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16,501 | Special Lagrangian submanifolds and cohomogeneity one actions on the complex projective space | We construct examples of cohomogeneity one special Lagrangian submanifolds in
the cotangent bundle over the complex projective space, whose Calabi-Yau
structure was given by Stenzel. For each example, we describe the condition of
special Lagrangian as an ordinary differential equation. Our method is based on
a moment map technique and the classification of cohomogeneity one actions on
the complex projective space classified by Takagi.
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16,502 | Linear Regression with Sparsely Permuted Data | In regression analysis of multivariate data, it is tacitly assumed that
response and predictor variables in each observed response-predictor pair
correspond to the same entity or unit. In this paper, we consider the situation
of "permuted data" in which this basic correspondence has been lost. Several
recent papers have considered this situation without further assumptions on the
underlying permutation. In applications, the latter is often to known to have
additional structure that can be leveraged. Specifically, we herein consider
the common scenario of "sparsely permuted data" in which only a small fraction
of the data is affected by a mismatch between response and predictors. However,
an adverse effect already observed for sparsely permuted data is that the least
squares estimator as well as other estimators not accounting for such partial
mismatch are inconsistent. One approach studied in detail herein is to treat
permuted data as outliers which motivates the use of robust regression
formulations to estimate the regression parameter. The resulting estimate can
subsequently be used to recover the permutation. A notable benefit of the
proposed approach is its computational simplicity given the general lack of
procedures for the above problem that are both statistically sound and
computationally appealing.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,503 | Matrix Scaling and Balancing via Box Constrained Newton's Method and Interior Point Methods | In this paper, we study matrix scaling and balancing, which are fundamental
problems in scientific computing, with a long line of work on them that dates
back to the 1960s. We provide algorithms for both these problems that, ignoring
logarithmic factors involving the dimension of the input matrix and the size of
its entries, both run in time $\widetilde{O}\left(m\log \kappa \log^2
(1/\epsilon)\right)$ where $\epsilon$ is the amount of error we are willing to
tolerate. Here, $\kappa$ represents the ratio between the largest and the
smallest entries of the optimal scalings. This implies that our algorithms run
in nearly-linear time whenever $\kappa$ is quasi-polynomial, which includes, in
particular, the case of strictly positive matrices. We complement our results
by providing a separate algorithm that uses an interior-point method and runs
in time $\widetilde{O}(m^{3/2} \log (1/\epsilon))$.
In order to establish these results, we develop a new second-order
optimization framework that enables us to treat both problems in a unified and
principled manner. This framework identifies a certain generalization of linear
system solving that we can use to efficiently minimize a broad class of
functions, which we call second-order robust. We then show that in the context
of the specific functions capturing matrix scaling and balancing, we can
leverage and generalize the work on Laplacian system solving to make the
algorithms obtained via this framework very efficient.
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16,504 | AP17-OLR Challenge: Data, Plan, and Baseline | We present the data profile and the evaluation plan of the second oriental
language recognition (OLR) challenge AP17-OLR. Compared to the event last year
(AP16-OLR), the new challenge involves more languages and focuses more on short
utterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two
types of baselines are constructed to assist the participants, one is based on
the i-vector model and the other is based on various neural networks. We report
the baseline results evaluated with various metrics defined by the AP17-OLR
evaluation plan and demonstrate that the combined database is a reasonable data
resource for multilingual research. All the data is free for participants, and
the Kaldi recipes for the baselines have been published online.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,505 | First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies | The IllustrisTNG project is a new suite of cosmological
magneto-hydrodynamical simulations of galaxy formation performed with the Arepo
code and updated models for feedback physics. Here we introduce the first two
simulations of the series, TNG100 and TNG300, and quantify the stellar mass
content of about 4000 massive galaxy groups and clusters ($10^{13} \leq M_{\rm
200c}/M_{\rm sun} \leq 10^{15}$) at recent times ($z \leq 1$). The richest
clusters have half of their total stellar mass bound to satellite galaxies,
with the other half being associated with the central galaxy and the diffuse
intra-cluster light. The exact ICL fraction depends sensitively on the
definition of a central galaxy's mass and varies in our most massive clusters
between 20 to 40% of the total stellar mass. Haloes of $5\times 10^{14}M_{\rm
sun}$ and above have more diffuse stellar mass outside 100 kpc than within 100
kpc, with power-law slopes of the radial mass density distribution as shallow
as the dark matter's ( $-3.5 < \alpha_{\rm 3D} < -3$). Total halo mass is a
very good predictor of stellar mass, and vice versa: at $z=0$, the 3D stellar
mass measured within 30 kpc scales as $\propto (M_{\rm 500c})^{0.49}$ with a
$\sim 0.12$ dex scatter. This is possibly too steep in comparison to the
available observational constraints, even though the abundance of TNG less
massive galaxies ($< 10^{11}M_{\rm sun}$ in stars) is in good agreement with
the measured galaxy stellar mass functions at recent epochs. The 3D sizes of
massive galaxies fall too on a tight ($\sim$0.16 dex scatter) power-law
relation with halo mass, with $r^{\rm stars}_{\rm 0.5} \propto (M_{\rm
500c})^{0.53}$. Even more fundamentally, halo mass alone is a good predictor
for the whole stellar mass profiles beyond the inner few kpc, and we show how
on average these can be precisely recovered given a single mass measurement of
the galaxy or its halo.
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16,506 | A Fast Implementation of Singular Value Thresholding Algorithm using Recycling Rank Revealing Randomized Singular Value Decomposition | In this paper, we present a fast implementation of the Singular Value
Thresholding (SVT) algorithm for matrix completion. A rank-revealing randomized
singular value decomposition (R3SVD) algorithm is used to adaptively carry out
partial singular value decomposition (SVD) to fast approximate the SVT operator
given a desired, fixed precision. We extend the R3SVD algorithm to a recycling
rank revealing randomized singular value decomposition (R4SVD) algorithm by
reusing the left singular vectors obtained from the previous iteration as the
approximate basis in the current iteration, where the computational cost for
partial SVD at each SVT iteration is significantly reduced. A simulated
annealing style cooling mechanism is employed to adaptively adjust the low-rank
approximation precision threshold as SVT progresses. Our fast SVT
implementation is effective in both large and small matrices, which is
demonstrated in matrix completion applications including image recovery and
movie recommendation system.
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16,507 | Surface magnetism of gallium arsenide nanofilms | Gallium arsenide (GaAs) is the widest used second generation semiconductor
with a direct band gap and increasingly used as nanofilms. However, the
magnetic properties of GaAs nanofilms have never been studied. Here we find by
comprehensive density functional theory calculations that GaAs nanofilms
cleaved along the <111> and <100> directions become intrinsically metallic
films with strong surface magnetism and magnetoelectric (ME) effect. The
surface magnetism and electrical conductivity are realized via a combined
effect of transferring charge induced by spontaneous electric-polarization
through the film thickness and spin-polarized surface states. The surface
magnetism of <111> nanofilms can be significantly and linearly tuned by
vertically applied electric field, endowing the nanofilms unexpectedly high ME
coefficients, which are tens of times higher than those of ferromagnetic metals
and transition metal oxides.
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16,508 | Monte Carlo study of the Coincidence Resolving Time of a liquid xenon PET scanner, using Cherenkov radiation | In this paper we use detailed Monte Carlo simulations to demonstrate that
liquid xenon (LXe) can be used to build a Cherenkov-based TOF-PET, with an
intrinsic coincidence resolving time (CRT) in the vicinity of 10 ps. This
extraordinary performance is due to three facts: a) the abundant emission of
Cherenkov photons by liquid xenon; b) the fact that LXe is transparent to
Cherenkov light; and c) the fact that the fastest photons in LXe have
wavelengths higher than 300 nm, therefore making it possible to separate the
detection of scintillation and Cherenkov light. The CRT in a Cherenkov LXe
TOF-PET detector is, therefore, dominated by the resolution (time jitter)
introduced by the photosensors and the electronics. However, we show that for
sufficiently fast photosensors (e.g, an overall 40 ps jitter, which can be
achieved by current micro-channel plate photomultipliers) the overall CRT
varies between 30 and 55 ps, depending of the detection efficiency. This is
still one order of magnitude better than commercial CRT devices and improves by
a factor 3 the best CRT obtained with small laboratory prototypes.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,509 | Detecting Hierarchical Ties Using Link-Analysis Ranking at Different Levels of Time Granularity | Social networks contain implicit knowledge that can be used to infer
hierarchical relations that are not explicitly present in the available data.
Interaction patterns are typically affected by users' social relations. We
present an approach to inferring such information that applies a link-analysis
ranking algorithm at different levels of time granularity. In addition, a
voting scheme is employed for obtaining the hierarchical relations. The
approach is evaluated on two datasets: the Enron email data set, where the goal
is to infer manager-subordinate relationships, and the Co-author data set,
where the goal is to infer PhD advisor-advisee relations. The experimental
results indicate that the proposed approach outperforms more traditional
approaches to inferring hierarchical relations from social networks.
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16,510 | Optimal stimulation protocol in a bistable synaptic consolidation model | Consolidation of synaptic changes in response to neural activity is thought
to be fundamental for memory maintenance over a timescale of hours. In
experiments, synaptic consolidation can be induced by repeatedly stimulating
presynaptic neurons. However, the effectiveness of such protocols depends
crucially on the repetition frequency of the stimulations and the mechanisms
that cause this complex dependence are unknown. Here we propose a simple
mathematical model that allows us to systematically study the interaction
between the stimulation protocol and synaptic consolidation. We show the
existence of optimal stimulation protocols for our model and, similarly to LTP
experiments, the repetition frequency of the stimulation plays a crucial role
in achieving consolidation. Our results show that the complex dependence of LTP
on the stimulation frequency emerges naturally from a model which satisfies
only minimal bistability requirements.
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16,511 | Parametric Polynomial Preserving Recovery on Manifolds | This paper investigates gradient recovery schemes for data defined on
discretized manifolds. The proposed method, parametric polynomial preserving
recovery (PPPR), does not require the tangent spaces of the exact manifolds
which have been assumed for some significant gradient recovery methods in the
literature. Another advantage is that superconvergence is guaranteed for PPPR
without the symmetric condition which has been asked in the existing
techniques. There is also numerical evidence that the superconvergence by PPPR
is high curvature stable, which distinguishes itself from the other methods. As
an application, we show that its capability of constructing an asymptotically
exact \textit{a posteriori} error estimator. Several numerical examples on
two-dimensional surfaces are presented to support the theoretical results and
make comparisons with state of the art methods.
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16,512 | Integral points on the complement of plane quartics | Let $Y$ be the complement of a plane quartic curve $D$ defined over a number
field. Our main theorem confirms the Lang-Vojta conjecture for $Y$ when $D$ is
a generic smooth quartic curve, by showing that its integral points are
confined in a curve except for a finite number of exceptions. The required
finiteness will be obtained by reducing it to the Shafarevich conjecture for K3
surfaces. Some variants of our method confirm the same conjecture when $D$ is a
reducible generic quartic curve which consists of four lines, two lines and a
conic, or two conics.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,513 | Modelling collective motion based on the principle of agency | Collective motion is an intriguing phenomenon, especially considering that it
arises from a set of simple rules governing local interactions between
individuals. In theoretical models, these rules are normally \emph{assumed} to
take a particular form, possibly constrained by heuristic arguments. We propose
a new class of models, which describe the individuals as \emph{agents}, capable
of deciding for themselves how to act and learning from their experiences. The
local interaction rules do not need to be postulated in this model, since they
\emph{emerge} from the learning process. We apply this ansatz to a concrete
scenario involving marching locusts, in order to model the phenomenon of
density-dependent alignment. We show that our learning agent-based model can
account for a Fokker-Planck equation that describes the collective motion and,
most notably, that the agents can learn the appropriate local interactions,
requiring no strong previous assumptions on their form. These results suggest
that learning agent-based models are a powerful tool for studying a broader
class of problems involving collective motion and animal agency in general.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,514 | Cosmic Microwave Background constraints for global strings and global monopoles | We present the first CMB power spectra from numerical simulations of the
global O(N) linear $\sigma$-model with N = 2,3, which have global strings and
monopoles as topological defects. In order to compute the CMB power spectra we
compute the unequal time correlators (UETCs) of the energy-momentum tensor,
showing that they fall off at high wave number faster than naive estimates
based on the geometry of the defects, indicating non-trivial
(anti-)correlations between the defects and the surrounding Goldstone boson
field. We obtain source functions for Einstein-Boltzmann solvers from the
UETCs, using a recent method that improves the modelling at the radiation-
matter transition. We show that the interpolation function that mimics the
transition is similar to other defect models, but not identical, confirming the
non-universality of the interpolation function. The CMB power spectra for
global strings and monopoles have the same overall shape as those obtained
using the non-linear $\sigma$-model approximation, which is well captured by a
large-N calculation. However, the amplitudes are larger than the large-N
calculation predict, and in the case of global strings much larger: a factor of
20 at the peak. Finally we compare the CMB power spectra with the latest CMB
data to put limits on the allowed contribution to the temperature power
spectrum at multipole $\ell$ = 10 of 1.7% for global strings and 2.4% for
global monopoles. These limits correspond to symmetry-breaking scales of
2.9x1015 GeV (6.3x1014 GeV with the expected logarithmic scaling of the
effective string tension between the simulation time and decoupling) and
6.4x1015 GeV respectively. The bound on global strings is a significant one for
the ultra-light axion scenario with axion masses ma 10-28 eV. These upper
limits indicate that gravitational wave from global topological defects will
not be observable at the GW observatory LISA.
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16,515 | A parallel implementation of the Synchronised Louvain method | Community detection in networks is a very actual and important field of
research with applications in many areas. But, given that the amount of
processed data increases more and more, existing algorithms need to be adapted
for very large graphs. The objective of this project was to parallelise the
Synchronised Louvain Method, a community detection algorithm developed by
Arnaud Browet, in order to improve its performances in terms of computation
time and thus be able to faster detect communities in very large graphs. To
reach this goal, we used the API OpenMP to parallelise the algorithm and then
carried out performance tests. We studied the computation time and speedup of
the parallelised algorithm and were able to bring out some qualitative trends.
We obtained a great speedup, compared with the theoretical prediction of Amdahl
law. To conclude, using the parallel implementation of the algorithm of Browet
on large graphs seems to give good results, both in terms of computation time
and speedup. Further tests should be carried out in order to obtain more
quantitative results.
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16,516 | Index coding with erroneous side information | In this paper, new index coding problems are studied, where each receiver has
erroneous side information. Although side information is a crucial part of
index coding, the existence of erroneous side information has not yet been
considered. We study an index code with receivers that have erroneous side
information symbols in the error-free broadcast channel, which is called an
index code with side information errors (ICSIE). The encoding and decoding
procedures of the ICSIE are proposed, based on the syndrome decoding. Then, we
derive the bounds on the optimal codelength of the proposed index code with
erroneous side information. Furthermore, we introduce a special graph for the
proposed index coding problem, called a $\delta_s$-cycle whose properties are
similar to those of the cycle in the conventional index coding problem.
Properties of the ICSIE are also discussed in the $\delta_s$-cycle and clique.
Finally, the proposed ICSIE is generalized to an index code for the scenario
having both additive channel errors and side information errors, called a
generalized error correcting index code (GECIC).
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16,517 | HATS-36b and 24 other transiting/eclipsing systems from the HATSouth - K2 Campaign 7 program | We report on the result of a campaign to monitor 25 HATSouth candidates using
the K2 space telescope during Campaign 7 of the K2 mission. We discover
HATS-36b (EPIC 215969174b), a hot Jupiter with a mass of 2.79$\pm$0.40 M$_J$
and a radius of 1.263$\pm$0.045 R$_J$ which transits a solar-type G0V star
(V=14.386) in a 4.1752d period. We also refine the properties of three
previously discovered HATSouth transiting planets (HATS-9b, HATS-11b, and
HATS-12b) and search the K2 data for TTVs and additional transiting planets in
these systems. In addition we also report on a further three systems that
remain as Jupiter-radius transiting exoplanet candidates. These candidates do
not have determined masses, however pass all of our other vetting observations.
Finally we report on the 18 candidates which we are now able to classify as
eclipsing binary or blended eclipsing binary systems based on a combination of
the HATSouth data, the K2 data, and follow-up ground-based photometry and
spectroscopy. These range in periods from 0.7 days to 16.7 days, and down to
1.5 mmag in eclipse depths. Our results show the power of combining
ground-based imaging and spectroscopy with higher precision space-based
photometry, and serve as an illustration as to what will be possible when
combining ground-based observations with TESS data.
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16,518 | The Carnegie-Chicago Hubble Program: Discovery of the Most Distant Ultra-faint Dwarf Galaxy in the Local Universe | Ultra-faint dwarf galaxies (UFDs) are the faintest known galaxies and due to
their incredibly low surface brightness, it is difficult to find them beyond
the Local Group. We report a serendipitous discovery of an UFD, Fornax UFD1, in
the outskirts of NGC 1316, a giant galaxy in the Fornax cluster. The new galaxy
is located at a projected radius of 55 kpc in the south-east of NGC 1316. This
UFD is found as a small group of resolved stars in the Hubble Space Telescope
images of a halo field of NGC 1316, obtained as part of the Carnegie-Chicago
Hubble Program. Resolved stars in this galaxy are consistent with being mostly
metal-poor red giant branch (RGB) stars. Applying the tip of the RGB method to
the mean magnitude of the two brightest RGB stars, we estimate the distance to
this galaxy, 19.0 +- 1.3 Mpc. Fornax UFD1 is probably a member of the Fornax
cluster. The color-magnitude diagram of these stars is matched by a 12 Gyr
isochrone with low metallicity ([Fe/H] ~ -2.4). Total magnitude and effective
radius of Fornax UFD1 are Mv ~ -7.6 +- 0.2 mag and r_eff = 146 +- 9 pc, which
are similar to those of Virgo UFD1 that was discovered recently in the
intracluster field of Virgo by Jang & Lee (2014).Fornax UFD1 is the most
distant known UFD that is confirmed by resolved stars. This indicates that UFDs
are ubiquitous and that more UFDs remain to be discovered in the Fornax
cluster.
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16,519 | An Exploratory Study on the Implementation and Adoption of ERP Solutions for Businesses | Enterprise Resource Planning (ERP) systems have been covered in both
mainstream Information Technology (IT) periodicals, and in academic literature,
as a result of extensive adoption by organisations in the last two decades.
Some of the past studies have reported operational efficiency and other gains,
while other studies have pointed out the challenges. ERP systems continue to
evolve, moving into the cloud hosted sphere, and being implemented by
relatively smaller and regional companies. This project has carried out an
exploratory study into the use of ERP systems, within Hawke's Bay New Zealand.
ERP systems make up a major investment and undertaking by those companies.
Therefore, research and lessons learned in this area are very important. In
addition to a significant initial literature review, this project has conducted
a survey on the local users' experience with Microsoft Dynamics NAV (a popular
ERP brand). As a result, this study will contribute new and relevant
information to the literature on business information systems and to ERP
systems, in particular.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,520 | Selective inference for the problem of regions via multiscale bootstrap | A general approach to selective inference is considered for hypothesis
testing of the null hypothesis represented as an arbitrary shaped region in the
parameter space of multivariate normal model. This approach is useful for
hierarchical clustering where confidence levels of clusters are calculated only
for those appeared in the dendrogram, thus subject to heavy selection bias. Our
computation is based on a raw confidence measure, called bootstrap probability,
which is easily obtained by counting how many times the same cluster appears in
bootstrap replicates of the dendrogram. We adjust the bias of the bootstrap
probability by utilizing the scaling-law in terms of geometric quantities of
the region in the abstract parameter space, namely, signed distance and mean
curvature. Although this idea has been used for non-selective inference of
hierarchical clustering, its selective inference version has not been discussed
in the literature. Our bias-corrected $p$-values are asymptotically
second-order accurate in the large sample theory of smooth boundary surfaces of
regions, and they are also justified for nonsmooth surfaces such as polyhedral
cones. The $p$-values are asymptotically equivalent to those of the iterated
bootstrap but with less computation.
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16,521 | Arbitrary Beam Synthesis of Different Hybrid Beamforming Systems | For future mmWave mobile communication systems the use of analog/hybrid
beamforming is envisioned be a key as- pect. The synthesis of beams is a key
technology of enable the best possible operation during beamsearch, data
transmission and MU MIMO operation. The developed method for synthesizing beams
is based on previous work in radar technology considering only phase array
antennas. With this technique it is possible to generate a desired beam of any
shape with the constraints of the desired target transceiver antenna frontend.
It is not constraint to a certain antenna array geometry, but can handle 1d, 2d
and even 3d antenna array geometries like cylindric arrays. The numerical
examples show that the method can synthesize beams by considering a user
defined tradeoff between gain, transition width and passband ripples.
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16,522 | The emergence of the concept of filter in topological categories | In all approaches to convergence where the concept of filter is taken as
primary, the usual motivation is the notion of neighborhood filter in a
topological space. However, these approaches often lead to spaces more general
than topological ones, thereby calling into question the need to use filters in
the first place. In this note we overturn the usual view and take as primary
the notion of convergence in the most general context of centered spaces. In
this setting, the notion of filterbase emerges from the concept of germ of a
function, while the concept of filter emerges from an amnestic modification of
the subcategory of centered spaces admitting germs at each point.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,523 | Memory-augmented Neural Machine Translation | Neural machine translation (NMT) has achieved notable success in recent
times, however it is also widely recognized that this approach has limitations
with handling infrequent words and word pairs. This paper presents a novel
memory-augmented NMT (M-NMT) architecture, which stores knowledge about how
words (usually infrequently encountered ones) should be translated in a memory
and then utilizes them to assist the neural model. We use this memory mechanism
to combine the knowledge learned from a conventional statistical machine
translation system and the rules learned by an NMT system, and also propose a
solution for out-of-vocabulary (OOV) words based on this framework. Our
experiments on two Chinese-English translation tasks demonstrated that the
M-NMT architecture outperformed the NMT baseline by $9.0$ and $2.7$ BLEU points
on the two tasks, respectively. Additionally, we found this architecture
resulted in a much more effective OOV treatment compared to competitive
methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,524 | RFID Localisation For Internet Of Things Smart Homes: A Survey | The Internet of Things (IoT) enables numerous business opportunities in
fields as diverse as e-health, smart cities, smart homes, among many others.
The IoT incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications.
Localisation in indoor positioning systems plays an important role in the IoT.
Location Based IoT applications range from tracking objects and people in
real-time, assets management, agriculture, assisted monitoring technologies for
healthcare, and smart homes, to name a few. Radio Frequency based systems for
indoor positioning such as Radio Frequency Identification (RFID) is a key
enabler technology for the IoT due to its costeffective, high readability
rates, automatic identification and, importantly, its energy efficiency
characteristic. This paper reviews the state-of-the-art RFID technologies in
IoT Smart Homes applications. It presents several comparable studies of RFID
based projects in smart homes and discusses the applications, techniques,
algorithms, and challenges of adopting RFID technologies in IoT smart home
systems.
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16,525 | Symplectic capacities from positive S^1-equivariant symplectic homology | We use positive S^1-equivariant symplectic homology to define a sequence of
symplectic capacities c_k for star-shaped domains in R^{2n}. These capacities
are conjecturally equal to the Ekeland-Hofer capacities, but they satisfy
axioms which allow them to be computed in many more examples. In particular, we
give combinatorial formulas for the capacities c_k of any "convex toric domain"
or "concave toric domain". As an application, we determine optimal symplectic
embeddings of a cube into any convex or concave toric domain. We also extend
the capacities c_k to functions of Liouville domains which are almost but not
quite symplectic capacities.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,526 | Synthesis versus analysis in patch-based image priors | In global models/priors (for example, using wavelet frames), there is a well
known analysis vs synthesis dichotomy in the way signal/image priors are
formulated. In patch-based image models/priors, this dichotomy is also present
in the choice of how each patch is modeled. This paper shows that there is
another analysis vs synthesis dichotomy, in terms of how the whole image is
related to the patches, and that all existing patch-based formulations that
provide a global image prior belong to the analysis category. We then propose a
synthesis formulation, where the image is explicitly modeled as being
synthesized by additively combining a collection of independent patches. We
formally establish that these analysis and synthesis formulations are not
equivalent in general and that both formulations are compatible with analysis
and synthesis formulations at the patch level. Finally, we present an instance
of the alternating direction method of multipliers (ADMM) that can be used to
perform image denoising under the proposed synthesis formulation, showing its
computational feasibility. Rather than showing the superiority of the synthesis
or analysis formulations, the contributions of this paper is to establish the
existence of both alternatives, thus closing the corresponding gap in the field
of patch-based image processing.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,527 | Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks | We prove new upper and lower bounds on the VC-dimension of deep neural
networks with the ReLU activation function. These bounds are tight for almost
the entire range of parameters. Letting $W$ be the number of weights and $L$ be
the number of layers, we prove that the VC-dimension is $O(W L \log(W))$, and
provide examples with VC-dimension $\Omega( W L \log(W/L) )$. This improves
both the previously known upper bounds and lower bounds. In terms of the number
$U$ of non-linear units, we prove a tight bound $\Theta(W U)$ on the
VC-dimension. All of these bounds generalize to arbitrary piecewise linear
activation functions, and also hold for the pseudodimensions of these function
classes.
Combined with previous results, this gives an intriguing range of
dependencies of the VC-dimension on depth for networks with different
non-linearities: there is no dependence for piecewise-constant, linear
dependence for piecewise-linear, and no more than quadratic dependence for
general piecewise-polynomial.
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16,528 | Benchmarking Numerical Methods for Lattice Equations with the Toda Lattice | We compare performances of well-known numerical time-stepping methods that
are widely used to compute solutions of the doubly-infinite
Fermi-Pasta-Ulam-Tsingou (FPUT) lattice equations. The methods are benchmarked
according to (1) their accuracy in capturing the soliton peaks and (2) in
capturing highly-oscillatory parts of the solutions of the Toda lattice
resulting from a variety of initial data. The numerical inverse scattering
transform method is used to compute a reference solution with high accuracy. We
find that benchmarking a numerical method on pure-soliton initial data can lead
one to overestimate the accuracy of the method.
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16,529 | Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT? | Due to its wide field of view, cone-beam computed tomography (CBCT) is
plagued by large amounts of scatter, where attenuated photons hit the detector,
and corrupt the linear models used for reconstruction. Given that one can
generate a good estimate of scatter however, then image accuracy can be
retained. In the context of adaptive radiotherapy, one usually has a
low-scatter planning CT image of the same patient at an earlier time.
Correcting for scatter in the subsequent CBCT scan can either be self
consistent with the new measurements or exploit the prior image, and there are
several recent methods that report high accuracy with the latter. In this
study, we will look at the accuracy of various scatter estimation methods, how
they can be effectively incorporated into a statistical reconstruction
algorithm, along with introducing a method for matching off-line Monte-Carlo
(MC) prior estimates to the new measurements. Conclusions we draw from testing
on a neck cancer patient are: statistical reconstruction that incorporates the
scatter estimate significantly outperforms analytic and iterative methods with
pre-correction; and although the most accurate scatter estimates can be made
from the MC on planning image, they only offer a slight advantage over the
measurement based scatter kernel superposition (SKS) in reconstruction error.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,530 | Proactive Defense Against Physical Denial of Service Attacks using Poisson Signaling Games | While the Internet of things (IoT) promises to improve areas such as energy
efficiency, health care, and transportation, it is highly vulnerable to
cyberattacks. In particular, distributed denial-of-service (DDoS) attacks
overload the bandwidth of a server. But many IoT devices form part of
cyber-physical systems (CPS). Therefore, they can be used to launch "physical"
denial-of-service attacks (PDoS) in which IoT devices overflow the "physical
bandwidth" of a CPS. In this paper, we quantify the population-based risk to a
group of IoT devices targeted by malware for a PDoS attack. In order to model
the recruitment of bots, we develop a "Poisson signaling game," a signaling
game with an unknown number of receivers, which have varying abilities to
detect deception. Then we use a version of this game to analyze two mechanisms
(legal and economic) to deter botnet recruitment. Equilibrium results indicate
that 1) defenders can bound botnet activity, and 2) legislating a minimum level
of security has only a limited effect, while incentivizing active defense can
decrease botnet activity arbitrarily. This work provides a quantitative
foundation for proactive PDoS defense.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,531 | The weakly compact reflection principle need not imply a high order of weak compactness | The weakly compact reflection principle $\text{Refl}_{\text{wc}}(\kappa)$
states that $\kappa$ is a weakly compact cardinal and every weakly compact
subset of $\kappa$ has a weakly compact proper initial segment. The weakly
compact reflection principle at $\kappa$ implies that $\kappa$ is an
$\omega$-weakly compact cardinal. In this article we show that the weakly
compact reflection principle does not imply that $\kappa$ is
$(\omega+1)$-weakly compact. Moreover, we show that if the weakly compact
reflection principle holds at $\kappa$ then there is a forcing extension
preserving this in which $\kappa$ is the least $\omega$-weakly compact
cardinal. Along the way we generalize the well-known result which states that
if $\kappa$ is a regular cardinal then in any forcing extension by
$\kappa$-c.c. forcing the nonstationary ideal equals the ideal generated by the
ground model nonstationary ideal; our generalization states that if $\kappa$ is
a weakly compact cardinal then after forcing with a `typical' Easton-support
iteration of length $\kappa$ the weakly compact ideal equals the ideal
generated by the ground model weakly compact ideal.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,532 | Large scale distributed neural network training through online distillation | Techniques such as ensembling and distillation promise model quality
improvements when paired with almost any base model. However, due to increased
test-time cost (for ensembles) and increased complexity of the training
pipeline (for distillation), these techniques are challenging to use in
industrial settings. In this paper we explore a variant of distillation which
is relatively straightforward to use as it does not require a complicated
multi-stage setup or many new hyperparameters. Our first claim is that online
distillation enables us to use extra parallelism to fit very large datasets
about twice as fast. Crucially, we can still speed up training even after we
have already reached the point at which additional parallelism provides no
benefit for synchronous or asynchronous stochastic gradient descent. Two neural
networks trained on disjoint subsets of the data can share knowledge by
encouraging each model to agree with the predictions the other model would have
made. These predictions can come from a stale version of the other model so
they can be safely computed using weights that only rarely get transmitted. Our
second claim is that online distillation is a cost-effective way to make the
exact predictions of a model dramatically more reproducible. We support our
claims using experiments on the Criteo Display Ad Challenge dataset, ImageNet,
and the largest to-date dataset used for neural language modeling, containing
$6\times 10^{11}$ tokens and based on the Common Crawl repository of web data.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,533 | New method to design stellarator coils without the winding surface | Finding an easy-to-build coils set has been a critical issue for stellarator
design for decades. Conventional approaches assume a toroidal "winding"
surface. We'll investigate if the existence of winding surface unnecessarily
constrains the optimization, and a new method to design coils for stellarators
is presented. Each discrete coil is represented as an arbitrary, closed,
one-dimensional curve embedded in three-dimensional space. A target function to
be minimized that covers both physical requirements and engineering constraints
is constructed. The derivatives of the target function are calculated
analytically. A numerical code, named FOCUS, has been developed. Applications
to a simple configuration, the W7-X, and LHD plasmas are presented.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,534 | An Analytical Framework for Modeling a Spatially Repulsive Cellular Network | We propose a new cellular network model that captures both deterministic and
random aspects of base station deployments. Namely, the base station locations
are modeled as the superposition of two independent stationary point processes:
a random shifted grid with intensity $\lambda_g$ and a Poisson point process
(PPP) with intensity $\lambda_p$. Grid and PPP deployments are special cases
with $\lambda_p \to 0$ and $\lambda_g \to 0$, with actual deployments in
between these two extremes, as we demonstrate with deployment data. Assuming
that each user is associated with the base station that provides the strongest
average received signal power, we obtain the probability that a typical user is
associated with either a grid or PPP base station. Assuming Rayleigh fading
channels, we derive the expression for the coverage probability of the typical
user, resulting in the following observations. First, the association and the
coverage probability of the typical user are fully characterized as functions
of intensity ratio $\rho_\lambda = \lambda_p/\lambda_g$. Second, the user
association is biased towards the base stations located on a grid. Finally, the
proposed model predicts the coverage probability of the actual deployment with
great accuracy.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,535 | Glycolaldehyde in Perseus young solar analogs | Aims: In this paper we focus on the occurrence of glycolaldehyde (HCOCH2OH)
in young solar analogs by performing the first homogeneous and unbiased study
of this molecule in the Class 0 protostars of the nearby Perseus star forming
region. Methods: We obtained sub-arcsec angular resolution maps at 1.3mm and
1.4mm of glycolaldehyde emission lines using the IRAM Plateau de Bure (PdB)
interferometer in the framework of the CALYPSO IRAM large program. Results:
Glycolaldehyde has been detected towards 3 Class 0 and 1 Class I protostars out
of the 13 continuum sources targeted in Perseus: NGC1333-IRAS2A1,
NGC1333-IRAS4A2, NGC1333-IRAS4B1, and SVS13-A. The NGC1333 star forming region
looks particularly glycolaldehyde rich, with a rate of occurrence up to 60%.
The glycolaldehyde spatial distribution overlaps with the continuum one,
tracing the inner 100 au around the protostar. A large number of lines (up to
18), with upper-level energies Eu from 37 K up to 375 K has been detected. We
derived column densities > 10^15 cm^-2 and rotational temperatures Trot between
115 K and 236 K, imaging for the first time hot-corinos around NGC1333-IRAS4B1
and SVS13-A. Conclusions: In multiple systems glycolaldehyde emission is
detected only in one component. The case of the SVS13-A+B and IRAS4-A1+A2
systems support that the detection of glycolaldehyde (at least in the present
Perseus sample) indicates older protostars (i.e. SVS13-A and IRAS4-A2), evolved
enough to develop the hot-corino region (i.e. 100 K in the inner 100 au).
However, only two systems do not allow us to firmly conclude whether the
primary factor leading to the detection of glycolaldehyde emission is the
environments hosting the protostars, evolution (e.g. low value of Lsubmm/Lint),
or accretion luminosity (high Lint).
| 0 | 1 | 0 | 0 | 0 | 0 |
16,536 | Comprehension-guided referring expressions | We consider generation and comprehension of natural language referring
expression for objects in an image. Unlike generic "image captioning" which
lacks natural standard evaluation criteria, quality of a referring expression
may be measured by the receiver's ability to correctly infer which object is
being described. Following this intuition, we propose two approaches to utilize
models trained for comprehension task to generate better expressions. First, we
use a comprehension module trained on human-generated expressions, as a
"critic" of referring expression generator. The comprehension module serves as
a differentiable proxy of human evaluation, providing training signal to the
generation module. Second, we use the comprehension module in a
generate-and-rerank pipeline, which chooses from candidate expressions
generated by a model according to their performance on the comprehension task.
We show that both approaches lead to improved referring expression generation
on multiple benchmark datasets.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,537 | Modeling the Multiple Sclerosis Brain Disease Using Agents: What Works and What Doesn't? | The human brain is one of the most complex living structures in the known
Universe. It consists of billions of neurons and synapses. Due to its intrinsic
complexity, it can be a formidable task to accurately depict brain's structure
and functionality. In the past, numerous studies have been conducted on
modeling brain disease, structure, and functionality. Some of these studies
have employed Agent-based approaches including multiagent-based simulation
models as well as brain complex networks. While these models have all been
developed using agent-based computing, however, to our best knowledge, none of
them have employed the use of Agent-Oriented Software Engineering (AOSE)
methodologies in developing the brain or disease model. This is a problem
because without due process, developed models can miss out on important
requirements. AOSE has the unique capability of merging concepts from
multiagent systems, agent-based modeling, artificial intelligence, besides
concepts from distributed systems. AOSE involves the various tested software
engineering principles in various phases of the model development ranging from
analysis, design, implementation, and testing phases. In this paper, we employ
the use of three different AOSE methodologies for modeling the Multiple
Sclerosis brain disease namely GAIA, TROPOS, and MASE. After developing the
models, we further employ the use of Exploratory Agent-based Modeling (EABM) to
develop an actual model replicating previous results as a proof of concept. The
key objective of this study is to demonstrate and explore the viability and
effectiveness of AOSE methodologies in the development of complex brain
structure and cognitive process models. Our key finding include demonstration
that AOSE methodologies can be considerably helpful in modeling various living
complex systems, in general, and the human brain, in particular.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,538 | Improved NN-JPDAF for Joint Multiple Target Tracking and Feature Extraction | Feature aided tracking can often yield improved tracking performance over the
standard multiple target tracking (MTT) algorithms with only kinematic
measurements. However, in many applications, the feature signal of the targets
consists of sparse Fourier-domain signals. It changes quickly and nonlinearly
in the time domain, and the feature measurements are corrupted by missed
detections and mis-associations. These two factors make it hard to extract the
feature information to be used in MTT. In this paper, we develop a
feature-aided nearest neighbour joint probabilistic data association filter
(NN-JPDAF) for joint MTT and feature extraction in dense target environments.
To estimate the rapidly varying feature signal from incomplete and corrupted
measurements, we use the atomic norm constraint to formulate the sparsity of
feature signal and use the $\ell_1$-norm to formulate the sparsity of the
corruption induced by mis-associations. Based on the sparse representation, the
feature signal are estimated by solving a semidefinite program (SDP) which is
convex. We also provide an iterative method for solving this SDP via the
alternating direction method of multipliers (ADMM) where each iteration
involves closed-form computation. With the estimated feature signal,
re-filtering is performed to estimate the kinematic states of the targets,
where the association makes use of both kinematic and feature information.
Simulation results are presented to illustrate the performance of the proposed
algorithm in a radar application.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,539 | KINETyS: Constraining spatial variations of the stellar initial mass function in early-type galaxies | The heavyweight stellar initial mass function (IMF) observed in the cores of
massive early-type galaxies (ETGs) has been linked to formation of their cores
in an initial swiftly-quenched rapid starburst. However, the outskirts of ETGs
are thought to be assembled via the slow accumulation of smaller systems in
which the star formation is less extreme; this suggests the form of the IMF
should exhibit a radial trend in ETGs. Here we report radial stellar population
gradients out to the half-light radii of a sample of eight nearby ETGs.
Spatially resolved spectroscopy at 0.8-1.35{\mu}m from the VLT's KMOS
instrument was used to measure radial trends in the strengths of a variety of
IMF-sensitive absorption features (including some which are previously
unexplored). We find weak or no radial variation in some of these which, given
a radial IMF trend, ought to vary measurably, e.g. for the Wing-Ford band we
measure a gradient of +0.06$\pm$0.04 per decade in radius.
Using stellar population models to fit stacked and individual spectra, we
infer that the measured radial changes in absorption feature strengths are
primarily accounted for by abundance gradients which are fairly consistent
across our sample (e.g. we derive an average [Na/H] gradient of
-0.53$\pm$0.07). The inferred contribution of dwarf stars to the total light
typically corresponds to a bottom heavy IMF, but we find no evidence for radial
IMF variations in the majority of our sample galaxies.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,540 | Multi-Agent Deep Reinforcement Learning with Human Strategies | Deep learning has enabled traditional reinforcement learning methods to deal
with high-dimensional problems. However, one of the disadvantages of deep
reinforcement learning methods is the limited exploration capacity of learning
agents. In this paper, we introduce an approach that integrates human
strategies to increase the exploration capacity of multiple deep reinforcement
learning agents. We also report the development of our own multi-agent
environment called Multiple Tank Defence to simulate the proposed approach. The
results show the significant performance improvement of multiple agents that
have learned cooperatively with human strategies. This implies that there is a
critical need for human intellect teamed with machines to solve complex
problems. In addition, the success of this simulation indicates that our
developed multi-agent environment can be used as a testbed platform to develop
and validate other multi-agent control algorithms. Details of the environment
implementation can be referred to
this http URL
| 0 | 0 | 0 | 1 | 0 | 0 |
16,541 | Deep into the Water Fountains: The case of IRAS 18043-2116 | (Abridged) The formation of large-scale (hundreds to few thousands of AU)
bipolar structures in the circumstellar envelopes (CSEs) of post-Asymptotic
Giant Branch (post-AGB) stars is poorly understood. The shape of these
structures, traced by emission from fast molecular outflows, suggests that the
dynamics at the innermost regions of these CSEs does not depend only on the
energy of the radiation field of the central star. Deep into the Water
Fountains is an observational project based on the results of programs carried
out with three telescope facilities: The Karl G. Jansky Very Large Array
(JVLA), The Australia Telescope Compact Array (ATCA), and the Very Large
Telescope (SINFONI-VLT). Here we report the results of the observations towards
the WF nebula IRAS 18043$-$2116: Detection of radio continuum emission in the
frequency range 1.5GHz - 8.0GHz; H$_{2}$O maser spectral features and radio
continuum emission detected at 22GHz, and H$_{2}$ ro-vibrational emission lines
detected at the near infrared. The high-velocity H$_{2}$O maser spectral
features, and the shock-excited H$_{2}$ emission detected could be produced in
molecular layers which are swept up as a consequence of the propagation of a
jet-driven wind. Using the derived H$_{2}$ column density, we estimated a
molecular mass-loss rate of the order of $10^{-9}$M$_{\odot}$yr$^{-1}$. On the
other hand, if the radio continuum flux detected is generated as a consequence
of the propagation of a thermal radio jet, the mass-loss rate associated to the
outflowing ionized material is of the order of 10$^{-5}$M$_{\odot}$yr$^{-1}$.
The presence of a rotating disk could be a plausible explanation for the
mass-loss rates estimated.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,542 | QAOA for Max-Cut requires hundreds of qubits for quantum speed-up | Computational quantum technologies are entering a new phase in which noisy
intermediate-scale quantum computers are available, but are still too small to
benefit from active error correction. Even with a finite coherence budget to
invest in quantum information processing, noisy devices with about 50 qubits
are expected to experimentally demonstrate quantum supremacy in the next few
years. Defined in terms of artificial tasks, current proposals for quantum
supremacy, even if successful, will not help to provide solutions to practical
problems. Instead, we believe that future users of quantum computers are
interested in actual applications and that noisy quantum devices may still
provide value by approximately solving hard combinatorial problems via hybrid
classical-quantum algorithms. To lower bound the size of quantum computers with
practical utility, we perform realistic simulations of the Quantum Approximate
Optimization Algorithm and conclude that quantum speedup will not be
attainable, at least for a representative combinatorial problem, until several
hundreds of qubits are available.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,543 | Mixed-Effect Modeling for Longitudinal Prediction of Cancer Tumor | In this paper, a mixed-effect modeling scheme is proposed to construct a
predictor for different features of cancer tumor. For this purpose, a set of
features is extracted from two groups of patients with the same type of cancer
but with two medical outcome: 1) survived and 2) passed away. The goal is to
build different models for the two groups, where in each group,
patient-specified behavior of individuals can be characterized. These models
are then used as predictors to forecast future state of patients with a given
history or initial state. To this end, a leave-on-out cross validation method
is used to measure the prediction accuracy of each patient-specified model.
Experiments show that compared to fixed-effect modeling (regression),
mixed-effect modeling has a superior performance on some of the extracted
features and similar or worse performance on the others.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,544 | Distributed Exact Shortest Paths in Sublinear Time | The distributed single-source shortest paths problem is one of the most
fundamental and central problems in the message-passing distributed computing.
Classical Bellman-Ford algorithm solves it in $O(n)$ time, where $n$ is the
number of vertices in the input graph $G$. Peleg and Rubinovich (FOCS'99)
showed a lower bound of $\tilde{\Omega}(D + \sqrt{n})$ for this problem, where
$D$ is the hop-diameter of $G$.
Whether or not this problem can be solved in $o(n)$ time when $D$ is
relatively small is a major notorious open question. Despite intensive research
\cite{LP13,N14,HKN15,EN16,BKKL16} that yielded near-optimal algorithms for the
approximate variant of this problem, no progress was reported for the original
problem.
In this paper we answer this question in the affirmative. We devise an
algorithm that requires $O((n \log n)^{5/6})$ time, for $D = O(\sqrt{n \log
n})$, and $O(D^{1/3} \cdot (n \log n)^{2/3})$ time, for larger $D$. This
running time is sublinear in $n$ in almost the entire range of parameters,
specifically, for $D = o(n/\log^2 n)$. For the all-pairs shortest paths
problem, our algorithm requires $O(n^{5/3} \log^{2/3} n)$ time, regardless of
the value of $D$.
We also devise the first algorithm with non-trivial complexity guarantees for
computing exact shortest paths in the multipass semi-streaming model of
computation.
From the technical viewpoint, our algorithm computes a hopset $G"$ of a
skeleton graph $G'$ of $G$ without first computing $G'$ itself. We then conduct
a Bellman-Ford exploration in $G' \cup G"$, while computing the required edges
of $G'$ on the fly. As a result, our algorithm computes exactly those edges of
$G'$ that it really needs, rather than computing approximately the entire $G'$.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,545 | Fingerprints of angulon instabilities in the spectra of matrix-isolated molecules | The formation of vortices is usually considered to be the main mechanism of
angular momentum disposal in superfluids. Recently, it was predicted that a
superfluid can acquire angular momentum via an alternative, microscopic route
-- namely, through interaction with rotating impurities, forming so-called
`angulon quasiparticles' [Phys. Rev. Lett. 114, 203001 (2015)]. The angulon
instabilities correspond to transfer of a small number of angular momentum
quanta from the impurity to the superfluid, as opposed to vortex instabilities,
where angular momentum is quantized in units of $\hbar$ per atom. Furthermore,
since conventional impurities (such as molecules) represent three-dimensional
(3D) rotors, the angular momentum transferred is intrinsically 3D as well, as
opposed to a merely planar rotation which is inherent to vortices. Herein we
show that the angulon theory can explain the anomalous broadening of the
spectroscopic lines observed for CH$_3$ and NH$_3$ molecules in superfluid
helium nanodroplets, thereby providing a fingerprint of the emerging angulon
instabilities in experiment.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,546 | Considering Multiple Uncertainties in Stochastic Security-Constrained Unit Commitment Using Point Estimation Method | Security-Constrained Unit Commitment (SCUC) is one of the most significant
problems in secure and optimal operation of modern electricity markets. New
sources of uncertainties such as wind speed volatility and price-sensitive
loads impose additional challenges to this large-scale problem. This paper
proposes a new Stochastic SCUC using point estimation method to model the power
system uncertainties more efficiently. Conventional scenario-based Stochastic
SCUC approaches consider the Mont Carlo method; which presents additional
computational burdens to this large-scale problem. In this paper we use point
estimation instead of scenario generating to detract computational burdens of
the problem. The proposed approach is implemented on a six-bus system and on a
modified IEEE 118-bus system with 94 uncertain variables. The efficacy of
proposed algorithm is confirmed, especially in the last case with notable
reduction in computational burden without considerable loss of precision.
| 0 | 1 | 1 | 0 | 0 | 0 |
16,547 | Gluing Delaunay ends to minimal n-noids using the DPW method | We construct constant mean curvature surfaces in euclidean space by gluing n
half Delaunay surfaces to a non-degenerate minimal n-noid, using the DPW
method.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,548 | Some Remarks on the Hyperkähler Reduction | We consider a hyperkähler reduction and describe it via frame bundles.
Tracing the connection through the various reductions, we recover the results
of Gocho and Nakajima. In addition, we show that the fibers of such a reduction
are necessarily totally geodesic. As an independent result, we describe
O'Neill's submersion tensors on principal bundles.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,549 | Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment | We introduce a novel approach to Maximum A Posteriori inference based on
discrete graphical models. By utilizing local Wasserstein distances for
coupling assignment measures across edges of the underlying graph, a given
discrete objective function is smoothly approximated and restricted to the
assignment manifold. A corresponding multiplicative update scheme combines in a
single process (i) geometric integration of the resulting Riemannian gradient
flow and (ii) rounding to integral solutions that represent valid labelings.
Throughout this process, local marginalization constraints known from the
established LP relaxation are satisfied, whereas the smooth geometric setting
results in rapidly converging iterations that can be carried out in parallel
for every edge.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,550 | Operator algebraic approach to inverse and stability theorems for amenable groups | We prove an inverse theorem for the Gowers $U^2$-norm for maps $G\to\mathcal
M$ from an countable, discrete, amenable group $G$ into a von Neumann algebra
$\mathcal M$ equipped with an ultraweakly lower semi-continuous, unitarily
invariant (semi-)norm $\Vert\cdot\Vert$. We use this result to prove a
stability result for unitary-valued $\varepsilon$-representations $G\to\mathcal
U(\mathcal M)$ with respect to $\Vert\cdot \Vert$.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,551 | Collective irregular dynamics in balanced networks of leaky integrate-and-fire neurons | We extensively explore networks of weakly unbalanced, leaky
integrate-and-fire (LIF) neurons for different coupling strength, connectivity,
and by varying the degree of refractoriness, as well as the delay in the spike
transmission. We find that the neural network does not only exhibit a
microscopic (single-neuron) stochastic-like evolution, but also a collective
irregular dynamics (CID). Our analysis is based on the computation of a
suitable order parameter, typically used to characterize synchronization
phenomena and on a detailed scaling analysis (i.e. simulations of different
network sizes). As a result, we can conclude that CID is a true thermodynamic
phase, intrinsically different from the standard asynchronous regime.
| 0 | 0 | 0 | 0 | 1 | 0 |
16,552 | Measurement of human activity using velocity GPS data obtained from mobile phones | Human movement is used as an indicator of human activity in modern society.
The velocity of moving humans is calculated based on position information
obtained from mobile phones. The level of human activity, as recorded by
velocity, varies throughout the day. Therefore, velocity can be used to
identify the intervals of highest and lowest activity. More specifically, we
obtained mobile-phone GPS data from the people around Shibuya station in Tokyo,
which has the highest population density in Japan. From these data, we observe
that velocity tends to consistently increase with the changes in social
activities. For example, during the earthquake in Kumamoto Prefecture in April
2016, the activity on that day was much lower than usual. In this research, we
focus on natural disasters such as earthquakes owing to their significant
effects on human activities in developed countries like Japan. In the event of
a natural disaster in another developed country, considering the change in
human behavior at the time of the disaster (e.g., the 2016 Kumamoto Great
Earthquake) from the viewpoint of velocity allows us to improve our planning
for mitigation measures. Thus, we analyze the changes in human activity through
velocity calculations in Shibuya, Tokyo, and compare times of disasters with
normal times.
| 1 | 1 | 0 | 0 | 0 | 0 |
16,553 | Concurrent Segmentation and Localization for Tracking of Surgical Instruments | Real-time instrument tracking is a crucial requirement for various
computer-assisted interventions. In order to overcome problems such as specular
reflections and motion blur, we propose a novel method that takes advantage of
the interdependency between localization and segmentation of the surgical tool.
In particular, we reformulate the 2D instrument pose estimation as heatmap
regression and thereby enable a concurrent, robust and near real-time
regression of both tasks via deep learning. As demonstrated by our experimental
results, this modeling leads to a significantly improved performance than
directly regressing the tool position and allows our method to outperform the
state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis
Challenge 2015.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,554 | Bobtail: A Proof-of-Work Target that Minimizes Blockchain Mining Variance (Draft) | Blockchain systems are designed to produce blocks at a constant average rate.
The most popular systems currently employ a Proof of Work (PoW) algorithm as a
means of creating these blocks. Bitcoin produces, on average, one block every
10 minutes. An unfortunate limitation of all deployed PoW blockchain systems is
that the time between blocks has high variance. For example, 5% of the time,
Bitcoin's inter-block time is at least 40 minutes. This variance impedes the
consistent flow of validated transactions through the system. We propose an
alternative process for PoW-based block discovery that results in an
inter-block time with significantly lower variance. Our algorithm, called
Bobtail, generalizes the current algorithm by comparing the mean of the k
lowest order statistics to a target. We show that the variance of inter-block
times decreases as k increases. If our approach were applied to Bitcoin, about
80% of blocks would be found within 7 to 12 minutes, and nearly every block
would be found within 5 to 18 minutes; the average inter-block time would
remain at 10 minutes. Further, we show that low-variance mining significantly
thwarts doublespend and selfish mining attacks. For Bitcoin and Ethereum
currently (k=1), an attacker with 40% of the mining power will succeed with 30%
probability when the merchant sets up an embargo of 8 blocks; however, when
k>=20, the probability of success falls to less than 1%. Similarly, for Bitcoin
and Ethereum currently, a selfish miner with 40% of the mining power will claim
about 66% of blocks; however, when k>=5, the same miner will find that selfish
mining is less successful than honest mining. The cost of our approach is a
larger block header.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,555 | Mobility Edges in 1D Bichromatic Incommensurate Potentials | We theoretically study a one-dimensional (1D) mutually incommensurate
bichromatic lattice system which has been implemented in ultracold atoms to
study quantum localization. It has been universally believed that the
tight-binding version of this bichromatic incommensurate system is represented
by the well-known Aubry-Andre model. Here we establish that this belief is
incorrect and that the Aubry-Andre model description, which applies only in the
extreme tight-binding limit of very deep primary lattice potential, generically
breaks down near the localization transition due to the unavoidable appearance
of single-particle mobility edges (SPME). In fact, we show that the 1D
bichromatic incommensurate potential system manifests generic mobility edges
which disappear in the tight-binding limit, leading to the well-studied
Aubry-Andre physics. We carry out an extensive study of the localization
properties of the 1D incommensurate optical lattice without making any
tight-binding approximation. We find that, for the full lattice system, an
intermediate phase between completely localized and completely delocalized
regions appears due to the existence of the SPME, making the system
qualitatively distinct from the Aubry-Andre prediction. Using the Wegner flow
approach, we show that the SPME in the real lattice system can be attributed to
significant corrections of higher-order harmonics in the lattice potential
which are absent in the strict tight-binding limit. We calculate the dynamical
consequences of the intermediate phase in detail to guide future experimental
investigations for the observation of 1D SPME and the associated intermediate
phase. We consider effects of interaction numerically, and conjecture the
stability of SPME to weak interaction effects, thus leading to the exciting
possibility of an experimentally viable nonergodic extended phase in
interacting 1D optical lattices.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,556 | Privacy Preserving Identification Using Sparse Approximation with Ambiguization | In this paper, we consider a privacy preserving encoding framework for
identification applications covering biometrics, physical object security and
the Internet of Things (IoT). The proposed framework is based on a sparsifying
transform, which consists of a trained linear map, an element-wise
nonlinearity, and privacy amplification. The sparsifying transform and privacy
amplification are not symmetric for the data owner and data user. We
demonstrate that the proposed approach is closely related to sparse ternary
codes (STC), a recent information-theoretic concept proposed for fast
approximate nearest neighbor (ANN) search in high dimensional feature spaces
that being machine learning in nature also offers significant benefits in
comparison to sparse approximation and binary embedding approaches. We
demonstrate that the privacy of the database outsourced to a server as well as
the privacy of the data user are preserved at a low computational cost, storage
and communication burdens.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,557 | Manifold Learning Using Kernel Density Estimation and Local Principal Components Analysis | We consider the problem of recovering a $d-$dimensional manifold $\mathcal{M}
\subset \mathbb{R}^n$ when provided with noiseless samples from $\mathcal{M}$.
There are many algorithms (e.g., Isomap) that are used in practice to fit
manifolds and thus reduce the dimensionality of a given data set. Ideally, the
estimate $\mathcal{M}_\mathrm{put}$ of $\mathcal{M}$ should be an actual
manifold of a certain smoothness; furthermore, $\mathcal{M}_\mathrm{put}$
should be arbitrarily close to $\mathcal{M}$ in Hausdorff distance given a
large enough sample. Generally speaking, existing manifold learning algorithms
do not meet these criteria. Fefferman, Mitter, and Narayanan (2016) have
developed an algorithm whose output is provably a manifold. The key idea is to
define an approximate squared-distance function (asdf) to $\mathcal{M}$. Then,
$\mathcal{M}_\mathrm{put}$ is given by the set of points where the gradient of
the asdf is orthogonal to the subspace spanned by the largest $n - d$
eigenvectors of the Hessian of the asdf. As long as the asdf meets certain
regularity conditions, $\mathcal{M}_\mathrm{put}$ is a manifold that is
arbitrarily close in Hausdorff distance to $\mathcal{M}$. In this paper, we
define two asdfs that can be calculated from the data and show that they meet
the required regularity conditions. The first asdf is based on kernel density
estimation, and the second is based on estimation of tangent spaces using local
principal components analysis.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,558 | Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach | The RGB-D camera maintains a limited range for working and is hard to
accurately measure the depth information in a far distance. Besides, the RGB-D
camera will easily be influenced by strong lighting and other external factors,
which will lead to a poor accuracy on the acquired environmental depth
information. Recently, deep learning technologies have achieved great success
in the visual SLAM area, which can directly learn high-level features from the
visual inputs and improve the estimation accuracy of the depth information.
Therefore, deep learning technologies maintain the potential to extend the
source of the depth information and improve the performance of the SLAM system.
However, the existing deep learning-based methods are mainly supervised and
require a large amount of ground-truth depth data, which is hard to acquire
because of the realistic constraints. In this paper, we first present an
unsupervised learning framework, which not only uses image reconstruction for
supervising but also exploits the pose estimation method to enhance the
supervised signal and add training constraints for the task of monocular depth
and camera motion estimation. Furthermore, we successfully exploit our
unsupervised learning framework to assist the traditional ORB-SLAM system when
the initialization module of ORB-SLAM method could not match enough features.
Qualitative and quantitative experiments have shown that our unsupervised
learning framework performs the depth estimation task comparable to the
supervised methods and outperforms the previous state-of-the-art approach by
$13.5\%$ on KITTI dataset. Besides, our unsupervised learning framework could
significantly accelerate the initialization process of ORB-SLAM system and
effectively improve the accuracy on environmental mapping in strong lighting
and weak texture scenes.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,559 | On the Properties of the Power Systems Nodal Admittance Matrix | This letter provides conditions determining the rank of the nodal admittance
matrix, and arbitrary block partitions of it, for connected AC power networks
with complex admittances. Furthermore, some implications of these properties
concerning Kron Reduction and Hybrid Network Parameters are outlined.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,560 | Isolation and connectivity in random geometric graphs with self-similar intensity measures | Random geometric graphs consist of randomly distributed nodes (points), with
pairs of nodes within a given mutual distance linked. In the usual model the
distribution of nodes is uniform on a square, and in the limit of infinitely
many nodes and shrinking linking range, the number of isolated nodes is Poisson
distributed, and the probability of no isolated nodes is equal to the
probability the whole graph is connected. Here we examine these properties for
several self-similar node distributions, including smooth and fractal, uniform
and nonuniform, and finitely ramified or otherwise. We show that nonuniformity
can break the Poisson distribution property, but it strengthens the link
between isolation and connectivity. It also stretches out the connectivity
transition. Finite ramification is another mechanism for lack of connectivity.
The same considerations apply to fractal distributions as smooth, with some
technical differences in evaluation of the integrals and analytical arguments.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,561 | Multi-SpaM: a Maximum-Likelihood approach to Phylogeny reconstruction based on Multiple Spaced-Word Matches | Motivation: Word-based or `alignment-free' methods for phylogeny
reconstruction are much faster than traditional approaches, but they are
generally less accurate. Most of these methods calculate pairwise distances for
a set of input sequences, for example from word frequencies, from so-called
spaced-word matches or from the average length of common substrings.
Results: In this paper, we propose the first word-based approach to tree
reconstruction that is based on multiple sequence comparison and Maximum
Likelihood. Our algorithm first samples small, gap-free alignments involving
four taxa each. For each of these alignments, it then calculates a quartet tree
and, finally, the program Quartet MaxCut is used to infer a super tree topology
for the full set of input taxa from the calculated quartet trees. Experimental
results show that trees calculated with our approach are of high quality.
Availability: The source code of the program is available at
this https URL
Contact: [email protected]
| 0 | 0 | 0 | 0 | 1 | 0 |
16,562 | Zermelo deformation of Finsler metrics by Killing vector fields | We show how geodesics, Jacobi vector fields and flag curvature of a Finsler
metric behave under Zermelo deformation with respect to a Killing vector field.
We also show that Zermelo deformation with respect to a Killing vector field of
a locally symmetric Finsler metric is also locally symmetric.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,563 | Robust estimation of mixing measures in finite mixture models | In finite mixture models, apart from underlying mixing measure, true kernel
density function of each subpopulation in the data is, in many scenarios,
unknown. Perhaps the most popular approach is to choose some kernel functions
that we empirically believe our data are generated from and use these kernels
to fit our models. Nevertheless, as long as the chosen kernel and the true
kernel are different, statistical inference of mixing measure under this
setting will be highly unstable. To overcome this challenge, we propose
flexible and efficient robust estimators of the mixing measure in these models,
which are inspired by the idea of minimum Hellinger distance estimator, model
selection criteria, and superefficiency phenomenon. We demonstrate that our
estimators consistently recover the true number of components and achieve the
optimal convergence rates of parameter estimation under both the well- and
mis-specified kernel settings for any fixed bandwidth. These desirable
asymptotic properties are illustrated via careful simulation studies with both
synthetic and real data.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,564 | Phase Noise and Jitter in Digital Electronics | This article explains phase noise, jitter, and some slower phenomena in
digital integrated circuits, focusing on high-demanding, noise-critical
applications. We introduce the concept of phase type and time type phase noise.
The rules for scaling the noise with frequency are chiefly determined by the
spectral properties of these two basic types, by the aliasing phenomenon, and
by the input and output circuits. Then, we discuss the parameter extraction
from experimental data and we report on the measured phase noise in some
selected devices of different node size and complexity. We observed flicker
noise between -80 and -130 dBrad^2/Hz at 1 Hz offset, and white noise down to
-165 dBrad^2/Hz in some fortunate cases and using the appropriate tricks. It
turns out that flicker noise is proportional to the reciprocal of the volume of
the transistor. This unpleasant conclusion is supported by a gedanken
experiment. Further experiments provide understanding on: (i) the interplay
between noise sources in the internal PLL, often present in FPGAs; (ii) the
chattering phenomenon, which consists in multiple bouncing at transitions; and
(iii) thermal time constants, and their effect on phase wander and on the Allan
variance.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,565 | Advanced Soccer Skills and Team Play of RoboCup 2017 TeenSize Winner NimbRo | In order to pursue the vision of the RoboCup Humanoid League of beating the
soccer world champion by 2050, new rules and competitions are added or modified
each year fostering novel technological advances. In 2017, the number of
players in the TeenSize class soccer games was increase to 3 vs. 3, which
allowed for more team play strategies. Improvements in individual skills were
also demanded through a set of technical challenges. This paper presents the
latest individual skills and team play developments used in RoboCup 2017 that
lead our team Nimbro winning the 2017 TeenSize soccer tournament, the technical
challenges, and the drop-in games.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,566 | Phase partitioning in a novel near equi-atomic AlCuFeMn alloy | A novel low cost, near equi-atomic alloy comprising of Al, Cu, Fe and Mn is
synthesized using arc-melting technique. The cast alloy possesses a dendritic
microstructure where the dendrites consist of disordered FCC and ordered FCC
phases. The inter-dendritic region is comprised of ordered FCC phase and
spinodally decomposed BCC phases. A Cu segregation is observed in the
inter-dendritic region while dendritic region is rich in Fe. The bulk hardness
of the alloy is ~ 380 HV, indicating significant yield strength.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,567 | Overview of Project 8 and Progress Towards Tritium Operation | Project 8 is a tritium endpoint neutrino mass experiment utilizing a phased
program to achieve sensitivity to the range of neutrino masses allowed by the
inverted mass hierarchy. The Cyclotron Radiation Emission Spectroscopy (CRES)
technique is employed to measure the differential energy spectrum of decay
electrons with high precision. We present an overview of the Project 8
experimental program, from first demonstration of the CRES technique to
ultimate sensitivity with an atomic tritium source. We highlight recent
advances in preparation for the first measurement of the continuous tritium
spectrum with CRES.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,568 | Proper efficiency and cone efficiency | In this report, two general concepts for proper efficiency in vector
optimization are studied. Properly efficient elements can be defined as
minimizers of functionals with certain monotonicity properties or as weakly
efficient elements with respect to sets that contain the domination set.
Interdependencies between both concepts are proved in topological vector spaces
by means of Gerstewitz functionals. The investigation includes proper
efficiency notions introduced by Henig and by Nehse and Iwanow. In contrary to
Henig's notion, proper efficiency by Nehse and Iwanow is defined as efficiency
with respect to certain convex sets which are not necessarily cones. For the
finite-dimensional case, we turn to Geoffrion's proper efficiency as a special
case of Henig's proper efficiency. It is characterized as efficiency with
regard to subclasses of the set of polyhedral cones. Conditions for the
existence of Geoffrion's properly efficient points are proved. For closed
feasible point sets, Geoffrion's properly efficient point set is empty or
coincides with that of Nehse and Iwanow. Properly efficient elements by Nehse
and Iwanow are the minimizers of continuous convex functionals with certain
monotonicity properties. Henig's proper efficiency can be described by means of
minimizers of continuous sublinear functionals with certain monotonicity
properties.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,569 | Sequential Skip Prediction with Few-shot in Streamed Music Contents | This paper provides an outline of the algorithms submitted for the WSDM Cup
2019 Spotify Sequential Skip Prediction Challenge (team name: mimbres). In the
challenge, complete information including acoustic features and user
interaction logs for the first half of a listening session is provided. Our
goal is to predict whether the individual tracks in the second half of the
session will be skipped or not, only given acoustic features. We proposed two
different kinds of algorithms that were based on metric learning and sequence
learning. The experimental results showed that the sequence learning approach
performed significantly better than the metric learning approach. Moreover, we
conducted additional experiments to find that significant performance gain can
be achieved using complete user log information.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,570 | Genetic interactions from first principles | We derive a general statistical model of interactions, starting from
probabilistic principles and elementary requirements. Prevailing interaction
models in biomedical researches diverge both mathematically and practically. In
particular, genetic interaction inquiries are formulated without an obvious
mathematical unity. Our model reveals theoretical properties unnoticed so far,
particularly valuable for genetic interaction mapping, where mechanistic
details are mostly unknown, distribution of gene variants differ between
populations, and genetic susceptibilities are spuriously propagated by linkage
disequilibrium. When applied to data of the largest interaction mapping
experiment on Saccharomyces Cerevisiae to date, our results imply less aversion
to positive interactions, detection of well-documented hubs and partial
remapping of functional regions of the currently known genetic interaction
landscape. Assessment of divergent annotations across functional categories
further suggests that positive interactions have a more important role on
ribosome biogenesis than previously realized. The unity of arguments elaborated
here enables the analysis of dissimilar interaction models and experimental
data with a common framework.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,571 | On the Log Partition Function of Ising Model on Stochastic Block Model | A sparse stochastic block model (SBM) with two communities is defined by the
community probability $\pi_0,\pi_1$, and the connection probability between
communities $a,b\in\{0,1\}$, namely $q_{ab} = \frac{\alpha_{ab}}{n}$. When
$q_{ab}$ is constant in $a,b$, the random graph is simply the
Erdős-Rény random graph. We evaluate the log partition function of the
Ising model on sparse SBM with two communities.
As an application, we give consistent parameter estimation of the sparse SBM
with two communities in a special case. More specifically, let $d_0,d_1$ be the
average degree of the two communities, i.e.,
$d_0\overset{def}{=}\pi_0\alpha_{00}+\pi_1\alpha_{01},d_1\overset{def}{=}\pi_0\alpha_{10}+\pi_1\alpha_{11}$.
We focus on the regime $d_0=d_1$ (the regime $d_0\ne d_1$ is trivial). In this
regime, there exists $d,\lambda$ and $r\geq 0$ with $\pi_0=\frac{1}{1+r},
\pi_1=\frac{r}{1+r}$, $\alpha_{00}=d(1+r\lambda), \alpha_{01}=\alpha_{10} =
d(1-\lambda), \alpha_{11} = d(1+\frac{\lambda}{r})$. We give a consistent
estimator of $r$ when $\lambda<0$. The estimator of $\lambda$ given by
\citep{mossel2015reconstruction} is valid in the general situation. We also
provide a random clustering algorithm which does not require knowledge of
parameters and which is positively correlated with the true community label
when $\lambda<0$.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,572 | Radial transonic shock solutions of Euler-Poisson system in convergent nozzles | Given constant data of density $\rho_0$, velocity $-u_0{\bf e}_r$, pressure
$p_0$ and electric force $-E_0{\bf e}_r$ for supersonic flow at the entrance,
and constant pressure $p_{\rm ex}$ for subsonic flow at the exit, we prove that
Euler-Poisson system admits a unique transonic shock solution in a two
dimensional convergent nozzle, provided that $u_0>0$, $E_0>0$, and that $E_0$
is sufficiently large depending on $(\rho_0, u_0, p_0)$ and the length of the
nozzle.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,573 | X-rays from Green Pea Analogs | X-ray observations of two metal-deficient luminous compact galaxies (LCG)
(SHOC~486 and SDSS J084220.94+115000.2) with properties similar to the
so-called Green Pea galaxies were obtained using the {\emph{Chandra X-ray
Observatory}}. Green Pea galaxies are relatively small, compact (a few kpc
across) galaxies that get their green color from strong [OIII]$\lambda$5007\AA\
emission, an indicator of intense, recent star formation. These two galaxies
were predicted to have the highest observed count rates, using the X-ray
luminosity -- star formation rate ($L_X$--SFR) relation for X-ray binaries,
from a statistically complete sample drawn from optical criteria. We determine
the X-ray luminosity relative to star-formation rate and metallicity for these
two galaxies. Neither exhibit any evidence of active galactic nuclei and we
suspect the X-ray emission originates from unresolved populations of high mass
X-ray binaries. We discuss the $L_X$--SFR--metallicity plane for star-forming
galaxies and show that the two LCGs are consistent with the prediction of this
relation. This is the first detection of Green Pea analogs in X-rays.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,574 | A deeper view of the CoRoT-9 planetary system. A small non-zero eccentricity for CoRoT-9b likely generated by planet-planet scattering | CoRoT-9b is one of the rare long-period (P=95.3 days) transiting giant
planets with a measured mass known to date. We present a new analysis of the
CoRoT-9 system based on five years of radial-velocity (RV) monitoring with
HARPS and three new space-based transits observed with CoRoT and Spitzer.
Combining our new data with already published measurements we redetermine the
CoRoT-9 system parameters and find good agreement with the published values. We
uncover a higher significance for the small but non-zero eccentricity of
CoRoT-9b ($e=0.133^{+0.042}_{-0.037}$) and find no evidence for additional
planets in the system. We use simulations of planet-planet scattering to show
that the eccentricity of CoRoT-9b may have been generated by an instability in
which a $\sim 50~M_\oplus$ planet was ejected from the system. This scattering
would not have produced a spin-orbit misalignment, so we predict that CoRoT-9b
orbit should lie within a few degrees of the initial plane of the
protoplanetary disk. As a consequence, any significant stellar obliquity would
indicate that the disk was primordially tilted.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,575 | Wave propagation characteristics of Parareal | The paper derives and analyses the (semi-)discrete dispersion relation of the
Parareal parallel-in-time integration method. It investigates Parareal's wave
propagation characteristics with the aim to better understand what causes the
well documented stability problems for hyperbolic equations. The analysis shows
that the instability is caused by convergence of the amplification factor to
the exact value from above for medium to high wave numbers. Phase errors in the
coarse propagator are identified as the culprit, which suggests that
specifically tailored coarse level methods could provide a remedy.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,576 | Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets | Deep learning models (aka Deep Neural Networks) have revolutionized many
fields including computer vision, natural language processing, speech
recognition, and is being increasingly used in clinical healthcare
applications. However, few works exist which have benchmarked the performance
of the deep learning models with respect to the state-of-the-art machine
learning models and prognostic scoring systems on publicly available healthcare
datasets. In this paper, we present the benchmarking results for several
clinical prediction tasks such as mortality prediction, length of stay
prediction, and ICD-9 code group prediction using Deep Learning models,
ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA
scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III)
(v1.4) publicly available dataset, which includes all patients admitted to an
ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the
benchmarking tasks. Our results show that deep learning models consistently
outperform all the other approaches especially when the `raw' clinical time
series data is used as input features to the models.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,577 | Modeling of Persistent Homology | Topological Data Analysis (TDA) is a novel statistical technique,
particularly powerful for the analysis of large and high dimensional data sets.
Much of TDA is based on the tool of persistent homology, represented visually
via persistence diagrams. In an earlier paper we proposed a parametric
representation for the probability distributions of persistence diagrams, and
based on it provided a method for their replication. Since the typical
situation for big data is that only one persistence diagram is available, these
replications allow for conventional statistical inference, which, by its very
nature, requires some form of replication. In the current paper we continue
this analysis, and further develop its practical statistical methodology, by
investigating a wider class of examples than treated previously.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,578 | Auto-Encoding Sequential Monte Carlo | We build on auto-encoding sequential Monte Carlo (AESMC): a method for model
and proposal learning based on maximizing the lower bound to the log marginal
likelihood in a broad family of structured probabilistic models. Our approach
relies on the efficiency of sequential Monte Carlo (SMC) for performing
inference in structured probabilistic models and the flexibility of deep neural
networks to model complex conditional probability distributions. We develop
additional theoretical insights and introduce a new training procedure which
improves both model and proposal learning. We demonstrate that our approach
provides a fast, easy-to-implement and scalable means for simultaneous model
learning and proposal adaptation in deep generative models.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,579 | Einstein's accelerated reference systems and Fermi-Walker coordinates | We show that the uniformly accelerated reference systems proposed by Einstein
when introducing acceleration in the theory of relativity are Fermi-Walker
coordinate systems. We then consider more general accelerated motions and, on
the one hand we obtain Thomas precession and, on the other, we prove that the
only accelerated reference systems that at any time admit an instantaneously
comoving inertial system belong necessarily to the Fermi-Walker class.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,580 | Premise Selection for Theorem Proving by Deep Graph Embedding | We propose a deep learning-based approach to the problem of premise
selection: selecting mathematical statements relevant for proving a given
conjecture. We represent a higher-order logic formula as a graph that is
invariant to variable renaming but still fully preserves syntactic and semantic
information. We then embed the graph into a vector via a novel embedding method
that preserves the information of edge ordering. Our approach achieves
state-of-the-art results on the HolStep dataset, improving the classification
accuracy from 83% to 90.3%.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,581 | Goldbach Representations in Arithmetic Progressions and zeros of Dirichlet L-functions | Assuming a conjecture on distinct zeros of Dirichlet L-functions we get
asymptotic results on the average number of representations of an integer as
the sum of two primes in arithmetic progression. On the other hand the
existence of good error terms gives information on the the location of zeros of
L-functions and possible Siegel zeros. Similar results are obtained for an
integer in a congruence class expressed as the sum of two primes.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,582 | Estimable group effects for strongly correlated variables in linear models | It is well known that parameters for strongly correlated predictor variables
in a linear model cannot be accurately estimated. We look for linear
combinations of these parameters that can be. Under a uniform model, we find
such linear combinations in a neighborhood of a simple variability weighted
average of these parameters. Surprisingly, this variability weighted average is
more accurately estimated when the variables are more strongly correlated, and
it is the only linear combination with this property. It can be easily computed
for strongly correlated predictor variables in all linear models and has
applications in inference and estimation concerning parameters of such
variables.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,583 | Online Structure Learning for Sum-Product Networks with Gaussian Leaves | Sum-product networks have recently emerged as an attractive representation
due to their dual view as a special type of deep neural network with clear
semantics and a special type of probabilistic graphical model for which
inference is always tractable. Those properties follow from some conditions
(i.e., completeness and decomposability) that must be respected by the
structure of the network. As a result, it is not easy to specify a valid
sum-product network by hand and therefore structure learning techniques are
typically used in practice. This paper describes the first online structure
learning technique for continuous SPNs with Gaussian leaves. We also introduce
an accompanying new parameter learning technique.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,584 | Rapid Design of Wide-Area Heterogeneous Electromagnetic Metasurfaces beyond the Unit-Cell Approximation | We propose a novel numerical approach for the optimal design of wide-area
heterogeneous electromagnetic metasurfaces beyond the conventionally used
unit-cell approximation. The proposed method exploits the combination of
Rigorous Coupled Wave Analysis (RCWA) and global optimization techniques (two
evolutionary algorithms namely the Genetic Algorithm (GA) and a modified form
of the Artificial Bee Colony (ABC with memetic search phase method) are
considered). As a specific example, we consider the design of beam deflectors
using all-dielectric nanoantennae for operation in the visible wavelength
region; beam deflectors can serve as building blocks for other more complicated
devices like metalenses. Compared to previous reports using local optimization
approaches our approach improves device efficiency; transmission efficiency is
especially improved for wide deflection angle beam deflectors. The ABC method
with memetic search phase is also an improvement over the more commonly used GA
as it reaches similar efficiency levels with upto 35% reduction in computation
time. The method described here is of interest for the rapid design of a wide
variety of electromagnetic metasurfaces irrespective of their operational
wavelength.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,585 | Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks | Autonomous driving presents one of the largest problems that the robotics and
artificial intelligence communities are facing at the moment, both in terms of
difficulty and potential societal impact. Self-driving vehicles (SDVs) are
expected to prevent road accidents and save millions of lives while improving
the livelihood and life quality of many more. However, despite large interest
and a number of industry players working in the autonomous domain, there is
still more to be done in order to develop a system capable of operating at a
level comparable to best human drivers. One reason for this is high uncertainty
of traffic behavior and large number of situations that an SDV may encounter on
the roads, making it very difficult to create a fully generalizable system. To
ensure safe and efficient operations, an autonomous vehicle is required to
account for this uncertainty and to anticipate a multitude of possible
behaviors of traffic actors in its surrounding. In this work, we address this
critical problem and present a method to predict multiple possible trajectories
of actors while also estimating their probabilities. The method encodes each
actor's surrounding context into a raster image, used as input by deep
convolutional networks to automatically derive relevant features for the task.
Following extensive offline evaluation and comparison to state-of-the-art
baselines, as well as closed course tests, the method was successfully deployed
to a fleet of SDVs.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,586 | Phase-Resolved Two-Dimensional Spectroscopy of Electronic Wavepackets by Laser-Induced XUV Free Induction Decay | We present a novel time- and phase-resolved, background-free scheme to study
the extreme ultraviolet dipole emission of a bound electronic wavepacket,
without the use of any extreme ultraviolet exciting pulse. Using multiphoton
transitions, we populate a superposition of quantum states which coherently
emit extreme ultraviolet radiation through free induction decay. This emission
is probed and controlled, both in amplitude and phase, by a time-delayed
infrared femtosecond pulse. We directly measure the laser-induced dephasing of
the emission by using a simple heterodyne detection scheme based on two-source
interferometry. This technique provides rich information about the interplay
between the laser field and the Coulombic potential on the excited electron
dynamics. Its background-free nature enables us to use a large range of gas
pressures and to reveal the influence of collisions in the relaxation process.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,587 | Optimized Spatial Partitioning via Minimal Swarm Intelligence | Optimized spatial partitioning algorithms are the corner stone of many
successful experimental designs and statistical methods. Of these algorithms,
the Centroidal Voronoi Tessellation (CVT) is the most widely utilized. CVT
based methods require global knowledge of spatial boundaries, do not readily
allow for weighted regions, have challenging implementations, and are
inefficiently extended to high dimensional spaces. We describe two simple
partitioning schemes based on nearest and next nearest neighbor locations which
easily incorporate these features at the slight expense of optimal placement.
Several novel qualitative techniques which assess these partitioning schemes
are also included. The feasibility of autonomous uninformed sensor networks
utilizing these algorithms are considered. Some improvements in particle swarm
optimizer results on multimodal test functions from partitioned initial
positions in two space are also illustrated. Pseudo code for all of the novel
algorithms depicted here-in is available in the supplementary information of
this manuscript.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,588 | Learning Correspondence Structures for Person Re-identification | This paper addresses the problem of handling spatial misalignments due to
camera-view changes or human-pose variations in person re-identification. We
first introduce a boosting-based approach to learn a correspondence structure
which indicates the patch-wise matching probabilities between images from a
target camera pair. The learned correspondence structure can not only capture
the spatial correspondence pattern between cameras but also handle the
viewpoint or human-pose variation in individual images. We further introduce a
global constraint-based matching process. It integrates a global matching
constraint over the learned correspondence structure to exclude cross-view
misalignments during the image patch matching process, hence achieving a more
reliable matching score between images. Finally, we also extend our approach by
introducing a multi-structure scheme, which learns a set of local
correspondence structures to capture the spatial correspondence sub-patterns
between a camera pair, so as to handle the spatial misalignments between
individual images in a more precise way. Experimental results on various
datasets demonstrate the effectiveness of our approach.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,589 | Inner Product and Set Disjointness: Beyond Logarithmically Many Parties | A basic goal in complexity theory is to understand the communication
complexity of number-on-the-forehead problems
$f\colon(\{0,1\}^n)^{k}\to\{0,1\}$ with $k\gg\log n$ parties. We study the
problems of inner product and set disjointness and determine their randomized
communication complexity for every $k\geq\log n$, showing in both cases that
$\Theta(1+\lceil\log n\rceil/\log\lceil1+k/\log n\rceil)$ bits are necessary
and sufficient. In particular, these problems admit constant-cost protocols if
and only if the number of parties is $k\geq n^{\epsilon}$ for some constant
$\epsilon>0.$
| 1 | 0 | 0 | 0 | 0 | 0 |
16,590 | Topological classification of time-asymmetry in unitary quantum processes | Effective gauge fields have allowed the emulation of matter under strong
magnetic fields leading to the realization of Harper-Hofstadter, Haldane
models, and led to demonstrations of one-way waveguides and topologically
protected edge states. Central to these discoveries is the chirality induced by
time-symmetry breaking. Due to the discovery of quantum search algorithms based
on walks on graphs, recent work has discovered new implications the effect of
time-reversal symmetry breaking has on the transport of quantum states and has
brought with it a host of new experimental implementations. We provide a full
classification of the unitary operators defining quantum processes which break
time-reversal symmetry in their induced transition properties between basis
elements in a preferred site-basis. Our results are furthermore proven in terms
of the geometry of the corresponding Hamiltonian support graph and hence
provide a topological classification. A quantum process of this type is
necessarily time-symmetric for any choice of time-independent Hamiltonian if
and only if the underlying support graph is bipartite. Moreover, for
non-bipartite support, there exists a time-independent Hamiltonian with
necessarily complex edge weights that induces time-asymmetric transition
probabilities between edge(s). We further prove that certain bipartite graphs
give rise to transition probability suppression, but not broken time-reversal
symmetry. These results fill an important missing gap in understanding the role
this omnipresent effect has in quantum physics. Furthermore, through our
development of a general framework, along the way to our results we completely
characterize gauge potentials on combinatorial graphs.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,591 | A Light Modality for Recursion | We investigate the interplay between a modality for controlling the behaviour
of recursive functional programs on infinite structures which are completely
silent in the syntax. The latter means that programs do not contain "marks"
showing the application of the introduction and elimination rules for the
modality. This shifts the burden of controlling recursion from the programmer
to the compiler. To do this, we introduce a typed lambda calculus a la Curry
with a silent modality and guarded recursive types. The typing discipline
guarantees normalisation and can be transformed into an algorithm which infers
the type of a program.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,592 | Counting the number of metastable states in the modularity landscape: Algorithmic detectability limit of greedy algorithms in community detection | Modularity maximization using greedy algorithms continues to be a popular
approach toward community detection in graphs, even after various better
forming algorithms have been proposed. Apart from its clear mechanism and ease
of implementation, this approach is persistently popular because, presumably,
its risk of algorithmic failure is not well understood. This Rapid
Communication provides insight into this issue by estimating the algorithmic
performance limit of modularity maximization. This is achieved by counting the
number of metastable states under a local update rule. Our results offer a
quantitative insight into the level of sparsity at which a greedy algorithm
typically fails.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,593 | Infinite rank surface cluster algebras | We generalise surface cluster algebras to the case of infinite surfaces where
the surface contains finitely many accumulation points of boundary marked
points. To connect different triangulations of an infinite surface, we consider
infinite mutation sequences.
We show transitivity of infinite mutation sequences on triangulations of an
infinite surface and examine different types of mutation sequences. Moreover,
we use a hyperbolic structure on an infinite surface to extend the notion of
surface cluster algebras to infinite rank by giving cluster variables as lambda
lengths of arcs. Furthermore, we study the structural properties of infinite
rank surface cluster algebras in combinatorial terms, namely we extend "snake
graph combinatorics" to give an expansion formula for cluster variables. We
also show skein relations for infinite rank surface cluster algebras.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,594 | Machine learning prediction errors better than DFT accuracy | We investigate the impact of choosing regressors and molecular
representations for the construction of fast machine learning (ML) models of
thirteen electronic ground-state properties of organic molecules. The
performance of each regressor/representation/property combination is assessed
using learning curves which report out-of-sample errors as a function of
training set size with up to $\sim$117k distinct molecules. Molecular
structures and properties at hybrid density functional theory (DFT) level of
theory used for training and testing come from the QM9 database [Ramakrishnan
et al, {\em Scientific Data} {\bf 1} 140022 (2014)] and include dipole moment,
polarizability, HOMO/LUMO energies and gap, electronic spatial extent, zero
point vibrational energy, enthalpies and free energies of atomization, heat
capacity and the highest fundamental vibrational frequency. Various
representations from the literature have been studied (Coulomb matrix, bag of
bonds, BAML and ECFP4, molecular graphs (MG)), as well as newly developed
distribution based variants including histograms of distances (HD), and angles
(HDA/MARAD), and dihedrals (HDAD). Regressors include linear models (Bayesian
ridge regression (BR) and linear regression with elastic net regularization
(EN)), random forest (RF), kernel ridge regression (KRR) and two types of
neural net works, graph convolutions (GC) and gated graph networks (GG). We
present numerical evidence that ML model predictions deviate from DFT less than
DFT deviates from experiment for all properties. Furthermore, our out-of-sample
prediction errors with respect to hybrid DFT reference are on par with, or
close to, chemical accuracy. Our findings suggest that ML models could be more
accurate than hybrid DFT if explicitly electron correlated quantum (or
experimental) data was available.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,595 | Invariant tori for the Nosé Thermostat near the High-Temperature Limit | Let H(q,p) = p^2/2 + V(q) be a 1-degree of freedom mechanical Hamiltonian
with a C^n periodic potential V where n>4. The Nosé-thermostated system
associated to H is shown to have invariant tori near the infinite temperature
limit. This is shown to be true for all thermostats similar to Nosé's. These
results complement the result of Legoll, Luskin and Moeckel who proved the
existence of such tori near the decoupling limit.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,596 | Dyadic Green's function formalism for photo-induced forces in tip-sample nanojunctions | A comprehensive theoretical analysis of photo-induced forces in an
illuminated nanojunction, formed between an atomic force microscopy tip and a
sample, is presented. The formalism is valid within the dipolar approximation
and includes multiple scattering effects between the tip, sample and a planar
substrate through a dyadic Green's function approach. This physically intuitive
description allows a detailed look at the quantitative contribution of multiple
scattering effects to the measured photo-induced force, effects that are
typically unaccounted for in simpler analytical models. Our findings show that
the presence of the planar substrate and anisotropy of the tip have a
substantial effect on the magnitude and the spectral response of the
photo-induced force exerted on the tip. Unlike previous models, our
calculations predict photo-induced forces that are within range of
experimentally measured values in photo-induced force microscopy (PiFM)
experiments.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,597 | Semi-supervised Embedding in Attributed Networks with Outliers | In this paper, we propose a novel framework, called Semi-supervised Embedding
in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
representation that systematically captures the topological proximity,
attribute affinity and label similarity of vertices in a partially labeled
attributed network (PLAN). Our method is designed to work in both transductive
and inductive settings while explicitly alleviating noise effects from
outliers. Experimental results on various datasets drawn from the web, text and
image domains demonstrate the advantages of SEANO over state-of-the-art methods
in semi-supervised classification under transductive as well as inductive
settings. We also show that a subset of parameters in SEANO is interpretable as
outlier score and can significantly outperform baseline methods when applied
for detecting network outliers. Finally, we present the use of SEANO in a
challenging real-world setting -- flood mapping of satellite images and show
that it is able to outperform modern remote sensing algorithms for this task.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,598 | Kernel partial least squares for stationary data | We consider the kernel partial least squares algorithm for non-parametric
regression with stationary dependent data. Probabilistic convergence rates of
the kernel partial least squares estimator to the true regression function are
established under a source and an effective dimensionality condition. It is
shown both theoretically and in simulations that long range dependence results
in slower convergence rates. A protein dynamics example shows high predictive
power of kernel partial least squares.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,599 | Doubled Khovanov Homology | We define a homology theory of virtual links built out of the direct sum of
the standard Khovanov complex with itself, motivating the name doubled Khovanov
homology. We demonstrate that it can be used to show that some virtual links
are non-classical, and that it yields a condition on a virtual knot being the
connect sum of two unknots. Further, we show that doubled Khovanov homology
possesses a perturbation analogous to that defined by Lee in the classical case
and define a doubled Rasmussen invariant. This invariant is used to obtain
various cobordism obstructions; in particular it is an obstruction to
sliceness. Finally, we show that the doubled Rasmussen invariant contains the
odd writhe of a virtual knot, and use this to show that knots with non-zero odd
writhe are not slice.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,600 | On the multi-dimensional elephant random walk | The purpose of this paper is to investigate the asymptotic behavior of the
multi-dimensional elephant random walk (MERW). It is a non-Markovian random
walk which has a complete memory of its entire history. A wide range of
literature is available on the one-dimensional ERW. Surprisingly, no references
are available on the MERW. The goal of this paper is to fill the gap by
extending the results on the one-dimensional ERW to the MERW. In the diffusive
and critical regimes, we establish the almost sure convergence, the law of
iterated logarithm and the quadratic strong law for the MERW. The asymptotic
normality of the MERW, properly normalized, is also provided. In the
superdiffusive regime, we prove the almost sure convergence as well as the mean
square convergence of the MERW. All our analysis relies on asymptotic results
for multi-dimensional martingales.
| 0 | 0 | 1 | 1 | 0 | 0 |
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