text:
Q: Recursive function: Syracuse I want to print a list of the syracuse sequence.
My recursive function only gives back [1]
I know the syracuse_sequence in syracuse(n) is a local variable, but I don't know how to return the list.
import sys

def syracuse(n):
        syracuse_sequence = []
        if n == 1:
                syracuse_sequence.append(round(n))
                print(syracuse_sequence)
                return 0
        elif n % 2 == 0:
                n /= 2
                syracuse_sequence.append(round(n))
                return 1 + syracuse(n)
        else:
                n = (n * 3 ) + 1
                syracuse_sequence.append(round(n))
                return 1 + syracuse(n)

def main(argv):
        n = int(argv[1])
        syracuse(n)
if __name__ == "__main__":
        main(sys.argv)

The result I expect is:
$python3 syracuse.py 17
[17, 52, 26, 13, 40, 20, 10, 5, 16, 8, 4, 2, 1]

A: You want a loop:
import sys

def syracuse(n):
    syracuse_sequence = [n]
    while True:
        if n == 1:
            print(syracuse_sequence)
            return 0
        elif n % 2 == 0:
            n /= 2
            syracuse_sequence.append(round(n))
            continue
        else:
            n = (n * 3 ) + 1
            syracuse_sequence.append(round(n))
            continue

def main(argv):
    n = int(argv[1])
    syracuse(n)
if __name__ == "__main__":
    main(sys.argv)

This will run the program until it gets to 1 (which I assume is what you want)
This is what it outputs for 17:

Hope this helps!

A: There are two ways to implement this recursively. One is to make the accumulator an argument to the recursive function.
def syracuse(n, acc=None):
    if acc is None:
        acc = []  # why do this? Search "mutable default arguments"
                  # and the side effects that come with!
    if n == 1:
        acc.append(n)
        return acc
    elif n % 2 == 0:
        n /= 2
        acc.append(n)
        return syracuse(n, acc)
    else:
        n = (n*3) + 1
        acc.append(n)
        return syracuse(n, acc)

The other way is to embed the accumulator in each function call.
def syracuse(n):
    if n == 1:
        return [1]
    elif n % 2:
        return [n] + syracuse(n/2)
    else:
        return [n] + syracuse(n*3+1)

This works because [1] + [2] + [3, 4, 5] == [1, 2, 3, 4, 5]. Since each call to syracuse produces another list to be added with the previous, we get:
syracuse(5) =
[5] + syracuse(16) =
[5] + [16] + syracuse(8) =
[5] + [16] + [8] + syracuse(4) =
[5] + [16] + [8] + [4] = syracuse(2) =
[5] + [16] + [8] + [4] + [2] + syracuse(1) =
[5] + [16] + [8] + [4] + [2] + [1] =
[5, 16, 8, 4, 2, 1]

Do note, however, that recursion is something Python does particularly poorly. You should greatly prefer iteration here.

A: One way to do this with recursion is passing and returning the list, rather than n. n is then just the last element of the list. This reuses the same list object throughout the recursion, rather than creating new ones constantly, so has memory and performance advantages over e.g. Adam Smith's second version.
def syracuse(seq):
    n = seq[-1]
    if n == 1:            # done
        return seq
    if n % 2:             # odd
        seq.append(n * 3 + 1)
        return syracuse(seq)
    seq.append(n // 2)    # even
    return syracuse(seq)

I have also removed a few things you don't need in your code and rearranged it a bit to simplify it.
To call it, you must pass n as a single-item list:
syracuse([17])

If you don't want to have to pass in a list, just add a test to the beginning:
def syracuse(seq):
    if type(seq) is not list:
       seq = [seq]
    n = seq[-1]
    if n == 1:            # done
        return seq
    if n % 2:             # odd
        seq.append(n * 3 + 1)
        return syracuse(seq)
    seq.append(n // 2)    # even
    return syracuse(seq)

Or add a helper function:
def syracuse2(seq):
    n = seq[-1]
    if n == 1:            # done
        return seq
    if n % 2:             # odd
        seq.append(n * 3 + 1)
        return syracuse2(seq)
    seq.append(n // 2)    # even
    return syracuse2(seq)

def syracuse(n):
    return syracuse2([n])


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Recently, there was a tweet by Ned Batchelder that illustrated how to make Python REPL output prettier. I went to implement it, and wanted to put together some instructions for the future.
The PYTHONSTARTUP referenced here is an environment variable that points to a python file that we can place a series of command into that will be evaluated when we launch a Python REPL. I have the following code in a file called .python-startup.py.
The code above will set the >>> to a light blue and the ... to a darker blue, but this isn't the part you are here for probably. You want the next line, which sets the displayhook for output to pretty print.
Next, you can export the PYTHONSTARTUP environment variable pointing to your file as shown here.
You can also add this to your .bashrc or .zshrc depending on which shell you are using, and it will always make your python repl output be pretty.


text:
\section{Introduction}
Many non-equilibrium phenomena in physics involve
random fluctuations with a wide range of spatial and/or temporal scales~\cite{haken1983,prigogineeltit}.
The theory of stochastic processes provides a conceptual framework to
describe such phenomena~\cite{Wax,Friedrich2011a}.
The most emblematic example is provided by Brownian motion, which
results from random uncorrelated collisions acting on a particle, and
can be described by a Wiener process~\cite{Einstein1905}. Nonetheless, several complex systems in
nature~\cite{Kolmogorov1941,Onsager1949,Weizsacker1948,Goldstein1995,Makarava2014,Peng1993} also involve long- or short-range correlations which can be described in
terms of fractional Brownian motion~\cite{levy1965processus,mandelbrot1968}.
Furthermore, the description of
a broad range of problems in physics or in the general field of complex systems requires the fractional Brownian motion (fBm)
process to be \emph{constrained}, by passing through a certain number of prescribed points.
Typical examples include the tracking of animal movement~\cite{kranstauber2012}, information-based financial models~\cite{Brody2008}, the number of neutrons involved in reactor diffusion~\cite{de_Mulatier_2015,Mazzolo2017}, or cosmic ray propagation in astrophysical turbulence~\cite{batista-etal:2016}.

Current stochastic interpolation  methods, e.g.,
kriging~\cite{cressie1990origins,Delhomme_1978}, which is used for data reconstruction/spatial enhancement of particle image velocimetry~\cite{doi:10.1063/1.3003069}, interpolations of buoy location in the tropical pacific~\cite{hansen1989temporal},
and interpolation of rainfall data from rain gages and radars~\cite{seo1990stochastic,seo1990stochastic1},
or other various types of polynomial interpolation act often to smooth the underlying data~\cite{griffa2004predictability}.
A more promising method to interpolate  sparse data sets
has been introduced in
the field of surface hydrology~\cite{molz1993fractal,molz1997fractional},
using the L\'evy-Ciesielski
construction~\cite{levy1965processus,ciesielski1961holder}. This construction,
however, is exact only in the very special case when the random processes are
describable by
ordinary Brownian motion~\cite{saupe1988algorithms,Voss_1988}.

Providing a more faithful representation of the data requires the development of exact stochastic interpolations on the basis of fBm while at the same time giving an estimate for the optimal Hurst exponent which characterizes the roughness of the sparsely sampled data.

Here, we stress the important example of cosmic ray propagation in turbulent magnetic fields~\cite{schlickeiser:2015, zweibel:2013}. In order to overcome the overwhelming problem of resolving the wide range of scales involved in these extremely high Reynolds number astrophysical flows,
several methods for generating synthetic turbulent fields were developed in the last decades~\cite{giacalone-jokipii:1999, zimbardo-amato-etal:2015,
snodin-shukurov-etal:2016,reichherzer-etal:2019}. Such methods are frequently  implemented and used in major cosmic ray propagation codes (see e.g.~\cite{batista-etal:2016}). To capture large anisotropies due to the geometry of galaxies (spiral arms, outflow regions, bow shocks), synthetic turbulent fields must be embedded in large-scale magnetohydrodynamic (MHD) simulations of the turbulent interstellar or intergalactic plasma. Therefore, in this problem we face two challenges: i.)\;fBm, which is used to emulate turbulent flow properties, must be constrained to match the values on the numerical grid of the MHD simulation and ii.)\;scaling properties, represented by the Hurst exponent of fluctuations of the coarse-grained MHD simulation must be determined
from sparse grid data, in order to allow for an ``optimal stochastic interpolation'' of sparsely-sampled
data.

In the following, we will restrict ourselves to temporal
processes, or more generally to processes depending on only one variable.
The fBm $X(t)$
is a \emph{nonstationary centered Gaussian process}, and thus
entirely characterized by its covariance
\begin{equation}
  \langle X(t)X(t') \rangle = \frac{1}{2}\left(|t|^{2H}+|t'|^{2H}-|t-t'|^{2H}\right)\;.
  \label{eq:fBm_corr}
\end{equation}
which implies that $\langle (X(t) - X(t'))^2 \rangle = |t - t'|^{2H}$, where $H$ denotes the Hurst exponent.
We first present a method to construct a fBm $X(t)$, which
takes specific values $X_i$ at times $t_i$, and subsequently discuss
its application as an optimal stochastic interpolation of a sparsely-sampled real time signal. Accordingly,
we start with the well-known notion of a
fractional Brownian bridge~\cite{delorme:2016,sottinen:2014,Walter:2020aa}, which is defined as fBm starting from zero at $t=0$, ending at $X_1$ at $t=t_1$, \emph{and} possessing the same statistical (including scaling (\ref{eq:fBm_corr})) properties as $X(t)$. Such a fractional Brownian bridge (fBb) can be constructed from $X(t)$ according to
\begin{equation}
  X^B(t)=X(t)- (X(t_1)-X_1) \frac{\langle X(t)X(t_1) \rangle}{\langle X(t_1)^2 \rangle}\;.
  \label{eq:fBb_ordinary}
\end{equation}
It is possible to generalize this ordinary fBb to an arbitrary number of prescribed intermediate grid points in the following manner: First, we consider the $n$-times conditional moments
\begin{align}
  \label{eq:cond1}
  \langle X(t)|\{X_i, t_i\} \rangle =& \frac{\langle X(t) \prod_{i=1}^n \delta(X(t_i)-X_i) \rangle}{\prod_{i=1}^n \langle \delta(X(t_i)-X_i) \rangle}\;,\\
  \langle X(t)X(t')|\{X_i, t_i\}\rangle =& \frac{\langle X(t)X(t') \prod_{i=1}^n \delta(X(t_i)-X_i) \rangle}{\prod_{i=1}^n \langle \delta(X(t_i)-X_i) \rangle}\;.
  \label{eq:cond2}
\end{align}
We then demand that our bridge process $X^B(t)$ is conditional on $X_i$ at $t_i$ for $i=1,\ldots,n$, which is equivalent to the process possessing the conditional moments (\ref{eq:cond1}-\ref{eq:cond2}).
For a Gaussian process with zero mean and covariance
$\langle X(t)X(t') \rangle$, the conditional moments read
\begin{equation}
  \langle X(t)|\{X_i, t_i\} \rangle = \left \langle X(t)X(t_i) \right \rangle\sigma_{ij}^{-1}   X_j  \;,
  \label{eq:cond1_calculated}
\end{equation}
and
\begin{align}\label{eq:cond2_calculated}
  \lefteqn{\langle X(t)X(t')|\{X_i, t_i\}\rangle
   = \left \langle X(t)X(t') \right \rangle }\\ \nonumber
  &~~-\left \langle X(t)X(t_i) \right \rangle \left[\sigma_{ij}^{-1} - \sigma_{ik}^{-1} X_k X_l \sigma_{jl}^{-1}  \right]
  \left \langle X(t')X(t_j) \right \rangle\;,
\end{align}
where we implied summation over equal indices and where $\sigma_{ij}=\langle X(t_i)X(t_j)\rangle$ denotes the covariance matrix.
As shown in~\cite{sm},\nocite{lim2002,grebenkov2015multiscale,beran1994statistics,flandrin1989spectrum,fbm,Risken,Mazzolo_2017,stevens1995six,Kolmogorov1962,Oboukhov1962,Friedrich:1997aa,yakhot:2006}
the \emph{multi-point fractional Brownian bridge}
\begin{equation}
  X^B(t) =X(t) -  (X(t_i)-X_i) \sigma_{ij}^{-1}
  \left \langle X(t)X(t_j) \right \rangle\;,
  \label{eq:gen_bridge}
\end{equation}
possesses one- and two-point moments
which are identical to
(\ref{eq:cond1_calculated}-\ref{eq:cond2_calculated}) and we thus conclude that
$X^{B}(t)$ is the stochastic process $X(t)$ conditioned on points $X_i$ at times $t_i$. We indeed obtain $X^B(t_k)= X(t_k)-(X(t_i)-X_i)\sigma_{ij}^{-1}\sigma_{jk}= X(t_k)-(X(t_i)-X_i)\delta_{ik}= X_k$.
Fig.~\ref{fig:fBb}(a) depicts the multi-point fBb (\ref{eq:gen_bridge}) for three different Hurst exponents with $16$ equidistant fixed points (black).
Here, fBm realizations $X(t)$ were generated by the Davies-Harte method~\cite{Davies1987}, which is an exact numerical implementation of fBm (by contrast to other methods, e.g., Fourier representations; see also~\cite{sm} for further specifications).
\begin{figure}[h!]
\centering
\includegraphics[width=0.4 \textwidth]{gen_fbb_4096_16_grid_points_new.pdf}
\includegraphics[width=0.4 \textwidth]{struc_2_res_32768_32_new.pdf}
\includegraphics[width=0.4 \textwidth]{fbb_opti_all_H_new.pdf}
\caption{(a) Multi-point fractional Brownian bridge (\ref{eq:gen_bridge}) for different Hurst exponents $H$ but identical grid points (black). The $16$ equidistant grid points $(t_i, X_i)$ were drawn randomly from the interval $X_i \in [-0.5,0.5]$. The full temporal resolution comprises $4096$ points
and the inset zooms into the vicinity of one of the prescribed points. (b) Second order structure function $S_2(\tau)= \langle (X^B(t+\tau)-X^B(t))^2 \rangle$ calculated from $100$ realizations of the multi-point fractional Brownian bridge (\ref{eq:gen_bridge}) for $N=32768$ total grid points and $\tilde N=32$ prescribed grid points. Dashed lines correspond to the explicit formula (\ref{eq:s2_exp}), whereas
thin grey lines correspond to the ordinary scaling of fBm $|\tau|^{2H}$ and extend up to the grid length of the coarse grid $\textrm{d}\tilde t=1/32$.
The prescribed grid points were drawn as fBm with $\tilde H=0.5$. Accordingly, the fBb with $H=0.5$ (orange curve) is the \emph{optimal bridge} that belongs to the prescribed points. (c) Cost curve $H_{emp}(H)-H$ for three under-sampled time series $\tilde N=128$ (fBm with $\tilde H=0.33, 0.5, 0.66$ for samples 1-3). Each time series is embedded into bridges (\ref{eq:gen_bridge}) with varying Hurst exponents $H$ and resolution $N=4096$. Therefore, the fit for $H_{emp}$ relies on the left part of (b).}
\label{fig:fBb}
\end{figure}

In order to check that the simulated bridge processes possess the desired properties (\ref{eq:fBm_corr}) and
(\ref{eq:cond1_calculated}-\ref{eq:cond2_calculated}), we have carried out numerical calculations of the second order structure functions
$S_2(\tau)= \left \langle (X^B(t+\tau)-X^B(t))^2 \right \rangle$
for three different Hurst exponents ($H=0.33, 0.5, 0.66$) with a total number of  $N=32768$ total grid points. From these we prescribed $\tilde{N} = 32$ equidistant points generated from fBm (\ref{eq:fBm_corr}) with a Hurst exponent $\tilde{H} = 0.5$.
The results are shown in Fig.~\ref{fig:fBb}(b) and are in agreement with the prediction $S_2(\tau)=|\tau|^{2H}$ (dashed black lines) for small $\tau$. For such a case, where the prescribed points also follow fBm with Hurst exponent $\tilde H$, we can obtain an explicit formula for $S_2(\tau)$ from Eq. (\ref{eq:gen_bridge}), namely
\begin{align}
  \label{eq:s2_exp}
  &S_2(\tau)=
   |\tau|^{2H}\\ \nonumber
   &- \langle \delta_{\tau} X(t) X(t_i)\rangle \left[\sigma_{ij}^{-1}-\sigma_{ik}^{-1}\tilde \sigma_{kl} \sigma_{lj}^{-1}\right]
  \langle
  \delta_{\tau} X(t) X(t_j)\rangle\;,
\end{align}
where $\delta_{\tau} X(t)=X(t+\tau)-X(t)$
and where
$\tilde \sigma_{ij}=\langle X_i X_j\rangle$
denotes the covariance matrix of the prescribed points with $\tilde H$. Consequently, $H=\tilde H$ implies
$\sigma =\tilde \sigma$, which yields $
S_2(\tau)= |\tau|^{2H}$. Hence, Eq. (\ref{eq:s2_exp}) entails that strict self-similarity of the multi-point fBb is only guaranteed if the bridge fBm process $X(t)$ characterized by $H$ matches the Hurst exponent $\tilde H$ of the prescribed points $X_i$. Other bridge multi-point fBbs with $H \neq \tilde H$ correspond to multifractional Brownian motion with a time-dependent Hurst exponent, as shown in Fig.~\ref{fig:fBb}(b)
(we also refer to~\cite{peltier1995multifractional,benassi1997elliptic} for further references).
In other words, given a certain time series $\{X_i,t_i\}$ that possesses a self-similar part governed by $\tilde H$, the bridge with $H=\tilde H$ can be considered as the \emph{optimal stochastic interpolation} of this time series.

Therefore, as already highlighted in the introduction, we are now in the position to describe an optimization procedure that allows estimating Hurst exponents from sparsely sampled time series. The basic idea is to embed a given time series $\{X_i, t_i\}$ into fBbs (\ref{eq:gen_bridge}) with varying Hurst exponents $H$.
For each of these bridges we determine the empirical Hurst exponent $H_{emp}$ as a function of $H$ by fitting the second order structure function up to the smallest time scale of the time series $\textrm{d}\tilde t$,
(i.e., fitting only the left part up to $\textrm{d}t_{coarse}$ in Fig.~\ref{fig:fBb}(b)). This procedure ensures that we only measure deviations from the scaling $|\tau|^{2H}$ in the interpolated region (grey lines in Fig.~\ref{fig:fBb}(b)) and are not directly contaminated by correlations contained in
$\{X_i, t_i\}$.
\begin{figure}[h]
\centering
\includegraphics[width=0.49 \textwidth]{shrek_data_bridge.pdf}
\includegraphics[width=0.49 \textwidth]{shrek_data_dephased_bridge.pdf}
\caption{(a) Turbulent velocity field measurements (blue) in a von K\'arm\'an experiment using normal Helium. The number of points $N=16384$ corresponds roughly to one integral length scale $L$. The corresponding fBb (orange) was constructed from $\tilde N=64$ points of the signal (black) and possesses a resolution of $1024$ points. The smallest scale of the bridge process therefore corresponds approximately to the Taylor scale of the flow which ensures that the fBb and the velocity field $u(x)$ possess comparable inertial ranges. Small-scale turbulent fluctuations in the velocity field (blue) cannot be reproduced by the fBb due to its restricting Gaussian properties. The Hurst exponent for the fBb $H=0.376$ was determined from randomized samples of the original turbulent signal such as the one depicted in (b). Due to the self-similarity of these samples, an optimization procedure similar to the one depicted in Fig.~\ref{fig:fBb}(c) could be applied. The bridge (orange) is in much better agreement with the self-similar signal (blue) in (b) than with the original signal (blue) in (a).}
\label{fig:wind}
\end{figure}
We have tested our optimization for three different samples of fBm with Hurst exponents $\tilde H=0.33, 0.5, 0.66$ and $\tilde N=128$ grid points. Each of the samples was embedded in fBbs with varying Hurst exponents $H$ and $N=4096$ grid points. Fig.~\ref{fig:fBb}(c) depicts the minimization of $H_{emp}(H)$ for the three samples. It can be clearly seen that optimal Hurst exponents $H_{opt}$ are recovered with high accuracy, although slight variations ($\Delta H_{opt} \approx 0.015$ from 20 different samples of the same $\tilde H$) between different samples can be observed~\cite{sm}. This effect can be attributed to finite sample sizes, and the corresponding deviations remain rather small,
which is quite appealing given the fact that common methods (rescaled range analysis~\cite{Caccia1997} or wavelets~\cite{Simonsen1998}) yield erroneous results for such sparsely sampled time series. The proposed method can thus roughly be considered as the extrapolation of self-similar properties of a given time series to finer scales.

In the examples discussed so far, we have systematically chosen the synthetic signal $X_i$, as well as the process $X(t)$ used in Eqs. (\ref{eq:fBm_corr},\ref{eq:gen_bridge}), to be normalized in the same manner.
In order to apply our optimization to real signals, we examine a turbulent velocity time series
obtained from hot wire anemometry in the superfluid high Reynolds von K\'arm\'an experiment (SHREK) at CEA-Grenoble~\cite{Rousset2014}. The particular experimental setup
is a von K\'arm\'an cell with two-counter rotating disks (-0.12 Hz on top, +0.18 Hz on bottom) in normal Helium (see~\cite{Kharche2018} for further specifications). The temporal resolution is 50kHz and the attained Taylor-Reynolds number was $\textrm{Re}_{\lambda}
=2737$. We applied Taylor's hypothesis of frozen turbulence~\cite{monin} to relate single-point velocity measurement at time $t$ to scales $x=\langle u\rangle t$, where $\langle u \rangle$ is the mean velocity. Furthermore, a key prerequisite for the above mentioned optimization procedure is the standardization of the signal by
$\lim_{r\rightarrow L}
\sqrt{\left \langle (u(x+r)-u(x))^2 \right \rangle}  = \sqrt{2\left \langle u(x))^2 \right \rangle}= \sqrt{2} \sigma$. This standardization ensures the correct large-scale limit of the second-order structure function in Fig.~\ref{fig:fBb}(b), which was necessarily fulfilled by the synthetic samples of fBm (\ref{eq:fBm_corr}) in the previous study.
The blue curve in Fig.~\ref{fig:wind}(a)
depicts an extract of the velocity field $u(x)$ standardized by $\sigma$ of the total signal for around one integral length scale $L$.

By contrast to the above optimization procedure for data from synthetic samples, the present analysis is complicated by: i.) the existence of different scaling regimes in the flow, namely a dissipative and integral range of scales, and ii.) non-self similar (intermittent) features in the signal. Turning to point i.), we chose sample sizes of length $L$ and determined the subset of points in order to guarantee that their grid length lies within the inertial range of scales (here we choose $\approx 100 \eta$).
As far as point ii.) is concerned, intermittent features manifest themselves in form of strongly varying $H_{opt}$ for different samples~\cite{sm}. An example of such an intermittent fluctuation is well visible in the signal shown in Fig.~\ref{fig:wind}(a), at $x \approx 0.8L$. In this region our interpolation procedure does not work very well and the bridge process (\ref{eq:gen_bridge}) has to be generalized to a non-Gaussian process. In this letter, however, we are solely interested in the self-similar part of the signal and thus perform a randomization of Fourier phases of the turbulent signal. A snapshot of the resulting randomized signal is depicted in Fig.~\ref{fig:wind}(b). Strikingly, and contrary to Fig.~\ref{fig:wind}(a), our interpolation procedure leads to much better results than for the original signal. The randomization procedure has effectively suppressed intermittency, and made the signal essentially Gaussian. We fed several samples of this randomized signal into our optimization routine which reduced fluctuations of the Hurst exponent to an extend comparable to the ones observed in synthetic signals~\cite{sm}. Moreover, we obtain a Hurst exponent of the randomized signal
$H_{opt}=0.3786 \pm 0.0251$ (evaluated from the optimization of 100 snapshots~\cite{sm}). To put this result into context, we consider the log-normal model of turbulence~\cite{Kolmogorov1962,Oboukhov1962} which suggests scaling exponents $\zeta_n=n/3-\mu n (n-3)/18$
for the scaling of structure functions $\langle (u(x+r)-u(x))^n \rangle \sim |r|^{\zeta_n}$
where $\mu$ denotes the intermittency coefficient.
Hence, the self-similar part in the K62 model
is given by $H_{K62}= (2+\mu)/6$ and can be compared to our analysis. The above mentioned value from the optimization procedure of the randomized signal $H_{opt}=0.3786 \pm 0.0251$ thus suggests that $\mu = 0.2716 \pm 0.1506$. Latter value for the intermittency coefficient is comparable to $\mu = 0.2913 \pm 0.0853$ acquired from an analysis of the entire turbulent signal~\cite{sm}.
It is thus important to stress here, that the combination of both the Fourier phase randomization and the optimization procedure via the multi-point fBbs is capable of reproducing the monofractal/self-similar part of the turbulent signal, which is also strongly supported by Fig.~\ref{fig:wind}(b).

To conclude, we have presented a generalization of a fractional Brownian bridge to a stochastic process with an arbitrary number of prescribed points.
Furthermore, we devised an optimization method which allowed us to estimate the Hurst exponent of a sparsely sampled time series. Our method
has proven reliable even in the presence of strong anomalous fluctuations, i.e., non-self-similar features, at small scales. In order to address such features, which are visibly not captured by the multi-point fBb in Fig.~\ref{fig:wind}(a), it will be a task for the future to construct multi-point bridge processes with \emph{multiscaling properties} (i.e., which are non-self-similar and potentially possess a dissipative scale~\cite{Chevillard2019,Viggiano2019,rosales2008anomalous,juneja1994synthetic,malara2016fast}).
A generalization of the bridge process (\ref{eq:gen_bridge}) to
an arbitrary number of dimensions is straight-forward and might be of potential interest for the construction of various synthetic fields in several physical contexts.
In turbulence, the full spatio-temporal (though non-intermittent) Eulerian velocity field $\mathbf{u}(\mathbf{x},t)$ can possibly be reconstructed from a set of Lagrangian trajectories $\mathbf{X}(\mathbf{y},t)$ where $\mathbf{\dot X}(\mathbf{y},t)=
\mathbf{u}(\mathbf{X}(\mathbf{y},t),t)$.
Such applications could be of considerable interest for particle tracking measurements, which sometimes require a certain knowledge of the flow field in the vicinity of tracer particles~\cite{Machicoane2019}. Furthermore, the optimization procedure may help to shed light into the ongoing discussion about the inertial range power spectrum in the solar wind~\cite{Boldyrev2005,Goldstein1995}.
To this end, the ideas proposed in this work should be
extended to take into account the existence of several ranges of scales,
involving different power law behaviors~\cite{sm}. Last, we mention possible extensions of our method which might apply or even improve concepts borrowed from machine learning~\cite{brenner2019perspective,doi:10.1190/geo2016-0300.1,APPELHANS201591,rasmussen2003gaussian}. Moreover, the prior assignment of hot- and cold-spot clusters by a simple
$k$-means algorithm might be suitable to model land price fields by multi-point fractional Brownian scalar fields in the widely different domain of urban decision making~\cite{Lengyel2020}.\\
\\
We are grateful to the SHREK collaboration~\cite{Rousset2014,Kharche2018} for providing us with their hot wire anemometry measurements.
J.F. acknowledges funding from the Humboldt Foundation within a Feodor-Lynen fellowship and also benefited from financial support of the Project IDEXLYON of the University of Lyon in the framework of the French program ``Programme Investissements d'Avenir'' (ANR-16-IDEX-0005). S.G.'s visit to Lyon was financially supported by the German Academic Exchange Service through a PROMOS scholarship.

\bibliographystyle{apsrev4-1}

\section{Representations of fractional Brownian motion}
The literature on fractional Brownian motion typically distinguishes between two different representations of fBm. In the original work of L\'evy~\cite{levy1965processus}, fractional Brownian motion was introduced in form of a Riemann-Liouville integral with respect to a Gaussian white noise measure $\textrm{d}W(t)$
\begin{equation}
  X_{RL}(t)= \frac{1}{\Gamma\left(H+\frac{1}{2} \right)} \int_0^t
  \frac{1}{(t-t')^{\frac{1}{2}-H}}\textrm{d}W(t')\;,
  \label{eq:levy_fbm}
\end{equation}
where $\Gamma(z)$ denotes the Gamma function and $H<0<1$ the Hurst exponent.
This corresponds to a self-similar Gaussian stochastic process with zero mean whose increments, however, are nonstationary. Furthermore, its covariance
possesses a rather complicated functional form~\cite{lim2002}.
Therefore, Mandelbrot and van Ness~\cite{mandelbrot1968} (see also~\cite{grebenkov2015multiscale}) suggested a representation of fBm which is based on the Weyl integral
\begin{equation}
  X_{W}(t)= \frac{1}{\Gamma\left(H+\frac{1}{2} \right)} \left\{ \int_{-\infty}^0
  \left[\frac{1}{(t-t')^{\frac{1}{2}-H}} - \frac{1}{(-t')^{\frac{1}{2}-H}} \right]\textrm{d}W(t') + \int_0^t \frac{1}{(t-t')^{\frac{1}{2}-H}}\textrm{d}W(t')\right\}\;,
\end{equation}
for $t>0$. By contrast to the Riemann-Liouville representation (\ref{eq:levy_fbm}), this stochastic process possesses stationary increments \textrm{and} implies a simpler structure for the covariance
\begin{equation}
 \langle X_{W}(t)X_{W}(t') \rangle = \frac{\Gamma\left(1-2H \right) \cos(H\pi)}{2H\pi} \left(|t|^{2H}+|t'|^{2H}-|t-t'|^{2H}\right)\;,
\end{equation}
which was used throughout the constructions of fBm bridge processes in the main paper. Furthermore, as opposed to fractional Gaussian noise which exhibits a clear spectral density $\sim f^{1-2H}$~(we also refer the reader to the monograph~\cite{beran1994statistics}), here, the notion of spectral density has to be extended in order to capture the nonstationarity of fBm~\cite{flandrin1989spectrum}.

In addition, sample paths of fractional Brownian motion were constructed on the basis of the Davies-Harte algorithm~\cite{Davies1987}, which is similar to a Cholesky decomposition of the incremental covariance matrix.
The decomposition in the Davies-Harte algorithm~\cite{Davies1987} is achieved by embedding the original covariance matrix into a circulant matrix, which can readily be decomposed. Numerical implementations of this algorithm are available in form of Python packages~\cite{fbm}. The circulant embedding has the advantage that it is an exact numerical implementation of fBm, by contrast to other methods, e.g., Fourier representations of the integral (\ref{eq:levy_fbm}) which require a periodization of the kernels.

Due to the nonstationarity of the increments of fBm discussed above, some time series are also modeled in terms of Ornstein-Uhlenbeck processes due to its stationarity and mean-reverting properties~\cite{Risken}.
In the present study, however, we purely focused on the scaling range of the time series (i.e., the inertial range for the turbulence time signal). Therefore, no requirements for the saturation of the second order structure function at large scales (e.g., in Fig.1(b) in the main paper) had to be imposed. Consequently, we could rely on this rather simple method of the multi-point bridge construction on the basis of fBm. Nonetheless, a similar method that allows to constrain ordinary Ornstein-Uhlenbeck processes has been discussed recently~\cite{Mazzolo_2017}. It will be a task for the future to extend this treatment and introduce constrained fractional Ornstein-Uhlenbeck processes.

Whereas the use of Ornstein-Uhlenbeck processes appears as
a natural way to take into account the saturation of the structure function at
large scales, other methods need to be developed to account for the existence
of ranges of scales, where the process is known to develop other power laws,
as it occurs in the dissipative range in turbulent flows~\cite{monin}.

\section{Calculation of conditional moments for centered Gaussian process}
\label{app:gauss}
Although the conditional moments of a multivariate Gaussian distribution are
well known, in what follows, we want to give a brief derivation. To this end, we consider the characteristic functional
\begin{equation}
  \varphi[\alpha]= \left \langle e^{i \int \textrm{d}t \alpha(t)X(t)} \right \rangle\;.
\end{equation}
For a centered Gaussian process $X(t)$, the characteristic functional reduces to (see, for instance~\cite{monin,stevens1995six})
\begin{equation}
  \varphi[\alpha] = e^{-\frac{1}{2}\int \textrm{d}t \int  \textrm{d}t' \alpha(t) \left \langle X(t)X(t') \right \rangle \alpha(t')}\;.
\end{equation}
The $n$-point probability density function (PDF)
\begin{equation}
f_n(X_1, t_1; \ldots; X_n ,t_n)=
\prod_{i=1}^n \langle \delta(X(t_i)-X_i) \rangle\;,
\end{equation}
can thus be re-expressed according to
\begin{align}\nonumber
  \lefteqn{f_n(X_1, t_1; \ldots; X_n ,t_n)
  = \int \frac{\textrm{d} k_1}{2 \pi} \ldots \frac{\textrm{d} k_n}{2 \pi}
  e^{-i \sum_{i=1}^n k_i X_i} \left \langle e^{i \sum_{i=1}^n k_i X(t_i)} \right \rangle} \\ \nonumber
  &= \int \frac{\textrm{d} k_1}{2 \pi} \ldots \frac{\textrm{d} k_n}{2 \pi}
  e^{-i \sum_{i=1}^nk_i X_i} \varphi \left[\alpha(t)= \sum_{i=1}^n k_i \delta(t-t_i) \right]\\
  &= \int \frac{\textrm{d} k_1}{2 \pi} \ldots \frac{\textrm{d} k_n}{2 \pi}
  e^{-i \sum_{i=1}^n k_i X_i} \exp \left [-\frac{1}{2}\sum_{i=1}^n \sum_{i'=1}^n
  k_i k_{i'} \left \langle X(t_i)X(t_{i'}) \right \rangle \right ]\;.
\end{align}
Therefore, we obtain a multivariate Gaussian distribution
\begin{equation}
f_n(X_1, t_1; \ldots; X_n ,t_n)
  =\frac{1}{\sqrt{(2 \pi)^n \det(\sigma)}} e^{-\frac{1}{2} \mathbf{ X}^T \sigma^{-1} \mathbf{ X}}\;,
\end{equation}
where
\begin{equation}
  \mathbf{X}=(X_1, \ldots, X_n)^T\;,
\end{equation}
and where
\begin{equation}
  \sigma_{ij} = \langle X(t_i)X(t_j)\rangle\;,
\end{equation}
denotes the covariance matrix. Next, we can calculate the $n+1$-point quantity,
\begin{align}\nonumber
  \lefteqn{\prod_{i=1}^n \langle X(t)\delta(X(t_i)-X_i) \rangle
  =\int \frac{\textrm{d} k_1}{2 \pi} \ldots \frac{\textrm{d} k_n}{2 \pi}
  e^{-i \sum_{i=1}^n k_i X_i} \left \langle X(t) e^{i \sum_{i=1}^n k_i X(t_i)} \right \rangle} \\ \nonumber
  &= \int \frac{\textrm{d} k_1}{2 \pi} \ldots \frac{\textrm{d} k_n}{2 \pi}
  e^{-i \sum_{i=1}^nk_i X_i} \left. \frac{ \delta \varphi[\alpha] }{\delta i \alpha(t)} \right |_{\alpha(t)=\sum_{i=1}^n k_i \delta(t-t_i)}\\ \nonumber
  &=  i \int \frac{\textrm{d} k_1}{2 \pi} \ldots \frac{\textrm{d} k_n}{2 \pi}
  e^{-i \sum_{i=1}^n k_i X_i}  \sum_{i=1}^n k_i \left \langle X(t)X(t_i) \right \rangle e^{-\frac{1}{2}\sum_{i=1}^n \sum_{i'=1}^n
  k_i k_{i'} \left \langle X(t_i)X(t_{i'}) \right \rangle}\\
  &= -\sum_{i=1}^n  \left \langle X(t)X(t_i) \right \rangle \frac{\textrm{d}}{\textrm{d}X_i} f_n(X_1, t_1; \ldots; X_n ,t_n)\;,
\end{align}
\newpage
as well as the $n+2$-point quantity
\begin{align}\nonumber
  \lefteqn{\prod_{i=1}^n \langle X(t)X(t')\delta(X(t_i)-X_i) \rangle=\int \frac{\textrm{d} k_1}{2 \pi} \ldots \frac{\textrm{d} k_n}{2 \pi}
  e^{-i \sum_{i=1}^n k_i X_i} \left \langle X(t)X(t') e^{i \sum_{i=1}^n k_i X(t_i)} \right \rangle} \\ \nonumber
  &= \int \frac{\textrm{d} k_1}{2 \pi} \ldots \frac{\textrm{d} k_n}{2 \pi}
  e^{-i \sum_{i=1}^nk_i X_i} \left. \frac{ \delta^2 \varphi[\alpha] }{\delta i \alpha(t)\delta i \alpha(t')} \right |_{\alpha(t)=\sum_{i=1}^n k_i \delta(t-t_i)}\\ \nonumber
  &=  \int \frac{\textrm{d} k_1}{2 \pi} \ldots \frac{\textrm{d} k_n}{2 \pi}
  e^{-i \sum_{i=1}^n k_i X_i} \left [\left \langle X(t)X(t') \right \rangle  -\sum_{i=1}^n \sum_{i'=1}^n k_i k_{i'}\left \langle X(t)X(t_i) \right \rangle
  \left \langle X(t')X(t_{i'}) \right \rangle \right]e^{-\frac{1}{2}\sum_{i=1}^n \sum_{i'=1}^n
  k_i k_{i'} \left \langle X(t_i)X(t_{i'}) \right \rangle }\\
  &= \left[ \left \langle X(t)X(t') \right \rangle +\sum_{i=1}^n \sum_{i'=1}^n   \left \langle X(t)X(t_i) \right \rangle
  \left \langle X(t')X(t_{i'}) \right \rangle \frac{\textrm{d}^2}{\textrm{d}X_i \textrm{d}X_{i'}} \right] f_n(X_1, t_1; \ldots; X_n ,t_n)\;.
  \;,
\end{align}
The $n$-times conditional moments
\begin{align}
  \label{eq:cond1}
  \langle X(t)|\{X_i, t_i\} \rangle =& \frac{\langle X(t) \prod_{i=1}^n \delta(X(t_i)-X_i) \rangle}{\prod_{i=1}^n \langle \delta(X(t_i)-X_i) \rangle}\;,\\
  \langle X(t)X(t')|\{X_i, t_i\}\rangle =& \frac{\langle X(t)X(t') \prod_{i=1}^n \delta(X(t_i)-X_i) \rangle}{\prod_{i=1}^n \langle \delta(X(t_i)-X_i) \rangle}\;,
  \label{eq:cond2}
\end{align}
thus read
\begin{equation}
  \langle X(t)|\{X_i, t_i\} \rangle =  \sigma_{ij}^{-1} \left \langle X(t)X(t_i) \right \rangle X_j \;,
  \label{eq:cond1_gauss}
\end{equation}
and
\begin{align}
\langle X(t)X(t')|\{X_i, t_i\}\rangle= \left \langle X(t)X(t') \right \rangle-\left[\sigma_{ij}^{-1} - \sigma_{ik}^{-1}X_k X_l\sigma_{jl}^{-1} \right] \left \langle X(t)X(t_i) \right \rangle
  \left \langle X(t')X(t_j) \right \rangle \;.
  \label{eq:cond2_gauss}
\end{align}
where we made use of the symmetry of the covariance matrix and where we imply summation over equal indices.
\section{Proof for moments of multi-point fractional Brownian bridge}
\label{app:fBb}
In this section, we want to proof that the multi-point fBb
\begin{equation}
  X^B(t) =X(t) -  (X(t_i)-X_i) \sigma_{ij}^{-1}
  \left \langle X(t)X(t_j) \right \rangle\;,
  \label{eq:gen_bridge}
\end{equation}
possesses identical one and two-points moments to the moments fBm process $X(t)$ \emph{conditioned on} $\{X_i, t_i\}$, i.e., eqs. (\ref{eq:cond1_gauss}-\ref{eq:cond2_gauss}).
Therefore, we first calculate the mean of the fBb in eq. (\ref{eq:gen_bridge})
\begin{align}\nonumber
  \left \langle X^B(t) \right \rangle&=
  \underbrace{\left \langle X(t) \right \rangle}_{=0}-
  ( \underbrace{\left \langle X(t) \right \rangle}_{=0} -X_i) \sigma_{jk}^{-1}
  \langle X(t)X(t_j)\rangle \\
  &= X_i \sigma_{jk}^{-1}
  \langle X(t)X(t_j)\rangle\;,
\end{align}
where we assumed that the fBm possesses zero mean. Next, the correlation function of the generalized fBb reads
\begin{align}\nonumber
    \lefteqn{\left \langle X^B(t) X^B(t') \right \rangle}\\ \nonumber
    &= \langle X(t)X(t') \rangle-
    2 \langle X(t)X(t_i) \rangle \sigma_{ij}^{-1} \langle X(t)X(t_j) \rangle +\underbrace{\langle X(t_k)X(t_l) \rangle}_{\sigma_{kl}} \sigma_{ik}^{-1} \langle X(t)X(t_i) \rangle \langle X(t')X(t_j) \rangle \sigma_{jl}^{-1}\\ \nonumber
    &~~+ \sigma_{ik}^{-1}X_k \sigma_{jl}^{-1} X_l \left \langle X(t)X(t_i) \right \rangle
    \left \langle X(t')X(t_j) \right \rangle\\
    &= \left \langle X(t)X(t') \right \rangle-\sigma_{ij}^{-1} \left \langle X(t)X(t_i) \right \rangle  \left \langle X(t')X(t_j) \right \rangle+ \sigma_{ik}^{-1}X_k \sigma_{jl}^{-1} X_l \left \langle X(t)X(t_i) \right \rangle
      \left \langle X(t')X(t_j) \right \rangle \;,
\end{align}
where we made use of the identity $\sigma_{kl}\sigma_{ki}^{-1}=\delta_{il}$.
\section{Error analysis of the optimal Hurst exponent determination}
\begin{figure}[h]
\centering
\includegraphics[width=0.32 \textwidth]{hist_syn.pdf}
\includegraphics[width=0.32 \textwidth]{hist_shrek_dephased.pdf}
\includegraphics[width=0.32 \textwidth]{hist_shrek.pdf}
\caption{(a) Histogram of the optimal Hurst exponent evaluated from 100 synthetic samples of fBm with $\tilde H=0.3786$. (b) Same plot as in (a), but now for 100 sub-samples of the randomized turbulent signal (Fig. 2(b) in main paper). (c) Same plot, but now for 100 sub-samples of the turbulent signal (Fig. 2(a) in main paper).}
\label{fig:hist}
\end{figure}

An optimization procedure similar to the one used to generate Fig. 1(c) of the main paper for different synthetic samples drawn as fBm with Hurst index $\tilde H$ yields slightly different results for the optimal Hurst exponent $H_{opt}$. In order to compare to the optimization procedure for the turbulent signal later, we choose 100 synthetic samples of fBm with Hurst exponent $\tilde H=0.3786$ with a spatial resolution of $\tilde N =128$ points. Fig.~\ref{fig:hist}(a) depicts the histograms for the optimal Hurst exponent obtained via optimization procedured as discussed in the main paper. The histogram is clearly peaked at the prescribed values $\tilde H$ and we obtain $\langle H_{opt} \rangle =0.3783 \pm 0.013$.
\begin{figure}[h]
\centering
\includegraphics[width=0.32 \textwidth]{pdf_dephased.pdf}
\includegraphics[width=0.32 \textwidth]{pdf_intermittent.pdf}
\includegraphics[width=0.32 \textwidth]{pdf_intermittent_sym.pdf}
\caption{(a) PDFs of velocity increments $\delta_r u$ ($\log(r/L)=   -6.41, -5.60, -4.66, -3.64, -2.75, -1.86, -0.87, 0.12$ from top to bottom) of the randomized turbulent signal which has been obtained by random rotation of Fourier phases from the original turbulent signal in (b). The PDFs in (a) exhibit self-similarity across different scales and the randomized signal can thus be used in the described optimization procedure via fBbs. (c) symmetrization of the PDFs in (b). The dashed curves correspond to the prediction of the K62 model of turbulence with $\mu= $ and are only shown for PDFs with scale separation $r$ in the inertial range.}
\label{fig:pdfs}
\end{figure}
These variations are due to finite-size effects as well as noise due to the numerical implementation of the Davies-Harte algorithm. Turning next to the turbulent signal in Fig. 2(a) of the main paper, we first perform a Fourier transform of the entire signal and then randomize Fourier phases in order to get rid of strong (intermittent) correlations. The resulting signal possesses Gaussian statistics which can be verified by computing the PDF of velocity increments $\delta_r u = u(x+r)-u(x)$ at different scales $r$, as it has been done in  Fig.~\ref{fig:pdfs}(a). For comparison, Fig.~\ref{fig:pdfs}(b) depicts the velocity increment PDF of the original turbulent signal for the same scale separations $r$ as in (a). As to be expected the PDFs develop pronounced tails at small scales, a key signature of small-scale intermittency. Due to the self-similar property of the randomized signal in Fig.~\ref{fig:pdfs}(a), the corresponding fluctuations can be treated by the same optimization routine as the synthetic fBm. However,
as explained in the main text, one has to take care of the existence of a dissipation scale in both the turbulent and randomized signal. To this end, we consider plateaus in log-derivatives of structure functions and determine the small-scale cut-off to be approximately $96 \eta$, where $\eta$ denotes the Kolmogorov microscale.
We thus perform the optimization procedure for 100 different sub-samples of the randomized signal.
The result is shown in Fig.~\ref{fig:hist}(b) and shows many similarities to the one obtained from synthetic samples in Fig.~\ref{fig:hist}(a), although the standard deviation is slightly higher. In fact, we obtain $\langle H_{opt} \rangle=0.3786 \pm 0.0251$.
\begin{figure}[h]
\centering
\includegraphics[width=0.4 \textwidth]{joint_pdf.pdf}
\includegraphics[width=0.5 \textwidth]{eps_pdf.pdf}
\caption{(a) Joint histogram of energy dissipation rate and Hurst exponent evaluated from 320 different sub-samples of the turbulent signal. The axis of the energy dissipation has been divided by the standard deviation determined from all sub-samples. (b) Histogram of the energy dissipation rate.}
\label{fig:joint}
\end{figure}

In order to assess this result, we consider the K62 model of turbulence~\cite{Kolmogorov1962,Oboukhov1962}, which predicts structure functions
\begin{equation}
  \langle (\delta_r u)^n \rangle \sim |r|^{\zeta_n} \quad \textrm{with scaling exponents} \quad \zeta_n = \frac{n}{3}-\frac{\mu}{18} n(n-3)=an-bn^2\;.
\label{eq:k62}
\end{equation}
Latter model can also be cast as a stochastic process for the velocity increment $\delta_r u$ which evolves from large scales $L$ to smaller scales~\cite{Friedrich:1997aa}. More precisely, the Langevin equation with
\begin{equation}
  -\textrm{d}(\delta_r u) = -\frac{2+\mu}{6r} \delta_r u \textrm{d}r + \sqrt{\frac{\mu}{18r}}\textrm{d}W(r)\;,
\end{equation}
where $W(r)$ is white noise. Hence, intermittency in the lognormal model of turbulence is described in terms of a multiplicative noise term in the corresponding Langevin equation for the velocity increments. As, by virtue of the randomization of Fourier phases described above, we actually suppress this part of the signal, we are able to compare the remaining self-similar part in the K62 model, which
is given by $H_{K62}= (2+\mu)/6$ to our analysis.
The estimated value $\langle H_{opt} \rangle $ thus implies that $\mu = 0.2716 \pm 0.1506$. This result, which has been obtained from the optimization procedure of the randomized signal can now be compared to the original turbulent case as follows: First, the velocity increment PDF of the K62 model is accessible via a Mellin transform of the scaling exponents (\ref{eq:k62}), which yields~\cite{yakhot:2006}
\begin{equation}
    f(\delta_r u) = \frac{1}{2\pi \delta_r u\sqrt{\ln r^b}}
    \int_{-\infty}^{\infty} dx \; e^{-x^2} \exp\left[- \frac{\left(\ln \frac{\delta_r u}{r^a\sqrt{2}x}\right)^2}{4 b \ln r}\right]\;,
    \label{eq:pdfK62}
\end{equation}
This PDF is now used as a fit for the velocity increment PDFs in Fig.~\ref{fig:pdfs}(b) after a symmetrization (the PDFs in Fig.~\ref{fig:pdfs}(b) are strongly skewed, a direct consequence of the turbulent energy transfer). The symmetrization is depicted in Fig.~\ref{fig:pdfs}(c) and fitting the whole range of scales that were used for the optimization procedure from above, we obtain $\mu = 0.2913 \pm 0.0833$. Examples of the corresponding PDF (\ref{eq:pdfK62}) for different scales $r$ are indicated as the dashed curves in Fig.~\ref{fig:pdfs}(c). Hence, the intermittency coefficient determined from the optimization procedure of the randomized signal and the intermittency coefficient from the turbulent signal agree fairly well.

Finally, we applied the optimization procedure to the original turbulent signal in keeping the same range of scales and number of sub-samples as for the randomized signal. The result is shown in Fig.~\ref{fig:hist}(c). Compared to (a) and (b) the optimal Hurst exponent exhibits stronger sample-to-sample fluctuations. The mean value, however, agrees very well with the Hurst exponent of the randomized signal and we obtain $\langle H_{opt} \rangle= 0.3797 \pm 0.0368$. In order to further categorize these fluctuations, which can be considered as a direct signature of intermittency (i.e., non-self-similarity), we determined the joint PDF of the optimal Hurst parameter and the energy dissipation rate $\varepsilon = 2\nu  \left(\frac{\partial u(x)}{\partial x} \right)^2 $ averaged over each sample. The result is depicted in Fig.~\ref{fig:joint} (a). Even with limited statistics, we observe a clear negative correlation between energy dissipation and the determined value of the optimal Hurst exponent.
\bibliographystyle{apsrev4-1}

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Hamani Tanginoa
First published Fri 21 Aug 2020, 3:05pm
University student and musician
Hamani Tanginoa is a proud Wiradjuri, Gooreng Gooreng and Tongan man from South West Sydney.
He was born and raised in Campbelltown surrounded by the Aboriginal community.
Hamani is a musician, student and activist and raises issues regarding Aboriginal Affairs, Education, LGBTQI+ Rights and Mental Health. Hamani is currently studying a Bachelor of Music (Contemporary Performance) at JMC Academy, Sydney. His university course was disrupted due to COVID-19 and has transitioned his performance degree online.
In 2019, Hamani made history by getting elected the first Aboriginal Youth Premier in the NSW YMCA Youth Parliament Program. Also in 2019, Hamani was a member of the Youth Advisory Council at the Office of the Advocate for Children and Young People.
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5 year-old shot, parents refuse to help police
5-yar-old boy shot in Sydney's south west
by The Sydney Telegraph
2nd May 2017 6:32 AM
A FIVE-year-old boy has been taken to hospital after being shot in a home in Sydney's south west last night.
Police said they were called to a home on Morison Dr, Lurnea about 9pm after reports a five-year-old boy had suffered a gunshot wound to the hip.
A forensic service officer at the scene, following the shooting, late last night. Bill Hearne
The boy was taken to Liverpool Hospital by his parents - where he remains in a stable condition.
Police this morning said the family car is being forensically exmained as a matter of procedure.
A crime scene has been established at the home as detectives carry out their investigation.
The five-year-old boy suffered a single gunshot wound. Bill Hearne
Police said the motive for the shooting was not yet clear.
Police are still piecing together what occurred. Bill Hearne
Anyone with further information should contact Crime Stoppers on 1800 333 000.
Originally published as Boy, 5, shot in Sydney's south west
editors picks injury police shooting


Commercially available conductive paint was tested by stirring and painting lines in the same manner as the other samples. The commercially available conductive paint was much more liquid so it produced thinner traces. All traces were dried for at least five hours in the order to test their resistance as it would be in a finished project. Each substance was measured again with fixed-width probes. Close-up pictures were taken of each sample using a macro lens. The lens has a very shallow depth of field which is not flat so the samples are not entirely visible. Acrylic paint with graphite powder is the most conductive sample in this experiment when painted in a line like a circuit trace.
Did you do any testing with different COLORS of acrylic paint? I don't know what they make the pigments out of, but isn't it possible that some are more conductive than others? Some might be metal-based.
Also, some very inconsistent results in readings.
Glue all resistance goes up only 6.6% from thick to thin line.
Tite Bond III actually does DOWN with a thinner line. Doesn't make sense.
Acrylic goes up by 1935 times from thick to thin line.
Wire Glue goes up by 7700 times from thick to thin line.
Since in every case the conductive medium is graphite, I would expect more consistent results. I suspect the Titebond results are reversed.
I only tested a single color of paint which I had on hand but I suspect some metallic sheen paints would conduct differently. If I were going to make a large batch I would likely choose those paints over plain colors now that you mention it. You got my brain ticking again and here I thought I was done with this project.
The pictures for the readings tell the story and you are right that the test results didn't make logical sense. All I was looking for was for was a rough idea of which medium conducted well while maintaining a usable consistency. The paint stood out as a clear winner so I called it. The tests I ran were not scientifically rigorous in fact I would put them on par with a high school science experiment.
I was thinking more in terms of normal paint pigments based on metal compounds, not metallic ones. For example, cadmium, antimony and chrome compounds are used for different colors. I can't take that information too far because I'm not really knowledgeable about pigment chemistry. But the thought occurred to me the pigment might matter. I don't even know if any of those compounds are even conductive even though they are metal compounds.
Anyway, you're the first guy I know who has actually done anything about this application, and I applaud you for that.
I've been a ham for 57 years and spend most of my time designing and building circuits, not yakking on the radio. So the idea of a reasonably conducting paint looks like an interesting direction to spend my time on.
I see what you're saying. I'm probably less of a chemist than you but I think some reds may use iron oxide as a pigment and that has a lot of potential. At least it would when I applied voltage. Sorry, I couldn't resist that joke. Or that one.
I wonder how the hobby store would feel about me opening all their paints and dipping my probe leads in. They probably wouldn't be too keen on it and I don't think paint inspector is a real thing.
My analog/digital communications teacher was a HAM operator and had been for years. When they finally dropped the necessity to learn Morse Code he really pushed us to get our licenses. Sadly I didn't and now the hack space I go to has a HAM station I could use if I were licensed.
If you make a winning recipe I will gladly link from this site. Good luck.
Not too late, Brian. A couple weekends of study (mostly FCC regs and a little theory), and take a test with a Volunteer Examiner through any of a number of ham radio clubs, and you're licensed. Your hack space guys will know exactly what you need to do. No code, but lots of new hams are discovering code is fun and VERY practical for weak signal work because of very low bandwidth, and the fact it is the only digital mode that the human brain can copy. Anyway, glad I found your blog and your work with conductive paints.
When I give it a try, I'll post here as I don't have a blog. You can use the info any way you choose then.
Maybe you can give the sales pitch for ham radio to me and anyone else following this comment thread. The internet can provide all the communication I need so what's the appeal of doing it over analog?
I have heard that when tragedies strike that civilian radio operators are some of the first to respond and instrumental as a communication hub. To date that's the most appealing thing I've heard.
It would be awesome if you found a recipe that tested better than what I've done and I think the readers would appreciate another set of hands testing!
Those of us who fly RC sailplanes could easily put in motors and bore holes in the sky, but we'd probably fall asleep out of boredom. My guess is that HAM operators would feel the same way about the internet. I imagine they like playing around with antennas and mastering all sorts of technical info. Somewhere I have a stack of radio contact cards that my ex's father gave me. Rather interesting, from a bunch of different far away places.
On the conductive paint, my guess is that a lot of the resistance is at the junction between the voltmeter probe and the material. You've got a variably shaped surface with very little contact area. It's also not clear that the proportion of graphite to paint is constant all the way through the material. Maybe in some cases the paint/glue comes to the outside. You might try making some standard size copper pads and running tracks between them. Or figure out how to make a really consistent, large surface, and use a good sized chunk of metal placed on it instead of a voltmeter probe. Maybe you can just built it up thick and sand it down flat?
Someday, I am going to get my van de Graaff generator going again, and it needs a better terminal. Metal spinning is kind of expensive.


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Russian carrier, Transaero Airlines, has signed a letter of intent to purchase 20 A330 aircraft from Airbus, including eight A330ceos and 12 A330neos.
The carrier, which announced the deal at the Farnborough Airshow last week, will use the aircraft to modernize its fleet and increase its medium and long-haul international and domestic services. The deal is worth $5.3bn at list prices.
The airline has not confirmed which engine will power its new A330ceos.


text:
// .NAME vtkHyperOctreeSampleFunction - sample an implicit function over an
// hyperoctree
// .SECTION Description
// vtkHyperOctreeSampleFunction is a source object that evaluates an implicit
// function to drive the subdivision process. The user can specify
// the threshold over which a subdivision occurs, the maximum and minimum
// level of subdivisions and the dimension of the hyperoctree.
//
// .SECTION See Also
// vtkSampleFunction

#ifndef vtkHyperOctreeSampleFunction_h
#define vtkHyperOctreeSampleFunction_h

#include "vtkFiltersHyperTreeModule.h" // For export macro
#include "vtkHyperOctreeAlgorithm.h"

class vtkImplicitFunction;

class VTKFILTERSHYPERTREE_EXPORT vtkHyperOctreeSampleFunction : public vtkHyperOctreeAlgorithm
{
public:
  vtkTypeMacro(vtkHyperOctreeSampleFunction,vtkHyperOctreeAlgorithm);
  void PrintSelf(ostream& os, vtkIndent indent);

  static vtkHyperOctreeSampleFunction *New();

  // Description:
  // Return the maximum number of levels of the hyperoctree.
  // \post positive_result: result>=1
  int GetLevels();

  // Description:
  // Set the maximum number of levels of the hyperoctree. If
  // GetMinLevels()>=levels, GetMinLevels() is changed to levels-1.
  // \pre positive_levels: levels>=1
  // \post is_set: this->GetLevels()==levels
  // \post min_is_valid: this->GetMinLevels()<this->GetLevels()
  void SetLevels(int levels);

  // Description:
  // Return the minimal number of levels of systematic subdivision.
  // \post positive_result: result>=0
  int GetMinLevels();

  // Description:
  // Set the minimal number of levels of systematic subdivision.
  // \pre positive_minLevels: minLevels>=0
  // \post is_set: this->GetMinLevels()==minLevels
  void SetMinLevels(int minLevels);

  // Description:
  // Return the threshold over which a subdivision is required.
  // \post positive_result: result>0
  double GetThreshold();

  // Description:
  // Set the threshold over which a subdivision is required.
  // \pre positive_threshold: threshold>=0
  // \post is_set: this->GetThreshold()==threshold
  void SetThreshold(double threshold);

  // Description:
  // Return the dimension of the tree (1D:binary tree(2 children), 2D:quadtree(4 children),
  // 3D:octree (8 children))
  // \post valid_result: result>=1 && result<=3
  int GetDimension();

   // Set the dimension of the tree with `dim'. See GetDimension() for details.
  // \pre valid_dim: dim>=1 && dim<=3
  // \post dimension_is_set: GetDimension()==dim
  void SetDimension(int dim);

  // Description:
  // Set the size on each axis.
  vtkSetVector3Macro(Size,double);

  // Description:
  // Return the size on each axis.
  vtkGetVector3Macro(Size,double);

  // Description:
  // Set the origin (position of corner (0,0,0) of the root.
  vtkSetVector3Macro(Origin,double);
  // Return the origin (position of corner (0,0,0) ) of the root.
  vtkGetVector3Macro(Origin,double);

  // Description:
  // Return the length along the x-axis.
  // \post positive_result: result>0
  double GetWidth();

  // Description:
  // Set the length along the x-axis.
  // \pre positive_width: width>0
  // \post width_is_set: GetWidth()==width
  void SetWidth(double width);

  // Description:
  // Return the length along the y-axis.
  // Relevant only if GetDimension()>=2
  // \post positive_result: result>0
  double GetHeight();

  // Description:
  // Set the length along the y-axis.
  // Relevant only if GetDimension()>=2
  // \pre positive_height: height>0
  // \post height_is_set: GetHeight()==height
  void SetHeight(double height);

  // Description:
  // Return the length along the z-axis.
  // Relevant only if GetDimension()>=3
  // \post positive_result: result>0
  double GetDepth();

  // Description:
  // Return the length along the z-axis.
  // Relevant only if GetDimension()>=3
  // \pre positive_depth: depth>0
  // \post depth_is_set: GetDepth()==depth
  void SetDepth(double depth);

  // Description:
  // Specify the implicit function to use to generate data.
  virtual void SetImplicitFunction(vtkImplicitFunction*);
  vtkGetObjectMacro(ImplicitFunction,vtkImplicitFunction);

  // Description:
  // Set what type of scalar data this source should generate.
  vtkSetMacro(OutputScalarType,int);
  vtkGetMacro(OutputScalarType,int);
  void SetOutputScalarTypeToDouble()
    {this->SetOutputScalarType(VTK_DOUBLE);}
  void SetOutputScalarTypeToFloat()
    {this->SetOutputScalarType(VTK_FLOAT);}
  void SetOutputScalarTypeToLong()
    {this->SetOutputScalarType(VTK_LONG);}
  void SetOutputScalarTypeToUnsignedLong()
    {this->SetOutputScalarType(VTK_UNSIGNED_LONG);};
  void SetOutputScalarTypeToInt()
    {this->SetOutputScalarType(VTK_INT);}
  void SetOutputScalarTypeToUnsignedInt()
    {this->SetOutputScalarType(VTK_UNSIGNED_INT);}
  void SetOutputScalarTypeToShort()
    {this->SetOutputScalarType(VTK_SHORT);}
  void SetOutputScalarTypeToUnsignedShort()
    {this->SetOutputScalarType(VTK_UNSIGNED_SHORT);}
  void SetOutputScalarTypeToChar()
    {this->SetOutputScalarType(VTK_CHAR);}
  void SetOutputScalarTypeToUnsignedChar()
    {this->SetOutputScalarType(VTK_UNSIGNED_CHAR);}

  // Description:
  // Return the MTime also considering the implicit function.
  vtkMTimeType GetMTime();

protected:
  // Description:
  // Default constructor.
  // Set dimension to 3, width, height and depth to 1, levels to 5, minLevels
  // to 1, implicitFunction to 0, OutputScalarType to VTK_DOUBLE,
  // Threshold is 0.1.
  vtkHyperOctreeSampleFunction();
  ~vtkHyperOctreeSampleFunction();

  int RequestInformation (vtkInformation * vtkNotUsed(request),
                          vtkInformationVector ** vtkNotUsed( inputVector ),
                          vtkInformationVector *outputVector);

  virtual int RequestData(vtkInformation *, vtkInformationVector **, vtkInformationVector *);

  void Subdivide(vtkHyperOctreeCursor *cursor,
                 int level,
                 vtkHyperOctree *output);

  int Dimension;
  double Size[3]; // size on each axis
  double Origin[3]; // position of corner (0,0,0) of the root.
  int Levels;
  int MinLevels;

  int OutputScalarType;
  vtkImplicitFunction *ImplicitFunction;
  double Threshold;

private:
  vtkHyperOctreeSampleFunction(const vtkHyperOctreeSampleFunction&) VTK_DELETE_FUNCTION;
  void operator=(const vtkHyperOctreeSampleFunction&) VTK_DELETE_FUNCTION;
};

#endif

redpajama_set_name: RedPajamaGithub


A new training program from Canada's BC SPCA is a model for all to follow.
"Our hope is that programs like AnimalKind will draw attention to the evidence for humane dog training, and help move the dog training profession towards using more evidence-based methods."
I wanted to know more about AnimalKind dog training, so I asked Dr. Dubois if she could answer a few questions. She deferred to Dr. Karen van Haaften, the BC SPCA's senior manager of behavior and welfare and a diplomate of the American College of Veterinary Behaviorists. Dr. van Haaften answered the questions I sent except for number 6. Our interview went as follows.
Why did you establish AnimalKind Dog Training? How did your backgrounds move you in this direction?
Working with an effective, humane dog trainer can help many dogs and relationships between dogs and their owners. Evidence on which training methods are most humane & effective is growing, but the dog-owning public is not always aware of the findings. Additionally, the dog training industry in North America is unregulated, and although several high-quality dog training schools and certification programs exist, members of the public are often confused about which certifications indicate humane, evidence-based training expertise.
The AnimalKind team is made up of animal welfare scientists, professional dog trainers, and a board-certified veterinary behaviorist. Behavior concerns are a leading cause of dog relinquishment to animal shelters, and we (the BC SPCA) get hundreds of requests for dog trainer referrals every year. Until now, we haven't had a way to objectively evaluate the quality of dog training businesses.
What are the basic guidelines for the methods you use?
Aversive training tools and methods have been shown in many studies to be associated with poor welfare consequences and undesirable behavioral outcomes for the dog (increased fear-based behavior, including aggression). In contrast, rewards-based training methods are associated with better obedience, lower rates of aggression and other fear-based behaviors, and increased attention to owner during training.
Full details on our position on methods used to train animals are available here, and our literature review on dog training methods can be seen here.
How do you assess each dog and their human who come to you to solve a given problem or problems? Of course, each dog is an individual and requires personal attention.
Based on the individual dog's temperament and history, skilled trainers are able to determine a comprehensive behavior plan that may involve environmental management and training new behaviors. Trainers can also help dog owners determine the cause of undesirable behaviors, which can be addressed by removing the reinforcer for the undesirable behavior, and making an alternative behavior consistently more rewarding.
An example: let's say a dog is jumping on people for attention. You could remove the reinforcer for the jumping behavior (turn away and ignore the dog whenever they jump on you), and train an alternative behavior (for example, stand calmly with 4 feet on the floor), and reward the dog with attention or treats whenever they perform that behavior. Over time, the dog will learn not to engage in the jumping behavior and instead stand calmly for attention.
How much emphasis do you put on working with the human companion, as so many "dog problems" are really just as much "human problems?"
Good dog trainers need excellent communication skills. This is why our standards require trainers to demonstrate not only theoretical knowledge of dog behavior and learning science, but also be proficient in the practical skills of training and client communication.
Why do you think that some people remain resistant to reward-based training, including the use of food?
This is multi-factorial. Research has shown many dog owners do not reach out to professionals for advice on dog training methodology. Even if they do reach out to dog trainers, the profession is unregulated, and many trainers are still using and promoting outdated methods. In addition, cultural factors such as family history or celebrity trainers influence how owners interact with their dogs. The evidence on negative welfare and behavioral consequences of training dogs using aversive tools and methods is relatively recent, and it may take time for the general public to become aware of the risks.
AnimalKind accreditation programs launched in March 2018 to initially respond to the need to help the public navigate the unregulated "pest" control industry and find humane wildlife and rodent control operators the BC SPCA could recommend based on current evidence and practices in the field. We currently have two accredited companies in this space which otherwise struggles to define "humane" appropriately, since it is often a marketing term used by companies that still use harmful methods.
In the future, given that other pet service industries are also unregulated, and the public frequently contacts the BC SPCA for referrals, we will be creating evidence-based standards for doggie daycares, kennels and boarding facilities. We look forward to recommending companies that are committed to humane and transparent practices!
Our hope is that programs like AnimalKind will draw attention to the evidence for humane dog training, and help move the dog training profession towards using more evidence-based methods.
Thank you Karen and Sara for taking the time to answer my questions. Your program surely is a model that can clearly help dogs and their humans, and I hope it receives a global audience. Humans who choose to bring dogs (or other companion animals) into their homes and hearts are obligated to give them the very best lives possible. (See Canine Confidential: Why Dogs Do What They Do and Unleashing Your Dog: A Field Guide to Giving Your Canine Companion the Best Life Possible.) I found your "Review of dog training methods: welfare, learning ability, and current standards" to be an incredibly important read, one that should put to rest ideas that aversive conditioning, including the use of shock collars, work well in the long term. (See "Is It Time to Ban Shock Collars for Dogs In All Situations?" and "What and Who Dogs Want and Need: Love, Not Shocks.") I hope that everyone who works in the dog training field will read it. For those who don't or won't, I've provided some snippets in Note 1. And, as I mentioned above, your summary of dog training standards should be required reading for anyone who chooses to work in the area of dog training/teaching. Dog who have trouble fitting in the world of dogs or an increasingly human-dominated world need all the help they can get, and your program will help make it a win-win for all involved, the dogs and their humans.
Here is a brief overview of the BC SPCA's report titled "Review of dog training methods: welfare, learning ability, and current standards" by Dr. I. J. Makowska. Concerning comparisons between reward-based vs. aversive-based methods and dog welfare (Page 6) we learn that "two empirical studies found that training with aversive-based techniques led to more stress-related behaviours in the dogs compared to training with reward-based techniques, Stress-related behaviours persisted even after the dogs were trained and the aversive stimulus was no longer used, suggesting that the verbal cues themselves had become aversive, 5 in 5 surveys found that more frequent reported use of aversive-based techniques, whether alone or in combination with reward-based techniques, was associated with more frequent reporting of aggression and other problem behaviors, and More frequent use of R+ [reward-based positive reinforcement] alone was associated with less frequent reporting of aggression and other problem behaviors." Concerning dog-human relationships we learn that "Dogs trained with R+ were more likely to gaze at their guardians during training than dogs trained with R-, but dogs trained with R+ may have simply been looking to their guardians for treats, Dogs whose guardians reported using P+, P- or R- were less likely to interact with their guardian and with a stranger during a play session than dogs of guardians who reported using R+, and with respect to training success, "More frequent reported use of P+, R- or P- was associated with lower obedience and learning ability and More frequent reported use of R+ was associated with better obedience and learning ability. The summary of research conducted on the use of shock collars shows "3 in 3 empirical studies reported yelping and other vocalizations in response to shock, 2 in 2 empirical studies found more immediate stress-related behaviors in dogs trained with vs. without a shock collar (e.g. lowered ears, lip licking, lifting of front paw), 2 in 2 empirical studies reported long-term negative effects in dogs trained with vs. without a shock collar (increased alertness; persistent stress-related behaviors around the handler), and 2 in 3 surveys found lower success in training by guardians who used shock vs. other reward- or aversive-based methods; the third found no difference."
Punishment-Based Dog Training a Risk Factor for Euthanasia?


text:
Q: Lower bound on minimal eigenvalue of $B(A+B)^{-1}A$ Matrix $A$ and $B$ are symmetric positive semidefinite. I wish to lower bound the minimal eigenvalue of the matrix product:
$$B(A+B)^{-1}A$$
The first lower bound I came up with is:
$$\lambda_{\min} \left( B (A+B)^{-1} A \right) \geq \frac{\lambda_{\min}(A) \cdot \lambda_{\min}(B)}{\lambda_{\max}(A+B)}\geq\frac{\lambda_{\min}(A) \cdot \lambda_{\min}(B)}{\lambda_{\max}(A)+\lambda_{\max}(B)}$$
According to this lower bound, if we do rank-one update on $A$ and $B$ in such a way that $\lambda_{\min}(A)$ and $\lambda_{min}(B)$ remain the same, but $\lambda_{\max}(A)$ and $\lambda_{\max}(B)$ increase (by updating A and B using eigenvectors corresponding to their maximum eigenvalues), then the lower bound will decrease.
However, my simulation results show that the minimum eigenvalue of this matrix product actually always increases as I rank-one updating $A$ and $B$.
Then my question is, whether there is any tighter lower bound of the minimum eigenvalue of this product?

A: Assume both $A$ and $B$ are invertible.
To lower bound the minimum eigenvalue, we need the following result.
$$B(A+B)^{-1}A=(A^{-1}+B^{-1})^{-1}$$
Then it directly leads to:
$$\lambda_{\min} \left( B (A+B)^{-1} A \right) = \frac{1}{\lambda_{max}(A^{-1}+B^{-1})}\geq \frac{1}{\lambda_{max}(A^{-1})+\lambda_{max}(B^{-1})}=\frac{1}{\frac{1}{\lambda_{min}(A)}+\frac{1}{\lambda_{min}(B)}}$$
To show that $B(A+B)^{-1}A=(A^{-1}+B^{-1})^{-1}$, we start from:
$$A^{-1}(A+B)B^{-1}=A^{-1}AB^{-1}+A^{-1}BB^{-1}=B^{-1}+A^{-1}$$
Then taking inverse of both sides gives the result.

redpajama_set_name: RedPajamaStackExchange


These statements don't have any natural question tags, because the verbs "do" and "let" are in the imperative mood. There's no question tag for a command ("Go to bed, ______?!"). The closest thing is that the second statement could be made into "Let's sing now, shall we?"


The Chancel Choir is directed by Laura Bigley. The choir sings a special anthem most Sundays during the worship service.
The Choir practices on Wednesday evenings at 7:30 p.m. and is open for youth and adults who would like to share in the music ministry of the church.


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Vimeo®, an operating business of IAC [NASDAQ: IACI], today announced its Apps for Android, Amazon Kindle Fire, and Windows Phone. The Apps are free to download from the Android Market, Amazon App Store for Android, and Windows Phone Marketplace.
Available today, both the Vimeo for Android and Vimeo for Windows Phone Apps enable users to browse and watch the full range of videos on Vimeo, in addition to the ability to like, comment on, or add videos to their Watch Later queue. Personalized access to member's Inbox, Watch Later list or prior Liked videos is also available in the new beautiful, easy-to-use interfaces.
Leading video sharing site Vimeo® provides the easiest way for people to host and share their videos in high quality. The site provides great privacy features and inspiring videos from a vibrant, respectful community of creative users who care about how and where they show their work. Launched in 2004 and headquartered in New York, NY, Vimeo offers users a video sharing experience that is both entertaining and easy to use. Vimeo is a 2010 & 2009 Time Magazine Top 50 Website. Vimeo, LLC is a subsidiary of IAC (NASDAQ: IACI).
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Haha I love your guys' costumes!! That is awesome!
You guys had a busy month!
It took forever to paint those runes on, but it was a lot of fun. The kids were nice enough to share some of their candy with me. They know my weakness- Reese's Peanut Butter Cups!
Cool costumes! I skipped on it this year.
I loooove the series! When I finished reading Talon's book (loved it!), I was quite upset to find out that the next book was checked out already at the library. Can't wait to read more about Zarek!
Thanks! :) We decided to do this at almost the last minute, but it worked out pretty well.


Using qualitative methods this research uses Aristotelian theory as a framework to explore the rhetorical strategies used in the discussion and portrayal of biotechnology within Canada's seed industry. Using Aristotle's modes of persuasion (ethos, logos, and pathos) and his three types of rhetoric (deliberative, forensic, and epideictic) this research analyzed an example of each type of rhetoric. As an example of deliberative rhetoric this research analyzed a House of Commons debate on agricultural policy and biotechnology. The lawsuit between Monsanto and Percy Schmeiser was analyzed as an example of forensic rhetoric. Lastly, as an example of epidictic rhetoric, Monsanto's Canadian website was analyzed. This research also highlights the approaches which need to be taken by anti-GMO groups in order for their messages to be received by the general public.
Fraser, Meghan, "Understanding the Rhetoric Used in the Discussion of GM Seeds and Biotechnology" (2013). Electronic Theses and Dissertations. 4975.


text:
Q: Create History via single file and without edited my created model All.
I want track history of my whole admin panel via LARAVEL. And, already my project was done so now I don't want to go for change each and every model file like don't want to change model event or don't want create observer or trait.
So I have already tried with Trait, Observer and also via model event.
Using those option I have to add / update my code for each and every module so, Without change all files. How can I do that?


Home Staircase Design Ideas 1 is free HD wallpaper. This wallpaper was upload at September 06, 2018 upload by Farica Gaby in Interior Design.
Home Staircase Design Ideas 1 in your computer by clicking resolution image in Download by size:. Don't forget to rate and comment if you interest with this wallpaper.
Stairs are so commonplace in architecture that you might not give them a second thought. But as evidenced by modern stair design, it's something that we should pay attention to more. When done well, they transcend their primary function and become sculptural works of art that make ordinary places come alive.
24 photos of the "24 Gorgeous Staircase Design For Amazing Home Interior Ideas"
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Does it feel too much to talk about? When our depression, grief, anxiety and other painful emotions become too much, we can turn inward and isolate. Art therapy helps people heal. This is not an art class. No skill level is needed since every piece of art is an expression of emotion. For example, some of the projects I do with clients include: worry boxes, gratitude trees, angry postcards, collages, acrylic paintings and watercolors.
Click here to read more about the experience and benefits.
A typical experience inclues meditative music play while art is created. I guide my clients through the process, helping them create and share their story. Unleash your creativity and find your voice. Contact me today to schedule an appointment.


text:
University of Texas at Austin Home
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UT Directory
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Provost's Office Org. Chart
Provost Appearance Request Form
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Institutional Accreditation and Effectiveness 2015-2016 Year in Review
The Office of Institutional Accreditation and Effectiveness (IAE) led many initiatives this past academic year designed to improve organizational processes and the student experience. Collaborating with hundreds of faculty, staff, and administrators and leading multiple cross-campus committees, the IAE team worked hard to ensure the university's success in accreditation and assessment.
Here are some of the major accomplishments of IAE's initiatives over the past year:
Launching the University's 2018 Reaffirmation of Accreditation.
Every ten years, UT Austin undergoes reaffirmation of accreditation with the Southern Association of Colleges and Schools Commission on Colleges (SACSCOC). Besides supporting internal quality control and demonstrating accountability to constituents, accreditation entitles UT Austin to receive federal funds, including research monies and student scholarships and loans.
From fall 2015 to spring 2018, campus partners will collaborate to prepare the institution for its reaffirmation; this year, IAE unveiled a new website to keep the campus community informed about accreditation.
Also this year, President Fenves convened an Accreditation Steering Team that provides oversight of reaffirmation. SACSCOC President Dr. Belle Wheelan and Vice-President Dr. Mike Johnson accepted our invitation to visit campus in March. During the successful one-day event, the SACSCOC representatives met with the president, provost, and members of the steering team to discuss accreditation benefits and processes.
Last semester, IAE met with over 60 faculty members, staffers, and administrators who are writing the Compliance Certification Report (CCR), which documents institutional accomplishments and practices across 94 SACSCOC standards of quality.
Selecting the University's Quality Enhancement Plan.
As part of UT Austin's reaffirmation, the university must create a Quality Enhancement Plan (QEP) that proposes a major, well-defined, evidence-based, five-year initiative that impacts student learning. In the spring, the IAE team kicked off the QEP selection process by convening the QEP Development Committee. This group of faculty and administrators conducted an environmental scan that included reviewing structures and resources, identifying constituencies, and assessing strategic documents and reports. The committee also reviewed substantial state and national data related to student learning initiatives. Committee members have identified potential topics and will discuss their recommendations with the provost over the summer.
Revising the University's Assessment Framework.
The university began assessing all academic programs and many non-academic services and departments in 2006 with the goal of engaging in continuous improvement. In 2014, IAE reflected on institutional assessment processes, concluding that the university would benefit from a new approach that acknowledges the evolution of assessment over the past eight years. Consequently, the team led a 2015 initiative to adopt a new assessment framework. Along with faculty, staff, and administrators who served on the Institutional Effectiveness Committee, they conducted extensive research, including benchmarking, case studies, literature reviews, and a campus needs assessment.
The findings led IAE to launch a new assessment framework in the fall 2015 that focuses on use, utility, and meaningful and sustainable practices; it also creates more uniform language and expectations across academic and non-academic units. The intention is that the framework will provide more flexibility to units, especially allowing them to capture more comprehensive and strategic changes to curriculum, pedagogy, processes, operations, or services.
Early feedback indicates that campus partners think the framework is simpler, more comprehensive, and more meaningful.
Generating Assessment Principles and Guidelines.
IAE, along with the Institutional Effectiveness Committee (IEC), crafted and ratified principles and guidelines to support the university's assessment culture. The IAE team is pleased to have these statements, which can serve as touchpoints about why and how we conduct outcomes assessment. Units can look to the principles and guidelines in order to further develop their own processes and structures. True to the guidelines, many colleges/schools, departments, and units created assessment committees this year that ensure local ownership of assessment and provide opportunities for collaborative reflection and decision making.
110 Inner Campus Dr STOP G1000
Public Access to Course information (HB 2504)


"Seven billion souls that move around the sun, rolling faster, faster and not a chance to slow down" 🎶 One of my favorite songs of the past decade. If this blows up, it could be the "Marvin Gaye - What's Going On" of my generation.
I like the way the message was artistically done. I think this has got to be the best video of 2018 thus far!
This is just beautiful and dope!


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Texas Woman Competes For "Extreme Huntress" Title
A lady hunter from Dripping Springs has her eye set on becoming an "Extreme Huntress." She also wants to help more women and children get involved with hunting. Olivia Nalos was recently selected as one of 10 finalists in the Extreme Huntress Contest. "The goal for the Extreme Huntress Contest is to create positive roles models for other woman who are considering involvement in the outdoor field sports. Our outdoor heritage's future is at stake," Nalos said. "With divorce rates at 50 percent and the latest Census stating 41 percent of all children in the USA are born out of wedlock, we feel there must be more woman involved in outdoor field sports. What it comes down to is if mom goes hunting — then her kids will too — ensuring our outdoor heritage," she added. Nalos' passion is to educate the public about the important role hunters play as stewards of nature. Because of her appreciation for wildlife, and enthusiasm to help promote conservation, she's involved in the hunting industry as a hunting consultant and adventure coordinator for The Global Hunting Network. In addition, she has appeared on various hunting television shows; has an online blog for NBC Sports; was published as a featured huntress in French author Maxime van Hanswijck de Jonge's book titled, "La chasse au feminine;" acted as a co-host of "Inside Texas Outdoors" radio show; and is a certified measurer for Boone and Crockett, Pope & Young, Rowland Ward, and a Master Measurer/Certified Measuring Instructor for SCI. Furthermore, she established the Nebraska Panhandle SCI Chapter where she served as chapter president and acted as SCIF Sables marketing committee chair. As the 2003 Mrs. Nebraska, America, her platform was to promote the importance of hunters as conservationists, humanitarians and stewards of renewable wildlife resources. With such a controversial platform she understood that criticism and adverse reaction would result, but remained eager to provide factual insight. Nalos hopes that fellow Texas Trophy Hunters might help by giving online voting support in this competition. Voting has already begun and will end Jan. 1, 2012. Besides winning the title, the winner will travel to Africa to hunt Cape buffalo in Zimbabwe and be filmed for a future episode of "Eye Of The Hunter" on NBC Sports starting August 2012. Visit http://tahoefilms.com/contests/extreme-huntress-contest/ to learn more about the competition.
QDMA to hold its 12th Annual National Convention in August
Quebec To Ban Caribou Hunting In 2018
Lawman Faces Poaching Charges


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Lanvin Robe de Style Savoir Faire
The 1920's were an interesting times in Paris, especially where fashion was concerned. World War I had ended and the devastating effects of the Depression were a long way off. Paul Poiret, who had helped liberate women on the fashion stage, was on the way out and Chanel was in. le garcons ruled, with her shapeless body and shingled hair dancing the Charleston and drinking cocktails.
All the couturiers adopted this look, except one! The amazing Jeanne Lanvin, whose signature look was the "robe de style". Other couture houses such as Callot Soeurs and Lucille had their own versions, however none were as successful as Lanvin's. This was an alternative to the rather shapeless, straight cut chemise which was the height of fashion. Lanvin's "robe de styles" were characterised by full skirts, with a dropped waist and fitted bodice. Petticoats, panniers and hoops emphasised the fullness of the skirts. These were then further embellished with intricate trimmings and extraordinary embroideries.
This was a look for a woman who did not have the figure to wear the tubular lines of the likes of Chanel or Patou. This was couture with an historical reference, harking back to the Infanta dresses of Spain. It was meant for afternoon teas, and tango dancing in the park; a kind of a cocktail, semi-formal ensemble. Lanvin made hundreds of variations on "the robe de style"; each one possesses its own unique charm.
Of course Lanvin produced fabulous outfits in keeping with the times and just did not limit herself the "robe de style", however these stand apart to the savoir faire that Lanvin had.
The amazing thing is she never adopted the "robe de style" look for herself, but limited herself to discreet and simple outfits with minimal detail.
Labels: Lanvin 9 comments


Where the Hell is my Hard Drive?
I'm doing this little helper writeup for anyone else who has ever felt the pain of trying to install Gentoo on a box with the HP Compaq Smart Array RAID controller. The machine that this was in was an HP Proliant DL380 G3. I've installed Gentoo many, many times on lots of different hardware and I've never had the level of problems that I had with this one. Below, I will detail my woes and workarounds to get this machine up and running with a Gentoo install.
This was an adventure. Why? I don't know. What I do know is that my 2008.0 Gentoo minimal cd would not boot. I also couldn't boot off of an Ubuntu 8.10 Desktop CD. I was able to boot off of an 8.10 Server CD and install Ubuntu server, but the machine wouldn't boot after that.
I'm not sure about everyone else, but usually expect my first hard drive (or LUN, in this case) to be /dev/sda. Not so with this wonderful RAID card. Your first drive is /dev/cciss/c0d0. I found some good documentation about the cciss kernel driver at http://www.mjmwired.net/kernel/Documentation/cciss.txt.
When you boot up off of your liveCD, you should be able to now run fdisk on /dev/cciss/c0d0 and set up your partitions. Note that the first partition on the disk will be /dev/cciss/c0d0p1, not /dev/cciss/c0d0a or something of that nature. Then just mount those and do the normal install procedure for Gentoo as documented on http://gentoo.org.
This is the driver you need so change the "m", or whatever is after "=", to a "y" and save the config file. This will assure that your kernel has support built right in for the RAID controller.
Ok, grub is installed. Yay!
If you setup your /boot/grub/grub.conf with the value you would assume for root=, you will probably be wrong. Whether you use genkernel or build your kernel manually, the root= portion of your "kernel" line should be root=/dev/cciss!c0d0p3 and NOT root=/dev/cciss/c0d0p3 as you would expect. I have no clue why this is, but it seems to be some oddity of the driver. This will cause your bootup to fail if this is not set correctly in /boot/grub/grub.conf.
Hopefully this is indexed by Google sooner, rather than later, and saves others the pain and unhappiness that I went through installing to my Proliant DL380 G3. Happy serving.


Another Bush has landed in Texas. Heisman winner is worthy of going number 1.
Top defensive end on the board. Must stop Pro Bowl QB's Vick & Delhomme.
O-Coordinator Norm Chow gets his prized pupil he helped win a Heisman.
Probably the first to make the Pro Bowl of the 2006 class.
Playmaker. The Packers don't have many on defense right now.
Niners QB Alex Smith needs a target over the middle.
Young to Randy Moss… it could be scary.
The Bills QB situation is still influx, but at least they'll have time to throw.
Lions add the best defensive back in the draft.
Cards gave up rushing 22 TD's. Ngata plugs the hole.
Marc Bulger is injury prone. Cutler becomes the understudy.
LB Ray Lewis plays better when the big boys upfront let him roam free.
Andy Reid loves to throw… he needs another target.
Ashley Lelie has been a bust and Rod Smith needs some help.
Fins are lucky to get the draft's second best corner this low.
Vikes forget to slow 40 time and look at the production on tape.
The next Carl Banks lines up on the outside with DeMarcus Ware on the other side.
Chargers must improve on the NFL's 28th ranked pass D.
KC's pass D is worse than San Diego's…. 30th!
Surprise pick, but Corey Dillon was hurt last year and entering his 10th season.
Williams will bring the run game back to Clinton Portis level… 1,500 yards.
Must stop division sackmasters Julius Peppers, Mario Williams, and John Abraham.
Carson Palmer needs a safety net over the middle.
His dad is Rob Carpenter, former Giants fullback.
The Monsters of the Midway need help in the defensive backfield.
DeShaun Foster gets hurt year in and year out, Maroney is insurance.
Marcellus Wiley should be a backup, not a starter. Enter Wimbley.
Edgerrin James replacement… Peyton Manning hopes.
Will take up blocks to free Steeler linebackers.