SUTROFOR Spring School "Preparing Field Work in the Tropics"
The "Tropical Forestry and Management" (MSc) Programme at TU Dresden, together with its SUTROFOR partner universities, is organizing a Spring School on "Preparing Field Work in the Tropics". To be held in Pokhara, Nepal, between 5 and 19 March 2019, the Spring School will be preceeded by a preparation course at home institutions between 28 January and 1 March 2019.
The Spring School aims to impart a thorough understanding of the importance of field work preparation. This is planned to be achieved through acquisition of in-depth factual location specific knowledge, building on theoretical knowledge obtained in other courses, relevant to tropical forestry issues and development of practical skills.
This even shall serve as a preparatory course for the Sustainable Tropical Forestry Summer School, with the special focus on developing a minor research project (that will then be implemented during the spring school taking place in a developing country). The course thus covers the entire research process until the implementation phase. Particular attention is paid to practical considerations in connection to research design and implementation, group work, field work, e.g. ethics, personal safely, training and managing enumerators.


The Show and Share portion of a meeting is where members share up to five of their images with those in attendance via digital projection.
Sizing: When sizing images, please use the same conventions used for the Holiday Show, which is 1,800 pixels on the longest side.
Timing: Images must be submitted by midnight the Saturday preceding the monthly meeting.
Ten great ways to break the rule against middle-of-the-day photography recommended by past speaker Darrell Gulin.


Our rigorously trained Private Wealth Advisors provide clients with a direct line to all of the resources and capabilities of a truly global investment firm – in ways that are more in keeping with a small, very exclusive boutique. Concierge services, family meetings, private seminars and events are all part of the experience.
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Carl and his wife, Jan, have been married for 33 years and have three sons. In his free time, he takes advantage of the Colorado lifestyle by hunting, fishing, skiing and spending time outdoors — and enjoys sharing these activities with clients.
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A dedicated community member, Scott serves on the Board and Investment Committee of the Colorado State University Foundation as well as the board at CASA Advocates for Children. He is married with three children and is an avid volleyball player, golfer and skier.
*Source: Barron's "Top 1,200 Financial Advisors" list, March 11, 2019. For more information about the selection criteria, go to http://details-he.re/1u7KVH. Barron's is a trademark of Dow Jones & Company, Inc. All rights reserved. These rankings and ratings are not representative nor indicative of any one client's experience, future performance, or investment outcome and such rankings should not be construed as an endorsement of the advisor. Years prior to 2014, this Barron's list consisted of 1,000 advisors.
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Mitch is a graduate of Colorado State University, where he led their ACHA Division ll Hockey team to three top 8 national finishes. Mitch is also an avid skier, having worked three years at Vail Resorts.
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A Tulsa native, Jennifer is an avid Oklahoma football fan. She is married and enjoys outdoor activities, traveling and attending concerts.
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Cheryl and her husband moved to Denver from Austin in early 2016. Away from the office she enjoys running, cycling and yoga. She and her husband have two golden retrievers who they look forward to exploring the Rocky Mountains with.
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At the Hoover-Thisted Team, we help executives, business owners and affluent families foster lasting wealth with sophisticated support and strategies. We serve clients during personal and professional transitions — such as business sales, IPOs and retirement — and have the experience and resources to guide them through these events while helping them build a strong foundation for the future.
With more than 115 years of combined Merrill tenure, our team has supported investors across multiple generations and market environments. We have deep Firm knowledge and relationships with specialists, and we believe Merrill Lynch offers the greatest resources and opportunities for high-net-worth investors.
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Licensed Realtor since 2006. I am married to my best friend Linda. We have 5 children, and 12 grandchildren, and 3 great grandchildren. I am a graduate of Rider University in New Jersey with a BS in Business Administration. My career started in sales, selling Pharmaceuticals. I was very successful, earning top sales awards at Marion Lab in Kansas City, MO. Now using my knowledge as a Professional Realtor, I help people find their dream home in this Paradise in South West Florida.


In the early hours of a Sunday afternoon in August, 300 or so of us randonneurs in the 5:30PM "G" group left the suburban confines and cheering crowds of St. Quentin-en-Yvelines bound for Brest. The nondescript tracts of concrete buildings soon gave way to the rolling terrain of countryside. I'd read in the event literature that the route used to be flatter, but the organizers had diverted it to quieter, rural roads with less traffic (due to some unfortunate fatalities on busier thoroughfares) and thus added more hills to the overall route. While there's no terribly steep, mountainous climbs on Paris-Brest-Paris, there's hardly any flatlands to rest your weary legs, either. It's a relentless up-and-down course with some long ascents near the western coast; it's a deceptively fatiguing route as you accumulate close to 36,000 ft. of elevation gain overall by the time you return to the start.
About 10 miles from the velodrome, we passed through a tunnel of tall sycamore trees that loomed over the road. Now we'd entered the gateway to another realm and teleported back into time to a tranquil countryside dotted with a charming succession of stone villages, castles and patches of cobblestoned streets. With overcast skies but no threat of rain, we were able to travel peacefully through the summer evening past the fields of wheat, corn and lush green meadows pockmarked with hay bales.
My riding group and I even had a lovely encounter with a husband-and-wife walking their cows down a narrow road, guiding them gently along with just a walking stick. And as we crossed over to the northwest region of Brittany, or Bretagne, signs began appearing in both English and the ancient Breton language with Celtic origins.
I wasn't able to truly capture the experience of riding at night with my small point-and-shoot camera. If I'd had a tripod and a more robust set-up, it would have wonderful to properly document the endless stream of red tail lights that twinkled off into the horizon.
Aside from the Pont de l'Iroise at Brest, Roc'h Trevezel was another stunning highlight of PBP for me. A gradual two-mile climb that's the highest peak en route, we reached it just at sunrise on our second day. It was a breathtaking vista, with the valley aglow with pastel tints of pink and orange light. The luminous packs of riders in their bright yellow fluorescent vests and sparkling headlights ascended in the soft morning mist. And we happily zoomed down from the summit, flattening our bodies against our bicycles so we could go as fast as possible, and relishing this hard-won reward for our efforts.
There were some glorious, postcard-perfect sunsets during Paris-Brest-Paris. And as the course was so well-marked by signs with reflective arrows, I didn't need to squint at my cue sheet at night. One did have to keep a vigilant eye out for these reliable markers, which were sometimes hard to spot. There were a few missed turns here and there, but we were always able to backtrack quite easily. (I later heard that some riders had trouble towards the end of the ride when some had of the signs had disappeared; they'd been snatched up by eager souvenir seekers. I managed to find a stray sign at the Brest control, but with no way to carry it back, passed it along to my friend Ian.) Most of the time, however, I could relax and just appreciate the scenery and the stars.


Dr. Greenhouse received his undergraduate degrees from MIT in mathematics and music prior to obtaining his medical degree from Cornell. He completed his general surgery residency at the University of Vermont. Following this he performed his thoracic surgery residency at The Massachusetts General Hospital. He completed fellowship training at Boston Children's Hospital and Children's Healthcare of Atlanta (Emory) specializing in congenital cardiac surgery.


text:
Q: How to allocate arrays inside a kernel using numba? I am trying to allocate some arrays inside a for loop that is inside a Kernel. The Kernel looks like this:
@cuda.jit
def forcecudatiling(p_num,d_num,r,force):
    threadsInBlock=cuda.blockDim.x
    threadsInGrid=threadsInBlock*cuda.gridDim.x
    tid=cuda.threadIdx.x + cuda.blockIdx.x*cuda.blockDim.x
    tiles=p_num/cuda.blockDim.x + 1
    shared_p_mx = cuda.shared.array(0,dtype=np.float32)
    shared_p_my = cuda.shared.array(0,dtype=np.float32)
    alpha=(1.5)
    rho=(1.0)
    beta=(1.5*(1.0+alpha))
    for k in range(tid,p_num,threadsInGrid):
        r_k=cuda.device_array((d_num,p_num))
        forcetemp=cuda.device_array((d_num,p_num))
        r_k[0,k]=r[0,k]
        r_k[1,k]=r[1,k]
        forcetemp[0,k]=0.0
        forcetemp[1,k]=0.0

The arrays that I am trying to allocate are r_k, and forcetemp but with the above code I get the following error:

TypingError: Failed in nopython mode pipeline (step: nopython
  frontend) Unknown attribute 'device_array' of type Module()
File "", line 117: def
  forcecudatiling(p_num,d_num,r,force):

      for k in range(tid,p_num,threadsInGrid):
          r_k=cuda.device_array((d_num,p_num))

A:
I am trying to allocate some arrays inside a for loop that is inside a
  Kernel

You can't do that. No memory allocation or array creation is  supported in Numba CUDA kernels.

redpajama_set_name: RedPajamaStackExchange


001 Project Shoal - A 12 kiloton nuclear device was detonated below this site October 26, 1963. The nuclear test was conducted as part of the Vela Uniform Program sponsored jointly by the US Department of Defense and the Atomic Energy Commission. The nuclear device was detonated in a granite formation at a depth of 1,211 feet. Access was through a horizontal drift that extended from a vertical shaft 1,000 feet west of this location. Objectives of the nuclear detonation were to provide data for detection studies and fundamental research in seismology within a granite geologic media in an active seismic area.
No excavation drilling and/or removal of materials is permitted between a level of plus 5,050 feet and plus 3,530 feet above mean sea level and out a horizontal distance of 3,300 feet from this surface ground zero location. Any reentry into drill holes or the shaft within this horizontal restricted area is prohibited.


Indicating LED Blinking or shining output indicators.
Current Consumption Transmitter:45 mA Max.
Receiver:35 mA Max. Transmitter:60 mA Max.
Receiver:40 mA Max. Transmitter:70 mA Max.
Receiver:45 mA Max. Transmitter:90 mA Max.
Response time Off to On:15 ms Max.
On to Off:20 ms Max.
Voltage withstandabiligy AC1000V 60Hz for 60 Sec.


text:
\section*{Abstract}
In this paper we investigate Charon's craters size distribution using a deep learning model. This is motivated by the recent results of \cite{singer} who, using manual cataloging, found a change in the size distribution slope of craters smaller than 12 km in diameter, translating into a paucity of small Kuiper Belt objects. {These results were corroborated by \cite{robbins2021}, but {opposed} by \cite{morby}, necessitating an independent review.} Our MaskRCNN-based ensemble of models was trained on Lunar, Mercurian, and Martian crater catalogues and both optical and digital elevation images. We use a robust image augmentation scheme to force the model to generalize and transfer-learn into icy objects. With no prior bias  or exposure to Charon, our model  {find best fit slopes of $q=$-1.47$\pm$0.33  for craters smaller than 10 km, and $q=$-2.91$\pm$0.51 for craters larger than 15 km. These values indicate a clear change in slope around 15 km as suggested by \cite{singer} and thus independently confirm their conclusions. Our slopes however are both slightly flatter than those found more recently by  \cite{robbins2021}. Our trained models and relevant codes are available online on github.com/malidib/ACID . }

\section{Introduction}
Craters are the dominant morphological features on airless solar system bodies. Their frequency measurements are routinely used to infer the relative ages of different regions of an object, and absolute ages if calibration is possible \citep{murchie,pat}. They can moreover be compared between the apex and antapex regions of synchronous satellites to constrain whether the impacting debris had heliocentric or planetocentric orbits \citep{zahnle2}.
Craters diameters distribution reflect the size distribution of the impactors, and that can be compared to known small bodies populations to constrain the nature of the asteroidal or cometary impactors reservoirs \citep{strom,dones,minton}. This is turn can be compared to different dynamical evolution models of the early solar system \citep{levison,zahnle}. On the other hand, the depth-to-Diameter distribution of craters holds valuable information on the crust's thickness and composition. \cite{schenk} for example used the d-to-D distribution slope on Europa to constrain its icy shell thickness. Finally, craters ellipticity distribution is directly related to the impact angles \citep{herrick}, and that can be used to constrain the obliquity evolution of the body \citep{holo}.

In the past few years, \textit{New Horizons} images allowed for the first detailed studies of the surfaces of Pluto and Charon.
Using high resolution {LORRI (LOng Range Reconnaissance Imager, \cite{cheng2008}) and Ralph/MVIC imager \citep{reuter2008} data}, \cite{singer} investigated the size-frequency distributions of Pluto and Charon's craters. They concluded that this distribution has a {shallower slope} for craters smaller than $\sim$ 12 km, indicating a paucity in small craters and therefore {in small Trans-Neptunian Objects (TNOs), which is inconsistent with the main belt asteroids size distribution. {This was corroborated further by \cite{robbins2021} who generated an improved craters catalogue with manual counting, and came to the same conclusions.}
 {On the other hand, \cite{morby} combined the} \cite{singer} dataset with images of Arrokoth, a {cold classical Kuiper Belt Object} that was also imaged at medium resolution during \textit{New Horizons's} extended mission. Using the new dataset and a numerical model, \cite{morby} found a craters cumulative power law slope consistent with main belt asteroids.}

Identifying craters and refining  their size distributions has always been a manual process (as done for example by \cite{robbins} and \cite{singer}). This is cumbersome, specially for the small-radii end of the distribution where identification of craters becomes more time consuming. An automatized method has been proposed recently by \cite{ad5,ad4} who trained convolutional neural networks (CNNs) to {identify craters on the surface of the moon with a recall higher than 90\%.}  These algorithms were then successfully used to identify craters on Mercury \citep{ad5}, Mars \citep{lee}, as well as historic mining pits on Earth \citep{pits}. In this paper we investigate Charon's craters size-frequency distribution (SFD) using these automated techniques. {This allows us to have a robust and self-consistent catalogue of craters, with consistent size measurements.} In section \ref{methods} we present our machine learning model, and the results are discussed in section \ref{results}. We finally summarize and conclude in section \ref{summary}.

\section{Methods}
\label{methods}
Our deep learning model of choice is the semantic segmentation framework MaskRCNN, \citep{he} \footnote{\url{https://github.com/facebookresearch/Detectron} and \url{https://github.com/matterport/Mask_RCNN}}. This algorithm has been successfully used by \cite{ad4} to constrain the size and ellipticity distribution of Lunar craters, and hence we refer the readers to that paper for more detailed background information on the framework. Here we discuss the main aspects of the model training and the differences with \cite{ad4}.

\subsection{Training dataset}
The aim of this work is to investigate Charon's SFD using an unbiased model. For this end, we choose to train the model exclusively on a  dataset of Lunar, Mercurian, and Martian craters, containing digital elevation maps and optical images. The model is never exposed to icy objects at training or testing, {and is thus less prone to overfitting}. The massive size ($\sim$ 4 times larger than \cite{ad4}) of the dataset and its diversity that we increase furthermore using extensive image augmentation allows the final model to generalize very well through transfer learning to new objects, including Charon (this work), and comet 67P/Churyumov–Gerasimenko (future work).

{Our images dataset is generated from the Moon's LRO/LOLA (Lunar Reconnaissance Orbiter/Lunar Orbiter Laser Altimeter) global DEM (Digital Elevation Map) and LRO/LROC WAC (wide Angle Camera) Global Optical Mosaic, Mercury's MESSENGER/MDIS (Mercury Dual Imaging System) Global Mosaic and Global DEM, and finally Mars' MGS/MOLA (Mars Global Surveyor/Mars Orbiter Laser Altimeter) DEM and Viking Global Mosaic V2 (we use Viking data instead of the newer THEMIS since it covers a larger area and it is simpler to use).  These are all available at \\ https://astrogeology.usgs.gov/search .\\ We supplemented the Lunar craters catalogues used by \cite{ad4} (\cite{Povilaitis} and \cite{Head}) with that of \cite{robbinsMoon} for smaller diameters. We used the \cite{robbinsMars} catalogue for Mars, and finally the \cite{mercurycat} catalogue for Mercury. The final dataset consists of 5411 ``poststamp'' 512$\times$512 pixel images that were generated through random cropping of the global maps, using the same procedure outlines in \cite{ad4}. {Random cropping allows us to generate on demand a practically infinite amount of unique training data from a finite map and catalogue.} {In our training set, as each individual catalogued crater is contained in a separate target mask, and thus considered as 1 training example, our dataset consists in total of 205618 training examples.} }

\subsection{Image augmentation}

Our pipeline also includes extensive image augmentation to limit overfitting by increasing the diversity of the dataset in general, and  negate the directionally-biased effects of shadows in the optical images in particular. Our image augmentation routine is called during training when a poststamp image is loaded, and includes the following distinct effects:

\begin{itemize}
\item Image color inversion, { with 25\% probability of being applied to the image.} This allows the algorithm to detect craters no matter if they are darker or brighter than their immediate environment.
\item Image rotation in multiples of 90 degrees, with a 33\% probability. This randomizes the direction of shadows, preventing any correlations between the incidence angle and detection efficiency.
\item Image flipping, with 33\% probability.
\item Adding Gaussian noise with a mean of 0 and standard-deviation of 0.7, with 17\% probability. This allows the model to generalize better to lower quality data.
\item Adding ``salt and pepper'' noise with a pixel-alteration chance of 0.05, with 17\% probability. {The pixel-alteration chance, in addition to the mean and standard deviation of the Gaussian noise, were chosen through error and trial to add visible noise without overly distorting small craters. }
\item Random image perspective warping that adds a viewing angle to the image, instead of being orthogonal, with 17\% probability. This helps the algorithm detect highly elliptical craters.
\end{itemize}
{Therefore, on average, any given image undergoes 1.5 random augmentations, with the median being 2. }

\subsection{Machine learning model}
Instead of relying on a single model, {we choose to implement an ensemble of models.} Ensembling is known to limit overfitting and improve model generalization \citep{ensemble,ensemble2}. Our ensemble is simply a collection of 14 MaskRCNN models with different parameters trained on the entirety or a subset of the data, each taken at a specific epoch {(how many
times to pass the input dataset through the network during training)}. The models parameters are summarized in Table 1.  We initially started by training a total of $\sim$ 100 models, and these 14 models were selected for having very low false positive rates when tested on a small (10 poststamps) random sample of Charon's images. The rationale is to have many models with low false positive rates even though for most of them the false negative rate is relatively high, as the ensemble of these models will have both a low false positive and negative rates. These rates were recalculated more precisely for Charon after the new catalogues have been generated, as discussed more in section \ref{inference}.

\begin{table}[]
\footnotesize

\begin{tabular}{llllll}
Model \# & epochs &loss weights & head only &  augmentation & data  \\
 \hline
1\footnote{Same model as \cite{ad4}}       & 13     & 1,1,1,1,1         & no         & no        & Moon only   \\
2       & 12      &  1,1,1,1,1 &  no         &   yes       & Moon only    \\
3      & 13      & 1,1,1,1,1  &   no        &   no  &   full dataset   \\
4      & 20      & 1,1,1,1,1&     no      &    rotation only      & Moon only      \\
5       & 18      &  1,1,1,1,1  &       no    &    no      &  full dataset  \\
6 \footnote{ \texttt{RPN\_ANCHOR\_SCALES} = (2, 4, 16, 64,128)  instead of (4, 8, 16, 32, 64), and \texttt{RNP\_NMS\_THRESOHOLD = 0.3
} instead of 0.7        }      & 7     & 1,1,0.1,0.1,1  &       no    &     no     &  full dataset    \\
7 \footnote{Training initially with loss weights = (1,1,1,1,1), then resumed with (1,4,1,1,1)}  & 40      &  (1,4,1,1,1)   &       no    &    no      &  full dataset    \\
8         & 255      & 1,1,1,1,1   &       no    &    no      &  full dataset   \\
9        & 450      &  1,1,1,1,1   &       no    &    no      &   full dataset   \\
10      & 153      &   1,1,1,1,1    &       no    &    no      & full dataset   \\
11\footnote{{head only: freeze (do not update the weights of) all layers except the dense (fully connected), non convolutional ``head'' layers.}}        & 135      & 1,1,1,1,1   &        yes   &    no      &  full dataset   \\
12        & 11      &  1,1,1,1,1  &       no    &    yes      & full dataset   \\
13       & 24      &  1,1,1,1,1 &       no    &   yes       &  full dataset   \\
14      & 18     &1,1,1,1,1   & no        & rotation only        &  full dataset

\label{tablemodels}
\end{tabular}
\caption{Models ensemble parameters. Loss weights, are the weights given to the different (multi-)loss functions of MaskRCNN, and they are respectively \texttt{rpn\_class\_loss}, \texttt{rpn\_bbox\_loss}, \texttt{mrcnn\_class\_loss}, \texttt{mrcnn\_bbox\_loss}, \texttt{mrcnn\_mask\_loss}. These are all defined in \cite{he}, and explained in the Appendix below. All models were trained with the same learning rate of 10$^{-3}$. {``yes'' under augmentation means that the full augmentation routine was used. In models 1,2,4 only the Lunar data was used as it is the highest quality data available for training. }
{For models 8,9,10,11 we randomly subsampled the data, then train the models for 15 epochs for 8,9 and 9 epochs for 10,11. This process was repeated with new subsamples till reaching the quotes total number of epochs. } }
\end{table}

\subsection{Inference on Charon's images}
\label{inference}
\subsubsection{Methodology}
To generate Charon's craters catalogues, we use a sliding window on the New Horizons LORRI MVIC  300m/pixel \href{https://astrogeology.usgs.gov/search/map/Charon/NewHorizons/Charon_NewHorizons_Global_Mosaic_300m_Jul2017}{Mosaic}
  to generate unique poststamp images on which {we make predictions using all 14 models}. {We however limit our predictions exclusively to the Vulcan Planitia region of Charon, and do not use the rest of the map (mainly Oz Terra). This is motivated by two reasons: 1- the planar nature of Vulcan Planitia facilitates craters identification and limits ambiguous surfaces, and 2- to maintain a reasonably homogeneous resolution across the map, as the imagery is not homogeneous with different regions and instruments having a variety of resolutions, incidence and emission angles, and terrain quality.}

  MaskRCNN models works best on image 512$\times$512 pixels, as this is the size of the training set images. However, we found that taking smaller poststamps (256$\times$256) then upscaling them to 512$\times$512 pixels improve the detection efficiency of small craters, with negligible effects on false positive rates. {The upscaling is done with \texttt{OpenCV}'s \texttt{resize} method, using bilinear interpolation .} Therefore, we generate catalogues using both 256$\times$256 and 512$\times$512 pixels sized sliding windows. In total, this result in 28 individual catalogues for the global map. We subsequently merge all of the catalogues, and filter out duplicates {by keeping track of the global coordinates for each detection}.

{Duplicates filtration is done through calculating the intersection-over-union (IoU) of the bounding boxes of every pair combination, and removing those where the score is higher than {0.5 IoU (a value we found optimal by trial and error)}, also known as the Jaccard index, calculates the extent of overlap between the bounding boxes of objects pairs, then exclude one of them if the overlap is larger than a given parametric fraction. Note that two embedded craters where one is much larger than the other would have a small overlapping region (equal to the area of the smaller crater), and thus neither would be removed.} We finally end up with one definitive catalogue for the map that we use for to generate the SFD. For all inferences, we use a \texttt{DETECTION\_MIN\_CONFIDENCE} parameter\footnote{{MaskRCNN parameter defined as the confidence level threshold, above which the object will be classified. }} of 0.95, implying that it is a conservative catalogue.

{At this stage we end up with a catalogue containing 1088 craters. We finally inspect the entire catalogue for false positives (non-crater identified as one) and false negatives (missed craters). We hence remove a total of 33 detections (or 3\% of the entire catalogue), and add 8 missed craters.  }

\section{Results}
\label{results}

{To study the craters size distribution, and for ease of comparison with \cite{singer}, we choose to follow them in representing the SFD as an R-plot. In these plots the The differential number of craters per diameter ($dN/dD \propto D^q$ where q is the slope) is divided by $D^{-3}$.
To calculate the slopes below we fit the R-plots directly, then subtract 3 to obtain the differential distribution slope $q$. }

{Our  automatically generated but {hand-curated} craters catalogue is shown in Fig. \ref{fig:charonGlobal} and Charon's R-plot is shown in \ref{fig:charon1}. Visually inspecting Fig. \ref{fig:charon1}, a change in the slope between 10 and 15 km is visible. {Our {slopes} are also moderately consistent with the catalogue of \cite{robbins2021}, also plotted. Using $\chi^2$ minimization, we find best fit slopes for $q$ of -1.47$\pm$ 0.33  for craters smaller than 10 km, and -2.91$\pm$ 0.51 for craters larger than 15 km. {Both of our slopes are slightly shallower than those found by \cite{robbins2021} who found $q\sim -3.9 \pm 0.6$ for large craters and $q\sim -2.0 \pm 0.2$ for small ones in the Vulcan Planitia region. Our slopes are however consistent with those found for some Pluto regions, where \cite{robbins2021} reported for the mid-latitude strip $q\sim -2.4 \pm 0.5$ and $q\sim -1.9 \pm 0.5$ for large and small craters respectively. }}

We verify this change in slope using Bayesian information criterion (BIC) analysis. {BIC analysis is used in models selection by assigning a coefficient value to each model. The actual values have no real meaning. However, models with smaller coefficients (where negative numbers are smaller than positive ones), are preferred over models with larger coefficients}. Our analysis needs to account for two properties of the data: the error bars, and the fact that small craters are much more common than large ones (this is true in both cumulative and non-cumulative SFDs). Our procedure hence consists of splitting the data at 10-15 km diameter, then calculating the BICs for two models with $q$ = -2.91 and $q$ = -1.47 while accounting for the uncertainties. This is done by replacing each individual data point with one sampled randomly from its uncertainty interval, then calculating the average BIC of 10$^4$ repetitions. Splitting the data prevents small craters from dominating the BIC no matter what slope is preferred for the far less numerous larger ones. We get final BIC values for craters larger than 15 km of 89.28 for $q$ = -2.91, and 177.20 for $q$ = -1.47. Therefore, for large craters, the $q$ = -2.91 model is strongly preferred over $q$ = -1.47. Considering small craters we get a BIC of 223.35 for  $q$ = -2.91 and 161.66 for $q$ = -1.47, implying that $q$ = -1.47 is strongly preferred over $q$ = -2.91 for craters smaller than 10 km. Bayesian analysis hence favors a change in Charon's SFD slope between 10 and 15 km diameter.  Our results strengthen the conclusions of \cite{singer}, as this slope change translates into a paucity of impactors smaller than 1.1 km.

\begin{figure*}
	\begin{center}
		{\includegraphics[scale=0.30]{chrome_IE0YilM4ck.png}}
		\caption{{A map of Charon's Vulcan Planitia region, with the final craters catalogue plotted.  }  }
		\label{fig:charonGlobal}
	\end{center}
\end{figure*}

\begin{figure*}
	\begin{center}

		{\includegraphics[scale=0.42]{charon_rplot.pdf}}
		\caption{Charon's R-plot (differential size-frequency distribution normalized to D$^{-3}$), compared to \cite{robbins2021}. Both curves show a change in slope {between 10 and 15 km}. {Note that in R-plots a horizontal line corresponds to q=-3.} }
		\label{fig:charon1}
	\end{center}
\end{figure*}

\section{Summary \& Conclusions}
\label{summary}
In this paper we investigated the crater size-frequency distribution of the Vulcan Planitia region of Charon using a MaskRCNN deep learning ensemble of 14 models. After training the model on a plethora of rocky objects images and catalogues, and augmenting the dataset through random rotation, flipping, perspective change, and noise addition, we obtain a model capable of transfer-learning to icy objects. Applying the model to Charon, we independently confirm the results of \cite{singer}  and \cite{robbins2021} who found a change of slope {between 10 and 15 km} diameter, implying a paucity of KBOs smaller than $\sim$ 1.1 km. {The slopes we found are however slightly shallower than \cite{robbins2021}.} We do not investigate Pluto's craters, as we found that its more complex and rugged terrain poses challenges for our model.

\subsection*{Implications for future solar system missions}
{Multiple} other major icy objects in the solar system have been partially or near completely mapped at kilometre or sub-km resolutions. The amount of information present in these images, in addition to complementary measurements, is overwhelming.
This will only get exacerbated with upcoming missions providing higher resolution imagery. The Europa Clipper's instrument EIS for example will map Europa's surface at a maximum resolution of 50 m, down to possibly 0.5 m \citep{turtle}. JUICE's JANUS camera on the other hand will map Ganymede and parts of Callisto with a resolution of 400 m, down to possibly 2.4 m \citep{pal}. Properly analyzing the existent amount of data is already challenging, so one can quickly see that new techniques altogether will be necessary for the Clipper and JUICE. {Fortunately, computer vision techniques such as the one used in this work proved to be incredibly powerful tools for planetary surfaces objects identification.} These techniques should be developed and tested on Jupiter and Saturn moons with published catalogues, in preparation for the upcoming data stream.

\section*{Acknowledgments}
We thank S. Robbins and an anonymous referee for their useful comments that helped
us improve our manuscript significantly. This work was supported by Tamkeen under the NYU Abu Dhabi Research Institute grant CAP3, and M.A.-D. was supported through a CAP3 fellowship.

\section*{Appendix}
\subsection*{Some MaskRCNN terminology}
\texttt{rpn\_class\_loss}: loss function accounting for the improper classification of anchor boxes. It is higher when multiple objects are not detected.

\texttt{rpn\_bbox\_loss}: loss function accounting for the localization accuracy. It is higher if objects are detected but the bounding boxes are incorrect.

\texttt{mrcnn\_class\_loss}: loss function that increases if the object is detected but classified incorrectly.

\texttt{mrcnn\_bbox\_loss}: loss function that increases if the object is correctly classified, but its localization is imprecise.

 \texttt{mrcnn\_mask\_loss}: loss function that increases if the masks at the pixel level are not accurate.

redpajama_set_name: RedPajamaArXiv


text:
require  File.expand_path(File.dirname(__FILE__)) + "/../../../../../spec/spec_helper"

describe UserProfileEntry do

  reset_domain_tables :content_publications, :content_publication_fields, :user_profile_types, :content_model_features, :user_profile_entries, :end_users, :user_profile_type_user_classes, :content_nodes, :content_types, :site_modules

  before do
    test_activate_module('user_profile')
    @prof_type =  UserProfileType.create(:name => 'Test',
                                          :user_classes => [ { 'id' =>  UserClass.default_user_class_id } ])
  end

  after do
    SiteModule.destroy_all
  end

  it 'should create entries' do
    assert_difference "UserProfileEntry.count", 2 do
      EndUser.push_target('test@webiva.org')
      EndUser.push_target('test2@webiva.org')
    end
  end

  it 'should delete profile entries when the type is deleted' do
    EndUser.push_target('test@webiva.org')
    assert_difference "UserProfileEntry.count", -1 do
     @prof_type.destroy
    end
  end

end

redpajama_set_name: RedPajamaGithub


text:
Q: Responsive scaling of images within table cells causes gaps in Chrome I'm trying to set up my company's emails to be more mobile-friendly. And unfortunately, because this is email-based, I have to build `em like it's 1999: tables and image slices, mostly.
I have tables that are set to a specific width, no defined height. Never more than one row per table, and usually 2-3 columns. One image per table cell. Currently, I'm not defining the dimensions of the table cells or images.
Here's my new responsive code I'm working on:
@media (max-width: 670px) {
    body {
        background-color: red !important; /* for testing */
    }
    table {
        width: 100% !important;
        height: auto !important;
    }
    table img {
        width: 100% !important;
        height: auto !important;
    }
}
@media (max-width: 480px) {
    body {
        background-color: blue !important; /* for testing */
    }
}

http://codepen.io/BevansDesign/pen/CnAfB
As you can see on my Codepen page, if you shrink the width of the window, the graphics will shrink down appropriately. However in Chrome, I get a lot of gaps between the images (green lines), due to pixel-based scaling being a bit weird. (Works fine in FF, doesn't work at all in IE8 of course, and I haven't tested it on anything else yet.)
Does anyone know how I can get rid of those gaps? I've tried setting the image height to 100%, but that just leaves the images at their original height and squishes them horizontally.
I know I can fix this by setting all the dimensions manually, but I'm looking for a solution with as little tweaking as possible, since I'm pumping out these emails 2-3 times each week.
Thanks for your help!

A: It might help you
source
Option 1.) Add style="display:block" to your image

Options 2.) If your image height is greater than 16px, set the "align" attribute in the image to any of the following: absmiddle, middle, left, right, absbottom, texttop OR top

Option 3.) Place the image in a block element with a style="line-height:10px" (or lower)
Example:

Option 4.) Place the image in a block element with a style="font-size:2px" (or lower)

Option 5.) Add style="float:left" OR style="float:right"

A: Best solution I ever found to this problem is to use a background image with the slices saved as a grid of transparent GIFs instead. The gaps still appear but they're not visible.
In my example below, the first table uses regular slices and you'll see the narrow 1 or 2 pixel gaps appearing as you scale it in a browser. Chrome seems to be the worst but it happens in other browsers too.
The second table uses my background and GIFs technique. Responsive styles switch the image to a Retina-friendly hi-res version.
<!DOCTYPE html>

<title>Slices</title>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta http-equiv="X-UA-Compatible" content="IE=edge" />


The USSF (United States Soccer Federation) needed a wake-up call… and boy did they ever receive one.
On Tuesday night, a perfect storm of ineptness and shock occurred — as the United States Men's National Team lost its final World Cup qualifier to tiny Trinidad and Tobago by a score of 2-1. The defeat, coupled with wins by Panama and Honduras, knocked the USMNT out of World Cup contention. It will be the first time since 1986 in which the United States will not be a part of the globe's most significant tournament.
This piece will attempt to delve into the intricacies as to why this happened. Warning: it will not be for the faint of heart.
Let us begin with the current player pool. Of the 20 outfield players from this past squad, 10 of them are age 30 or older. Only five players were under the age of 25. Traditionally a side rooted in quickness and pace, this current USMNT team looked old and uninspired. The reliance upon the "old guard" impeded the potential emergence of other players. Jozy Altidore, Michael Bradley, and Tim Howard were shoe-ins for the starting lineup despite playing mediocre football over the past year. The bench was littered with questionable selections — namely Chris Wondolowski, Dax McCarty, Graham Zusi, DeMarcus Beasley, Nick Rimando, Brad Guzan, Alejandro Bedoya, and Benny Feilhaber.
Manager Bruce Arena has never been known as a progressive manager — nor has he been one to take any sort of risk. As a result, he put together an MLS-heavy roster of players he had been familiar with. The team in general played with a lack of inventiveness, and an archaic directness only seen in the bowels of lower division, less-skilled European leagues.
19-year-old Christian Pulisic is without question the best player in the program. Hell, he's on track to become the best American player ever produced. The United States scored 17 goals during the World Cup qualifying cycle. Pulisic — whether it was a goal or an assist — was involved in 12 of them. He's truly a transcendent talent, and one in which the youth across the country can emulate.
With that said, he's in desperate need of some help. Pulisic needs like-minded creators in the center of the pitch to link with. The duo of Altidore-Bobby Wood up top simply cannot work together — as both are far too similar in nature. Garnering enough width on the flanks has also been a massive issue. Fabian Johnson is out of form, and may be ending his career with the National Team sooner than later. Other than that, the team lacks even average wing play.
This current National Team program has more resources, funding, and money than it's ever had before. The country's domestic league (MLS) is setting records for attendance and viewership within the United States. We've got a greater collection of young talent in Europe than we've ever had. However…we couldn't qualify for the World Cup — instead losing to an island nation with a population (1.3 million) smaller than Phoenix.
Bruce…you're operating with a twisted, distorted view of reality. This was the most embarrassing (and damaging) loss in the history of the program. "Crazy changes" should be DEMANDED at this point.
We are significantly below-average when it comes to developing young players in the United States — particularly when it comes to the rest of the World. Some attribute the disproportion to our culture — as American football, basketball, and baseball have traditionally been more popular sports. This isn't the case virtually anywhere else. However, our gigantic population indicates this theory to be inherently flawed.
Uruguay is a nation with a current population of roughly 3.5 million people. Los Angeles County alone houses more than 10 million Americans. Yet, this tiny South American country has won two World Cups. Even with a large chunk of the youth participating in other sports, we should have enough to field a competitive squad.
To put it into a larger perspective, Iceland recently qualified for the World Cup — besting European competition in arguably the toughest qualifying process. Population-wise, Iceland is the size of Corpus Christi, Texas and Anaheim, California.
The question persists: why aren't we earning success at the highest level? One can point to MLS — which truly isn't considered a top league. The competition level simply isn't up to par when compared to the likes of England, Germany, Spain, Italy, the Netherlands, France, and other countries. This does account for the lack of sharpness and technical prowess pertaining to those plying their trade domestically.
Unequivocally, the single biggest issue stunting the growth of success soccer-wise in this country is the developmental aspect.
We simply do not have the educators in place to teach the true fundamentals of the game. Other countries have spent tons of money in setting up youth academies — where players as young as six can go to school, and essentially be groomed as professional players. This includes proper instruction from both a technical and maturity standpoint. The United States simply doesn't have that luxury for those at such a young age.
A player in the States at 10 or 11 years of age likely won't understand the game at the same level as a seven-year-old in Belgium or Denmark. There are knowledgeable trainers and coaches residing in the States. However, there's a collective saturation of these individuals in specific geographic areas — rather than dispersed evenly within the sport. Across the board, an enhancement in credible training is imperative for the future growth of our young players.
The "pay for play" model is a disgusting money-making sham. Those in the States are led to believe that the only path towards earning a college scholarship or an opportunity abroad is to play club soccer. Parents of these youngsters spend thousands of dollars annually to send their children to weekend tournaments.
Simply put, this premise strips away the organic integrity of the sport. It also happens to feast upon the socioeconomic divide in this country. Though a player's potential value should be based upon their talent on the field, the opposite seemingly happens. Instead, a family's income level often determines whether a child can play at an advanced level or not. The next Landon Donovan could currently be living in the south side of Chicago, or in El Paso, Texas. However, he may never get the chance to realize his vast potential. The grassroots efforts in both predominantly African-American and Hispanic areas is grossly lacking.
This leads to thousands of quality players falling through the proverbial cracks. It also waters down the talent pool tremendously from what it could ultimately be.
For a talented player coming from an impoverished area, there's simply a lack of opportunity and visibility. Marcelo — one of the world's best defenders — grew up in a notoriously dangerous Rio de Janeiro slum. Edinson Cavani and Luis Suarez are considered two of the best strikers in this current generation. Each came from the same small mountainous village in Uruguay. There are countless examples of professional footballers emanating from abhorrent economic conditions. None of them had to "pay to play" the sport. Rather, they grew up playing in the streets, and in parks with their peers.
Marcelo, Cavani, and Suarez are all products of local academies cultivating their respective talents. Normally, each city will have multiple teams backed by the clubs in the said city. These youth sides will then benefit monetarily once the bigger clubs gobble up promising prospects. MLS has made tremendous strides in creating youth academies — which then act as essentially feeders into the professional club. We also have youth setups without any sort of affiliation to a professional team.
These youth clubs don't garner any sort of monetary gain once their products move on to professional clubs. A rapid overhaul must occur in terms of incentivizing development. These institutions need to reap the fruits of their work. A revamping of the MLS stranglehold over youth clubs truly could foster greater development across the board. In many cases, development in non-MLS-affiliated youth clubs is far greater than its counterpart. For young players wanting to make the leap across the pond, landing with an MLS club — whether on a professional or youth level — crushingly limits the opportunity to do so. We are truly in need of an open market model.
Lastly, we get to USSF President Sunil Gulati.
Gulati, do us a favor…resign.
Any other self-respecting footballing country would have cleaned-house after the shambolic buffoonery of Tuesday night. None of the world's powerhouses would stand to accept the current spot in which the USMNT finds itself. As an example, Germany finished dead-last in its group during Euro 2000. The country went through an extensive overhaul which included building 52 soccer-centric centers and 366 regional coaching bases. It also hired 1,300 full-time professional coaches to teach the youth the true basics of the game.
Duly, the German Football Association worked hand-in-hand with every level of the sport — starting with the Bundesliga. Cultivating an organizational plan allowed German football to flourish — as we saw in the 2014 World Cup and in this current generation.
Germany recognized a system problem in its fundamental way of operating, and thus acted accordingly. While it's foolish to believe the United States will immediately churn out players such as Thomas Muller, Mats Hummels, Manuel Neuer, Mesut Ozil, Julian Draxler, and Toni Kroos, a plan must be put in place immediately for the long-term health of the program.
Much like Arena, Gulati is operating under a pretense. It's somewhat frightening to see the most significant individuals in the United States' soccer pyramid displaying a level of obliviousness to this magnitude. A most fascinating time for the direction of the program will come in February — when Gulati's current four-year tenure is slated to end. As an elected position, Gulati could opt to run again — which would signal the final four-year window in which he could be President. Though he ran against no competition in the past, ESPN is reporting that a number of individuals are expected to throw their hat into the ring — including USSF board member John Motta, entrepreneur Paul Lapointe, Boston attorney Steve Gans, and former professional footballer Eric Wynalda.
The "scariest" proposition for the anti-Gulati crowd is Carlos Cordeiro. The former Goldman Sachs executive is currently the Vice President of the USSF under Gulati. Conventional wisdom suggests Cordeiro functioning quite similarly when compared to his boss.
Regardless of whether Gulati is back or not, wholesale changes must occur. Arena was always a stop-gap for this cycle — though he did nothing more than to make supporters of the side gag. The prudent thing would be to bring in a manager without any ties to the United States. Ushering in fresh ideas technically and strategically should be the goal. In addition, hiring a full-time technical director to act as a buffer between the President and the manager would be ideal. The director would be in charge of calling up players for matches and camps, evaluating the said pool of players, and hiring coaches at all levels of the program. Essentially, he'd be what a general manager would be to an NFL or NBA franchise.
Above all else, player selection MUST see some change. The urgency to qualify for the World Cup resulted in a utilization of veteran players as opposed to younger ones with higher ceilings. With no World Cup possibility until 2022, now is the time to experiment with young talent. There's simply no need to call in the same roster for every single friendly or tournament. The USSF should be scheduling as many friendlies as possible to garner young players valuable international experience. It's time to see the likes of Kenny Saief, Ethan Horvath, Matt Miazga, Weston McKennie, Jonathan Gonzalez, Jesse Gonzalez, Lyndon Gooch, Tyler Adams, Cameron Carter-Vickers, and Brandon Vincent.
There's been an air of vociferous arrogance permeating throughout the program. We saw it on Tuesday night, as the team truly thought it could walk into the match and come away with a World Cup berth. Simply put, the United States isn't as good as it thinks it is. They've made it to the knockout stage in two-straight World Cups. It's taken equal parts grit, guile, dogged determination, and athleticism for this to occur. However, it was a bit of fool's gold when looking at the bigger picture.
The lack of development from a young age has caused a group of players to possess subpar technical abilities. We thought we could challenge the big boys in the World, though at this point we're clearly behind both Costa Rica and Mexico in the hierarchy of CONCACAF. We'll have the occasional outlier (Donovan, Pulisic), but this is not a recipe for success. Systemically, things must change immediately. The ego must be stripped away — as we are not good enough (yet) to function with such a brashness in this sport. We must build from the ground up if we want to sustain long periods of success at the highest level. Developing talent the right way is integral going forward. No longer can we opt to "develop" players based on size and speed. It must be inverted — to rather first focus on skill level and headiness.
The loss to Trinidad and Tobago will go down as one of the worst days in the history of American soccer. As the sport continues to grow exponentially, a major opportunity has now been lost. Sports Illustrated intimated that the fan base within the country has grown from 59 million to 79 million (34-percent) from 2010 to 2016. 78-percent of Americans aged 12-to-17 consider themselves a fan of the sport — which is a higher percentage than the NFL (59-percent), the NBA (44-percent), MLB (42-percent), and the NHL (28-percent).
Most impressively, 120 million people in the United States tuned in for World Cup games in 2014. With the United States out of the tournament, the casual fan likely won't tune in. Within that same token, the children of the casual fan — who could be turned onto the sport with a fantastic performance from Pulisic — won't have that opportunity. The World Cup is the grandest spectacle in the sport. It provides a chance for young kids across the country to learn and love the sport. In turn, this will lead to the next generation of players on the National Team. In these formative years, those same children could opt to play a different sport solely based upon the unavailability of seeing the USMNT feature.
While we won't know the lasting impact of this embarrassment for quite some time, it could ultimately cause an adverse ripple effect.


text:
YVONNE GREEN vs. TOWN OF WEST SPRINGFIELD.
Present: QUA, C.J., LUMMUS, DOLAN, RONAN, & WILKINS, JJ.
An action at common law might be maintained against a town for personal injuries sustained in a fall into an excavation dug by its employees in a public way "for the construction and maintenance of sewers and drains" and by them negligently left open and in a dangerous condition.
TORT. Writ in the Superior Court dated January 26, 1948.
A demurrer to the declaration was heard and sustained by Donnelly, J.
In this court the case was submitted on briefs.
J. E. Kerigan, for the plaintiff.
F. T. Raleigh, Town Counsel, for the defendant.
RONAN, J. This is an action of tort to recover damages for personal injuries received on the night of November 3, 1947, resulting from falling into an excavation, located in the sidewalk of a public way in the defendant town, which its agents and servants dug "for the construction and maintenance of sewers and drains" and negligently and carelessly permitted to remain open and in a dangerous condition. The declaration contained the usual allegations of due care upon the part of the plaintiff and damages. The plaintiff appealed from an order sustaining the defendant's demurrer.
The declaration contains no averment that written notice of the time, place and cause of the injury was given to the defendant as provided for by G. L. (Ter. Ed.) c. 84, Section 18, as appearing in St. 1933, c. 114, Section 1. Such a notice would be a condition precedent to the maintenance of an action against the defendant town for breach of its statutory duty
to keep the public ways reasonably safe and convenient for travellers. G. L. (Ter. Ed.) c. 84, Sections 1, 15. Gay v. Cambridge, 128 Mass. 387. Brown v. Winthrop, 275 Mass. 43. Lucas v. Boston, 313 Mass. 495. The instant action, however, is not brought for breach of this statutory duty but is an action at common law for the negligence of the defendant's agents and servants in the construction of a sewer or drain. A town is not acting in a governmental capacity in the construction of a sewer but is engaged in a commercial undertaking for its own benefit and profit, and while not liable for negligent defects in the plan of the sewer it is responsible for the negligent construction and maintenance of a sewer. We need not discuss the grounds upon which liability is predicated, as the subject has been recently discussed with a full citation of authorities in Galluzzi v. Beverly, 309 Mass. 135, and Harvard Furniture Co. Inc. v. Cambridge, 320 Mass. 227.
Order sustaining the demurrer reversed.
Order to be entered overruling the demurrer.

redpajama_set_name: RedPajamaCommonCrawl


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text:
Орі́хівський зака́зник — гідрологічний заказник місцевого значення в Україні. Об'єкт розташований на території Ковельського району Волинської області, біля сіл Межисить, Залисиця, Самари-Оріхові, Мале Оріхове.

Площа — 824 га, статус отриманий у 1996 році. Перебуває у віданні: Самарівська сільська громада (805 га) та ДП СЛАП «Ратнеагроліс», Щедрогірське л-во, кв. 4, вид. 26–34, 37–39 (19,0 га).

Статус надано з метою охорони та збереження у природному стані озер льодовикового походження: Велике Оріхове, Оріховець, Засвяття з прибережними угрупованнями лучно-болотної рослинності, де зростають очерет звичайний (Phragmites australis), рогіз вузьколистий (Typha angustifolia), осоки: чорна (Carex nigra), здута (C. rostrata), пухирчаста (C. vesicaria), висока (C. elata), щавель прибережний (Rumex hydrolapathum), щучка дерниста (Deschampsia cespitosa), тимофіївка лучна (Phleum pratense), верба біла (Salix alba). У заказнику також розташоване болото «Урочище Дупло» площею 21,5 га, де росте журавлина болотяна Vaccinium oxycoccos. Озера Велике Оріхове і Оріховець інтенсивно замулюються.

Фауна водойм і водно-болотних комплексів заказника представлена такими видами риб: лящ (Abramis brama), щука (Esox lucius), карась сріблястий (Carassius gibelio), лин (Tinca tinca), лиска (Fulica atra), птахів: чирянки велика (Anas querquedula) і мала (A. crecca), шилохвіст (Anas acuta), широконіска (Anas clypeata), крижень (Anas platyrhynchos), лебідь-шипун (Cygnus olor), лелека білий (Ciconia ciconia), ссавців: ондатра (Ondatra zibethicus), бобер європейський (Castor fiber).

У заказнику охороняються рідкісні види тварин: лелека чорний (Ciconia nigra), журавель сірий (Grus grus), горностай (Mustela erminea) та видра річкова (Lutra lutra), які занесені до Червоної книги України, Європейського червоного списку, додатків конвенції CITES, Бернської конвенції, Боннської конвенції та Угоди AEWA.

Галерея

Джерела

 Розпорядження облдержадміністрації від 16.10.96 № 551
 Природно-заповідний фонд Волинської області. Гідрологічний заказник Оріхівський

Гідрологічні заказники Волинської області
Природоохоронні об'єкти, засновані 1996

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