dataesr/scientific-paragraphs-categorization huggingface dataset:
On Figure  16  is represented the graph of the function α 2 → t(α 2 ) along the three splitting curves for times t ≤ 3. 2, the homotopic parameter α 2 being strictly monotone along the splitting curves. Since Σ 1 is below Σ 2 and Σ 3 on the figure,  4  it is clear that only Σ 1 has a chance to be part of the cut locus, compared to Σ 2 and Σ 3. Note that there may exist others splitting curves but the numerical experiments suggest that Σ 1 is below any of them. Remark 3. 22. Note that the extremities of a splitting curve are the vortex and infinity according to the numerical experiments. Hence, we cannot compute all the curve. Numerically, we use an option of the HamPath code to stop the homotopy if the curve goes out the annulus of smaller circle of radius 0. 005 and of larger one of radius 100.
A third, heuristic explanation is that humans are subject to a 'repetition bias' according to which they repeat their response irrespective of their estimate of the state. Regardless of its origin, the high repetition propensity in data raises the question of whether it dominates the behavior of the average learning rate. As a control, we excluded all occurrences of repetitions in subjects' data and carried out the same analyses on the truncated dataset. We reached identical conclusions, namely, significant discrepancies between the HI and HD learning rates at short and long run-lengths, albeit with, naturally, higher average rates overall (see Supplementary Fig. B2 ).
Acknowledgements This work was supported by the  CNRS  through  UMR8550 , the  Global Scholar Program at Princeton University , and the  Visiting Faculty Program  at the  Weizmann Institute of Science. A. P. C. was supported by a  PhD fellowship of the Fondation Pierre-Gilles de Gennes pour la Recherche. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the program Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche.
Digital pathology image data is ripe ground for "data hungry" deep learning methods, which have made significant advances in the areas of image analysis, computer vision, speech recognition and speech translation, to name only a few. In histopathology, deep learning based algorithms have been shown to detect metastatic tumor in sentinel lymph nodes of breast cancer  14  , predict mutation status in lung cancer  15  and predict microsatellite instability (MSI)  4  , consensus molecular subtypes  18  and outcome  17  in CRC. More recently, Kather et al. 18  extended their proposed computational pipeline  4  to detect clinically actionable genetic alterations from H&E images. Echle et al. 12  recently evaluated four models and showed that three of those models achieved a strong performance in intra-cohort validation on three big international training sets (n=1770, n=2013, and n=2197), in addition to a multi-centric dataset of The Cancer Genome Atlas (TCGA, n=426).
However, both intra-and inter-cohort validations on the multi-centric TCGA dataset could only match the previous state-of-the-art performance  4  (AUROC=0. 77). Weakly supervised learning of slide-level labels for whole-slide images (WSIs) of H&E stained tissue slides and detection of clinically actionable genetic alterations from the analysis of H&E WSIs have been shown to be possible using deep learning. However, two fundamental questions remain to be answered: 1) can we employ deep learning to predict the status of molecular pathways and relevant genetic mutations from images of H&E stained sections with a high degree of accuracy, and 2) can these predictions be mapped onto known or novel histological features? The labeling of a digitized WSI as positive or negative without resorting to detailed annotations at cellular and regional levels would be termed as 'weakly supervised' learning in the language of machine learning.
Data collection and preparation The first cohort used in this study is the multi-centric cohort consisting of 502 diagnostic slides (DX) of 499 patients taken from The Cancer Genome Atlas (TCGA) colon and rectal cancer (TCGA-CRC-DX) cohort which has also been used in recent studies. 4, 12, 18  This cohort has slides contributed from thirty-six different centers, mimicking a challenging real-world setting. The IDaRS deep neural network model is trained for binary classification. For instance, in the case of HM tumor prediction, the two classes are high mutation density (HMD) and low mutation density (LMD). For MSI pathway prediction, the two classes are MSI-High (MSI) and Microsatellite stable (MSS) tumors; MSI-Low are combined with MSS in line with general literature as well as another recent study.
Tumor segmentation To perform the subsequent analysis in tumor regions of the slides, a deep neural network is trained to extract tumor tiles. These tiles are taken from seven randomly picked TCGA slides and two publicly available datasets. 4, 33  Seventy percent of the data is used for training, fifteen percent for validation to fine tune for the best model and the remaining fifteen percent is held out to test the network performance with an unseen set of tiles. The model achieved an accuracy of 99% for unseen tiles. This model is then used to extract tumor tiles (a total of nearly 450K tiles) from the entire TCGA-CRC-DX cohort.
Histological feature discovery of CRC pathways IDaRS offers opportunities of objective quantitative analyses of differential histological features of CRC pathways at cellular levels in the H&E histology landscape in a systematic manner. We have analyzed nuclear composition of IDaRS-predicted top tiles for each pathway and HM tumors. This is determined independently by the nucleus segmentation and classification network HoVer-Net  29  , also termed as CNN-3 in Fig 1c. This step mimics the visual analysis of an expert pathologist who analyzes nuclear composition in the tissue microenvironment subjectively by looking on the slide under the microscope at high power field. We use a deep neural network trained on a publicly available dataset for the analysis of tumor microenvironment and nuclei types to enable an objective and systematic analysis of the cellular composition.
For this purpose, we first performed nucleus segmentation of the top predicted tiles using HoVer-Net  29  , a recent state-of-the-art nuclear segmentation and classification model. We employed the HoVer-Net model pre-trained on the PanNuke dataset  37  for nucleus segmentation and classification. The model processed each of the top predictive IDaRS tiles to analyze their nuclear composition by segmenting and classifying tile nuclei into one of the following five categories: neoplastic and nonneoplastic epithelial, inflammatory, mesenchymal, and dead cells. The cell segmentation and classification results of HoVer-Net were visually examined and agreed by expert pathologists for all cell types. Non-neoplastic epithelial cells identified by the network were found to be different from those identified as neoplastic cells, but all of those were categorized as tumor cells by the experts.
Existing state-of-the-art results To the best of our knowledge, Kather et al. 4  were the first to propose a deep learning based automated prediction of MSI status from H&E images. They use the same deep learning pipeline with more data and multiple cohorts in  12  to predict MSI status from H&E images. The authors used standard fully supervised image classification by transfer learning the ResNet18 model  28  pre-trained on ImageNet. Standard CE loss was used to train the model with Adam optimizer with learning rate and weight decay of 0. 4×10 -4. The training dataset of color normalized tiles was balanced by under sampling of the majority class (MSS). Tile scores obtained from the deep learning models are aggregated into slide score using average or majority voting. Again to the best of our knowledge, the study by Kather et al.
Acknowledgments The research reported in this publication was supported by the  UK Medical Research Council  (award  MR/P015476/1 ). The results shown here are in whole or in part based upon data generated by the TCGA Research Network [https://www. cancer. gov/tcga]. De-identified pathology images and annotations in the PAIP cohort (used as external validation cohort in this study) were prepared and provided by the  Seoul National University Hospital  by a grant of the  Korea Health Technology R&D  Project through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  (grant number:  HI18C0316 ).
). The solution needs to be efficient enough to work on real-world scenes and simple enough to run on a smart device in a real-time application. The proposed method has to detect the obstacles, on the user's path and to predict the time until the obstacle reaches the user or until the distance between the user and the obstacle is less than one meter. In this work, we propose a model that extracts spatial features from the egocentric pedestrian video stream in a static data extractor module such as the scene's depth maps and the obstacles' location from state-of-the-art light weighted algorithms. These features stacked on multiple frames are fed in a dynamic data extractor that introduces our novel obstacle Time-to-Collision prediction neural network. We present the different datasets used for training and testing the new network.
A few datasets for autonomous car driving tasks contain context appropriate annotations for outdoor pedestrian obstacle detection  [14, 15]. The risk estimator is usually tackled with the "relative distance" from the camera focal and knowledge of obstacle width, or the "relative velocity"  [5, 6, 10]. These methods use temporal cues and rely on a few frames in order to predict a possible risk. The authors of  [5]  presented a method that detects the SIFT (Scale-Invariant Feature Transform) feature points of the obstacles and decides if the detected obstacle may cause a collision by comparing the area ratio of the obstacle and the position of the drone. The authors of  [10]  presented a method that detects the vehicles with Faster-RCNN  [11]  and uses the bounding box coordinates with the camera intrinsic parameters to compute the relative distance and velocity.
Static Data Extractor This module (Figure  2 ) exploits spatial features from each color video frame in order to help identify and localize the user's environment. It computes from the monocular color video the obstacles position (bounding box coordinates and dimensions) and the depth map. The localization and object recognition tasks are performed by a retrained version of the YOLOv3  [12]  real-time object detection neural network. The network, initially designed and trained in order to recognize 20 classes, was retrained from the Darknet-53 backbone on the COCO  [26]  and SUN-RGB  [27]  datasets to detect only 3 classes to limit the task to more context appropriate outputs: • "Person": this class represents the mobile obstacles the smartglasses user would encounter (pedestrians, vehicles).
• "Chair", "desk": these classes illustrate some examples of "static" obstacles the smartglasses user would have to avoid on his/her path. Those classes were selected according to the data available in existing accessible datasets and the testing environment we have chosen to test our solution (indoor office). The depth map estimation is performed by the lightweight robust real-time FastDepth neural network proposed in  [8]. From the data computed by the static data extractor, we select five compact pieces of information (cf. Figure  3 ) which are the coordinates x and y of the center of each object bounding box, w and h its dimensions and D the minimum depth value estimated within each bounding box. For the purpose of Time-to-Collision estimation, some data are normalized as follows: X = x/W I Y = y/H I W = w/W I H = h/H I (1).
• The Time-to-Collision value corresponding to each frame where the prediction should be (as we consider the Time-to-Collision task to be a regression problem). The dataset used for training and evaluation is the Near-Collision set  [25]  consisting of indoor hallways egocentric videos with time to collision annotations of incoming pedestrians (Figure  6 ). The videos were taken from an embedded camera mounted on a suitcase shaped prototype. The context of this setup is very similar to the one of the smartglasses navigation system. The dataset contains also the raw data used to annotate the Time-to-Collision which are the stereo images and the 3D point cloud data from a LIDAR. The bounding box data are generated from the Faster-RCNN person detection  [11]  and completed manually for missing data and false-positive.
The Time-to-Collision is annotated from zero to six seconds. For each frame with an incoming pedestrian, a value is calculated as the time it takes for the pedestrian to be less than one meter away from the camera. The distance to the pedestrian is the minimal value of the depth map in the detected bounding box area. Because their official test split contains only the input sequence and the annotated Time-to-Collision but not the corresponding depth data, a new test split was regenerated from the raw data available. In fact, the whole dataset has been reformatted so that the bounding box and depth data could be used as input when labeling the Time-to-Collision. Ninety sequences of pedestrian near-collision cases are used in total for training and testing. A new dataset more suitable in the smartglasses navigation system context has been acquired (Figure  7 ).
As a matter of fact, the Near-Collision dataset contains only mobile pedestrians as possible obstacles. No static obstacles are available. Note that this new dataset is used for testing purposes only. It contains similar images of indoor hallways videos. However, the path contains incoming pedestrians but also static obstacles like chairs and desks. These additional obstacle types simulate the variety of size, shape, and relative movement of the applicative real obstacle the system must detect and process. The images have been collected from an embedded stereo camera coupled with a NVIDIA Jetson TX1 development board. The videos were filmed while holding the camera at head level height to simulate the egocentric video from a camera embedded in smartglasses. This dataset contains the left-right stereo images with the associated depth maps computed directly by the ZED stereo camera API.
More than 20 trips have been acquired with 17 instances of collision. Time-to-Collision labels were generated in the same way as in the case of the Near-Collision dataset that is to say "collision" is labeled when the obstacle is estimated to be less than one meter away from the user from the depth map (minimal value in the bounding box area). The detection algorithm used to recognize the obstacles and localize their bounding box is the retrained YOLOv3 network from the Static Data Extractor module that is able to use the "person", "chair", and "desk" classes as obstacles. This dataset is only used as a test set because of the limited number of acquired samples and also to be able to see if the SimpleTTC network can generalize the task from one dataset to another (dataset cross-validation) and from one type of obstacle to another (from mobile objects to static ones).
Network Configuration The architecture chosen for this task is a simple fully connected network (Figure  5 ). The network parameters to be considered in this experiment are the number of hidden layers M and the number of neurons K per hidden layer. The number of input frames is fixed in this particular experiment at six (the same as for the Near-Collision network  [25] ). The network is trained for 100 epochs with each configuration and the results in Table  1  are always the best out of 100 epochs. We compute the mean absolute error and the standard deviation for the test set of the Near Collision dataset to quantify the performance. As can be seen in Table  1 , the best configuration with 6 input frames is 8 layers and 60 neurons per layer. The number of hidden layers matters. However, the number of neurons per hidden layer does not have a huge influence on the performance.
In the case of the Near-Collision dataset, the speed between the camera and the incoming pedestrians is relatively constant. This helped our network generalize the predictions for these particular situations in a small-time history (0. 3 s). Nevertheless, if the speed changes a lot as in the case of a smartglasses user with possible pauses in his/her trip, the influence of the time history size should be more substantial. The results in Table  1  show that the depth of the network is more relevant than its thickness. The number of non-linear computations from the data acquired on multiple frames to make a linear regression prediction has yet a direct effect on the performance.
Time to Collision Estimation Results on the Near-Collision Test Set Figure  8  depicts predictions of 12 different pedestrian "collision" encounters. Each spike of the ground truth's curve represents the first frame of the sequence the prediction is made on. Predictions from our SimpleTTC model with 3 input frames, 8 hidden layers, and 60 neurons per layer show that the network is able to predict the Time-to-Collision consistently. However, the network struggles to predict correctly when the pedestrian is far from the camera (Time-to-Collision above 3 s). Some prediction results of our generated dataset can be seen in Figure  9. The four first sequences are encountered with walking pedestrians. The next one is from a static position (the camera is not moving) with a scooter coming forward.
The last two sequences are again from a walking point of view with the scooter coming forward. The goal of this experiment is to see if the solution can be cross-validated based on a very different dataset distribution with the same type of "near collision" events and also how it reacts to faster obstacles (a person on a scooter for instance). The relative speed is bigger in the last two sequences. The SimpleTTC network predicts well on pedestrians, as the ground truth Time-to-Collision shape is reproduced by the prediction with small undesired fluctuations. For the predictions on the scooter with movement (last two slopes in Figure  9 ), the prediction slope starting point is twice higher than it should be. This is because the network assumes that the person on the scooter would take twice the time to reach the "collision" threshold and so on for all the following frames.
Results on Static Obstacles The second purpose of the proposed test set is to study the generalization of the trained model for different kinds of obstacles, more precisely for a static "chair" and "desk". The obtained predictions for those static obstacles can be observed in Figure  10. For some collisions sequences the prediction seems to have a normal behavior with a slight detection gap (about 0. 3 s) but the detection seems to fail most of the time. The network is hardly able to correctly predict the Time-to-Collision of static objects. In the Near-Collision dataset, the pedestrians leave the frame from the left or right as the camera is placed at about one meter high. In our dataset the static obstacles leave the frame more in the bottom corner of the frame as the camera is leveled at eye height.
At the end part of the network, the features from each stream are flattened and stacked together to process the data from all the frames in a last fully connected layer. The network outputs the Time-to-Collision. Reprinted with permission from ref. [25]. Copyright 2019 IEEE. However, their method does not generalize at all when dealing with our dataset (Figure  13 ) as the results of their prediction seem random and nonlinear. The Near-Collision network  [25]  can accurately predict the Time-to-Collision on the Near-Collision dataset, but it does not generalize to our dataset (Figure  13 ). When restricting the data to the bounding box of the obstacles and their relative distance to the camera, our model generalizes the task well on a different dataset that does not even have the same camera height (about 0.
Comparison with an Improved Version of the NearCollision Network Initially the Near-Collision network is trained on the whole image. However, in the case where there are multiple objects in the same image, as it is the case when dealing with our dataset, it can introduce some noise in estimating the Time-to-Collision for a given object. That is why we conducted a new experiment to check how much better the Near-Collision architecture could improve while restricting the input layer to the bounding box around each obstacle (Figure  14 ). As the input data is gradually reduced, the efficiency of the features selected as input can be confirmed as improvement or not. In this experiment we retrained the Near-Collision network by changing the input frames with different forms of the data outputs from the static data extractor module (Figure  14 ).
Conclusions The SimpleTTC network is a 10-layer deep fully connected neural network that is fed with the obstacle's location and depth data as input on three frames. It performs well on the Near Collision test set  [25]  as it was trained from the same dataset but is also able to generalize well on pedestrian collisions on a dataset with similar context but different locations. It is, however, unable to predict well for different kinds of obstacles as the shape and relative speed may differ too much from the data originally trained on (incoming walking pedestrians). Restricting the input data to the obstacle spatial coordinates instead of the input video frames enables better training for the Time-to-Collision task. The proposed method shows that obstacle Time-to-Collision forecasting on monocular smartglasses can be addressed while restricting the context scenario to specific situations.
Wallet attack In order to transact with a cryptocurrency, users need to control a cryptocurrency wallet. A wallet can be managed by a hardware device, a software program, or an online service which stores the private and public keys corresponding to the addresses associated with the user. An attack on a wallet software or wallet service provider and its users can produce a big impact, resulting in a massive theft of coins and decreasing the trust in the system overall. Besides traditional wallet service providers (e. g. , Trinity, MyEther, Edge) cryptocurrrency holdings can be stored on other online platforms such as exchanges (e. g. , Coinbase, Bitfinex, Bithumb, Bittrex). Coinbase is a digital exchange and online cryptocurrency wallet provider which, contrary to single-coin wallets, allows for holding and trading several cryptocurrencies under the same account.
Contribution As noted before, the event study literature is mostly focused on corporate stock performance analysis. There are few prior event studies applying to cryptocurrencies attacks. In the following, we will adapt existing event study methodologies to specific crypto-related event scenarios so that we can analyze and understand their impact. We agree with Campbell et al. [37]  about the importance of dividing the attacks into different categories depending on their type to estimate the economic effect. For the 51% attack, we utilize data based on the intrinsic feature of blockchain technology that tracks the exact time and date at which an attack happened by spotting the sudden changes in the network's longest chain and determining whether a transaction was been double spent. This technical scenario introduces a different set of challenges compared to company-related event studies, where the exact timeline of the propagation of information about the event (e.