nils-herrmann/clean_scirepeval huggingface dataset:
Toxidrome Recognition in Chemical-Weapons Attacks.

Terrorist attacks are increasing in both frequency and complexity around the world. In 2016 alone, there were more than 13,400 terrorist attacks globally, killing more than 34,000  Of equal concern, chemical-warfare agents that were developed for the battlefield are being used on civilians in major cities and conflict zones. The recent sarin attacks in Syria,2,3 the latest in a series of chemical attacks in that region,3,4 along with the use of the nerve agent VX in the assassination of Kim Jong-nam in Malaysia and the Sovietera agent Novichok in the poisoning of Sergei Skripal in the United Kingdom,5 all represent a worrisome trend in the use of deadly chemical agents by various rogue groups in civilian settings. In light of the rise in coordinated, multimodal terrorist attacks in Western urban centers,6,7 concern has been expressed about an increase in the use of chemical agents by terrorists on civilian targets around the world. Such attacks entail unique issues in on-the-scene safety8,9 and also require a rapid medical response.10 As health care providers, we must be proactive in how we prepare for and respond to this new threat. This article reviews the toxidromes (constellations of signs and symptoms that are characteristic of a given class of agents) for known and suspected chemicalwarfare agents that have properties that are well suited for terrorist attacks — namely, high volatility and rapid onset of incapacitating or lethal effects.11 Poisoncontrol procedures currently use toxidromes to identify specific classes of agents. Although symptoms such as eye irritation and coughing are common to a number of classes, specific clinical findings, including fasciculations, hypersecretions, early seizure, and miosis or mydriasis, can be rapidly identified as part of an acutephase triage system and used to differentiate among classes of agents. This should lead to reduced morbidity and mortality while also decreasing the risk to responding health care workers.12 The combined group of chemical-warfare agents examined here includes nerve agents, asphyxiants (blood agents), opioid agents, anesthetic agents, anticholinergic (antimuscarinic) agents, botulinum toxin, pulmonary agents, caustic agents (acids), riot-control agents, T-2 toxin, and vesicants (Table 1).
Injuries of the fingers and thumb in the athlete.

Injuries of the fingers and thumb in the athlete are common. Mallet finger, jersey finger, boutonniere deformity, Bennett and Rolando fractures, and gamekeeper's thumb are just a few of the injuries that can occur in athletes. A thorough understanding of the mechanism of injury, osseous and soft tissue abnormalities, imaging features, and treatment is important in the care of athletes. Prompt and accurate diagnosis is important and may help minimize outcomes of malunion, posttraumatic arthritis, and debility.
Relationship Queries on Extended Knowledge Graphs

Entity search over text corpora is not geared for relationship queries where answers are tuples of related entities and where a query often requires joining cues from multiple documents. With large knowledge graphs, structured querying on their relational facts is an alternative, but often suffers from poor recall because of mismatches between user queries and the knowledge graph or because of weakly populated relations.
 This paper presents the TriniT search engine for querying and ranking on extended knowledge graphs that combine relational facts with textual web contents. Our query language is designed on the paradigm of SPO triple patterns, but is more expressive, supporting textual phrases for each of the SPO arguments. We present a model for automatic query relaxation to compensate for mismatches between the data and a user's query. Query answers -- tuples of entities -- are ranked by a statistical language model. We present experiments with different benchmarks, including complex relationship queries, over a combination of the Yago knowledge graph and the entity-annotated ClueWeb'09 corpus.
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission

In machine learning often a tradeoff must be made between accuracy and intelligibility. More accurate models such as boosted trees, random forests, and neural nets usually are not intelligible, but more intelligible models such as logistic regression, naive-Bayes, and single decision trees often have significantly worse accuracy. This tradeoff sometimes limits the accuracy of models that can be applied in mission-critical applications such as healthcare where being able to understand, validate, edit, and trust a learned model is important. We present two case studies where high-performance generalized additive models with pairwise interactions (GA2Ms) are applied to real healthcare problems yielding intelligible models with state-of-the-art accuracy. In the pneumonia risk prediction case study, the intelligible model uncovers surprising patterns in the data that previously had prevented complex learned models from being fielded in this domain, but because it is intelligible and modular allows these patterns to be recognized and removed. In the 30-day hospital readmission case study, we show that the same methods scale to large datasets containing hundreds of thousands of patients and thousands of attributes while remaining intelligible and providing accuracy comparable to the best (unintelligible) machine learning methods.
Evaluating and Informing the Design of Chatbots

Text messaging-based conversational agents (CAs), popularly called chatbots, received significant attention in the last two years. However, chatbots are still in their nascent stage: They have a low penetration rate as 84% of the Internet users have not used a chatbot yet. Hence, understanding the usage patterns of first-time users can potentially inform and guide the design of future chatbots. In this paper, we report the findings of a study with 16 first-time chatbot users interacting with eight chatbots over multiple sessions on the Facebook Messenger platform. Analysis of chat logs and user interviews revealed that users preferred chatbots that provided either a 'human-like' natural language conversation ability, or an engaging experience that exploited the benefits of the familiar turn-based messaging interface. We conclude with implications to evolve the design of chatbots, such as: clarify chatbot capabilities, sustain conversation context, handle dialog failures, and end conversations gracefully.
Direction-Aware Spatial Context Features for Shadow Detection

Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper presents a novel network for shadow detection by analyzing image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting shadows. This design is developed into the DSC module and embedded in a CNN to learn DSC features at different levels. Moreover, a weighted cross entropy loss is designed to make the training more effective. We employ two common shadow detection benchmark datasets and perform various experiments to evaluate our network. Experimental results show that our network outperforms state-of-the-art methods and achieves 97% accuracy and 38% reduction on balance error rate.
Driving Using End-to-End Deep Learning

This work explores the problem of autonomously driving a vehicle given live videos obtained from cameras mounted around it. The current approach to this problem is to utilize hand-coded rules that explicitly tell a vehicle how to react given various stimuli (ex. stop signs, lane markings, etc). We instead seek to solve it using a single, end-to-end deep convolutional neural net trained on pairs of images and vehicle control parameters (ex. steering angle, throttle, etc) recorded from an actual driving sequence by a human. When visualizing the class activation maps for end-to-end models that currently exist for this task, it can be seen that the convolutional layers do not activate upon many regions of the image other than just the road. This is a major issue because driving requires understanding of the entire image to properly react to objects like pedestrians, traffic lights, and even other vehicles. We propose a new model that takes advantage of object detectors and multi-task learning to create a network that helps pays more attention to significant areas of an image in an unsupervised manner.
A Joint Model of Entity Linking and Predicate Recognition for Knowledge Base Question Answering

In the paper, we build a QA system which can automatically find the right answers from Chinese knowledge base. In particular, we first identify all possible topic entities in the knowledge base for a question. Then some predicate scores are utilized to pre-rank all candidate triple paths of topic entities by logistic model. Second, we use a joint training entity linking and predicate recognition model to re-rank candidate triple paths for the question. Finally, the paper selects the answer component from matched triple path based on heuristic rules. Our approach achieved the averaged F1-score of 57.67% on test data which obtained the second place in the contest of CCKS 2018 COQA task.
Learning Semantic Sentence Embeddings using Pair-wise Discriminator

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant.
In vitro regulatory models for systems biology.

The reductionist approach has revolutionized biology in the past 50 years. Yet its limits are being felt as the complexity of cellular interactions is gradually revealed by high-throughput technology. In order to make sense of the deluge of "omic data", a hypothesis-driven view is needed to understand how biomolecular interactions shape cellular networks. We review recent efforts aimed at building in vitro biochemical networks that reproduce the flow of genetic regulation. We highlight how those efforts have culminated in the rational construction of biochemical oscillators and bistable memories in test tubes. We also recapitulate the lessons learned about in vivo biochemical circuits such as the importance of delays and competition, the links between topology and kinetics, as well as the intriguing resemblance between cellular reaction networks and ecosystems.
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an Abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications.
Probabilistic Terrain Analysis For High-Speed Desert Driving

The ability to perceive and analyze terrain is a key problem in mobile robot navigation. Terrain perception problems arise in planetary robotics, agriculture, mining, and, of course, self-driving cars. Here, we introduce the PTA (probabilistic terrain analysis) algorithm for terrain classification with a fastmoving robot platform. The PTA algorithm uses probabilistic techniques to integrate range measurements over time, and relies on efficient statistical tests for distinguishing drivable from nondrivable terrain. By using probabilistic techniques, PTA is able to accommodate severe errors in sensing, and identify obstacles with nearly 100% accuracy at speeds of up to 35mph. The PTA algorithm was an essential component in the DARPA Grand Challenge, where it enabled our robot Stanley to traverse the entire course in record time.
An Intelligent System for False Alarm Reduction in Infrared Forest-Fire Detection

FOREST FIRES CAUSE MANY ENVIronmental disasters, creating economical and ecological damage as well as endangering people’s lives. Heightened interest in automatic surveillance and early forest-fire detection has taken precedence over traditional human surveillance because the latter’s subjectivity affects detection reliability, which is the main issue for forest-fire detection systems. In current systems, the process is tedious, and human operators must manually validate many false alarms. Our approach—the False Alarm Reduction system—proposes an alternative realtime infrared–visual system that overcomes this problem. The FAR system consists of applying new infrared-image processing techniques and Artificial Neural Networks (ANNs), using additional information from meteorological sensors and from a geographical information database, taking advantage of the information redundancy from visual and infrared cameras through a matching process, and designing a fuzzy expert rule base to develop a decision function. Furthermore, the system provides the human operator with new software tools to verify alarms.
Orientation-boosted Voxel Nets for 3D Object Recognition

Recent work has shown good recognition results in 3D object recognition using 3D convolutional networks. In this paper, we show that the object orientation plays an important role in 3D recognition. More specifically, we argue that objects induce different features in the network under rotation. Thus, we approach the category-level classification task as a multi-task problem, in which the network is trained to predict the pose of the object in addition to the class label as a parallel task. We show that this yields significant improvements in the classification results. We test our suggested architecture on several datasets representing various 3D data sources: LiDAR data, CAD models, and RGB-D images. We report state-of-the-art results on classification as well as significant improvements in precision and speed over the baseline on 3D detection.
Early Recognition of 3D Human Actions

Action recognition is an important research problem of human motion analysis (HMA). In recent years, 3D observation-based action recognition has been receiving increasing interest in the multimedia and computer vision communities, due to the recent advent of cost-effective sensors, such as depth camera Kinect. This work takes this one step further, focusing on early recognition of ongoing 3D human actions, which is beneficial for a large variety of time-critical applications, e.g., gesture-based human machine interaction, somatosensory games, and so forth. Our goal is to infer the class label information of 3D human actions with partial observation of temporally incomplete action executions. By considering 3D action data as multivariate time series (m.t.s.) synchronized to a shared common clock (frames), we propose a stochastic process called dynamic marked point process (DMP) to model the 3D action as temporal dynamic patterns, where both timing and strength information are captured. To achieve even more early and better accuracy of recognition, we also explore the temporal dependency patterns between feature dimensions. A probabilistic suffix tree is constructed to represent sequential patterns among features in terms of the variable-order Markov model (VMM). Our approach and several baselines are evaluated on five 3D human action datasets. Extensive results show that our approach achieves superior performance for early recognition of 3D human actions.
Semantic Feature Structure Extraction from Documents Based on Extended Lexical Chains

The meaning of a sentence in a document is more easily determined if its constituent words exhibit cohesion with respect to their individual semantics. This paper explores the degree of cohesion among a document's words using lexical chains as a semantic representation of its meaning. Using a combination of diverse types of lexical chains, we develop a text document representation that can be used for semantic document retrieval. For our approach, we develop two kinds of lexical chains: (i) a multilevel flexible chain representation of the extracted semantic values, which is used to construct a fixed segmentation of these chains and constituent words in the text; and (ii) a fixed lexical chain obtained directly from the initial semantic representation from a document. The extraction and processing of concepts is performed using WordNet as a lexical database. The segmentation then uses these lexical chains to model the dispersion of concepts in the document. Representing each document as a high-dimensional vector, we use spherical k-means clustering to demonstrate that our approach performs better than previ-
Realization of depth first search algorithm on line maze solver robot

In this paper, described depth first search algorithm being implemented on line maze solver robot. Right hand rule will be used to select the turn priority. Some mazes will be used to test the reliability of this depth first search maze solver robot. In the result, the system is able to use depth first search algorithm well. The robot able to finish non-loop maze with 84.97 s average time and with-loop maze in 63,4 s average time. But, the robot fails to finish in the maze with-loop where the finish is located at the non-priority turn and the next junction is loop.
Basic survey on Malware Analysis, Tools and Techniques

The term malware stands for malicious software. It is a program installed on a system without the knowledge of owner of the system. It is basically installed by the third party with the intention to steal some private data from the system or simply just to play pranks. This in turn threatens the computer’s security, wherein computer are used by one’s in day-to-day life as to deal with various necessities like education, communication, hospitals, banking, entertainment etc. Different traditional techniques are used to detect and defend these malwares like Antivirus Scanner (AVS), firewalls, etc. But today malware writers are one step forward towards then Malware detectors. Day-by-day they write new malwares, which become a great challenge for malware detectors. This paper focuses on basis study of malwares and various detection techniques which can be used to detect malwares.
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge

Adversarial examples are inputs to machine learning models designed to cause the model to make a mistake. They are useful for understanding the shortcomings of machine learning models, interpreting their results, and for regularisation. In NLP, however, most example generation strategies produce input text by using known, pre-specified semantic transformations, requiring significant manual effort and in-depth understanding of the problem and domain. In this paper, we investigate the problem of automatically generating adversarial examples that violate a set of given First-Order Logic constraints in Natural Language Inference (NLI). We reduce the problem of identifying such adversarial examples to a combinatorial optimisation problem, by maximising a quantity measuring the degree of violation of such constraints and by using a language model for generating linguisticallyplausible examples. Furthermore, we propose a method for adversarially regularising neural NLI models for incorporating background knowledge. Our results show that, while the proposed method does not always improve results on the SNLI and MultiNLI datasets, it significantly and consistently increases the predictive accuracy on adversarially-crafted datasets – up to a 79.6% relative improvement – while drastically reducing the number of background knowledge violations. Furthermore, we show that adversarial examples transfer among model architectures, and that the proposed adversarial training procedure improves the robustness of NLI models to adversarial examples.
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

Knowledge bases (KB), both automatically and manually constructed, are often incomplete — many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pair of entities. Given the enormous size of KBs and the exponential number of paths, previous path-based models have considered only the problem of predicting a missing relation given two entities, or evaluating the truth of a proposed triple. Additionally, these methods have traditionally used random paths between fixed entity pairs or more recently learned to pick paths between them. We propose a new algorithm, MINERVA1, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity. Since random walks are impractical in a setting with combinatorially many destinations from a start node, we present a neural reinforcement learning approach which learns how to navigate the graph conditioned on the input query to find predictive paths. Empirically, this approach obtains state-of-the-art results on several datasets, significantly outperforming prior methods.