nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Perspective Context Matching for Machine Comprehension

Previous machine comprehension (MC) datasets are either too small to train endto-end deep learning models, or not difficult enough to evaluate the ability of current MC techniques. The newly released SQuAD dataset alleviates these limitations, and gives us a chance to develop more realistic MC models. Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage. Our model first adjusts each word-embedding vector in the passage by multiplying a relevancy weight computed against the question. Then, we encode the question and weighted passage by using bi-directional LSTMs. For each point in the passage, our model matches the context of this point against the encoded question from multiple perspectives and produces a matching vector. Given those matched vectors, we employ another bi-directional LSTM to aggregate all the information and predict the beginning and ending points. Experimental result on the test set of SQuAD shows that our model achieves a competitive result on the leaderboard.
Suicidal decapitation by guillotine: case report and review of the literature.

Suicidal decapitation is seldom encountered in forensic medicine practice. This study reports the analysis of a suicide committed by a 31-year-old man with a self-fabricated guillotine. The construction of the guillotine was very interesting and sophisticated. The guillotine-like blade with additional weight was placed in a large metal frame. The movement of the blade was controlled by the frame rails. The steel blade was triggered by a tensioned rubber band after releasing the safety catch. The cause of death was immediate exsanguination after complete severance of the neck. The suicide motive was most likely emotional distress after the death of his father. In medico-legal literature, there has been only one similar case of suicidal complete decapitation by a guillotine described.
Partial Shading Detection and Smooth Maximum Power Point Tracking of PV Arrays Under PSC

One of the most important issues in the operation of a photovoltaic (PV) system is extracting maximum power from the PV array, especially in partial shading condition (PSC). Under PSC, P-V characteristic of PV arrays will have multiple peak points, only one of which is global maximum. Conventional maximum power point tracking (MPPT) methods are not able to extract maximum power in this condition. In this paper, a novel two-stage MPPT method is presented to overcome this drawback. In the first stage, a method is proposed to determine the occurrence of PSC, and in the second stage, using a new algorithm that is based on ramp change of the duty cycle and continuous sampling from the P-V characteristic of the array, global maximum power point (MPP) of array is reached. Perturb and observe algorithm is then re-activated to trace small changes of the new MPP. Open-loop operation of the proposed method makes its implementation cheap and simple. The method is robust in the face of changing environmental conditions and array characteristics, and has minimum negative impact on the connected power system. Simulations in Matlab/Simulink and experimental results validate the performance of the proposed methods.
Boosting Question Answering by Deep Entity Recognition

In this paper an open-domain factoid question answering system for Polish, RAFAEL, is presented. The system goes beyond finding an answering sentence; it also extracts a single string, corresponding to the required entity. Herein the focus is placed on different approaches to entity recognition, essential for retrieving information matching question constraints. Apart from traditional approach, including named entity recognition (NER) solutions, a novel technique, called Deep Entity Recognition (DeepER), is introduced and implemented. It allows a comprehensive search of all forms of entity references matching a given WordNet synset (e.g. an impressionist), based on a previously assembled entity library. It has been created by analysing the first sentences of encyclopaedia entries and disambiguation and redirect pages. DeepER also provides automatic evaluation, which makes possible numerous experiments, including over a thousand questions from a quiz TV show answered on the grounds of Polish Wikipedia. The final results of a manual evaluation on a separate question set show that the strength of DeepER approach lies in its ability to answer questions that demand answers beyond the traditional categories of named entities.
A Focused Dynamic Attention Model for Visual Question Answering

Visual Question and Answering (VQA) problems are attracting increasing interest from multiple research disciplines. Solving VQA problems requires techniques from both computer vision for understanding the visual contents of a presented image or video, as well as the ones from natural language processing for understanding semantics of the question and generating the answers. Regarding visual content modeling, most of existing VQA methods adopt the strategy of extracting global features from the image or video, which inevitably fails in capturing fine-grained information such as spatial configuration of multiple objects. Extracting features from auto-generated regions – as some region-based image recognition methods do – cannot essentially address this problem and may introduce some overwhelming irrelevant features with the question. In this work, we propose a novel Focused Dynamic Attention (FDA) model to provide better aligned image content representation with proposed questions. Being aware of the key words in the question, FDA employs off-the-shelf object detector to identify important regions and fuse the information from the regions and global features via an LSTM unit. Such question-driven representations are then combined with question representation and fed into a reasoning unit for generating the answers. Extensive evaluation on a large-scale benchmark dataset, VQA, clearly demonstrate the superior performance of FDA over wellestablished baselines.
The Prediction of Students’ Academic Performance With Fluid
Intelligence in Giving Special Consideration to the Contribution of
Learning

The present study provides a new account of how fluid intelligence influences academic performance. In this account a complex learning component of fluid intelligence tests is proposed to play a major role in predicting academic performance. A sample of 2, 277 secondary school students completed two reasoning tests that were assumed to represent fluid intelligence and standardized math and verbal tests assessing academic performance. The fluid intelligence data were decomposed into a learning component that was associated with the position effect of intelligence items and a constant component that was independent of the position effect. Results showed that the learning component contributed significantly more to the prediction of math and verbal performance than the constant component. The link from the learning component to math performance was especially strong. These results indicated that fluid intelligence, which has so far been considered as homogeneous, could be decomposed in such a way that the resulting components showed different properties and contributed differently to the prediction of academic performance. Furthermore, the results were in line with the expectation that learning was a predictor of performance in school.
Drugging the undruggables: exploring the ubiquitin system for drug development

Dynamic modulation of protein levels is tightly controlled in response to physiological cues. In mammalian cells, much of the protein degradation is carried out by the ubiquitin-proteasome system (UPS). Similar to kinases, components of the ubiquitin system are often dysregulated, leading to a variety of diseases, including cancer and neurodegeneration, making them attractive drug targets. However, so far there are only a handful of drugs targeting the ubiquitin system that have been approved by the FDA. Here, we review possible therapeutic intervention nodes in the ubiquitin system, analyze the challenges, and highlight the most promising strategies to target the UPS.
An Approach to Automatically Extract Predictive Properties from Nominal Attributes in Relational Databases

Feature engineering is a fundamental step in data mining and yet it is both difficult and expensive. Hand-crafting features is not only a time-consuming task that requires specific domain knowledge, it also may prevent new information to emerge. The extraction of meaningful features from relational data is particularly difficult due to complex relationships between tables. In the last decade there is an emerging trend towards automating the process of constructing propositional features from relational data and such approaches have been successfully used for solving numerous real-world problems. Despite their success, most of them lack an adequate support of nominal attributes. We present a new approach helping propositionalization methods to extract meaningful features from nominal attributes and improve their predictive performance. In an experimental evaluation on three datasets we demonstrate that the proposed technique is capable of producing novel features that are highly correlated with the target attribute. Furthermore, those features can reveal relationships among the distinct categorical values allowing to compare and order them. Finally, experimental results show that those new features can significantly improve the predictive performance in classification tasks.
A Sequence-to-Sequence Model for Semantic Role Labeling

We explore a novel approach for Semantic Role Labeling (SRL) by casting it as a sequence-to-sequence process. We employ an attention-based model enriched with a copying mechanism to ensure faithful regeneration of the input sequence, while enabling interleaved generation of argument role labels. Here, we apply this model in a monolingual setting, performing PropBank SRL on English language data. The constrained sequence generation set-up enforced with the copying mechanism allows us to analyze the performance and special properties of the model on manually labeled data and benchmarking against state-of-the-art sequence labeling models. We show that our model is able to solve the SRL argument labeling task on English data, yet further structural decoding constraints will need to be added to make the model truly competitive. Our work represents a first step towards more advanced, generative SRL labeling setups.
Clustering feature decision trees for semi-supervised classification from high-speed data streams

Most stream data classification algorithms apply the supervised learning strategy which requires massive labeled data. Such approaches are impractical since labeled data are usually hard to obtain in reality. In this paper, we build a clustering feature decision tree model, CFDT, from data streams having both unlabeled and a small number of labeled examples. CFDT applies a micro-clustering algorithm that scans the data only once to provide the statistical summaries of the data for incremental decision tree induction. Micro-clusters also serve as classifiers in tree leaves to improve classification accuracy and reinforce the any-time property. Our experiments on synthetic and real-world datasets show that CFDT is highly scalable for data streams while generating high classification accuracy with high speed.
EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants

A re-emergence of research on EEG-neurofeedback followed controlled evidence of clinical benefits and validation of cognitive/affective gains in healthy participants including correlations in support of feedback learning mediating outcome. Controlled studies with healthy and elderly participants, which have increased exponentially, are reviewed including protocols from the clinic: sensory-motor rhythm, beta1 and alpha/theta ratios, down-training theta maxima, and from neuroscience: upper-alpha, theta, gamma, alpha desynchronisation. Outcome gains include sustained attention, orienting and executive attention, the P300b, memory, spatial rotation, RT, complex psychomotor skills, implicit procedural memory, recognition memory, perceptual binding, intelligence, mood and well-being. Twenty-three of the controlled studies report neurofeedback learning indices along with beneficial outcomes, of which eight report correlations in support of a meditation link, results which will be supplemented by further creativity and the performing arts evidence in Part II. Validity evidence from optimal performance studies represents an advance for the neurofeedback field demonstrating that cross fertilisation between clinical and optimal performance domains will be fruitful. Theoretical and methodological issues are outlined further in Part III.
Gratitude as a psychotherapeutic intervention.

Gratitude practice can be a catalyzing and relational healing force, often untapped in clinical practice. In this article, we provide an overview of current thinking about gratitude's defining and beneficial properties, followed by a brief review of the research on mental health outcomes that result from gratitude practice. Following an analysis of our case study of the use of gratitude as a psychotherapeutic intervention, we present various self-strategies and techniques for consciously choosing and cultivating gratitude. We conclude by describing ways in which gratitude might be capitalized upon for beneficial outcomes in therapeutic settings.
Video Game Genre as a Predictor of Problem Use

This study assessed how problem video game playing (PVP) varies with game type, or "genre," among adult video gamers. Participants (n=3,380) were adults (18+) who reported playing video games for 1 hour or more during the past week and completed a nationally representative online survey. The survey asked about characteristics of video game use, including titles played in the past year and patterns of (problematic) use. Participants self-reported the extent to which characteristics of PVP (e.g., playing longer than intended) described their game play. Five percent of our sample reported moderate to extreme problems. PVP was concentrated among persons who reported playing first-person shooter, action adventure, role-playing, and gambling games most during the past year. The identification of a subset of game types most associated with problem use suggests new directions for research into the specific design elements and reward mechanics of "addictive" video games and those populations at greatest risk of PVP with the ultimate goal of better understanding, preventing, and treating this contemporary mental health problem.
Behavior-Based Control: Main Properties and Implications

In addition to its computational and sensory hardware , the architecture of an autonomous robot determines how that robot will behave by imposing key constraints on the way it can sense, reason, and act in order to achieve its task. In this paper we deene the basic types of control architectures: purely reac-tive, behavior-based, and planner-based. We clarify the fundamental diierences between the rst two approaches , which are commonly confused in the literature. Next we describe the properties of behavior-based systems in detail and compare them to the two alternative approaches. Finally, we describe the application of the behavior-based paradigm in two very diierent domains.
Cholinesterase Inhibitors for Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common age-related neurodegenerative disease and has become an urgent public health problem in most areas of the world. Substantial progress has been made in understanding the basic neurobiology of AD and, as a result, new drugs for its treatment have become available. Cholinesterase inhibitors (ChEIs), which increase the availability of acetylcholine in central synapses, have become the main approach to symptomatic treatment. ChEIs that have been approved or submitted to the US Food and Drug Administration (FDA) include tacrine, donepezil, metrifonate, rivastigmine and galantamine. In this review we discuss their pharmacology, clinical experience to date with their use and their potential benefits or disadvantages. ChEIs have a significant, although modest, effect on the cognitive status of patients with AD. In addition to their effect on cognition, ChEIs have a positive effect on mood and behaviour. Uncertainty remains about the duration of the benefit because few studies of these compounds beyond one year have been published. Although ChEIs are generally well tolerated, all patients should be followed closely for possible adverse effects. There is no substantial difference in the effectivenes of the various ChEIs, however, they may have different safety profiles. We believe the benefits of their use outweigh the risks and costs and, therefore, ChEIs should be considered as primary therapy for patients with mild to moderate AD.
An Empirical Examination of Individual Traits as Antecedents to Computer Anxiety and Computer Self-Efficacy

To better understand how individual differences influence the use of information technoiogy (IT), this study models and tests relationships among dynamic, IT-specific individual differences (i.e.. computer self-efficacy and computer anxiety). stable, situation-specific traits (i.e., personal innovativeness in IT) and stable, broad traits (i.e.. ''Cynthia Beath was the accepting senior editor for this paper. trait anxiety and negative affectivity). When compared to broad traits, the model suggests that situation-specific traits exert a more pervasive influence on IT situation-specific individual differences. Further, the modei suggests that computer anxiety mediates the influence of situationspecific traits (i.e., personal innovativeness) on computer self-efficacy. Results provide support for many of the hypothesized relationships. From a theoretical perspective, the findings help to further our understanding of the nomological network among individual differences that lead to computer self-efficacy. From a practical perspective, the findings may help IT managers design training programs that more effectiveiy increase the computer self-efficacy of users with different dispositional characteristics.
Recent Advances in Active Noise Control Inside Automobile Cabins: Toward quieter cars

In this article, a compact tutorial of ANC techniques was presented with a review of their application in reducing undesired noise inside automobiles. Some of the recent advances have demonstrated significant improvements in the noise reduction levels as well as the cost and implementation complexity. While the aforementioned techniques discussed may individually focus on a particular noise field (e.g., road noise only, engine noise only), it is proven through research and commercial products that a combination of these strategies can deliver significant benefits in realistic conditions.
Helicopter Performance and Vibration Enhancement by Twist-Actuated Blades

This paper investigates potential improvements that can be accomplished by integral twist-actuated rotor blades regarding helicopter performance and its vibration. The twist deformation is obtained using anisotropic piezocomposite actuators embedded in the composite blade construction. A four-bladed fullyarticulated Active Twist Rotor (ATR) system was built and tested at Langley Transonic Dynamics Tunnel. From these tests, the integral twist control authority exerted upon the fixed system was determined. Significant control authority in hub vertical shear load component is observed from different blade actuation modes. Similar control authority is found in the other components of the fixed-system loads. Exploiting those authorities, vehicle performance enhancement can be achieved using low-frequency actuation, for example, 0P, 1P and 2P. Payload increase in hover can be obtained with a steady collective actuation of blade twist. Power consumption can be reduced by employing a certain mode of blade actuation at 2P in forward flight. Vehicle pitch and roll moments are generated by longitudinal and lateral blade actuation mode at 1P frequency. On the other hand, actuation at higher frequencies can be used to reduce hub vibratory loads. The closed-loop control algorithm used for this reduction is an improved version of the traditional Higher Harmonic Control. Multiharmonic and multi-mode controller is designed and tested as part of the present study.
Efficient Online Novelty Detection in News Streams

Novelty detection in text streams is a challenging task that emerges in quite a few different scenarii, ranging from email threads to RSS news feeds on a cell phone. An efficient novelty detection algorithm can save the user a great deal of time when accessing interesting information. Most of the recent research for the detection of novel documents in text streams uses either geometric distances or distributional similarities with the former typically performing better but being slower as we need to compare an incoming document with all the previously seen ones. In this paper, we propose a new novelty detection algorithm based on the Inverse Document Frequency (IDF) scoring function. Computing novelty based on IDF enables us to avoid similarity comparisons with previous documents in the text stream, thus leading to faster execution times. At the same time, our proposed approach outperforms several commonly used baselines when applied on a real-world news articles dataset.
Synthesis Lectures on Artificial Intelligence andMachine Learning

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. e field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have only recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 that gives a brief survey of psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 on interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides on best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects for this domain.