nils-herrmann/clean_scirepeval huggingface dataset:
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network

Recent work by Cohen et al.  has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis. In this paper we propose a generalization of this work that generally exhibits improved performace, but from an implementation point of view is actually simpler. An unusual feature of the proposed architecture is that it uses the Clebsch–Gordan transform as its only source of nonlinearity, thus avoiding repeated forward and backward Fourier transforms. The underlying ideas of the paper generalize to constructing neural networks that are invariant to the action of other compact groups.
Multilingual text-to-speech synthesis : the Bell Labs approach

This collection is a very welcome addition to the literature on automatic speech synthesis, also known as "text-to-speech" (TTS). It has been more than a decade since a comprehensive, edited collection of chapters on this topic has been published. Because much has changed in TTS research over the last ten years, this text will prove very useful to workers in the field. Along with Dutoit's recent book (An Introduction to Textto-Speech Synthesis [1997], also published by Kluwer), it is essential reading for anyone serious about TTS research. (Other recent works have included chapters on TTS, but their main focus has been on other aspects of speech processing, and thus the TTS details there are far fewer. While Bailly and Benoit's collection Talking Machines [1992] has synthesis as its sole focus, it derives from numerous presentations at a workshop and suffers accordingly due to its many uneven short chapters.) A more direct comparison can be made to Allen, Hunnicutt, and Klatt's book on MITalk (1987), especially as both that book and the one currently under review describe in detail specific TTS systems developed at two of the major research centres involved in speech synthesis over the years. As the foreword of the present book notes, the MITalk system was largely based on morphological decomposition of words and synthesis via a Klatt formant architecture, while the modular Bell system is distinguished by its regular relations for text analysis, its use of concatenation of diphone units, and its emphasis on the importance of careful selection of texts and recording conditions. While they cover similar ground, the newer Sproat book is quite different from Dutoit's. It has seven authors, all contributors to the Bell Labs system that is described in detail in the book. Often in multiauthor books, one finds a significant unevenness in coverage and style across chapters due to lack of coordination among the authors. This is much less apparent in Sproat's b o o k because all authors worked at the same lab and because one author (van Santen) was involved in seven of the chapters; at least one of the two principal authors (Sproat and van Santen) contributed to all nine chapters. Further distinguishing this book is its emphasis on multilingual TTS: the Bell system exists for ten diverse languages, and the book provides many specific examples of interesting problems in different languages. While several multilingual synthesizers are available commercially, most technical literature has focused on one language at a time. Given that all speech synthesis is based on the same human speech production mechanism and that the world's languages share many aspects of phonetics, it is prudent to examine how a uniform methodology can be applied to many different languages for TTS. Unlike speech coders, which normally function equally well for all languages without adjustment, speech recognizers and synthesizers necessarily need training for individual languages. One of the foci of this book is minimizing the work to
A review of inverse reinforcement learning theory and recent advances

A major challenge faced by machine learning community is the decision making problems under uncertainty. Reinforcement Learning (RL) techniques provide a powerful solution for it. An agent used by RL interacts with a dynamic environment and finds a policy through a reward function, without using target labels like Supervised Learning (SL). However, one fundamental assumption of existing RL algorithms is that reward function, the most succinct representation of the designer's intention, needs to be provided beforehand. In practice, the reward function can be very hard to specify and exhaustive to tune for large and complex problems, and this inspires the development of Inverse Reinforcement Learning (IRL), an extension of RL, which directly tackles this problem by learning the reward function through expert demonstrations. IRL introduces a new way of learning policies by deriving expert's intentions, in contrast to directly learning policies, which can be redundant and have poor generalization ability. In this paper, the original IRL algorithms and its close variants, as well as their recent advances are reviewed and compared.
A method of a lightning surge analysis recommended in Japan using EMTP

EMTP has been widely used in Japan to analyze switching and lightning overvoltages to design power stations and substations from the viewpoint of insulation coordination. The Japanese standard of high voltage testing, JEC-0102-1994, and the insulation design and the coordination of an 1100-kV line of Tokyo Electric Power Company were mostly based on EMTP simulations. This paper presents a method of a lightning surge analysis using the EMTP recommended in the Japanese standard and related guidelines, and suggests further work to be done based on discussions of applicable limits and problems of the recommended models.
Probiotics as modulators of the gut flora.

Probiotic ingestion can be recommended as a preventative approach to maintaining the balance of the intestinal microflora and thereby enhance 'well-being'. Research into the use of probiotic intervention in specific illnesses and disorders has identified certain patient populations that may benefit from the approach. Undoubtedly, probiotics will vary in their efficacy and it may not be the case that the same results occur with all species. Those that prove most efficient will likely be strains that are robust enough to survive the harsh physico-chemical conditions present in the gastrointestinal tract. This includes gastric acid, bile secretions and competition with the resident microflora. A survey of the literature indicates positive results in over fifty human trials, with prevention/treatment of infections the most frequently reported output. In theory, increased levels of probiotics may induce a 'barrier' influence against common pathogens. Mechanisms of effect are likely to include the excretion of acids (lactate, acetate), competition for nutrients and gut receptor sites, immunomodulation and the formation of specific antimicrobial agents. As such, persons susceptible to diarrhoeal infections may benefit greatly from probiotic intake. On a more chronic basis, it has been suggested that some probiotics can help maintain remission in the inflammatory conditions, ulcerative colitis and pouchitis. They have also been suggested to repress enzymes responsible for genotoxin formation. Moreover, studies have suggested that probiotics are as effective as anti-spasmodic drugs in the alleviation of irritable bowel syndrome. The approach of modulating the gut flora for improved health has much relevance for the management of those with acute and chronic gut disorders. Other target groups could include those susceptible to nosocomial infections, as well as the elderly, who have an altered microflora, with a decreased number of beneficial microbial species. For the future, it is imperative that mechanistic interactions involved in probiotic supplementation be identified. Moreover, the survival issues associated with their establishment in the competitive gut ecosystem should be addressed. Here, the use of prebiotics in association with useful probiotics may be a worthwhile approach. A prebiotic is a dietary carbohydrate selectively metabolised by probiotics. Combinations of probiotics and prebiotics are known as synbiotics.
Mobile Cloud Computing: A Comparison of Application Models

Cloud computing is an emerging concept combining many fields of computing. The foundation of cloud computing is the delivery of services, software and processing capacity over the Internet, reducing cost, increasing storage, automating systems, decoupling of service delivery from underlying technology, and providing flexibility and mobility of information. However, the actual realization of these benefits is far from being achieved for mobile applications and open many new research questions. In order to better understand how to facilitate the building of mobile cloud-based applications, we have surveyed existing work in mobile computing through the prism of cloud computing principles. We give a definition of mobile cloud coputing and provide an overview of the results from this review, in particular, models of mobile cloud applications. We also highlight research challenges in the area of mobile cloud computing. We conclude with recommendations for how this better understanding of mobile cloud computing can help building more powerful mobile applications.
Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures.

Integrating biomechanics, behavior and ecology requires a mechanistic understanding of the processes producing the movement of animals. This calls for contemporaneous biomechanical, behavioral and environmental data along movement pathways. A recently formulated unifying movement ecology paradigm facilitates the integration of existing biomechanics, optimality, cognitive and random paradigms for studying movement. We focus on the use of tri-axial acceleration (ACC) data to identify behavioral modes of GPS-tracked free-ranging wild animals and demonstrate its application to study the movements of griffon vultures (Gyps fulvus, Hablizl 1783). In particular, we explore a selection of nonlinear and decision tree methods that include support vector machines, classification and regression trees, random forest methods and artificial neural networks and compare them with linear discriminant analysis (LDA) as a baseline for classifying behavioral modes. Using a dataset of 1035 ground-truthed ACC segments, we found that all methods can accurately classify behavior (80-90%) and, as expected, all nonlinear methods outperformed LDA. We also illustrate how ACC-identified behavioral modes provide the means to examine how vulture flight is affected by environmental factors, hence facilitating the integration of behavioral, biomechanical and ecological data. Our analysis of just over three-quarters of a million GPS and ACC measurements obtained from 43 free-ranging vultures across 9783 vulture-days suggests that their annual breeding schedule might be selected primarily in response to seasonal conditions favoring rising-air columns (thermals) and that rare long-range forays of up to 1750 km from the home range are performed despite potentially heavy energetic costs and a low rate of food intake, presumably to explore new breeding, social and long-term resource location opportunities.
Culturally Responsive Teaching : Implications for Educational Justice

Educational justice is a major global challenge. In most underdeveloped countries, many students do not have access to education and in most advanced democracies, school attainment and success are still, to a large extent, dependent on a student’s social background. However, it has often been argued that social justice is an essential part of teachers’ work in a democracy. This article raises an important overriding question: how can we realize the goal of educational justice in the field of teaching? In this essay, I examine culturally responsive teaching as an educational practice and conclude that it is possible to realize educational justice in the field of teaching because in its true implementation, culturally responsive teaching conceptualizes the connection between education and social justice and creates the space needed for discussing social change in society.
Spatial Presence and Emotions during Video Game Playing: Does It Matter with Whom You Play?

The authors examined whether the nature of the opponent (computer, friend, or stranger) influences spatial presence, emotional responses, and threat and challenge appraisals when playing video games. In a within-subjects design, participants played two different video games against a computer, a friend, and a stranger. In addition to self-report ratings, cardiac interbeat intervals (IBIs) and facial electromyography (EMG) were measured to index physiological arousal and emotional valence. When compared to playing against a computer, playing against another human elicited higher spatial presence, engagement, anticipated threat, post-game challenge appraisals, and physiological arousal, as well as more positively valenced emotional responses. In addition, playing against a friend elicited greater spatial presence, engagement, and self-reported and physiological arousal, as well as more positively valenced facial EMG responses, compared to playing against a stranger. The nature of the opponent influences spatial presence when playing video games, possibly through the mediating influence on arousal and attentional processes.
Teacher-Student Curriculum Learning

We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic curriculum learning, where the Student tries to learn a complex task and the Teacher automatically chooses subtasks from a given set for the Student to train on. We describe a family of Teacher algorithms that rely on the intuition that the Student should practice more those tasks on which it makes the fastest progress, i.e. where the slope of the learning curve is highest. In addition, the Teacher algorithms address the problem of forgetting by also choosing tasks where the Student’s performance is getting worse. We demonstrate that TSCL matches or surpasses the results of carefully hand-crafted curricula in two tasks: addition of decimal numbers with LSTM and navigation in Minecraft. Using our automatically generated curriculum enabled to solve a Minecraft maze that could not be solved at all when training directly on solving the maze, and the learning was an order of magnitude faster than uniform sampling of subtasks.
Seed dormancy and the control of germination.

Seed dormancy is an innate seed property that defines the environmental conditions in which the seed is able to germinate. It is determined by genetics with a substantial environmental influence which is mediated, at least in part, by the plant hormones abscisic acid and gibberellins. Not only is the dormancy status influenced by the seed maturation environment, it is also continuously changing with time following shedding in a manner determined by the ambient environment. As dormancy is present throughout the higher plants in all major climatic regions, adaptation has resulted in divergent responses to the environment. Through this adaptation, germination is timed to avoid unfavourable weather for subsequent plant establishment and reproductive growth. In this review, we present an integrated view of the evolution, molecular genetics, physiology, biochemistry, ecology and modelling of seed dormancy mechanisms and their control of germination. We argue that adaptation has taken place on a theme rather than via fundamentally different paths and identify similarities underlying the extensive diversity in the dormancy response to the environment that controls germination.
A comparative study of open source deep learning frameworks

Deep Learning (DL) is one of the hottest trends in machine learning as DL approaches produced results superior to the state-of-the-art in problematic areas such as image processing and natural language processing (NLP). To foster the growth of the DL community, several open source frameworks appeared providing implementations of the most common DL algorithms. These frameworks vary in the algorithms they support and in the quality of their implementations. The purpose of this work is to provide a qualitative and quantitative comparison among three of the most popular and most comprehensive DL frameworks (namely Google's TensorFlow, University of Montreal's Theano, and Microsoft's CNTK). The ultimate goal of this work is to help end users make an informed decision about the best DL framework that suits their needs and resources. To ensure that our study is as comprehensive as possible, we conduct several experiments using multiple benchmark datasets and measure the performance of the frameworks' implementation of different DL algorithms. For most of our experiments, we find out that CNTK's implementations are superior to the other ones under consideration.
Fast Kernel Classifiers with Online and Active Learning

Very high dimensional learning systems become theoretical ly possible when training examples are abundant. The computing cost then becomes the limiting fact or. Any efficient learning algorithm should at least take a brief look at each example. But should a ll ex mples be given equal attention? This contribution proposes an empirical answer. We first pre sent an online SVM algorithm based on this premise. LASVM yields competitive misclassifi cation rates after a single pass over the training examples, outspeeding state-of-the-art SVM s olvers. Then we show how active example selection can yield faster training, higher accuracies , and simpler models, using only a fraction of the training example labels.
Sparse code multiple access: An energy efficient uplink approach for 5G wireless systems

The rapid traffic growth and ubiquitous access requirements make it essential to explore the next generation (5G) wireless communication networks. In the current 5G research area, non-orthogonal multiple access has been proposed as a paradigm shift of physical layer technologies. Among all the existing non-orthogonal technologies, the recently proposed sparse code multiple access (SCMA) scheme is shown to achieve a better link level performance. In this paper, we extend the study by proposing an unified framework to analyze the energy efficiency of SCMA scheme and a low complexity decoding algorithm which is critical for prototyping. We show through simulation and prototype measurement results that SCMA scheme provides extra multiple access capability with reasonable complexity and energy consumption, and hence, can be regarded as an energy efficient approach for 5G wireless communication systems.
Efficient Online Multi-Person 2D Pose Tracking with Recurrent Spatio-Temporal Affinity Fields

We present an online approach to efficiently and simultaneously detect and track the 2D pose of multiple people in a video sequence. We build upon Part Affinity Field (PAF) representation designed for static images, and propose an architecture that can encode and predict Spatio-Temporal Affinity Fields (STAF) across a video sequence. In particular, we propose a novel temporal topology cross-linked across limbs which can consistently handle body motions of a wide range of magnitudes. Additionally, we make the overall approach recurrent in nature, where the network ingests STAF heatmaps from previous frames and estimates those for the current frame. Our approach uses only online inference and tracking, and is currently the fastest and the most accurate bottom-up approach that is runtime invariant to the number of people in the scene and accuracy invariant to input frame rate of camera. Running at ∼30 fps on a single GPU at single scale, it achieves highly competitive results on the PoseTrack benchmarks.
The Cyber Aggression in Relationships Scale: A New Multidimensional Measure of Technology-Based Intimate Partner Aggression.

The purpose of this study was to develop and provide initial validation for a measure of adult cyber intimate partner aggression (IPA): the Cyber Aggression in Relationships Scale (CARS). Drawing on recent conceptual models of cyber IPA, items from previous research exploring general cyber aggression and cyber IPA were modified and new items were generated for inclusion in the CARS. Two samples of adults 18 years or older were recruited online. We used item factor analysis to test the factor structure, model fit, and invariance of the measure structure across women and men. Results confirmed that three-factor models for both perpetration and victimization demonstrated good model fit, and that, in general, the CARS measures partner cyber aggression similarly for women and men. The CARS also demonstrated validity through significant associations with in-person IPA, trait anger, and jealousy. Findings suggest the CARS is a useful tool for assessing cyber IPA in both research and clinical settings.
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering

Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intraand inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that the proposed dynamic intra-modality attention flow conditioned on the other modality can dynamically modulate the intramodality attention of the target modality, which is vital for multimodality feature fusion. Experimental evaluations on the VQA 2.0 dataset show that the proposed method achieves state-of-the-art VQA performance. Extensive ablation studies are carried out for the comprehensive analysis of the proposed method.
Biomedical Magnesium Alloys : A Review of Material Properties , Surface Modifications and Potential as a Biodegradable Orthopaedic Implant

Magnesium and magnesium based alloys are lightweight metallic materials that are extremely biocompatib le and have similar mechanical properties to natural bone. These materials have the potential to function as an osteoconductive and biodegradable substitute in load bearing applicat ions in the field of hard t issue engineering. However, the effects of corrosion and degradation in the physiological environment of the body has prevented their wide spread applicat ion to date. The aim of this review is to examine the properties, chemical stability, degradation in situ and methods of improving the corrosion resistance of magnesium and its alloys for potential application in the orthopaedic field. To be an effective implant, the surface and sub-surface properties of the material needs to be carefully selected so that the degradation kinetics of the implant can be efficiently controlled. Several surface modification techniques are presented and their effectiveness in reducing the corrosion rate and methods of controlling the degradation period are discussed. Ideally, balancing the gradual loss of material and mechanical strength during degradation, with the increasing strength and stability of the newly forming bone tissue is the ultimate goal. If this goal can be achieved, then orthopaedic implants manufactured from magnesium based alloys have the potential to deliver successful clinical outcomes without the need for revision surgery.