nils-herrmann/clean_scirepeval huggingface dataset:
A Recursive Born Approach to Nonlinear Inverse Scattering

The iterative Born approximation (IBA) is a well-known method for describing waves scattered by semitransparent objects. In this letter, we present a novel nonlinear inverse scattering method that combines IBA with an edge-preserving total variation regularizer. The proposed method is obtained by relating iterations of IBA to layers of an artificial multilayer neural network and developing a corresponding error backpropagation algorithm for efficiently estimating the permittivity of the object. Simulations illustrate that, by accounting for multiple scattering, the method successfully recovers the permittivity distribution where the traditional linear inverse scattering fails.
On the Evaluation of Dialogue Systems with Next Utterance Classification

An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data. Recent work has proposed NextUtterance-Classification (NUC) as a surrogate task for building dialogue systems from text data. In this paper we investigate the performance of humans on this task to validate the relevance of NUC as a method of evaluation. Our results show three main findings: (1) humans are able to correctly classify responses at a rate much better than chance, thus confirming that the task is feasible, (2) human performance levels vary across task domains (we consider 3 datasets) and expertise levels (novice vs experts), thus showing that a range of performance is possible on this type of task, (3) automated dialogue systems built using state-of-the-art machine learning methods have similar performance to the human novices, but worse than the experts, thus confirming the utility of this class of tasks for driving further research in automated dialogue systems.
Psychoneuroimmunology and psychosomatic medicine: back to the future.

Although psychological modulation of immune function is now a well-established phenomenon, much of the relevant literature has been published within the last decade. This article speculates on future directions for psychoneuroimmunology research, after reviewing the history of the field.



This review focuses on human psychoneuroimmunology studies published since 1939, particularly those that have appeared in Psychosomatic Medicine. Studies were clustered according to key themes, including stressor duration and characteristics (laboratory stressors, time-limited naturalistic stressors, or chronic stress), as well as the influences of psychopathology, personality, and interpersonal relationships; the responsiveness of the immune system to behavioral interventions is also addressed. Additionally, we describe trends in populations studied and the changing nature of immunological assessments. The final section focuses on health outcomes and future directions for the field.



There are now sufficient data to conclude that immune modulation by psychosocial stressors or interventions can lead to actual health changes, with the strongest direct evidence to date in infectious disease and wound healing. Furthermore, recent medical literature has highlighted a spectrum of diseases whose onset and course may be influenced by proinflammatory cytokines, from cardiovascular disease to frailty and functional decline; proinflammatory cytokine production can be directly stimulated by negative emotions and stressful experiences and indirectly stimulated by chronic or recurring infections. Accordingly, distress-related immune dysregulation may be one core mechanism behind a diverse set of health risks associated with negative emotions.



We suggest that psychoneuroimmunology may have broad implications for the basic biological sciences and medicine.
Effects of stabilization exercise using a ball on mutifidus cross-sectional area in patients with chronic low back pain.

The purpose of this study was to compare the effects of lumbar stabilization exercises using balls to the effects of general lumbar stabilization exercises with respect to changes in the cross section of the multifidus (MF), weight bearing, pain, and functional disorders in patients with non-specific chronic low back pain. Twelve patients participated in either a 8 week (3 days per week) stabilization exercise program using balls and control group (n = 12). The computer tomography (CT) was used to analyze MF cross-sectional areas (CSA) and Tetrax balancing scale was used to analyze left and right weight bearing differences. Both groups had significant changes in the CSA of the MF by segment after training (p < 0.05) and the experimental group showed greater increases at the L4 (F = 9.854, p = 0.005) and L5 (F = 39. 266, p = 0.000). Both groups showed significant decreases in weight bearing, from 9.25% to 5.83% in the experimental group and from 9.33% to 4.25% in the control group (p < 0.05), but did not differ significantly between the two groups. These results suggests that stabilization exercises using ball can increases in the CSA of the MF segments, improvement in weight bearing, pain relief, and recovery from functional disorders, and the increases in the CSA of the MF of the L4 and L5 segments for patients with low back pain.  PointsCompared with the stabilization exercise using a ball and general stabilization exercise increased the CSA of the MF, weight bearing, pain, and functional ability in patients with low back pain.We verified that increases in the CSA of the MF of the L4 and L5 segments and functional ability during the stabilization exercise using a ball.The stabilization exercise using a ball was shown to be an effective exercise method for patients with low back pain in a rehabilitation program by increasing functional ability and the CSA of the MF.
Generalization Bounds for Neural Networks: Kernels, Symmetry, and Sample Compression

Even though Deep Neural Networks (DNNs) are widely celebrated for their practical performance, they possess many intriguing properties related to depth that are difficult to explain both theoretically and intuitively. Understanding how weights in deep networks coordinate together across layers to form useful learners has proven challenging, in part because of the repeated composition of nonlinearities induced by depth. This paper presents a reparameterization of DNNs as a linear function of a feature map that is locally independent of the weights. This feature map transforms depth-dependencies into simple tensor products and maps each input to a discrete subset of the feature space. Then, using a max-margin assumption, the paper develops a sample compression representation of the neural network in terms of the discrete activation state of neurons induced by s “support vectors”. The papers shows that the number of support vectors s relates with learning guarantees for neural networks through sample compression bounds, yielding a sample complexity of O(ns/ ) for networks with n neurons. Finally, the number of support vectors s is found to have monotonic dependence on width, depth, and label noise for simple networks trained on the MNIST dataset.
A comparative study on security features of Indian , Canadian and Dubai cheques

Cheque is a document that orders the bank to pay a specific amount of money from the holder’s account to the person whose details are drafted on the cheque. Cheque is one of the most prevailing documents of the bank whose chances of being counterfeited is maximum, therefore in order to prevent counterfeiting the cheque are being embedded with security features. Cheque is essentially considered as the bill of exchange that is manufactured in order to make money transactions without having cash in  Section 13 of Negotiable Instruments Act, 1881 (Act no. 26 of 1881) of Indian constitution states that a negotiable instrument is a promissory note, bill of exchange or a cheque payable either to payee or to a bearer and Section 6 of this Act exclusively defines a cheque as a bill of exchange drawn on a specified banker, and not expressed to be payable otherwise than on demand.2 Cheque is an instrument in writing containing an unconditional order, addressed to a banker, signed by the person who has deposited money with the banker, requiring him to pay on demand a certain sum of money only two or to the order of a certain person or to the bearer of
Modeling Intergovernmental Collaboration: A System Dynamics Approach

This paper describes the development and testing of a system dynamics model of collaboration, trust building, and knowledge sharing in a complex, intergovernmental information system project. The model building and testing activity was an exploration of the feasibility of applying these modeling methods to a complex interorganizational process about which only qualitative data were available. The process to be modeled was the subject of qualitative field research studying knowledge and information sharing in interorganizational networks. This research had produced a large volume of observational and interview data and analyses about the technology project. In the course of collecting and analyzing data from this project, the researchers noted evidence of what appeared to be important feedback effects. The feedback loops appeared to influence the collaboration and knowledge sharing, critical parts of how the information system design and construction progressed. These observations led to conversations with colleagues who have extensive experience in dynamic modeling. All agreed that applying dynamic modeling methods to this process had considerable potential to yield valuable insights into collaboration. As a novel application of the methods it could yield new modeling insights as well. The modeling experience supported both propositions and was judged a success that will lead to continued exploration of these questions.
Lagrangian modeling and flight control of articulated-winged bat robot

This paper presents a systematic flight controller design based on the mathematics of parametrized manifolds and calculus of variations for the Bat Bot (B2), which possesses many articulated wings. Wing kinematics and morphological properties are crucial in the powered flight of flying vertebrates. The articulated skeleton of these mammals, which contains many degrees of actuation and underactuation, has made it difficult to understand the connection between the bat's flight dynamics and its intricate array of physiological and morphological specializations. B2 is a biomimetic micro aerial vehicle (MAV) that possesses similar morphological properties to a bat in order to duplicate bats powered ballistic motion. In an effort to design the advanced flight control algorithm for B2, this paper reports two major contributions. First, a systematic mathematical framework is introduced that evaluates the holonomically-constrained Lagrangian model of a flapping robot with specified active and passive degrees of freedom (DoF) in order to locate physically feasible and biologically meaningful periodic solutions using optimization. These are parametrized constraint manifolds; the flapping wing dynamics are governed by these manifolds. Second, calculus of variations and the well-recognized method of inverse dynamics are applied in order to synthesize the flight control algorithm for the flapping wings.
Big Data Computing and Clouds : Trends and Future Directions

Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. Cloud computing is a type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., computer networks, servers, storage, applications and services), [1][2] which can be rapidly provisioned and released with minimal management effort. Cloud computing and storage solutions provide users and enterprises with various capabilities to store and process their data in either privately owned, or third-party data centers  that may be located far from the user–ranging in distance from across a city to across the world. Cloud computing relies on sharing of resources to achieve coherence and economy of scale, similar to a utility (like the electricity grid) over an electricity network. This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications. It revolves around four important areas of analytics and Big Data, namely (i) data management and supporting architectures; (ii) model development and scoring; (iii) visualisation and user interaction; and (iv) business models. Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions.
ATR4S: toolkit with state-of-the-art automatic terms recognition methods in Scala

Automatically recognized terminology is widely used for various domain-specific texts processing tasks, such as machine translation, information retrieval or ontology construction. However, there is still no agreement on which methods are best suited for particular settings and, moreover, there is no reliable comparison of already developed methods. We believe that one of the main reasons is the lack of state-of-the-art methods implementations, which are usually non-trivial to recreate. In order to address these issues, we present ATR4S, an open-source software written in Scala that comprises more than 15 methods for automatic terminology recognition (ATR) and implements the whole pipeline from text document preprocessing, to term candidates collection, term candidates scoring, and finally, term candidates ranking. It is highly scalable, modular and configurable tool with support of automatic caching. We also compare 13 state-of-the-art methods on 7 open datasets by average precision and processing time. Experimental comparison reveals that no single method demonstrates best average precision for all datasets and that other available tools for ATR do not contain the best methods.
Providing all global energy with wind , water , and solar power , Part I : Technologies , energy resources , quantities and areas of infrastructure , and materials

Climate change, pollution, and energy insecurity are among the greatest problems of our time. Addressing them requires major changes in our energy infrastructure. Here, we analyze the feasibility of providing worldwide energy for all purposes (electric power, transportation, heating/cooling, etc.) from wind, water, and sunlight (WWS). In Part I, we discuss WWS energy system characteristics, current and future energy demand, availability of WWS resources, numbers of WWS devices, and area and material requirements. In Part II, we address variability, economics, and policy of WWS energy. We estimate that !3,800,000 5 MW wind turbines, !49,000 300 MW concentrated solar plants, !40,000 300 MW solar PV power plants, !1.7 billion 3 kW rooftop PV systems, !5350 100 MWgeothermal power plants, !270 new 1300 MW hydroelectric power plants, !720,000 0.75 MWwave devices, and !490,000 1 MW tidal turbines can power a 2030 WWS world that uses electricity and electrolytic hydrogen for all purposes. Such a WWS infrastructure reduces world power demand by 30% and requires only !0.41% and !0.59% more of the world’s land for footprint and spacing, respectively. We suggest producing all new energy withWWSby 2030 and replacing the pre-existing energy by 2050. Barriers to the plan are primarily social and political, not technological or economic. The energy cost in a WWS world should be similar to
Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation

Recently, a variety of representation learning approaches have been developed in the literature to induce latent generalizable features across two domains. In this paper, we extend the standard hidden Markov models (HMMs) to learn distributed state representations to improve cross-domain prediction performance. We reformulate the HMMs by mapping each discrete hidden state to a distributed representation vector and employ an expectationmaximization algorithm to jointly learn distributed state representations and model parameters. We empirically investigate the proposed model on cross-domain part-ofspeech tagging and noun-phrase chunking tasks. The experimental results demonstrate the effectiveness of the distributed HMMs on facilitating domain adaptation.
The blockchain: a new framework for robotic swarm systems

Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. However, several of the heterogeneous characteristics that make them ideal for certain future applications — robot autonomy, decentralized control, collective emergent behavior, etc. — hinder the evolution of the technology from academic institutions to real-world problems. Blockchain, an emerging technology originated in the Bitcoin field, demonstrates that by combining peer-topeer networks with cryptographic algorithms a group of agents can reach an agreement on a particular state of affairs and record that agreement without the need for a controlling authority. The combination of blockchain with other distributed systems, such as robotic swarm systems, can provide the necessary capabilities to make robotic swarm operations more secure, autonomous, flexible and even profitable. This work explains how blockchain technology can provide innovative solutions to four emergent issues in the swarm robotics research field. New security, decision making, behavior differentiation and business models for swarm robotic systems are described by providing case scenarios and examples. Finally, limitations and possible future problems that arise from the combination of these two technologies are described.
Adaptive Augmented Reality Serious Game to Foster Problem Solving Skills

This paper describes the design of an adaptive intelligent augmented reality serious game which aims to foster problem solving skills in young learners. Studies show that our students lack computational thinking skills in high school, which raises the need to establish new methods to develop these skills in our younger learners. We believe that problem solving skills are the fundamental skills of computational thinking and are critical for STEM, in addition to a broad range of other fields. Therefore we decided to focus on those meta-cognitive skills acquired to foster problem solving, such as strategic knowledge. The game described in this paper provides a unique adaptive learning environment that aims to develop learners’ meta-cognitive skills by utilizing augmented reality technology, believable pedagogical agents and intelligent tutoring modules. It offers a great user experience and entertainment which we hope will encourage learners to invest more time in the learning process. This paper describes the architecture and design of the game from the viewpoint of educational pedagogies and frameworks for serious game design.
Effective Environmental Factors on Designing Productive Learning Environments

Educational spaces play an important role in enhancing learning productivity levels of society people as the most important places to human train. Considering the cost, time and energy spending on these spaces, trying to design efficient and optimized environment is a necessity. Achieving efficient environments requires changing environmental criteria so that they can have a positive impact on the activities and learning in users. Therefore, creating suitable conditions for promoting learning in users requires full utilization of the comprehensive knowledge of architecture and the design of the physical environment with respect to the environmental, social and aesthetic dimensions; Which will naturally increase the usefulness of people in space and make optimal use of the expenses spent on building schools and the time spent on education and training.The main aim of this study was to find physical variables affecting on increasing productivity in learning environments. This study is quantitative-qualitative and was done in two research methods: a) survey research methods (survey) b) correlation method. The samples were teachers and students in secondary schools’ in Zahedan city, the sample size was 310 people. Variables were extracted using the literature review and deep interviews with professors and experts. The questionnaire was obtained using variables and it is used to collect the views of teachers and students. Cronbach’s alpha coefficient was 0.89 which indicates that the information gathering tool is acceptable. The findings shows that there are four main physical factor as: 1. Physical comfort, 2. Space layouts, 3. Psychological factors and 4. Visual factors thet they are affecting positively on space productivity. Each of the environmental factors play an important role in improving the learning quality and increasing interest in attending learning environments; therefore, the desired environment improves the productivity of the educational spaces by improving the components of productivity.
Role of Self-Efficacy and Self-Concept Beliefs in Mathematical Problem Solving : A Path Analysis

Path analysis was used to test the predictive and mediational role of self-efficacy beliefs in mathematical problem solving. Results revealed that math self-efficacy was more predictive of problem solving than was math self-concept, perceived usefulness of mathematics, prior experience with mathematics, or gender (N = 350). Self-efficacy also mediated the effect of gender and prior experience on self-concept, perceived usefulness, and problem solving. Gender and prior experience influenced self-concept, perceived usefulness, and problem solving largely through the mediational role of self-efficacy. Men had higher performance, self-efficacy, and self-concept and lower anxiety, but these differences were due largely to the influence of self-efficacy, for gender had a direct effect only on self-efficacy and a prior experience variable. Results support the hypothesized role of self-efficacy in A. Bandura's (1986) social cognitive theory.
MODEL-AGNOSTIC HIERARCHICAL EXPLANATIONS

Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models. We propose Mahé, a novel approach to provide Model-agnostic hierarchical éxplanations of how powerful machine learning models, such as deep neural networks, capture these interactions as either dependent on or free of the context of data instances. Specifically, Mahé provides context-dependent explanations by a novel local interpretation algorithm that effectively captures any-order interactions, and obtains context-free explanations through generalizing contextdependent interactions to explain global behaviors. Experimental results show that Mahé obtains improved local interaction interpretations over state-of-the-art methods and successfully provides explanations of interactions that are contextfree.
pix2code: Generating Code from a Graphical User Interface Screenshot

Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically reverse engineer user interfaces and generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies).