nils-herrmann/clean_scirepeval huggingface dataset:
A Simple Neural Attentive Meta-Learner

Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.
Musical motivation : Towards a model synthesising the research

This article explores motivation to participate in music making activities. It examines historical and current theories of motivation and sets out a model describing the way that the characteristics of the individual including, personality, self-concept and personal goals interact with the environment to influence motivation and subsequent behaviour. The environment may include culture and sub-cultures, society, time, place, institutions, family and peers and the rewards and punishments that these offer. The article then explores research relating to motivation to actively participate in music. This supports the notion that musical motivation is determined by complex interactions between the individual and the environment within which they find themselves. The article concludes with a consideration of directions for future research.
Power electronics intensive solutions for advanced electric, hybrid electric, and fuel cell vehicular power systems

There is a clear trend in the automotive industry to use more electrical systems in order to satisfy the ever-growing vehicular load demands. Thus, it is imperative that automotive electrical power systems will obviously undergo a drastic change in the next 10-20 years. Currently, the situation in the automotive industry is such that the demands for higher fuel economy and more electric power are driving advanced vehicular power system voltages to higher levels. For example, the projected increase in total power demand is estimated to be about three to four times that of the current value. This means that the total future power demand of a typical advanced vehicle could roughly reach a value as high as 10 kW. In order to satisfy this huge vehicular load, the approach is to integrate power electronics intensive solutions within advanced vehicular power systems. In view of this fact, this paper aims at reviewing the present situation as well as projected future research and development work of advanced vehicular electrical power systems including those of electric, hybrid electric, and fuel cell vehicles (EVs, HEVs, and FCVs). The paper will first introduce the proposed power system architectures for HEVs and FCVs and will then go on to exhaustively discuss the specific applications of dc/dc and dc/ac power electronic converters in advanced automotive power systems
Iterative deep learning for image set based face and object recognition

We present a novel technique for image set based face/object recognition, where each gallery and query example contains a face/object image set captured from different viewpoints, background, facial expressions, resolution and illumination levels. While several image set classification approaches have been proposed in recent years, most of them represent each image set as a single linear subspace, assumptions in regards to the specific category of the geometric surface on which images of the set are believed to lie. This could result in a loss of discriminative information for classification. This paper alleviates these limitations by proposing an Iterative Deep Learning Model (IDLM) that automatically and hierarchically learns discriminative representations from raw face and object images. In the proposed approach, low level translationally invariant features are learnt by the Pooled Convolutional Layer (PCL). The latter is followed by Artificial Neural Networks (ANNs) applied iteratively in a hierarchical fashion to learn a discriminative non-linear feature representation of the input image sets. The proposed technique was extensively evaluated for the task of image set based face and object recognition on YouTube Celebrities, Honda/UCSD, CMU Mobo and ETH-80 (object) dataset, respectively. Experimental results and comparisons with state-of-the-art methods show that our technique achieves the best performance on all these datasets. & 2015 Elsevier B.V. All rights reserved.
Similarities and Differences in Chinese and Caucasian Adults' Use of Facial Cues for Trustworthiness Judgments

All cultural groups in the world place paramount value on interpersonal trust. Existing research suggests that although accurate judgments of another's trustworthiness require extensive interactions with the person, we often make trustworthiness judgments based on facial cues on the first encounter. However, little is known about what facial cues are used for such judgments and what the bases are on which individuals make their trustworthiness judgments.



In the present study, we tested the hypothesis that individuals may use facial attractiveness cues as a "shortcut" for judging another's trustworthiness due to the lack of other more informative and in-depth information about trustworthiness. Using data-driven statistical models of 3D Caucasian faces, we compared facial cues used for judging the trustworthiness of Caucasian faces by Caucasian participants who were highly experienced with Caucasian faces, and the facial cues used by Chinese participants who were unfamiliar with Caucasian faces. We found that Chinese and Caucasian participants used similar facial cues to judge trustworthiness. Also, both Chinese and Caucasian participants used almost identical facial cues for judging trustworthiness and attractiveness.



The results suggest that without opportunities to interact with another person extensively, we use the less racially specific and more universal attractiveness cues as a "shortcut" for trustworthiness judgments.
An Investigation into the Dispersion Mechanisms of Ternary Dry Powder Inhaler Formulations by the Quantification of Interparticulate Forces

To investigate the dispersion mechanism(s) of ternary dry powder inhaler (DPI) formulations by comparison of the interparticulate adhesions and in vitro performance of a number of carrier–drug–fines combinations. The relative levels of adhesion and cohesion between a lactose carrier and a number of drugs and fine excipients were quantified using the cohesion–adhesion balance (CAB) approach to atomic force microscopy. The in vitro performance of formulations produced using these materials was quantified and the particle size distribution of the aerosol clouds produced from these formulations determined by laser diffraction. Comparison between CAB ratios and formulation performance suggested that the improvement in performance brought about by the addition of fines to which the drug was more adhesive than cohesive might have been due to the formation of agglomerates of drug and fines particles. This was supported by aerosol cloud particle size data. The mechanism(s) underlying the improved performance of ternary formulations where the drug was more cohesive than adhesive to the fines was unclear. The performance of ternary DPI formulations might be increased by the preferential formation of drug–fines agglomerates, which might be subject to greater deagglomeration forces during aerosolisation than smaller agglomerates, thus producing better formulation performance.
How does foreign direct investment affect economic 1 growth ?

We test the effect of foreign direct investment (FDI) on economic growth in a cross-country regression framework, utilizing data on FDI flows from industrial countries to 69 developing countries over the last two decades. Our results suggest that FDI is an important vehicle for the transfer of technology, contributing relatively more to growth than domestic investment. However, the higher productivity of FDI holds only when the host country has a minimum threshold stock of human capital. Thus, FDI contributes to economic growth only when a sufficient absorptive capability of the advanced technologies is available in the host economy.
Fano's inequality for random variables

We extend Fano’s inequality, which controls the average probability of events in terms of the average of some f–divergences, to work with arbitrary events (not necessarily forming a partition) and even with arbitrary [0, 1]–valued random variables, possibly in continuously infinite number. We provide two applications of these extensions, in which the consideration of random variables is particularly handy: we offer new and elegant proofs for existing lower bounds, on Bayesian posterior concentration (minimax or distribution-dependent) rates and on the regret in non-stochastic sequential learning. MSC 2000 subject classifications. Primary-62B10; secondary-62F15, 68T05.
Supplier Development at Honda , Nissan and Toyota : Comparative Case Studies of Organizational Capability Enhancement *

What factors facilitate and constrain the development and replication of organizational capabilities at suppliers? This question is addressed in a comparison of historical case studies at Toyota, Nissan and Honda in Japan. First, as expected, replication difficulty is overcome by enabling companies to share the process of accumulating tacit knowledge. Second, the interdependence in the hierarchy of routines, that constitute organizational capabilities, has led companies to broaden the scope of supplier development. Third, this broadening challenges suppliers to accept buyer companies’ intervention in internal investment decisions, requiring a certain type of corporate governance. It is argued that the boundary of a capability-based organization may go beyond legally distinct units of financial control when firms are subjected to a cumulative process of capability enhancement.
Recent Advances on Neuromorphic Systems Using Phase-Change Materials

Realization of brain-like computer has always been human's ultimate dream. Today, the possibility of having this dream come true has been significantly boosted due to the advent of several emerging non-volatile memory devices. Within these innovative technologies, phase-change memory device has been commonly regarded as the most promising candidate to imitate the biological brain, owing to its excellent scalability, fast switching speed, and low energy consumption. In this context, a detailed review concerning the physical principles of the neuromorphic circuit using phase-change materials as well as a comprehensive introduction of the currently available phase-change neuromorphic prototypes becomes imperative for scientists to continuously progress the technology of artificial neural networks. In this paper, we first present the biological mechanism of human brain, followed by a brief discussion about physical properties of phase-change materials that recently receive a widespread application on non-volatile memory field. We then survey recent research on different types of neuromorphic circuits using phase-change materials in terms of their respective geometrical architecture and physical schemes to reproduce the biological events of human brain, in particular for spike-time-dependent plasticity. The relevant virtues and limitations of these devices are also evaluated. Finally, the future prospect of the neuromorphic circuit based on phase-change technologies is envisioned.
The development of a symptom questionnaire for assessing virtual reality viewing using a head-mounted display.

Virtual reality devices, including virtual reality head-mounted displays, are becoming increasingly accessible to the general public as technological advances lead to reduced costs. However, there are numerous reports that adverse effects such as ocular discomfort and headache are associated with these devices. To investigate these adverse effects, questionnaires that have been specifically designed for other purposes such as investigating motion sickness have often been used. The primary purpose of this study was to develop a standard questionnaire for use in investigating symptoms that result from virtual reality viewing. In addition, symptom duration and whether priming subjects elevates symptom ratings were also investigated.



A list of the most frequently reported symptoms following virtual reality viewing was determined from previously published studies and used as the basis for a pilot questionnaire. The pilot questionnaire, which consisted of 12 nonocular and 11 ocular symptoms, was administered to two groups of eight subjects. One group was primed by having them complete the questionnaire before immersion; the other group completed the questionnaire postviewing only. Postviewing testing was carried out immediately after viewing and then at 2-min intervals for a further 10 min.



Priming subjects did not elevate symptom ratings; therefore, the data were pooled and 16 symptoms were found to increase significantly. The majority of symptoms dissipated rapidly, within 6 min after viewing. Frequency of endorsement data showed that approximately half of the symptoms on the pilot questionnaire could be discarded because <20% of subjects experienced them.



Symptom questionnaires to investigate virtual reality viewing can be administered before viewing, without biasing the findings, allowing calculation of the amount of change from pre- to postviewing. However, symptoms dissipate rapidly and assessment of symptoms needs to occur in the first 5 min postviewing. Thirteen symptom questions, eight nonocular and five ocular, were determined to be useful for a questionnaire specifically related to virtual reality viewing using a head-mounted display.
Shadow effect on Photovoltaic potentiality analysis using 3 D city models

Three dimensional city models are presently being used in many sectors. The potentiality of three dimensional data has been exploited by many researchers and applications. It has been realized that 3D data are not only for visualization or navigation but to support solving more complex problems in urban planning, disaster management, facility management etc. In this paper a 3D city model is used to perform a solar energy potentiality analysis. In contrast to existing methods shadowing effects on the roof surface and façades are taken into account to achieve better simulation results. Of course, geometric details of the building geometry directly effect the calculation of shadows. In principle, 3D city models or point clouds, which contain roof structure, vegetation, thematically differentiated surface and texture, are suitable to simulate exact real-time shadow. Solar radiation data during the whole day and around the year and photovoltaic cells response to the radiation can be modeled using available simulation environment. However, the real impact on geometric details has to be investigated in further research. ings and shadow, which are the important parameters for predicting the energy production from the photovoltaic cells, can also be derived from these 3D models. Among all these factors shadow is most difficult to determine. People spend a lot of money for photovoltaic cells and if it is placed at a wrong place where due to shadow, the production is much lower than it was measured from potentiality analysis, they will lose money. Therefore it is of great importance to be investigated is to measure exact shadow effect and sunlight intensity on each surface. The computation should also include the direct and diffuse component of light after reflection and absorption by surrounding objects and at real time automatically from the 3D city models. During this research, the quality of the current city models will be checked. It will also be determined how detailed data is the minimum for calculating exact shadows. Cloud, Air quality, humidity, vegetation, color, material and other weather related and geographical aspects which causes, effects and controls shadow will also be investigated. This paper has been organized with a brief introduction at the beginning. Then some related works has been mentioned and drawback has been used to identify the gap in the literature and the good things have been integrated. A case study area has been selected for availability of data. The types of data suitable for the research have been discussed. Then the system architecture has been presented. The effect of shadow on photovoltaic cells and a methodology for detecting shadow caused by blocking of direct beam radiation have been explained. Finally the result has been shown applying the methodology on a small sample city model to have a idea of the final result.
Measure it? Manage it? Ignore it? software practitioners and technical debt

The technical debt metaphor is widely used to encapsulate numerous software quality problems. The metaphor is attractive to practitioners as it communicates to both technical and nontechnical audiences that if quality problems are not addressed, things may get worse. However, it is unclear whether there are practices that move this metaphor beyond a mere communication mechanism. Existing studies of technical debt have largely focused on code metrics and small surveys of developers. In this paper, we report on our survey of 1,831 participants, primarily software engineers and architects working in long-lived, software-intensive projects from three large organizations, and follow-up interviews of seven software engineers. We analyzed our data using both nonparametric statistics and qualitative text analysis. We found that architectural decisions are the most important source of technical debt. Furthermore, while respondents believe the metaphor is itself important for communication, existing tools are not currently helpful in managing the details. We use our results to motivate a technical debt timeline to focus management and tooling approaches.
Happiness Is Everything , or Is It ? Explorations on the Meaning of Psychological Well-Being

Reigning measures of psychological well-being have little theoretical grounding, despite an extensive literature on the contours of positive functioning. Aspects of well-being derived from this literature (i.e., self-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life, and personal growth) were operationalized. Three hundred and twenty-one men and women, divided among young, middle-aged, and older adults, rated themselves on these measures along with six instruments prominent in earlier studies (i.e., affect balance, life satisfaction, self-esteem, morale, locus of control, depression). Results revealed that positive relations with others, autonomy, purpose in life, and personal growth were not strongly tied to prior assessment indexes, thereby supporting the claim that key aspects of positive functioning have not been represented in the empirical arena. Furthermore, age profiles revealed a more differentiated pattern of well-being than is evident in prior research.
The Complex Relationship between Virulence and Antibiotic Resistance

Antibiotic resistance, prompted by the overuse of antimicrobial agents, may arise from a variety of mechanisms, particularly horizontal gene transfer of virulence and antibiotic resistance genes, which is often facilitated by biofilm formation. The importance of phenotypic changes seen in a biofilm, which lead to genotypic alterations, cannot be overstated. Irrespective of if the biofilm is single microbe or polymicrobial, bacteria, protected within a biofilm from the external environment, communicate through signal transduction pathways (e.g., quorum sensing or two-component systems), leading to global changes in gene expression, enhancing virulence, and expediting the acquisition of antibiotic resistance. Thus, one must examine a genetic change in virulence and resistance not only in the context of the biofilm but also as inextricably linked pathologies. Observationally, it is clear that increased virulence and the advent of antibiotic resistance often arise almost simultaneously; however, their genetic connection has been relatively ignored. Although the complexities of genetic regulation in a multispecies community may obscure a causative relationship, uncovering key genetic interactions between virulence and resistance in biofilm bacteria is essential to identifying new druggable targets, ultimately providing a drug discovery and development pathway to improve treatment options for chronic and recurring infection.
Code Recommendation with Natural Language Tags and Other Heterogeneous Data

Recommender systems solve the problem of information overload by efficiently utilizing huge quantities of data and trying its best to predict potential preference which aims at a certain user. They are widely applied in numerous fields. However, hardly can we see a code recommender system for programmers though it is desperately expected. Raw data of the code are not so convenient to handle for the difference in structure and lack of relevance. Fortunately, in real world, there are abundant data affiliated to the code, such as context, tags, social relations of users and view histories. In this paper, we firstly formulate a new task of code recommendation. Then, we propose a hybrid linear algorithm for recommending source codes, in which we maximize the utility of multivariate heterogeneous auxiliary data with code. Experiments on the dataset from Code Review Community show that our proposed method works for the new code recommendation task. Our system is hopefully designed to be adaptive to new source of heterogeneous information, and hopefully performs better with more significant data and new inspired components.
Multicast Routing for Multimedia Communications in the Internet of Things

Multicast routing that meets multiple quality of service constraints is important for supporting multimedia communications in the Internet of Things (IoT). Existing multicast routing technologies for IoT mainly focus on ad hoc sensor networking scenarios; thus, are not responsive and robust enough for supporting multimedia applications in an IoT environment. In order to tackle the challenging problem of multicast routing for multimedia communications in IoT, in this paper, we propose two algorithms for the establishing multicast routing tree for multimedia data transmissions. The proposed algorithms leverage an entropy-based process to aggregate all weights into a comprehensive metric, and then uses it to search a multicast tree on the basis of the spanning tree and shortest path tree algorithms. We conduct theoretical analysis and extensive simulations for evaluating the proposed algorithms. Both analytical and experimental results demonstrate that one of the proposed algorithms is more efficient than a representative multiconstrained multicast routing algorithm in terms of both speed and accuracy; thus, is able to support multimedia communications in an IoT environment. We believe that our results are able to provide in-depth insight into the multicast routing algorithm design for multimedia communications in IoT.