nils-herrmann/clean_scirepeval huggingface dataset:
SINGA: Putting Deep Learning in the Hands of Multimedia Users

Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Two key factors behind deep learning's remarkable achievement are the immense computing power and the availability of massive training datasets, which enable us to train large models to capture complex regularities of the data. There are two challenges to overcome before deep learning can be widely adopted in multimedia and other applications. One is usability, namely the implementation of different models and training algorithms must be done by non-experts without much effort. The other is scalability, that is the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets. To address these two challenges, in this paper, we design a distributed deep learning platform called SINGA which has an intuitive programming model and good scalability. Our experience with developing and training deep learning models for real-life multimedia applications in SINGA shows that the platform is both usable and scalable.
Performance evaluation of solid state transformer based microgrid in FREEDM systems

A new concept of solid state transformer based microgrid system is presented in this paper. By utilizing 400V DC bus generated from Gen-I solid state transformer proposed by FREEDM systems center, integration issues of DC microgrid and solid state transformer are analyzed. Zonal DC microgrid concept is applied to this novel system with the consideration of burden minimization to the existing AC grid. The future grid architecture is described by using this solid state transformer based integrated microgrid system.
LoRea: A Backscatter Reader for Everyone!

Computational RFID (CRFID) platforms have enabled reconfigurable, battery-free applications for close to a decade. However, several factors have impeded their wide-spread adoption: low communication range, low throughput, and expensive infrastructure— CRFID readers usually cost upwards of $1000. This paper presents LoRea, a backscatter reader that achieves an order of magnitude higher range than existing CRFID readers, while costing a fraction of their price. LoRea achieves this by diverging from existing designs of CRFID readers and more specifically, how self-interference is tackled. LoRea builds on recent work that frequency-shifts backscatter transmissions away from the carrier signal to reduce selfinterference. LoRea also decouples the carrier generation from the reader, helping to further decrease self-interference. Decoupling carrier generation also enables the use of deployed infrastructure of smartphones and sensor nodes to provide the carrier signal. Together these methods reduce cost and complexity of the reader. LoRea also purposefully operates at lower bitrates than recent backscatter systems which enables high sensitivity and longer range. We evaluate LoRea experimentally and find that it achieves a communication range of up to 225 m in lineof-sight scenarios. In indoor environments, where the signal traverses several walls separating the reader and the backscatter tag, LoRea achieves a range of 30 m. These results illustrate how LoRea outperforms stateof-art backscatter systems and CRFID platforms.
Ontology-driven Information Extraction

Homogeneous unstructured data (HUD) are collections of unstructured documents that share common properties, such as similar layout, common file format, or common domain of values. Building on such properties, it would be desirable to automatically process HUD to access the main information through a semantic layer – typically an ontology – called semantic view. Hence, we propose an ontology-based approach for extracting semantically rich information from HUD, by integrating and extending recent technologies and results from the fields of classical information extraction, table recognition, ontologies, text annotation, and logic programming. Moreover, we design and implement a system, named KnowRex, that has been successfully applied to curriculum vitae in the Europass style to offer a semantic view of them, and be able, for example, to select those which exhibit required skills.
MRG-OHTC Database for Online Handwritten Tibetan Character Recognition

A handwritten Tibetan database, MRG-OHTC, is presented to facilitate the research of online handwritten Tibetan character recognition. The database contains 910 Tibetan character classes written by 130 persons from Tibetan ethnic minority. These characters are selected from basic set and extension set A of Tibetan coded character set. The current version of this database is collected using electronic pen on digital tablet. We investigate some characteristic of writing style from different writers. We evaluate MRG-OHTC database using existing algorithms as a baseline. Experimental results reveal a big challenge to higher recognition performance. To our knowledge, MRG-OHTC is the first publicly available database for handwritten Tibetan research. It provides a basic database to compare empirically different algorithms for handwritten Tibetan character recognition.
Robo-Advising as a Human-Machine Interaction System

Robo-advising can substantially enhance a human’s efficiency in investment decisions. Since machines are unable to observe the preferences of the humans that they serve, it becomes important to establish an effective interaction protocol. While the investor’s and machine’s objectives are aligned, asymmetric information, along with heterogeneous sensitivities to risk by the investor and machine, make their joint optimization process a game with strategic interactions. We propose a framework based on risk-sensitive dynamic games, where the investor seeks to optimize her risk-sensitive criterion while the machine adaptively learns the investor’s preferences. We consider an investor with myopic mean-variance preferences and reduce the game to a partially observed Markov decision process (POMDP). The human-machine interaction protocol features a trade-off between allowing the robo-advisor to learn the investor’s preferences through costly communications and optimizing the investor’s objective relying on outdated information.
A framework for consciousness

Here we summarize our present approach to the problem of consciousness. After an introduction outlining our general strategy, we describe what is meant by the term 'framework' and set it out under ten headings. This framework offers a coherent scheme for explaining the neural correlates of (visual) consciousness in terms of competing cellular assemblies. Most of the ideas we favor have been suggested before, but their combination is original. We also outline some general experimental approaches to the problem and, finally, acknowledge some relevant aspects of the brain that have been left out of the proposed framework.
Time-Domain Finite-Difference and Finite-Element Methods for Maxwell Equations in Complex Media

Extensions of finite-difference time-domain (FDTD) and finite-element time-domain (FETD) algorithms are reviewed for solving transient Maxwell equations in complex media. Also provided are a few representative examples to illustrate the modeling capabilities of FDTD and FETD for complex media. The term complex media refers here to media with dispersive, (bi)anisotropic, inhomogeneous, and/or nonlinear properties present in the constitutive tensors.
Moving from syntactic to semantic organizations using JXML2OWL

Today’s enterprises face critical needs in integrating disparate information spread over several data sources inside and even outside the organization. Most organizations already rely on XML standard to define their data models. Unfortunately, even when using XML to represent data, problems arise when it is necessary to integrate different data sources. Emerging Semantic Web technologies, such as ontologies, RDF, RDFS, and OWL, can play an important role in the semantic definition and integration of data. The purpose of our study is to present a framework to assist organizations to move from a syntactic data infrastructure defined in XML to a semantic data infrastructure using OWL. The framework supports mappings and fully automated instance transformation from syntactic data sources in XML format to a common shared global model defined by an ontology using Semantic Web technologies. The presented framework, JXML2OWL, allows organizations to automatically convert their XML data sources to a semantic model defined in OWL.
A short introduction to probabilistic soft logic

Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a continuous optimization task, which can be solved efficiently. This paper provides an overview of the PSL language and its techniques for inference and weight learning. An implementation of PSL is available at http://psl.umiacs.umd.edu/.
Current Status of Bioinks for Micro-Extrusion-Based 3D Bioprinting

Recent developments in 3D printing technologies and design have been nothing short of spectacular. Parallel to this, development of bioinks has also emerged as an active research area with almost unlimited possibilities. Many bioinks have been developed for various cells types, but bioinks currently used for 3D printing still have challenges and limitations. Bioink development is significant due to two major objectives. The first objective is to provide growth- and function-supportive bioinks to the cells for their proper organization and eventual function and the second objective is to minimize the effect of printing on cell viability, without compromising the resolution shape and stability of the construct. Here, we will address the current status and challenges of bioinks for 3D printing of tissue constructs for in vitro and in vivo applications.
Transfer Learning for Heterogeneous One-Class Collaborative Filtering

Various memory- and model-based collaborative filtering algorithms have been designed for multiclass feedback (such as grade scores) in the past two decades. Recently, one-class feedback (such as positive feedback and implicit examination) has been recognized as a more pervasive and important source of information in many real recommendation systems. Previous work along these lines mainly focus on homogenous one-class positive feedback, such as likes on Facebook or transactions on Amazon, which might not capture a user's true preferences due to the sparsity of such data. To alleviate this sparsity problem, the authors study positive feedback and implicit examinations simultaneously, coined as heterogeneous one-class collaborative filtering (HOCCF). Specifically, they designed a novel transfer learning algorithm for HOCCF, called transfer via joint similarity learning (TJSL), that jointly learns a similarity between a candidate item and a preferred item, and a similarity between a candidate item and an identified likely-to-prefer examined item. Joint similarity learning has the merit of being able to connect two seemingly unrelated items along sparse positive feedback only. Empirical studies on three real-world datasets show that TJSL can recommend items more accurately than other state-of-the-art methods.
Convolutional Neural Networks for Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control

Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with partial observability. This paper proposes a novel approach that uses a convolutional neural network (CNN) to extract hidden state-time features to mitigate the curse of partial observability. More specific, a CNN is used as a function approximator to estimate the state-action value function or <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-function in the supervised learning step of fitted <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-iteration. The approach is evaluated in a qualitative simulation, comprising a cluster of thermostatically controlled loads that only share their air temperature, while their envelope temperature remains hidden. The simulation results show that the presented approach is able to capture the underlying hidden features and able to successfully reduce the electricity cost the cluster.
Improving Interactional Organizational Research : A Model of Person-Organization Fit

In order for researchers to understand and predict behavior, they must consider both person and situation factors and how these factors interact. Even though organization researchers have developed interactional models, many have overemphasized either person or situation components, and most have failed to consider the effects that persons have on situations. This paper presents criteria for improving interactional models and a model of person-organization fit, which satisfies these criteria. Using a Q-sort methodology, individual value profiles are compared to organizational value profiles to determine fit and to predict changes in values, norms, and behaviors.
Context-Free Transductions with Neural Stacks

This paper analyzes the behavior of stackaugmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, contextfree language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.
Expanded Semantic Graph Representation for Matching Related Information of Interest across Free Text Documents

This research proposes an expanded semantic graph definition that serves as a basis for an expanded semantic graph representation and graph matching approach. This representation separates the content and context and adds a number of semantic structures that encapsulate inferred information. The expanded semantic graph approach facilitates finding additional matches, identifying and eliminating poor matches, and prioritizing matches based on how much new knowledge is provided. By focusing on information of interest, doing pre-processing, and reducing processing requirements, the approach is applicable to problems where related information of interest is sought across a massive body of free text documents. Key aspects of the approach include (1) expanding the nodes and edges through inference using DL-Safe rules, abductive hypotheses, and syntactic patterns, (2) separating semantic content into nodes and semantic context into edges, and (3) applying relatedness measures on a node, edge, and sub graph basis. Results from tests using a ground-truthed subset of a large dataset of law enforcement investigator emails illustrate the benefits of these approaches.
Deep Face Recognition: A Survey

Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief that this technique reached human performance, it still remains an open problem in unconstrained environments as demonstrated by the newly released IJB datasets. This survey aims to summarize the main advances in deep face recognition and, more in general, in learning face representations for verification and identification. The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues. The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc.); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. The survey concludes with an overview of the SOTA results at a glance along with some open issues currently overlooked by the community.
Challenging the public stigma of mental illness: a meta-analysis of outcome studies.

Public stigma and discrimination have pernicious effects on the lives of people with serious mental illnesses. Given a plethora of research on changing the stigma of mental illness, this article reports on a meta-analysis that examined the effects of antistigma approaches that included protest or social activism, education of the public, and contact with persons with mental illness.



The investigators heeded published guidelines for systematic literature reviews in health care. This comprehensive and systematic review included articles in languages other than English, dissertations, and population studies. The search included all articles from the inception of the databases until October 2010. Search terms fell into three categories: stigma, mental illness (such as schizophrenia and depression), and change program (including contact and education). The search yielded 72 articles and reports meeting the inclusion criteria of relevance to changing public stigma and sufficient data and statistics to complete analyses. Studies represented 38,364 research participants from 14 countries. Effect sizes were computed for all studies and for each treatment condition within studies. Comparisons between effect sizes were conducted with a weighted one-way analysis of variance.



Overall, both education and contact had positive effects on reducing stigma for adults and adolescents with a mental illness. However, contact was better than education at reducing stigma for adults. For adolescents, the opposite pattern was found: education was more effective. Overall, face-to-face contact was more effective than contact by video.



Future research is needed to identify moderators of the effects of both education and contact.
Three-dimensional integrated circuits

integrated circuits A. W. Topol D. C. La Tulipe, Jr. L. Shi D. J. Frank K. Bernstein S. E. Steen A. Kumar G. U. Singco A. M. Young K. W. Guarini M. Ieong Three-dimensional (3D) integrated circuits (ICs), which contain multiple layers of active devices, have the potential to dramatically enhance chip performance, functionality, and device packing density. They also provide for microchip architecture and may facilitate the integration of heterogeneous materials, devices, and signals. However, before these advantages can be realized, key technology challenges of 3D ICs must be addressed. More specifically, the processes required to build circuits with multiple layers of active devices must be compatible with current state-of-the-art silicon processing technology. These processes must also show manufacturability, i.e., reliability, good yield, maturity, and reasonable cost. To meet these requirements, IBM has introduced a scheme for building 3D ICs based on the layer transfer of functional circuits, and many process and design innovations have been implemented. This paper reviews the process steps and design aspects that were developed at IBMto enable the formation of stacked device layers. Details regarding an optimized layer transfer process are presented, including the descriptions of 1) a glass substrate process to enable through-wafer alignment; 2) oxide fusion bonding and wafer bow compensation methods for improved alignment tolerance during bonding; 3) and a single-damascene patterning and metallization method for the creation of high-aspect-ratio (6:1 , AR , 11:1) contacts between two stacked device layers. This process provides the shortest distance between the stacked layers (,2 lm), the highest interconnection density (.10 vias/cm), and extremely aggressive wafer-to-wafer alignment (submicron) capability.
Deception through telling the truth ? ! : Experimental evidence from individuals and teams

Informational asymmetries abound in economic decision making and often provide an incentive for deception through telling a lie or misrepresenting information. In this paper I use a cheap-talk sender-receiver experiment to show that telling the truth should be classified as deception too if the sender chooses the true message with the expectation that the receiver will not follow the sender’s (true) message. The experimental data reveal a large degree of ‘sophisticated’ deception through telling the truth. The robustness of my broader definition of deception is confirmed in an experimental treatment where teams make decisions.
Mindfulness-based psychotherapies: a review of conceptual foundations, empirical evidence and practical considerations.

This paper, composed by an interest group of clinicians and researchers based in Melbourne, presents some background to the practice of mindfulness-based therapies as relevant to the general professional reader. We address the empirical evidence for these therapies, the principles through which they might operate, some practical questions facing those wishing to commence practice in this area or to refer patients into mindfulness-based therapies, and some considerations relevant to the conduct and interpretation of research into the therapeutic application of mindfulness.



Databases (e.g. PsycINFO, MEDLINE) were searched for literature on the impact of mindfulness interventions, and the psychological and biological mechanisms that underpin the effects of mindfulness practice. This paper also draws upon the clinical experience of the author group.



Mindfulness practice and principles have their origins in many contemplative and philosophical traditions but individuals can effectively adopt the training and practice of mindfulness in the absence of such traditions or vocabulary. A recent surge of interest regarding mindfulness in therapeutic techniques can be attributed to the publication of some well-designed empirical evaluations of mindfulness-based cognitive therapy. Arising from this as well as a broader history of clinical integration of mindfulness and Western psychotherapies, a growing number of clinicians have interest and enthusiasm to learn the techniques of mindfulness and to integrate them into their therapeutic work. This review highlights the importance of accurate professional awareness and understanding of mindfulness and its therapeutic applications.



The theoretical and empirical literatures on therapeutic applications of mindfulness are in states of significant growth and development. This group suggests, based on this review, that the combination of some well-developed conceptual models for the therapeutic action of mindfulness and a developing empirical base, justifies a degree of optimism that mindfulness-based approaches will become helpful strategies to offer in the care of patients with a wide range of mental and physical health problems.