nils-herrmann/clean_scirepeval huggingface dataset:
A highly scalable 8-layer 3D vertical-gate (VG) TFT NAND Flash using junction-free buried channel BE-SONOS device

An 8-layer, 75 nm half-pitch, 3D stacked vertical-gate (VG) TFT BE-SONOS NAND Flash array is fabricated and characterized. We propose a buried-channel (n-type well) device to improve the read current of TFT NAND, and it also allows the junction-free structure which is particularly important for 3D stackable devices. Large self-boosting disturb-free memory window (6V) can be obtained in our device, and for the first time the “Z-interference” between adjacent vertical layers is studied. The proposed buried-channel VG NAND allows better X, Y pitch scaling and is a very attractive candidate for ultra high-density 3D stackable NAND Flash.
Generic Physiological Features as Predictors of Player Experience

This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and selfreported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.
An Interpretable Model with Globally Consistent Explanations for Credit Risk

We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment. Rather than present a black box model and explain it afterwards, we provide a globally interpretable model that is as accurate as other neural networks. Our "two-layer additive risk model" is decomposable into subscales, where each node in the second layer represents a meaningful subscale, and all of the nonlinearities are transparent. We provide three types of explanations that are simpler than, but consistent with, the global model. One of these explanation methods involves solving a minimum set cover problem to find high-support globally-consistent explanations. We present a new online visualization tool to allow users to explore the global model and its
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems

We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems. Our agents explore via Thompson sampling, drawing Monte Carlo samples from a Bayes-by-Backprop neural network. Our algorithm learns much faster than common exploration strategies such as -greedy, Boltzmann, bootstrapping, and intrinsic-reward-based ones. Additionally, we show that spiking the replay buffer with experiences from just a few successful episodes can make Q-learning feasible when it might otherwise fail.
Transfer Learning for Low-Resource Neural Machine Translation

The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages. We present a transfer learning method that significantly improves BLEU scores across a range of low-resource languages. Our key idea is to first train a high-resource language pair (the parent model), then transfer some of the learned parameters to the low-resource pair (the child model) to initialize and constrain training. Using our transfer learning method we improve baseline NMT models by an average of 5.6 BLEU on four low-resource language pairs. Ensembling and unknown word replacement add another 2 BLEU which brings the NMT performance on low-resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair. Additionally, using the transfer learning model for re-scoring, we can improve the SBMT system by an average of 1.3 BLEU, improving the state-of-the-art on low-resource machine translation.
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced data sets and their broad application domains in data mining, and then summarizes the evaluation metrics and the existing methods to evaluate and solve the imbalance problem. Synthetic minority oversampling technique (SMOTE) is one of the over-sampling methods addressing this problem. Based on SMOTE method, this paper presents two new minority over-sampling methods, borderline-SMOTE1 and borderline-SMOTE2, in which only the minority examples near the borderline are over-sampled. For the minority class, experiments show that our approaches achieve better TP rate and F-value than SMOTE and random over-sampling methods.
Zero-Voltage-Switching PWM Resonant Full-Bridge Converter With Minimized Circulating Losses and Minimal Voltage Stresses of Bridge Rectifiers for Electric Vehicle Battery Chargers

This paper presents a zero-voltage-switching (ZVS) full-bridge dc-dc converter combing resonant and pulse-width-modulation (PWM) power conversions for electric vehicle battery chargers. In the proposed converter, a half-bridge LLC resonant circuit shares the lagging leg with a phase-shift full-bridge (PSFB) dc-dc circuit to guarantee ZVS of the lagging-leg switches from zero to full load. A secondary-side hybrid-switching circuit, which is formed by the leakage inductance, output inductor of the PSFB dc-dc circuit, a small additional resonant capacitor, and two additional diodes, is integrated at the secondary side of the PSFB dc-dc circuit. With the clamp path of a hybrid-switching circuit, the voltage overshoots that arise during the turn off of the rectifier diodes are eliminated and the voltage of bridge rectifier is clamped to the minimal achievable value, which is equal to secondary-reflected input voltage of the transformer. The sum of the output voltage of LLC resonant circuit and the resonant capacitor voltage of the hybrid-switching circuit is applied between the bridge rectifier and the output inductor of the PSFB dc-dc circuit during the freewheeling phases. As a result, the primary-side circulating current of the PSFB dc-dc circuit is instantly reset to zero, achieving minimized circulating losses. The effectiveness of the proposed converter was experimentally verified using a 4-kW prototype circuit. The experimental results show 98.6% peak efficiency and high efficiency over wide load and output voltage ranges.
Introducing a gender-neutral pronoun in a natural gender language: the influence of time on attitudes and behavior

The implementation of gender fair language is often associated with negative reactions and hostile attacks on people who propose a change. This was also the case in Sweden in 2012 when a third gender-neutral pronoun hen was proposed as an addition to the already existing Swedish pronouns for she (hon) and he (han). The pronoun hen can be used both generically, when gender is unknown or irrelevant, and as a transgender pronoun for people who categorize themselves outside the gender dichotomy. In this article we review the process from 2012 to 2015. No other language has so far added a third gender-neutral pronoun, existing parallel with two gendered pronouns, that actually have reached the broader population of language users. This makes the situation in Sweden unique. We present data on attitudes toward hen during the past 4 years and analyze how time is associated with the attitudes in the process of introducing hen to the Swedish language. In 2012 the majority of the Swedish population was negative to the word, but already in 2014 there was a significant shift to more positive attitudes. Time was one of the strongest predictors for attitudes also when other relevant factors were controlled for. The actual use of the word also increased, although to a lesser extent than the attitudes shifted. We conclude that new words challenging the binary gender system evoke hostile and negative reactions, but also that attitudes can normalize rather quickly. We see this finding very positive and hope it could motivate language amendments and initiatives for gender-fair language, although the first responses may be negative.
Understanding current causes of women's underrepresentation in science.

Explanations for women's underrepresentation in math-intensive fields of science often focus on sex discrimination in grant and manuscript reviewing, interviewing, and hiring. Claims that women scientists suffer discrimination in these arenas rest on a set of studies undergirding policies and programs aimed at remediation. More recent and robust empiricism, however, fails to support assertions of discrimination in these domains. To better understand women's underrepresentation in math-intensive fields and its causes, we reprise claims of discrimination and their evidentiary bases. Based on a review of the past 20 y of data, we suggest that some of these claims are no longer valid and, if uncritically accepted as current causes of women's lack of progress, can delay or prevent understanding of contemporary determinants of women's underrepresentation. We conclude that differential gendered outcomes in the real world result from differences in resources attributable to choices, whether free or constrained, and that such choices could be influenced and better informed through education if resources were so directed. Thus, the ongoing focus on sex discrimination in reviewing, interviewing, and hiring represents costly, misplaced effort: Society is engaged in the present in solving problems of the past, rather than in addressing meaningful limitations deterring women's participation in science, technology, engineering, and mathematics careers today. Addressing today's causes of underrepresentation requires focusing on education and policy changes that will make institutions responsive to differing biological realities of the sexes. Finally, we suggest potential avenues of intervention to increase gender fairness that accord with current, as opposed to historical, findings.
Advances in Computational Stereo

Extraction of three-dimensional structure of a scene from stereo images is a problem that has been studied by the computer vision community for decades. Early work focused on the fundamentals of image correspondence and stereo geometry. Stereo research has matured significantly throughout the years, and many advances in computational stereo continue to be made, allowing stereo to be applied to new and more demanding problems. In this paper, we review recent advances in computational stereo, focusing primarily on three important topics: correspondence methods, methods for occlusion and real-time implementations. Throughout, we present tables that summarize and draw distinctions among key ideas and approaches. Where available, we provide comparative analyses, and we make suggestions for analyses yet to be
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs

In this paper, we present subgraph2vec, a novel approach for learning latent representations of rooted subgraphs from large graphs inspired by recent advancements in Deep Learning and Graph Kernels. These latent representations encode semantic substructure dependencies in a continuous vector space, which is easily exploited by statistical models for tasks such as graph classification, clustering, link prediction and community detection. subgraph2vec leverages on local information obtained from neighbourhoods of nodes to learn their latent representations in an unsupervised fashion. We demonstrate that subgraph vectors learnt by our approach could be used in conjunction with classifiers such as CNNs, SVMs and relational data clustering algorithms to achieve significantly superior accuracies. Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel. Our experiments on several benchmark and large-scale real-world datasets reveal that subgraph2vec achieves significant improvements in accuracies over existing graph kernels on both supervised and unsupervised learning tasks. Specifically, on two realworld program analysis tasks, namely, code clone and malware detection, subgraph2vec outperforms state-of-the-art kernels by more than 17% and 4%, respectively.
Carrier aggregation for LTE-advanced mobile communication systems

In order to achieve up to 1 Gb/s peak data rate in future IMT-Advanced mobile systems, carrier aggregation technology is introduced by the 3GPP to support very-high-data-rate transmissions over wide frequency bandwidths (e.g., up to 100 MHz) in its new LTE-Advanced standards. This article first gives a brief review of continuous and non-continuous CA techniques, followed by two data aggregation schemes in physical and medium access control layers. Some technical challenges for implementing CA technique in LTE-Advanced systems, with the requirements of backward compatibility to LTE systems, are highlighted and discussed. Possible technical solutions for the asymmetric CA problem, control signaling design, handover control, and guard band setting are reviewed. Simulation results show Doppler frequency shift has only limited impact on data transmission performance over wide frequency bands in a high-speed mobile environment when the component carriers are time synchronized. The frequency aliasing will generate much more interference between adjacent component carriers and therefore greatly degrades the bit error rate performance of downlink data transmissions.
A weakly-supervised approach for discovering new user intents from search query logs

State-of-the art spoken language understanding models that automatically capture user intents in human to machine dialogs are trained with manually annotated data, which is cumbersome and time-consuming to prepare. For bootstrapping the learning algorithm that detects relations in natural language queries to a conversational system, one can rely on publicly available knowledge graphs, such as Freebase, and mine corresponding data from the web. In this paper, we present an unsupervised approach to discover new user intents using a novel Bayesian hierarchical graphical model. Our model employs search query click logs to enrich the information extracted from bootstrapped models. We use the clicked URLs as implicit supervision and extend the knowledge graph based on the relational information discovered from this model. The posteriors from the graphical model relate the newly discovered intents with the search queries. These queries are then used as additional training examples to complement the bootstrapped relation detection models. The experimental results demonstrate the effectiveness of this approach, showing extended coverage to new intents without impacting the known intents.
Knowledge Enhanced Hybrid Neural Network for Text Matching

Long text brings a big challenge to neural network based text matching approaches due to their complicated structures. To tackle the challenge, we propose a knowledge enhanced hybrid neural network (KEHNN) that leverages prior knowledge to identify useful information and filter out noise in long text and performs matching from multiple perspectives. The model fuses prior knowledge into word representations by knowledge gates and establishes three matching channels with words, sequential structures of text given by Gated Recurrent Units (GRUs), and knowledge enhanced representations. The three channels are processed by a convolutional neural network to generate high level features for matching, and the features are synthesized as a matching score by a multilayer perceptron. In this paper, we focus on exploring the use of taxonomy knowledge for text matching. Evaluation results from extensive experiments on public data sets of question answering and conversation show that KEHNN can significantly outperform state-of-the-art matching models and particularly improve matching accuracy on pairs with long text.
An Explication of Social Norms

This article identifies four factors for consideration in norms-based research to enhance the predictive ability of theoretical models. First, it makes the distinction between perceived and collective norms and between descriptive and injunctive norms. Second, the article addresses the role of important moderators in the relationship between descriptive norms and behaviors, including outcome expectations, group identity, and ego involvement. Third, it discusses the role of both interpersonal and mass communication in normative influences. Lastly, it outlines behavioral attributes that determine susceptibility to normative influences, including behavioral ambiguity and the public or private nature of the behavior.
Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised adaptation

Tracking the user's intention throughout the course of a dialog, called dialog state tracking, is an important component of any dialog system. Most existing spoken dialog systems are designed to work in a static, well-defined domain, and are not well suited to tasks in which the domain may change or be extended over time. This paper shows how recurrent neural networks can be effectively applied to tracking in an extended domain with new slots and values not present in training data. The method is evaluated in the third Dialog State Tracking Challenge, where it significantly outperforms other approaches in the task of tracking the user's goal. A method for online unsupervised adaptation to new domains is also presented. Unsupervised adaptation is shown to be helpful in improving word-based recurrent neural networks, which work directly from the speech recognition results. Word-based dialog state tracking is attractive as it does not require engineering a spoken language understanding system for use in the new domain and it avoids the need for a general purpose intermediate semantic representation.
Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data

Depression is a serious health disorder. In this study, we investigate the feasibility of depression screening using sensor data collected from smartphones. We extract various behavioral features from smartphone sensing data and investigate the efficacy of various machine learning tools to predict clinical diagnoses and PHQ-9 scores (a quantitative tool for aiding depression screening in practice). A notable feature of our study is that we leverage a dataset that includes clinical ground truth. We find that behavioral data from smartphones can predict clinical depression with good accuracy. In addition, combining behavioral data and PHQ-9 scores can provide prediction accuracy significantly exceeding each in isolation, indicating that behavioral data captures relevant features that are not reflected by PHQ-9 scores. Finally, we develop multi-feature regression models for PHQ-9 scores that achieve significantly improved accuracy compared to direct regression models based on single features.
Staying connected while on the move: Cell phone use and social connectedness

As people integrate use of the cell phone into their lives, do they view it as just an update of the fixed telephone or assign it special values? This study explores that question in the framework of gratifications sought and their relationship both to differential cell phone use and to social connectedness. Based on a survey of Taiwanese college students, we found that the cell phone supplements the fixed telephone as a means of strengthening users’ family bonds, expanding their psychological neighborhoods, and facilitating symbolic proximity to the people they call. Thus, the cell phone has evolved from a luxury for businesspeople into an important facilitator of many users’ social relationships. For the poorly connected socially, the cell phone offers a unique advantage: it confers instant membership in a community. Finally, gender was found to mediate how users exploit the cell phone to maintain social ties.
Recursive self-organizing maps

This paper explores the combination of self-organizing map (SOM) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities. It has been difficult to apply this approach to SOM, because feedback generates instability during learning. We demonstrate a solution to this problem, based on a nonlinearity. The result is a generalization of SOM that learns to represent sequences recursively. We demonstrate that the resulting representations are adapted to the temporal statistics of the input series.
Suicide and suicidal behavior.

Suicidal behavior is a leading cause of injury and death worldwide. Information about the epidemiology of such behavior is important for policy-making and prevention. The authors reviewed government data on suicide and suicidal behavior and conducted a systematic review of studies on the epidemiology of suicide published from 1997 to 2007. The authors' aims were to examine the prevalence of, trends in, and risk and protective factors for suicidal behavior in the United States and cross-nationally. The data revealed significant cross-national variability in the prevalence of suicidal behavior but consistency in age of onset, transition probabilities, and key risk factors. Suicide is more prevalent among men, whereas nonfatal suicidal behaviors are more prevalent among women and persons who are young, are unmarried, or have a psychiatric disorder. Despite an increase in the treatment of suicidal persons over the past decade, incidence rates of suicidal behavior have remained largely unchanged. Most epidemiologic research on suicidal behavior has focused on patterns and correlates of prevalence. The next generation of studies must examine synergistic effects among modifiable risk and protective factors. New studies must incorporate recent advances in survey methods and clinical assessment. Results should be used in ongoing efforts to decrease the significant loss of life caused by suicidal behavior.
Kernel sparse representation based classification

Sparse representation has attracted great attention in the past few years. Sparse representation based classification (SRC) algorithm was developed and successfully used for classification. In this paper, a kernel sparse representation based classification (KSRC) algorithm is proposed. Samples are mapped into a high dimensional feature space first and then SRC is performed in this new feature space by perform KSRC directly. In order to overcome this difficulty, we give the method to solve the problem of sparse representation in the high dimensional feature space. If an appropriate kernel is selected, in the high dimensional feature space, a test sample is probably represented as the linear combination of training samples of the same class more accurately. Therefore, KSRC has more powerful classification ability than SRC. Experiments of face recognition, palmprint recognition and finger-knuckle-print recognition demonstrate the effectiveness of KSRC. & 2011 Elsevier B.V. All rights reserved.