nils-herrmann/clean_scirepeval huggingface dataset:
Robust time delay estimation exploiting redundancy among multiple microphones

To find the position of an acoustic source in a room, typically, a set of relative delays among different microphone pairs needs to be determined. The generalized cross-correlation (GCC) method is the most popular to do so and is well explained in a landmark paper by Knapp and Carter. In this paper, the idea of cross-correlation coefficient between two random signals is generalized to the multichannel case by using the notion of spatial prediction. The multichannel spatial correlation matrix is then deduced and its properties are discussed. We then propose a new method based on the multichannel spatial correlation matrix for time delay estimation. It is shown that this new approach can take advantage of the redundancy when more than two microphones are available and this redundancy can help the estimator to better cope with noise and reverberation.
Molecular Mechanisms Underlying Bacterial Persisters

All bacteria form persisters, cells that are multidrug tolerant and therefore able to survive antibiotic treatment. Due to the low frequencies of persisters in growing bacterial cultures and the complex underlying molecular mechanisms, the phenomenon has been challenging to study. However, recent technological advances in microfluidics and reporter genes have improved this scenario. Here, we summarize recent progress in the field, revealing the ubiquitous bacterial stress alarmone ppGpp as an emerging central regulator of multidrug tolerance and persistence, both in stochastically and environmentally induced persistence. In several different organisms, toxin-antitoxin modules function as effectors of ppGpp-induced persistence.
Mono-and Biarticular Muscle Activity During Jumping in Different Directions

This study examined the role of monoand biarticular muscles in control of countermovement jumps (CMJ) in different directions. It was hypothesized that monoarticular muscles would demonstrate the same activity regardless of jump direction, based on previous studies which suggest their role is to generate energy to maximize center-of-mass (CM) velocity. In contrast, biarticular activity patterns were expected to change to control the direction of the ground reaction force (GRF) and CM velocity vectors. Twelve participants performed maximal CMJs in four directions: vertical, forward, intermediate forward, and backward. Electromyographical data from 4 monoarticular and 3 biarticular lower extremity muscles were analyzed with respect to segmental kinematics and kinetics during the jumps. The biarticular rectus femoris (RF), hamstrings (HA), and gastrocnemius all exhibited changes in activity magnitude and pattern as a function of jump angle. In particular, HA and RF demonstrated reciprocal trends, with HA activity increasing as jump angle changed from backward to forward, while RF activity was reduced in the forward jump condition. The vastus lateralis and gluteus maximus both demonstrated changes in activity patterns, although the former was the only monoarticular muscle to change activity level with jump direction. Monoand biarticular muscle activities therefore did not fit with their hypothesized roles. CM and segmental kinematics suggest that jump direction was initiated early in the countermovement, and that in each jump direction the propulsion phase began from a different position with unique angular and linear momentum. Issues that dictated the muscle activity patterns in each jump direction were the early initiation of appropriate forward momentum, the transition from countermovement to propulsion, the control of individual segment rotations, the control of GRF location and direction, and the influence of the subsequent landing.
ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search

Neural network architectures found by sophistic search algorithms achieve strikingly good test performance, surpassing most human-crafted network models by significant margins. Although computationally efficient, their design is often very complex, impairing execution speed. Additionally, finding models outside of the search space is not possible by design. While our space is still limited, we implement undiscoverable expert knowledge into the economic search algorithm Efficient Neural Architecture Search (ENAS), guided by the design principles and architecture of ShuffleNet V2. While maintaining baselinelike 2.85% test error on CIFAR-10, our ShuffleNASNets are significantly less complex, require fewer parameters, and are two times faster than the ENAS baseline in a classification task. These models also scale well to a low parameter space, achieving less than 5% test error with little regularization and only 236K parameters.
Management of large class II lesions in molars: how to restore and when to perform surgical crown lengthening?

The restoration of endodontic tooth is always a challenge for the clinician, not only due to excessive loss of tooth structure but also invasion of the biological width due to large decayed lesions. In this paper, the 7 most common clinical scenarios in molars with class II lesions ever deeper were examined. This includes both the type of restoration (direct or indirect) and the management of the cavity margin, such as the need for deep margin elevation (DME) or crown lengthening. It is necessary to have the DME when the healthy tooth remnant is in the sulcus or at the epithelium level. For caries that reaches the connective tissue or the bone crest, crown lengthening is required. Endocrowns are a good treatment option in the endodontically treated tooth when the loss of structure is advanced.
Memory systems of the brain: A brief history and current perspective

The idea that memory is composed of distinct systems has a long history but became a topic of experimental inquiry only after the middle of the 20th century. Beginning about 1980, evidence from normal subjects, amnesic patients, and experimental animals converged on the view that a fundamental distinction could be drawn between a kind of memory that is accessible to conscious recollection and another kind that is not. Subsequent work shifted thinking beyond dichotomies to a view, grounded in biology, that memory is composed of multiple separate systems supported, for example, by the hippocampus and related structures, the amygdala, the neostriatum, and the cerebellum. This article traces the development of these ideas and provides a current perspective on how these brain systems operate to support behavior.
A CRD-WEL System for Chemical-disease Relations Extraction

As one task of the BioCreative V competition, the chemical-disease relations (CDR) include two subtasks: DNER and CID. We participated in this track and designed two separate systems for each subtask. The CRD-WEL system consists of two subsystems: CRD-DNER and WEL-CID. For DNER, the CRD-DNER system is proposed, which is a combined system for disease named entity recognition based on shallow and deep models. For CID, WELCID system uses a novel word embedding model and logistic regression classifier to extract Chemical-induced Diseases from text.
The thresholds for statistical and clinical significance – a five-step procedure for evaluation of intervention effects in randomised clinical trials

Thresholds for statistical significance are insufficiently demonstrated by 95% confidence intervals or P-values when assessing results from randomised clinical trials. First, a P-value only shows the probability of getting a result assuming that the null hypothesis is true and does not reflect the probability of getting a result assuming an alternative hypothesis to the null hypothesis is true. Second, a confidence interval or a P-value showing significance may be caused by multiplicity. Third, statistical significance does not necessarily result in clinical significance. Therefore, assessment of intervention effects in randomised clinical trials deserves more rigour in order to become more valid.



Several methodologies for assessing the statistical and clinical significance of intervention effects in randomised clinical trials were considered. Balancing simplicity and comprehensiveness, a simple five-step procedure was developed.



For a more valid assessment of results from a randomised clinical trial we propose the following five-steps: (1) report the confidence intervals and the exact P-values; (2) report Bayes factor for the primary outcome, being the ratio of the probability that a given trial result is compatible with a 'null' effect (corresponding to the P-value) divided by the probability that the trial result is compatible with the intervention effect hypothesised in the sample size calculation; (3) adjust the confidence intervals and the statistical significance threshold if the trial is stopped early or if interim analyses have been conducted; (4) adjust the confidence intervals and the P-values for multiplicity due to number of outcome comparisons; and (5) assess clinical significance of the trial results.



If the proposed five-step procedure is followed, this may increase the validity of assessments of intervention effects in randomised clinical trials.
Interannual variations of monthly and seasonal normalized difference vegetation index ( NDVI ) in China from 1982 to 1999

In this paper, we analyzed interannual variations of normalized difference vegetation index (NDVI) and their relationships with climatic variables (temperature and precipitation) and human activity in China between 1982 and 1999. Monthly and seasonal NDVI increased significantly at both the country and biome scales over the study period. NDVI shows the largest increase (14.4% during the 18 years and a trend of 0.0018 yr ) over 85.9% of the total study area in spring and the smallest increase (5.2% with a trend of 0.0012 yr ) over 72.2% of the area in summer. The NDVI trends show a marked heterogeneity corresponding to regional and seasonal variations in climates. There is about a 3-month lag for the period between the maximum trend in temperature (February) and that in NDVI (April or May) at the country and biome scales. Human activity (urbanization and agricultural practices) also played an important role in influencing the NDVI trends over some regions. Rapid urbanization resulted in a sharp decrease in NDVI in the Yangtze River and Pearl River deltas, while irrigation and fertilization may have contributed to the increased NDVI in the North China plain.
The evolutionary psychology of facial beauty.

What makes a face attractive and why do we have the preferences we do? Emergence of preferences early in development and cross-cultural agreement on attractiveness challenge a long-held view that our preferences reflect arbitrary standards of beauty set by cultures. Averageness, symmetry, and sexual dimorphism are good candidates for biologically based standards of beauty. A critical review and meta-analyses indicate that all three are attractive in both male and female faces and across cultures. Theorists have proposed that face preferences may be adaptations for mate choice because attractive traits signal important aspects of mate quality, such as health. Others have argued that they may simply be by-products of the way brains process information. Although often presented as alternatives, I argue that both kinds of selection pressures may have shaped our perceptions of facial beauty.
LAYOUTGAN: GENERATING GRAPHIC LAYOUTS

Layouts are important for graphic design and scene generation. We propose a novel generative adversarial network, named as LayoutGAN, that synthesizes graphic layouts by modeling semantic and geometric relations of 2D elements. The generator of LayoutGAN takes as input a set of randomly placed 2D graphic elements and uses self-attention modules to refine their semantic and geometric parameters jointly to produce a meaningful layout. Accurate alignment is critical for good layouts. We thus propose a novel differentiable wireframe rendering layer that maps the generated layout to a wireframe image, upon which a CNNbased discriminator is used to optimize the layouts in visual domain. We validate the effectiveness of LayoutGAN in various experiments including MNIST digit generation, document layout generation, clipart abstract scene generation and tangram graphic design.
Accelerating Reduction and Scan Using Tensor Core Units

Driven by deep learning, there has been a surge of specialized processors for matrix multiplication, referred to as Tensor Core Units (TCUs). These TCUs are capable of performing matrix multiplications on small matrices (usually 4× 4 or 16×16) to accelerate the convolutional and recurrent neural networks in deep learning workloads. In this paper we leverage NVIDIA’s TCU to express both reduction and scan with matrix multiplication and show the benefits — in terms of program simplicity, efficiency, and performance. Our algorithm exercises the NVIDIA TCUs which would otherwise be idle, achieves 89%− 98% of peak memory copy bandwidth, and is orders of magnitude faster (up to 100× for reduction and 3× for scan) than state-of-the-art methods for small segment sizes — common in machine learning and scientific applications. Our algorithm achieves this while decreasing the power consumption by up to 22% for reduction and 16% for scan.
Benchmarking Multimodal Sentiment Analysis

We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities. We obtain a performance improvement of 10% over the state of the art by combining visual, text and audio features. We also discuss some major issues frequently ignored in multimodal sentiment analysis research: the role of speakerindependent models, importance of the modalities and generalizability. The paper thus serve as a new benchmark for further research in multimodal sentiment analysis and also demonstrates the different facets of analysis to be considered while performing such tasks.
Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis

The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems. While their image generation technique uses a slow optimization process, recently several authors have proposed to learn generator neural networks that can produce similar outputs in one quick forward pass. While generator networks are promising, they are still inferior in visual quality and diversity compared to generation-by-optimization. In this work, we advance them in two significant ways. First, we introduce an instance normalization module to replace batch normalization with significant improvements to the quality of image stylization. Second, we improve diversity by introducing a new learning formulation that encourages generators to sample unbiasedly from the Julesz texture ensemble, which is the equivalence class of all images characterized by certain filter responses. Together, these two improvements take feed forward texture synthesis and image stylization much closer to the quality of generation-via-optimization, while retaining the speed advantage.
Deep learning for time series classification: a review

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.
Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition

The proliferation of wearable devices has contributed to the emergence of mobile crowdsensing, which leverages the power of the crowd to collect and report data to a third party for large-scale sensing and collaborative learning. However, since the third party may not be honest, privacy poses a major concern. In this paper, we address this concern with a two-stage privacy-preserving scheme called RG-RP: the first stage is designed to mitigate maximum a posteriori (MAP) estimation attacks by perturbing each participant's data through a nonlinear function called repeated Gompertz (RG); while the second stage aims to maintain accuracy and reduce transmission energy by projecting high-dimensional data to a lower dimension, using a row-orthogonal random projection (RP) matrix. The proposed RG-RP scheme delivers better recovery resistance to MAP estimation attacks than most state-of-the-art techniques on both synthetic and real-world datasets. For collaborative learning, we proposed a novel LSTM-CNN model combining the merits of Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). Our experiments on two representative movement datasets captured by wearable sensors demonstrate that the proposed LSTM-CNN model outperforms standalone LSTM, CNN and Deep Belief Network. Together, RG+RP and LSTM-CNN provide a privacy-preserving collaborative learning framework that is both accurate and privacy-preserving.
Depression and Social Identity: An Integrative Review.

Social relationships play a key role in depression. This is apparent in its etiology, symptomatology, and effective treatment. However, there has been little consensus about the best way to conceptualize the link between depression and social relationships. Furthermore, the extensive social-psychological literature on the nature of social relationships, and in particular, research on social identity, has not been integrated with depression research. This review presents evidence that social connectedness is key to understanding the development and resolution of clinical depression. The social identity approach is then used as a basis for conceptualizing the role of social relationships in depression, operationalized in terms of six central hypotheses. Research relevant to these hypotheses is then reviewed. Finally, we present an agenda for future research to advance theoretical and empirical understanding of the link between social identity and depression, and to translate the insights of this approach into clinical practice.
Business and Social Networks in International Trade

Today . . . new transportation and communications technologies allow even the smallest firms to build partnerships with foreign producers to tap overseas expertise, cost-savings, and markets . . . The scarce resource in this new environment is the ability to locate foreign partners quickly and to manage complex business relationships across cultural and linguistic boundaries . . . [T]he Chinese and Indian entrepreneurs of Silicon Valley . . . are creating social structures that enable even the smallest producers to locate and maintain mutually beneficial collaborations across long distances.
Violent Media Content and Aggressiveness in Adolescents A Downward Spiral Model

Theory and research on media violence provides evidence that aggressive youth seek out media violence and that media violence prospectively predicts aggression in youth.The authors argue that both relationships,when modeled over time, should be mutually reinforcing, in what they call a downward spiral model. This study uses multilevel modeling to examine individual growth curves in aggressiveness and violent media use. The measure of use of media violence included viewing action films, playing violent computer and video games, and visiting violence-oriented Internet sites by students from 20 middle schools in 10 different regions in the United States. The findings appear largely consistent with the proposed model. In particular, concurrent effects of aggressiveness on violent-media use and concurrent and lagged effects of violent media use on aggressiveness were found. The implications of this model for theorizing about media effects on youth, and for bridging active audience with media effects perspectives, are discussed.
A Neural Chatbot with Personality

Conversational modeling is an important task in natural language processing as well as machine learning. Like most important tasks, it’s not easy. Previously, conversational models have been focused on specific domains, such as booking hotels or recommending restaurants. They were built using hand-crafted rules, like ChatScript [11], a popular rule-based conversational model. In 2014, the sequence to sequence model being used for translation opened the possibility of phrasing dialogues as a translation problem: translating from an utterance to its response. The systems built using this principle, while conversing fairly fluently, aren’t very convincing because of their lack of personality and inconsistent persona [10] . In this paper, we experiment building open-domain response generator with personality and identity. We built chatbots that imitate characters in popular TV shows: Barney from How I Met Your Mother, Sheldon from The Big Bang Theory, Michael from The Office, and Joey from Friends. A successful model of this kind can have a lot of applications, such as allowing people to speak with their favorite celebrities, creating more life-like AI assistants, or creating virtual alter-egos of ourselves. The model was trained end-to-end without any hand-crafted rules. The bots talk reasonably fluently, have distinct personalities, and seem to have learned certain aspects of their identity. The results of standard automated translation model evaluations yielded very low scores. However, we designed an evaluation metric with a human judgment element, for which the chatbots performed well. We are able to show that for a bot’s response, a human is more than 50% likely to believe that the response actually came from the real character. Seq2seq, attentional mechanism, chatbot, dialogue system.
Performance management and employee engagement

Many contemporary organizations are placing a greater emphasis on their performance management systems as a means of generating higher levels of job performance. We suggest that producing performance increments may be best achieved by orienting the performance management system to promote employee engagement. To this end, we describe a new approach to the performance management process that includes employee engagement and the key drivers of employee engagement at each stage. We present a model of engagement management that incorporates the main ideas of the paper and suggests a new perspective for thinking about how to foster and manage employee engagement to achieve high levels of job