nils-herrmann/clean_scirepeval huggingface dataset:
Zika Virus Vaccine Development.

The emergence of Zika virus in Brazil and its association with microcephaly and Guillain-Barré syndrome led to accelerated vaccine development efforts. Based on prior flavivirus vaccine development programs, knowledge of flavivirus particle structure, definition of E dimers as the key antigenic target, and deep understanding of neutralizing mechanisms, multiple vaccine strategies have advanced to the stage of clinical evaluation with unprecedented speed. These include nucleic acid (DNA and messenger RNA), whole-inactivated virus, live-attenuated or chimeric virus, and protein or viruslike particle vaccines. Within a year from the declaration by the World Health Organization of Zika virus as a Public Health Emergency of International Concern, multiple vaccine candidates entered clinical trials, now totaling 7 products with an additional 40-plus candidate vaccines in preclinical development. The rapid progress in vaccine development demonstrates the capacity of governments, public health organizations, and the scientific community to respond to pandemic threats when sufficient prior knowledge exists, emergency funding is made available, and interagency cooperation is achieved and serves as a paradigm for preparing for future emerging infectious diseases.
A PWM LLC Type Resonant Converter Adapted to Wide Output Range in PEV Charging Applications

In conventional LLC-based plug-in electric vehicle (PEV) onboard chargers, the battery pack voltage varies in a wide range with the change of state of charge. This makes it difficult to optimally design the pulse frequency modulated LLC resonant converter. Besides, the voltage regulation of the LLC converter is highly dependent on the load conditions. In this paper, a modified pulse width modulated (PWM) LLC type resonant topology (PWM-LLC) is proposed and investigated in PEV charging applications. The switching frequency of the primary LLC network is constant and equal to the resonant frequency. The voltage regulation is achieved by modulating the duty cycle of the secondary side auxiliary mosfet. Compared with the conventional LLC topology, the proposed topology shrinks the magnetic component size and achieves a wide and fixed voltage gain range independent of load conditions. Meanwhile, zero-voltage-switching and zero-current-switching are realized among all MOSFETs and diodes, respectively. A 100-kHz, 1-kW converter prototype, generating 250–420 V output from the 390-V dc link, is designed and tested to verify the proof of concept. The prototype demonstrates 96.7% peak efficiency and robust performance over wide voltage and load ranges.
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification

Person re-identification (reID) is an important task that requires to retrieve a person’s images from an image dataset, given one image of the person of interest. For learning robust person features, the pose variation of person images is one of the key challenges. Existing works targeting the problem either perform human alignment, or learn human-region-based representations. Extra pose information and computational cost is generally required for inference. To solve this issue, a Feature Distilling Generative Adversarial Network (FD-GAN) is proposed for learning identity-related and pose-unrelated representations. It is a novel framework based on a Siamese structure with multiple novel discriminators on human poses and identities. In addition to the discriminators, a novel same-pose loss is also integrated, which requires appearance of a same person’s generated images to be similar. After learning pose-unrelated person features with pose guidance, no auxiliary pose information and additional computational cost is required during testing. Our proposed FD-GAN achieves state-of-the-art performance on three person reID datasets, which demonstrates that the effectiveness and robust feature distilling capability of the proposed FD-GAN.
Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond

We exploit the prevalence of malicious review writers on crowdsourcing platforms like RapidWorkers to identify actual fraud reviews on Amazon. Complementary to previous efforts which often rely on proxies for fraud reviews, we present a long-term study of actual fraudulent behaviors in online review manipulation. We find that these malicious reviewers – though often providing seemingly legitimate opinions – do exhibit significant differences from normal reviewers in terms of ratings distribution, length of the reviews, and the burstiness of the reviews themselves. We additionally study the evolution of these reviews, and find striking temporal changes that could support future discovery of these reviewers who may be “hiding in plain sight”.
From PSNR to perceived quality in H.264 encoded video sequences

This paper describes and compares a set of no-reference quality assessment algorithms for H.264/AVC encoded video sequences. These algorithms have in common a module that estimates the error due to lossy encoding of the video signals, using only information available on the compressed bitstream. In order to obtain perceived quality scores from the estimated error, three methods are presented: i) to weight the error estimates according to a perceptual model; ii) to linearly combine the mean squared error (MSE) estimates with additional video features; iii) to use MSE estimates as the input of a logistic function. The performances of the algorithms are evaluated using cross-validation procedures and the one showing the best performance is also in a preliminary study of quality assessment in the presence of transmission losses.
Stereo Vision-Based Semantic 3D Object and Ego-Motion Tracking for Autonomous Driving

We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios. Instead of directly regressing the 3D bounding box using end-to-end approaches, we propose to use the easy-to-labeled 2D detection and discrete viewpoint classification together with a light-weight semantic inference method to obtain rough 3D object measurements. Based on the object-aware-aided camera pose tracking which is robust in dynamic environments, in combination with our novel dynamic object bundle adjustment (BA) approach to fuse temporal sparse feature correspondences and the semantic 3D measurement model, we obtain 3D object pose, velocity and anchored dynamic point cloud estimation with instance accuracy and temporal consistency. The performance of our proposed method is demonstrated in diverse scenarios. Both the ego-motion estimation and object localization are compared with the state-of-of-theart solutions.
Chinese herbal medicine for infertility with anovulation: a systematic review.

The aim of this systematic review is to assess the effectiveness and safety of Chinese herbal medicine (CHM) in treatment of anovulation and infertility in women. Eight (8) databases were extensively retrieved. The Chinese electronic databases included VIP Information, CMCC, and CNKI. The English electronic databases included AMED, CINAHL, Cochrane Library, Embase, and MEDLINE(®). Randomized controlled trials using CHM as intervention were included in the study selection. The quality of studies was assessed by the Jadad scale and the criteria referred to Cochrane reviewers' handbook. The efficacy of CHM treatment for infertility with anovulation was evaluated by meta-analysis. There were 692 articles retrieved according to the search strategy, and 1659 participants were involved in the 15 studies that satisfied the selection criteria. All the included trials were done in China. Meta-analysis indicated that CHM significantly increased the pregnancy rate (odds ratio [OR] 3.12, 95% confidence interval [CI] 2.50-3.88) and reduced the miscarriage rate (OR 0.2, 95% CI 0.10-0.41) compared to clomiphene. In addition, CHM also increased the ovulation rate (OR 1.55, 95% CI 1.06-2.25) and improved the cervical mucus score (OR 3.82, 95% CI 1.78-8.21) compared to clomiphene, while there were no significant difference between CHM and clomiphene combined with other medicine. CHM is effective in treating infertility with anovulation. Also, no significant adverse effects were identified for the use of CHM from the studies included in this review. However, owing to the low quality of the studies investigated, more randomized controlled trials are needed before evidence-based recommendation regarding the effectiveness and safety of CHM in the management of infertility with anovulation can be provided.
An Analysis of the t-SNE Algorithm for Data Visualization

A first line of attack in exploratory data analysis is data visualization, i.e., generating a 2-dimensional representation of data that makes clusters of similar points visually identifiable. Standard JohnsonLindenstrauss dimensionality reduction does not produce data visualizations. The t-SNE heuristic of van der Maaten and Hinton, which is based on non-convex optimization, has become the de facto standard for visualization in a wide range of applications. This work gives a formal framework for the problem of data visualization – finding a 2-dimensional embedding of clusterable data that correctly separates individual clusters to make them visually identifiable. We then give a rigorous analysis of the performance of t-SNE under a natural, deterministic condition on the “ground-truth” clusters (similar to conditions assumed in earlier analyses of clustering) in the underlying data. These are the first provable guarantees on t-SNE for constructing good data visualizations. We show that our deterministic condition is satisfied by considerably general probabilistic generative models for clusterable data such as mixtures of well-separated log-concave distributions. Finally, we give theoretical evidence that t-SNE provably succeeds in partially recovering cluster structure even when the above deterministic condition is not met.
Speech Emotion Recognition: A Review

Field of emotional content recognition of speech signals has been gaining increasing interest during recent years. Several emotion recognition systems have been constructed by different researchers for recognition of human emotions in spoken utterances. This paper describes speech emotion recognition based on the previous technologies which uses different methods of feature extraction and different classifiers for the emotion recognition are reviewed. The database for the speech emotion recognition system is the emotional speech samples and the features extracted from these speech samples are the energy, pitch, linear prediction cepstrum coefficient (LPCC), Mel frequency cepstrum coefficient (MFCC). Different wavelet decomposition structures can also used for feature vector extraction. The classifiers are used to differentiate emotions such as anger, happiness, sadness, surprise, fear, neutral state, etc. The classification performance is based on extracted features. Conclusions drawn from performance and limitations of speech emotion recognition system based on different methodologies are also discussed.
Identification of genomic regions determining the phenological development leading to floral transition in wheat (Triticum aestivum L.)

Autumn-seeded winter cereals acquire tolerance to freezing temperatures and become vernalized by exposure to low temperature (LT). The level of accumulated LT tolerance depends on the cold acclimation rate and factors controlling timing of floral transition at the shoot apical meristem. In this study, genomic loci controlling the floral transition time were mapped in a winter wheat (T. aestivum L.) doubled haploid (DH) mapping population segregating for LT tolerance and rate of phenological development. The final leaf number (FLN), days to FLN, and days to anthesis were determined for 142 DH lines grown with and without vernalization in controlled environments. Analysis of trait data by composite interval mapping (CIM) identified 11 genomic regions that carried quantitative trait loci (QTLs) for the developmental traits studied. CIM analysis showed that the time for floral transition in both vernalized and non-vernalized plants was controlled by common QTL regions on chromosomes 1B, 2A, 2B, 6A and 7A. A QTL identified on chromosome 4A influenced floral transition time only in vernalized plants. Alleles of the LT-tolerant parent, Norstar, delayed floral transition at all QTLs except at the 2A locus. Some of the QTL alleles delaying floral transition also increased the length of vegetative growth and delayed flowering time. The genes underlying the QTLs identified in this study encode factors involved in regional adaptation of cold hardy winter wheat.
Supply networks and complex adaptive systems : control versus emergence

In much of the current literature on supply chain management, supply networks are recognized as a system. In this paper, we take this observation to the next level by arguing the need to recognize supply networks as a complex adaptive system (CAS). We propose that many supply networks emerge rather than result from purposeful design by a singular entity. Most supply chain management literature emphasizes negative feedback for purposes of control; however, the emergent patterns in a supply network can much better be managed through positive feedback, which allows for autonomous action. Imposing too much control detracts from innovation and flexibility; conversely, allowing too much emergence can undermine managerial predictability and work routines. Therefore, when managing supply networks, managers must appropriately balance how much to control and how much to let emerge.
Neural Architecture Construction using EnvelopeNets

In recent years, advances in the design of convolutional neural networks have resulted in signicant improvements on the image classication and object detection problems. One of the advances is networks built by stacking complex cells, seen in such networks as InceptionNet and NasNet. ese cells are either constructed by hand, generated by generative networks or discovered by search. Unlike conventional networks (where layers consist of a convolution block, sampling and non linear unit), the new cells feature more complex designs consisting of several lters and other operators connected in series and parallel. Recently, several cells have been proposed or generated that are supersets of previously proposed custom or generated cells. Inuenced by this, we introduce a network construction method based on EnvelopeNets. An EnvelopeNet is a deep convolutional neural network of stacked EnvelopeCells. EnvelopeCells are supersets (or envelopes) of previously proposed handcraed and generated cells. We propose a method to construct improved network architectures by restructuring EnvelopeNets. e algorithm restructures an EnvelopeNet by rearranging blocks in the network. It identies blocks to be restructured using metrics derived from the featuremaps collected during a partial training run of the EnvelopeNet. e method requires less computation resources to generate an architecture than an optimized architecture search over the entire search space of blocks. e restructured networks have higher accuracy on the image classication problem on a representative dataset than both the generating EnvelopeNet and an equivalent arbitrary network.
Survey of Flocking Algorithms in Multi-agent Systems

Flocking behaviour in Multi-agent Systems (MAS) has attracted tremendous attention amongst researchers in the recent past due to its potential applications in various fields where distributed work environment is desired. The flocking algorithms have the potential to introduce self-organizing, self-healing and selfconfiguring capabilities in the functioning of a distributed system. The flocking algorithms exploit various artificial intelligence techniques, mathematical potential functions and geometric approaches to realize the global objectives by controlling local parameters. The main parameters of characterization of any flocking algorithm consist of mathematical models of agents, their hierarchical or flat control structures and the control approach by which these agents are controlled to exhibit flocking behaviour along with any type of formational constraints. A rigorous survey study of flocking algorithms for agents in MAS in the perspective of various instances of agents shows that there lies a huge scope for the researchers to apply, experiment and analyse various techniques locally to achieve global objectives. This paper surveys the flocking algorithms in perspective of these parameters.
Interpretable VAEs for nonlinear group factor analysis

Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data. Far less attention has been paid to making these generative models interpretable. In many scenarios, ranging from scientific applications to finance, the observed variables have a natural grouping. It is often of interest to understand systems of interaction amongst these groups, and latent factor models (LFMs) are an attractive approach. However, traditional LFMs are limited by assuming a linear correlation structure. We present an output interpretable VAE (oi-VAE) for grouped data that models complex, nonlinear latent-to-observed relationships. We combine a structured VAE comprised of group-specific generators with a sparsity-inducing prior. We demonstrate that oi-VAE yields meaningful notions of interpretability in the analysis of motion capture and MEG data. We further show that in these situations, the regularization inherent to oi-VAE can actually lead to improved generalization and learned generative processes.
Relationshopping : Investigating the market metaphor in online dating

In this manuscript we explore the ways in which the marketplace metaphor resonates with online dating participants and how this conceptual framework influences how they assess themselves, assess others, and make decisions about whom to pursue. Taking a metaphor approach enables us to highlight the ways in which participants’ language shapes their self-concept and interactions with potential partners. Qualitative analysis of in-depth interviews with 34 participants from a large online dating site revealed that the marketplace metaphor was salient for participants, who employed several strategies that reflected the assumptions underlying the marketplace perspective (including resisting the metaphor). We explore the implications of this metaphor for romantic relationship development, such as the objectification of potential partners. Journal of Social and Personal Relationships © The Author(s), 2010.   10.1177/0265407510361614 This research was funded by Affirmative Action Grant 111579 from the Office of Research and Sponsored Programs at California State University, Stanislaus. An earlier version of this paper was presented at the International Communication Association, 2005. We would like to thank Jack Bratich, Art Ramirez, Lamar Reinsch, Jeanine Turner, and three anonymous reviewers for their helpful comments. All correspondence concerning this article should be addressed to Rebecca D. Heino, Georgetown University, McDonough School of Business, Washington D.C. 20057, USA [e-mail: rdh26@georgetown.edu]. Larry Erbert was the Action Editor on this article. at MICHIGAN STATE UNIV LIBRARIES on June 9, 2010 http://spr.sagepub.com Downloaded from
Computation of All Rational Solutions of First-Order Algebraic ODEs

In this paper, we consider the class of first-order algebraic ordinary differential equations (AODEs), and study their rational solutions in three different approaches. A combinatorial approach gives a degree bound for rational solutions of a class of AODEs which do not have movable poles. Algebraic considerations yield an algorithm for computing rational solutions of quasilinear AODEs. And finally ideas from algebraic geometry combine these results to an algroithm for finding all rational solutions of a class of firstorder AODEs which covers all examples from the collection of Kamke. In particular, parametrizations of algebraic curves play an important role for a transformation of a parametrizable first-order AODE to a quasi-linear differential equation.
A Single-shot Object Detector with Feature Aggragation and Enhancement

For many real applications, it’s equally important to detect objects accurately and quickly. In this paper, we propose an accurate and efficient single shot object detector with feature aggregation and enhancement (FAENet). Our motivation is to enhance and exploit the shallow and deep feature maps of the whole network simultaneously. To achieve it we introduce a pair of novel feature aggregation modules and two feature enhancement blocks, and integrate them into the original structure of SSD. Extensive experiments on both the PASCAL VOC and MS COCO datasets demonstrate that the proposed method achieves much higher accuracy than SSD. In addition, our method performs better than the state-of-the-art one-stage detector RefineDet on small objects and can run at a faster speed.
Hybrid software and system development in practice: waterfall, scrum, and beyond

Software and system development faces numerous challenges of rapidly changing markets. To address such challenges, companies and projects design and adopt specific development approaches by combining well-structured comprehensive methods and flexible agile practices. Yet, the number of methods and practices is large, and available studies argue that the actual process composition is carried out in a fairly ad-hoc manner. The present paper reports on a survey on hybrid software development approaches. We study which approaches are used in practice, how different approaches are combined, and what contextual factors influence the use and combination of hybrid software development approaches. Our results from 69 study participants show a variety of development approaches used and combined in practice. We show that most combinations follow a pattern in which a traditional process model serves as framework in which several fine-grained (agile) practices are plugged in. We further show that hybrid software development approaches are independent from the company size and external triggers. We conclude that such approaches are the results of a natural process evolution, which is mainly driven by experience, learning, and pragmatism.
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models

Neural language models (NLMs) have recently gained a renewed interest by achieving state-of-the-art performance across many natural language processing (NLP) tasks. However, NLMs are very computationally demanding largely due to the computational cost of the softmax layer over a large vocabulary. We observe that, in decoding of many NLP tasks, only the probabilities of the top-K hypotheses need to be calculated preciously and K is often much smaller than the vocabulary size. This paper proposes a novel softmax layer approximation algorithm, called Fast Graph Decoder (FGD), which quickly identifies, for a given context, a set of K words that are most likely to occur according to a NLM. We demonstrate that FGD reduces the decoding time by an order of magnitude while attaining close to the full softmax baseline accuracy on neural machine translation and language modeling tasks. We also prove the theoretical guarantee on the softmax approximation quality.