nils-herrmann/clean_scirepeval huggingface dataset:
Origins and evolution of antibiotic resistance.

Antibiotics have always been considered one of the wonder discoveries of the 20th century. This is true, but the real wonder is the rise of antibiotic resistance in hospitals, communities, and the environment concomitant with their use. The extraordinary genetic capacities of microbes have benefitted from man's overuse of antibiotics to exploit every source of resistance genes and every means of horizontal gene transmission to develop multiple mechanisms of resistance for each and every antibiotic introduced into practice clinically, agriculturally, or otherwise. This review presents the salient aspects of antibiotic resistance development over the past half-century, with the oft-restated conclusion that it is time to act. To achieve complete restitution of therapeutic applications of antibiotics, there is a need for more information on the role of environmental microbiomes in the rise of antibiotic resistance. In particular, creative approaches to the discovery of novel antibiotics and their expedited and controlled introduction to therapy are obligatory.
Multiple-supplier inventory models in supply chain management : A review

This paper reviews inventory models with multiple supply options and discusses their contribution to supply chain management. After discussing strategic aspects of supplier competition and the role of operational flexibility in global sourcing, inventory models which use several suppliers in order to avoid or reduce the effects of shortage situations are outlined. Further, related inventory problems from the fields of reverse logistics and multi-echelon systems are presented. Finally, issues for future research and a synthesis of available supply chain management and multiple supplier inventory models are discussed. r 2002 Elsevier Science B.V. All rights reserved.
Sudden cardiac death in heart failure. The role of abnormal repolarization.

Congestive heart failure is a common, highly lethal cardiovascular disorder claiming over 200,000 lives a year in the United States alone. Some 50% of the deaths in heart failure patients are sudden, and most of these are probably the result of ventricular tachyarrhythmias. Methods designed to identify patients at risk have been remarkably unrewarding, as have attempts to intervene and prevent sudden death in these patients. The failure to impact favorably on the incidence of sudden death in heart failure patients stems largely from a lack of understanding of the underlying mechanisms of arrhythmogenesis. This article explores the role of abnormalities of ventricular repolarization in heart failure patients. We will examine evidence for the hypothesis that alteration of repolarizing K+ channel expression in failing myocardium predisposes to abnormalities in repolarization that are arrhythmogenic. The possible utility of novel electrophysiological and ECG measures of altered ventricular repolarization will be explored. Understanding the mechanism of sudden death in heart failure may lead to effective therapy and more accurate identification of patients at greatest risk.
Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning

The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits. Thus, neural network decoding (NND) is currently only feasible for very short block lengths. In this work, we show that the conventional iterative decoding algorithm for polar codes can be enhanced when sub-blocks of the decoder are replaced by neural network (NN) based components. Thus, we partition the encoding graph into smaller sub-blocks and train them individually, closely approaching maximum a posteriori (MAP) performance per sub-block. These blocks are then connected via the remaining conventional belief propagation decoding stage(s). The resulting decoding algorithm is non-iterative and inherently enables a highlevel of parallelization, while showing a competitive bit error rate (BER) performance. We examine the degradation through partitioning and compare the resulting decoder to state-of-the art polar decoders such as successive cancellation list and belief propagation decoding.
Robust Backstepping control of ball and beam system with external disturbance estimator

This paper presents a robust feedback controller for ball and beam system (BBS). The BBS is a nonlinear system in which a ball has to be balanced on a particular beam position. The proposed nonlinear controller designed for the BBS is based upon Backstepping control technique which guarantees the boundedness of tracking error. To tackle the unknown disturbances, an external disturbance estimator (EDE) has been employed. The stability analysis of the overall closed loop robust control system has been worked out in the sense of Lyapunov theory. Finally, the simulation studies have been done to demonstrate the suitability of proposed scheme.
Empirical study of topic modeling in Twitter

Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of information for a wide spectrum of users. In Twitter, popular information that is deemed important by the community propagates through the network. Studying the characteristics of content in the messages becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, sentiment analysis and others. While many researchers wish to use standard text mining tools to understand messages on Twitter, the restricted length of those messages prevents them from being employed to their full potential.
 We address the problem of using standard topic models in micro-blogging environments by studying how the models can be trained on the dataset. We propose several schemes to train a standard topic model and compare their quality and effectiveness through a set of carefully designed experiments from both qualitative and quantitative perspectives. We show that by training a topic model on aggregated messages we can obtain a higher quality of learned model which results in significantly better performance in two real-world classification problems. We also discuss how the state-of-the-art Author-Topic model fails to model hierarchical relationships between entities in Social Media.
Unsupervised Video Object Segmentation Using Motion Saliency-Guided Spatio-Temporal Propagation

Unsupervised video segmentation plays an important role in a wide variety of applications from object identification to compression. However, to date, fast motion, motion blur and occlusions pose significant challenges. To address these challenges for unsupervised video segmentation, we develop a novel saliency estimation technique as well as a novel neighborhood graph, based on optical flow and edge cues. Our approach leads to significantly better initial foreground-background estimates and their robust as well as accurate diffusion across time. We evaluate our proposed algorithm on the challenging DAVIS, SegTrack v2 and FBMS-59 datasets. Despite the usage of only a standard edge detector trained on 200 images, our method achieves state-of-the-art results outperforming deep learning based methods in the unsupervised setting. We even demonstrate competitive results comparable to deep learning based methods in the semi-supervised setting on the DAVIS dataset.
Document-Level Neural Machine Translation with Hierarchical Attention Networks

TED Talks Subtitles News Zh–En Es–En Zh–En Es–En Es–En NMT transformer 16.87 35.44 28.60 35.20 21.36 + cache 17.32 ∗∗∗ 36.46 ∗∗∗ 28.86 35.49 22.36 ∗∗∗ + HAN encoder 17.61 ∗∗∗ †† 36.91 ∗∗∗ †† 29.35 † 35.96 † 22.36 ∗∗∗ + HAN decoder 17.39 ∗∗∗ 37.01 ∗∗∗ ††† 29.21 ∗ 35.50 22.62 ∗∗∗ ††† + HAN joint 17.79 ∗∗∗ ††† 37.24 ∗∗∗ ††† 29.67 ∗∗ † 36.23 ∗∗ †† 22.76 ∗∗∗ ††† BLEU scores. Significance with respect to NMT ∗, and to cache model †. P-values: † < .05,∗∗ †† < .01,∗∗∗ ††† < .001. •Significant improvement over strong baselines on multiple data sets. •Context from source and target sides are complementary.
Joint Attention in Autonomous Driving (JAAD)

In this paper we present a novel dataset for a critical aspect of autonomous driving, the joint attention that must occur between drivers and of pedestrians, cyclists or other drivers. This dataset is produced with the intention of demonstrating the behavioral variability of traffic participants. We also show how visual complexity of the behaviors and scene understanding is affected by various factors such as different weather conditions, geographical locations, traffic and demographics of the people involved. The ground truth data conveys information regarding the location of participants (bounding boxes), the physical conditions (e.g. lighting and speed) and the behavior of the parties involved.
Deep Packet Inspection as a Service

Middleboxes play a major role in contemporary networks, as forwarding packets is often not enough to meet operator demands, and other functionalities (such as security, QoS/QoE provisioning, and load balancing) are required. Traffic is usually routed through a sequence of such middleboxes, which either reside across the network or in a single, consolidated location. Although middleboxes provide a vast range of different capabilities, there are components that are shared among many of them.
 A task common to almost all middleboxes that deal with L7 protocols is Deep Packet Inspection (DPI). Today, traffic is inspected from scratch by all the middleboxes on its route. In this paper, we propose to treat DPI as a service to the middleboxes, implying that traffic should be scanned only once, but against the data of all middleboxes that use the service. The DPI service then passes the scan results to the appropriate middleboxes. Having DPI as a service has significant advantages in performance, scalability, robustness, and as a catalyst for innovation in the middlebox domain. Moreover, technologies and solutions for current Software Defined Networks (SDN) (e.g., SIMPLE [41]) make it feasible to implement such a service and route traffic to and from its instances.
Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data

How to model distribution of sequential data, including but not limited to speech and human motions, is an important ongoing research problem. It has been demonstrated that model capacity can be significantly enhanced by introducing stochastic latent variables in the hidden states of recurrent neural networks. Simultaneously, WaveNet, equipped with dilated convolutions, achieves astonishing empirical performance in natural speech generation task. In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure. We argue that Stochastic WaveNet enjoys powerful distribution modeling capacity and the advantage of parallel training from dilated convolutions. In order to efficiently infer the posterior distribution of the latent variables, a novel inference network structure is designed based on the characteristics of WaveNet architecture. State-of-the-art performances on benchmark datasets are obtained by Stochastic WaveNet on natural speech modeling and high quality human handwriting samples can be generated as well.
Inaccuracy in Inventory Systems — Stock Loss and Stockout

Many companies have automated their inventory management processes and rely on an information system in critical decision making. However, if the information is inaccurate, the ability of the system to provide high availability of products at the minimal operating cost can be compromised. In this paper, analytical and simulation modelling demonstrate that even a small rate of stock loss undetected by the information system can lead to inventory inaccuracy that disrupts the replenishment process and creates severe out-of-stocks. In fact, revenue losses due to out-of-stocks can far outweigh the stock losses themselves. This sensitivity of performance to the inventory inaccuracy becomes even higher in systems operating in lean environments. Motivated by an automatic product identification technology under development at the Auto-ID Center, various methods of compensating for the inventory inaccuracy are presented and evaluated. Comparisons of the methods reveal that the inventory inaccuracy problem can be effectively treated even without automatic product identification technologies in some situations.
Biopesticides from Natural Products : Current Development , Legislative Framework , and Future Trends

The importance of biomass as a source of chemicals, biofuels, and energy is widely accepted. Currently, the attention is mainly focused on the valorisation of by-products from lignocellulosic materials. Chemical compounds derived from plants and microorganisms that provide good protection for crops against weeds, pests, and diseases (biopesticide active substances) have been used to formulate pesticides. Their use is increasingly encouraged by new pesticide regulations that discourage the use of harmful active substances. This article reviews the current and future situation of biopesticides, especially natural chemical products, and focuses on their potential within the European pesticide legislative framework. Moreover, this article highlights the importance of the different modes/mechanisms of action of the active substances obtained from natural sources, the role of chemistry in biopesticide development, and how the adoption of integrated pest management practices contributes to a greater trend towards biopesticides.
A Tutorial on Principal Component Analysis

Principal component analysis (PCA) is a mainstay of modern data analysis a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works; furthermore, it crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA . This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.
RECURRENT NEURAL NETWORKS

Modeling the structure of coherent texts is a task of great importance in NLP. The task of organizing a given set of sentences into a coherent order has been commonly used to build and evaluate models that understand such structure. In this work we propose an end-to-end neural approach based on the recently proposed set to sequence mapping framework to address the sentence ordering problem. Our model achieves state-of-the-art performance in the order discrimination task on two datasets widely used in the literature. We also consider a new interesting task of ordering abstracts from conference papers and research proposals and demonstrate strong performance against recent methods. Visualizing the sentence representations learned by the model shows that the model has captured high level logical structure in these paragraphs. The model also learns rich semantic sentence representations by learning to order texts, performing comparably to recent unsupervised representation learning methods in the sentence similarity and paraphrase detection tasks.
Automatic Classification of Single Facial Images

ÐWe propose a method for automatically classifying facial images based on labeled elastic graph matching, a 2D Gabor wavelet representation, and linear discriminant analysis. Results of tests with three image sets are presented for the classification of sex, arace,o and expression. A visual interpretation of the discriminant vectors is provided. Index TermsÐComputer vision, face recognition, facial expression recognition, Gabor wavelets, principal component analysis, discriminant analysis.
Multiplicative LSTM for sequence modelling

We introduce multiplicative LSTM (mLSTM), a novel recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible input, which we argue makes it more expressive for autoregressive density estimation. We demonstrate empirically that mLSTM outperforms standard LSTM and its deep variants for a range of character level modelling tasks, and that this improvement increases with the complexity of the task. This model achieves a test error of 1.19 bits/character on the last 4 million characters of the Hutter prize dataset when combined with dynamic evaluation.
Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches

The motor system comprises a network of cortical and subcortical areas interacting via excitatory and inhibitory circuits, thereby governing motor behaviour. The balance within the motor network may be critically disturbed after stroke when the lesion either directly affects any of these areas or damages-related white matter tracts. A growing body of evidence suggests that abnormal interactions among cortical regions remote from the ischaemic lesion might also contribute to the motor impairment after stroke. Here, we review recent studies employing models of functional and effective connectivity on neuroimaging data to investigate how stroke influences the interaction between motor areas and how changes in connectivity relate to impaired motor behaviour and functional recovery. Based on such data, we suggest that pathological intra- and inter-hemispheric interactions among key motor regions constitute an important pathophysiological aspect of motor impairment after subcortical stroke. We also demonstrate that therapeutic interventions, such as repetitive transcranial magnetic stimulation, which aims to interfere with abnormal cortical activity, may correct pathological connectivity not only at the stimulation site but also among distant brain regions. In summary, analyses of connectivity further our understanding of the pathophysiology underlying motor symptoms after stroke, and may thus help to design hypothesis-driven treatment strategies to promote recovery of motor function in patients.
Muscle creatine loading in men.

The effect of dietary creatine and supplementation on skeletal muscle creatine accumulation and subsequent degradation and on urinary creatinine excretion was investigated in 31 male subjects who ingested creatine in different quantities over varying time periods. Muscle total creatine concentration increased by approximately 20% after 6 days of creatine supplementation at a rate of 20 g/day. This elevated concentration was maintained when supplementation was continued at a rate of 2 g/day for a further 30 days. In the absence of 2 g/day supplementation, total creatine concentration gradually declined, such that 30 days after the cessation of supplementation the concentration was no different from the presupplementation value. During this period, urinary creatinine excretion was correspondingly increased. A similar, but more gradual, 20% increase in muscle total creatine concentration was observed over a period of 28 days when supplementation was undertaken at a rate of 3 g/day. In conclusion, a rapid way to "creatine load" human skeletal muscle is to ingest 20 g of creatine for 6 days. This elevated tissue concentration can then be maintained by ingestion of 2 g/day thereafter. The ingestion of 3 g creatine/day is in the long term likely to be as effective at raising tissue levels as this higher dose.
Large-scale malware classification using random projections and neural networks

Automatically generated malware is a significant problem for computer users. Analysts are able to manually investigate a small number of unknown files, but the best large-scale defense for detecting malware is automated malware classification. Malware classifiers often use sparse binary features, and the number of potential features can be on the order of tens or hundreds of millions. Feature selection reduces the number of features to a manageable number for training simpler algorithms such as logistic regression, but this number is still too large for more complex algorithms such as neural networks. To overcome this problem, we used random projections to further reduce the dimensionality of the original input space. Using this architecture, we train several very large-scale neural network systems with over 2.6 million labeled samples thereby achieving classification results with a two-class error rate of 0.49% for a single neural network and 0.42% for an ensemble of neural networks.
Fear of feedback.

Nobody likes performance reviews. Subordinates are terrified they'll hear nothing but criticism. Bosses think their direct reports will respond to even the mildest criticism with anger or tears. The result? Everyone keeps quiet. That's unfortunate, because most people need help figuring out how to improve their performance and advance their careers. This fear of feedback doesn't come into play just during annual reviews. At least half the executives with whom the authors have worked never ask for feedback. Many expect the worst: heated arguments, even threats of dismissal. So rather than seek feedback, people try to guess what their bosses are thinking. Fears and assumptions about feedback often manifest themselves in psychologically maladaptive behaviors such as procrastination, denial, brooding, jealousy, and self-sabotage. But there's hope, say the authors. Those who learn adaptive techniques can free themselves from destructive responses. They'll be able to deal with feedback better if they acknowledge negative emotions, reframe fear and criticism constructively, develop realistic goals, create support systems, and reward themselves for achievements along the way. Once you've begun to alter your maladaptive behaviors, you can begin seeking regular feedback from your boss. The authors take you through four steps for doing just that: self-assessment, external assessment, absorbing the feedback, and taking action toward change. Organizations profit when employees ask for feedback and deal well with criticism. Once people begin to know how they are doing relative to management's priorities, their work becomes better aligned with organizational goals. What's more, they begin to transform a feedback-averse environment into a more honest and open one, in turn improving performance throughout the organization.
Conditional variable importance for random forests

Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables. We identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure. The resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach.