nils-herrmann/clean_scirepeval huggingface dataset:
The Burrows-Wheeler Transform : Theory and Practice

In this paper we describe the Burrows-Wheeler Transform (BWT) a completely new approach to data compression which is the basis of some of the best compressors available today. Although it is easy to intuitively understand why the BWT helps compression, the analysis of BWT-based algorithms requires a careful study of every single algorithmic component. We describe two algorithms which use the BWT and we show that their compression ratio can be bounded in terms of the k-th order empirical entropy of the input string for any k ≥ 0. Intuitively, this means that these algorithms are able to make use of all the regularity which is in the input string. We also discuss some of the algorithmic issues which arise in the computation of the BWT, and we describe two variants of the BWT which promise interesting developments.
Effect of Iyengar yoga therapy for chronic low back pain

Low back pain is a significant public health problem and one of the most commonly reported reasons for the use of Complementary Alternative Medicine. A randomized control trial was conducted in subjects with non-specific chronic low back pain comparing Iyengar yoga therapy to an educational control group. Both programs were 16 weeks long. Subjects were primarily self-referred and screened by primary care physicians for study of inclusion/exclusion criteria. The primary outcome for the study was functional disability. Secondary outcomes including present pain intensity, pain medication usage, pain-related attitudes and behaviors, and spinal range of motion were measured before and after the interventions. Subjects had low back pain for 11.2+/-1.54 years and 48% used pain medication. Overall, subjects presented with less pain and lower functional disability than subjects in other published intervention studies for chronic low back pain. Of the 60 subjects enrolled, 42 (70%) completed the study. Multivariate analyses of outcomes in the categories of medical, functional, psychological and behavioral factors indicated that significant differences between groups existed in functional and medical outcomes but not for the psychological or behavioral outcomes. Univariate analyses of medical and functional outcomes revealed significant reductions in pain intensity (64%), functional disability (77%) and pain medication usage (88%) in the yoga group at the post and 3-month follow-up assessments. These preliminary data indicate that the majority of self-referred persons with mild chronic low back pain will comply to and report improvement on medical and functional pain-related outcomes from Iyengar yoga therapy.
The Relationship between Softball Student-Athletes' Motivation, Self-Confidence, and Perception of Coach Leadership

This study advanced the understanding of relationships among coach leadership and collegiate softball players' self-confidence and motivation. The purpose of this study was to examine if there was a correlation between perceived coach leadership and softball player' s self­ confidence and motivation. Ninety-seven U.S. collegiate female softball student-athletes completed a Basic Demographic Survey, the Trait Sport-Confident Inventory (TSCI), Leadership Scale for Sport (LSS), and Sport Motivation Scale (SMS). Multiple linear regression analyses results indicated that LSS and SMS subscales explained 45 percent of the variance in TSCI scores. Pearson correlation coefficients were computed and significant moderate positive relationships were found between the LSS subscales, SMS subscales, and TSCI scores. These results indicate that there is a relationship between perceived coach leadership and a softball student-athlete ' s self-confidence and motivation. The research findings can be used to aid coaches in developing a coaching style that is well perceived by softball athletes. This will assist coaches in developing techniques (i.e. , communication skills, motivational factors, correctional feedback, mental toughness) that will motivate and give their softball athletes high levels of self­ confidence while performing.
Security Best Practices and Risk Assessment of SCADA and Industrial Control Systems

The nation's critical infrastructures, such as those found in Supervisory Control and Data Acquisition (SCADA) and industrial control systems (ICS), are increasingly at risk and vulnerable to internal and external threats. Security best practices on these systems come at a very opportune time. Further, the value of risk assessment of these systems is something that cannot just be relegated as irrelevant. In this paper, we present a review of security best practices and risk assessment of SCADA and ICS and report our research findings on an on-going risk modeling of a prototypical industrial control system using the CORAS framework tool.
Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts

A text corpus typically contains two types of context information -- global context and local context. Global context carries topical information which can be utilized by topic models to discover topic structures from the text corpus, while local context can train word embeddings to capture semantic regularities reflected in the text corpus. This encourages us to exploit the useful information in both the global and the local context information. In this paper, we propose a unified language model based on matrix factorization techniques which 1) takes the complementary global and local context information into consideration simultaneously, and 2) models topics and learns word embeddings collaboratively. We empirically show that by incorporating both global and local context, this collaborative model can not only significantly improve the performance of topic discovery over the baseline topic models, but also learn better word embeddings than the baseline word embedding models. We also provide qualitative analysis that explains how the cooperation of global and local context information can result in better topic structures and word embeddings.
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

In this paper, we propose a new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments. DEPICT generally consists of a multinomial logistic regression function stacked on top of a multi-layer convolutional autoencoder. We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of cluster assignments. An alternating strategy is then derived to optimize the objective by updating parameters and estimating cluster assignments. Furthermore, we employ the reconstruction loss functions in our autoencoder, as a data-dependent regularization term, to prevent the deep embedding function from overfitting. In order to benefit from end-to-end optimization and eliminate the necessity for layer-wise pre-training, we introduce a joint learning framework to minimize the unified clustering and reconstruction loss functions together and train all network layers simultaneously. Experimental results indicate the superiority and faster running time of DEPICT in real-world clustering tasks, where no labeled data is available for hyper-parameter tuning.
Eigenwords: spectral word embeddings

Spectral learning algorithms have recently become popular in data-rich domains, driven in part by recent advances in large scale randomized SVD, and in spectral estimation of Hidden Markov Models. Extensions of these methods lead to statistical estimation algorithms which are not only fast, scalable, and useful on real data sets, but are also provably correct. Following this line of research, we propose four fast and scalable spectral algorithms for learning word embeddings – low dimensional real vectors (called Eigenwords) that capture the “meaning” of words from their context. All the proposed algorithms harness the multi-view nature of text data i.e. the left and right context of each word, are fast to train and have strong theoretical properties. Some of the variants also have lower sample complexity and hence higher statistical power for rare words. We provide theory which establishes relationships between these algorithms and optimality criteria for the estimates they provide. We also perform thorough qualitative and quantitative evaluation of Eigenwords showing that simple linear approaches give performance comparable to or superior than the state-of-the-art non-linear deep learning based methods.
Distinct immune responses of juvenile and adult oysters (Crassostrea gigas) to viral and bacterial infections

Since 2008, massive mortality events of Pacific oysters (Crassostrea gigas) have been reported worldwide and these disease events are often associated with Ostreid herpesvirus type 1 (OsHV-1). Epidemiological field studies have also reported oyster age and other pathogens of the Vibrio genus are contributing factors to this syndrome. We undertook a controlled laboratory experiment to simultaneously investigate survival and immunological response of juvenile and adult C. gigas at different time-points post-infection with OsHV-1, Vibrio tasmaniensis LGP32 and V. aestuarianus. Our data corroborates epidemiological studies that juveniles are more susceptible to OsHV-1, whereas adults are more susceptible to Vibrio. We measured the expression of 102 immune-genes by high-throughput RT-qPCR, which revealed oysters have different transcriptional responses to OsHV-1 and Vibrio. The transcriptional response in the early stages of OsHV-1 infection involved genes related to apoptosis and the interferon-pathway. Transcriptional response to Vibrio infection involved antimicrobial peptides, heat shock proteins and galectins. Interestingly, oysters in the later stages of OsHV-1 infection had a transcriptional response that resembled an antibacterial response, which is suggestive of the oyster's microbiome causing secondary infections (dysbiosis-driven pathology). This study provides molecular evidence that oysters can mount distinct immune response to viral and bacterial pathogens and these responses differ depending on the age of the host.
Promoting empathy among medical students: A two-site randomized controlled study.

To assess the effects of Balint groups on empathy measured by the Consultation And Relational Empathy Measure (CARE) scale rated by standardized patients during objective structured clinical examination and self-rated Jefferson's School Empathy Scale - Medical Student (JSPE-MS©) among fourth-year medical students.



A two-site randomized controlled trial were planned, from October 2015 to December 2015 at Paris Diderot and Paris Descartes University, France. Eligible students were fourth-year students who gave their consent to participate. Participants were allocated in equal proportion to the intervention group or to the control group. Participants in the intervention group received a training of 7 sessions of 1.5-hour Balint groups, over 3months. The main outcomes were CARE and the JSPE-MS© scores at follow-up.



Data from 299 out of 352 randomized participants were analyzed: 155 in the intervention group and 144 in the control group, with no differences in baseline measures. There was no significant difference in CARE score at follow-up between the two groups (P=0.49). The intervention group displayed significantly higher JSPE-MS© score at follow-up than the control group [Mean (SD): 111.9 (10.6) versus 107.7 (12.7), P=0.002]. The JSPE-MS© score increased from baseline to follow-up in the intervention group, whereas it decreased in the control group [1.5 (9.1) versus -1.8 (10.8), P=0.006].



Balint groups may contribute to promote clinical empathy among medical students.
Big Data Analytics for Information Security

Big Data Analytics (BDA) is one of themainstream technologies that change our perspectives on processing of information. Together with information security, BDA could be an extremely effective tool to learn more about communication and social networks.There will be infinite possibilities to find new methods of tracking cybercrimes using big data from different sources. BDA in information security also changes our thinking about security algorithms; they must change from a small data paradigm to big ones. This special issue is to analyze how the latest trends in this area help learn more about cyberspace and new threats using big data approaches. It contains seven papers and the details were listed as follows:
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification

We present a novel multi-task attentionbased neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attentionbased neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.
Discriminative Multi-modal Feature Fusion for RGBD Indoor Scene Recognition

RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios. While many research has been conducted, most work used hand-crafted features which are difficult to capture high-level semantic structures. Recently, the feature extracted from deep convolutional neural network has produced state-of-the-art results for various computer vision tasks, which inspire researchers to explore incorporating CNN learned features for RGBD scene understanding. On the other hand, most existing work combines rgb and depth features without adequately exploiting the consistency and complementary information between them. Inspired by some recent work on RGBD object recognition using multi-modal feature fusion, we introduce a novel discriminative multi-modal fusion framework for rgbd scene recognition for the first time which simultaneously considers the inter-and intra-modality correlation for all samples and meanwhile regularizing the learned features to be discriminative and compact. The results from the multimodal layer can be back-propagated to the lower CNN layers, hence the parameters of the CNN layers and multimodal layers are updated iteratively until convergence. Experiments on the recently proposed large scale SUN RGB-D datasets show that our method achieved the state-of-the-art without any image segmentation.
Bridgeless SEPIC Rectifier With Unity Power Factor and Reduced Conduction Losses

In this paper, a new bridgeless single-phase AC-DC converter with an automatic power factor correction (PFC) is proposed. The proposed rectifier is based on the single-ended primary inductance converter (SEPIC) topology and it utilizes a bidirectional switch and two fast diodes. The absence of an input diode bridge and the presence of only one diode in the flowing-current path during each switching cycle result in less conduction loss and improved thermal management compared to existing PFC rectifiers. Other advantages include simple control circuitry, reduced switch voltage stress, and low electromagnetic-interference noise. Performance comparison between the proposed and the conventional SEPIC PFC rectifier is performed. Simulation and experimental results are presented to demonstrate the feasibility of the proposed technique.
3D Volumetric Modeling with Introspective Neural Networks

In this paper, we study the 3D volumetric modeling problem by adopting the Wasserstein introspective neural networks method (WINN) that was previously applied to 2D static images. We name our algorithm 3DWINN which enjoys the same properties as WINN in the 2D case: being simultaneously generative and discriminative. Compared to the existing 3D volumetric modeling approaches, 3DWINN demonstrates competitive results on several benchmarks in both the generation and the classification tasks. In addition to the standard inception score, the Fréchet Inception Distance (FID) metric is also adopted to measure the quality of 3D volumetric generations. In addition, we study adversarial attacks for volumetric data and demonstrate the robustness of 3DWINN against adversarial examples while achieving appealing results in both classification and generation within a single model. 3DWINN is a general framework and it can be applied to the emerging tasks for 3D object and scene modeling.
Macroeconomic Conditions and Corporate Tax Planning

This study investigates whether macroeconomic conditions are associated with corporate tax planning. We predict that changes in expectations about economic growth and market-wide discount rates are associated with the net present value of tax planning, thus affecting managers’ incentives to engage in tax planning. Consistent with this prediction, we find that tax planning is associated with proxies for both expected economic growth and the discount rate. We conduct a series of cross-sectional analysis, and find the association between GDP growth forecasts (a proxy for expectations about economic growth) and tax planning is more pronounced for firms with highly cyclical operations, firms that are financially constrained, and firms facing greater competition. Finally, we show that the decline in corporate tax rates over the past twenty years for both U.S. domestic and multinational firms is explained largely by intertemporal variation in macroeconomic conditions. We appreciate helpful comments from Andrew Kitto, Allison Koester, Terry Shevlin, Milda Tylaite, and workshop participants at the Stockholm School of Economics and the 2017 UBCOW conference. McGuire acknowledges funding from the Mays Business School and the Ernst & Young Professorship.
Advances in Parabolic Trough Solar Power

Parabolic trough solar technology is the most proven and lowest cost large-scale power technology available today, primarily because of the nine large commercial-s solar power plants that are operating in the California Mojave Desert. These pla developed by Luz International Limited and referred to as Solar Electric Genera Systems (SEGS), range in size from 14 –80 MW and represent 354 MW of installe electric generating capacity. More than 2,000,000 m 2 of parabolic trough collector technology has been operating daily for up to 18 years, and as the year 2001 ended, plants had accumulated 127 years of operational experience. The Luz collector tec ogy has demonstrated its ability to operate in a commercial power plant environmen no other solar technology in the world. Although no new plants have been built s 1990, significant advancements in collector and plant design have been made poss the efforts of the SEGS plants operators, the parabolic trough industry, and solar rese laboratories around the world. This paper reviews the current state of the art of parab trough solar power technology and describes the R&D efforts that are in progres enhance this technology. The paper also shows how the economics of future par trough solar power plants are expected to improve.
An Instrument for the Development of the Enterprise Architecture Practice

In this paper we introduce an architecture maturity model for the domain of enterprise architecture. The model differs from other existing models in that it departs from the standard 5-level approach. It distinguishes 18 factors, called key areas, which are relevant to developing an architectural practice. Each key area has its own maturity development path that is balanced against the maturity development paths of the other key areas. Two real-life case studies are presented to illustrate the use of the model. Usage of the model in these cases shows that the model delivers recognizable results, that the results can be traced back to the basic approach to architecture taken by the organizations investigated and that the key areas chosen bear relevance to the architectural practice of the organizations. 1 MATURITY IN ENTERPRISE
Remote monitoring multi-point temperature system based on SMT16030 and DataSocket technology

For remote monitoring of multi-point temperature in the low temperature field, a remote temperature monitoring system without the slave computer is realized in a LAN, which is based on LabVIEW. This system uses Client/Server (C/S) network mode. The temperatures of multiple digital integrated temperature sensors SMT16030 are acquired by NI USB-6251 multifunction data acquisition (DAQ) card with counters on the server. DataSocket technology is used to transmit temperature information to the client, and the temperatures are extracted on the client, thus realizing remote monitoring of multi-point temperature. This paper presents a remote multi-point temperature measurement circuit and the corresponding programs with LabVIEW. This system has many advantages of building simply, fast, high accuracy etc.
Cloud-Based Commissioning of Constrained Devices using Permissioned Blockchains

In this paper we describe a privacy-preserving method for commissioning an IoT device into a cloud ecosystem. The commissioning consists of the device proving its manufacturing provenance in an anonymous fashion without reliance on a trusted third party, and for the device to be anonymously registered through the use of a blockchain system. We introduce the ChainAnchor architecture that provides device commissioning in a privacy-preserving fashion. The goal of ChainAnchor is (i) to support anonymous device commissioning, (ii) to support device-owners being remunerated for selling their device sensor-data to service providers, and (iii) to incentivize device-owners and service providers to share sensor-data in a privacy-preserving manner.
A novel decision-making process for tooth retention or extraction.

Implant-supported restorations have become the most popular therapeutic option for professionals and patients for the treatment of total and partial edentulism. When implants are placed in an ideal position, with adequate prosthetic loading and proper maintenance, they can have success rates >90% over 15 years of function. Implants may be considered a better therapeutic alternative than performing more extensive conservative procedures in an attempt to save or maintain a compromised tooth. Inadequate indication for tooth extraction has resulted in the sacrifice of many sound savable teeth. This article presents a chart that can assist clinicians in making the right decision when they are deciding which route to take.



Articles published in peer-reviewed English journals were selected using several scientific databases and subsequently reviewed. Book sources were also searched. Individual tooth- and patient-related features were thoroughly analyzed, particularly when determining if a tooth should be indicated for extraction.



A color-based decision-making chart with six different levels, including several factors, was developed based upon available scientific literature. The rationale for including these factors is provided, and its interpretation is justified with literature support.



The decision-making chart provided may serve as a reference guide for dentists when making the decision to save or extract a compromised tooth.