nils-herrmann/clean_scirepeval huggingface dataset:
Genetic analysis of cerebral malaria in the mouse model infected with Plasmodium berghei

Malaria is a common and sometimes fatal disease caused by infection with Plasmodium parasites. Cerebral malaria (CM) is a most severe complication of infection with Plasmodium falciparum parasites which features a complex immunopathology that includes a prominent neuroinflammation. The experimental mouse model of cerebral malaria (ECM) induced by infection with Plasmodium berghei ANKA has been used abundantly to study the role of single genes, proteins and pathways in the pathogenesis of CM, including a possible contribution to neuroinflammation. In this review, we discuss the Plasmodium berghei ANKA infection model to study human CM, and we provide a summary of all host genetic effects (mapped loci, single genes) whose role in CM pathogenesis has been assessed in this model. Taken together, the reviewed studies document the many aspects of the immune system that are required for pathological inflammation in ECM, but also identify novel avenues for potential therapeutic intervention in CM and in diseases which feature neuroinflammation.
Depression, anxiety and stress in dental students

To measure the occurrence and levels of depression, anxiety and stress in undergraduate dental students using the Depression, Anxiety and Stress Scale (DASS-21).



This cross-sectional study was conducted in November and December of 2014. A total of 289 dental students were invited to participate, and 277 responded, resulting in a response rate of 96%. The final sample included 247 participants. Eligible participants were surveyed via a self-reported questionnaire that included the validated DASS-21 scale as the assessment tool and questions about demographic characteristics and methods for managing stress.



Abnormal levels of depression, anxiety and stress were identified in 55.9%, 66.8% and 54.7% of the study participants, respectively. A multiple linear regression analysis revealed multiple predictors: gender (for anxiety b=-3.589, p=.016 and stress b=-4.099, p=.008), satisfaction with faculty relationships (for depression b=-2.318, p=.007; anxiety b=-2.213, p=.004; and stress b=-2.854, p<.001), satisfaction with peer relationships (for depression b=-3.527, p<.001; anxiety b=-2.213, p=.004; and stress b=-2.854, p<.001), and dentistry as the first choice for field of study (for stress b=-2.648, p=.045). The standardized coefficients demonstrated the relationship and strength of the predictors for each subscale. To cope with stress, students engaged in various activities such as reading, watching television and seeking emotional support from others.



The high occurrence of depression, anxiety and stress among dental students highlights the importance of providing support programs and implementing preventive measures to help students, particularly those who are most susceptible to higher levels of these psychological conditions.
Financial Knowledge and Credit Card Behavior of College Students

This study examined the relationship between financial knowledge and credit card behavior of college students. The widespread availability of credit cards has raised concerns over how college students might use those cards given the negative consequences (both immediate and long-term) associated with credit abuse and mismanagement. Using a sample of 1,354 students from a major southeastern university, results suggest that financial knowledge is a significant factor in the credit card decisions of college students. Students with higher scores on a measure of personal financial knowledge are more likely to engage in more responsible credit card use. Specific behaviors chosen have been associated with greater costs of borrowing and adverse economic consequences in the past.
Body-Based Gender Recognition Using Images from Visible and Thermal Cameras

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems.
Recognizing realistic actions from videos “in the wild”

In this paper, we present a systematic framework for recognizing realistic actions from videos “in the wild”. Such unconstrained videos are abundant in personal collections as well as on the Web. Recognizing action from such videos has not been addressed extensively, primarily due to the tremendous variations that result from camera motion, background clutter, changes in object appearance, and scale, etc. The main challenge is how to extract reliable and informative features from the unconstrained videos. We extract both motion and static features from the videos. Since the raw features of both types are dense yet noisy, we propose strategies to prune these features. We use motion statistics to acquire stable motion features and clean static features. Furthermore, PageRank is used to mine the most informative static features. In order to further construct compact yet discriminative visual vocabularies, a divisive information-theoretic algorithm is employed to group semantically related features. Finally, AdaBoost is chosen to integrate all the heterogeneous yet complementary features for recognition. We have tested the framework on the KTH dataset and our own dataset consisting of 11 categories of actions collected from YouTube and personal videos, and have obtained impressive results for action recognition and action localization.
A pre-crash discrimination system for an airbag deployment algorithm

The airbag system has been a standard safety equipment for vehicles and it is efficient for enhancing the safety of a driver and passengers from crash. However, inadvertent injuries have been caused by airbag deployment in a rough and uncertain temporal interval. In this paper, a pre-crash discrimination system is proposed to prevent airbag deployment from malfunction. The system consists of a radar sensor of ACC system and vehicle state sensors of VDC system. The pre-crash information includes crash probability, time-to-crash and crash type. Using the information, the host vehicle recognizes crash situation and airbags are deployed accurately at the predefined moment for each crash situation.
A Word-Embedding-based Sense Index for Regular Polysemy Representation

We present a method for the detection and representation of polysemous nouns, a phenomenon that has received little attention in NLP. The method is based on the exploitation of the semantic information preserved in Word Embeddings. We first prove that polysemous nouns instantiating a particular sense alternation form a separate class when clustering nouns in a lexicon. Such a class, however, does not include those polysemes in which a sense is strongly predominant. We address this problem and present a sense index that, for a given pair of lexical classes, defines the degree of membership of a noun to each class: polysemy is hence implicitly represented as an intermediate value on the continuum between two classes. We finally show that by exploiting the information provided by the sense index it is possible to accurately detect polysemous nouns in the dataset.
Decision Frameworks for Using Uncertain Predictions for Cut-In Detections in (Semi-) Automated Driving

This work examines how a classifier's output of a cut-in prediction can be mapped to a (semi-) automated car's reaction to it. Several approaches of decision making are compared using real world data of a lane change predictor for an automated longitudinal guidance system, similar to an adaptive cruise control system, as an example. We show how the decision algorithms affect the time when a new lead vehicle is selected and how much more comfortable we can decelerate given different selection strategies. We propose a novel decision algorithm and conducted a case study with a prototype research car to evaluate the subjective quality of the different approaches.
Optimal bounding cones of vectors in three dimensions

The problem of computing the minimum-angle bounding cone of a set of three-dimensional vectors has numero cations in computer graphics and geometric modeling. One such application is bounding the tangents of space cur vectors normal to a surface in the computation of the intersection of two surfaces. No optimal-time exact solution to this problem has been yet given. This paper presents a roadmap for a few strate provide optimal or near-optimal (time-wise) solutions to this problem, which are also simple to implement. Specifica worst-case running time is required, we provide an O ( logn)-time Voronoi-diagram-based algorithm, where n is the number of vectors whose optimum bounding cone is sought. Otherwise, i f one is willing to accept an, in average, efficient algorithm, we show that the main ingredient of the algorithm of Shirman and Abi- (1993) 261–272 implemented to run in optimal (n) expected time. Furthermore, if the vectors (as points on the sphere of directions) are to occupy no more than a hemisphere, we show how to simplify this ingredient (by reducing the dimension of the p without affecting the asymptotic expected running time. Both versions of this algorithm are based on computing (as an problem) the minimum spanning circle (respectively, ball) of a two-dimensional (respectively, three-dimensional) set o  2004 Elsevier B.V. All rights reserved.
Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology.

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.
Deep Attention Recurrent Q-Network

A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels. Its creators at the Google DeepMind’s team called the approach: Deep Q-Network (DQN). We present an extension of DQN by “soft” and “hard” attention mechanisms. Tests of the proposed Deep Attention Recurrent Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of performance superior to that of DQN. Moreover, built-in attention mechanisms allow a direct online monitoring of the training process by highlighting the regions of the game screen the agent is focusing on when making decisions.
Frontalis Suspension Surgery in Upper Eyelid Blepharoptosis

Frontalis suspension is a commonly used surgery that is indicated in patients with blepharoptosis and poor levator muscle function. The surgery is based on connecting the tarsal plate to the eyebrow with various sling materials. Although fascia lata is most commonly used due to its long-lasting effect and low rate of complications, it has several limitations such as difficulty of harvesting, insufficient amounts in small children, and postoperative donor-site complications. Other sling materials have overcome these limitations, but on the other hand, have been reported to be associated with other complications. In this review we focus on the different techniques and materials which are used in frontalis suspension surgeries, as well as the advantage and disadvantage of these techniques.
Broadband Millimeter-Wave Single Balanced Mixer and Its Applications to Substrate Integrated Wireless Systems

This paper presents an innovative broadband millimeter-wave single balanced diode mixer that makes use of a substrate integrated waveguide (SIW)-based 180 hybrid. It has low conversion loss of less than 10 dB, excellent linearity, and high port-to-port isolations over a wide frequency range of 20 to 26 GHz. The proposed mixer has advantages over previously reported millimeter-wave mixer structures judging from a series of aspects such as cost, ease of fabrication, planar construction, and broadband performance. Furthermore, a receiver front-end that integrates a high-performance SIW slot-array antenna and our proposed mixer is introduced. Based on our proposed receiver front-end structure, a K-band wireless communication system with M-ary quadrature amplitude modulation is developed and demonstrated for line-of-sight channels. Excellent overall error vector magnitude performance has been obtained.
Adaptive Online Learning

We propose a general framework for studying adaptive regret bounds in the online learning framework, including model selection bounds and data-dependent bounds. Given a dataor model-dependent bound we ask, “Does there exist some algorithm achieving this bound?” We show that modifications to recently introduced sequential complexity measures can be used to answer this question by providing sufficient conditions under which adaptive rates can be achieved. In particular each adaptive rate induces a set of so-called offset complexity measures, and obtaining small upper bounds on these quantities is sufficient to demonstrate achievability. A cornerstone of our analysis technique is the use of one-sided tail inequalities to bound suprema of offset random processes. Our framework recovers and improves a wide variety of adaptive bounds including quantile bounds, second-order data-dependent bounds, and small loss bounds. In addition we derive a new type of adaptive bound for online linear optimization based on the spectral norm, as well as a new online PAC-Bayes theorem that holds for countably infinite sets.
Miniaturized Microstrip-Fed Tapered-Slot Antenna With Ultrawideband Performance

A method to design a microstrip-fed antipodal tapered-slot antenna, which has ultrawideband (UWB) performance and miniaturized dimensions, is presented. The proposed method modifies the antenna's structure to establish a direct connection between the microstrip feeder and the radiator. That modification, which removes the need to use any transitions and/or baluns in the feeding structure, is the first step in the proposed miniaturization. In the second step of miniaturization, the radiator and ground plane are corrugated to enable further reduction in the antenna's size without jeopardizing its performance. The simulated and measured results confirm the benefits of the adopted method in reducing the surface area of the antenna, while maintaining the ultrawideband performance.
Stem vs non-stem cell origin of colorectal cancer

Colorectal cancer (CRC) is one of the most common cancers in the western world and is characterised by deregulation of the Wnt signalling pathway. Mutation of the adenomatous polyposis coli (APC) tumour suppressor gene, which encodes a protein that negatively regulates this pathway, occurs in almost 80% of CRC cases. The progression of this cancer from an early adenoma to carcinoma is accompanied by a well-characterised set of mutations including KRAS, SMAD4 and TP53. Using elegant genetic models the current paradigm is that the intestinal stem cell is the origin of CRC. However, human histology and recent studies, showing marked plasticity within the intestinal epithelium, may point to other cells of origin. Here we will review these latest studies and place these in context to provide an up-to-date view of the cell of origin of CRC.
How Does Batch Normalization Help Optimization?

2018 How Does Batch Normalization Help Optimization? S. Santurkar*, D. Tsipras*, A. Ilyas*, & A. Mądry NIPS 2018  2018 Adversarially Robust Generalization Requires More Data L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, & A. Mądry NIPS 2018 (Spotlight presentation) 2018 A Classification–Based Study of Covariate Shift in GAN Distributions S. Santurkar, L. Schmidt, & A. Mądry ICML 2018 2018 Generative Compression S. Santurkar, D. Budden, & N. Shavit PCS 2018 2017 Deep Tensor Convolution on Multicores D. Budden, A. Matveev, S. Santurkar, S. R. Chaudhuri, & N. Shavit ICML 2017
Merged Computation for Whirlpool Hashing

This paper presents an improved hardware structure for the computation of the Whirlpool hash function. By merging the round key computation with the data compression and by using embedded memories to perform part of the Galois Field (28) multiplication, a core can be implemented in just 43% of the area of the best current related art while achieving a 12% higher throughput. The proposed core improves the Throughput per Slice compared to the state of the art by 160%, achieving a throughput of 5.47 Gbit/s with 2110 slices and 32 BRAMs on a VIRTEX II Pro FPGA. Results for a real application are also presented by considering a polymorphic computational approach.
Managing medical and psychiatric comorbidity in individuals with major depressive disorder and bipolar disorder.

Most individuals with mood disorders experience psychiatric and/or medical comorbidity. Available treatment guidelines for major depressive disorder (MDD) and bipolar disorder (BD) have focused on treating mood disorders in the absence of comorbidity. Treating comorbid conditions in patients with mood disorders requires sufficient decision support to inform appropriate treatment.



The Canadian Network for Mood and Anxiety Treatments (CANMAT) task force sought to prepare evidence- and consensus-based recommendations on treating comorbid conditions in patients with MDD and BD by conducting a systematic and qualitative review of extant data. The relative paucity of studies in this area often required a consensus-based approach to selecting and sequencing treatments.



Several principles emerge when managing comorbidity. They include, but are not limited to: establishing the diagnosis, risk assessment, establishing the appropriate setting for treatment, chronic disease management, concurrent or sequential treatment, and measurement-based care.



Efficacy, effectiveness, and comparative effectiveness research should emphasize treatment and management of conditions comorbid with mood disorders. Clinicians are encouraged to screen and systematically monitor for comorbid conditions in all individuals with mood disorders. The common comorbidity in mood disorders raises fundamental questions about overlapping and discrete pathoetiology.
A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

After about a decade of intense research, spurred by both economic and operational considerations, and by environmental concerns, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, with millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribution to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.
Flexible, High Performance Convolutional Neural Networks for Image Classification

We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.
Analysis and Performance of Axial Flux Permanent-Magnet Machine With Air-Cored Nonoverlapping Concentrated Stator Windings

In this paper, the performance of air-cored (ironless) stator axial flux permanent magnet machines with different types of concentrated-coil nonoverlapping windings is evaluated. The evaluation is based on theoretical analysis and is confirmed by finite-element analysis and measurements. It is shown that concentrated-coil winding machines can have a similar performance as that of normal overlapping winding machines using less copper.
Solid State Transformer based on cascade multilevel converter for distribution network

Solid State Transformer (SST), a power electronics based transformer, tends to replace conventional bulk size transformer for distribution of electric power. In this paper a SST has been designed in which cascading has been done for interfacing high voltage power using low power switching devices. Pulse Width Modulation (PWM) has been used for switching technique. The configuration has been simulated in MATLAB Simulink software and its performance at different stage has been shown. Results shows that voltage and current stress is equal to each bridge.
Author Profiling with Word+Character Neural Attention Network

This paper describes neural network models that we prepared for the author profiling task of PAN@CLEF 2017. In previous PAN series, statistical models using a machine learning method with a variety of features have shown superior performances in author profiling tasks. We decided to tackle the author profiling task using neural networks. Neural networks have recently shown promising results in NLP tasks. Our models integrate word information and character information with multiple neural network layers. The proposed models have marked joint accuracies of 64–86% in the gender identification and the language variety identification of four languages.
The Digital Divide, E-Commerce, and ODR: Constructing the Egyptian Information Society

The revolutionary advancement in Information and Communication Technologies (“ICT”) has intrinsically altered the scale on which human affairs take place; it has fostered an interdependence of local, national, and international communities that is far greater than any previously experienced. The most significant feature of this technological revolution is the invention of the Internet: this network of interactive and global communications complex that has resulted in a compression of space and time, transmogrified the classic models of commercial interaction, business, and economic paradigms, and reshaped our lives and communities accordingly.