nils-herrmann/clean_scirepeval huggingface dataset:
The sliding mode control based on extended state observer for skid steering of 4-wheel-drive electric vehicle

Skid steering of 4-wheel-drive electric vehicles has good maneuverability and mobility as a result of the application of differential torque to wheels on opposite sides. For path following, the paper utilizes the techniques of sliding mode control based on extended state observer which not only has robustness against the system dynamics not modeled and uncertain parameter but also reduces the switch gain effectively, so as to obtain a predictable behavior for the instantaneous center of rotation thus preventing excessive skidding. The efficiency of the algorithm is validated on a vehicle model with 14 degree of freedom. The simulation results show that the control law is robust against to the evaluation error of parameter and to the variation of the friction force within the wheel-ground interaction, what's more, it is easy to be carried out in controller.
Update on bacterial vaccines: Photobacterium damselae subsp. piscicida.

Photobacterium damselae subsp. piscicida is the causative agent of pasteurellosis in wild and farmed marine fish worldwide. Although serologically homogeneous, recent molecular advances have led to the discovery of distinct genetic clades, depending on geographical origin. Further details of the strategies for host colonisation have arisen including information on the role of capsule, susceptibility to oxidative stress, confirmation of intracellular survival in host epithelial cells, and induced apoptosis of host macrophages. This improved understanding has given rise to new ideas and advances in vaccine technologies, which are reviewed in this paper.
Improved direct torque control of induction motor with dither injection

In this paper, a three-level inverter-fed induction motor drive operating under Direct Torque Control (DTC) is presented. A triangular wave is used as dither signal of minute amplitude (for torque hysteresis band and flux hysteresis band respectively) in the error block. This method minimizes flux and torque ripple in a three-level inverter fed induction motor drive while the dynamic performance is not affected. The optimal value of dither frequency and magnitude is found out under free running condition. The proposed technique reduces torque ripple by 60% (peak to peak) compared to the case without dither injection, results in low acoustic noise and increases the switching frequency of the inverter. A laboratory prototype of the drive system has been developed and the simulation and experimental results are reported.
Structure-antioxidant activity relationships of flavonoids and phenolic acids.

The recent explosion of interest in the bioactivity of the flavonoids of higher plants is due, at least in part, to the potential health benefits of these polyphenolic components of major dietary constituents. This review article discusses the biological properties of the flavonoids and focuses on the relationship between their antioxidant activity, as hydrogen donating free radical scavengers, and their chemical structures. This culminates in a proposed hierarchy of antioxidant activity in the aqueous phase. The cumulative findings concerning structure-antioxidant activity relationships in the lipophilic phase derive from studies on fatty acids, liposomes, and low-density lipoproteins; the factors underlying the influence of the different classes of polyphenols in enhancing their resistance to oxidation are discussed and support the contention that the partition coefficients of the flavonoids as well as their rates of reaction with the relevant radicals define the antioxidant activities in the lipophilic phase.
Anomaly Detection for Skin Disease Images Using Variational Autoencoder

In this paper, we demonstrate the potential of applying Variational Autoencoder (VAE)  for anomaly detection in skin disease images. VAE is a class of deep generative models which is trained by maximizing the evidence lower bound of data distribution . When trained on only normal data, the resulting model is able to perform efficient inference and to determine if a test image is normal or not. We perform experiments on ISIC2018 Challenge Disease Classification dataset (Task 3)[4, 13] and compare different methods to use VAE to detect anomaly. The model is able to detect all diseases with 0.779 AUCROC. If we focus on specific diseases, the model is able to detect melanoma with 0.864 AUCROC and detect actinic keratosis with 0.872 AUCROC, even if it only sees the images of nevus. To the best of our knowledge, this is the first applied work of deep generative models for anomaly detection in dermatology.
The health of aging populations in China and India.

China and India are home to two of the world's largest populations, and both populations are aging rapidly. Our data compare health status, risk factors, and chronic diseases among people age forty-five and older in China and India. By 2030, 65.6 percent of the Chinese and 45.4 percent of the Indian health burden are projected to be borne by older adults, a population with high levels of noncommunicable diseases. Smoking (26 percent in both China and India) and inadequate physical activity (10 percent and 17.7 percent, respectively) are highly prevalent. Health policy and interventions informed by appropriate data will be needed to avert this burden.
Automatic Classification of Bird Species From Their Sounds Using Two-Dimensional Cepstral Coefficients

This paper presents a method for automatic classification of birds into different species based on the audio recordings of their sounds. Each individual syllable segmented from continuous recordings is regarded as the basic recognition unit. To represent the temporal variations as well as sharp transitions within a syllable, a feature set derived from static and dynamic two-dimensional Mel-frequency cepstral coefficients are calculated for the classification of each syllable. Since a bird might generate several types of sounds with variant characteristics, a number of representative prototype vectors are used to model different syllables of identical bird species. For each bird species, a model selection method is developed to determine the optimal mode between Gaussian mixture models (GMM) and vector quantization (VQ) when the amount of training data is different for each species. In addition, a component number selection algorithm is employed to find the most appropriate number of components of GMM or the cluster number of VQ for each species. The mean vectors of GMM or the cluster centroids of VQ will form the prototype vectors of a certain bird species. In the experiments, the best classification accuracy is 84.06% for the classification of 28 bird species.
Friends FTW! friendship and competition in halo: reach

How important are friendships in determining success by individuals and teams in complex collaborative environments? By combining a novel data set containing the dynamics of millions of ad hoc teams from the popular multiplayer online first person shooter Halo: Reach with survey data on player demographics, play style, psychometrics and friendships derived from an anonymous online survey, we investigate the impact of friendship on collaborative and competitive performance. In addition to finding significant differences in player behavior across these variables, we find that friendships exert a strong influence, leading to both improved individual and team performance - even after controlling for the overall expertise of the team - and increased pro-social behaviors. Players also structure their in-game activities around social opportunities, and as a result hidden friendship ties can be accurately inferred directly from behavioral time series. Virtual environments that enable such friendship effects will thus likely see improved collaboration and competition.
Effects of acetyl-L-carnitine in diabetic neuropathy and other geriatric disorders.

A long history of diabetes mellitus and increasing age are associated with the onset of diabetic neuropathy, a painful and highly disabling complication with a prevalence peaking at 50% among elderly diabetic patients. Acetyl-L-carnitine (ALC) is a molecule derived from the acetylation of carnitine in the mitochondria that has an essential role in energy production. It has recently been proposed as a therapy to improve the symptoms of diabetic neuropathy. ALC is widely distributed in mammalian tissues, including the brain, blood-brain barrier, brain neurons, and astrocytes. Aside from its metabolic activity, ALC has demonstrated cytoprotective, antioxidant, and antiapoptotic effects in the nervous system. It exerts an analgesic action by reducing the concentration of glutamate in the synapses. It facilitates nerve regeneration and damage repair after primary trauma: its positive effects on metabolism promote the synthesis, fluidity, and functionality of neuronal membranes, increase protein synthesis, and improve the axonal transport of neurofilament proteins and tubulin. It also amplifies nerve growth factor responsiveness, an effect that is believed to enhance overall neurite growth. ALC has been proposed for the treatment of various neurological and psychiatric diseases, such as mood disorders and depression, dementias, Alzheimer's disease, and Parkinson's disease, because synaptic energy states and mitochondrial dysfunction are core factors in their pathogenesis.
Feature Space Transfer for Data Augmentation

The problem of data augmentation in feature space is considered. A new architecture, denoted the FeATure TransfEr Network (FATTEN), is proposed for the modeling of feature trajectories induced by variations of object pose. This architecture exploits a parametrization of the pose manifold in terms of pose and appearance. This leads to a deep encoder/decoder network architecture, where the encoder factors into an appearance and a pose predictor. Unlike previous attempts at trajectory transfer, FATTEN can be efficiently trained end-to-end, with no need to train separate feature transfer functions. This is realized by supplying the decoder with information about a target pose and the use of a multi-task loss that penalizes category- and pose-mismatches. In result, FATTEN discourages discontinuous or non-smooth trajectories that fail to capture the structure of the pose manifold, and generalizes well on object recognition tasks involving large pose variation. Experimental results on the artificial ModelNet database show that it can successfully learn to map source features to target features of a desired pose, while preserving class identity. Most notably, by using feature space transfer for data augmentation (w.r.t. pose and depth) on SUN-RGBD objects, we demonstrate considerable performance improvements on one/few-shot object recognition in a transfer learning setup, compared to current state-of-the-art methods.
Siegel Modular Forms and Representations

This paper explicitly describes the procedure of associating an automorphic representation of PGSp(2n, A) with a Siegel modular form of degree n for the full modular group Γn = Sp(2n, Z), generalizing the well-known procedure for n = 1. This will show that the so-called “standard” and “spin” L-functions associated with such forms are obtained as Langlands L-functions. The theory of Euler products, developed by Langlands, applied to a Levi subgroup of the exceptional group of type F4, is then used to establish meromorphic continuation for the spin L-function when n = 3.  Let f be a Siegel modular form of degree n for the full modular group Γn = Sp(2n, Z). If f is an eigenfunction for the action of the Hecke algebra, then there are two L-functions associated with f . Let a0, a1, . . . , an be the Satake parameters of f , and define the standard L-function L1(s, f) = ∏
Learning Compact Geometric Features

We present an approach to learning features that represent the local geometry around a point in an unstructured point cloud. Such features play a central role in geometric registration, which supports diverse applications in robotics and 3D vision. Current state-of-the-art local features for unstructured point clouds have been manually crafted and none combines the desirable properties of precision, compactness, and robustness. We show that features with these properties can be learned from data, by optimizing deep networks that map high-dimensional histograms into low-dimensional Euclidean spaces. The presented approach yields a family of features, parameterized by dimension, that are both more compact and more accurate than existing descriptors.
Building fast Bayesian computing machines out of intentionally stochastic, digital parts

The brain interprets ambiguous sensory information faster and more reliably than modern computers, using neurons that are slower and less reliable than logic gates. But Bayesian inference, which underpins many computational models of perception and cognition, appears computationally challenging even given modern transistor speeds and energy budgets. The computational principles and structures needed to narrow this gap are unknown. Here we show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude. We find that by connecting stochastic digital components according to simple mathematical rules, one can build massively parallel, low precision circuits that solve Bayesian inference problems and are compatible with the Poisson firing statistics of cortical neurons. We evaluate circuits for depth and motion perception, perceptual learning and causal reasoning, each performing inference over 10,000+ latent variables in real time — a 1,000x speed advantage over commodity microprocessors. These results suggest a new role for randomness in the engineering and reverse-engineering of intelligent computation.
Policy Gradient Based Reinforcement Learning Approach for Autonomous Highway Driving

The paper presents the application of the Policy Gradient reinforcement learning method in the area of vehicle control. The purpose of the research presented is to design an end-to-end behavior control of a kinematic vehicle model placed in a simulated highway environment, by using reinforcement learning approach. The environment model for the surrounding traffic uses microscopic simulation to provide different situations for the agent. The environment sensing model is based on high-level sensor information, e.g. the odometry, lane position and surrounding vehicle states that can be reached from the current automotive sensors, such as camera and radar based systems. The objectives were reached through the definition of a rewarding system, with subrewards and penalties enforcing desired speed, lane keeping, keeping right and avoiding collision. After the description of the theoretical basis the environment model with the reward function is detailed. Finally the experiments with the learning process are presented and the results of the evaluation are given from some aspects of control quality and safety.
[What is the prognosis in unclassified colitis? Results of a cohort study of 104 patients in the Northern-Pas-de-Calais region].

Acute unclassified colitis could be the first attack of inflammatory bowel disease, particularly chronic ulcerative colitis or acute non specific colitis regarded as being of infectious origin without recurrence. The aim of this work was to determine the outcome of 104 incidental cases of acute unclassified colitis diagnosed during the year 1988 at a census point made 2.5 to 3 years later and to search for demographic and clinical discriminating data for final diagnosis. Thirteen patients (12.5%) were lost to follow up. Another final diagnosis was made in three other patients: two had salmonellosis and one diverticulosis. Of the remaining 88 patients, 46 (52.3%) relapsed and were subsequently classified as inflammatory bowel disease: 54% ulcerative colitis, 33% Crohn's disease and 13% chronic unclassified colitis. Forty-two (47.7%) did not relapse and were considered to have acute non specific colitis. The mean age at onset was significantly lower in patients with inflammatory bowel disease (32.3 years) than in patients with acute non specific colitis (42.6 years) (P < 0.001). No clinical data (diarrhea, abdominal pain, bloody stool, mucus discharge fever, weight loss) was predictive of the final diagnosis. In this series, 52.3% of patients initially classified as having an acute unclassified colitis had a final diagnosis of inflammatory bowel disease after a 2.5-3 years follow-up. These data warrant a thorough follow up of acute unclassified colitis, especially when it occurs in patients < 40 years.
Microscale Strategies for Generating Cell-Encapsulating Hydrogels.

Hydrogels in which cells are encapsulated are of great potential interest for tissue engineering applications. These gels provide a structure inside which cells can spread and proliferate. Such structures benefit from controlled microarchitectures that can affect the behavior of the enclosed cells. Microfabrication-based techniques are emerging as powerful approaches to generate such cell-encapsulating hydrogel structures. In this paper we introduce common hydrogels and their crosslinking methods and review the latest microscale approaches for generation of cell containing gel particles. We specifically focus on microfluidics-based methods and on techniques such as micromolding and electrospinning.
Introducing Time into RDF

The resource description framework (RDF) is a metadata model and language recommended by the W3C. This paper presents a framework to incorporate temporal reasoning into RDF, yielding temporal RDF graphs. We present a semantics for these kinds of graphs which includes the notion of temporal entailment and a syntax to incorporate this framework into standard RDF graphs, using the RDF vocabulary plus temporal labels. We give a characterization of temporal entailment in terms of RDF entailment and show that the former does not yield extra asymptotic complexity with respect to nontemporal RDF graphs. We also discuss temporal RDF graphs with anonymous timestamps, providing a theoretical framework for the study of temporal anonymity. Finally, we sketch a temporal query language for RDF, along with complexity results for query evaluation that show that the time dimension preserves the tractability of answers
Lexicon-based sentiment analysis of Indian Union Budget 2016–17

In this work sentiment analysis of annual budget for Financial year 2016–17 is done. Text mining is used to extract text data from the budget document and to compute the word association of significant words and their correlation in computed with the associated words. Word frequency and the corresponding word cloud is plotted. The analysis is done in R software. The corresponding sentiment score is computed and analyzed. This analysis is of significant importance keeping in mind the sentiment reflected about the budget in the official budget document.
Neural Computation and the Computational Theory of Cognition

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism-neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.
Crystal Structure of the Catalytic α Subunit of E. coli Replicative DNA Polymerase III

Bacterial replicative DNA polymerases such as Polymerase III (Pol III) share no sequence similarity with other polymerases. The crystal structure, determined at 2.3 A resolution, of a large fragment of Pol III (residues 1-917), reveals a unique chain fold with localized similarity in the catalytic domain to DNA polymerase beta and related nucleotidyltransferases. The structure of Pol III is strikingly different from those of members of the canonical DNA polymerase families, which include eukaryotic replicative polymerases, suggesting that the DNA replication machinery in bacteria arose independently. A structural element near the active site in Pol III that is not present in nucleotidyltransferases but which resembles an element at the active sites of some canonical DNA polymerases suggests that, at a more distant level, all DNA polymerases may share a common ancestor. The structure also suggests a model for interaction of Pol III with the sliding clamp and DNA.
Lichen planopilaris: Epidemiology and prevalence of subtypes - a retrospective analysis in 104 patients.

Management of patients with lichen planopilaris (LPP) and frontal fibrosing alopecia (FFA) is rendered difficult as robust epidemiologic data, insights on pathogenesis, associated diseases, possible relevance of concomitant medications or environmental factors are lacking.



Retrospective analysis of demography, skin status, concomitant medication and diagnostic procedures were performed on 104 medical records (71 classic LPP, 32 FFA, and one Graham-Little-Piccardi-Lassueur syndrome).



Women were more often affected (distribution F: M classic LPP 4.9: 1; FFA: 31: 1). Compared to LPP patients, patients with FFA were significantly older (p < 0.001), more often postmenopausal, and more frequently on hormone replacement therapy. No other specific associations were identified. An association with lichen planus, other autoimmune diseases, or hepatitis virus infection was found only in individual patients. Clinically, FFA patients were significantly more often reported to have reduced eyebrows (p < 0.005), axillary, and/or pubic hair (p = 0.050).



The findings obtained from this study, with currently largest LPP/FFA patient cohort in Germany, encouraged us to set up a national FFA patient registry. Prospective data collected from larger numbers of patients with standardized questionnaires will help to assess assumed associations and influencing factors and to develop, in the long-term, recommendations for diagnosis and treatment.