nils-herrmann/clean_scirepeval huggingface dataset:
3300 V HiPak 2 modules with Enhanced Trench ( TSPT + ) IGBTs and Field Charge Extraction Diodes rated up to 1800 A

In this paper, we introduce the new generation 3300V HiPak2 IGBT module (130x190)mm employing the recently developed TSPT+ IGBT with Enhanced Trench MOS technology and Field Charge Extraction (FCE) diode. The new chip-set enables IGBT modules with improved electrical performance in terms of low losses, good controllability, high robustness and soft diode recovery. Due to the lower losses and the excellent SOA, the current rating of the 3300V HiPak2 module can be increased from 1500A for the current SPT+ generation to 1800A for the new TSPT+ version.
Self-Organization of Very Large Document Collections: State of the Art

The Self-Organizing Map (SOM) forms a nonlinear projection from a high-dimensional data manifold onto a low-dimensional grid. A representative model of some subset of data is associated with each grid point. The SOM algorithm computes an optimal collection of models that approximates the data in the sense of some error criterion and also takes into account the similarity relations of the models. The models then become ordered on the grid according to their similarity. When the SOM is used for the exploration of statistical data, the data vectors can be approximated by models of the same dimensionality. When mapping documents, one can represent them statistically by their word frequency histograms or some reduced representations of the histograms that can be regarded as data vectors. We have made SOMs of collections of over one million documents. Each document is mapped onto some grid point, with a link from this point to the document database. The documents are ordered on the grid according to their contents and neighboring documents can be browsed readily. Keywords or key texts can be used to search for the most relevant documents rst. New eeective coding and computing schemes of the mapping are described.
Recognition of Handwritten Mathematical Symbols with PHOG Features

Converting handwritten formulas to LaTex is a challenging machine learning problem. An essential step in the recognition of mathematical formulas is the symbol recognition. In this paper we show that pyramids of oriented gradients (PHOG) are effective features for recognizing mathematical symbols. Our best results are obtained using PHOG features along with a one-againstone SVM classifier. We train our classifier using images extracted from XY coordinates of online data from the CHROHME dataset, which contains 22000 character samples. We limit our analysis to 59 characters. The classifier achieves a 96% generalization accuracy on these characters and makes reasonable mistakes. We also demonstrate that our classifier is able to generalize gracefully to phone images of mathematical symbols written by a new user. On a small experiment performed on images of 75 handwritten symbols, the symbol recognition rates is 92 %. The code is available at: https://github.com/nicodjimenez/
BNN+: Improved Binary Network Training

Deep neural networks (DNN) are widely used in many applications. However, their deployment on edge devices has been difficult because they are resource hungry. Binary neural networks (BNN) help to alleviate the prohibitive resource requirements of DNN, where both activations and weights are limited to 1-bit. We propose an improved binary training method (BNN+), by introducing a regularization function that encourages training weights around binary values. In addition to this, to enhance model performance we add trainable scaling factors to our regularization functions. Furthermore, we use an improved approximation of the derivative of the sign activation function in the backward computation. These additions are based on linear operations that are easily implementable into the binary training framework. We show experimental results on CIFAR-10 obtaining an accuracy of 86.5%, on AlexNet and 91.3% with VGG network. On ImageNet, our method also outperforms the traditional BNN method and XNOR-net, using AlexNet by a margin of 4% and 2% top-1 accuracy respectively.
The EMF Parsley DSL for developing EMF applications

The Eclipse Modeling Framework (EMF) is the official Eclipse modeling framework. It provides code generation facilities for building tools and applications based on structured data models. The Eclipse project EMF Parsley enhances the EMF reflective mechanisms in order to make the development of EMF applications easier by hiding most EMF internal details and by using dependency injection for customizing all the aspects of such applications. In this paper we show the main features of the EMF Parsley DSL that aims at making the development of EMF applications even easier and faster. The DSL is built with Xtext and Xbase, thus it features full Eclipse IDE support and full interoperability with Java.
Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption

Unsupervised domain adaption aims to learn a powerful classifier for the target domain given a labeled source data set and an unlabeled target data set. To alleviate the effect of ‘domain shift’, the major challenge in domain adaptation, studies have attempted to align the distributions of the two domains. Recent research has suggested that generative adversarial network (GAN) has the capability of implicitly capturing data distribution. In this paper, we thus propose a simple but effective model for unsupervised domain adaption leveraging adversarial learning. The same encoder is shared between the source and target domains which is expected to extract domain-invariant representations with the help of an adversarial discriminator. With the labeled source data, we introduce the center loss to increase the discriminative power of feature learned. We further align the conditional distribution of the two domains to enforce the discrimination of the features in the target domain. Unlike previous studies where the source features are extracted with a fixed pre-trained encoder, our method jointly learns feature representations of two domains. Moreover, by sharing the encoder, the model does not need to know the source of images during testing and hence is more widely applicable. We evaluate the proposed method on several unsupervised domain adaption benchmarks and achieve superior or comparable performance to state-of-the-art results.
Short-chain fatty acid fermentation products of the gut microbiome: implications in autism spectrum disorders

Recent evidence suggests potential, but unproven, links between dietary, metabolic, infective, and gastrointestinal factors and the behavioral exacerbations and remissions of autism spectrum disorders (ASDs). Propionic acid (PPA) and its related short-chain fatty acids (SCFAs) are fermentation products of ASD-associated bacteria (Clostridia, Bacteriodetes, Desulfovibrio). SCFAs represent a group of compounds derived from the host microbiome that are plausibly linked to ASDs and can induce widespread effects on gut, brain, and behavior. Intraventricular administration of PPA and SCFAs in rats induces abnormal motor movements, repetitive interests, electrographic changes, cognitive deficits, perseveration, and impaired social interactions. The brain tissue of PPA-treated rats shows a number of ASD-linked neurochemical changes, including innate neuroinflammation, increased oxidative stress, glutathione depletion, and altered phospholipid/acylcarnitine profiles. These directly or indirectly contribute to acquired mitochondrial dysfunction via impairment in carnitine-dependent pathways, consistent with findings in patients with ASDs. Of note, common antibiotics may impair carnitine-dependent processes by altering gut flora favoring PPA-producing bacteria and by directly inhibiting carnitine transport across the gut. Human populations that are partial metabolizers of PPA are more common than previously thought. PPA has further bioactive effects on neurotransmitter systems, intracellular acidification/calcium release, fatty acid metabolism, gap junction gating, immune function, and alteration of gene expression that warrant further exploration. These findings are consistent with the symptoms and proposed underlying mechanisms of ASDs and support the use of PPA infusions in rats as a valid animal model of the condition. Collectively, this offers further support that gut-derived factors, such as dietary or enteric bacterially produced SCFAs, may be plausible environmental agents that can trigger ASDs or ASD-related behaviors and deserve further exploration in basic science, agriculture, and clinical medicine.
Smart real-time healthcare monitoring and tracking system using GSM/GPS technologies

Health monitoring systems have rapidly evolved recently, and smart systems have been proposed to monitor patient current health conditions, in our proposed and implemented system, we focus on monitoring the patient's blood pressure, and his body temperature. Based on last decade statistics of medical records, death rates due to hypertensive heart disease, shows that the blood pressure is a crucial risk factor for atherosclerosis and ischemic heart diseases; thus, preventive measures should be taken against high blood pressure which provide the ability to track, trace and save patient's life at appropriate time is an essential need for mankind. Nowadays, Globalization demands Smart cities, which involves many attributes and services, such as government services, Intelligent Transportation Systems (ITS), energy, health care, water and waste. This paper proposes a system architecture for smart healthcare based on GSM and GPS technologies. The objective of this work is providing an effective application for Real Time Health Monitoring and Tracking. The system will track, trace, monitor patients and facilitate taking care of their health; so efficient medical services could be provided at appropriate time. By Using specific sensors, the data will be captured and compared with a configurable threshold via microcontroller which is defined by a specialized doctor who follows the patient; in any case of emergency a short message service (SMS) will be sent to the Doctor's mobile number along with the measured values through GSM module. furthermore, the GPS provides the position information of the monitored person who is under surveillance all the time. Moreover, the paper demonstrates the feasibility of realizing a complete end-to-end smart health system responding to the real health system design requirements by taking in consideration wider vital human health parameters such as respiration rate, nerves signs ... etc. The system will be able to bridge the gap between patients - in dramatic health change occasions- and health entities who response and take actions in real time fashion.
THE USE OF CRYOTHERAPY IN THE TREATMENT OF TEMPOROMANDIBULAR DISORDERS O uso da crioterapia no tratamento das disfunções temporomandibulares

when the patient has painful symptoms. The local effects of cryotherapy include: vasoconstriction; decreasing of inflammation and pain; myorelaxation, reduction of muscle spasm and stiffness 3. Analgesia occurs due to the decreasing of activity of the muscle spindles, the neuromuscular junction, the conduction velocity of the peripheral nerves and hence of the transmission of nociceptive information. The reduction of blood flow also helps to reduce pain by preventing excessive release of the chemical mediators that activate nociceptors 4. Although very effective in relieving pain, it is rarely used, since most speech therapy professionals are not familiar with its usage. However, among the cutaneous stimuli, it is considered one of the most effective pain relief. Cold provides analgesia, often more effective, early and lasting than heat 3.
Circadian Clock Model Supports Molecular Link Between PER3 and Human Anxiety

Generalized anxiety and major depression have become increasingly common in the United States, affecting 18.6 percent of the adult population. Mood disorders can be debilitating, and are often correlated with poor general health, life dissatisfaction, and the need for disability benefits due to inability to work. Recent evidence suggests that some mood disorders have a circadian component, and disruptions in circadian rhythms may even trigger the development of these disorders. However, the molecular mechanisms of this interaction are not well understood. Polymorphisms in a circadian clock-related gene, PER3, are associated with behavioral phenotypes (extreme diurnal preference in arousal and activity) and sleep/mood disorders, including seasonal affective disorder (SAD). Here we show that two PER3 mutations, a variable number tandem repeat (VNTR) allele and a single-nucleotide polymorphism (SNP), are associated with diurnal preference and higher Trait-Anxiety scores, supporting a role for PER3 in mood modulation. In addition, we explore a potential mechanism for how PER3 influences mood by utilizing a comprehensive circadian clock model that accurately predicts the changes in circadian period evident in knock-out phenotypes and individuals with PER3-related clock disorders.
Analysis of Emergency Medical Transport Datasets using Machine Learning JOSEFINE LETZNER

The selection of hospital once an ambulance has picked up its patient is today decided by the ambulance staff. This report describes a supervised machine learning approach for predicting hospital selection. This is a multi-class classification problem. The performance of random forest, logistic regression and neural network were compared to each other and to a baseline, namely the one rule-algorithm. The algorithms were applied to real world data from SOS-alarm, the company that operate Sweden’s emergency call services. Performance was measured with accuracy and f1-score. Random Forest got the best result followed by neural network. Logistic regression exhibited slightly inferior results but still performed far better than the baseline. The results point toward machine learning being a suitable method for learning the problem of hospital selection. Analys av ambulanstransport medelst maskininlärning
Reciprocal Concessions Procedure for Inducing Compliance : The Door-inthe-Face Technique

Three experiments were conducted to test the effectiveness of a rejection-thenmoderation procedure for inducing compliance with a request for a favor. All three experiments included a condition in which a requester first asked for an extreme favor (which was refused to him) and then for a smaller favor. In each instance, this procedure produced more compliance with the smaller favor than a procedure in which the requester asked solely for the smaller favor. Additional control conditions in each experiment supported the hypothesis that the effect is mediated by a rule for reciprocation of concessions. Several advantages to the use of the rejection-then-moderation procedure for producing compliance are discussed.
A Multilingual, Multi-style and Multi-granularity Dataset for Cross-language Textual Similarity Detection

In this paper we describe our effort to create a dataset for the evaluation of cross-language textual similarity detection. We present preexisting corpora and their limits and we explain the various gathered resources to overcome these limits and build our enriched dataset. The proposed dataset is multilingual, includes cross-language alignment for different granularities (from chunk to document), is based on both parallel and comparable corpora and contains human and machine translated texts. Moreover, it includes texts written by multiple types of authors (from average to professionals). With the obtained dataset, we conduct a systematic and rigorous evaluation of several state-of-the-art cross-language textual similarity detection methods. The evaluation results are reviewed and discussed. Finally, dataset and scripts are made publicly available on GitHub: http://github.com/FerreroJeremy/Cross-Language-Dataset.
Effects of intermittent feeding upon body weight and lifespan in inbred mice: interaction of genotype and age

Beginning at either 1.5, 6 or 10 months of age, male mice from the A/J and C57BL/6J strains and their F1 hybrid, B6AF1/J were fed a diet (4.2 kcal/g) either ad libitum every day or in a restricted fashion by ad libitum feeding every other day. Relative to estimates for ad libitum controls, the body weights of the intermittently-fed restricted C57BL/6J and hybrid mice were reduced and mean and maximum life span were incremented when the every-other-day regimen was initiated at 1.5 or 6 months of age. When every-other-day feeding was introduced at 10 months of age, again both these genotypes lost body weight relative to controls; however, mean life span was not significantly affected although maximum life span was increased. Among A/J mice, intermittent feeding did not reduce body weight relative to ad libitum controls when introduced at 1.5 or 10 months of age; however, this treatment did increase mean and maximum life span when begun at 1.5 months, while it decreased mean and maximum life span when begun at 10 months. When restricted feeding was introduced to this genotype at 6 months of age, body weight reduction compared to control values was apparent at some ages, but the treatment had no significant effects on mean or maximum life span. These results illustrate that the effects of particular regimens of dietary restriction on body weight and life span are greatly dependent upon the genotype and age of initiation. Moreover, when examining the relationship of body weight to life span both between and within the various groups, it was clear that the complexity of this relationship made it difficult to predict that lower body weight would induce life span increment.
Frankenstack: Toward real-time Red Team feedback

Cyber Defense Exercises have received much attention in recent years, and are increasingly becoming the cornerstone for ensuring readiness in this new domain. Crossed Swords is an exercise directed at training Red Team members for responsive cyber defense. However, prior iterations have revealed the need for automated and transparent real-time feedback systems to help participants improve their techniques and understand technical challenges. Feedback was too slow and players did not understand the visibility of their actions. We developed a novel and modular open-source framework to address this problem, dubbed Frankenstack. We used this framework during Crossed Swords 2017 execution and evaluated its effectiveness by interviewing participants and conducting an online survey. Due to the novelty of Red Team-centric exercises, very little academic research exists on providing real-time feedback during such exercises. Thus, this paper serves as a first foray into a novel research field.
Electrical Interactions via the Extracellular Potential Near Cell Bodies

Ephaptic interactions between a neuron and axons or dendrites passing by its cell body can be, in principle, more significant than ephaptic interactions among axons in a fiber tract. Extracellular action potentials outside axons are small in amplitude and spatially spread out, while they are larger in amplitude and much more spatially confined near cell bodies. We estimated the extracellular potentials associated with an action potential in a cortical pyramidal cell using standard one-dimensional cable theory and volume conductor theory. Their spatial and temporal pattern reveal much about the location and timing of currents in the cell, especially in combination with a known morphology, and simple experiments could resolve questions about spike initiation. From the extracellular potential we compute the ephaptically induced polarization in a nearby passive cable. The magnitude of this induced voltage can be several mV, does not spread electrotonically, and depends only weakly on the passive properties of the cable. We discuss their possible functional relevance.
Biologically Inspired Joint Stiffness Control

Biological systems are able to perform movements in unpredictable environments more elegantly than traditionally engineered robotic systems. A current limitation of robotic systems is their inability to simultaneously and independently control both joint angle and joint stiffness without electromechanical feedback loops, which can reduce system stability. In this paper, we describe the development and physical implementation of a servo-actuated robotic joint that uses antagonistic, series-elastic actuation with novel nonlinear spring mechanisms. These mechanisms form a real-time mechanical feedback loop that provides the joint with angle and stiffness control through differential and common-mode actuation of the servos, respectively. This approach to joint control emulates the mechanics of antagonistic muscle groups used by animals, and we experimentally show that it is capable of independently controlling both joint angle and joint stiffness using a simple open-loop control algorithm.
Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized
Dermoscopic Skin Lesion Classification

Melanoma mortality rates are the highest amongst skin cancer patients. Melanoma is life threating when it grows beyond the dermis of the skin. Hence, depth is an important factor to diagnose melanoma. This paper introduces a non-invasive computerized dermoscopy system that considers the estimated depth of skin lesions for diagnosis. A 3-D skin lesion reconstruction technique using the estimated depth obtained from regular dermoscopic images is presented. On basis of the 3-D reconstruction, depth and 3-D shape features are extracted. In addition to 3-D features, regular color, texture, and 2-D shape features are also extracted. Feature extraction is critical to achieve accurate results. Apart from melanoma, in-situ melanoma the proposed system is designed to diagnose basal cell carcinoma, blue nevus, dermatofibroma, haemangioma, seborrhoeic keratosis, and normal mole lesions. For experimental evaluations, the PH2, ISIC: Melanoma Project, and ATLAS dermoscopy data sets is considered. Different feature set combinations is considered and performance is evaluated. Significant performance improvement is reported the post inclusion of estimated depth and 3-D features. The good classification scores of sensitivity = 96%, specificity = 97% on PH2 data set and sensitivity = 98%, specificity = 99% on the ATLAS data set is achieved. Experiments conducted to estimate tumor depth from 3-D lesion reconstruction is presented. Experimental results achieved prove that the proposed computerized dermoscopy system is efficient and can be used to diagnose varied skin lesion dermoscopy images.