nils-herrmann/clean_scirepeval huggingface dataset:
A contemporary update on Fournier's gangrene

Despite advances in the evaluation, treatment, and pathophysiological understanding of necrotizing soft-tissue infections, Fournier's gangrene remains a life-threatening urological emergency. Although the condition can affect patients of any age and gender, it might be more prevalent in some high-risk groups with certain comorbidities. Several prognostic and diagnostic tools have been developed to assist with clinical decision-making once the diagnosis is made — primarily based on the physician's physical exam and potentially supported by laboratory and imaging findings. Expedited treatment with resuscitation, antibiotic administration, and rapid, wide surgical debridement are key elements of the initial management. These procedures must be followed by meticulous wound care and liberal use of planned subsequent surgical debridements. Once the patient has overcome the associated systemic illness, several reconstructive options for the genitalia and perineum can be considered to improve functionality and cosmesis.
Are We Ready for Real-world Neuroscience?

Real-world environments are typically dynamic, complex, and multisensory in nature and require the support of top–down attention and memory mechanisms for us to be able to drive a car, make a shopping list, or pour a cup of coffee. Fundamental principles of perception and functional brain organization have been established by research utilizing well-controlled but simplified paradigms with basic stimuli. The last 30 years ushered a revolution in computational power, brain mapping, and signal processing techniques. Drawing on those theoretical and methodological advances, over the years, research has departed more and more from traditional, rigorous, and well-understood paradigms to directly investigate cognitive functions and their underlying brain mechanisms in real-world environments. These investigations typically address the role of one or, more recently, multiple attributes of real-world environments. Fundamental assumptions about perception, attention, or brain functional organization have been challenged—by studies adapting the traditional paradigms to emulate, for example, the multisensory nature or varying relevance of stimulation or dynamically changing task demands. Here, we present the state of the field within the emerging heterogeneous domain of real-world neuroscience. To be precise, the aim of this Special Focus is to bring together a variety of the emerging “real-world neuroscientific” approaches. These approaches differ in their principal aims, assumptions, or even definitions of “real-world neuroscience” research. Here, we showcase the commonalities and distinctive features of the different “real-world neuroscience” approaches. To do so, four early-career researchers and the speakers of the Cognitive Neuroscience Society 2017 Meeting symposium under the same title answer questions pertaining to the added value of such approaches in bringing us closer to accurate models of functional brain organization and cognitive functions.
Reconstruction of Hidden Representation for Robust Feature Extraction

This article aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: (1) The reconstruction error of the input cannot be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also cannot be lower than a lower bound. (2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. (3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pretraining methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models.
RCMAC-Based Adaptive Control for Uncertain Nonlinear Systems

An adaptive control system, using a recurrent cerebellar model articulation controller (RCMAC) and based on a sliding mode technique, is developed for uncertain nonlinear systems. The proposed dynamic structure of RCMAC has superior capability to the conventional static cerebellar model articulation controller in an efficient learning mechanism and dynamic response. Temporal relations are embedded in RCMAC by adding feedback connections in the association memory space so that the RCMAC provides a dynamical structure. The proposed control system consists of an adaptive RCMAC and a compensated controller. The adaptive RCMAC is used to mimic an ideal sliding mode controller, and the compensated controller is designed to compensate for the approximation error between the ideal sliding mode controller and the adaptive RCMAC. The online adaptive laws of the control system are derived based on the Lyapunov stability theorem, so that the stability of the system can be guaranteed. In addition, in order to relax the requirement of the approximation error bound, an estimation law is derived to estimate the error bound. Finally, the simulation and experimental studies demonstrate the effectiveness of the proposed control scheme for the nonlinear systems with unknown dynamic functions
Generalized Sliced Wasserstein Distances

The Wasserstein distance and its variations, e.g., the sliced-Wasserstein (SW) distance, have recently drawn attention from the machine learning community. The SW distance, specifically, was shown to have similar properties to the Wasserstein distance, while being much simpler to compute, and is therefore used in various applications including generative modeling and general supervised/unsupervised learning. In this paper, we first clarify the mathematical connection between the SW distance and the Radon transform. We then utilize the generalized Radon transform to define a new family of distances for probability measures, which we call generalized slicedWasserstein (GSW) distances. We also show that, similar to the SW distance, the GSW distance can be extended to a maximum GSW (max-GSW) distance. We then provide the conditions under which GSW and max-GSW distances are indeed distances. Finally, we compare the numerical performance of the proposed distances on several generative modeling tasks, including SW flows and SW auto-encoders.
Fasting: molecular mechanisms and clinical applications.

Fasting has been practiced for millennia, but, only recently, studies have shed light on its role in adaptive cellular responses that reduce oxidative damage and inflammation, optimize energy metabolism, and bolster cellular protection. In lower eukaryotes, chronic fasting extends longevity, in part, by reprogramming metabolic and stress resistance pathways. In rodents intermittent or periodic fasting protects against diabetes, cancers, heart disease, and neurodegeneration, while in humans it helps reduce obesity, hypertension, asthma, and rheumatoid arthritis. Thus, fasting has the potential to delay aging and help prevent and treat diseases while minimizing the side effects caused by chronic dietary interventions.
Craniocervical junction venous anatomy around the suboccipital cavernous sinus: evaluation by MR imaging

The present study evaluated the venous anatomy of the craniocervical junction, focusing on the suboccipital cavernous sinus (SCS), a vertebral venous plexus surrounding the horizontal portion of the vertebral artery at the skull base. MR imaging was reviewed to clarify the venous anatomy of the SCS in 33 patients. Multiplanar reconstruction MR images were obtained using contrast-enhanced three-dimensional fast spoiled gradient–recalled acquisition in the steady state (3-D fast SPGR) with fat suppression. Connections with the SCS were evaluated for the following venous structures: anterior condylar vein (ACV); posterior condylar vein (PCV); lateral condylar vein (LCV); vertebral artery venous plexus (VAVP); and anterior internal vertebral venous plexus (AVVP). The SCS connected with the ACV superomedially, with the VAVP inferolaterally, and with the AVVP medially. The LCV connected with the external orifice of the ACV and superoanterior aspect of the SCS. The PCV connected with the posteromedial aspect of the jugular bulb and superoposterior aspect of the SCS. The findings of craniocervical junction venography performed in eight patients corresponded with those on MR imaging, other than with regard to the PCV. Contrast-enhanced 3-D fast SPGR allows visualization of the detailed anatomy of these venous structures, and this technique facilitates interventions and description of pathologies occurring in this area.
Hymenal anomalies in twins—review of the literature and case report

Subocclusive hymenal variants, such as microperforate or septate hymen, impair somatic functions (e.g., vaginal intercourse or menstrual hygiene) and can negatively impact the quality of life of young women. We know little about the prevalence and inheritance of subocclusive hymenal variants. So far, eight cases of familial occurrence of occlusive hymenal anomalies (imperforate hymen) have been reported. In one of these cases, monozygotic twins were affected. We are reporting the first case of subocclusive hymenal variants (microperforate hymen and septate hymen) in 16-year-old white dizygotic twins. In addition, we review and discuss the current evidence.  The mode of inheritance of hymenal variants has not been determined so far. Because surgical corrections of hymenal variants should be carried out in asymptomatic patients (before menarche), gynecologists and pediatricians should keep in mind that familial occurrences may occur.
HappyJIT: a tracing JIT compiler for PHP

Current websites are a combination of server-generated dynamic content with client-side interactive programs. Dynamically - typed languages have gained a lot of ground in both of these domains. The growth of Web 2.0 has introduced a myriad of websites which contain personalized content, which is specific to the user. PHP or Python programs generate the actual HTML page after querying a database and processing the results, which are then presented by the browser. It is becoming more and more vital to accelerate the execution of these programs, as this is a significant part of the total time needed to present the page to the user.
 This paper presents a novel interpreter for the PHP language written in RPython, which the PyPy translator then translates into C. The translator integrates into the interpreter a tracing just-in-time compiler which optimizes the hottest loops in the interpreted programs. We also describe a data model that supports all the data types in the PHP language, such as references and iterators. We evaluate the performance of this interpreter, showing that speedups up to a factor of 8 are observed using this approach.
Learning Steering Bounds for Parallel Autonomous Systems

Deep learning has been successfully applied to “end-to-end” learning of the autonomous driving task, where a deep neural network learns to predict steering control commands from camera data input. However, the learned representations do not support higher-level decision making required for autonomous navigation, nor the uncertainty estimates required for parallel autonomy, where vehicle control is shared between human and robot. This paper tackles the problem of learning a representation to predict a continuous control probability distribution, and thus steering control options and bounds for those options, which can be used for autonomous navigation. Each mode of the distribution encodes a possible macro-action that the system could execute at that instant, and the covariances of the modes place bounds on safe steering control values. Our approach has the added advantage of being trained on unlabeled data collected from inexpensive cameras. The deep neural network based algorithm generates a probability distribution over the space of steering angles, from which we leverage Variational Bayesian methods to extract a mixture model and compute the different possible actions in the environment. A bound, which the autonomous vehicle must respect in our parallel autonomy setting, is then computed for each of these actions. We evaluate our approach on a challenging dataset containing a wide variety of driving conditions, and show that our algorithm is capable of parameterizing Gaussian Mixture Models for possible actions, and extract steering bounds with a mean error of only 2 degrees. Additionally, we demonstrate our system working on a full scale autonomous vehicle and evaluate its ability to successful handle various different parallel autonomy situations.
Detecting Retries of Voice Search Queries

When a system fails to correctly recognize a voice search query, the user will frequently retry the query, either by repeating it exactly or rephrasing it in an attempt to adapt to the system’s failure. It is desirable to be able to identify queries as retries both offline, as a valuable quality signal, and online, as contextual information that can aid recognition. We present a method than can identify retries offline with 81% accuracy using similarity measures between two subsequent queries as well as system and user signals of recognition accuracy. The retry rate predicted by this method correlates significantly with a gold standard measure of accuracy, suggesting that it may be useful as an offline predictor of accuracy.
Cramer-Rao lower bounds for the time delay estimation of UWB signals

In this paper, we present the Cramer-Rao lower bounds (CKLBs) for the time delay estimation of UWB signals which could be tight lower bounds for the theoretical performance limits of UWB synchronizers. The CRLBs are investigated for both single pulse systems and time hopping systems in AWGN and multipath channels. Insights are given into the relationship between CRLBs for different Gaussian monocycles. It is found that larger number of multipath signals implies higher CRLBs and inferior performance of synchronizers, and multipath interference on CRLBs can not be eliminated completely except in very special cases. As every estimate of time delay could not be perfect, the least influence of the synchronization error on the performance of receivers is quantified.
LATINIZED, IMPROVED LHS, AND CVT POINT SETS IN HYPERCUBES

It is shown how an arbitrary set of points in the hypercube can be Latinized, i.e., can be transformed into a point set that has the Latin hypercube property. The effect of Latinization on the star discrepancy and other uniformity measures of a point set is analyzed. For a few selected but representative point sampling methods, evidence is provided to show that Latinization lowers the star discrepancy measure. A novel point sampling method is presented based on centroidal Voronoi tessellations of the hypercube. These point sets have excellent volumetric distributions, but have poor star discrepancies. Evidence is given that the Latinization of CVT points sets greatly lowers their star discrepancy measure but still preserves superior volumetric uniformity. As a result, means for determining improved Latin hypercube point samples are given.
Ecosystem stability and compensatory effects in the Inner Mongolia grassland

Numerous studies have suggested that biodiversity reduces variability in ecosystem productivity through compensatory effects; that is, a species increases in its abundance in response to the reduction of another in a fluctuating environment. But this view has been challenged on several grounds. Because most studies have been based on artificially constructed grasslands with short duration, long-term studies of natural ecosystems are needed. On the basis of a 24-year study of the Inner Mongolia grassland, here we present three key findings. First, that January–July precipitation is the primary climatic factor causing fluctuations in community biomass production; second, that ecosystem stability (conversely related to variability in community biomass production) increases progressively along the hierarchy of organizational levels (that is, from species to functional group to whole community); and finally, that the community-level stability seems to arise from compensatory interactions among major components at both species and functional group levels. From a hierarchical perspective, our results corroborate some previous findings of compensatory effects. Undisturbed mature steppe ecosystems seem to culminate with high biodiversity, productivity and ecosystem stability concurrently. Because these relationships are correlational, further studies are necessary to verify the causation among these factors. Our study provides new insights for better management and restoration of the rapidly degrading Inner Mongolia grassland.
Bilingually-constrained Phrase Embeddings for Machine Translation

We propose Bilingually-constrained Recursive Auto-encoders (BRAE) to learn semantic phrase embeddings (compact vector representations for phrases), which can distinguish the phrases with different semantic meanings. The BRAE is trained in a way that minimizes the semantic distance of translation equivalents and maximizes the semantic distance of nontranslation pairs simultaneously. After training, the model learns how to embed each phrase semantically in two languages and also learns how to transform semantic embedding space in one language to the other. We evaluate our proposed method on two end-to-end SMT tasks (phrase table pruning and decoding with phrasal semantic similarities) which need to measure semantic similarity between a source phrase and its translation candidates. Extensive experiments show that the BRAE is remarkably effective in these two tasks.
Design of Multi-Stage Power Divider Based on the Theory of Small Reflections

This paper presents a novel multi-way multi-stage power divider design method based on the theory of small reflections. Firstly, the application of the theory of small reflections is extended from transmission line to microwave network. Secondly, an explicit closed-form analytical formula of the input reflection coefficient, which consists of the scattering parameters of power divider elements and the lengths of interconnection lines between each element, is derived. Thirdly, the proposed formula is applied to determine the lengths of interconnection lines. A prototype of a 16-way 4-stage power divider working at 4 GHz is designed and fabricated. Both the simulation and measurement results demonstrate the validity of the proposed method.
How sex and age affect immune responses, susceptibility to infections, and response to vaccination

Do men die young and sick, or do women live long and healthy? By trying to explain the sexual dimorphism in life expectancy, both biological and environmental aspects are presently being addressed. Besides age-related changes, both the immune and the endocrine system exhibit significant sex-specific differences. This review deals with the aging immune system and its interplay with sex steroid hormones. Together, they impact on the etiopathology of many infectious diseases, which are still the major causes of morbidity and mortality in people at old age. Among men, susceptibilities toward many infectious diseases and the corresponding mortality rates are higher. Responses to various types of vaccination are often higher among women thereby also mounting stronger humoral responses. Women appear immune-privileged. The major sex steroid hormones exhibit opposing effects on cells of both the adaptive and the innate immune system: estradiol being mainly enhancing, testosterone by and large suppressive. However, levels of sex hormones change with age. At menopause transition, dropping estradiol potentially enhances immunosenescence effects posing postmenopausal women at additional, yet specific risks. Conclusively during aging, interventions, which distinctively consider the changing level of individual hormones, shall provide potent options in maintaining optimal immune functions.
5-DOF manipulator simulation based on MATLAB-Simulink methodology

This work presents a novel simulation methodology applied to a 5-DOF manipulator. The work includes mathematical modeling of the direct, inverse and differential kinematics as well as the dynamics of the manipulator. The method implements the path following in the 3D space and uses the Matlab-Simulink approach. Several paths were tested to verify the method. This methodology can be used with different robots to test the behavior and control laws.
An effective voting method for circle detection

The drawbacks of the standard Hough transform (SHT) are the computational time spending and the large storage requirement. Moreover, the voting method of SHT affects accuracy of detection. In this paper, a new voting method is proposed to reduce the computation and storage requirements of SHT for circle detection. This method improves the efficiency of circle detection by letting each pixel only belong to one candidate of circle parameters. Synthetic images are used to show the capability of the proposed method.
A New Nine-Phase Permanent Magnet Synchronous Motor With Consequent Pole Rotor for High-Power Traction Applications

Although three-phase permanent magnet (PM) motors are quite common in industry, multi-phase PM motors are used in special applications where high power and redundancy are required. Multi-phase PM motors offer higher torque/power density than conventional three-phase PM motors. In this paper, a novel multi-phase consequent pole PM (CPPM) synchronous motor is proposed. The constant power–speed range of the proposed motor is quite wide as opposed to conventional PM motors. The design and the detailed finite-element analysis of the proposed nine-phase CPPM motor and performance comparison with a nine-phase surface mounted PM motor are completed to illustrate the benefits of the proposed motor.