nils-herrmann/clean_scirepeval huggingface dataset:
Bidirectional Learning for Robust Neural Networks

A multilayer perceptron can behave as a generative classifier by applying bidirectional learning (BL). It consists of training an undirected neural network to map input to output and vice-versa; therefore it can produce a classifier in one direction, and a generator in the opposite direction for the same data. The learning process of BL tries to reproduce the neuroplasticity stated in Hebbian theory using only backward propagation of errors. In this paper, two novel learning techniques are introduced which use BL for improving robustness to white noise static and adversarial examples. The first method is bidirectional propagation of errors, which the error propagation occurs in backward and forward directions. Motivated by the fact that its generative model receives as input a constant vector per class, we introduce as a second method the hybrid adversarial networks (HAN). Its generative model receives a random vector as input and its training is based on generative adversarial networks (GAN). To assess the performance of BL, we perform experiments using several architectures with fully and convolutional layers, with and without bias. Experimental results show that both methods improve robustness to white noise static and adversarial examples, and even increase accuracy, but have different behavior depending on the architecture and task, being more beneficial to use the one or the other. Nevertheless, HAN using a convolutional architecture with batch normalization presents outstanding robustness, reaching state-of-the-art accuracy on adversarial examples of hand-written digits.
A Domain-Independent Algorithm for Plan Adaptation

The paradigms of transformational planning, case-based planning, and plan debugging all involve a process known as plan adaptation | modifying or repairing an old plan so it solves a new problem. In this paper we provide a domain-independent algorithm for plan adaptation, demonstrate that it is sound, complete, and systematic, and compare it to other adaptation algorithms in the literature. Our approach is based on a view of planning as searching a graph of partial plans. Generative planning starts at the graph's root and moves from node to node using planre nement operators. In planning by adaptation, a library plan|an arbitrary node in the plan graph|is the starting point for the search, and the plan-adaptation algorithm can apply both the same re nement operators available to a generative planner and can also retract constraints and steps from the plan. Our algorithm's completeness ensures that the adaptation algorithm will eventually search the entire graph and its systematicity ensures that it will do so without redundantly searching any parts of the graph.
Liveness Detection for Embedded Face Recognition System

To increase reliability of face recognition system, the system must be able to distinguish real face from a copy of face such as a photograph. In this paper, we propose a fast and memory efficient method of live face detection for embedded face recognition system, based on the analysis of the movement of the eyes. We detect eyes in sequential input images and calculate variation of each eye region to determine whether the input face is a real face or not. Experimental results show that the proposed approach is competitive and promising for live face detection. Liveness Detection, Eye detection, SQI.
Minimizing Energy Consumption of Wheeled Mobile Robots via Optimal Motion Planning

This paper presents a new optimal motion planning aiming to minimize the energy consumption of a wheeled mobile robot in robot applications. A model that can be used to formulate the energy consumption for kinetic energy transformation and for overcoming traction resistance is developed first. This model will provide a base for minimizing the robot energy consumption through a proper motion planning. To design the robot path, the A* algorithm is employed to generate an energy-efficient path where a new energy-related criterion is utilized in the cost function. To achieve a smooth trajectory along the generated path, the appropriate arrival time and velocity at the defined waypoints are selected for minimum energy consumption. Simulations and experiments are performed to demonstrate the energy-saving efficiency of the proposed motion planning approach.
Data Management in Machine Learning: Challenges, Techniques, and Systems

Large-scale data analytics using statistical machine learning (ML), popularly called advanced analytics, underpins many modern data-driven applications. The data management community has been working for over a decade on tackling data management-related challenges that arise in ML workloads, and has built several systems for advanced analytics. This tutorial provides a comprehensive review of such systems and analyzes key data management challenges and techniques. We focus on three complementary lines of work: (1) integrating ML algorithms and languages with existing data systems such as RDBMSs, (2) adapting data management-inspired techniques such as query optimization, partitioning, and compression to new systems that target ML workloads, and (3) combining data management and ML ideas to build systems that improve ML lifecycle-related tasks. Finally, we identify key open data management challenges for future research in this important area.
Parallelizing Linear Recurrent Neural Nets Over Sequence Length

Recurrent neural networks (RNNs) are widely used to model sequential data but their non-linear dependencies between sequence elements prevent parallelizing training over sequence length. We show the training of RNNs with only linear sequential dependencies can be parallelized over the sequence length using the parallel scan algorithm, leading to rapid training on long sequences even with small minibatch size. We develop a parallel linear recurrence CUDA kernel and show that it can be applied to immediately speed up training and inference of several state of the art RNN architectures by up to 9x. We abstract recent work on linear RNNs into a new framework of linear surrogate RNNs and develop a linear surrogate model for the long short-term memory unit, the GILR-LSTM, that utilizes parallel linear recurrence. We extend sequence learning to new extremely long sequence regimes that were previously out of reach by successfully training a GILR-LSTM on a synthetic sequence classification task with a one million timestep dependency.
Quantitative Comparison of Xen and KVM

We present initial results from and quantitative analysis of two leading open source hypervisors, Xen and KVM. This study focuses on the overall performance, performance isolation, and scalability of virtual machines running on these hypervisors. Our comparison was carried out using a benchmark suite that we developed to make the results easily repeatable. Our goals are to understand how the different architectural decisions taken by different hypervisor developers affect the resulting hypervisors, to help hypervisor developers realize areas of improvement for their hypervisors, and to help users make informed decisions about their choice of hypervisor.
Use of dermal fat graft for augmentation of the labia majora.

Dermal fat grafts have been utilized in plastic surgery for both reconstructive and aesthetic purposes of the face, breast, and body. There are multiple reports in the literature on the male phallus augmentation with the use of dermal fat grafts. Few reports describe female genitalia aesthetic surgery, in particular rejuvenation of the labia majora. In this report we describe an indication and use of autologous dermal fat graft for labia majora augmentation in a patient with loss of tone and volume in the labia majora. We found that this procedure is an option for labia majora augmentation and provides a stable result in volume-restoration.
Ehlers-Danlos syndrome--a historical review.

Ehlers-Danlos syndrome is an inherited heterogeneous group of connective tissue disorders, characterized by abnormal collagen synthesis, affecting skin, ligaments, joints, blood vessels and other organs. It is one of the oldest known causes of bruising and bleeding and was first described by Hipprocrates in 400 BC. Edvard Ehlers, in 1901, recognized the condition as a distinct entity. In 1908, Henri-Alexandre Danlos suggested that skin extensibility and fragility were the cardinal features of the syndrome. In 1998, Beighton published the classification of Ehlers-Danlos syndrome according to the Villefranche nosology. From the 1960s the genetic make up was identified. Management of bleeding problems associated with Ehlers-Danlos has been slow to progress.
Associations between socioeconomic status and major complications in type 1 diabetes: the Pittsburgh epidemiology of diabetes complication (EDC) Study.

To understand the effect of socioeconomic status (SES) on the risk of complications in type 1 diabetes (T1D), we explored the relationship between SES and major diabetes complications in a prospective, observational T1D cohort study.



Complete data were available for 317 T1D persons within 4 years of age 28 (ages 24-32) in the Pittsburgh Epidemiology of Diabetes Complications Study. Age 28 was selected to maximize income, education, and occupation potential and to minimize the effect of advanced diabetes complications on SES.



The incidences over 1 to 20 years' follow-up of end-stage renal disease and coronary artery disease were two to three times greater for T1D individuals without, compared with those with a college degree (p < .05 for both), whereas the incidence of autonomic neuropathy was significantly greater for low-income and/or nonprofessional participants (p < .05 for both). HbA(1c) was inversely associated only with income level. In sex- and diabetes duration-adjusted Cox models, lower education predicted end-stage renal disease (hazard ratio [HR], 2.9; 95% confidence interval [95% CI], 1.1-7.7) and coronary artery disease (HR, 2.5, 95% CI, 1.3-4.9), whereas lower income predicted autonomic neuropathy (HR, 1.7; 95% CI, 1.0-2.9) and lower-extremity arterial disease (HR, 3.7; 95% CI, 1.1-11.9).



These associations, partially mediated by clinical risk factors, suggest that lower SES T1D individuals may have poorer self-management and, thus, greater complications from diabetes.
Hand Gesture Recognition System

Gestures are a major form of human communication. Hence gestures can be found to be an appealing way to interact with computers, since they are already a natural part of how people communicate. A primary goal of gesture recognition is to create a system which can identify specific human gestures and use them to convey information for controlling device and by implementing real time gesture recognition a user can control a computer by doing a specific gesture in front of a video camera which is linked to the computer. A primary goal of this gesture recognition research is to create a system which can identify specific human gestures for the control the traffic signals and mouse. This project also covers various issues like what are gesture, their classification, their role in implementing a gesture recognition system for traffic and mouse control, system architecture concepts for implementing a gesture recognition system, major issues involved in implementing gesture recognition system, and future scope of gesture recognition system. For implementation of this system real-time hand tracking and extraction algorithm, and feature extraction are used.
Suspended plate antennas with shorting strips and slots

The suspended plate antennas (SPAs) with shorting strips and slots are presented numerically and validated experimentally. Effects of narrow shorting strips on the impedance and radiation performances of the SPAs are studied. The symmetric use of shorting and feeding strips makes the SPAs structurally symmetrical, which is conducive to the radiation performance of the SPAs. Also, effects of narrow rectangular slots cut in the center or the edges of the plates are investigated. Cutting the slots reduces size, realizes dual-band operation, and improves radiation performance of SPAs.
Pre- and postsynaptic effects of glutamate in the frog labyrinth

The role of glutamate in quantal release at the cytoneural junction was examined by measuring mEPSPs and afferent spikes at the posterior canal in the intact frog labyrinth. Release was enhanced by exogenous glutamate, or dl-TBOA, a blocker of glutamate reuptake. Conversely, drugs acting on ionotropic glutamate receptors did not affect release; the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPA-R) blocker CNQX decreased mEPSP size in a dose-dependent manner; the NMDA-R blocker d-AP5 at concentrations <200 µM did not affect mEPSP size, either in the presence or absence of Mg and glycine. In isolated hair cells, glutamate did not modify Ca currents. Instead, it systematically reduced the compound delayed potassium current, IKD, whereas the metabotropic glutamate receptor (mGluR)-II inverse agonist, (2S)-2-amino-2-[(1S,2S)-2-carboxycycloprop-1-yl]-3-(xanth-9-yl)propanoic acid (LY341495), increased it. Given mGluR-II decrease cAMP production, these finding are consistent with the reported sensitivity of IKD to protein kinase A (PKA)-mediated phosphorylation. LY341495 also enhanced transmitter release, presumably through phosphorylation-mediated facilitation of the release machinery. The observed enhancement of release by glutamate confirms previous literature data, and can be attributed to activation of mGluR-I that promotes Ca release from intracellular stores. Glutamate-induced reduction in the repolarizing IKD may contribute to facilitation of release. Overall, glutamate exerts both a positive feedback action on mGluR-I, through activation of the phospholipase C (PLC)/IP3 path, and the negative feedback, by interfering with substrate phosphorylation through Gi/0-coupled mGluRs-II/III. The positive feedback prevails, which may explain the increase in overall rates of release observed during mechanical stimulation (symmetrical in the excitatory and inhibitory directions). The negative feedback may protect the junction from over-activation.
Natural Language Processing in Oncology: A Review.

Natural language processing (NLP) has the potential to accelerate translation of cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of personalized medicine. This technology can harvest important clinical variables trapped in the free-text narratives within electronic medical records.



Natural language processing can be used as a tool for oncological evidence-based research and quality improvement. Oncologists interested in applying NLP for clinical research can play pivotal roles in building NLP systems and, in doing so, contribute to both oncological and clinical NLP research. Herein, we provide an introduction to NLP and its potential applications in oncology, a description of specific tools available, and a review on the state of the current technology with respect to cancer case identification, staging, and outcomes quantification.



More automated means of leveraging unstructured data from daily clinical practice is crucial as therapeutic options and access to individual-level health information increase. Research-minded oncologists may push the avenues of evidence-based research by taking advantage of the new technologies available with clinical NLP. As continued progress is made with applying NLP toward oncological research, incremental gains will lead to large impacts, building a cost-effective infrastructure for advancing cancer care.
Clomipramine Treatment and Repeated Restraint Stress Alter Parameters of Oxidative Stress in Brain Regions of Male Rats

This study aimed to compare the effects of repeated restraint stress alone and the combination with clomipramine treatment on parameters of oxidative stress in cerebral cortex, striatum and hippocampus of male rats. Animals were divided into control and repeated restraint stress, and subdivided into treated or not with clomipramine. After 40 days of stress and 27 days of clomipramine treatment with 30 mg/kg, the repeated restraint stress alone reduced levels of Na+, K+-ATPase in all tissues studied. The combination of repeated restraint stress and clomipramine increased the lipid peroxidation, free radicals and CAT activity as well as decreased levels of NP-SH in the tissues studied. However, Na+, K+-ATPase level decreased in striatum and cerebral cortex and the SOD activity increased in hippocampus and striatum. Results indicated that clomipramine may have deleterious effects on the central nervous system especially when associated with repeated restraint stress and chronically administered in non therapeutic levels.
Exploring Causes of Frustration for Software Developers

When learning to program, frustrating experiences contribute to negative learning outcomes and poor retention in the field. Defining a common framework that explains why these experiences occur can lead to better interventions and learning mechanisms. To begin constructing such a framework, we asked 45 software developers about the severity of their frustration and to recall their most recent frustrating programming experience. As a result, 67% considered their frustration to be severe. Further, we distilled the reported experiences into 11 categories, which include issues with mapping behaviors to code and broken programming tools. Finally, we discuss future directions for defining our framework and designing future interventions.
A hybrid algorithm for modeling ice formation

We present a novel algorithm that simulates ice formation. Motivated by the physical process of ice growth, we develop a novel hybrid algorithm by synthesizing three techniques: diffusion limited aggregation, phase field methods, and stable fluid solvers. Each technique maps to one of the three stages of solidification. The visual realism of the resulting algorithm appears to surpass that of each technique alone, particularly in animations of freezing. In addition, we present a faster, simplified phase field method, as well as a unified parameterization that enables artistic manipulation of the simulation. We illustrate the results on arbitrary 3D surfaces.
Speeding up Web Page Loads with Shandian

Web page loads are slow due to intrinsic inefficiencies in the page load process. Our study shows that the inefficiencies are attributable not only to the contents and structure of the Web pages (e.g., three-fourths of the CSS resources are not used during the initial page load) but also the way that pages are loaded (e.g., 15% of page load times are spent waiting for parsing-blocking resources to be loaded). To address these inefficiencies, this paper presents Shandian (which means lightening in Chinese) that restructures the page load process to speed up page loads. Shandian exercises control over what portions of the page gets communicated and in what order so that the initial page load is optimized. Unlike previous techniques, Shandian works on demand without requiring a training period, is compatible with existing latency-reducing techniques (e.g., caching and CDNs), supports security features that enforce same-origin policies, and does not impose additional privacy risks. Our evaluations show that Shandian reduces page load times by more than half for both mobile phones and desktops while incurring modest overheads to data usage.
Antimicrobial activity and stability of the d-amino acid substituted derivatives of antimicrobial peptide polybia-MPI

Antimicrobial peptide has the potential to be developed as new kind of antimicrobial agents with novel action mechanism. However, the susceptibility to protease is a drawback for potential peptides to be clinical used. d-amino acid substitution can be one way to increase the proteolytic stability of peptides. In the present study, we synthesized the d-lysines substituted analog (d-lys-MPI) and the d-enantiomer of polybia-MPI (D-MPI) to improve the proteolytic resistance of polybia-MPI. Our results showed that, the stability of its d-amino acid partially substituted analog d-lys-MPI was increased. However, it lost antimicrobial activity at the tested concentration with the loss of α-helix content. As shown in the CD spectra, after substitution, the spectra of D-MPI is symmetrical to MPI, indicated it turned into left hand α-helical conformation. Excitingly, the stability of D-MPI toward the tested protease was improved greatly. Notably, the antimicrobial activity of D-MPI was comparable to its L-counterpart MPI, even improved. In addition, the hemolytic activity of D-MPI was lowered. This also indicated that the action target of antimicrobial peptide polybia-MPI was not chiral specific. So, D-MPI may offer a therapeutic strategy to defend the infection of microbes, considering its stability to protease and relatively lower cytotoxicity to human erythrocytes.
Interaction of famotidine, an H2 histamine receptor antagonist, with conventional antiepileptic drugs in mice.

H2 histamine receptors are localized postsynaptically in the CNS. The aim of this study was to evaluate the effects of acute (1 day) and prolonged (7 day) administration of the H2 histamine receptor antagonist, famotidine, on the anticonvulsant activity of conventional antiepileptic drugs (AEDs; valproate, carbamazepine, diphenylhydantoin and phenobarbital) against maximal electroshock (MES)-induced seizures in mice. In addition, the effects of these drugs alone or in combination with famotidine were studied on motor performance and long-term memory. The influence of H2 receptor antagonist on brain concentrations and free plasma levels of the antiepileptic drugs was also evaluated. After acute or prolonged administration of famotidine (at dose of 10mg/kg) the drug raised the threshold for electroconvulsions. No effect was observed on this parameter at lower doses. Famotidine (5mg/kg), given acutely, significantly enhanced the anticonvulsant activity of valproate, which was expressed by a decrease in ED50. After the 7-day treatment, famotidine (5mg/kg) increased the anticonvulsant activity of diphenylhydantoin against MES. Famotidine (5mg/kg), after acute and prolonged administration, combined with valproate, phenobarbital, diphenylhydantoin and carbamazepine did not alter their free plasma levels. In contrast, brain concentrations of valproate were elevated for 1-day treatment with famotidine (5mg/kg). Moreover, famotidine co-applied with AEDs, given prolonged, worsened motor coordination in mice treated with carbamazepine or diphenylhydantoin. In contrast this histamine antagonist, did not impair the performance of mice evaluated in the long-term memory task. The results of this study indicate that famotidine modifies the anticonvulsant activity of some antiepileptic drugs.