nils-herrmann/clean_scirepeval huggingface dataset:
Simulated microgravity, Mars gravity, and 2g hypergravity affect cell cycle regulation, ribosome biogenesis, and epigenetics in Arabidopsis cell cultures

Gravity is the only component of Earth environment that remained constant throughout the entire process of biological evolution. However, it is still unclear how gravity affects plant growth and development. In this study, an in vitro cell culture of Arabidopsis thaliana was exposed to different altered gravity conditions, namely simulated reduced gravity (simulated microgravity, simulated Mars gravity) and hypergravity (2g), to study changes in cell proliferation, cell growth, and epigenetics. The effects after 3, 14, and 24-hours of exposure were evaluated. The most relevant alterations were found in the 24-hour treatment, being more significant for simulated reduced gravity than hypergravity. Cell proliferation and growth were uncoupled under simulated reduced gravity, similarly, as found in meristematic cells from seedlings grown in real or simulated microgravity. The distribution of cell cycle phases was changed, as well as the levels and gene transcription of the tested cell cycle regulators. Ribosome biogenesis was decreased, according to levels and gene transcription of nucleolar proteins and the number of inactive nucleoli. Furthermore, we found alterations in the epigenetic modifications of chromatin. These results show that altered gravity effects include a serious disturbance of cell proliferation and growth, which are cellular functions essential for normal plant development.
Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop.

Criteria for the diagnosis of vascular dementia (VaD) that are reliable, valid, and readily applicable in a variety of settings are urgently needed for both clinical and research purposes. To address this need, the Neuroepidemiology Branch of the National Institute of Neurological Disorders and Stroke (NINDS) convened an International Workshop with support from the Association Internationale pour la Recherche et l'Enseignement en Neurosciences (AIREN), resulting in research criteria for the diagnosis of VaD. Compared with other current criteria, these guidelines emphasize (1) the heterogeneity of vascular dementia syndromes and pathologic subtypes including ischemic and hemorrhagic strokes, cerebral hypoxic-ischemic events, and senile leukoencephalopathic lesions; (2) the variability in clinical course, which may be static, remitting, or progressive; (3) specific clinical findings early in the course (eg, gait disorder, incontinence, or mood and personality changes) that support a vascular rather than a degenerative cause; (4) the need to establish a temporal relationship between stroke and dementia onset for a secure diagnosis; (5) the importance of brain imaging to support clinical findings; (6) the value of neuropsychological testing to document impairments in multiple cognitive domains; and (7) a protocol for neuropathologic evaluations and correlative studies of clinical, radiologic, and neuropsychological features. These criteria are intended as a guide for case definition in neuroepidemiologic studies, stratified by levels of certainty (definite, probable, and possible). They await testing and validation and will be revised as more information becomes available.
HordeQBF: A Modular and Massively Parallel QBF Solver

The recently developed massively parallel satisfiability (SAT) solver HordeSAT was designed in a modular way to allow the integration of any sequential CDCL-based SAT solver in its core. We integrated the QCDCL-based quantified Boolean formula (QBF) solver DepQBF in HordeSAT to obtain a massively parallel QBF solver—HordeQBF. In this paper we describe the details of this integration and report on results of the experimental evaluation of HordeQBF’s performance. HordeQBF achieves superlinear average and median speedup on the hard application instances of the 2014 QBF Gallery.
Fano Resonance and Spectrally Modified Photoluminescence Enhancement in Monolayer MoS2 Integrated with Plasmonic Nanoantenna Array.

The manipulation of light-matter interactions in two-dimensional atomically thin crystals is critical for obtaining new optoelectronic functionalities in these strongly confined materials. Here, by integrating chemically grown monolayers of MoS2 with a silver-bowtie nanoantenna array supporting narrow surface-lattice plasmonic resonances, a unique two-dimensional optical system has been achieved. The enhanced exciton-plasmon coupling enables profound changes in the emission and excitation processes leading to spectrally tunable, large photoluminescence enhancement as well as surface-enhanced Raman scattering at room temperature. Furthermore, due to the decreased damping of MoS2 excitons interacting with the plasmonic resonances of the bowtie array at low temperatures stronger exciton-plasmon coupling is achieved resulting in a Fano line shape in the reflection spectrum. The Fano line shape, which is due to the interference between the pathways involving the excitation of the exciton and plasmon, can be tuned by altering the coupling strengths between the two systems via changing the design of the bowties lattice. The ability to manipulate the optical properties of two-dimensional systems with tunable plasmonic resonators offers a new platform for the design of novel optical devices with precisely tailored responses.
Deep Learning for Conversational AI

Spoken Dialogue Systems (SDS) have great commercial potential as they promise to revolutionise the way in which humans interact with machines. The advent of deep learning led to substantial developments in this area of NLP research, and the goal of this tutorial is to familiarise the research community with the recent advances in what some call the most difficult problem in NLP. From a research perspective, the design of spoken dialogue systems provides a number of significant challenges, as these systems depend on: a) solving several difficult NLP and decision-making tasks; and b) combining these into a functional dialogue system pipeline. A key long-term goal of dialogue system research is to enable open-domain systems that can converse about arbitrary topics and assist humans with completing a wide range of tasks. Furthermore, such systems need to autonomously learn on-line to improve their performance and recover from errors using both signals from their environment and from implicit and explicit user feedback. While the design of such systems has traditionally been modular, domain and language-specific, advances in deep learning have alleviated many of the design problems. The main purpose of this tutorial is to encourage dialogue research in the NLP community by providing the research background, a survey of available resources, and giving key insights to application of state-of-the-art SDS methodology into industry-scale systems. We plan to introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems, which includes three key components: 1) Language Understanding; 2) Dialogue Management; and 3) Language Generation. The differences between goal-oriented dialogue systems and chat-bot style conversational agents will be explained in order to show the motivation behind the design of both, with the main focus on the pipeline SDS framework. For each key component, we will define the research problem, provide a brief literature review and introduce the current state-of-the-art approaches. Complementary resources (e.g. available datasets and toolkits) will also be discussed. Finally, future work, outstanding challenges, and current industry practices will be presented. All of the presented material will be made available online for future reference.
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption

We consider the phase retrieval problem of recovering the unknown signal from the magnitudeonly measurements, where the measurements can be contaminated by both sparse arbitrary corruption and bounded random noise. We propose a new nonconvex algorithm for robust phase retrieval, namely Robust Wirtinger Flow, to jointly estimate the unknown signal and the sparse corruption. We show that our proposed algorithm is guaranteed to converge linearly to the unknown true signal up to a minimax optimal statistical precision in such a challenging setting. Compared with existing robust phase retrieval methods, we improved the statistical error rate by a factor of √ n/m where n is the dimension of the signal and m is the sample size, provided a refined characterization of the corruption fraction requirement, and relaxed the lower bound condition on the number of corruption. In the noise-free case, our algorithm converges to the unknown signal at a linear rate and achieves optimal sample complexity up to a logarithm factor. Thorough experiments on both synthetic and real datasets corroborate our theory.
New Directions on Agile Methods: A Comparative Analysis

Agile software development methods have caught the attention of software engineers and researchers worldwide. Scientific research is yet scarce. This paper reports results from a study, which aims to organize, analyze and make sense out of the dispersed field of agile software development methods. The comparative analysis is performed using the method's life-cycle coverage, project management support, type of practical guidance, fitness-for-use and empirical evidence as the analytical lenses. The results show that agile software development methods, without rationalization, cover certain/different phases of the software development life-cycle and most of the them do not offer adequate support for project management. Yet, many methods still attempt to strive for universal solutions (as opposed to situation appropriate) and the empirical evidence is still very limited Based on the results, new directions are suggested In principal it is suggested to place emphasis on methodological quality -- not method quantity.
Cost-Effective Object Detection: Active Sample Mining With Switchable Selection Criteria

Though quite challenging, leveraging large-scale unlabeled or partially labeled data in learning systems (e.g., model/classifier training) has attracted increasing attentions due to its fundamental importance. To address this problem, many active learning (AL) methods have been proposed that employ up-to-date detectors to retrieve representative minority samples according to predefined confidence or uncertainty thresholds. However, these AL methods cause the detectors to ignore the remaining majority samples (i.e., those with low uncertainty or high prediction confidence). In this paper, by developing a principled active sample mining (ASM) framework, we demonstrate that cost-effective mining samples from these unlabeled majority data are a key to train more powerful object detectors while minimizing user effort. Specifically, our ASM framework involves a switchable sample selection mechanism for determining whether an unlabeled sample should be manually annotated via AL or automatically pseudolabeled via a novel self-learning process. The proposed process can be compatible with mini-batch-based training (i.e., using a batch of unlabeled or partially labeled data as a one-time input) for object detection. In this process, the detector, such as a deep neural network, is first applied to the unlabeled samples (i.e., object proposals) to estimate their labels and output the corresponding prediction confidences. Then, our ASM framework is used to select a number of samples and assign pseudolabels to them. These labels are specific to each learning batch based on the confidence levels and additional constraints introduced by the AL process and will be discarded afterward. Then, these temporarily labeled samples are employed for network fine-tuning. In addition, a few samples with low-confidence predictions are selected and annotated via AL. Notably, our method is suitable for object categories that are not seen in the unlabeled data during the learning process. Extensive experiments on two public benchmarks (i.e., the PASCAL VOC 2007/2012 data sets) clearly demonstrate that our ASM framework can achieve performance comparable to that of the alternative methods but with significantly fewer annotations.
A particle swarm optimization approach for optimum design of PID controller in AVR system

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.
Milk with Honey Heals after Tonsillectomy ; Bleeding , Pain and Wound Healing are in a Better Condition : An Experimental Study with Control Group

To assess the effect of milk with honey in childre n undergoing tonsillectomy on bleeding, pain and wound healing.  The experimental study wit contol group was conduct ed out ear, nose and throat clinic and outpatient clinic in a public hospital. In the study, it were studied with children undergoing tonsillectomy who are 6-17 years of age (N=68). The standardized natural flowe r honey was applied to children in the experimental group after tonsillectomy, every day, in addition to the standard diet in clinical routine. The children wer e assigned randomly the experimental and control groups accord ing to the operation sequence. In collecting the da ta, questionnaire, pain, wound healing and visual analo g scales was used. The data were analyzed by percen tage distributions, means, chi-square test, variance ana lysis, and correlation analysis. It was depended on ethical principles.  In the study, it was determined that not bleeding, is significant less pain and the level of wound he aling of children in group milk with honey than children i milk group (p<.001). It has been found that a st rong negative correlation between the level of pain and wound healing of children in milk with honey and mi lk groups (p<.001).  It has been determined that milk with honey was ef fective in prevent bleeding, reducing pain, and accelerate wound healing. Honey, which is a natural nutrient is a safe care tool that can be applied i n children undergoing tonsillectomy without diabetes and aller gic to honey and oral feeding in addition to routin e clinical the diet.
CloudSimDisk: Energy-Aware Storage Simulation in CloudSim

The cloud computing paradigm is continually evolving, and with it, the size and the complexity of its infrastructure. Assessing the performance of a cloud environment is an essential but an arduous task. Further, the energy consumed by data centers is steadily increasing and major components such as the storage systems need to be more energy efficient. Cloud simulation tools have proved quite useful to study these issues. However, these simulation tools lack mechanisms to study energy efficient storage in cloud systems. This paper contributes in the area of cloud computing by extending the widely used cloud simulator CloudSim. In this paper, we propose CloudSimDisk, a scalable module for modeling and simulation of energy-aware storage in cloud systems. We show how CloudSimDisk can be used to simulate energy-aware storage, and can be extended to study new algorithms for energy-awareness in cloud systems. Our simulation results proved to be in accordance with the analytical models that were developed to model energy consumption of hard disk drives in cloud systems. The source code of CloudSimDisk is also made available for the research community for further testing and development.
GMAD: Graph-based Malware Activity Detection by DNS traffic analysis

Malicious activities on the Internet are one of the most dangerous threats to Internet users and organizations. Malicious software controlled remotely is addressed as one of the most critical methods for executing the malicious activities. Since blocking domain names for command and control (C&C) of the malwares by analyzing their Domain Name System (DNS) activities has been the most effective and practical countermeasure, attackers attempt to hide their malwares by adopting several evasion techniques, such as client sub-grouping and domain flux on DNS activities. A common feature of the recently developed evasion techniques is the utilization of multiple domain names for render malware DNS activities temporally and spatially more complex. In contrast to analyzing the DNS activities for a single domain name, detecting the malicious DNS activities for multiple domain names is not a simple task. The DNS activities of malware that uses multiple domain names, termed multi-domain malware, are sparser and less synchronized with respect to space and time. In this paper, we introduce a malware activity detection mechanism, GMAD: Graph-based Malware Activity Detection that utilizes a sequence of DNS queries in order to achieve robustness against evasion techniques. GMAD uses a graph termed Domain Name Travel Graph which expresses DNS query sequences to detect infected clients and malicious domain names. In addition to detecting malware C&C domain names, GMAD detects malicious DNS activities such as blacklist checking and fake DNS querying. To detect malicious domain names utilized to malware activities, GMAD applies domain name clustering using the graph structure and determines malicious clusters by referring to public blacklists. Through experiments with four sets of DNS traffic captured in two ISP networks in the U.S. and South Korea, we show that GMAD detected thousands of malicious domain names that had neither been blacklisted nor detected through group activity of DNS clients. In a detection accuracy evaluation, GMAD showed an accuracy rate higher than 99% on average, with a higher than 90% precision and lower than 0:5% false positive rate. It is shown that the proposed method is effective for detecting multi-domain malware activities irrespective of evasion techniques.
Design Criteria for Stimulating Emotions in Web Applications

This work aims to identify the main aspects of Web design responsible for eliciting specific emotions. For this purpose, we performed a user study with 40 participants testing a Web application designed by applying a set of criteria for stimulating various emotions. In particular, we considered six emotions (hate, anxiety, boredom, fun, serenity, love), and for each of them a specific set of design criteria was exploited. The purpose of the study was to reach a better understanding regarding what design techniques are most important to stimulate each emotion. We report on the results obtained and discuss their implications. Such results can inform the development of guidelines for Web applications able to stimulate users’ emotions.
A Delphi study of knowledge management systems: Scope and requirements

We empirically explored the roles and scope of knowledge management systems in organizations. Building on a knowledgebased view of the firm, we hypothesized and empirically tested our belief that more integration is needed between technologies intended to support knowledge and those supporting business operations. Findings from a Delphi study and in-depth interviews illustrated this and led us to suggest a revised approach to developing organizational knowledge management systems.
State-Space Based Simulink Modeling of BLDC Motor and its Speed Control using Fuzzy PID

C. P. Singh, SS Kulkarni, S.C. Rana, Kapil Deo, Scientists, Avionics Group, ARDE, Pune (DRDO), India Email-erchandravishwa02@gmail.com  In this paper, an efficient simulation model for Fuzzy logic controlled Brushless DC Motor drives using Matlab / Simulink is presented. Here Model of BLDC motor is based on State-space and its speed loop control is based on Fuzzy logic PID controller. A three phase inverter model is implemented for Motor commutation in six steps and PWM based motor Current Control with help of three Hall Sensors which are placed at 120 electrical degrees apart around the motor shaft. Dynamic performances (i.e. Torque and Speed) and currents and voltages of the inverter components are analyzed for this model. The modeling, simulation and two loop control of BLDC have been done in MATLAB\SIMULINK software.
A comparative study of the classification techniques in isolated Mandarin syllable tone recognition

Tonal languages, such as Chinese, use systematic variations of pitch to distinguish lexical or grammatical meaning. Thus, tone recognition is essential for tonal languages. Typically, tone recognition for isolated syllables involves three major steps: fundamental frequency (F0) detection, feature extraction, and classification. The work compares different techniques for these three steps and to answer the questions: for Mandarin Chinese syllables, what combination of fundamental frequency detection and feature extraction methods best prepare data for classification, and what is the most effective classification method for tone recognition. Three types of F0 detection methods (autocorrelation, cross-correlation and cepstrum), two feature extraction schemes (sampled F0 and average F0, slope and energy from three subsegments), four normalization methods (slope only, 0--100 scaled, z-score and T1 shift), and two classification methods (Support Vector Machine (SVM) and Multilayer Perceptron (MLP)) were experimentally studied using 700 collected data samples.
Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data

Electronic health records (EHRs) have contributed to the computerization of patient records and can thus be used not only for efficient and systematic medical services, but also for research on biomedical data science. However, there are many missing values in EHRs when provided in matrix form, which is an important issue in many biomedical EHR applications. In this paper, we propose a two-stage framework that includes missing data imputation and disease prediction to address the missing data problem in EHRs. We compared the disease prediction performance of generative adversarial networks (GANs) and conventional learning algorithms in combination with missing data prediction methods. As a result, we obtained a level of accuracy of 0.9777, sensitivity of 0.9521, specificity of 0.9925, area under the receiver operating characteristic curve (AUC-ROC) of 0.9889, and F-score of 0.9688 with a stacked autoencoder as the missing data prediction method and an auxiliary classifier GAN (AC-GAN) as the disease prediction method. The comparison results show that a combination of a stacked autoencoder and an AC-GAN significantly outperforms other existing approaches. Our results suggest that the proposed framework is more robust for disease prediction from EHRs with missing data.