nils-herrmann/clean_scirepeval huggingface dataset:
When accuracy hurts, and when it helps: a test of the empathic accuracy model in marital interactions.

This study tested predictions from W. Ickes and J. A. Simpson's (1997, 2001) empathic accuracy model. Married couples were videotaped as they tried to resolve a problem in their marriage. Both spouses then viewed a videotape of the interaction, recorded the thoughts and feelings they had at specific time points, and tried to infer their partner's thoughts and feelings. Consistent with the model, when the partner's thoughts and feelings were relationship-threatening (as rated by both the partners and by trained observers), greater empathic accuracy on the part of the perceiver was associated with pre-to-posttest declines in the perceiver's feelings of subjective closeness. The reverse was true when the partner's thoughts and feelings were nonthreatening. Exploratory analyses revealed that these effects were partially mediated through observer ratings of the degree to which partners tried to avoid the discussion issue.
Deep Reinforcement Learning for Multimedia Traffic Control in Software Defined Networking

Software Defined Networking (SDN) is a promising paradigm to provide centralized traffic control. Multimedia traffic control based on SDN is crucial but challenging for Quality of Experience (QoE) optimization. It is very difficult to model and control multimedia traffic because solutions mainly depend on an understanding of the network environment, which is complicated and dynamic. Inspired by the recent advances in artificial intelligence (AI) technologies, we study the adaptive multimedia traffic control mechanism leveraging Deep Reinforcement Learning (DRL). This paradigm combines deep learning with reinforcement learning, which learns solely from rewards by trial-and-error. Results demonstrate that the proposed mechanism is able to control multimedia traffic directly from experience without referring to a mathematical model.
THE MCKINSEY 7S MODEL FRAMEWORK FOR E-LEARNING SYSTEM READINESS ASSESSMENT

These study it is necessary to can be used as a theoretical foundation upon which to base decision-making and strategic thinking about e-learning system. This paper proposes a new framework for assessing readiness of an organization to implement the e-learning system project on the basis of McKinsey 7S model using fuzzy logic analysis. The study considers 7 dimensions as approach to assessing the current situation of the organization prior to system implementation to identify weakness areas which may encounter the project with failure. Adopted was focus on Questionnaires and group interviews to specific data collection from three colleges in Mosul University in Iraq. This can be achieved success in building an e-learning system at the University of Mosul by readiness assessment according to the model of multidimensional based on the framework of 7S is selected by 23 factors, and thus can avoid failures or weaknesses facing the implementation process before the start of the project and a step towards enabling the administration to make decisions that achieve success in this area, as well as to avoid the high cost associated with the implementation process.
A CMOS linear-in-dB VGA based on exponential current generator

This paper proposes a simple CMOS exponential current circuit that is capable to control a Variable Gain Amplifier with a linear-in-dB manner. The proposed implementation is based on a Taylor's series approximation of the exponential function. A simple VGA architecture has been designed in a CMOS 90nm technology, in order to validate the theoretical analysis. The approximation achieves a 17dB linear range with less than 0.5dB approximation error, while the overall power consumption is less than 300μW.
The female rat reproductive cycle: a practical histological guide to staging.

During preclinical investigations into the safety of drugs and chemicals, many are found to interfere with reproductive function in the female rat. This interference is commonly expressed as a change in normal morphology of the reproductive tract or a disturbance in the duration of particular phases of the estrous cycle. Such alterations can be recognized only if the pathologist has knowledge of the continuously changing histological appearance of the various components of the reproductive tract during the cycle and can accurately and consistently ascribe an individual tract to a particular phase of the cycle. Unfortunately, although comprehensive reports illustrating the normal appearance of the tract during the rat estrous cycle have been available over many years, they are generally somewhat ambiguous about distinct criteria for defining the end of one stage and the beginning of another. This detail is absolutely essential to achieve a consistent approach to staging the cycle. For the toxicologic pathologist, this report illustrates a pragmatic and practical approach to staging the estrous cycle in the rat based on personal experience and a review of the literature from the last century.
Wideband power divider using radial stub for six-port interferometer

This paper presents the development of wideband power divider using radial stub for six-port interferometer. The performance of designed power divider is evaluated over Ultrawideband (UWB) frequency range across 3.1 GHz to 10.6 GHz. The design of power divider is started with conventional design of Wilkinson power divider. The operating bandwidth of the power divider is improved by introducing radial stub to the design. To observe the significant bandwidth enhancement, the performance of the power divider with radial stub is compared with the conventional design of power divider. The comparison is investigated using CST Microwave Studio 2010 simulation tool. The overall simulated percentage bandwidth of the radial stub power divider is 37.5%, ranging from 5 to 11 GHz frequency band. To validate the proposed design, the design is fabricated and its S-parameter performances are measured using vector network analyzer. The simulated and measured results of the proposed Wilkinson power divider is compared and analyzed.
Inattentional blindness for a gun during a simulated police vehicle stop

People often fail to notice unexpected objects and events when they are focusing attention on something else. Most studies of this "inattentional blindness" use unexpected objects that are irrelevant to the primary task and to the participant (e.g., gorillas in basketball games or colored shapes in computerized tracking tasks). Although a few studies have examined noticing rates for personally relevant or task-relevant unexpected objects, few have done so in a real-world context with objects that represent a direct threat to the participant. In this study, police academy trainees (n = 100) and experienced police officers (n = 75) engaged in a simulated vehicle traffic stop in which they approached a vehicle to issue a warning or citation for running a stop sign. The driver was either passive and cooperative or agitated and hostile when complying with the officer's instructions. Overall, 58% of the trainees and 33% of the officers failed to notice a gun positioned in full view on the passenger dashboard. The driver's style of interaction had little effect on noticing rates for either group. People can experience inattentional blindness for a potentially dangerous object in a naturalistic real-world context, even when noticing that object would change how they perform their primary task and even when their training focuses on awareness of potential threats.
Competitive probabilistic neural network

Probabilistic neural networks (PNNs) are artificial neural network algorithms widely used in pattern recognition and classification problems. In the traditional PNN algorithm, the probability density function (PDF) is approximated using the entire training dataset for each class. In some complex datasets, classmate clusters may be located far from each other and these distances between clusters may cause a reduction in the correct class’s posterior probability and lead to misclassification. This paper presents a novel PNN algorithm, the competitive probabilistic neural network (CPNN). In the CPNN, a competitive layer ranks kernels for each class and an optimum fraction of kernels are selected to estimate the class-conditional probability. Using a stratified, repeated, random subsampling cross-validation procedure and 9 benchmark classification datasets, CPNN is compared to both traditional PNN and the state of the art (e.g. enhanced probabilistic neural network, EPNN). These datasets are examined with and without noise and the algorithm is evaluated with several ratios of training to testing data. In all datasets (225 simulation categories), performance percentages of both CPNN and EPNN are greater than or equivalent to that of the traditional PNN; in 73% of simulation categories, the CPNN analyses show modest improvement in performance over the state of the art.
Wiring the Vascular Network with Neural Cues: A CNS Perspective

The vascular and the nervous system are responsible for oxygen, nutrient, and information transfer and thereby constitute highly important communication systems in higher organisms. These functional similarities are reflected at the anatomical, cellular, and molecular levels, where common developmental principles and mutual crosstalks have evolved to coordinate their action. This resemblance of the two systems at different levels of complexity has been termed the "neurovascular link." Most of the evidence demonstrating neurovascular interactions derives from studies outside the CNS and from the CNS tissue of the retina. However, little is known about the specific properties of the neurovascular link in the brain. Here, we focus on regulatory effects of molecules involved in the neurovascular link on angiogenesis in the periphery and in the brain and distinguish between general and CNS-specific cues for angiogenesis. Moreover, we discuss the emerging molecular interactions of these angiogenic cues with the VEGF-VEGFR-Delta-like ligand 4 (Dll4)-Jagged-Notch pathway.
Offline signature recognition using neural networks approach

The fact that the signature is widely used as a means of personal verification emphasizes the need for an automatic verification system because of the unfortunate side-effect of being easily abused by those who would feign the identification or intent of an individual. A great deal of work has been done in the area of off-line signature verification over the past few decades. Verification can be performed either Offline or Online based on the application. Online systems use dynamic information of a signature captured at the time the signature is made. Offline systems work on the scanned image of a signature. In this paper, we present a method for Offline Verification of signatures using a set of simple shape based geometric features. The features that are used are Area, Center of gravity, Eccentricity, Kurtosis and Skewness. Before extracting the features, preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. The system is initially trained using a database of signatures obtained from those individuals whose signatures have to be authenticated by the system. The details of preprocessing as well as the features depicted above are described throughout the discussion. Then artificial neural network (ANN) was used to verify and classify the signatures: exact or forged, and a classification ratio of about 93% was obtained under a threshold of 90%.The implementation details and simulation results are discussed in the thesis.
Medial epicanthoplasty using the palpebral margin incision method.

Epicanthal deformity is one of the most frequently encountered cosmetic problems in Asian people. Herein, we introduce a new method for correction of epicanthal folds, which always is performed in combination with double eyelidplasty.



First, through upper and lower palpebral margin incisions, we release and excise the connective and orbicularis oculi muscle dense fibres underlying the epicanthal folds, as well as the superficial head of the medial canthal ligament. After repositioning the medial canthus in a double eyelidplastic procedure, we cut off the redundant skin tissue and close the incisions.



82 epicanthoplasties have been performed during the past 2 years. Follow-up time ranged from 1 to 32 months. Postsurgery scars were invisible in all cases. All patients were satisfied with the results. No recurrence of the epicanthal fold was observed.



The new method introduced has advantages in avoiding scar formation and is an especially suitable approach for epicanthoplasty in Asian patients.
Circular Polarization and Polarization Reconfigurable Designs for Annular Slot Antennas

A simple design for circularly-polarized (CP) annular slot antennas is first described. The antenna is fed by a V-shaped coupling strip loaded with a small resistance, and it can generate CP radiation as long as the inclined angle of the V-shaped coupling strip is properly adjusted. Numerical analyses to the effects of varying the angle on CP axial ratio are performed. Several CP prototypes are fabricated. Both simulated and measured results demonstrate that the proposed feeding mechanism can give good CP performances for the case that the slot width is varied from 0.008 to 0.09 λ0. Then, a design for polarization reconfigurable antennas is developed from the feeding mechanism. Only two PIN diodes are involved in the reconfigurable design that can offer the switching among three different polarizations, including one linear polarization and dual orthogonal circular polarizations. Details of the designs and experimental results are shown.
Bioconversion of dairy manure by black soldier fly (Diptera: Stratiomyidae) for biodiesel and sugar production.

Modern dairies cause the accumulation of considerable quantity of dairy manure which is a potential hazard to the environment. Dairy manure can also act as a principal larval resource for many insects such as the black soldier fly, Hermetia illucens. The black soldier fly larvae (BSFL) are considered as a new biotechnology to convert dairy manure into biodiesel and sugar. BSFL are a common colonizer of large variety of decomposing organic material in temperate and tropical areas. Adults do not need to be fed, except to take water, and acquired enough nutrition during larval development for reproduction. Dairy manure treated by BSFL is an economical way in animal facilities. Grease could be extracted from BSFL by petroleum ether, and then be treated with a two-step method to produce biodiesel. The digested dairy manure was hydrolyzed into sugar. In this study, approximately 1248.6g fresh dairy manure was converted into 273.4 g dry residue by 1200 BSFL in 21 days. Approximately 15.8 g of biodiesel was gained from 70.8 g dry BSFL, and 96.2g sugar was obtained from the digested dairy manure. The residual dry BSFL after grease extraction can be used as protein feedstuff.
Compact UWB Bandpass Filter With Improved Upper-Stopband Performance

In this letter, a novel ultra-wideband (UWB) bandpass filter with compact size and improved upper-stopband performance has been studied and implemented using multiple-mode resonator (MMR). The MMR is formed by attaching three pairs of circular impedance-stepped stubs in shunt to a high impedance microstrip line. By simply adjusting the radius of the circles of the stubs, the resonant modes of the MMR can be roughly allocated within the 3.1-10.6 GHz UWB band while suppressing the spurious harmonics in the upper-stopband. In order to enhance the coupling degree, two interdigital coupled-lines are used in the input and output sides. Thus, a predicted UWB passband is realized. Meanwhile, the insertion loss is higher than 30.0 dB in the upper-stopband from 12.1 to 27.8 GHz. Finally, the filter is successfully designed and fabricated. The EM-simulated and the measured results are presented in this work where excellent agreement between them is obtained.
Genetic association study of circadian genes with seasonal pattern in bipolar disorders

About one fourth of patients with bipolar disorders (BD) have depressive episodes with a seasonal pattern (SP) coupled to a more severe disease. However, the underlying genetic influence on a SP in BD remains to be identified. We studied 269 BD Caucasian patients, with and without SP, recruited from university-affiliated psychiatric departments in France and performed a genetic single-marker analysis followed by a gene-based analysis on 349 single nucleotide polymorphisms (SNPs) spanning 21 circadian genes and 3 melatonin pathway genes. A SP in BD was nominally associated with 14 SNPs identified in 6 circadian genes: NPAS2, CRY2, ARNTL, ARNTL2, RORA and RORB. After correcting for multiple testing, using a false discovery rate approach, the associations remained significant for 5 SNPs in NPAS2 (chromosome 2:100793045-100989719): rs6738097 (pc = 0.006), rs12622050 (pc = 0.006), rs2305159 (pc = 0.01), rs1542179 (pc = 0.01), and rs1562313 (pc = 0.02). The gene-based analysis of the 349 SNPs showed that rs6738097 (NPAS2) and rs1554338 (CRY2) were significantly associated with the SP phenotype (respective Empirical p-values of 0.0003 and 0.005). The associations remained significant for rs6738097 (NPAS2) after Bonferroni correction. The epistasis analysis between rs6738097 (NPAS2) and rs1554338 (CRY2) suggested an additive effect. Genetic variations in NPAS2 might be a biomarker for a seasonal pattern in BD.
When being right is not enough: four-year-olds distinguish knowledgeable informants from merely accurate informants.

Recent evidence demonstrates that children are selective in their social learning, preferring to learn from a previously accurate speaker than from a previously inaccurate one. We examined whether children assessing speakers' reliability take into account how speakers achieved their prior accuracy. In Study 1, when faced with two accurate informants, 4- and 5-year-olds (but not 3-year-olds) were more likely to seek novel information from an informant who had previously given the answers unaided than from an informant who had always relied on help from a third party. Similarly, in Study 2, 4-year-olds were more likely to trust the testimony of an unaided informant over the testimony provided by an assisted informant. Our results indicate that when children reach around 4 years of age, their selective trust extends beyond simple generalizations based on informants' past accuracy to a more sophisticated selectivity that distinguishes between truly knowledgeable informants and merely accurate informants who may not be reliable in the long term.
Efficient Realization of Householder Transform Through Algorithm-Architecture Co-Design for Acceleration of QR Factorization

QR factorization is a ubiquitous operation in many engineering and scientific applications. In this paper, we present efficient realization of Householder Transform (HT) based QR factorization through algorithm-architecture co-design where we achieve performance improvement of 3-90x in-terms of Gflops/watt over state-of-the-art multicore, General Purpose Graphics Processing Units (GPGPUs), Field Programmable Gate Arrays (FPGAs), and ClearSpeed CSX700. Theoretical and experimental analysis of classical HT is performed for opportunities to exhibit higher degree of parallelism where parallelism is quantified as a number of parallel operations per level in the Directed Acyclic Graph (DAG) of the transform. Based on theoretical analysis of classical HT, an opportunity to re-arrange computations in the classical HT is identified that results in Modified HT (MHT) where it is shown that MHT exhibits 1.33x times higher parallelism than classical HT. Experiments in off-the-shelf multicore and General Purpose Graphics Processing Units (GPGPUs) for HT and MHT suggest that MHT is capable of achieving slightly better or equal performance compared to classical HT based QR factorization realizations in the optimized software packages for Dense Linear Algebra (DLA). We implement MHT on a customized platform for Dense Linear Algebra (DLA) and show that MHT achieves 1.3x better performance than native implementation of classical HT on the same accelerator. For custom realization of HT and MHT based QR factorization, we also identify macro operations in the DAGs of HT and MHT that are realized on a Reconfigurable Data-path (RDP). We also observe that due to re-arrangement in the computations in MHT, custom realization of MHT is capable of achieving 12 percent better performance improvement over multicore and GPGPUs than the performance improvement reported by General Matrix Multiplication (GEMM) over highly tuned DLA software packages for multicore and GPGPUs which is counter-intuitive.
Neutrosophic Association Rule Mining Algorithm for Big Data Analysis

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules.
5G: Adaptable Networks Enabled by Versatile Radio Access Technologies

The requirements and key areas for 5G are gradually becoming more apparent, and it is becoming clear that 5G will need to be able to deal with increased levels of diversity in both the requirements it must fulfil and the technologies that it uses to fulfil them. The diverse and demanding requirements for 5G necessitate a shift away from the rigid networks of previous generations, toward a more versatile and adaptable network. Essential to enabling this level of adaptability in 5G networks will be the new radio access technologies that are employed. In previous generations, the radio access network (RAN) was composed of technologies and techniques that were tailored to satisfy the killer application of that era. In contrast, 5G will require versatile solutions that can be adapted to satisfy many different services and applications. The core network will also undergo fundamental changes, with increased levels of abstraction allowing for further reconfiguration of the network. The relationship between the RAN and core network will have a key role to play in managing and enabling adaptable networks. In this paper, we survey the choices and adaptability afforded by some of the radio access technologies being considered for 5G and explore how several system-level techniques, such as software-defined networking and cloud-RAN, can be utilized to enable and manage versatile 5G networks. Specifically, we focus on the relationship between new radio access technologies and emerging system-level techniques, examining how they may assist and complement each other. In this regard, we examine some tools such as virtualization and cognitive networks that can bridge this relationship. This paper is not intended to be a general survey on 5G, but rather a survey on how the requirements of flexibility and adaptability may be achieved in 5G through the coupling of versatile radio access technologies and emerging system-level techniques.