nils-herrmann/clean_scirepeval huggingface dataset:
Isolated craniosynostosis: prenatal ultrasound of scaphocephaly with polyhydramnios.

Craniosynostosis is an abnormal shape or dimension of the skull caused by premature closure of one or more skull sutures. It includes scaphocephaly, brachycephaly, oxycephaly, plagiocephaly, trigonocephaly, turricephaly, and a cloverleaf-shaped head. The only cases of craniosynostosis that have been reported concern fetuses with complex and marked craniosynostosis syndromes such as acrocraniofacial dysostosis, Apert's syndrome, Beare-Stevenson cutis gyratum syndrome, Calabro's syndrome, etc. Isolated or simple craniosynostosis, however, is a developmental anomaly rarely found on routine ultrasound examination. We present a case that was diagnosed as scaphocephaly with moderate polyhydramnios by prenatal ultrasound. No other structural anomaly was detected, and the karyotyping was normal. So isolated sagittal craniosynostosis was diagnosed prenatally and was confirmed by postnatal skull radiography as well as 3-dimensional computed tomography. The infant underwent bilateral parietal craniectomy at the age of 3 months. He obtained a good result with no mental problems 4 years after the operation. To our knowledge, this is the second report on the prenatal detection of an isolated form of craniosynostosis.
Are computer hacker break-ins ethical?

Recent incidents of unauthorized computer intrusion have brought about discussion of the ethics of breaking into computers. Some individuals have argued that as long as no significant damage results, break-ins may serve a useful purpose. Others counter with the expression that the break-ins are almost always harmful and wrong. This article lists and refutes many of the reasons given to justify computer intrusions. It is the author’s contention that break-ins are ethical only in extreme situations, such as a lifecritical emergency. The article also discusses why no break-in is “harmless.”
Prenatal Diagnosis of Isolated Agnathia-Otocephaly: A Case Report and Review of the Literature

Agnathia is a rare disease characterized by the absence of a mandible. Few cases of prenatally diagnosed isolated agnathia have been reported. We present a case report and review of the literature of prenatally diagnosed agnathia. A 38-year-old woman (gravida 0, para 0) was referred to our hospital at 28 weeks and 3 days of gestation for fetal evaluation because of polyhydramnios and suspected facial anomalies. Three-dimensional ultrasonography and MRI indicated agnathia. Premature rupture of the membranes occurred before the parents could reach a decision on the postnatal treatment. We performed emergency cesarean section on the second day of the 33rd week of gestation. The neonate was deemed nonresuscitable and he died of airway obstruction shortly after birth. Because agnathia is associated with very poor prognosis, accurate prenatal diagnosis and detailed counseling should be promptly provided before unexpected delivery to the parents for the determination of postnatal treatment.
Model Predictive Controller of Boost Converter with RLE Load

This paper discuss about the new approach of control mechanism for dc to dc converters. Using this technique fast response and steady state output can be achieved. Model predictive controller will predict the future output by suitable training of the present and past occurrences. Conventional PI controllers will get the output at lesser response and vast deviation from the output. In this algorithm single input and single output is developed to describe the boost converter. The control objectives of voltage tracking are used by weighted cost function. The proposed algorithm of Model Predictive Controller is done by simulation using MATLAB. Comparative analysis and results of MPC and PI control is observed.
Coffee Silverskin Extract Protects against Accelerated Aging Caused by Oxidative Agents

Nowadays, coffee beans are almost exclusively used for the preparation of the beverage. The sustainability of coffee production can be achieved introducing new applications for the valorization of coffee by-products. Coffee silverskin is the by-product generated during roasting, and because of its powerful antioxidant capacity, coffee silverskin aqueous extract (CSE) may be used for other applications, such as antiaging cosmetics and dermaceutics. This study aims to contribute to the coffee sector's sustainability through the application of CSE to preserve skin health. Preclinical data regarding the antiaging properties of CSE employing human keratinocytes and Caenorhabditis elegans are collected during the present study. Accelerated aging was induced by tert-butyl hydroperoxide (t-BOOH) in HaCaT cells and by ultraviolet radiation C (UVC) in C. elegans. Results suggest that the tested concentrations of coffee extracts were not cytotoxic, and CSE 1 mg/mL gave resistance to skin cells when oxidative damage was induced by t-BOOH. On the other hand, nematodes treated with CSE (1 mg/mL) showed a significant increased longevity compared to those cultured on a standard diet. In conclusion, our results support the antiaging properties of the CSE and its great potential for improving skin health due to its antioxidant character associated with phenols among other bioactive compounds present in the botanical material.
Fuzzy logic modeling for hydrothermal gold mineralization mapping using geochemical, geological, ASTER imageries and other geo-data, a case study in Central Alborz, Iran

The study area is located ~50 km in the north of Tehran capital city, Iran, and is a part of central Alborz Mountain. The intrusive bodies aged post Eocene have intruded in the Eocene volcanic units causing hydrothermal alterations in these units. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used to map hydrothermal alteration zones. The propylitic, phyllic and argillic alteration and iron oxide minerals identified using Spectral Angle Mapper (SAM) method. Structural lineaments were extracted from ASTER images by applying automatic lineament extraction processes and visual interpretations. An exploration model was considered based on previous studies, and appropriate evidence maps were generated, weighted and reclassified. Ore Forming Potential (OFP) map was generated by applying Fuzzy SUM operator on alteration and Pb, Cu, Ag, and Au geochemical anomaly maps. Finally, Host rock, geological structures and OFP were combined using Fuzzy Gamma operator (γ ) to produce mineral prospectivity map. Eventually, the conceptual model discussed here, fairly demonstrated the known hydrothermal gold deposits in the study area and could be a source for future detailed explorations.
Event-based feature tracking with probabilistic data association

Asynchronous event-based sensors present new challenges in basic robot vision problems like feature tracking. The few existing approaches rely on grouping events into models and computing optical flow after assigning future events to those models. Such a hard commitment in data association attenuates the optical flow quality and causes shorter flow tracks. In this paper, we introduce a novel soft data association modeled with probabilities. The association probabilities are computed in an intertwined EM scheme with the optical flow computation that maximizes the expectation (marginalization) over all associations. In addition, to enable longer tracks we compute the affine deformation with respect to the initial point and use the resulting residual as a measure of persistence. The computed optical flow enables a varying temporal integration different for every feature and sized inversely proportional to the length of the flow. We show results in egomotion and very fast vehicle sequences and we show the superiority over standard frame-based cameras.
Blind, Greedy, and Random: Algorithms for Matching and Clustering Using Only Ordinal Information

We study the Maximum Weighted Matching problem in a partial information setting where the agents’ utilities for being matched to other agents are hidden and the mechanism only has access to ordinal preference information. Our model is motivated by the fact that in many settings, agents cannot express the numerical values of their utility for different outcomes, but are still able to rank the outcomes in their order of preference. Specifically, we study problems where the ground truth exists in the form of a weighted graph, and look to design algorithms that approximate the true optimum matching using only the preference orderings for each agent (induced by the hidden weights) as input. If no restrictions are placed on the weights, then one cannot hope to do better than the simple greedy algorithm, which yields a half optimal matching. Perhaps surprisingly, we show that by imposing a little structure on the weights, we can improve upon the trivial algorithm significantly: we design a 1.6-approximation algorithm for instances where the hidden weights obey the metric inequality. Our algorithm is obtained using a simple but powerful framework that allows us to combine greedy and random techniques in unconventional ways. These results are the first non-trivial ordinal approximation algorithms for such problems, and indicate that we can design robust matchings even when we are agnostic to the precise agent utilities.
A2C: Self Destructing Exploit Executions via Input Perturbation

Malicious payload injection attacks have been a serious threat to software for decades. Unfortunately, protection against these attacks remains challenging due to the ever increasing diversity and sophistication of payload injection and triggering mechanisms used by adversaries. In this paper, we develop A2C, a system that provides general protection against payload injection attacks. A2C is based on the observation that payloads are highly fragile and thus any mutation would likely break their functionalities. Therefore, A2C mutates inputs from untrusted sources. Malicious payloads that reside in these inputs are hence mutated and broken. To assure that the program continues to function correctly when benign inputs are provided, A2C divides the state space into exploitable and post-exploitable sub-spaces, where the latter is much larger than the former, and decodes the mutated values only when they are transmitted from the former to the latter. A2C does not rely on any knowledge of malicious payloads or their injection and triggering mechanisms. Hence, its protection is general. We evaluate A2C with 30 realworld applications, including apache on a real-world work-load, and our results show that A2C effectively prevents a variety of payload injection attacks on these programs with reasonably low overhead (6.94%).
Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development—Part 2: Introduction to Pharmacokinetic Modeling Methods

Population pharmacokinetics is the study of pharmacokinetics at the population level, in which data from all individuals in a population are evaluated simultaneously using a nonlinear mixedeffects model. “Nonlinear” refers to the fact that the dependent variable (e.g., concentration) is nonlinearly related to the model parameters and independent variable(s). “Mixed-effects” refers to the parameterization: parameters that do not vary across individuals are referred to as “fixed effects,” parameters that vary across individuals are called “random effects.” There are five major aspects to developing a population pharmacokinetic model: (i) data, (ii) structural model, (iii) statistical model, (iv) covariate models, and (v) modeling software. Structural models describe the typical concentration time course within the population. Statistical models account for “unexplainable” (random) variability in concentration within the population (e.g., betweensubject, between-occasion, residual, etc.). Covariate models explain variability predicted by subject characteristics (covariates). Nonlinear mixed effects modeling software brings data and models together, implementing an estimation method for finding parameters for the structural, statistical, and covariate models that describe the  A primary goal of most population pharmacokinetic modeling evaluations is finding population pharmacokinetic parameters and sources of variability in a population. Other goals include relating observed concentrations to administered doses through identification of predictive covariates in a target population. Population pharmacokinetics does not require “rich” data (many observations/subject), as required for analysis of single-subject data, nor is there a need for structured sampling time schedules. “Sparse” data (few observations/ subject), or a combination, can be used. We examine the fundamentals of five key aspects of population pharmacokinetic modeling together with methods for comparing and evaluating population pharmacokinetic models.
Development of Gaussian Minimum Shift Keying ( GMSK ) Demodulator for Satellite Communication A

ISSN 2277 5080 | This paper discusses DSP based implementation of Gaussian Minimum Shift Keying (GMSK) demodulator using Polarity type Costas loop. The demodulator consists of a Polarity type Costas loop for carrier recovery, data recovery, and phase detection. Carrier has been recovered using a loop of center-frequency locking scheme as in M-ary Phase Shift Keying (MPSK) Polarity type Costas-loop. Phase unwrapping and Bit-Reconstruction is presented in detail. All the modules are first modeled in MATLAB (Simulink) and Systemview. After bit true simulation, the design is coded in VHDL and code simulation is done using QuestaSim 6.3c. The design is targeted to Virtex-4 XC4VSX35-10FF668 Xilinx FPGA (Field programmable gate array) for real time testing, which is carried out on Xtreme DSP development platform.
Protein Folding Optimization using Differential Evolution Extended with Local Search and Component Reinitialization

This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and that the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers, within a budget of 10 solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper.
Job characteristics and employee well-being : a test of Warr ' s Vitamin Model in health care workers using structural equation modelling

Warr's (1987) Vitamin Model was investigated in a representative sample of 1437 Dutch health care workers (i.e. nurses and nurses' aides). According to this model, it was hypothesized that three job characteristics (i.e. job demands, job autonomy, and workplace social support) are curvilinearly related with three key indicators of employee well-being (i.e. job satisfaction, job-related anxiety, and emotional exhaustion). Structural equation modelling (LISREL 8) was employed to test the comprehensive Vitamin Model. The results showed that the ®t of the non-linear model is superior to that of the linear model. Except for the relationship between job autonomy and emotional exhaustion, the curvilinear relationships followed the predicted U-shaped or inverted U-shaped curvilinear pattern. Moreover, it appeared that the three job characteristics are dierentially related with various indicators of employee well-being. In conclusion, this study partially supports the assertion of the Vitamin Model that non-linear relationships exist between job characteristics and employee well-being.
Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

Performing cellular long term evolution (LTE) communications in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome wireless spectrum scarcity. However, to reap the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and f ractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-LAA operators. Adopting a proactive coexistence mechanism enables future delay-tolerant LTE-LAA data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-LAA traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as homo egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with wireless local area network and other LTE-LAA operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium, when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-LAA network.
Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique

These days, microarray gene expression data are playing an essential role in cancer classifications. However, due to the availability of small number of effective samples compared to the large number of genes in microarray data, many computational methods have failed to identify a small subset of important genes. Therefore, it is a challenging task to identify small number of disease-specific significant genes related for precise diagnosis of cancer sub classes. In this paper, particle swarm optimization (PSO) method along with adaptive K-nearest neighborhood (KNN) based gene selection technique are proposed to distinguish a small subset of useful genes that are sufficient for the desired classification purpose. A proper value of K would help to form the appropriate numbers of neighborhood to be explored and hence to classify the dataset accurately. Thus, a heuristic for selecting the optimal values of K efficiently, guided by the classification accuracy is also proposed. This proposed technique of finding minimum possible meaningful set of genes is applied on three benchmark microarray datasets, namely the small round blue cell tumor (SRBCT) data, the acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) data and the mixed-lineage leukemia (MLL) data. Results demonstrate the usefulness of the proposed method in terms of classification accuracy on blind test samples, number of informative genes and computing time. Further, the usefulness and universal characteristics of the identified genes are reconfirmed by using different classifiers, such as support vector machine (SVM). 2014
Friendship clique and peer influences on body image concerns, dietary restraint, extreme weight-loss behaviors, and binge eating in adolescent girls.

This study explored friendship variables in relation to body image, dietary restraint, extreme weight-loss behaviors (EWEBs), and binge eating in adolescent girls. From 523 girls, 79 friendship cliques were identified using social network analysis. Participants completed questionnaires that assessed body image concerns, eating, friendship relations, and psychological family, and media variables. Similarity was greater for within than for between friendship cliques for body image concerns, dietary restraint, and EWLBs, but not for binge eating. Cliques high in body image concerns and dieting manifested these concerns in ways consistent with a high weight/shape-preoccupied subculture. Friendship attitudes contributed significantly to the prediction of individual body image concern and eating behaviors. Use of EWLBs by friends predicted an individual's own level of use.
Publishing Danish Agricultural Government Data as Semantic Web Data

Recent advances in Semantic Web technologies have led to a growing popularity of the Linked Open Data movement. Only recently, the Danish government has joined the movement and published several datasets as Open Data. These raw datasets are difficult to process automatically and combine with other data sources on the Web. Hence, our goal is to convert such data into RDF and make it available to a broader range of users and applications as Linked Open Data. In this paper, we discuss our experiences based on the particularly interesting use case of agricultural data as agriculture is one of the most important industries in Denmark. We describe the process of converting the data and discuss the particular problems that we encountered with respect to the considered datasets. We additionally evaluate our result based on several queries that could not be answered based on existing sources before.
Vocal Experimentation in the Juvenile Songbird Requires a Basal Ganglia Circuit

Songbirds learn their songs by trial-and-error experimentation, producing highly variable vocal output as juveniles. By comparing their own sounds to the song of a tutor, young songbirds gradually converge to a stable song that can be a remarkably good copy of the tutor song. Here we show that vocal variability in the learning songbird is induced by a basal-ganglia-related circuit, the output of which projects to the motor pathway via the lateral magnocellular nucleus of the nidopallium (LMAN). We found that pharmacological inactivation of LMAN dramatically reduced acoustic and sequence variability in the songs of juvenile zebra finches, doing so in a rapid and reversible manner. In addition, recordings from LMAN neurons projecting to the motor pathway revealed highly variable spiking activity across song renditions, showing that LMAN may act as a source of variability. Lastly, pharmacological blockade of synaptic inputs from LMAN to its target premotor area also reduced song variability. Our results establish that, in the juvenile songbird, the exploratory motor behavior required to learn a complex motor sequence is dependent on a dedicated neural circuit homologous to cortico-basal ganglia circuits in mammals.