nils-herrmann/clean_scirepeval huggingface dataset:
A synthetic kisspeptin analog that triggers ovulation and advances puberty
Venue: Scientific reports

The neuropeptide kisspeptin and its receptor, KiSS1R, govern the reproductive timeline of mammals by triggering puberty onset and promoting ovulation by stimulating gonadotrophin releasing hormone (GnRH) secretion. To overcome the drawback of kisspeptin short half life we designed kisspeptin analogs combining original modifications, triazole peptidomimetic and albumin binding motif, to reduce proteolytic degradation and to slow down renal clearance, respectively. These analogs showed improved in vitro potency and dramatically enhanced pharmacodynamics. When injected intramuscularly into ewes (15 nmol/ewe) primed with a progestogen, the best analog (compound 6, C6) induced synchronized ovulations in both breeding and non breeding seasons. Ovulations were fertile as demonstrated by the delivery of lambs at term. C6 was also fully active in both female and male mice but was completely inactive in KiSS1R KO mice. Electrophysiological recordings of GnRH neurons from brain slices of GnRH GFP mice indicated that C6 exerted a direct excitatory action on GnRH neurons. Finally, in prepubertal female mice daily injections (0.3 nmol/mouse) for five days significantly advanced puberty. C6 ability to trigger ovulation and advance puberty demonstrates that kisspeptin analogs may find application in the management of livestock reproduction and opens new possibilities for the treatment of reproductive disorders in humans.
Business Process Improvement through Data Mining Techniques: An Experimental Approach

The chapter proposes a general methodology on how to use data mining techniques to support total quality management especially related to the quality tools. The effectiveness of the proposed general methodology is demonstrated through their application. The goal of this chapter is to build the 7 new quality tools based on the rules that are "hidden" in the raw data of a database and to propose solutions and actions that will lead the organization under study to improve its business processes by evaluating the results. Four popular data mining approaches (rough sets, association rules, classification rules and Bayesian networks) were applied on a set of 12.477 case records concerning vehicles damages. The set of rules and patterns that was produced by each algorithm was used as input in order to dynamically form each of the quality tools. This would enable the creation of the quality tools starting from the raw data and passing through the stage of data mining, using automatic software was employed.
Predicting statistics of asynchronous SGD parameters for a large scale distributed deep learning system on GPU supercomputers
Venue: 2016 IEEE International Conference on Big Data (Big Data)

Many studies have shown that Deep Convolutional Neural Networks (DCNNs) exhibit great accuracies given large training datasets in image recognition tasks. Optimization technique known as asynchronous mini batch Stochastic Gradient Descent (SGD) is widely used for deep learning because it gives fast training speed and good recognition accuracies, while it may increases generalization error if training parameters are in inappropriate ranges. We propose a performance model of a distributed DCNN training system called SPRINT that uses asynchronous GPU processing based on mini batch SGD. The model considers the probability distribution of mini batch size and gradient staleness that are the core parameters of asynchronous SGD training. Our performance model takes DCNN architecture and machine specifications as input parameters, and predicts time to sweep entire dataset, mini batch size and staleness with 5% 9% and 19% error in average respectively on several supercomputers with up to thousands of GPUs. Experimental results on two different supercomputers show that our model can steadily choose the fastest machine configuration that nearly meets a target mini batch size.
How to scale distributed deep learning?
Venue: ArXiv

Training time on large datasets for deep neural networks is the principal workflow bottleneck in a number of important applications of deep learning, such as object classification and detection in automatic driver assistance systems (ADAS) To minimize training time, the training of a deep neural network must be scaled beyond a single machine to as many machines as possible by distributing the optimization method used for training. While a number of approaches have been proposed for distributed stochastic gradient descent (SGD) at the current time synchronous approaches to distributed SGD appear to be showing the greatest performance at large scale. Synchronous scaling of SGD suffers from the need to synchronize all processors on each gradient step and is not resilient in the face of failing or lagging processors. In asynchronous approaches using parameter servers, training is slowed by contention to the parameter server. In this paper we compare the convergence of synchronous and asynchronous SGD for training a modern ResNet network architecture on the ImageNet classification problem. We also propose an asynchronous method, gossiping SGD, that aims to retain the positive features of both systems by replacing the all reduce collective operation of synchronous training with a gossip aggregation algorithm. We find, perhaps counterintuitively, that asynchronous SGD, including both elastic averaging and gossiping, converges faster at fewer nodes (up to about 32 nodes) whereas synchronous SGD scales better to more nodes (up to about 100 nodes)
Customer Clustering Using a Combination of Fuzzy C Means and Genetic Algorithms

This study intends to combine the fuzzy c means clustering and genetic algorithms to cluster the customers of steel industry. The customers were divided into two clusters by using the variables of the LRFM (length, recency, frequency, monetary value) model. Results indicated that customers belonging to the first cluster had a higher length of the relationship, recency of trade, and frequency of trade but lower monetary value compared to the average values of these criteria for all customers. The results also showed that customers belonging to the secondcluster had a higher recency of trade and monetary value but lower length of the relationship and frequency of trade compared to the average values of these criteria for all customers. It was also found that the combined algorithm (i.e. fuzzy c means clustering and genetic algorithm) used in this study had a lower mean squared error (MSE) compared to fuzzy c means clustering.
WSN DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks
Venue: J. Sensors

Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS 2) and then processed to produce 23 features. The collected dataset is called WSN DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10 Fold Cross Validation methods. The best results were achieved with 10 Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8% 99.4% 92.2% 75.6% and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks) respectively.
A game theoretic approach for combating node compromise attack in Wireless Sensor Network
Venue: 2016 International Conference on Communication and Signal Processing (ICCSP)

Wireless Sensor Network(WSN) due to their popularity have become common in many domains like defense, traffic management, health applications, environment monitoring etc. With these wide range of applications and increasing popularity of WSN, that carry sensitive information are vulnerable to various threats and attacks. In this paper, an intelligent Intruder Detection System(IDS) is proposed that uses sophisticated data fusion technique, involving the use Neural Network as decision making tool and game theory model for the interaction between victim and attacker node. In specific Bayesian game theory is used in formulating the scenario of the intruder, in IDS. These combined effects make IDS robust to combat against the packets attempting to hack some sensitive information residing in the legitimate node. This paper presents the study and combined implementation of both game theory concept and Neural Network to function as an intelligent IDS, which is trained to produce optimal output strategy with varying parameters of intruder. The obtained results, shows that the defence mechanism can be considered as one of the solution concept towards security concerns of WSN.
Relationships of nativity and length of residence in the U.S. with favorable cardiovascular health among Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL)
Venue: Preventive medicine

Individuals with favorable levels of all readily measured major CVD risk factors (low CV risk) during middle age incur lower cardiovascular morbidity and mortality, lower all cause mortality, and lower Medicare costs at older ages compared to adults with one or more unfavorable CVD risk factors. Studies on predictors of low CV risk in Hispanics/Latinos have focused solely on Mexican Americans. The objective of this study was to use data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL; enrolled 2008 to 2011) to assess relationships of nativity and length of residence in the US, a commonly used proxy for acculturation, with low CV risk (not currently smoking; no diabetes; untreated total cholesterol <200mg/dL; untreated blood pressure<120/<80; body mass index <25kg/m(2) and no major ECG abnormalities) in 15,047 Central American, South American, Cuban, Dominican, Mexican, Puerto Rican men and women, and Hispanic/Latino men and women identifying as other or >1 heritage. We also tested whether associations varied by Hispanic/Latino background. Women living in the US<10years were 1.96 (95% confidence interval: 1.37, 2.80) times more likely to be low CV risk than US born women after adjusting for sociodemographic characteristics, diet, physical activity, and self reported experiences of ethnic discrimination. Findings varied in men by Hispanic/Latino background, but length of residence was largely unrelated to low CV risk. These findings highlight the role acculturative processes play in shaping cardiovascular health in Hispanics/Latinos.
Perceived mutual impact of strategy and organizational structure: Findings from the high technology enterprises

The paper aims to investigate the relationship between strategy and structure in the high technology enterprises. The study attempts to ascertain how chief executive officers perceive the impact of strategy on organizational structure, and likewise impact of structure on strategy, at two phases in the innovation process: the phase of innovation exploration; and the phase of innovation exploitation. The research was conducted in 61 high technology companies based in Poland that operate either in Poland or in the global marketplace. The results show that, during the exploration of innovation, chief executive officers consider that the impact of organizational structure on strategy is stronger than the impact of strategy on structure. During the exploitation of innovations, the impact of strategy on structure is stronger.
Lower track students' sense of academic futility: Selection or effect?

Given the importance of students' judgement of the future rewarding character of studying for their willingness to deliver effort at school, we propose the concept of sense of academic futility to understand track differences in achievement. As research is inconclusive in this matter, this study examines whether negative attitudes such as sense of futility follow from track enrolment. Based on new and unique Flemish data of 4500 first grade secondary school students clustered in 119 tracks in 57 schools, gathered in 2012 and 2014, this study shows by means of multilevel analysis (HLM7) that students' higher sense of academic futility in technical and vocational tracks versus academic tracks exists irrespective of background features and prior feelings of futility. Furthermore, it suggests a higher increment in sense of futility in vocational tracks compared with academic tracks, establishing the usefulness of this concept for tracking research.
An Alkaline Stable, Metal Hydroxide Mimicking Metal Organic Framework for Efficient Electrocatalytic Oxygen Evolution.
Venue: Journal of the American Chemical Society

Postsynthetic ion exchange of [Co2(m Cl)2(btta) (MAF X27 Cl, H2bbta =1H,5H benzo(1,2 d:4,5 d')bistriazole) possessing open metal sites on its pore surface yields a material [Co2(m ) (MAF X27 OH) functionalized by both open metal sites and hydroxide ligands, giving drastically improved electrocatalytic activities for the oxygen evolution reaction (an overpotential of 292 mV at 10.0 mA cm( 2) in 1.0 M KOH solution) Isotope tracing experiments further confirm that the hydroxide ligands are involved in the OER process to provide a low energy intraframework coupling pathway.
Review: Nonstationary Spatial Modeling, with Emphasis on Process Convolution and Covariate Driven Approaches

In many environmental applications involving spatially referenced data, limitations on the number and locations of observations motivate the need for practical and efficient models for spatial interpolation, or kriging. A key component of models for continuously indexed spatial data is the covariance function, which is traditionally assumed to belong to a parametric class of stationary models. While convenient, the assumption of stationarity is rarely realistic; as a result, there is a rich literature on alternative methodologies which capture and model the nonstationarity present in most environmental processes. This review document provides a rigorous and concise description of the existing literature on nonstationary methods, paying particular attention to process convolution (also called kernel smoothing or moving average) approaches. A summary is also provided of more recent methods which leverage covariate information and yield both interpretational and computational benefits.
CBMIR: Content Based Medical Image Retrieval System Using Texture and Intensity for Eye Images

This paper aims to develop an efficient visual content based technique to search, and retrieve images from the medical image database. Various features such as texture, color, intensity, shape, resolution, global and local are extracted from the medical images for the retrieval. In this work; we concentrate on the retrieval of eye images. Intensity and texture feature are used to retrieve images. Both these features are combined to form the single feature vector of the image by using fusion method. Query images are used to retrieve similar images from the database. Euclidean Distance method gives top images matching to our query images.
Multi feature extraction of CBMIR using Fuzzy Inference System
Venue: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

During recent years, the use of analysis and dynamical system modeling methods to solve biological and medical problems has considerably increased. The application of these techniques to physiological systems can generate a deep and diversified understanding of the general nature of these systems and of the complex processes that govern them. Medical Engineering studies the application of engineering and technology concepts to the development of instrumentation, materials, diagnostic and therapeutic devices, artificial organs, and other medical equipment. A content based approach is followed for medical images of CBIR. The purpose of this study is to access the stability of these methods for medical image retrieval. The methods used texture based retrieval GLCM (gray level co occurrence matrix) and local binary pattern (LBP) The work developed in this paper establishes a contribution to modeling and simulation efforts in soft sciences, such as biomedical engineering. Fuzzy Inference System (FIS) constitute yet another qualitative reasoning paradigm. Fuzzy controllers have successfully been applied to various medical systems, and they therefore deserve to be mentioned in this context. Some of the more important contributions to the field of fuzzy systems as related to medical systems have been obtained and reported. To this end, a qualitative methodology based on inference and fuzziness is proposed that addresses some of the methodology inherent in dealing with these types of systems. To illustrate the results obtained in this research effort, two kinds of feature extraction technique of biomedical image retrieval are being used and better performance is observed using Fuzzy Inference system with 83.33 value of sensitivity as compared to ANN.
Mutual Information and Diverse Decoding Improve Neural Machine Translation
Venue: ArXiv

Sequence to sequence neural translation models learn semantic and syntactic relations between sentence pairs by optimizing the likelihood of the target given the source, i.e. $p(y|x) an objective that ignores other potentially useful sources of information. We introduce an alternative objective function for neural MT that maximizes the mutual information between the source and target sentences, modeling the bi directional dependency of sources and targets. We implement the model with a simple re ranking method, and also introduce a decoding algorithm that increases diversity in the N best list produced by the first pass. Applied to the WMT German/English and French/English tasks, the proposed models offers a consistent performance boost on both standard LSTM and attention based neural MT architectures.
On cross domain feature fusion in gearbox fault diagnosis under various operating conditions based on Transfer Component Analysis
Venue: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM)

This paper addresses the cross domain feature extraction and fusion from time domain and frequency domain with spectrum envelop preprocessing and time domain synchronization average principle using Transfer Component Analysis (TCA) for gearbox fault diagnosis. Considering TCA is developed based on kernel methods, the effects of different kernels including Gaussian kernel, Linear kernel, Polynomial kernel and PolyPlus kernel on the performance of TCA are investigated and evaluated in comprehensive experiments of gearbox testbed under various operating conditions. The experimental results show that the presented method can extract and fuse the cross domain features of gearbox conditions by enhancing the reuse of historical data under various operating conditions efficiently, compared with other baseline dimension reduction methods. In addition, TCA with Gaussian kernel presents best performance, especially for low frequency levels of operation.
Fundamental Limits of Caching in Wireless D2D Networks
Venue: IEEE Transactions on Information Theory

We consider a wireless device to device (D2D) network where communication is restricted to be single hop. Users make arbitrary requests from a finite library of files and have pre cached information on their devices, subject to a per node storage capacity constraint. A similar problem has already been considered in an infrastructure setting, where all users receive a common multicast (coded) message from a single omniscient server (e.g. a base station having all the files in the library) through a shared bottleneck link. In this paper, we consider a D2D infrastructureless version of the problem. We propose a caching strategy based on deterministic assignment of subpackets of the library files, and a coded delivery strategy where the users send linearly coded messages to each other in order to collectively satisfy their demands. We also consider a random caching strategy, which is more suitable to a fully decentralized implementation. Under certain conditions, both approaches can achieve the information theoretic outer bound within a constant multiplicative factor. In our previous work, we showed that a caching D2D wireless network with one hop communication, random caching, and uncoded delivery (direct file transmissions) achieves the same throughput scaling law of the infrastructure based coded multicasting scheme, in the regime of large number of users and files in the library. This shows that the spatial reuse gain of the D2D network is order equivalent to the coded multicasting gain of single base station transmission. It is, therefore, natural to ask whether these two gains are cumulative, i.e. if a D2D network with both local communication (spatial reuse) and coded multicasting can provide an improved scaling law. Somewhat counterintuitively, we show that these gains do not cumulate (in terms of throughput scaling law) This fact can be explained by noticing that the coded delivery scheme creates messages that are useful to multiple nodes, such that it benefits from broadcasting to as many nodes as possible, while spatial reuse capitalizes on the fact that the communication is local, such that the same time slot can be reused in space across the network. Unfortunately, these two issues are in contrast with each other.
The development and psychometric properties of the Turkish death anxiety scale (TDAS)
Venue: Death studies

The purpose of the present study was to develop the Turkish Death Anxiety Scale (TDAS) and test its initial psychometric properties. Four independent samples participated in the study: 943 college students in item generation, 388 college students in validation, 171 college students in reliability investigation, and 338 adults in cross validation. Principal component analysis with a varimax rotation revealed that 20 items of the scale contained three factors (i.e. Ambiguity of Death, Exposure to Death, and Agony of Death) which explained over 67% of the total variability. Confirmatory factor analyses indicated acceptable fit. Significant correlations were found between the scale and death anxiety, state anxiety, trait anxiety, depression, and hopelessness in the student sample. In the adult sample, death anxiety significantly correlated with trait anxiety. Reliability coefficients were also found acceptable. The authors conclude that the TDAS is a promising instrument in assessing the death anxiety levels in Turkey.