nils-herrmann/clean_scirepeval huggingface dataset:
Simultaneous assessment of plasmatic , acrosomal , and mitochondrial membranes in ram sperm by fluorescent probes

In this experiment, it was defined a protocol of fluorescent probes combination: propidium iodide (PI), fluorescein isothiocyanate-conjugated Pisum sativum agglutinin (FITC-PSA), and JC-1. For this purpose, four ejaculates from three different rams (n=12), all showing motility 80% and abnormal morphology 10%, were diluted in TALP medium and split into two aliquots. One of the aliquots was flash frozen and thawed in three continuous cycles, to induce damage in cellular membranes and to disturb mitochondrial function. Three treatments were prepared with the following fixed ratios of fresh semen:flash frozen semen: 0:100 (T0), 50:50 (T50), and 100:0 (T100). Samples were stained in the proposal protocol and evaluated by epifluorescence microscopy. For plasmatic membrane integrity, detected by PI probe, it was obtained the equation: Ŷ=1.09+0.86X (R 2 =0.98). The intact acrosome, verified by the FITC-PSA probe, produced the equation: Ŷ=2.76+0.92X (R 2 =0.98). The high mitochondrial membrane potential, marked in red-orange by JC-1, was estimated by the equation: Ŷ=1.90+0.90X (R 2 =0.98). The resulting linear equations demonstrate that this technique is efficient and practical for the simultaneous evaluations of the plasmatic, acrosomal, and mitochondrial membranes in ram spermatozoa.
An Effective Method of Probe Calibration in Phase-Resolved Near-Field Scanning for EMI Application

Near-field scanning can be used to determine the far-field emissions of electronic devices. In general, this requires phase-resolved electric and magnetic near-field data. To capture a broad frequency range relatively quickly, a multichannel oscilloscope can be used for data capture. The phase relationship of the fields between different space points and between the electric and the magnetic field needs to be known. Consequently, it is required to determine the complex-valued probe factor (PF) of the probe, cable, and amplifier chain. This paper presents a fast and efficient calibration method which uses the same setup and instruments during calibration and measurement, and it allows for easy and economical integration of the calibration hardware and software into the scanning system. Known fields are created by a microstrip trace driven with a comb generator. By referencing measured data to this known field, the PF is obtained over a broad frequency range by capturing one time-domain waveform.
Development of extensible open information extraction

Open information extraction is used to extract information from open-domain documents such as articles in the Internet. Most extraction techniques have common extraction flow starting from collecting documents, preprocessing documents, extracting informations, postprocessing extracted relations and evaluation of extracted relations. Implementation of a new technique requires the user to build the whole process from scratch. Consequently, implementation becomes a complex and time-consuming process. Therefore, a prototype acting as a framework to abstract and split the whole extraction processes into multiple separate processes implemented as components or plugins will allow users to reuse already existing extraction subprocess.
Use of cervical vertebral maturation to determine skeletal age.

The purpose of this study was to evaluate the validity of the cervical vertebral maturation (CVM) method as an indicator of skeletal age in the circumpubertal period by correlating it to the hand-wrist method (HWM).



Hand-wrist and lateral cephalometric radiographs of 400 Chinese subjects were randomly selected. Their ages were 10 to 15 years for girls and 12 to 17 years for boys, so they were within the circumpubertal period. Skeletal ages were assessed according to the CVM method and the HWM.



The CVM was significantly correlated with HWM skeletal age (Spearman r = 0.9521 [boys] and 0.9408 [girls]). All patients in cervical vertebral stage 3 of the CVM corresponded to stages MP3-FG or MP3-G (around the peak of the growth spurt) in the HWM.



The CVM is a valid indicator of skeletal growth during the circumpubertal period, providing information for timing of growth modification.
Developing user interfaces with EMF parsley

In this paper we describe the main features of EMF Parsley, a new Eclipse project for implementing applications based on the Eclipse Modeling Framework (EMF). EMF Parsley aims at complementing the EMF reflective mechanisms with respect to rapidly creating user interfaces based on models, without having to deal with internal details and setup code. In particular, EMF Parsley uses injection mechanisms to easily customize all the aspects of such applications. Moreover, it provides a set of reusable user interface components like trees, tables and detail forms that manage the model with the introspective EMF capabilities, together with reusable views, editors and dialogs. Besides project wizards, to easily create projects based on EMF Parsley, the main developing tool is a DSL, implemented with Xtext/Xbase, that provides a rapid customization mechanism.
T APL Environmental Assessment for Navy Anti-Air Warfare

he role of the ocean environment in defense of U.S. Navy ships is amplified when they operate in coastal regions. To address this, APL has developed a unique capability to characterize critical aspects of the ocean environment, particularly those that impact Navy shipboard combat systems through their effects on microwave and infrared propagation. APL produces analysis techniques, instrumentation, models, simulations, and tactical decision aids that support a surface ship’s defense against seaskimming missiles. The environmental characterization skill grew out of a need to understand the capabilities and limitations of the Aegis weapon system in the 1980s. Success led to support of a wide variety of Navy and NATO programs for system analysis, at-sea testing, and sensor development. This article provides an overview of the story of environmental assessment and the successes that sprang from the capability to measure and analyze the ocean environment for the Surface Fleet.
On Teaching Big Data Query Languages

Big data computing systems (e.g., Hadoop) have recently seen tremendous intake as computing platforms for data-intensive applications. The emergence of such big data computing systems has triggered a plenty of new techniques for data management. For example, several new query paradigms have been introduced including map-reduce, HiveQL, Impala, Pig Latin, and Spark. In order to cope with this big-data surge and hence meet the current job market requirements, computer science students need to have a good understanding of the big-data technologies. In this paper, we give a module to teach the basics of three big-data query languages. First, we give a categorization of big-data languages into three categories: procedural, declarative, and scripting. Then we pick one language from each category and show how a given query can be expressed by the three syntactically-different languages. We mainly focus on queries that are composed of one or more of the various relational algebra operators (e.g., select, project, join, and group by). We believe that the process of contrasting the languages helps students to gain deeper understanding of the expressive power similarities between the various languages, and hence it will be easier to learn new languages as they are being introduced. Throughout this paper we will give various teaching resources that can be used by instructors to teach the proposed module.
A cell culture model for T lymphocyte clonal anergy.

T lymphocytes respond to foreign antigens both by producing protein effector molecules known as lymphokines and by multiplying. Complete activation requires two signaling events, one through the antigen-specific receptor and one through the receptor for a costimulatory molecule. In the absence of the latter signal, the T cell makes only a partial response and, more importantly, enters an unresponsive state known as clonal anergy in which the T cell is incapable of producing its own growth hormone, interleukin-2, on restimulation. Our current understanding at the molecular level of this modulatory process and its relevance to T cell tolerance are reviewed.
Small Changes, Big Changes: An Updated View on the Android Permission System

Since the appearance of Android, its permission system was central to many studies of Android security. For a long time, the description of the architecture provided by Enck et al. in  was immutably used in various research papers. The introduction of highly anticipated runtime permissions in Android 6.0 forced us to reconsider this model. To our surprise, the permission system evolved with almost every release. After analysis of 16 Android versions, we can confirm that the modifications, especially introduced in Android 6.0, considerably impact the aptness of old conclusions and tools for newer releases. For instance, since Android 6.0 some signature permissions, previously granted only to apps signed with a platform certificate, can be granted to third-party apps even if they are signed with a non-platform certificate; many permissions considered before as threatening are now granted by default. In this paper, we review in detail the updated system, introduced changes, and their security implications. We highlight some bizarre behaviors, which may be of interest for developers and security researchers. We also found a number of bugs during our analysis, and provided patches to AOSP where possible.
Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays.

A tetrazolium salt has been used to develop a quantitative colorimetric assay for mammalian cell survival and proliferation. The assay detects living, but not dead cells and the signal generated is dependent on the degree of activation of the cells. This method can therefore be used to measure cytotoxicity, proliferation or activation. The results can be read on a multiwell scanning spectrophotometer (ELISA reader) and show a high degree of precision. No washing steps are used in the assay. The main advantages of the colorimetric assay are its rapidity and precision, and the lack of any radioisotope. We have used the assay to measure proliferative lymphokines, mitogen stimulations and complement-mediated lysis.
Electronic Muscles and Skins: A Review of Soft Sensors and Actuators.

This article reviews several classes of compliant materials that can be utilized to fabricate electronic muscles and skins. Different classes of materials range from compliant conductors, semiconductors, to dielectrics, all of which play a vital and cohesive role in the development of next generation electronics. This paper covers recent advances in the development of new materials, as well as the engineering of well-characterized materials for the repurposing in applications of flexible and stretchable electronics. In addition to compliant materials, this article further discusses the use of these materials for integrated systems to develop soft sensors and actuators. These new materials and new devices pave the way for a new generation of electronics that will change the way we see and interact with our devices for decades to come.
Learning Word-Class Lattices for Definition and Hypernym Extraction

Definition extraction is the task of automatically identifying definitional sentences within texts. The task has proven useful in many research areas including ontology learning, relation extraction and question answering. However, current approaches – mostly focused on lexicosyntactic patterns – suffer from both low recall and precision, as definitional sentences occur in highly variable syntactic structures. In this paper, we propose WordClass Lattices (WCLs), a generalization of word lattices that we use to model textual definitions. Lattices are learned from a dataset of definitions from Wikipedia. Our method is applied to the task of definition and hypernym extraction and compares favorably to other pattern generalization methods proposed in the literature.
A friction differential and cable transmission design for a 3-DOF haptic device with spherical kinematics

We present a new mechanical design for a 3-DOF haptic device with spherical kinematics (pitch, yaw, and prismatic radial). All motors are grounded in the base to decrease inertia and increase compactness near the user's hand. An aluminum-aluminum friction differential allows for actuation of pitch and yaw with mechanical robustness while allowing a cable transmission to route through its center. This novel cabling system provides simple, compact, and high-performance actuation of the radial DOF independent of motions in pitch and yaw. We show that the device's capabilities are suitable for general haptic rendering, as well as specialized applications of spherical kinematics such as laparoscopic surgery simulation.
Active Zero-Shot Learning

In multi-label classification in the big data age, the number of classes can be in thousands, and obtaining sufficient training data for each class is infeasible. Zero-shot learning aims at predicting a large number of unseen classes using only labeled data from a small set of classes and external knowledge about class relations. However, previous zero-shot learning models passively accept labeled data collected beforehand, relinquishing the opportunity to select the proper set of classes to inquire labeled data and optimize the performance of unseen class prediction. To resolve this issue, we propose an active class selection strategy to intelligently query labeled data for a parsimonious set of informative classes. We demonstrate two desirable probabilistic properties of the proposed method that can facilitate unseen classes prediction. Experiments on 4 text datasets demonstrate that the active zero-shot learning algorithm is superior to a wide spectrum of baselines. We indicate promising future directions at the end of this paper.
Forecasting Non-Stationary Time Series : From Theory to Algorithms

Generalization bounds for time series prediction and other non-i.i.d. learning scenarios that can be found in the machine learning and statistics literature assume that observations come from a (strictly) stationary distribution. The first bounds for completely non-stationary setting were proved in . In this work we present an extension of these results and derive novel algorithms for forecasting nonstationary time series. Our experimental results show that our algorithms significantly outperform standard autoregressive models commonly used in practice.
Alcohol-Induced Blackouts: A Review of Recent Clinical Research with Practical Implications and Recommendations for Future Studies.

Alcohol-induced blackouts, or memory loss for all or portions of events that occurred during a drinking episode, are reported by approximately 50% of drinkers and are associated with a wide range of negative consequences, including injury and death. As such, identifying the factors that contribute to and result from alcohol-induced blackouts is critical in developing effective prevention programs. Here, we provide an updated review (2010 to 2015) of clinical research focused on alcohol-induced blackouts, outline practical and clinical implications, and provide recommendations for future research.



A comprehensive, systematic literature review was conducted to examine all articles published between January 2010 through August 2015 that focused on vulnerabilities, consequences, and possible mechanisms for alcohol-induced blackouts.



Twenty-six studies reported on alcohol-induced blackouts. Fifteen studies examined prevalence and/or predictors of alcohol-induced blackouts. Six publications described the consequences of alcohol-induced blackouts, and 5 studies explored potential cognitive and neurobiological mechanisms underlying alcohol-induced blackouts.



Recent research on alcohol-induced blackouts suggests that individual differences, not just alcohol consumption, increase the likelihood of experiencing an alcohol-induced blackout, and the consequences of alcohol-induced blackouts extend beyond the consequences related to the drinking episode to include psychiatric symptoms and neurobiological abnormalities. Prospective studies and a standardized assessment of alcohol-induced blackouts are needed to fully characterize factors associated with alcohol-induced blackouts and to improve prevention strategies.
A Data Mining Approach for Retailing Bank Customer Attrition Analysis

Deregulation within the financial service industries and the widespread acceptance of new technologies is increasing competition in the finance marketplace. Central to the business strategy of every financial service company is the ability to retain existing customers and reach new prospective customers. Data mining is adopted to play an important role in these efforts. In this paper, we present a data mining approach for analyzing retailing bank customer attrition. We discuss the challenging issues such as highly skewed data, time series data unrolling, leaker field detection etc, and the procedure of a data mining project for the attrition analysis for retailing bank customers. We use lift as a proper measure for attrition analysis and compare the lift of data mining models of decision tree, boosted naïve Bayesian network, selective Bayesian network, neural network and the ensemble of classifiers of the above methods. Some interesting findings are reported. Our research work demonstrates the effectiveness and efficiency of data mining in attrition analysis for retailing bank.
On Predictive Patent Valuation: Forecasting Patent Citations and Their Types

Patents are widely regarded as a proxy for inventive output which is valuable and can be commercialized by various means. Individual patent information such as technology field, classification, claims, application jurisdictions are increasingly available as released by different venues. This work has relied on a long-standing hypothesis that the citation received by a patent is a proxy for knowledge flows or impacts of the patent thus is directly related to patent value. This paper does not fall into the line of intensive existing work that test or apply this hypothesis, rather we aim to address the limitation of using so-far received citations for patent valuation. By devising a point process based patent citation type aware (self-citation and non-self-citation) prediction model which incorporates the various information of a patent, we open up the possibility for performing predictive patent valuation which can be especially useful for newly granted patents with emerging technology. Study on real-world data corroborates the efficacy of our approach. Our initiative may also have policy implications for technology markets, patent systems and all other stakeholders. The code and curated data will be available to the research community.
Inverse Reinforcement Learning for Marketing

Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of Stochastic Optimal Control. We suggest an alternative approach to study dynamic consumer demand, based on Inverse Reinforcement Learning (IRL). We develop a version of the Maximum Entropy IRL that leads to a highly tractable model formulation that amounts to low-dimensional convex optimization in the search for optimal model parameters. Using simulations of consumer demand, we show that observational noise for identical customers can be easily confused with an apparent consumer heterogeneity.
On Modeling Low-Power Wireless Protocols Based on Synchronous Packet Transmissions

Mathematical models play a pivotal role in understanding and designing advanced low-power wireless systems. However, the distributed and uncoordinated operation of traditional multi-hop low-power wireless protocols greatly complicates their accurate modeling. This is mainly because these protocols build and maintain substantial network state to cope with the dynamics of low-power wireless links. Recent protocols depart from this design by leveraging synchronous transmissions (ST), whereby multiple nodes simultaneously transmit towards the same receiver, as opposed to pair wise link-based transmissions (LT). ST improve the one-hop packet reliability to an extent that efficient multi-hop protocols with little network state are feasible. This paper studies whether ST also enable simple yet accurate modeling of these protocols. Our contribution to this end is two-fold. First, we show, through experiments on a 139-node test bed, that characterizing packet receptions and losses as a sequence of independent and identically distributed (i.i.d.) Bernoulli trials-a common assumption in protocol modeling but often illegitimate for LT-is largely valid for ST. We then show how this finding simplifies the modeling of a recent ST-based protocol, by deriving (i) sufficient conditions for probabilistic guarantees on the end-to-end packet reliability, and (ii) a Markovian model to estimate the long-term energy consumption. Validation using test bed experiments confirms that our simple models are also highly accurate, for example, the model error in energy against real measurements is 0.25%, a figure never reported before in the related literature.
Random Gradient-Free Minimization of Convex Functions

In this paper, we prove the complexity bounds for methods of Convex Optimization based only on computation of the function value. The search directions of our schemes are normally distributed random Gaussian vectors. It appears that such methods usually need at most n times more iterations than the standard gradient methods, where n is the dimension of the space of variables. This conclusion is true both for nonsmooth and smooth problems. For the later class, we present also an accelerated scheme with the expected rate of convergence O(n/k), where k is the iteration counter. For Stochastic Optimization, we propose a zero-order scheme and justify its expected rate of convergence O(n/k). We give also some bounds for the rate of convergence of the random gradient-free methods to stationary points of nonconvex functions, both for smooth and nonsmooth cases. Our theoretical results are supported by preliminary computational experiments.
A Sparse Recurrent Neural Network for Trajectory Prediction of Atlantic Hurricanes

Hurricanes constitute major natural disasters that lead to destruction and loss of lives. Therefore, to reduce economic loss and to save human lives, an accurate forecast of hurricane occurrences is crucial. Despite the availability of data and advanced forecasting techniques, there is a need for effective methods with higher accuracy of prediction. We propose a sparse Recurrent Neural Network (RNN) with flexible topology for trajectory prediction of the Atlantic hurricanes. Topology of the RNN along with the strength of the connections are evolved by a customized Genetic Algorithm. The network is particularly suitable for modeling of hurricanes which have complex systems with unknown dynamics. For prediction of the future trajectories of a target hurricane, the Dynamic Time Warping (DTW) distances between direction of the target hurricane over time, and other hurricanes in the dataset are determined and compared. The most similar hurricanes to the target hurricane are then used for training of the network. Comparisons between the actual tracks of the hurricanes DEAN, SANDY, ISSAC and HUMBERTO, and the generated predictions by the sparse RNN for one and two steps ahead of time show that our approach is quite promising for this aim.