nils-herrmann/clean_scirepeval huggingface dataset:
IP3 receptor in the hair cells of frog semicircular canal and its possible functional role.

The presence and functional role of inositol trisphosphate receptors (IP3R) was investigated by electrophysiology and immunohistochemistry in hair cells from the frog semicircular canal. Intracellular recordings were performed from single fibres of the posterior canal in the isolated, intact frog labyrinth, at rest and during rotation, in the presence of IP3 receptor inhibitors and drugs known to produce Ca2+ release from the internal stores or to increase IP3 production. Hair cell immunolabelling for IP3 receptor was performed by standard procedures. The drug 2-aminoethoxydiphenyl borate (2APB), an IP3 receptor inhibitor, produced a marked decrease of mEPSP and spike frequency at low concentration (0.1 mm), without affecting mEPSP size or time course. At high concentration (1 mm), 2APB is reported to block the sarcoplasmic-endoplasmic reticulum Ca2+-ATPase (SERCA pump) and increase [Ca2+]i; at the labyrinthine cytoneural junction, it greatly enhanced the resting and mechanically evoked sensory discharge frequency. The selective agonist of group I metabotropic glutamate receptors (RS)-3,5-dihydroxyphenylglycine (DHPG, 0.6 mm), produced a transient increase in resting mEPSP and spike frequency at the cytoneural junction, with no effects on mEPSP shape or amplitude. Pretreatment with cyclopiazonic acid (CPA, 0.1 mm), a SERCA pump inhibitor, prevented the facilitatory effect of both 2APB and DHPG, suggesting a link between Ca2+ release from intracellular stores and quantal emission. Consistently, diffuse immunoreactivity for IP3 receptors was observed in posterior canal hair cells. Our results indicate the presence and a possibly relevant functional role of IP3-sensitive stores in controlling [Ca2+]i and modulating the vestibular discharge.
An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems

Graph processing is increasingly used in knowledge economies and in science, in advanced marketing, social networking, bioinformatics, etc. A number of graph-processing systems, including the GPU-enabled Medusa and Totem, have been developed recently. Understanding their performance is key to system selection, tuning, and improvement. Previous performance evaluation studies have been conducted for CPU-based graph-processing systems, such as Graph and GraphX. Unlike them, the performance of GPU-enabled systems is still not thoroughly evaluated and compared. To address this gap, we propose an empirical method for evaluating GPU-enabled graph-processing systems, which includes new performance metrics and a selection of new datasets and algorithms. By selecting 9 diverse graphs and 3 typical graph-processing algorithms, we conduct a comparative performance study of 3 GPU-enabled systems, Medusa, Totem, and MapGraph. We present the first comprehensive evaluation of GPU-enabled systems with results giving insight into raw processing power, performance breakdown into core components, scalability, and the impact on performance of system-specific optimization techniques and of the GPU generation. We present and discuss many findings that would benefit users and developers interested in GPU acceleration for graph processing.
IFA-Based Metal-Frame Antenna Without Ground Clearance for the LTE/WWAN Operation in the Metal-Casing Tablet Computer

A metal-frame antenna for the long-term evolution/wireless wide area network (LTE/WWAN) operation in the metal-casing tablet computer is presented. The antenna is formed by using two inverted-F antenna (IFA) structures to provide a low band and a high band to, respectively, cover the LTE/WWAN operation in the 824-960 and 1710-2690 MHz bands. The larger IFA has a longer radiating metal strip for the low band, and the smaller IFA has a shorter radiating metal strip for the high band. The two radiating metal strips are configured to be a portion of the metal frame disposed around the edges of the metal back cover of the tablet computer. The projection of the metal frame lies on the edges of the metal back cover, such that there is no ground clearance between the projection and the metal back cover. Furthermore, the feeding and shorting strips with matching networks therein for the two IFAs are disposed on a small dielectric substrate (feed circuit board), which is separated from the system circuit board and the metal back cover. In this case, there is generally no planar space of the metal back cover and system circuit board occupied, and the antenna can cover the 824-960/1710-2690 MHz bands. Results of the proposed antenna are presented. An extended study is also presented to show that the antenna's low-band coverage can be widened from 824-960 to 698-960 MHz. The wider bandwidth coverage is obtained when a switchable inductor bank is applied in the larger IFA.
Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns

This study aimed to classify different emotional states by means of EEG-based functional connectivity patterns. Forty young participants viewed film clips that evoked the following emotional states: neutral, positive, or negative. Three connectivity indices, including correlation, coherence, and phase synchronization, were used to estimate brain functional connectivity in EEG signals. Following each film clip, participants were asked to report on their subjective affect. The results indicated that the EEG-based functional connectivity change was significantly different among emotional states. Furthermore, the connectivity pattern was detected by pattern classification analysis using Quadratic Discriminant Analysis. The results indicated that the classification rate was better than chance. We conclude that estimating EEG-based functional connectivity provides a useful tool for studying the relationship between brain activity and emotional states.
Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadataoblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions.
Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data.

Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers, and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data.



Here we describe 'Dhaka', a variational autoencoder method which transforms single cell genomic data to a reduced dimension feature space that is more efficient in differentiating between (hidden) tumor subpopulations. Our method is general and can be applied to several different types of genomic data including copy number variation from scDNA-Seq and gene expression from scRNA-Seq experiments. We tested the method on synthetic and 6 single cell cancer datasets where the number of cells ranges from 250 to 6000 for each sample. Analysis of the resulting feature space revealed subpopulations of cells and their marker genes. The features are also able to infer the lineage and/or differentiation trajectory between cells greatly improving upon prior methods suggested for feature extraction and dimensionality reduction of such data.



All the datasets used in the paper are publicly available and developed software package and supporting info is available on Github https://github.com/MicrosoftGenomics/Dhaka.
Golden Age for Filter Design: Innovative and Proven Approaches for Acoustic Filter, Duplexer, and Multiplexer Design

Mobile communication is already an integral part of our lives, and its role will only continue to grow. While early mobile phone functions were limited mainly to simple address books and text messaging, today's predominant smartphones and tablets support almost endless functionality via third-party application software, bringing an insatiable hunger for higher download rates. Telecommunication associations all around the world  are, therefore, continuously working on new cellular network technologies. This comes along with the introduction of advanced digital modulation schemes with higher spectral efficiency; multiple-input, multiple-output (MIMO)-based radio interfaces; and carrier aggregation (CA) .
Die präperitoneale Netzplastik in der Reparation der Narbenhernie Eine vergleichende retrospektive Studie an 272 operierten Narbenhernien

In a retrospective study on 245 patients we evaluated the results of 272 incisional hernia repairs in the Department Surgery of the University Hospital Aachen. The group consisted of 58 % male and 42 % female patients with a mean age of 61.1 years and 111 primary and 161 recurrent incisional hernias. Conventional techniques (simple closure, Mayo) and alloplastic repairs were performed in 69.9 and 30.1 %, respectively. During the last 4 years we predominantly used the preperitoneal mesh repair with polypropylene mesh (Marlex®). The results of 87 % of our group of patients were evaluated by questionnaire and information from the family physicians (mean follow-up period 64 months). The patients who underwent preperitoneal mesh repair were examined clinically and with ultrasound. In comparison to the results of conventional hernia repair, early complications (seroma, hematoma) were higher. The recurrence rate, however, was significantly lower in this group with mesh repair (6.8 %) than in patients without alloplastic augmentation (32.6 %). Whereas preperitoneal mesh repair is convincingly the ideal surgical technique, optimization of the alloplastic materials by reduction of the amount of foreign substance and improvement of elasticity and biocompatibility is mandatory. In einer retrospektiven Studie an 245 Patienten mit 272 Narbenhernien wurden die Ergebnisse der Narbenhernienoperationen an der Chirurgischen Universitätsklinik Aachen von 1987 bis 1994 untersucht. Dabei handelte es sich um 111 Primär- und 161 Rezidiv-Narbenhernien, 58 % Männer und 42 % Frauen, mit einem Altersdurchschnitt von 61,1 Jahren. An operativen Verfahren kamen bei 69,9 % der Patienten konventionelle Verfahren (Stoß auf Stoß, Mayo) und bei 30,1 % der Fälle eine Reparation mit alloplastischen Materialien zur Anwendung. Dabei verwendeten wir in den letzten 4 Jahren vornehmlich die präperitoneale Netzplastik (PNP) mit einem Polypropylen-Netz (Marlex®). Mittels Fragebogen und Befragung der Hausärzte konnten die Ergebnisse von 87 % der Patienten erfaßt werden (durchschnittlicher Nachbeobachtungszeitraum 64 Monate). Die Patienten mit PNP wurden klinisch und sonographisch nachuntersucht. Der Vergleich der PNP-Patienten mit den konventionellen Verfahren, zeigte eine erhöhte Rate von Frühkomplikationen (Serome, Hämatome), die Rezidivrate war mit 6,8 % jedoch signifikant niedriger als bei den Reparationen ohne alloplastische Materialien (32,6 %). Während die PNP als Operationstechnik als ideales Verfahren überzeugt, scheint eine Optimierung der verwendeten alloplastischen Materialien, mit Reduktion des Fremdmaterials, vermehrter Elastizität und verbesserter Biokompatibilität notwendig.
An Empirical Study of Credit Scoring Model for Credit Card

The purpose of this paper is to propose an optimal credit scoring model to reassess the default risk of credit card holders for credit card issuing banks in Taiwan. This paper adopted four credit scoring models which are the linear discriminant analysis, decision tree, back-propagation neural network, and a hybrid method to evaluate the default risk. By comparing the evaluation results of these models, it shows that the decision tree method has the best classification performance in terms of accuracy and sensitivity. These results of this empirical study will be provided to credit card issuing banks for achieving efficient automatically credit reassessment of default risk.
Vibration Isolation for Active Suspensions With Performance Constraints and Actuator Saturation

This paper investigates the problem of vibration isolation for vehicle active suspension systems, where parameter uncertainties, external disturbances, actuator saturation, and performance constraints are considered in an unified framework. A constrained adaptive robust control technology is developed to not only stabilize the attitude of vehicle in the context of parameter uncertainties and external disturbances, but also cover the problems of actuator saturation and performance constraints. Furthermore, the performance analysis of the closed-loop systems is given, by means of rigorous mathematical derivations. Extensive comparative experimental results are obtained to illustrate the effectiveness of the proposed control law.
A Learning Design Recommendation System Based on Markov Decision Processes

As learning environments are gaining in features and in complexity, the e-learning industry is more and more interested in features easing teachers’ work. Learning design being a critical and time consuming task could be facilitated by intelligent components helping teachers build their learning activities. The Intelligent Learning Design Recommendation System (ILD-RS) is such a software component, designed to recommend learning paths during the learning design phase in a Learning Management System (LMS). Although ILD-RS exploits several parameters which are sometimes subject to controversy, such as learning styles and teaching styles, the main interest of the component lies on its algorithm based on Markov decision processes that takes into account the teacher’s use to refine its accuracy.
Four Perspectives on Human Bias in Visual Analytics

Visual analytic systems, especially mixed-initiative systems, can steer analytical models and adapt views by making inferences from users’ behavioral patterns with the system. Because such systems rely on incorporating implicit and explicit user feedback, they are particularly susceptible to the injection and propagation of human biases. To ultimately guard against the potentially negative effects of systems biased by human users, we must first qualify what we mean by the term bias. Thus, in this paper we describe four different perspectives on human bias that are particularly relevant to visual analytics. We discuss the interplay of human and computer system biases, particularly their roles in mixed-initiative systems. Given that the term bias is used to describe several different concepts, our goal is to facilitate a common language in research and development efforts by encouraging researchers to mindfully choose the perspective(s) considered in their work.
A 5.8nW, 45ppm/°C on-chip CMOS wake-up timer using a constant charge subtraction scheme

This work presents an ultra-low power oscillator designed for wake-up timers in compact wireless sensors. A constant charge subtraction scheme removes continuous comparator delay from the oscillation period, which is the source of temperature dependence in conventional RC relaxation oscillators. This relaxes comparator design constraints, enabling low power operation. In 0.18μm CMOS, the oscillator consumes 5.8nW at room temperature with temperature stability of 45ppm/°C (-10°C to 90°C) and 1%V line sensitivity.
On Cloud Service Reliability Enhancement with Optimal Resource Usage

An increasing number of companies are beginning to deploy services/applications in the cloud computing environment. Enhancing the reliability of cloud service has become a critical and challenging research problem. In the cloud computing environment, all resources are commercialized. Therefore, a reliability enhancement approach should not consume too much resource. However, existing approaches cannot achieve the optimal effect because of checkpoint image-sharing neglect, and checkpoint image inaccessibility caused by node crashing. To address this problem, we propose a cloud service reliability enhancement approach for minimizing network and storage resource usage in a cloud data center. In our proposed approach, the identical parts of all virtual machines that provide the same service are checkpointed once as the service checkpoint image, which can be shared by those virtual machines to reduce the storage resource consumption. Then, the remaining checkpoint images only save the modified page. To persistently store the checkpoint image, the checkpoint image storage problem is modeled as an optimization problem. Finally, we present an efficient heuristic algorithm to solve the problem. The algorithm exploits the data center network architecture characteristics and the node failure predicator to minimize network resource usage. To verify the effectiveness of the proposed approach, we extend the renowned cloud simulator Cloudsim and conduct experiments on it. Experimental results based on the extended Cloudsim show that the proposed approach not only guarantees cloud service reliability, but also consumes fewer network and storage resources than other approaches.
Fake and Spam Messages: Detecting Misinformation During Natural Disasters on Social Media

During natural disasters or crises, users on social media tend to easily believe contents of postings related to the events, and retweet the postings with hoping them to be reached to many other users. Unfortunately, there are malicious users who understand the tendency and post misinformation such as spam and fake messages with expecting wider propagation. To resolve the problem, in this paper we conduct a case study of 2013 Moore Tornado and Hurricane Sandy. Concretely, we (i) understand behaviors of these malicious users, (ii) analyze properties of spam, fake and legitimate messages, (iii) propose flat and hierarchical classification approaches, and (iv) detect both fake and spam messages with even distinguishing between them. Our experimental results show that our proposed approaches identify spam and fake messages with 96.43% accuracy and 0.961 F-measure.
Collaboration and coordination in architectural design : approaches to computer mediated team work

The paper reports on three projects at our laboratory that deal respectively with synchronous collaborative design, asynchronous collaborative design, and design coordination. The Electronic Cocktail Napkin and its mobile extension that runs on hand-held computers supports synchronous design with shared freehand drawing environments. The PHIDIAS hypermedia system supports long-term, asynchronous collaboration by enabling designers of large complex artifacts to store Ž . and retrieve rationale about design decisions and the Construction Kit Builder CKB supports team design by supporting a priori agreements among team members to avoid conflicts. q 1998 Elsevier Science B.V. All rights reserved.
An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation

Financial fraud under IoT environment refers to the unauthorized use ofmobile transaction usingmobile platform through identity theft or credit card stealing to obtain money fraudulently. Financial fraud under IoT environment is the fast-growing issue through the emergence of smartphone and online transition services. In the real world, a highly accurate process of financial fraud detection under IoT environment is needed since financial fraud causes financial loss. Therefore, we have surveyed financial fraud methods using machine learning and deep learning methodology, mainly from 2016 to 2018, and proposed a process for accurate fraud detection based on the advantages and limitations of each research. Moreover, our approach proposed the overall process of detecting financial fraud based on machine learning and compared with artificial neural networks approach to detect fraud and process large amounts of financial data. To detect financial fraud and process large amounts of financial data, our proposed process includes feature selection, sampling, and applying supervised and unsupervised algorithms. The final model was validated by the actual financial transaction data occurring in Korea, 2015.
Managing Phenol Contents in Crop Plants by Phytochemical Farming and Breeding—Visions and Constraints

Two main fields of interest form the background of actual demand for optimized levels of phenolic compounds in crop plants. These are human health and plant resistance to pathogens and to biotic and abiotic stress factors. A survey of agricultural technologies influencing the biosynthesis and accumulation of phenolic compounds in crop plants is presented, including observations on the effects of light, temperature, mineral nutrition, water management, grafting, elevated atmospheric CO(2), growth and differentiation of the plant and application of elicitors, stimulating agents and plant activators. The underlying mechanisms are discussed with respect to carbohydrate availability, trade-offs to competing demands as well as to regulatory elements. Outlines are given for genetic engineering and plant breeding. Constraints and possible physiological feedbacks are considered for successful and sustainable application of agricultural techniques with respect to management of plant phenol profiles and concentrations.
Scaling Machine Learning for Target Prediction in Drug Discovery using Apache Spark

In the context of drug discovery, a key problem is the identification of candidate molecules that affect proteins associated with diseases. Inside Janssen Pharmaceutical, the Chemo genomics project aims to derive new candidates from existing experiments through a set of machine learning predictor programs, written in single-node C++. These programs take a long time to run and are inherently parallel, but do not use multiple nodes. We show how we reimplementation the pipeline using Apache Spark, which enabled us to lift the existing programs to a multi-node cluster without making changes to the predictors. We have benchmarked our Spark pipeline against the original, which shows almost linear speedup up to 8 nodes. In addition, our pipeline generates fewer intermediate files while allowing easier check pointing and monitoring.