nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating ODE-Based Simulation of General and Heterogeneous Biophysical Models Using a GPU

Flint is a simulator that numerically integrates heterogeneous biophysical models described by a large set of ordinary differential equations. It uses an internal bytecode representation of simulation-related expressions to handle various biophysical models built for general purposes. We propose two acceleration methods for Flint using a graphics processing unit (GPU). The first method interprets multiple bytecodes in parallel on the GPU. It automatically parallelizes the simulation using a level scheduling algorithm. We implement an interpreter of the Flint bytecode that is suited for running on the GPU, which reduces both the number of memory accesses and divergent branches to achieve higher performance. The second method translates a model into a source code for both the CPU and the GPU through the internal bytecode, which speeds up the compilation of the generated source codes, because the code size is diminished because of bytecode unification. With large models such that tens of thousands or more expressions can be evaluated simultaneously, the translated code running on the GPU achieves computational performance of up to 2.7 higher than that running on a CPU. Otherwise, with small models, the CPU is faster than the GPU. Therefore, the translated code dynamically determines on which to run either the CPU or the GPU by profiling initial few iterations of the simulation.
Role-based model for Named Entity Recognition

Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. The proposed model has been implemented in two use cases using Spanish drug Summary of Product Characteristics: identification of therapeutic indications and identification of adverse reactions. The results show how precision is increased using a NER model that is oriented towards a specific role and discards entities out of scope.
Learning a Predictable and Generative Vector Representation for Objects

What is a good vector representation of an object? We believe that it should be generative in 3D, in the sense that it can produce new 3D objects; as well as be predictable from 2D, in the sense that it can be perceived from 2D images. We propose a novel architecture, called the TL-embedding network, to learn an embedding space with these properties. The network consists of two components: (a) an autoencoder that ensures the representation is generative; and (b) a convolutional network that ensures the representation is predictable. This enables tackling a number of tasks including voxel prediction from 2D images and 3D model retrieval. Extensive experimental analysis demonstrates the usefulness and versatility of this embedding.
Standardization of marketing strategy 107 Standardization of international marketing strategy by firms from a developing country

A major debate in the international marketing literature deals with the globalization of markets and the extent to which a company’s international marketing strategy can be standardized (Buzzell, 1968; Cavusgil et al., 1993; Douglas and Wind, 1987; Hill and Still, 1984; Jain, 1989; Levitt, 1983; Sorenson and Wiechmann, 1975). Significant progress has been made with respect to the extent to which international marketing strategy can be standardized across national borders (Cavusgil et al., 1993; Jain, 1989). However, almost all previous research has been conducted from a US-firm perspective. Even though a few studies dealt with this debate in the context of less developed economies, the issues studied were viewed from the perspective of US firms’ operating activities in those economies (e.g. Grosse and Zinn, 1990; Hill and Still, 1984;  1972). A major gap in the literature is whether our current knowledge can be generalized to foreign companies in other nations, especially the developing world. Firms from developing countries assume an increasingly important role in international competition, since many of the fastest growing economies in the world can be found in these nations. In addition, since developing countries are often culturally different from developed countries, they provide a suitable context to assess the generalizability of the existing knowledge in the standardization literature. Consequently, it is of interest to researchers and practitioners of international marketing to know the perspective of firms from developing countries regarding the standardization and adaptation of marketing strategy. Another void in the literature is that previous studies implicitly assume that standardization concepts are unidimensional at either the overall marketing programme level or the 4-P level (e.g. Akaah, 1991; Boddewyn et al., 1986;
Adaptive exploration for large-scale protein analysis in the molecular dynamics database

Molecular dynamics (MD) simulations generate detailed time-series data of all-atom motions. These simulations are leading users of the world's most powerful supercomputers, and are standard-bearers for a wide range of high-performance computing (HPC) methods. However, MD data exploration and analysis is in its infancy in terms of scalability, ease-of-use, and ultimately its ability to answer 'grand challenge' science questions. This demonstration introduces the Molecular Dynamics Database (MDDB) project at Johns Hopkins, to study the co-design of database methods for deep on-the-fly exploratory MD analyses with HPC simulations. Data exploration in MD suffers from a "human bottleneck", where the laborious administration of simulations leaves little room for domain experts to focus on tackling science questions. MDDB exploits the data-rich nature of MD simulations to provide adaptive control of the exploration process with machine learning techniques, specifically reinforcement learning (RL). We present MDDB's data and queries, architecture, and its use of RL methods. Our audience will co-operate with our steering algorithm and science partners, and witness MDDB's abilities to significantly reduce exploration times and direct computation resources to where they best address science questions.
New Robot for Power Line Inspection

Power line inspection is of the utmost importance for the reliability and stability of electric power distribution. However, manual inspection is a hazardous, slow, expensive and unreliable task. Therefore, new highly specialized robots are required to improve the overall quality and safety of the power line inspection. The research conducted so far has been mainly focused on the development of climbing and flying robots. This paper first addresses the main achievements in the field of robotic power line inspection. The proposed solutions are critically assessed and the associated problems are outlined. Based on these findings, a new concept for robot-assisted power line inspection, combining both climbing and flying principles, is proposed in the second part of the paper. The proposed concept is critically assessed and related to the other established concepts so as to demonstrate its advantages and feasibility for a routine power line inspection.
Comparing Images Using the Hausdorff Distance

Absfmct-The Hausdorffdistance measures the extent to which each point of a “model” set lies near some point of an “image” set and vice versa. Thus, thii distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. In this paper, we provide efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model. We focus primarily on the case in which the model is only allowed to translate with respect to the image. Then, we consider how to extend the techniques to rigid motion (translation and rotation). The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods. Moreover, we show how the method extends naturally to the problem of comparing II portion of a model against an image.
Internet of Things: A Comprehensive Study of Security Issues and Defense Mechanisms

The Internet of Things (IoT) is an evolving global trend in Web-based information architecture aiding in the exchange of services and goods over a network without necessitating human-to-human or human-to-computer interaction. It has the potential to revolutionize physical world interaction of individuals and the organizations. The application of IoT can be recognized significantly in many areas such as in healthcare, resource management, learning, knowledge processing, and many more. The practical realization of IoT is met with a plethora of security and privacy challenges that need to be tackled for IoT’s successful deployment on a commercially viable large scale. This paper analyzes the security issues related to IoT networks through an analysis of the existing empirical researches to get an insight on the security requirements of the IoT networks. The findings of the study revealed that security threats are one of the biggest and ever-growing challenges for IoT, and it is essential to substantially mitigate them for the success of this platform.
The Romance of Learning from Disagreement. The Effect of Cohesiveness and Disagreement on Knowledge Sharing Behavior and Individual Performance Within Teams

The purpose of this study was to explore the effects of disagreement and cohesiveness on knowledge sharing in teams, and on the performance of individual team members.  Data were obtained from a survey among 1,354 employees working in 126 teams in 17 organizations.  The results show that cohesiveness has a positive effect on the exchange of advice between team members and on openness for sharing opinions, whereas disagreement has a negative effect on openness for sharing opinions. Furthermore, the exchange of advice in a team has a positive effect on the performance of individual team members and acts as a mediator between cohesiveness and individual performance.  Managers who want to stimulate knowledge sharing processes and performance within work teams may be advised to take measures to prevent disagreement between team members and to enhance team cohesiveness.  Although some gurus in organizational learning claim that disagreement has a positive effect on group processes such as knowledge sharing and team learning, this study does not support this claim.
Inferring activities from interactions with objects

A key aspect of pervasive computing is using computers and sensor networks to effectively and unobtrusively infer users' behavior in their environment. This includes inferring which activity users are performing, how they're performing it, and its current stage. Recognizing and recording activities of daily living is a significant problem in elder care. A new paradigm for ADL inferencing leverages radio-frequency-identification technology, data mining, and a probabilistic inference engine to recognize ADLs, based on the objects people use. We propose an approach that addresses these challenges and shows promise in automating some types of ADL monitoring. Our key observation is that the sequence of objects a person uses while performing an ADL robustly characterizes both the ADL's identity and the quality of its execution. So, we have developed Proactive Activity Toolkit (PROACT).
The Weighted Majority Algorithm

We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials with a prediction to be made in each and the goal of the learner is to make few mistakes We are interested in the case that the learner has reason to believe that one of some pool of known algorithms will perform well but the learner does not know which one A simple and e ective method based on weighted voting is introduced for constructing a compound algorithm in such a circumstance We call this method the Weighted Majority Algorithm We show that this algorithm is robust in the presence of errors in the data We discuss various versions of the Weighted Majority Algorithm and prove mistake bounds for them that are closely related to the mistake bounds of the best algorithms of the pool For example given a sequence of trials if there is an algorithm in the pool A that makes at most m mistakes then the Weighted Majority Algorithm will make at most c log jAj m mistakes on that sequence where c is xed constant
Characterization and Implementation of Dual-SiC MOSFET Modules for Future Use in Traction Converters

Silicon (Si) insulated-gate bipolar transistors are widely used in railway traction converters. In the near future, silicon carbide (SiC) technology will push the limits of switching devices in three directions: higher blocking voltage, higher operating temperature, and higher switching speeds. The first silicon carbide (SiC) MOSFET modules are available on the market and look promising. Although they are still limited in breakdown voltage, these wide-bandgap components should improve traction-chain efficiency. Particularly, a significant reduction in the switching losses is expected which should lead to improvements in power-weight ratios. Nevertheless, because of the high switching speed and the high current levels required by traction applications, the implementation of these new modules is critical. An original method is proposed to compare, in terms of stray inductance, several dc bus-bar designs. To evaluate the potential of these new devices, a first set of measurements, based on a single-pulse test-bench, was obtained. The switching behavior of SiC devices was well understood at turn-off and turn-on. To complete this work, the authors use an opposition method to compare Si-IGBT and SiC-MOSFET modules in voltage source inverter operation. For this purpose, a second test-bench, allowing electrical and thermal measurements, was developed. Experimental results confirm the theoretical loss-calculation of the single-pulse tests and the correct operation of up to three modules directly connected in parallel. This analysis provides guidelines for a full SiC inverter design, and prospects for developments in traction applications are presented.
Pattern recognition: Historical perspective and future directions

Pattern recognition is one of the most important functionalities for intelligent behavior and is displayed by both biological and artificial systems. Pattern recognition systems have four major components: data acquisition and collection, feature extraction and representation, similarity detection and pattern classifier design, and performance evaluation. In addition, pattern recognition systems are successful to the extent that they can continuously adapt and learn from examples; the underlying framework for building such systems is predictive learning. The pattern recognition problem is a special case of the more general problem of statistical regression; it seeks an approximating function that minimizes the probability of misclassification. In this framework, data representation requires the specification of a basis set of approximating functions. Classification requires an inductive principle to design and model the classifier and an optimization or learning procedure for classifier parameter estimation. Pattern recognition also involves categorization: making sense of patterns not previously seen. The sections of this paper deal with the categorization and functional approximation problems; the four components of a pattern recognition system; and trends in predictive learning, feature selection using “natural” bases, and the use of mixtures of experts in classification.
Team-based learning: systematic research review.

Team-based learning (TBL) is an active learning method developed to help students achieve course objectives while learning how to function in teams. Many faculty members have adopted TBL because it is a unique teaching method, but evidence about its effectiveness is unclear. Seventeen original studies on TBL are presented in this systematic review of research. The studies include descriptive, explanatory, and experimental research published from 2003 to 2011 in the nursing, medical, education, and business literature. Generally, students are satisfied with TBL and student engagement is higher in TBL classes. Evidence also exists that students in TBL classes score higher on examinations. However, further high-quality experimental studies are needed to confirm that TBL positively affects examination scores and other learning outcomes and to determine whether TBL produces students who have the ability to function well in groups.
ERP Projects: Key Success Factors and Risk of Failure A Proposed Model of Governance of Enterprise Resource Planning

The ERP (Enterprise Resource Planning) is a major innovation in the Field of Information Systems and Organization Management. Including their ability to make the organization more integrated and consistent, and absolute control of information. But the implementation of these projects remains a disturbing adventure that may lead to success or deadly failure of the organization that is embarking on this project. The aims of this article are to determine the necessary critical success factors for the ERP and causes of failures,through a comparative study of large organizations that have adopted ERP , for addressing the convergence and divergence points in implementation ERP. Such a study will allow us to develop a governance model for ERP
BLIS: A Framework for Rapidly Instantiating BLAS Functionality

The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels. While others have had similar insights, BLIS reduces the necessary kernels to what we believe is the simplest set that still supports the high performance that the computational science community demands. Higher-level framework code is generalized and implemented in ISO C99 so that it can be reused and/or reparameterized for different operations (and different architectures) with little to no modification. Inserting high-performance kernels into the framework facilitates the immediate optimization of any BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are given a choice of using the traditional Fortran-77 BLAS interface, a generalized C interface, or any other higher level interface that builds upon this latter API. Preliminary performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL).
 Replicated Computational Results Certified: This article was subjected to an optional supplementary review process in which the computational results leading to the main conclusions of the paper were independently replicated. A separate report with the results of this process is published in this issue . The certification process is described in Michael Heroux's editorial: http://dx.doi.org/10.1145/2743015.
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

We propose the hierarchical Dirichlet process (HDP), a nonp arametric Bayesian model for clustering problems involving multiple groups of data. Each group of data is modeled with a mixture, with the nu mber of components being open-ended and inferred automatically by the model. Further, components can be shared across groups, allowing d epen encies across groups to be modeled effectively as well as conferrin g generalization to new groups. Such grouped clustering problems occur o ften in practice, e.g. in the problem of topic discovery in document corpora. We report experimental results on three text corpora showing t he effective and superior performance of the HDP over previous models.
A content analysis of depression-related tweets

This study examines depression-related chatter on Twitter to glean insight into social networking about mental health. We assessed themes of a random sample (n=2,000) of depression-related tweets (sent 4-11 to 5-4-14). Tweets were coded for expression of DSM-5 symptoms for Major Depressive Disorder (MDD). Supportive or helpful tweets about depression was the most common theme (n=787, 40%), closely followed by disclosing feelings of depression (n=625; 32%). Two-thirds of tweets revealed one or more symptoms for the diagnosis of MDD and/or communicated thoughts or ideas that were consistent with struggles with depression after accounting for tweets that mentioned depression trivially. Health professionals can use our findings to tailor and target prevention and awareness messages to those Twitter users in need.
External-Memory Graph Algorithms

Michael T. GoodrichjS Edward F. Grovefll Roberto Tamassia*t Darren Erik Vengrofftt Jeffrey Scott Vitterqt We present a collection of new techniques for designing and analyzing efficient external-memory algorithms for graph problems and illustrate how these techniques can be applied to a wide variety of specific problems. Our results include: Proximate-neighboring. We present a simple method for deriving external-memory lower bounds via reductions from a problem we call the “proximate neighbors” problem. We use this technique to derive non-trivial lower bounds for such problems as list ranking, expression tree evaluation, and connected components. PRAM simulation. We give methods for efficiently simulating PRAM computations in external memory, even for some cases in which the PRAM algorithm is not work-optimal. We apply this to derive a number of optimal (and simple) external-memory graph algorithms. Time-forward processing. We present a general technique for evaluating circuits (or “circuit-like” computations) in external memory. We also use *Department of Computer Science, Box 1910, Brown University, Providence, RI 02912-1910. Supported in part by the National Science Foundation, by the U.S. Army Research Office, and by the Advanced Research
CloudFlame: Cyberinfrastructure for Combustion Research

Combustion experiments and chemical kinetics simulations generate huge data that is computationally and data intensive. A cloud-based cyber infrastructure known as Cloud Flame is implemented to improve the computational efficiency, scalability and availability of data for combustion research. The architecture consists of an application layer, a communication layer and distributed cloud servers running in a mix environment of Windows, Macintosh and Linux systems. The application layer runs software such as CHEMKIN modeling application. The communication layer provides secure transfer/archive of kinetic, thermodynamic, transport and gas surface data using private/public keys between clients and cloud servers. A robust XML schema based on the Process Informatics Model (Prime) combined with a workflow methodology for digitizing, verifying and uploading data from scientific graphs/tables to Prime is implemented for chemical molecular structures of compounds. The outcome of using this system by combustion researchers at King Abdullah University of Science and Technology (KAUST) Clean Combustion Research Center and its collaborating partners indicated a significant improvement in efficiency in terms of speed of chemical kinetics and accuracy in searching for the right chemical kinetic data.