nils-herrmann/clean_scirepeval huggingface dataset:
Deep Belief Networks Are Compact Universal Approximators

Deep belief networks (DBN) are generative models with many layers of hidden causal variables, recently introduced by Hinton, Osindero, and Teh (2006), along with a greedy layer-wise unsupervised learning algorithm. Building on Le Roux and Bengio (2008) and Sutskever and Hinton (2008), we show that deep but narrow generative networks do not require more parameters than shallow ones to achieve universal approximation. Exploiting the proof technique, we prove that deep but narrow feedforward neural networks with sigmoidal units can represent any Boolean expression.
FPGA-GPU-CPU heterogenous architecture for real-time cardiac physiological optical mapping

Real-time optical mapping technology is a technique that can be used in cardiac disease study and treatment technology development to obtain accurate and comprehensive electrical activity over the entire heart. It provides a dense spatial electro-physiology. Each pixel essentially plays the role of a probe on that location of the heart. However, the high throughput nature of the computation causes significant challenges in implementing a real-time optical mapping algorithm. This is exacerbated by high frame rate video for many medical applications (order of 1000 fps). Accelerating optical mapping technologies using multiple CPU cores yields modest improvements, but still only performs at 3.66 frames per second (fps). A highly tuned GPU implementation achieves 578 fps. A FPGA-only implementation is infeasible due to the resource requirements for processing intermediate data arrays generated by the algorithm. We present a FPGA-GPU-CPU architecture that is a real-time implementation of the optical mapping algorithm running at 1024 fps. This represents a 273× speed up over a multi-core CPU implementation.
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

As data science continues to grow in popularity, there will be an increasing need to make data science tools more scalable, flexible, and accessible. In particular, automated machine learning (AutoML) systems seek to automate the process of designing and optimizing machine learning pipelines. In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem. Further, we analyze a large database of pipelines that were previously used to solve various supervised classification problems and identify 100 short series of machine learning operations that appear the most frequently, which we call the building blocks of machine learning pipelines. We harness these building blocks to initialize TPOT with promising solutions, and find that this sensible initialization method significantly improves TPOT’s performance on one benchmark at no cost of significantly degrading performance on the others. Thus, sensible initialization with machine learning pipeline building blocks shows promise for GP-based AutoML systems, and should be further refined in future work.
A right hemisphere role in cognitive reserve

High levels of education, occupational complexity, and/or premorbid intelligence are associated with lower levels of cognitive impairment than would be expected from a given brain pathology. This has been observed across a range of conditions including Alzheimer's disease (Roe et al., 2010), stroke (Ojala-Oksala et al., 2012), traumatic brain injury (Kesler et al., 2003), and penetrating brain injury (Grafman, 1986). This cluster of factors, which seemingly protect the brain from expressing symptoms of damage, has been termed "cognitive reserve" (Stern, 2012). The current review considers one possible neural network, which may contribute to cognitive reserve. Based on the evidence that the neurotransmitter, noradrenaline mediates cognitive reserve's protective effects (Robertson, 2013) this review identifies the neurocognitive correlates of noradrenergic (NA) activity. These involve a set of inter-related cognitive processes (arousal, sustained attention, response to novelty, and awareness) with a strongly right hemisphere, fronto-parietal localization, along with working memory, which is also strongly modulated by NA. It is proposed that this set of processes is one plausible candidate for partially mediating the protective effects of cognitive reserve. In addition to its biological effects on brain structure and function, NA function may also facilitate networks for arousal, novelty, attention, awareness, and working memory, which collectively provide for a set of additional, cognitive, mechanisms that help the brain adapt to age-related changes and disease. It is hypothesized that to the extent that the lateral surface of the right prefrontal lobe and/or the right inferior parietal lobe maintain structural (white and gray matter) and functional integrity and connectivity, cognitive reserve should benefit and behavioral expression of pathologic damage should thus be mitigated.
Design and development of a glove for post-stroke hand rehabilitation

Numerous endeavors have been made in creating automated post-stroke hand rehabilitation and assistive devices over the last decade. An extensive variety of actuators have been investigated in directing the patients' finger to maneuver in a natural path. Despite the fact that their execution is considered satisfactory for some typical undertakings, there is ample room for improvement by applying late advances in lightweight, soft and flexible bending actuator. This paper presents an innovative design of a skinny rehabilitation glove by using a bi-directional soft pneumatic bending actuator. It describes the prototyping of a complete hand rehabilitation system, including the design of glove, control kit and user interface. Some preliminary test results demonstrate good repeatability and controllability of the prototype. Attributed to its light weight, low profile, anthropomorphic form, satisfactory output force and angular displacement in both contraction and extension, it shows a pragmatic solution to an automatic robot-aided post-stroke hand rehabilitation system.
Using fMRI to study reward processing in humans: past, present, and future.

Functional magnetic resonance imaging (fMRI) is a noninvasive tool used to probe cognitive and affective processes. Although fMRI provides indirect measures of neural activity, the advent of fMRI has allowed for1) the corroboration of significant animal findings in the human brain, and2) the expansion of models to include more common human attributes that inform behavior. In this review, we briefly consider the neural basis of the blood oxygenation level dependent signal to set up a discussion of how fMRI studies have applied it in examining cognitive models in humans and the promise of using fMRI to advance such models. Specifically, we illustrate the contribution that fMRI has made to the study of reward processing, focusing on the role of the striatum in encoding reward-related learning signals that drive anticipatory and consummatory behaviors. For instance, we discuss how fMRI can be used to link neural signals (e.g., striatal responses to rewards) to individual differences in behavior and traits. While this functional segregation approach has been constructive to our understanding of reward-related functions, many fMRI studies have also benefitted from a functional integration approach that takes into account how interconnected regions (e.g., corticostriatal circuits) contribute to reward processing. We contend that future work using fMRI will profit from using a multimodal approach, such as combining fMRI with noninvasive brain stimulation tools (e.g., transcranial electrical stimulation), that can identify causal mechanisms underlying reward processing. Consequently, advancements in implementing fMRI will promise new translational opportunities to inform our understanding of psychopathologies.
Second screen applications and tablet users: constellation, awareness, experience, and interest

This study investigates how tablet users incorporate multiple media in their television viewing experience. Three patterns are found: (a) only focusing on television, (b) confounding television viewing with other screen media (e.g. laptop, tablet) and (c) confounding television viewing with various media, including print and screen media. Furthermore, we question how the incorporation of screen media in this experience affects the practice of engaging in digital commentary on television content. Also, we inquire the uptake and interest in so-called 'second screen applications'. These applications allow extensions of the primary screen experience on secondary screens (e.g. tablet). The results, based on a sample of 260 tablet users, indicate that there is only a modest uptake and interest in using secondary screens to digitally share opinions. However, the use of second screen interaction with television content is not discarded: although there is still little awareness and experience, we notice a moderate interest in these apps.
GPU ray casting of virtual globes

Accurately rendering an ellipsoid is a fundamental problem for virtual globes in GIS and aerospace applications where the Earth's standard reference surface is non-spherical. The traditional approach of tessellating an ellipsoid into triangles and rendering via rasterization has several drawbacks [Miller and Gaskins 2009]. Geodetic grid tessellations oversample at the poles (2a), which leads to shading artifacts and ineffective culling. Tessellations based on subdividing an inscribed platonic solid lead to problematic triangles crossing the International Date Line and poles (2b).
Wideband Tapered-Slot Antenna with Corrugated Edges for GPR Applications

A novel Wideband tapered-slot antenna for GPR applications is proposed. A conventional approach to reduce unwanted interactions between the antenna and the ground surface requires extensive microwave absorbers treatment. For the same purpose we propose to use corrugations along the antenna edges. The corrugations improve directivity and wideband matching of the antenna with free space retaining also good radiation efficiency. Further improvement of the antenna performance has been achieved by patching the corrugated areas of the antenna with the resistance card strips. The elimination of the foam-type absorbers makes the antenna more robust and easier in fabrication.
Panoramic video stitching from commodity HDTV cameras

Digital camera and smartphone technologies have made high-quality images and video pervasive and abundant. Combining or stitching collections of images from a variety of viewpoints into an extended panoramic image is a common and popular function for such devices. Extending this functionality to video however, poses many new challenges due to the demand for both spatial and temporal continuity. Multi-view video stitching (also called panoramic video stitching) is an emerging, common research area in computer vision, image/video processing and computer graphics and has wide applications in virtual reality, virtual tourism, surveillance, and human computer interaction. In this paper, we study and solve the major technical and practical problems in the complete process of stitching a high-resolution multi-view video into a high-resolution panoramic video. The challenges addressed include video stabilization, efficient high-definition multi-view video alignment and stitching, color correction, and blurred frame detection and repair. The proposed approaches have been successfully applied in a high-quality virtual reality system—the Virtual Exercise Environment (VEE) system.
Osteoprotegerin: A Novel Secreted Protein Involved in the Regulation of Bone Density

A novel secreted glycoprotein that regulates bone resorption has been identified. The protein, termed Osteoprotegerin (OPG), is a novel member of the TNF receptor superfamily. In vivo, hepatic expression of OPG in transgenic mice results in a profound yet nonlethal osteopetrosis, coincident with a decrease in later stages of osteoclast differentiation. These same effects are observed upon administration of recombinant OPG into normal mice. In vitro, osteoclast differentiation from precursor cells is blocked in a dose-dependent manner by recombinant OPG. Furthermore, OPG blocks ovariectomy-associated bone loss in rats. These data show that OPG can act as a soluble factor in the regulation of bone mass and imply a utility for OPG in the treatment of osteoporosis associated with increased osteoclast activity.
The influences of electronic word-of-mouth message appeal and message source credibility on brand attitude

Based on message source theory, the purpose of this paper is to use positive electronic word-of-mouth (eWOM) settings to examine the influence of message source credibility on brand attitude (brand trust, brand affection, and purchase intention). The current study also uses the elaboration likelihood model to examine the moderating effect of product involvement on the relationships between message appeals (rational vs emotional) and brand attitude, as well as between message source credibility and brand attitude.  Data were collected via internet questionnaires. Based on the internet investigation and pretest, this study chose the stimuli for research products, virtual brands, eWOM, and message source credibility. Via a 2 (notebooks vs shampoo) *2 (appeal: rational vs emotional) experimental design, 211 effective samples were collected to verify the hypotheses of this study.  The results, for both notebooks and shampoo, are: the positive eWOM message with higher message source credibility indicates a better brand attitude than the eWOM message with lower message source credibility, and this effect is not moderated by the degree of product involvement, indicating its robustness. Second, with a high degree of product involvement, the rational appeal indicates a better brand attitude than the emotional appeal; no significant difference is found when product involvement drops to a low level.  Previous research investigating the moderating effect of involvement on the effects of message-appeal types and the message source credibility of eWOM is rare. The main contribution of this study is to fill this gap.
Selection from alphabetic and numeric menu trees using a touch screen: breadth, depth, and width

Goal items were selected by a series of touch-menu choices among sequentially subdivided ranges of integers or alphabetically ordered words. The number of alternatives at each step, <italic>b</italic>, was varied, and, inversely, the size of the target area for the touch. Mean response time for each screen was well described by <italic>T</italic> = <italic>k</italic>+<italic>c</italic>log<italic>b</italic>, in agreement with the Hick-Hyman and Fitts' laws for decision and movement components in series. It is shown that this function favors breadth over depth in menus, whereas others might not. Speculations are offered as to when various functions could be expected.
An improved Opposition-Based Sine Cosine Algorithm for global optimization

Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces.
Measuring and predicting software productivity: A systematic map and review

0950-5849/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.infsof.2010.12.001 ⇑ Address: School of Computing, Blekinge Institute 372 25, Sweden. E-mail addresses: kai.petersen@bth.se, kai.petersen URLs: http://www.bth.se/besq,  Software productivity measurement is essential in order to control and improve the performance of software development. For example, by identifying role models (e.g. projects, individuals, tasks) when comparing productivity data. The prediction is of relevance to determine whether corrective actions are needed, and to discover which alternative improvement action would yield the best results.  In this study we identify studies for software productivity prediction and measurement. Based on the identified studies we first create a classification scheme and map the studies into the scheme (systematic map). Thereafter, a detailed analysis and synthesis of the studies is conducted.  As a research method for systematically identifying and aggregating the evidence of productivity measurement and prediction approaches systematic mapping and systematic review have been used.  In total 38 studies have been identified, resulting in a classification scheme for empirical research on software productivity. The mapping allowed to identify the rigor of the evidence with respect to the different productivity approaches. In the detailed analysis the results were tabulated and synthesized to provide recommendations to practitioners.  Risks with simple ratio-based measurement approaches were shown. In response to the problems data envelopment analysis seems to be a strong approach to capture multivariate productivity measures, and allows to identify reference projects to which inefficient projects should be compared. Regarding simulation no general prediction model can be identified. Simulation and statistical process control are promising methods for software productivity prediction. Overall, further evidence is needed to make stronger claims and recommendations. In particular, the discussion of validity threats should become standard, and models need to be compared with each other.
Perspectives on the Productivity Dilemma

Editor’s note The authors of this paper presented an All-academy session at the 2008 Academy of Management annual meeting in Anaheim, California. We were excited by the dynamic nature of the debate and felt that it related closely to critical issues in the areas of operations management, strategy, product development and international business. We thus invited the authors to write an article offering their individual and joint views on the productivity dilemma. We trust you will find it to be stimulating and thought provoking. We invite you to add your voice to the discussion by commenting on this article at the Operations and Supply Chain (OSM) Forum at http://www.journaloperationsmanagement.org/OSM.asp. – Kenneth K. Boyer and Morgan L. Swink
Detecting Flying Objects Using a Single Moving Camera

We propose an approach for detecting flying objects such as Unmanned Aerial Vehicles (UAVs) and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. We argue that solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approach for object-centric motion stabilization of image patches that allows us to achieve effective classification on spatio-temporal image cubes and outperform state-of-the-art techniques. As this problem has not yet been extensively studied, no test datasets are publicly available. We therefore built our own, both for UAVs and aircrafts, and will make them publicly available so they can be used to benchmark future flying object detection and collision avoidance algorithms.
A Knowledge Representation Method to Implement A Taiwanese Tone Group Parser

A tone group parser could be one of the most important components of the Taiwanese text-to-speech system. In this paper, we offered the hypothesis of tonal government to emphasis the idea that if the allotone selection can be made for each word in a sentence then the tone groups will be separated within the sentence and supported our viewpoint with the implementation of a Taiwanese tone group parser. In addition to the description of using the symbol system to convert language expertise and heuristic knowledge into a knowledge base to cope with a frame-based corpus and a tone sandhi processor, the procedure of connecting the inference engine and the knowledge 1 The 2017 Conference on Computational Linguistics and Speech Processing ROCLING 2017, pp. 1-4  The Association for Computational Linguistics and Chinese Language Processing
Asynchronous Design—Part 1: Overview and Recent Advances

An asynchronous design paradigm is capable of addressing the impact of increased process variability, power and thermal bottlenecks, high fault rates, aging, and scalability issues prevalent in emerging densely packed integrated circuits. The first part of the two-part article on asynchronous design presents a chronicle of past and recent commercial advances, as well as technical foundations, and highlights the enabling role of asynchronous design in two application areas: GALS systems and networks-on-chip.
Optical Proximity Correction with Hierarchical Bayes Model

Optical proximity correction (OPC) is one of the most important techniques in today’s optical lithography-based manufacturing process. Although the most widely used model-based OPC is expected to achieve highly accurate correction, it is also known to be extremely time-consuming. This paper proposes a regression model for OPC using a hierarchical Bayes model (HBM). The goal of the regression model is to reduce the number of iterations in model-based OPC. Our approach utilizes a Bayes inference technique to learn the optimal parameters from given data. All parameters are estimated by the Markov Chain Monte Carlo method. Experimental results show that utilizing HBM can achieve a better solution than other conventional models, e.g., linear regression-based model, or nonlinear regression-based model. In addition, our regression results can be used as the starting point of conventional model-based OPC, through which we are able to overcome the runtime bottleneck.