nils-herrmann/clean_scirepeval huggingface dataset:
An evolutionary optimization framework for neural networks and neuromorphic architectures

As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.
Employee Engagement From a Self-Determination Theory Perspective

Macey and Schneider (2008) draw on numerous theories to explain what engagement is and how it is similar to and different from related constructs in the organizational behavior literature. As a result, we now have a better understanding of some of the key ‘‘components’’ of engagement. What appears to be missing, however, is a strong unifying theory to guide research and practice. We believe that such a theory exists in the form of self-determination theory (SDT; Deci & Ryan, 1985; Ryan & Deci, 2000) and its various corollaries, self-concordance theory (SCT; Sheldon & Elliot, 1999), hierarchical theory (Vallerand, 1997), and passion theory (Vallerand et al., 2003). Although Macey and Schneider acknowledged the relevance of SDT and SCT, we believe that much greater use of these theories could be made to justify and extend their conceptual model.
Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010

Depressive disorders were a leading cause of burden in the Global Burden of Disease (GBD) 1990 and 2000 studies. Here, we analyze the burden of depressive disorders in GBD 2010 and present severity proportions, burden by country, region, age, sex, and year, as well as burden of depressive disorders as a risk factor for suicide and ischemic heart disease.



Burden was calculated for major depressive disorder (MDD) and dysthymia. A systematic review of epidemiological data was conducted. The data were pooled using a Bayesian meta-regression. Disability weights from population survey data quantified the severity of health loss from depressive disorders. These weights were used to calculate years lived with disability (YLDs) and disability adjusted life years (DALYs). Separate DALYs were estimated for suicide and ischemic heart disease attributable to depressive disorders. Depressive disorders were the second leading cause of YLDs in 2010. MDD accounted for 8.2% (5.9%-10.8%) of global YLDs and dysthymia for 1.4% (0.9%-2.0%). Depressive disorders were a leading cause of DALYs even though no mortality was attributed to them as the underlying cause. MDD accounted for 2.5% (1.9%-3.2%) of global DALYs and dysthymia for 0.5% (0.3%-0.6%). There was more regional variation in burden for MDD than for dysthymia; with higher estimates in females, and adults of working age. Whilst burden increased by 37.5% between 1990 and 2010, this was due to population growth and ageing. MDD explained 16 million suicide DALYs and almost 4 million ischemic heart disease DALYs. This attributable burden would increase the overall burden of depressive disorders from 3.0% (2.2%-3.8%) to 3.8% (3.0%-4.7%) of global DALYs.



GBD 2010 identified depressive disorders as a leading cause of burden. MDD was also a contributor of burden allocated to suicide and ischemic heart disease. These findings emphasize the importance of including depressive disorders as a public-health priority and implementing cost-effective interventions to reduce its burden. Please see later in the article for the Editors' Summary.
Point Pair Feature Based Object Detection for Random Bin Picking

Point pair features are a popular representationfor free form 3D object detection and pose estimation. Inthis paper, their performance in an industrial random binpicking context is investigated. A new method to generaterepresentative synthetic datasets is proposed. This allows toinvestigate the influence of a high degree of clutter and thepresence of self similar features, which are typical to ourapplication. We provide an overview of solutions proposedin literature and discuss their strengths and weaknesses. Asimple heuristic method to drastically reduce the computationalcomplexity is introduced, which results in improved robustness, speed and accuracy compared to the naive approach.
The induction of apoptosis and autophagy by Wasabia japonica extract in colon cancer.

Wasabia japonica (wasabi) has been shown to exhibit properties of detoxification, anti-inflammation and the induction of apoptosis in cancer cells. This study aimed to investigate the molecular mechanism of the cytotoxicity of wasabi extract (WE) in colon cancer cells to evaluate the potential of wasabi as a functional food for chemoprevention.



Colo 205 cells were treated with different doses of WE, and the cytotoxicity was analyzed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide. Apoptosis and autophagy were detected by 4',6-diamidino-2-phenylindole, 5,5',6,6'-tetrachloro-1,1',3,3'-tetraethyl-imidacarbo-yanine iodide and staining for acidic vascular organelles (AVOs), along with Western blotting.



The results demonstrated that WE induced the extrinsic pathway and mitochondrial death machinery through the activation of TNF-α, Fas-L, caspases, truncated Bid and cytochrome C. WE also induced autophagy by decreasing the phosphorylation of Akt and mTOR and promoting the expression of microtubule-associated protein 1 light chain 3-II and AVO formation. An in vivo xenograft model verified that tumor growth was delayed by WE treatment.



Our studies revealed that WE exhibits anti-colon cancer properties through the induction of apoptosis and autophagy. These results provide support for the application of WE as a chemopreventive functional food and as a prospective treatment of colon cancer.
Global outer-urban navigation with OpenStreetMap

Publicly available map services are widely used by humans for navigation and nowadays provide almost complete road network data. When utilizing such maps for autonomous navigation with mobile robots one is faced with the problem of inaccuracies of the map and the uncertainty about the position of the robot relative to the map. In this paper, we present a probabilistic approach to autonomous robot navigation using data from OpenStreetMap that associates tracks from Open-StreeetMap with the trails detected by the robot based on its 3D-LiDAR data. It combines semantic terrain information, derived from the 3D-LiDAR data, with a Markov-Chain Monte-Carlo technique to match the tracks from OpenStreetMap with the sensor data. This enables our robot to utilize OpenStreetMap for navigation planning and to still stay on the trails during the execution of these plans. We present the results of extensive experiments carried out in real world settings that demonstrate the robustness of our system regarding the alignment of the vehicle pose relative to the OpenStreetMap data.
Maximizing rumor containment in social networks with constrained time

The spread of rumor or misinformation in social networks may cause bad effects among the public. Thus, it is necessary to find effective strategies to control the spread of rumor. Specifically, in our paper, we consider such a setting: initially, a subset of nodes is chosen as the set of protectors, and the influence of protector diffuses competitively with the diffusion of rumor. However, in real world, we generally have limited budget (limited number of protectors) and time to fight with rumor. Therefore, we study the problem of maximizing rumor containment within a fixed number of initial protectors and a given time deadline. Generalizing two seminal models in the field—the Independent Cascade (IC) model and the Linear Threshold (LT) model—we propose two new models of competitive influence diffusion in social networks with the following three factors: a time deadline for information diffusion, random time delay of information exchange and personal interests regarding the acceptance of information. Under these two models, we show that the optimization problems are NP-hard. Furthermore, we prove that the objective functions are submodular. As a result, the greedy algorithm is used as constant-factor approximation algorithms with performance guarantee $$1-\frac{1}{e}$$ 1 - 1 e for the two optimization problems.
Intelligent Method for Face Recognition of Infant

Mixing, abduction and illegal adoption of infant is a global challenge and the research done to solve this problem is minimal. Traditional methods of biometric and non biometric techniques have their own limitations and fail to provide required level of security. The task of face recognition has been actively researched in recent year and automatic face recognition of infant is not reported in the literature. The contributions of this research are: (i) Design and implementation of intelligent face recognition technique to recognize infant. (ii) Preparing an infant face database of 150 individuals with slight variations in pose, illumination; expression and quality. The results on the infant database are very encouraging.
A Study on Knowledge , Perceptions and Attitudes about Screening and Diagnosis of Diabetes in Saudi Population

Timely screening and treatment of diabetes can considerably reduce associated health adverse effects. The purpose of the study was to evaluate the knowledge about diabetes and perceptions and attitude about screening and early diagnosis of diabetes among the population.  A cross-sectional questionnaire-based study conducted among non-diabetes adult people attending primary health care centers in Jeddah, Saudi Arabia. Participants’ knowledge about diabetes risk factors and complications and perceptions and attitude regarding screening and early diagnosis of diabetes was assessed and different scores were calculated.  Total 202 patients were included: mean (SD) age was 38.19 (13.25) years and 55.0% were females. Knowledge about diabetes risk factors and complications was 60.41%84.77% and 44.67%-67.51%, respectively, depending on the item. Perceptions about screening and early diagnosis showed that 81.19% believed that screening tests exist and 75.14% believed that it is possible to diagnose diabetes before complication stage; while 84.16% believed that early diagnosis increases treatment efficacy, decreases incidence of complications (83.66%), and allows early treatment (83.66%). Regarding attitude, 86.14% agreed to undergo diabetes screening if advised by the physician and 60.40% would do on their own initiative. Linear regression showed a positive correlation of attitude score with knowledge about diabetes risk factors (OR=1.87; p<0.0001) and complications (OR=1.46; p<0.0001); perception about feasibility of screening (OR=1.93; p<0.0001) and benefits of early diagnosis (OR=1.69; p<0.0001).  The improvement of knowledge about diabetes risk factors and complications as well as the perception about feasibility and benefits of screening are prerequisites for the promotion of diabetes screening among the population.
Graph Indexing: Tree + Delta >= Graph

Recent scientific and technological advances have witnessed an abundance of structural patterns modeled as graphs. As a result, it is of special interest to process graph containment queries effectively on large graph databases. Given a graph database G, and a query graph q, the graph containment query is to retrieve all graphs in G which contain q as subgraph(s). Due to the vast number of graphs in G and the nature of complexity for subgraph isomorphism testing, it is desirable to make use of high-quality graph indexing mechanisms to reduce the overall query processing cost. In this paper, we propose a new cost-effective graph indexing method based on frequent tree-features of the graph database. We analyze the effectiveness and efficiency of tree as indexing feature from three critical aspects: feature size, feature selection cost, and pruning power. In order to achieve better pruning ability than existing graph-based indexing methods, we select, in addition to frequent tree-features (Tree), a small number of discriminative graphs (∆) on demand, without a costly graph mining process beforehand. Our study verifies that (Tree+∆) is a better choice than graph for indexing purpose, denoted (Tree+∆ ≥Graph), to address the graph containment query problem. It has two implications: (1) the index construction by (Tree+∆) is efficient, and (2) the graph containment query processing by (Tree+∆) is efficient. Our experimental studies demonstrate that (Tree+∆) has a compact index structure, achieves an order of magnitude better performance in index construction, and most importantly, outperforms up-to-date graphbased indexing methods: gIndex and C-Tree, in graph containment query processing.
Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk.

Most decisions must be made without advance knowledge of their consequences. Economists and psychologists have devoted much attention to modeling decisions made under conditions of risk in which options can be characterized by a known probability distribution over possible outcomes. The descriptive shortcomings of classical economic models motivated the development of prospect theory (D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk. Econometrica, 4 (1979) 263-291; A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5 (4) (1992) 297-323) the most successful behavioral model of decision under risk. In the prospect theory, subjective value is modeled by a value function that is concave for gains, convex for losses, and steeper for losses than for gains; the impact of probabilities are characterized by a weighting function that overweights low probabilities and underweights moderate to high probabilities. We outline the possible neural bases of the components of prospect theory, surveying evidence from human imaging, lesion, and neuropharmacology studies as well as animal neurophysiology studies. These results provide preliminary suggestions concerning the neural bases of prospect theory that include a broad set of brain regions and neuromodulatory systems. These data suggest that focused studies of decision making in the context of quantitative models may provide substantial leverage towards a fuller understanding of the cognitive neuroscience of decision making.
Efficient semantic image segmentation with superpixel pooling

In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement for other pooling strategies that incorporates spatial prior information. We propose a simple and efficient GPU-implementation of the layer and explore several designs for the integration of the layer into existing network architectures. We provide experimental results on the IBSR and Cityscapes dataset, demonstrating that superpixel pooling can be leveraged to consistently increase network accuracy with minimal computational overhead. Source code is available at https://github.com/bermanmaxim/superpixPool.
A Decomposed Dual-Cross Generative Adversarial Network for Image Rain Removal

Rain removal is important for many computer vision applications, such as surveillance, autonomous car, etc. Traditionally, rain removal is regarded as a signal removal problem which usually causes over-smoothing by removing texture details in non-rain background regions. This paper considers the issue of rain removal from a completely different perspective, to treat rain removal as a signal decomposition problem. Specifically, we decompose the rain image into two components, namely non-rain background image and rain streaks image. Then, we introduce an adversarial training mechanism to synthesize non-rain background image and rain streaks image in a Dual-Cross manner, which makes the two adversarial branches interact with each other, archiving a win-win result ultimately. The proposed Decomposed Dual-Cross Generative Adversarial Network (DDC-GAN) shows significantly performance improvement compared with stateof-the-art methods on both synthetic and real-world images in terms of qualitative and quantitative measures (over 3dB gains in PSNR).
Sensory-motor transformations for speech occur bilaterally

Historically, the study of speech processing has emphasized a strong link between auditory perceptual input and motor production output. A kind of ‘parity’ is essential, as both perception- and production-based representations must form a unified interface to facilitate access to higher-order language processes such as syntax and semantics, believed to be computed in the dominant, typically left hemisphere. Although various theories have been proposed to unite perception and production, the underlying neural mechanisms are unclear. Early models of speech and language processing proposed that perceptual processing occurred in the left posterior superior temporal gyrus (Wernicke’s area) and motor production processes occurred in the left inferior frontal gyrus (Broca’s area). Sensory activity was proposed to link to production activity through connecting fibre tracts, forming the left lateralized speech sensory–motor system. Although recent evidence indicates that speech perception occurs bilaterally, prevailing models maintain that the speech sensory–motor system is left lateralized and facilitates the transformation from sensory-based auditory representations to motor-based production representations. However, evidence for the lateralized computation of sensory–motor speech transformations is indirect and primarily comes from stroke patients that have speech repetition deficits (conduction aphasia) and studies using covert speech and haemodynamic functional imaging. Whether the speech sensory–motor system is lateralized, like higher-order language processes, or bilateral, like speech perception, is controversial. Here we use direct neural recordings in subjects performing sensory–motor tasks involving overt speech production to show that sensory–motor transformations occur bilaterally. We demonstrate that electrodes over bilateral inferior frontal, inferior parietal, superior temporal, premotor and somatosensory cortices exhibit robust sensory–motor neural responses during both perception and production in an overt word-repetition task. Using a non-word transformation task, we show that bilateral sensory–motor responses can perform transformations between speech-perception- and speech-production-based representations. These results establish a bilateral sublexical speech sensory–motor system.
Agile Big Data Analytics Development: An Architecture-Centric Approach

Agile development for big data analytics has become the new normal. However, research questions remain: 1) how should a big data system be designed and developed to effectively support advanced analytics? and 2) how should the agile process be adapted for big data analytics development? This article contributes an Architecture-centric Agile Big data Analytics (AABA) development methodology evolved and validated in 10 case studies through Collaborative Practice Research. Our studies showed that architecture agility is the key for successful agile big data analytics development. Employing an architecture-centric approach, the AABA methodology integrates the Big Data system Design (BDD) method and Architecture-centric Agile Analytics with architecture-supported DevOps (AAA) model for effective value discovery and rapid continuous delivery of value. The uses of a design concepts catalog and architectural spikes are advancements to architecture design methods that have proven to be critical to agile big data analytics development.
The Analysis of the Disease Spectrum in China

Analysis of the related risks of disease provides a scientific basis for disease prevention and treatment, hospital management, and policy formulation by the changes in disease spectrum of patients in hospital. Retrospective analysis was made to the first diagnosis, age, gender, daily average cost of hospitalized patients, and other factors in the First Affiliated Hospital of Nanjing Medical University during 2006-2013. The top 4 cases were as follows: cardiovascular disease, malignant tumors, lung infections, and noninsulin dependent diabetes mellitus. By the age of disease analysis, we found a younger age trend of cardiovascular disease, and the age of onset of cancer or diabetes was somewhat postponed. The average daily cost of hospitalization and the average daily cost of the main noncommunicable diseases were both on the rise. Noncommunicable diseases occupy an increasingly important position in the constitution of the disease, and they caused an increasing medical burden. People should pay attention to health from the aspects of lifestyle changing. Hospitals should focus on building the appropriate discipline. On the other hand, an integrated government response is required to tackle key risks. Multiple interventions are needed to lower the burden of these diseases and to improve national health.
Nirikshan: process mining software repositories to identify inefficiencies, imperfections, and enhance existing process capabilities

Process mining is to extract knowledge about business processes from data stored implicitly in ad-hoc way or explicitly by information systems. The aim is to discover runtime process, analyze performance and perform conformance verification, using process mining tools like ProM and Disco, for single software repository and processes spanning across multiple repositories. Application of process mining to software repositories has recently gained interest due to availability of vast data generated during software development and maintenance. Process data are embodied in repositories which can be used for analysis to improve the efficiency and capability of process, however, involves a lot of challenges which have not been addressed so far. Project team defines workflow, design process and policies for tasks like issue tracking (defect or feature enhancement), peer code review (review the submitted patch to avoid defects before they are injected) etc. to streamline and structure the activities. The reality may not be the same as defined because of imperfections so the extent of non-conformance needs to be measured. We propose a research framework `Nirikshan' to process mine the data of software repositories from multiple perspectives like process, organizational, data and time. We apply process mining on software repositories to derive runtime process map, identify and remove inefficiencies and imperfections, extend the capabilities of existing software engineering tools to make them more process aware, and understand interaction pattern between various contributors to improve the efficiency of project.