nils-herrmann/clean_scirepeval huggingface dataset:
Diet induced epigenetic changes and their implications for health.

Dietary exposures can have consequences for health years or decades later and this raises questions about the mechanisms through which such exposures are 'remembered' and how they result in altered disease risk. There is growing evidence that epigenetic mechanisms may mediate the effects of nutrition and may be causal for the development of common complex (or chronic) diseases. Epigenetics encompasses changes to marks on the genome (and associated cellular machinery) that are copied from one cell generation to the next, which may alter gene expression, but which do not involve changes in the primary DNA sequence. These include three distinct, but closely inter-acting, mechanisms including DNA methylation, histone modifications and non-coding microRNAs (miRNA) which, together, are responsible for regulating gene expression not only during cellular differentiation in embryonic and foetal development but also throughout the life-course. This review summarizes the growing evidence that numerous dietary factors, including micronutrients and non-nutrient dietary components such as genistein and polyphenols, can modify epigenetic marks. In some cases, for example, effects of altered dietary supply of methyl donors on DNA methylation, there are plausible explanations for the observed epigenetic changes, but to a large extent, the mechanisms responsible for diet-epigenome-health relationships remain to be discovered. In addition, relatively little is known about which epigenomic marks are most labile in response to dietary exposures. Given the plasticity of epigenetic marks and their responsiveness to dietary factors, there is potential for the development of epigenetic marks as biomarkers of health for use in intervention studies.
Author-level metrics in the new academic profile platforms: The online behaviour of the Bibliometrics community

The new web-based academic communication platforms do not only enable researchers to better advertise their academic outputs, making them more visible than ever before, but they also provide a wide supply of metrics to help authors better understand the impact their work is making. This study has three objectives: a) to analyse the uptake of some of the most popular platforms (Google Scholar Citations, ResearcherID, ResearchGate, Mendeley and Twitter) by a specific scientific community (bibliometrics, scientometrics, informetrics, webometrics, and altmetrics); b) to compare the metrics available from each platform; and c) to determine the meaning of all these new metrics. To do this, the data available in these platforms about a sample of 811 authors (researchers in bibliometrics for whom a public profile Google Scholar Citations was found) were extracted. A total of 31 metrics were analysed. The results show that a high number of the analysed researchers only had a profile in Google Scholar Citations (159), or only in Google Scholar Citations and ResearchGate (142). Lastly, we find two kinds of metrics of online impact. First, metrics related to connectivity (followers), and second, all metrics associated to academic impact. This second group can further be divided into usage metrics (reads, views), and citation metrics. The results suggest that Google Scholar Citations is the source that provides more comprehensive citation-related data, whereas Twitter stands out in connectivity-related metrics.
A unified search federation system based on online user feedback

Today's popular web search engines expand the search process beyond crawled web pages to specialized corpora ("verticals") like images, videos, news, local, sports, finance, shopping etc., each with its own specialized search engine. Search federation deals with problems of the selection of search engines to query and merging of their results into a single result set. Despite a few recent advances, the problem is still very challenging. First, due to the heterogeneous nature of different verticals, how the system merges the vertical results with the web documents to serve the user's information need is still an open problem. Moreover, the scale of the search engine and the increasing number of vertical properties requires a solution which is efficient and scaleable. In this paper, we propose a unified framework for the search federation problem. We model the search federation as a contextual bandit problem. The system uses reward as a proxy for user satisfaction. Given a query, our system predicts the expected reward for each vertical, then organizes the search result page (SERP) in a way which maximizes the total reward. Instead of relying on human judges, our system leverages implicit user feedback to learn the model. The method is efficient to implement and can be applied to verticals of different nature. We have successfully deployed the system to three different markets, and it handles multiple verticals in each market. The system is now serving hundreds of millions of queries live each day, and has improved user metrics considerably.
A Climate-Based Malaria Transmission Model with Structured Vector Population

In this paper, we present a malaria transmission model with periodic birth rate and age structure for the vector population. We first introduce the basic reproduction ratio for this model and then show that there exists at least one positive periodic state and that the disease persists when R0 > 1. It is also shown that the disease will die out if R0 < 1, provided that the invasion intensity is not strong. We further use these analytic results to study the malaria transmission cases in KwaZulu-Natal Province, South Africa. Some sensitivity analysis of R0 is performed, and in particular, the potential impact of climate change on seasonal transmission and populations at risk of the disease is analyzed.
A fast comparator with integrated small dual hysteresis and offset control

This paper presents the design of a fast comparator, where the dual hysteresis and the digital offset control are integrated in a source degenerated preamplifier stage. The selected architecture of the preamplifier stage, followed by a differential symmetrical OTA allowed easily to be achieved design requirements as 8mV hysteresis, 1σ input random offset of 0.58mV, range of ±35mV additional digital offset control with 5mV step, 30ns propagation delay, while the power dissipation is 90μW and the block area is 0.015mm2 at 0.18μm CMOS technology. The source degenerating resistors in the preamplifier stage can be used to create dual or single, digitally programmed hysteretic comparators.
Ontology extraction from MongoDB using formal concept analysis

Using formal concept analysis, we propose a method for engineering ontology from MongoDB to effectively represent unstructured data. Our method consists of three main phases: (1) generating formal context from a MongoDB, (2) applying formal concept analysis to derive a concept lattice from that formal context, and (3) converting the obtained concept lattice to the first prototype of an ontology. We apply our method on NorthWind database and demonstrate how the proposed mapping rules can be used for learning an ontology from such database. At the end, we discuss about suggestions by which we can improve and generalize the method for more complex database examples.
An anatomical study of the levator veli palatini and superior constrictor with special reference to their nerve supply

We dissected 50 head halves of 25 Japanese cadavers (10 males, 15 females) to investigate the innervations of the levator veli palatini (LVP) and superior constrictor pharyngis. The branches supplying the LVP were classified into the following three types according to their origins: supplying branches that originated from the pharyngeal branch of the glossopharyngeal nerve (type I, four sides, 8%), branches that originated from a communicating branch between the pharyngeal branches of the glossopharyngeal and vagus nerves (type II, 36 sides, 72%), and those that originated from the pharyngeal branch of the vagus nerve (type III, 10 sides, 20%). In previous studies, supplying branches of type I were seldom described. Regarding the innervation of the superior constrictor, some variations were observed, and we consider it likely that there is a close relationship between these variations and the type of innervation of the LVP.
A Dynamic Programming Approach to De Novo Peptide Sequencing via Tandem Mass Spectrometry

Tandem mass spectrometry fragments a large number of molecules of the same peptide sequence into charged molecules of prefix and suffix peptide subsequences and then measures mass/charge ratios of these ions. The de novo peptide sequencing problem is to reconstruct the peptide sequence from a given tandem mass spectral data of k ions. By implicitly transforming the spectral data into an NC-spectrum graph G (V, E) where /V/ = 2k + 2, we can solve this problem in O(/V//E/) time and O(/V/2) space using dynamic programming. For an ideal noise-free spectrum with only b- and y-ions, we improve the algorithm to O(/V/ + /E/) time and O(/V/) space. Our approach can be further used to discover a modified amino acid in O(/V//E/) time. The algorithms have been implemented and tested on experimental data.
Adsorption-desorption mediated separation of low concentrated D2O from water with hydrophobic activated carbon fiber.

The adsorption and desorption of D2O on hydrophobic activated carbon fiber (ACF) occurs at a smaller pressure than the adsorption and desorption of H2O. The behavior of the critical desorption pressure difference between D2O and H2O in the pressure range of 1.25-1.80kPa is applied to separate low concentrated D2O from water using the hydrophobic ACF, because the desorption branches of D2O and H2O drop almost vertically. The deuterium concentration of all desorbed water in the above pressure range is lower than that of water without adsorption-treatment on ACF. The single adsorption-desorption procedure on ACF at 1.66kPa corresponding to the maximum difference of adsorption amount between D2O and H2O reduced the deuterium concentration of desorbed water to 130.6ppm from 143.0ppm. Thus, the adsorption-desorption procedure of water on ACF is a promising separation and concentration method of low concentrated D2O from water.
Adolescent suicide and suicidal behavior.

This review examines the descriptive epidemiology, and risk and protective factors for youth suicide and suicidal behavior. A model of youth suicidal behavior is articulated, whereby suicidal behavior ensues as a result of an interaction of socio-cultural, developmental, psychiatric, psychological, and family-environmental factors. On the basis of this review, clinical and public health approaches to the reduction in youth suicide and recommendations for further research will be discussed.
A waterfall model for knowledge management and experience management

This paper examines experience and knowledge, experience management (EM) and knowledge management (KM), and their interrelationships. It then proposes waterfall models for both EM and KM. The models characterize EM and KM as the integration of experience processing and corresponding management, that of knowledge processing and corresponding management respectively. The proposed approach facilitates research and development of KM, EM, and hybrid intelligent systems.
A smart bed platform for monitoring & Ulcer prevention

The focus of this paper is to develop a software-hardware platform that addresses one of the most costly, acute health conditions, pressure ulcers — or bed sores. Caring for pressure ulcers is extremely costly, increases the length of hospital stays and is very labor intensive. The proposed platform collects information from various sensors incorporated into the bed, analyzes the data to create a time-stamped, whole-body pressure distribution map, and commands the bed's actuators to periodically adjust its surface profile to redistribute pressure over the entire body. These capabilities are combined to form a cognitive support system, that augments the ability of a care giver, allowing them to provide better care to more patients in less time. For proof of concept, we have implemented algorithms and architectures that cover four key aspects of this platform: 1) data collection, 2) modeling & profiling, 3) machine learning, and 4) acting.
Enhancing Freebase Question Answering Using Textual Evidence

Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like information extraction techniques are robust to data scarcity, they are less expressive than deep understanding methods, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a novel neural network based relation extractor to retrieve the candidate answers from Freebase, and then develop a refinement model to validate answers using Wikipedia. We achieve 53.3 F1 on WEBQUESTIONS, a substantial improvement over the state-of-theart.
Measuring the Size of Large No-Limit Poker Games

In the field of computational game theory, games are often compared in terms of their size. This can be measured in several ways, including the number of unique game states, the number of decision points, and the total number of legal actions over all decision points. These numbers are either known or estimated for a wide range of classic games such as chess and checkers. In the stochastic and imperfect information game of poker, these sizes are easily computed in “limit” games which restrict the players’ available actions, but until now had only been estimated for the more complicated “no-limit” variants. In this paper, we describe a simple algorithm for quickly computing the size of two-player no-limit poker games, provide an implementation of this algorithm, and present for the first time precise counts of the number of game states, information sets, actions and terminal nodes in the no-limit poker games played in the Annual Computer Poker Competition.
Studying cooperation and conflict between authors with history flow visualizations

The Internet has fostered an unconventional and powerful style of collaboration: "wiki" web sites, where every visitor has the power to become an editor. In this paper we investigate the dynamics of Wikipedia, a prominent, thriving wiki. We make three contributions. First, we introduce a new exploratory data analysis tool, the history flow visualization, which is effective in revealing patterns within the wiki context and which we believe will be useful in other collaborative situations as well. Second, we discuss several collaboration patterns highlighted by this visualization tool and corroborate them with statistical analysis. Third, we discuss the implications of these patterns for the design and governance of online collaborative social spaces. We focus on the relevance of authorship, the value of community surveillance in ameliorating antisocial behavior, and how authors with competing perspectives negotiate their differences.
Expertise in fingerprint identification.

Although fingerprint experts have presented evidence in criminal courts for more than a century, there have been few scientific investigations of the human capacity to discriminate these patterns. A recent latent print matching experiment shows that qualified, court-practicing fingerprint experts are exceedingly accurate (and more conservative) compared with novices, but they do make errors. Here, a rationale for the design of this experiment is provided. We argue that fidelity, generalizability, and control must be balanced to answer important research questions; that the proficiency and competence of fingerprint examiners are best determined when experiments include highly similar print pairs, in a signal detection paradigm, where the ground truth is known; and that inferring from this experiment the statement "The error rate of fingerprint identification is 0.68%" would be unjustified. In closing, the ramifications of these findings for the future psychological study of forensic expertise and the implications for expert testimony and public policy are considered.
Urban Land Use Information Retrieval Based on Scene Classification of Google Street View Images

Land use maps are very important references for the urban planning and management. However, it is difficult and time-consuming to get high-resolution urban land use maps. In this study, we propose a new method to derive land use information at building block level based on machine learning and geo-tagged street-level imagery – Google Street View images. Several commonly used generic image features (GIST, HoG, and SIFT-Fisher) are used to represent street-level images of different cityscapes in a case study area of New York City. Machine learning is further used to categorize different images based on the calculated image features of different street-level images. Accuracy assessment results show that the method developed in this study is a promising method for land use mapping at building block level in future.
Alternating Minimization for Mixed Linear Regression

Mixed linear regression involves the recovery of two (or more) unknown vectors from unlabeled linear measurements; that is, where each sample comes from exactly one of the vectors, but we do not know which one. It is a classic problem, and the natural and empirically most popular approach to its solution has been the EM algorithm. As in other settings, this is prone to bad local minima; however, each iteration is very fast (alternating between guessing labels, and solving with those labels). In this paper we provide a new initialization procedure for EM, based on finding the leading two eigenvectors of an appropriate matrix. We then show that with this, a re-sampled version of the EM algorithm provably converges to the correct vectors, under natural assumptions on the sampling distribution, and with nearly optimal (unimprovable) sample complexity. This provides not only the first characterization of EM’s performance, but also much lower sample complexity as compared to both standard (randomly initialized) EM, and other methods for this problem.
SAT-based analysis of large real-world feature models is easy

Modern conflict-driven clause-learning (CDCL) Boolean SAT solvers provide efficient automatic analysis of real-world feature models (FM) of systems ranging from cars to operating systems. It is well-known that solver-based analysis of real-world FMs scale very well even though SAT instances obtained from such FMs are large, and the corresponding analysis problems are known to be NP-complete. To better understand why SAT solvers are so effective, we systematically studied many syntactic and semantic characteristics of a representative set of large real-world FMs. We discovered that a key reason why large real-world FMs are easy-to-analyze is that the vast majority of the variables in these models are unrestricted, i.e., the models are satisfiable for both true and false assignments to such variables under the current partial assignment. Given this discovery and our understanding of CDCL SAT solvers, we show that solvers can easily find satisfying assignments for such models without too many backtracks relative to the model size, explaining why solvers scale so well. Further analysis showed that the presence of unrestricted variables in these real-world models can be attributed to their high-degree of variability. Additionally, we experimented with a series of well-known nonbacktracking simplifications that are particularly effective in solving FMs. The remaining variables/clauses after simplifications, called the core, are so few that they are easily solved even with backtracking, further strengthening our conclusions. We explain the connection between our findings and backdoors, an idea posited by theorists to explain the power of SAT solvers. This connection strengthens our hypothesis that SAT-based analysis of FMs is easy. In contrast to our findings, previous research characterizes the difficulty of analyzing randomly-generated FMs in terms of treewidth. Our experiments suggest that the difficulty of analyzing real-world FMs cannot be explained in terms of treewidth.
ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide☆☆☆★

In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.
Efficient quantum algorithms for analyzing large sparse electrical networks

Analyzing large sparse electrical networks is a fundamental task in physics, electrical engineering and computer science. We propose two classes of quantum algorithms for this task. The first class is based on solving linear systems, and the second class is based on using quantum walks. These algorithms compute various electrical quantities, including voltages, currents, dissipated powers and effective resistances, in time poly(d,c, log(N),1/λ,1/ε), where N is the number of vertices in the network, d is the maximum unweighted degree of the vertices, c is the ratio of largest to smallest edge resistance, λ is the spectral gap of the normalized Laplacian of the network, and ε is the accuracy. Furthermore, we show that the polynomial dependence on 1/λ is necessary. This implies that our algorithms are optimal up to polynomial factors and cannot be signficantly improved.
3D Human Pose Estimation from a Single Image via Distance Matrix Regression

This paper addresses the problem of 3D human pose estimation from a single image. We follow a standard two-step pipeline by first detecting the 2D position of the N body joints, and then using these observations to infer 3D pose. For the first step, we use a recent CNN-based detector. For the second step, most existing approaches perform 2N-to-3N regression of the Cartesian joint coordinates. We show that more precise pose estimates can be obtained by representing both the 2D and 3D human poses using NxN distance matrices, and formulating the problem as a 2D-to-3D distance matrix regression. For learning such a regressor we leverage on simple Neural Network architectures, which by construction, enforce positivity and symmetry of the predicted matrices. The approach has also the advantage to naturally handle missing observations and allowing to hypothesize the position of non-observed joints. Quantitative results on Humaneva and Human3.6M datasets demonstrate consistent performance gains over state-of-the-art. Qualitative evaluation on the images in-the-wild of the LSP dataset, using the regressor learned on Human3.6M, reveals very promising generalization results.