nils-herrmann/clean_scirepeval huggingface dataset:
Compositionality and the angular gyrus: A multi-voxel similarity analysis of the semantic composition of nouns and verbs

The cognitive and neural systems that enable conceptual processing must support the ability to combine (and recombine) concepts to form an infinite number of ideas. Two candidate neural systems for conceptual combination-the left anterior temporal lobe (ATL) and the left angular gyrus (AG)-have been characterized as "semantic hubs" due to both functional and anatomical properties; however, these two regions likely support different aspects of composition. Here we consider two hypotheses for the role of AG in conceptual combination, both of which differ from a putative role for the ATL in "feature-based" combinatorics (i.e., meaning derived by combining concepts' features). Firstly, we examine whether AG is more sensitive to function-argument relations of the sort that arise when a predicate is combined with its arguments. Secondly, we examine the non-mutually exclusive possibility that AG represents information carried on a verb in particular, whether this be information about event composition or about thematic relations denoted uniquely by verbs. We identified voxels that respond differentially to two-word versus one-word stimuli, and we measured the similarity of the patterns in these voxels evoked by (1) pairs of two-word phrases that shared a noun that was an argument, thus sharing function-argument composition (e.g. eats meat and with meat), in comparison with two-word phrases that shared only a noun, not an argument (e.g., eats meat and tasty meat); and (2) stimulus pairs that shared only an event (operationalized here as sharing a verb; e.g. eats meat and eats quickly), in comparison to both of the above. We found that activity patterns in left AG tracked information relating to the presence of an event-denoting verb in a pair of two-word phrases. We also found that the neural similarity in AG voxel patterns between two phrases sharing a verb correlated with subjects' ratings of how similar the meanings of those two verb phrases were. These findings indicate that AG represents information specific to verbs, perhaps event structure or thematic relations mediated by verbs, as opposed to argument structure in general.
Personal Safety is More Important Than Cost of Damage During Robot Failure

As robots become more common in everyday life it will be increasingly important to understand how non-experts will view robot failure. In this study, we found that severity of failure seems to be tightly coupled with perceived risk to self rather than risk to the robot's task and object. We initially thought perceived severity would be tied to the cost of damage. Instead, participants placed falling drinking glasses above a laptop when rating the severity of the failure. Related results reinforce the primacy of personal safety over the financial cost of damage and suggest the results were tied to proximity to breaking glass.
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

Early detection of pulmonary cancer is the most promising way to enhance a patient’s chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection(average FROC-score of 0.891, ranking the 1st place over all submitted results).
EM*: An EM Algorithm for Big Data

Existing data mining techniques, more particularly iterative learning algorithms, become overwhelmed with big data. While parallelism is an obvious and, usually, necessary strategy, we observe that both (1) continually revisiting data and (2) visiting all data are two of the most prominent problems especially for iterative, unsupervised algorithms like Expectation Maximization algorithm for clustering (EM-T). Our strategy is to embed EM-T into a non-linear hierarchical data structure(heap) that allows us to (1) separate data that needs to be revisited from data that does not and (2) narrow the iteration toward the data that is more difficult to cluster. We call this extended EM-T, EM*. We show our EM* algorithm outperform EM-T algorithm over large real world and synthetic data sets. We lastly conclude with some theoretic underpinnings that explain why EM* is successful.
Neurocognitive Mechanisms of Synesthesia

Synesthesia is a condition in which stimulation of one sensory modality causes unusual experiences in a second, unstimulated modality. Although long treated as a curiosity, recent research with a combination of phenomenological, behavioral, and neuroimaging methods has begun to identify the cognitive and neural basis of synesthesia. Here, we review this literature with an emphasis on grapheme-color synesthesia, in which viewing letters and numbers induces the perception of colors. We discuss both the substantial progress that has been made in the past fifteen years and some open questions. In particular, we focus on debates in the field relating to the neural basis of synesthesia, including the relationship between synesthesia and attention and the role of meaning in synesthetic colors. We propose that some, but probably not all, of these differences can be accounted for by differences in the synesthetes studied and discuss some methodological implications of these individual differences.
Why can't a man be more like a woman? Sex differences in Big Five personality traits across 55 cultures.

Previous research suggested that sex differences in personality traits are larger in prosperous, healthy, and egalitarian cultures in which women have more opportunities equal with those of men. In this article, the authors report cross-cultural findings in which this unintuitive result was replicated across samples from 55 nations (N = 17,637). On responses to the Big Five Inventory, women reported higher levels of neuroticism, extraversion, agreeableness, and conscientiousness than did men across most nations. These findings converge with previous studies in which different Big Five measures and more limited samples of nations were used. Overall, higher levels of human development--including long and healthy life, equal access to knowledge and education, and economic wealth--were the main nation-level predictors of larger sex differences in personality. Changes in men's personality traits appeared to be the primary cause of sex difference variation across cultures. It is proposed that heightened levels of sexual dimorphism result from personality traits of men and women being less constrained and more able to naturally diverge in developed nations. In less fortunate social and economic conditions, innate personality differences between men and women may be attenuated.
New Semi-Supervised and Active Learning Combination Technique for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is a procedure that is used to disaggregate the contributions of different appliances in a building. Various traditional techniques use supervised and unsupervised learning for disaggregation. Very few papers utilize the semi-supervised or active approaches. The two approaches are used in case of training data scarcity. The semi-supervised approach is prone to errors as training dataset is small, while the active leaning approach relies completely on the user to get correct labels. A technique that leverages the semi-supervised and active learning together is applied. This can reduce the number of samples required to the users, also can increase the accuracy of the semi-supervised algorithm while more reliable samples can be added. Many cases are studied and the results showed the robustness and reliability of the new combination. The proposed combined technique showed that the number of queries can be reduced to fifth without losing more than 4% of the accuracy. Also, by combining the semi-supervised and active techniques, the accuracy increased by 6% compared to the stand alone semi-supervised approach.
ON STAGNATION OF THE DIFFERENTIAL EVOLUTION ALGORITHM

This article discusses the stagnation of an evolutionary optimization algorithm called Differential Evolution. Stagnation problem refers to a situation in which the optimum seeking process stagnates before finding a globally optimal solution. Typically, stagnation occurs virtually without any obvious reason. The stagnation differs from the premature convergence so that the population remains diverse and unconverged after stagnation, but the optimization process does not progress anymore. The reasons for this problem have remained unknown so far. This article uncovers this problem describing the basic nature of stagnation phenomena, a mechanism behind it and some reasons for stagnation. Advices for reducing the risk of stagnation are concluded on basis of the new findings.
Single-Frame Vulnerable Road Users Classification with a 77 GHz FMCW Radar Sensor and a Convolutional Neural Network

Road traffic accidents accounted in 2013 for over a million deaths worldwide. Pedestrians and cyclists are especially vulnerable in road accidents and therefore it is essential to identify them in a timely manner to foresee dangerous situations. Radar sensors are excellent candidates for this task since they are able to simultaneously measure range, radial velocity and angle while remaining robust in adverse weather conditions. In this paper, a method to classify moving subjects as pedestrians, cyclists or cars using single radar measurement frames from a 77 GHz FMCW radar sensor is proposed. To perform the classification the range-Doppler-angle power spectrum is run through a convolutional neural network. A dataset of around 9.1k frames gathered in urban scenarios is used to train the convolutional neural network. A classification accuracy as high as 97.3(%) is achieved on a set consisting of tracks not seen during training but on known locations. The classification accuracy drops to 84.2(%) when tested on unseen tracks gathered in an unseen location.
Congestion Prediction With Big Data for Real-Time Highway Traffic

By collecting and analyzing a vast quantity and different categories of information, traffic flow and road congestion can be predicted and avoided in intelligent transportation system. However, how to tackle with these big data is vital but challenging. Most of the existing literatures utilized batch method to process a bunch of road data that cannot achieve real-time traffic prediction. In this paper, we use the spouts and bolts in Apache Storm to implement a real-time traffic prediction model by analyzing enormous streaming data, such as road density, traffic events, and rainfall volume. The proposed SVM-based real-time highway traffic congestion prediction (SRHTCP) model collects the road data from the Taiwan Area National Freeway Bureau, the traffic events reported by road users from the Police Broadcasting Service in Taiwan, and the weather data from the Central Weather Bureau in Taiwan. We use fuzzy theory to evaluate the traffic level of road section in real time with considering road speed, road density, road traffic volume, and the rainfall of road sections. In addition, the SRHTCP model predicts the road speed of next time period by exploring streaming traffic and weather data. Results showed that the proposed SRHTCP model improves 25.6% prediction accuracy than the prediction method based on weighted exponential moving average method under the measurement of mean absolute relative error.
From Theory to Practice: Plug and Play with Succinct Data Structures

Engineering efficient implementations of compact and succinct structures is time-consuming and challenging, since there is no standard library of easy-to-use, highly optimized, and composable components. One consequence is that measuring the practical impact of new theoretical proposals is difficult, since older baseline implementations may not rely on the same basic components, and reimplementing from scratch can be time-consuming. In this paper we present a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements. We demonstrate the functionality of the framework by recomposing two succinct solutions for top-k document retrieval which can operate on both character and integer alphabets.
Modeling of Lithium-Ion Battery for Energy Storage System Simulation

Batteries are the power providers for almost all portable computing devices. They can also be used to build energy storage systems for large-scale power applications. In order to design battery systems for energy-optimal architectures and applications with maximized battery lifetime, system designers require computer aided design tools that can implement mathematical battery models, predict the battery behavior and thus help the designers search for the optimal schemes. This paper presentss a lithium-ion battery model which can be used on SIMPLORER software to simulate the behavior of the battery under dynamic conditions. Based on measured battery data, a mathematical model of the battery is developed which takes into account battery operating temperature and the rates of the battery charge/discharge currents. In addition, thermal characteristics of the battery are also studied. KeywordsLithium-ion battery; dynamic model; energy storage system;
Nested Named Entity Recognition

Many named entities contain other named entities inside them. Despite this fact, the field of named entity recognition has almost entirely ignored nested named entity recognition, but due to technological, rather than ideological reasons. In this paper, we present a new technique for recognizing nested named entities, by using a discriminative constituency parser. To train the model, we transform each sentence into a tree, with constituents for each named entity (and no other syntactic structure). We present results on both newspaper and biomedical corpora which contain nested named entities. In three out of four sets of experiments, our model outperforms a standard semi-CRF on the more traditional top-level entities. At the same time, we improve the overall F-score by up to 30% over the flat model, which is unable to recover any nested entities.
Membrane Bioreactor (MBR) Technology for Wastewater Treatment and Reclamation: Membrane Fouling

The membrane bioreactor (MBR) has emerged as an efficient compact technology for municipal and industrial wastewater treatment. The major drawback impeding wider application of MBRs is membrane fouling, which significantly reduces membrane performance and lifespan, resulting in a significant increase in maintenance and operating costs. Finding sustainable membrane fouling mitigation strategies in MBRs has been one of the main concerns over the last two decades. This paper provides an overview of membrane fouling and studies conducted to identify mitigating strategies for fouling in MBRs. Classes of foulants, including biofoulants, organic foulants and inorganic foulants, as well as factors influencing membrane fouling are outlined. Recent research attempts on fouling control, including addition of coagulants and adsorbents, combination of aerobic granulation with MBRs, introduction of granular materials with air scouring in the MBR tank, and quorum quenching are presented. The addition of coagulants and adsorbents shows a significant membrane fouling reduction, but further research is needed to establish optimum dosages of the various coagulants/adsorbents. Similarly, the integration of aerobic granulation with MBRs, which targets biofoulants and organic foulants, shows outstanding filtration performance and a significant reduction in fouling rate, as well as excellent nutrients removal. However, further research is needed on the enhancement of long-term granule integrity. Quorum quenching also offers a strong potential for fouling control, but pilot-scale testing is required to explore the feasibility of full-scale application.
Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations

Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia. The short texts have a limited contextual information, and they are sparse, noisy and ambiguous, and hence, automatically learning topics from them remains an important challenge. To tackle this problem, in this paper, we propose a semantics-assisted non-negative matrix factorization (SeaNMF) model to discover topics for the short texts. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of the corpus. The SeaNMF model is solved using a block coordinate descent algorithm. We also develop a sparse variant of the SeaNMF model which can achieve a better model interpretability. Extensive quantitative evaluations on various realworld short text datasets demonstrate the superior performance of the proposed models over several other state-of-the-art methods in terms of topic coherence and classification accuracy. The qualitative semantic analysis demonstrates the interpretability of our models by discovering meaningful and consistent topics. With a simple formulation and the superior performance, SeaNMF can be an effective standard topic model for short texts.
Relationship between the cayley-dickson fourier transform and the hartley transform of multidimensional real signals

The paper shows relations between two different frequency representations of n-dimensional signals (n = 1, 2, 3): the Cayley-Dickson Fourier transform and the Hartley transform. The formulas relating the n-D complex Fourier transform and the n-D Hartley transform are presented. New formulas relating the Quaternion and Octonion Fourier transforms and respectively the 2-D and 3-D Hartley transforms are developed. The paper is illustrated with examples of Hartley transforms of 1-D, 2-D and 3-D Gaussian signals.
Premarital Sexual Behavior among male college students of Kathmandu, Nepal

In Nepal, as in other Asian countries, the issue of sexuality still remains a taboo. Despite this fact, an increasing number of sexual activities is being reported by Nepalese students. This trend warrants serious and timely attention. Due to the sensitivity of the topic of premarital sexuality, youth receive inadequate education, guidance and services on reproductive health. The main objectives of this paper are to explore the sexual behavior especially focusing on prevalence of premarital sex among college men and to investigate the factors surrounding premarital sexual behavior.



A cross-sectional survey of college students was conducted in April-May 2006. A self-administered questionnaire was completed by 573 male students. Association between premarital sex and the explanatory variables was assessed in bivariate analysis using Chi-square tests. The associations were further explored using multivariate logistic analysis.



Despite the religious and cultural restrictions, about two-fifths of survey respondents (39%) reported that they have had premarital sex. The study has also shown that substantial proportions of students indulge in sexual activities as well as risky sexual behavior. Sex with commercial sex workers, multiple sex partners, and inconsistence use of condom with non-regular partner was common among the students. Less than two in five male students (57%) had used condom at the first sexual intercourse.The prevalence of premarital sex varied on different settings. Older students aged 20 and above were more likely to have premarital sex compared with younger students aged 15-19. Men who had liberal attitude towards male virginity at marriage were almost two times more likely to have engaged in premarital sex compared to their counterparts who have conservative attitude towards male virginity at marriage. Moreover, those students who believe in Hindu religion were more than two times (OR = 2.5) more likely to have premarital sex compared with those who follow other religions. Furthermore, those men who have close unmarried friends who have experienced premarital sexual intercourse were eight times (OR = 8.4) more likely to be sexually active compared to those who did not have such sexually active friends.



Prevalence of premarital sexual intercourse and risky sexual behavior are not uncommon in Nepal. Young people are exposed to health hazards due to their sexual behavior; hence sex education should be provided. School or college based sexuality education could benefit even out-of-school youths, because their partners often are students.
Extractive Summarisation Based on Keyword Profile and Language Model

We present a statistical framework to extract information-rich citation sentences that summarise the main contributions of a scientific paper. In a first stage, we automatically discover salient keywords from a paper’s citation summary, keywords that characterise its main contributions. In a second stage, exploiting the results of the first stage, we identify citation sentences that best capture the paper’s main contributions. Experimental results show that our approach using methods rooted in quantitative statistics and information theory outperforms the current state-of-the-art systems in scientific paper summarisation.
Revisiting the Bullwhip Effect under Economic Uncertainty

For the past decade, economic uncertainties were the only certainty. At the breakout of the global financial crisis, sales managers at the air conditioning industry noticed two strange distortions in the supply chain. At the downstream, factories were increasing their levels of inventories of specific raw materials mainly copper pipes and power cables whereas at the upper stream the distribution channels were logically making lower orders of the finished products compared to the actual market demands. This paper aims at analyzing some of the situational factors that lead to these two types of distortions. In doing so, it also proposes that the model of the Bullwhip Effect (BWE) will have an inverse shape under economic uncertainty compared to the conventional shape reported in supply chain traditional literature. Despite the fact that a number of BWE distortions were reported by several scholars, the phenomenon has never been studied under the special situational factors during an economic uncertainty.
A Linked Data wrapper for CrunchBase

CrunchBase is a database about startups and technology companies. The database can be searched, browsed, and edited via a website, but is also accessible via an entity-centric HTTP API in JSON format. We present a wrapper around the API that provides the data as Linked Data. The wrapper provides schema-level links to schema.org, Friend-of-a-Friend and Vocabulary-of-a-Friend, and entity-level links to DBpedia for organization entities. We describe how to harvest the RDF data to obtain a local copy of the data for further processing and querying that goes beyond the access facilities of the CrunchBase API. Further, we describe the cases in which the Linked Data API for CrunchBase and the crawled CrunchBase RDF data have been used in other works.