nils-herrmann/clean_scirepeval huggingface dataset:
Gated Neural Networks for Targeted Sentiment Analysis

Targeted sentiment analysis classifies the sentiment polarity towards each target entity mention in given text documents. Seminal methods extract manual discrete features from automatic syntactic parse trees in order to capture semantic information of the enclosing sentence with respect to a target entity mention. Recently, it has been shown that competitive accuracies can be achieved without using syntactic parsers, which can be highly inaccurate on noisy text such as tweets. This is achieved by applying distributed word representations and rich neural pooling functions over a simple and intuitive segmentation of tweets according to target entity mentions. In this paper, we extend this idea by proposing a sentencelevel neural model to address the limitation of pooling functions, which do not explicitly model tweet-level semantics. First, a bi-directional gated neural network is used to connect the words in a tweet so that pooling functions can be applied over the hidden layer instead of words for better representing the target and its contexts. Second, a three-way gated neural network structure is used to model the interaction between the target mention and its surrounding contexts. Experiments show that our proposed model gives significantly higher accuracies compared to the current best method for targeted sentiment analysis.
Accelerating Stochastic Gradient Descent

There is widespread sentiment that fast gradient methods (e.g. Nesterov’s acceleration, conjugate gradient, heavy ball) are not effective for the purposes of stochastic optimization due to their instability and error accumulation. Numerous works have attempted to quantify these instabilities in the face of either statistical or non-statistical errors (Paige, 1971; Proakis, 1974;  1987; Greenbaum, 1989; Roy and Shynk, 1990; Sharma et al., 1998; d’Aspremont, 2008; Devolder et al., 2014; Yuan et al., 2016). This work considers these issues for the special case of stochastic approximation for the least squares regression problem, and our main result refutes this conventional wisdom by showing that acceleration can be made robust to statistical errors. In particular, this work introduces an accelerated stochastic gradient method that provably achieves the minimax optimal statistical risk faster than stochastic gradient descent. Critical to the analysis is a sharp characterization of accelerated stochastic gradient descent as a stochastic process. We hope this characterization gives insights towards the broader question of designing simple and effective accelerated stochastic methods for more general convex and non-convex optimization problems.
Comparison Between Analog Radio-Over-Fiber and Sigma Delta Modulated Radio-Over-Fiber

With the continuously increasing demand of cost effective, broadband wireless access, radio-over-fiber (RoF) starts to gain more and more momentum. Various techniques already exist, using analog (ARoF) or digitized (DRoF) radio signals over fiber; each with their own advantages and disadvantages. By transmitting a sigma delta modulated signal over fiber (SDoF), a similar immunity to impairments as DRoF can be obtained while maintaining the low complexity of ARoF. This letter describes a detailed experimental comparison between ARoF and SDoF that quantifies the improvement in linearity and error vector magnitude (EVM) of SDoF over ARoF. The experiments were carried out using a 16-QAM constellation with a baudrate from 20 to 125 MBd modulated on a central carrier frequency of 1 GHz. The sigma delta modulator runs at 8 or 13.5 Gbps. A high-speed vertical-cavity surface-emitting laser (VCSEL) operating at 850 nm is used to transmit the signal over 200-m multimode fiber. The receiver amplifies the electrical signals and subsequently filters to recover the original RF signal. Compared with ARoF, improvements exceeding 40 dB were measured on the third order intermodulation products when SDoF was employed, the EVM improves between 2.4 and 7.1 dB.
Sleep duration, positive attitude toward life, and academic achievement: the role of daytime tiredness, behavioral persistence, and school start times.

Sleep timing undergoes profound changes during adolescence, often resulting in inadequate sleep duration. The present study examines the relationship of sleep duration with positive attitude toward life and academic achievement in a sample of 2716 adolescents in Switzerland (mean age: 15.4 years, SD = 0.8), and whether this relationship is mediated by increased daytime tiredness and lower self-discipline/behavioral persistence. Further, we address the question whether adolescents who start school modestly later (20 min; n = 343) receive more sleep and report better functioning. Sleeping less than an average of 8 h per night was related to more tiredness, inferior behavioral persistence, less positive attitude toward life, and lower school grades, as compared to longer sleep duration. Daytime tiredness and behavioral persistence mediated the relationship between short sleep duration and positive attitude toward life and school grades. Students who started school 20 min later received reliably more sleep and reported less tiredness.
Depression among Chinese University Students: Prevalence and Socio-Demographic Correlates

The purpose of the present study was to estimate the prevalence of depression in Chinese university students, and to identify the socio-demographic factors associated with depression in this population. A multi-stage stratified sampling procedure was used to select university students (N = 5245) in Harbin (Heilongjiang Province, Northeastern China), who were aged 16-35 years. The Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores of 14 or higher were categorized as depressive for logistic regression analysis. Depression was diagnosed by the Structured Clinical Interview (SCID) for the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV). 11.7% of the participants had a BDI score 14 or higher. Major Depressive Disorder was seen in 4.0% of Chinese university students. There were no statistical differences in the incidence of depression when gender, ethnicity, and university classification were analyzed. Multivariate analysis showed that age, study year, satisfaction with major, family income situation, parental relationship and mother's education were significantly associated with depression. Moderate depression is prevalent in Chinese university students. The students who were older, dissatisfied with their major, had a lower family income, poor parental relationships, and a lower level of mother's education were susceptible to depression.
Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins

Protein folding is a complex process that can lead to disease when it fails. Especially poorly understood are the very early stages of protein folding, which are likely defined by intrinsic local interactions between amino acids close to each other in the protein sequence. We here present EFoldMine, a method that predicts, from the primary amino acid sequence of a protein, which amino acids are likely involved in early folding events. The method is based on early folding data from hydrogen deuterium exchange (HDX) data from NMR pulsed labelling experiments, and uses backbone and sidechain dynamics as well as secondary structure propensities as features. The EFoldMine predictions give insights into the folding process, as illustrated by a qualitative comparison with independent experimental observations. Furthermore, on a quantitative proteome scale, the predicted early folding residues tend to become the residues that interact the most in the folded structure, and they are often residues that display evolutionary covariation. The connection of the EFoldMine predictions with both folding pathway data and the folded protein structure suggests that the initial statistical behavior of the protein chain with respect to local structure formation has a lasting effect on its subsequent states.
Payday Loans and Consumer Financial Health

The annualized interest rate for a payday loan often exceeds 10 times that of a typical credit card, yet this market grew immensely in the 1990s and 2000s, elevating concerns about the risk payday loans pose to consumers and whether payday lenders target minority neighborhoods. This paper employs individual credit record data, and Census data on payday lender store locations, to assess these concerns. Taking advantage of several state law changes since 2006 and, following previous work, within-state-year differences in access arising from proximity to states that allow payday loans, I find little to no effect of payday loans on credit scores, new delinquencies, or the likelihood of overdrawing credit lines. The analysis also indicates that neighborhood racial composition has little influence on payday lender store locations conditional on income, wealth and demographic characteristics.
Deep Local Video Feature for Action Recognition

We investigate the problem of representing an entire video using CNN features for human action recognition. End-to-end learning of CNN/RNNs is currently not possible for whole videos due to GPU memory limitations and so a common practice is to use sampled frames as inputs along with the video labels as supervision. However, the global video labels might not be suitable for all of the temporally local samples as the videos often contain content besides the action of interest. We therefore propose to instead treat the deep networks trained on local inputs as local feature extractors. The local features are then aggregated to form global features which are used to assign video-level labels through a second classification stage. We investigate a number of design choices for this local feature approach. Experimental results on the HMDB51 and UCF101 datasets show that a simple maximum pooling on the sparsely sampled local features leads to significant performance improvement.
A Three-Way Model for Collective Learning on Multi-Relational Data

Relational learning is becoming increasingly important in many areas of application. Here, we present a novel approach to relational learning based on the factorization of a three-way tensor. We show that unlike other tensor approaches, our method is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factorization. We substantiate our theoretical considerations regarding the collective learning capabilities of our model by the means of experiments on both a new dataset and a dataset commonly used in entity resolution. Furthermore, we show on common benchmark datasets that our approach achieves better or on-par results, if compared to current state-of-the-art relational learning solutions, while it is significantly faster to compute.
Online Adaptation for Joint Scene and Object Classification

The proposed active learning framework learns scene and object classification models simultaneously. Both scene and object classification models take advantage of the interdependence between them to select the most informative samples with the least manual labeling cost. To the best of our knowledge, any previous work using active learning to classify scene and objects together is unknown. Leveraging upon the inter-relationships between scene and objects, we propose a new information-theoretic sample selection strategy.  This figure presents a pictorial representation of the proposed framework. Overview of Our Joint Active Learning Framework
Lightning performance of 275 kV transmission lines

This paper presents a comparative lightning performance study conducted on a 275 kV double circuit shielded transmission line using two software programs, TFlash and Sigma-Slp. The line performance was investigated by using both a single stroke and a statistical performance analysis and considering cases of shielding failure and backflashover. A sensitivity analysis was carried out to determine the relationship between the flashover rate and the parameters influencing it. To improve the lightning performance of the line, metal oxide surge arresters were introduced using different phase and line locations. Optimised arrester arrangements are proposed.
The Ambiguous Intentions Hostility Questionnaire (AIHQ): a new measure for evaluating hostile social-cognitive biases in paranoia.

This study reports on the development of a new measure of hostile social-cognitive biases for use in paranoia research, the Ambiguous Intentions Hostility Questionnaire (AIHQ). The AIHQ is comprised of a variety of negative situations that differ in terms of intentionality. Items were developed to reflect causes that were ambiguous, intentional, and accidental in nature.



Participants were 322 college students who completed the AIHQ along with measures of paranoia, hostility, attributional style, and psychosis proneness. The reliability and validity of the AIHQ was evaluated using both correlational and multiple regression methods.



The AIHQ had good levels of reliability (internal consistency and interrater reliability). The AIHQ was positively correlated with paranoia and hostility and was not correlated with measures of psychosis proneness, which supported the convergent and discriminant validity of the scale. In addition, the AIHQ predicted incremental variance in paranoia scores as compared to the attributional, hostility, and psychosis proneness measures. Ambiguous items showed the most consistent relationships with paranoia.



The AIHQ appears to be a reliable and valid measure of hostile social cognitive biases in paranoia. Recommendations for using the AIHQ in the study of paranoia are discussed.
Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies

Grounded in the diffusion of innovation theory and the technology-organization-environment framework, we develop an integrative model to study the determinants of post-adoption stages of innovation diffusion, using enterprise digital transformation as an example of technology-enabled innovations. We specify four innovation characteristics (relative advantage, compatibility, costs and security concern) and four contextual factors (technology competence, organization size, competitive pressure and partner readiness) as determinants of post-adoption usage, and postulate usage as an intermediate link to impact on firm performance. We test the proposed model using a dataset of 1415 companies from six European countries. We find that the innovation needs to be used extensively in value-chain activities before its impact can be realized. Among the innovation characteristics, we find that compatibility is the strongest driver, and security concern outweighs cost as a usage inhibitor.
Differential effects of strength training leading to failure versus not to failure on hormonal responses, strength, and muscle power gains.

The purpose of this study was to examine the efficacy of 11 wk of resistance training to failure vs. nonfailure, followed by an identical 5-wk peaking period of maximal strength and power training for both groups as well as to examine the underlying physiological changes in basal circulating anabolic and catabolic hormones. Forty-two physically active men were matched and then randomly assigned to either a training to failure (RF; n = 14), nonfailure (NRF; n = 15), or control groups (C; n = 13). Muscular and power testing and blood draws to determine basal hormonal concentrations were conducted before the initiation of training (T0), after 6 wk of training (T1), after 11 wk of training (T2), and after 16 wk of training (T3). Both RF and NRF resulted in similar gains in 1-repetition maximum bench press (23 and 23%) and parallel squat (22 and 23%), muscle power output of the arm (27 and 28%) and leg extensor muscles (26 and 29%), and maximal number of repetitions performed during parallel squat (66 and 69%). RF group experienced larger gains in the maximal number of repetitions performed during the bench press. The peaking phase (T2 to T3) after NRF resulted in larger gains in muscle power output of the lower extremities, whereas after RF it resulted in larger gains in the maximal number of repetitions performed during the bench press. Strength training leading to RF resulted in reductions in resting concentrations of IGF-1 and elevations in IGFBP-3, whereas NRF resulted in reduced resting cortisol concentrations and an elevation in resting serum total testosterone concentration. This investigation demonstrated a potential beneficial stimulus of NRF for improving strength and power, especially during the subsequent peaking training period, whereas performing sets to failure resulted in greater gains in local muscular endurance. Elevation in IGFBP-3 after resistance training may have been compensatory to accommodate the reduction in IGF-1 to preserve IGF availability.
Formal Verification of Neural Network Controlled Autonomous Systems

In this paper, we consider the problem of formally verifying the safety of an autonomous robot equipped with a Neural Network (NN) controller that processes LiDAR images to produce control actions. Given a workspace that is characterized by a set of polytopic obstacles, our objective is to compute the set of safe initial conditions such that a robot trajectory starting from these initial conditions is guaranteed to avoid the obstacles. Our approach is to construct a finite state abstraction of the system and use standard reachability analysis over the finite state abstraction to compute the set of the safe initial states. The first technical problem in computing the finite state abstraction is to mathematically model the imaging function that maps the robot position to the LiDAR image. To that end, we introduce the notion of imaging-adapted sets as partitions of the workspace in which the imaging function is guaranteed to be affine. Based on this notion, and resting on efficient algorithms in the literature of computational geometry, we develop a polynomialtime algorithm to partition theworkspace into imaging-adapted sets along with computing the corresponding affine imaging functions. Given this workspace partitioning, a discrete-time linear dynamics of the robot, and a pre-trained NN controller with Rectified Linear Unit (ReLU) nonlinearity, the second technical challenge is to analyze the behavior of the neural network. To that end, and thanks to the ReLU functions being piecewise linear functions, we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible segments of different ReLUs. SMC solvers then use a Boolean satisfiability solver and a convex programming solver and decompose the problem into smaller subproblems. At each iteration, the Boolean satisfiability solver searches for a candidate assignment for the different ReLU segments while completely abstracting the robot dynamics. Convex programming is then used to check the feasibility of the proposed ReLU phases against the dynamic and imagining constraints, or generate succinct explanations for their infeasibility to reduce the search space. To accelerate this process, we develop a pre-processing algorithm that could rapidly prune the space feasible ReLU segments. Finally, we demonstrate the efficiency of the proposed algorithms using numerical simulations with increasing complexity of the neural network controller.
Comparing the Complexity of Wayfinding Tasks in Built Environments

Wayfinding is a basic activity that people do throughout their entire lives as they navigate from one place to another. Many theories of spatial cognition have been developed to account for this behavior; however, most of the computational models focus on knowledge representation (e.g., cognitive maps) and do not consider the process of how people structure wayfinding tasks and space. This paper presents a computational method to compare the complexity of wayfinding tasks in built environments. As a measure for such complexity we use a simple wayfinding model that consists of two critical elements: choices and clues. We show that elements of people’s perception and cognition can be used to determine the elements of the wayfinding model and, therefore, to compare the complexity of wayfinding tasks in built environments. A case study of wayfinding in airports demonstrates the applicability of the method. The integration of this method into the computational design process of built environments will help to identify architectural problems with regard to wayfinding prior to construction.
The spacing effect in children’s memory and category induction

The spacing effect describes the robust phenomenon whereby memory is enhanced when learning events are distributed, instead of being presented in succession. We investigated the effect of spacing on children's memory and category induction. Three-year-old children were presented with two tasks, a memory task and a category induction task. In the memory task, identical instances of an object were presented and then tested in a multiple choice test. In the category induction task, different instances of a category were presented and tested in a multiple choice test. In both tasks, presenting the instances in a spaced sequence resulted in more learning than presenting the instances in a massed sequence, despite the difficulty created by the spaced sequence. The spaced sequence increased the difficulty of the task by allowing children time to forget the previous instance during the spaced interval.
Financial Fraud Detection u

The ubiquitous cases of abnormal transactions with intent to defraud is a global phenomenon. An architecture that enhances fraud detection using a radial basis function network was designed using a supervised data mining technique― radial basis function (RBF) network, interpolation approximation method. Several base models were thus created, and in turn used in aggregation to select the optimum model using the misclassification erro accuracy, sensitivity, specificity and receiver operating characteristics (ROC) metrics. The results shows model has a zero-tolerance for fraud with better especially in cases where there were no fraud doubtful cases were rather flagged than to allow a fraud incident to pass undetected. Expectedly, the model’s computations converge faster at 200 iterations. generic with similar characteristics with other classification methods but distinct parameters thereby minimizing the time and cost of fraud detection by adopting computationally efficient algorithm.
Knowledge discovery from road traffic accident data in Ethiopia: Data quality, ensembling and trend analysis for improving road safety

Descriptive analysis of the magnitude and situation of road safety in general and road accidents in particular is important, but understanding of data quality, factors related with dangerous situations and various interesting patterns in data is of even greater importance. Under the umbrella of information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore data quality issues, analyze trends and predict the role of road users on possible injury risks. The research employed TreeNet, Classification and Adaptive Regression Trees (CART), Random Forest (RF) and hybrid ensemble approach. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is subject to several analyses. Empirical results illustrate that data quality is a major problem that needs architectural guideline and the prototype models could classify accidents with promising accuracy. In addition, an ensemble technique proves to be better in terms of predictive accuracy in the domain under study.