nils-herrmann/clean_scirepeval huggingface dataset:
NESTA, The NICTA Energy System Test Case Archive

In recent years the power systems research community has seen an explosion of work applying operations research techniques to challenging power network optimization problems. Regardless of the application under consideration, all of these works rely on power system test cases for evaluation and validation. However, many of the well established power system test cases were developed as far back as the 1960s with the aim of testing AC power flow algorithms. It is unclear if these power flow test cases are suitable for power system optimization studies. This report surveys all of the publicly available AC transmission system test cases, to the best of our knowledge, and assess their suitability for optimization tasks. It finds that many of the traditional test cases are missing key network operation constraints, such as line thermal limits and generator capability curves. To incorporate these missing constraints, data driven models are developed from a variety of publicly available data sources. The resulting extended test cases form a compressive archive, NESTA, for the evaluation and validation of power system optimization algorithms.
A Survey of Text Clustering Algorithms

Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.
Ultra Small OLEd Pico Projector Ground-breaking , active illumination-free concept enables new projection applications

An ultra compact projection system based on a high brightness OLEd micro display is developed. System design and realization of a prototype are presented. This OLEd pico projector with a volume of about 10 cm3 can be integrated into portable systems like mobile phones or PdAs. The Fraunhofer IPMS developed the high brightness monochrome OLEd micro display. The Fraunhofer IOF desig­ ned the specific projection lens  and in tegrated the OLEd and the projection optic to a full functional pico projection system. This article provides a closer look on the technology and its possibilities.
Assignment Algorithms for Modeling Resource Contention in Multirobot Task Allocation

This paper considers multirobot task allocation problems where the estimated costs for performing tasks are interrelated, and the overall team objective need not be a standard sum-of-costs (or utilities) model, enabling straightforward treatment of the additional costs incurred by resource contention. In the model we introduce, a team may choose one of a set of shared resources to perform a task (e.g., several routes to reach a destination), and interference is modeled when multiple robots use the same resource. We show that the general problem is NP-hard, and investigate specialized subinstances with particular cost structures. For the general problem, we describe an exact algorithm which finds an optimal assignment in a reasonable time on small instances. Aiming at larger problems, we turn two particular subinstances, introducing an two algorithms that find assignments quickly even for problems of considerable size, the first being optimal, the second being an approximation algorithm but also producing high-quality solutions with bounded suboptimality.
Identifying authoritative actors in question-answering forums: the case of Yahoo! answers

We consider the problem of identifying authoritative users in Yahoo! Answers. A common approach is to use link analysis techniques in order to provide a ranked list of users based on their degree of authority. A major problem for such an approach is determining how many users should be chosen as authoritative from a ranked list. To address this problem, we propose a method for automatic identification of authoritative actors. In our approach, we propose to model the authority scores of users as a mixture of gamma distributions. The number of components in the mixture is estimated by the Bayesian Information Criterion (BIC) while the parameters of each component are estimated using the Expectation-Maximization (EM) algorithm. This method allows us to automatically discriminate between authoritative and non-authoritative users. The suitability of our proposal is demonstrated in an empirical study using datasets from Yahoo! Answers.
Learning N-Gram Language Models from Uncertain Data

We present a new algorithm for efficiently training n-gram language models on uncertain data, and illustrate its use for semisupervised language model adaptation. We compute the probability that an n-gram occurs k times in the sample of uncertain data, and use the resulting histograms to derive a generalized Katz back-off model. We compare three approaches to semisupervised adaptation of language models for speech recognition of selected YouTube video categories: (1) using just the one-best output from the baseline speech recognizer or (2) using samples from lattices with standard algorithms versus (3) using full lattices with our new algorithm. Unlike the other methods, our new algorithm provides models that yield solid improvements over the baseline on the full test set, and, further, achieves these gains without hurting performance on any of the set of video categories. We show that categories with the most data yielded the largest gains. The algorithm has been released as part of the OpenGrm n-gram library .
An improvement of rotation invariant 3D-shape based on functions on concentric spheres

In this paper, we consider 3D-shape descriptors generated by using functions on a sphere. The descriptors are engaged for retrieving polygonal mesh models. Invariance of descriptors with respect to rotation of a model can be achieved either by using the Principle Component Analysis (PCA) or defining features in which the invariance exists. The contribution of the paper is twofold: firstly, we define a new rotation invariant feature vector based on functions on concentric spheres, that outperforms a recently proposed descriptor; secondly, we compare the two approaches for achieving rotation invariance as well as options to use a single function or several functions on concentric spheres to generate feature vectors. We conclude that descriptors, which use the PCA, outperform others, while capturing the internal structure of a 3D-model with functions on concentric spheres can improve retrieval effectiveness.
Big Data Analytics in Chemical Engineering.

Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.
Large-scale detection of non-technical losses in imbalanced data sets

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion

We exploit the versatile framework of Riemannian optimization on quotient manifolds to develop R3MC, a nonlinear conjugate-gradient method for low-rank matrix completion. The underlying search space of fixed-rank matrices is endowed with a novel Riemannian metric that is tailored to the least-squares cost. Numerical comparisons suggest that R3MC robustly outperforms state-of-the-art algorithms across different problem instances, especially those that combine scarcely sampled and ill-conditioned data.
On the position accuracy of mobile robot localization based on particle filters combined with scan matching

Many applications in mobile robotics and especially industrial applications require that the robot has a precise estimate about its pose. In this paper, we analyze the accuracy of an integrated laser-based robot pose estimation and positioning system for mobile platforms. For our analysis, we used a highly accurate motion capture system to precisely determine the error in the robot's pose. We are able to show that by combining standard components such as Monte-Carlo localization, KLD sampling, and scan matching, an accuracy of a few millimeters at taught-in reference locations can be achieved. We believe that this is an important analysis for developers of robotic applications in which pose accuracy matters.
VFH*: Local Obstacle Avoidance with Look-Ahead Verification

This paper presents an enhancement to the earlier developed Vector Field Histogram (VFH) method for mobile robot obstacle avoidance. The enhanced method, called VFH*, successfully deals with situations that are problematic for purely local obstacle avoidance algorithms. The VFH* method verifies that a particular candidate direction guides the robot around an obstacle. The verification is performed by using the A* search algorithm and appropriate cost and heuristic functions.
Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction

Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does not consider interactions across mentions, requires redundant computation for each mention pair, and ignores relationships expressed across sentence boundaries. These problems are exacerbated by the document(rather than sentence-) level annotation common in biological text. In response, we propose a model which simultaneously predicts relationships between all mention pairs in a document. We form pairwise predictions over entire paper abstracts using an efficient self-attention encoder. Allpairs mention scores allow us to perform multi-instance learning by aggregating over mentions to form entity pair representations. We further adapt to settings without mention-level annotation by jointly training to predict named entities and adding a corpus of weakly labeled data. In experiments on two Biocreative benchmark datasets, we achieve state of the art performance on the Biocreative V Chemical Disease Relation dataset for models without external KB resources. We also introduce a new dataset an order of magnitude larger than existing human-annotated biological information extraction datasets and more accurate than distantly supervised alternatives.
Preschool program improves cognitive control.

E xecutive functions (EFs), also called cognitive control, are critical for success in school and life. Although EF skills are rarely taught, they can be. The Tools of the Mind (Tools) curriculum improves EFs in preschoolers in regular classrooms with regular teachers at minimal expense. Core EF skills are (i) inhibitory control (resisting habits, temptations, or distractions), (ii) working memory (mentally holding and using information), and (iii) cognitive flexibility (adjusting to change) (1, 2).
Sparse Phase Retrieval via Truncated Amplitude Flow

This paper develops a novel algorithm, termed <italic>SPARse Truncated Amplitude flow</italic> (SPARTA), to reconstruct a sparse signal from a small number of magnitude-only measurements. It deals with what is also known as sparse phase retrieval (PR), which is <italic>NP-hard</italic> in general and emerges in many science and engineering applications. Upon formulating sparse PR as an amplitude-based nonconvex optimization task, SPARTA works iteratively in two stages: In stage one, the support of the underlying sparse signal is recovered using an analytically well-justified rule, and subsequently a sparse orthogonality-promoting initialization is obtained via power iterations restricted on the support; and in the second stage, the initialization is successively refined by means of hard thresholding based gradient-type iterations. SPARTA is a simple yet effective, scalable, and fast sparse PR solver. On the theoretical side, for any <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-dimensional <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-sparse (<inline-formula> <tex-math notation="LaTeX">$k\ll n$</tex-math></inline-formula>) signal <inline-formula><tex-math notation="LaTeX"> $\boldsymbol {x}$</tex-math></inline-formula> with minimum (in modulus) nonzero entries on the order of <inline-formula> <tex-math notation="LaTeX">$(1/\sqrt{k})\Vert \boldsymbol {x}\Vert _2$</tex-math></inline-formula>, SPARTA recovers the signal exactly (up to a global unimodular constant) from about <inline-formula><tex-math notation="LaTeX">$k^2\log n$ </tex-math></inline-formula> random Gaussian measurements with high probability. Furthermore, SPARTA incurs computational complexity on the order of <inline-formula><tex-math notation="LaTeX">$k^2n\log n$</tex-math> </inline-formula> with total runtime proportional to the time required to read the data, which improves upon the state of the art by at least a factor of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>. Finally, SPARTA is robust against additive noise of bounded support. Extensive numerical tests corroborate markedly improved recovery performance and speedups of SPARTA relative to existing alternatives.
Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr

Online freelancing marketplaces have grown quickly in recent years. In theory, these sites offer workers the ability to earn money without the obligations and potential social biases associated with traditional employment frameworks. In this paper, we study whether two prominent online freelance marketplaces - TaskRabbit and Fiverr - are impacted by racial and gender bias. From these two platforms, we collect 13,500 worker profiles and gather information about workers' gender, race, customer reviews, ratings, and positions in search rankings. In both marketplaces, we find evidence of bias: we find that gender and race are significantly correlated with worker evaluations, which could harm the employment opportunities afforded to the workers. We hope that our study fuels more research on the presence and implications of discrimination in online environments.
An analysis of reinforcement learning with function approximation

We address the problem of computing the optimal Q-function in Markov decision problems with infinite state-space. We analyze the convergence properties of several variations of Q-learning when combined with function approximation, extending the analysis of TD-learning in (Tsitsiklis & Van Roy, 1996a) to stochastic control settings. We identify conditions under which such approximate methods converge with probability 1. We conclude with a brief discussion on the general applicability of our results and compare them with several related works.
Poisson shape interpolation

In this paper, we propose a novel shape interpolation approach based on Poisson equation. We formulate the trajectory problem of shape interpolation as solving Poisson equations defined on a domain mesh. A non-linear gradient field interpolation method is proposed to take both vertex coordinates and surface orientation into account. With proper boundary conditions, the in-between shapes are reconstructed implicitly from the interpolated gradient fields, while traditional methods usually manipulate vertex coordinates directly. Besides of global shape interpolation, our method is also applicable to local shape interpolation, and can be further enhanced by incorporating with deformation. Our approach can generate visual pleasing and physical plausible morphing sequences with stable area and volume changes. Experimental results demonstrate that our technique can avoid the shrinkage problem appeared in linear shape interpolation.
Mindfulness and emotion regulation difficulties in generalized anxiety disorder: preliminary evidence for independent and overlapping contributions.

Diminished levels of mindfulness (awareness and acceptance/nonjudgment) and difficulties in emotion regulation have both been proposed to play a role in symptoms of generalized anxiety disorder (GAD); the current studies investigated these relationships in nonclinical and clinical samples. In the first study, among a sample of 395 individuals at an urban commuter campus, self-reports of both emotion regulation difficulties and aspects of mindfulness accounted for unique variance in GAD symptom severity, above and beyond variance shared with depressive and anxious symptoms, as well as variance shared with one another. In the second study, individuals with GAD (n=16) reported significantly lower levels of mindfulness and significantly higher levels of difficulties in emotion regulation than individuals in a nonanxious control group (n=16). Results are discussed in terms of directions for future research and potential implications for treatment development.
Row-less Universal Schema

Universal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In the past, entity pairs and relations were represented as learned vectors with compatibility determined by a scoring function, limiting generalization to unseen text patterns and entities. Recently, ‘column-less’ versions of Universal Schema have used compositional pattern encoders to generalize to all text patterns. In this work we take the next step and propose a ‘row-less’ model of universal schema, removing explicit entity pair representations. Instead of learning vector representations for each entity pair in our training set, we treat an entity pair as a function of its relation types. In experimental results on the FB15k-237 benchmark we demonstrate that we can match the performance of a comparable model with explicit entity pair representations using a model of attention over relation types. We further demonstrate that the model performs with nearly the same accuracy on entity pairs never seen during training.
Operations in Financial Services — An Overview

We provide an overview of the state of the art in research on operations in financial services. We start by highlighting a number of specific operational features that differentiate financial services from other service industries, and discuss how these features affect the modeling of financial services. We then consider in more detail the various different research areas in financial services, namely systems design, performance analysis and productivity, forecasting, inventory and cash management, waiting line analysis for capacity planning, personnel scheduling, operational risk management, and pricing and revenue management. In the last section, we describe the most promising research directions for the near future.
Survey of SCADA security challenges and potential attack vectors

SCADA systems run silently in the background of our power generation plants, manufacturing facilities, and many other critical infrastructure resources. Even though these SCADA systems are critical in nature, the IT community often poorly understands them. As a result, security of SCADA systems has been neglected, even though companies have improved their security posture to meet regulatory demands. This paper will explore some of the weaknesses in SCADA based system security and the effectiveness of current security techniques. In addition, new methods and tools will be proposed to augment SCADA system security strategies.
Optimization of parallel regenerative braking control strategy

Regenerative braking system is the own system of electric and hybrid electric vehicle. The system can restore the kinetic energy and potential energy, used during start and accelerating, into battery through electrical machine. The total brake force is composed of friction brake force on front axel, friction brake force on rear axel and regenerative brake force when a vehicle equipped with regenerative braking system brakes. A control strategy, parallel regenerative brake strategy, was proposed to resolve the distribution of the three forces. The parallel regenerative brake strategy was optimized on Saturn SL1 and then simulated under urban 15 drive cycle. As a result, through optimization the parallel brake strategy is not only safe enough but also can restore the largest amount of the brake energy.
One-class support vector machines - an application in machine fault detection and classification

Fast incipient machine fault diagnosis is becoming one of the key requirements for economical and optimal process operation management. Artificial neural networks have been used to detect machine faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for machine fault detection and classification in electro-mechanical machinery from vibration measurements using one-class support vector machines (SVMs). In order to evaluate one-class SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data. q 2005
Image Segmentation to Distinguish Between Overlapping Human Chromosomes

In medicine, visualizing chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosomes often overlap and it is necessary to identify and distinguish between the overlapping chromosomes. A segmentation solution that is fast and automated will enable scaling of cost effective medicine and biomedical research. We apply neural network-based image segmentation to the problem of distinguishing between partially overlapping DNA chromosomes. A convolutional neural network is customized for this problem. The results achieved intersection over union (IOU) scores of 94.7% for the overlapping region and 88-94% on the non-overlapping chromosome regions.