nils-herrmann/clean_scirepeval huggingface dataset:
MANAGING WITH STYLE : THE EFFECT OF MANAGERS ON FIRM POLICIES ∗

This paper investigates whether and how individual managers affect corporate behavior and performance. We construct a manager-firm matched panel data set which enables us to track the top managers across different firms over time. We find that manager fixed effects matter for a wide range of corporate decisions. A significant extent of the heterogeneity in investment, financial and organizational practices of firms can be explained by the presence of manager fixed effects. We identify specific patterns in managerial decision making that appear to indicate general differences in “style” across managers. Moreover, we show that management style is significantly related to manager fixed effects in performance and that managers with higher performance fixed effects receive higher compensation and are more likely to be found in better governed firms. In a final step, we tie back these findings to observable managerial characteristics. We find that executives from earlier birth cohorts appear on average to be more conservative; on the other hand, managers who hold an MBA degree seem to follow on average more aggressive strategies. ∗We thank the editors (Lawrence Katz and Edward Glaeser), three anonymous referees, Kent Daniel, Rebecca Henderson, Steven Kaplan, Kevin J. Murphy, Sendhil Mullainathan, Canice Prendergast, David Scharfstein, Jerry Warner, Michael Weisbach, seminar participants at Harvard University, the Kellogg Graduate School of Management, the Massachusetts Institute of Technology, the University of Chicago Graduate School of Business, the University of Illinois at Urbana-Champaign, Rochester University and the Stockholm School of Economics for many helpful comments. We thank Kevin J. Murphy and Robert Parrino for generously providing us with their data. Jennifer Fiumara and Mike McDonald provided excellent research assistance. E-mail: marianne.bertrand@gsb.uchicago.edu;
Traveling salesman problem heuristics: Leading methods, implementations and latest advances

0377-2217/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.ejor.2010.09.010 ⇑ Corresponding author.  E-mail addresses: crego@bus.olemiss.edu (C. R (D. Gamboa), fred.glover@colorado.edu (F. Glover), colin.j.osterman@navy.mil (C. Osterman). Heuristics for the traveling salesman problem (TSP) have made remarkable advances in recent years. We survey the leading methods and the special components responsible for their successful implementations, together with an experimental analysis of computational tests on a challenging and diverse set of symmetric and asymmetric TSP benchmark problems. The foremost algorithms are represented by two families, deriving from the Lin–Kernighan (LK) method and the stem-and-cycle (S&C) method. We show how these families can be conveniently viewed within a common ejection chain framework which sheds light on their similarities and differences, and gives clues about the nature of potential enhancements to today’s best methods that may provide additional gains in solving large and difficult TSPs.
Training an End-to-End System for Handwritten Mathematical Expression Recognition by Generated Patterns

Motivated by recent successes in neural machine translation and image caption generation, we present an end-to-end system to recognize Online Handwritten Mathematical Expressions (OHMEs). Our system has three parts: a convolution neural network for feature extraction, a bidirectional LSTM for encoding extracted features, and an LSTM and an attention model for generating target LaTex. For recognizing complex structures, our system needs large data for training. We propose local and global distortion models for generating OHMEs from the CROHME database. We evaluate the end-to-end system on the CROHME database and the generated databases. The experiential results show that the end-to-end system achieves 28.09% and 35.19% recognition rates on CROHME without and with the generated data, respectively.
Sentiment Analysis in News Articles Using Sentic Computing

Fine-grained sentiment analysis in news articles is a challenging problem with many potential applications. The difficulties of performing sentiment analysis in this domain can be overcome by leveraging on common-sense knowledge bases. In this paper, we present an opinion-mining engine that exploits common-sense knowledge extracted from ConceptNet and SenticNet to perform sentiment analysis in news articles. We have tested our engine on a large corpus of sentences from news articles. Our results show 71% accuracy in classification, with 91% precision for neutral sentences and F-measures 59%, 66% and 79% for positive, negative and neutral sentences, respectively. Our method can potentially be applied to reputation management in text-based media such as newspapers.
Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser

This paper presents a system that compositionally maps outputs of a wide-coverage Japanese CCG parser onto semantic representations and performs automated inference in higher-order logic. The system is evaluated on a textual entailment dataset. It is shown that the system solves inference problems that focus on a variety of complex linguistic phenomena, including those that are difficult to represent in the standard first-order logic.
Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges

Text is a very important type of data within the biomedical domain. For example, patient records contain large amounts of text which has been entered in a non-standardized format, consequently posing a lot of challenges to processing of such data. For the clinical doctor the written text in the medical findings is still the basis for decision making – neither images nor multimedia data. However, the steadily increasing volumes of unstructured information need machine learning approaches for data mining, i.e. text mining. This paper provides a short, concise overview of some selected text mining methods, focusing on statistical methods, i.e. Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation, Hierarchical Latent Dirichlet Allocation, Principal Component Analysis, and Support Vector Machines, along with some examples from the biomedical domain. Finally, we provide some open problems and future challenges, particularly from the clinical domain, that we expect to stimulate future research.
Gender differences in emotional responses: A psychophysiological study

Gender differences in emotional responses have been investigated in two groups of students, 22 males and 21 females. Participants watched a set of sixty emotional standardized slides divided into pleasant, neutral and unpleasant, while Startle reflex, Evoked Potentials, Heart Rate, facial EMG and Skin Conductance were recorded. Startle reflex amplitude, an index modulated by amygdala and orbitofrontal cortex and sensitive to aversive emotional stimuli, was overall larger in women. In addition, startle emotion modulation was greater in women with respect to men. Slow Evoked Potentials (400-800 ms), a measure representing the cognitive component of the emotional response, revealed gender differences in the left prefrontal site, with women showing greater positivity to unpleasant compared with pleasant slides while men had greater positivity to pleasant vs. neutral slides. Women, compared with men, perceived all slides as less pleasant and reported greater arousal to unpleasant condition. Results are in line with known functional brain differences, at level of limbic and paralimbic structures, between men and women, and point to biologically grounded greater sensitivity and vulnerability of women to adverse/stressful events.
Control and analysis of multi-level type multi-phase resonant converter for wireless EV charging

In this study, a multi-level type multi-phase resonant converter is presented for high power wireless EV charging applications. As an alternative to the traditional frequency and phase shift control methods, a hybrid phase-frequency control strategy is implemented to improve the system efficiency. In order to confirm the proposed converter and control technique, a laboratory prototype wireless EV charger is designed using 8 inches air gap coreless transformer and rectifier. The proposed control is compared with the conventional control methods for various load conditions at the different power levels. The experimental results show that the proposed converter is within the desired frequency range while regulating output from 0 to 15 kW with 750 V input DC bus voltage.
Privacy Enhancing Technologies

Privacy-enhancing technologies (PETs), which constitute a wide array of technical means for protecting users’ privacy, have gained considerable momentum in both academia and industry. However, existing surveys of PETs fail to delineate what sorts of privacy the described technologies enhance, which makes it difficult to differentiate between the various PETs. Moreover, those surveys could not consider very recent important developments with regard to PET solutions. The goal of this chapter is two-fold. First, we provide an analytical framework to differentiate various PETs. This analytical framework consists of high-level privacy principles and concrete privacy concerns. Secondly, we use this framework to evaluate representative up-to-date PETs, specifically with regard to the privacy concerns they address, and how they address them (i.e., what privacy principles they follow). Based on findings of the evaluation, we outline several future research directions.
TapLogger: inferring user inputs on smartphone touchscreens using on-board motion sensors

Today's smartphones are shipped with various embedded motion sensors, such as the accelerometer, gyroscope, and orientation sensors. These motion sensors are useful in supporting the mobile UI innovation and motion-based commands. However, they also bring potential risks of leaking user's private information as they allow third party applications to monitor the motion changes of smartphones.
 In this paper, we study the feasibility of inferring a user's tap inputs to a smartphone with its integrated motion sensors. Specifically, we utilize an installed trojan application to stealthily monitor the movement and gesture changes of a smartphone using its on-board motion sensors. When the user is interacting with the trojan application, it learns the motion change patterns of tap events. Later, when the user is performing sensitive inputs, such as entering passwords on the touchscreen, the trojan application applies the learnt pattern to infer the occurrence of tap events on the touchscreen as well as the tapped positions on the touchscreen.
 For demonstration, we present the design and implementation of TapLogger, a trojan application for the Android platform, which stealthily logs the password of screen lock and the numbers entered during a phone call (e.g., credit card and PIN numbers). Statistical results are presented to show the feasibility of such inferences and attacks.
OCR Post-Processing Error Correction Algorithm using Google Online Spelling Suggestion

With the advent of digital optical scanners, a lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into an electronic version that can be manipulated by a computer. For this purpose, OCR, short for Optical Character Recognition was developed to translate scanned graphical text into editable computer text. Unfortunately, OCR is still imperfect as it occasionally mis-recognizes letters and falsely identifies scanned text, leading to misspellings and linguistics errors in the OCR output text. This paper proposes a post-processing context-based error correction algorithm for detecting and correcting OCR non-word and real-word errors. The proposed algorithm is based on Google‟s online spelling suggestion which harnesses an internal database containing a huge collection of terms and word sequences gathered from all over the web, convenient to suggest possible replacements for words that have been misspelled during the OCR process. Experiments carried out revealed a significant improvement in OCR error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized and executed over multiprocessing platforms.
A Cloud-based monitoring framework for Smart Home

Today, Smart Home monitoring services have attracted much attention from both academia and industry. However, in the conventional monitoring mechanism the remote camera can not be accessed for remote monitoring anywhere and anytime. Besides, traditional approaches might have the limitation in local storage due to lack of device elasticity. In this paper, we proposed a Cloud-based monitoring framework to implement the remote monitoring services of Smart Home. The main technical issues considered include Data-Cloud storage, Local-Cache mechanism, Media device control, NAT traversal, etc. The implementation shows three use scenarios: (a) operating and controlling video cameras for remote monitoring through mobile devices or sound sensors; (b) streaming live video from cameras and sending captured image to mobile devices; (c) recording videos and images on a cloud computing platform for future playback. This system framework could be extended to other applications of Smart Home.
Noncontact Monitoring Breathing Pattern, Exhalation Flow Rate and Pulse Transit Time

We present optical imaging-based methods to measure vital physiological signals, including breathing frequency (BF), exhalation flow rate, heart rate (HR), and pulse transit time (PTT). The breathing pattern tracking was based on the detection of body movement associated with breathing using a differential signal processing approach. A motion-tracking algorithm was implemented to correct random body movements that were unrelated to breathing. The heartbeat pattern was obtained from the color change in selected region of interest (ROI) near the subject's mouth, and the PTT was determined by analyzing pulse patterns at different body parts of the subject. The measured BF, exhaled volume flow rate and HR are consistent with those measured simultaneously with reference technologies (r = 0.98, p <; 0.001 for HR; r = 0.93, p <; 0.001 for breathing rate), and the measured PTT difference (30-40 ms between mouth and palm) is comparable to the results obtained with other techniques in the literature. The imaging-based methods are suitable for tracking vital physiological parameters under free-living condition and this is the first demonstration of using noncontact method to obtain PTT difference and exhalation flow rate.
Acquisition of Localization Confidence for Accurate Object Detection

Modern CNN-based object detectors rely on bounding box regression and non-maximum suppression to localize objects. While the probabilities for class labels naturally reflect classification confidence, localization confidence is absent. This makes properly localized bounding boxes degenerate during iterative regression or even suppressed during NMS. In the paper we propose IoU-Net learning to predict the IoU between each detected bounding box and the matched ground-truth. The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes. Furthermore, an optimization-based bounding box refinement method is proposed, where the predicted IoU is formulated as the objective. Extensive experiments on the MS-COCO dataset show the effectiveness of IoU-Net, as well as its compatibility with and adaptivity to several state-of-the-art object detectors.
Adversarial Attack on Graph Structured Data

Deep learning on graph structures has shown exciting results in various applications. However, few attentions have been paid to the robustness of such models, in contrast to numerous research work for image or text adversarial attack and defense. In this paper, we focus on the adversarial attacks that fool deep learning models by modifying the combinatorial structure of data. We first propose a reinforcement learning based attack method that learns the generalizable attack policy, while only requiring prediction labels from the target classifier. We further propose attack methods based on genetic algorithms and gradient descent in the scenario where additional prediction confidence or gradients are available. We use both synthetic and real-world data to show that, a family of Graph Neural Network models are vulnerable to these attacks, in both graph-level and node-level classification tasks. We also show such attacks can be used to diagnose the learned classifiers.
Real-Time Long-Range Lane Detection and Tracking for Intelligent Vehicle

This paper presents a real-time long-range lane detection and tracking approach to meet the requirements of the high-speed intelligent vehicles running on highway roads. Based on a linear-parabolic two-lane highway road model and a novel strong lane marking feature named Lane Marking Segmentation, the maximal lane detection distance of this approach is up to 120 meters. Then the lane lines are selected and tracked by estimating the ego vehicle lateral offset with a Kalman filter. Experiment results with test dataset extracted from real traffic scenes on highway roads show that the approaches proposed in this paper can achieve a high detection rate with a low time cost.
Emerging Memory Technologies: Recent Trends and Prospects

This tutorial introduces the basics of emerging nonvolatile memory (NVM) technologies including spin-transfer-torque magnetic random access memory (STTMRAM), phase-change random access memory (PCRAM), and resistive random access memory (RRAM). Emerging NVM cell characteristics are summarized, and device-level engineering trends are discussed. Emerging NVM array architectures are introduced, including the one-transistor-one-resistor (1T1R) array and the cross-point array with selectors. Design challenges such as scaling the write current and minimizing the sneak path current in cross-point array are analyzed. Recent progress on megabit-to gigabit-level prototype chip demonstrations is summarized. Finally, the prospective applications of emerging NVM are discussed, ranging from the last-level cache to the storage-class memory in the memory hierarchy. Topics of three-dimensional (3D) integration and radiation-hard NVM are discussed. Novel applications beyond the conventional memory applications are also surveyed, including physical unclonable function for hardware security, reconfigurable routing switch for field-programmable gate array (FPGA), logic-in-memory and nonvolatile cache/register/flip-flop for nonvolatile processor, and synaptic device for neuro-inspired computing.
Planar Multi-Band Microwave Components Based on the Generalized Composite Right/Left Handed Transmission Line Concept

This paper is focused on the design of generalized composite right/left handed (CRLH) transmission lines in a fully planar configuration, that is, without the use of surface-mount components. These artificial lines exhibit multiple, alternating backward and forward-transmission bands, and are therefore useful for the synthesis of multi-band microwave components. Specifically, a quad-band power splitter, a quad-band branch line hybrid coupler and a dual-bandpass filter, all of them based on fourth-order CRLH lines (i.e., lines exhibiting 2 left-handed and 2 right-handed bands alternating), are presented in this paper. The accurate circuit models, including parasitics, of the structures under consideration (based on electrically small planar resonators), as well as the detailed procedure for the synthesis of these lines using such circuit models, are given. It will be shown that satisfactory results in terms of performance and size can be obtained through the proposed approach, fully compatible with planar technology.
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning

Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-toend model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.
Modeling with Euler-Lagrang Equation and Cybernetical Analysis for a Unicycle Robot

In this paper we study the dynamic modeling of a unicycle robot composed of a wheel, a frame and a disk. The unicycle can reach longitudinal stability by appropriate control to the wheel and lateral stability by adjusting appropriate torque imposed by the disk. The dynamic modeling of the unicycle robot is derived by Euler-Lagrange method. The stability and controllability of the system are analyzed according to the mathematic model. Independent simulation using MATLAB and ODE methods are then proposed respectively. Through the simulation, we confirm the validity of the two obtained models of the unicycle robot system, and provide two experimental platforms for the designing of the balance controller.
Flow Graph Corpus from Recipe Texts

Academic Center for Computing and Media Studies, Kyoto University Graduate School of Informatics, Kyoto University Yoshidahonmachi, Sakyo-ku, Kyoto, Japan forest@i.kyoto-u.ac.jp, maeta@ar.media.kyoto-u.ac.jp, yamakata@dl.kuis.kyoto-u.ac.jp, sasada@ar.media.kyoto-u.ac.jp Abstract In this paper, we present our attempt at annotating procedural texts with a flow graph as a representation of understanding. The domain we focus on is cooking recipe. The flow graphs are directed acyclic graphs with a special root node corresponding to the final dish. The vertex labels are recipe named entities, such as foods, tools, cooking actions, etc. The arc labels denote relationships among them. We converted 266 Japanese recipe texts into flow graphs manually. 200 recipes are randomly selected from a web site and 66 are of the same dish. We detail the annotation framework and report some statistics on our corpus. The most typical usage of our corpus may be automatic conversion from texts to flow graphs which can be seen as an entire understanding of procedural texts. With our corpus, one can also try word segmentation, named entity recognition, predicate-argument structure analysis, and coreference resolution.
Synthesizing Entity Matching Rules by Examples

Entity matching (EM) is a critical part of data integration. We study how to synthesize entity matching rules from positive-negative matching examples. The core of our solution is program synthesis, a powerful tool to automatically generate rules (or programs) that satisfy a given highlevel specification, via a predefined grammar. This grammar describes a General Boolean Formula (GBF) that can include arbitrary attribute matching predicates combined by conjunctions ( Ź