nils-herrmann/clean_scirepeval huggingface dataset:
Deep Counterfactual Networks with Propensity-Dropout

We propose a novel approach for inferring the individualized causal effects of a treatment (intervention) from observational data. Our approach conceptualizes causal inference as a multitask learning problem; we model a subject’s potential outcomes using a deep multitask network with a set of shared layers among the factual and counterfactual outcomes, and a set of outcome-specific layers. The impact of selection bias in the observational data is alleviated via a propensity-dropout regularization scheme, in which the network is thinned for every training example via a dropout probability that depends on the associated propensity score. The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers. Experiments conducted on data based on a real-world observational study show that our algorithm outperforms the state-of-the-art.
Lightweight Multilingual Entity Extraction and Linking

Text analytics systems often rely heavily on detecting and linking entity mentions in documents to knowledge bases for downstream applications such as sentiment analysis, question answering and recommender systems. A major challenge for this task is to be able to accurately detect entities in new languages with limited labeled resources. In this paper we present an accurate and lightweight, multilingual named entity recognition (NER) and linking (NEL) system. The contributions of this paper are three-fold: 1) Lightweight named entity recognition with competitive accuracy; 2) Candidate entity retrieval that uses search click-log data and entity embeddings to achieve high precision with a low memory footprint; and 3) efficient entity disambiguation. Our system achieves state-of-the-art performance on TAC KBP 2013 multilingual data and on English AIDA CONLL data.
Nonlinear trajectory generation for the Caltech Multi-Vehicle Wireless Testbed

The Caltech Multi-Vehicle Wireless Testbed (MVWT) is a platform designed to explore theoretical advances in multi-vehicle coordination and control, networked control systems and high confidence distributed computation. The contribution of this report is to present simulation and experimental results on the generation and implementation of optimal trajectories for the MVWT vehicles. The vehicles are nonlinear, spatially constrained and their input controls are bounded. The trajectories are generated using the NTG software package developed at Caltech. Minimum time trajectories and the application of Model Predictive Control (MPC) are investigated.
UNIVERSALS IN THE CONTENT AND STRUCTURE OF VALUES : THEORETICAL ADVANCES AND EMPIRICAL TESTS IN 20 COUNTRIES

“The value concept, more than any other, should occupy a central position . . . able to unify the apparently diverse interests of all the sciences concerned with human behavior.” These words, proclaiming the centrality of the value concept, were written by a psychologist (Rokeach, 1973, p. 3), but similar stands have been taken by sociologists (e.g., Williams, 1968) and anthropologists (e.g., Kluckhohn, 1951). These theorists view values as the criteria people use to select and justify actions and to evaluate people (including the self) and events. We, too, adopt this view of values as criteria rather than as qualities inherent in objects. This article discusses work that is part of a larger project intended to explore the importance of values in a wide variety of contexts. The project addresses three broad questions about values. First, how are the value priorities of individuals affected by their social experiences? That is, how do the common experiences people have, because of their shared locations in the social structure (their education, age, gender, occupation, etc.), influence their value priorities? And, how do individuals’ unique experiences (trauma, relations with parents, immigration, etc.) affect their value priorities? Second, how do the value priorities held by individuals affect their behavioral orientations and choices? That is, how do value priorities influence ideologies, attitudes, and actions in the political, religious, environmental, and other domains?
Compliant actuator designs

In the growing fields of wearable robotics, rehabilitation robotics, prosthetics, and walking k robots, variable stiffness actuators (VSAs) or adjustable compliant actuators are being designed and implemented because of their ability to minimize large forces due to shocks, to safely interact with the user, and their ability to store and release energy in passive elastic elements. This review article describes the state of the art in the design of actuators with adaptable passive compliance. This new type of actuator is not preferred for classical position-controlled applications such as pick and place operations but is preferred in novel robots where safe human- robot interaction is required or in applications where energy efficiency must be increased by adapting the actuator's resonance frequency. The working principles of the different existing designs are explained and compared. The designs are divided into four groups: equilibrium-controlled stiffness, antagonistic-controlled stiffness, structure-controlled stiffness (SCS), and mechanically controlled stiffness.
The Design, Analysis and Interpretation of Repertory Grids

It is argued that because the technique is quite powerful and the basic principles of its design are easy to grasp there is some danger in it being used inappropriately. Inappropriate applications may be harmful both to those involved directly, and to the general reputation of the technique itself. The paper therefore surveys a range of alternatives in the design of grids, and discusses the factors that are important to consider in these cases. But even if a design has been produced which is inherently "good", any applications based on this will be of doubtful value unless prior thought has been given to the availability of analytic techniques, and to the means of interpretation of the results. Hence the paper outlines a number of approaches to the analysis of grids (both manual and computer based), and it also illustrates the possible process of interpretation in a number of cases.
Segmenting Chinese Microtext: Joint Informal-Word Detection and Segmentation with Neural Networks

State-of-the-art Chinese word segmentation systems typically exploit supervised models trained on a standard manually-annotated corpus, achieving performances over 95% on a similar standard testing corpus. However, the performances may drop significantly when the same models are applied onto Chinese microtext. One major challenge is the issue of informal words in the microtext. Previous studies show that informal word detection can be helpful for microtext processing. In this work, we investigate it under the neural setting, by proposing a joint segmentation model that integrates the detection of informal words simultaneously. In addition, we generate training corpus for the joint model by using existing corpus automatically. Experimental results show that the proposed model is highly effective for segmentation of Chinese microtext.
Question Answering over Knowledge Base using Factual Memory Networks

In the task of question answering, Memory Networks have recently shown to be quite effective towards complex reasoning as well as scalability, in spite of limited range of topics covered in training data. In this paper, we introduce Factual Memory Network, which learns to answer questions by extracting and reasoning over relevant facts from a Knowledge Base. Our system generate distributed representation of questions and KB in same word vector space, extract a subset of initial candidate facts, then try to find a path to answer entity using multi-hop reasoning and refinement. Additionally, we also improve the run-time efficiency of our model using various computational heuristics.
A portable 24-GHz FMCW radar based on six-port for short-range human tracking

A 24-GHz portable FMCW radar for short-range human tracking is designed, fabricated, and tested. The complete radar system weights 17.3 grams and has a dimension of 65mm×60mm×25mm. It has an on-board chirp generator, which generates a 45.7 Hz sawtooth signal to control the VCO. A 1.8GHz bandwidth ranging from 22.8 GHz to 24.6 GHz is transmitted. A pair of Vivaldi antennas with a bandwidth of 3.8 GHz, ranging from 22.5 GHz to 26.3 GHz, are implemented on the same board with the RF transceiver. A six-port structure is employed to down-convert the RF signal to baseband. Measurement result has validated its promising ability to for short-range human tracking.
Mapping actor roles in social media: Different perspectives on value creation in theories of user participation

While media studies have been locked into a classic producer-text-audience model, most theories of social media suggest some degree of collapse between the producer and audience. In this article, we address social media in terms of processes of value creation. The aim of the article is to demonstrate that social media are either addressed in terms of economic and socio-political value creation, that is, power, exploitation and business revenues, or in terms of value creation as sense-making, that is, creative explorations of the self and management of social relationships in everyday life. These different interests in value creation, we argue, have consequences for the conceptualization of the media user as a participatory agent. With specific focus on the notion of value creation in social media, we uncover implicit conceptions of the social media user guiding industry and user-centric perspectives, respectively. We demonstrate that while studying the same phenomenon, the two perspectives operate with very different conceptions of the producer/user nexus. We then discuss whether the literature is inconsequential in the analytic treatment of its own suggested collapse by questioning if, and if so how, this collapse is in fact taking place. Finally, we offer a mapping of the multifarious actor roles identified in the literature review to nuance the understanding of the producer/ user nexus in social media and use it to identify and discuss possible opportunities Corresponding author: Anja Bechmann, Department of Aethetics and Communication, Aarhus University, Denmark.  anjabechmann@gmail.com 462853 NMS0010.1177/1461444812462853new media & societyBechmann and Lomborg 2012
Mixed Reality: Future Dreams Seen at the Border between Real and Virtual Worlds

ixed reality (MR) is a kind of virtual reality (VR) but a broader concept than augmented reality (AR), which augments the real world with synthetic electronic data. On the opposite side, there is a term, augmented virtuality (AV), which enhances or augments the virtual environment (VE) with data from the real world. Mixed reality covers a continuum from AR to AV. This concept embraces the definition of MR stated by Paul Milgram. 1 We participated in the Key Technology Research Project on Mixed Reality Systems (MR Project) in Japan. The Japanese government and Canon funded the Mixed Reality Systems Laboratory (MR Lab) and launched it in January 1997. We completed this national project in March 2001. At the end of the MR Project, an event called MiRai-01 (mirai means future in Japanese) was held at Yokohama, Japan, to demonstrate this emerging technology all over the world. This event was held in conjunction with two international conferences,  Reality 2001 and the Second International Symposium on Mixed Reality (ISMR) and aggregated about 3,000 visitors for two days. This project aimed to produce an innovative information technology that could be used in the first decade of the 21st century while expanding the limitations of traditional VR technology. The basic policy we maintained throughout this project was to emphasize a pragmatic system development rather than a theory and to make such a system always available to people. Since MR is an advanced form of VR, the MR system inherits a VR char-acteristic—users can experience the world of MR interactively. According to this policy, we tried to make the system work in real time. Then, we enhanced each of our systems in their response speed and image quality in real time to increase user satisfaction. We describe the aim and research themes of the MR Project in Tamura et al. 2 To develop MR systems along this policy, we studied the fundamental problems of AR and AV and developed several methods to solve them in addition to system development issues. For example, we created a new image-based rendering method for AV systems, hybrid registration methods, and new types of see-through head-mounted displays (ST-HMDs) for AR systems. Three universities in Japan—University of Tokyo (Michi-taka Hirose), University of Tsukuba (Yuichic Ohta), and Hokkaido University (Tohru Ifukube)—collaborated with us to study the broad research area of MR. The side-bar, " Four Types of MR Visual Simulation, …
Investigation of a Wideband 90 $^{\circ}$ Hybrid Coupler With an Arbitrary Coupling Level

This paper presents a new class of wideband 90° hybrid coupler with an arbitrary coupling level. The physical size of the proposed coupler is close to that of a conventional two-section branch-line coupler, but it has an additional phase inverter. The impedance bandwidth of the proposed coupler is close to that of a four-section branch-line coupler. The proposed coupler is a backward-wave coupler with a port assignment different from that of a conventional branch-line coupler. The design formulas of the proposed coupler are proved based on its even- and odd-mode half structures. We demonstrated three couplers at the center frequency of 2 GHz with different design parameters.
Spreadsheet as a relational database engine

Spreadsheets are among the most commonly used applications for data management and analysis. Perhaps they are even among the most widely used computer applications of all kinds. However, the spreadsheet paradigm of computation still lacks sufficient analysis.
 In this paper we demonstrate that a spreadsheet can play the role of a relational database engine, without any use of macros or built-in programming languages, merely by utilizing spreadsheet formulas. We achieve that by implementing all operators of relational algebra by means of spreadsheet functions.
 Given a definition of a database in SQL, it is therefore possible to construct a spreadsheet workbook with empty worksheets for data tables and worksheets filled with formulas for queries. From then on, when the user enters, alters or deletes data in the data worksheets, the formulas in query worksheets automatically compute the actual results of the queries. Thus, the spreadsheet serves as data storage and executes SQL queries, and therefore acts as a relational database engine.
 The paper is based on Microsoft Excel (TM), but our constructions work in other spreadsheet systems, too. We present a number of performance tests conducted in the beta version of Excel 2010. Their conclusion is that the performance is sufficient for a desktop database with a couple thousand rows.
ANN-Based Forecasting of Foreign Currency Exchange Rates

In this paper, we have investigated artificial neural networks based prediction modeling of foreign currency rates using three learning algorithms, namely, Standard Backpropagation (SBP), Scaled Conjugate Gradient (SCG) and Backpropagation with Bayesian Regularization (BPR). The models were trained from historical data using five technical indicators to predict six currency rates against Australian dollar. The forecasting performance of the models was evaluated using a number of widely used statistical metrics and compared. Results show that significantly close prediction can be made using simple technical indicators without extensive knowledge of market data. Among the three models, SCG based model outperforms other models when measured on two commonly used metrics and attains comparable results with BPR based model on other three metrics. The effect of network architecture on the performance of the forecasting model is also presented. Future research direction outlining further improvement of the model is discussed. Neural network, ARIMA, financial forecasting, foreign exchange
Hybrid Page Segmentation with Efficient Whitespace Rectangles Extraction and Grouping

Page segmentation is still a challenging problem due to the large variety of document layouts. Methods examining both foreground and background regions are among the most effective to solve this problem. However, their performance is influenced by the implementation of two key steps: the extraction and selection of background regions, and the grouping of background regions into separators. This paper proposes an efficient hybrid method for page segmentation. The method extracts white space rectangles based on connected component analysis, and filters white space rectangles progressively incorporating foreground and background information such that the remaining rectangles are likely to form column separators. Experimental results on the ICDAR2009 page segmentation competition test set demonstrate the effectiveness and superiority of the proposed method.
Expert finding on social network with link analysis approach

With the appearance of social networks in the Internet, the communications between people took a new form. Nowadays, lots of people with different goals are registered in social networks and do wide range of activities. One of the most important feature of social networks is knowledge sharing. The main problem regarding to this issue is a wide range of shared knowledge and there is no mechanism to determine their validity. So, the knowledge shared on social networks could not be trusted. By finding experts in social networks and determining their level of knowledge, the validity of their posts could be determined. Therefore a solution to the mentioned problem is to provide a method for expert finding. In this research a novel model based on social network analysis is proposed to find the experts who are the members of social networks by means of business intelligence approach. This model is verified by real data from Friendfeed social network. First, data is extracted, transformed and loaded to data warehouse with ETL processes. Then a new ranking algorithm is proposed for finding the experts, and finally the obtained results are compared to the experts' opinions utilizing spearman's correlation function.
Objective Reduction in Evolutionary Multiobjective Optimization: Theory and Applications

Many-objective problems represent a major challenge in the field of evolutionary multiobjective optimizationin terms of search efficiency, computational cost, decision making, visualization, and so on. This leads to various research questions, in particular whether certain objectives can be omitted in order to overcome or at least diminish the difficulties that arise when many, that is, more than three, objective functions are involved. This study addresses this question from different perspectives.First, we investigate how adding or omitting objectives affects the problem characteristics and propose a general notion of conflict between objective sets as a theoretical foundation for objective reduction. Second, we present both exact and heuristic algorithms to systematically reduce the number of objectives, while preserving as much as possible of the dominance structure of the underlying optimization problem. Third, we demonstrate the usefulness of the proposed objective reduction method in the context of both decision making and search for a radar waveform application as well as for well-known test functions.
IoT technologies for embedded computing: A survey

Emergence of Internet-of-Things brings a whole new class of applications and higher efficiency for existing services. Application-specific requirements, as well as connectivity and communication ability of devices have introduced new challenges for IoT applications.
 This paper provides an overview of IoT technologies required from an embedded design perspective and specific properties associated with IoT in embedded systems' landscape. We investigate essential technologies for development of IoT systems, existing trends, and its distinguishing properties. By discussing the key characteristics, main application domains, and major research issues in IoT, this paper provides a comprehensive IoT perspective for embedded system design.
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech

In this work, we propose an alternative solution for parallel wave generation by WaveNet. In contrast to parallel WaveNet (Oord et al., 2018), we distill a Gaussian inverse autoregressive flow from the autoregressive WaveNet by minimizing a novel regularized KL divergence between their highly-peaked output distributions. Our method computes the KL divergence in closed-form, which simplifies the training algorithm and provides very efficient distillation. In addition, we propose the first text-to-wave neural architecture for speech synthesis, which is fully convolutional and enables fast end-to-end training from scratch. It significantly outperforms the previous pipeline that connects a text-to-spectrogram model to a separately trained WaveNet (Ping et al., 2018). We also successfully distill a parallel waveform synthesizer conditioned on the hidden representation in this end-to-end model.
Compact Eight-Band Frequency Reconfigurable Antenna for LTE/WWAN Tablet Computer Applications

A novel eight-band LTE/WWAN frequency reconfigurable antenna for tablet computer applications is proposed in this communication. With a small dimension of 40 × 12 × 4 mm3, the proposed antenna comprises a loop feeding strip and a shorting strip in which a single-pole four-throw RF switch is embedded. The RF switch is used to change the resonant modes of lower band among four different working states, so that the antenna can provide a multiband operation of LTE700/GSM850 /900/1800/1900/UMTS2100/LTE2300/2500 with return loss better than 6 dB. Reasonably good radiating efficiency and antenna gain are also achieved for the practical tablet computer.
A Low-Noise, Wide-BW 3.6GHz Digital ΔΣ Fractional-N Frequency Synthesizer with a Noise-Shaping Time-to-Digital Converter and Quantization Noise Cancellation

A digital fractional-N frequency synthesizer is presented that leverages a noise-shaping time-to-digital converter (TDC) and a simple quantization noise cancellation technique to achieve low phase noise with a wide PLL bandwidth of 500kHz. In contrast to previous cancellation techniques, this structure requires no analog components and is straightforward to implement with standard-cell digital logic.
SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration

In this paper we present SimpleElastix, an extension of SimpleITK designed to bring the Elastix medical image registration library to a wider audience. Elastix is a modular collection of robust C++ image registration algorithms that is widely used in the literature. However, its command-line interface introduces overhead during prototyping, experimental setup, and tuning of registration algorithms. By integrating Elastix with SimpleITK, Elastix can be used as a native library in Python, Java, R, Octave, Ruby, Lua, Tcl and C# on Linux, Mac and Windows. This allows Elastix to intregrate naturally with many development environments so the user can focus more on the registration problem and less on the underlying C++ implementation. As means of demonstration, we show how to register MR images of brains and natural pictures of faces using minimal amount of code. SimpleElastix is open source, licensed under the permissive Apache License Version 2.0 and available at https://github.com/kaspermarstal/SimpleElastix.