nils-herrmann/clean_scirepeval huggingface dataset:
General Compensation Method for a Marchand Balun With an Arbitrary Connecting Segment Between the Balance Ports

For a Marchand balun, the output imbalance due to the inevitable physical separation between the balance ports is a problem. In this paper, a general compensation method to cope with this imbalance issue is proposed with rigorous analysis and design formulas. The compensation relies on two intentionally shortened coupling sections and a pair of short-circuited transmission lines as the terminations. The proposed method is able to deal with a long connecting segment between the balance ports as long as the coupling sections are tight enough at the desired frequencies. The theory and formulation are first treated using transient analysis with multiple reflections/couplings between the networks. Design graphs are summarized and three examples are fabricated, validated, and discussed to demonstrate the design flexibility the proposed method provides.
MovieClouds: content-based overviews and exploratory browsing of movies

Movies are one of the biggest sources of entertainment, in individual and social contexts. By combining diverse symbol systems, such as images, texts, music and narration to tell stories, they often engage the viewers perceptually, cognitively and emotionally. Advances in digitalization and networking are enabling the access to enormous collections of videos and movies over the Internet, in social media, and through video on demand services on iTV. The development of video content-based analysis and classification techniques is also allowing the access to more information about or contained in the movies, demanding for new ways to search, browse and view videos and movies in this scenario. In this paper, we present and evaluate MovieClouds, an interactive web application designed to access, explore and visualize movies based on the information conveyed in the different tracks or perspectives of its content, especially audio and subtitles where most of the semantics is expressed, and with a special focus on the emotional dimensions expressed in the movies or felt by the viewers. For the overview, analysis, and exploratory browsing of the movies collection and the individual movies, it adopts a tag cloud unifying-paradigm, that gained popularity in Web 2.0, with the aim to extend to movies the power, flexibility, engagement and fun usually associated with clouds.
GraphNet: Learning Image Pseudo Annotations for Weakly-Supervised Semantic Segmentation

Weakly-supervised semantic image segmentation suffers from lacking accurate pixel-level annotations. In this paper, we propose a novel graph convolutional network-based method, called GraphNet, to learn pixel-wise labels from weak annotations. Firstly, we construct a graph on the superpixels of a training image by combining the low-level spatial relation and high-level semantic content. Meanwhile, scribble or bounding box annotations are embedded into the graph, respectively. Then, GraphNet takes the graph as input and learns to predict high-confidence pseudo image masks by a convolutional network operating directly on graphs. At last, a segmentation network is trained supervised by these pseudo image masks. We comprehensively conduct experiments on the PASCAL VOC 2012 and PASCAL-CONTEXT segmentation benchmarks. Experimental results demonstrate that GraphNet is effective to predict the pixel labels with scribble or bounding box annotations. The proposed framework yields state-of-the-art results in the community.
Domain Adaptation for Visual Applications: A Comprehensive Survey

The aim of this paper1 is to give an overview of domain adaptation and transfer learning with a specific view on visual applications. After a general motivation, we first position domain adaptation in the larger transfer learning problem. Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and the heterogeneous domain adaptation methods. Third, we discuss the effect of the success of deep convolutional architectures which led to new type of domain adaptation methods that integrate the adaptation within the deep architecture. Fourth, we overview the methods that go beyond image categorization, such as object detection or image segmentation, video analyses or learning visual attributes. Finally, we conclude the paper with a section where we relate domain adaptation to other machine learning solutions.
Statistical inference to advance network models in epidemiology.

Contact networks are playing an increasingly important role in the study of epidemiology. Most of the existing work in this area has focused on considering the effect of underlying network structure on epidemic dynamics by using tools from probability theory and computer simulation. This work has provided much insight on the role that heterogeneity in host contact patterns plays on infectious disease dynamics. Despite the important understanding afforded by the probability and simulation paradigm, this approach does not directly address important questions about the structure of contact networks such as what is the best network model for a particular mode of disease transmission, how parameter values of a given model should be estimated, or how precisely the data allow us to estimate these parameter values. We argue that these questions are best answered within a statistical framework and discuss the role of statistical inference in estimating contact networks from epidemiological data.
Enterprise resource planning: Managing the implementation process

Over the past few years, thousands of companies around the world have implemented enterprise resource planning (ERP) systems. Implementing an ERP system is generally a formidable challenge, with a typical ERP implementation taking anywhere from one to five years. The story of the success of ERP systems in achieving the stated objectives is mixed. Some companies have had very successful implementations while others have struggled. This paper empirically investigates and identifies key differences in the approaches used by companies that managed their implementations ontime and/or on/under-budget versus the ones that did not using data collected through a survey of US manufacturing companies that have implemented ERP systems. Logistic regressions are used to classify on-time and on/under-budget firm groups based on the survey responses and to identify the significant variables that contribute to on-time and on/ under-budget implementation performance. The results indicate that many different factors ranging from pre-implementation planning to system configuration influence performance, which managers should be sensitive about when implementing major systems like ERP.
A Language Modeling Approach to Information Retrieval

Models of document indexing and document retrieval have been extensively studied. The integration of these two classes of models has been the goal of several researchers but it is a very diicult problem. We argue that much of the reason for this is the lack of an adequate indexing model. This suggests that perhaps a better indexing model would help solve the problem. However, we feel that making unwarranted parametric assumptions will not lead to better retrieval performance. Furthermore, making prior assumptions about the similarity of documents is not warranted either. Instead, we propose an approach to retrieval based on probabilistic language modeling. We estimate models for each document individually. Our approach to model-ing is non-parametric and integrates document indexing and document retrieval into a single model. One advantage of our approach is that collection statistics which are used heuristically in many other retrieval models are an integral part of our model. We have implemented our model and tested it empirically. Our approach sig-niicantly outperforms standard tf.idf weighting on two diierent collections and query sets.
EthoHand: A dexterous robotic hand with ball-joint thumb enables complex in-hand object manipulation

Our dexterous hand is a fundmanetal human feature that distinguishes us from other animals by enabling us to go beyond grasping to support sophisticated in-hand object manipulation. Our aim was the design of a dexterous anthropomorphic robotic hand that matches the human hand's 24 degrees of freedom, under-actuated by seven motors. With the ability to replicate human hand movements in a naturalistic manner including in-hand object manipulation. Therefore, we focused on the development of a novel thumb and palm articulation that would facilitate in-hand object manipulation while avoiding mechanical design complexity. Our key innovation is the use of a tendon-driven ball joint as a basis for an articulated thumb. The design innovation enables our under-actuated hand to perform complex in-hand object manipulation such as passing a ball between the fingers or even writing text messages on a smartphone with the thumb's end-point while holding the phone in the palm of the same hand. We then proceed to compare the dexterity of our novel robotic hand design to other designs in prosthetics, robotics and humans using simulated and physical kinematic data to demonstrate the enhanced dexterity of our novel articulation exceeding previous designs by a factor of two. Our innovative approach achieves naturalistic movement of the human hand, without requiring translation in the hand joints, and enables teleoperation of complex tasks, such as single (robot) handed messaging on a smartphone without the need for haptic feedback. Our simple, under-actuated design outperforms current state-of-the-art prostheses or robotic and prosthetic hands regarding abilities that encompass from grasps to activities of daily living which involve complex in-hand object manipulation.
Developing Model-Based Reasoning in Mathematics and Science

It is essential to base instruction on a foundation of understanding of children’s thinking, but it is equally important to adopt the longer-term view that is needed to stretch these early competencies into forms of thinking that are complex, multifaceted, and subject to development over years, rather than weeks or months. We pursue this topic through our studies of model-based reasoning. We have identified four forms of models and related modeling practices that show promise for developing model-based reasoning. Models have the fortuitous feature of making forms of student reasoning public and inspectable—not only among the community of modelers, but also to teachers. Modeling provides feedback about student thinking that can guide teaching decisions, an important dividend for improving professional practice.
FRETTING CORROSION IN ELECTRIC CONTACTS

Fretting corrosion is a form of accelerated atmospheric oxidation which occurs at the interface of contacting materials undergoing slight, cyclic relative motion. In electric contacts involving non-noble metals, fretting action can cause rapid increases in contact resistance, proceeding to virtual open circuits in a matter of minutes in the worst cases. Experimental results of resistance increase due to fretting action are given for 36 different contact plating combinations, including hard and soft materials and noble as well as non-noble metals. Effects of contact load, both mechanical and electrical, were studied. The results show that all nonnoble metals are susceptible to some degree to fretting corrosion and will suffer contact resistance increases. The beneficial effects of boundary lubricants are demonstrated. Fretting corrosion phenomena were studied on a laboratory test rig designed to produce a particularly damaging fretting motion. Actual and simulated connector configurations were also tested and found to exhibit similar trends in resistance increase, but at a slower rate. Differential thermal expansion is identified as one source of fretting motion in connectors.
Cannabis Oil : chemical evaluation of an upcoming cannabis-based medicine

Concentrated cannabis extracts, also known as Cannabis oils because of their sticky and viscous appearance, are becoming increasingly popular among self-medicating patients as a claimed cure for cancer. In general, preparation methods for Cannabis oils are relatively simple and do not require particular instruments. The most well-known example of such a product is called ‘Simpson oil’. The purpose of the extraction, often followed by a solvent evaporation step, is to make cannabinoids and other beneficial components such as terpenes available in a highly concentrated form. Although various preparation methods have been recommended for Cannabis oils, so far no studies have reported on the chemical composition of such products. Recognizing the need for more information on quality and safety issues regarding Cannabis oils, an analytical study was performed to compare several generally used preparation methods on the basis of content of cannabinoids, terpenes, and residual solvent components. Solvents used include ethanol, naphtha, petroleum ether, and olive oil. The obtained results are not intended to support or deny the therapeutic properties of these products, but may be useful for better understanding the experiences of self-medicating patients through chemical analysis of this popular medicine.
The Loss Surfaces of Multilayer Networks

We study the connection between the highly non-convex loss function of a simple model of the fully-connected feed-forward neural network and the Hamiltonian of the spherical spin-glass model under the assumptions of: i) variable independence, ii) redundancy in network parametrization, and iii) uniformity. These assumptions enable us to explain the complexity of the fully decoupled neural network through the prism of the results from random matrix theory. We show that for large-size decoupled networks the lowest critical values of the random loss function form a layered structure and they are located in a well-defined band lower-bounded by the global minimum. The number of local minima outside that band diminishes exponentially with the size of the network. We empirically verify that the mathematical model exhibits similar behavior as the computer simulations, despite the presence of high dependencies in real networks. We conjecture that both simulated annealing and SGD converge to the band of low critical points, and that all critical points found there are local minima of high quality measured by the test error. This emphasizes a major difference between largeand small-size networks where for the latter poor quality local minima have nonzero probability of being recovered. Finally, we prove that recovering the global minimum becomes harder as the network size increases and that it is in practice irrelevant as global minimum often leads to overfitting.
Occupational therapy with children with pervasive developmental disorders.

Although the prevalence of children with pervasive developmental disorders (PDD) has increased, empirical data about the role and practices of occupational therapists have not been reported in the literature. This descriptive study investigated the practice of occupational therapists with children with PDD.



A survey was mailed to 500 occupational therapists in the Sensory Integration Special Interest Section or School System Special Interest Section of the American Occupational Therapy Association in eastern and midwestern United States. The valid return rate was 58% (292 respondents). The survey used Likert scale items to measure frequency of performance problems observed in children with PDD, performance areas addressed in intervention, perceived improvement in performance, and frequency of use of and competency in intervention approaches.



The respondents primarily worked in schools and reported that in the past 5 years they had served an increasing number of children with PDD. Most respondents provided direct services and appeared to use holistic approaches in which they addressed multiple performance domains. They applied sensory integration and environmental modification approaches most frequently and believed that they were most competent in using these approaches. Respondents who reported more frequent use of and more competence in sensory integration approaches perceived more improvement in children's sensory processing. Respondents who reported more frequent use of and more competence in child-centered play perceived more improvement in children's sensory integration and play skills.
Business Intelligence and Analytics: From Big Data to Big Impact

Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
Secure Deduplication of Encrypted Data without Additional Independent Servers

Encrypting data on client-side before uploading it to a cloud storage is essential for protecting users' privacy. However client-side encryption is at odds with the standard practice of deduplication. Reconciling client-side encryption with cross-user deduplication is an active research topic. We present the first secure cross-user deduplication scheme that supports client-side encryption without requiring any additional independent servers. Interestingly, the scheme is based on using a PAKE (password authenticated key exchange) protocol. We demonstrate that our scheme provides better security guarantees than previous efforts. We show both the effectiveness and the efficiency of our scheme, via simulations using realistic datasets and an implementation.
Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning

Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency. An efficient fleet management strategy not only can significantly improve the utilization of transportation resources but also increase the revenue and customer satisfaction. It is a challenging task to design an effective fleet management strategy that can adapt to an environment involving complex dynamics between demand and supply. Existing studies usually work on a simplified problem setting that can hardly capture the complicated stochastic demand-supply variations in high-dimensional space. In this paper we propose to tackle the large-scale fleet management problem using reinforcement learning, and propose a contextual multi-agent reinforcement learning framework including two concrete algorithms, namely contextual deep Q-learning and contextual multi-agent actor-critic, to achieve explicit coordination among a large number of agents adaptive to different contexts. We show significant improvements of the proposed framework over state-of-the-art approaches through extensive empirical studies.
2D Human Pose Estimation: New Benchmark and State of the Art Analysis

Human pose estimation has made significant progress during the last years. However current datasets are limited in their coverage of the overall pose estimation challenges. Still these serve as the common sources to evaluate, train and compare different models on. In this paper we introduce a novel benchmark "MPII Human Pose" that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future developments in human body models. This comprehensive dataset was collected using an established taxonomy of over 800 human activities . The collected images cover a wider variety of human activities than previous datasets including various recreational, occupational and householding activities, and capture people from a wider range of viewpoints. We provide a rich set of labels including positions of body joints, full 3D torso and head orientation, occlusion labels for joints and body parts, and activity labels. For each image we provide adjacent video frames to facilitate the use of motion information. Given these rich annotations we perform a detailed analysis of leading human pose estimation approaches and gaining insights for the success and failures of these methods.
Evaluation of header metadata extraction approaches and tools for scientific PDF documents

This paper evaluates the performance of tools for the extraction of metadata from scientific articles. Accurate metadata extraction is an important task for automating the management of digital libraries. This comparative study is a guide for developers looking to integrate the most suitable and effective metadata extraction tool into their software. We shed light on the strengths and weaknesses of seven tools in common use. In our evaluation using papers from the arXiv collection, GROBID delivered the best results, followed by Mendeley Desktop. SciPlore Xtract, PDFMeat, and SVMHeaderParse also delivered good results depending on the metadata type to be extracted.
Recent advances in the automatic recognition of audiovisual speech

Visual speech information from the speaker’s mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audio-visual automatic speech recognition and present novel contributions in two main areas: First, the visual front end design, based on a cascade of linear image transforms of an appropriate video region-of-interest, and subsequently, audio-visual speech integration. On the latter topic, we discuss new work on feature and decision fusion combination, the modeling of audio-visual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audio-visual adaptation. We apply our algorithms to three multi-subject bimodal databases, ranging from smallto largevocabulary recognition tasks, recorded in both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves automatic speech recognition over all conditions and data considered, though less so for visually challenging environments and large vocabulary tasks.