nils-herrmann/clean_scirepeval huggingface dataset:
Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions

This paper presents coordination algorithms for networks of mobile autonomous agents. The objective of the proposed algorithms is to achieve rendezvous, that is, agreement over the location of the agents in the network. We provide analysis and design results for multiagent networks in arbitrary dimensions under weak requirements on the switching and failing communication topology. The novel correctness proof relies on proximity graphs and their properties and on a general LaSalle invariance principle for nondeterministic discrete-time dynamical systems
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning

Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant. In such cases, it is sometimes easier to learn which actions not to take. In this work, we propose the Action-Elimination Deep Q-Network (AE-DQN) architecture that combines a Deep RL algorithm with an Action Elimination Network (AEN) that eliminates sub-optimal actions. The AEN is trained to predict invalid actions, supervised by an external elimination signal provided by the environment. Simulations demonstrate a considerable speedup and added robustness over vanilla DQN in text-based games with over a thousand discrete actions.
System of Systems Interoperability ( SOSI ) : Final Report

...................................................................................................................v 1 Purpose of the Research and Development Effort........................................1 2 Defining Interoperability .................................................................................3 3 Models of Interoperability ...............................................................................5 3.1 Levels of Information System Interoperability ............................................5 3.2 Organizational Interoperability Maturity Model ...........................................6 3.3 NATO C3 Technical Architecture (NC3TA) Reference Model for Interoperability...........................................................................................7 3.4 Levels of Conceptual Interoperability (LCIM) Model...................................8 3.5 Layers of Coalition Interoperability.............................................................9 3.6 The System of Systems Interoperability (SOSI) Model ..............................9 4 Approach........................................................................................................13 4.1 Method ....................................................................................................13 4.2 Collaborators...........................................................................................14 5  Current State ...................................................................................15 5.1 Observations on the SOSI Model ............................................................15 6 DoD Interoperability Initiatives .....................................................................17 6.1 Commands, Directorates and Centers.....................................................17 6.2 Standards ................................................................................................20 6.3 Strategies ................................................................................................20 6.4 Demonstrations, Exercises and Testbeds ................................................21 6.5 Joint and Coalition Force Integration Initiatives........................................22 6.6 DoD-Sponsored Research.......................................................................25 6.7 Other Initiatives .......................................................................................26 7 Interview and Workshop Findings................................................................27 7.1 General Themes......................................................................................27
Optimally combining sampling techniques for Monte Carlo rendering

Monte Carlo integration is a powerful technique for the evaluation of difficult integrals. Applications in rendering include distribution ray tracing, Monte Carlo path tracing, and form-factor computation for radiosity methods. In these cases variance can often be significantly reduced by drawing samples from several distributions, each designed to sample well some difficult aspect of the integrand. Normally this is done by explicitly partitioning the integration domain into regions that are sampled differently. We present a powerful alternative for constructing robust Monte Carlo estimators, by combining samples from several distributions in a way that is provably good. These estimators are unbiased, and can reduce variance significantly at little additional cost. We present experiments and measurements from several areas in rendering: calculation of glossy highlights from area light sources, the “final gather” pass of some radiosity algorithms, and direct solution of the rendering equation using bidirectional path tracing. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism; I.3.3 [Computer Graphics]: Picture/Image Generation; G.1.9 [Numerical Analysis]: Integral Equations— Fredholm equations.
Role of extracellular RNA-carrying vesicles in cell differentiation and reprogramming

Growing evidence suggests that transcriptional regulators and secreted RNA molecules encapsulated within membrane vesicles modify the phenotype of target cells. Membrane vesicles, actively released by cells, represent a mechanism of intercellular communication that is conserved evolutionarily and involves the transfer of molecules able to induce epigenetic changes in recipient cells. Extracellular vesicles, which include exosomes and microvesicles, carry proteins, bioactive lipids, and nucleic acids, which are protected from enzyme degradation. These vesicles can transfer signals capable of altering cell function and/or reprogramming targeted cells. In the present review we focus on the extracellular vesicle-induced epigenetic changes in recipient cells that may lead to phenotypic and functional modifications. The relevance of these phenomena in stem cell biology and tissue repair is discussed.
Human Safety Mechanisms of Human-Friendly Robots: Passive Viscoelastic Trunk and Passively Movable Base

We would like to give robots the ability to secure human safety in human-robot collisions capable of arising in our living and working environments. However, unfortunately, not much attention has been paid to the technologies of human robot symbiosis to date because almost all robots have been designed and constructed on the assumption that the robots are physically separated from humans. A robot with a new concept will be required to deal with human-robot contact. In this article, we propose a passively movable human-friendly robot that consists of an elastic material-covered manipulator, passive compliant trunk, and passively movable base. The compliant trunk is equipped with springs and dampers, and the passively movable base is constrained by friction developed between the contact surface of the base and the ground. During unexpected collisions, the trunk and base passively move in response to the produced collision force. We describe the validity of the movable base and compliant trunk for collision force suppression, and it is demonstrated in several collision situations. passive viscoelastic trunk, passive base, collision force suppression, compliance ellipsoid, redundancy
Patterns of persuasion for sustainability

Research into the values motivating unsustainable behavior has generated unique insight into how NGOs and environmental campaigns contribute toward successfully fostering significant and long-term behavior change, yet thus far this research has not been applied to the domain of sustainable HCI. We explore the implications of this research as it relates to the potential limitations of current approaches to persuasive technology, and what it means for designing higher impact interventions. As a means of communicating these implications to be readily understandable and implementable, we develop a set of antipatterns to describe persuasive technology approaches that values research suggests are unlikely to yield significant sustainability wins, and a complementary set of patterns to describe new guidelines for what may become persuasive technology best practice.
Adaptive Fault-Tolerant Synchronization Control of a Class of Complex Dynamical Networks With General Input Distribution Matrices and Actuator Faults

This paper is concerned with the problem of adaptive fault-tolerant synchronization control of a class of complex dynamical networks (CDNs) with actuator faults and unknown coupling weights. The considered input distribution matrix is assumed to be an arbitrary matrix, instead of a unit one. Within this framework, an adaptive fault-tolerant controller is designed to achieve synchronization for the CDN. Moreover, a convex combination technique and an important graph theory result are developed, such that the rigorous convergence analysis of synchronization errors can be conducted. In particular, it is shown that the proposed fault-tolerant synchronization control approach is valid for the CDN with both time-invariant and time-varying coupling weights. Finally, two simulation examples are provided to validate the effectiveness of the theoretical results.
Effect of peer support on prevention of postnatal depression among high risk women: multisite randomised controlled trial

To evaluate the effectiveness of telephone based peer support in the prevention of postnatal depression.



Multisite randomised controlled trial.



Seven health regions across Ontario, Canada.



701 women in the first two weeks postpartum identified as high risk for postnatal depression with the Edinburgh postnatal depression scale and randomised with an internet based randomisation service.



Proactive individualised telephone based peer (mother to mother) support, initiated within 48-72 hours of randomisation, provided by a volunteer recruited from the community who had previously experienced and recovered from self reported postnatal depression and attended a four hour training session.



Edinburgh postnatal depression scale, structured clinical interview-depression, state-trait anxiety inventory, UCLA loneliness scale, and use of health services.



After web based screening of 21 470 women, 701 (72%) eligible mothers were recruited. A blinded research nurse followed up more than 85% by telephone, including 613 at 12 weeks and 600 at 24 weeks postpartum. At 12 weeks, 14% (40/297) of women in the intervention group and 25% (78/315) in the control group had an Edinburgh postnatal depression scale score >12 (chi(2)=12.5, P<0.001; number need to treat 8.8, 95% confidence interval 5.9 to 19.6; relative risk reduction 0.46, 95% confidence interval 0.24 to 0.62). There was a positive trend in favour of the intervention group for maternal anxiety but not loneliness or use of health services. For ethical reasons, participants identified with clinical depression at 12 weeks were referred for treatment, resulting in no differences between groups at 24 weeks. Of the 221 women in the intervention group who received and evaluated their experience of peer support, over 80% were satisfied and would recommend this support to a friend.



Telephone based peer support can be effective in preventing postnatal depression among women at high risk.
A variational approach for Bayesian blind image deconvolution

In this paper, the blind image deconvolution (BID) problem is addressed using the Bayesian framework. In order to solve for the proposed Bayesian model, we present a new methodology based on a variational approximation, which has been recently introduced for several machine learning problems, and can be viewed as a generalization of the expectation maximization (EM) algorithm. This methodology reaps all the benefits of a "full Bayesian model" while bypassing some of its difficulties. We present three algorithms that solve the proposed Bayesian problem in closed form and can be implemented in the discrete Fourier domain. This makes them very cost effective even for very large images. We demonstrate with numerical experiments that these algorithms yield promising improvements as compared to previous BID algorithms. Furthermore, the proposed methodology is quite general with potential application to other Bayesian models for this and other imaging problems.
Quantified Markov Logic Networks

Markov Logic Networks (MLNs) are well-suited for expressing statistics such as “with high probability a smoker knows another smoker” but not for expressing statements such as “there is a smoker who knows most other smokers”, which is necessary for modeling, e.g. influencers in social networks. To overcome this shortcoming, we study quantified MLNs which generalize MLNs by introducing statistical universal quantifiers, allowing to express also the latter type of statistics in a principled way. Our main technical contribution is to show that the standard reasoning tasks in quantified MLNs, maximum a posteriori and marginal inference, can be reduced to their respective MLN counterparts in polynomial time.
Brain Tumor Detection Using Anisotropic Filtering, SVM Classifier and Morphological Operation from MR Images

Tumor is a pre-stage of cancer which has become a serious problem in this era. Researchers are trying to develop methods and treatments to round it. Brain tumor is an exceptional cell enhancement in brain tissue and may not always be seen in imaging tricks. Magnetic Resonance Imaging (MRI) is a technique which is applied to display the detailed image of the attacked brain location. The medical imaging trick plays a significant behavior in identification of the disease. In this paper, the brain MRI image is chosen to investigate and a method is targeted for more clear view of the location attacked by tumor. An MRI abnormal brain images as input in the introduced method, Anisotropic filtering for noise removal, SVM classifier for segmentation and morphological operations for separating the affected area from normal one are the key stages if the presented method. Attaining clear MRI images of the brain are the base of this method. The classification of the intensities of the pixels on the filtered image identifies the tumor. Experimental result showed that the SVM has obtained 83% accuracy in segmentation. Finally, the segmented region of the tumor is put on the original image for a distinct identification.
What makes dietary restraint problematic? Development and validation of the Inflexible Eating Questionnaire

This study presents the Inflexible Eating Questionnaire (IEQ), which measures the inflexible adherence to subjective eating rules. The scale's structure and psychometric properties were examined in distinct samples from the general population comprising both men and women. IEQ presented an 11-item one-dimensional structure, revealed high internal consistency, construct and temporal stability, and discriminated eating psychopathology cases from non-cases. The IEQ presented significant associations with dietary restraint, eating psychopathology, body image inflexibility, general psychopathology symptoms, and decreased intuitive eating. IEQ was a significant moderator on the association between dietary restraint and eating psychopathology symptoms. Findings suggested that the IEQ is a valid and useful instrument with potential implications for research on psychological inflexibility in disordered eating.
How to write a paper for publication.

Engaging in the scientific publication process can be for both altruistic and egotistical reasons; publication advances the state of scientific knowledge while advancing your institution and your career. Writing for publication means setting aside a location and time dedicated entirely to the process of planning and writing. It is easiest to begin with the Methods section, then the Results, followed by the Discussion, which is the most challenging part of a paper. A realistic assessment of the value of the article will determine the level of journal into which it is likely to gain acceptance. If your article is rejected by a journal, be consoled by the fact that 50% of articles that are initially rejected are eventually published. Following the steps outlined here can reduce the daunting task of writing to one of manageable proportions and can help overcome the mental block and procrastination that all of us have experienced when we set out to write a scientific paper.
Review article: the global emergence of Helicobacter pylori antibiotic resistance

Helicobacter pylori is one of the most prevalent global pathogens and can lead to gastrointestinal disease including peptic ulcers, gastric marginal zone lymphoma and gastric carcinoma.



To review recent trends in H. pylori antibiotic resistance rates, and to discuss diagnostics and treatment paradigms.



A PubMed literature search using the following keywords: Helicobacter pylori, antibiotic resistance, clarithromycin, levofloxacin, metronidazole, prevalence, susceptibility testing.



The prevalence of bacterial antibiotic resistance is regionally variable and appears to be markedly increasing with time in many countries. Concordantly, the antimicrobial eradication rate of H. pylori has been declining globally. In particular, clarithromycin resistance has been rapidly increasing in many countries over the past decade, with rates as high as approximately 30% in Japan and Italy, 50% in China and 40% in Turkey; whereas resistance rates are much lower in Sweden and Taiwan, at approximately 15%; there are limited data in the USA. Other antibiotics show similar trends, although less pronounced.



Since the choice of empiric therapies should be predicated on accurate information regarding antibiotic resistance rates, there is a critical need for determination of current rates at a local scale, and perhaps in individual patients. Such information would not only guide selection of appropriate empiric antibiotic therapy but also inform the development of better methods to identify H. pylori antibiotic resistance at diagnosis. Patient-specific tailoring of effective antibiotic treatment strategies may lead to reduced treatment failures and less antibiotic resistance.
Extended Use Case Points Method for Software Cost Estimation

Software cost estimation is a key open issue for the software industry, which suffers from cost overruns frequently. As the most popular technique for object-oriented software cost estimation, Use Case Points(UCP) method, however, has two major drawbacks: the uncertainty of the cost factors and the abrupt classification. To address these two issues, we propose the Extended Use Case Points (EUCP) method. With a probabilistic cost model constructed from integrating fuzzy set theory and Bayesian Belief Networks(BBNs) with the UCP method, EUCP provides a probability distribution of cost and a refined gradual classification, which mitigate the uncertainty of cost factors and improve the accuracy of classification. In this paper, we provides two case studies to demonstrate the effectiveness of EUCP in the real life.
Context Awareness by Case-Based Reasoning in a Music Recommendation System

The recommendation system is one of the core technologies for implementing personalization services. Recommendation systems in ubiquitous computing environment should have the capability of context-awareness. In this research, we developed a music recommendation system, which we shall call C_Music, which utilizes not only the user’s demographics and behavioral patterns but also the user’s context. For a specific user in a specific context, the C_Music recommends the music that the similar users listened most in the similar context. In evaluating the performance of C_Music using a real world data, it outperforms the comparative system that utilizes the user’s demographics and behavioral patterns only.
KisBot : New Spherical Robot with Arms

A new type of spherical robot, called KisBot, is presented that includes arms and two types of driving mode: rolling and wheeling. In the rolling mode, the robot uses its arms as pendulums and works like a pendulum-driven robot, while in the wheeling mode, it extends its arms to the ground and works like a one-wheel car. The basic design idea of KisBot is introduced and a prototype is implemented. The robot has a wheel-shaped body between two rotating semi-spheres. Each semi-sphere contains one DC motor for propulsion in the rolling mode and wheeling mode, one RC motor for arm extension, a speed controller for changing the direction of the arm rotation, a battery as the power source, and the mechanical components of the arm. Experiments using the rolling mode and wheeling mode verify the driving efficiency of the proposed spherical robot.  Spherical robot, Rolling robot, Deformable robot, Locomotion, Motion generation.
A Comparison of Two Paraphrase Models for Taxonomy Augmentation

Taxonomies are often used to look up the concepts they contain in text documents (for instance, to classify a document). The more comprehensive the taxonomy, the higher recall the application has that uses the taxonomy. In this paper, we explore automatic taxonomy augmentation with paraphrases. We compare two state-of-the-art paraphrase models based on Moses, a statistical Machine Translation system, and a sequence-to-sequence neural network, trained on a paraphrase datasets with respect to their abilities to add novel nodes to an existing taxonomy from the risk domain. We conduct component-based and task-based evaluations. Our results show that paraphrasing is a viable method to enrich a taxonomy with more terms, and that Moses consistently outperforms the sequence-to-sequence neural model. To the best of our knowledge, this is the first approach to augment taxonomies with paraphrases.
Neural Machine Translation for Bilingually Scarce Scenarios: A Deep Multi-task Learning Approach

Neural machine translation requires large amounts of parallel training text to learn a reasonable-quality translation model. This is particularly inconvenient for language pairs for which enough parallel text is not available. In this paper, we use monolingual linguistic resources in the source side to address this challenging problem based on a multi-task learning approach. More specifically, we scaffold the machine translation task on auxiliary tasks including semantic parsing, syntactic parsing, and named-entity recognition. This effectively injects semantic and/or syntactic knowledge into the translation model, which would otherwise require a large amount of training bitext. We empirically evaluate and show the effectiveness of our multi-task learning approach on three translation tasks: English-to-French, English-to-Farsi, and English-to-Vietnamese.