nils-herrmann/clean_scirepeval huggingface dataset:
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted. Learning joint actionvalues conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralised policies is unclear. Our solution is QMIX, a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations. We structurally enforce that the joint-action value is monotonic in the per-agent values, which allows tractable maximisation of the joint action-value in off-policy learning, and guarantees consistency between the centralised and decentralised policies. We evaluate QMIX on a challenging set of StarCraft II micromanagement tasks, and show that QMIX significantly outperforms existing value-based multi-agent reinforcement learning methods.
Simulating Action Dynamics with Neural Process Networks

Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics. Our model complements existing memory architectures with dynamic entity tracking by explicitly modeling actions as state transformers. The model updates the states of the entities by executing learned action operators. Empirical results demonstrate that our proposed model can reason about the unstated causal effects of actions, allowing it to provide more accurate contextual information for understanding and generating procedural text, all while offering more interpretable internal representations than existing alternatives.
Convolutional neural network-based real-time ROV detection using forward-looking sonar image

Agent system is strategy to enhance the underwater manipulation. The conventional manipulation is generally robot arm-based configuration which has singular points. On the other hand, the agent system is an armless manipulation that the agent vehicle works as the end-effector. If the location of the agent can be measured, the end effector is able to be place to any position. To implement this system, the method of an agent vehicle localization is proposed. The method uses the sonar images of moving agent obtained by forward-looking sonar. To detect the location of the agent in the sonar images, the convolutional neural network is applied. We applied the state-of-art object-detection algorithm to the agent vehicle system. The fast object-detection algorithm based on neural network can fulfil the real-time detection and show the remarkable validity. It means the underwater robot can begin navigation under its feed-back. Through field experiment, we confirm the proposed method can detect and track the agent in the successive sonar images.
Visual Detection and Tracking System for a Spherical Amphibious Robot

With the goal of supporting close-range observation tasks of a spherical amphibious robot, such as ecological observations and intelligent surveillance, a moving target detection and tracking system was designed and implemented in this study. Given the restrictions presented by the amphibious environment and the small-sized spherical amphibious robot, an industrial camera and vision algorithms using adaptive appearance models were adopted to construct the proposed system. To handle the problem of light scattering and absorption in the underwater environment, the multi-scale retinex with color restoration algorithm was used for image enhancement. Given the environmental disturbances in practical amphibious scenarios, the Gaussian mixture model was used to detect moving targets entering the field of view of the robot. A fast compressive tracker with a Kalman prediction mechanism was used to track the specified target. Considering the limited load space and the unique mechanical structure of the robot, the proposed vision system was fabricated with a low power system-on-chip using an asymmetric and heterogeneous computing architecture. Experimental results confirmed the validity and high efficiency of the proposed system. The design presented in this paper is able to meet future demands of spherical amphibious robots in biological monitoring and multi-robot cooperation.
Data Ingestion for the Connected World

In this paper, we argue that in many “Big Data” applications, getting data into the system correctly and at scale via traditional ETL (Extract, Transform, and Load) processes is a fundamental roadblock to being able to perform timely analytics or make real-time decisions. The best way to address this problem is to build a new architecture for ETL which takes advantage of the push-based nature of a stream processing system. We discuss the requirements for a streaming ETL engine and describe a generic architecture which satisfies those requirements. We also describe our implementation of streaming ETL using a scalable messaging system (Apache Kafka), a transactional stream processing system (S-Store), and a distributed polystore (Intel’s BigDAWG), as well as propose a new time-series database optimized to handle ingestion internally.
Optimal Photovoltaic Array Reconfiguration to Reduce Partial Shading Losses

Partial shading of a photovoltaic array is the condition under which different modules in the array experience different irradiance levels due to shading. This difference causes mismatch between the modules, leading to undesirable effects such as reduction in generated power and hot spots. The severity of these effects can be considerably reduced by photovoltaic array reconfiguration. This paper proposes a novel mathematical formulation for the optimal reconfiguration of photovoltaic arrays to minimize partial shading losses. The paper formulates the reconfiguration problem as a mixed integer quadratic programming problem and finds the optimal solution using a branch and bound algorithm. The proposed formulation can be used for an equal or nonequal number of modules per row. Moreover, it can be used for fully reconfigurable or partially reconfigurable arrays. The improvement resulting from the reconfiguration with respect to the existing photovoltaic interconnections is demonstrated by extensive simulation results.
A new feather mite species of the genus Picalgoides Černý, 1974 (Astigmata: Psoroptoididae) from a passerine host in Costa Rica

A new feather mite species, Picalgoides giganteus n. sp. , is described from the tawny-throated leaftosser Sclerurus mexicanus Sclater (Passeriformes: Furnariidae) in Costa Rica. Among the 10 species of Picalgoides Černý, 1974, including the new one, this is the third recorded from a passerine host; the remaining seven nominal species are associated with hosts of the order Piciformes. Brief data on the host-parasite associations of Picalgoides spp. are provided. Megninia megalixus Trouessart, 1885 from the short-tailed green magpie Cissa thalassina (Temminck) is transferred to Picalgoides as P. megalixus (Trouessart, 1885) n. comb.
Microfluidic devices for nucleic acid (NA) isolation, isothermal NA amplification, and real-time detection.

Molecular (nucleic acid)-based diagnostics tests have many advantages over immunoassays, particularly with regard to sensitivity and specificity. Most on-site diagnostic tests, however, are immunoassay-based because conventional nucleic acid-based tests (NATs) require extensive sample processing, trained operators, and specialized equipment. To make NATs more convenient, especially for point-of-care diagnostics and on-site testing, a simple plastic microfluidic cassette ("chip") has been developed for nucleic acid-based testing of blood, other clinical specimens, food, water, and environmental samples. The chip combines nucleic acid isolation by solid-phase extraction; isothermal enzymatic amplification such as LAMP (Loop-mediated AMPlification), NASBA (Nucleic Acid Sequence Based Amplification), and RPA (Recombinase Polymerase Amplification); and real-time optical detection of DNA or RNA analytes. The microfluidic cassette incorporates an embedded nucleic acid binding membrane in the amplification reaction chamber. Target nucleic acids extracted from a lysate are captured on the membrane and amplified at a constant incubation temperature. The amplification product, labeled with a fluorophore reporter, is excited with a LED light source and monitored in situ in real time with a photodiode or a CCD detector (such as available in a smartphone). For blood analysis, a companion filtration device that separates plasma from whole blood to provide cell-free samples for virus and bacterial lysis and nucleic acid testing in the microfluidic chip has also been developed. For HIV virus detection in blood, the microfluidic NAT chip achieves a sensitivity and specificity that are nearly comparable to conventional benchtop protocols using spin columns and thermal cyclers.
Adjusting Word Embeddings with Semantic Intensity Orders

Semantic lexicons such as WordNet and PPDB have been used to improve the vector-based semantic representations of words by adjusting the word vectors. However, such lexicons lack semantic intensity information, inhibiting adjustment of vector spaces to better represent semantic intensity scales. In this work, we adjust word vectors using the semantic intensity information in addition to synonyms and antonyms from WordNet and PPDB, and show improved performance on judging semantic intensity orders of adjective pairs on three different human annotated datasets.
TREESVIS-A SOFTWARE SYSTEM FOR SIMULTANEOUS 3 D-REAL-TIME VISUALISATION OF DTM , DSM , LASER RAW DATA , MULTISPECTRAL DATA , SIMPLE TREE AND BUILDING MODELS

A software package has been developed, allowing to visualise simultaneously several data-sets in real-time without the need of special graphic hardware. This is achieved by combining DirectX routines together with methods like CLOD, culling, indicated triangles, ROAM, Quad tree, GeoMorphing and Geo-Mipmapping. This has been done within the framework of the NATSCAN project.
Exfoliative cheilitis.

Exfoliative cheilitis is an uncommon condition affecting the vermilion zone of the upper, lower or both lips. It is characterized by the continuous production and desquamation of unsightly, thick scales of keratin; when removed, these leave a normal appearing lip beneath. The etiology is unknown, although some cases may be factitious. Attempts at treatment by a wide variety of agents and techniques have been unsuccessful. Three patients with this disease are reported and its relationship to factitious cheilitis and candidal cheilitis is discussed.
Keyframe Extraction for Human Motion Capture Data Based on Joint Kernel Sparse Representation

Human motion capture data, which are used to animate animation characters, have been widely used in many areas. To satisfy the high-precision requirement, human motion data are captured with a high frequency (120 frames/s) by a high-precision capture system. However, the high frequency and nonlinear structure make the storage, retrieval, and browsing of motion data challenging problems, which can be solved by keyframe extraction. Current keyframe extraction methods do not properly model two important characteristics of motion data, i.e., sparseness and Riemannian manifold structure. Therefore, we propose a new model called joint kernel sparse representation (SR), which is in marked contrast to all current keyframe extraction methods for motion data and can simultaneously model the sparseness and the Riemannian manifold structure. The proposed model completes the SR in a kernel-induced space with a geodesic exponential kernel, whereas the traditional SR cannot model the nonlinear structure of motion data in the Euclidean space. Meanwhile, because of several important modifications to traditional SR, our model can also exploit the relations between joints and solve two problems, i.e., the unreasonable distribution and redundancy of extracted keyframes, which current methods do not solve. Extensive experiments demonstrate the effectiveness of the proposed method.
Analysis of Roadway Traffic using Data Mining Techniques for providing safety Measures to Avoid Fatal Accidents

Roadway traffic safety is a major concern for transportation governing agencies as well as ordinary citizens. Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Globalization has affected many countries. There has been a drastic increase in the economic activities and consumption level, leading to expansion of travel and transportation. The increase in the vehicles, traffic lead to road accidents. Considering the importance of the road safety, government is trying to identify the causes of road accidents to reduce the accidents level. The exponential increase in the accidents data is making it difficult to analyse the constraints causing the road accidents. The paper describes how to mine frequent patterns causing road accidents from collected data set. We find associations among road accidents and predict the type of accidents for existing as well as for new roads. We make use of association and classification rules to discover the patterns between road accidents and as well as predict road accidents for new roads.
Analysis of a Very Large Altavista Query Log" SRC Technical note #1998-14

In this paper we present an analysis of a 280 GB AltaVista Sear ch Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents approximately 28 5 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplica tion, and query sessions. Furthermore we present results of a correlation a nalysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ signi fica tly from the user assumed in the standard information retrieval lite rature. Specifically, we show that web users type in short queries, mostly look at th e first 10 results only, and seldom modify the query. This suggests that t raditional information retrieval techniques might not work well for answeri ng web search requests. The correlation analysis showed that the most highly correl ated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if t he user did not explicitly specify them as such.
Job Stressors and Coping Mechanisms among Emergency Department Nurses in the Armed Force Hospitals of Taiwan

Nurses in an Armed Force Hospital (AFH) expose to stronger stress than those in a civil hospital, especially in an emergency department (ED). Ironically, stresses of these nurses received few if any attention in academic research in the past. This study collects 227 samples from the emergency departments of four armed force hospitals in central and southern Taiwan. The research indicates that the top five stressors are a massive casualty event, delayed physician support, overloads of routine work, overloads of assignments, and annoying paper work. Excessive work loading was found to be the primary source of stress. Nurses who were perceived to have greater stress levels were more inclined to deploy emotion-oriented approaches and more likely to seek job rotations. Professional stressors and problem-oriented approaches were positively correlated. Unlike other local studies, this study concludes that the excessive work-loading is more stressful in an AFH. Emergency nurse; Job stressor; Coping behavior; Armed force hospital.
Bilinear CNNs for Fine-grained Visual Recognition

We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and Stanford cars  dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at http://vis-www.cs.umass.edu/bcnn.
Interleukin-33 biology with potential insights into human diseases

Interleukin (IL)-33 is a member of the IL-1 family of cytokines. IL-33 is a nuclear protein that is also released into the extracellular space, and thus acts as a dual-function molecule, as does IL-1α. Extracellular IL-33 binds to the cell-surface receptor ST2, leading to the activation of intracellular signaling pathways similar to those used by IL-1. Unlike conventional cytokines, IL-33 might be secreted via unconventional pathways, and can be released upon cell injury as an alarmin. IL-33 is expressed in cells that are in contact with the environment, and acts as an early inducer of inflammation. Its production is then upregulated in inflamed tissues, thus contributing to the further amplification of inflammatory responses. Studies of IL-33-deficient mice will provide more information on intracellular functions of this cytokine. A large body of evidence supports the pathogenic role of IL-33 in asthma and possibly other inflammatory airway conditions. Furthermore, IL-33 has been shown to be involved in experimental models of arthritis and potentially has a pathogenic role in ulcerative colitis and fibrotic conditions, suggesting that IL-33 antagonists might be of interest for the treatment of asthma, rheumatoid arthritis and ulcerative colitis. However, IL-33 also appears to exert important functions in host defense against pathogens and to display cardioprotective properties, which might have implications for the clinical use of IL-33 blockade.
New Parsimonious Multivariate Spatial Model : Spatial Envelope

Dimension reduction provides a useful tool for analyzing high dimensional data. The recently developed Envelope method is a parsimonious version of the classical multivariate regression model through identifying a minimal reducing subspace of the responses. However, existing envelope methods assume an independent error structure in the model. While the assumption of independence is convenient, it does not address the additional complications associated with spatial or temporal correlations in the data. In this article, we introduce a Spatial Envelope method for dimension reduction in the presence of dependencies across space. We study the asymptotic properties of the proposed estimators and show that the asymptotic variance of the estimated regression coefficients under the spatial envelope model is smaller than that from the traditional maximum likelihood estimation. Furthermore, we present a computationally efficient approach for inference. The efficacy of the new approach is investigated through simulation studies and an analysis of an Air Quality Standard (AQS) dataset from the Environmental Protection Agency (EPA).  Dimension reduction, Grassmanian manifold, Matern covariance function, Spatial dependency.
Analysis of Permission-based Security in Android through Policy Expert, Developer, and End User Perspectives

Being one of the major operating system in smartphone industry, security in Android is paramount importance to end users. Android applications are published through Google Play Store which is an official marketplace for Android. If we have to define the current security policy implemented by Google Play Store for publishing Android applications in one sentence then we can write it as “all are suspect but innocent until proven guilty.” It means an application does not have to go through rigorous security review to be accepted for publication. It is assumed that all the applications are benign which does not mean it will remain so in future. If any application is found doing suspicious activities then the application will be categorized as malicious and it will be removed from the Play Store. Though filtering of malicious applications is performed at Play Store, some malicious applications escape the filtering process. Thus, it becomes necessary to take strong security measures at other levels. Security in Android can be enforced at system and application levels. At system level Android uses sandboxing technique while at application level it uses permission. In this paper, we analyze the permission-based security implemented in Android through three different perspectives – policy expert, developer, and end user.
Metaprogramming with Traits

In many domains, classes have highly regular internal structure. For example, so-called business objects often contain boilerplate code for mapping database fields to class members. The boilerplate code must be repeated per-field for every class, because existing mechanisms for constructing classes do not provide a way to capture and reuse such member-level structure. As a result, programmers often resort to ad hoc code generation. This paper presents a lightweight mechanism for specifying and reusing member-level structure in Java programs. The proposal is based on a modest extension to traits that we have termed trait-based metaprogramming. Although the semantics of the mechanism are straightforward, its type theory is difficult to reconcile with nominal subtyping. We achieve reconciliation by introducing a hybrid structural/nominal type system that extends Java’s type system. The paper includes a formal calculus defined by translation to Featherweight Generic Java.
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling

We study probability measures induced by set functions with constraints. Such measures arise in a variety of real-world settings, where prior knowledge, resource limitations, or other pragmatic considerations impose constraints. We consider the task of rapidly sampling from such constrained measures, and develop fast Markov chain samplers for them. Our first main result is for MCMC sampling from Strongly Rayleigh (SR) measures, for which we present sharp polynomial bounds on the mixing time. As a corollary, this result yields a fast mixing sampler for Determinantal Point Processes (DPPs), yielding (to our knowledge) the first provably fast MCMC sampler for DPPs since their inception over four decades ago. Beyond SR measures, we develop MCMC samplers for probabilistic models with hard constraints and identify sufficient conditions under which their chains mix rapidly. We illustrate our claims by empirically verifying the dependence of mixing times on the key factors governing our theoretical bounds.