nils-herrmann/clean_scirepeval huggingface dataset:
Contextual Bandits in a Collaborative Environment

Contextual bandit algorithms provide principled online learning solutions to find optimal trade-offs between exploration and exploitation with companion side-information. They have been extensively used in many important practical scenarios, such as display advertising and content recommendation. A common practice estimates the unknown bandit parameters pertaining to each user independently. This unfortunately ignores dependency among users and thus leads to suboptimal solutions, especially for the applications that have strong social components.
 In this paper, we develop a collaborative contextual bandit algorithm, in which the adjacency graph among users is leveraged to share context and payoffs among neighboring users while online updating. We rigorously prove an improved upper regret bound of the proposed collaborative bandit algorithm comparing to conventional independent bandit algorithms. Extensive experiments on both synthetic and three large-scale real-world datasets verified the improvement of our proposed algorithm against several state-of-the-art contextual bandit algorithms.
A learning framework using Green's function and kernel regularization with application to recommender system

Green's function for the Laplace operator represents the propagation of influence of point sources and is the foundation for solving many physics problems. On a graph of pairwise similarities, the Green's function is the inverse of the combinatorial Laplacian; we resolve the zero-mode difficulty by showing its physical origin as the consequence of the Von Neumann boundary condition. We propose to use Green's function to propagate label information for both semi-supervised and unsupervised learning. We also derive this learning framework from the kernel regularization using Reproducing Kernel Hilbert Space theory at strong regularization limit. Green's function provides a well-defined distance metric on a generic weighted graph, either as the effective distance on the network of electric resistors, or the average commute time in random walks. We show that for unsupervised learning this approach is identical to Ratio Cut and Normalized Cut spectral clustering algorithms. Experiments on newsgroups and six UCI datasets illustrate the effectiveness of this approach. Finally, we propose a novel item-based recommender system using Green's function and show its effectiveness.
Indexing by Latent Dirichlet Allocation and an Ensemble Model

The contribution of this paper is two-fold. First, we presen t indexing by Latent Dirichlet Allocation(LDI), an automatic document indexing method with a probabilistic concept search. The pr obability distributions in LDI utilizes those in Latent Dir ichlet Allocation (LDA), which is a generative topic model that has been pr eviously used in applications for document indexing tasks. However, those ad hoc applications, or their variants with smoothing techniques as prompted by previous studies in LDA-based lan guage modeling, would result in unsatisfactory performance as th e terms in documents may not properly reflect concept space. T o improve the performances, we introduce a new definition of docu ment probability vectors in the context of LDA and present a n ovel scheme for automatic document indexing based on it. Second, we propose an ensemble model (EnM) for document indexing. Th e EnM combines basis indexing models by assigning di fferent weights and tries to uncover the optimal weights with w hich the mean average precision (MAP) is maximized. To solve the optimiza tion problem, we propose three algorithms, EnM.B, EnM,CD an d EnM.PCD. EnM.B is derived based on the boosting method, EnM. CD the coordinate descent method, and EnM.PCD the parallel property of the EnM.CD. The results of our computational exp eriment on a benchmark data set indicate that both the propos ed approaches are viable options in the document indexing task s.
A developmental psychopathology model of childhood traumatic stress and intersection with anxiety disorders

Empirical findings regarding childhood traumatic stress are placed within a developmental life-trajectory model that incorporates a tripartite etiology of posttrauma distress. This approach recognizes an intricate matrix of child-intrinsic factors, developmental maturation and experience, life events, and evolving family and social ecologies. Of central developmental importance in the field of traumatic stress is the ontogenesis of appraisal, emotional response, emotional and physiological regulation, and consideration of protective action with regard to danger. The complexity of traumatic situations and their aftermath suggests the relevance of multiple stress diatheses in understanding individual variability in proximal and distal effects. Neurobiological systems that subserve danger mature over childhood and adolescence. Neurophysiological and neurohormonal studies among traumatized children and adolescents suggest potential neurodevelopmental stage-related vulnerabilities within these systems. Advances in child development and traumatic stress provide tools for investigating proximal and distal interplay of psychopathology, disturbances in the acquisition and maintenance of developmental competencies, and life-trajectory outcomes. A developmental psychopathology model suggests different avenues by which dangerous circumstances, childhood traumatic experiences, and posttraumatic stress disorder (PTSD) can intersect with other anxiety disorders over the life span.
Current Status and Prospects for Cannabidiol Preparations as New Therapeutic Agents.

States and the federal government are under growing pressure to legalize the use of cannabis products for medical purposes in the United States. Sixteen states have legalized (or decriminalized possession of) products high in cannabidiol (CBD) and with restricted ∆(9) -tetrahydrocannabinol (∆(9) -THC) content. In most of these states, the intent is for use in refractory epileptic seizures in children, but in a few states, the indications are broader. This review provides an overview of the pharmacology and toxicology of CBD; summarizes some of the regulatory, safety, and cultural issues relevant to the further exploitation of its antiepileptic or other pharmacologic activities; and assesses the current status and prospects for clinical development of CBD and CBD-rich preparations for medical use in the United States. Unlike Δ(9) -THC, CBD elicits its pharmacologic effects without exerting any significant intrinsic activity on the cannabinoid receptors, whose activation results in the psychotropic effects characteristic of Δ(9) -THC, and CBD possesses several pharmacologic activities that give it a high potential for therapeutic use. CBD exhibits neuroprotective, antiepileptic, anxiolytic, antipsychotic, and antiinflammatory properties. In combination with Δ(9) -THC, CBD has received regulatory approvals in several European countries and is currently under study in trials registered by the U.S. Food and Drug Administration in the United States. A number of states have passed legislation to allow for the use of CBD-rich, limited Δ(9) -THC-content preparations of cannabis for certain pathologic conditions. CBD is currently being studied in several clinical trials and is at different stages of clinical development for various medical indications. Judging from clinical findings reported so far, CBD and CBD-enriched preparations have great potential utility, but uncertainties regarding sourcing, long-term safety, abuse potential, and regulatory dilemmas remain.
Spectral Norm Regularization for Improving the Generalizability of Deep Learning

We investigate the generalizability of deep learning based on the sensitivity to input perturbation. We hypothesize that the high sensitivity to the perturbation of data degrades the performance on it. To reduce the sensitivity to perturbation, we propose a simple and effective regularization method, referred to as spectral norm regularization, which penalizes the high spectral norm of weight matrices in neural networks. We provide supportive evidence for the abovementioned hypothesis by experimentally confirming that the models trained using spectral norm regularization exhibit better generalizability than other baseline methods.
Data-Driven Ghosting using Deep Imitation Learning

Current state-of-the-art sports statistics compare players and teams to league average performance. For example, metrics such as “Wins-above-Replacement” (WAR) in baseball [1], “Expected Point Value” (EPV) in basketball [2] and “Expected Goal Value” (EGV) in soccer  and hockey  are now commonplace in performance analysis. Such measures allow us to answer the question “how does this player or team compare to the league average?” Even “personalized metrics” which can answer how a “player’s or team’s current performance compares to its expected performance” have been used to better analyze and improve prediction of future outcomes .
Recognizing Textual Entailment using Dependency Analysis and Machine Learning

This paper presents a machine learning system that uses dependency-based features and lexical features for recognizing textual entailment. The proposed system evaluates the feature values automatically. The performance of the proposed system is evaluated by conducting experiments on RTE1, RTE2 and RTE3 datasets. Further, a comparative study of the current system with other ML-based systems for RTE to check the performance of the proposed system is also presented. The dependency-based heuristics and lexical features from the current system have resulted in significant improvement in accuracy over existing state-of-art ML-based solutions for RTE.
Gigantomastia: A problem of local recurrence.

Gigantomastia by definition means bilateral benign progressive breast enlargement to a degree that requires breast reduction surgery to remove more than 1800 g of tissue on each side. It is seen at puberty or during pregnancy. The etiology for this condition is still not clear, but surgery remains the mainstay of treatment. We present a unique case of Gigantomastia, which was neither related to puberty nor pregnancy and has undergone three operations so far for recurrence.
Explaining perceived performance of the World Wide Web: uncertainty and the task-technology fit model

The performance of the World Wide Web is evaluated as an information resource in a specific information domain. The theoretical framework underpinning this approach recognises the contribution of information-seeking behaviour from the discipline of information science and models of information systems success from the discipline of information systems in explaining World Wide Web usage as an information resource. A model integrating the construct of uncertainty and the task-technology fit model is presented. A questionnaire-based empirical study is used to test this integrated approach. Our results confirm that richer models representing the broad context of World Wide Web usage are required to evaluate usage of the World Wide Web as an information resource. Usage for travel tasks, uncertainty reduction, the Web as an information resource, and mediation all have a significant impact on users’ perception of performance, explaining 46 per cent of the variance.
A Low-Cost Open-Source 3-D-Printed Three-Finger Gripper Platform for Research and Educational Purposes

Robotics research and education have gained significant attention in recent years due to increased development and commercial deployment of industrial and service robots. A majority of researchers working on robot grasping and object manipulation tend to utilize commercially available robot-manipulators equipped with various end effectors for experimental studies. However, commercially available robotic grippers are often expensive and are not easy to modify for specific purposes. To extend the choice of robotic end effectors freely available to researchers and educators, we present an open-source low-cost three-finger robotic gripper platform for research and educational purposes. The 3-D design model of the gripper is presented and manufactured with a minimal number of 3-D-printed components and an off-the-shelf servo actuator. An underactuated finger and gear train mechanism, with an overall gripper assembly design, are described in detail, followed by illustrations and a discussion of the gripper grasping performance and possible gripper platform modifications. The presented open-source gripper platform computer-aided design model is released for downloading on the authors research lab website (<;uri xlink:href="http://www.alaris.kz" xlink:type="simple">www.alaris.kz<;/uri>) and can be utilized by robotics researchers and educators as a design platform to build their own robotic end effector solutions for research and educational purposes.
Stable Non-Gaussian Random Processes: Stochastic Models with In nite Variance

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Power Consumption Breakdown on a Modern Laptop

The purpose of this work was to obtain a componentwise breakdown of the power consumption a modern laptop. We measured the power usage of the key components in an IBM ThinkPad R40 laptop using an Agilent Oscilloscope and current probes. We obtained the power consumption for the CPU, optical drive, hard disk, display, graphics card, memory, and wireless card subsystems--either through direct measurement or subtractive measurement and calculation. Moreover, we measured the power consumption of each component for a variety of workloads. We found that total system power consumption varies a lot (8 W to 30 W) depending on the workload, and moreover that the distribution of power consumption among the components varies even more widely. We also found that though power saving techniques such as DVS can reduce CPU power considerably, the total system power is still dominated by CPU power in the case of CPU intensive workloads. The display is the other main source of power consumption in a laptop; it dominates when the CPU is idle. We also found that reducing the backlight brightness can reduce the system power significantly, more than any other display power saving techniques. Finally, we observed OS differences in the power consumption.
Data glove embedded with 9-axis IMU and force sensing sensors for evaluation of hand function

A hand injury can greatly affect a person's daily life. Physicians must evaluate the state of recovery of a patient's injured hand. However, current manual evaluations of hand functions are imprecise and inconvenient. In this paper, a data glove embedded with 9-axis inertial sensors and force sensitive resistors is proposed. The proposed data glove system enables hand movement to be tracked in real-time. In addition, the system can be used to obtain useful parameters for physicians, is an efficient tool for evaluating the hand function of patients, and can improve the quality of hand rehabilitation.
Multi-Tasking with Joint Semantic Spaces for Large-Scale Music Annotation and Retrieval

Music prediction tasks range from predicting tags given a song or clip of audio, predicting the name of the artist, or predicting related songs given a song, clip, artist name or tag. That is, we are interested in every semantic relationship between the different musical concepts in our database. In realistically sized databases, the number of songs is measured in the hundreds of thousands or more, and the number of artists in the tens of thousands or more, providing a considerable challenge to standard machine learning techniques. In this work, we propose a method that scales to such datasets which attempts to capture the semantic similarities between the database items by modeling audio, artist names, and tags in a single low-dimensional semantic embedding space. This choice of space is learnt by optimizing the set of prediction tasks of interest jointly using multi-task learning. Our single model learnt by training on the joint objective function is shown experimentally to have improved accuracy over training on each task alone. Our method also outperforms the baseline methods tried and, in comparison to them, is faster and consumes less memory. We also demonstrate how our method learns an interpretable model, where the semantic space captures well the similarities of interest.
A Person Identification Method Using a Top-View Head Image from an Overhead Camera

In this paper, we describe a novel imagebased person identification task. Traditional facebased person identification methods have a low tolerance for occluded situation, such as overlapping of people in an image. We focus on an image from an overhead camera. Using the overhead camera reduces a restriction of the installation location of a camera and solves the problem of occluded images. First, our method identifies the person’s area in a captured image by using background subtraction. Then, it extracts four features from the area; (1) body size, (2) hair color, (3) hairstyle and (4) hair whorl. We apply the four features into the AdaBoost algorithm. Experimental result shows the effectiveness of our method.
APT: Accurate outdoor pedestrian tracking with smartphones

This paper presents APT, a localization system for outdoor pedestrians with smartphones. APT performs better than the built-in GPS module of the smartphone in terms of accuracy. This is achieved by introducing a robust dead reckoning algorithm and an error-tolerant algorithm for map matching. When the user is walking with the smartphone, the dead reckoning algorithm monitors steps and walking direction in real time. It then reports new steps and turns to the map-matching algorithm. Based on updated information, this algorithm adjusts the user's location on a map in an error-tolerant manner. If location ambiguity among several routes occurs after adjustments, the GPS module is queried to help eliminate this ambiguity. Evaluations in practice show that the error of our system is less than 1/2 that of GPS.
Real-time bag of words, approximately

We start from the state-of-the-art Bag of Words pipeline that in the 2008 benchmarks of TRECvid and PASCAL yielded the best performance scores. We have contributed to that pipeline, which now forms the basis to compare various fast alternatives for all of its components: (i) For descriptor extraction we propose a fast algorithm to densely sample SIFT and SURF, and we compare several variants of these descriptors. (ii) For descriptor projection we compare a k-means visual vocabulary with a Random Forest. As a preprojection step we experiment with PCA on the descriptors to decrease projection time. (iii) For classification we use Support Vector Machines and compare the x2 kernel with the RBF kernel. Our results lead to a 10-fold speed increase without any loss of accuracy and to a 30-fold speed increase with 17% loss of accuracy, where the latter system does real-time classification at 26 images per second.
"Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding"

An overview on state of the art automotive radar usage is presented and the changing requirements from detection and ranging towards radar based environmental understanding for highly automated and autonomous driving deduced. The traditional segmentation in driving, manoeuvering and parking tasks vanishes at the driver less stage. Situation assessment and trajectory/manoeuver planning need to operate in a more thorough way. Hence, fast situational up-date, motion prediction of all kind of dynamic objects, object dimension, ego-motion estimation, (self)-localisation and more semantic/classification information, which allows to put static and dynamic world into correlation/context with each other is mandatory. All these are new areas for radar signal processing and needs revolutionary new solutions. The article outlines the benefits that make radar essential for autonomous driving and presents recent approaches in radar based environmental perception.
Short-circuit behavior of high-voltage IGBTs

A load fault or a semiconductor breakdown in a high-power inverter results in very challenging shorts for the remaining semiconductors. Understanding the short-circuit behavior improves the robustness of the semiconductor and the inverters. However, this important topic is not very well analyzed and only few literature exist. Up to now, five different short-circuit types are known which can occur in two or multilevel inverters. The five short-circuit types differ in the starting condition and the dominating semiconductor during the short. Despite the different initial conditions, the behavior of the high-voltage IGBT and diode during short-circuit can be explained with the help of a capacitive equivalent circuit. The resulting dv/dt, the current distribution between the semiconductors, and the influence of the gate-drive unit on the short-circuit behavior can be explained. To validate the results retrieved from the equivalent circuit, measurements on a high voltage IGBT and simulations with a semiconductor simulator are presented.
Residual Reinforcement Learning for Robot Control

Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. Hence, applying control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment. Reinforcement learning (RL) methods have been demonstrated to be capable of learning continuous robot controllers from interactions with the environment, even for problems that include friction and contacts. In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL. The final control policy is a superposition of both control signals. We demonstrate our approach by training an agent to successfully perform a real-world block assembly task involving contacts and unstable objects.
GeoSpark: a cluster computing framework for processing large-scale spatial data

This paper introduces GeoSpark an in-memory cluster computing framework for processing large-scale spatial data. GeoSpark consists of three layers: Apache Spark Layer, Spatial RDD Layer and Spatial Query Processing Layer. Apache Spark Layer provides basic Spark functionalities that include loading / storing data to disk as well as regular RDD operations. Spatial RDD Layer consists of three novel Spatial Resilient Distributed Datasets (SRDDs) which extend regular Apache Spark RDDs to support geometrical and spatial objects. GeoSpark provides a geometrical operations library that accesses Spatial RDDs to perform basic geometrical operations (e.g., Overlap, Intersect). System users can leverage the newly defined SRDDs to effectively develop spatial data processing programs in Spark. The Spatial Query Processing Layer efficiently executes spatial query processing algorithms (e.g., Spatial Range, Join, KNN query) on SRDDs. GeoSpark also allows users to create a spatial index (e.g., R-tree, Quad-tree) that boosts spatial data processing performance in each SRDD partition. Preliminary experiments show that GeoSpark achieves better run time performance than its Hadoop-based counterparts (e.g., SpatialHadoop).