nils-herrmann/clean_scirepeval huggingface dataset:
Impure Altruism and Donations to Public Goods : A Theory of Warm-Glow Giving

When people make donations to privately provided public goods, such as charity, there may be many factors infiuencing their decisions other than altruism. As Olson (1965) noted, 'people are sometimes motivated by a desire to win prestige, respect, friendship, and other social and psychological objectives' (p. 60) or as Becker (1974) observed, 'apparent "charitable" behavior can also be motivated by a desire to avoid scorn of others or to receive social acclaim' (p. 1083). Clearly social pressure, guilt, sympathy, or simply a desire for a 'warm glow' may play important roles in the decisions of agents. While such warm-glow giving has been acknowledged in the literature/ the most common approach has been to assume that preferences depend only on private consumption and the total supply of the public good and not on individual donations per se. Recent research reveals, however, that this ' pure altruism' model lacks predictive power. First, Warr (1982) and Roberts (1984) demonstrate theoretically that government grants should crowd out voluntary gifts dollarfor-dollar,^ a finding that has been extended to subsidies by Bernheim (1986) and Andreoni (1988). However, empirical studies by Abrams and Schmitz (1978, 1984) and Clotfelter (1985) show that crowding out is quite small. Second, Warr (1983) and Bergstrom et al. (1986) show theoretically that the total supply of the public good is independent of the distribution of income, while an empirical study by Hochman and Rodgers (1973) shows that giving to local charities is highly sensitive to the distribution of income within the
Effect of Swastika Slot in Square Fractal Antenna

In the modern wireless communication systems, antennas of multiple bands, smaller size and wider bandwidth are high in demands to meet the requirement of multiple applications. This has initiated the research in new antenna fields; fractal antenna theory is one among them. Its unique properties such as selfsimilarity and space filling lead to multiband and size reduction characteristics. Due to smaller size and multiband characteristics fractal antenna has drawn the attention of researchers. Fractal antennas have more electrical length, improved SWR and impedance in a reduced physical area. The aim of this work is to design an antenna of square shape with swastika slot for multiband characteristics and efficient gain. The proposed fractal antenna has the dimensions of 22mm x 22mm. The antenna is fabricated on FR4epoxy substrate with dielectric constant (r) =4.4 and thickness 1.5676mm, which is easily available in market and cheap, thus making antenna cost effective. The structure has been designed using four swastika shapes in between two square rings on two sides. In this antenna only single iteration with the scaling factor 0.5 has been done to see the effect of iteration on the geometry. The designing and simulation of the antenna has been done using Ansoft HFSS software. The designed antenna resonates at seven different frequencies 4.9GHz, 5.8GHz, 8GHz, 13.8GHz, 22.3GHz, 26GHz and 27.6GHz. The Gains at these frequencies are 5.8099dBi, 1.6628dBi, -2.0782dBi, 3.6563dBi, 4.7219dBi, 5.2740dBi and 7.0985dBi respectively. This antenna can be used in different C and Ku band applications such as satellite and long distance telecommunication.
Foundations of non-Bayesian Social Learning ∗

In this paper, we study the problem of non-Bayesian learning over social networks by taking an axiomatic approach. As our main behavioral assumption, we postulate that agents follow social learning rules that satisfy imperfect recall, according to which they treat the current beliefs of their neighbors as sufficient statistics for all the information available to them. We establish that as long as imperfect recall represents the only point of departure from Bayesian rationality, agents’ social learning rules take a log-linear form. Our approach also enables us to provide a taxonomy of behavioral assumptions that underpin various non-Bayesian models of learning, including the canonical model of DeGroot. We then show that for a fairly large class of learning rules, the form of bounded rationality represented by imperfect recall is not an impediment to asymptotic learning, as long as agents assign weights of equal orders of magnitude to every independent piece of information. Finally, we show how the dispersion of information among different individuals in the social network determines the rate of learning.
Empirical Bounds on Linear Regions of Deep Rectifier Networks

One form of comparing the expressiveness of rectifier networks is by the number of linear regions, or pieces, of the piecewise linear functions modeled by such networks. However, enumerating these regions is prohibitive in practice and the known analytical bounds on their numbers are identical for networks having the same dimensions. In this work, we approximate the number of linear regions of rectifier networks through empirical bounds based on features of the trained network and probabilistic inference. Our first contribution is an algorithm for probabilistic lower bounds of mixed-integer linear sets, which is several orders of magnitude faster than exact counting and obtain values reaching similar orders of magnitude. Our second contribution is a tighter activation-based bound for the maximum number of linear regions, which is particularly stronger in networks with narrow layers. Combined, these bounds yield a reasonable proxy for the number of linear regions and the accuracy of the networks.
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform a multitude of tasks in human-centric environments. However, effectively controlling them remains challenging due to their high dimensionality and large number of potential contacts. Deep reinforcement learning (DRL) provides a model-agnostic approach to control complex dynamical systems, but has not been shown to scale to highdimensional dexterous manipulation. Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency. Consequently, the success of DRL in robotics has thus far been limited to simpler manipulators and tasks. In this work, we show that model-free DRL can effectively scale up to complex manipulation tasks with a high-dimensional 24-DoF hand, and solve them from scratch in simulated experiments. Furthermore, with the use of a small number of human demonstrations, the sample complexity can be significantly reduced, which enables learning with sample sizes equivalent to a few hours of robot experience. The use of demonstrations result in policies that exhibit very natural movements and, surprisingly, are also substantially more robust. We demonstrate successful policies for object relocation, in-hand manipulation, tool use, and door opening, which are shown in the supplementary video.
Implementation of Enhanced Web Crawler for Deep-Web Interfaces

1Student, Department of Computer Science & Engineering, G.H.Raisoni Institute of Engineering & Management, Jalgaon, Maharashtra, India. 2Assistant Professor, Department of Information and Technology , G.H.Raisoni Institute of Engineering & Management, Jalgaon, Maharashtra, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract As deep web grows at a very fast pace, there has been increased interest in techniques that help efficiently locate deep-web interfaces. However, due to the large volume of web resources and the dynamic nature of deep web, achieving wide coverage and high efficiency is a challenging issue. Therefore a two-stage enhanced web crawler framework is proposed for efficiently harvesting deep web interfaces. The proposed enhanced web crawler is divided into two stages. In the first stage, site locating is performed by using reverse searching which finds relevant content. In the second stage, enhanced web crawler achieves fast in site searching by excavating most relevant links of site. It uses a novel deep web crawling framework based on reinforcement learning which is effective for crawling the deep web. The experimental results show that the method outperforms the state of art methods in terms of crawling capability and achieves higher harvest rates than other crawlers.
Integrated Forensic Accounting Investigative Process Model in Digital Environment

A new field of forensic accounting has emerged as current practices have been changed in electronic business environment and rapidly increasing fraudulent activities. Despite taking many forms, the fraud is usually theft of funds and information or misuse of someone's information assets. As financial frauds prevail in digital environment, accountants are the most helpful people to investigate them. However, forensic accountants in digital environment, usually called fraud investigators or fraud examiners, must be specially trained to investigate and report digital evidences in the courtroom. In this paper, the authors researched the case of financial fraud forensic analysis of the Microsoft Excel file, as it is very often used in financial reporting. We outlined some of the well-known difficulties involved in tracing the fraudster activities throughout extracted Excel file metadata, and applied a different approach from that well-described in classic postmortem computer system forensic analysis or in data mining techniques application. In the forensic examination steps we used open source code, Deft 7.1 (Digital evidence & forensic toolkit) and verified results by the other forensic tools, Meld a visual diff and merge tool to compare files and directories and KDiff tool, too. We proposed an integrated forensic accounting, functional model as a combined accounting, auditing and digital forensic investigative process. Before this approach can be properly validated some future work needs to be done, too.
Integration of nanoscale memristor synapses in neuromorphic computing architectures

Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient computation using massively parallel arrays of limited precision, highly variable, and unreliable components. Recent developments in nano-technologies are making available extremely compact and low power, but also variable and unreliable solid-state devices that can potentially extend the offerings of availing CMOS technologies. In particular, memristors are regarded as a promising solution for modeling key features of biological synapses due to their nanoscale dimensions, their capacity to store multiple bits of information per element and the low energy required to write distinct states. In this paper, we first review the neuro- and neuromorphic computing approaches that can best exploit the properties of memristor and scale devices, and then propose a novel hybrid memristor-CMOS neuromorphic circuit which represents a radical departure from conventional neuro-computing approaches, as it uses memristors to directly emulate the biophysics and temporal dynamics of real synapses. We point out the differences between the use of memristors in conventional neuro-computing architectures and the hybrid memristor-CMOS circuit proposed, and argue how this circuit represents an ideal building block for implementing brain-inspired probabilistic computing paradigms that are robust to variability and fault tolerant by design.
The nursing shortage: solutions for the short and long term.

With predictions that this nursing shortage will be more severe and have a longer duration than has been previously experienced, traditional strategies implemented by employers will have limited success. The aging nursing workforce, low unemployment, and the global nature of this shortage compound the usual factors that contribute to nursing shortages. For sustained change and assurance of an adequate supply of nurses, solutions must be developed in several areas: education, healthcare deliver systems, policy and regulations, and image. This shortage is not solely nursing's issue and requires a collaborative effort among nursing leaders in practice and education, health care executives, government, and the media. This paper poses several ideas of solutions, some already underway in the United States, as a catalyst for readers to initiate local programs.
Tensile properties of five commonly used mid-urethral slings relative to the TVT™

We characterized the tensile properties of five mid-urethral slings relative to the Gynecare TVT™. Slings were divided and loaded to failure. The heat-sealed Boston Scientific mid-section and the American Medical Systems (AMS) tensioning suture were examined separately. Analysis of the resulting nonlinear load elongation curves included calculation of low and high stiffness, the transition point between them (inflection point), load at failure, and relative elongation. Permanent elongation was measured after repetitive loads. Mean values were compared using a one-way analysis of variance. The curves of the Gynecare, Boston Scientific (no midsection) and AMS (no suture) were nonlinear with similar low stiffness and inflection points. The Bard, Caldera, and Mentor slings were stiffer. Heat sealing the Boston Scientific mid-section increased stiffness, while the AMS suture had negligible effect. Cyclical loading induced permanent elongation that was similar for Gynecare, AMS, and Boston Scientific (without mid-section) and lower for Bard, Caldera, and Mentor. With the exception of AMS, the overall effect of newer sling modifications was an increase in tensile stiffness.
GRAPHENE: Packing and Dependency-Aware Scheduling for Data-Parallel Clusters

We present anew cluster scheduler,Graphene, aimed at jobs that have a complex dependency structure and heterogeneous resource demands. Relaxing either of these challenges, i.e., scheduling a DAG of homogeneous tasks or an independent set of heterogeneous tasks, leads to NP-hard problems. Reasonable heuristics exist for these simpler problems, but they perform poorly when scheduling heterogeneous DAGs. Our key insights are: (1) focus on the long-running tasks and thosewith toughto-pack resource demands, (2) compute a DAG schedule, oøine, by ûrst scheduling such troublesome tasks and then scheduling the remaining tasks without violating dependencies. _ese oøine schedules are distilled to a simple precedence order and are enforced by an online component that scales to many jobs. _e online component also uses heuristics to compactly pack tasks and to trade-oò fairness for faster job completion. Evaluation on a 200-server cluster and using traces of productionDAGs at Microso , shows that Graphene improves median job completion time by 25% and cluster throughput by 30%.
A novel multilevel carrier-based PWM-control method for GTO inverter in low index modulation region

Gate turn offs (GTOs) have an inherent minimum ON-OFF time, which is needed for their safe operation. For GTO-based three-level or neutral-point-clamped (NPC) inverters, this minimum ON-OFF pulsewidth limitation results in a distortion of the output voltage and current waveforms, especially in the low index modulation region. Some approaches have been previously proposed to compensate for the minimum ON pulse. However, these methods increase the inverter switching losses. Two new methods of pulsewidth-modulation (PWM) control based on: 1) adding a bias to the reference voltage of the inverter and 2) switching patterns are presented. The former method improves the output waveforms, but increases the switching losses; while the latter improves the output waveforms without increasing the switching losses. The fluctuations of the neutral-point voltage are also reduced using this method. The theoretical and practical aspects as well as the experimental results are presented in this paper.
Effect of Wideband Beam Squint on Codebook Design in Phased-Array Wireless Systems

Analog beamforming with phased arrays is a promising technique for 5G wireless communication at millimeter wave frequencies. Using a discrete codebook consisting of multiple analog beams, each beam focuses on a certain range of angles of arrival or departure and corresponds to a set of fixed phase shifts across frequency due to practical hardware considerations. However, for sufficiently large bandwidth, the gain provided by the phased array is actually frequency dependent, which is an effect called beam squint, and this effect occurs even if the radiation pattern of the antenna elements is frequency independent. This paper examines the nature of beam squint for a uniform linear array (ULA) and analyzes its impact on codebook design as a function of the number of antennas and system bandwidth normalized by the carrier frequency. The criterion for codebook design is to guarantee that each beam's minimum gain for a range of angles and for all frequencies in the wideband system exceeds a target threshold, for example 3 dB below the array's maximum gain. Analysis and numerical examples suggest that a denser codebook is required to compensate for beam squint. For example, 54% more beams are needed compared to a codebook design that ignores beam squint for a ULA with 32 antennas operating at a carrier frequency of 73 GHz and bandwidth of 2.5 GHz. Furthermore, beam squint with this design criterion limits the bandwidth or the number of antennas of the array if the other one is fixed.
Reliability of diagnosis and clinical efficacy of visceral osteopathy: a systematic review

In 2010, the World Health Organization published benchmarks for training in osteopathy in which osteopathic visceral techniques are included. The purpose of this study was to identify and critically appraise the scientific literature concerning the reliability of diagnosis and the clinical efficacy of techniques used in visceral osteopathy.



Databases MEDLINE, OSTMED.DR, the Cochrane Library, Osteopathic Research Web, Google Scholar, Journal of American Osteopathic Association (JAOA) website, International Journal of Osteopathic Medicine (IJOM) website, and the catalog of Académie d'ostéopathie de France website were searched through December 2017. Only inter-rater reliability studies including at least two raters or the intra-rater reliability studies including at least two assessments by the same rater were included. For efficacy studies, only randomized-controlled-trials (RCT) or crossover studies on unhealthy subjects (any condition, duration and outcome) were included. Risk of bias was determined using a modified version of the quality appraisal tool for studies of diagnostic reliability (QAREL) in reliability studies. For the efficacy studies, the Cochrane risk of bias tool was used to assess their methodological design. Two authors performed data extraction and analysis.



Eight reliability studies and six efficacy studies were included. The analysis of reliability studies shows that the diagnostic techniques used in visceral osteopathy are unreliable. Regarding efficacy studies, the least biased study shows no significant difference for the main outcome. The main risks of bias found in the included studies were due to the absence of blinding of the examiners, an unsuitable statistical method or an absence of primary study outcome.



The results of the systematic review lead us to conclude that well-conducted and sound evidence on the reliability and the efficacy of techniques in visceral osteopathy is absent.
Graph Sparsification Approaches for Laplacian Smoothing

Given a statistical estimation problem where regularization is performed according to the structure of a large, dense graph G, we consider fitting the statistical estimate using a sparsified surrogate graph G̃, which shares the vertices of G but has far fewer edges, and is thus more tractable to work with computationally. We examine three types of sparsification: spectral sparsification, which can be seen as the result of sampling edges from the graph with probabilities proportional to their effective resistances, and two simpler sparsifiers, which sample edges uniformly from the graph, either globally or locally. We provide strong theoretical and experimental results, demonstrating that sparsification before estimation can give statistically sensible solutions, with significant computational savings.
ANALYSIS OF SYNCHRONOUS-REFERENCE-FRAME-BASED CONTROL METHOD FOR UPQC UNDER UNBALANCED AND DISTORTED LOAD CONDITIONS

This paper presents a new synchronous-reference frame (SRF)-based control method to compensate power-quality (PQ) problems through a three-phase four-wire unified PQ conditioner (UPQC) under unbalanced and distorted load conditions. The proposed UPQC system can improve the power quality at the point of common coupling on power distribution systems under unbalanced and distorted load conditions. The simulation results based on Matlab/Simulink are discussed in detail to support the SRF-based control method presented in this paper. The proposed approach is also validated through experimental study with the UPQC hardware prototype.
Hierarchical Reinforcement Learning for Multi-agent MOBA Game

Although deep reinforcement learning has achieved great success recently, there are still challenges in Real Time Strategy (RTS) games. Due to its large state and action space, as well as hidden information, RTS games require macro strategies as well as micro level manipulation to obtain satisfactory performance. In this paper, we present a novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games. In this hierarchical framework, agents make macro strategies by imitation learning and do micromanipulations through reinforcement learning. Moreover, we propose a simple self-learning method to get better sample efficiency for reinforcement part and extract some global features by multi-target detection method in the absence of game engine or API. In 1v1 mode, our agent successfully learns to combat and defeat built-in AI with 100% win rate, and experiments show that our method can create a competitive multi-agent for a kind of mobile MOBA game King of Glory (KOG) in 5v5 mode.
An automated, complete, structural test solution for SERDES

Gigahertz serialization and deserialization (SERDES) has become a dominant inter-chip and inter-board data transmission technique. Signal integrity is the primary factor determining its bit error rate, typically less than 10/sup -12/, so the primary production test challenges are testing picosecond jitter and the signal eye opening. Off-chip jitter and rise/fall time measurements are limited by hardware complexity, access, bandwidth, and noise. Published on-chip measurement techniques are limited by delay line jitter. This paper presents a new jitter test technique that has been demonstrated on an FPGA to achieve less than 1 ps RMS self-jitter, and a new signal eye test that has unlimited bandwidth; neither test uses high speed circuitry. The all-digital technique uses the receiver itself to demodulate the signal jitter to a low-speed bit stream that is analyzed by a single-clock domain, synthesizable circuit. This is combined with logic BIST and 1149.6 boundary scan to completely test an IC.