nils-herrmann/clean_scirepeval huggingface dataset:
Least Squares Temporal Difference Learning and Galerkin ’ s Method

The problem of estimating the value function underlying a Markovian reward process is considered. As it is well known, the value function underlying a Markovian reward process satisfied a linear fixed point equation. One approach to learning the value function from finite data is to find a good approximation to the value function in a given (linear) subspace of the space of value functions. We review some of the issues that arise when following this approach, as well as some results that characterize the finite-sample performance of some of the algorithms. 1 Markovian Reward Processes Let X be a measurable space and consider a stochastic process (X0, R1, X1, R2, X2, . . .), where Xt ∈ X and Rt+1 ∈ R, t = 0, 1, 2, . . .. The process is called a Markovian Reward process if • (X0, X1, . . .) is a Markov process, and • for any t ≥ 0, given Xt, Xt+1 the distribution of Rt+1 is independent of the history of the process. Here, Xt is called the state of the system at time t, while Rt+1 is the reward associated to transitioning from Xt to Xt+1. We shall denote by P the Markovian kernel underlying the process: Thus, the distribution of (Xt+1, Rt+1) given Xt is given by P(·, ·|Xt), t = 0, 1, . . .. Fix the so-called discount factor 0 ≤ γ ≤ 1 and define the (total discounted) return associated to the process R = ∞ ∑
An Analysis and Comparison of CDN-P2P-hybrid Content Delivery System and Model

In order to fully utilize the stable edge transmission capability of CDN and the scalable last-mile transmission capability of P2P, while at the same time avoiding ISP-unfriendly policies and unlimited usage of P2P delivery, some researches have begun focusing on CDN-P2P-hybrid architecture and ISP-friendly P2P content delivery technology in recent years. In this paper, we first survey CDN-P2P-hybrid architecture technology, including current industry efforts and academic efforts in this field. Second, we make comparisons between CDN and P2P. And then we explore and analyze main issues, including overlay route hybrid issues, and playing buffer hybrid issues. After that we focus on CDN-P2P-hybrid model analysis and design, we compare the tightlycoupled hybrid model with the loosely-coupled hybrid model, and we propose that there are some main common models which need further study. At last, we analyze the prospective research direction and propose our future work. KeywordsCDN, P2P, P2P Streaming, CDN-P2P-hybrid Architecture, Live Streaming, VoD  This work is supported by 2009 National Science Foundation of China (60903164): Research on Model and Algorithm of New-Generation Controllable, Trustworthy, Network-friendly CDN-P2P hybrid Content Delivery.
Characterization of PicoGreen reagent and development of a fluorescence-based solution assay for double-stranded DNA quantitation.

A sensitive assay for detecting double-stranded (ds) DNA in solution is described. This assay employs a new dye, PicoGreen dsDNA quantitation reagent, which becomes intensely fluorescent upon binding nucleic acids. The brightness of this reagent is due to its high quantum yield (approximately 0.5, bound to ds calf thymus DNA) and large molar extinction coefficient (approximately 70,000 cm-1 M-1). The fluorescence enhancement of this dye upon binding dsDNA is > 1000-fold, with excitation and emission maxima near those of fluorescein. Unlike Hoechst 33258, PicoGreen reagent fluorescence intensity was the same upon binding to poly(dA).poly(dT) and poly(dG).poly(dC) homopolymers. The PicoGreen assay allowed the detection of 25 pg/ml dsDNA, surpassing the sensitivity achieved with Hoechst 33258 by 400-fold. The linear concentration range for DNA quantitation extended over four orders of magnitude-25 pg/ml to 1 microgram/ml-with a single dye concentration. Assay linearity was maintained even in the presence of salts, proteins, poly(ethylene glycol), urea, chloroform, ethanol, and agarose; some ionic detergents and heparin interfered. Linear DNAs yielded slightly brighter signals than supercoiled plasmids. Finally, the assay showed greater dsDNA:RNA selectivity than Hoechst 33258 in low ionic strength buffer and better dsDNA:single-stranded DNA selectivity in 1 M NaCl.
Architecture optimization of a 3-DOF translational parallel mechanism for machining applications, the orthoglide

This paper addresses the architecture optimization of a 3-DOF translational parallel mechanism designed for ma chining applications. The design optimization is conducte d on the basis of a prescribed Cartesian workspace with prescrib ed kinetostatic performances. The resulting machine, the Ort hoglide, features three fixed parallel linear joints which are mounted orthogonally and a mobile platform which moves in the Cartesian x-y-z space with fixed orientation. The interesting features of the Orthoglide are a regular Cartesian workspace shape, unifor m performances in all directions and good compactness. A smal lscale prototype of the Orthoglide under development is pres ented at the end of this paper.
Intrusion detection using clustering of network traffic flows

This paper investigates the continued need for intrusion detection systems (IDS) in computer networks. It explores some of the ways that data mining techniques can be used to improve IDS, and looks at how others have implemented those techniques. It then highlights a method for developing an intrusion detection model using DBSCAN clustering and presents the results of the clustering algorithm as applied to a real-world data set. Finally, the paper concludes that clustering as an intrusion detection technique produces accurate results, but that special considerations must be made both with regard to outliers and the type of traffic flowing across the network.
A lane detection algorithm based on reliable lane markings

This paper proposes a robust and effective vision-based lane detection approach. First, two binary images are obtained from the region of interest of gray-scale images. The obtained binary images are merged by a novel neighborhood AND operator and then transformed to a bird's eye view (BEV) via inverse perspective mapping. Then, gaussian probability density functions are fit to the left and right regions of a histogram image acquired from the BEV. Finally, a polynomial lane model is estimated from the identified regions. Experimental results show that the proposed method accurately detects lanes in complex situations including worn-out and curved lanes.
A comprehensive study of visual event computing

This paper contains a survey on aspects of visual event computing. We start by presenting events and their classifications, and continue with discussing the problem of capturing events in terms of photographs, videos, etc, as well as the methodologies for event storing and retrieving. Later, we review an extensive set of papers taken from well-known conferences and journals in multiple disciplines. We analyze events, and summarize the procedure of visual event actions. We introduce each component of a visual event computing system, and its computational aspects, we discuss the progress of each component and review its overall status. Finally, we suggest future research trends in event computing and hope to introduce a comprehensive profile of visual event computing to readers.
Mechanical design for reinforcing miniature mobile robots' anti-impact capability and experimental verification

Secret reconnaissance and surveillance in spatially constrained spot are demanded in many antiterrorism activities. The special applications require a kind of miniature mobile robot to function covertly in highly confined environments. Thrown deployment mode for robots could be adopted. However, the crucial problem coping with the impact resulted from thrown miniature mobile robots' falling to the ground, is the difficulty of designing anti-impact machine. This paper presents a kind of mechanical design for reinforcing miniature mobile robots' anti-impact capability so that miniature mobile robots (“BMS-2”) carry out reconnaissance and surveillance tasks in confined region by thrown deployment mode. The inflatable wheels with damping spring and damping lid are designed as the crucially damping devices for robots' anti-impact machine. Also the principle for design and the relatively auxiliary design are discussed. Finally, the finite element analysis and several experiments show that the anti-impact mechanical design of BMS-2 is useful for attenuating the impact resulting from robots' falling to the ground, when Miniature Mobile Robots are thrown for carrying out missions in confined spaces.
Online Gaming Adoption in Competitive Social Networks: Combining the Theory of Planned Behavior and Social Network Theory

Online gaming has been the most rapidly growing segment of the video game market within the last ten years. Its market penetration has increased steadily attracting consumers worldwide. Even though, it occurs in a particular environment of competitive social networks, its adoption has gained only little scientific interest. To fill that gap, we suggest a model of online gaming adoption in competitive social networks. For that purpose, we combine the Theory of Planned Behavior with Social Network Theory, introducing external influences in terms of social interaction design and network exposure.
Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks

Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. We provide a simple yet rigorous explanation for this behaviour by introducing the concept of an optimal representation space, in which semantically close symbols are mapped to representations that are close under a similarity measure induced by the model’s objective function. In addition, we present a straightforward procedure that, without any retraining or architectural modifications, allows deep recurrent models to perform equally well (and sometimes better) when compared to shallow models. To validate our analysis, we conduct a set of consistent empirical evaluations and introduce several new sentence embedding models in the process. Even though this work is presented within the context of natural language processing, the insights are readily applicable to other domains that rely on distributed representations for transfer tasks.
Beats: Tapping Gestures for Smart Watches

Interacting with smartwatches poses new challenges. Although capable of displaying complex content, their extremely small screens poorly match many of the touchscreen interaction techniques dominant on larger mobile devices. Addressing this problem, this paper presents beating gestures, a novel form of input based on pairs of simultaneous or rapidly sequential and overlapping screen taps made by the index and middle finger of one hand. Distinguished simply by their temporal sequence and relative left/right position these gestures are designed explicitly for the very small screens (approx. 40mm square) of smartwatches and to operate without interfering with regular single touch input. This paper presents the design of beating gestures and a rigorous empirical study that characterizes how users perform them -- in a mean of 355ms and with an error rate of 5.5%. We also derive thresholds for reliably distinguishing between simultaneous (under 30ms) and sequential (under 400ms) pairs of screen touches or releases. We then present five interface designs and evaluate them in a qualitative study in which users report valuing the speed and ready availability of beating gestures.
Multi-stage beamforming codebook for 60GHz WPAN

Beamforming(BF) based on codebook is regarded as an attractive solution to resolve the poor link budget of millimeter-wave 60GHz wireless communication. Because the number of the elements of antenna array in 60GHz increases, the beam patterns generated are more than common BF, and it causes long set-up time during beam patterns searching. In order to reduce the set-up time, three stages protocol, namely the device (DEV) to DEV linking, sector-level searching and beam-level searching has been adopted by the IEEE 802.15.3c as an optional functionality to realize Gbps communication systems. However, it is still a challenge to create codebook of different patterns to support three stages protocol from common codebook of beam pattern. In this paper, we proposes a multi-stage codebook design and the realization architecture to support three stages BF. The multi-stage codebook can create different granularity of beam patterns and realize progressive searching. Simulation results for eight elements uniform linear array (ULA) show that this design can divide the beam searching to three stages searching without increasing the system complexity.
Design and Sensorless Control of a Novel Axial-Flux Permanent Magnet Machine for In-Wheel Applications

This paper proposes an axial-flux-modulation permanent magnet (PM) machine and its sensorless control strategy for in-wheel application in electrical vehicles. A Vernier structure is integrated with the axial-flux PM machine to include the magnetic gear effect and improve output torque. The sensorless control strategy of the proposed machine, including initial rotor position estimation and rotating position estimation, is proposed for flux-modulation motors in this paper. The initial rotor position estimation is based on the technique of rectangular pulse voltage injection and the rotating rotor position estimation is based on the sliding mode observer (SMO). The saturation effect on inductances, which is the theoretical basis of the rectangular pulse voltage injection, makes the stator parameter variation in different loads and affects the SMO estimation. To overcome this problem, a novel online parameter self-adjustment procedure for the SMO is introduced. The machine design and its sensorless control performance are verified by simulation and prototype experiments.
Computation of the characteristics of a claw pole alternator through the finite element method

The paper presents the analysis of a 3-D numerical model developed for a claw pole alternator. The complex structure of the claw-pole magnetic circuit required a 3D FEM model and a double scalar potential magnetic φ-φred formulation, in order to reduce computing time and memory. The no-load and magnetization characteristics and the e.m.f. time dependence have been calculated. Working characteristics and the induced voltage in the static winding can be calculated by knowing the 3D distribution of the field in stationary magnetic regime for successive positions of the rotor considering the stator.
Diverse and Controllable Image Captioning with Part-of-Speech Guidance

Automatically describing an image is an important capability for virtual assistants. Significant progress has been achieved in recent years on this task of image captioning. However, classical prediction techniques based on maximum likelihood trained LSTM nets don’t embrace the inherent ambiguity of image captioning. To address this concern, recent variational auto-encoder and generative adversarial network based methods produce a set of captions by sampling from an abstract latent space. But, this latent space has limited interpretability and therefore, a control mechanism for captioning remains an open problem. This paper proposes a captioning technique conditioned on part-of-speech. Our method provides human interpretable control in form of part-of-speech. Importantly, part-of-speech is a language prior, and conditioning on it provides: (i) more diversity as evaluated by counting n-grams and the novel sentences generated, (ii) achieves high accuracy for the diverse captions on standard captioning metrics.
A neural network approach to predicting urban building energy consumption

As the world rapidly urbanizes, the built environment will become increasingly responsible for the world’s primary energy usage and greenhouse gas emissions. However, designers and engineers are unable to predict the energy performance of these buildings at a high accuracy because of their inability to account for the intra-building energy dynamics and interdependencies that can have a substantial impact on building energy use. This paper tests two methods: a multilayer perceptron (MLP) and residual network (ResNet) to learn the hidden context affecting building energy consumption and predict at a high accuracy. The results show that a Deep Residual Network with 2 channels in each block and 128 output channels outperforms other algorithms and shows a 74.9% prediction improvement over traditional energy simulation approaches.
Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization

Military operations are turning to more complex and advanced automation technologies for minimum risk and maximum efficiency.A critical piece to this strategy is unmanned aerial vehicles. Unmanned aerial vehicles require the intelligence to safely maneuver along a path to an intended target and avoiding obstacles such as other aircrafts or enemy threats. This paper presents a unique three-dimensional path planning problem formulation and solution approach using particle swarm optimization. The problem formulation was designed with three objectives: 1) minimize risk owing to enemy threats, 2) minimize fuel consumption incurred by deviating from the original path, and 3) fly over defined reconnaissance targets. The initial design point is defined as the original path of the unmanned aerial vehicles. Using particle swarm optimization, alternate paths are generated using B-spline curves, optimized based on the three defined objectives. The resulting paths can be optimized with a preference toward maximum safety, minimum fuel consumption, or target reconnaissance. This method has been implemented in a virtual environment where the generated alternate paths can be visualized interactively to better facilitate the decision-making process. The problem formulation and solution implementation is described along with the results from several simulated scenarios demonstrating the effectiveness of the method.
An Efficient and Trustworthy Resource Sharing Platform for Collaborative Cloud Computing

Advancements in cloud computing are leading to a promising future for collaborative cloud computing (CCC), where globally-scattered distributed cloud resources belonging to different organizations or individuals (i.e., entities) are collectively used in a cooperative manner to provide services. Due to the autonomous features of entities in CCC, the issues of resource management and reputation management must be jointly addressed in order to ensure the successful deployment of CCC. However, these two issues have typically been addressed separately in previous research efforts, and simply combining the two systems generates double overhead. Also, previous resource and reputation management methods are not sufficiently efficient or effective. By providing a single reputation value for each node, the methods cannot reflect the reputation of a node in providing individual types of resources. By always selecting the highest-reputed nodes, the methods fail to exploit node reputation in resource selection to fully and fairly utilize resources in the system and to meet users' diverse QoS demands. We propose a CCC platform, called Harmony, which integrates resource management and reputation management in a harmonious manner. Harmony incorporates three key innovations: integrated multi-faceted resource/reputation management, multi-QoS-oriented resource selection, and price-assisted resource/reputation control. The trace data we collected from an online trading platform implies the importance of multi-faceted reputation and the drawbacks of highest-reputed node selection. Simulations and trace-driven experiments on the real-world PlanetLab testbed show that Harmony outperforms existing resource management and reputation management systems in terms of QoS, efficiency and effectiveness.
A 24-GHz SiGe phased-array receiver-LO phase-shifting approach

A local-oscillator phase-shifting approach is introduced to implement a fully integrated 24-GHz phased-array receiver using SiGe technology. Sixteen phases of the local oscillator are generated in one oscillator core, resulting in a raw beam-forming accuracy of 4 bits. These phases are distributed to all eight receiving paths of the array by a symmetric network. The appropriate phase for each path is selected using high-frequency analog multiplexers. The raw beam-steering resolution of the array is better than 10deg for a forward-looking angle, while the array spatial selectivity, without any amplitude correction, is better than 20 dB. The overall gain of the array is 61 dB, while the array improves the input signal-to-noise ratio by 9 dB
Discriminatively trained recurrent neural networks for single-channel speech separation

This paper describes an in-depth investigation of training criteria, network architectures and feature representations for regression-based single-channel speech separation with deep neural networks (DNNs). We use a generic discriminative training criterion corresponding to optimal source reconstruction from time-frequency masks, and introduce its application to speech separation in a reduced feature space (Mel domain). A comparative evaluation of time-frequency mask estimation by DNNs, recurrent DNNs and non-negative matrix factorization on the 2nd CHiME Speech Separation and Recognition Challenge shows consistent improvements by discriminative training, whereas long short-term memory recurrent DNNs obtain the overall best results. Furthermore, our results confirm the importance of fine-tuning the feature representation for DNN training.
Apollonian Circle Packings: Geometry and Group Theory II. Super-Apollonian Group and Integral Packings

Apollonian circle packings arise by repeatedly filling the interstices between four mutually tangent circles with further tangent circles. Such packings can be described in terms of the Descartes configurations they contain, where a Descartes configuration is a set of four mutually tangent circles in the Riemann sphere, having disjoint interiors. Part I showed there exists a discrete group, the Apollonian group, acting on a parameter space of (ordered, oriented) Descartes configurations, such that the Descartes configurations in a packing formed an orbit under the action of this group. It is observed there exist infinitely many types of integral Apollonian packings in which all circles had integer curvatures, with the integral structure being related to the integral nature of the Apollonian group. Here we consider the action of a larger discrete group, the super-Apollonian group, also having an integral structure, whose orbits describe the Descartes quadruples of a geometric object we call a super-packing. The circles in a super-packing never cross each other but are nested to an arbitrary depth. Certain Apollonian packings and super-packings are strongly integral in the sense that the curvatures of all circles are integral and the curvature×centers of all circles are integral. We show that (up to scale) there are exactly 8 different (geometric) strongly integral super-packings, and that