nils-herrmann/clean_scirepeval huggingface dataset:
Phase-Gradient Meta-Dome for Increasing Grating-Lobe-Free Scan Range in Phased Arrays

This paper presents a simple and effective way to increase the scan range of phased array antennas by using a metasurface (MTS) curved dome. The technique is based on a phase-gradient passive MTS that deflects the incident rays coming from the array aperture, and on a predistortion of amplitude and phase applied to the original phased array. We assume, therefore, to have at disposal an array capable of electronic beam scanning over a limited angular range with control of amplitude and phase. Geometrical optics ray tracing is used for the design of the metadome and physical optics for the analysis. For the sake of simplicity, the idea is illustrated here for a 2-D scan case; however, the extension to full 3-D scanning appears to be straightforward.
Eight Key Elements of a Business Model

The growth of e-commerce and mobile services has been creating business opportunities. Among these, mobile marketing is predicted to be a new trend of marketing. As mobile devices are personal tools such that the services ought to be unique, in the last few years, researches have been conducted studies related to the user acceptance of mobile services and produced results. This research aims to develop a model of mobile e-commerce mobile marketing system, in the form of computer-based information system (CBIS) that addresses the recommendations or criteria formulated by researchers. In this paper, the criteria formulated by researches are presented then each of the criteria is resolved and translated into mobile services. A model of CBIS, which is an integration of a website and a mobile application, is designed to materialize the mobile services. The model is presented in the form of business model, system procedures, network topology, software model of the website and mobile application, and database models.
Planar Leaky-Wave Antenna Designs Offering Conical-Sector Beam Scanning and Broadside Radiation Using Surface-Wave Launchers

Two planar antenna designs that utilize surface-waves for leaky-wave excitation are investigated. Specifically, a surface-wave launcher is implemented to excite cylindrical surface-waves, which are bound to a grounded dielectric slab. By the addition of circular metallic strip gratings, a partially reflecting surface is realized, providing appropriate conditions for 2-D leaky-wave radiation. In particular, two designs are investigated: a continuous circular strip and a segmented circular strip grating. Results illustrate conical-sector beam scanning for the continuous circular strip grating between 2022 GHz, while broadside radiation is observed at 21.2 GHz by the segmented circular strip design.
Where Do Rewards Come From ?

Reinforcement learning has achieved broad and successful application in cognitive science in part because of its general formulation of the adaptive control problem as the maximization of a scalar reward function. The computational reinforcement learning framework is motivated by correspondences to animal reward processes, but it leaves the source and nature of the rewards unspecified. This paper advances a general computational framework for reward that places it in an evolutionary context, formulating a notion of an optimal reward function given a fitness function and some distribution of environments. Novel results from computational experiments show how traditional notions of extrinsically and intrinsically motivated behaviors may emerge from such optimal reward functions. In the experiments these rewards are discovered through automated search rather than crafted by hand. The precise form of the optimal reward functions need not bear a direct relationship to the fitness function, but may nonetheless confer significant advantages over rewards based only on fitness.
Message Understanding Conference- 6: A Brief History

We have recently completed the sixth in a series of "Message Understanding Conferences" which are designed to promote and evaluate research in information extraction. MUC-6 introduced several innovations over prior MUCs, most notably in the range of different tasks for which evaluations were conducted. We describe some of the motivations for the new format and briefly discuss some of the results of the evaluations. 1 The M U C Evaluations We have just completed the sixth in a series of Message Understanding Conferences, which have been organized by NRAD, the RDT&E division of the Naval Command, Control and Ocean Surveillance Center (formerly NOSC, the Naval Ocean Systems Center) with the support of DARPA, the Defense Advanced Research Projects Agency. This paper looks briefly at the history of these Conferences and then examines the considerations which led to the structure of MUC-6} The Message Understanding Conferences were initiated by NOSC to assess and to foster research on the automated analysis of mili tary messages containing textual information. Although called "conferences", the distinguishing characteristic of the MUCs are not the conferences themselves, but the evaluations to which participants must submit in order to be permit ted to at tend the conference. For each MUC, participating groups have been given sample messages and instructions on the type of information to be extracted, and have developed a system to process such messages. Then, shortly before the conference, participants are given a set of test messages to be run through their system (without making any changes to the system); the output of each part icipant 's system 1The full proceedings of the conference are to be distributed by Morgan Kaufmann Publishers, San Mateo, California; earlier MUC proeeedings~ for MUC-3, 4, and 5, are also available from Morgan Kaufmann. Beth Sundhe im Naval Command, Control and Ocean Surveillance Center Research, Development, Test and Evaluation Division (NRaD) Code 44208 53140 Gatchell Road San Diego, CMifornia 92152-7420 s u n d h e i m @ p o j k e . n o s c . m i l is then evaluated against a manual ly-prepared answer key. The MUCs are remarkable in part because of the degree to which these evaluations have defined a prograin of research and development. DARPA has a number of information science and technology programs which are driven in large part, by regular evaluations. The MUCs are notable, however, in that they in large par t have shaped the research program in information extraction and brought it to its current s ta te}
Automatic Bug Triage using Semi-Supervised Text Classification

In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectationmaximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the approach iteratively labels numerous unlabeled bug reports and trains a new classifier with labels of all the bug reports. We also employ a weighted recommendation list to boost the performance by imposing the weights of multiple developers in training the classifier. Experimental results on bug reports of Eclipse show that our new approach outperforms existing supervised approaches in terms of classification accuracy. Keywordsautomatic bug triage; expectation-maximization; semi-supervised text classification; weighted recommendation list
Parallel processing in retinal ganglion cells: how integration of space-time patterns of excitation and inhibition form the spiking output.

Our goal was to understand how patterns of excitation and inhibition, interacting across arrays of ganglion cells in space and time, generate the spiking output pattern for each ganglion cell type. We presented the retina with a 1-s flashed square, 600 microm on a side, and measured patterns of excitation and inhibition over an 1,800-microm-wide region encompassing many ganglion cells. Excitatory patterns of on ganglion cells resembled rectified versions of the voltage patterns of on bipolar cells. Inhibitory patterns in on ganglion cells resembled the rectified versions of voltage patterns of off bipolar cells. off ganglion cells received off excitation and on inhibition. Many ganglion cells also received an additional wide field transient inhibition derived from the activity of both on and off bipolar cells. Ganglion cell spiking was suppressed in those space-time regions dominated by inhibition. We classified each ganglion cell type by correlating its space-time patterns with its dendritic morphology. These studies suggest the bipolar and amacrine cell circuitry underlying the interplay between on and off signals that generate spiking patterns in ganglion cells. They reveal a surprising synergistic interaction between excitation and inhibition in most ganglion cells.
Clustering items for collaborative filtering

This short paper reports on work in progress related to applying data partitioning/clustering algorithms to ratings data in collaborative filtering. We use existing data partitioning and clustering algorithms to partition the set of items based on user rating data. Predictions are then computed independently within each partition. Ideally, partitioning will improve the quality of collaborative filtering predictions and increase the scalability of collaborative filtering systems. We report preliminary results that suggest that partitioning algorithms can greatly increase scalability, but we have mixed results on improving accuracy. However, partitioning based on ratings data does result in more accurate predictions than random partitioning, and the results are similar to those when the data is partitioned based on a known content classification.
Probabilistic Weighted Automata

Nondeterministic weighted automata are finite automata wit h numerical weights on transitions. They define quantitative languages L that assign to each word w a real numberL(w). The value of an infinite wordw is computed as the maximal value of all runs over w, and the value of a run as the maximum, limsup, liminf, limit av erage, or discounted sum of the transition weights. We introduce probabilistic weighted a utomata, in which the transitions are chosen in a randomized (rather than nondeterministic) fash ion. Under almost-sure semantics (resp. positive semantics), the value of a word w is the largest real v such that the runs over w have value at least v with probability 1 (resp. positive probability). We study the classical questions of automata theory for prob abilistic weighted automata: emptiness and universality, expressiveness, and closure under v arious operations on languages. For quantitative languages, emptiness and universality are de fined as whether the value of some (resp. every) word exceeds a given threshold. We prove some o f these questions to be decidable, and others undecidable. Regarding expressive power, we show that probabilities allow us to define a wide variety of new classes of quantitative langua ges, except for discounted-sum automata, where probabilistic choice is no more expressive than nondeterminism. Finally, we give an almost complete picture of the closure of various cla ses of probabilistic weighted automata for the following pointwise operations on quantitat ive languages: max, min, sum, and numerical complement.
Beyond Exchangeability: The Chinese Voting Process

Many online communities present user-contributed responses such as reviews of products and answers to questions. User-provided helpfulness votes can highlight the most useful responses, but voting is a social process that can gain momentum based on the popularity of responses and the polarity of existing votes. We propose the Chinese Voting Process (CVP) which models the evolution of helpfulness votes as a self-reinforcing process dependent on position and presentation biases. We evaluate this model on Amazon product reviews and more than 80 StackExchange forums, measuring the intrinsic quality of individual responses and behavioral coefficients of different communities.
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception

While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neuronal systems. Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks may be more than superficial. However, while CNNs capture key properties of the average responses of cortical neurons, they fail to explain other properties of these neurons. For one, CNNs typically require large quantities of labeled input data for training. Our own brains, in contrast, rarely have access to this kind of supervision, so to the extent that representations are similar between CNNs and brains, this similarity must arise via different training paths. In addition, neurons in visual cortex produce complex time-varying responses even to static inputs, and they dynamically tune themselves to temporal regularities in the visual environment. We argue that these differences are clues to fundamental differences between the computations performed in the brain and in deep networks. To begin to close the gap, here we study the emergent properties of a previouslydescribed recurrent generative network that is trained to predict future video frames in a self-supervised manner. Remarkably, the model is able to capture a wide variety of seemingly disparate phenomena observed in visual cortex, ranging from single unit response dynamics to complex perceptual motion illusions. These results suggest potentially deep connections between recurrent predictive neural network models and the brain, providing new leads that can enrich both fields.
Electric Flight – Potential and Limitations

During the last years, the development of electric propulsion systems is pushed strongly, most notably for automotive applications. For ground based transportation, the system mass is relevant, but of relatively low importance. This paper addresses the application of electric propulsion systems in aeronautics, where all systems are much more constrained by their mass. After comparing different propulsion system architectures the focus is cast on the energy storage problem and here especially on battery storage systems. Using first order principles the limitations and required technology developments are demonstrated by introducing battery powered electric propulsion systems to small and medium sized aircraft. Abbreviations and Symbols b = wing span [m] C = capacity [Wh] D = drag force [N] * E = mass specific energy content [Wh/kg] e f = empty mass fraction e m /m g = gravity acceleration [m/s] L = lift force [N] m = mass [kg] initial m = initial mass at takeoff [kg] e m = empty mass without fuel and payload [kg] final m = final mass at landing [kg] battery m = battery mass [kg] payload m = payload mass [kg] pax m = mass per passenger [kg] PAX = number of passengers P = power [W] battery P = electric power provided by battery [W] R = range [m] t = time [s] T = thrust [N] * V = volume specific energy content [Wh/m] v¥ = flight speed [m/s] h = efficiency ¥ r = density of air [kg/m ]
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA

Feature Extraction Approaches Based On Matrix Pattern: MatPCA and MatFLDA Songcan Chen 1* Yulian Zhu 1 Daoqiang Zhang 1 Jing-Yu Yang 2 Dept. of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, People’s Republic of China. Dept. of Computer Science, Nanjing University of Science & Technology, Nanjing 210094, People’s Republic of China Abstract Principle component analysis (PCA) and Fisher linear discriminant analysis (FLDA), as two popular feature extraction approaches in Pattern recognition and data analysis, extract so-needed features directly based on vector patterns, i.e., before applying them, any non-vector pattern such as an image is first vectorized into a vector pattern by some technique like concatenation. However, such a vectorization has been proved not to be beneficial for image recognition due to consequences of both the algebraic feature extraction approach and 2DPCA. In this paper, inspired by the above two approaches, we try an opposite direction to extract features for any vector pattern by first matrixizing it into a matrix pattern and then applying the matrixized versions of PCA and FLDA, MatPCA and MatFLDA, to the pattern. MatFLDA uses, in essence, the same principle as the algebraic feature extraction approach and is constructed in terms of similar objective function to FLDA while MatPCA uses a minimization of the reconstructed error for the training samples like PCA to obtain a set of projection vectors, which is somewhat different derivation from 2DPCA despite of equivalence. Finally experiments on 10 publicly obtainable datasets show that both MatPCA and MatFLDA gain performance improvement in different degrees respectively on 7 and 5
One-Class Kernel Spectral Regression for Outlier Detection

The paper introduces a new efficient nonlinear oneclass classifier formulated as the Rayleigh quotient criterion optimisation. The method, operating in a reproducing kernel Hilbert subspace, minimises the scatter of target distribution along an optimal projection direction while at the same time keeping projections of positive observations distant from the mean of the negative class. We provide a graph embedding view of the problem which can then be solved efficiently using the spectral regression approach. In this sense, unlike previous similar methods which often require costly eigen-computations of dense matrices, the proposed approach casts the problem under consideration into a regression framework which is computationally more efficient. In particular, it is shown that the dominant complexity of the proposed method is the complexity of computing the kernel matrix. Additional appealing characteristics of the proposed one-class classifier are: 1-the ability to be trained in an incremental fashion (allowing for application in streaming data scenarios while also reducing the computational complexity in a non-streaming operation mode); 2-being unsupervised, but providing the option for refining the solution using negative training examples, when available; Last but not least, 3-the use of the kernel trick which facilitates a nonlinear mapping of the data into a high-dimensional feature space to seek better solutions. Extensive experiments conducted on several datasets verify the merits of the proposed approach in comparison with other alternatives.
Thermoresponsive Melamine Sponges with Switchable Wettability by Interface-Initiated Atom Transfer Radical Polymerization for Oil/Water Separation.

Here we have obtained a temperature responsive melamine sponge with a controllable wettability between superhydrophilicity and superhydrophobicity by grafting the octadecyltrichlorosilane and thermoresponsive poly(N-isopropylacrylamide) (PNIPAAm) onto the surface of melamine sponge skeletons. The whole process included the silanization in which step the rough surface with low surface energy and the NH2 were provided, and the atom transfer radical polymerization which ensured the successful grafting of PNIPAAm onto the skeleton's surface. The product exhibits a good reversible switch between superhydrophilicity and superhydrophobicity by changing the temperature below or above the lower critical solution temperature (LCST, about 32 °C) of PNIPAAm, and the modified sponge still retains a good responsiveness after undergoing two temperature switches for 20 cycles. Simultaneously, the functionalized sponges could be used to absorb the oil under water at 37 °C, and they released the absorbed oil in various ways under water at 20 °C, showing wide potential applications including oil/water separation.
Forecasting Next-Day Electricity Prices by Time Series Models

In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real world case studies based on the electricity markets of mainland Spain and Califomia are presented.
Affordances in Grounded Language Learning

We present a novel methodology involving mappings between different modes of semantic representations. We propose distributional semantic models as a mechanism for representing the kind of world knowledge inherent in the system of abstract symbols characteristic of a sophisticated community of language users. Then, motivated by insight from ecological psychology, we describe a model approximating affordances, by which we mean a language learner’s direct perception of opportunities for action in an environment. We present a preliminary experiment involving mapping between these two representational modalities, and propose that our methodology can become the basis for a cognitively inspired model of grounded language learning.
A flow-guided streamline seeding strategy

The paper presents a seed placement strategy for streamlines based on flow features in the dataset. The primary goal of our seeding strategy is to capture flow patterns in the vicinity of critical points in the flow field, even as the density of streamlines is reduced. Secondary goals are to place streamlines such that there is sufficient coverage in non-critical regions, and to vary the streamline placements and lengths so that the overall presentation is aesthetically pleasing (avoid clustering of streamlines, avoid sharp discontinuities across several streamlines, etc.). The procedure is straightforward and non-iterative. First, critical points are identified. Next, the flow field is segmented into regions, each containing a single critical point. The critical point in each region is then seeded with a template depending on the type of critical point. Finally, additional seed points are randomly distributed around the field using a Poisson disk distribution to minimize closely spaced seed points. The main advantage of this approach is that it does not miss the features around critical points. Since the strategy is not image-guided, and hence not view dependent, significant savings are possible when examining flow fields from different viewpoints, especially for 3D flow fields.