nils-herrmann/clean_scirepeval huggingface dataset:
A Low-Cost 60-GHz Switched-Beam Patch Antenna Array With Butler Matrix Network

In this letter, a low-cost 60-GHz switched-beam patch antenna array with Butler matrix network is developed and experimentally demonstrated. In order to improve integration with the patch elements, a 4 times 4 planar Butler matrix is implemented in a low-dielectric substrate. The four rectangular patches fed by inset microstrip lines are connected to the outputs of the Butler matrix for the 60-GHz operation. Because of the fabrication tolerances, the operating frequency of the fabricated antenna shifts to 62 GHz. The radiation patterns measured at 62 GHz are in good agreement with the theoretical array factors. The antenna developed in this letter provides a cost-effective approach to implement an adaptive antenna for 60-GHz wireless communications.
The New Frontier of Smart Grids

The power grid is a massive interconnected network used to deliver electricity from suppliers to consumers and has been a vital energy supply. To minimize the impact of climate change while at the same time maintaining social prosperity, smart energy must be embraced to ensure a balanced economical growth and environmental sustainability. There fore, in the last few years, the new concept of a smart grid (SG) became a critical enabler in the contempo rary world and has attracted increas ing attention of policy makers and engineers. This article introduces the main concepts and technological challenges of SGs and presents the authors' views on some required challenges and opportunities pre sented to the IEEE Industrial Electronics Society (IES) in this new and exciting frontier.
A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback

Implicit feedback based recommendation has recently been an important task with the accumulated user-item interaction data. However, it is very challenging to produce recommendations from implicit feedback due to the sparseness of data and the lack of negative feedback/rating. Although various factor models have been proposed to tackle this problem, they either focus on rating prediction that may lead to inaccurate top-k recommendations or are dependent on the sampling of negative feedback that often results in bias. To this end, we propose a Relaxed Ranking-based Factor Model, RRFM, to relax pairwise ranking into a SVM-like task, where positive and negative feedbacks are separated by the soft boundaries, and their non-separate property is employed to capture the characteristic of unobserved data. A smooth and scalable algorithm is developed to solve groupand instancelevel’s optimization and parameter estimation. Extensive experiments based on real-world datasets demonstrate the effectiveness and advantage of our approach.
Ultrafast response humidity sensor using supramolecular nanofibre and its application in monitoring breath humidity and flow

Measuring humidity in dynamic situations calls for highly sensitive fast response sensors. Here we report, a humidity sensor fabricated using solution processed supramolecular nanofibres as active resistive sensing material. The nanofibres are built via self- assembly of donor and acceptor molecules (coronene tetracarboxylate and dodecyl methyl viologen respectively) involved in charge transfer interactions. The conductivity of the nanofibre varied sensitively over a wide range of relative humidity (RH) with unprecedented fast response and recovery times. Based on UV-vis, XRD and AFM measurements, it is found that the stacking distance in the nanofibre decreases slightly while the charge transfer band intensity increases, all observations implying enhanced charge transfer interaction and hence the conductivity. It is demonstrated to be as a novel breath sensor which can monitor the respiration rate. Using two humidity sensors, a breath flow sensor was made which could simultaneously measure RH and flow rate of exhaled nasal breath. The integrated device was used for monitoring RH in the exhaled breath from volunteers undergoing exercise and alcohol induced dehydration.
A Semi-automatic Method for Efficient Detection of Stories on Social Media

Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be lifesaving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-
Ultra wideband antennas: past and present

Since the release by the Federal Communications Commission (FCC) of a bandwidth of 7.5GHz (from 3.1GHz to 10.6GHz) for ultra wideband (UWB) wireless communications, UWB is rapidly advancing as a high data rate wireless communication technology. As is the case in conventional wireless communication systems, an antenna also plays a very crucial role in UWB systems. However, there are more challenges in designing a UWB antenna than a narrow band one. A suitable UWB antenna should be capable of operating over an ultra wide bandwidth as allocated by the FCC. At the same time, satisfactory radiation properties over the entire frequency range are also necessary. This paper focuses on UWB planar printed circuit board (PCB) antenna design and analysis. Studies have been undertaken covering the areas of UWB fundamentals and antenna theory. Extensive investigations were also carried out on the development of UWB antennas from the past to present. First, the planar PCB antenna designs for UWB system is introduced and described. Next, the special design considerations for UWB antennas are summarized. State-of-the-art UWB antennas are also reviewed. Finally, a new concept for the design of a UWB antenna with a bandwidth ranging from 2GHz-11.3GHz is introduced, which satisfies the system requirements for S-DMB, WiBro, WLAN, CMMB and the entire UWB . KeywordsUltra wideband, planar PCB Antenna and bandwidth.
PaDNet: Pan-Density Crowd Counting

Crowd counting in varying density scenes is a challenging problem in artificial intelligence (AI) and pattern recognition. Recently, deep convolutional neural networks (CNNs) are used to tackle this problem. However, the single-column CNN cannot achieve high accuracy and robustness in diverse density scenes. Meanwhile, multi-column CNNs lack effective way to accurately learn the features of different scales for estimating crowd density. To address these issues, we propose a novel pan-density level deep learning model, named as Pan-Density Network (PaDNet). Specifically, the PaDNet learns multi-scale features by three steps. First, several sub-networks are pre-trained on crowd images with different density-levels. Then, a Scale Reinforcement Net (SRN) is utilized to reinforce the scale features. Finally, a Fusion Net fuses all of the scale features to generate the final density map. Experiments on four crowd counting benchmark datasets, the ShanghaiTech, the UCF CC 50, the UCSD, and the UCF-QRNF, indicate that the PaDNet achieves the best performance and has high robustness in pan-density crowd counting compared with other state-of-the-art algorithms.
Bricolage: example-based retargeting for web design

The Web provides a corpus of design examples unparalleled in human history. However, leveraging existing designs to produce new pages is often difficult. This paper introduces the Bricolage algorithm for transferring design and content between Web pages. Bricolage employs a novel, structured-prediction technique that learns to create coherent mappings between pages by training on human-generated exemplars. The produced mappings are then used to automatically transfer the content from one page into the style and layout of another. We show that Bricolage can learn to accurately reproduce human page mappings, and that it provides a general, efficient, and automatic technique for retargeting content between a variety of real Web pages.
TransG : A Generative Model for Knowledge Graph Embedding

Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence. This paper proposes a novel generative model (TransG) to address the issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples. The new model can discover latent semantics for a relation and leverage a mixture of relationspecific component vectors to embed a fact triple. To the best of our knowledge, this is the first generative model for knowledge graph embedding, and at the first time, the issue of multiple relation semantics is formally discussed. Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines.
A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems

We consider programmable matter as a collection of simple computational elements (or particles) with limited (constant-size) memory that self-organize to solve system-wide problems of movement, configuration, and coordination. Here, we focus on the compression problem, in which the particle system gathers as tightly together as possible, as in a sphere or its equivalent in the presence of some underlying geometry. More specifically, we seek fully distributed, local, and asynchronous algorithms that lead the system to converge to a configuration with small perimeter. We present a Markov chain based algorithm that solves the compression problem under the geometric amoebot model, for particle systems that begin in a connected configuration with no holes. The algorithm takes as input a bias parameter λ, where λ > 1 corresponds to particles favoring inducing more lattice triangles within the particle system. We show that for all λ > 5, there is a constant α > 1 such that at stationarity with all but exponentially small probability the particles are <i>α-compressed</i>, meaning the perimeter of the system configuration is at most α ⋅ <i>p</i><sub><i>min</i></sub>, where <i>p</i><sub><i>min</i></sub> is the minimum possible perimeter of the particle system. We additionally prove that the same algorithm can be used for expansion for small values of λ in particular, for all <i>0</i> < λ < √2, there is a constant β < 1 such that at stationarity, with all but an exponentially small probability, the perimeter will be at least β ⋅ <i>p</i><sub><i>max</i></sub>, where <i>p</i><sub><i>max</i></sub> is the maximum possible perimeter.
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT

In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
2-Stripes Block-Circulant LDPC Codes for Single Bursts Correction

In this paper the low-density parity-check (LDPC) codes are considered applied to correction of error bursts. Errors grouping and forming of so-called bursts are typical effect in real communication and data storage systems, however, this effect is typically ignored, and the coding task is reduced to correction of independent errors, which makes the practical characteristics of coding systems worse comparing to possibly reachable. Nevertheless, LDPC codes are able to protect from burst errors as well as independent ones. The main result of the paper is dedicated to evaluation of maximum correctable burst length of Gilbert codes, which are the 2-stripes special case of LDPC block-permutation codes, the construction which is often used in modern practical applications and research.
Numerical Relation Extraction with Minimal Supervision

We study a novel task of numerical relation extraction with the goal of extracting relations where one of the arguments is a number or a quantity (e.g., atomic number(Aluminium, 13), inflation rate(India, 10.9%)). This task presents peculiar challenges not found in standard Information Extraction (IE), such as the difficulty of matching numbers in distant supervision and the importance of units. We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. We find that both systems dramatically outperform MultiR, a state-of-the-art non-numerical IE model, obtaining up to 25 points F-score
Identification and control of dynamic systems using recurrent fuzzy neural networks

This paper proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN). The RFNN expands the basic ability of the FNN to cope with temporal problems. In addition, results for the FNNfuzzy inference engine, universal approximation, and convergence analysis are extended to the RFNN. For the control problem, we present the direct and indirect adaptive control approaches using the RFNN. Based on the Lyapunov stability approach, rigorous proofs are presented to guarantee the convergence of the RFNN by choosing appropriate learning rates. Finally, the RFNN is applied in several simulations (time series prediction, identification, and control of nonlinear systems). The results confirm the effectiveness of the RFNN.
The Enigmatic temporal pole: a review of findings on social and emotional processing.

The function of the anterior-most portion of the temporal lobes, the temporal pole, is not well understood. Anatomists have long considered it part of an extended limbic system based on its location posterior to the orbital frontal cortex and lateral to the amygdala, along with its tight connectivity to limbic and paralimbic regions. Here we review the literature in both non-human primates and humans to assess the temporal pole's putative role in social and emotional processing. Reviewed findings indicate that it has some role in both social and emotional processes, including face recognition and theory of mind, that goes beyond semantic memory. We propose that the temporal pole binds complex, highly processed perceptual inputs to visceral emotional responses. Because perceptual inputs remain segregated into dorsal (auditory), medial (olfactory) and ventral (visual) streams, the integration of emotion with perception is channel specific.
Fish gut microbiota analysis differentiates physiology and behavior of invasive Asian carp and indigenous American fish

Gut microbiota of invasive Asian silver carp (SVCP) and indigenous planktivorous gizzard shad (GZSD) in Mississippi river basin were compared using 16S rRNA gene pyrosequencing. Analysis of more than 440 000 quality-filtered sequences obtained from the foregut and hindgut of GZSD and SVCP revealed high microbial diversity in these samples. GZSD hindgut (GZSD_H) samples (n=23) with >7000 operational taxonomy units (OTUs) exhibited the highest alpha-diversity indices followed by SVCP foregut (n=15), GZSD foregut (n=9) and SVCP hindgut (SVCP_H) (n=24). UniFrac distance-based non-metric multidimensional scaling (NMDS) analysis showed that the microbiota of GZSD_H and SVCP_H were clearly separated into two clusters: samples in the GZSD cluster were observed to vary by sampling location and samples in the SVCP cluster by sampling date. NMDS further revealed distinct microbial community between foregut to hindgut for individual GZSD and SVCP. Cyanobacteria, Proteobacteria, Actinobacteria and Bacteroidetes were detected as the predominant phyla regardless of fish or gut type. The high abundance of Cyanobacteria observed was possibly supported by their role as the fish’s major food source. Furthermore, unique and shared OTUs and OTUs in each gut type were identified, three OTUs from the order Bacteroidales, the genus Bacillariophyta and the genus Clostridium were found significantly more abundant in GZSD_H (14.9–22.8%) than in SVCP_H (0.13–4.1%) samples. These differences were presumably caused by the differences in the type of food sources including bacteria ingested, the gut morphology and digestion, and the physiological behavior between GZSD and SVCP.
Vector space model adaptation and pseudo relevance feedback for content-based image retrieval

Image retrieval is an important problem for researchers in computer vision and content-based image retrieval (CBIR) fields. Over the last decades, many image retrieval systems were based on image representation as a set of extracted low-level features such as color, texture and shape. Then, systems calculate similarity metrics between features in order to find similar images to a query image. The disadvantage of this approach is that images visually and semantically different may be similar in the low level feature space. So, it is necessary to develop tools to optimize retrieval of information. Integration of vector space models is one solution to improve the performance of image retrieval. In this paper, we present an efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Wang, Oxford5k and Inria Holidays datasets to show the performance of our proposed framework.
Fast Keypoint Recognition in Ten Lines of Code

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well to handle large number of classes. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.
ZigBee based parameters monitoring system for induction motor

In this paper, a wireless monitoring system for three phase induction motor is realized using ZigBee protocol, where wired communication is either more expensive or impossible due to physical conditions and human hazards for safe and economic data communication in industrial fields. A low cost system for measuring the parameters of induction motor such as phase voltages, phase currents, active power, reactive power, motor temperature with ZigBee protocol connectivity is described in this paper. Moreover, a database is built to execute online monitoring and to save the motor parameters received by radio frequency (RF) data acquisition system. The aim of this paper is to monitor and acquire the remote electrical parameters like Voltages, Currents, Powers, Temperature, and send these real values over wireless network. To implement this, a ZigBee model is connected to a programmed digital signal controller which would transmit the data to ZigBee coordinator which is connected to a PC through RS232 serial communication. Experimental results show that the proposed system is less costly, provides higher accuracy as well as safe and gives visual environment.
New Concepts and Perspectives on Micro-Rotorcraft and Small Autonomous Rotary-Wing Vehicles

This paper summarizes ongoing work concerning micro-rotorcraft (MRC) – i.e., rotary-wing micro air vehicles (MAV) – research and development. Technology trends involving microelectronic miniaturization, vehicle autonomy systems, electric propulsion and power electronics are contributing to an ongoing revolution in MAV and MRC aerial vehicle concepts and applications. New vehicle configurations are being developed, as well as old concepts being reassessed, for MAV and MRC vehicles.
Prediction of Rainfall Time Series Using Modular Artificial Neural Networks Coupled with Data Preprocessing Techniques

1 Dept. of Civil and Structural Engineering, Hong Kong Polytechnic University, 4 Hung Hom, Kowloon, Hong Kong, People’s Republic of China 5 6 2 Changjiang Institute of Survey, Planning, Design and Research, 7 Changjiang Water Resources Commission, 8 430010, Wuhan, HuBei, People’s Republic of China 9 10 3 Department of Civil Engineering, Ryerson University, 11 350 Victoria Street, Toronto, Ontario, Canada, M5B 2K3 12 13 *Email: cekwchau@polyu.edu.hk 14 15
Multi-threshold CMOS design for low power digital circuits

Multi-threshold CMOS (MTCMOS) power gating is a design technique in which a power gating transistor is connected between the logic transistors and either power or ground, thus creating a virtual supply rail or virtual ground rail, respectively. Power gating transistor sizing, transition (sleep mode to active mode) current, short circuit current and transition time are design issues for power gating design. The use of power gating design results in the delay overhead in the active mode. If both nMOS and pMOS sleep transistor are used in power gating, delay overhead will increase. This paper proposes the design methodology for reducing the delay of the logic circuits during active mode. This methodology limits the maximum value of transition current to a specified value and eliminates short circuit current. Experiment results show 16.83% reduction in the delay.
Immersion, presence and performance in virtual environments: an experiment with tri-dimensional chess

This paper describes an experiment to assess the influence of immersion on performance in immersive virtual environments. The task involved Tri-Dimensional Chess, and required subjects to reproduce on a real chess board the state of the board learned from a sequence of moves witnessed in a virtual environment. Twenty four subjects were allocated to a factorial design consisting of two levels of immersion (exocentric screen based, and egocentric HMD based), and two kinds of environment (plain and realistic. The results suggest that egocentric subjects performed better than exocentric, and those in the more realistic environment performed better than those in the less realistic environment. Previous knowledge of chess, and amount of virtual practice were also significant, and may be considered as control variables to equalise these factors amongst the subjects. Other things being equal, males remembered the moves better than females, although female performance improved with higher spatial ability test score. The paper also attempts to clarify the relationship between immersion, presence and performance, and locates the experiment within such a theoretical framework.