nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating the Nonuniform Fast Fourier Transform

The nonequispaced Fourier transform arises in a variety of application areas, from medical imaging to radio astronomy to the numerical solution of partial differential equations. In a typical problem, one is given an irregular sampling of N data in the frequency domain and one is interested in reconstructing the corresponding function in the physical domain. When the sampling is uniform, the fast Fourier transform (FFT) allows this calculation to be computed in O(N logN) operations rather than O(N2) operations. Unfortunately, when the sampling is nonuniform, the FFT does not apply. Over the last few years, a number of algorithms have been developed to overcome this limitation and are often referred to as nonuniform FFTs (NUFFTs). These rely on a mixture of interpolation and the judicious use of the FFT on an oversampled grid [A. Dutt and V. Rokhlin,  (1993), pp. 1368–1383]. In this paper, we observe that one of the standard interpolation or “gridding” schemes, based on Gaussians, can be accelerated by a significant factor without precomputation and storage of the interpolation weights. This is of particular value in twoand threedimensional settings, saving either 10dN in storage in d dimensions or a factor of about 5–10 in CPU time (independent of dimension).
Analysis and Practical Considerations in Implementing Multiple Transmitters for Wireless Power Transfer via Coupled Magnetic Resonance

Multiple transmitters can be used to simultaneously transmit power wirelessly to a single receiver via strongly coupled magnetic resonance. A simple circuit model is used to help explain the multiple-transmitter wireless power transfer system. Through this particular scheme, there is an increase in gain and “diversity” of the transmitted power according to the number of transmit coils. The effect of transmitter resonant coil coupling is also shown. Resonant frequency detuning due to nearby metallic objects is observed, and the extent of how much tuning can be done is demonstrated. A practical power line synchronization technique is proposed to synchronize all transmit coils, which reduces additional dedicated synchronization wiring or the addition of an RF front-end module to send the reference driving signal.
AutoConnect: computational design of 3D-printable connectors

We present AutoConnect, an automatic method that creates customized, 3D-printable connectors attaching two physical objects together. Users simply position and orient virtual models of the two objects that they want to connect and indicate some auxiliary information such as weight and dimensions. Then, AutoConnect creates several alternative designs that users can choose from for 3D printing. The design of the connector is created by combining two holders, one for each object. We categorize the holders into two types. The first type holds standard objects such as pipes and planes. We utilize a database of parameterized mechanical holders and optimize the holder shape based on the grip strength and material consumption. The second type holds free-form objects. These are procedurally generated shell-gripper designs created based on geometric analysis of the object. We illustrate the use of our method by demonstrating many examples of connectors and practical use cases.
Estimating Human Movement Parameters Using a Software Radio-based Radar

Radar is an attractive technology for long term monitoring of human movement as it operates remotely, can be placed behind walls and is able to monitor a large area depending on its operating parameters. A radar signal reflected off a moving person carries rich information on his or her activity pattern in the form of a set of Doppler frequency signatures produced by the specific combination of limbs and torso movements. To enable classification and efficient storage and transmission of movement data, unique parameters have to be extracted from the Doppler signatures. Two of the most important human movement parameters for activity identification and classification are the velocity profile and the fundamental cadence frequency of the movement pattern. However, the complicated pattern of limbs and torso movement worsened by multipath propagation in indoor environment poses a challenge for the extraction of these human movement parameters. In this paper, three new approaches for the estimation of human walking velocity profile in indoor environment are proposed and discussed. The first two methods are based on spectrogram estimates whereas the third method is based on phase difference computation. In addition, a method to estimate the fundamental cadence frequency of the gait is suggested and discussed. The accuracy of the methods are evaluated and compared in an indoor experiment using a flexible and low-cost software defined radar platform. The results obtained indicate that the velocity estimation methods are able to estimate the velocity profile of the person’s translational motion with an error of less than 10%. The results also showed that the fundamental cadence is estimated with an error of 7%.
Low-Rank Common Subspace for Multi-view Learning

Multi-view data is very popular in real-world applications, as different view-points and various types of sensors help to better represent data when fused across views or modalities. Samples from different views of the same class are less similar than those with the same view but different class. We consider a more general case that prior view information of testing data is inaccessible in multi-view learning. Traditional multi-view learning algorithms were designed to obtain multiple view-specific linear projections and would fail without this prior information available. That was because they assumed the probe and gallery views were known in advance, so the correct view-specific projections were to be applied in order to better learn low-dimensional features. To address this, we propose a Low-Rank Common Subspace (LRCS) for multi-view data analysis, which seeks a common low-rank linear projection to mitigate the semantic gap among different views. The low-rank common projection is able to capture compatible intrinsic information across different views and also well-align the within-class samples from different views. Furthermore, with a low-rank constraint on the view-specific projected data and that transformed by the common subspace, the within-class samples from multiple views would concentrate together. Different from the traditional supervised multi-view algorithms, our LRCS works in a weakly supervised way, where only the view information gets observed. Such a common projection can make our model more flexible when dealing with the problem of lacking prior view information of testing data. Two scenarios of experiments, robust subspace learning and transfer learning, are conducted to evaluate our algorithm. Experimental results on several multi-view datasets reveal that our proposed method outperforms state-of-the-art, even when compared with some supervised learning methods.
LMF-Based Control Algorithm for Single Stage Three-Phase Grid Integrated Solar PV System

This paper proposes the use of a least mean fourth (LMF)-based algorithm for single-stage three-phase grid-integrated solar photovoltaic (SPV) system. It consists of an SPV array, voltage source converter (VSC), three-phase grid, and linear/nonlinear loads. This system has an SPV array coupled with a VSC to provide three-phase active power and also acts as a static compensator for the reactive power compensation. It also conforms to an IEEE-519 standard on harmonics by improving the quality of power in the three-phase distribution network. Therefore, this system serves to provide harmonics alleviation, load balancing, power factor correction and regulating the terminal voltage at the point of common coupling. In order to increase the efficiency and maximum power to be extracted from the SPV array at varying environmental conditions, a single-stage system is used along with perturb and observe method of maximum power point tracking (MPPT) integrated with the LMF-based control technique. The proposed system is modeled and simulated using MATLAB/Simulink with available simpower system toolbox and the behaviour of the system under different loads and environmental conditions are verified experimentally on a developed system in the laboratory.
Indoor Positioning and Navigation with Camera Phones

This low-cost indoor navigation system runs on off-the-shelf camera phones. More than 2,000 users at four different large-scale events have already used it. The system uses built-in cameras to determine user location in real time by detecting unobtrusive fiduciary markers. The required infrastructure is limited to paper markers and static digital maps, and common devices are used, facilitating quick deployment in new environments. The authors have studied the application quantitatively in a controlled environment and qualitatively during deployment at four large international events. According to test users, marker-based navigation is easier to use than conventional mobile digital maps. Moreover, the users' location awareness in navigation tasks improved. Experiences drawn from questionnaires, usage log data, and user interviews further highlight the benefits of this approach.
Selective removal of impulse noise based on homogeneity level information

In this paper, we propose a decision-based, signal-adaptive median filtering algorithm for removal of impulse noise. Our algorithm achieves accurate noise detection and high SNR measures without smearing the fine details and edges in the image. The notion of homogeneity level is defined for pixel values based on their global and local statistical properties. The cooccurrence matrix technique is used to represent the correlations between a pixel and its neighbors, and to derive the upper and lower bound of the homogeneity level. Noise detection is performed at two stages: noise candidates are first selected using the homogeneity level, and then a refining process follows to eliminate false detections. The noise detection scheme does not use a quantitative decision measure, but uses qualitative structural information, and it is not subject to burdensome computations for optimization of the threshold values. Empirical results indicate that our scheme performs significantly better than other median filters, in terms of noise suppression and detail preservation.
Introduction to the Dirichlet Distribution and Related Processes

This tutorial covers the Dirichlet distribution, Dirichlet process, Pólya urn (and the associated Chinese restaurant process), hierarchical Dirichlet Process, and the Indian buffet process. Apart from basic properties, we describe and contrast three methods of generating samples: stick-breaking, the Pólya urn, and drawing gamma random variables. For the Dirichlet process we first present an informal introduction, and then a rigorous description for those more comfortable with probability theory.
Experimental performance evaluation of LoRaWAN: A case study in Bangkok

The Internet of Things (IoT) is a vision which real-world objects are part of the internet. Every object is uniquely identified, and accessible to the network. There are various types of communication protocol for connect the device to the Internet. One of them is a Low Power Wide Area Network (LPWAN) which is a novel technology use to implement IoT applications. There are many platforms of LPWAN such as NB-IoT, LoRaWAN. In this paper, the experimental performance evaluation of LoRaWAN over a real environment in Bangkok, Thailand is presented. From these experimental results, the communication ranges in both an outdoor and an indoor environment are limited. Hence, the IoT application with LoRaWAN technology can be reliable in limited of communication ranges.
Attribute Learning in Large-Scale Datasets

We consider the task of learning visual connections between object categories using the ImageNet dataset, which is a large-scale dataset ontology containing more than 15 thousand object classes. We want to discover visual relationships between the classes that are currently missing (such as similar colors or shapes or textures). In this work we learn 20 visual attributes and use them both in a zero-shot transfer learning experiment as well as to make visual connections between semantically unrelated object categories.
A Novel Inverter-Output Passive Filter for Reducing Both Differential- and Common-Mode $dv/dt$ at the Motor Terminals in PWM Drive Systems

A novel inverter-output filter, which cannot only filter the differential-mode voltage dv/dt but also suppress the common-mode voltage dv/dt and their rms values, is proposed in this paper. The filter is in combination with a conventional RLC filter and a common-mode transformer. The main advantage is that the functions of filtering a differential-mode voltage and suppressing a common-mode voltage can be integrated into a single system. Furthermore, the structure and design of the proposed filter are rather simple because only passive components are used. Simulations and experiments are conducted to validate the performance of the proposed filter. Both of their results indicate that about 80% of the rms value of the common-mode voltage are suppressed, while the demand of differential-mode voltage filtering is still met
Scalable Graph Hashing with Feature Transformation

Hashing has been widely used for approximate nearest neighbor (ANN) search in big data applications because of its low storage cost and fast retrieval speed. The goal of hashing is to map the data points from the original space into a binary-code space where the similarity (neighborhood structure) in the original space is preserved. By directly exploiting the similarity to guide the hashing code learning procedure, graph hashing has attracted much attention. However, most existing graph hashing methods cannot achieve satisfactory performance in real applications due to the high complexity for graph modeling. In this paper, we propose a novel method, called scalable graph hashing with feature transformation (SGH), for large-scale graph hashing. Through feature transformation, we can effectively approximate the whole graph without explicitly computing the similarity graph matrix, based on which a sequential learning method is proposed to learn the hash functions in a bit-wise manner. Experiments on two datasets with one million data points show that our SGH method can outperform the state-of-the-art methods in terms of both accuracy and scalability.
Optimal Investment , Growth Options , and Security Returns

As a consequence of optimal investment choices, a firm’s assets and growth options change in predictable ways. Using a dynamic model, we show that this imparts predictability to changes in a firm’s systematic risk, and its expected return. Simulations show that the model simultaneously reproduces: ~i! the time-series relation between the book-to-market ratio and asset returns; ~ii! the cross-sectional relation between book-to-market, market value, and return; ~iii! contrarian effects at short horizons; ~iv! momentum effects at longer horizons; and ~v! the inverse relation between interest rates and the market risk premium. RECENT EMPIRICAL RESEARCH IN FINANCE has focused on regularities in the cross section of expected returns that appear anomalous relative to traditional models. Stock returns are related to book-to-market, and market value.1 Past returns have also been shown to predict relative performance, through the documented success of contrarian and momentum  Existing explanations for these results are that they are due to behavioral biases or risk premia for omitted state variables.3 These competing explanations are difficult to evaluate without models that explicitly tie the characteristics of interest to risks and risk premia. For example, with respect to book-to-market, Lakonishok et al. ~1994! argue: “The point here is simple: although the returns to the B0M strategy are impressive, B0M is not a ‘clean’ variable uniquely associated with eco* Berk is at the University of California, Berkeley, and NBER; Green is at Carnegie Mellon University; and Naik is with the University of British Columbia. We acknowledge the research assistance of Robert Mitchell and Dave Peterson. We have benefited from and are grateful for comments by seminar participants at Berkeley, British Columbia, Carnegie Mellon, Dartmouth, Duke, Michigan, Minnesota, North Carolina, Northwestern, Rochester, Utah, Washington at St. Louis, Washington, Wharton, Wisconsin, Yale, the 1996 meetings of the Western Finance Association, and the 1997 Utah Winter Finance Conference and the suggestions from an anonymous referee and from the editor, René Stulz. We also acknowledge financial support for this research from the Social Sciences and Humanities Research Council of Canada and the Bureau of Asset Management at University of British Columbia. The computer programs used in this paper are available on this journal’s web page: http:00www.afajof.org 1 See Fama and French ~1992! for summary evidence. 2 See Conrad and Kaul ~1998! for a recent summary of evidence on this subject. 3 See Lakonishok, Shleifer, and Vishny ~1994! for arguments in favor of behavioral biases and Fama and French ~1993! for an interpretation in terms of state variable risks.
RCNF: Real-time Collaborative Network Forensic Scheme for Evidence Analysis

Network forensic techniques help in tracking different types of cyber attack by monitoring and inspecting network traffic. However, with the high speed and large sizes of current networks, and the sophisticated philosophy of attackers, in particular mimicking normal behaviour and/or erasing traces to avoid detection, investigating such crimes demands intelligent network forensic techniques. This paper suggests a real-time collaborative network Forensic scheme (RCNF) that can monitor and investigate cyber intrusions. The scheme includes three components of capturing and storing network data, selecting important network features using chi-square method and investigating abnormal events using a new technique called correntropy-variation. We provide a case study using the UNSW-NB15 dataset for evaluating the scheme, showing its high performance in terms of accuracy and false alarm rate compared with three recent state-of-the-art mechanisms.
Applications of Causally Defined Direct and Indirect Effects in Mediation Analysis using SEM in Mplus

This paper summarizes some of the literature on causal effects in mediation analysis. It presents causally-defined direct and indirect effects for continuous, binary, ordinal, nominal, and count variables. The expansion to non-continuous mediators and outcomes offers a broader array of causal mediation analyses than previously considered in structural equation modeling practice. A new result is the ability to handle mediation by a nominal variable. Examples with a binary outcome and a binary, ordinal or nominal mediator are given using Mplus to compute the effects. The causal effects require strong assumptions even in randomized designs, especially sequential ignorability, which is presumably often violated to some extent due to mediator-outcome confounding. To study the effects of violating this assumption, it is shown how a sensitivity analysis can be carried out. This can be used both in planning a new study and in evaluating the results of an existing study.
Fingerprint Indexing Based on Minutia Cylinder-Code

This paper proposes a new hash-based indexing method to speed up fingerprint identification in large databases. A Locality-Sensitive Hashing (LSH) scheme has been designed relying on Minutiae Cylinder-Code (MCC), which proved to be very effective in mapping a minutiae-based representation (position/angle only) into a set of fixed-length transformation-invariant binary vectors. A novel search algorithm has been designed thanks to the derivation of a numerical approximation for the similarity between MCC vectors. Extensive experimentations have been carried out to compare the proposed approach against 15 existing methods over all the benchmarks typically used for fingerprint indexing. In spite of the smaller set of features used (top performing methods usually combine more features), the new approach outperforms existing ones in almost all of the cases.
Real time facial expression recognition in video using support vector machines

Enabling computer systems to recognize facial expressions and infer emotions from them in real time presents a challenging research topic. In this paper, we present a real time approach to emotion recognition through facial expression in live video. We employ an automatic facial feature tracker to perform face localization and feature extraction. The facial feature displacements in the video stream are used as input to a Support Vector Machine classifier. We evaluate our method in terms of recognition accuracy for a variety of interaction and classification scenarios. Our person-dependent and person-independent experiments demonstrate the effectiveness of a support vector machine and feature tracking approach to fully automatic, unobtrusive expression recognition in live video. We conclude by discussing the relevance of our work to affective and intelligent man-machine interfaces and exploring further improvements.
A bandgap voltage reference design for high power supply

Based on the CSMC 0.6um 40V BCD process and the bandgap principle a reference circuit used in high voltage chip is designed. The simulation results show that a temperature coefficient of 26.5ppm/°C in the range of 3.5∼40V supply, the output voltage is insensitive to the power supply, when the supply voltage rages from 3.5∼40V, the output voltage is equal to 1.2558V to 1.2573V at room temperature. The circuit we designed has high precision and stability, thus it can be used as stability reference voltage in power management IC.
Leader Election Using NewSQL Database Systems

Leader election protocols are a fundamental building block for replicated distributed services. They ease the design of leader-based coordination protocols that tolerate failures. In partially synchronous systems, designing a leader election algorithm, that does not permit multiple leaders while the system is unstable, is a complex task. As a result many production systems use third-party distributed coordination services, such as ZooKeeper and Chubby, to provide a reliable leader election service. However, adding a third-party service such as ZooKeeper to a distributed system incurs additional operational costs and complexity. ZooKeeper instances must be kept running on at least three machines to ensure its high availability. In this paper, we present a novel leader election protocol using NewSQL databases for partially synchronous systems, that ensures at most one leader at any given time. The leader election protocol uses the database as distributed shared memory. Our work enables distributed systems that already use NewSQL databases to save the operational overhead of managing an additional third-party service for leader election. Our main contribution is the design, implementation and validation of a practical leader election algorithm, based on NewSQL databases, that has performance comparable to a leader election implementation using a state-of-the-art distributed coordination service, ZooKeeper.
One-point calibration gaze tracking based on eyeball kinematics using stereo cameras

This paper presents a one-point calibration gaze tracking method based on eyeball kinematics using stereo cameras. By using two cameras and two light sources, the optic axis of the eye can be estimated. One-point calibration is required to estimate the angle of the visual axis from the optic axis. The eyeball rotates with optic and visual axes based on the eyeball kinematics (Listing's law). Therefore, we introduced eyeball kinematics to the one-point calibration process in order to properly estimate the visual axis. The prototype system was developed and it was found that the accuracy was under 1° around the center and bottom of the display.