Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlocal robust tensor recovery with nonconvex regularization

The robust tensor recovery problem consists in reconstructing a tensor from a sample of entries corrupted by noise, which has attracted great interest in a wide range of practical situations such as image processing and computer vision. In this paper, we study robust tensor recovery for third-order tensors with different degradations, which aims to recover a tensor from partial observations corrupted by Gaussian noise and sparse noise simultaneously. In contrast to traditional approaches based on the tensor nuclear norm penalty for the low-rank component and the tensor ℓ 1 norm penalty for the sparse component, we propose a nonlocal robust low-rank tensor recovery model with nonconvex regularization (NRTRM) to explore the global low-rankness and nonlocal self-similarity of the underlying tensor. The NRTRM method is first to extract similar patched-tubes to form a third-order sub-tensor. Then a class of nonconvex low-rank penalties and nonconvex sparse penalties are employed to explore the low-rank component and the sparse corruptions for such sub-tensor, respectively. Moreover, a proximal alternating linearized minimization algorithm is developed to solve the resulting model in each group and its convergence is established under very mild conditions. Extensive numerical experiments on both multispectral images and video datasets demonstrate the superior performance of NRTRM in comparison with several state-of-the-art methods.
Bilevel Generalized Least Squares Estimation of Dynamic Origin–Destination Matrix for Urban Network with Probe Vehicle Data

Methods of estimating dynamic origin–destination (O-D) matrices for urban networks from probe vehicle data are explored. A speed–density function is derived and fitted for different types of roads with the use of the maximum likelihood method. Both a Bayesian method that carefully incorporates prior information and an ordinary method are used to estimate link flows from probe vehicle speed. A dynamic traffic assignment–based bilevel generalized least squares (GLS) estimator considering the distance between the estimated and target O-D matrices as well as the distance between the calculated and observed link flows is formulated to estimate dynamic O-D matrices from estimated link flows. In the iterative solution procedure, the upper level is solved with the extended Bell algorithm, and the microscopic dynamic traffic assignment system VISSIM is applied to produce the assignment matrix in the lower level. A medium-sized signalized network in Tokyo is modeled in a case study in which Bayesian and ordinary methods are compared both in link flow estimation and O-D matrix estimation. Further, the bilevel GLS estimator and bilevel ordinary least squares estimator are implemented and then compared in O-D estimation. The results validate the proposed bilevel GLS estimator.
Positivity in electron-positron scattering: testing the axiomatic quantum field theory principles and probing the existence of UV states

We consider the positivity bounds on dimension-8 four-electron operators and study two related phenomenological aspects at future lepton colliders. First, if positivity is violated, probing such violations will revolutionize our understanding of the fundamental pillars of quantum field theory and the S-matrix theory. We observe that positivity violation at scales of 1-10 TeV can potentially be probed at future lepton colliders even if one assumes that dimension-6 operators are also present. Second, the positive nature of the dimension-8 parameter space often allows us to either directly infer the existence of UV-scale particles together with their quantum numbers or exclude them up to certain scales in a model-independent way. In particular, dimension-8 positivity plays an important role in the test of the Standard Model. If no deviations from the Standard Model are observed, it allows for simultaneous exclusion limits on all kinds of potential UV-complete models. Unlike the dimension-6 case, these limits apply regardless of the UV model setup and cannot be removed by possible cancellations among various UV contributions. This thus consists of a novel and universal test to confirm the Standard Model. We demonstrate with realistic examples how all the previously mentioned possibilities, including the test of positivity violation, can be achieved. Hence, we provide an important motivation for studying dimension-8 operators more comprehensively.
Matrix formulation of electromagnetic scattering

A new method is proposed for the computation of the radar cross section and other associated field quantities arising when a smooth, perfectly conducting obstacle is illuminated by an incident electromagnetic wave. The scattered wave is first represented by a distribution of electric dipoles over the surface in question, with the response from any dipole proportional to the induced surface current density at that point. The surface current is then determined by the "boundary condition" that the scattered wave, through interference, precisely cancels the incident wave inside the obstacle. One obtains in this mariner a pair of coupled (infinite) matrix equations for the surface current. Green's identity permits decoupling of the equations, reducing the problem to roughly the equivalent of two independent scalar problems. The equations have been specialized to axially symmetric obstacles and then solved numerically on the IBM 7094 for several examples of interest. Reciprocity and energy conservation are also examined and the resonant mode (interior) problem set up explicitly in matrix form.
Kindness By Post: A Mixed-Methods Evaluation of A Participatory Public Mental Health Project

Background: Random acts of kindness can improve wellbeing. However, less is known about the impacts of giving and receiving acts of kindness with strangers on wellbeing and loneliness. Therefore, the study objectives were to evaluate a participatory public mental health project involving sending and receiving a card with goodwill messages, to understand how such acts of kindness influence wellbeing and loneliness, and to investigate the potential mechanisms underlying the impacts of the project. Materials and methods: This study was a secondary analysis of anonymised service evaluation data collected in the Kindness by Post (KBP) project in 2020. It used a mixed-methods single-group design and data from 289 participants. Changes in wellbeing, loneliness, sense of belonging and hope from baseline to follow-up were analysed using linear or multinomial logistic regression. Regression models also examined the associations between changes in wellbeing and baseline loneliness or participation level. Free text responses about experiences and suggestions for the project were analysed using thematic analysis. Results: Participants had a small, but statistically significant improvement, in wellbeing equating to 0.21 standard deviations (SD) (95% CI: 0.12 to 0.30) after taking part in the project, as well as improvements in loneliness, sense of belonging and hope. How lonely a participant was at baseline and whether participants both sent and received a kindness card were not associated with improvements in wellbeing. In the qualitative analysis, a desire to help others emerged as the main motivator to take part in the card exchange. Participants reported enhanced personal fulfilment, leading to improvements in wellbeing. Receiving a card could make people feel special and cherished, which was reported to establish a sense of connection with others, with potential benefits for reducing loneliness. Conclusions: This study provided preliminary evidence that the KBP project might improve wellbeing, loneliness, sense of belonging and hope. Sending a kindness card in this project played a predominant role in wellbeing enhancement, and receiving a kindness card could reduce loneliness. This study suggests that the KBP project can be replicated in more contexts in the future, and might improve wellbeing and loneliness in large communities.
Planning Cooperation in Inter-Organizational Systems

The purpose of this chapter is to present a systematic reasoning framework (called e-Planning) to plan cooperation between organizations in a network. Thorough assessment of opportunities for and obstacles to cooperation is of paramount importance, as setting up electronic networks usually requires considerable up-front investments in information technology (IT) specific for this cooperation. E-Planning offers an action plan for decision makers to determine with whom to establish cooperation first, and on which topic to cooperate more closely. Following a method for critical problem solving, e-Planning provides guidance to analyze different potential partners and to reason about obstacles to and opportunities for cooperation. To illustrate and validate the framework, it is applied in the area of cooperation between customs organizations of member states of the European Union. Applying the reasoning framework in practice revealed several potential benefits, such as fast and successful assessment of cooperation needs that result in increased re-use of knowledge and software applications. In particular, this way of reasoning may prove to help decision makers cut down unnecessary expenses by, for instance, avoiding duplicated projects.
StarcNet: Machine Learning for Star Cluster Identification

We present a machine learning (ML) pipeline to identify star clusters in the multicolor images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic Ultraviolet Survey). StarcNet (STAR Cluster classification NETwork) is a multiscale convolutional neural network (CNN) that achieves an accuracy of 68.6% (four classes)/86.0% (two classes: cluster/noncluster) for star cluster classification in the images of the LEGUS galaxies, nearly matching human expert performance. We test the performance of StarcNet by applying a pre-trained CNN model to galaxies not included in the training set, finding accuracies similar to the reference one. We test the effect of StarcNet predictions on the inferred cluster properties by comparing multicolor luminosity functions and mass–age plots from catalogs produced by StarcNet and by human labeling; distributions in luminosity, color, and physical characteristics of star clusters are similar for the human and ML classified samples. There are two advantages to the ML approach: (1) reproducibility of the classifications: the ML algorithm’s biases are fixed and can be measured for subsequent analysis; and (2) speed of classification: the algorithm requires minutes for tasks that humans require weeks to months to perform. By achieving comparable accuracy to human classifiers, StarcNet will enable extending classifications to a larger number of candidate samples than currently available, thus increasing significantly the statistics for cluster studies.
Input power factor control of three‐phase to three‐phase matrix converters

This paper presents input power factor control of three‐phase to three‐phase matrix converters. The authors propose three kinds of switching patterns for the matrix converters that can realize an arbitrary input‐power‐factor angle between 0 and 2π and reduce the number of commutations in all three phases to four during the control period. Also, the control range of the output voltage reference in the proposed control scheme is derived. The improvement of the input power factor by using the proposed control algorithm has been verified by experiments. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 178(3): 42–52, 2012; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21199
ACUTE EFFECT OF QUADRICEPS STRETCHING ON PERFORMANCE AND MOVEMENT TECHNIQUE DURING SQUAT JUMPS

The aim of this study was to analyze the acute effects of stretching on jumping performance (vertical velocity of the center of mass), vertical ground reaction force (peak force, rate of force development and impulse) and movement technique (joint angles and angular velocities) during the squat jump (SJ). Thirty male individuals performed the SJ test before and after one quadriceps stretching session. This session consisted of four 20-second repetitions of passive static stretching of quadriceps. The results indicated no significant difference in the performance, vertical ground reaction force or movement technique.
Modeling and Characterization of Microstrip-to-Coaxial Transitions

A simple circuit model for the transition from a Iossy microstrip to coaxial line has been developed on an experimental basis. The proposed model can be used to predict accurately the insertion loss and insertion phase over a wide frequency range. Since explicit formulas for the model element values are given, these elements, representing the parasitic of the transitions, can be taken into account very easify when the microstrip is used as a test fixture for measuring the parameters of solid-state devices. The practical use of the model has been examined for several Z/sub 0/=50-omega lines on both Epsilam-10 and 99-percent alumina substrates with standard SMA coaxial connectors.
Photonic Network Coding and Partial Protection for Optical Core Networks: Two for a Tango

The digital transformation is creating basically a digital version of our physical world and the currency in that digital space is data. Massive amount of data has been generated ranging from wearable devices monitoring our physical health every single millisecond to autonomous vehicles generating roughly 5Tb hourly to even astronomical activities producing an order of Exabytes on daily basis and then ultra-broadband Internet comes into play, moving such data to the cloud. Internet traffic therefore has been experiencing explosive growth and in this context, optical transport networks forming the backbone of the Internet are pushed for transformation in system capacity. While the intuitive solution of deploying multiple fibers can address the pressing demand for increased capacity, doing so does not bring improvement in economic of scales in terms of cost, power consumption and spectral efficiency. This necessitates for a different approach so that the fiber capacity could be utilized in a more efficient manner. In this paper, we focus on innovative techniques, that is, photonic network coding and partial protection, to reduce the effective traffic load in order to achieve greater capacity efficiency for optical transport networks. Specifically, the application of network coding is examined by upgrading the functionalities of intermediate nodes with all-optical processing (i.e., encoding and decoding) capabilities. Besides, partial protection relying on the premise of providing just enough bandwidth in case of failure events is investigated for saving the redundant protection capacity. That it takes two to tango, combining photonic network coding and partial protection therefore bring to light new opportunities and challenges. In mining such new avenue, we present insights on how to derive compounding gains to maximize spectral efficiency via a case study.
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network

This paper presents an efficient variational inference framework for deriving a family of structured gaussian process regression network (SGPRN) models. The key idea is to incorporate auxiliary inducing variables in latent functions and jointly treats both the distributions of the inducing variables and hyper-parameters as variational parameters. Then we propose structured variable distributions and marginalize latent variables, which enables the decomposability of a tractable variational lower bound and leads to stochastic optimization. Our inference approach is able to model data in which outputs do not share a common input set with a computational complexity independent of the size of the inputs and outputs and thus easily handle datasets with missing values. We illustrate the performance of our method on synthetic data and real datasets and show that our model generally provides better imputation results on missing data than the state-of-the-art. We also provide a visualization approach for time-varying correlation across outputs in electrocoticography data and those estimates provide insight to understand the neural population dynamics.
How memory architecture affects learning in a simple POMDP: the two-hypothesis testing problem

Reinforcement learning is generally difficult for partially observable Markov decision processes (POMDPs), which occurs when the agent's observation is partial or noisy. To seek good performance in POMDPs, one strategy is to endow the agent with a finite memory, whose update is governed by the policy. However, policy optimization is non-convex in that case and can lead to poor training performance for random initialization. The performance can be empirically improved by constraining the memory architecture, then sacrificing optimality to facilitate training. Here we study this trade-off in a two-hypothesis testing problem, akin to the two-arm bandit problem. We compare two extreme cases: (i) the random access memory where any transitions between $M$ memory states are allowed and (ii) a fixed memory where the agent can access its last $m$ actions and rewards. For (i), the probability $q$ to play the worst arm is known to be exponentially small in $M$ for the optimal policy. Our main result is to show that similar performance can be reached for (ii) as well, despite the simplicity of the memory architecture: using a conjecture on Gray-ordered binary necklaces, we find policies for which $q$ is exponentially small in $2^m$, i.e. $q\sim\alpha^{2^m}$ with $\alpha<1$. In addition, we observe empirically that training from random initialization leads to very poor results for (i), and significantly better results for (ii) thanks to the constraints on the memory architecture.
Mitochondrial fission is increased in macrophages during mROS production in response to S. pneumoniae

Immunometabolism and regulation of mitochondrial reactive oxygen species (mROS) are critical determinants of the immune effector phenotype of differentiated macrophages. Mitochondrial function requires dynamic fission and fusion, but whether effector function is associated with altered dynamics during bacterial responses is unknown. We show that macrophage mitochondria undergo fission after 12 h of progressive ingestion of live Streptococcus pneumoniae (pneumococci). Fission is associated with progressive reduction in oxidative phosphorylation but increased mROS generation. Fission is enhanced by mROS production, PI3Kγ signaling and by cathepsin B, but not by inflammasome activation or IL-1β generation. Reduced fission following PI3Kγ or cathepsin B inhibition is associated with reduced mROS generation and bacterial killing. Fission is associated with Parkin recruitment to mitochondria, but not mitophagy. Fission occurs upstream of apoptosis induction and independently of caspase activation. During macrophage innate responses to live bacteria mitochondria shift from oxidative phosphorylation and ATP generation to mROS production and microbicidal responses with a progressive shift towards mitochondrial fission.
Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows

Water lost through leakage from water distribution networks is often appreciable. As pressure increases on water resources, there is a growing emphasis for water service providers to minimize this loss. The objective of the work presented in this paper was to assess the online application and resulting benefits of an artificial intelligence system for detection of leaks/bursts at district meter area (DMA) level. An artificial neural network model, a mixture density network, was trained using a continually updated historic database that constructed a probability density model of the future flow profile. A fuzzy inference system was used for classification; it compared latest observed flow values with predicted flows over time windows such that in the event of abnormal flow conditions alerts are generated. From the probability density functions of predicted flows, the fuzzy inference system provides confidence intervals associated with each detection, these confidence values provide useful information for f.
Commutation analysis of self-driven synchronous rectifiers in an active-clamp forward converter

The effectiveness of using self-driven synchronous rectifiers in forward converters strongly depends on the current commutations, which are determined by the parasitic elements-the PCB trace inductance and the transformer leakage inductance. These parasitic elements are modeled as equivalent circuits using the partial element equivalent circuit (PEEC) method and the two-winding transformer model. The current commutation waveforms for different PCB layouts are compared to evaluate the parasitic effects on rectifier commutations. PCB design guidelines are presented for an optimum low-voltage synchronous rectification performance.
A frequency reconfigurable planar inverted‐F antenna for WWAN mobile handset applications

In this article, a reconfigurable antenna for wireless wide area network (WWAN) mobile handset application is proposed. The proposed antenna consists of a conventional planar inverted‐F antenna (PIFA), parasitic line, PIN diode, and bias line. The PIN diode is located between the parasitic line and ground, and its connection is electrically controlled by the on/off state of the PIN diode. The antenna's wide low band is formed by switching the quarter‐wavelength resonance mode of a conventional PIFA and covers the GSM850/900 bands. The antenna's high band is formed by the harmonic mode of a conventional PIFA and a new generated loop mode and covers the GSM1800/1900/UMTS bands. A stable and omnidirectional radiation pattern with a reasonable gain is observed over the operating bandwidth. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2888–2893, 2014
Crack damage detection of structures using distributed electrical-time-domain reflectometry sensors

Electrical time domain reflectometry (ETDR) stress/strain sensing technique has been successfully used in geotechnical applications to detect rock deformation and longwall movement. The ETDR sensing method appears to be practical for health monitoring applications of civil concrete structures since durable sensor media can be used. In this paper, feasibility of using an embedded coaxial ETDR cable to detect crack damages in a structure is investigated. Tension and bending tests were performed on edge-notched photoelastic epoxy specimens with embedded coaxial ETDR sensing cables. The test results show that crack lines passing through the embedded sensing cable can be detected. The TDR signal response of the sensing cable reveals not only the location of the crack damage site but also indicates the relative magnitude of the crack opening. The results of the current study strongly suggest that the ETDR sensing technique possesses a great potential for the application of health monitoring of large civil concrete structures.