Miracle-dz/UniFAR-FTUs huggingface dataset:
Forward-backward algorithm for functions with locally Lipschitz gradient: applications to mean field games

In this paper, we provide a generalization of the forward-backward splitting algorithm for minimizing the sum of a proper convex lower semicontinuous function and a differentiable convex function whose gradient satisfies a locally Lipschitztype condition. We prove the convergence of our method and derive a linear convergence rate when the differentiable function is locally strongly convex. We recover classical results in the case when the gradient of the differentiable function is globally Lipschitz continuous and an already known linear convergence rate when the function is globally strongly convex. We apply the algorithm to approximate equilibria of variational mean field game systems with local couplings. Compared with some benchmark algorithms to solve these problems, our numerical tests show similar performances in terms of the number of iterations but an important gain in the required computational time.
Fuzzy c-means algorithm based on adaptive Mahalanobis distances

The classical Fuzzy C-Means algorithm(FCM) is based on Euclidean distance function,which can only be used to detect spherical structural clusters.When FCM processes some dataset of high dimension,error probability will be increased. Focusing on above two problems,this paper proposes an improved new algorithm called Fuzzy C-Means based on Mahalanobis distance function(FCM-M),and adds a regulating factor of covariance matrix to each class in objective function.Using the advantage of Mahalanobis distance,FCM-M algorithm effectively solves the shortcomings of FCM algorithm.There are efficient methods to solve singular values problem for finding Eigen value and eigenvectors of a symmetric matrix or computing pseudoinvertion involved in finding the Mahalanobis distance.Experimental results of data clustering and image segmentation illustrate its effectiveness and feasibility.
A possible common explanation for several cosmic microwave background (CMB) anomalies: A strong impact of nearby galaxies on observed large-scale CMB fluctuations

Context. A new and hitherto unknown cosmic microwave background (CMB) foreground has recently been detected. A systematic decrease in CMB temperatures around nearby large spiral galaxies points to an unknown interaction with CMB photons in a sphere up to several projected megaparsecs around these galaxies. Aims. We investigate to what extent this foreground may impact the CMB fluctuation map and create the so-called CMB anomalies. Methods. Using the observed temperature decrements around the galaxies, and making some general assumptions about the unknown interaction, we propose a common radial temperature profile. By assigning this profile to nearby galaxies in the redshift range z = [0.004, 0.02], we created a foreground map model. Results. We find a remarkable resemblance between this temperature model map, based on nearby galaxies, and the Planck CMB map. Compared to 1000 simulated maps, we find that none of them show such a strong correlation with the foreground map over both large and small angular scales. In particular, the quadrupole, octopole, and ℓ = 4 and ℓ = 5 modes correlate with the foreground map to a high significance. Furthermore, one of the most prominent temperature decrements in the foreground map coincides with the position of the CMB cold spot. Conclusions. The largest scales of the CMB, and thereby the cosmological parameters, may change significantly after this foreground component is properly corrected. However, a reliable corrected CMB map can only be derived when suitable physical mechanisms are proposed and tested.
Study on Collective Ball behind Wooden Bucket Principle

Set off from the angle that economics and modern business administration learn, law and analysis wielding the document data,business administration in resisting the kind motion item group to the collective and field developing mimics law and their wooden bucket principle having been in progress expounding,principle resists application in being similar to the motion item group in the collective and field having made the meticulous but all-round thinking,development of high tone provides the new theory and visual angle to the person more to the wooden bucket.
Estimation of Structural Breaks in Large Panels with Cross-Sectional Dependence

This paper considers modelling and detecting structure breaks associated with cross-sectional dependence for large dimensional panel data models, which are popular in many fields including economics and finance. We propose a dynamic factor structure to measure the degree of cross-sectional dependence. The extent of such cross-sectional dependence is parameterized as an unknown parameter, which is defined by assuming that a small proportion of the total factor loadings are important. Compared with the usual parameterized style, this exponential description of extent covers the case of small proportion of the total sections being cross-sectionally dependent. We established a 'moment' criterion to estimate the unknown based on the covariance of cross-sectional averages at different time lags. By taking into account the fact that the serial dependence of common factors is stronger than that of idiosyncratic components, the proposed criterion is able to capture weak cross-sectional dependence that is reflected on relatively small values of the unknown parameter. Due to the involvement of some unknown parameter, both joint and marginal estimators are constructed. This paper then establishes that the joint estimators of a pair of unknown parameters converge in distribution to bivariate normal. In the case where the other unknown parameter is being assumed to be known, an asymptotic distribution for an estimator of the original unknown parameter is also established, which naturally coincides with the joint asymptotic distribution for the case where the other unknown parameter is assumed to be known. Simulation results show the finite-sample effectiveness of the proposed method. Empirical applications to cross-country macro-variables and stock returns in SP500 market are also reported to show the practical relevance of the proposed estimation theory.
On the rising and sinking motion of bouncing oil drops in strongly stratified liquids

Abstract When an immiscible oil drop is immersed in a stably stratified ethanol–water mixture, the Marangoni flow on the surface of the drop can experience an oscillatory instability, so that the drop undergoes a transition from levitating to bouncing. The onset of the instability and its mechanisms have been studied previously (Li et al., Phys. Rev. Lett., vol. 126, 2021, 124502; Li et al., J. Fluid Mech., vol. 932, 2022, A11), yet the bouncing motion of the drop itself, which is a completely different problem, has not yet been investigated. Here we study how the bouncing characteristics (jumping height, rising and sinking time) depend on the control parameters (drop radius, stratification strength, drop viscosity). We first record experimentally the bouncing trajectories of drops of different viscosities in different stratifications. Then a simplified dynamical analysis is performed to get the scaling relations of the jumping height and the rising and sinking times. The rising and sinking time scales are found to depend on the drag coefficient $C_D^S$ of the drop in the stratified liquid, which is determined empirically for the current parameter space (Zhang et al., J. Fluid Mech., vol. 875, 2019, 622–656). For low-viscosity (5 cSt) oil drops, the results on the drag coefficient match those from the literature (Yick et al., J. Fluid Mech., vol. 632, 2009, pp. 49–68; Candelier et al., J. Fluid Mech., vol. 749, 2014, pp. 184–200). For high-viscosity (100 cSt) oil drops, the parameter space had not been explored and the drag coefficients are not readily available. Numerical simulations are therefore performed to provide external verification for the drag coefficients, which well match with the experimental results.
COMPORTAMENTO DE FUGA DE COLÊMBOLOS EXPOSTOS A SOLOS CONTAMINADOS COM CIPERMETRINA

Avaliar a toxicidade da cipermetrina para organismos do solo e uma forma de entender os efeitos deste farmaco sobre organismos nao-alvo e a utilizacao da ecotoxicologia terrestre, que possui normas padronizadas ISO para a realizacao dos ensaios, permite entender esses efeitos em um âmbito mais proximo de um ecossistema natural. O presente trabalho teve como objetivo determinar o efeito da aplicacao do farmaco cipermetrina no comportamento de colembolos da especie Folsomia candida. Para a realizacao dos ensaios foi utilizado um Latossolo Vermelho distrofico proveniente da regiao oeste de Santa Catarina e o Solo Artificial Tropical (SAT). Os ensaios foram conduzidos em delineamento inteiramente casualizado com cinco repeticoes. Os tratamentos consistiam em concentracoes de cipermetrina de 0; 7,5; 15; 22,5 e 30 mg kg-1. O numero de individuos no teste de comportamento de fuga foi analisado atraves do “Fisher Exact Test”. Tanto o solo natural como o solo artificial apresentaram fuga de colembolos desde a menor dose de cipermetrina testada (7,5 mg kg-1), resultados que evidenciam a toxicidade do produto utilizado. Os valores de AC50 (Avoidance concentration - concentracao efetiva de 50% de fuga) para Latossolo e SAT foram de 8,58 mg kg-1 e 6,47 mg kg-1, respectivamente. Deste modo, a presenca de cipermetrina afeta negativamente o comportamento de colembolos F. candida. Palavras-chave: ecotoxicologia terrestre, Folsomia candida, Latossolo Vermelho distrofico, piretroide.
Simulation-based study of low-Reynolds-number flow around a ventilated cavity

Abstract Ventilated cavitating flows are investigated via direct numerical simulations, using a coupled level set and volume of fluid method to capture the interface between the air and water phases. A ventilated disk cavitator is used to create the cavity and is modelled by a sharp-interface immersed boundary method. The simulation data provide a comprehensive description of the two-phase flow and the air leakage and vortex shedding processes in the cavitating flow. The mean velocity of the air phase suggests the existence of three characteristic flow structures, namely the shear layer (SL), recirculating area (RA) and jet layer (JL). The turbulent kinetic energy (TKE) is concentrated in the JL in the closure region, and streamwise turbulent fluctuations dominate transverse fluctuations in both SL and JL. Budget analyses of the TKE show that the production term causes the TKE to increase in the SL due to the high velocity gradients, and decrease in the JL due to streamwise stretching effects. Air leakage and vortex shedding occur periodically in the closure region, and the one-to-one correspondence between these two processes is confirmed by the velocity and volume fluid spectra results, and the autocorrelation function of the air volume fraction. Moreover, the coherent flow structures are analysed using the spectral proper orthogonal decomposition method. We identify several fine coherent structures, including $SL_{KH}$ induced by the Kelvin–Helmholtz instability, $SL_{out}$ associated with large-scale vortex shedding, $SL_{in}$ associated with small-scale vortex shedding, and $SL_{r}$ associated with upstream turbulent convection. The present study complements previous research by providing detailed descriptions of the turbulent motions associated with the violent mixing of air and water, and the complex interactions between different characteristic structures in cavitating flows.
Transcatheter closure of atrial septal defect in the elderly: a systematic review and meta-analysis

Objective Despite the establishment of transcatheter closure as the treatment of choice in adults with secundum atrial septal defects (ASDs), the effectiveness of this approach in the elderly is disputed. This systematic review and meta-analysis aims to explore the impact of transcatheter ASD closure in patients ≥60 years old. Methods We systematically searched four major electronic databases (PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), Scopus and Web of Science), ClinicalTrials.gov, article references and grey literature. Primary outcomes were the right ventricular end-diastolic diameter (RVEDD) and the New York Heart Association functional class change, whereas secondary outcomes included systolic pulmonary arterial pressure (sPAP), left ventricular end-diastolic diameter (LVEDD), brain natriuretic peptide (BNP), tricuspid valve regurgitation (TR) change, as well as the rate of atrial arrhythmias and all-cause mortality. Results In total, 18 single-arm cohorts comprising 1184 patients were included. RVEDD was reduced after ASD closure (standardised mean difference (SMD) −0.9, 95% CI −1.2 to −0.7). Elderly patients had 9.5 times higher odds of being asymptomatic after ASD closure (95% CI 5.06 to 17.79). Furthermore, ASD closure improved sPAP (mean difference (MD) −10.8, 95% CI −14.6 to −7), LVEDD (SMD 0.8, 95% CI 0.7 to 1.0), TR severity (OR 0.39, 95% CI 0.25 to 0.60) and BNP (MD −68.3, 95% CI −114.4 to −22.1). There was a neutral effect of ASD closure on atrial arrhythmias. Conclusions Transcatheter ASD closure is beneficial for the elderly population since it improves functional capacity, biventricular dimensions, pulmonary pressures, TR severity and BNP. However, the incidence of atrial arrhythmias did not change significantly after the intervention. PROSPERO registration number CRD42022378574.
Item Matching Model in E-Commerce: How Users Benefit

Abstract Research purpose. During the last decades, e-commerce sales have been rocketing, and this tendency is expected to increase over the following years. Due to the digital nature of e-commerce, one actual item can be sold on various e-commerce platforms, which leads to the exponential growth of the number of propositions. At the same time, the title and description of this item might differ. All these facts make more complicated for customers the process of searching on online platforms and change business approaches to the development of competitive strategy by e-commerce companies. The research question is how we can apply a machine learning algorithm to detect, based on the product information such as title and description, whether the items are actually relevant to the same product. Methodology. We suggest an approach that is based on a flexible textual data pipeline and the usage of a machine-learning model ensemble. Each step of the data processing is adjustable in dependence on domain issues and data features because we can achieve better results in solving the item-matching task. Findings. The item-matching model is developed. The proposed model is based on the semantic closeness of text descriptions of items and the usage of the core of keywords to present the reference item. Practical implications. We suggest an approach to improving the item searching process on different e-commerce platforms by dividing the process into two steps. The first step is searching for the related items among the set of reference items according to user preferences. The reference item description is created based on our item-matching model. The second step is surfing proposals of similar items on chosen e-commerce platforms. This approach can benefit buyers and sellers in various aspects, such as a low-price guarantee, a flexible strategy of similar products shown, and appropriate category-choosing recommendations.
What is Your Location Privacy Worth? Monetary Valuation of Different Location Types and Privacy Influencing Factors

Nowadays, many apps use location data to estimate the user's behavior for targeted advertising, predicting significant locations, personal preferences, state of health, and sports activities. Users of location-based services are often left with no other choice than to accept or reject location tracking when they want to use various applications. Especially, users with higher privacy concerns may reduce the frequency of location tracking by turning it off in the settings. However, most users are unaware that many applications installed on their phones are continuously tracking them. Therefore, this study attempts to answer how (obviously) being tracked over one-week influences a user's privacy concerns. The study was implemented using an iOS app, which participants could install on their smartphones. Moreover, over one week, the participants were requested to answer daily mini-questionnaires about how much they would be willing to pay for the protection of their location information on a monthly basis and how much money they were willing to accept in exchange for their location information. Hereby, the context was an important criterion to determine how the monetary values vary among different location types for, among others, home location, work location, and meeting family and friends. The participants (N=51) interacted with the app on a daily basis by filling out various daily mini-surveys based on their significant locations visited. The results show a significant difference between the monetary valuating of willingness to pay and to accept for all location types except work location and sharing scenarios contributing to further empirical evidence for the endowment effect. The obvious fact of continuously being tracked did not increase the privacy concern of participants.
Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between?

The generation of time-series profiles of building operation requires expensive and time-consuming data consolidation and modeling efforts that rely on extensive domain knowledge and need frequent revisions due to evolving energy systems, user behavior, and environmental conditions. Generative deep learning may be used to provide an automatic, scalable, data-source-agnostic, and efficient method to synthesize these artificial time-series profiles by learning the distribution of the original data. While a range of generative neural networks have been proposed, generative adversarial networks (GANs) and variational autoencoders (VAEs) are most popular models; GANs typically require considerable customization to stabilize the training procedure, while VAEs are often reported to generate lower-quality samples compared to GANs. In this paper, we propose a network architecture and training procedure that combines the strengths of VAEs and GANs by incorporating Regularized Adversarial Fine-Tuning (RAFT). We imbue the architecture with conditional inputs to reflect ambient/outdoor conditions and operating conditions, and demonstrate its effectiveness by using operational data collected over 585 days from SUSTIE: Mitsubishi Electric’s net-zero energy building. Comparing against classical GAN, VAE, Wasserstein-GAN, and VAE-GAN, our proposed conditional RAFT-VAE-GAN outperforms its competitors in terms of mean accuracy, training stability, and several metrics that ascertain how close the synthetic distribution is to the measured data distribution.
Machine Learning-Based Jamming Detection and Classification in Wireless Networks

The development of novel tools to detect, classify and counteract the new generation of smart jammers in Internet of Things (IoT) is of paramount importance. Detection and classification have to be performed in a short time, with high reliability, and preserving the privacy of network users. In this work, we propose a novel machine learning (ML)-based jamming detection and classification algorithm which can be implemented in the network gateway (GW). The proposed method is based on energy detector (ED), the extraction of specific problem-tailored features, dimensionality reduction, and multi-class classification. Extensive numerical results have been carried out to evaluate the performance of detection and classification, varying the number of principal components selected through dimensionality reduction, the observation window length, the shadowing intensity, and the signal-to-jammer ratio (SJR). Our solution reaches remarkably high accuracy, i.e., up to 99%, outperforming a state-of-the-art solution. That is a very promising result considering that the approach does not need to inspect the decoded information, thus preserving the privacy of the network users.
Machine Learning Assisted Physical Layer Secret Key Generation in the One-Time-Pad Encryption Scheme

The one-time-pad (OTP) technique is widely used in the encryption to achieve the information-theoretic security, and the physical-layer secret key generation (SKG) technique can provide the random keys for OTP. However, the keys are susceptible to eavesdroppers in the correlated channel scenarios. To address this issue, this paper proposes a machine learning (ML) assisted SKG in the OTP encryption scheme. We make a complete and rigorous analysis of the information leakage under the influence of space-time correlation. The derivation based on information theory proves the security of the key generation scheme. To simplify the computational complexity, we generate the initial key based on ML. The simulation results show that the probability of information being leaked in the next frame increases if information is leaked in the current frame. Meanwhile, the simulation results verify that our ML assisted SKG scheme can generate keys securely for OTP under different space-time correlation.
Improving Building Energy Efficiency through Data Analysis

For many lawmakers, energy-efficient buildings have been the main focus in large cities across the United States. Buildings consume the largest amount of energy and produce the highest amounts of greenhouse emissions. This is especially true for New York City (NYC)’s public and private buildings, which alone emit more than two-thirds of the city’s total greenhouse emissions. Therefore, improvements in building energy efficiency have become an essential target to reduce the amount of greenhouse gas emissions and fossil fuel consumption. NYC’s buildings’ historical energy consumption data was used in machine learning models to determine their ENERGY STAR scores for time series analysis and future prediction. Machine learning models were used to predict future energy use and answer the question of how to incorporate machine learning for effective decision-making to optimize energy usage within the largest buildings in a city. The results show that grouping buildings by property type, rather than by location, provides better predictions for ENERGY STAR scores.
How Can the Adversary Effectively Identify Cellular IoT Devices Using LSTM Networks?

The Internet of Things (IoT) has become a key enabler for connecting edge devices with each other and the internet. Massive IoT services provided by cellular networks offer various applications such as smart metering and smart cities. Security of the massive IoT devices working alongside traditional devices such as smartphones and laptops has become a major concern. Protecting these IoT devices from being identified by malicious attackers is often the first line of defense for cellular IoT devices. In this paper, we provide an effective attacking method for identifying cellular IoT devices from cellular networks. Inspired by the characteristics of Long Short-Term Memory (LSTM) networks, we have developed a method that can not only capture context information but also adapt to the dynamic changes of the environment over time. Experimental validation shows a high detection rate with less than 10 epochs of training on public datasets.