Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle Interior Sound Classification Based on Local Quintet Magnitude Pattern and Iterative Neighborhood Component Analysis

ABSTRACT Nowadays, environmental sound classification (ESC) has become one of the most studied research areas. Sound signals that are indistinguishable from the human auditory systems have been classified by computer-aided systems and machine learning methods. Therefore, ESC has been used in signal processing and sound forensics applications. A novel ESC type is presented in this paper, and it is named as vehicle interior sound classification (VISC). VISC is defined as one of the sub-branches of the ESC, and it is utilized as sound-based biometrics for vehicles. A hand-crafted feature-based VISC method is presented. The proposed method has multileveled feature generation by using maximum pooling and the proposed local quintet magnitude pattern (LQMP), feature selection with iterative neighborhood component analysis (INCA), and classification phases. A novel VISC dataset was collected from YouTube and the proposed LQMP and INCA based method applied to the collected sounds. The results denoted that following: the accuracy, F1-score, and geometric mean of the proposed LQMP and INCA based VISC method were calculated as 98.38%,98.23%, and 98.21% by using support vector machine classifier respectively. The contribution of the proposed VISC method is to denote that the vehicles can be classified by using sound.
Multi‐component instantaneous frequency estimation using locally adaptive directional time frequency distributions

This paper presents a locally adaptive time‐frequency (t,f) method for estimating the instantaneous frequency (IF) of multi‐component signals. A high‐resolution adaptive directional time‐frequency distribution (ADTFD) is defined by locally adapting the direction of its smoothing kernel at each (t,f) point based on the direction of the energy distribution in the (t,f) domain. The IF of signal components is then estimated from the ADTFD using an image processing algorithm. Using the mean square error between the original IF and estimated IF as a performance criterion, experimental results indicate that the ADTFD gives better IF estimation performance compared with other TFDs for a multi‐component signal. For example, for signal‐to‐noise ratio of 12dB, the IF estimate obtained using the ADTFD achieves a mean square error of −42dB for a weak signal component, which is an improvement of −12dB compared with other TFDs. Copyright © 2015 John Wiley & Sons, Ltd.
Progress in two-loop Master Integrals computation

Over the last years, master integral families at one, two and three loops, with up to ﬁve external particles, including oﬀ-shell legs and internal masses have been computed analytically based on the Simpliﬁed Diﬀerential Equations approach. In this presentation we focus on the latest results for two-loop ﬁve-point Feynman Integrals with one oﬀ-shell leg. The three planar and one of the non-planar families have been fully expressed in terms of Goncharov polylogarithms. For the other two non-planar families, we introduce a new approach to obtain the boundary terms and establish a one-dimensional integral representation of the master integrals in terms of generalised polylogarithms, when the alphabet contains non-factorizable square roots. The results are relevant to the study of NNLO QCD corrections for W , Z and Higgs-boson production in association with two hadronic jets. and in -
Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.
TOWARDS USABILITY INTERFACE TRUSTWORTHINESS IN E-COMMERCE SYSTEMS

The Internet gives vendors an easy way to set up shop for electronic commerce throughout the world. However, lack of consumer trust is a critical impediment to the success of e-commerce since users avoid systems they do not trust. In this paper we present the outcome of a literature review in which we investigated personal aspects that impact the sense of trust regarding user experience when interacting with e-commerce systems. We identified and complemented a set of attributes that characterizes the perceived feeling of trust by the users and formalized a set of related trustworthiness measures. The work makes clear a process that goes from the sub attributes identification to the calculation of their values based on answers provided by the users. The goal is to help users to measure how trustworthy an e-commerce application is, toward a future benchmark.
Maneuvering trajectory recognition based on COA-LSSVM

Aiming at the problem of low recognition accuracy of maneuvering trajectories, this paper constructs a relatively complete maneuvering unit library by analyzing the characteristics of maneuvering trajectory, and expresses complex trajectories with simple units; Combine the coyote optimization algorithm(COA) with the Least Squares Support Vector Machine (LSSVM) classifier, and use the COA algorithm to adaptively adjust the width factor and the penalty factor δ of the kernel function in the LSSVM according to the error; Five classification algorithms, LSSVM, SSA-LSSVM, HHO-LSSVM, AOA-LSSVM, and AEO-LSSVM are selected for comparative experiments. The results show that the method proposed in this paper has higher accuracy in maneuvering trajectory recognition.
Microgrid to smart grid's evolution: Technical challenges, current solutions, and future scopes

Renewable energy (RE) sources play an important role that not only reduces the pressure on fossil fuels but also produces safe and clean energy by developing the microgrid (MG) technology that fulfills the excess demand for power throughout the world. The technological development and the blessing of information and communication technology converts the MG technology to a smarter one, termed as smart grid (SG) and virtual power plant, by establishing a two‐way communication between the consumers and service provider with the aid of smart metering infrastructure, dynamic pricing scheme, energy management system, smart transmission and distribution system. The transformation of the traditional power system to SG enhances the efficiency, flexibility, resiliency, and robustness of the power system due to the increasing level of cyber security and automation. A directional pathway from conventional to smart power system has been carried out in this paper by addressing the present status of the power system, challenges during the operations, and possible solutions. Initially, the control operation of MG with RE and energy storage system has been investigated which is essential to understand the operation complexity and challenges due to the variable nature of the RE, load dynamics, and uncertainties. But the facilities of end‐user feedback, dynamic pricing scheme, cyber security, and smart metering system are not present in MG technology that hampers the proper regulation of controlling operation, generating and distributing energy as well as energy losses. The development of SG with smart technologies overcomes these problems by proper controlling of power usage. Finally, this paper provides a path to move the MG toward SG by analyzing the technical and critical challenges of smart devices, equipment integration, and their control issues and provides possible solutions to overcome the integration and control challenges for a reliable and sustainable operation.
Generalized additive models for location, scale and shape - Discussion

Generalized Additive Models for Location, Scale and Shape (GAMLSS) were introduced by Rigby and Stasinopoulos (2005). GAMLSS is a general framework for univariate regression type statistical problems. In GAMLSS the exponential family distribution assumption used in Generalized Linear Model (GLM) and Generalized Additive Model (GAM), (see Nelder and Wedderburn, 1972 and Hastie and Tibshirani, 1990, respectively) is relaxed and replaced by a very general distribution family including highly skew and kurtotic discrete and continuous distributions. The systematic part of the model is expanded to allow modelling not only the mean (or location) but other parameters of the distribution of y as linear parametric, non-linear parametric or additive non-parametric functions of explanatory variables and/or random effects terms. Maximum (penalized) likelihood estimation is used to fit the models. The algorithms used to fit the model are described in detail in Rigby and Stasinopoulos (2005). For medium to large size data, GAMLSS allow flexibility in statistical modelling far beyond other currently available methods. The most important application of GAMLSS up to now is its use by the Department of Nutrition for Health and Development of the World Health Organization to construct the worldwide standard growth curves. The range of possible applications for GAMLSS though is a lot more general and examples will be given of its usefulness in modelling medical and insurance data. In the talk we will describe the GAMLSS model, the variety of different (two, three and four) distributions that are implemented within the GAMLSS package and the variety of different additive terms that can be used in the current implementation. New distributions and new additive terms can be easily added to the package. We shall also discuss the difference of GAMLSS with other available packages in R such as gam and mgcv. More recent work, for example the inclusion of non linear parameter components as additive terms and the inclusion of truncated distributions and censored data within the GAMLSS family, will be also discussed. Rigby, R. Stasinopoulos, D. Akanztiliotou, C. 2
Testing for foreground residuals in the Planck foreground cleaned maps: A new method for designing confidence masks

We test for foreground residuals in the foreground cleaned Planck Cosmic Microwave Background (CMB) maps outside and inside U73 mask commonly used for cosmological analysis. The aim of this paper is to introduce a new method to validate masks by looking at the differences in cleaned maps obtained by different component separation methods. By analyzing the power spectrum as well as the mean, variance and skewness of needlet coefficients on bands outside and inside the U73 mask we first confirm that the pixels already masked by U73 are highly contaminated and cannot be used for cosmological analysis. We further find that the U73 mask needs extension in order to reduce large scale foreground residuals to a level of less than $20\%$ of the standard deviation of CMB fluctuations within the bands closest to the galactic equator. We also find 276 point sources in the cleaned foreground maps which are currently not masked by the U73 mask. Our final publicly available extended mask leaves $65.9\%$ of the sky for cosmological analysis. Note that this extended mask may be important for analyses on local sky patches; in full sky analyses the additional residuals near the galactic equator may average out.
A Lateral Flow Device for Point-of-Care Detection of Doxorubicin

A simple, rapid, and sensitive point-of-care (POC) device for the on-site detection of doxorubicin was developed. The proposed method relies on the naked-eye detection of the intrinsic fluorescence of the drug in a lateral flow device (LFD) configuration, exploiting the biological recognition of DNA probes and avoiding the use of expensive antibodies and sophisticated instrumentations. The POC assay does not require any pre-treatment or purification step and provides an immediate visual readout, achieving a limit of detection as low as ca. 1 ng doxorubicin, outperforming several laboratory-based instrumental techniques. The POC method was proven useful for the detection of trace amounts of the drug both in the case of water solutions (to simulate surface contaminations) and in urine samples, opening promising perspectives for routine monitoring of doxorubicin, with potential benefit to healthcare workers and personalized chemotherapies.
Tuning the spin Hall effect in a two-dimensional electron gas

We provide a theoretical framework for the electric field control of the electron spin in systems with diffusive electron motion. The approach is valid in the experimentally important case where both intrinsic and extrinsic spin-orbit interaction in a two-dimensional electron gas are present simultaneously. Surprisingly, even when the extrinsic mechanism is the dominant driving force for spin Hall currents, the amplitude of the spin Hall conductivity may be considerably tuned by varying the intrinsic spin-orbit coupling via a gate voltage. Furthermore we provide an explanation for the mechanism giving rise to the experimentally observed out-of-plane spin polarization in a (110) GaAs quantum well.
Optimization of a Novel Mandelamide-Derived Pyrrolopyrimidine Series of PERK Inhibitors

The protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK) is one of three endoplasmic reticulum (ER) transmembrane sensors of the unfolded protein response (UPR) responsible for regulating protein synthesis and alleviating ER stress. PERK has been implicated in tumorigenesis, cancer cell survival as well metabolic diseases such as diabetes. The structure-based design and optimization of a novel mandelamide-derived pyrrolopyrimidine series of PERK inhibitors as described herein, resulted in the identification of compound 26, a potent, selective, and orally bioavailable compound suitable for interrogating PERK pathway biology in vitro and in vivo, with pharmacokinetics suitable for once-a-day oral dosing in mice.
Backreacted Kähler geometry of wrapped branes

For supersymmetric solutions of D3(M2) branes with AdS3(AdS2) factor, it is known that the internal space is expressible as U(1) fibration over Kahler space which satisfies a specific partial differential equation involving the Ricci tensor. In this paper we study the wrapped brane solutions of D3 and M2-branes which were originally constructed using gauged supergravity and uplifted to D=10 and D=11. We rewrite the solutions in canonical form, identify the backreacted Kahler geometry, and present a class of solutions which satisfy the Killing spinor equation.
Material configurations for n-type silicon-based terahertz quantum cascade lasers

Silicon-based quantum cascade lasers (QCLs) offer the prospect of integrating coherent terahertz (THz) radiation sources with silicon microelectronics. Theoretical studies have proposed a variety of $n$-type SiGe-based heterostructures as design candidates; however, the optimal material configuration remains unclear. In this work, an optimization algorithm is used to design equivalent THz QCLs in three recently proposed configurations [(001) Ge/GeSi, (001) Si/SiGe, and (111) Si/SiGe], with emission frequencies of 3 and 4 THz. A systematic comparison of the electronic and optical properties is presented. A semiclassical electron transport simulation is used to model the charge carrier dynamics and calculate the peak gain, the corresponding current density, and the maximum operating temperature. It is shown that (001) Ge/GeSi structures yield the best simulated performance at both emission frequencies.
Discrete modeling analysis of enterprise supply chain management optimization based on big data analysis

With the advent of the era of massive data, algorithmic optimization and data mining have been applied in many industries in an unprecedented and innovative way. In the field of enterprise supply chain management, big data and algorithmic optimization technologies have been well applied. The establishment of the innovative model of supply chain management efficiency optimization can improve the management efficiency in the supply process and can greatly promote market development. The use of big data technology can make supply chain management more informative and intelligent. This study proposes a discrete modeling analysis of enterprise supply chain management optimization based on big data analysis. The main use of big data technology is to analyze the obtained data information and quantify the abstract features of the supply process. The discrete dynamic modeling technique is also used to develop a three-level intelligent planning algorithm, focusing on the quality issues in the upstream supply process and the cost issues in ordering and forwarding for management optimization. In addition, this study also have innovatively developed intelligently selected time series for forecasting the supply potential of companies.
Characteristics of Exosomes and the Vascular Landscape Regulate Exosome Sequestration by Peripheral Tissues and Brain

Exosomes mediate intercellular communication, shuttling messages between cells and tissues. We explored whether exosome tissue sequestration is determined by the exosomes or the tissues using ten radiolabeled exosomes from human or murine, cancerous or noncancerous cell lines. We measured sequestration of these exosomes by the liver, kidney, spleen, and lung after intravenous injection into male CD-1 mice. Except for kidney sequestration of three exosomes, all exosomes were incorporated by all tissues, but sequestration levels varied greatly among exosomes and tissues. Species of origin (mouse vs. human) or source (cancerous vs. noncancerous cells) did not influence tissue sequestration. Sequestration of J774A.1 exosomes by liver involved the mannose-6 phosphate (M6P) receptor. Wheatgerm agglutinin (WGA) or lipopolysaccharide (LPS) treatments enhanced sequestration of exosomes by brain and lung but inhibited sequestration by liver and spleen. Response to LPS was not predictive of response to WGA. Path and heat map analyses included our published results for brain and found distinct clusters among the exosomes and the tissues. In conclusion, we found no evidence for a universal binding site controlling exosome-tissue interactions. Instead, sequestration of exosomes by tissues is differentially regulated by both exosomes and tissues and may be stimulated or inhibited by WGA and inflammation.
Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France

Current Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim was to forecast the land cover class of a group of pixels as a multi-class single-label classification problem given their time series gathered using satellite images. In this article, we exploit SITS to assess the capability of several spatial and temporal deep learning models with the proposed architecture. The models implemented are the bidirectional gated recurrent unit (GRU), temporal convolutional neural networks (TCNN), GRU + TCNN, attention on TCNN, and attention of GRU + TCNN. The proposed architecture integrates univariate, multivariate, and pixel coordinates for the Reunion Island’s landcover classification (LCC). the evaluation of the proposed architecture with deep neural networks on the test dataset determined that blending univariate and multivariate with a recurrent neural network and pixel coordinates achieved increased accuracy with higher F1 scores for each class label. The results suggest that the models also performed exceptionally well when executed in a partitioned manner for the LCC task compared to the temporal models. This study demonstrates that using deep learning approaches paired with spatiotemporal SITS data addresses the difficult task of cost-effectively classifying land cover, contributing to a sustainable environment.
THE RISING STELLAR VELOCITY DISPERSION OF M87 FROM INTEGRATED STARLIGHT

We have measured the line-of-sight velocity distribution from integrated stellar light at two points in the outer halo of M87 (NGC 4486), the second-rank galaxy in the Virgo Cluster. The data were taken at R = 480″ (∼41.5 kpc) and R = 526″ (∼45.5 kpc) along the SE major axis. The second moment for a non-parametric estimate of the full velocity distribution is 420 ± 23 km s−1 and 577 ± 35 km s−1, respectively. There is intriguing evidence in the velocity profiles for two kinematically distinct stellar components at the position of our pointing. Under this assumption, we employ a two-Gaussian decomposition and find the primary Gaussian having rest velocities equal to M87 (consistent with zero rotation) and second moments of 383 ± 32 km s−1 and 446 ± 43 km s−1, respectively. The asymmetry seen in the velocity profiles suggests that the stellar halo of M87 is not in a relaxed state and confuses a clean dynamical interpretation. That said, either measurement (full or two component model) shows a rising velocity dispersion at large radii, consistent with previous integrated light measurements, yet significantly higher than globular cluster measurements at comparable radial positions. These integrated light measurements at large radii, and the stark contrast they make to the measurements of other kinematic tracers, highlight the rich kinematic complexity of environments like the center of the Virgo Cluster and the need for caution when interpreting kinematic measurements from various dynamical tracers.