Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the efficacy of different data processing methods in diagnosing precancerous skin conditions in a preclinical in-vivo model using bimodal spectroscopy

Non-invasive diagnosis of skin pathologies as skin cancer using optical methods has become increasingly common in recent years. However, the related skin data processing is often quite complex, and the way in which this step is carried out can significantly affect the final results. This study presents the results of diffuse reflectance spectra (with spectral range of the emission source is 300-800 nm) and autofluorescence spectra (with 7 autofluorescence excitation wavelengths in the 360-430 nm range) obtained in vivo from precancerous and benign skin lesions of various types (compensatory hyperplasia, atypical hyperplasia and dysplasia). The skin lesions were modelled using a preclinical model in mice. Spectra were taken in the range of 317 - 789 nm at three different source-detector separations: 271, 536 and 834 μm. The spectra obtained were processed using statistical feature extraction techniques, traditional machine learning (support vector machine, linear discriminant analysis, k-nearest neighbors) and deep learning methods (artificial neural network, convolutional neural network, autoencoder). This study presents a comparison of the performance of these methods and their combinations for multiclass classification of skin lesions.
Probabilities of Large Deviations for Sums ofIndependent Random Variables with a Common Distribution Functionfrom the Domain of Attraction of an Asymmetric Stable Law

Consider a sequence of independent random variables $\{X_i\}$ with a common distribution function $V(x)$ from the domain of attraction of a stable law with an index $\ap\in(1,2)$ and suppose that $\bE X_1=0$~and $$ 0 x_0\ \hbox{and some}\ \dt x\}$, which is true uniformly with respect to all positive x for n tending to infinity. The case $\ap=2$ was earlier carefully investigated in [10].
Angle dependent surface enhanced Raman scattering obtained from a Ag nanorod array substrate

The angular dependence of surface enhanced Raman scattering (SERS) has been investigated for molecules adsorbed onto Ag nanorod array substrates fabricated using an oblique angle deposition technique. The strong SERS signal of trans-1,2-bis(4-pyridyl) ethene (BPE) was found to be strongly dependent on the incident angle of the excitation laser, with the maximum SERS intensity appearing at approximately 45° relative to the surface normal. A scattering model based on classical electrodynamic dipole radiation has been developed for BPE adsorbed onto these Ag nanorod substrates, and the theoretical SERS scattering intensity was found to be in good agreement with the experimental results.
One-Dimensional Constitutive Behavior of Shape Memory Alloys: Thermomechanical Derivation with Non-Constant Material Functions and Redefined Martensite Internal Variable

A one-dimensional constitutive model for the thermomechanical behavior of shape memory alloys is developed based on previous work by Liang and Tanaka. An internal variable ap proach is used to derive a comprehensive constitutive law for shape memory alloy materials from first principles without the assumption of constant material functions. This constitutive law is of such a form that it is well suited to further practical engineering applications and calculations. A separation of the martensite fraction internal variable into temperature-induced and stress- induced parts is presented and justified, which then allows the derived constitutive law to accurately represent both the pseudoelastic and shape memory effects at all temperatures. Several numerical ex amples are given that illustrate the ability of the constitutive law to capture the unique thermome chanical behavior of shape memory alloys due to their internal phase transformations with stress and temperature.
The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of Experimental Use for Medium-Range Weather Forecasts

AbstractSince August 2009, the GLOBO atmospheric general circulation model has been running experimentally at the Institute of Atmospheric Sciences and Climate (ISAC) of the National Council of Research of Italy. GLOBO is derived from the Bologna Limited Area Model (BOLAM), a gridpoint limited-area meteorological model that was developed at the same institute and that has been extended to the entire earth atmosphere. The main dynamical features and physical parameterizations of GLOBO are presented. Starting from initial conditions obtained from the analysis of the NCEP Global Forecast System (GFS) model valid at 0000 UTC, 6-day forecasts with average horizontal resolution of 32 km were performed on a daily basis and in real time. The assessment of the forecast skill during the 1.5-yr period included the calculation of the monthly averaged root-mean-square errors (model prediction versus gridded analyses) of geopotential height at 500 hPa and mean sea level pressure for the northern and southern extratropi.
Partial Matching Algorithm of 3D CAD Models Based on the Constraints of Transition Features

The CAD models of mechanical parts usually have many blends and chamfers, and the existence of these machining features can greatly change the geometric and topological patterns of the CAD models, but the existing partial matching algorithms cannot match the CAD models which contain machining features such as blends and chamfers. In this paper, a new approach to partial matching based on the constraints of transition features is proposed. Firstly, the transition features are identified by feature recognition, and then these machining features are removed to eliminate the impacts to the geometric and topological information of the CAD models, and the attribute adjacent graph is reconstructed, finally, the sub-graph isomorphism approach is used to achieve the partial matching. Experimental results show that this method can achieve partial matching of CAD models which contain machining features such as blends and chamfers, and the matching efficiency can satisfy the requirement of the engineering retrieval.
Role of Metabolism and Metabolic Pathways in Prostate Cancer

Prostate cancer (PCa) is the common cause of death in men. The pathophysiological factors contributing to PCa are not well known. PCa cells gain a protective mechanism via abnormal lipid signaling and metabolism. PCa cells modify their metabolism in response to an excessive intake of nutrients to facilitate advancement. Metabolic syndrome (MetS) is inextricably linked to the carcinogenic progression of PCa, which heightens the severity of the disease. It is hypothesized that changes in the metabolism of the mitochondria contribute to the onset of PCa. The studies of particular alterations in the progress of PCa are best accomplished by examining the metabolome of prostate tissue. Due to the inconsistent findings written initially, additional epidemiological research is required to identify whether or not MetS is an aspect of PCa. There is a correlation between several risk factors and the progression of PCa, one of which is MetS. The metabolic symbiosis between PCa cells and the tumor milieu and how this type of crosstalk may aid in the development of PCa is portrayed in this work. This review focuses on in-depth analysis and evaluation of the metabolic changes that occur within PCa, and also aims to assess the effect of metabolic abnormalities on the aggressiveness status and metabolism of PCa.
National press monitoring using Natural Language Processing as an early warning signal for prediction of asylum applications flows in Europe

Europe, during the last decade, is facing major migration flows, due to several crises out the border of its territory. In this regard, the prediction of migration flows place a significant r ole. The development of the internet and the rise of Big Data pave the path for the development of early warning systems with promising forecasting power. This paper presents a novel approach to migration prediction, using neural network architectures, in combination with Natural Language Processing techniques, that monitor and search for early signals in the national press of countries of origin and destination. This is the first study to use topic modeling to monitor the national press to predict migration. The pipeline proposed in this paper uses the following three major datasets: EUROSTAT, GDELT and ALL-NEWS. The topic classifier, which is proposed, consists of a Latent Dirichlet Allocation(LDA) model trained using the ALL-NEWS dataset. Big Data in the form of national press articles can be of tremendous value to the problem of migration prediction.
Big data techniques for industrial problems with little data

Technicians and maintenance managers in industrial environments would benefit from automatically extracting entities and relationships from different text data sources such as logs, event reports, and manuals. Extracting components from pieces of text and classifying them to the right failure type is not trivial in the domain specific setting where the vocabulary has specific meaning to the industry or domain, and labeled data set is very small. In this paper we address how to overcome these challenges in named entity recognition and classification of text, and present a way to improve the model iteratively and quickly. This interaction between components and related failures in the system can be represented in a knowledge graph, which enables further investigations such as Root Cause Analysis and Problem Diagnosis.
Fidelity and diversity metrics for validating hierarchical synthetic data: Application to residential energy demand

Synthetic data is gaining rapid importance in many application domains due to privacy issues, bias, lack, or simply unavailability of real data. It is important that the synthetic data be a good representation of real data for successfully completing the task at hand. Thus, devising characteristic validation metrics is crucial and remains an open problem in many domains (e.g., image generation). Good validation metrics must be able to disentangle the differences between the quality and the variability coverage of the synthetic data. We propose to use a 3-dimensional metric (precision α, recall β, coverage γ) to describe the fidelity and diversity of the synthetic data. In this paper, we improve on existing definitions of precision, recall, and coverage to extend to large scale time series data. Traditional nearest neighbor manifolds from the literature are replaced by unsupervised learning techniques such as clustering to deal with large scale fine resolution time series while computing the validation metrics. The proposed metrics are employed to validate synthetic data in the domain of residential energy demand. In addition, we extend these definitions to datasets that have a natural hierarchical structure. We propose a hierarchical data-tree model in which precision, recall, and coverage can be computed at multiple inherent (and/or custom) levels of groupings of the data.
Transcendence of Elliptic Modular Functions in Characteristicp

Classical transcendence theory began with the study of special values of the exponential function and later the study of special values of the logarithm and, more generally elliptic and abelian logarithms, was added (see [B]). Characteristic p analogues have been considered, first with the analogues of the exponential function arising from the theory of Drinfeld modules (see [Y]) and also with p-adic exponentiation and its abelian analogue [MP, V]. Let us also note the general transcendence criterion obtained in [CKMR]. The purpose of this paper is to study the transcendence properties in positive characteristic of the power series introduced by Tate which give a non-archimedian analogue of elliptic functions. Let p be a prime number, k be the algebraic closure of Fp , and q be a transcendental over k, i.e., a variable. The following series determine elements of k q :
Functional Mapping of the Brain for Brain–Computer Interfacing: A Review

Brain–computer interfacing has been applied in a range of domains including rehabilitation, neuro-prosthetics, and neurofeedback. Neuroimaging techniques provide insight into the structural and functional aspects of the brain. There is a need to identify, map and understand the various structural areas of the brain together with their functionally active roles for the accurate and efficient design of a brain–computer interface. In this review, the functionally active areas of the brain are reviewed by analyzing the research available in the literature on brain–computer interfacing in conjunction with neuroimaging experiments. This review first provides an overview of various approaches of brain–computer interfacing and basic components in the BCI system and then discuss active functional areas of the brain being utilized in non-invasive brain–computer interfacing performed with hemodynamic signals and electrophysiological recording-based signals. This paper also discusses various challenges and limitations in BCI becoming accessible to a novice user, including security issues in the BCI system, effective ways to overcome those issues, and design implementations.
Simulating paraxial optical systems using the linear canonical transform: properties, issues, and applications

The linear canonical transform (also known as the quadratic phase integral and the special affine Fourier transform, among others) is an important tool for the modeling of quadratic phase systems for coherent optical signal processing, as it is a generalization of a number of important and widely used transforms such as the Fresnel transform, the Fourier transform and the fractional Fourier transform. We consider properties of the linear canonical transform which are important for numerical approximation of the integral transform, and thus for simulation of the related paraxial optical systems. Some of these properties have been previously developed in the literature, but are analyzed here in the context of linear canonical transform simulations, others are developed here for the first time. We examine these properties analytically, including how the support and bandwidth of the signal are related to transform parameters, a review of sampling issues and some new proposals in this area. Finally, we examine the effect of the linear canonical transform on the sparsity of signals, which is useful for efficient transmission or storage or to aid certain signal processing tools such as blind source separation.
A Comparative Study of Auto-tuned PID Controller based on Different SMC Schemes for a DC Motor Speed Control: Practical Validation

This paper represents a comparative study of autotuned proportional-integral-derivative (PID) controller based on different sliding mode control (SMC) schemes for the DC motor speed control such as traditional SMC, fuzzy SMC (Fuzzy-SMC), fuzzy terminal SMC (Fuzzy-TSMC) and fuzzy fast terminal SMC (Fuzzy-FTSMC) schemes to treat uncertainties existing in the DC motor model in practical validation. The DC motor have little change in its parameters, in addition, surrounding conditions vary from one time to another, this makes a very difficult to choose an appropriate controller, so that the researchers resort to using an adaptive controller to control the DC motor to improve the performance and increase the stability against the uncertainties. The auto tuned PID controller automatically compensates for variations in system dynamics by adjusting the controller characteristics so that the overall system performance remains the same, or rather maintained at optimum level. The experimental results were obtained to verify the applicability and effectiveness of the proposed control scheme against external disturbance and model uncertainties with a comparative study.
Dependent Task List Scheduling Algorithm with Function Configuration in Edge Computing

In Mobile Edge omputing (MEC), the applic-ations offloaded from mobile devices to edge servers become more complicated, consisting of multiple interdependent tasks, and which are typically modeled as a Directed Acyclic Graph (DAG). But due to the limited capacity of resources, all functions cannot be configured in one server, so the usual practice is to configure functions on-demand when tasks arrive. We refer to the idea of classic heuristic list scheduling algorithm and jointly consider the problem of dependent task scheduling in MEC and the function configuration on edge servers. Our objective is to minimize the application completion time. Specifically, before the tasks start scheduling, we perform virtual function configuration work to determine the candidate execution edge servers for each task. Then, like the list scheduling algorithm, we define the priority calculation method, which jointly considers the computing overhead and communication overhead of the task. Finally, we propose a new heuristic algorithm, named DDoc, to determine the scheduling position and execution time of the task, and propose the function configuration strategy. Our extensive experiment on the DAG synthesis graphs show that the DDoc algorithm effectively reduces the completion time of the application. Moreover, DDoc consistently performs well on various setting of key parameters.
DSServe - Data Science using Serverless

AI Applications uses various data science tools such as Jupyter notebook to prescribe a series of steps, commonly referred as workflow, for building AI Solutions. The steps in workflow can be as simple as loading the data from remote storage, visualize the data for better understanding or conducting data quality study, or it can be as complex as generating features for modeling, best model discovery processes, etc. Clearly, different steps of the data science workflow has varying requirement of compute resources. Moreover, the execution of steps in workflow are Adhoc and Subjective. With wider availability of various Serverless technology, in this paper, we demonstrate a generalized framework that can be used to provide on demand scale out capability for the Data Science Workflow. In particular, we selected the most common AI operation, namely Automatic Model Selection, as an example to demonstrate benefits of serverless computing. We conducted a detailed experimental results using IBM Code Engine technology to validate the benefits of our proposed approach.
Analysis of some localized boundary-domian integral equations

Some direct segregated localized boundary-domain integral equation (LBDIE) systems associated with the Dirichlet and Neumann boundary value problems (BVP) for a scalar ”Laplace” PDE with variable coefficient are formulated and analysed. The parametrix is localized by multiplication with a radial localizing function. Mapping and jump properties of surface and volume integral potentials based on a localized parametrix and constituting the LBDIE systems are studied in a scale of Sobolev (Bessel potential) spaces. The main results established in the paper are the LBDIEs equivalence to the original variable-coefficient BVPs and the invertibility of the LBDIE operators in the corresponding Sobolev spaces.
Stepwise DOA Estimation for Coherent and Uncorrelated Mixed Signals on Coprime Array

The coprime array constructs virtual array to obtain higher degrees of freedom (DOF), but the premise of doing so is that the signals are independent of each other. Once there are coherent signals, the signal model of virtual array is destroyed and the method fails. To solve these problems, we propose the stepwise estimation of the Direction of Arrival (DOA) for a mixture of coherent and uncorrelated signals. Firstly, the symmetry of the two coprime subarrays is used to eliminate the uncorrelated signals, and combined with the properties of prime numbers, the coherent source DOA is jointly estimated. The obtained coherent source DOA was combined with the optimization algorithm to remove the coherent signal in the mixed signal. Finally, the virtual domain signal processing method was used to estimate the independent source DOA. This method can effectively reduce the influence of coherent signals on the accuracy of DOA estimation without loss of degrees of freedom. In this paper, the feasibility of the proposed method is proved by theoretical derivation, and the effectiveness of the method is also verified by experimental simulations.
Standing shocks in adiabatic black hole accretion of rotating matter

This paper discusses the solutions for stationary, axisymmetric, transonic, adiabatic flows with polytropic, rotating fluid configurations of small transverse thickness in an arbitrarily chosen potential. Special attention is paid to the formation of the standing shocks in the case of black hole accretion and winds. The possibility of three types of shocks depending upon three extreme physical conditions at the shocks is pointed out. These are Rankine-Hugoniot shocks, isentropic compression waves, and isothermal shocks. The shock conditions for these three cases are written down, and the physical situations under which they may form are discussed. 21 refs.
A Hybrid Model Based on NeuralProphet and Long Short-Term Memory for Time Series Forecasting

Time series forecasting has historically been a popular research area, attracting widespread interest in academia and industry. Recently, Deep Learning based models like RNN, LSTM, and NeuralProphet have been successfully applied for time series forecasting. However, single forecasting models are unable to capture full information and learn complex patterns in data. The combination of models has proved to be an effective strategy to address this issue. Traditional hybrid structures still limit the forecasting ability of hybrid methods. In this paper, we propose a novel hybrid forecasting model based on LSTM and NeuralProphet (NP-LSTM), which is constructed by a parallel-series hybrid structure. The proposed model uses LSTM to model diverse nonlinear relationships and NeuralProphet is designed to extract primary trends and seasonal effects and provide interpretability. In the experiments of this paper, we compare our model with other single models and hybrid models of traditional structures using four real-world datasets. The experimental results show that our NP-LSTM hybrid model obtains superior performance in various metrics for time series forecasting.