Miracle-dz/UniFAR-FTUs huggingface dataset:
Mutual Coupling Mitigation in Array Dense Regime by an Antenna Distribution Optimization in SKA1‐LOW

The Aperture Array Verification System 2.0 is the latest technological demonstrator designed for the Square Kilometer Array radio telescope operating at low frequency (50–350 MHz). This is an aperture phased array, known as a “station” of the radio telescope, with a pseudo‐random distribution of 256 dual polarized log‐periodic antennas. Mutual coupling effects among the antennas are known to produce detrimental effects especially at the lower end of the operating frequency band. This paper presents a relationship between the antennas electromagnetic performance and their physical position inside the station, followed by an identification of the most critical distances and alignment‐angles between antennas. The calibration accuracy of the station and its directivity within the field of view of the radio telescope are recalculated after repositioning ∼20% of the 256 antennas showing clear improvements in dense regime with respect to the actual AAVS2.0 layout.
Toward a 'Standard Model' of Machine Learning

Machine learning (ML) is about computational methods that enable machines to learn concepts from experience. In handling a wide variety of experience ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interaction in an ever-growing spectrum of tasks, contemporary ML/AI (artificial intelligence) research has resulted in a multitude of learning paradigms and methodologies. Despite the continual progresses on all different fronts, the disparate narrowly focused methods also make standardized, composable, and reusable development of ML approaches difficult, and preclude the opportunity to build AI agents that panoramically learn from all types of experience. This article presents a standardized ML formalism, in particular a `standard equation' of the learning objective, that offers a unifying understanding of many important ML algorithms in the supervised, unsupervised, knowledge-constrained, reinforcement, adversarial, and online learning paradigms, respectively -- those diverse algorithms are encompassed as special cases due to different choices of modeling components. The framework also provides guidance for mechanical design of new ML approaches and serves as a promising vehicle toward panoramic machine learning with all experience.
A geometric framework for transfer learning using manifold alignment

Many machine learning problems involve dealing with a large amount of high-dimensional data across diverse domains. In addition, annotating or labeling the data is expensive as it involves significant human effort. This dissertation explores a joint solution to both these problems by exploiting the property that high-dimensional data in real-world application domains often lies on a lower-dimensional structure, whose geometry can be modeled as a graph or manifold. In particular, we propose a set of novel manifold-alignment based approaches for transfer learning. The proposed approaches transfer knowledge across different domains by finding low-dimensional embeddings of the datasets to a common latent space, which simultaneously match corresponding instances while preserving local or global geometry of each input dataset. We develop a novel two-step transfer learning method called Procrustes alignment. Procrustes alignment first maps the datasets to low-dimensional latent spaces reflecting their intrinsic geometries and then removes the translational, rotational and scaling components from one set so that the optimal alignment between the two sets can be achieved. This approach can preserve either global geometry or local geometry depending on the dimensionality reduction approach used in the first step. We propose a general one-step manifold alignment framework called manifold projections that can find alignments, both across instances as well as across features, while preserving local domain geometry. We develop and mathematically analyze several extensions of this framework to more challenging situations, including (1) when no correspondences across domains are given; (2) when the global geometry of each input domain needs to be respected; (3) when label information rather than correspondence information is available. A final contribution of this thesis is the study of multiscale methods for manifold alignment. Multiscale alignment automatically generates alignment results at different levels by discovering the shared intrinsic multilevel structures of the given datasets, providing a common representation across all input datasets.
Physics of Lakes: Volume 2: Lakes as Oscillators

The overwhelming focus of this 2nd volume of 'Physics of Lakes' is adequately expressed by its subtitle 'Lakes as Oscillators'. It deals with barotropic and baroclinic waves in homogeneous and stratified lakes on the rotating Earth and comprises 12 chapters, starting with rotating shallow-water waves, demonstrating their classification into gravity and Rossby waves for homogeneous and stratified water bodies. This leads to gravity waves in bounded domains of constant depth, Kelvin, Poincare and Sverdrup waves, reflection of such waves in gulfs and rectangles and their description in sealed basins as barotropic 'inertial waves proper'. The particular application to gravity waves in circular and elliptical basins of constant depth leads to the description of Kelvin-type and Poincare-type waves and their balanced description in basins of arbitrary geometry on the rotating Earth. Consideration of two-, three- and n-layer fluids with sharp interfaces give rise to the description of gravity waves of higher order baroclinicity with experimental corroboration in a laboratory flume and e.g. in Lake of Lugano, Lake Banyoles and Lake Biwa. Barotropic wave modes in Lake Onega with complex geometry show that data and computational output require careful interpretation. Moreover, a summer field campaign in Lake of Lugano and its two-layer modal analysis show that careful statistical analyses of the data are requested to match data with computational results. Three chapters are devoted to topographic Rossby waves. Conditions are outlined for which these waves are negligibly affected by baroclinicity. Three classes of these large period modes are identified: channel modes, so-called Ball modes and bay modes, often with periods which lie very close together. The last chapter deals with an entire class of Chrystal-type equations for barotropic waves in elongated basins which incorporate the effects of the rotation of the Earth.
GENERATIVE DESIGN FOR PRECISION GEO-INTERVENTIONS

Abstract. The purpose of this research is to develop an approach for a Spatial Decision Support System (SDSS) that integrates Geographic Information Systems (GIS), Automated Machine Learning (AutoML), and Hyperparameter Optimization (HPO) to generate precision geo-interventions based on standardized geospatial data and user design constraints. The geo-intervention generation approach involves three steps: (1) Geo-binning, (2) AutoML, and (3) Prediction Optimization. Geo-binning is used to standardize geospatial data into regularized grids as inputs into AutoML models. Prediction optimization generates geo-interventions by applying user-design constraints and optimizing AutoML model output to find optimized input variables that form precise geo-interventions. An experiment in reducing road traffic collisions using infrastructural changes in Toronto, Ontario, Canada was done to evaluate the geo-intervention generation approach. The results of the experiment found that changing the number of schools, red light cameras, and transit shelters in high traffic areas could potentially halve the total number of traffic collisions according to a 80 by 80 geo-binned grid Auto-Sklearn model with a Mean Absolute Error (MAE) of 117.68. It was also found that user design constraints heavily affected the prediction optimization step as when the areas were altered to an alternative grid of cells with scarce infrastructure, the number of predicted collisions rose by 6127 collisions. Thus, limitations of this study included subjectivity in user design constraints, scalability, and interactivity. Future work involves improving modelling/optimization efficiency and developing an interactive interface for exploring generated precision geo-interventions.
Intrusion detection framework using stacked auto encoder based deep neural network in IOT network

Security is of paramount importance in the number of systems affiliated with increased IoT. Therefore, in this manuscript, a Stacked Auto Encoder based Deep Neural Network (DNN) fostered Intrusion Detection Framework is proposed to secure the IoT Environment. Here, the data is given to the preprocessing stage, in which redundancy elimination and replacement of missing value are done. Then, the preprocessed output is given to the feature selection process. Wherein, the Golden eagle optimization (GEO) algorithm selects the optimum features from pre‐processed data sets. Then selected features are given to the Stacked Auto encoder based deep neural network for classification, which classified the data, like normal, anomalies. Here, the proposed approach is implemented in Python language. To check the robustness of the proposed approach, the performance metrics, like accuracy, specificity, sensitivity, F‐measure, precision, and recall is measured. The simulation outcome show that the proposed Stacked Auto Encoder based Deep Neural Network based Intrusion Detection Framework (IDS‐FS‐GEO‐SAENN) method attains higher accuracy 99.75%, 97.85%, 95.13%, and 98.79, higher sensitivity 96.34%, 91.23%, 89.12%, and 87.25%, higher specificity 93.67%, 92.37%, 98.47%, and 94.78% compared with the existing methods, like FS‐SMO‐SDPN, FS‐WO‐RNNLSTM, FS‐hybrid GWOPSO‐RF, and FS‐CNNLSTMGRU, respectively.
Candidate Multi-Epitope Vaccine against Corona B.1.617 Lineage: In Silico Approach

Various mutations have accumulated since the first genome sequence of SARS-CoV2 in 2020. Mutants of the virus carrying the D614G and P681R mutations in the spike protein are increasingly becoming dominant all over the world. The two mutations increase the viral infectivity and severity of the disease. This report describes an in silico design of SARS-CoV-2 multi-epitope carrying the spike D614G and P681R mutations. The designed vaccine harbors the D614G mutation that increases viral infectivity, fitness, and the P681R mutation that enhances the cleavage of S to S1 and S2 subunits. The designed multi-epitope vaccine showed an antigenic property with a value of 0.67 and the immunogenicity of the predicted vaccine was calculated and yielded 3.4. The vaccine construct is predicted to be non-allergenic, thermostable and has hydrophilic nature. The combination of the selected CTL and HTL epitopes in the vaccine resulted in 96.85% population coverage globally. Stable interactions of the vaccine with Toll-Like Receptor 4 were tested by docking studies. The multi-epitope vaccine can be a good candidate against highly infecting SARS-CoV-2 variants.
Exposure Assessment of Air Pollution in Lungs

In this article, a comprehensive literature survey on air pollution and its effects on the human respiratory system is carried out. Based on the knowledge gaps, a computational assessment is proposed to find the impact of air quality on respiratory suspended particulate matter (RSPM) deposition in the human airways. A realistic 3D geometric model of the human airway was constructed to study the airflow characteristics and RSPM (PM2.5 and PM10) transport and deposition in it for normal and moderate inhalation patterns (corresponding to natural breathing) of air having an unhealthy air quality index (AQI). The results identify inertial impact as the primary mechanism of particle deposition in the human airways. They also reveals the significant differences in the deposition patterns of PM2.5 and PM10 in the right and left bronchial airways.
Underlying Dimensions of Lifestyle and Their Relation with Creative Ideation Among Undergraduate Students

In higher education, a limited number of studies have been conducted on the relationship between lifestyle and creativity and dating back tomore than 50 years ago. This study investigates the up-to-date results of the relationship between university students' lifestyles and their creative ideation. The data was collected two times in order to perform Exploratory Factor Analysis (EFA) and Hierarchical Regression Analysis. The participants consisted of 233 students for EFA, whereas regression analysis consisted of 239 students from five public universities across Thailand. The data was analyzed using 'R Programing Language' to obtain the results. The study revealed that in a collectivist culture society like Thailand, student lifestyles consist of five dimensions, i.e., family-oriented, community consciousness, modern, family concern, and traditional lifestyle. The correlation results showed that community consciousness and a modern lifestyle were significantly associated with creative ideation after controlling for gender and age variables. In contrast, family concern, family-oriented, and a traditional lifestyle were not. These findings suggest that undergraduate students who endorse modern and community consciousness lifestyles were likely to perform better in generating creative ideas.
Astrophysical Limits on the Evolution of Dimensionless Physical Constants over Cosmological Time

We report new upper limits on any possible long-term time variation of the ratio of the electron to proton masses, $(m_e/m_p)$, the fine-structure constant, ($\alpha$), and the quantity $\alpha^2 g_p(m_e/m_p)$, where $g_p$ is the proton gyromagnetic ratio. These limits are based on extremely high precision observations of H$_2$, Si$^{3+}$, C$^0$ and H$^0$ in high-redshift quasar absorption lines. They amount to 95\% confidence ranges of $(-7.6\to 9.7) \times 10^{-14}$ yr$^{-1}$ for ($m_e/m_p$), $(-4.6\to 4.2)\times 10^{-14}$ yr$^{-1}$ for $\alpha$\ and $(-2.2\to 4.2) \times 10^{-15}$ yr$^{-1}$ for $\alpha^2 g_p(m_e/m_p)$, where the elapsed time has been computed for a cosmology with \hnought = 75 km s$^{-1}$ Mpc$^{-1}$ and \qnought = 0.5.
Multi-scale Riemann-Finsler geometry : applications to diffusion tensor imaging and high angular resolution diffusion imaging

In this brief summary, we reflect the goals and the achievements of this PhD-project by citing three sentences in the original project proposal. "This project investigates the exploitation of the scale degree of freedom in images from the vantage point of differential geometry and tensor calculus in scale space. " Indeed, this thesis introduces the necessary tools for differential geometric tensor calculus and derives some theoretical as well as practical results. The scale is considered separately in two different settings. In this thesis, the weight is somewhat shifted from the problem of scale to problems in differential geometry and applications. The applications considered here are multi-directional medical images, namely diffusion weighted magnetic resonance images. The measurements are modeled using symmetric tensors, which is equivalent to a polynomial approximation. This thesis can be roughly separated in to two parts: one that studies second order tensor fields and one that uses higher order tensor fields. The second order tensor fields are studied with tools from Riemann geometry, and the higher order ones respectively with those from Finsler geometry. Each of these can be separated to a theoretical part, that contain the mathematical definitions and derivations and an applied part, where some geometric properties of synthetic, simulated or real data are computed and analyzed. "The goal is to couple geometry to image content based on a specific task. " By attaching geometric meaning to the physical properties (of the imaged object) represented by data, we have derived some measures and algorithms to extract information from images. For example a novel method to do fiber tractography, i.e. extract neural connections from diffusion weighted images of brain, is introduced. This method has the special property, that it can propagate through voxels with complex fiber orientations. Techniques to measure relative diffusivity along a curve and to detect inhomogeneities in tensor field are some of the other examples. Since the real data is discrete, the interpolation of tensor fields is also considered. "The objective is to foster specific applications in biomedical image analysis, and to extend these to multiple scales. " In the Riemannian framework, the concept of scale is introduced in a Gaussian derivative scheme to second order tensor fields. In Finslerian context, a scale parameter was attached to higher order tensors by applying Laplace-Beltrami smoothing, solving the heat equation on the sphere.
Evaluation of a Peak-Free Chemometric Laser-Induced Breakdown Spectroscopy Method for Direct Rapid Cancer Detection via Trace Metal Biomarkers in Tissue

The ability to perform direct rapid analysis in air and at atmospheric pressure is a remarkable attraction of laser-induced breakdown spectroscopy (LIBS) for the diagnostic quantification of disease biomarker metals in body tissue. However, accurate trace analysis is limited by matrix effects and a pronounced background that masks the subtle (peak-free) analyte signals because tissue plasma is dense and most lines are optically thick. In this work, a peak-free chemometric LIBS method based on a single-shot (for rapidity and nondestructiveness) and an artificial neural network multivariate calibration strategy with spectral feature selection was evaluated for its utility for direct trace quantitative analysis of copper (Cu), iron (Fe), manganese (Mg), magnesium (Mg), and zinc (Zn) in model soft body tissue. The spectral signatures corresponding to the biometals (so-called because the metals are intrinsic to tissue biochemistry) were generated by spiking their known human-body-representative concentrations in molten paraffin wax. The developed multivariate analytical model achieved ≥95% accuracy as determined from the analysis of oyster tissue-certified reference material. The analytical models were tested on the liver, breast, and abdominal tissue biopsies. The results of applying the model to the clinical tissues indicated the absence or presence (including severity) of cancer as either malignant or benign, in agreement with the pathological examination report.
The neo-Nazi doxa, the deep state, and COVID-19: neo-Nazis’ ambivalent relations to Big Pharma and the anti-vaccine movement

ABSTRACT This article explores the role of conspiracy theories in the neo-Nazi movement. The focal points of the study are the core conspiracy theories within the movement and the interface with other conspiracy theories of less ideological importance such as ‘Big Pharma. ’ The study is based on interviews with six active neo-Nazis in the context of the Swedish-dominated Nordic resistance movement. The informants were all senior in the movement with long experience as neo-Nazi officials. In addition, the empirical material also depicts a unique internal strategy document from the movement that instructs all activists on how to talk and how to avoid talking about various forms of conspiracy theories. The study shows that conspiracy theories, such as the claim of a ‘great replacement’ and ‘the deep state,’ serve as a doxa for the movement. This doxa is not the consequence of the movement but rather a precondition for it.
TWO ALGORITHMS FOR SYMMETRIC LINEAR SYSTEMS WITH MULTIPLE RIGHT-HAND SIDES

TWOALGORITHMSFORSYMMETRICLINEARSYSTEMSWITHMULTIPLERIGHT-HANDSIDESHUA DAI1;2Department of MathematicsNanjing University of Aeronautics and AstronauticsNanjing 210016, People's Republic of ChinaCERFACS TechnicalRep ort - TR/PA/98/17AbstractIn this pap er, we investigate the blo ck Lanczos algorithm for solving large sparse symmet-riclinearsystems withmultipleright-handsides,andshow how toincorp oratedeationtodropconvergedlinearsystemsusinganaturalergencecriterion,andpresentanadaptiveblo ckLanczosalgorithm.Wprop osealsoaversionofPaigeandSaun-ders'MINRESmetho dforiterativesolutionofsymmetriclinearsystems,anddescrib eimp ortantimplementationdetails.Weestablisharelationshipb etweentheblo ckLanc-zos algorithm andblo ck MINRESalgorithm, and compare the numericalp erformanceoftheLanczosalgorithmandMINRESmetho dforsymmetriclinearsystemsappliedtoasequence of right-hand sides with that of the blo ck Lanczos algorithm and blo ck MINRESalgorithm for multiplelinearsystems simultaneously.Key words:symmetric linear systems, multiple right-hand sides, blo ck Lanczos algorithm,blo ck MINRES metho dAMS(MOS) sub ject classi cation:65F10, 65F151The research supp orted in part by the National Natural Science Foundation of China and theJiangsu Province Natural Science Foundation.2The work of this author was done during a visit to CERFACS, France in March-August 1998.1
A HIGHER-ORDER PANEL METHOD BASED ON B-SPLINES

A two-dimensional higher order panel method using B-splines has been developed, and the results of this method show that it is not only accurate, but also robust and efficient. In this method, both the panel geometry and singularity distributions are defined by B- splines, and Green's theorem is then applied at selected collocation points. The integral of the influence functions are expressed in terms of polynomials of a parametric coordinate, and calculated analytically. Both the collocation method and the Galerkin method are presented to solve the system of equations.
A dataset of standard precipitation index reconstructed from multi-proxies over Asia for the past 300 years

Abstract. Proxy-based precipitation reconstruction is essential to study the inter-annual to decadal variability and underlying mechanisms beyond the instrumental period that is critically needed for climate modeling, prediction and attribution. Based on 2912 annually resolved proxy series mainly derived from tree rings and historical documents, we present a set of standard precipitation index (SPI) reconstructions for each year (November–October), covering the whole of Asia, and for the wet season (i.e., November–April for western Asia and May–October for the others) since 1700, with the spatial resolution of 2.5∘. To screen the optimal candidate proxies for SPI reconstruction in each grid from available proxies in its connected region with a homogeneous rainfall regime and similar precipitation variability, a new approach is developed by adopting the grid-location-dependent division derived from the instrumental SPI data. The validation shows that these reconstructions are effective for most of Asia. The assessment of data quality compared with gauge precipitation before calibration time indicates that our reconstruction has high quality to show the precipitation variability in most of the study areas, except for a few grids in western Russia, the coastal area of southeast Asia and northern Japan. The full dataset can be obtained from https://doi.org/10.57760/sciencedb.01829 (Y. Liu et al., 2022).
Is MLOps different in Industry 4.0? General and Specific Challenges

: An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collection of practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed in the context of pure software systems, Industry 4.0 systems received much less attention. So far, there is no specialized research for Industry 4.0 in this regard. In this position paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We identify both context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specific challenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicates that existing tools and approaches are also mostly suited for the Industry 4.0 context.