Miracle-dz/UniFAR-FTUs huggingface dataset:
Stellar classification with convolutional neural networks and photometric images: a new catalogue of 50 million SDSS stars without spectra

Stellar classification is a central topic in astronomical research that relies mostly on the use of spectra. However, with the development of large sky surveys, spectra are becoming increasingly scarce compared to photometric images. Numerous observed stars lack spectral types. In SDSS, there are more than hundreds of millions of such stars. In this paper, we propose a convolutional neural network based stellar classification network (SCNet) in an attempt to solve the stellar classification task from photometric images alone, distinguishing between seven classes, i.e., O, B, A, F, G, K, and M. A total of 46 245 identified stellar objects were collected from the SDSS as the training samples for our network. Compared to many typical classification networks in deep learning, SCNet achieves the best classification accuracy of 0.861. When we allow an error to be within three neighboring subtypes for SCNet, the accuracy even reaches 0.907. We apply the final SCNet model to 50 245 638 SDSS stars without corresponding spectra and present a new star classification catalogue, containing 7 438 O-type stars, 31 433 B-type stars, 201 189 A-type stars, 910 007 F-type stars, 10 986 055 G-type stars, 18 941 155 K-type stars, and 19 168 361 M-type stars.
Non-syndromic Intellectual Disability: An Experimental In-Depth Exploration of Inheritance Pattern, Phenotypic Presentation, and Genomic Composition

Background Intellectual disability (ID), also termed mental retardation (MR), is a neurodevelopmental disorder characterized by an intelligence quotient (IQ) of 70 or below and a deficit in at least two behaviors associated with adaptive functioning. The condition is further classified into syndromic intellectual disability (S-ID) and non-syndromic intellectual disability (NS-ID). This study highlights the genes associated with NS-ID. Objectives A genetic study was performed on two Pakistani families to know the inheritance patterns, clinical phenotypes, and molecular genetics of affected individuals with NS-ID. Methodology Samples were collected from two families: families A and B. All affected individuals in both families were diagnosed by a neurologist. Written informed consent was taken from the affected individuals and guardians before collecting the data and sample. Family A belongs to the Swabi District of Pakistan having four affected individuals, out of whom three were male and one was female. Family B also belongs to the Swabi District of Pakistan having two affected individuals, out of whom one was male and one was female. A total of 10 candidate genes were selected and were further screened by microarray analysis. Results In family A, this analysis identified a region of 9.6 Mb on chromosome 17q11.2-q12 between the single nucleotide polymorphisms (SNPs) rs953527 and rs2680398. The region was genotyped using microsatellite markers to confirm the haplotypes in all family members. Based on the phenotype-genotype relationship, 10 possible candidate genes were selected out of more than 140 genes in this critical region of 9.6 Mb. In family B, homozygosity mapping through microarray identified four homozygous areas of affected individuals: two (27,324,822-59,122,062 and 96,423,252123,656,241) on chromosome 8, one (14,785,224-19,722,760) on chromosome 9, and one (126173647-126215644) on chromosome 11. Conclusion An autosomal recessive pattern was found in the pedigrees of both families A and B. Phenotypically affected individuals showed IQ levels below 70. Three genes, CDK5R1, OMG, and EV12A, were found on chromosome 17q11.2-q12 region of affected individuals in family A with high expression in the frontal cortex of the brain, hippocampus, and spinal cord, respectively. Other regions on chromosomes 8, 9, and 11 as evident from the affected individuals in family B can also contribute to the non-syndromic autosomal recessive intellectual disability (NS-ARID). Further research is needed to find the association of these genes with intelligence and other neuropsychiatric conditions.
An Efficient and Unified Recognition Method for Multiple License Plates in Unconstrained Scenarios

License plate recognition is an important research topic in image recognition, which plays a vital role in helping the government effectively manage vehicles. At present, most license plate recognition methods focus on a single category and specific scenes. The requirements on distance, illumination, angle and other conditions are pretty high, which are not conducive to the application in practice. This paper proposes a real-time and accurate automatic license plate recognition (ALPR) method to overcome these challenges. The ALPR method contains a license plate detection network (LPDNet) and character recognition network (CRNet). LPDNet is based on the anchor-free method to detect the bounding box and four corners of license plates in an unconstrained environment. The proposed Rotating Gaussian Kernel and centrality loss enable LPDNet to improve performance in complex environments. CRNet is composed of a full convolution network, which can identify many kinds of license plates. In this network, feature extraction and compression technology have been proposed, effectively identifying multiple license plates in combination with a Character Classification module. Experiments on multiple public datasets show that the method outperforms existing methods in speed and accuracy, especially in complex and challenging environments.
Optimizing team science in an academic medical center: A qualitative examination of investigator perspectives

Abstract Introduction: Optimizing the effectiveness of a team-based approach to unite multiple disciplines in advancing specific translational areas of research is foundational to improving clinical practice. The current study was undertaken to examine investigators’ experiences of participation in transdisciplinary team science initiatives, with a focus on challenges and recommendations for improving effectiveness. Methods: Qualitative interviews were conducted with investigators from twelve multidisciplinary teams awarded pilot research funding by the University of Kentucky College of Medicine to better understand the barriers and facilitators to effective team science within an academic medical center. An experienced qualitative researcher facilitated one-on-one interviews, which lasted about one hour. Structured consensus coding and thematic analysis were conducted. Results: The sample was balanced by gender, career stage (five were assistant professor at the time of the award, seven were senior faculty), and training (six were PhDs; six were MD physicians). Key themes at the team-level centered on the tension between clinical commitments and research pursuits and the limitations for effective team functioning. Access to tangible support from home departments and key university centers was identified as a critical organizational facilitator of successful project completion. Organizational barriers centered on operationalizing protected time for physicians, gaps in effective mentoring, and limitations in operational support. Conclusions: Prioritizing tailored mentoring and career development support for early career faculty, and particularly physician faculty, emerged as a key recommendation for improving team science in academic medical centers. The findings contribute to establishing best practices and policies for team science in academic medical centers.
Non-Myopic Knowledge Gradient Policy for Ranking and Selection

We consider the ranking and selection (R&S) problem with fixed simulation budget, in which the budget is assumed to be allocated sequentially. Deriving the optimal sampling procedure for this problem amounts to solving a stochastic dynamic program that is highly intractable. To overcome this difficulty, the existing R&S procedures are often designed from a myopic viewpoint. However, these myopic procedures are only single-step optimal and may have a poor performance for general sequential R&S problems. Therefore, in this paper, we combine two popular lookahead strategies and design a non-myopic knowledge gradient (KG) procedure. Meanwhile, to streamline the computation of procedure, we propose a modified Monte Carlo tree search method specifically designed under the R&S context. We show that the new procedure can exhibit a performance superior to the classic KG.
Understanding the Role of ATP Release through Connexins Hemichannels during Neurulation

Neurulation is a crucial process in the formation of the central nervous system (CNS), which begins with the folding and fusion of the neural plate, leading to the generation of the neural tube and subsequent development of the brain and spinal cord. Environmental and genetic factors that interfere with the neurulation process promote neural tube defects (NTDs). Connexins (Cxs) are transmembrane proteins that form gap junctions (GJs) and hemichannels (HCs) in vertebrates, allowing cell-cell (GJ) or paracrine (HCs) communication through the release of ATP, glutamate, and NAD+; regulating processes such as cell migration and synaptic transmission. Changes in the state of phosphorylation and/or the intracellular redox potential activate the opening of HCs in different cell types. Cxs such as Cx43 and Cx32 have been associated with proliferation and migration at different stages of CNS development. Here, using molecular and cellular biology techniques (permeability), we demonstrate the expression and functionality of HCs-Cxs, including Cx46 and Cx32, which are associated with the release of ATP during the neurulation process in Xenopus laevis. Furthermore, applications of FGF2 and/or changes in intracellular redox potentials (DTT), well known HCs-Cxs modulators, transiently regulated the ATP release in our model. Importantly, the blockade of HCs-Cxs by carbenoxolone (CBX) and enoxolone (ENX) reduced ATP release with a concomitant formation of NTDs. We propose two possible and highly conserved binding sites (N and E) in Cx46 that may mediate the pharmacological effect of CBX and ENX on the formation of NTDs. In summary, our results highlight the importance of ATP release mediated by HCs-Cxs during neurulation.
A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data

Whether the computer is driving your car or you are, advanced driver assistance systems (ADAS) come into play on all levels, from weather monitoring to safety. These modern-day ADASs use various assisting tools for drivers to keep the journey safe; these sophisticated tools provide early signals of numerous events, such as road conditions, emerging traffic scenarios, and weather warnings. Many urban applications, such as car-sharing and logistics, rely on accurate and up-to-date road map data. Map generation methods use a variety of data sources, including but not limited to global positioning systems (GPS). In this research we propose a GPS-only data trajectory analysis and a novel scheme to convert GPS trajectory data to image-based data to train a custom Convolutional Neural Network (CNN) model. The empirical results with an extensive 5-fold cross-validation show that the proposed scheme identifies turn and not turn with more than 94% recall. It outperforms the existing turn detection schemes on two major frontiers, the required data and the accuracy achieved in detecting different driving behaviors.
A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model

Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.
Scale Dependence of Contact Line Computations

The shape and velocity of a sliding droplet are computed by solving the Navier{ Stokes equation with free interface boundary conditions. The Galerkin flnite element method is implemented in a 2D computation domain discretized using an unstructured mesh with triangular elements. The mesh is reflned recursively at the corners (contact points). The stationary sliding velocity is found to be strongly dependent on grid reflnement, which is a consequence of the contact line singularity resolved through the efiective slip across the flnite elements adjacent to the contact point. For small droplets, this dependence is well approximated by a theoretical estimates obtained using multiscale expansion and matching technique in lubrication approximation, where the corner element size is used as a microscale parameter. For larger droplets, the shape is also dependent on grid reflnement. This ques- tions the validity of numerous computations of ∞ows with moving contact line where grids are invariably much more coarse than molecular scales on which the singularity is resolved. It is suggested that extrapolation to molecular scales should be used to obtain realistic results.
Comparison of Lasso Type Estimators for High-Dimensional Data

Abstract This paper compares of lasso type estimators in various high-dimensional data situations with sparse param-eters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are comparedvia simulation in linear models with correlated and uncorrelated covariates and binary regression models withcorrelated covariates and discrete covariates. Each method is shown to have advantages with diﬀerent penaltyconditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Ara-bidopsis microarray gene expression data to ﬁnd the strongly signiﬁcant genes to distinguish two groups.Keywords: Adaptive Lasso, elastic net, fused lasso, high-dimensional data, lasso, ridge. 1. Introduction High-dimensional data refers to a situation where the number of unknown parameters p are estimatedto be larger than the number of samples n in the data ( p >> n ). High-dimensional data arise in theareas like information technology, bioinformatics, astronomy and brain research. Classical statisticalinference cannot be used for high-dimensional problems. High-dimensional statistical inference isimpossible without additional assumptions or restrictions to a certain class of models. For example,least-squaresﬁttingofalinearmodelhavingmanyunknownparametersthanobservationsisill-posed.A well-posed framework for ﬁtting is based on assuming structural smoothness. Shifting the focusfromsmoothnesstosparsityforhigh-dimensionaldataopensthewayformanymoreapplicationsthatinvolve complex data.The lasso, proposed by Tibshirani (1996), is an acronym for Least Absolute Shrinkage and Selec-tion Operator. The lasso estimates a vector of regression coeﬃcients by minimizing the residual sumof squares subject to a constraint on
Integral operators and the compactness of induced representations

Integral operators are investigated which are compositions of multiplication by bounded vector valued functions and convolutions with vector valued functions. All of the functions are based on a fixed locally compact group. Conditions are given under which certain of these operators are compact. As an application of these conditions we consider induced representations of twisted group algebras (these are generalizations of representations of groups induced from closed normal subgroups in the sense of Mackey and include these as special cases) and we give necessary and sufficient conditions for these representations to be compact (that is, to consist entirely of compact operators).
Drag reduction by rotary oscillation for flow past a circular cylinder

With the help of computational results of incompressible flow past a circular cylinder at Reynolds numbers of Re = 150 and 1000, we explain two possible mechanisms for the experimentally observed drag reduction by rotary oscillation. Here, detailed computed results are compared with available experimental and computational results in Thiria et al. (J Fluid Mech 2006; 560:123-147) for Re = 150. The time-varying loads and moments for various cases have been analyzed first to study bluff body flow control at low Reynolds numbers. We specifically focus upon the effects of amplitude and frequency of the rotary oscillation. Furthermore, to study the effects of Reynolds number, we report another case for a higher Reynolds number of Re = 1000. Proper orthogonal decomposition of computational data for these two Re cases have been performed to explain physical mechanisms behind drag reduction by rotary oscillation and reduced order modeling for different parameter combinations. We show that the drag reduction at th.
Suppression of energetic particle driven instabilities with HHFW heating

In plasmas in the National Spherical Torus Experiment (NSTX) (Ono et al 2000 Nucl. Fusion 40 557) heated with neutral beams, the beam ions typically excite energetic particle modes (or fishbones), and toroidal, global or compressional Alfvén eigenmodes. These modes can redistribute the energetic beam ions, altering the beam driven current profile and the plasma heating profile, or they may affect electron thermal transport or cause losses of the beam ions. In this paper we present experimental results where these instabilities, driven by the super-thermal beam ions, are suppressed with the application of high harmonic fast wave heating.
Discriminating between unresolved point sources and 'negative' Sunyaev-Zel'dovich clusters in cosmic microwave background maps

Clusters of galaxies produce negative features at wavelengths λ > 1.25 mm in CMB maps, by means of the thermal SZ effect, while point radio sources produce positive peaks. This fact implies that a distribution of unresolved SZ clusters could be detected using the negative asymmetry introduced in the odd-moments of the brightness map (skewness and higher), or in the probability distribution function (PDF) for the fluctuations, once the map has been filtered in order to remove the contribution from primordial CMB fluctuations from large scales. This property provides a consistency check to the recent detections from CBI and BIMA experiments of an excess of power at small angular scales, in order to confirm that they are produced by a distribution of unresolved SZ clusters. However it will require at least 1.5 - 2 times more observing time than detection of corresponding power signal. This approach could also be used with the data of the planned SZ experiments (e.g. ACT, AMI, AMIBA, APEX, 8 m South Pole telescope).
Optimization of the Design of Modular Production Systems

The desire for more flexibility in manufacturing systems, especially when different products or many product variants are manufactured in one production system is leading to a move away from the manufacturing principle of classic line production to more flexible and workshop-oriented production systems, particularly in the automotive industry. One of the challenges in these so-called modular assembly or production systems is the system design, especially the allocation of activities to the individual production cells. One approach to improve this allocation is offered by simulation-based optimization. In this paper, a concept for simulation-based optimization of the design of modular production systems is presented and demonstrated by means of a small academic case study. Classical genetic algorithms and additionally the NSGA-II algorithm, which also allows multi-objective optimization, are used.
The Effect of Citrus aurantium on Non-Small-Cell Lung Cancer: A Research Based on Network and Experimental Pharmacology

Purpose To screen the main active components of Citrus aurantium through a network pharmacology approach, construct a component-disease target network, explore its molecular mechanism for the treatment of non-small-cell lung cancer (NSCLC), and validate it experimentally. Methods The active ingredients in Citrus aurantium and the targets of Citrus aurantium and NSCLC were collected through the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP), GeneCards, and OMIM databases. The protein interaction network was constructed using the STRING database, and the component-disease relationship network graph was analyzed using Cytoscape 3.9. 1. The Metascape database can be used for GO and KEGG enrichment analyses. The Kaplan-Meier plotter was applied for overall survival analysis of key targets of Citrus aurantium in the treatment of NSCLC. Real-time PCR (RT-PCR) and Western blotting were used to determine the mRNA and protein levels of key targets of Citrus aurantium for the treatment of NSCLC. Results Five active ingredients of Citrus aurantium were screened, and 54 potential targets for the treatment of NSCLC were found, of which the key ingredient was nobiletin and the key targets are TP53, CXCL8, ESR1, PPAR-α, and MMP9. GO and KEGG enrichment analyses indicated that the mechanism of nobiletin in treating NSCLC may be related to the regulation of cancer signaling pathway, phosphatidylinositol-3 kinase (PI3K)/protein kinase B (Akt) signaling pathway, lipid and atherosclerosis signaling pathway, and neurodegenerative signaling pathway. The experimental results showed that nobiletin could inhibit the proliferation of NSCLC cells and upregulate the levels of P53 and PPAR-α and suppress the expression of MMP9 (P < 0.05). Conclusion Citrus aurantium can participate in the treatment of NSCLC through multiple targets and pathways.
Development of Porous-Polyacrylonitrile-Based Fibers Using Nanocellulose Additives as Precursor for Carbon Fiber Manufacturing

Cellulose is a renewable and environmentally friendly raw material that has an important economic and technical impact in several applications. Recently, nanocellulose (NC) presented a promising road to support the manufacturing of functional carbon fibers (CFs), which are considered superior materials for several applications because of their outstanding properties. However, the smooth and limited effective surface areas make CFs virtually useless in some applications, such as energy storage. Therefore, strategies to increase the porosity of CFs are highly desirable to realize their potential. Within this article, we present an approach that focuses on the designing of porous CF precursors using polyacrilonitrile (PAN) and NC additives using a wet spinning method. To enhance the porosity, two jet stretching (50% and 100%) and four NC additive amounts (0 wt.%, 0.1 wt.%, 0.4 wt.% and 0.8 wt.%) have been applied and investigated. In comparison with the reference PAN fibers (without NC additives and stretching), the results showed an increase in specific surface area from 10.45 m2/g to 138.53 m2/g and in total pore volume from 0.03 cm3/g to 0.49 cm3/g. On the other hand, mechanical properties have been affected negatively by NC additives and the stretching process. Stabilization and carbonization processes could be applied in a future study to support the production of multifunctional porous CF.