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This paper presents properties of the intracluster medium (ICM) in the environment of a cool core cluster Abell 2566 (redshift $z$ = 0.08247) based on the analysis of 20 ks Chandra X-ray data. 2D imaging analysis of the Chandra data from this cluster revealed spiral structures in the morphology of X-ray emission from within the central 109 kpc formed due to gas sloshing. This analysis also witness sharp edges in the surface brightness distribution along the south-east and north-west of the X-ray peaks at 41.6 kpc and 77.4 kpc, respectively. Spectral analysis of 0.5 - 7 keV X-ray photons along these discontinuities exhibited sharp temperature jumps from 2.3 to 3.1 keV and 1.8 to 2.8 keV, respectively, with consistency in the pressure profiles, implying their association with cold fronts due to gas sloshing of the gas. Further confirmation for such an association was provided by the deprojected broken power-law density function fit to the surface brightness distribution along these wedge shaped sectorial regions. This study also witness an offset of 4.6 arcsec (6.8 kpc) between the BCG and the X-ray peak, and interaction of the BCG with a sub-system in the central region, pointing towards the origin of the spiral structure due to a minor merger., Comment: 10 pages, 7 figures, Accepted for New Astronomy

The observation of gravitational waves from compact binary coalescences is a promising tool to test the validity of general relativity (GR) in a highly dynamical strong-field regime. There are now a variety of tests of GR performed on the observed compact binary signals. In this paper, we propose a new test of GR that compares the results of these individual tests. This meta inspiral-merger-ringdown consistency test (IMRCT) involves inferring the final mass and spin of the remnant black hole obtained from the analyses of two different tests of GR and checking for consistency. If there is a deviation from GR, we expect that different tests of GR will recover different values for the final mass and spin, in general. We check the performance of the meta IMRCT using a standard set of null tests used in various gravitational-wave analyses: the original IMRCT, parameterized phasing tests (TIGER and FTI) and the modified dispersion test. However, the meta IMRCT is applicable to any tests of GR that infer the initial masses and spins or the final mass and spin, including ones that are applied to binary neutron star or neutron star--black hole signals. We apply the meta IMRCT to simulated quasi-circular GR and non-GR binary black hole (BBH) signals as well as to eccentric BBH signals in GR (analyzed with quasicircular waveforms). We find that the meta IMRCT gives consistency with GR for the quasi-circular GR signals and picks up a deviation from GR in the other cases, as do other tests. In some cases, the meta IMRCT finds a significant GR deviation for a given pair of tests (and specific testing parameters) while the individual tests do not, showing that it is more sensitive than the individual tests to certain types of deviations. In addition, we also apply this test to a few selected real compact binary signals and find them consistent with GR., Comment: 27 pages, 30 figures

A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling individual-level computer simulations of human health, which can be used to conduct more efficient clinical trials or to recommend personalized treatment options. Due to the overwhelming complexity of human biology, machine learning approaches that leverage large datasets of historical patients' longitudinal health records to generate patients' digital twins are more tractable than potential mechanistic models. In this manuscript, we describe a neural network architecture that can learn conditional generative models of clinical trajectories, which we call Digital Twin Generators (DTGs), that can create digital twins of individual patients. We show that the same neural network architecture can be trained to generate accurate digital twins for patients across 13 different indications simply by changing the training set and tuning hyperparameters. By introducing a general purpose architecture, we aim to unlock the ability to scale machine learning approaches to larger datasets and across more indications so that a digital twin could be created for any patient in the world.

This paper presents a comprehensive analysis of 30 ks Chandra and 46.8 ks (13 Hr) 1.4 GHz GMRT radio data on the cool-core cluster RXCJ0352.9+1941 with an objective to investigate AGN activities at its core. This study confirms a pair of X-ray cavities at projected distances of about 10.30 kpc and 20.80 kpc, respectively, on the NW and SE of the X-ray peak. GMRT L band (1.4 GHz) data revealed a bright radio source associated with the core of this cluster hosting multiple jet-like emissions. The spatial association of the X-ray cavities with the inner pair of radio jets confirm their origin due to AGN outbursts. The 1.4 GHz radio power ${\rm 7.4 \pm 0.8 \times 10^{39} \, erg\, s^{-1}}$ is correlated with the mechanical power stored in the X-ray cavities ($\sim7.90\times 10^{44}$ erg s$^{-1}$), implying that the power injected by radio jets in the ICM is sufficient enough to offset the radiative losses. The X-shaped morphology of diffuse radio emission seems to be comprised of two pairs of orthogonal radio jets, likely formed due to a spin-flip of jets due to the merger of two systems. The X-ray surface brightness analysis of the ICM in its environment revealed two non-uniform, extended spiral-like emission structures on either side of the core, pointing towards the sloshing of gas due to a minor merger and might have resulted in a cold front at $\sim$31 arcsec (62 kpc) with a temperature jump of 1.44 keV., Comment: 18 pages, 10 figures, Accepted for Journal of Astrophysics and Astronomy

In recent years, Vision Transformers (ViTs) have shown promising classification performance over Convolutional Neural Networks (CNNs) due to their self-attention mechanism. Many researchers have incorporated ViTs for Hyperspectral Image (HSI) classification. HSIs are characterised by narrow contiguous spectral bands, providing rich spectral data. Although ViTs excel with sequential data, they cannot extract spectral-spatial information like CNNs. Furthermore, to have high classification performance, there should be a strong interaction between the HSI token and the class (CLS) token. To solve these issues, we propose a 3D-Convolution guided Spectral-Spatial Transformer (3D-ConvSST) for HSI classification that utilizes a 3D-Convolution Guided Residual Module (CGRM) in-between encoders to "fuse" the local spatial and spectral information and to enhance the feature propagation. Furthermore, we forego the class token and instead apply Global Average Pooling, which effectively encodes more discriminative and pertinent high-level features for classification. Extensive experiments have been conducted on three public HSI datasets to show the superiority of the proposed model over state-of-the-art traditional, convolutional, and Transformer models. The code is available at https://github.com/ShyamVarahagiri/3D-ConvSST., Comment: Accepted in IEEE Conference on Artificial Intelligence, 2024

The growth in availability of large-scale GPS mobility data from mobile devices has the potential to aid traditional travel demand models (TDMs) such as the four-step planning model, but those processing methods are not commonly used in practice. In this study, we show the application of trip generation and trip distribution modeling using GPS data from smartphones in the state of Indiana. This involves extracting trip segments from the data and inferring the phone users' home locations, adjusting for data representativeness, and using a data-driven travel time-based cost function for the trip distribution model. The trip generation and interchange patterns in the state are modeled for 2025, 2035, and 2045. Employment sectors like industry and retail are observed to influence trip making behavior more than other sectors. The travel growth is predicted to be mostly concentrated in the suburban regions, with a small decline in the urban cores. Further, although the majority of the growth in trip flows over the years is expected to come from the corridors between the major urban centers of the state, relative interzonal trip flow growth will likely be uniformly spread throughout the state. We also validate our results with the forecasts of two travel demand models, finding a difference of 5-15% in overall trip counts. Our GPS data-based demand model will contribute towards augmenting the conventional statewide travel demand model developed by the state and regional planning agencies., Comment: This paper constitutes a chapter of project SPR-4608 (DOI: 10.5703/128828431768) sponsored by the Joint Transportation Research Program and the Indiana Department of Transportation. It has not been published elsewhere

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements., Comment: Summary of the 8th AI City Challenge Workshop in conjunction with CVPR 2024

Locational measures of accessibility are widely used in urban and transportation planning to understand the impact of the transportation system on influencing people's access to places. However, there is a considerable lack of measurement standards and publicly available data. We propose a generalized measure of locational accessibility that has a comprehensible form for transportation planning analysis. This metric combines the cumulative opportunities approach with gravity-based measures and is capable of catering to multiple trip purposes, travel modes, cost thresholds, and scales of analysis. Using data from multiple publicly available datasets, this metric is computed by trip purpose and travel time threshold for all block groups in the United States, and the data is made publicly accessible. Further, case studies of three large metropolitan areas reveal substantial inefficiencies in transportation infrastructure, with the most inefficiency observed in sprawling and non-core urban areas, especially for bicycling. Subsequently, it is shown that targeted investment in facilities can contribute to a more equitable distribution of accessibility to essential shopping and service facilities. By assigning greater weights to socioeconomically disadvantaged neighborhoods, the proposed metric formally incorporates equity considerations into transportation planning, contributing to a more equitable distribution of accessibility to essential services and facilities., Comment: Article originally submitted to the Journal of Transport Geography. Article contains 5 main figures. Supplementary Material is prepared but not uploaded in this submission