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This article explores charge diffusion in relativistic hydrodynamics using kinetic theory with a modified collision kernel that incorporates the momentum dependence of the particle relaxation time. Starting from the Boltzmann equation within the extended relaxation time approximation (ERTA), we derive second-order evolution equations for the dissipative charge current and calculate the associated transport coefficients. The sensitivity of transport coefficients to the particle momentum dependence of the collision time scale of the microscopic interactions in the hot QCD medium is analyzed. For a conformal charge-conserved system, we compare the ERTA-modified transport coefficients for particle diffusion with exact results derived from scalar field theory. With an appropriate parameterization of the relaxation time, we demonstrate the consistency of our analysis and assess the degree of agreement of the results with the exact solutions. The relaxation times for the shear and number diffusion modes are seen to be distinct in general when the momentum dependence of the relaxation time is taken into consideration., Comment: 14 pages, revtex4-2, 1 figure, 1 table

Large-scale Ads recommendation and auction scoring models at Google scale demand immense computational resources. While specialized hardware like TPUs have improved linear algebra computations, bottlenecks persist in large-scale systems. This paper proposes solutions for three critical challenges that must be addressed for efficient end-to-end execution in a widely used production infrastructure: (1) Input Generation and Ingestion Pipeline: Efficiently transforming raw features (e.g., "search query") into numerical inputs and streaming them to TPUs; (2) Large Embedding Tables: Optimizing conversion of sparse features into dense floating-point vectors for neural network consumption; (3) Interruptions and Error Handling: Minimizing resource wastage in large-scale shared datacenters. To tackle these challenges, we propose a shared input generation technique to reduce computational load of input generation by amortizing costs across many models. Furthermore, we propose partitioning, pipelining, and RPC (Remote Procedure Call) coalescing software techniques to optimize embedding operations. To maintain efficiency at scale, we describe novel preemption notice and training hold mechanisms that minimize resource wastage, and ensure prompt error resolution. These techniques have demonstrated significant improvement in Google production, achieving a 116% performance boost and an 18% reduction in training costs across representative models., Comment: 13 pages, 7 figures

The second Hot QCD Matter 2024 conference at IIT Mandi focused on various ongoing topics in high-energy heavy-ion collisions, encompassing theoretical and experimental perspectives. This proceedings volume includes 19 contributions that collectively explore diverse aspects of the bulk properties of hot QCD matter. The topics encompass the dynamics of electromagnetic fields, transport properties, hadronic matter, spin hydrodynamics, and the role of conserved charges in high-energy environments. These studies significantly enhance our understanding of the complex dynamics of hot QCD matter, the quark-gluon plasma (QGP) formed in high-energy nuclear collisions. Advances in theoretical frameworks, including hydrodynamics, spin dynamics, and fluctuation studies, aim to improve theoretical calculations and refine our knowledge of the thermodynamic properties of strongly interacting matter. Experimental efforts, such as those conducted by the ALICE and STAR collaborations, play a vital role in validating these theoretical predictions and deepening our insight into the QCD phase diagram, collectivity in small systems, and the early-stage behavior of strongly interacting matter. Combining theoretical models with experimental observations offers a comprehensive understanding of the extreme conditions encountered in relativistic heavy-ion and proton-proton collisions., Comment: Compilation of the 19 contributions in Bulk Matter presented at the second 'Hot QCD Matter 2024 Conference' held from July 1-3, 2024, organized by IIT Mandi, Himachal Pradesh, India

In the present paper we address the general problem of selective electrodynamic interactions between DNA and protein, which is motivated by decades of theoretical study and our very recent experimental findings (M. Lechelon et al, \textit{Sci Adv} \textbf{8,} eabl5855 (2022)). Inspired by the Davydov and Holstein-Fr\"{o}hlich models describing electron motion along biomolecules, and using a model Hamiltonian written in second quantization, the time-dependent variational principle (TDVP) is used to derive the dynamical equations of the system. We demonstrate the efficacy of this {second-quantized} model for a well-documented biochemical system consisting of a restriction enzyme, \textit{Eco}RI, which binds selectively to a palindromic six-base-pair target within a DNA oligonucleotide sequence to catalyze a DNA double-strand cleavage. The time-domain Fourier spectra of the electron currents numerically computed for the DNA fragment and for the \textit{Eco}RI enzyme, respectively, exhibit a cross-correlation spectrum with a sharp co-resonance peak. When the target DNA recognition sequence is randomized, this sharp co-resonance peak is replaced with a broad and noisy spectrum. Such a sequence-dependent charge transfer phenomenology is suggestive of a potentially rich variety of selective electrodynamic interactions influencing the coordinated activity of DNA substrates, enzymes, transcription factors, ligands, and other proteins under realistic biochemical conditions characterized by electron-phonon excitations., Comment: 28 pages, 7 figures

Patient-centered research is increasingly important in narrowing the gap between research and patient care, yet incorporating patient perspectives into health research has been inconsistent. We propose an automated framework leveraging innovative natural language processing (NLP) and artificial intelligence (AI) with patient portal messages to generate research ideas that prioritize important patient issues. We further quantified the quality of AI-generated research topics. To define patient clinical concerns, we analyzed 614,464 patient messages from 25,549 individuals with breast or skin cancer obtained from a large academic hospital (2013 to 2024), constructing a 2-staged unsupervised NLP topic model. Then, we generated research topics to resolve the defined issues using a widely used AI (ChatGPT-4o, OpenAI Inc, April 2024 version) with prompt-engineering strategies. We guided AI to perform multi-level tasks: 1) knowledge interpretation and summarization (e.g., interpreting and summarizing the NLP-defined topics), 2) knowledge generation (e.g., generating research ideas corresponding to patients issues), 3) self-reflection and correction (e.g., ensuring and revising the research ideas after searching for scientific articles), and 4) self-reassurance (e.g., confirming and finalizing the research ideas). Six highly experienced breast oncologists and dermatologists assessed the significance and novelty of AI-generated research topics using a 5-point Likert scale (1-exceptional, 5-poor). One-third of the AI-suggested research topics were highly significant and novel when both scores were lower than the average. Two-thirds of the AI-suggested topics were novel in both cancers. Our findings demonstrate that AI-generated research topics reflecting patient perspectives via a large volume of patient messages can meaningfully guide future directions in patient-centered health research.

Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology data - is costly and inaccessible. We present the cross-modal genomic feature translation and alignment network for enhanced survival prediction from histopathology images (PathoGen-X). It is a deep learning framework that leverages both genomic and imaging data during training, relying solely on imaging data at testing. PathoGen-X employs transformer-based networks to align and translate image features into the genomic feature space, enhancing weaker imaging signals with stronger genomic signals. Unlike other methods, PathoGen-X translates and aligns features without projecting them to a shared latent space and requires fewer paired samples. Evaluated on TCGA-BRCA, TCGA-LUAD, and TCGA-GBM datasets, PathoGen-X demonstrates strong survival prediction performance, emphasizing the potential of enriched imaging models for accessible cancer prognosis.

We introduce a black-box auxiliary field quantum Monte Carlo (AFQMC) approach to perform highly accurate electronic structure calculations using configuration interaction singles and doubles (CISD) trial states. This method consistently provides more accurate energy estimates than coupled cluster singles and doubles with perturbative triples (CCSD(T)), often regarded as the gold standard in quantum chemistry. This level of precision is achieved at a lower asymptotic computational cost, scaling as $O(N^6)$ compared to the $O(N^7)$ scaling of CCSD(T). We provide numerical evidence supporting these findings through results for challenging main group and transition metal-containing molecules., Comment: 19 pages, 13 figures

Active galactic nuclei (AGN) feedback and its impact on their host galaxies are critical to our understanding of galaxy evolution. Here, we present a combined analysis of new high resolution ultraviolet (UV) data from the Ultraviolet Imaging Telescope (UVIT) on AstroSat and archival optical spectroscopic data from VLT/MUSE, for the Seyfert galaxy, NGC 1365. Concentrating on the central 5 kpc region, the UVIT images in the far and near UV show bright star forming knots in the circumnuclear ring as well as a faint central source. After correcting for extinction, we found the star formation rate (SFR) surface density of the circumnuclear 2 kpc ring to be similar to other starbursts, despite the presence of an AGN outflow, as seen in [OIII] 5007 Angstrom. On the other hand, we found fainter UV and thus lower SFR in the direction south-east of the AGN relative to north-west in agreement with observations at other wavelengths from JWST and ALMA. The AGN outflow velocity is found to be lesser than the escape velocity, suggesting that the outflowing gas will rain back into the galaxy. The deep UV data has also revealed diffuse UV emission in the direction of the AGN outflow. By combining [OIII] and UV data, we found the diffuse emission to be of AGN origin., Comment: 12 Pages, 7 figures, Accepted for publication in ApJ

Histopathology whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic softwares are vulnerable to biases and impurities in the training and test data which can lead to inaccurate diagnoses. For instance, WSIs contain multiple types of tissue regions, at least some of which might not be relevant to the diagnosis. We introduce HistoROI, a robust yet lightweight deep learning-based classifier to segregate WSI into six broad tissue regions -- epithelium, stroma, lymphocytes, adipose, artifacts, and miscellaneous. HistoROI is trained using a novel human-in-the-loop and active learning paradigm that ensures variations in training data for labeling-efficient generalization. HistoROI consistently performs well across multiple organs, despite being trained on only a single dataset, demonstrating strong generalization. Further, we have examined the utility of HistoROI in improving the performance of downstream deep learning-based tasks using the CAMELYON breast cancer lymph node and TCGA lung cancer datasets. For the former dataset, the area under the receiver operating characteristic curve (AUC) for metastasis versus normal tissue of a neural network trained using weakly supervised learning increased from 0.88 to 0.92 by filtering the data using HistoROI. Similarly, the AUC increased from 0.88 to 0.93 for the classification between adenocarcinoma and squamous cell carcinoma on the lung cancer dataset. We also found that the performance of the HistoROI improves upon HistoQC for artifact detection on a test dataset of 93 annotated WSIs. The limitations of the proposed model are analyzed, and potential extensions are also discussed., Comment: 18 pages

We present phenomenological findings on charm quark transport while including its energy loss in both pre-equilibrium and hydrodynamic stages of the evolution. We employed the MARTINI event generator for the production and evolution of heavy quarks in the relativistic heavy-ion collisions. The sensitivity of the heavy meson nuclear modification factor and flow coefficient to the early stage of heavy-ion collisions and bulk medium evolution is analyzed for Pb+Pb collisions at 5.02 TeV. Our study provides insights into the interaction strength of charm quarks during the early phase and within the quark-gluon plasma., Comment: 4 pages, 3 figures, Conference proceedings for 21st International Conference on Strangeness in Quark Matter (SQM2024), Strasbourg (France), June 3-7, 2024

This work demonstrates high-performance vertical NiO/Ga2O3 heterojunction diodes (HJDs) with a 2-step space-modulated junction termination extension. Distinct from the current state-of-the-art Ga2O3 HJDs, we achieve breakdown voltage exceeding 3 kV with a low turn on voltage (VON) of 0.8V, estimated at a forward current density (IF) of 1 A-cm-2. The measured devices exhibit excellent turn-on characteristics achieving 100 A-cm-2 current density at a forward bias of 1.5V along with a low differential specific on-resistance (Ron,sp) of 4.4 m{\Omega}-cm2. The SM-JTE was realized using concentric NiO rings with varying widths and spacing that approximates a gradual reduction in JTE charge. The unipolar figure of merit (FOM) calculated exceeds 2 GW-cm2 and is among the best reported for devices with a sub-1V turn-on. The fabricated devices also displayed minimal change in forward I-V characteristics post reverse bias stress of 3 kV applied during breakdown voltage testing.

In this work, we report on the anisotropic etching characteristics of \b{eta}-Ga2O3 using triethylgallium (TEGa) performed in-situ within an MOCVD chamber. At sufficiently high substrate temperature, TEGa can act as a strong etchant for \b{eta}-Ga2O3 utilizing the suboxide reaction between Ga and Ga2O3. We observe that due to monoclinic crystal structure of \b{eta}-Ga2O3, TEGa etching on both (010) and (001) substrates is highly anisotropic in nature, both in terms of sidewall roughness and lateral etch rate. Smooth sidewalls are only obtained along crystal orientations that minimize sidewall surface energy. Utilizing this technique we also demonstrate deep sub-micron fins with smooth sidewalls and high aspect ratios. Furthermore, we also demonstrate the damage free nature of TEGa etching by fabricating Schottky diodes on the etched surface which display no change in net donor concentration., Comment: 14 pages, 5 figures, journal paper

We report here our comparative analysis of active galactic nucleus (AGN) and star formation (SF) characteristics of a sample of narrow-line Seyfert 1 (NLS1) and broad-line Seyfert 1 (BLS1) galaxies. Our sample consisted of 373 BLS1 and 240 NLS1 galaxies spanning the redshift 0.02 to 0.8. The broad-band spectral energy distribution, constructed using data from the ultra-violet to the far-infrared, was modelled using CIGALE to derive the basic properties of our sample. We searched for differences in stellar mass, star formation rate (SFR), and AGN luminosity in the two populations. We also estimated new radiation-pressure corrected black hole masses for our sample of BLS1 and NLS1 galaxies. While the virial black hole mass (MBH) of BLS1 galaxies is similar to their radiation-pressure corrected MBH values, the virial MBH values of NLS1 galaxies are underestimated. We found that NLS1 galaxies have a lower MBH of log (MBH) = 7.45 +/- 0.27 and a higher log (Eddington ratio) of -0.72 +/- 0.22 than BLS1 galaxies, which have log (MBH) and log (Eddington ratio) of 8.04 +/- 0.26 and -1.08 +/- 0.24, respectively. The distributions of stellar mass, SFR, and specific star formation (sSFR = SFR/stellar mass) for the two populations are indistinguishable. This analysis is based on an independent approach and contradicts reports in the literature that NLS1 galaxies have a higher SF than BLS1 galaxies. While we found that AGN luminosity increases with stellar mass, luminosity of SF flattens at high stellar mass for both BLS1 and NLS1 galaxies. The reason may be that SF is suppressed by AGN feedback at stellar mass higher than 10^11 solar mass or that the AGN fuelling mechanism is decoupled from SF. Separating the sample into radio-detected and radio-undetected subsamples, we found no difference in their SF properties suggesting that the effect of AGN jets on SF is negligible., Comment: 15 pages, accepted for publication in A&A

In this paper, we define two operations, neighbourhood m-splitting hypergraph $NS_m(\mathscr{G}^*)$ and non-neighbourhood splitting hypergraph $NNS(\mathscr{G}^*)$, and obtain several properties of their adjacency spectrum. We also estimate the energies of $NS_m(\mathscr{G}^*)$ and $NNS(\mathscr{G}^*)$. Moreover, we introduce two new join operations on $k$-uniform hypergraphs: the neighbourhood splitting V-vertex join $\mathscr{G}_1^*\veebar \mathscr{G}_2^*$ and the S-vertex join $\mathscr{G}_1^*\barwedge \mathscr{G}_2^*$ of hypergraphs $\mathscr{G}_1^*$ and $\mathscr{G}_2^*$, and determine their adjacency spectrum. As an application, we obtain infinite families of singular hypergraphs and infinite pairs of non-regular non-isomorphic cospectral hypergraphs.

We study here the collective quantum optical effect of superradiance in neuroprotein architectures. This phenomenon arises from the interaction of the electromagnetic field with an organized network of tryptophan chromophores, where each can be effectively modeled as a two-level quantum emitter. Building on our prior experimental confirmation of single-photon superradiance in microtubules, we predict that bright superradiant states will also emerge in simulated actin filament bundles and amyloid fibrils, which manifests in the form of high quantum yields that are robust to static disorder up to five times that of room temperature. For microtubules and amyloid fibrils, the quantum yield is enhanced with increasing system size, contrary to the conventional expectations of quantum effects in a thermally equilibrated environment. We conduct our analyses using a non-Hermitian Hamiltonian derived from the Lindblad equation for an open quantum system that describes the interaction of a chromophore network with the electromagnetic field, in the single-photon limit. Our detailed quantum yield predictions in realistic neuroprotein structures -- including analysis of the potential information-processing applications of correlated superradiant and subradiant states at divergent timescales -- provide motivation for a range of in vitro experiments to confirm these quantum enhancements, which can serve in vivo as a mechanism for dissipating or downconverting high-energy UV metabolic photon emissions in intensely oxidative pathological environments, including those found in Alzheimer's disease and related dementias., Comment: 41 pages, 11 figures, 2 tables

This study aims to develop second-order relativistic viscous magnetohydrodynamics (MHD) derived from kinetic theory within an extended relaxation time approximation (momentum/energy dependent) for the collision kernel. The investigation involves a detailed examination of shear stress tensor evolution equations and associated transport coefficients. The Boltzmann equation is solved using a Chapman-Enskog-like gradient expansion for a charge-conserved conformal system, incorporating a momentum-dependent relaxation time. The derived relativistic non-resistive, viscous second-order MHD equations for the shear stress tensor reveal significant modifications in the coupling with dissipative charge current and magnetic field due to the momentum dependence of the relaxation time. By utilizing a power law parametrization to quantify the momentum dependence of the relaxation time, the anisotropic magnetic field-dependent shear coefficients in the Navier-Stokes limit have been investigated. The resulting viscous coefficients are seen to be sensitive to the momentum dependence of the relaxation time., Comment: 25 pages, 3 figures, 1 table, version accepted in Physical Review D

The corona of hypergraphs is an extension of the corona operation applied to graphs. The corona $G_0^* \odot_1^n G_1^*$ of two hypergraphs is obtained by taking $n$ copies of $G_1^*$ (where $n$ is the order of $G_0^*$) and by joining the $i$-th vertex of $G_0^*$ with the $i$-th copy of $G_1^*$. In this paper, we estimate the complete spectrum(adjacency and Seidel) and eigenvectors of the corona $G_0^* \odot_1^n G_1^*$ of two hypergraphs when $G_1^*$ is regular. Additionally, we define the corona hypergraph $G_0^{*(m)}=G_0^{*(m-1)} \odot_1^n G_0^*$ and determined its adjacency spectrum. Also, we extend the definition coronal of the adjacency matrix. Moreover, we estimate the characteristic polynomial of Seidel matrix of the generalised corona of hypergraphs. Applying these results, we obtain infinitely many non-regular non-isomorphic adjacency and Seidel cospectral hypergraphs.

The cloud computing landscape is rapidly expanding and growing in complexity. It has witnessed the emergence of Cloud Computing as a widely adopted model for efficiently processing large volumes of data by harnessing clusters of commodity computers. This evolution enables the handling of massive data through on-demand services, relying on numerous microservices with diverse dependencies. The technology of containers ensures secure storage, allowing for largescale data processing with high scalability and portability. Container technology, particularly exemplified by Docker in the last decade, plays a pivotal role in this scenario. It empowers microservices to process data swiftly, enabling developers to dynamically scale these services in real-time. This paper initiates by establishing a comprehensive taxonomy for delineating container architecture. Focusing specifically on Docker containers, we scrutinize various existing container related literature. Through this taxonomy and survey, we not only discern similarities and disparities in the architectural approaches of Docker container technology but also pinpoint areas necessitating further research.

Crowdfunding in the realm of the Social Web has received substantial attention, with prior research examining various aspects of campaigns, including project objectives, durations, and influential project categories for successful fundraising. These factors are crucial for entrepreneurs seeking donor support. However, the terrain of charity crowdfunding within the Social Web remains relatively unexplored, lacking comprehension of the motivations driving donations that often lack concrete reciprocation. Distinct from conventional crowdfunding that offers tangible returns, charity crowdfunding relies on intangible rewards like tax advantages, recognition posts, or advisory roles. Such details are often embedded within campaign narratives, yet, the analysis of textual content in charity crowdfunding is limited. This study introduces an inventive text analytics framework, utilizing Latent Dirichlet Allocation (LDA) to extract latent themes from textual descriptions of charity campaigns. The study has explored four different themes, two each in campaign and incentive descriptions. Campaign description themes are focused on child and elderly health mainly the ones who are diagnosed with terminal diseases. Incentive description themes are based on tax benefits, certificates, and appreciation posts. These themes, combined with numerical parameters, predict campaign success. The study was successful in using Random Forest Classifier to predict success of the campaign using both thematic and numerical parameters. The study distinguishes thematic categories, particularly medical need-based charity and general causes, based on project and incentive descriptions. In conclusion, this research bridges the gap by showcasing topic modelling utility in uncharted charity crowdfunding domains.

We study the transport dynamics of charm quarks within an expanding quark-gluon plasma for Pb+Pb collisions at 2.76 TeV. The analysis incorporates the hydrodynamical approach-MUSIC with fluctuating IP-Glasma initial state and Bayesian-quantified viscous coefficients. We study the interaction strength of charm quarks in the medium, including elastic collisional processes with medium constituents, gluon emission processes, and the impact of non-perturbative interactions on heavy quark transport. Further, we analyze the dynamics of heavy flavors using a hybrid framework that incorporates the MARTINI event generator, with PYTHIA8.1 for the initial production of heavy quarks, and Langevin dynamics to describe the evolution of heavy quarks., Comment: 4 pages, 2 figures, Contribution to the proceedings of the "Hot Quarks 2022 (HQ2022)"