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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

During the last decade, scholars have studied the behavior of decision-making in newsvendor settings and have identified numerous behavior patterns for deviating from normative behavior. However, there is a dearth of research which have examined the influence of individual heterogeneity on decision-making in newsvendor settings. This study examines the level of construal (Abstract and concrete) using construal level theory (CLT) on performance in newsvendor games. In addition, this study measures the cognitive reflection of individuals using cognitive reflection test (CRT) ex-ante to analyze the true impact of how people construe a problem and its impact on their decision-making.

The Dead Internet Theory (DIT) suggests that much of today's internet, particularly social media, is dominated by non-human activity, AI-generated content, and corporate agendas, leading to a decline in authentic human interaction. This study explores the origins, core claims, and implications of DIT, emphasizing its relevance in the context of social media platforms. The theory emerged as a response to the perceived homogenization of online spaces, highlighting issues like the proliferation of bots, algorithmically generated content, and the prioritization of engagement metrics over genuine user interaction. AI technologies play a central role in this phenomenon, as social media platforms increasingly use algorithms and machine learning to curate content, drive engagement, and maximize advertising revenue. While these tools enhance scalability and personalization, they also prioritize virality and consumption over authentic communication, contributing to the erosion of trust, the loss of content diversity, and a dehumanized internet experience. This study redefines DIT in the context of social media, proposing that the commodification of content consumption for revenue has taken precedence over meaningful human connectivity. By focusing on engagement metrics, platforms foster a sense of artificiality and disconnection, underscoring the need for human-centric approaches to revive authentic online interaction and community building.

This study aims to investigate the factors influencing the Human Development Index (HDI). Five variables-GDP per capita, health expenditure, education expenditure, infant mortality rate (per 1,000 live births), and average years of schooling-were analyzed to develop a regression model assessing their impact on HDI. The results indicate that GDP per capita, infant mortality rate, and average years of schooling are significant predictors of HDI. Specifically, the study finds a positive relationship between GDP per capita and average years of schooling with HDI, while infant mortality rate is negatively associated with HDI.

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)"

The Gribov-Zwanziger prescription applied within Yang-Mills theory is demonstrated to be an efficient method for refining the theory's infrared dynamics. We study the collisional energy loss experienced by a high-energetic test parton as it traverses through the Grivov plasma at finite temperature. To achieve this, we employ a semi-classical approach that considers the parton's energy loss while accounting for the back-reaction induced by the polarization effects due to its motion in the medium. The polarization tensor of the medium is estimated within a non-perturbative resummation considering the Gribov-Zwanziger approach. The modification of the gluon and ghost loops due to the presence of the Gribov parameter plays a vital role in our estimation. We observe that the non-perturbative interactions have a sizable effect on the parton energy loss. Further, we discuss the implications of our findings in the context of relativistic heavy-ion collisions., Comment: 6 pages, 3 figures, 1 Supplementary material

In potential flow networks, the equilibrium flow rates are usually not proportional to the demands and flow control elements are required to regulate the flow. The control elements can broadly be classified into two types - discrete and continuous. Discrete control elements can have only two operational states: fully open or fully closed. On the other hand, continuous control elements may be operated in any intermediate position in addition to the fully open and fully closed states. Naturally, with their increased flexibility, continuous control elements can provide better network performance, but $to~what~extent$? We consider a class of branched networks with a single source and multiple sinks. The potential drop across edges ($\Delta H$) is assumed to be proportional to the $n^{th}$ power of flow rate ($Q$), i.e., $\Delta H=kQ^n$ , ($n>=1$). We define $\textbf{R}$ as the ratio of minimal operational times required to transport a given quantum of material with either type of control element and show that $1\leq \textbf{R}\leq m^{\left(1-1/n\right)}$, where $m$ is the maximum depth of the network. The results point to the role of network topology in the variations in operational time. Further analysis reveals that the selfish operation of a network with continuous control valves has the same bounds on the price of anarchy.

This study conducts a thorough examination of the research stream focusing on AI risks in healthcare, aiming to explore the distinct genres within this domain. A selection criterion was employed to carefully analyze 39 articles to identify three primary genres of AI risks prevalent in healthcare: clinical data risks, technical risks, and socio-ethical risks. Selection criteria was based on journal ranking and impact factor. The research seeks to provide a valuable resource for future healthcare researchers, furnishing them with a comprehensive understanding of the complex challenges posed by AI implementation in healthcare settings. By categorizing and elucidating these genres, the study aims to facilitate the development of empirical qualitative and quantitative research, fostering evidence-based approaches to address AI-related risks in healthcare effectively. This endeavor contributes to building a robust knowledge base that can inform the formulation of risk mitigation strategies, ensuring safe and efficient integration of AI technologies in healthcare practices. Thus, it is important to study AI risks in healthcare to build better and efficient AI systems and mitigate risks.

A hypergraph generalizes the concept of an ordinary graph. In an ordinary graph, edges connect pairs of vertices, whereas in a hypergraph, hyperedges can connect multiple vertices at a time. In this paper, we obtain a relationship between the characteristic polynomial of Seidel and adjacency matrices of hypergraph and also compute all the eigenvalues of some k-uniform hypergraphs. Moreover, we estimate the adjacency and Seidel spectra of the uniform double hyperstar and sunflower hypergraph. In addition to that, we determine the Seidel spectrum and main Seidel eigenvalues of hyperstar.

We develop a local correlation variant of auxiliary field quantum Monte Carlo (AFQMC) that is based on local natural orbitals (LNO-AFQMC). In LNO-AFQMC, independent AFQMC calculations are performed for each localized occupied orbital using a truncated set of tailored orbitals. Because the size of this space does not grow with system size for a target accuracy, the method has linear scaling. Applying LNO AFQMC to molecular problems containing a few hundred to a thousand orbitals, we demonstrate convergence of total energies with significantly reduced costs. The savings are more significant for larger systems and larger basis sets. However, even for our smallest system studied, we find that LNO-AFQMC is cheaper than canonical AFQMC, in contrast with many other reduced-scaling methods. Perhaps most significantly, we show that energy differences converge much more quickly than total energies, making the method ideal for applications in chemistry and material science. Our work paves the way for linear scaling AFQMC calculations of strongly correlated systems, which would have a transformative effect on ab initio quantum chemistry., Comment: 9 pages, 2 figures

Academic research has shown significant interest in international student mobility, with previous literature primarily focusing on the migration industry from a political and public policy perspective. For many countries, international student mobility plays a crucial role in bolstering their economies through financial gains and attracting skilled immigrants. While previous studies have explored the determinants of mobility and country economic policies, only a few have examined the impact of policy changes on mobility trends. In this study, the researchers investigate the influence of immigration policy changes, particularly the optional practical training (OPT) extension on STEM programs, on Asian students' preference for enrolling in STEM majors at universities. The study utilizes observational data and employs a quasi-experimental design, analysing the information using the difference-in-difference technique. The findings of the research indicate that the implementation of the STEM extension policy in 2008 has a significant effect on Asian students' decisions to enroll in a STEM major. Additionally, the study highlights the noteworthy role of individual factors such as the specific STEM major, terminal degree pursued, and gender in influencing Asian students' enrollment decisions.

We present a method for calculating first-order response properties in phaseless auxiliary field quantum Monte Carlo (AFQMC) through the application of automatic differentiation (AD). Biases and statistical efficiency of the resulting estimators are discussed. Our approach demonstrates that AD enables the calculation of reduced density matrices (RDMs) with the same computational cost scaling as energy calculations, accompanied by a cost prefactor of less than four in our numerical calculations. We investigate the role of self-consistency and trial orbital choice in property calculations., Comment: 10 pages, 6 figures

We present the Virtual Human Benchmark (VHB) game to evaluate and improve physical and cognitive acuity. VHB simulates in 3D the BATAK lightboard game, which is designed to improve physical reaction and hand-eye coordination, on the \textit{Oculus Rift} and \textit{Quest} headsets. The game comprises the \textit{reaction}, \textit{accumulator} and \textit{sequence} modes; \bj{along} with the \textit{reaction} and \textit{accumulator} modes which mimic BATAK functionalities, the \textit{sequence} mode involves the user repeating a sequence of illuminated targets with increasing complexity to train visual memory and cognitive processing. A first version of the game (VHB v1) was evaluated against the real-world BATAK by 20 users, and their feedback was utilized to improve game design and obtain a second version (VHB v2). Another study to evaluate VHB v2 was conducted with 20 users, whose results confirmed that the deign improvements enhanced game usability and user experience in multiple respects. Also, logging and visualization of performance data such as \textit{reaction time}, \textit{speed between targets} and \textit{completed sequence patterns} provides useful data for coaches/therapists monitoring sports/rehabilitation regimens., Comment: 5 Figures

The present study aims to explore the feasibility of language translation using quantum natural language processing algorithms on noisy intermediate-scale quantum (NISQ) devices. Classical methods in natural language processing (NLP) struggle with handling large-scale computations required for complex language tasks, but quantum NLP on NISQ devices holds promise in harnessing quantum parallelism and entanglement to efficiently process and analyze vast amounts of linguistic data, potentially revolutionizing NLP applications. Our research endeavors to pave the way for quantum neural machine translation, which could potentially offer advantages over classical methods in the future. We employ Shannon entropy to demonstrate the significant role of some appropriate angles of rotation gates in the performance of parametrized quantum circuits. In particular, we utilize these angles (parameters) as a means of communication between quantum circuits of different languages. To achieve our objective, we adopt the encoder-decoder model of classical neural networks and implement the translation task using long short-term memory (LSTM). Our experiments involved 160 samples comprising English sentences and their Persian translations. We trained the models with different optimisers implementing stochastic gradient descent (SGD) as primary and subsequently incorporating two additional optimizers in conjunction with SGD. Notably, we achieved optimal results-with mean absolute error of 0.03, mean squared error of 0.002, and 0.016 loss-by training the best model, consisting of two LSTM layers and using the Adam optimiser. Our small dataset, though consisting of simple synonymous sentences with word-to-word mappings, points to the utility of Shannon entropy as a figure of merit in more complex machine translation models for intricate sentence structures., Comment: 27 pages, 10 figures

The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection. Convolutional neural networks often neglect the spatial relationships within histopathological images, which can limit their accuracy. Graph neural networks (GNNs) offer a promising solution by coding the spatial relationships within images. Prior studies have investigated the modeling of histopathological images as cell and tissue graphs, but they have not fully tapped into the potential of extracting interrelationships between these biological entities. In this paper, we present a novel approach using a heterogeneous GNN that captures the spatial and hierarchical relations between cell and tissue graphs to enhance the extraction of useful information from histopathological images. We also compare the performance of a cross-attention-based network and a transformer architecture for modeling the intricate relationships within tissue and cell graphs. Our model demonstrates superior efficiency in terms of parameter count and achieves higher accuracy compared to the transformer-based state-of-the-art approach on three publicly available breast cancer datasets -- BRIGHT, BreakHis, and BACH.

We study the dependence of collisional energy loss of a test parton moving with a high velocity on the chiral imbalance and magnetic field in the QCD medium. A semi-classical approach is adopted to estimate the parton energy loss that takes into account the back-reaction on the parton due to the polarization effects of the QCD medium while traversing through the medium. We find that the motion of the parton is sensitive to the chiral asymmetry in the medium. Further, we investigate the effect of magnetic field-induced anisotropy on the energy transfer between the moving parton and the medium. Our results show that the energy loss of the parton is strongly influenced by the strength of the magnetic field as well as the relative orientation of the motion of the parton and the direction of the magnetic field in the medium., Comment: 9 pages, 5 figures

We study open charm flavor observables in Pb+Pb collision at $\sqrt{s_{NN}}= 2.76$ TeV within the MARTINI framework. The space-time expansion of the quark-gluon plasma is described using the hydrodynamical approach-MUSIC with IP-Glasma initial conditions. The model parameters, including the viscous coefficients, were obtained from a recent Bayesian model-to-data comparison. We evolve heavy quarks in this background using Langevin dynamics while incorporating their collisional and radiative processes in the medium. The sensitivity of charm observables to the IP-Glasma initial state, bulk evolution, and centrality of the collision is studied. We find that the elliptic flow of open charm flavor has a strong dependence on the fluctuating initial conditions in addition to the strength of the interaction of heavy quarks with the medium constituents. Within this framework, the nuclear suppression factor and elliptic flow of D-mesons act as efficient probes to study the initial stages of heavy-ion collisions, transport coefficients associated with QGP medium as well as heavy quark interactions., Comment: 7 pages, 4 figures; Version published in Phys. Rev. C

In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its third supercomputer for such ML models. Optical circuit switches (OCSes) dynamically reconfigure its interconnect topology to improve scale, availability, utilization, modularity, deployment, security, power, and performance; users can pick a twisted 3D torus topology if desired. Much cheaper, lower power, and faster than Infiniband, OCSes and underlying optical components are <5% of system cost and <3% of system power. Each TPU v4 includes SparseCores, dataflow processors that accelerate models that rely on embeddings by 5x-7x yet use only 5% of die area and power. Deployed since 2020, TPU v4 outperforms TPU v3 by 2.1x and improves performance/Watt by 2.7x. The TPU v4 supercomputer is 4x larger at 4096 chips and thus ~10x faster overall, which along with OCS flexibility helps large language models. For similar sized systems, it is ~4.3x-4.5x faster than the Graphcore IPU Bow and is 1.2x-1.7x faster and uses 1.3x-1.9x less power than the Nvidia A100. TPU v4s inside the energy-optimized warehouse scale computers of Google Cloud use ~3x less energy and produce ~20x less CO2e than contemporary DSAs in a typical on-premise data center., Comment: 15 pages; 16 figures; to be published at ISCA 2023 (the International Symposium on Computer Architecture)

Semi-supervised learning (semi-SL) is a promising alternative to supervised learning for medical image analysis when obtaining good quality supervision for medical imaging is difficult. However, semi-SL assumes that the underlying distribution of unaudited data matches that of the few labeled samples, which is often violated in practical settings, particularly in medical images. The presence of out-of-distribution (OOD) samples in the unlabeled training pool of semi-SL is inevitable and can reduce the efficiency of the algorithm. Common preprocessing methods to filter out outlier samples may not be suitable for medical images that involve a wide range of anatomical structures and rare morphologies. In this paper, we propose a novel pipeline for addressing open-set supervised learning challenges in digital histology images. Our pipeline efficiently estimates an OOD score for each unlabelled data point based on self-supervised learning to calibrate the knowledge needed for a subsequent semi-SL framework. The outlier score derived from the OOD detector is used to modulate sample selection for the subsequent semi-SL stage, ensuring that samples conforming to the distribution of the few labeled samples are more frequently exposed to the subsequent semi-SL framework. Our framework is compatible with any semi-SL framework, and we base our experiments on the popular Mixmatch semi-SL framework. We conduct extensive studies on two digital pathology datasets, Kather colorectal histology dataset and a dataset derived from TCGA-BRCA whole slide images, and establish the effectiveness of our method by comparing with popular methods and frameworks in semi-SL algorithms through various experiments.

$\beta$-Ga$_2$O$_3$ trench Schottky barrier diodes fabricated through a Gallium atomic beam etching technique, with excellent field strength and power device figure of merit, are demonstrated. Trench formation was accomplished by a low-damage Ga flux etch that enables near-ideal forward operating characteristics that are independent of fin orientation. The reverse breakdown field strength of greater than 5.10 MV/cm is demonstrated at breakdown voltage as of 1.45 kV. This result demonstrates the potential for Ga atomic beam etching and high-quality dielectric layers for improved performance in $\beta$-Ga$_2$O$_3$ vertical power devices., Comment: 10 pages, 5 figures

Convolution Neural Networks (CNNs) are widely used in medical image analysis, but their performance degrade when the magnification of testing images differ from the training images. The inability of CNNs to generalize across magnification scales can result in sub-optimal performance on external datasets. This study aims to evaluate the robustness of various deep learning architectures in the analysis of breast cancer histopathological images with varying magnification scales at training and testing stages. Here we explore and compare the performance of multiple deep learning architectures, including CNN-based ResNet and MobileNet, self-attention-based Vision Transformers and Swin Transformers, and token-mixing models, such as FNet, ConvMixer, MLP-Mixer, and WaveMix. The experiments are conducted using the BreakHis dataset, which contains breast cancer histopathological images at varying magnification levels. We show that performance of WaveMix is invariant to the magnification of training and testing data and can provide stable and good classification accuracy. These evaluations are critical in identifying deep learning architectures that can robustly handle changes in magnification scale, ensuring that scale changes across anatomical structures do not disturb the inference results., Comment: 6 pages, 3 figures

We study the problem of progressive ensemble distillation: Given a large, pretrained teacher model $g$, we seek to decompose the model into smaller, low-inference cost student models $f_i$, such that progressively evaluating additional models in this ensemble leads to improved predictions. The resulting ensemble allows for flexibly tuning accuracy vs. inference cost at runtime, which is useful for a number of applications in on-device inference. The method we propose, B-DISTIL , relies on an algorithmic procedure that uses function composition over intermediate activations to construct expressive ensembles with similar performance as $g$ , but with smaller student models. We demonstrate the effectiveness of B-DISTIL by decomposing pretrained models across standard image, speech, and sensor datasets. We also provide theoretical guarantees in terms of convergence and generalization.

Hybrid magnets are currently under consideration as an economically viable option towards 20 T dipole magnets for next generation of particle accelerators. In these magnets, High Temperature Superconducting (HTS) materials are used in the high field part of the coil with so-called insert coils, and Low Temperature Superconductors (LTS) like Nb3Sn and Nb-Ti superconductors are used in the lower field region with so-called outsert coils. The attractiveness of the hybrid option lays on the fact that, on the one hand, the 20 T field level is beyond the Nb3Sn practical limits of 15-16 T for accelerator magnets and can be achieved only via HTS materials; on the other hand, the high cost of HTS superconductors compared to LTS superconductors makes it advantageous exploring a hybrid approach, where the HTS portion of the coil is minimized. We present in this paper an overview of different design options aimed at generating 20 T field in a 50 mm clear aperture. The coil layouts investigated include the Cos-theta design (CT), with its variations to reduce the conductor peak stress, namely the Canted Cos-theta design (CCT) and the Stress Management Cos-theta design (SMCT), and, in addition, the Block-type design (BL) including a form of stress management and the Common-Coil design (CC). Results from a magnetic and mechanical analysis are discussed, with particular focus on the comparison between the different options regarding quantity of superconducting material, field quality, conductor peak stress, and quench protection.

Networks of tryptophan -- an aromatic amino acid with strong fluorescent response -- are ubiquitous in biological systems, forming diverse architectures in transmembrane proteins, cytoskeletal filaments, sub-neuronal elements, photoreceptor complexes, virion capsids, and other cellular structures. We analyze the cooperative effects induced by ultraviolet (UV) excitation of several biologically relevant tryptophan mega-networks, thus giving insight into novel mechanisms for cellular signalling and control. Our theoretical analysis in the single-excitation manifold predicts the formation of strongly superradiant states due to collective interactions among organized arrangements of up to more than $10^5$ tryptophan UV-excited transition dipoles in microtubule architectures, which leads to an enhancement of the fluorescence quantum yield that is confirmed by our experiments. We demonstrate the observed consequences of this superradiant behavior in the fluorescence quantum yield for hierarchically organized tubulin structures, which increases in different geometric regimes at thermal equilibrium before saturation -- highlighting the effect's persistence in the presence of disorder.

He$^+$ ion irradiation is used to pattern multiple areas of Pt/Co/W films with different irradiation doses in Hall bars. The resulting perpendicular magnetic anisotropy landscape enables selective multilevel current-induced switching, with full deterministic control of the position and order of the individual switching elements. Key pattern design parameters are specified, opening a way to scalable multilevel switching devices., Comment: 7 pages, 3 figures

Chronic Obstructive Pulmonary Disease (COPD) is a progressive lung disease characterized by airflow limitation. This study develops a systems engineering framework for representing important mechanistic details of COPD in a model of the cardio-respiratory system. In this model, we present the cardio-respiratory system as an integrated biological control system responsible for regulating breathing. Four engineering control system components are considered: sensor, controller, actuator, and the process itself. Knowledge of human anatomy and physiology is used to develop appropriate mechanistic mathematical models for each component. Following a systematic analysis of the computational model, we identify three physiological parameters associated with reproducing clinical manifestations of COPD - changes in the forced expiratory volume (FEV), lung volumes, and pulmonary hypertension. We quantify the changes in these parameters (airway resistance, lung elastance, and pulmonary resistance) as the ones that result in a systemic response that is diagnostic of COPD. A multivariate analysis reveals that the changes in airway resistance have a broad impact on the human cardio-respiratory system, and that the pulmonary circuit is stressed beyond normal under hypoxic environments in most COPD patients., Comment: 25 pages, 15 figures

This paper presents a new shield configuration for cryogenic current comparators (CCCs), namely the folded coaxial geometry. An analytical model describing its shielding performance is first developed, and then validated by means of finite element simulations. Thanks to this model, the fundamental properties of the new shield are highlighted. Additionally, this paper compares the volumetric performance of the folded coaxial shield to the one of a ring shield, the latter being installed in many CCCs for measuring particle beam currents in accelerator facilities., Comment: 13 pages, 15 figures

Electric charge transport of hadronic matter at finite temperature and magnetic field is studied within the linear sigma model. Anisotropic transport coefficients associated with the charge transport are estimated both in the weak and strong regimes of the magnetic field using the transport theory approach. In a weakly magnetized medium, the magnetic field effects are incorporated through the Lorentz force term in the Boltzmann equation. Strong magnetic field puts further constraints on the motion of charged particles through Landau quantization. Magnetic field-dependent thermal relaxation time is obtained from interaction rates of hadrons with the S-matrix approach by considering the Landau level kinematics of the charged hadrons. Mean-field effects are embedded in the analysis through the temperature-dependent hadron masses. Further, the hadronic medium response to a time-varying external electric field is studied in weak and strong magnetic field regimes. It is seen that electromagnetic responses of the hadronic matter have a strong dependence on the mean-field effects, sigma mass, the strength of the external fields, and its evolution in the medium., Comment: 22 pages, 7 figures

The microscopic origins of terahertz (THz) vibrational modes in biological systems are an active and open area of current research. Recent experiments [Physical Review X 8, 031061 (2018)] have revealed the presence of a pronounced mode at $\sim$0.3 THz in fluorophore-decorated bovine serum albumin (BSA) protein in aqueous solution under nonequilibrium conditions induced by optical pumping. This result was heuristically interpreted as a collective elastic fluctuation originating from the activation of a low-frequency phonon mode. In this work, we show that the sub-THz spectroscopic response emerges in a statistically significant manner (> 2$\sigma$) from such collective behavior, illustrating how specific THz vibrational modes can be triggered through optical excitations and other charge reorganization processes. We revisit the theoretical analysis with proof-of-concept molecular dynamics that introduce optical excitations into the simulations. Using information theory techniques, we show that these excitations can induce a multiscale response involving the two optically excited chromophores (tryptophans), other amino acids in the protein, ions, and water. Our results motivate new experiments and fully nonequilibrium simulations to probe these phenomena, as well as the refinement of atomistic models of Fr\"ohlich condensates that are fundamentally determined by nonlinear interactions in biology., Comment: 47 pages in total: consisting of 43 of main manuscipt and 4 pages of supporting information

Automated test generation based on symbolic execution can be beneficial for systematically testing safety-critical software, to facilitate test engineers to pursue the strict testing requirements mandated by the certification standards, while controlling at the same time the costs of the testing process. At the same time, the development of safety-critical software is often constrained with programming languages or coding conventions that ban linguistic features which are believed to downgrade the safety of the programs, e.g., they do not allow dynamic memory allocation and variable-length arrays, limit the way in which loops are used, forbid recursion, and bound the complexity of control conditions. As a matter of facts, these linguistic features are also the main efficiency-blockers for the test generation approaches based on symbolic execution at the state of the art. This paper contributes new evidence of the effectiveness of generating test cases with symbolic execution for a significant class of industrial safety critical-systems. We specifically focus on Scade, a largely adopted model-based development language for safety-critical embedded software, and we report on a case study in which we exploited symbolic execution to automatically generate test cases for a set of safety-critical programs developed in Scade. To this end, we introduce a novel test generator that we developed in a recent industrial project on testing safety-critical railway software written in Scade, and we report on our experience of using this test generator for testing a set of Scade programs that belong to the development of an on-board signaling unit for high-speed rail. The results provide empirically evidence that symbolic execution is indeed a viable approach for generating high-quality test suites for the safety-critical programs considered in our case study.

The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade. Automated mitosis detection through deep learning-oriented image analysis often fails on unseen patient data due to inherent domain biases. This paper proposes a domain homogenizer for mitosis detection that attempts to alleviate domain differences in histology images via adversarial reconstruction of input images. The proposed homogenizer is based on a U-Net architecture and can effectively reduce domain differences commonly seen with histology imaging data. We demonstrate our domain homogenizer's effectiveness by observing the reduction in domain differences between the preprocessed images. Using this homogenizer, along with a subsequent retina-net object detector, we were able to outperform the baselines of the 2021 MIDOG challenge in terms of average precision of the detected mitotic figures.

We present a single-beam atomic magnetometer operating at room temperature for the measurement of ac magnetic fields. The magnetometer functions in the non-linear regime of magneto-optical rotation of $^{85}$Rb atomic vapour. We demonstrate a sensitivity of $\sim 0.9$ pT$/ \sqrt{Hz}$ at 2 kHz and a large bandwidth of 24 kHz. The dynamic range of measurement is $10^6$, making the sensor effective even in Earth's field. We present the signal-to-noise and bandwidth characteristics of the system for both shielded and unshielded modes of operation. Moreover, we perform theoretical analysis for the atom-light system for the single laser beam configuration. The effect of light intensity and detuning on the magnetometer are studied theoretically as well as experimentally to understand the strengths and limitations of the technique., Comment: 7 pages, 6 Figures