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Energy management decreases energy expenditures and consumption while simultaneously increasing energy efficiency, reducing carbon emissions, and enhancing operational performance. Smart grids are a type of sophisticated energy infrastructure that increase the generation and distribution of electricity's sustainability, dependability, and efficiency by utilizing digital communication technologies. They combine a number of cutting-edge techniques and technology to improve energy resource management. A large amount of research study on the topic of smart grids for energy management has been completed in the last several years. The authors of the present study want to cover a number of topics, including smart grid benefits and components, technical developments, integrating renewable energy sources, using artificial intelligence and data analytics, cybersecurity, and privacy. Smart Grids for Energy Management are an innovative field of study aiming at tackling various difficulties and magnifying the efficiency, dependability, and sustainability of energy systems, including: 1) Renewable sources of power like solar and wind are intermittent and unpredictable 2) Defending smart grid system from various cyber-attacks 3) Incorporating an increasing number of electric vehicles into the system of power grid without overwhelming it. Additionally, it is proposed to use AI and data analytics for better performance on the grid, reliability, and energy management. It also looks into how AI and data analytics can be used to optimize grid performance, enhance reliability, and improve energy management. The authors will explore these significant challenges and ongoing research. Lastly, significant issues in this field are noted, and recommendations for further work are provided.

The paper investigates a pivotal condition for the Bobkov-Tanaka type spectrum for double-phase operators. This condition is satisfied if either the weight $w$ driving the double-phase operator is strictly positive in the whole domain or the domain is convex and fulfils a suitable symmetry condition.

In this article we show how certain irreducible unitary representation $ \Pi_\lambda $ of the twisted Heisenberg group $ \He_\lambda^n(\C)$ leads to the twisted modulation spaces $ M_\lambda^{p,q}(\R^{2n}).$ These $ \Pi_\lambda $ also turn out to be irreducible unitary representations of another nilpotent Lie group $ G_n $ which contains two copies of the Heisenberg group $ \He^n.$ By lifting $ \Pi_\lambda $ we obtain another unitary representation $ \Pi $ of $ G_n $ acting on $ L^2(\He^n).$ We define our modulation spaces $ M^{p,q}(\He^n) $ in terms of the matrix coefficients associated to $ \Pi.$ These spaces are shown to be invariant under Heisenberg translations and Heisenberg modulations which are different from euclidean modulations. We also establish some of the basic properties of $ M_\lambda^{p,q}(\R^{2n})$ and $ M^{p,q}(\He^n) $ such as completeness and invariance under suitable Fourier transforms., Comment: 28 pages

Substance use disorders (SUDs) are a serious public health concern in the United States. Alcohol and cannabis are two of the most widely used substances. For adolescent/youth users of alcohol or cannabis, we propose a joint Bayesian learning model to predict their risks of developing alcohol use disorder (AUD) and cannabis use disorder (CUD) in adulthood based on their personal risk factors. The model is trained on nationally representative longitudinal data from Add Health (n = 12503). It consists of sub-models that predict the two SUDs for three groups of users-those who use alcohol only, cannabis only, and both substances - based on shared as well as unique risk factors. The model comprises of ten predictors. We externally validate the model on two independent datasets. The areas under the receiver operating characteristic curves for AUD and CUD, respectively, are: (a) 0.719 and 0.690 based on 5-fold cross-validation, (b) 0.748 and 0.710 based on validation dataset 1, and (c) 0.650 and 0.750 based on validation dataset 2. A simulation study shows that the proposed joint modeling approach generally performs better than separate univariate modeling of the corresponding dependent outcomes in terms of predictive accuracy. Our model may help in identifying adolescent substance users at high risk of developing SUD in adulthood, who can then be helped with appropriate intervention.

The Standard Model Effective Field Theory (SMEFT) provides a robust framework for probing deviations in the couplings of Standard Model particles from their theoretical predictions. This framework relies on an expansion in higher-dimensional operators, often truncated at dimension-six. In this work, we compute the effective dimension-eight operators generated by integrating out heavy scalar fields at one-loop order in the Green's basis within two extended scalar sector models: the Two Higgs Doublet Model and the Complex Triplet Scalar Model. We also investigate the impact of heavy scalar fields on the fermion sector, deriving the fermionic effective operators up to dimension eight for these models, and detail how contributions can be mapped onto non-redundant bases. To assess the importance of higher-order contributions in the SMEFT expansion, we analyze the dimension-eight effects for electroweak precision observables at the next frontier of precision lepton machines such as GigaZ., Comment: 47 pages, 3 figures, 9 tables

HR 5907 (HD 142184) stands out among magnetic OB stars for its rapid rotation, exceptionally hard X-ray emission, and strong magnetic field. High-frequency (>5 GHz) radio emission from the star exhibits an approximately flat spectrum that can be attributed to gyrosynchrotron emission from a dense centrifugal magnetosphere. In a survey of radio emission from massive stars at sub-GHz frequencies, we noticed remarkable low-frequency radio emission from this star, characterized by high circular polarization and brightness temperature, which is inconsistent with the gyrosynchrotron model. We present a follow-up low-frequency radio study of this star with the upgraded Giant Metrewave Radio Telescope (uGMRT) in search of emission mechanisms that can go undiagnosed at higher frequencies. We detect variable radio emission characterized by varying degrees of circular polarization (15-45%) throughout the rotation cycle. The broad-band spectral fitting also suggests additional emission components at lower frequencies. We show that the observed emission is likely auroral emission via electron cyclotron maser emission (ECME), and identify this star as a Main-sequence Radio Pulse emitter (MRP). For MRPs, ECME is usually observed as short polarized enhancements near the magnetic nulls of the star. The detection of a high degree of circular polarization (>15%) at all times makes HR 5907 unique among MRPs. This is only the second MRP after $\rho$ Oph C (detected polarization fraction: 0-60%) that exhibits persistent coherent radio emission attributed to the nearly aligned stellar magnetic and rotational axes., Comment: 14 pages, 7 figures, 2 tables, accepted for publication in ApJ

The growing use of autonomous mobile service robots (AMSRs) in dynamic environments requires flexible management of compute resources to optimize the performance of diverse tasks such as navigation, localization, perception, and so on. Current robot deployments, which oftentimes rely on static configurations (of the OS, applications, etc.) and system over-provisioning, fall short since they do not account for the tasks' performance variations resulting in poor system-wide behavior such as robot instability and/or inefficient resource use. This paper presents ConfigBot, a system designed to adaptively reconfigure AMSR applications to meet a predefined performance specification by leveraging runtime profiling and automated configuration tuning. Through experiments on a Boston Dynamics Spot robot equipped with NVIDIA AGX Orin, we demonstrate ConfigBot's efficacy in maintaining system stability and optimizing resource allocation across diverse scenarios. Our findings highlight the promise of tailored and dynamic configurations for robot deployments., Comment: 14 pages, 13 figures, 6 tables

Muon-Induced X-ray Emission (MIXE) is a non-destructive analytical technique that leverages negative muons to probe elemental and isotopic compositions by detecting characteristic muonic X-rays emitted during atomic cascades and gamma rays from nuclear capture processes. By controlling the muon beam momentum, MIXE enables depth-resolved analysis, spanning microns to centimeters, making it ideal for studying compositional variations in fragile, valuable, or operando samples. To enhance its capabilities, we integrated a twin Time Projection Chamber (TPC) tracker with Gas Electron Multiplier (GEM) amplification stages, allowing precise measurement of muon trajectories. A custom-built fiber detector with scintillating fibers and a Silicon Photomultiplier (SiPM) provided sub-percent-level accuracy in drift velocity calibration, essential for accurate spatial reconstruction. This advanced setup correlates muon stopping points with X-ray emissions, paving the way towards element-sensitive imaging and establishing MIXE as a unique tool for high-resolution, depth-specific elemental analysis across diverse scientific applications., Comment: 4 pages, 3 figures, conference proceeding MPGD2024

Recent advancements in language-guided diffusion models for image editing are often bottle-necked by cumbersome prompt engineering to precisely articulate desired changes. An intuitive alternative calls on guidance from in-the-wild image exemplars to help users bring their imagined edits to life. Contemporary exemplar-based editing methods shy away from leveraging the rich latent space learnt by pre-existing large text-to-image (TTI) models and fall back on training with curated objective functions to achieve the task. Though somewhat effective, this demands significant computational resources and lacks compatibility with diverse base models and arbitrary exemplar count. On further investigation, we also find that these techniques restrict user control to only applying uniform global changes over the entire edited region. In this paper, we introduce a novel framework for progressive exemplar-driven editing with off-the-shelf diffusion models, dubbed PIXELS, to enable customization by providing granular control over edits, allowing adjustments at the pixel or region level. Our method operates solely during inference to facilitate imitative editing, enabling users to draw inspiration from a dynamic number of reference images, or multimodal prompts, and progressively incorporate all the desired changes without retraining or fine-tuning existing TTI models. This capability of fine-grained control opens up a range of new possibilities, including selective modification of individual objects and specifying gradual spatial changes. We demonstrate that PIXELS delivers high-quality edits efficiently, leading to a notable improvement in quantitative metrics as well as human evaluation. By making high-quality image editing more accessible, PIXELS has the potential to enable professional-grade edits to a wider audience with the ease of using any open-source image generation model.

The Gaseous Electron Multiplier-based Time Projection Chamber (GEM-TPC) in TWIN configuration for particle tracking has been consolidated after extensive investigations in different facilities to study its tracking performance. The most attractive feature of this detector is its ultra-low material budget, which is 0.28\% X/X$_0$ and can be further reduced by decreasing the thickness of the gas traversed by the incident particles. Thus, it provides excellent position reconstruction and reduced multi-scattering. This detector consists of two GEM-TPCs with drift fields in opposite directions, achieved by rotating one 180 degrees in the middle plane with respect to the other. These two GEM-TPCs share the same gas volume, i.e., inside a single vessel. This configuration is called a TWIN configuration. The results presented in this work were measured using the newly integrated VMM3a/SRS readout electronics, an important milestone in improving overall performance and capabilities. In 2024, this detector was tested at the H4 beamline of the SPS at CERN, using muons and pions and with different gas mixtures like, for instance: Ar/CO$_2$ (70/30 \%), He/CO$_2$ (70/30 \%) and He/CO$_2$ (90/10 \%). The helium-based mixtures were used to commission the detector to track low momenta muons required in the PSI muon-induced X-ray emission (MIXE) experiment. The results obtained from these measurements, a brief discussion of the methodology used for the data analysis, and a comparison of the spatial resolution for different gas mixtures will be presented., Comment: 5 pages, 5 figures

Estimating rare event kinetics from molecular dynamics simulations is a non-trivial task despite the great advances in enhanced sampling methods. Weighted Ensemble (WE) simulation, a special class of enhanced sampling techniques, offers a way to directly calculate kinetic rate constants from biased trajectories without the need to modify the underlying energy landscape using bias potentials. Conventional WE algorithms use different binning schemes to partition the collective variable (CV) space separating the two metastable states of interest. In this work, we have developed a new "binless" WE simulation algorithm to bypass the hurdles of optimizing binning procedures. Our proposed protocol (WeTICA) uses a low-dimensional CV space to drive the WE simulation toward the specified target state. We have applied this new algorithm to recover the unfolding kinetics of three proteins: (A) TC5b Trp-cage mutant, (B) TC10b Trp-cage mutant, and (C) Protein G, with unfolding times spanning the range between 3 and 40 {\mu}s using projections along predefined fixed Time-lagged Independent Component Analysis (TICA) eigenvectors as CVs. Calculated unfolding times converge to the reported values with good accuracy with more than one order of magnitude less cumulative WE simulation time than the unfolding time scales with or without a priori knowledge of the CVs that can capture unfolding. Our algorithm can be used with other linear CVs, not limited to TICA. Moreover, the new walker selection criteria for resampling employed in this algorithm can be used on more sophisticated nonlinear CV space for further improvements of binless WE methods., Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in The Journal of Chemical Physics (J. Chem. Phys. 162, 034106 (2025)) and may be found at https://doi.org/10.1063/5.0239713

This paper introduces an Unmanned Aerial Vehicle - enabled content management architecture that is suitable for critical content access in communities of users that are communication-isolated during diverse types of disaster scenarios. The proposed architecture leverages a hybrid network of stationary anchor UAVs and mobile Micro-UAVs for ubiquitous content dissemination. The anchor UAVs are equipped with both vertical and lateral communication links, and they serve local users, while the mobile micro-ferrying UAVs extend coverage across communities with increased mobility. The focus is on developing a content dissemination system that dynamically learns optimal caching policies to maximize content availability. The core innovation is an adaptive content dissemination framework based on distributed Federated Multi-Armed Bandit learning. The goal is to optimize UAV content caching decisions based on geo-temporal content popularity and user demand variations. A Selective Caching Algorithm is also introduced to reduce redundant content replication by incorporating inter-UAV information sharing. This method strategically preserves the uniqueness in user preferences while amalgamating the intelligence across a distributed learning system. This approach improves the learning algorithm's ability to adapt to diverse user preferences. Functional verification and performance evaluation confirm the proposed architecture's utility across different network sizes, UAV swarms, and content popularity patterns., Comment: 25 pages, 11 figures, 1 table, 4 algorithms, journal

Introduction: Substance use disorders (SUDs) have emerged as a pressing public health crisis in the United States, with adolescent substance use often leading to SUDs in adulthood. Effective strategies are needed to prevent this progression. To help in filling this need, we develop a novel and the first-ever absolute risk prediction model for cannabis use disorder (CUD) for adolescent or young adult cannabis users. Methods: We train a Bayesian machine learning model that provides a personalized CUD absolute risk for adolescent or young adult cannabis users using data from the National Longitudinal Study of Adolescent to Adult Health. Model performance is assessed using 5-fold cross-validation (CV) with area under the curve (AUC) and ratio of the expected to observed number of cases (E/O). External validation of the final model is conducted using two independent datasets. Results: The proposed model has five risk factors: biological sex, delinquency, and scores on personality traits of conscientiousness, neuroticism, and openness. For predicting CUD risk within five years of first cannabis use, AUC and E/O, computed via 5-fold CV, were 0.68 and 0.95, respectively. For the same type of prediction in external validation, AUC values were 0.64 and 0.75, with E/O values of 0.98 and 1, indicating good discrimination and calibration performances of the model. Discussion and Conclusion: The proposed model is the first absolute risk prediction model for an SUD. It can aid clinicians in identifying adolescent/youth substance users at a high risk of developing CUD in future for clinically appropriate interventions.

We describe the development of a one-credit course to promote AI literacy at The University of Texas at Austin. In response to a call for the rapid deployment of class to serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinformation and employment. University students, faculty, and staff, and even community members outside of the University, were invited to enroll in this online offering: The Essentials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final survey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quantitative and qualitative analysis, which uncovered challenges in designing a course for this general audience. We utilized the course feedback to design a three-credit version of the course that is being offered in Fall of 2024. The lessons we learned and our plans for this new iteration may serve as a guide to instructors designing AI courses for a broad audience., Comment: Accepted to EAAI-25: The 15th Symposium on Educational Advances in Artificial Intelligence, collocated with AAAI-25

This study introduces an advanced methodology for automatically identifying minor deformations in flat walls caused by vibrations from nearby railway tracks. It leverages high-density Terrestrial Laser Scanner (TLS) LiDAR surveys and AI/ML techniques to collect and analyze data. The scan data is processed into a detailed point cloud, which is segmented to distinguish ground points, trees, buildings, and other objects. The analysis focuses on identifying sections along flat walls and estimating their deformations relative to the ground orientation. Findings from the study, conducted at the RGIPT campus, reveal significant deformations in walls close to the railway corridor, with the highest deformations ranging from 7 to 8 cm and an average of 3 to 4 cm. In contrast, walls further from the corridor show negligible deformations. The developed automated process for feature extraction and deformation monitoring demonstrates potential for structural health monitoring. By integrating LiDAR data with machine learning, the methodology provides an efficient system for identifying and analyzing structural deformations, highlighting the importance of continuous monitoring for ensuring structural integrity and public safety in urban infrastructure. This approach represents a substantial advancement in automated feature extraction and deformation analysis, contributing to more effective management of urban infrastructure., Comment: IEEE Conference Paper

Self-assembled nano and micro-structures, particularly those capable of responsive erasure and regeneration, have garnered significant interest for their applications in smart photonics and electronics. However, current techniques for modulating these architectures largely depend on network rearrangement, posing challenges for in situ regeneration. Furthermore, their common fabrication techniques are complex and uncontrolled with the structures formed not being amenable for large area applications, thus compromising their economic viability. Herein, we present a controlled thermal process strategy for fabricating large-area, dynamically tunable, 1D,2D and 3D micro and nanostructures on a wide range of compatible materials including metals, semiconductors and polymers. By tuning the temperature changes in the system, thermal expansion coefficients of thin films and substrates, surface energy, Youngs modulus and thickness of the thin films we achieve robust, uniform, periodic structures over extensive areas on soft and stretchable substrates. The process is further supported by a theoretical model that we developed and validated by experiments and simulations. To showcase the robustness of our approach, we present prototypes of dynamically tunable diffraction gratings, optical diffusers, large-area reflective displays, camouflage devices, out-coupling efficiency enhancers, wearable devices and mechanochromic sensors., Comment: The legend to figure 1 was missing in the previous version

This paper deals with the following nonlocal system of equations: \begin{align}\tag{$\mathcal S$}\label{MAT1} (-\Delta_p)^s u = \frac{\alpha}{p_s^*}|u|^{\alpha-2}u|v|^{\beta}+f(x) \text{ in } \mathbb{R}^{d}, \, (-\Delta_p)^s v = \frac{\beta}{p_s^*}|v|^{\beta-2}v|u|^{\alpha}+g(x) \text{ in } \mathbb{R}^{d},\, u,v >0 \mbox{ in } \mathbb{R}^{d}, \end{align} where $s\in(0,1)$, $p\in(1,\infty)$, $d>sp$, $\alpha,\beta>1$, $\alpha+\beta=dp/(d-sp)$, and $f,\, g$ are nonnegative functionals in the dual space of $\mathcal{D}^{s,p}(\mathbb{R}^{d})$. When $f=g=0$, we prove a uniqueness result for the ground state solution of \eqref{MAT1}. On the other hand, when $f \not \equiv 0, g \not \equiv 0$, the primary objective is to present a global compactness result that offers a complete characterization of the Palais-Smale sequences of the energy functional associated with \eqref{MAT1}. Using this characterization, within a certain range of $s$, we establish the existence of a solution with negative energy for \eqref{MAT1} when $\ker(f)=\ker(g)$., Comment: 35 pages

Let $X_0$ be an irreducible smooth projective curve defined over $\overline{\mathbb Q}$ and $f_0 : X_0 \rightarrow \mathbb{P}^1_{\overline{\mathbb Q}}$ a nonconstant morphism whose branch locus is contained in the subset $\{0,1, \infty\} \subset \mathbb{P}^1_{\overline{\mathbb Q}}$. For any vector bundle $E$ on $X = X_0\times_{{\rm Spec}\,\overline{\mathbb Q}} {\rm Spec} \mathbb{C}$, consider the direct image $f_*E$ on $\mathbb{P}^1_{\mathbb C}$, where $f= (f_0)_{\mathbb C}$. It decomposes into a direct sum of line bundles and also it has a natural parabolic structure. We prove that $E$ is the base change, to $\mathbb C$, of a vector bundle on $X_0$ if and only if there is an isomorphism $f_*E \stackrel{\sim}{\rightarrow} \bigoplus_{i=1}^r {\mathcal O}_{{\mathbb P}^1_{\mathbb C}}(m_i)$, where $r = {\rm rank}(f_*E)$, that takes the parabolic structure on $f_*E$ to a parabolic structure on $\bigoplus_{i=1}^r {\mathcal O}_{{\mathbb P}^1_{\mathbb C}}(m_i)$ defined over $\overline{\mathbb Q}$., Comment: Final version; to appear in Michigan Mathematical Journal

In this study, the intermittency behavior of emitted particles produced in heavy ion collisions has been studied using both modes (default $\&$ string melting) of A Multi Phase Transport (AMPT) model-generated data. We adopted one of the most conventional and successful techniques, the Scaled Factorial Moment (SFM) method, using Monte Carlo (MC) data for 10 AGeV Au+Au collisions in search of intermittency in the model-generated data. Our interest is to search for intermittency behavior of particles that leads to multiplicity fluctuations and that would reveal a phase transition from hadronic matter to QGP. In this article, the intermittency values for both modes of AMPT data are presented. The results obtain some insight into the dynamics of heavy ion collisions and the formation of QGP., Comment: A complete article with eight figures

We investigate the formation of helical multifilament bundles and the torque required to achieve them as a function of applied twist. Hyperelastic filaments with circular cross sections are mounted parallel in a uniform circle onto end-clamps that can move along the twist axis depending on the applied axial load. With increasing twist, the filaments describe a hyperbolic hyperboloid surface before coming into contact in a circle, and then packing in a tight helical bundle in the center with increasing twist. While the bundle appears ordered for sufficiently small number of filaments, they are disordered for large enough number of filaments and applied twist. We reveal with x-ray tomography, that the packing of the filaments becomes disordered following a radial-instability which leads to a decrease in bundle radius, and migration of filaments relative to each other in the bundle. Nonetheless, the helical angle of the filaments in the bundle are found to be essentially constant, resulting in inclination angles which increase with distance from axis of rotation. We develop energy minimization analysis to capture the observed variations in bundle length and torque as a function of number of filaments considering the neo-Hookean nature of the filaments. We show that the bundle geometry and the applied load can be used to describe the non-linear torque profile measured as a function of twist angle.

The superheated emulsion detector consisting of the droplets of tetra-fluoroethane (C2HC$_2$H$_2$F$_4$2F4) has been fabricated at the laboratory and installed at the 555m deep underground laboratory, JUSL during July to Dec 2022. The 500ml detector ran for an effective period of 48.6 days at a threshold of 5.87 keV with an exposure of 2.47 kg-days. The acoustic signals produced due to the bubble nucleation were collected by the acoustic sensor and FPGAbased data acquisition system. The data shows a minimum sensitivity of SI-nucleon for carbon at WIMP mass of 22.81 GeV/c$^2$ and SD (p) for fluorine at 30.67 GeV/c$^2$. The threshold of WIMP mass is 5.16 GeV/c$^2$ for F and 4.44 GeV/c$^2$ for C at the operating threshold of 5.87 keV. The first result of the dark matter direct search experiment named InDEx with tetra-fluoro-ethane active liquid from JUSL underground laboratory is reported in this article.

Algorithmic fairness of machine learning (ML) models has raised significant concern in the recent years. Many testing, verification, and bias mitigation techniques have been proposed to identify and reduce fairness issues in ML models. The existing methods are model-centric and designed to detect fairness issues under static settings. However, many ML-enabled systems operate in a dynamic environment where the predictive decisions made by the system impact the environment, which in turn affects future decision-making. Such a self-reinforcing feedback loop can cause fairness violations in the long term, even if the immediate outcomes are fair. In this paper, we propose a simulation-based framework called FairSense to detect and analyze long-term unfairness in ML-enabled systems. Given a fairness requirement, FairSense performs Monte-Carlo simulation to enumerate evolution traces for each system configuration. Then, FairSense performs sensitivity analysis on the space of possible configurations to understand the impact of design options and environmental factors on the long-term fairness of the system. We demonstrate FairSense's potential utility through three real-world case studies: Loan lending, opioids risk scoring, and predictive policing., Comment: In Proceedings of the 47th International Conference on Software Engineering (ICSE 2025)

Let $\mathcal{H}$ be the class of harmonic functions $f=h+\overline{g}$ in the unit disk $\mathbb{D}:=\{z\in\mathbb{C}:|z|<1\}$, where $h$ and $g$ are analytic in $\mathbb{D}$ with the normalization $h(0)=g(0)=h'(0)-1=0$. Let $\mathcal{P}_{\mathcal{H}}^0(\alpha,M)$ denote the subclass of $\mathcal{H}$ in $\mathbb{D}$ satisfying $\text{Re}\left((1-\alpha)h'(z)+\alpha zh''(z)\right)>-M+\left|(1-\alpha)g'(z)+\alpha zg''(z)\right|$ with $g'(0)=0$, $M>0$ and $\alpha\in(0,1]$. In this paper, we investigate some fundamental properties of functions belonging to the class $\mathcal{P}_{\mathcal{H}}^0(\alpha,M)$, including coefficient bounds, growth estimates, convexity, starlikeness, convex combinations, and convolution., Comment: 15 Pages

The objective of this paper is to investigate the existence and the forms of the pair of finite order entire and meromorphic solutions of some certain systems of Fermat-type partial differential-difference equations of several complex variables. These results represent some refinements and generalizations of the earlier findings, especially the results due to Xu {\it et al.} (J. Math. Anal. Appl. 483(2) (2020)). We provide some examples to support the results., Comment: 24 Pages

Let $C$ be a smooth projective curve defined over the field of complex numbers. Let $E$ be a vector bundle on $C$, and fix an integer $d\geqslant 1$. Let $\mc Q:={\rm Quot}(E,d)$ be the Quot Scheme which parameterizes all torsion quotients of $E$ of degree $d$. In this article, we survey some recent results on various invariants of $\mc Q$., Comment: Final version; to appear in ICTS Conference proceedings. arXiv admin note: text overlap with arXiv:2203.13150, arXiv:2006.16666

Over the past decade, several image-processing methods and algorithms have been proposed for identifying plant diseases based on visual data. DNN (Deep Neural Networks) have recently become popular for this task. Both traditional image processing and DNN-based methods encounter significant performance issues in real-time detection owing to computational limitations and a broad spectrum of plant disease features. This article proposes a novel technique for identifying and localising plant disease based on the Quad-Tree decomposition of an image and feature learning simultaneously. The proposed algorithm significantly improves accuracy and faster convergence in high-resolution images with relatively low computational load. Hence it is ideal for deploying the algorithm in a standalone processor in a remotely operated image acquisition and disease detection system, ideally mounted on drones and robots working on large agricultural fields. The technique proposed in this article is hybrid as it exploits the advantages of traditional image processing methods and DNN-based models at different scales, resulting in faster inference. The F1 score is approximately 0.80 for four disease classes corresponding to potato and tomato crops.

We present a measurement of the branching fraction and fraction of longitudinal polarization of $B^0 \to \rho^+ \rho^-$ decays, which have two $\pi^0$'s in the final state. We also measure time-dependent $CP$ violation parameters for decays into longitudinally polarized $\rho^+ \rho^-$ pairs. This analysis is based on a data sample containing $(387\pm6) \times 10^6$ \BBbar pairs collected with the Belle~II detector at the SuperKEKB asymmetric-energy $e^+e^-$ collider in 2019-2022. We obtain ${B}(B^0\to\rho^+\rho^-) = (2.88 ^{+0.23}_{-0.22} {}^{+0.29}_{-0.27}) \times 10^{-5}, f_{L} = 0.921 ^{+0.024}_{-0.025} {}^{+0.017}_{-0.015}$, $S = -0.26\pm0.19\pm0.08$, and $C = -0.02\pm0.12^{+0.06}_{-0.05}$, where the first uncertainties are statistical and the second are systematic. We use these results to perform an isospin analysis to constrain the CKM angle $\phi_2$ and obtain two solutions; the result consistent with other Standard Model constraints is $\phi_2 = (92.6^{+4.5}_{-4.8})^\circ$.

Quantum computation based on superconducting circuits utilizes superconducting qubits with Josephson tunnel junctions. Engineering high-coherence qubits requires materials optimization. In this work, we present two superconducting thin film systems, grown on silicon (Si), and one obtained from the other via annealing. Cobalt (Co) thin films grown on Si were found to be superconducting [EPL 131 (2020) 47001]. These films also happen to be a self-organised hybrid superconductor/ferromagnet/superconductor (S/F/S) structure. The S/F/S hybrids are important for superconducting $\pi$-qubits [PRL 95 (2005) 097001] and in quantum information processing. Here we present our results on the superconductivity of a hybrid Co film followed by the superconductivity of a CoSi$_2$ film, which was prepared by annealing the Co film. CoSi$_2$, with its $1/f$ noise about three orders of magnitude smaller compared to the most commonly used superconductor aluminium (Al), is a promising material for high-coherence qubits. The hybrid Co film revealed superconducting transition temperature $T_c$ = 5 K and anisotropy in the upper critical field between the in-plane and out-of-plane directions. The anisotropy was of the order of ratio of lateral dimensions to thickness of the superconducting Co grains, suggesting a quasi-2D nature of superconductivity. On the other hand, CoSi$_2$ film showed a $T_c$ of 900 mK. In the resistivity vs. temperature curve, we observe a peak near $T_c$. Magnetic field scan as a function of $T$ shows a monotonic increase in intensity of this peak with temperature. The origin of the peak has been explained in terms of parallel resistive model for the particular measurement configuration. Although our CoSi$_2$ film contains grain boundaries, we observed a perpendicular critical field of 15 mT and a critical current density of 3.8x10$^7$ A/m$^2$, comparable with epitaxial CoSi$_2$ films., Comment: 15 pages, 7 figures, 2 tables

In this paper, we solve certain Fermat-type partial differential-difference equations for finite order entire functions of several complex variables. These results are significant generalizations of some earlier findings, especially those of Haldar and Ahamed (Entire solutions of several quadratic binomial and trinomial partial differential-difference equations in $\mathbb{C}^2$, Anal. Math. Phys., 12 (2022)). In addition, the results improve the previous results from the situation with two complex variables to the situation with several complex variables. To support our results, we have included several examples., Comment: Pages-24, Latex-V1

The objective of this study is to ascertain the existence and forms of the finite order meromorphic and entire functions of several complex variables satisfying some certain Fermat-type partial differential-difference equations by considering the more general forms of the PDDEs in an open problem on $\mathbb{C}^2$ due to Xu and Wang (Notes on the existence of entire solutions for several partial differential-difference equations, Bull. Iran. Math. Soc., 47, 1477-1489 (2020)). We provide examples to illustrate the results., Comment: 17 Pages, Latex-V1

We propose and demonstrate numerically a fast classical annealing scheme for the Sherrington-Kirkpatrick (SK) spin glass model, employing the Suzuki-Kubo meanfield Ising dynamics (supplemented by a modified Thouless-Anderson-Palmer reaction field). The resultant dynamics, starting from any arbitrary paramagnetic phase (with local $m_i=\pm 1$ for the $i^{th}$ spin, and the global magnetization $m=0$), takes the system quickly to an appropriate state with small local values of magnetization ($m_i$) commensurate with the (frustrated) interactions. As the temperature decreases with the annealing, the configuration practically remain (in an effective adiabatic way) close to the ground state as the $m_i$'s and the spin glass order parameter $q$ grow to unity. For an $N$-spin SK model (with $N$ up to 10000) the deviation in the annealed ground state energy per spin $E^0_N - E^0$ is found to scale as $N^{-2/3}$, with $E^0 = -0.7633\pm 0.0002 $ (analytical estimate being $E^0 =-0.7631667265 \dots$), fluctuation $\sigma_N $ in $E^0_N$ decreases as $\sim N^{-3/4}$ and the annealing time $\tau_N \sim N$, making this protocol highly efficient in estimating the ground state of the SK model., Comment: 7 pages, 7 figures, 1 table

We study the recently-proposed hyperbolic approximation of the Korteweg-de Vries equation (KdV). We show that this approximation, which we call KdVH, possesses a rich variety of solutions, including solitary wave solutions that approximate KdV solitons, as well as other solitary and periodic solutions that are related to higher-order water wave models, and may include singularities. We analyze a class of implicit-explicit Runge-Kutta time discretizations for KdVH that are asymptotic preserving, energy conserving, and can be applied to other hyperbolized systems. We also develop structure-preserving spatial discretizations based on summation-by-parts operators in space including finite difference, discontinuous Galerkin, and Fourier methods. We use the relaxation approach to make the fully discrete schemes energy-preserving. Numerical experiments demonstrate the effectiveness of these discretizations., Comment: 29 pages

We present the first search for the lepton flavor-violating decay modes $B^0 \rightarrow K_S^0 \tau^\pm \ell^\mp~(\ell=\mu, e)$ using the 711 fb$^{-1}$ and 365 fb$^{-1}$ data samples recorded by the Belle and Belle II detectors, respectively. We use a hadronic $B$-tagging technique, and search for the signal decay in the system recoiling against the fully reconstructed $B$ meson. We find no evidence for $B^0 \rightarrow K_S^0 \tau^\pm \ell^\mp$ decays and set 90\% confidence level upper limits on the branching fractions in the range of $[0.8,\,3.6]\times10^{-5}$.

This work proposes a novel framework, Uncertainty-Guided Cross Attention Ensemble Mean Teacher (UG-CEMT), for achieving state-of-the-art performance in semi-supervised medical image segmentation. UG-CEMT leverages the strengths of co-training and knowledge distillation by combining a Cross-attention Ensemble Mean Teacher framework (CEMT) inspired by Vision Transformers (ViT) with uncertainty-guided consistency regularization and Sharpness-Aware Minimization emphasizing uncertainty. UG-CEMT improves semi-supervised performance while maintaining a consistent network architecture and task setting by fostering high disparity between sub-networks. Experiments demonstrate significant advantages over existing methods like Mean Teacher and Cross-pseudo Supervision in terms of disparity, domain generalization, and medical image segmentation performance. UG-CEMT achieves state-of-the-art results on multi-center prostate MRI and cardiac MRI datasets, where object segmentation is particularly challenging. Our results show that using only 10\% labeled data, UG-CEMT approaches the performance of fully supervised methods, demonstrating its effectiveness in exploiting unlabeled data for robust medical image segmentation. The code is publicly available at \url{https://github.com/Meghnak13/UG-CEMT}, Comment: Accepted in WACV 2025

We measure the branching fraction and $\it CP$-violating flavor-dependent rate asymmetry of $B^{0} \to \pi^{0} \pi^{0}$ decays reconstructed using the Belle II detector in an electron-positron collision sample containing $387 \times 10^{6}$ $B\overline{B}$ pairs. Using an optimized event selection, we find $126\pm 20$ signal decays in a fit to background-discriminating and flavor-sensitive distributions. The resulting branching fraction is $(1.25 \pm 0.23)\times 10^{-6}$ and the $\it CP$-violating asymmetry is $0.03 \pm 0.30$.

Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.

We measure the branching fraction of the decay $B^0 \to J/\psi \omega$ using data collected with the Belle II detector at the SuperKEKB collider. The data contain $(387 \pm 6) \times 10^6$ $B\overline{B}$ meson pairs produced in energy-asymmetric $e^+e^-$ collisions at the $\Upsilon (4S)$ resonance. The measured branching fraction $\mathcal{B}(B^0 \to J/\psi \omega) = \left( 2.16 \pm 0.30 \pm 0.14 \right) \times 10^{-5}$, where the first uncertainty is statistical and the second is systematic, is more precise than previous results and constitutes the first observation of the decay with a significance of $6.5$ standard deviations.

We have analytically examined the integral form of Gauss' law for arbitrarily moving charges inside and outside an arbitrarily expanding (or contracting) and deforming Gaussian surface with explicit use of the motion of the Gaussian surface and the Maxwell equations under consideration. We have obtained a simple-looking differential equation $\frac{\text{d}}{\text{d}\text{t}}\oint_{s(t)}\vec{E}\cdot\text{d}\vec{s}(t)=\frac{I^{(s)}_{\text{in}}(t)}{\epsilon_0}$ for time-dependent Gauss' flux-integral. We have explicitly calculated time-dependent Gauss' flux-integral for the moving surface and have found no changes in the original result which was obtained without explicit use of the motion. Our explicit derivation of Gauss' flux-integral for the moving surface can be directly useful in an undergraduate/postgraduate physics class for a better understanding of Gauss' law for electrodynamics., Comment: 4 pages, 1 figure

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

Using data samples of 983.0~$\rm fb^{-1}$ and 427.9~$\rm fb^{-1}$ accumulated with the Belle and Belle~II detectors operating at the KEKB and SuperKEKB asymmetric-energy $e^+e^-$ colliders, singly Cabibbo-suppressed decays $\Xi_c^{+} \to pK_{S}^{0}$, $\Xi_c^+ \to \Lambda \pi^+$, and $\Xi_c^+ \to \Sigma^{0} \pi^+$ are observed for the first time. The ratios of branching fractions of $\Xi_{c}^{+}\to p K_{S}^{0}$, $\Xi_{c}^{+}\to \Lambda \pi^{+}$, and $\Xi_{c}^{+}\to \Sigma^{0} \pi^{+}$ relative to that of $\Xi_c^+ \to \Xi^- \pi^{+} \pi^{+}$ are measured to be \begin{equation} \frac{{\cal B}(\Xi_c^+ \to pK_S^0)}{{\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+ \pi^+)} = (2.47 \pm 0.16 \pm 0.07)\% \notag, \end{equation} \begin{equation} \frac{{\cal B}(\Xi_c^+ \to \Lambda \pi^+)}{{\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+ \pi^+)} = (1.56 \pm 0.14 \pm 0.09)\% \notag, \end{equation} \begin{equation} \frac{{\cal B}(\Xi_c^+ \to \Sigma^0 \pi^+)}{{\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+ \pi^+)} = (4.13 \pm 0.26 \pm 0.22)\% \notag. \end{equation} Multiplying these values by the branching fraction of the normalization channel, ${\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+\pi^+) = (2.9 \pm 1.3)\%$, the absolute branching fractions are determined to be \begin{equation} {\cal B}(\Xi_c^{+} \to p K_{S}^{0}) = (7.16 \pm 0.46 \pm 0.20 \pm 3.21) \times 10^{-4} \notag, \end{equation} \begin{equation} {\cal B}(\Xi_c^{+} \to \Lambda \pi^+) = (4.52 \pm 0.41 \pm 0.26 \pm 2.03) \times 10^{-4} \notag, \end{equation} \begin{equation} {\cal B}(\Xi_c^{+} \to \Sigma^0 \pi^+) = (1.20 \pm 0.08 \pm 0.07 \pm 0.54) \times 10^{-3} \notag. \end{equation} The first and second uncertainties above are statistical and systematic, respectively, while the third ones arise from the uncertainty in ${\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^{+} \pi^{+})$., Comment: 21 pages, 5 pages

We present the first example where length scale for the growth of ordered regions and the correlation length for the two point correlations of persistent sites scale differently with time. We do so by studying a global spin exchange dynamics in one dimension where a selected spin interacts with its two nearest domains. We found domain growth exponent $z=2.47\pm 0.03$ and the persistence exponent $\theta=0.445\pm0.002$, making $z\theta > d=1$. Unlike any previous study, we found correlation length of two point correlation of persistent sites grows in a power law with exponent $\zeta=1.00\pm 0.03$ by studying the fractal structure created by the persistent sites at the different stages of the dynamics and shown that fractal dimension is not related with any growth exponents., Comment: 6 pages, 8 figures

We examine the behavior of a colloidal particle immersed in a viscoelastic bath undergoing stochastic resetting at a rate $r$. Microscopic probes suspended in viscoelastic environment do not follow the classical theory of Brownian motion. This is primarily because the memory from successive collisions between the medium particles and the probes does not necessarily decay instantly as opposed to the classical Langevin equation. To treat such a system one needs to incorporate the memory effects to the Langevin equation. The resulting equation formulated by Kubo, known as the Generalized Langevin equation (GLE), has been instrumental to describe the transport of particles in inhomogeneous or viscoelastic environments. The purpose of this work, henceforth, is to study the behavior of such a colloidal particle governed by the GLE under resetting dynamics. To this end, we extend the renewal formalism to compute the general expression for the position variance and the correlation function of the resetting particle driven by the environmental memory. These generic results are then illustrated for the prototypical example of the Jeffreys viscoelastic fluid model. In particular, we identify various timescales and intermittent plateaus in the transient phase before the system relaxes to the steady state; and further discuss the effect of resetting pertaining to these behaviors. Our results are supported by numerical simulations showing an excellent agreement., Comment: 14 pages, 5 figures

Concerns about the environmental footprint of machine learning are increasing. While studies of energy use and emissions of ML models are a growing subfield, most ML researchers and developers still do not incorporate energy measurement as part of their work practices. While measuring energy is a crucial step towards reducing carbon footprint, it is also not straightforward. This paper introduces the main considerations necessary for making sound use of energy measurement tools and interpreting energy estimates, including the use of at-the-wall versus on-device measurements, sampling strategies and best practices, common sources of error, and proxy measures. It also contains practical tips and real-world scenarios that illustrate how these considerations come into play. It concludes with a call to action for improving the state of the art of measurement methods and standards for facilitating robust comparisons between diverse hardware and software environments., Comment: Released as NREL Tech Report

This article is concerned regarding the nonexistence of positive solutions for nonlinear fractional elliptic inequalities in exterior domains of $\mathbb R^n$, $n\geq1$. We would like to highlight the fact that our results are new with very weak assumption on the nonlinear term appearing in the inequalities. The results are also novel in this generality even if the whole space is considered.