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Our study explores the mixture of polar particles in apolar environment. We employ a coarse-grained approach to model the mixture, where polar particles are in minority. The interaction between polar and apolar components is incorporated via a coupling term in the free energy. Coupling generates local interaction in the system which results in the formation of string like structures connecting a pair of half integer topological defects. The increase in the coupling strength or the density of polar particles results in the: Sharper strings with larger probability of connecting the topological defects of same charge and the enhanced dynamics of topological defects. However, the ordering kinetics of the system shows the delayed coarsening for larger coupling or polar density. Our results can be used to develop controlled kinetics as well as to detect the impurities in liquid crystals.

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

Gender biases in scholarly metrics remain a persistent concern, despite numerous bibliometric studies exploring their presence and absence across productivity, impact, acknowledgment, and self-citations. However, methodological inconsistencies, particularly in author name disambiguation and gender identification, limit the reliability and comparability of these studies, potentially perpetuating misperceptions and hindering effective interventions. A review of 70 relevant publications over the past 12 years reveals a wide range of approaches, from name-based and manual searches to more algorithmic and gold-standard methods, with no clear consensus on best practices. This variability, compounded by challenges such as accurately disambiguating Asian names and managing unassigned gender labels, underscores the urgent need for standardized and robust methodologies. To address this critical gap, we propose the development and implementation of ``Scholarly Data Analysis (SoDA) Cards." These cards will provide a structured framework for documenting and reporting key methodological choices in scholarly data analysis, including author name disambiguation and gender identification procedures. By promoting transparency and reproducibility, SoDA Cards will facilitate more accurate comparisons and aggregations of research findings, ultimately supporting evidence-informed policymaking and enabling the longitudinal tracking of analytical approaches in the study of gender and other social biases in academia., Comment: 33 pg, 7 figures. Soda Cards: https://github.com/HaeJinLee41/scholarly_bias_study

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

In this brief, the authors examine changes in enrollments at the Los Angeles Community College District between fall 2017 and summer 2023 to document the extent of pandemic-era enrollment declines overall and across student populations.

Ensuring safety of Generative AI requires a nuanced understanding of pluralistic viewpoints. In this paper, we introduce a novel data-driven approach for calibrating granular ratings in pluralistic datasets. Specifically, we address the challenge of interpreting responses of a diverse population to safety expressed via ordinal scales (e.g., Likert scale). We distill non-parametric responsiveness metrics that quantify the consistency of raters in scoring the varying levels of the severity of safety violations. Using safety evaluation of AI-generated content as a case study, we investigate how raters from different demographic groups (age, gender, ethnicity) use an ordinal scale to express their perception of the severity of violations in a pluralistic safety dataset. We apply our metrics across violation types, demonstrating their utility in extracting nuanced insights that are crucial for developing reliable AI systems in a multi-cultural contexts. We show that our approach offers improved capabilities for prioritizing safety concerns by capturing nuanced viewpoints across different demographic groups, hence improving the reliability of pluralistic data collection and in turn contributing to more robust AI evaluations.

Effective trajectory stitching for long-horizon planning is a significant challenge in robotic decision-making. While diffusion models have shown promise in planning, they are limited to solving tasks similar to those seen in their training data. We propose CompDiffuser, a novel generative approach that can solve new tasks by learning to compositionally stitch together shorter trajectory chunks from previously seen tasks. Our key insight is modeling the trajectory distribution by subdividing it into overlapping chunks and learning their conditional relationships through a single bidirectional diffusion model. This allows information to propagate between segments during generation, ensuring physically consistent connections. We conduct experiments on benchmark tasks of various difficulties, covering different environment sizes, agent state dimension, trajectory types, training data quality, and show that CompDiffuser significantly outperforms existing methods., Comment: Project page: https://comp-diffuser.github.io/

Response consistency-based, reference-free hallucination detection (RFHD) methods do not depend on internal model states, such as generation probabilities or gradients, which Grey-box models typically rely on but are inaccessible in closed-source LLMs. However, their inability to capture query-response alignment patterns often results in lower detection accuracy. Additionally, the lack of large-scale benchmark datasets spanning diverse domains remains a challenge, as most existing datasets are limited in size and scope. To this end, we propose HalluCounter, a novel reference-free hallucination detection method that utilizes both response-response and query-response consistency and alignment patterns. This enables the training of a classifier that detects hallucinations and provides a confidence score and an optimal response for user queries. Furthermore, we introduce HalluCounterEval, a benchmark dataset comprising both synthetically generated and human-curated samples across multiple domains. Our method outperforms state-of-the-art approaches by a significant margin, achieving over 90\% average confidence in hallucination detection across datasets., Comment: 30 pages, 4 figures

Browser extensions are additional tools developed by third parties that integrate with web browsers to extend their functionality beyond standard capabilities. However, the browser extension platform is increasingly being exploited by hackers to launch sophisticated cyber threats. These threats encompass a wide range of malicious activities, including but not limited to phishing, spying, Distributed Denial of Service (DDoS) attacks, email spamming, affiliate fraud, malvertising, and payment fraud. This paper examines the evolving threat landscape of malicious browser extensions in 2025, focusing on Mozilla Firefox and Chrome. Our research successfully bypassed security mechanisms of Firefox and Chrome, demonstrating that malicious extensions can still be developed, published, and executed within the Mozilla Add-ons Store and Chrome Web Store. These findings highlight the persisting weaknesses in browser's vetting process and security framework. It provides insights into the risks associated with browser extensions, helping users understand these threats while aiding the industry in developing controls and countermeasures to defend against such attacks. All experiments discussed in this paper were conducted in a controlled laboratory environment by the researchers, adhering to proper ethical guidelines. The sole purpose of these experiments is to raise security awareness among the industry, research community, and the general public.

One of the most prolific methods of studying exoplanet atmospheres is transmission spectroscopy, which measures the difference between the depth of an exoplanet's transit signal at various wavelengths and attempts to correlate the depth changes to potential features in the exoplanet's atmosphere. Here we present reconnaissance observations of 21 exoplanet atmospheres measured with the Exoplanet Transmission Spectroscopy Imager (ETSI), a recently deployed spectro-photometer on the McDonald Observatory Otto Struve 2.1 m telescope. ETSI measurements are mostly free of systematics through the use of a novel observing technique called common-path multi-band imaging (CMI), which has been shown to achieve photometric color precision on-par with space-based observations (300ppm or 0.03%). This work also describes the various statistical tests performed on the data to evaluate the efficacy of the CMI method and the ETSI instrument in combination. We find that none of the 8 comparisons of exoplanet atmospheres measured with ETSI and other observatories (including the Hubble Space Telescope) provide evidence that the spectra are statistically dissimilar. These results suggest that ETSI can provide initial transmission spectroscopy observations for a fraction of the observational and monetary overhead previously required to detect an exoplanet's atmosphere. Ultimately these reconnaissance observations increase the number of planets with transmission spectroscopy measurements by ~10% and provide an immediate prioritization of 21 exoplanets for future follow-up with more precious observatories, such as the James Webb Space Telescope. The reconnaissance spectra are available through the Filtergraph visualization portal at the URL: https://filtergraph.com/etsi/., Comment: 28 pages, 8 figures, 11 tables, article published in AJ. ETSI data is available on request and spectra can be found at the URL https://filtergraph.com/etsi/

The Dark Matter (DM) distribution in dwarf galaxies provides crucial insights into both structure formation and the particle nature of DM. GraphNPE (Graph Neural Posterior Estimator), first introduced in Nguyen et al. (2023), is a novel simulation-based inference framework that combines graph neural networks and normalizing flows to infer the DM density profile from line-of-sight stellar velocities. Here, we apply GraphNPE to satellite dwarf galaxies in the FIRE-2 Latte simulation suite of Milky Way-mass halos, testing it against both Cold and Self-Interacting DM scenarios. Our method demonstrates superior precision compared to conventional Jeans-based approaches, recovering DM density profiles to within the 95% confidence level even in systems with as few as 30 tracers. Moreover, we present the first evaluation of mass modeling methods in constraining two key parameters from realistic simulations: the peak circular velocity, $V_\mathrm{max}$, and the peak virial mass, $M_\mathrm{200m}^\mathrm{peak}$. Using only line-of-sight velocities, GraphNPE can reliably recover both $V_\mathrm{max}$ and $M_\mathrm{200m}^\mathrm{peak}$ within our quoted uncertainties, including those experiencing tidal effects ($\gtrsim$ 63% of systems are recovered with our 68% confidence intervals and $\gtrsim$ 92% within our 95% confidence intervals). The method achieves 10-20% accuracy in $V_\mathrm{max}$ recovery, while $M_\mathrm{200m}^\mathrm{peak}$ is recovered to 0.1-0.4 dex accuracy. This work establishes GraphNPE as a robust tool for inferring DM density profiles in dwarf galaxies, offering promising avenues for constraining DM models. The framework's potential extends beyond this study, as it can be adapted to non-spherical and disequilibrium models, showcasing the broader utility of simulation-based inference and graph-based learning in astrophysics., Comment: Submitted to MNRAS. Comments welcomed. 20 + 12 pages, 13 + 11 figures, 6 tables

Integral field unit (IFU) spectroscopic observations of resolved galaxies provide an optimal experimental setting for determination of stellar population properties, in particular - age, metallicity and $\alpha$-enhancement, which are key to understanding evolution of galaxies across diverse physical environments. We determine these properties for the edge-on disc galaxy IC 1553, through stellar population models fitted to MUSE IFU observations. From our determined spatial distributions of metallicity and [$\alpha$/Fe], we serendipitiously identify the unique chemical signature of a dwarf galaxy that is co-spatial with the luminous disc of IC 1553. The dwarf galaxy is characterized by the presence of higher [$\alpha$/Fe] and metal-poor stellar populations relative to the disc of IC 1553. The identified dwarf is dynamically cold from its determined kinematics, consistent with being a satellite of IC 1553. From modeling the Spitzer IRAC 3.6 $\mu m$ image of IC 1553, we confirmed the presence of the dwarf galaxy and calculated its stellar mass to be $\sim1.28\times 10^{9} \rm M_{\odot}$. This is the first such identification of a dwarf galaxy from its unique chemical signature in such integrated light IFU observations, even though its hidden by the luminous body of its massive host., Comment: In submission at MNRAS letters; 4 figures, 6 pages

Physics-Informed Neural Networks (PINNs) offer a promising approach to simulating physical systems. Still, their application is limited by optimization challenges, mainly due to the lack of activation functions that generalize well across several physical systems. Existing activation functions often lack such flexibility and generalization power. To address this issue, we introduce Rational Exponential Activation (REAct), a generalized form of tanh consisting of four learnable shape parameters. Experiments show that REAct outperforms many standard and benchmark activations, achieving an MSE three orders of magnitude lower than tanh on heat problems and generalizing well to finer grids and points beyond the training domain. It also excels at function approximation tasks and improves noise rejection in inverse problems, leading to more accurate parameter estimates across varying noise levels., Comment: 5 pages, 5 tables, 1 figure; Accepted at ICASSP 2025

Llama-3.1-Sherkala-8B-Chat, or Sherkala-Chat (8B) for short, is a state-of-the-art instruction-tuned open generative large language model (LLM) designed for Kazakh. Sherkala-Chat (8B) aims to enhance the inclusivity of LLM advancements for Kazakh speakers. Adapted from the LLaMA-3.1-8B model, Sherkala-Chat (8B) is trained on 45.3B tokens across Kazakh, English, Russian, and Turkish. With 8 billion parameters, it demonstrates strong knowledge and reasoning abilities in Kazakh, significantly outperforming existing open Kazakh and multilingual models of similar scale while achieving competitive performance in English. We release Sherkala-Chat (8B) as an open-weight instruction-tuned model and provide a detailed overview of its training, fine-tuning, safety alignment, and evaluation, aiming to advance research and support diverse real-world applications., Comment: Technical Report

The rapid advancements in generative AI models, such as ChatGPT, have introduced both significant benefits and new risks within the cybersecurity landscape. This paper investigates the potential misuse of the latest AI model, ChatGPT-4o Mini, in facilitating social engineering attacks, with a particular focus on phishing, one of the most pressing cybersecurity threats today. While existing literature primarily addresses the technical aspects, such as jailbreaking techniques, none have fully explored the free and straightforward execution of a comprehensive phishing campaign by novice users using ChatGPT-4o Mini. In this study, we examine the vulnerabilities of AI-driven chatbot services in 2025, specifically how methods like jailbreaking and reverse psychology can bypass ethical safeguards, allowing ChatGPT to generate phishing content, suggest hacking tools, and assist in carrying out phishing attacks. Our findings underscore the alarming ease with which even inexperienced users can execute sophisticated phishing campaigns, emphasizing the urgent need for stronger cybersecurity measures and heightened user awareness in the age of AI.

The large majority of topological phases in one dimensional many-body systems are known to inherit from the corresponding single-particle Hamiltonian. In this work, we go beyond this assumption and find a new example of topological order induced through specific interactions couplings. Specifically, we consider a fermionic mixture where one component experiences a staggered onsite potential and it is coupled through density dependent hopping interactions to the other fermionic component. Crucially, by varying the sign of the staggered potential, we show that this latter fermionic component can acquire topological properties. Thanks to matrix product state simulations, we prove this result both at the equilibrium by extracting the behavior of correlation functions and in an out-of-equilibrium scheme by employing a Thouless charge pumping. Notably, we further discuss how our results can be probed in quantum simulators made up of ultracold atoms. Our results reveal an important and alternative mechanism that can give rise to topological order., Comment: 5 pages, 4 figures

Pulsar timing arrays (PTAs) are designed to detect nanohertz-frequency gravitational waves (GWs). Since GWs are anticipated from cosmic strings, PTAs offer a viable approach to testing their existence. We present the results of the first Bayesian search for gravitational-wave bursts from cosmic string cusps (GWCS) using the third PPTA data release for 30 millisecond pulsars. In this data collection, we find no evidence for GWCS signals. We compare a model with a GWCS signal to one with only noise, including a common spatially uncorrelated red noise (CURN), and find that our data is more consistent with the noise-only model.We then establish upper limits on the strain amplitude of GWCS at the pulsar term, based on the analysis of 30 millisecond pulsars, after finding no compelling evidence. We find the addition of a CURN with different spectral indices into the noise model has a negligible impact on the upper limits. And the upper limit range of the amplitude of the pulsar-term GWCS is concentrated between 10^{-12} and 10^{-11}. Finally, we set upper limits on the amplitude of GWCS events, parametrized by width and event epoch, for a single pulsar PSR J1857+0943. Moreover, we derive upper limit on the cosmic string tension as a function of burst width and compare it with previous results.

We consider the Chern-Simons theory coupled to massive fundamental matter in three spacetime dimensions, focusing on the Higgsed phase of the bosonic matter in the large N limit. To study the characteristics of the Z-boson in the Higgsed phase and address an apparent puzzle regarding its fermionic analogue, we employ the 3d Bose-Fermi duality to derive the exact large N result for the two-point function of the Z-boson. We study the spectral representation of the Z-boson and find that, in the large N limit, it behaves as an unstable resonance with a non-perturbatively large lifetime at weak coupling., Comment: 41 pages, 6 figures

Studies for inferring the global characteristics of coronal mass ejections (CMEs) from its multipoint local in situ observations have been undertaken earlier, but there are limited studies utilizing measurements from multiple spacecraft with sufficiently small radial and angular separations. In the present study, we investigate a magnetic cloud (MC) region of a CME observed in situ during 2023 September 24-26, at STEREO-A and Wind spacecraft near 1 AU, which had radial and angular separations of 0.03 AU and 3.4 degrees, respectively. We examine the disparities in the estimates of the arrival times of CME substructures, the MC axis, and its orientation between the two spacecraft. We also propose an approach for identifying the MC axis's arrival and have compared it with the arrival of the size/time center to understand the non-isotropic compression of the MC along its angular extent. Using minimum variance analysis (MVA), we note that the orientation of the MC is slightly out-of-ecliptic at Wind but not at STEREO-A. We also compare the magnetic field parameters over the start to end of the MC at both spacecraft and note a significant non-coherency in the MC towards its trailing portion. Our analysis confirms that MC has a stronger rear side compression at STEREO-A than at Wind, with its trailing edge arriving later at Wind. Our study highlights substantial differences in CME characteristics even at mesoscales across the angular extent, and therefore, one needs to analyze several such cases to better understand the flux rope structure., Comment: 17 pages, 6 figures; accepted for publication in The Astrophysical Journal (ApJ)

Most Mn-rich cathodes are known to undergo phase transformation into structures resembling spinel-like ordering upon electrochemical cycling. Recently, the irreversible transformation of Ti-containing Mn-rich disordered rock-salt cathodes into a phase -- named $\delta$ -- with nanoscale spinel-like domains has been shown to increase energy density, capacity retention, and rate capability. However, the nature of the boundaries between domains and their relationship with composition and electrochemistry are not well understood. In this work, we discuss how the transformation into the multi-domain structure results in eight variants of Spinel domains, which is crucial for explaining the nanoscale domain formation in the $\delta$-phase. We study the energetics of crystallographically unique boundaries and the possibility of Li-percolation across them with a fine-tuned CHGNet machine learning interatomic potential. Energetics of $16d$ vacancies reveal a strong affinity to segregate to the boundaries, thereby opening Li-pathways at the boundary to enhance long-range Li-percolation in the $\delta$ structure. Defect calculations of the relatively low-mobility Ti show how it can influence the extent of Spinel ordering, domain morphology and size significantly; leading to guidelines for engineering electrochemical performance through changes in composition.

The process of measuring a qubit and re-initializing it to the ground state practically lead to long qubit idle times between re-runs of experiments on a superconducting quantum computer. Here, we propose a protocol for a demolition measurement of a transmon qubit that integrates qubit readout with the reset process to minimize qubit idle time. We present a three-staged implementation of this protocol, involving a combined qubit readout and resonator reset scheme that unconditionally resets the resonator at the end of the readout; a leakage removal scheme that can be integrated with the measurement stage; and an unconditional qubit reset. We demonstrate that this protocol could be implemented in $1 \mu$s with greater than $95 \%$ reset fidelity and a $99 \%$ readout fidelity without any hardware overhead beyond those commonly used. This provides at least a tenfold speed-up compared to the passive decay of the qubit, hence significantly increasing the data-acquisition rate., Comment: 12 pages, 8 figures

Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. However, prompt engineering remains an impediment for end users due to rapid advances in models, tasks, and associated best practices. To mitigate this, Automatic Prompt Optimization (APO) techniques have recently emerged that use various automated techniques to help improve the performance of LLMs on various tasks. In this paper, we present a comprehensive survey summarizing the current progress and remaining challenges in this field. We provide a formal definition of APO, a 5-part unifying framework, and then proceed to rigorously categorize all relevant works based on their salient features therein. We hope to spur further research guided by our framework., Comment: 8 main pages, 31 total pages, 1 figure

Zero-shot multi-label recognition (MLR) with Vision-Language Models (VLMs) faces significant challenges without training data, model tuning, or architectural modifications. Existing approaches require prompt tuning or architectural adaptations, limiting zero-shot applicability. Our work proposes a novel solution treating VLMs as black boxes, leveraging scores without training data or ground truth. Using large language model insights on object co-occurrence, we introduce compound prompts grounded in realistic object combinations. Analysis of these prompt scores reveals VLM biases and ``AND''/``OR'' signal ambiguities, notably that maximum compound scores are surprisingly suboptimal compared to second-highest scores. We address these through a debiasing and score-fusion algorithm that corrects image bias and clarifies VLM response behaviors. Our method enhances other zero-shot approaches, consistently improving their results. Experiments show superior mean Average Precision (mAP) compared to methods requiring training data, achieved through refined object ranking for robust zero-shot MLR.

Enhancing light-matter interactions at the nanoscale is foundational to nanophotonics, with epsilon near zero (ENZ) materials demonstrating significant potential.High-quality (Q) factor resonances maximizing these interactions are typically realized in photonic crystals requiring sub-50 nm precision nanofabrication over large areas, limiting scalability and increasing complexity. Mie resonances offer an alternative but are constrained by low Q factors due to the scarcity of high refractive index materials, necessitating large refractive index changes for effective resonance switching and limiting dynamic reconfigurability. We overcome these limitations by embedding Mie resonators within ENZ media, thereby enhancing Q- factors, mitigating geometric dispersion and fabrication challenges, and maximizing optical reconfigurability. We introduce three resonator-ENZ configurations - voids in AlN, Ge in $SiO_2$, and intrinsic InSb in doped InSb - spanning from low-loss phononic to lossy plasmonic ENZ modes.Using novel epitaxial regrowth techniques, we achieve significant Q-factor improvements over non-embedded resonators. Notably, an air-based Mie resonator embedded in AlN supports resonant Q-factors exceeding 100, with negligible geometric dispersion across sizes from 800 nm to 2,800 nm. Additionally, we demonstrate dynamic reconfigurability of intrinsic InSb resonators by thermally tuning the ENZ wavelength over a 2$\mu$m range in the mid-infrared (11-16 $\mu$m) wavelength regime. These results showcase the potential of Mie resonators embedded in ENZ media for high-fidelity sensors, thermal emitters, and reconfigurable metasurfaces, bridging theoretical predictions with practical applications, advancing the development of dynamic, high-Q optical devices., Comment: 27 pages, 4 figures, 2 Supplmentary information figures

With the widespread deployment of large language models (LLMs) such as GPT4, BART, and LLaMA, the need for a system that can intelligently select the most suitable model for specific tasks while balancing cost, latency, accuracy, and ethical considerations has become increasingly important. Recognizing that not all tasks necessitate models with over 100 billion parameters, we introduce OptiRoute, an advanced model routing engine designed to dynamically select and route tasks to the optimal LLM based on detailed user-defined requirements. OptiRoute captures both functional (e.g., accuracy, speed, cost) and non-functional (e.g., helpfulness, harmlessness, honesty) criteria, leveraging lightweight task analysis and complexity estimation to efficiently match tasks with the best-fit models from a diverse array of LLMs. By employing a hybrid approach combining k-nearest neighbors (kNN) search and hierarchical filtering, OptiRoute optimizes for user priorities while minimizing computational overhead. This makes it ideal for real-time applications in cloud-based ML platforms, personalized AI services, and regulated industries.

In this work, we present and analyze a novel stabilized virtual element formulation for the coupled Stokes-Temperature equation on polygonal meshes, employing equal-order element pairs where viscosity depends on temperature. The main objective of the proposed virtual elements is to develop a stabilized virtual element problem that avoids higher-order derivative terms and bilinear forms involving velocity, pressure and temperature, thereby avoiding the coupling between virtual element pairs. Moreover, it also reduces the violation of divergence-free constraints and offers reasonable control over the gradient of temperature. We derive the stability of the continuous solution using the Banach fixed-point theorem under sufficiently small data. The stabilized coupled virtual element problem is formulated using the local projection-based stabilization methods. We demonstrate the existence and uniqueness of the stabilized discrete solution using the Brouwer fixed-point theorem and the contraction theorem under the assumption of sufficient small data by showing the well-posedness of the stabilized decoupled virtual element problems. Furthermore, we derive the error estimates with optimal convergence rates in the energy norms. We present several numerical examples to confirm the theoretical findings. Additionally, the numerical behavior of the proposed stabilized method is shown to be robust with respect to linear and non-linear thermal conductivity.

Recent agent frameworks and inference-time algorithms often struggle with complex planning problems due to limitations in verifying generated plans or reasoning and varying complexity of instances within a single task. Many existing methods for these tasks either perform task-level verification without considering constraints or apply inference-time algorithms without adapting to instance-level complexity. To address these limitations, we propose PlanGEN, a model-agnostic and easily scalable agent framework with three key components: constraint, verification, and selection agents. Specifically, our approach proposes constraint-guided iterative verification to enhance performance of inference-time algorithms--Best of N, Tree-of-Thought, and REBASE. In PlanGEN framework, the selection agent optimizes algorithm choice based on instance complexity, ensuring better adaptability to complex planning problems. Experimental results demonstrate significant improvements over the strongest baseline across multiple benchmarks, achieving state-of-the-art results on NATURAL PLAN ($\sim$8%$\uparrow$), OlympiadBench ($\sim$4%$\uparrow$), DocFinQA ($\sim$7%$\uparrow$), and GPQA ($\sim$1%$\uparrow$). Our key finding highlights that constraint-guided iterative verification improves inference-time algorithms, and adaptive selection further boosts performance on complex planning and reasoning problems., Comment: 30 pages

In this paper, we present a control design methodology, stability criteria, and performance bounds for autonomous helicopter aerial refueling. Autonomous aerial refueling is particularly difficult due to the aerodynamic interaction between the wake of the tanker, the contact-sensitive nature of the maneuver, and the uncertainty in drogue motion. Since the probe tip is located significantly away from the helicopter's center-of-gravity, its position (and velocity) is strongly sensitive to the helicopter's attitude (and angular rates). In addition, the fact that the helicopter is operating at high speeds to match the velocity of the tanker forces it to maintain a particular orientation, making the docking maneuver especially challenging. In this paper, we propose a novel outer-loop position controller that incorporates the probe position and velocity into the feedback loop. The position and velocity of the probe tip depend both on the position (velocity) and on the attitude (angular rates) of the aircraft. We derive analytical guarantees for docking performance in terms of the uncertainty of the drogue motion and the angular acceleration of the helicopter, using the ultimate boundedness property of the closed-loop error dynamics. Simulations are performed on a high-fidelity UH60 helicopter model with a high-fidelity drogue motion under wind effects to validate the proposed approach for realistic refueling scenarios. These high-fidelity simulations reveal that the proposed control methodology yields an improvement of 36% in the 2-norm docking error compared to the existing standard controller.

We study the replacement paths problem in the $\mathsf{CONGEST}$ model of distributed computing. Given an $s$-$t$ shortest path $P$, the goal is to compute, for every edge $e$ in $P$, the shortest-path distance from $s$ to $t$ avoiding $e$. For unweighted directed graphs, we establish the tight randomized round complexity bound for this problem as $\widetilde{\Theta}(n^{2/3} + D)$ by showing matching upper and lower bounds. Our upper bound extends to $(1+\epsilon)$-approximation for weighted directed graphs. Our lower bound applies even to the second simple shortest path problem, which asks only for the smallest replacement path length. These results improve upon the very recent work of Manoharan and Ramachandran (SIROCCO 2024), who showed a lower bound of $\widetilde{\Omega}(n^{1/2} + D)$ and an upper bound of $\widetilde{O}(n^{2/3} + \sqrt{n h_{st}} + D)$, where $h_{st}$ is the number of hops in the given $s$-$t$ shortest path $P$.

Given a simple, unweighted, undirected graph $G=(V,E)$ with $|V|=n$ and $|E|=m$, and parameters $0 < \varepsilon, \delta <1$, along with \texttt{Degree}, \texttt{Neighbour}, \texttt{Edge} and \texttt{RandomEdge} query access to $G$, we provide a query based randomized algorithm to generate an estimate $\widehat{T}$ of the number of triangles $T$ in $G$, such that $\widehat{T} \in [(1-\varepsilon)T , (1+\varepsilon)T]$ with probability at least $1-\delta$. The query complexity of our algorithm is $\widetilde{O}\left({m \alpha \log(1/\delta)}/{\varepsilon^3 T}\right)$, where $\alpha$ is the arboricity of $G$. Our work can be seen as a continuation in the line of recent works [Eden et al., SIAM J Comp., 2017; Assadi et al., ITCS 2019; Eden et al. SODA 2020] that considered subgraph or triangle counting with or without the use of \texttt{RandomEdge} query. Of these works, Eden et al. [SODA 2020] considers the role of arboricity. Our work considers how \texttt{RandomEdge} query can leverage the notion of arboricity. Furthermore, continuing in the line of work of Assadi et al. [APPROX/RANDOM 2022], we also provide a lower bound of $\widetilde{\Omega}\left({m \alpha \log(1/\delta)}/{\varepsilon^2 T}\right)$ that matches the upper bound exactly on arboricity and the parameter $\delta$ and almost on $\varepsilon$., Comment: 21 pages

Instruction-following LLMs have recently allowed systems to discover hidden concepts from a collection of unstructured documents based on a natural language description of the purpose of the discovery (i.e., goal). Still, the quality of the discovered concepts remains mixed, as it depends heavily on LLM's reasoning ability and drops when the data is noisy or beyond LLM's knowledge. We present Instruct-LF, a goal-oriented latent factor discovery system that integrates LLM's instruction-following ability with statistical models to handle large, noisy datasets where LLM reasoning alone falls short. Instruct-LF uses LLMs to propose fine-grained, goal-related properties from documents, estimates their presence across the dataset, and applies gradient-based optimization to uncover hidden factors, where each factor is represented by a cluster of co-occurring properties. We evaluate latent factors produced by Instruct-LF on movie recommendation, text-world navigation, and legal document categorization tasks. These interpretable representations improve downstream task performance by 5-52% than the best baselines and were preferred 1.8 times as often as the best alternative, on average, in human evaluation., Comment: NAACL 2025

We introduce MONSTER-the MONash Scalable Time Series Evaluation Repository-a collection of large datasets for time series classification. The field of time series classification has benefitted from common benchmarks set by the UCR and UEA time series classification repositories. However, the datasets in these benchmarks are small, with median sizes of 217 and 255 examples, respectively. In consequence they favour a narrow subspace of models that are optimised to achieve low classification error on a wide variety of smaller datasets, that is, models that minimise variance, and give little weight to computational issues such as scalability. Our hope is to diversify the field by introducing benchmarks using larger datasets. We believe that there is enormous potential for new progress in the field by engaging with the theoretical and practical challenges of learning effectively from larger quantities of data., Comment: 45 pages; 38 figures

Efficient and privacy-preserving multimodal interaction is essential as AR, VR, and modern smartphones with powerful cameras become primary interfaces for human-computer communication. Existing powerful large vision-language models (VLMs) enabling multimodal interaction often rely on cloud-based processing, raising significant concerns about (1) visual privacy by transmitting sensitive vision data to servers, and (2) their limited real-time, on-device usability. This paper explores Visual Instruction Rewriting, a novel approach that transforms multimodal instructions into text-only commands, allowing seamless integration of lightweight on-device instruction rewriter VLMs (250M parameters) with existing conversational AI systems, enhancing vision data privacy. To achieve this, we present a dataset of over 39,000 examples across 14 domains and develop a compact VLM, pretrained on image captioning datasets and fine-tuned for instruction rewriting. Experimental results, evaluated through NLG metrics such as BLEU, METEOR, and ROUGE, along with semantic parsing analysis, demonstrate that even a quantized version of the model (<500MB storage footprint) can achieve effective instruction rewriting, thus enabling privacy-focused, multimodal AI applications., Comment: 12 pages, 7 figures, 3 tables

We investigate the phase diagram of a one-dimensional model of hardcore bosons or spinless fermions with tunable nearest-neighbor interactions. By introducing alternating repulsive and attractive interactions on consecutive bonds, we show that the system undergoes a transition from a bond-ordered (BO) phase to a charge-density wave-II (CDW-II) phase as the attractive interaction strength increases at a fixed repulsive interaction. For a specific interaction pattern, the BO phase exhibits topological properties, which vanish when the pattern is altered, leading to a transition from a topological BO phase to a trivial BO phase through a gap-closing point where both interactions vanish. We identify these phases using a combination of order parameters, topological invariants, edge-state analysis and Thouless charge pumping. By extending our analysis beyond half-filling, we explore the phase diagram across all densities and identify the superfluid (SF) and the pair-superfluid (PSF) phases, characterized by single-particle and bound-pair excitations at incommensurate densities. The proposed model is experimentally realizable in platforms such as Rydberg excited or ultracold atoms in optical lattices, offering a versatile framework to study such interplay between topology and interactions in low-dimensional systems., Comment: 17 pages, 18 figures

Concept drift refers to gradual or sudden changes in the properties of data that affect the accuracy of machine learning models. In this paper, we address the problem of concept drift detection in the malware domain. Specifically, we propose and analyze a clustering-based approach to detecting concept drift. Using a subset of the KronoDroid dataset, malware samples are partitioned into temporal batches and analyzed using MiniBatch $K$-Means clustering. The silhouette coefficient is used as a metric to identify points in time where concept drift has likely occurred. To verify our drift detection results, we train learning models under three realistic scenarios, which we refer to as static training, periodic retraining, and drift-aware retraining. In each scenario, we consider four supervised classifiers, namely, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest, and XGBoost. Experimental results demonstrate that drift-aware retraining guided by silhouette coefficient thresholding achieves classification accuracy far superior to static models, and generally within 1% of periodic retraining, while also being far more efficient than periodic retraining. These results provide strong evidence that our clustering-based approach is effective at detecting concept drift, while also illustrating a highly practical and efficient fully automated approach to improved malware classification via concept drift detection.

We construct a new dilaton Weyl multiplet for $\mathcal{N}=3$ conformal supergravity in four dimensions. The R-symmetry realized on this dilaton Weyl multiplet is $SU(2) \times U(1) \times U(1)$. The construction follows a two-step procedure. Firstly, two on-shell vector multiplets are coupled to the standard Weyl multiplet. Secondly, using the field equations of the vector multiplets, some of the auxiliary fields of the standard Weyl multiplet are solved in terms of the fields belonging to the vector multiplets and some dual gauge fields. The remaining fields of the standard Weyl multiplet combine with the vector multiplet fields and the dual gauge fields to constitute the new dilaton Weyl multiplet., Comment: 16 pages, added new references

Beginning with high-$T_c$ cuprate materials, it has been observed that many superconductors exhibit so-called "Homes scaling", in which the zero-temperature superfluid density, $\rho_{s0}$, is proportional to the product of the normal-state dc conductivity and the superconducting transition temperature, $\sigma_\mathrm{dc} T_c$. For conventional, s-wave superconductors, such scaling has been shown to be a natural consequence of elastic-scattering disorder, not only in the extreme dirty limit but across a broad range of scattering parameters. Here we show that when an analogous calculation is carried out for elastic scattering in d-wave superconductors, a stark contrast emerges, with $\rho_{s0} \propto \left(\sigma_\mathrm{dc} T_c \right)^2$ in the dirty limit, in apparent violation of Homes scaling. Within a simple approximate Migdal--Eliashberg treatment of inelastic scattering, we show how Homes scaling is recovered. The normal-state behavior of near optimally doped cuprates is dominated by inelastic scattering, but significant deviations from Homes scaling occur for disorder-dominated cuprate systems, such as underdoped YBCO and overdoped LSCO, and in very clean materials with little inelastic scattering, such as Sr$_2$RuO$_4$. We present a revised analysis where both axes of the original Homes scaling plot are normalized by the Drude plasma weight, $\omega_{p,D}^2$, and show that new universal scaling emerges, in which the superfluid fractions of dirty s-wave and dirty d-wave superconductors coalesce to a single point at which normal-state scattering is occurring at the Planckian bound. The combined result is a new tool for classifying superconductors in terms of order parameter symmetry, as well as scattering strength and character. Although our model starts from a Fermi-liquid assumption it describes underdoped cuprates surprisingly well., Comment: 15 pages, 5 figures

We explore how private synthetic text can be generated by suitably prompting a large language model (LLM). This addresses a challenge for organizations like hospitals, which hold sensitive text data like patient medical records, and wish to share it in order to train machine learning models for medical tasks, while preserving patient privacy. Methods that rely on training or finetuning a model may be out of reach, either due to API limits of third-party LLMs, or due to ethical and legal prohibitions on sharing the private data with the LLM itself. We propose Differentially Private Keyphrase Prompt Seeding (DP-KPS), a method that generates a private synthetic text corpus from a sensitive input corpus, by accessing an LLM only through privatized prompts. It is based on seeding the prompts with private samples from a distribution over phrase embeddings, thus capturing the input corpus while achieving requisite output diversity and maintaining differential privacy. We evaluate DP-KPS on downstream ML text classification tasks, and show that the corpora it generates preserve much of the predictive power of the original ones. Our findings offer hope that institutions can reap ML insights by privately sharing data with simple prompts and little compute.

We use data from the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) to study the most likely formation of a forced reconnection region and associated plasma blobs, triggered by jet-like structures in a prominence segment. Around 05:44 UT on December 16$^{th}$, 2017, hot jet-like structures lifted from a nearby active region and fell obliquely on one side of the prominence segment with velocities of $\approx$45--65 km s$^{-1}$. These eruptions compressed the boundaries of the prominence and flux rope, forming an elongated reconnection region with inflow velocities of 47--52 km s$^{-1}$ and 36--49 km s$^{-1}$ in the projected plane. A thin, elongated reconnection region was formed, with multiple magnetic plasma blobs propagating bidirectionally at velocities of 91--178 km s$^{-1}$. These dense blobs, associated with ongoing reconnection, may also be linked to the onset of Kelvin-Helmholtz (K-H) instability. The blobs are attributed to plasmoids, moving at slower speeds (91--178 km s$^{-1}$) due to the high density in the prominence segment. The dimensionless reconnection rate varied from 0.57--0.28, 0.53--0.26, and 0.41--0.20, indicating reconnection rate enhancement and supporting the forced reconnection scenario. After reconnection, the prominence plasma heated to 6 MK, releasing significant thermal energy ($\approx$5.4$\times$10$^{27}$ erg), which drained cool prominence plasma and heated it to coronal temperatures. The ubiquity of jets and outflows in the solar atmosphere makes the aforementioned of reconnection and possible co-existence of K-H instability potentially important for the magnetic energy release and heating in the solar atmosphere., Comment: 10 Figures and Accepted for publications in ApJ

Electron irradiation of water-rich ices plays a significant role in initiating the chemical and physical processes on the surface of airless icy bodies in radiation environments such as Europa and Enceladus, as well as on the Moon, comets, and asteroids interacting with the solar wind. The sputtering process by electrons and ions leads to chemical modification and outgassing of their icy surfaces and the subsequent formation of a tenuous atmosphere. Though electron-sputtering yields are known to be lower compared to ion-sputtering yields, one needs to also account for their differential fluxes. In our experiments, the electron-induced sputtering yields of all the gaseous species H2, O, OH, H2O, and O2 are investigated for electron energies lower than 2 keV in terms of partial pressure vs. time of irradiation. The effective averaged change in partial pressure of the desorbed species is converted to the number of sputtered atoms or molecules per second per cm2 from the ice, and then to the sputtering yields (number of species sputtered per electron). Our data agrees well with the previously reported data for the sputtering of O2 and H2O yield for the amorphous ice. We also find that crystalline ice shows significantly lower sputtering yields when compared to amorphous ice, in agreement with the observation of similar trends in the literature. Our work indicates that sputtering yields per keV of O, OH, O2, and H2O drop with increasing electron energy from 0.5 keV to 2 keV.

Integration of Brain-Computer Interfaces (BCIs) and Generative Artificial Intelligence (GenAI) has opened new frontiers in brain signal decoding, enabling assistive communication, neural representation learning, and multimodal integration. BCIs, particularly those leveraging Electroencephalography (EEG), provide a non-invasive means of translating neural activity into meaningful outputs. Recent advances in deep learning, including Generative Adversarial Networks (GANs) and Transformer-based Large Language Models (LLMs), have significantly improved EEG-based generation of images, text, and speech. This paper provides a literature review of the state-of-the-art in EEG-based multimodal generation, focusing on (i) EEG-to-image generation through GANs, Variational Autoencoders (VAEs), and Diffusion Models, and (ii) EEG-to-text generation leveraging Transformer based language models and contrastive learning methods. Additionally, we discuss the emerging domain of EEG-to-speech synthesis, an evolving multimodal frontier. We highlight key datasets, use cases, challenges, and EEG feature encoding methods that underpin generative approaches. By providing a structured overview of EEG-based generative AI, this survey aims to equip researchers and practitioners with insights to advance neural decoding, enhance assistive technologies, and expand the frontiers of brain-computer interaction.

Tuberculosis (TB) remains a formidable global health challenge, driven by complex spatiotemporal transmission dynamics and influenced by factors such as population mobility and behavioral changes. We propose an Epidemic-Guided Deep Learning (EGDL) approach that fuses mechanistic epidemiological principles with advanced deep learning techniques to enhance early warning systems and intervention strategies for TB outbreaks. Our framework is built upon a networked Susceptible-Infectious-Recovered (SIR) model augmented with a saturated incidence rate and graph Laplacian diffusion, capturing both long-term transmission dynamics and region-specific population mobility patterns. Compartmental model parameters are rigorously estimated using Bayesian inference via the Markov Chain Monte Carlo (MCMC) approach. Theoretical analysis leveraging the comparison principle and Green's formula establishes global stability properties of the disease-free and endemic equilibria. Building on these epidemiological insights, we design two forecasting architectures, EGDL-Parallel and EGDL-Series, that integrate the mechanistic outputs of the networked SIR model within deep neural networks. This integration mitigates the overfitting risks commonly encountered in data-driven methods and filters out noise inherent in surveillance data, resulting in reliable forecasts of real-world epidemic trends. Experiments conducted on TB incidence data from 47 prefectures in Japan demonstrate that our approach delivers robust and accurate predictions across multiple time horizons (short to medium-term forecasts). Additionally, incorporating uncertainty quantification through conformal prediction enhances the model's practical utility for guiding targeted public health interventions.

Traditional solar forecasting models are based on several years of site-specific historical irradiance data, often spanning five or more years, which are unavailable for newer photovoltaic farms. As renewable energy is highly intermittent, building accurate solar irradiance forecasting systems is essential for efficient grid management and enabling the ongoing proliferation of solar energy, which is crucial to achieve the United Nations' net zero goals. In this work, we propose SPIRIT, a novel approach leveraging foundation models for solar irradiance forecasting, making it applicable to newer solar installations. Our approach outperforms state-of-the-art models in zero-shot transfer learning by about 70%, enabling effective performance at new locations without relying on any historical data. Further improvements in performance are achieved through fine-tuning, as more location-specific data becomes available. These findings are supported by statistical significance, further validating our approach. SPIRIT represents a pivotal step towards rapid, scalable, and adaptable solar forecasting solutions, advancing the integration of renewable energy into global power systems.

The counterexamples that Catlin used to disprove Haj\'os' conjecture (that every graph $G$ contains a subdivision of $K_{\chi(G)}$) are the lexicographic product of cliques and cycles. In 1989, Lescure and Meyniel made a conjecture that is a weakening of Haj\'os' and that remains open: every graph contains an immersion of $K_{\chi(G)}$. Can minimal counterexamples to this conjecture be produced through graph products? Collins, Heenehan, and McDonald recently gave a negative answer to this question for the lexicographic and Cartesian product. We consider a strengthening of the Lescure and Meyniel conjecture, different from that of Haj\'os, based on the notion of totally odd immersions. We study the largest totally odd immersion appearing in the graph product of two graphs. Our results imply that no minimal counterexample to this strengthened conjecture can be obtained from the Cartesian, lexicographic, direct (tensor) or strong product of graphs.

The transportation of essential goods in mountainous regions faces severe logistical challenges and frequent disruptions. To mitigate these difficulties, transport companies often overload trucks, which, though cost-saving, significantly heightens the risk of accidents and mechanical failures. This paper presents the development of a device that detects overloaded and insecurely fastened loads on trucks and commercial vehicles. Using advanced load sensors, the device offers real-time monitoring of cargo weight distribution, alerting drivers and authorities to unsafe conditions. The initial prototype utilised two basic load cells and an Arduino microcontroller. The second version was enhanced with four load cells and extended sensors. This version was tested by placing an electric golf cart onto the prototype. Various loads were then added to the cart in different orientations to assess whether the system could accurately detect improper or excessive load conditions., Comment: Accepted at SCEECS 2025 at MANIT Bhopal

Verifying scientific claims presents a significantly greater challenge than verifying political or news-related claims. Unlike the relatively broad audience for political claims, the users of scientific claim verification systems can vary widely, ranging from researchers testing specific hypotheses to everyday users seeking information on a medication. Additionally, the evidence for scientific claims is often highly complex, involving technical terminology and intricate domain-specific concepts that require specialized models for accurate verification. Despite considerable interest from the research community, there is a noticeable lack of large-scale scientific claim verification datasets to benchmark and train effective models. To bridge this gap, we introduce two large-scale datasets, SciClaimHunt and SciClaimHunt_Num, derived from scientific research papers. We propose several baseline models tailored for scientific claim verification to assess the effectiveness of these datasets. Additionally, we evaluate models trained on SciClaimHunt and SciClaimHunt_Num against existing scientific claim verification datasets to gauge their quality and reliability. Furthermore, we conduct human evaluations of the claims in proposed datasets and perform error analysis to assess the effectiveness of the proposed baseline models. Our findings indicate that SciClaimHunt and SciClaimHunt_Num serve as highly reliable resources for training models in scientific claim verification.

We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document understanding. Our model is trained on a comprehensive instruction-following dataset, including document-related tasks, such as content extraction from tables, charts, diagrams, sketches, and infographics, as well as general image tasks. The architecture of Granite Vision is centered around visual modality alignment with a decoder-only, 2 billion parameter Granite large language model. Additionally, we introduce a dedicated safety classification approach in test-time that leverages a sparse set of attention vectors to identify potential harmful inputs. Despite its lightweight architecture, Granite Vision achieves strong results in standard benchmarks related to visual document understanding, as well as on the LiveXiv benchmark, which is designed to avoid test set contamination by using a constantly updated corpus of recently published Arxiv papers. We are releasing the model under the Apache-2 license, allowing for both research and commercial use, while offering complete visibility into the training data and other relevant details. See https://huggingface.co/ibm-granite/ for model weights.

Noncollinear metallic antiferromagnets, with their rapid spin dynamics, efficient spin transport, and distinctive spin textures, play a pivotal role in advancing the field of spintronics. In this study, we report a comprehensive investigation of the structural, magnetic, and transport properties of cubic Mn$_{1.7}$Fe$_{1.3}$Si Heusler compound. Temperature-dependent magnetization measurement reveals a paramagnetic to ferromagnetic transition at $T_C$ = 85 K, followed by a spin reorientation transition. Neutron diffraction data, analyzed as a function of temperature, demonstrates that the occurrence of a spin-reorientation transition is accompanied by magnetoelastic coupling, as evidenced by a change in unit cell volume below $T_C$. Magnetic structure refinement of the low-temperature neutron powder diffraction data confirms the canted antiferromagnetic ordering below 55 K. The metallic nature of the sample is confirmed by the gradual decrease in the $\rho$(T) with decreasing temperature. At low temperatures, a field-induced metamgnetic transition is observed in both, magnetization and magneto-transport measurements. The $H-T$ phase diagram shows a phase-coexistence region emerging at low temperatures for H $<$ 2.5 T. These findings provide valuable insights into the magnetic and transport behavior of the Heusler compounds, underscoring their potential for spintronic applications., Comment: 11 pages, 10 figures

The properties (masses and decay widths) of the $\phi$ meson are investigated in nuclear matter from the $\phi$ meson self-energy, using the tree-level $\phi K\bar{K}$ Lagrangian, and, incorporating in-medium masses of (anti)kaons calculated within the quark meson coupling (QMC) model. These mass shifts and decay widths are incorporated in the Breit-Wigner spectral function of the $\phi$ meson to calculate the production cross-section of $\phi$ in asymmetric nuclear matter. Considerable modifications to the production cross-section are observed at normal nuclear matter density, driven by the in-medium mass reduction and the increase in the decay width of $\phi$ meson. The potential experienced by $\phi$ meson in nuclear matter is used to study the possibility of formation of the $\phi$ mesic bound state with atomic nuclei. We explore the potential formation of $\phi$-mesic bound states in ${\rm{^{4}He}}$, ${\rm{^{12}C}}$, ${\rm{^{16}O}}$, ${\rm{^{40}Ca}}$, ${\rm{^{90}Zr}}$, ${\rm{^{197}Au}}$ and ${\rm{^{208}Pb}}$ nuclei by investigating their binding energies and absorption widths based on the corresponding $\phi$-nucleus potentials. Our study shows shallow bound states with the light nuclei and deeply bound states in heavy nuclei. Among the investigated nuclei, a particularly distinct signal for a $\phi$-mesic bound state is identified in ${\rm{^{16}O}}$, suggesting its potential experimental observability. The work provides valuable insights into $\phi$ meson interactions in infinite nuclear matter and the potential formation of exotic $\phi$-mesic nuclear states, offering promising probes for strongly interacting matter in the upcoming experiments at J-PARC, JLab, and ${\rm{\bar{P}}ANDA}$@FAIR physics program., Comment: 11 pages, 10 figures