Search

This study investigates the transport parameters in multi-Weyl semimetals, focusing on their magneto-optical properties and the role of chiral states. The tilting parameter is identified as a key factor in higher-order Weyl nodes, significantly influencing the magneto-optical response. We obtain a generic Landau-level expression for multi-Weyl semimetals, establishing a robust framework for analyzing their quantum transport properties. A comprehensive expression for the conductivity tensor components is presented, uncovering distinctive low-frequency peaks and other features shaped by the tilting parameter. Our findings reveal that the signatures of chiral states in the conductivity tensors become increasingly pronounced with the Weyl node order. Particularly, the tilting parameter is shown to impact Faraday rotation, at energies near the tilted Dirac cone energies. These results provide critical insights into the magneto-optical behavior of multi-Weyl semimetals and their potential for exploring topological phenomena., Comment: 8 pages, 6 Figures

Despite recent advancements in speech processing, zero-resource speech translation (ST) and automatic speech recognition (ASR) remain challenging problems. In this work, we propose to leverage a multilingual Large Language Model (LLM) to perform ST and ASR in languages for which the model has never seen paired audio-text data. We achieve this by using a pre-trained multilingual speech encoder, a multilingual LLM, and a lightweight adaptation module that maps the audio representations to the token embedding space of the LLM. We perform several experiments both in ST and ASR to understand how to best train the model and what data has the most impact on performance in previously unseen languages. In ST, our best model is capable to achieve BLEU scores over 23 in CoVoST2 for two previously unseen languages, while in ASR, we achieve WERs of up to 28.2\%. We finally show that the performance of our system is bounded by the ability of the LLM to output text in the desired language., Comment: ICASSP 2025, 5 pages, 2 figures, 2 tables

We explore and analyze bulk geometric aspects corresponding to a driven two-dimensional holographic CFT, where the drive Hamiltonian is constructed from the $sl^{(q)}(2,\mathbb{R})$ generators. In particular, we demonstrate that starting with a thermal initial state, the evolution of the event horizon is characterized by distinct geometric transformations in the bulk which are associated to the conjugacy classes of the corresponding transformations on the CFT. Namely, the bulk evolution of the horizon is geometrically classified into an oscillatory (non-heating) behaviour, an exponentially growing (heating) behaviour and a power-law growth with an angular rotation (the phase boundary), all as a function of the stroboscopic time. We also show that the explicit symmetry breaking of the drive is manifest in a flowery structure of the event horizon that displays a $U(1) \to {\mathbb Z}_q$ symmetry breaking. In the $q\to \infty$ limit, the $U(1)$ symmetry is effectively restored. Furthermore, by analyzing the integral curves generated by the asymptotic Killing vectors, we also demonstrate how the fixed points of these curves approximate a bulk Ryu-Takayanagi surface corresponding to a modular Hamiltonian for a sub-region in the CFT. Since the CFT modular Hamiltonian has an infinitely many in-equivalent extensions in the bulk, the fixed points of the integral curves can also lie outside the entanglement wedge of the CFT sub-region., Comment: v2: 42 pages, 16 figures, typos corrected, references added

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

The inert doublet model (IDM), a minimal extension of the Standard Model (SM), provides a scalar dark matter (DM) candidate that belongs to the additional Higgs doublet. The model faces challenges in achieving the correct relic abundance for compressed spectra and DM masses in the high-mass range. In this work we introduce a $Z_2$-odd singlet vector-like quark (VLQ) into the IDM framework that helps us alleviate these issues and provide new channels of contributions to the relic abundance. The VLQ not only enhances the DM relic abundance for masses above $~550$ GeV but also eases constraints from direct detection experiments by enabling smaller couplings between the inert scalars and the SM Higgs. We analyze the impact of the VLQ on DM phenomenology, including relic density, direct and indirect detection constraints. The results demonstrate that the extended IDM framework not only resolves existing limitations in the compressed spectrum but also offers exciting prospects for detection in current and future collider experiments., Comment: 23 pages, 11 figures, 3 tables

Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data regime, e.g. few-shot learning. Prior approaches have employed only nearest-neighbor based strategies for data selection, which retrieve auxiliary samples with high similarity to instances in the target task. However, these approaches are prone to selecting highly redundant samples, since they fail to incorporate any notion of diversity. In our work, we first demonstrate that data selection strategies used in prior retrieval-augmented few-shot learning settings can be generalized using a class of functions known as Combinatorial Mutual Information (CMI) measures. We then propose COBRA (COmBinatorial Retrieval Augmentation), which employs an alternative CMI measure that considers both diversity and similarity to a target dataset. COBRA consistently outperforms previous retrieval approaches across image classification tasks and few-shot learning techniques when used to retrieve samples from LAION-2B. COBRA introduces negligible computational overhead to the cost of retrieval while providing significant gains in downstream model performance.

The dynamic environment context necessitates harnessing digital technologies, including artificial intelligence and the Internet of Things, to supply high-resolution, real-time meteorological data to support agricultural decision-making and improve overall farm productivity and sustainability. This study investigates the potential application of various AI-powered, IoT-based, low-cost platforms for local weather forecasting to enable smart farming. Despite the increasing demand for this topic, a few promising studies have explored this area. This paper developed a conceptual research framework based on a systematic review of relevant literature and employed a case study method to validate the framework. The framework comprised five key components: the Data Acquisition Layer, Data Storage Layer, Data Processing Layer, Application Layer, and Decision-Making Layer. This paper contributes to the literature by exploring the integration of AI-ML and IoT techniques for weather prediction tasks to support agriculture, and the incorporation of IoT technologies that provide real-time, high-resolution meteorological data, representing a step forward. Furthermore, this paper discusses key research gaps, such as the significant obstacles impeding the adoption of AI in agriculture and local weather forecasting, including the lack of straightforward solutions and the lack of digital skills among farmers, particularly those in rural areas. Further empirical research is needed to enhance the existing frameworks and address these challenges., Comment: 14 pages, 1 figure, One table

The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences. Despite recent advancements, there is limited research addressing hate speech detection in Devanagari-scripted languages, where resources and tools are scarce. While large language models (LLMs) have shown promise in language-related tasks, traditional fine-tuning approaches are often infeasible given the size of the models. In this paper, we propose a Parameter Efficient Fine tuning (PEFT) based solution for hate speech detection and target identification. We evaluate multiple LLMs on the Devanagari dataset provided by (Thapa et al., 2025), which contains annotated instances in 2 languages - Hindi and Nepali. The results demonstrate the efficacy of our approach in handling Devanagari-scripted content.

We study the general relativistic transonic accretion flow around the primary black hole, which forms the circumprimary disc (CPD), within a BBH system. The BBH spacetime is characterized by the mass ratio ($q$) and the separation distance ($z_2$) between the two black holes. We numerically solve the radial momentum and energy equations to obtain the accretion solutions. It is observed that the CPD can exhibit shock solutions, which exist for a wide range parameter space spanned by flow angular momentum ($\lambda$) and energy ($E$). We find that the shock parameter space is modified by $q$ and $z_2$. Investigations show that $q$ and $z_2$ also affect various shock properties, such as density compression and temperature compression across the shock fronts. Moreover, we calculate the spectral energy distributions (SEDs) of the CPD and examine how the SEDs are modified by $q$ and $z_2$ for both shock-free and shock-induced accretion solutions. SED is found to be nearly independent of the binary parameters. We depict that the mutual interaction between two black holes in a binary system hardly alters their individual accretion features. This study replicates the existing predictions in a different context of binary accretion., Comment: LaTeX, 14 pages, 10 figures, 2 tables

Quantum teleportation efficiently transfers quantum information between distant locations by utilizing a pre-established composite system. Assessing the effectiveness of teleportation hinges on its fidelity, representing the similarity between input and output states. This fidelity, in turn, relies on a singlet fraction, quantifying the resemblance of the composite system to maximally entangled states. The relation between teleportation fidelity and singlet fraction given by [Horodecki \textit{et al}., Phy. Rev. A \textbf{60}, 1888 (1999)] does not hold for distinguishable particles with multiple degrees of freedom or indistinguishable particles with single or multiple degrees of freedom. In this paper, we propose generalized expressions for teleportation fidelity and singlet fraction and derive their relations, applicable for both distinguishable and indistinguishable particles with single or multiple degrees of freedom. We derive an upper bound for the generalized singlet fraction for distinguishable particles using the monogamy of singlet fraction by [Kay \textit{et al.} Phys. Rev. Lett. \textbf{103}, 050501 (2009)]. We also show how our relation helps to characterize different types of composite states in terms of their distinguishability, separability, presence of maximally entangled structure, and the number of degrees of freedom. We complement our theory with two practical illustrations. First, we demonstrate two counter-intuitive values of generalized singlet fraction using our optical circuit and the circuit of [Li \textit{et al.}, Phys. Rev. Lett. \textbf{120}, 050404 (2018)]. Finally, we show that using an additional degree of freedom as an ancilla instead of a particle can be advantageous in quantum cryptographic protocols.

We use Full Counting Statistics to study fluctuations and optimal control in a three-terminal Floquet quantum thermal transistor. We model the setup using three qubits (termed as the emitter, collector and base) coupled to three thermal baths. As shown in Phys. Rev. E 106, 024110 (2022), one can achieve significant change in the emitter and collector currents through a small change in the base current, thereby achieving a thermal transistor operation. Using sinusoidal and pi-flip modulations of the base qubit frequency, we show that the variance of the base current is much less compared to those of the emitter and collector currents, while the opposite is true in case of the Fano factor. We then apply optimal control through the Chopped Random Basis optimization protocol, in order to significantly enhance the amplification obtained in the transistor. In contrast, a reduction in the Fano factor of the setup through optimal control is associated with a large base current, thereby suggesting a trade-off between precision and base current. We expect our results will be relevant for developing heat modulation devices in near-term quantum technologies., Comment: 14 pages, 8 figures

In this work we propose a Visual Mamba (ViM) based architecture, to dissolve the existing trade-off for real-time and accurate model with low computation overhead for disparity map generation (DMG). Moreover, we proposed a performance measure that can jointly evaluate the inference speed, computation overhead and the accurateness of a DMG model. The code implementation and corresponding models are available at: https://github.com/MBora/ViM-Disparity.

We investigate the electronic transport behavior of Fano-Anderson (FA) systems, consisting of a one-dimensional finite backbone chain and an attached side-group of varying length. The tight-binding model within the non-equilibrium Green's function (NEGF) reveals distinct interference patterns between the frontier orbitals of odd and even side-group systems that lead to distinct transport mechanisms across various configurations. The even side-group systems exhibit resonant tunneling peaks, while the odd ones demonstrate significant destructive interference at Fermi energy leading to antiresonance and reduced current responses. Further exploration of the role of electron-electron interactions within mean-field Hubbard Hamiltonian indicates the emergence of negative differential resistance (NDR) in odd side-group systems at lower biases, a phenomenon absent in the non-interacting case. Our study demonstrates the critical role of quantum interferences among the frontier molecular orbitals and electron correlations in shaping the transport behavior of FA systems, providing insights for the design of molecular-scale electronic devices.

Magnetic fields are ubiquitous across different physical systems of current interest; from the early Universe, compact astrophysical objects and heavy-ion collisions to condensed matter systems. A proper treatment of the effects produced by magnetic fields during the dynamical evolution of these systems, can help to understand observables that otherwise show a puzzling behavior. Furthermore, when these fields are comparable to or stronger than \Lambda_QCD, they serve as excellent probes to help elucidate the physics of strongly interacting matter under extreme conditions of temperature and density. In this work we provide a comprehensive review of recent developments on the description of QED and QCD systems where magnetic field driven effects are important. These include the modification of meson static properties such as masses and form factors, the chiral magnetic effect, the description of anomalous transport coefficients, superconductivity in extreme magnetic fields, the properties of neutron stars, the evolution of heavy-ion collisions, as well as effects on the QCD phase diagram. We describe recent theory and phenomenological developments using effective models as well as LQCD methods. The work represents a state-of-the-art review of the field, motivated by presentations and discussions during the "Workshop on Strongly Interacting Matter in Strong Electromagnetic Fields" that took place in the European Centre for Theoretical Studies in Nuclear Physics and Related Areas (ECT*) in the city of Trento, Italy, September 25-29, 2023., Comment: 325 pages-long review of recent topics of interest in the field of magnetic field effects on QED and QCD matter. To be susbmitted to PNPP

Previous research on LLM vulnerabilities often relied on nonsensical adversarial prompts, which were easily detectable by automated methods. We address this gap by focusing on human-readable adversarial prompts, a more realistic and potent threat. Our key contributions are situation-driven attacks leveraging movie scripts to create contextually relevant, human-readable prompts that successfully deceive LLMs, adversarial suffix conversion to transform nonsensical adversarial suffixes into meaningful text, and AdvPrompter with p-nucleus sampling, a method to generate diverse, human-readable adversarial suffixes, improving attack efficacy in models like GPT-3.5 and Gemma 7B. Our findings demonstrate that LLMs can be tricked by sophisticated adversaries into producing harmful responses with human-readable adversarial prompts and that there exists a scope for improvement when it comes to robust LLMs.

Gender diversity enhances research by bringing diverse perspectives and innovative approaches. It ensures equitable solutions that address the needs of diverse populations. However, gender disparity persists in research where women remain underrepresented, which might limit diversity and innovation. Many even leave scientific careers as their contributions often go unnoticed and undervalued. Therefore, understanding gender-based contributions and collaboration dynamics is crucial to addressing this gap and creating a more inclusive research environment. In this study, we analyzed 2,000 articles published over the past decade in the Journal of Systems and Software (JSS). From these, we selected 384 articles that detailed authors' contributions and contained both female and male authors to investigate gender-based contributions. Our contributions are fourfold. First, we analyzed women's engagement in software systems research. Our analysis showed that only 32.74% of the total authors are women and female-led or supervised studies were fewer than those of men. Second, we investigated female authors' contributions across 14 major roles. Interestingly, we found that women contributed comparably to men in most roles, with more contributions in conceptualization, writing, and reviewing articles. Third, we explored the areas of software systems research and found that female authors are more actively involved in human-centric research domains. Finally, we analyzed gender-based collaboration dynamics. Our findings revealed that female supervisors tended to collaborate locally more often than national-level collaborations. Our study highlights that females' contributions to software systems research are comparable to those of men. Therefore, the barriers need to be addressed to enhance female participation and ensure equity and inclusivity in research.

Composite asymmetric dark matter (ADM) is the framework that naturally explains the coincidence of the baryon density and the dark matter density of the Universe. Through a portal interaction sharing particle-antiparticle asymmetries in the Standard Model and dark sectors, dark matter particles, which are dark-sector counterparts of baryons, can decay into antineutrinos and dark-sector counterparts of mesons (dark mesons) or dark photon. Subsequent cascade decay of the dark mesons and the dark photon can also provide electromagnetic fluxes at late times of the Universe. We derive constraints on the lifetime of dark matter decay in the composite ADM scenario from the astrophysical observations of the $e^+$, $e^-$, and $\gamma$-ray fluxes. The constraints from cosmic-ray positron measurements by AMS-02 are the most stringent at $\gtrsim2$ GeV: a lifetime should be larger than the order of $10^{26}$ s, corresponding to the cutoff scale of the portal interaction of about $10^8 \text{--} 10^9 \, \mathrm{GeV}$. We also show the importance of neutrino observations with Super-Kamiokande and Hyper-Kamiokande, which give conservative bounds., Comment: 14 + 7 pages, 10 figures

Our primary aim in this paper is to introduce and study the cohomology of a Nijenhuis operator and of a Nijenhuis algebra. Our cohomology of a Nijenhuis algebra controls the simultaneous deformations of the underlying associative structure and the Nijenhuis operator. We interpret the second cohomology group as the space of all isomorphism classes of abelian extensions. Then we study the inducibility of a pair of Nijenhuis algebra automorphisms in a given abelian extension and show that the corresponding obstruction can be seen as the image of a suitable Wells-type map. We also consider skeletal and strict $2$-term homotopy Nijenhuis algebras and characterize them by third cocycles of Nijenhuis algebras and crossed modules of Nijenhuis algebras, respectively. Finally, we introduce strict homotopy Nijenhuis operators on $A_\infty$-algebras and show that they induce $NS_\infty$-algebras., Comment: 30 pages

The Gromov-Wasserstein (GW) distance is an effective measure of alignment between distributions supported on distinct ambient spaces. Calculating essentially the mutual departure from isometry, it has found vast usage in domain translation and network analysis. It has long been shown to be vulnerable to contamination in the underlying measures. All efforts to introduce robustness in GW have been inspired by similar techniques in optimal transport (OT), which predominantly advocate partial mass transport or unbalancing. In contrast, the cross-domain alignment problem being fundamentally different from OT, demands specific solutions to tackle diverse applications and contamination regimes. Deriving from robust statistics, we discuss three contextually novel techniques to robustify GW and its variants. For each method, we explore metric properties and robustness guarantees along with their co-dependencies and individual relations with the GW distance. For a comprehensive view, we empirically validate their superior resilience to contamination under real machine learning tasks against state-of-the-art methods.

Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk of eviction. We propose a novel active geospatial search (AGS) modeling framework for this problem. AGS integrates property-level information in a search policy that identifies a sequence of rental units to canvas to both determine their eviction risk and provide support if needed. We propose a hierarchical reinforcement learning approach to learn a search policy for AGS that scales to large urban areas containing thousands of parcels, balancing exploration and exploitation and accounting for travel costs and a budget constraint. Crucially, the search policy adapts online to newly discovered information about evictions. Evaluation using eviction data for a large urban area demonstrates that the proposed framework and algorithmic approach are considerably more effective at sequentially identifying eviction cases than baseline methods., Comment: Accepted to AAAI 2025 (AI for Social Impact Track)

Fault-tolerant quantum computing (FTQC) is essential for executing reliable quantum computations of meaningful scale. Widely adopted QEC codes for FTQC, such as the surface code and color codes, utilize Clifford+T gate sets, where T gates are generally considered as the primary bottleneck due to their high resource costs. Recent advances in T gate optimization have significantly reduced this overhead, making Clifford gate complexity an increasingly critical bottleneck that remains largely unaddressed in present FTQC compiler and architecture designs. To address this new bottleneck, this paper introduces TACO, a \textbf{T}ranspiler-\textbf{A}rchitecture \textbf{C}odesign \textbf{O}ptimization framework, to reduce Clifford cost. Specifically, we observe that, through codesign, insights rooted in the FTQC architecture can inform novel circuit-level optimizations for FTQC compilers. These optimizations, in turn, provide new opportunities to redesign and improve the underlying architecture. Evaluations show that TACO achieves an average 91.7% reduction in Clifford gates across diverse quantum circuits and significantly enhances gate parallelism compared to Pauli-based approaches. These improvements enable an efficient FTQC architecture that can achieve single-gate-per-cycle throughput using only $1.5n+4$ logical qubit tiles, considerably pushing forward upon previously proposed designs that require $2n+\sqrt{8n}+1$ tiles. These results highlight the benefits of bidirectional optimization through codesign. TACO will be open-source.

In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e.g., I can vent about my in-laws to understanding strangers), but the privacy risks are more abstract (e.g., will my partner be able to tell that this is me?). Prior work has sought to develop natural language processing (NLP) tools that help users identify potentially risky self-disclosures in their text, but none have been designed for or evaluated with the users they hope to protect. Absent this assessment, these tools will be limited by the social-technical gap: users need assistive tools that help them make informed decisions, not paternalistic tools that tell them to avoid self-disclosure altogether. To bridge this gap, we conducted a study with N = 21 Reddit users; we had them use a state-of-the-art NLP disclosure detection model on two of their authored posts and asked them questions to understand if and how the model helped, where it fell short, and how it could be improved to help them make more informed decisions. Despite its imperfections, users responded positively to the model and highlighted its use as a tool that can help them catch mistakes, inform them of risks they were unaware of, and encourage self-reflection. However, our work also shows how, to be useful and usable, AI for supporting privacy decision-making must account for posting context, disclosure norms, and users' lived threat models, and provide explanations that help contextualize detected risks., Comment: 31 pages, 5 figues, Accepted for publication at CSCW 2025

We explore pattern formation in an active fluid system involving two chemical species that regulate active stress: a fast-diffusing species ($A$) and a slow-diffusing species ($I$). The growth of species $A$ is modelled using a nonlinear logistic term. Through linear stability analysis, we derive phase diagrams illustrating the various dynamical regimes in parameter space. Our findings indicate that an increase in the P\'eclet number results in the destabilisation of the uniform steady state. In contrast, counter-intuitively, an increase in the nonlinear growth parameter of $A$ actually stabilises the homogeneous steady-state regime. Additionally, we observe that greater asymmetry between the species leads to three distinct dynamical phases, while low asymmetry fails to produce oscillatory instability. Numerical simulations conducted in instability regimes show patterns that range from irregular, arrhythmic configurations at high P\'eclet numbers to both transient and robust symmetry-breaking chimera states. Notably, these chimera patterns are more prevalent in the oscillatory instability regime, and our stability analysis indicates that this regime is the most extensive for high nonlinear growth parameters and moderately high P\'eclet numbers. Further, we also find soliton-like structures where aggregations of species $A$ merge, and new aggregations spontaneously emerge, and these patterns are prevalent in the phase of stationary instability. Overall, our study illustrates that a diverse array of patterns can emerge in active matter influenced by nonlinear growth in a chemical species, with chimeras being particularly dominant when the nonlinear growth parameter is elevated.

This paper considers the problem of fair probabilistic binary classification with binary protected groups. The classifier assigns scores, and a practitioner predicts labels using a certain cut-off threshold based on the desired trade-off between false positives vs. false negatives. It derives these thresholds from the ROC of the classifier. The resultant classifier may be unfair to one of the two protected groups in the dataset. It is desirable that no matter what threshold the practitioner uses, the classifier should be fair to both the protected groups; that is, the $\mathcal{L}_p$ norm between FPRs and TPRs of both the protected groups should be at most $\varepsilon$. We call such fairness on ROCs of both the protected attributes $\varepsilon_p$-Equalized ROC. Given a classifier not satisfying $\varepsilon_1$-Equalized ROC, we aim to design a post-processing method to transform the given (potentially unfair) classifier's output (score) to a suitable randomized yet fair classifier. That is, the resultant classifier must satisfy $\varepsilon_1$-Equalized ROC. First, we introduce a threshold query model on the ROC curves for each protected group. The resulting classifier is bound to face a reduction in AUC. With the proposed query model, we provide a rigorous theoretical analysis of the minimal AUC loss to achieve $\varepsilon_1$-Equalized ROC. To achieve this, we design a linear time algorithm, namely \texttt{FROC}, to transform a given classifier's output to a probabilistic classifier that satisfies $\varepsilon_1$-Equalized ROC. We prove that under certain theoretical conditions, \texttt{FROC}\ achieves the theoretical optimal guarantees. We also study the performance of our \texttt{FROC}\ on multiple real-world datasets with many trained classifiers., Comment: 51 pages, The 39th Annual AAAI Conference on Artificial Intelligence

Human mesh recovery (HMR) is crucial in many computer vision applications; from health to arts and entertainment. HMR from monocular images has predominantly been addressed by deterministic methods that output a single prediction for a given 2D image. However, HMR from a single image is an ill-posed problem due to depth ambiguity and occlusions. Probabilistic methods have attempted to address this by generating and fusing multiple plausible 3D reconstructions, but their performance has often lagged behind deterministic approaches. In this paper, we introduce GenHMR, a novel generative framework that reformulates monocular HMR as an image-conditioned generative task, explicitly modeling and mitigating uncertainties in the 2D-to-3D mapping process. GenHMR comprises two key components: (1) a pose tokenizer to convert 3D human poses into a sequence of discrete tokens in a latent space, and (2) an image-conditional masked transformer to learn the probabilistic distributions of the pose tokens, conditioned on the input image prompt along with randomly masked token sequence. During inference, the model samples from the learned conditional distribution to iteratively decode high-confidence pose tokens, thereby reducing 3D reconstruction uncertainties. To further refine the reconstruction, a 2D pose-guided refinement technique is proposed to directly fine-tune the decoded pose tokens in the latent space, which forces the projected 3D body mesh to align with the 2D pose clues. Experiments on benchmark datasets demonstrate that GenHMR significantly outperforms state-of-the-art methods. Project website can be found at https://m-usamasaleem.github.io/publication/GenHMR/GenHMR.html

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

The hot and dense QCD matter, known as the Quark-Gluon Plasma (QGP), is explored through heavy-ion collision experiments at the LHC and RHIC. Jets and heavy flavors, produced from the initial hard scattering, are used as hard probes to study the properties of the QGP. Recent experimental observations on jet quenching and heavy-flavor suppression have strengthened our understanding, allowing for fine-tuning of theoretical models in hard probes. The second conference, HOT QCD Matter 2024, was organized to bring the community together for discussions on key topics in the field. This article comprises 15 sections, each addressing various aspects of hard probes in relativistic heavy-ion collisions, offering a snapshot of current experimental observations and theoretical advancements. The article begins with a discussion on memory effects in the quantum evolution of quarkonia in the quark-gluon plasma, followed by an experimental review, new insights on jet quenching at RHIC and LHC, and concludes with a machine learning approach to heavy flavor production at the Large Hadron Collider., Comment: Compilation of the 15 contributions in Hard Probes presented at the second 'Hot QCD Matter 2024 Conference' held from July 1-3, 2024, organized by IIT Mandi, India

The extensive dissemination of false information in social networks affects netizens social lives, morals, and behaviours. When a neighbour expresses strong emotions (e.g., fear, anger, excitement) based on a false statement, these emotions can be transmitted to others, especially through interactions on social media. Therefore, exploring the mechanism that explains how an individuals emotions change under the influence of a neighbours false statement is a practically important task. In this work, we systematically examining the publics personal, interpersonal, and historical emotional influence based on social context, content, and emotional based features. The contribution of this paper is to build an emotionally infused model called the Emotional based User Sequential Influence Model(EUSIM) to understand users temporal emotional propagation patterns and predict future emotions against false information., Comment: 6 pages, 1 figure, The 25th International Conference on Intelligent Data Engineering and Automated Learning

Interstellar molecules are excellent tools for studying the physical and chemical environments of massive star-forming regions. In particular, vibrationally excited HC$_3$N (HC$_3$N*) lines are the key tracers for probing hot cores environments. We present the Atacama Large Millimeter/submillimeter Array (ALMA) 3 mm observations of HC$_3$N* lines in 60 hot cores, aiming to investigate how physical conditions affect the excitation of HC$_3$N* transitions. We have used the XCLASS for line identification. Under the assumption of local thermodynamic equilibrium (LTE), we derived the rotation temperature and column density of HC$_3$N* transitions in hot cores. Additionally, we calculated the H$_2$ column density and number density, along with the abundance of HC$_3$N* relative to H$_2$, to enable a comparison of the physical properties of hot cores with different numbers of HC$_3$N* states. We have detected HC$_3$N* lines in 52 hot cores, in which 29 cores showing more than one vibrationally excited state. Hot cores with higher gas temperatures have more detections of these vibrationally excited lines. The excitation of HC$_3$N* requires dense environments, with its spatial distribution influenced by the presence of UC Hii regions. The observed column density of HC$_3$N* contributes to the number of HC$_3$N* states in hot core environments. After analyzing the various factors influencing HC$_3$N* excitation in hot cores, we conclude that the excitation of HC$_3$N* is mainly driven by mid-IR pumping, while collisional excitation is ineffective., Comment: 14 pages, 9 figures

Reconstructing a 3D hand mesh from a single RGB image is challenging due to complex articulations, self-occlusions, and depth ambiguities. Traditional discriminative methods, which learn a deterministic mapping from a 2D image to a single 3D mesh, often struggle with the inherent ambiguities in 2D-to-3D mapping. To address this challenge, we propose MMHMR, a novel generative masked model for hand mesh recovery that synthesizes plausible 3D hand meshes by learning and sampling from the probabilistic distribution of the ambiguous 2D-to-3D mapping process. MMHMR consists of two key components: (1) a VQ-MANO, which encodes 3D hand articulations as discrete pose tokens in a latent space, and (2) a Context-Guided Masked Transformer that randomly masks out pose tokens and learns their joint distribution, conditioned on corrupted token sequences, image context, and 2D pose cues. This learned distribution facilitates confidence-guided sampling during inference, producing mesh reconstructions with low uncertainty and high precision. Extensive evaluations on benchmark and real-world datasets demonstrate that MMHMR achieves state-of-the-art accuracy, robustness, and realism in 3D hand mesh reconstruction. Project website: https://m-usamasaleem.github.io/publication/MMHMR/mmhmr.html

Whether in search of better trade opportunities or escaping wars, humans have always been on the move. For almost a century, mathematical models of human mobility have been instrumental in the quantification of commuting patterns and migratory fluxes. Equity is a common premise of most of these mathematical models, such that living conditions and job opportunities are assumed to be equivalent across cities. Growing inequalities in modern urban economy and pressing effects of climate change significantly strain this premise. Here, we propose a mobility model that is aware of inequalities across cities in terms of living conditions and job opportunities. Comparing results with real datasets, we show that the proposed model outperforms the state-of-the-art in predicting migration patterns in South Sudan and commuting fluxes in the United States. This model paves the way to critical research on resilience and sustainability of urban systems.

Let $p$ be a prime and $F$ a non-archimedean local field of residue characteristic $p$. In this paper, we study the restriction of smooth irreducible $\bar{\mathbb{F}}_p$-representations of $\mathrm{SL}_2(F)$ to its Borel subgroup. In essence, we show that the action of $\mathrm{SL}_2(F)$ on its irreducibles is controlled by the action of the Borel subgroup. The results of this paper constitute the $\mathrm{SL}_2$-analogue of a work of Pa\v{s}k\=unas\cite{PaskunasRestriction}., Comment: 19 pages

Parameter estimation of gravitational wave data is often computationally expensive, requiring simplifying assumptions such as circularisation of binary orbits. Although, if included, the sub-dominant effects like orbital eccentricity may provide crucial insights into the formation channels of compact binary mergers. To address these challenges, we present a pipeline strategy leveraging minimally modelled waveform reconstruction to identify the presence of eccentricity in real time. Using injected signals, we demonstrate that ignoring eccentricity ($e_{\rm 20Hz} \gtrsim 0.1$) leads to significant biases in parameter recovery, including chirp mass estimates falling outside the 90% credible interval. Waveform reconstruction shows inconsistencies increase with eccentricity, and this behaviour is consistent for different mass ratios. Our method enables low-latency inferences of binary properties supporting targeted follow-up analyses and can be applied to identify any physical effect of measurable strength.

This paper proposes a novel federated algorithm that leverages momentum-based variance reduction with adaptive learning to address non-convex settings across heterogeneous data. We intend to minimize communication and computation overhead, thereby fostering a sustainable federated learning system. We aim to overcome challenges related to gradient variance, which hinders the model's efficiency, and the slow convergence resulting from learning rate adjustments with heterogeneous data. The experimental results on the image classification tasks with heterogeneous data reveal the effectiveness of our suggested algorithms in non-convex settings with an improved communication complexity of $\mathcal{O}(\epsilon^{-1})$ to converge to an $\epsilon$-stationary point - compared to the existing communication complexity $\mathcal{O}(\epsilon^{-2})$ of most prior works. The proposed federated version maintains the trade-off between the convergence rate, number of communication rounds, and test accuracy while mitigating the client drift in heterogeneous settings. The experimental results demonstrate the efficiency of our algorithms in image classification tasks (MNIST, CIFAR-10) with heterogeneous data., Comment: FLUID Workshop@AAAI 2025

$Ar^+$ ion has been used regularly for the cleaning of semiconductor, metal surfaces for epitaxial nanostructures growth. We have investigated the effect of low-energy $Ar^+$ ion sputtering and subsequent annealing on the $Si(111)-(7\times7)$ surfaces under ultrahigh vacuum (UHV) condition. Using $in-situ$ scanning tunnelling microscopy (STM) we have compared the morphological changes to the $Si(111)-(7\times7)$ surfaces before and after the sputtering process. Following $500~eV Ar^+$ ion sputtering, the atomically flat $Si(111)-(7\times7)$ surface becomes amorphous. The average root mean square (rms) surface roughness $({\sigma}_{avg})$ of the sputtered surface and that following post-annealing at different temperatures $(500^\circ-700^\circ)C$ under UHV have been measured as a function of STM scan size. While, annealing at $\sim 500^\circ C$ shows no detectable changes in the surface morphology, recrystallization process starts at $\sim 600^\circ C$. For the sputtered samples annealed at temperatures $\geq 600^\circ C, \,log~\sigma_{avg}$ varies linearly at lower length scales and approaches a saturation value of $\sim 0.6 nm$ for the higher length scales confirming the self-affine fractal nature. The correlation length increases with annealing temperature indicating gradual improvement in crystallinity. For the present experimental conditions, $650^\circ C$ is the optimal annealing temperature for recrystallization. The results offer a method to engineer the crystallinity of sputtered surface during nanofabrication process., Comment: 20 Pages, 4 Figures

Neuromorphic computing aims to revolutionize large-scale data processing by developing efficient methods and devices inspired by neural networks. Among these, the control of magnetism through ion migration has emerged as a promising approach due to the inherent memory and nonlinearity of ionically conducting and magnetic materials. In this work, we present a lithium-ion-based magneto-ionic device that uses applied voltages to control the magnetic domain state of a perpendicularly magnetized ferromagnetic layer. This behavior emulates the analog and non-volatile properties of biological synapses and enables the creation of a simple reservoir computing system. To illustrate its capabilities, the device is used in a waveform classification task, where the voltage amplitude range and magnetic bias field are tuned to optimize the recognition accuracy., Comment: 11 pages, 7 figures

Large Language Models (LLMs) have significantly improved personalized conversational capabilities. However, existing datasets like Persona Chat, Synthetic Persona Chat, and Blended Skill Talk rely on static, predefined personas. This approach often results in dialogues that fail to capture human personalities' fluid and evolving nature. To overcome these limitations, we introduce a novel dataset with around 400,000 dialogues and a framework for generating personalized conversations using long-form journal entries from Reddit. Our approach clusters journal entries for each author and filters them by selecting the most representative cluster, ensuring that the retained entries best reflect the author's personality. We further refine the data by capturing the Big Five personality traits --openness, conscientiousness, extraversion, agreeableness, and neuroticism --ensuring that dialogues authentically reflect an individual's personality. Using Llama 3 70B, we generate high-quality, personality-rich dialogues grounded in these journal entries. Fine-tuning models on this dataset leads to an 11% improvement in capturing personality traits on average, outperforming existing approaches in generating more coherent and personality-driven dialogues., Comment: Accepted in COLING 2025

Individual fairness guarantees are often desirable properties to have, but they become hard to formalize when the dataset contains outliers. Here, we investigate the problem of developing an individually fair $k$-means clustering algorithm for datasets that contain outliers. That is, given $n$ points and $k$ centers, we want that for each point which is not an outlier, there must be a center within the $\frac{n}{k}$ nearest neighbours of the given point. While a few of the recent works have looked into individually fair clustering, this is the first work that explores this problem in the presence of outliers for $k$-means clustering. For this purpose, we define and solve a linear program (LP) that helps us identify the outliers. We exclude these outliers from the dataset and apply a rounding algorithm that computes the $k$ centers, such that the fairness constraint of the remaining points is satisfied. We also provide theoretical guarantees that our method leads to a guaranteed approximation of the fair radius as well as the clustering cost. We also demonstrate our techniques empirically on real-world datasets., Comment: 12 pages

A new mechanism of enhanced laser energy absorption in plasma microglobules is demonstrated with the help of two-dimensional Particle-In-Cell (PIC) simulations. The mechanism relies on the excitation of surface and bulk waves and the occurrence of repeated collisions in the confines of the finite-sized microglobular target. The episodic increase in the average particle energy correlates with the repeated collision of the surface and bulk waves that get excited by the laser on the target. It is shown that the size of the microglobular target governs the efficiency of absorption and the timings of episodic events of energy enhancement. This study thereby illustrates the novel efficient possibility that a closed plasma target provides for energy extraction. Parallels of such colliding waves creating havoc in terms of wave breaking etc. can be witnessed on the ocean surface, seismic disturbances traversing as body waves traverse reflecting and refracting in the interior of the Earth along with surface waves (propagating on the curved surface of the Earth) converge at the antipode to create destruction. Our studies here show the importance of choosing closed targets which aid in the process of repeated energy transfer to particles and often their thermalization. The waves keep propagating in the closed confines rather than getting dissipated over an extended region as would happen for extended targets.

As quantum computing rapidly advances, its near-term applications are becoming increasingly evident. However, the high cost and under-utilization of quantum resources are prompting a shift from single-user to multi-user access models. In a multi-tenant environment, where multiple users share one quantum computer, protecting user confidentiality becomes crucial. The varied uses of quantum computers increase the risk that sensitive data encoded by one user could be compromised by others, rendering the protection of data integrity and confidentiality essential. In the evolving quantum computing landscape, it is imperative to study these security challenges within the scope of realistic threat model assumptions, wherein an adversarial user can mount practical attacks without relying on any heightened privileges afforded by physical access to a quantum computer or rogue cloud services. In this paper, we demonstrate the potential of crosstalk as an attack vector for the first time on a Noisy Intermediate Scale Quantum (NISQ) machine, that an adversarial user can exploit within a multi-tenant quantum computing model. The proposed side-channel attack is conducted with minimal and realistic adversarial privileges, with the overarching aim of uncovering the quantum algorithm being executed by a victim. Crosstalk signatures are used to estimate the presence of CNOT gates in the victim circuit, and subsequently, this information is encoded and classified by a graph-based learning model to identify the victim quantum algorithm. When evaluated on up to 336 benchmark circuits, our attack framework is found to be able to unveil the victim's quantum algorithm with up to 85.7\% accuracy.

Neuro-symbolic Artificial Intelligence (AI) models, blending neural networks with symbolic AI, have facilitated transparent reasoning and context understanding without the need for explicit rule-based programming. However, implementing such models in the Internet of Things (IoT) sensor nodes presents hurdles due to computational constraints and intricacies. In this work, for the first time, we propose a near-sensor neuro-symbolic AI computing accelerator named Neuro-Photonix for vision applications. Neuro-photonix processes neural dynamic computations on analog data while inherently supporting granularity-controllable convolution operations through the efficient use of photonic devices. Additionally, the creation of an innovative, low-cost ADC that works seamlessly with photonic technology removes the necessity for costly ADCs. Moreover, Neuro-Photonix facilitates the generation of HyperDimensional (HD) vectors for HD-based symbolic AI computing. This approach allows the proposed design to substantially diminish the energy consumption and latency of conversion, transmission, and processing within the established cloud-centric architecture and recently designed accelerators. Our device-to-architecture results show that Neuro-Photonix achieves 30 GOPS/W and reduces power consumption by a factor of 20.8 and 4.1 on average on neural dynamics compared to ASIC baselines and photonic accelerators while preserving accuracy., Comment: 12 pages, 15 figures

In this article, we introduce finite mixture models (FMMs) renowned for capturing population heterogeneity. Our focus lies in establishing stochastic comparisons between two arithmetic (finite) mixture models, employing the vector majorization concept in the context of various univariate orders of magnitude, transform, and variability. These comparisons are conducted within the framework of multiple-outlier location-scale models. Specifically, we derive sufficient conditions for comparing two finite arithmetic mixture models with components distributed in a multiple-outlier location-scale model., Comment: 26 pages, 2 figures

A sheared cube is made out of a cube by giving a shear to the body in one direction keeping one of the faces fixed. We investigate here the thermodynamic phase behavior of a family of such regular hard sheared cubes, each of the members of the family having a distinct angle made by the faces with the perpendicular on the fixed face. Hard particle Monte Carlo (HPMC) has been performed with these anisotropic building blocks resulting entropy-driven self assembly. Thereby computational evidence of discrete plastic crystal phase has been found in crystal. The discrete plastic crystal phase is known to form through the spontaneous self-assembly of certain polyhedra. Throughout the entire solid regime particle orientations exhibit strong specific correlations before melting into a liquid, without any evidence of freely rotating plastic crystal at lower density solid. It has been thoroughly observed that geometrical attributes of the shapes don't determine any of the properties that designate this orientational disorder phase reported here. We also find that particle's rotational symmetric axes and one of the rotational symmetric axes of the unit cell of the crystal have a strong relationship in their alignment in space. These results, achieved with shapes having crystallographic point group symmetry, are investigated as being consistent with the phenomenology of discrete plastic crystal phase established in earlier works with hard particles having non-crystallographic point group symmetry., Comment: 37 pages, 65 references and 11 figures in the main text. 4 pages, 4 figures in the supplementary information

The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times, and difficulty selecting appropriate CPs. To address these challenges, we propose a novel end-to-end framework called Stable Matching EV Charging Assignment (SMEVCA) that efficiently assigns charge-seeking EVs to CPs with assistance from roadside units (RSUs). The proposed framework operates within a subscription-based model, ensuring that the subscribed EVs complete their charging within a predefined time limit enforced by a service level agreement (SLA). The framework SMEVCA employs a stable, fast, and efficient EV-CP assignment formulated as a one-to-many matching game with preferences. The matching process identifies the preferred coalition (a subset of EVs assigned to the CPs) using two strategies: (1) Preferred Coalition Greedy (PCG) that offers an efficient, locally optimal heuristic solution and (2) Preferred Coalition Dynamic (PCD) that is more computation-intensive but delivers a globally optimal coalition. Extensive simulations reveal that PCG and PCD achieve a gain of 14.6% and 20.8% over random elimination for in-network charge transferred with only 3% and 0.1% EVs unserved within the RSUs vicinity., Comment: This paper has been accepted for presentation at the 26th International Conference on Distributed Computing and Networking (ICDCN), 2025

Determining the robustness of deep learning models is an established and ongoing challenge within automated decision-making systems. With the advent and success of techniques that enable advanced deep learning (DL), these models are being used in widespread applications, including high-stake ones like healthcare, education, border-control. Therefore, it is critical to understand the limitations of these models and predict their regions of failures, in order to create the necessary guardrails for their successful and safe deployment. In this work, we revisit robustness, specifically investigating the sufficiency of robust accuracy (RA), within the context of deepfake detection. We present robust ratio (RR) as a complementary metric, that can quantify the changes to the normalized or probability outcomes under input perturbation. We present a comparison of RA and RR and demonstrate that despite similar RA between models, the models show varying RR under different tolerance (perturbation) levels., Comment: Extended abstract at Northern Lights Deep Learning (NLDL) Conference 2025

The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range of tasks. Many existing approaches to automating prompt engineering rely exclusively on textual feedback, refining prompts based solely on inference errors identified by large, computationally expensive LLMs. Unfortunately, smaller models struggle to generate high-quality feedback, resulting in complete dependence on large LLM judgment. Moreover, these methods fail to leverage more direct and finer-grained information, such as gradients, due to operating purely in text space. To this end, we introduce GReaTer, a novel prompt optimization technique that directly incorporates gradient information over task-specific reasoning. By utilizing task loss gradients, GReaTer enables self-optimization of prompts for open-source, lightweight language models without the need for costly closed-source LLMs. This allows high-performance prompt optimization without dependence on massive LLMs, closing the gap between smaller models and the sophisticated reasoning often needed for prompt refinement. Extensive evaluations across diverse reasoning tasks including BBH, GSM8k, and FOLIO demonstrate that GReaTer consistently outperforms previous state-of-the-art prompt optimization methods, even those reliant on powerful LLMs. Additionally, GReaTer-optimized prompts frequently exhibit better transferability and, in some cases, boost task performance to levels comparable to or surpassing those achieved by larger language models, highlighting the effectiveness of prompt optimization guided by gradients over reasoning. Code of GReaTer is available at https://github.com/psunlpgroup/GreaTer., Comment: 32 pages, 8 figures

Complexity is a fundamental characteristic of states within a quantum system. Its use is however mostly limited to bosonic systems, inhibiting its present applicability to supersymmetric theories. This is also relevant to its application to the AdS/CFT correspondence. To address this limitation, we extend the framework of spread complexity beyond bosonic systems to include fermionic and supercoherent states. This offers a gateway to compute spread complexity analytically for any semiclassical system governed by a Hamiltonian associated with a Lie (super)algebra. This requires extending the Krylov chain to a Krylov path in a higher-dimensional lattice. A detailed analysis of supercoherent states within the super Heisenberg-Weyl and OSp$(2|1)$ algebras elucidates distinct contributions from bosonic and fermionic degrees of freedom to the complexity. This generalisation allows us to access the semiclassical regime of the planar limit of the holographic correspondence. We then compute the spread complexity of large charge superstring states on the gravity side, which are equivalent to the dual gauge states. The resulting complexity leads to Krylov paths capturing the geometry in which the string propagates., Comment: 36 pages plus appendices, 10 Figures

We carry out an extensive light curve comparison of BL Her stars using observations from Gaia DR3 and stellar pulsation models computed using MESA-RSP with the goal to obtain the best-matched modeled-observed pairs for BL Her stars in the LMC. We use the Fourier decomposition technique to analyse the light curves in the G band obtained from Gaia DR3 and from MESA-RSP and use a robust light curve fitting approach to score the modeled-observed pairs with respect to their pulsation periods and over their Fourier parameter space. We obtain the best-fit models for 48 BL Her stars in the LMC and thereby provide the stellar parameter estimates of these stars, 30 of which are labelled as the gold sample with superior light curve fits. We find a relatively flat distribution of stellar masses between 0.5-0.65 Msolar for the gold sample of modeled-observed pairs. An interesting result is that the majority of the best-matched models in the gold sample are computed using the convection parameter sets without radiative cooling. The period-Wesenheit relation for the best-matched gold sample of 30 BL Her models exhibits a slope of $-2.805 \pm 0.164$ while the corresponding period-radius relation exhibits a slope of $0.565 \pm 0.035$, both in good agreement with the empirical PW and PR slopes from BL Her stars in the LMC, respectively. We also used the Wesenheit magnitudes of the 30 best-matched modeled-observed pairs to estimate a distance modulus of $\mu_{\rm LMC} = 18.582 \pm 0.067$ to the LMC, which lies within the bounds of previous literature values. We also discuss the degeneracy in the stellar parameters of the BL Her models that result in similar pulsation periods and light curve structure, and highlight that caution must be exercised while using the stellar parameter estimates., Comment: 20 pages, 10 main text figures, 5 appendix figures, 1 main text table, 1 appendix table. Accepted for publication in Astronomy & Astrophysics

The rise of digital platforms has led to an increasing reliance on technology-driven, home-based healthcare solutions, enabling individuals to monitor their health and share information with healthcare professionals as needed. However, creating an efficient care plan management system requires more than just analyzing hospital summaries and Electronic Health Records (EHRs). Factors such as individual user needs and social determinants of health, including living conditions and the flow of healthcare information between different settings, must also be considered. Challenges in this complex healthcare network involve schema diversity (in EHRs, personal health records, etc.) and terminology diversity (e.g., ICD, SNOMED-CT) across ancillary healthcare operations. Establishing interoperability among various systems and applications is crucial, with the European Interoperability Framework (EIF) emphasizing the need for patient-centric access and control of healthcare data. In this paper, we propose an integrated ontological model, the Common Semantic Data Model for Social Determinants of Health (CSSDH), by combining ISO/DIS 13940:2024 ContSys with WHO Social Determinants of Health. CSSDH aims to achieve interoperability within the Continuity of Care Network., Comment: 6 pages, 3 figures, conference-The 25th International Conference on Intelligent Data Engineering and Automated Learning