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Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufacturing industry. These anomaly detection paradigms predominantly focus on training detection models using only clean, unlabeled normal samples, assuming an absence of contamination; a condition often unmet in real-world scenarios. The performance of these methods significantly depends on the quality of the data and usually decreases when exposed to noise. We introduce a systematic adaptive method that employs deviation learning to compute anomaly scores end-to-end while addressing data contamination by assigning relative importance to the weights of individual instances. In this approach, the anomaly scores for normal instances are designed to approximate scalar scores obtained from the known prior distribution. Meanwhile, anomaly scores for anomaly examples are adjusted to exhibit statistically significant deviations from these reference scores. Our approach incorporates a constrained optimization problem within the deviation learning framework to update instance weights, resolving this problem for each mini-batch. Comprehensive experiments on the MVTec and VisA benchmark datasets indicate that our proposed method surpasses competing techniques and exhibits both stability and robustness in the presence of data contamination., Comment: Accepted to IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025)

Based on previous works, in this article we systematically analyze the implications of the explicit normal modes of a probe scalar sector in a BTZ background with a Dirichlet wall, in an asymptotically AdS-background. This is a Fuzzball-inspired geometric model, at least in an effective sense. We demonstrate explicitly that in the limit when the Dirichlet wall approaches the event horizon, the normal modes condense fast to yield an effective branch cut along the real line in the complex frequency plane. In turn, in this approximation, quasi-normal modes associated to the BTZ black hole emerge and the corresponding two-point function is described by a thermal correlator, associated with the Hawking temperature in the general case and with the right-moving temperature in the extremal limit. We further show, analytically, that the presence of a non-vanishing angular momentum non-perturbatively enhances this condensation. The consequences are manifold: {\it e.g.}~there is an emergent {\it strong thermalization} due to these modes, adding further support to a quantum chaotic nature associated to the spectral form factor. We explicitly demonstrate, by considering a classical collapsing geometry, that the one-loop scalar determinant naturally inherits a Dirichlet boundary condition, as the shell approaches the scale of the event horizon. This provides a plausible dynamical mechanism in the dual CFT through a global quench, that can create an emergent Dirichlet boundary close to the horizon-scale. We offer comments on how this simple model can describe salient features of Fuzzball-geometries, as well as of extremely compact objects. This also provides an explicit realization of how an effective thermal physics emerges from a non-thermal microscopic description, within a semi-classical account of gravity, augmented with an appropriate boundary condition., Comment: 68 pages, multiple figures

In this article we study a reaction diffusion system with $m$ unknown concentration. The non-linearity in our study comes from an underlying reversible chemical reaction and triangular in nature. Our objective is to understand the large time behaviour of solution where there are degeneracies. In particular we treat those cases when one of the diffusion coefficient is zero and others are strictly positive. We prove convergence to equilibrium type of results under some condition on stoichiometric coefficients in dimension $1$,$2$ and $3$ in correspondence with the existence of classical solution. For dimension greater than 3 we prove similar result under certain closeness condition on the non-zero diffusion coefficients and with the same condition imposed on stoichiometric coefficients. All the constant occurs in the decay estimates are explicit., Comment: 45 pages, comments welcome

While recent theoretical studies have positioned noncollinear polar magnets with $C_{nv}$ symmetry as compelling candidates for realizing topological magnetic phases and substantial intrinsic anomalous Hall conductivity, experimental realizations of the same in strongly correlated systems remain rare. Here, we present a large intrinsic anomalous Hall effect and extended topological magnetic ordering in Gd$_3$Ni$_8$Sn$_4$ with hexagonal $C_{6v}$ symmetry. Observation of topological Hall response, corroborated by metamagnetic anomalies in isothermal magnetization, peak/hump features in field-evolution of ac susceptibility and longitudinal resistivity, attests to the stabilization of skyrmion $A$-phase. The anomalous Hall effect is quantitatively accounted for by the intrinsic Berry curvature-mediated mechanism. Our results underscore polar magnets as a promising platform to investigate a plethora of emergent electrodynamic responses rooted in the interplay between magnetism and topology.

Integrating autonomous contact-based robotic characterization into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose a self-supervised convolutional neural network with a spatially differentiable loss function, incorporating shape priors to refine predictions of optimal robot contact poses for semiconductor characterization. This network improves valid pose generation by 20.0%, relative to existing models. We demonstrate our network's performance by driving a 4-degree-of-freedom robot to characterize photoconductivity at 3,025 predicted poses across a gradient of perovskite compositions, achieving throughputs over 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals regions of inhomogeneity. With this self-supervised deep learning-driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing the measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories.

In this paper, we develop the concept of multiple cylinder of relations which is a generalization of the relation cylinder, extending the multiple non-Hausdorff mapping cylinder to sequences of finite T0-spaces linked by a series of relations. This construction is important in capturing complex homotopical structures across chains of finite spaces and, when the relations are induced by maps, it serves as a third space that collapses to two distinct finite spaces. Additionally, we introduce the concept of a strong-good cover for simplicial complexes and finite spaces, char acterized by collapsible (rather than merely contractible) intersections. This leads to a strengthened version of the Nerve Theorem, which we develop for simplicial complexes as well as for finite spaces with strong-good covers, demonstrating that these complexes and spaces and their associated nerves maintain the same simple homotopy type, thereby refining classical results for finite simplicial complexes and finite topological structures.

Computing ground-state properties of molecules is a promising application for quantum computers operating in concert with classical high-performance computing resources. Quantum embedding methods are a family of algorithms particularly suited to these computational platforms: they combine high-level calculations on active regions of a molecule with low-level calculations on the surrounding environment, thereby avoiding expensive high-level full-molecule calculations and allowing to distribute computational cost across multiple and heterogeneous computing units. Here, we present the first density matrix embedding theory (DMET) simulations performed in combination with the sample-based quantum diagonalization (SQD) method. We employ the DMET-SQD formalism to compute the ground-state energy of a ring of 18 hydrogen atoms, and the relative energies of the chair, half-chair, twist-boat, and boat conformers of cyclohexane. The full-molecule 41- and 89-qubit simulations are decomposed into 27- and 32-qubit active-region simulations, that we carry out on the ibm_cleveland device, obtaining results in agreement with reference classical methods. Our DMET-SQD calculations mark a tangible progress in the size of active regions that can be accurately tackled by near-term quantum computers, and are an early demonstration of the potential for quantum-centric simulations to accurately treat the electronic structure of large molecules, with the ultimate goal of tackling systems such as peptides and proteins.

The Atacama Large Millimeter/submillimeter Array Survey of Orion Planck Galactic Cold Clumps (ALMASOP) reveals complex nested morphological and kinematic features of molecular outflows through the CO (J = 2 - 1) and SiO (J = 5 - 4) emission. We characterize the jet and outflow kinematics of the ALMASOP sample in four representative sources (HOPS 10, 315, 358, and G203.21-11.20W2) through channel maps and position-velocity diagrams (PVDs) parallel and transverse to the outflow axes. The combined CO and SiO emission exhibits the coexistence of the conventional extremely-high-velocity (EHV) jets and shell-like low-velocity (LV) cavity walls and new features. More complex, nested bubble-like and filamentary structures in the images and channel maps, triangle-shaped regions near the base of the parallel PVDs, and regions composed of rhombus/oval shapes in the transverse PVDs, are also evident. Such features find natural explanations within the bubble structure of the unified model of jet, wind, and ambient medium. The reverse shock cavity is revealed on the PVD base regions, and other features naturally arise within the dynamic postshock region of magnetic interaction. The finer nested shells observed within the compressed wind region reveal previously unnoticed shocked emission between the jet and the conventional large cavity walls. These pseudopulse-produced filamentary features connect to the jet-like knotty blobs, creating an impression of episodicity in mass ejection. SiO emission is enhanced downstream of the reverse shock boundary, with jet-like excitation conditions. Combined, these observed features reveal the extended structures induced by the magnetic interplay between a jet-bearing magnetized wide-angle wind and its ambient magnetized surrounding medium., Comment: 27 pages, 11 figures, accepted by ApJ

For a natural number n, let M(n) denote the maximum exponent of any prime power dividing n, and let m(n) denote the minimum exponent of any prime power dividing n. We study the second moments of these arithmetic functions and establish their limiting distributions. We introduce a new discrete probabilistic distribution dependent on a function f taking values in [0,1], study its first two moments, and provide examples of several arithmetic functions satisfying such distribution as their limiting behavior., Comment: 18 pages, Comments welcome

Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant computational challenges, limiting their practical utility. Our novel approach addresses these challenges by leveraging various encoders for intelligent data reduction and employing a different classification model to ensure robust, permutation-invariant representations of WSIs. A key innovation of our method is the ability to distill the complex information of an entire WSI into a single vector, effectively capturing the essential features needed for accurate analysis. This approach significantly enhances the computational efficiency of WSI analysis, enabling more accurate pathological assessments without the need for extensive computational resources. This breakthrough equips us with the capability to effectively address the challenges posed by large image resolutions in whole-slide imaging, paving the way for more scalable and effective utilization of WSIs in medical diagnostics and research, marking a significant advancement in the field., Comment: arXiv admin note: text overlap with arXiv:2411.08530

We discuss the nonclassical properties of photon subtracted compass states (PSCS). Nonclassical behavior is studied using various parameters like the Wigner function, squeezing, and photon statistical parameters like Mandels Q function, secondorder correlation function, Agarwal Tara $A_3$ criterion, and photon number distribution. Further analysis is being done to investigate the subPlanck structures in the Wigner functions of these PSCS. We also show that the photon subtraction causes the loss of sensitivity due to displacement of the states in phase space.

We introduce a novel Graph Attention Autoencoder (GAE) with spatial regularization to address the challenge of scalable anomaly detection in spatiotemporal rainfall data across India from 1990 to 2015. Our model leverages a Graph Attention Network (GAT) to capture spatial dependencies and temporal dynamics in the data, further enhanced by a spatial regularization term ensuring geographic coherence. We construct two graph datasets employing rainfall, pressure, and temperature attributes from the Indian Meteorological Department and ERA5 Reanalysis on Single Levels, respectively. Our network operates on graph representations of the data, where nodes represent geographic locations, and edges, inferred through event synchronization, denote significant co-occurrences of rainfall events. Through extensive experiments, we demonstrate that our GAE effectively identifies anomalous rainfall patterns across the Indian landscape. Our work paves the way for sophisticated spatiotemporal anomaly detection methodologies in climate science, contributing to better climate change preparedness and response strategies.

Dark matter is one of the pillars of the current standard model of structure formation: it is assumed to constitute most of the matter in the Universe. However, it can so far only be probed indirectly through its gravitational effects, and its nature remains elusive. In this focus meeting, we discussed different methods used to estimate galaxies' visible and dark matter masses in the nearby and distant Universe. We reviewed successes of the standard model relying on cold dark matter, confronted observations with simulations, and highlighted inconsistencies between the two. We discussed how robust mass measurements can help plan, perform, and refine particle dark matter searches. We further exchanged about alternatives to cold dark matter, such as warm, self-interacting, and fuzzy dark matter, as well as modified gravity. Finally, we discussed prospects and strategies that could be implemented to reveal the nature of this crucial component of the Universe., Comment: Rationale and highlights of the Focus Meeting 9 "Measures of luminous and dark matter in galaxies across time" at the XXXIInd IAU General Assembly in Cape Town, August 2024. 12 pages

We characterise the co-evolution of radio-loud AGN and their galaxies by mapping the dependence of radio-loud AGN activity on stellar mass and star-formation rate (SFR) across cosmic time (out to $z \sim 1.5$). Deep LOFAR radio observations are combined with large galaxy samples to study the incidence of radio-loud AGN across the galaxy population; the AGN are further split into low-excitation radio galaxies (LERGs) and high-excitation radio galaxies (HERGs). We find that LERG activity occurs over a wide range of SFRs, whereas HERGs are typically found in galaxies with ongoing star formation. The LERGs are then split based on their SFRs relative to the main sequence, across redshift. Within quiescent galaxies, LERG activity shows a steep stellar mass dependence with the same normalisation across the past $\sim$ 10 Gyr; this indicates that hot gas fuels LERGs in quiescent galaxies across cosmic time. In massive galaxies ($\log_{10}(M/\rm{M_{\odot}}) \gtrsim 11$), the incidence of LERGs is roughly constant across the galaxy population, suggesting that LERGs in massive galaxies may be fuelled by hot gas regardless of the star-formation activity. At lower masses, however, LERG activity is significantly more enhanced (by a factor of up to 10) in star-forming galaxies compared to quiescent galaxies; this suggests that an additional fuelling mechanism, likely associated with cold gas, may fuel the LERGs in galaxies with higher SFRs. We find that HERGs typically accrete above 1 per cent of the Eddington-scaled accretion rate, and the LERGs typically accrete below this level., Comment: Accepted for publication in MNRAS, 18 pages, 10 figures

Properties of the Higgs boson ($H$) at current and future particle colliders are crucial to explore new physics beyond the standard model. In particular, experimental and theoretical outlooks at future colliders drive interest in Higgs to gauge boson couplings. Single Higgs production via vector-boson fusion allows probing Higgs couplings with massive vector bosons ($V = W, Z$). We consider electron-proton (eP) collider to study these couplings due to the low background. In a recent study, we considered the most general anomalous Higgs-vector boson ($HVV$) couplings and explored the potential of eP collider in constraining the parameters of $HVV$ couplings. Our results were based on leading order predictions in perturbation theory. We include further Next to Leading Order (NLO) corrections of Quantum Chromodynamic (QCD) in Standard Model signal to make precise predictions. In this talk, I will present the effect of NLO QCD corrections on the standard model and anomalous $HVV$ couplings., Comment: 6 pages, 3 figures, proceedings of ICHEP 2024

When unsure about an answer, humans often respond with more words than necessary, hoping that part of the response will be correct. We observe a similar behavior in large language models (LLMs), which we term "Verbosity Compensation" (VC). VC is harmful because it confuses the user understanding, leading to low efficiency, and influences the LLM services by increasing the latency and cost of generating useless tokens. In this paper, we present the first work that defines and analyzes Verbosity Compensation, explores its causes, and proposes a simple mitigating approach. We define Verbosity Compensation as the behavior of generating responses that can be compressed without information loss when prompted to write concisely. Our experiments, conducted on five datasets of knowledge and reasoning-based QA tasks with 14 newly developed LLMs, reveal three conclusions. 1) We reveal a pervasive presence of verbosity compensation across all models and all datasets. Notably, GPT-4 exhibits a VC frequency of 50.40%. 2) We reveal the large performance gap between verbose and concise responses, with a notable difference of 27.61% on the Qasper dataset. We also demonstrate that this difference does not naturally diminish as LLM capability increases. Both 1) and 2) highlight the urgent need to mitigate the frequency of VC behavior and disentangle verbosity with veracity. We propose a simple yet effective cascade algorithm that replaces the verbose responses with the other model-generated responses. The results show that our approach effectively alleviates the VC of the Mistral model from 63.81% to 16.16% on the Qasper dataset. 3) We also find that verbose responses exhibit higher uncertainty across all five datasets, suggesting a strong connection between verbosity and model uncertainty. Our dataset and code are available at https://github.com/psunlpgroup/VerbosityLLM., Comment: 19 pages, 6 figures

There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effective merging-based alignment method called \textsc{MergeAlign} that interpolates the domain and alignment vectors, creating safer domain-specific models while preserving their utility. We apply \textsc{MergeAlign} on Llama3 variants that are experts in medicine and finance, obtaining substantial alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged. We hope our findings open new research avenues and inspire more efficient development of safe expert LLMs.

The difficulty of exploring and training online on real production systems limits the scope of real-time online data/feedback-driven decision making. The most feasible approach is to adopt offline reinforcement learning from limited trajectory samples. However, after deployment, such policies fail due to exogenous factors that temporarily or permanently disturb/alter the transition distribution of the assumed decision process structure induced by offline samples. This results in critical policy failures and generalization errors in sensitive domains like Real-Time Communication (RTC). We solve this crucial problem of identifying robust actions in presence of domain shifts due to unseen exogenous stochastic factors in the wild. As it is impossible to learn generalized offline policies within the support of offline data that are robust to these unseen exogenous disturbances, we propose a novel post-deployment shaping of policies (Streetwise), conditioned on real-time characterization of out-of-distribution sub-spaces. This leads to robust actions in bandwidth estimation (BWE) of network bottlenecks in RTC and in standard benchmarks. Our extensive experimental results on BWE and other standard offline RL benchmark environments demonstrate a significant improvement ($\approx$ 18% on some scenarios) in final returns wrt. end-user metrics over state-of-the-art baselines.

Complexity is an important metric for appropriate characterization of different classes of irregular signals, observed in the laboratory or in nature. The literature is already rich in the description of such measures using a variety of entropy and disequilibrium measures, separately or in combination. Chaotic signal was given prime importance in such studies while no such measure was proposed so far, how complex were the extreme events when compared to non-extreme chaos. We address here this question of complexity in extreme events and investigate if we can distinguish them from non-extreme chaotic signal. The normalized Shannon entropy in combination with disequlibrium is used for our study and it is able to distinguish between extreme chaos and non-extreme chaos and moreover, it depicts the transition points from periodic to extremes via Pomeau-Manneville intermittency and, from small amplitude to large amplitude chaos and its transition to extremes via interior crisis. We report a general trend of complexity against a system parameter that increases during a transition to extreme events, reaches a maximum, and then starts decreasing. We employ three models, a nonautonomous Lienard system, 2-dimensional Ikeda map and a 6-dimensional coupled Hindmarh-Rose system to validate our proposition., Comment: 9 pages, 6 figures, Accepted for publication in Chaos: An Interdisciplinary Journal of Nonlinear Science (2024)

Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset. In this work, we develop TimeText Corpus (TTC), a carefully curated, time-aligned text and time dataset for multimodal forecasting. Our dataset is composed of sequences of numbers and text aligned to timestamps, and includes data from two different domains: climate science and healthcare. Our data is a significant contribution to the rare selection of available multimodal datasets. We also propose the Hybrid Multi-Modal Forecaster (Hybrid-MMF), a multimodal LLM that jointly forecasts both text and time series data using shared embeddings. However, contrary to our expectations, our Hybrid-MMF model does not outperform existing baselines in our experiments. This negative result highlights the challenges inherent in multimodal forecasting. Our code and data are available at https://github.com/Rose-STL-Lab/Multimodal_ Forecasting., Comment: 21 pages, 4 tables, 2 figures

Each year, hundreds of clinical trials are conducted to evaluate new medical interventions, but sharing patient records from these trials with other institutions can be challenging due to privacy concerns and federal regulations. To help mitigate privacy concerns, researchers have proposed methods for generating synthetic patient data. However, existing approaches for generating synthetic clinical trial data disregard the usage requirements of these data, including maintaining specific properties of clinical outcomes, and only use post hoc assessments that are not coupled with the data generation process. In this paper, we propose SynRL which leverages reinforcement learning to improve the performance of patient data generators by customizing the generated data to meet the user-specified requirements for synthetic data outcomes and endpoints. Our method includes a data value critic function to evaluate the quality of the generated data and uses reinforcement learning to align the data generator with the users' needs based on the critic's feedback. We performed experiments on four clinical trial datasets and demonstrated the advantages of SynRL in improving the quality of the generated synthetic data while keeping the privacy risks low. We also show that SynRL can be utilized as a general framework that can customize data generation of multiple types of synthetic data generators. Our code is available at https://anonymous.4open.science/r/SynRL-DB0F/.

This study investigates the efficacy of Multi-Agent Systems in eliciting cross-agent communication and enhancing collective intelligence through group decision-making in a decentralized setting. Unlike centralized mechanisms, where a fixed hierarchy governs social choice, decentralized group decision-making allows agents to engage in joint deliberation. Our research focuses on the dynamics of communication and decision-making within various social choice methods. By applying different voting rules in various environments, we find that moderate decision flexibility yields better outcomes. Additionally, exploring the linguistic features of agent-to-agent conversations reveals indicators of effective collaboration, offering insights into communication patterns that facilitate or hinder collaboration. Finally, we propose various methods for determining the optimal stopping point in multi-agent collaborations based on linguistic cues. Our findings contribute to a deeper understanding of how decentralized decision-making and group conversation shape multi-agent collaboration, with implications for the design of more effective MAS environments., Comment: preprint

Understanding animal behaviour is central to predicting, understanding, and mitigating impacts of natural and anthropogenic changes on animal populations and ecosystems. However, the challenges of acquiring and processing long-term, ecologically relevant data in wild settings have constrained the scope of behavioural research. The increasing availability of Unmanned Aerial Vehicles (UAVs), coupled with advances in machine learning, has opened new opportunities for wildlife monitoring using aerial tracking. However, limited availability of datasets with wild animals in natural habitats has hindered progress in automated computer vision solutions for long-term animal tracking. Here we introduce BuckTales, the first large-scale UAV dataset designed to solve multi-object tracking (MOT) and re-identification (Re-ID) problem in wild animals, specifically the mating behaviour (or lekking) of blackbuck antelopes. Collected in collaboration with biologists, the MOT dataset includes over 1.2 million annotations including 680 tracks across 12 high-resolution (5.4K) videos, each averaging 66 seconds and featuring 30 to 130 individuals. The Re-ID dataset includes 730 individuals captured with two UAVs simultaneously. The dataset is designed to drive scalable, long-term animal behaviour tracking using multiple camera sensors. By providing baseline performance with two detectors, and benchmarking several state-of-the-art tracking methods, our dataset reflects the real-world challenges of tracking wild animals in socially and ecologically relevant contexts. In making these data widely available, we hope to catalyze progress in MOT and Re-ID for wild animals, fostering insights into animal behaviour, conservation efforts, and ecosystem dynamics through automated, long-term monitoring., Comment: 9 pages, 5 figures

Edge reconstruction of gate-tunable compressible quantum Hall fluids in the filling fraction range 1/3 to 2/3 is studied by measuring transmitted conductance of two individually excited fractional $e^2/3h$ edge modes of bulk 2/3 fractional quantum Hall fluid. Our findings reveal that the measured transmitted conductance deviates from the fully equilibrated value for the filling fraction range 1/3 to 2/3 of the gate-tunable compressible quantum Hall fluids at higher magnetic fields. This observation suggests that at the boundary of the compressible fluid a reconstructed $e^2/3h$ fractional edge mode is present and the mode does not completely equilibrate with the inner dissipative bulk region. Consequently, this outer reconstructed edge mode supports adiabatic charge transport, allowing non-equilibrated current transport through the compressible region. These studies open new avenues for achieving robust fractional edge modes even in compressible quantum Hall fluids under strong magnetic fields, enhancing our understanding of edge state dynamics in these complex systems., Comment: 9 pages, 5 figures

We show that a slowly varying Newton's constant, consistent with existing bounds, can potentially explain a host of observations pertaining to gravitational effects or phenomena across distances spanning from planetary to the cosmological, relying neither on the existence of Dark Matter or (and) Dark Energy, nor on any expected high proportions of either of them in the Universe. It may also have implications at very short distances or quantum gravity scales., Comment: 22 pages, 2 figures, 1 table; to appear in Eur. Phys. J. Plus

In this paper, we present a novel RRT*-based strategy for generating kinodynamically feasible paths that satisfy temporal logic specifications. Our approach integrates a robustness metric for Linear Temporal Logics (LTL) with the system's motion constraints, ensuring that the resulting trajectories are both optimal and executable. We introduce a cost function that recursively computes the robustness of temporal logic specifications while penalizing time and control effort, striking a balance between path feasibility and logical correctness. We validate our approach with simulations and real-world experiments in complex environments, demonstrating its effectiveness in producing robust and practical motion plans. This work represents a significant step towards expanding the applicability of motion planning algorithms to more complex, real-world scenarios.

Glucose monitoring plays a critical role in managing diabetes, one of the most prevalent diseases globally. The development of fast-responsive, cost-effective, and biocompatible glucose sensors is essential for improving patient care. In this study, a comparative analysis is conducted between pristine and Co-doped hematite samples, synthesized via the hydrothermal method, to evaluate their structural, morphological, and optical properties. The glucose sensing performance of both samples is assessed using a fiber-optic evanescent wave (FOEW) setup. While the sensitivity remains comparable for both pristine and Co-doped hematite, a reduction in the Limit of Detection (LoD) is observed in the Co-doped sample, suggesting enhanced interactions with glucose molecules at the surface. To gain further insights into the glucose adsorption mechanisms, Density Functional Theory (DFT) calculations are performed, revealing key details regarding charge transfer, electronic delocalization, and glucose binding on the hematite surfaces. These findings highlight the potential of Co-doped hematite for advanced glucose sensing applications, offering a valuable synergy between experimental and theoretical approaches for further exploration in biosensing technologies.

This systematic review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theorem. KANs distinguish themselves from traditional neural networks by using learnable, spline-parameterized functions instead of fixed activation functions, allowing for flexible and interpretable representations of high-dimensional functions. This review details KAN's architectural strengths, including adaptive edge-based activation functions that improve parameter efficiency and scalability in applications such as time series forecasting, computational biomedicine, and graph learning. Key advancements, including Temporal-KAN, FastKAN, and Partial Differential Equation (PDE) KAN, illustrate KAN's growing applicability in dynamic environments, enhancing interpretability, computational efficiency, and adaptability for complex function approximation tasks. Additionally, this paper discusses KAN's integration with other architectures, such as convolutional, recurrent, and transformer-based models, showcasing its versatility in complementing established neural networks for tasks requiring hybrid approaches. Despite its strengths, KAN faces computational challenges in high-dimensional and noisy data settings, motivating ongoing research into optimization strategies, regularization techniques, and hybrid models. This paper highlights KAN's role in modern neural architectures and outlines future directions to improve its computational efficiency, interpretability, and scalability in data-intensive applications.

We report the results of a comprehensive study of the spectroscopic binary (SB2) system HD 34736 hosting two chemically peculiar (CP) late B-type stars. Using new and archival observational data, we characterise the system and its components, including their rotation and magnetic fields. Fitting of the radial velocities yields $P_\mathrm{orb}=83.\!^\mathrm{d}219(3)$ and $e=0.8103(3)$. The primary component is a CP He-wk star with $T_{\mathrm{eff}A}=13000\pm500$ K and $\upsilon_\mathrm{e}\sin i\;=75\pm3$ km/s, while the secondary exhibits variability of Mg and Si lines, and has $T_{\mathrm{eff}B}=11500\pm1000$ K and $\upsilon_\mathrm{e}\sin i=110$-180 km/s. TESS and KELT photometry reveal clear variability of the primary component with a rotational period $P_{\mathrm{rot}A}=1.\!^\mathrm{d}279\,988\,5(11)$, which is lengthening at a rate of $1.26(6)$ s/yr. For the secondary, $P_{\mathrm{rot}B}=0.\!^\mathrm{d}522\,693\,8(5)$, reducing at a rate of $-0.14(3)$ s/yr. The longitudinal component $\langle B_\mathrm{z}\rangle$ of the primary's strongly asymmetric global magnetic field varies from $-6$ to +5 kG. Weak spectropolarimetric evidence of a magnetic field is found for the secondary star. The observed X-ray and radio emission of HD 34736 may equally be linked to a suspected T Tau-like companion or magnetospheric emission from the principal components. Given the presence of a possible third magnetically active body, one can propose that the magnetic characteristics of the protostellar environment may be connected to the formation of such systems., Comment: This preprint has 28 pages, 23 figures, and includes the complete version of Table 1. Published in MNRAS

Rare-earth diantimonides display intriguing ground states often associated with structural order, which can be manipulated in thin film geometries. In this study, we report epitaxial synthesis of one such compound, YbSb$_{2}$, on III-V substrates using molecular-beam epitaxy. The synthesized thin films exhibit large, non-saturating, linear magnetoresistance across a wide magnetic field range. Additionally, they demonstrate superconducting properties, with a critical temperature of $\approx$ 1.025 K and a critical field of $\approx$ 83.85 Oe, consistent with the reports in bulk single crystals. While YbSb$_{2}$ has been classified as a Type-I superconductor in its bulk form, our findings provide evidence of a mixed state in the epitaxial thin films. This work paves the way for controlling the electronic ground state in this class of materials through thin film engineering., Comment: 8 pages, 6 figures

This article presents a zeroth order probabilistic global optimization algorithm -- SwiftNav -- for (not necessarily convex) functions over a compact domain. A discretization procedure is deployed on the compact domain, starting with a small step-size $h > 0$ and subsequently adaptively refining it in the course of a simulated annealing routine utilizing the Walker slice and the Gibbs sampler, in order to identify a set of global optimizers up to good precision. SwiftNav is parallelizable, which helps with scalability as the dimension of decision variables increases. Several numerical experiments are included here to demonstrate the effectiveness and accuracy of SwiftNav in high-dimensional benchmark optimization problems., Comment: 18 pages, 16 figures

In industry, online randomized controlled experiment (a.k.a A/B experiment) is a standard approach to measure the impact of a causal change. These experiments have small treatment effect to reduce the potential blast radius. As a result, these experiments often lack statistical significance due to low signal-to-noise ratio. To improve the precision (or reduce standard error), we introduce the idea of trigger observations where the output of the treatment and the control model are different. We show that the evaluation with full information about trigger observations (full knowledge) improves the precision in comparison to a baseline method. However, detecting all such trigger observations is a costly affair, hence we propose a sampling based evaluation method (partial knowledge) to reduce the cost. The randomness of sampling introduces bias in the estimated outcome. We theoretically analyze this bias and show that the bias is inversely proportional to the number of observations used for sampling. We also compare the proposed evaluation methods using simulation and empirical data. In simulation, evaluation with full knowledge reduces the standard error as much as 85%. In empirical setup, evaluation with partial knowledge reduces the standard error by 36.48%., Comment: 11 pages, 3 page appendix, 6 figures

In this paper we compute first and second Zagreb indices of commuting conjugacy class graphs of finite groups and determine several classes of finite groups such that their commuting conjugacy class graphs satisfy Hansen-Vuki\v{c}evi\'c conjecture., Comment: 26 pages

Let $F$ be a multi-quadratic totally real number field. Let $\sigma_1,\dots, \sigma_r$ denote its distinct embeddings. Given $s \in F,$ we give an explicit formula for $\| \sigma(s)\|$ and $\sum_{i

Vision-Language Models (VLMs) have shown significant promise in Visual Question Answering (VQA) tasks by leveraging web-scale multimodal datasets. However, these models often struggle with continual learning due to catastrophic forgetting when adapting to new tasks. As an effective remedy to mitigate catastrophic forgetting, rehearsal strategy uses the data of past tasks upon learning new task. However, such strategy incurs the need of storing past data, which might not be feasible due to hardware constraints or privacy concerns. In this work, we propose the first data-free method that leverages the language generation capability of a VLM, instead of relying on external models, to produce pseudo-rehearsal data for addressing continual VQA. Our proposal, named as GaB, generates pseudo-rehearsal data by posing previous task questions on new task data. Yet, despite being effective, the distribution of generated questions skews towards the most frequently posed questions due to the limited and task-specific training data. To mitigate this issue, we introduce a pseudo-rehearsal balancing module that aligns the generated data towards the ground-truth data distribution using either the question meta-statistics or an unsupervised clustering method. We evaluate our proposed method on two recent benchmarks, \ie VQACL-VQAv2 and CLOVE-function benchmarks. GaB outperforms all the data-free baselines with substantial improvement in maintaining VQA performance across evolving tasks, while being on-par with methods with access to the past data.

The radio galaxy NGC 1275, located at the central region of Perseus cluster, is a well-known very high-energy (VHE) gamma-ray emitter. The Major Atmospheric Cherenkov Experiment Telescope has detected two distinct episodes of VHE (E > 80 GeV) gamma-ray emission from NGC 1275 during 2022 December and 2023 January. The second outburst, observed on 2023 January 10, was the more intense of the two, with flux reaching 58$\%$ of the Crab Nebula flux above 80 GeV. The differential energy spectrum measured between 80 GeV and 1.5 TeV can be described by a power law with a spectral index of $\Gamma = - 2.90 \pm 0.16_{stat}$ for both flaring events. The broadband spectral energy distribution derived from these flares, along with quasisimultaneous low-energy counterparts, suggests that the observed gamma-ray emission can be explained using a homogeneous single-zone synchrotron self-Compton model. The physical parameters derived from this model for both flaring states are similar. The intermediate state observed between two flaring episodes is explained by a lower Doppler factor or magnetic field, which subsequently returned to its previous value during the high-activity state observed on 2023 January 10., Comment: 7 Pages, 5 Figures, and 1 Table

Environmental monitoring and predictive modeling of the Water Quality Index (WQI) through the assessment of the water quality., Comment: 18 pages

This paper presents novel methods for tuning inverter controller gains using deep reinforcement learning (DRL). A Simulink-developed inverter model is converted into a dynamic link library (DLL) and integrated with a Python-based RL environment, leveraging the multi-core deployment and accelerated computing to significantly reduce RL training time. A neural network-based mechanism is developed to transform the cascaded PI controller into an actor network, allowing optimized gain tuning by an RL agent to mitigate scenarios such as subsynchronous oscillations (SSO) and initial transients. Two distinct tuning approaches are demonstrated: a fixed gain strategy, where controller gains are represented as RL policy (actor network) weights, and an adaptive gain strategy, where gains are dynamically generated as RL policy (actor network) outputs. A comparative analysis of these methods is provided, showcasing their effectiveness in stabilizing the transient performance of grid-forming and grid-following converters and deployment challenges in hardware. Experimental results are presented, demonstrating the enhanced robustness and practical applicability of the RL-tuned controller gains in real-world systems.

This work considers designing of reliability acceptance sampling plan (RASP) when the competing risk data are progressively interval-censored. The methodology uses the asymptotic results of the estimators of parameters of any lifetime distribution under progressive interval censored competing risk data. Therefore, we establish a simplified form of the Fisher information matrix and present the asymptotic properties of the maximum likelihood estimators (MLEs) under a set of regularity conditions. Next, we consider a special case to illustrate the proposed RASP. we assume that the lifetime of the item due to the individual cause follows Weibull distribution. Also, it is assumed that the components are dependent and the gamma frailty model describes the dependent structure between the components. Now, we obtain the optimal RASP in three different ways. First, We present the method for obtaining optimal sample size and acceptance limit using producer's and consumer's risks. Next, we determine the optimal RASP under C-optimal criteria without cost constraints and with cost constraints. Numerical example is performed for both independent and dependent cases. Also, Monte Carlo simulation study is conducted in order to show that the sampling plans meet the specified risks for finite sample size.

Recent advancements in text-to-image diffusion models have enabled the personalization of these models to generate custom images from textual prompts. This paper presents an efficient LoRA-based personalization approach for on-device subject-driven generation, where pre-trained diffusion models are fine-tuned with user-specific data on resource-constrained devices. Our method, termed Hollowed Net, enhances memory efficiency during fine-tuning by modifying the architecture of a diffusion U-Net to temporarily remove a fraction of its deep layers, creating a hollowed structure. This approach directly addresses on-device memory constraints and substantially reduces GPU memory requirements for training, in contrast to previous methods that primarily focus on minimizing training steps and reducing the number of parameters to update. Additionally, the personalized Hollowed Net can be transferred back into the original U-Net, enabling inference without additional memory overhead. Quantitative and qualitative analyses demonstrate that our approach not only reduces training memory to levels as low as those required for inference but also maintains or improves personalization performance compared to existing methods., Comment: NeurIPS 2024

Sound Event Detection (SED) is challenging in noisy environments where overlapping sounds obscure target events. Language-queried audio source separation (LASS) aims to isolate the target sound events from a noisy clip. However, this approach can fail when the exact target sound is unknown, particularly in noisy test sets, leading to reduced performance. To address this issue, we leverage the capabilities of large language models (LLMs) to analyze and summarize acoustic data. By using LLMs to identify and select specific noise types, we implement a noise augmentation method for noise-robust fine-tuning. The fine-tuned model is applied to predict clip-wise event predictions as text queries for the LASS model. Our studies demonstrate that the proposed method improves SED performance in noisy environments. This work represents an early application of LLMs in noise-robust SED and suggests a promising direction for handling overlapping events in SED. Codes and pretrained models are available at https://github.com/apple-yinhan/Noise-robust-SED., Comment: Submitted to ICASSP 2025 Workshop

We estimate the growth rates for the set of reciprocal conjugacy classes in the Hecke groups. In other words, this estimates the growth of the reciprocal geodesics in terms of word lengths on the hyperbolic orbifolds associated with the Hecke groups., Comment: 14 pages

We study the lifts of the closed curves in the bouquet of $n$ circles to a finite sheeted normal covering. We give necessary and sufficient conditions for a closed curve to admit a lift for a class of normal covers of odd degrees. As an application, we prove that, for any prime number $p$, a free group with $n$ $ (\geq 2)$ generators can be written as the union of subgroups of index $p$., Comment: 20 pages, 8 figures. Title revised. Lemma 2 added. A stronger version of Theorem 1 proved

We measure the time-integrated CP asymmetry in $D^{0} \rightarrow K^{0}_{S}K^{0}_{S}$ decays reconstructed in $e^{+}e^{-} \rightarrow c\overline{c}$ events collected by the Belle and Belle II experiments. The corresponding data samples have integrated luminosities of 980 fb$^{-1}$ and 428 fb$^{-1}$, respectively. The $D^{0}$ decays are required to originate from the $D^{*+} \rightarrow D^{0}\pi^{+}$ decay, which determines the charm flavor at production time. A control sample of $D^{0} \rightarrow K^{+}K^{-}$ decays is used to correct for production and detection asymmetries. The result, $(-1.4\pm1.3{\rm(stat)}\pm0.1{\rm (syst)})\%$, is consistent with previous determinations and with CP symmetry., Comment: 10 pages, 3 figures. arXiv admin note: text overlap with arXiv:2410.22961

Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality background template used to produce an anomaly score. Using the LHC Olympics R&D dataset, we demonstrate that this background template can also be repurposed to directly estimate the background expectation in a simple cut and count setup. In contrast to a traditional bump hunt, no fit to the invariant mass distribution is needed, thereby avoiding the potential problem of background sculpting. Furthermore, direct background estimation allows working with large background rejection rates, where resonant anomaly detection methods typically show their greatest improvement in significance., Comment: 26 pages, 9 figures, 2 tables

Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for $\textit{in-context fine-tuning}$ of a time-series foundation model. In particular, we design a pretrained foundation model that can be prompted (at inference time) with multiple time-series examples, in order to forecast a target time-series into the future. Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window (in addition to the history of the target time-series) to help it adapt to the specific distribution of the target domain at inference time. We show that such a foundation model that uses in-context examples at inference time can obtain much better performance on popular forecasting benchmarks compared to supervised deep learning methods, statistical models, as well as other time-series foundation models. Interestingly, our in-context fine-tuning approach even rivals the performance of a foundation model that is explicitly fine-tuned on the target domain.

We study a monolayer of metal balls under periodic chiral driving in the horizontal plane. Energy dissipation occurs in this system via (i) inelastic collisions and (ii) frictional interaction with the substrate. We show that below a density-dependent critical drive, the system phase separates into a fluid phase coexisting with a solid phase. Unlike ordinary coexistence, however, the system does not obey the lever rule, as the fluid-phase density depends on the overall particle density. Additionally, the pressure is discontinuous across the fluid-solid interface, accompanied by a chiral edge current at the interface.

Optical detection of magnetic resonance using quantum spin sensors (QSS) provides a spatially local and sensitive technique to probe spin dynamics in magnets. However, its utility as a probe of antiferromagnetic resonance (AFMR), wherein the characteristic resonant frequencies substantially exceed the QSS probing frequency, remains an open question. Here, using the nitrogen-vacancy center in diamond as a QSS, we report the first experimental demonstration of optically detected AFMR in layered van der Waals antiferromagnets up to frequencies of 24 GHz, significantly higher than the sensor's spin resonance frequency of 2.87 GHz. To achieve this, we leverage the enhancement of the QSS spin relaxation rate due to low-frequency magnetic field fluctuations that arise from collective late-time dynamics of finite-wavevector magnons excited by the driven uniform AFMR mode. Using optically detected AFMR, we first characterize the temperature and magnetic field dependence on the AFMR modes, which shed light on the intrinsic exchange fields and magnetic anisotropies. Second, we exploit the highly localized sensitivity of the QSS to demonstrate efficient magnon transport over tens of micrometers. Finally, we find that optical detection efficiency in fact increases with increasing frequency, enabling broadband detection of magnetization dynamics. Our work showcases the dual capabilities of QSS as detectors of both high frequency magnetization dynamics and magnon transport, paving the way for understanding and controlling magnetism in Neel states in antiferromagnets.

We perform a model-independent measurement of the $D^0$-$\overline{D}{}^0$ mixing parameters using samples of $e^+e^-$-collision data collected by the Belle and Belle II experiments that have integrated luminosities of $951\ \text{fb}^{-1}$ and $408\ \text{fb}^{-1}$, respectively. Approximately $2.05\times10^6$ neutral $D$ mesons are reconstructed in the $D^0\rightarrow K^0_{S}\pi^+\pi^-$ channel, with the neutral $D$ flavor tagged by the charge of the pion in the $D^{*+}\rightarrow D^0\pi^+$ decay. Assuming charge-parity symmetry, the mixing parameters are measured to be $ x = (4.0\pm1.7\pm0.4)\times 10^{-3} $ and $ y = (2.9\pm1.4\pm0.3)\times 10^{-3}$, where the first uncertainties are statistical and the second systematic. The results are consistent with previous determinations.

We consider a generalization of the so-called elephant random walk by introducing multiple elephants moving along the integer line, $\mathbb{Z}$. When taking a new step, each elephant considers not only its own previous steps but also the past steps of other elephants. The dynamics of "who follows whom" are governed by a directed graph, where each vertex represents an elephant, and the edges indicate that an elephant will consider the past steps of its in-neighbour elephants when deciding its next move. In other words, this model involves a collection of reinforced random walks evolving through graph-based interactions. We briefly investigate the first- and second-order asymptotic behaviour of the joint walks and establish connections with other network-based reinforced stochastic processes studied in the literature. We show that the joint walk can be expressed as a stochastic approximation scheme. In certain regimes, we employ tools from stochastic approximation theory to derive the asymptotic properties of the joint walks. Additionally, in a specific regime, we use better techniques to establish a strong invariance principle and a central limit theorem with improved rates compared to existing results in the stochastic approximation literature. These techniques can also be used to strengthen equivalent results in stochastic approximation theory. As a byproduct, we establish a strong invariance principle for the simple elephant random walk with significantly improved rates., Comment: 45 pages