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Photomechanically active materials undergo reversible deformation on illumination, making them ideal for remote, tether-free actuation. Much of the work on these materials has focused on one-dimensional structures, such as strips. In this paper, we explore photomechanically active two-dimensional structures such as sheets and shells. When illuminated, such structures undergo spontaneous bending due to the limited penetration of light. However, the geometry of the shell constrains possible deformation modes: changes in Gauss curvature lead to in-plane stretching, against which shells are very stiff. Therefore, there is a complex coupling between the photomechanical actuation and the mechanical behavior of a shell. We develop and implement a novel approach to study photomechananically active shells. This method is a discrete shell model which captures the interplay between actuation, stretching, bending, and geometric changes. Through a series of examples, we explore these complex interactions, demonstrating how one can design shells that deform to follow the source of illumination.

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC at Fermilab. We observe a concentration of low energy ($<$3 MeV) blips around fiberglass mechanical support struts along the TPC edges with energy spectrum features consistent with the Compton edge of 2.614 MeV $^{208}$Tl decay $\gamma$ rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE's data to few percent precision and to measure the specific activity of $^{208}$Tl in the fiberglass composing these struts, $(11.7 \pm 0.2 ~\text{(stat)} \pm 2.8~\text{(syst)})$ Bq/kg. Cosmogenically-produced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions. An enriched sample of low-energy protons selected using this new particle discrimination technique is found to be smaller in data than in dedicated CORSIKA cosmic ray simulations, suggesting either incorrect CORSIKA modeling of incident cosmic fluxes or particle transport modeling issues in Geant4., Comment: 19 pages, 15 figures total including the supplementary material section

Here, we report the full-fledged journey towards the material synthesis and characterization of few-layered/thin WSe$_2$ using sputtered W-films on SiO$_2$/Si substrates followed by electrical studies under dark and illumination conditions. Growth temperature 500oC and gas pressure 55 sccm are found to be the optimized parameters for formation of thermodynamically stable WSe$_{2-x}$ with dominant Raman peak at 265 cm-1. XRD and HR-TEM measurement clarify the formation of high crystallinity along the c-axis and quasi-crystallinity along a and b axes respectively. Lower intensities from Raman-measurement and PL-peak at 768 nm (with 532 nm excitation wavelength) infers the thin nature of the grown film, along with strong second harmonic emission with excitation wavelength varying from 350nm to 450 nm. This work also retracks the controlled etching by reactive ions to achieve large area bi/tri-layer films to fabricate advanced devices. We also have fabricated an advanced MOS structure on SiO$_2$/p-Si substrate which shows tremendous performance by means of photo-capacitance under illumination condition where photo-carriers can survive the higher probe frequencies (> 1MHz). Under illumination condition, HfO$_2$/WSe$_2$ embedded MOS shows its dominance showing a huge electron-inversion region over HfO$_2$/ SiO$_2$/p-Si and SiO$_2$/p-Si MOS devices even at high frequencies (1-10 MHz). Thereby, this work also reveals a possible route for capacitance based highly sensitive photodetection using conventional Si-technology with integration of such WSe$_2$/W as an active material., Comment: 22, 7

Dark matter (DM) genesis via Ultraviolet (UV) freeze-in embeds the seed of reheating temperature and dynamics in its relic density. Thus, discovery of such a DM candidate can possibly open the window for post-inflationary dynamics. However, there are several challenges in this exercise, as freezing-in DM possesses feeble interaction with the visible sector and therefore very low production cross-section at the collider. We show that mono-photon (and dilepton) signal at the ILC, arising from DM effective operators connected to the SM field strength tensors, can still warrant a signal discovery. We study both the scalar and fermionic DM production during reheating via UV freeze-in, when the inflaton oscillates at the bottom of a general monomial potential. Interestingly, we see, right DM abundance can be achieved only in the case of bosonic reheating scenario, satisfying bounds from big bang nucleosynthesis (BBN). This provides a unique correlation between collider signal and the post-inflationary dynamics of the Universe within single-field inflationary models., Comment: 28 pages, 10 figures and 4 table

The proliferation of machine learning models in diverse clinical applications has led to a growing need for high-fidelity, medical image training data. Such data is often scarce due to cost constraints and privacy concerns. Alleviating this burden, medical image synthesis via generative adversarial networks (GANs) emerged as a powerful method for synthetically generating photo-realistic images based on existing sets of real medical images. However, the exact image set size required to efficiently train such a GAN is unclear. In this work, we experimentally establish benchmarks that measure the relationship between a sample dataset size and the fidelity of the generated images, given the dataset's distribution of image complexities. We analyze statistical metrics based on delentropy, an image complexity measure rooted in Shannon's entropy in information theory. For our pipeline, we conduct experiments with two state-of-the-art GANs, StyleGAN 3 and SPADE-GAN, trained on multiple medical imaging datasets with variable sample sizes. Across both GANs, general performance improved with increasing training set size but suffered with increasing complexity., Comment: Accepted to ACCV, Workshop on Generative AI for Synthetic Medical Data

More than one dark sector particle transforming under the same symmetry provides one stable dark matter (DM) component which undergoes co-annihilation with the heavier particle(s) decaying to DM. Specific assumptions on the kinematics and on the coupling parameters may render the heavier component(s) stable and contribute as DM. The choices of the charges of the dark sector fields under transformation play a crucial role in the resultant phenomenology. In this paper, we systematically address the possibility of obtaining two scalar DM components under $\mathbb{Z}_N$ symmetry. We consider both the possibilities of DM being weakly interacting massive particle (WIMP) or pseudofeebly interacting massive particle (pFIMP). We elaborate upon $\mathbb{Z}_3$ symmetric model, confronting the relic density allowed parameter space with recent most direct and indirect search bounds and prospects. We also highlight the possible distinction of the allowed parameter space in single component and two component cases, as well as between WIMP-WIMP and WIMP-pFIMP scenarios., Comment: 35 pages and 12 figures

The synergy between real and reciprocal space topology is anticipated to yield a diverse array of topological properties in quantum materials. We address this pursuit by achieving topologically safeguarded magnetic order in novel Weyl metallic Heusler alloy, Mn$_{2}$Pd$_{0.5}$Ir$_{0.5}$Sn. The system possesses non-centrosymmetric D$_{2d}$ crystal symmetry with notable spin-orbit coupling effects. Our first principles calculations confirm the topological non-trivial nature of band structure, including 42 pairs of Weyl nodes at/near the Fermi level, offering deeper insights into the observed anomalous Hall effect mediated by intrinsic Berry curvature. A unique canted magnetic ordering facilitates such rich topological features, manifesting through an exceptionally large topological Hall effect at low fields. The latter is sustained even at room temperature and compared with other known topological magnetic materials. Detailed micromagnetic simulations demonstrate the possible existence of an antiskyrmion lattice. Our results underscore the $D_{2d}$ Heusler magnets as a possible platform to explore the intricate interplay of non-trivial topology across real and reciprocal spaces to leverage a plethora of emergent properties for spintronic applications.

We develop a robust Machine Learning classifier model utilizing the eXtreme-Gradient Boosting (XGB) algorithm for improved classification of Galactic Wolf-Rayet (WR) stars based on Infrared (IR) colors and positional attributes. For our study, we choose an extensive dataset of 6555 stellar objects (from 2MASS and AllWISE data releases) lying in the Milky Way (MW) with available photometric magnitudes of different types including WR stars. Our XGB classifier model can accurately (with an 86\% detection rate) identify a sufficient number of WR stars against a large sample of non-WR sources. The XGB model outperforms other ensemble classifier models such as the Random Forest. Also, using the XGB algorithm, we develop a WR sub-type classifier model that can differentiate the WR subtypes from the non-WR sources with a high model accuracy ($>60\%$). Further, we apply both XGB-based models to a selection of 6457 stellar objects with unknown object types, detecting 58 new WR star candidates and predicting sub-types for 10 of them. The identified WR sources are mainly located in the Local spiral arm of the MW and mostly lie in the solar neighborhood., Comment: 19 pages, Accepted to APJ

We consider the transition rate of a static Unruh-DeWitt detector in two $(2+1)$-dimensional black hole spacetimes that are isometric to the static Ba\~nados-Teitelboim-Zanelli black hole outside the horizon but have no asymptotically locally anti-de Sitter exterior behind the horizon. The spacetimes are the $\mathbb{R}\text{P}^{2}$ geon, with spatial topology $\mathbb{R}\text{P}^{2}\setminus\{\text{point at infinity}\}$, and the Swedish geon of \AA{}minneborg et al, with spatial topology $T^{2}\setminus\{\text{point at infinity}\}$. For a conformal scalar field, prepared in the Hartle-Hawking-type state that is induced from the global vacuum on the anti-de Sitter covering space, we show numerically that the detector's transition rate distinguishes the two spacetimes, particularly at late exterior times, and we trace this phenomenon to the differences in the isometries that are broken by the quotient construction from the universal covering space. Our results provide an example in which information about the interior topology of a black hole is accessible to a quantum observer outside the black hole., Comment: 16 pages, 10 figures

We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user's image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models. All videos from this paper are available at https://go.fb.me/MovieGenResearchVideos.

Vizing's Theorem from 1964 states that any $n$-vertex $m$-edge graph with maximum degree $\Delta$ can be {\em edge colored} using at most $\Delta + 1$ colors. For over 40 years, the state-of-the-art running time for computing such a coloring, obtained independently by Arjomandi [1982] and by Gabow, Nishizeki, Kariv, Leven and Terada~[1985], was $\tilde O(m\sqrt{n})$. Very recently, this time bound was improved in two independent works, by Bhattacharya, Carmon, Costa, Solomon and Zhang to $\tilde O(mn^{1/3})$, and by Assadi to $\tilde O(n^2)$. In this paper we present an algorithm that computes such a coloring in $\tilde O(mn^{1/4})$ time. Our key technical contribution is a subroutine for extending the coloring to one more edge within time $\tilde O(\Delta^2 + \sqrt{\Delta n})$. The best previous time bound of any color extension subroutine is either the trivial $O(n)$, dominated by the length of a Vizing chain, or the bound $\tilde{O}(\Delta^6)$ by Bernshteyn [2022], dominated by the length of {\em multi-step Vizing chains}, which is basically a concatenation of multiple (carefully chosen) Vizing chains. Our color extension subroutine produces significantly shorter multi-step Vizing chains than in previous works, for sufficiently large $\Delta$., Comment: To appear at SODA 2025

In this paper, we consider the \emph{metric $k$-center} problem in the fully dynamic setting, where we are given a metric space $(V,d)$ evolving via a sequence of point insertions and deletions and our task is to maintain a subset $S \subseteq V$ of at most $k$ points that minimizes the objective $\max_{x \in V} \min_{y \in S}d(x, y)$. We want to design our algorithm so that we minimize its \emph{approximation ratio}, \emph{recourse} (the number of changes it makes to the solution $S$) and \emph{update time} (the time it takes to handle an update). We give a simple algorithm for dynamic $k$-center that maintains a $O(1)$-approximate solution with $O(1)$ amortized recourse and $\tilde O(k)$ amortized update time, \emph{obtaining near-optimal approximation, recourse and update time simultaneously}. We obtain our result by combining a variant of the dynamic $k$-center algorithm of Bateni et al.~[SODA'23] with the dynamic sparsifier of Bhattacharya et al.~[NeurIPS'23].

In this paper, we introduce an equivariant analog of Weiss calculus of functors for all finite group $\mathrm{G}$. In our theory, Taylor approximations and derivatives are index by finite dimensional $\mathrm{G}$-representations, and homogeneous layers are classified by orthogonal $\mathrm{G}$-spectra. Further, our framework permits a notion of restriction as well as a notion of fixed-point at the level of Weiss functors. We establish various results comparing Taylor approximations and derivatives of fixed-point (resp. restrictions) functors to that of the fixed-point (resp. restrictions) of Taylor approximations and derivatives.

This study investigates judgment prediction in a realistic scenario within the context of Indian judgments, utilizing a range of transformer-based models, including InLegalBERT, BERT, and XLNet, alongside LLMs such as Llama-2 and GPT-3.5 Turbo. In this realistic scenario, we simulate how judgments are predicted at the point when a case is presented for a decision in court, using only the information available at that time, such as the facts of the case, statutes, precedents, and arguments. This approach mimics real-world conditions, where decisions must be made without the benefit of hindsight, unlike retrospective analyses often found in previous studies. For transformer models, we experiment with hierarchical transformers and the summarization of judgment facts to optimize input for these models. Our experiments with LLMs reveal that GPT-3.5 Turbo excels in realistic scenarios, demonstrating robust performance in judgment prediction. Furthermore, incorporating additional legal information, such as statutes and precedents, significantly improves the outcome of the prediction task. The LLMs also provide explanations for their predictions. To evaluate the quality of these predictions and explanations, we introduce two human evaluation metrics: Clarity and Linking. Our findings from both automatic and human evaluations indicate that, despite advancements in LLMs, they are yet to achieve expert-level performance in judgment prediction and explanation tasks., Comment: Accepted on NLLP at EMNLP 2024

Several measures exist for string similarity, including notable ones like the edit distance and the indel distance. The former measures the count of insertions, deletions, and substitutions required to transform one string into another, while the latter specifically quantifies the number of insertions and deletions. Many algorithmic solutions explicitly address one of these measures, and frequently techniques applicable to one can also be adapted to work with the other. In this paper, we investigate whether there exists a standardized approach for applying results from one setting to another. Specifically, we demonstrate the capability to reduce questions regarding string similarity over arbitrary alphabets to equivalent questions over a binary alphabet. Furthermore, we illustrate how to transform questions concerning indel distance into equivalent questions based on edit distance. This complements an earlier result of Tiskin (2007) which addresses the inverse direction.

Droplet growth and size spectra play a crucial role in the microphysics of atmospheric clouds. However, it is challenging to represent droplet growth rate accurately in cloud-resolving models such as Large Eddy Simulations (LESs). The assumption of "well-mixed" condition within each grid cell, often made by traditional LES solvers, typically falls short near the edges of clouds, where sharp gradients in water vapor supersaturation occur. This under-resolution of supersaturation gradients can lead to significant errors in prediction of droplet growth rate, which in turn affects the prediction of buoyancy at cloud edges, as well as forecast of precipitation. In "superdroplet" based LES model, a Lagrangian coarse-graining approach groups multiple droplets into superdroplets, each encompassing a specific number and size of actual droplets. The superdroplets are advected by the underlying LES velocity field, and the growth rate of these superdroplets is based on the filtered supersaturation field represented by the LES. To overcome the limitations of the "well-mixed" assumption, we propose a parameterization for superdroplet growth using high-fidelity Direct Numerical Simulation (DNS) data. We introduce a novel clustering algorithm to map droplets in DNS fields to superdroplets. The effective supersaturation at each superdroplet location is computed by averaging the unfiltered supersaturation of the associated droplets, which may differ from the value of filtered supersaturation at the superdroplet location. We then develop a machine learning-based parameterization to relate the effective growth rate of superdroplets to other filtered DNS flow variables. Preliminary results show a promising $R^2$ value of nearly 0.9 between the predicted and true effective supersaturation values for the superdroplets, for a range of superdroplet multiplicities., Comment: 17 pages, 10 figures

In this study, we investigate the performance of few-shot classification models across different domains, specifically natural images and histopathological images. We first train several few-shot classification models on natural images and evaluate their performance on histopathological images. Subsequently, we train the same models on histopathological images and compare their performance. We incorporated four histopathology datasets and one natural images dataset and assessed performance across 5-way 1-shot, 5-way 5-shot, and 5-way 10-shot scenarios using a selection of state-of-the-art classification techniques. Our experimental results reveal insights into the transferability and generalization capabilities of few-shot classification models between diverse image domains. We analyze the strengths and limitations of these models in adapting to new domains and provide recommendations for optimizing their performance in cross-domain scenarios. This research contributes to advancing our understanding of few-shot learning in the context of image classification across diverse domains.

We present an analysis of adaptive optics (AO) images from the Keck-I telescope of the microlensing event MOA-2011-BLG-262. The original discovery paper by Bennett et al. 2014 reports two distinct possibilities for the lens system; a nearby gas giant lens with an exomoon companion or a very low mass star with a planetary companion in the galactic bulge. The $\sim$10 year baseline between the microlensing event and the Keck follow-up observations allows us to detect the faint candidate lens host (star) at $K = 22.3$ mag and confirm the distant lens system interpretation. The combination of the host star brightness and light curve parameters yields host star and planet masses of $M_{\rm host} = 0.19 \pm 0.03M_{\odot}$ and $m_p = 28.92 \pm 4.75M_{\oplus}$ at a distance of $D_L = 7.49 \pm 0.91\,$kpc. We perform a multi-epoch cross reference to \textit{Gaia} DR3 and measure a transverse velocity for the candidate lens system of $v_L = 541.31 \pm 65.75$ km s$^{-1}$. We conclude this event consists of the highest velocity exoplanet system detected to date, and also the lowest mass microlensing host star with a confirmed mass measurement. The high-velocity nature of the lens system can be definitively confirmed with an additional epoch of high-resolution imaging at any time now. The methods outlined in this work demonstrate that the \textit{Roman} Galactic Exoplanet Survey (RGES) will be able to securely measure low-mass host stars in the bulge., Comment: 21 pages, 6 figures, 4 tables, submitted to AJ

We revisit the problem of spontaneous symmetry breaking (SSB), its restoration and phase transition for a self interacting quantum scalar field in a general curved background, at zero and finite temperature. To the best of our knowledge, most of the earlier computations in this context have been done in the linear order in curvature, which may not be very suitable for the Ricci flat spacetimes. One of our objectives is to see whether the higher order terms can bring in qualitatively new physical effects, and thereby attempting to fill in this gap in the literature. We use the Bunch-Parker local momentum space representation of the Feynman propagator. We compute the renormalised, background spacetime curvature (up to quadratic order) and temperature dependent one loop effective potential for $\phi^4$ plus $\phi^3$ self interaction. In particular for the de Sitter spacetime, we have shown for $\phi^4$-theory that we can have SSB for a scalar even with a positive rest mass squared and non-minimal coupling, at zero temperature. This cannot be achieved by the linear curvature term alone and the result remains valid for a very large range of renormalisation scale. For a phase transition, we have computed the leading curvature correction to the critical temperature. At finite temperature, symmetry restoration is also demonstrated. We also extend some of the above results to two loop level. The symmetry breaking in de Sitter at two loop remains present. We have further motivated the necessity of treating this problem non-perturbatively in some instances., Comment: v1; 32pp, 15 figs

Instantons and renormalons play important roles at the interface between perturbative and non-perturbative quantum field theory. They are both associated with branch points in the Borel transform of asymptotic series, and as such can be detected in perturbation theory. However, while instantons are associated with non-perturbative saddle points of the path integral, renormalons have mostly been understood in terms of Feynman diagrams and the operator product expansion. We provide a non-perturbative path integral explanation of how both instantons and renormalons produce singularities in the Borel plane using representative finite-dimensional integrals. In particular, renormalons can be understood as saddle points of the 1-loop effective action, enabled by a crucial contribution from the quantum scale anomaly. These results enable an exploration of renormalons from the path integral and thereby provide a new way to probe connections between perturbative and non-perturbative physics in QCD and other theories., Comment: 5 pages, 1 Appendix and 2 figures

Vizing's theorem states that any $n$-vertex $m$-edge graph of maximum degree $\Delta$ can be \emph{edge colored} using at most $\Delta + 1$ different colors [Vizing, 1964]. Vizing's original proof is algorithmic and shows that such an edge coloring can be found in $O(mn)$ time. This was subsequently improved to $\tilde O(m\sqrt{n})$ time, independently by [Arjomandi, 1982] and by [Gabow et al., 1985]. Very recently, independently and concurrently, using randomization, this runtime bound was further improved to $\tilde{O}(n^2)$ by [Assadi, 2024] and $\tilde O(mn^{1/3})$ by [Bhattacharya, Carmon, Costa, Solomon and Zhang, 2024] (and subsequently to $\tilde O(mn^{1/4})$ time by [Bhattacharya, Costa, Solomon and Zhang, 2024]). We present an algorithm that computes a $(\Delta+1)$-edge coloring in $\tilde O(m)$ time -- in fact, even $O(m\log{\Delta})$ time -- with high probability, \emph{giving a near-optimal algorithm for this fundamental problem}.

In this work, we present a lattice QCD calculation of the Mellin moments of the twist-2 axial-vector generalized parton distribution (GPD), $\widetilde{H}(x,\xi,t)$, at zero skewness, $\xi$, with multiple values of the momentum transfer, $t$. Our analysis employs the short-distance factorization framework on ratio-scheme renormalized quasi-GPD matrix elements. The calculations are based on an $N_f=2+1+1$ twisted mass fermions ensemble with clover improvement, a lattice spacing of $a = 0.093$ fm, and a pion mass of $m_\pi = 260$ MeV. We consider both the iso-vector and iso-scalar cases, utilizing next-to-leading-order perturbative matching while ignoring the disconnected contributions and gluon mixing in the iso-scalar case. For the first time, we determine the Mellin moments of $\widetilde{H}$ up to the fifth order. From these moments, we discuss the quark helicity and orbital angular momentum contributions to the nucleon spin, as well as the spin-orbit correlations of the quarks. Additionally, we perform a Fourier transform over the momentum transfer, which allows us to explore the spin structure in the impact-parameter space., Comment: 17 pages, 13 figures

Self-supervised learning has developed rapidly over the last decade and has been applied in many areas of computer vision. Decorrelation-based self-supervised pretraining has shown great promise among non-contrastive algorithms, yielding performance at par with supervised and contrastive self-supervised baselines. In this work, we explore the decorrelation-based paradigm of self-supervised learning and apply the same to learning disentangled stroke features for writer identification. Here we propose a modified formulation of the decorrelation-based framework named SWIS which was proposed for signature verification by standardizing the features along each dimension on top of the existing framework. We show that the proposed framework outperforms the contemporary self-supervised learning framework on the writer identification benchmark and also outperforms several supervised methods as well. To the best of our knowledge, this work is the first of its kind to apply self-supervised learning for learning representations for writer verification tasks.

This paper introduces two innovative convex optimization formulations to simultaneously optimize the H2/Hinf observer gain and sensing precision, and guarantee a specified estimation error bound for nonlinear systems in LPV form. Applied to the design of an onboard celestial navigation system for cislunar operations, these formulations demonstrate the ability to maintain accurate spacecraft positioning with minimal measurements and theoretical performance guarantees by design.

This article appeared in the September 2024 issue (Vol. 31, No. 3) of the Bulletin of the International Society for Bayesian Analysis (ISBA).

This paper addresses the sparse actuation problem for nonlinear systems represented in the Linear Parameter-Varying (LPV) form. We propose a convex optimization framework that concurrently determines actuator magnitude limits and the state-feedback law that guarantees a user-specified closed-loop performance in the $\mathcal{H}_2/\mathcal{H}_\infty$ sense. We also demonstrate that sparse actuation is achieved when the actuator magnitude-limits are minimized in the $l_1$ sense. This is the first paper that addresses this problem for LPV systems. The formulation is demonstrated in a vibration control problem for a flexible wing., Comment: Submitted to American Control Conference 2025

We report on the NuSTAR observations of the accreting pulsars IGR~J17480-2446 and IGR~J17511-3057 performed on March 4, 2023, and April 8, 2015, respectively. We describe the continuum emission of IGR~J17480-2446 with a combination of two soft thermal components and an additional hard X-ray emission described by a power-law. We suggest that the spectral properties of IGR~J17480-2446 are consistent with a soft state, different from many other accreting X-ray millisecond pulsars usually found in the hard spectral state. The source IGR~J17511-3057 exhibits a hard spectrum characterized by a Comptonized emission from the corona. The X-ray spectrum of both sources shows evidence of disc reflection. For the first time, we employ the self-consistent reflection models ({\tt relxill} and {\tt relxillNS}) to fit the reflection features in the NuSTAR spectrum. From the best-fit spectral model, we find an inner disc radius is precisely constrained to $(1.99-2.68)\:R_{ISCO}$ and inclination to $30\pm 1$ degree for IGR~J17480-2446. We determine an inner disc radius of $\lesssim 1.3\;R_{ISCO}$ and inclination of $44\pm 3$ degree for IGR~J17511-3057. A low inclination angle of the system is required for both sources. We further place an upper limit on the magnetic field strength of the sources, considering the disc is truncated at the magnetospheric radius., Comment: 23 pages, 11 figures, Submitted to Journal of High Energy Astrophysics (JHEAP). arXiv admin note: text overlap with arXiv:2408.06193

Graph isomorphism, a classical algorithmic problem, determines whether two input graphs are structurally identical or not. Interestingly, it is one of the few problems that is not yet known to belong to either the P or NP-complete complexity classes. As such, intelligent search-space pruning based strategies were proposed for developing isomorphism testing solvers like nauty and bliss, which are still, unfortunately, exponential in the worst-case scenario. Thus, the polynomial-time Weisfeiler-Lehman (WL) isomorphism testing heuristic, based on colour refinement, has been widely adopted in the literature. However, WL fails for multiple classes of non-isomorphic graph instances such as strongly regular graphs, block structures, and switched edges, among others. In this paper, we propose a novel polynomial-time graph isomorphism testing heuristic, RSVP, and depict its enhanced discriminative power compared to the Weisfeiler-Lehman approach for several challenging classes of graphs. Bounded by a run-time complexity of O(m^2+mn^2+n^3) (where n and m are the number of vertices and edges respectively), we show that RSVP can identify non-isomorphism in several 'hard' graph instance classes including Miyazaki, Paulus, cubic hypohamiltonian, strongly regular, Latin series and Steiner triple system graphs, where the 3-WL test fails. Similar to the WL test, our proposed algorithm is prone to only one-sided errors, where isomorphic graphs will never be determined to be non-isomorphic, although the reverse can happen.

In this work, we present RadGazeGen, a novel framework for integrating experts' eye gaze patterns and radiomic feature maps as controls to text-to-image diffusion models for high fidelity medical image generation. Despite the recent success of text-to-image diffusion models, text descriptions are often found to be inadequate and fail to convey detailed disease-specific information to these models to generate clinically accurate images. The anatomy, disease texture patterns, and location of the disease are extremely important to generate realistic images; moreover the fidelity of image generation can have significant implications in downstream tasks involving disease diagnosis or treatment repose assessment. Hence, there is a growing need to carefully define the controls used in diffusion models for medical image generation. Eye gaze patterns of radiologists are important visuo-cognitive information, indicative of subtle disease patterns and spatial location. Radiomic features further provide important subvisual cues regarding disease phenotype. In this work, we propose to use these gaze patterns in combination with standard radiomics descriptors, as controls, to generate anatomically correct and disease-aware medical images. RadGazeGen is evaluated for image generation quality and diversity on the REFLACX dataset. To demonstrate clinical applicability, we also show classification performance on the generated images from the CheXpert test set (n=500) and long-tailed learning performance on the MIMIC-CXR-LT test set (n=23550).

Resonance is a well-known phenomenon that happens in systems with second order dynamics. In this paper we address the fundamental question of making a network robust to signal being periodically pumped into it at or near a resonant frequency by an adversarial agent with the aim of saturating the network with the signal. Towards this goal, we develop the notion of network vulnerability, which is measured by the expected resonance amplitude on the network under a stochastically modeled adversarial attack. Assuming a second order dynamics model based on the network graph Laplacian matrix and a known stochastic model for the adversarial attack, we propose two methods for minimizing the network vulnerability that leverage the principle of eigenspectrum optimization. We provide extensive numerical results analyzing the effects of both methods., Comment: 13 pages, 18 figures

This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

We consider a class of non-standard, two-dimensional (2D) Hamiltonian models that may show features of active particle dynamics, and therefore, we refer to these models as active Hamiltonian (AH) systems. The idea is to consider a spin fluid where -- on top of spin-spin and particle-particle interactions -- spins are coupled to the particle's velocities via a vector potential. Continuous spin variables interact with each other as in a standard $XY$ model. Typically, the AH models exhibit non-standard thermodynamic properties (e.g., for temperature and pressure) and equations of motion with non-standard forces. This implies that the derivation of symplectic algorithms to solve Hamilton's equations of motion numerically, as well as the thermostatting for these systems, is not straightforward. Here, we derive a symplectic integration scheme and propose a Nos\'e-Poincar\'e thermostat, providing a correct sampling in the canonical ensemble. The expressions for AH systems that we find for temperature and pressure might have parallels with the ongoing debate about the definition of pressure and the equation of state in active matter systems. For a specific AH model, recently proposed by Casiulis et al. [Phys. Rev. Lett. {\bf 124}, 198001 (2020)], we rationalize the symplectic algorithm and the proposed thermostatting, and investigate the transition from a fluid at high temperature to a cluster phase at low temperature where, due to the coupling of velocities and spins, the cluster phase shows a collective motion that is reminiscent to that observed in a variety of active systems.

We investigate Large Language Models' (LLMs) ability to predict a user's stance on a target given a collection of his/her target-agnostic social media posts (i.e., user-level stance prediction). While we show early evidence that LLMs are capable of this task, we highlight considerable variability in the performance of the model across (i) the type of stance target, (ii) the prediction strategy and (iii) the number of target-agnostic posts supplied. Post-hoc analyses further hint at the usefulness of target-agnostic posts in providing relevant information to LLMs through the presence of both surface-level (e.g., target-relevant keywords) and user-level features (e.g., encoding users' moral values). Overall, our findings suggest that LLMs might offer a viable method for determining public stances towards new topics based on historical and target-agnostic data. At the same time, we also call for further research to better understand LLMs' strong performance on the stance prediction task and how their effectiveness varies across task contexts.

Time series anomaly detection (TSAD) is an evolving area of research motivated by its critical applications, such as detecting seismic activity, sensor failures in industrial plants, predicting crashes in the stock market, and so on. Across domains, anomalies occur significantly less frequently than normal data, making the F1-score the most commonly adopted metric for anomaly detection. However, in the case of time series, it is not straightforward to use standard F1-score because of the dissociation between `time points' and `time events'. To accommodate this, anomaly predictions are adjusted, called as point adjustment (PA), before the $F_1$-score evaluation. However, these adjustments are heuristics-based, and biased towards true positive detection, resulting in over-estimated detector performance. In this work, we propose an alternative adjustment protocol called ``Balanced point adjustment'' (BA). It addresses the limitations of existing point adjustment methods and provides guarantees of fairness backed by axiomatic definitions of TSAD evaluation., Comment: 5 pages, 6 figures

Medical coding is essential for standardizing clinical data and communication but is often time-consuming and prone to errors. Traditional Natural Language Processing (NLP) methods struggle with automating coding due to the large label space, lengthy text inputs, and the absence of supporting evidence annotations that justify code selection. Recent advancements in Generative Artificial Intelligence (AI) offer promising solutions to these challenges. In this work, we introduce MedCodER, a Generative AI framework for automatic medical coding that leverages extraction, retrieval, and re-ranking techniques as core components. MedCodER achieves a micro-F1 score of 0.60 on International Classification of Diseases (ICD) code prediction, significantly outperforming state-of-the-art methods. Additionally, we present a new dataset containing medical records annotated with disease diagnoses, ICD codes, and supporting evidence texts (https://doi.org/10.5281/zenodo.13308316). Ablation tests confirm that MedCodER's performance depends on the integration of each of its aforementioned components, as performance declines when these components are evaluated in isolation.

The recent advancements in artificial intelligence (AI), with the release of several large models having only query access, make a strong case for explainability of deep models in a post-hoc gradient free manner. In this paper, we propose a framework, named distillation aided explainability (DAX), that attempts to generate a saliency-based explanation in a model agnostic gradient free application. The DAX approach poses the problem of explanation in a learnable setting with a mask generation network and a distillation network. The mask generation network learns to generate the multiplier mask that finds the salient regions of the input, while the student distillation network aims to approximate the local behavior of the black-box model. We propose a joint optimization of the two networks in the DAX framework using the locally perturbed input samples, with the targets derived from input-output access to the black-box model. We extensively evaluate DAX across different modalities (image and audio), in a classification setting, using a diverse set of evaluations (intersection over union with ground truth, deletion based and subjective human evaluation based measures) and benchmark it with respect to $9$ different methods. In these evaluations, the DAX significantly outperforms the existing approaches on all modalities and evaluation metrics., Comment: 12 pages, 10 figures, Accepted in IEEE Journal of Selected Topics in Signal Processing (JSTSP), 2024

Audio token modeling has become a powerful framework for speech synthesis, with two-stage approaches employing semantic tokens remaining prevalent. In this paper, we aim to simplify this process by introducing a semantic knowledge distillation method that enables high-quality speech generation in a single stage. Our proposed model improves speech quality, intelligibility, and speaker similarity compared to a single-stage baseline. Although two-stage systems still lead in intelligibility, our model significantly narrows the gap while delivering comparable speech quality. These findings showcase the potential of single-stage models to achieve efficient, high-quality TTS with a more compact and streamlined architecture., Comment: Demo page: see https://narsistts.github.io

We investigate the evolutionary stages of four open clusters, Berkeley 39, Collinder 261, NGC 6819, and NGC 7789, of ages ranging from 1.6 -- 6 Gyr. These clusters have previously been classified into dynamically young and intermediate age groups based on the segregation level of BSS with respect to red giant branch stars and main sequence stars, respectively. We identify members of these four clusters using the ML-MOC algorithm on Gaia DR3 data. To examine the relative segregation of cluster members of different evolutionary stages, we utilize cumulative radial distributions, proper motion distributions, and spatial distributions in galactocentric coordinates. Our analysis shows that Berkeley 39 and NGC 6819 exhibit moderate signs of population-wise segregation from evolved to less-evolved members. NGC 7789 shows signs of mass segregation only in the cumulative radial distributions. On the other hand, Collinder 261 exhibits high segregation of BSS in the cumulative radial distribution, while other populations show the same level of segregation., Comment: 15 pages, 6 figures (+1 in Appendix), and 3 tables. Accepted for publication to AJ on 4th September 2024

The observation of large intrinsic anomalous Hall conductivity (AHC) in the non-collinear antiferromagnetic (AFM) phase of the Weyl semimetal Mn$_3$Sn generates enormous interest in uncovering the entanglement between the real space magnetic ordering and the momentum space band structure. Previous studies show that changes in the magnetic structure induced by the application of hydrostatic and chemical pressure can significantly affect the AHC of Mn$_{3+x}$Sn$_{1-x}$ system. Here, we employ the muon spin relaxation/rotation ($\mu^+$SR) technique to systematically investigate the evolution of different magnetic states in the Mn$_{3+x}$Sn$_{1-x}$ as a function of hydrostatic and chemical pressure. We find two muon sites experimentally, which is also supported by our \textit{ab initio} calculations. Our $\mu^+$SR experiments affirm that the $x = 0.05$ compound exhibits a commensurate magnetic state throughout the magnetically ordered phase below the Neel temperature $T_N \approx 420$~K in ambient pressure. In contrast, we observe an incommensurate magnetic state below $T_{IC} \sim 175$~K when a hydrostatic pressure of 1.5~GPa is applied. A similar transition from the commensurate to incommensurate state is also found with chemical pressure for $x = 0.04$ and $x = 0.03$, using $\mu^+$SR and elastic neutron scattering experiments. Using band structure calculations, we have shown the emergence of Fermi nesting in Mn$_3$Sn and the subsequent development of incommensurate magnetic ordering under hydrostatic/chemical pressure., Comment: 13 pages, 8 figures. Accepted at Physical Review B (2024)

In this paper, we present novel convex optimization formulations for designing full-state and output-feedback controllers with sparse actuation that achieve user-specified $\mathcal{H}_2$ and $\mathcal{H}_\infty$ performance criteria. For output-feedback control, we extend these formulations to simultaneously design control laws with sparse actuation and sensing. The sparsity is induced through the minimization of a weighted $\ell_1$ norm, promoting the efficient use of sensors and actuators while maintaining desired closed-loop performance. The proposed methods are applied to a nonlinear structural dynamics problem, demonstrating the advantages of simultaneous optimization of the control law, sensing, and actuation architecture in realizing an efficient closed-loop system.

Over the past decade, wearable computing devices (``smart glasses'') have undergone remarkable advancements in sensor technology, design, and processing power, ushering in a new era of opportunity for high-density human behavior data. Equipped with wearable cameras, these glasses offer a unique opportunity to analyze non-verbal behavior in natural settings as individuals interact. Our focus lies in predicting engagement in dyadic interactions by scrutinizing verbal and non-verbal cues, aiming to detect signs of disinterest or confusion. Leveraging such analyses may revolutionize our understanding of human communication, foster more effective collaboration in professional environments, provide better mental health support through empathetic virtual interactions, and enhance accessibility for those with communication barriers. In this work, we collect a dataset featuring 34 participants engaged in casual dyadic conversations, each providing self-reported engagement ratings at the end of each conversation. We introduce a novel fusion strategy using Large Language Models (LLMs) to integrate multiple behavior modalities into a ``multimodal transcript'' that can be processed by an LLM for behavioral reasoning tasks. Remarkably, this method achieves performance comparable to established fusion techniques even in its preliminary implementation, indicating strong potential for further research and optimization. This fusion method is one of the first to approach ``reasoning'' about real-world human behavior through a language model. Smart glasses provide us the ability to unobtrusively gather high-density multimodal data on human behavior, paving the way for new approaches to understanding and improving human communication with the potential for important societal benefits. The features and data collected during the studies will be made publicly available to promote further research., Comment: 22 pages, first three authors equal contribution

Non-invasive and continuous blood pressure (BP) monitoring is essential for the early prevention of many cardiovascular diseases. Estimating arterial blood pressure (ABP) from photoplethysmography (PPG) has emerged as a promising solution. However, existing deep learning approaches for PPG-to-ABP reconstruction (PAR) encounter certain information loss, impacting the precision of the reconstructed signal. To overcome this limitation, we introduce an invertible neural network for PPG to ABP reconstruction (INN-PAR), which employs a series of invertible blocks to jointly learn the mapping between PPG and its gradient with the ABP signal and its gradient. INN-PAR efficiently captures both forward and inverse mappings simultaneously, thereby preventing information loss. By integrating signal gradients into the learning process, INN-PAR enhances the network's ability to capture essential high-frequency details, leading to more accurate signal reconstruction. Moreover, we propose a multi-scale convolution module (MSCM) within the invertible block, enabling the model to learn features across multiple scales effectively. We have experimented on two benchmark datasets, which show that INN-PAR significantly outperforms the state-of-the-art methods in both waveform reconstruction and BP measurement accuracy.

The conventional ultralight QCD axion is typically rendered invisible at collider experiments by its large decay constant. What could also hint at its possible existence is the observation of other (heavy) particles that are characteristically related to the light axion. One such scenario is afforded within the framework of the clockwork mechanism where the axion can have suppressed couplings with the gluons or photons while its companion axion-like particles (ALPs) have relatively unsuppressed couplings. We study a minimal clockwork model for the QCD axion invoking a KSVZ-like setup and examine the visibility of the ALPs $(a_n)$ at the LHC through the process $p p \to a_n \, (+ \,{\rm additional \, jets})$, $a_n \to \gamma \gamma$. The model contains $N$ ALPs with a decay constant $f$ and masses defined by a scale $m$ characteristic of the nearest-neighbour interactions of the scalar fields. For $10\lesssim m \lesssim 100$ GeV, $f \sim 1$ TeV and $N \sim \mathcal{O}(10)$, the full spectrum of ALPs is accessible and the corresponding diphoton invariant mass distribution comprises a unique signature of a wide band of resonances. For the case of light ALPs $(m \sim \mathcal{O}(10 \,{\rm GeV}))$ with the axion being a dark matter candidate, the mass-splittings among the former are so small that the signal profile mimics that of a single broad resonance, or an $\textit{axion iceberg}$. The effect subsides for heavier ALPs, albeit still exhibiting undulating peaks. For light ALPs, the scenario is imminently testable by the end of LHC's Run 3 phase, with the estimated cumulative significance reaching the discovery threshold for an integrated luminosity of $\sim 300 {\rm \,fb^{-1}}$. While the signals for the heavier ALPs in this minimal setup may not be as prominent within the ongoing LHC operation, one could expect to probe a wider parameter space of the model at the forthcoming HL-LHC., Comment: 35 pages, 17 figures

We present a multi-wavelength spectral study of NGC 4151 based on five epochs of simultaneous AstroSat observations in the near ultra-violet (NUV) to hard X-ray band ($\sim 0.005-80$ keV) during $2017 - 2018$. We derived the intrinsic accretion disk continuum after correcting for internal and Galactic extinction, contributions from broad and narrow line regions, and emission from the host galaxy. We found a bluer continuum at brighter UV flux possibly due to variations in the accretion disk continuum or the UV reddening. We estimated the intrinsic reddening, $E(B-V) \sim 0.4$, using high-resolution HST/STIS spectrum acquired in March 2000. We used thermal Comptonization, neutral and ionized absorption, and X-ray reflection to model the X-ray spectra. We obtained the X-ray absorbing neutral column varying between $N_H \sim 1.2-3.4 \times 10^{23} cm^{-2}$, which are $\sim 100$ times larger than that estimated from UV extinction, assuming the Galactic dust-to-gas ratio. To reconcile this discrepancy, we propose two plausible configurations of the obscurer: (a) a two-zone obscurer consisting of dust-free and dusty regions, divided by the sublimation radius, or (b) a two-phase obscurer consisting of clumpy, dense clouds embedded in a low-density medium, resulting in a scenario where a few dense clouds obscure the compact X-ray source substantially, while the bulk of UV emission arising from the extended accretion disk passes through the low-density medium. Furthermore, we find a positive correlation between X-ray absorption column and $NUV-FUV$ color and UV flux, indicative of enhanced winds possibly driven by the 'bluer-when-brighter' UV continuum., Comment: 21 pages, 22 figures, 6 tables, accepted for publication in ApJ

This work explores the use of polarization-resolved high harmonic generation (HHG) spectroscopy to investigate the quantum phases and transitions in the correlated charge density wave (CDW) phase of TiSe$_2$. Unlike previous studies focusing on crystallographic changes, the research examines the reordering that occurs within the CDW phase as the material is cooled from room temperature to 14 K. By linking ultrafast field-driven dynamics to the material's potential landscape, the study demonstrates how HHG is sensitive to quantum phase transitions. The findings reveal an anisotropic component below the CDW transition temperature, providing new insights into the nature of this phase. The investigation highlights the interplay between linear and nonlinear optical responses and their departure from simple perturbative dynamics, offering a fresh perspective on correlated quantum phases in condensed matter systems., Comment: 3 figures

In both general relativity and Lanczos-Lovelock theories of gravity, it has been found that the Noether charge density in any bulk region of spacetime can be interpreted as the heat content of the boundary surface. In addition, it was found that the dynamical evolution of spacetime can be interpreted as the departure from an ``equipartition" between properly defined bulk and surface degrees of freedom. We find that the same interpretations are valid for scalar-tensor theories of gravity, in which case the gravity is mediated by the metric tensor as well as by the scalar field. Moreover, these results hold in both the frames associated with the scalar-tensor theory, namely the Jordan and the Einstein frames. However, it turns out that there are two dynamically equivalent representations of the scalar-tensor theory in the Jordan frame, differing by total derivatives in the action, which are thermodynamically inequivalent. Thus thermodynamics is sensitive to various representations of scalar-tensor theories. This not only implies the robustness of the thermodynamic description of gravity beyond general relativity, but also depicts the importance of having a thermodynamic description by distinguishing various dynamically equivalent representations., Comment: 27 Pages, No figure

The physics case for quarkonium-production studies accessible at the US Electron Ion Collider is described., Comment: Latex, 84 pages. Review prepared for Progress in Particle and Nuclear Physics

Quantum complexity has emerged as a central concept in diverse areas of physics, ranging from quantum computing to the theory of black holes. We perform a systematic study of complexity in random quantum circuits with and without measurements. We observe that complexity grows linearly before saturating to a constant value. For $N$ qubits without measurements, the saturation value scales as $2^{N-1}$, and the saturation time scales as $2^N$. This behaviour remains identical in the presence of random measurements with different probabilities, indicating that this notion of complexity is insensitive to the rate of measurement. We also study the behaviour of complexity in two variants of the random unitary floquet circuit, where we observe that complexity acts as a novel probe of Anderson localization and many-body localization., Comment: 6 pages, 3 figures

We consider the dynamical Morris-Thorne metric with radiating heat flow. By matching the interior Morris-Thorne metric with an exterior Vaidya metric we trace out the collapse solutions for the corresponding spherically symmetric inhomogeneous distribution of matter. The solutions obtained are broadly of four different types, giving different end state dynamics. Corresponding to three of the solutions we elaborate the collapsing dynamics of the Morris-Thorne type evolving wormhole. We show that for all those cases where collapse upto zero proper volume is obtained in finite time, the ensuing singularity is always a black hole type. However our solutions can also show other end states, like oscillating wormhole-black hole pair or infinite time contracting universe or a conformal past matter dominated universe. In all the cases we have worked out the background dynamics and physics of the solution. All our solutions are illustrated with appropriate graphical descriptions., Comment: Accepted for Publication in Annals of Physics