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The inverse diffusion flame (IDF) can experience thermoacoustic instability due to variations in power input or flow conditions. However, the dynamical transitions in IDF that lead to this instability when altering control parameters have not been thoroughly investigated. In this study, we explore the control parameters through two different approaches and employ multifractal detrended fluctuation analysis to characterize the transitions observed prior to the onset of thermoacoustic instability in the inverse diffusion flame. Our findings reveal a loss of multifractality near the region associated with thermoacoustic instability, which suggests a more ordered behavior. We determine that the singularity exponent, the width of the multifractal spectrum, and the Hurst exponent are reliable indicators of thermoacoustic instability and serve as effective classifiers of dynamical states in inverse diffusion flames., Comment: 17 pages, 10 figures

We present preliminary results on the susceptibilities involving the CP-violating (CPV) Weinberg three-gluon operator and the topological $\Theta$ term using the gradient flow scheme, and study their continuum and chiral extrapolations. These are used to provide an estimate of the $\Theta$ induced by the Weinberg operator in theories with the Peccei-Quinn (PQ) mechanism. Combined with the calculations of the matrix elements (MEs) of quark-bilinears between nucleon states, such calculations will enable estimates of the electric dipole moments (EDMs) and CPV pion-nucleon couplings due to the Weinberg operator, thereby providing robust constraints on beyond the standard model (BSM) physics., Comment: 11 pages, 2 figures

We study the threshold effects for the associated production of a Higgs boson with a massive vector boson $(V=Z,W)$ in the $q\bar{q} \rightarrow V^\star \rightarrow VH$ process at the LHC. By leveraging the universality of threshold logarithms and employing soft-virtual (SV) and next-to-soft virtual (NSV) resummation techniques, we compute threshold corrections to next-to-next-to-leading logarithmic accuracy. After matching the resummed predictions to the Next-to-Next-to-Leading order (NNLO) fixed order results, we present the invariant mass distribution to NNLO$+\overline{\text{NNLL}}$ accuracy in QCD for the current LHC energies and the total production cross sections. The $VH$ production channel is crucial for studying the couplings of the Higgs boson to the vector bosons $(W,Z)$ and understanding the mechanism of electroweak symmetry breaking. Precision measurements of this process help test the validity of the standard model (SM) and can reveal potential deviations indicating new physics., Comment: 8 figures , 15 Pages, 3 Tables

A glitch is a rare and sudden increase in the otherwise steadily decreasing rotation rate of a pulsar. Its cause is widely attributed to the transfer of angular momentum to the crust of the star from the array of superfluid vortices enclosed within. The magnitude of such an increase defines the size of the glitch. The distribution of glitch sizes in individual pulsars, the power-law being the most argued for, is shrouded in uncertainty due to the small sample size. From a Bayesian perspective, we revisit the data for PSR J0537-6910, the pulsar with the most glitches, and find a bimodality in the distribution, reminiscent of the Bactrian camel. To understand this bimodality, we use a superfluid vortex simulator and study three independent neutron star paradigms: (i) Annular variation in pinning strength to account for the predicted differences between the crust and the core; (ii) Sectorial triggers to mimic local disturbances; and (iii) Stress-waves to model global disturbances. We find that annular variation in pinning introduces a bimodality in the glitch-size distribution and that sectorial triggers do so weakly. Stress-waves do not lead to any such features for the range of parameters tested. This provides us with new insights into the effects of various perturbations on the vortex dynamics and the glitch statistics of neutron stars., Comment: 10 pages, 10 Figures. Submitted to MNRAS

Two-dimensional superconductors with spin-textured Fermi surfaces can be a platform for realizing unconventional pairing and are of substantial interest in the context of quantum information science, and superconducting spintronics/orbitronics. We find that the superconducting 2D electron gas (2DEG) formed at EuOx/KTaO3 (110) interfaces, where the EuOx is magnetic, has a spin-texture with an unusual in-plane Ising like uniaxial anisotropy that is revealed in measurements of the in-plane critical field in the superconducting state, as well as from quantum corrections to the magnetoresistance in the normal state. This spin texture is not evident in AlOx/KTaO3 (110) where the overlayer is non-magnetic. Our results are consistent with a highly anisotropic spin-textured Fermi surface in 2DEGs formed at the KTaO3 (110) interface that is hidden from external magnetic fields due to a near cancellation between orbital and spin moments but revealed by exchange interactions of the electrons in the 2DEG with Eu moments near the EuOx/KTaO3 (110) interface. Our findings demonstrate that magnetic overlayers provide a unique probe of spin textures and related phenomena in heterostructures., Comment: 17 pages, 5 figures

This paper examines various ways of improving the impact resilience of protective structures. Such structures' purpose is to dissipate an impact's energy while avoiding cracking and failure. We have tested the reaction of plane elastic-brittle lattices to an impulse. Four topologies are compared: periodic triangular, square, and hexagonal topologies, and aperiodic Penrose topology. Then, structures with random variations of the links' stiffness, node positions, and random holes are compared. Combinations of these random factors are also considered, as well as the resilience of bistable elastic-brittle lattices with sacrificial links. Several parameters are introduced to measure the structural resilience of the compared designs: (i) the amount of dissipated impact energy, (ii) the size of broken clusters of links, and (iii) the spread of damage. The results suggest new routes for rationally designing protective structures using nonperiodic topology, bistability, and structural randomness. In particular, we find that some quantities of interest can be maximized by tuning the randomized design appropriately -- for example, randomly removing 8\% of links maximizes energy dissipation. We also find that randomization of bistable lattices can offer superior energy dissipation while reducing the connectivity between broken clusters of links.

Hydrogen bonding plays a crucial role in biology and technology, yet it remains poorly understood and quantified despite its fundamental importance. Traditional models, which describe hydrogen bonds as electrostatic interactions between electropositive hydrogen and electronegative acceptors, fail to quantitatively capture bond strength, directionality, or cooperativity, and cannot predict the properties of complex hydrogen-bonded materials. Here, we introduce a novel approach that conceptualizes the effect of hydrogen bonds as elastic dipoles in an electric field, which captures a wide range of hydrogen bonding phenomena in various water systems. Using gypsum, a hydrogen bond heterostructure with two-dimensional structural crystalline water, we calibrate the hydrogen bond strength through an externally applied electric field. We show that our approach quantifies the strength of hydrogen bonds directly from spectroscopic measurements and reproduces a wide range of key properties of confined water reported in the literature. Using only the stretching vibration frequency of confined water, we can predict hydrogen bond strength, local electric field, O-H bond length, and dipole moment. Our work also introduces hydrogen bond heterostructures - a new class of electrically and chemically tunable materials that offer stronger, more directional bonding compared to van der Waals heterostructures, with potential applications in areas such as catalysis, separation, and energy storage.

In this work we study the {\it moment estimation} problem using weighted sampling. Given sample access to a set $A$ with $n$ weighted elements, and a parameter $t>0$, we estimate the $t$-th moment of $A$ given as $S_t=\sum_{a\in A} w(a)^t$. For t=1, this is the sum estimation problem. The moment estimation problem along with a number of its variants have been extensively studied in streaming, sublinear and distributed communication models. Despite being well studied, we don't yet have a complete understanding of the sample complexity of the moment estimation problem in the sublinear model and in this work, we make progress on this front. On the algorithmic side, our upper bounds match the known upper bounds for the problem for $t>1$. To the best of our knowledge, no sublinear algorithms were known for this problem for $01/2$ and show that no sublinear algorithms exist for $t\leq 1/2$. We prove a $\Omega(\frac{n^{1-1/t}\ln 1/\delta}{\epsilon^2})$ lower bound for moment estimation for $t>1$, and show optimal sample complexity bound $\Theta(\frac{n^{1-1/t}\ln 1/\delta}{\epsilon^2})$ for moment estimation for $t\geq 2$. Hence, we obtain a complete understanding of the sample complexity for moment estimation using proportional sampling for $t\geq 2$. We also study the moment estimation problem in the beyond worst-case analysis paradigm and identify a new {\it moment-density} parameter of the input that characterizes the sample complexity of the problem using proportional sampling and derive tight sample complexity bounds with respect to that parameter. We also study the moment estimation problem in the hybrid sampling framework in which one is given additional access to a uniform sampling oracle and show that hybrid sampling framework does not provide any additional gain over the proportional sampling oracle in the worst case., Comment: Abstract shortened to meet submission criteria

Traditional $\mathrm{SU}(N)$ lattice gauge theories (LGTs) can be formulated using an orthonormal basis constructed from the irreducible representations (irreps) $V_{\lambda}$ of the $\mathrm{SU}(N)$ gauge symmetry. On a lattice, the elements of this basis are tensor networks comprising dimer tensors on the links labeled by a set of irreps $\{\lambda_\ell\}$ and monomer tensors on sites labeled by $\{\lambda_s\}$. These tensors naturally define a local site Hilbert space, $\mathcal{H}^g_s$, on which gauge transformations act. Gauss's law introduces an additional index $\alpha_s = 1, 2, \dots, \mathcal{D}(\mathcal{H}_s^g)$ that labels an orthonormal basis of the gauge-invariant subspace of $\mathcal{H}^g_s$. This monomer-dimer tensor-network (MDTN) basis, $\left| \{\lambda_s\},\{\lambda_\ell\},\{\alpha_s\}\right\rangle$, of the physical Hilbert space enables the construction of new qubit-regularized $\mathrm{SU}(N)$ gauge theories that are free of sign problems while preserving key features of traditional LGTs. Here, we investigate finite-temperature confinement-deconfinement transitions in a simple qubit-regularized $\mathrm{SU}(2)$ and $\mathrm{SU}(3)$ gauge theory in $d=2$ and $d=3$ spatial dimensions, formulated using the MDTN basis, and show that they reproduce the universal results of traditional LGTs at these transitions. Additionally, in $d=1$, we demonstrate using a plaquette chain that the string tension at zero temperature can be continuously tuned to zero by adjusting a model parameter that plays the role of the gauge coupling in traditional LGTs., Comment: 17 pages, 11 figures

Structural guidance in an image-to-image translation allows intricate control over the shapes of synthesized images. Generating high-quality realistic images from user-specified rough hand-drawn sketches is one such task that aims to impose a structural constraint on the conditional generation process. While the premise is intriguing for numerous use cases of content creation and academic research, the problem becomes fundamentally challenging due to substantial ambiguities in freehand sketches. Furthermore, balancing the trade-off between shape consistency and realistic generation contributes to additional complexity in the process. Existing approaches based on Generative Adversarial Networks (GANs) generally utilize conditional GANs or GAN inversions, often requiring application-specific data and optimization objectives. The recent introduction of Denoising Diffusion Probabilistic Models (DDPMs) achieves a generational leap for low-level visual attributes in general image synthesis. However, directly retraining a large-scale diffusion model on a domain-specific subtask is often extremely difficult due to demanding computation costs and insufficient data. In this paper, we introduce a technique for sketch-to-image translation by exploiting the feature generalization capabilities of a large-scale diffusion model without retraining. In particular, we use a learnable lightweight mapping network to achieve latent feature translation from source to target domain. Experimental results demonstrate that the proposed method outperforms the existing techniques in qualitative and quantitative benchmarks, allowing high-resolution realistic image synthesis from rough hand-drawn sketches., Comment: Accepted in The International Conference on Pattern Recognition (ICPR) 2024

Human affordance learning investigates contextually relevant novel pose prediction such that the estimated pose represents a valid human action within the scene. While the task is fundamental to machine perception and automated interactive navigation agents, the exponentially large number of probable pose and action variations make the problem challenging and non-trivial. However, the existing datasets and methods for human affordance prediction in 2D scenes are significantly limited in the literature. In this paper, we propose a novel cross-attention mechanism to encode the scene context for affordance prediction by mutually attending spatial feature maps from two different modalities. The proposed method is disentangled among individual subtasks to efficiently reduce the problem complexity. First, we sample a probable location for a person within the scene using a variational autoencoder (VAE) conditioned on the global scene context encoding. Next, we predict a potential pose template from a set of existing human pose candidates using a classifier on the local context encoding around the predicted location. In the subsequent steps, we use two VAEs to sample the scale and deformation parameters for the predicted pose template by conditioning on the local context and template class. Our experiments show significant improvements over the previous baseline of human affordance injection into complex 2D scenes., Comment: 11 pages

Let $\mathrm{Mod}(S_g)$ be the mapping class group of the closed orientable surface $S_g$ of genus $g\geq 2$. Given a finite subgroup $H$ of $\mathrm{Mod}(S_g)$, let $\mathrm{Fix}(H)$ be the set of all fixed points induced by the action of $H$ on the Teichm\"{u}ller space $\mathrm{Teich}(S_g)$ of $S_g$. This paper provides a method to estimate the distance between the unique fixed points of certain irreducible cyclic actions on $S_g$. We begin by deriving an explicit description of a pants decomposition of $S_g$, the length of whose curves are bounded above by the Bers' constant. To obtain the estimate, our method then uses the quasi-isometry between $\mathrm{Teich}(S_g)$ and the pants graph $\mathcal{P}(S_g)$., Comment: 13 pages, 2 figures

Achieving low contact resistance in advanced electronic devices remains a significant challenge. As the demand for faster and more energy-efficient devices grows, 2D contact engineering emerges as a promising solution for next-generation electronics. Beyond graphene, 1T-WTe2 has gained attention due to its outstanding electrical transport properties, quantum phenomena, and Weyl semimetallic characteristics. We demonstrate the direct wafer-scale growth of 1T-WTe2 via molecular beam epitaxy (MBE) and use it as a 2D contact for layered materials like InSe, which exhibits broad photoresponsivity. The performance of this 2D electrode in InSe-based photodetectors is compared with conventional metal electrodes. Under near-infrared (NIR) to deep ultraviolet (DUV) illumination, the InSe/1T-WTe2 configuration shows a broad photoresponsivity range from 0.14 to 217.58 A/W, with fast rise/fall times of 42/126 ms in the visible region. In contrast, the InSe/Ti-Au configuration exhibits a peak photoresponsivity of 3.64 A/W in the DUV range, with an overall lower responsivity spanning from 0.000865 A/W to 3.64 A/W under NIR and DUV illumination, respectively. Additionally, in the visible regime, it exhibits slower rise and fall times of 150 ms and 144 ms, respectively, compared to InSe/1T-WTe2. These findings indicate that MBE-grown 1T-WTe2 serves as an effective 2D electrode, delivering higher photoresponsivity and faster photodetection compared to traditional metal contacts. This approach offers a simplified, high-performance alternative for layered material-based devices, eliminating the need for complex heterostructure configurations., Comment: 8 figures

We present the analysis of OGLE-2014-BLG-1760, a planetary system in the galactic bulge. We combine Keck Adaptive Optics follow-up observations in $K$-band with re-reduced light curve data to confirm the source and lens star identifications and stellar types. The re-reduced MOA dataset had an important impact on the light curve model. We find the Einstein ring crossing time of the event to be $\sim$ 2.5 days shorter than previous fits, which increases the planetary mass-ratio and decreases the source angular size by a factor of 0.25. Our OSIRIS images obtained 6 years after the peak of the event show a source-lens separation of 54.20 $\pm$ 0.23 mas, which leads to a relative proper motion of $\mu_{\rm rel}$ = 9.14 $\pm$ 0.05 mas/yr, larger than the previous light curve-only models. Our analysis shows that the event consists of a Jupiter-mass planet of $M_{\rm p}$ = 0.931 $\pm$ 0.117 $M_{\rm Jup}$ orbiting a K-dwarf star of $M_*$ = 0.803 $\pm$ 0.097 $M_{\odot}$ with a $K$-magnitude of $K_{\rm L}$ = 18.30 $\pm$ 0.05, located in the galactic bulge or bar. We also attempt to constrain the source properties using the source angular size $\theta_*$ and $K$-magnitude. Our results favor the scenario of the source being a younger star in the galactic disk, behind the galactic center, but future multicolor observations are needed to constrain the source and thus the lens properties., Comment: 20 pages, 8 figures. In review in AJ

In this paper, we study the motion of a massless, chargeless particle in Schwarzschild-de Sitter spacetime, revealing exponential radial growth and potential chaos in an integrable system. Poincar\'e sections show regular Kolmogorov-Arnold-Moser (KAM) tori when black hole and cosmological horizons are distant, but distortions and chaos emerge as they converge. As the horizons coincide, the Poincar\'e sections fully contract and vanish, marking the system's transition to Nariai spacetime. \textit{Our analysis also suggests that, within the parameter range explored, the event horizon exerts a comparatively more substantial chaotic influence on the system, primarily due to its consistent proximity.} Additionally, we analyze the Lyapunov exponents to quantify the degree of chaos in the system. Our findings indicate that as the closeness of the two horizons increases, the most prominent Lyapunov exponent also increases, signifying a rise in chaotic behavior. By examining the long-term saturation values of the Lyapunov exponents, we confirm that they consistently comply with the Maldacena-Shenker-Stanford (MSS) bound., Comment: 11 pages, 6 figures, 1 table

A global fit for $\alpha_s(m_Z)$ is performed on available $e^+e^-$ data for the heavy jet mass distribution. The state-of-the-art theory prediction includes $\mathcal{O}(\alpha_s^3)$ fixed-order results, N$^3$LL$^\prime$ dijet resummation, N$^2$LL Sudakov shoulder resummation, and a first-principles treatment of power corrections in the dijet region. Theoretical correlations are incorporated through a flat random-scan covariance matrix. The global fit results in $0.1145^{+0.0021}_{-0.0019}$, compatible with similar determinations from thrust and $C$-parameter. Dijet resummation is essential for a robust fit, as it engenders insensitivity to the fit-range lower cutoff; without resummation the fit-range sensitivity is overwhelming. In addition, we find evidence for a negative power correction in the trijet region if and only if Sudakov shoulder resummation is included., Comment: 4 pages + supplemental material, 8 figures, 3 tables

We present MicroBooNE's first search for dark sector $e^+e^-$ explanations of the long-standing MiniBooNE anomaly. The MiniBooNE anomaly has garnered significant attention over the past 20 years including previous MicroBooNE investigations into both anomalous electron and photon excesses, but its origin still remains unclear. In this letter, we provide the first direct test of dark sector models in which dark neutrinos, produced through neutrino-induced scattering, decay into missing energy and visible $e^+e^-$ pairs comprising the MiniBooNE anomaly. Many such models have recently gained traction as a viable solution to the anomaly while evading past bounds. Using an exposure of $6.87 \times 10^{20}$ protons-on-target in the Booster Neutrino Beam, we implement a selection targeting forward-going, coherently produced $e^+e^-$ events. After unblinding, we observe 95 events, which we compare with the constrained background-only prediction of $69.7 \pm 17.3$. This analysis sets the world's first direct limits on these dark sector models and, at the 95\% confidence level, excludes the majority of the parameter space viable as a solution to the MiniBooNE anomaly., Comment: 7 pages, 5 figures, Supplemental Materials included in Ancillary files

A smoking gun for our current paradigm of the early universe would be direct evidence for the quantum mechanical origin of density perturbations which are conjectured to seed the large scale structure of our universe. A recently-proposed novel phenomenon is that of \textit{recoherence}, wherein a specific interaction between the adiabatic and the entropic sector leads to the adiabatic mode retaining a coherent state after a transient increase in linear entropy. In this paper, we choose the most general Gaussian action and analyze the evolution of linear entropy, complexity of purification (COP), and complexity of formation (COF) to capture the interplay between decoherence and recoherence in this model. In the presence of two types of couplings that drive these two opposing characteristics, we highlight how COF is an efficient tool for diagnosing dynamics for such an open quantum system., Comment: 20 pages, 7 figures

Progress in AI has relied on human-generated data, from annotator marketplaces to the wider Internet. However, the widespread use of large language models now threatens the quality and integrity of human-generated data on these very platforms. We argue that this issue goes beyond the immediate challenge of filtering AI-generated content--it reveals deeper flaws in how data collection systems are designed. Existing systems often prioritize speed, scale, and efficiency at the cost of intrinsic human motivation, leading to declining engagement and data quality. We propose that rethinking data collection systems to align with contributors' intrinsic motivations--rather than relying solely on external incentives--can help sustain high-quality data sourcing at scale while maintaining contributor trust and long-term participation.

We consider a stochastic spectator scalar field coupled to fermion via the Yukawa interaction, in the inflationary de Sitter background. We consider the fermion to be massless, and take the one loop effective potential found earlier by using the exact fermion propagator in de Sitter spacetime. We take the potential for the spectator scalar to be quintessence-like, $V(\phi)=\alpha |\phi|^p$ ($\alpha \ensuremath{>} 0,\ p\ensuremath{>} 4$), so that the total effective potential is generically bounded from below for all values of the parameters and couplings, and a late time equilibrium state is allowed. Using next the stochastic spectral expansion method, we numerically investigate the two point correlation function, as well as the density fluctuations corresponding to the spectator field, with respect to the three parameters of the total effective potential, $\alpha,\ p$ and the Yukawa coupling, $g$. In particular, we find that the power spectrum and the spectral index corresponds to blue tilt with increasing $g$. The three point correlation function and non-Gaussianity corresponding to the density fluctuation has also been investigated. The increasing Yukawa coupling is shown to flatten the peak of the shape function in the squeezed limit. Also in this limit, the increase in the same is shown to increase the local non-Gaussianity parameter., Comment: v1; 24pp, 14figs

We present the first calculation of quantum electrodynamics (QED) nuclear medium effects under the experimental conditions of future Electron-Ion Collider (EIC) experiments. Our work offers numerical estimates, particularly in the context of inclusive deep inelastic scattering on a $^{208}_{82}\mathrm{Pb}$ nucleus. While prior studies have predominantly focused on elastic scattering, our investigation extends to the more complex scenarios of inelastic processes within a nuclear medium. Our findings suggest that the cross-section corrections due to QED nuclear medium effects could be substantial, reaching or exceeding the level of experimental precision. This work further compares the effects of single re-scattering events with those of multiple re-scatterings, as particles travel the nuclear volume. We estimate the dominant source of the uncertainties associated with our formalism by varying the scale of the atomic physics where the screening of the electric field of the nucleus happens. This calculation not only contributes to the understanding of QED nuclear medium effects, but also offers a path to a more precise extraction of the process-independent non-perturbative structure of nuclei., Comment: 16 pages, 8 figures

We demonstrate the potential of new adaptive optical technology to expand the detection horizon of gravitational-wave observatories. Achieving greater quantum-noise-limited sensitivity to spacetime strain hinges on achieving higher circulating laser power, in excess of 1~MW, in conjunction with highly-squeezed quantum states of light. The new technology will enable significantly higher levels of laser power and squeezing in gravitational-wave detectors, by providing high-precision, low-noise correction of limiting sources of thermal distortions directly to the core interferometer optics. In simulated projections for LIGO~A+, assuming an input laser power of 125~W and an effective injected squeezing level of 9~dB entering the interferometer, an initial concept of this technology can reduce the noise floor of the detectors by up to 20\% from 200~Hz to 5~kHz, corresponding to an increment of 4~Mpc in the sky-averaged detection range for binary neutron star mergers. This work lays the foundation for one of the key technology improvements essential to fully utilize the scientific potential of the existing 4-km LIGO facilities, to observe black hole merger events past a redshift of~5, and opens a realistic pathway towards a next-generation 40-km gravitational-wave observatory in the United States, Cosmic~Explorer., Comment: 8 pages, 5 figures

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

This article presents the first search for neutrino-induced neutral current coherent single-photon production (NC coherent 1$\gamma$). The search makes use of data from the MicroBooNE 85-tonne active volume liquid argon time projection chamber detector, situated in the Fermilab Booster Neutrino Beam (BNB), with an average neutrino energy of $\langle E_{\nu}\rangle \sim 0.8$ GeV. A targeted selection of candidate neutrino interactions with a single photon-like electromagnetic shower in the final state and no visible vertex activity was developed to search for the NC coherent 1$\gamma$ process, along with two auxiliary selections used to constrain the dominant background from NC$\pi^0$ production. With an integrated exposure of $6.87 \times 10^{20}$ protons on target delivered by the BNB, we set the world's first limit for this rare process, corresponding to an upper limit on the flux-averaged cross section of $\sigma<1.49 \times 10^{-41}\text{cm}^2$ at 90\% C.L., Comment: 20 pages, 17 figures

We present an inclusive search for anomalous production of single-photon events from neutrino interactions in the MicroBooNE experiment. The search and its signal definition are motivated by the previous observation of a low-energy excess of electromagnetic shower events from the MiniBooNE experiment. We use the Wire-Cell reconstruction framework to select a sample of inclusive single-photon final-state interactions with a final efficiency and purity of 7.0% and 40.2%, respectively. We leverage simultaneous measurements of sidebands of charged current $\nu_{\mu}$ interactions and neutral current interactions producing $\pi^{0}$ mesons to constrain signal and background predictions and reduce uncertainties. We perform a blind analysis using a dataset collected from February 2016 to July 2018, corresponding to an exposure of $6.34\times10^{20}$ protons on target from the Booster Neutrino Beam (BNB) at Fermilab. In the full signal region, we observe agreement between the data and the prediction, with a goodness-of-fit $p$-value of 0.11. We then isolate a sub-sample of these events containing no visible protons, and observe $93\pm22\text{(stat.)}\pm35\text{(syst.)}$ data events above prediction, corresponding to just above $2\sigma$ local significance, concentrated at shower energies below 600 MeV., Comment: 9 pages, 6 figures

In this paper, we address the task of semantic segmentation of legal documents through rhetorical role classification, with a focus on Indian legal judgments. We introduce LegalSeg, the largest annotated dataset for this task, comprising over 7,000 documents and 1.4 million sentences, labeled with 7 rhetorical roles. To benchmark performance, we evaluate multiple state-of-the-art models, including Hierarchical BiLSTM-CRF, TransformerOverInLegalBERT (ToInLegalBERT), Graph Neural Networks (GNNs), and Role-Aware Transformers, alongside an exploratory RhetoricLLaMA, an instruction-tuned large language model. Our results demonstrate that models incorporating broader context, structural relationships, and sequential sentence information outperform those relying solely on sentence-level features. Additionally, we conducted experiments using surrounding context and predicted or actual labels of neighboring sentences to assess their impact on classification accuracy. Despite these advancements, challenges persist in distinguishing between closely related roles and addressing class imbalance. Our work underscores the potential of advanced techniques for improving legal document understanding and sets a strong foundation for future research in legal NLP., Comment: Accepted on NAACL 2025

We report results from an updated search for neutral current (NC) resonant $\Delta$(1232) baryon production and subsequent $\Delta$ radiative decay (NC $\Delta\rightarrow N \gamma$). We consider events with and without final state protons; events with a proton can be compared with the kinematics of a $\Delta(1232)$ baryon decay, while events without a visible proton represent a more generic phase space. In order to maximize sensitivity to each topology, we simultaneously make use of two different reconstruction paradigms, Pandora and Wire-Cell, which have complementary strengths, and select mostly orthogonal sets of events. Considering an overall scaling of the NC $\Delta\rightarrow N \gamma$ rate as an explanation of the MiniBooNE anomaly, our data exclude this hypothesis at 94.4% CL. When we decouple the expected correlations between NC $\Delta\rightarrow N \gamma$ events with and without final state protons, and allow independent scaling of both types of events, our data exclude explanations in which excess events have associated protons, and do not exclude explanations in which excess events have no associated protons.

The theory of homogenization provides a systematic approach to the derivation of macroscale constitutive laws, obviating the need to repeatedly resolve complex microstructure. However, the unit cell problem that defines the constitutive model is typically not amenable to explicit evaluation. It is therefore of interest to learn constitutive models from data generated by the unit cell problem. Many viscoelastic and elastoviscoplastic materials are characterized by memory-dependent constitutive laws. In order to amortize the computational investment in finding such memory-dependent constitutive laws, it is desirable to learn their dependence on the material microstructure. While prior work has addressed learning memory dependence and material dependence separately, their joint learning has not been considered. This paper focuses on the joint learning problem and proposes a novel neural operator framework to address it. In order to provide firm foundations, the homogenization problem for linear Kelvin-Voigt viscoelastic materials is studied. The theoretical properties of the cell problem in this Kelvin-Voigt setting are used to motivate the proposed general neural operator framework; these theoretical properties are also used to prove a universal approximation theorem for the learned macroscale constitutive model. This formulation of learnable constitutive models is then deployed beyond the Kelvin-Voigt setting. Numerical experiments are presented showing that the resulting data-driven methodology accurately learns history- and microstructure-dependent linear viscoelastic and nonlinear elastoviscoplastic constitutive models, and numerical results also demonstrate that the resulting constitutive models can be deployed in macroscale simulation of material deformation., Comment: 48 pages, 11 figures

Drones or unmanned aerial vehicles are traditionally used for military missions, warfare, and espionage. However, the usage of drones has significantly increased due to multiple industrial applications involving security and inspection, transportation, research purposes, and recreational drone flying. Such an increased volume of drone activity in public spaces requires regulatory actions for purposes of privacy protection and safety. Hence, detection of illegal drone activities such as boundary encroachment becomes a necessity. Such detection tasks are usually automated and performed by deep learning models which are trained on annotated image datasets. This paper builds on a previous work and extends an already published open source dataset. A description and analysis of the entire dataset is provided. The dataset is used to train the YOLOv7 deep learning model and some of its minor variants and the results are provided. Since the detection models are based on a single image input, a simple cross-correlation based tracker is used to reduce detection drops and improve tracking performance in videos. Finally, the entire drone detection system is summarized.

Gravitational wave (GW) memory effect is studied in the context of generalised Ellis-Bronnikov (GEB) wormholes. We solved the geodesic equations in this wormhole spacetime, in the presence of a GW pulse. The resulting evolution of the geodesic separation shows the presence of displacement and velocity memory. Memory effect due to a gravitational wave ensures that there is a permanent effect on spacetime geometry. The corresponding geodesic evolution, being metric dependent, would display distinct results in each case. Motivated by the same, we study further aspects of memory effect on the geodesic congruences, known as the B-memory, by solving the Raychaudhuri equations. Since future GW detectors will be able to probe the memory effect, our work presents GEB spacetime as a black hole mimicker with distinguishing features., Comment: 10 pages, 16 figures

We report a rare realization of unconventional anomalous Hall effect (UAHE) both below and above the magnetic transition temperature (T_C) in a hexagonal noncentrosymmetric magnet Y_3Co_8Sn_4, using a combined experimental and ab-initio calculations. Occurrence of such UAHE is mainly attributed to the reciprocal (KS) topology (i.e. the presence of topological Weyl points at/near the Fermi level), along with some contribution from the topological magnetic texture, as inferred from the measured field-dependent ac susceptibility. The effect of UAHE on the measured transport behavior however evolves differently with temperature above and below T_C, suggesting different physical mechanism responsible in the two phases. A unique planar ferrimagnetic ordering is found to be the most stable state with ab-plane as the easy plane below TC, as observed experimentally. The simulated net magnetization and the moment per Co atom agrees fairly well with the measured values. A reasonably large AHC is also observed in both the phases (above and below and T_C) of the present compound, which is again not so ubiquitous. Our results underscore the family of R_3Co_8Sn_4 (R= rare earth) polar magnets as a compelling backdrop for exploring the synergy of topological magnetism and non-trivial electronic bands, pivotal for spintronic applications.

Stance detection has emerged as a popular task in natural language processing research, enabled largely by the abundance of target-specific social media data. While there has been considerable research on the development of stance detection models, datasets, and application, we highlight important gaps pertaining to (i) a lack of theoretical conceptualization of stance, and (ii) the treatment of stance at an individual- or user-level, as opposed to message-level. In this paper, we first review the interdisciplinary origins of stance as an individual-level construct to highlight relevant attributes (e.g., psychological features) that might be useful to incorporate in stance detection models. Further, we argue that recent pre-trained and large language models (LLMs) might offer a way to flexibly infer such user-level attributes and/or incorporate them in modelling stance. To better illustrate this, we briefly review and synthesize the emerging corpus of studies on using LLMs for inferring stance, and specifically on incorporating user attributes in such tasks. We conclude by proposing a four-point agenda for pursuing stance detection research that is theoretically informed, inclusive, and practically impactful.

Harvesting is a critical task in the tree fruit industry, demanding extensive manual labor and substantial costs, and exposing workers to potential hazards. Recent advances in automated harvesting offer a promising solution by enabling efficient, cost-effective, and ergonomic fruit picking within tight harvesting windows. However, existing harvesting technologies often indiscriminately harvest all visible and accessible fruits, including those that are unripe or undersized. This study introduces a novel foundation model-based framework for efficient apple ripeness and size estimation. Specifically, we curated two public RGBD-based Fuji apple image datasets, integrating expanded annotations for ripeness ("Ripe" vs. "Unripe") based on fruit color and image capture dates. The resulting comprehensive dataset, Fuji-Ripeness-Size Dataset, includes 4,027 images and 16,257 annotated apples with ripeness and size labels. Using Grounding-DINO, a language-model-based object detector, we achieved robust apple detection and ripeness classification, outperforming other state-of-the-art models. Additionally, we developed and evaluated six size estimation algorithms, selecting the one with the lowest error and variation for optimal performance. The Fuji-Ripeness-Size Dataset and the apple detection and size estimation algorithms are made publicly available, which provides valuable benchmarks for future studies in automated and selective harvesting.

The nearest spiral galaxy, M31, exhibits a kinematically hot stellar disc, a global star formation episode ~2-4 Gyr ago, and conspicuous substructures in its stellar halo, suggestive of a recent accretion event. Recent chemodynamical measurements in the M31 disc and inner halo can be used as additional constraints for N-body hydrodynamical simulations that successfully reproduce the disc age-velocity dispersion relation and star formation history, together with the morphology of the inner halo substructures. We combine an available N-body hydrodynamical simulation of a major merger (mass ratio 1:4) with a well-motivated chemical model to predict abundance distributions and gradients in the merger remnant at z=0. We computed the projected phase space and the [M/H] distributions for the substructures in the M31 inner halo, i.e. the GS, the NE-, W- Shelves. We compare these chemodynamical properties of the simulated M31 remnant with recent measurements for the M31 stars in the inner halo. This major merger model predicts (i) distinct multiple components within each of the substructure; (ii) a high mean metallicity and large spread in the GS, NE- and W- Shelves, explaining various photometric and spectroscopic metallicity measurements; (iii) simulated phase space diagrams that qualitatively reproduce various features identified in the projected phase space of the substructures in published data from the DESI; (iv) a large distance spread in the GS, as suggested by previous tip of the RGB measurements, and (v) phase space ridges caused by several wraps of the secondary, as well as up-scattered main M31 disc stars, that also have plausible counterparts in the observed phase spaces. These results provide further independent arguments for a major satellite merger in M31 ~3 Gyr ago and a coherent explanation for many of the observational results that make M31 look so different from the MW., Comment: 24 pages, 21 figures, 3 tables, submitted to A&A

Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential delays or inaccuracies in identifying clinically significant cancers. This leads to numerous unnecessary biopsies and risks of missing clinically significant cancers. Here we present prostate vision contrastive network (ProViCNet), prostate organ-specific vision foundation models for Magnetic Resonance Imaging (MRI) and Trans-Rectal Ultrasound imaging (TRUS) for comprehensive cancer detection. ProViCNet was trained and validated using 4,401 patients across six institutions, as a prostate cancer detection model on radiology images relying on patch-level contrastive learning guided by biopsy confirmed radiologist annotations. ProViCNet demonstrated consistent performance across multiple internal and external validation cohorts with area under the receiver operating curve values ranging from 0.875 to 0.966, significantly outperforming radiologists in the reader study (0.907 versus 0.805, p<0.001) for mpMRI, while achieving 0.670 to 0.740 for TRUS. We also integrated ProViCNet with standard PSA to develop a virtual screening test, and we showed that we can maintain the high sensitivity for detecting clinically significant cancers while more than doubling specificity from 15% to 38% (p<0.001), thereby substantially reducing unnecessary biopsies. These findings highlight that ProViCNet's potential for enhancing prostate cancer diagnosis accuracy and reduce unnecessary biopsies, thereby optimizing diagnostic pathways., Comment: 44pages

Multi-turn conversations with an Enterprise AI Assistant can be challenging due to conversational dependencies in questions, leading to ambiguities and errors. To address this, we propose an NLU-NLG framework for ambiguity detection and resolution through reformulating query automatically and introduce a new task called "Ambiguity-guided Query Rewrite." To detect ambiguities, we develop a taxonomy based on real user conversational logs and draw insights from it to design rules and extract features for a classifier which yields superior performance in detecting ambiguous queries, outperforming LLM-based baselines. Furthermore, coupling the query rewrite module with our ambiguity detecting classifier shows that this end-to-end framework can effectively mitigate ambiguities without risking unnecessary insertions of unwanted phrases for clear queries, leading to an improvement in the overall performance of the AI Assistant. Due to its significance, this has been deployed in the real world application, namely Adobe Experience Platform AI Assistant., Comment: Preprint

Recent advancements have made it possible to approximate light-cone correlation functions in lattice QCD by computing their Euclidean counterparts. In these proceedings, we review key developments in this approach and explore their direct implications for Generalized Parton Distributions (GPDs). Furthermore, we emphasize the pivotal role of GPDs in uncovering the internal structure of hadrons and beyond., Comment: 14 pages, 6 figures, Plenary talk presented at the 41st International Symposium on Lattice Field Theory (Lattice2024), July 28th - August 3rd, 2024, Liverpool, UK

Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target suspicious prostate lesions. This has led to artificial intelligence (AI) applications improving MRI-based detection of clinically significant prostate cancer (CsPCa). However, MRI-detected lesions must still be mapped to transrectal ultrasound (TRUS) images during biopsy, which results in missing CsPCa. This study systematically evaluates a multimodal AI framework integrating MRI and TRUS image sequences to enhance CsPCa identification. The study included 3110 patients from three cohorts across two institutions who underwent prostate biopsy. The proposed framework, based on the 3D UNet architecture, was evaluated on 1700 test cases, comparing performance to unimodal AI models that use either MRI or TRUS alone. Additionally, the proposed model was compared to radiologists in a cohort of 110 patients. The multimodal AI approach achieved superior sensitivity (80%) and Lesion Dice (42%) compared to unimodal MRI (73%, 30%) and TRUS models (49%, 27%). Compared to radiologists, the multimodal model showed higher specificity (88% vs. 78%) and Lesion Dice (38% vs. 33%), with equivalent sensitivity (79%). Our findings demonstrate the potential of multimodal AI to improve CsPCa lesion targeting during biopsy and treatment planning, surpassing current unimodal models and radiologists; ultimately improving outcomes for prostate cancer patients.

We study the approximation of eigenvalues and eigenfunctions for the Laplace-Beltrami operator on compact manifolds without boundary. The analysis is centered on manifolds characterized by specific geometric constraints: lower bounds on Ricci curvature and injectivity radius, and an upper bound on diameter. Using weighted graph techniques, we approximate the eigenvalues for manifolds having a uniform lower bound on the volume of small balls and also investigate uniform bounds of eigenvalues applicable across the entire class.

The $\beta$-delayed neutron emission in the $^{85, 86}$As $\beta$-decays has been measured at the Ion Guide Isotope Separator On Line facility of the Accelerator Laboratory of the University of Jyv\"askyl\"a. The complete $\beta$-decays have been studied with a complex setup that consists of a plastic scintillator for $\beta$-particles, MONSTER -- the MOdular Neutron time-of-flight SpectromeTER -- for neutrons, and a high-purity germanium and four LaBr$_3$ crystals for $\gamma$-rays. The $\beta$-delayed neutron energy distributions have been determined by unfolding the time-of-flight spectra with an innovative methodology based on the iterative Bayesian unfolding method and accurate Monte Carlo simulations. The results obtained for $^{85}$As are in excellent agreement with the existing evaluated data, validating the proposed methodology. In the case of $^{86}$As, a stronger neutron intensity at higher energies than previously predicted is discovered., Comment: 10 pages, 11 figures, to be submitted to Physical Review C

How do we know if the dark sector consists of more than one dark matter (DM) component is an important question, for which the answer is not very definite. In this article we study such a possibility in context of direct DM search. It was pointed out earlier in a model independent analysis that a kink in the nuclear recoil energy spectrum may indicate to the presence of two DM components. However, realising one such model was difficult due to experimental constraints. Here we propose and study a model containing a vector boson DM and a scalar DM, aided by a light scalar mediator, where a kink in the nuclear recoil spectrum arises after addressing individual relic densities, direct search limits, collider constraints and theoretical limits. We find out the allowed parameter space of the model and those regions likely to show such distinctive signal., Comment: 24 pages, 5 figures

Global, three-dimensional, magnetohydrodynamic simulations of Rayleigh-Taylor instabilities at the disk-magnetosphere boundary of rotating, magnetized, compact stellar objects reveal that accretion occurs in three regimes: the stable regime, the chaotic unstable regime, and the ordered unstable regime. Here we track stochastic fluctuations in the pulse period $P(t)$ and aperiodic X-ray luminosity $L(t)$ time series of 24 accretion-powered pulsars in the Small Magellanic Cloud using an unscented Kalman filter to analyze Rossi X-ray Timing Explorer data. We measure time-resolved histories of the magnetocentrifugal fastness parameter $\omega(t)$ and we connect $\omega(t)$ with the three Rayleigh-Taylor accretion regimes. The 24 objects separate into two distinct groups, with 10 accreting in the stable regime, and 14 accreting in the ordered unstable regime. None of the 24 objects except SXP 293 visit the chaotic unstable regime for sustained intervals, although several objects visit it sporadically. The Kalman filter output also reveals a positive temporal cross-correlation between $\omega(t)$ and the independently measured pulse amplitude $A(t)$, which agrees with simulation predictions regarding the pulse-forming behavior of magnetospheric funnel flows in the three accretion regimes., Comment: 41 pages, 25 figures, accepted for publication in The Astrophysical Journal

In this paper we prove some uncertainty bounds for states on a $C^*$-algebra. We generalize Robertson's Standard Uncertainty Principle to this setting, and give a short, elementary proof. We also prove some other uncertainty principles for which the lower bound doesn't vanish for an odd number of observables., Comment: 6 pages, 0 figures

We investigate the capacity region of multi-user interference channels (IC), where each user encodes multiple sub-user components. By unifying chain-rule decomposition with the Entropy Power Inequality (EPI), we reason that single-user Gaussian codebooks suffice to achieve optimal performance, thus obviating any need for intricate auxiliary variables or joint typicality arguments. Our partial-MAC formulation enumerates sub-user decoding orders while only imposing constraints for sub-users actually decoded. This significantly reduces complexity relative to enumerating all subsets or bruteforcing over all successive interference cancellation (SIC) decoding order combinations at all receivers. This leads to a finite but comprehensive construction of all achievable rate tuples under sum-power constraints, while guaranteeing that each receiver fully recovers its intended sub-user signals. Consequently, known single-user Gaussian capacity results generalize naturally to multi-user scenarios, revealing a cohesive framework for analyzing multi-user IC. Our results thus offer a streamlined, tractable pathway for designing next-generation cell-free wireless networks that rely on IC mechanisms, efficiently exploiting interference structure while minimizing overhead. Overall, this provides a unifying perspective.

Matrix extension of a scalar function of a single variable is well-studied in literature. Of particular interest is the trace of such functions. It is known that for diagonalizable matrices, $M$, the function $g(M) = \text{Tr}(f(M)) = \sum_{j=1}^n f(\mu_j)$ (where $\{\mu_j\}_{j=1,2,\cdots,n}$ are the eigenvalues of $M$) inherits the monotonocity and convexity properties of $f$ (i.e., for $g$ to be convex, $f$ need not be operator convex -- convexity is sufficient). In this paper we formalize the idea of matrix extension of a function of multiple variables, study the monotonicity and convexity properties of the trace, and thus show that a function of form $g(M) = \sum_{j_1=1}^n \sum_{j_2=1}^n \cdots \sum_{j_m=1}^n f(\mu_{j_1}, \mu_{j_2},\cdots, \mu_{j_m})$ also inherits the monotonocity and convexity properties of the multi-variable function, $f$. We apply these results to functions of the spectrum of the weighted Laplacian matrix of undirected, simple graphs., Comment: 11 pages

In this work we investigate the effect of complexity factor on the formation of photon spheres for spherically symmetric compact objects. The complexity factor obtained from the orthogonal splitting of the Riemann curvature tensor connects the geometric attributes of a compact spherically symmetric gravitating object with its matter inhomogeneity and pressure anisotropy via a scalar term. The novelty of the complexity factor is the inherent simple definition that identifies the evolution of matter tensors inside a given region of space-time. Such identification helps to obtain an equivalence class of gravitating compact objects based on their degree of complexity with zero complexity identified as the simplest system. On the other hand shadows and photon rings have become essential for identifying compact regions of space time characterised by massive gravity. Advanced observational data analysis tools augments the hope for identification of exotic gravitational objects, like the so called ``black hole mimickers" and may serve as testing ground for other gravity theories. In this context we explore how complexity of compact objects (a fundamentally theoretical classification) is connected to the photon ring (an astrophysical observable in the universe) and its stability. We consider zero complexity systems and discuss its significance with respect to (wrt) formation of photon rings and hence shadows.

The role of grain size in determining fracture toughness in metals is incompletely understood with apparently contradictory experimental observations. We study this grain-size dependence computationally by building a model that combines the phase-field formulation of fracture mechanics with dislocation density-based crystal plasticity. We apply the model to cleavage fracture of body-centered cubic materials in plane strain conditions, and find non-monotonic grain-size dependence of plastic-brittle transgranular fracture. We find two mechanisms at play. The first is the nucleation of failure due to cross-slip in critically located grains within transgranular band of localized deformation, and this follows the classical Hall-Petch law that predicts a higher failure stress for smaller grains. The second is the resistance to the propagation of a mode I crack, where grain boundaries can potentially pin a crack, and this follows an inverse Hall-Petch law with higher toughness for larger grains. The result of the competition between the two mechanisms gives rise to non-monotonic behavior and reconciles the apparently contradictory experimental observations.

Visible-infrared person re-identification (VI-ReID) aims to match individuals across different camera modalities, a critical task in modern surveillance systems. While current VI-ReID methods focus on cross-modality matching, real-world applications often involve mixed galleries containing both V and I images, where state-of-the-art methods show significant performance limitations due to large domain shifts and low discrimination across mixed modalities. This is because gallery images from the same modality may have lower domain gaps but correspond to different identities. This paper introduces a novel mixed-modal ReID setting, where galleries contain data from both modalities. To address the domain shift among inter-modal and low discrimination capacity in intra-modal matching, we propose the Mixed Modality-Erased and -Related (MixER) method. The MixER learning approach disentangles modality-specific and modality-shared identity information through orthogonal decomposition, modality-confusion, and ID-modality-related objectives. MixER enhances feature robustness across modalities, improving cross-modal and mixed-modal settings performance. Our extensive experiments on the SYSU-MM01, RegDB and LLMC datasets indicate that our approach can provide state-of-the-art results using a single backbone, and showcase the flexibility of our approach in mixed gallery applications.

The antiferromagnetic (AFM) materials are distinguished by zero net magnetic moment, high resistance to external magnetic disturbances, and ultrafast dynamic responses. For advancing AFM materials in spintronic and valleytronic applications, achieving spontaneous valley polarization and the anomalous valley Hall effect (AVHE) is pivotal. We predict an A-type AFM monolayer Fe$_2$C(OH)$_2$, which shows a significant spontaneous valley polarization of 157 meV. In Fe$_2$C(OH)$_2$, spatial inversion symmetry (P) and time-reversal symmetry (T) are individually broken, yet the combined PT symmetry is preserved. This symmetry conservation leads to spin degeneracy, resulting in zero Berry curvature in the momentum space and absence of AVHE. However, a layer-locked hidden Berry curvature is produced, leading to the observation of the valley layer-spin Hall effect. Further, an external out-of-plane electric field can induce spin splitting by introducing layer-dependent electrostatic potential, enabling the layer-locked AVHE. Additionally, the introduction of a built-in electric field caused by the Janus structure also induces spin splitting in monolayer Fe$_2$C(OH)F due to the electric-potential-difference-AFM mechanism. The high out-of-plane magnetic anisotropy and realization of AVHE, offer promising opportunities for next-generation spintronic technologies.