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The open-world assumption in model development suggests that a model might lack sufficient information to adequately handle data that is entirely distinct or out of distribution (OOD). While deep learning methods have shown promising results in handling OOD data through generalization techniques, they often require specialized hardware that may not be accessible to all users. We present TCL, a lightweight yet effective solution that operates efficiently on standard CPU hardware. Our approach adapts contrastive learning principles specifically for tabular data structures, incorporating full matrix augmentation and simplified loss calculation. Through comprehensive experiments across 10 diverse datasets, we demonstrate that TCL outperforms existing models, including FT-Transformer and ResNet, particularly in classification tasks, while maintaining competitive performance in regression problems. TCL achieves these results with significantly reduced computational requirements, making it accessible to users with limited hardware capabilities. This study also provides practical guidance for detecting and evaluating OOD data through straightforward experiments and visualizations. Our findings show that TCL offers a promising balance between performance and efficiency in handling OOD prediction tasks, which is particularly beneficial for general machine learning practitioners working with computational constraints.

Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce new capabilities while preserving strong generalizability. We introduce Hephaestus-Forge, the first large-scale pre-training corpus designed to enhance the fundamental capabilities of LLM agents in API function calling, intrinsic reasoning and planning, and adapting to environmental feedback. Hephaestus-Forge comprises 103B agent-specific data encompassing 76,537 APIs, including both tool documentation to introduce knowledge of API functions and function calling trajectories to strengthen intrinsic reasoning. To explore effective training protocols, we investigate scaling laws to identify the optimal recipe in data mixing ratios. By continual pre-training on Hephaestus-Forge, Hephaestus outperforms small- to medium-scale open-source LLMs and rivals commercial LLMs on three agent benchmarks, demonstrating the effectiveness of our pre-training corpus in enhancing fundamental agentic capabilities and generalization of LLMs to new tasks or environments., Comment: Accepted to NAACL 2025 main conference

Highly sensitive, broadband bolometers are of great interest because of their versatile usage in wide areas starting from dark matter search, radio astronomy, material science, and qubit readouts in cQED experiments. There have been different realizations of bolometers using superconducting thin films, nanowires, quantum dots, and various 2D materials in the recent past. The challenge is to have a single device that combines high sensitivity, broad bandwidth, a fast readout mechanism, and low noise. Here we demonstrate the first usage of a Josephson parametric amplifier (JPA) as a highly sensitive bolometer. Our key finding is the Kerr non-linearity of the JPA boosts the device's sensitivity. When the bolometer is biased in the non-linear regime, it enhances the sideband signals (~100 times), resulting in an order of magnitude improvement in sensitivity compared to the linear regime. In the non-linear biasing of the device, we achieve a NEP~500 aW/sqrt(Hz). Our bolometer offers a fast detection scheme with a thermal time constant of 4.26 us and an intrinsic JPA time constant of 70 ns. Our device's broadband and fast operation are key and new compared to previously studied graphene-based bolometers. In our device, the gate voltage tunability and the possibility of multiplexing combined with the sensitive bolometric performance offer an opportunity for integrated quantum sensor arrays. Our work demonstrates a way forward to enhance the performance of quantum sensors based on 2D materials by leveraging the inherent non-linear response.

The recently-discovered ferroelectric nematic ($\mathrm{N}_\mathrm{F}$) liquid crystal presents a host of defect phenomena due to its unique polar nature and long-ranged electrostatic interactions. Like the solid state ferroelectrics, the depolarization field in the material favors a spontaneous spatial variation of the polarization $\mathbf{P}$, manifesting in myriad ways including a twist in the bulk and different arrangements of alternating polarization domains. Unlike the solid-state ferroelectrics with a bulk crystalline structure, the configuration of the $\mathrm{N}_\mathrm{F}$ fluids is determined not only by the reduction of depolarization fields but also by the alignment of molecules at interfaces. In this work, we consider an $\mathrm{N}_\mathrm{F}$ confined to a thin cell, pre-patterned with various types of apolar surface anchoring produced by photoalignment. For uniform planar alignment, we find that the sample forms a series of striped domains. For a cell pre-patterned with a radial +1 defect pattern, the $\mathrm{N}_\mathrm{F}$ breaks up into "pie-slice" polarization domains. We calculate the elastic and electrostatic energy balance which determines the observed configurations and demonstrate that the electrostatic interactions tend to decrease the characteristic domain size $\lambda$ while the elastic and surface anchoring interactions facilitate a larger $\lambda$. We also demonstrate that ionic screening mitigates electrostatic interactions, increasing $\lambda$ and, above some critical concentration, eliminating the domains altogether., Comment: 14 pages, 8 figures

In this paper, we study an online algorithm in a reproducing kernel Hilbert spaces (RKHS) based on a class of dependent processes, called the mixing process. For such a process, the degree of dependence is measured by various mixing coefficients. As a representative example, we analyze a strictly stationary Markov chain, where the dependence structure is characterized by the \(\beta-\) and \(\phi-\)mixing coefficients. For these dependent samples, we derive nearly optimal convergence rates. Our findings extend existing error bounds for i.i.d. observations, demonstrating that the i.i.d. case is a special instance of our framework. Moreover, we explicitly account for an additional factor introduced by the dependence structure in the Markov chain.

The deepfake generation of singing vocals is a concerning issue for artists in the music industry. In this work, we propose a singing voice deepfake detection (SVDD) system, which uses noise-variant encodings of open-AI's Whisper model. As counter-intuitive as it may sound, even though the Whisper model is known to be noise-robust, the encodings are rich in non-speech information, and are noise-variant. This leads us to evaluate Whisper encodings as feature representations for the SVDD task. Therefore, in this work, the SVDD task is performed on vocals and mixtures, and the performance is evaluated in \%EER over varying Whisper model sizes and two classifiers- CNN and ResNet34, under different testing conditions., Comment: Accepted in ICASSP,2025

Vibrational properties of molecules are of widespread interest and importance in chemistry and biochemistry. The reliability of widely employed approximate computational methods is questioned here against the complex experimental spectrum of ethylene glycol. Comparisons between quantum vibrational self-consistent field and virtual-state configuration interaction (VSCF/VCI), adiabatically switched semiclassical initial value representation (AS SCIVR), and thermostatted ring polymer molecular dynamics (TRPMD) calculations are made using a full-dimensional machine-learned potential energy surface. Calculations are done for five low-lying conformers and compared with the experiment, with a focus on the high-frequency, OH-stretches, and CH-stretches, part of the spectrum. Fermi resonances are found in the analysis of VSCF/VCI eigenstates belonging to the CH-stretching band. Results of comparable accuracy, quality, and level of detail are obtained by means of AS SCIVR. The current VSCF/VCI and AS-SCIVR power spectra largely close the gaps between the experiment and TRPMD and classical MD calculations. Analysis of these results provide guidance on what level of accuracy to expect from TRPMD and classical MD calculations of the vibrational spectra for ubiquitous CH and OH-stretches bands. This work shows that even general vibrational features require a proper quantum treatment usually not achievable by the most popular theoretical approaches.

We theoretically investigate the thermoelectric properties (electronic contribution) of a normal-superconductor (NS) hybrid junction, where the normal region consists of magic-angle twisted bilayer graphene (MATBG). The superconducting region is characterized by a common $s$-wave superconductor closely proximitized to the MATBG. We compute various thermoelectric coefficients, including thermal conductance, thermopower, and the figure of merit ($zT$), using the scattering matrix formalism. These results are further supported by calculations based on a lattice-regularized version of the effective Hamiltonian. Additionally, we explore the impact of trigonal warping and valley polarization on the thermoelectric coefficients. Notably, we find a significant variation in $zT$ as a function of these parameters, reaching values as high as 2.5. Interestingly, we observe a violation of the Wiedemann-Franz law near the charge neutrality point with the superconducting correlation, indicating that MATBG electrons behave as slow Dirac fermions in this regime. This observation is further confirmed by the damped oscillatory behavior of the thermal conductance as a function of the barrier strength when an insulating barrier is modelled at the interface of the NS junction. Beyond theoretical insights, our findings suggest new possibilities for thermoelectric applications using MATBG based NS junctions., Comment: 12 Pages, 7 PDF Figures, Comments are welcome

Quantum coherence in bosonic systems is a fundamental resource for quantum technology applications. In this work, we introduce a framework for analyzing coherence in the Fock-state basis, utilizing context-dependent certification to reveal the quantum non-Gaussian nature of the tested coherence. Rather than relying on global coherence measures, our approach targets specific aspects of coherence, enabling a tailored hierarchical classification. We derive and compare two distinct hierarchies, each representing a different context for coherence in bosonic systems. Motivated by current advancements in optical quantum state engineering, we assess the feasibility and depth of these hierarchies under conditions of loss and thermal noise. The methodology introduced here is versatile and can be extended to multi-state and multi-mode coherences, making it adaptable to a wide range of experimental scenarios and platforms.

Non-Gaussianity, a distinctive characteristic of bosonic quantum states, is pivotal in advancing quantum networks, fault-tolerant quantum computing, and high-precision metrology. Verifying the quantum nature of a state, particularly its non-Gaussian features, is essential for ensuring the reliability and performance of these technologies. However, the specific properties required for each application demand tailored validation thresholds. Here, we introduce a hierarchical framework comprising absolute, relative, and qubit-specific thresholds to assess the non-Gaussianity of local coherences. We illustrate this framework using heralded optical non-Gaussian states with the highest purities available in optical platforms. This comprehensive framework presents the first detailed evaluation of number state coherences and can be extended to a wide range of bosonic states.

Spintronics has emerged as a field of vast applicability, and layered magnetic materials have served as a ground for advancement in this direction. Here, we report the synthesis and detailed magnetic and specific heat studies on NdSbSe, (a ZrSiS-based structure) magnetic topological material. The temperature-dependent magnetization shows the presence of competing magnetic interactions ($T_N

In this paper, we propose several methods that incorporate vocal tract length (VTL) warped features for spoken keyword spotting (KWS). The first method, VTL-independent KWS, involves training a single deep neural network (DNN) that utilizes VTL features with various warping factors. During training, a specific VTL feature is randomly selected per epoch, allowing the exploration of VTL variations. During testing, the VTL features with different warping factors of a test utterance are scored against the DNN and combined with equal weight. In the second method scores the conventional features of a test utterance (without VTL warping) against the DNN. The third method, VTL-concatenation KWS, concatenates VTL warped features to form high-dimensional features for KWS. Evaluations carried out on the English Google Command dataset demonstrate that the proposed methods improve the accuracy of KWS.

We employ deep learning (DL) to improve photometric redshifts (photo-$z$s) in the Kilo-Degree Survey Data Release 4 Bright galaxy sample (KiDS-Bright DR4). This dataset, used as a foreground for KiDS lensing and clustering studies, is flux-limited to $r<20$ mag with mean $z=0.23$ and covers 1000 deg$^2$. Its photo-$z$s were previously derived with artificial neural networks from the ANNz2 package, trained on the Galaxy And Mass Assembly (GAMA) spectroscopy. Here we considerably improve over these previous redshift estimations by building a DL model, Hybrid-z, which combines four-band KiDS images with nine-band magnitudes from KiDS+VIKING. The Hybrid-z framework provides photo-$z$s for KiDS-Bright, with negligible mean residuals of O($10^{-4}$) and scatter at the level of $0.014(1+z)$ -- reduction by 20% over the previous nine-band derivations with ANNz2. We check our photo-$z$ model performance on test data drawn from GAMA, as well as from other KiDS-overlapping wide-angle spectroscopic surveys, namely SDSS, 2dFLenS, and 2dFGRS. We find stable behavior and consistent improvement over ANNz2 throughout. We finally apply Hybrid-z trained on GAMA to the entire KiDS-Bright DR4 sample of 1.2 million galaxies. For these final predictions, we design a method of smoothing the input redshift distribution of the training set, to avoid propagation of features present in GAMA, related to its small sky area and large-scale structure imprint in its fields. Our work paves the way towards the best-possible photo-$z$s achievable with machine learning for any galaxy type both for the final KiDS-Bright DR5 data and for future deeper imaging, such as from the Legacy Survey of Space and Time., Comment: 14 pages, 7 figures

Mixture-of-Agents (MoA) has recently been proposed as a method to enhance performance of large language models (LLMs), enabling multiple individual LLMs to work together for collaborative inference. This collaborative approach results in improved responses to user prompts compared to relying on a single LLM. In this paper, we consider such an MoA architecture in a distributed setting, where LLMs operate on individual edge devices, each uniquely associated with a user and equipped with its own distributed computing power. These devices exchange information using decentralized gossip algorithms, allowing different device nodes to talk without the supervision of a centralized server. In the considered setup, different users have their own LLM models to address user prompts. Additionally, the devices gossip either their own user-specific prompts or augmented prompts to generate more refined answers to certain queries. User prompts are temporarily stored in the device queues when their corresponding LLMs are busy. Given the memory limitations of edge devices, it is crucial to ensure that the average queue sizes in the system remain bounded. In this paper, we address this by theoretically calculating the queuing stability conditions for the device queues under reasonable assumptions, which we validate experimentally as well. Further, we demonstrate through experiments, leveraging open-source LLMs for the implementation of distributed MoA, that certain MoA configurations produce higher-quality responses compared to others, as evaluated on AlpacaEval 2.0 benchmark. The implementation is available at: https://github.com/purbeshmitra/distributed_moa.

The current study examines the relationship between self-reported depression and the perception of affective speech within the Indian population. PANAS and PHQ-9 were used to assess current mood and depression, respectively. Participants' emotional reactivity was recorded on a valence and arousal scale against the affective speech audio presented in a sequence. No significant differences between the depression and no-depression groups were observed for any of the emotional stimuli, except the audio file depicting neutral emotion. Significantly higher PANAS scores by the depression than the no-depression group indicate the impact of pre-disposed mood on the current mood status. Contrary to previous findings, this study did not observe reduced positive emotional reactivity by the depression group. However, the results demonstrated consistency in emotional reactivity for speech stimuli depicting sadness and anger across all measures of emotion perception.

This paper provides the first incentive analysis of open radio access networks (ORAN) using game theory. We assess strategic interactions between telecom supply chain stakeholders: mobile network operators (MNOs), network infrastructure suppliers (NIS), and original equipment manufacturers (OEMs) across three procurement scenarios: (i) Traditional, (ii) Predatory as monolithic radio access networks (MRAN), and (iii) DirectOEM as ORAN. We use random forest and gradient boosting models to evaluate the optimal margins across urban, suburban, and rural U.S. regions. Results suggest that ORAN deployment consistently demonstrates higher net present value (NPV) of profits in urban and suburban regions, outperforming the traditional procurement strategy by 11% to 31%. However, rural areas present lower NPVs across all scenarios, with significant variability at the county level. This analysis offers actionable insights for telecom investment strategies, bridging technical innovation with economic outcomes and addressing strategic supply chain dynamics through a game-theoretic lens., Comment: 46 pages with 8 Figures

We report the detection of the [O III] auroral line in 42 galaxies within the redshift range of $3 < z < 10$. These galaxies were selected from publicly available JWST data releases, including the JADES and PRIMALsurveys, and observed using both the low-resolution PRISM/CLEAR configuration and medium-resolution gratings. The measured electron temperatures in the high-ionization regions of these galaxies range from $T_e$([O III]) = 12,000 to 24,000 K, consistent with temperatures observed in local metal-poor galaxies and previous JWST studies. In 10 galaxies, we also detect the [O II] auroral line, allowing us to determine electron temperatures in the low-ionization regions, which range between $T_e$([O II]) = 10,830 and 20,000 K. The direct-$T_e$-based metallicities of our sample span from 12 + log(O/H) = 7.2 to 8.4, indicating these high-redshift galaxies are relatively metal-poor. By combining our sample with 25 galaxies from the literature, we expand the dataset to a total of 67 galaxies within $3 < z < 10$, effectively more than doubling the previous sample size for direct-$T_e$ based metallicity studies. This larger dataset allow us to derive empirical metallicity calibration relations based exclusively on high-redshift galaxies, using six key line ratios: R3, R2, R23, Ne3O2, O32, and O3N2. Notably, we derive a novel metallicity calibration relation for the first time using high-redshift $T_e$-based metallicities: $\hat{R}$ = 0.18log $R2$ + 0.98log $R3$. This new calibration significantly reduces the scatter in high-redshift galaxies compared to the $\hat{R}$ relation previously calibrated for low-redshift galaxies., Comment: 23 pages, 10 figures, submitted to ApJ

Models predict that the well-studied mass-radius relation of white dwarf stars depends on the temperature of the star, with hotter white dwarfs having larger masses at a given radius than cooler stars. In this paper, we use a catalog of 26,041 DA white dwarfs observed in Sloan Digital Sky Survey Data Releases 1-19. We measure the radial velocity, effective temperature, surface gravity, and radius for each object. By binning this catalog in radius or surface gravity, we average out the random motion component of the radial velocities for nearby white dwarfs to isolate the gravitational redshifts for these objects and use them to directly measure the mass-radius relation. For gravitational redshifts measured from binning in either radius or surface gravity, we find strong evidence for a temperature-dependent mass-radius relation, with warmer white dwarfs consistently having greater gravitational redshifts than cool objects at a fixed radius or surface gravity. For warm white dwarfs, we find that their mean radius is larger and mean surface gravity is smaller than those of cool white dwarfs at 5.2{\sigma} and 6.0{\sigma} significance, respectively. Selecting white dwarfs with similar radii or surface gravities, the significance of the difference in mean gravitational redshifts between the warm and cool samples is >6.1{\sigma} and >3.6{\sigma} for measurements binned in radius and surface gravity, respectively, in the direction predicted by theory. This is an improvement over previous implicit detections, and our technique can be expanded to precisely test the white dwarf mass-radius relation with future surveys.

Let $\pi(A)$, $\xi(A)$ and $\nu(A)$, respectively, denote the number of positive, zero and negative eigenvalues of the matrix $A$. Then the triplet $(\pi(A), \xi(A), \nu(A))$ is called the \emph{inertia} of $A$ and is denoted by $\textup{Inertia(A)}$. Let $\beta$ be the beta function. The inertia of the matrix $\left[\beta(i,j )\right]$ is shown to be $\left(\frac{n}{2},0,\frac{n}{2}\right)$ if $n$ is even, and $\left(\frac{n+1}{2},0,\frac{n-1}{2}\right)$ if $n$ is odd. %Its connections with Birkhoff-James orthogonality are given. It is also shown that $\left[\beta(i,j)\right]$ is Birkhoff-James orthogonal to the $n\times n$ identity matrix $I$ in the trace norm if and only if $n$ is even. %We prove that the inverse of $\left[{\beta(i,j)}\right]$ is an integer matrix. For $0<\la_1<\cdots<\la_n, 0<\mu_1<\cdots<\mu_n$, it is shown that the matrix $\left[(\beta(\la_i,\mu_j))^m\right]$ is non singular if $\mu_{i+1}-\mu_{i}\in \N$ for all $1\leq i \leq n-1$. It is also shown that if $\mu_{i+1}-\mu_i \in \N$ for $1\leq i\leq n-1$, then for $m\in \mathbb N$, the matrix $\left[\frac{1}{\beta(\la_i,\mu_j)^m}\right]$ is totally positive., Comment: This manuscript contains 11 pages

This work introduces a novel approach to constructing DNA codes from linear codes over a non-chain extension of $\mathbb{Z}_4$. We study $(\text{\textbaro},\mathfrak{d}, \gamma)$-constacyclic codes over the ring $\mathfrak{R}=\mathbb{Z}_4+\omega\mathbb{Z}_4, \omega^2=\omega,$ with an $\mathfrak{R}$-automorphism $\text{\textbaro}$ and a $\text{\textbaro}$-derivation $\mathfrak{d}$ over $\mathfrak{R}.$ Further, we determine the generators of the $(\text{\textbaro},\mathfrak{d}, \gamma)$-constacyclic codes over the ring $\mathfrak{R}$ of any arbitrary length and establish the reverse constraint for these codes. Besides the necessary and sufficient criterion to derive reverse-complement codes, we present a construction to obtain DNA codes from these reversible codes. Moreover, we use another construction on the $(\text{\textbaro},\mathfrak{d},\gamma)$-constacyclic codes to generate additional optimal and new classical codes. Finally, we provide several examples of $(\text{\textbaro},\mathfrak{d}, \gamma)$ constacyclic codes and construct DNA codes from established results. The parameters of these linear codes over $\mathbb{Z}_4$ are better and optimal according to the codes available at \cite{z4codes}., Comment: 20

SHAPE (Spectro-polarimetry of HAbitable Planet Earth) is an experiment onboard the Chandrayaan-3 Mission, designed to study the spectro-polarimetric signatures of the habitable planet Earth in the near-infrared (NIR) wavelength range (1.0 - 1.7 $\mu$m). The spectro-polarimeter is the only scientific payload (experimental in nature) on the Propulsion Module (PM) of the Chandrayaan-3 mission. The instrument is a compact and lightweight spectro-polarimeter with an Acousto-Optic Tunable Filter (AOTF) at its core. The AOTF operates in the frequency range of 80 MHz to 135 MHz with a power of 0.5 - 2.0 Watts. The two output beams (e-beam and o-beam) from the AOTF are focused onto two InGaAs detectors (pixelated, 1D linear array) with the help of focusing optics. The primary (aperture) optics, with a diameter of $\sim$2 mm, collects the NIR light for input to the AOTF, defining the field of view (FOV) of 2.6$^\circ$. The payload has a mass of 4.8 kg and operates at a power of 25 Watts. This manuscript highlights some of the ground-based results, including the post-launch initial performance of the payload while orbiting around the Moon to observe Earth., Comment: Accepted for publication in Journal of Aerospace Sciences and Technologies

Barrow holographic dark energy model is an extension of holographic dark energy that incorporates modifications to entropy due to quantum gravitational effects. In this work we study the cosmological properties of interacting Barrow holographic dark energy model in the case of non-zero curvature universe. We construct the differential equations governing the evolution of the Barrow holographic dark energy density parameter and the dark matter density parameter in coupled form for both closed and open spatial geometry. Considering three different forms of coupling, we obtain the corresponding analytical expressions for the equation of state parameter for the dark energy component. We confront the scenario using recent observational datasets like cosmic chronometer and Pantheon data. It has been found that the strength of interaction as well as the curvature contribution come out to be nonzero which indicates that a non-flat interacting scenario is preferred by observational data.

We present first-principles results on the electronic and magnetic properties of the cubic bulk $\beta$-phase of iron(III) oxide (Fe$_2$O$_3$). Given that all Fe-Fe magnetic couplings are expected to be antiferromagnetic within this high-symmetry crystal structure, the system may exhibit some signature of magnetic frustration, making it challenging to identify its magnetic ground state. We have analyzed the possible magnetic phases of the $\beta$-phase among which there are ferrimagnets, altermagnets and Kramers antiferromagnets. While the $\alpha$-phase is an altermagnet and the $\gamma$-phase is a ferrimagnet, we conclude that the magnetic ground state for the bulk $\beta$-phase of Fe$_2$O$_3$ is a Kramers antiferromagnet, moreover, we find that close in energy there is a bulk d-wave altermagnetic phase. We report the density of states and the evolution band gap as a function of the electronic correlations, for suitable values of the Coulomb repulsion the system is a charge-transfer insulator with an indirect band gap of 1.5 eV. As the opposite to the $\gamma$-phase, the magnetic configuration between first-neighbor of the same kind is always antiferromagnetic while the magnetic configuration between Fe$_a$ and Fe$_b$ is ferro or antiferro. In this magnetic arrangement, first-neighbor interactions cancel out in the mean-field estimation of the N\'eel temperature, leaving second-neighbor magnetic exchanges as the primary contributors, resulting in a N\'eel temperature lower than that of other phases. Our work paves the way toward the ab initio study of nanoparticles and alloys for the $\beta$-phase of Fe$_2$O$_3$.

Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in atomistic energy decomposition and the computational efficiency of traditional force fields has not been fully achieved. Here, we present a novel method that combines aspects of both approaches, and achieves state-of-the-art balance of accuracy and force field-level speed. This method utilizes a monomer-centered representation, where the potential energy is decomposed into the sum of chemically meaningful monomeric energies. Without sophisticated neural network design, the structural descriptors of monomers are described by 1-body and 2-body effective interactions, enforced by appropriate sets of PIPs as inputs to the feed forward NN. We demonstrate the performance of this method through systematic assessments of models for gas-phase water trimer, liquid water, and also liquid CO2. The high accuracy, fast speed, and flexibility of this method provide a new route for constructing accurate ML potentials and enabling large-scale quantum and classical simulations for complex molecular systems.

We present two major contributions in this work: 1) we create a full HD resolution stereoscopic (S3D) video dataset comprised of 12 reference and 360 distorted videos. The test stimuli are produced by simulating the five levels of fog and haze ambiances on the pristine left and right video sequences. We perform subjective analysis on the created video dataset with 24 viewers and compute Difference Mean Opinion Scores (DMOS) as quality representative of the dataset, 2) an Opinion Unaware (OU) and Distortion Unaware (DU) video quality assessment model is developed for S3D videos. We construct cyclopean frames from the individual views of an S3D video and partition them into nonoverlapping blocks. We analyze the Natural Scene Statistics (NSS) of all patches of pristine and test videos, and empirically model the NSS features with Univariate Generalized Gaussian Distribution (UGGD). We compute UGGD model parameters ({\alpha}, \b{eta}) at multiple spatial scales and multiple orientations of spherical steerable pyramid decomposition and show that the UGGD parameters are distortion discriminable. Further, we perform Multivariate Gaussian (MVG) modeling on the pristine and distorted video feature sets and compute the corresponding mean vectors and covariance matrices of MVG fits. We compute the Bhattacharyya distance measure between mean vectors and covariance matrices to estimate the perceptual deviation of a test video from pristine video set. Finally, we pool both distance measures to estimate the overall quality score of an S3D video. The performance of the proposed objective algorithm is verified on the popular S3D video datasets such as IRCCYN, LFOVIAS3DPh1, LFOVIAS3DPh2 and the proposed VAD stereo dataset. The algorithm delivers consistent performance across all datasets and shows competitive performance against off-the-shelf 2D and 3D image and video quality assessment algorithms., Comment: 13 pages

Speech disfluencies in spontaneous communication can be categorized as either typical or atypical. Typical disfluencies, such as hesitations and repetitions, are natural occurrences in everyday speech, while atypical disfluencies are indicative of pathological disorders like stuttering. Distinguishing between these categories is crucial for improving voice assistants (VAs) for Persons Who Stutter (PWS), who often face premature cutoffs due to misidentification of speech termination. Accurate classification also aids in detecting stuttering early in children, preventing misdiagnosis as language development disfluency. This research introduces the IIITH-TISA dataset, the first Indian English stammer corpus, capturing atypical disfluencies. Additionally, we extend the IIITH-IED dataset with detailed annotations for typical disfluencies. We propose Perceptually Enhanced Zero-Time Windowed Cepstral Coefficients (PE-ZTWCC) combined with Shifted Delta Cepstra (SDC) as input features to a shallow Time Delay Neural Network (TDNN) classifier, capturing both local and wider temporal contexts. Our method achieves an average F1 score of 85.01% for disfluency classification, outperforming traditional features.

Domain structure of a fluid ferroelectric nematic is dramatically different from the domain structure of solid ferroelectrics since it is not restricted by rectilinear crystallographic axes and planar surface facets. We demonstrate that thin films of a ferroelectric nematic seeded by colloidal inclusions produce domain walls in the shape of conics such as a parabola. These conics reduce the bound charge within the domains and at the domain walls. An adequate description of the domain structures requires one to analyze the electrostatic energy, which is a challenging task. Instead, we demonstrate that a good approximation to the experimentally observed polydomain textures is obtained when the divergence of spontaneous polarization - which causes the bound charge is heavily penalized by assuming that the elastic constant of splay in the Oseen-Frank energy is much larger than those for twist and bend. The model takes advantage of the fact that the polarization vector is essentially parallel to the nematic director throughout the sample.

Medical image segmentation is crucial in robotic surgeries, disease diagnosis, and treatment plans. This research presents an innovative methodology that combines Kolmogorov-Arnold Networks (KAN) with an adapted Mamba layer for medical image segmentation. The proposed KAN-Mamba FusionNet framework improves image segmentation by integrating attention-driven mechanisms with convolutional parallel training and autoregressive deployment, while preserving interpretability, in contrast to the state-of-the-art techniques that depend exclusively on Mamba for ailment localization and accurate diagnosis. We evaluated our proposed KAN-Mamba FusionNet model on three distinct medical image segmentation datasets, BUSI, Kvasir-Seg and GlaS. The results indicated that the KAN-Mamba FusionNet consistently yields better IoU and F1 scores in comparison to the state-of-the-art methods. Further, we offer insights into the model's behavior via ablation studies, examining the effects of various components and assessing their contributions to the overall performance of the proposed model. The findings illustrate the strength and effectiveness of this methodology for dependable medical image segmentation, providing a unique approach to address intricate visual data issues in healthcare., Comment: 9 pages, 5 figures, 4 tables

Motivated by a case study of vegetation patterns, we introduce a mixture model with concomitant variables to examine the association between the orientation of vegetation stripes and wind direction. The proposal relies on a novel copula-based bivariate distribution for mixed axial and circular observations and provides a parsimonious and computationally tractable approach to examine the dependence of two environmental variables observed in a complex manifold. The findings suggest that dominant winds shape the orientation of vegetation stripes through a mechanism of neighbouring plants providing wind shelter to downwind individuals.

Training methods to perform robust 3D human pose and shape (HPS) estimation requires diverse training images with accurate ground truth. While BEDLAM demonstrates the potential of traditional procedural graphics to generate such data, the training images are clearly synthetic. In contrast, generative image models produce highly realistic images but without ground truth. Putting these methods together seems straightforward: use a generative model with the body ground truth as controlling signal. However, we find that, the more realistic the generated images, the more they deviate from the ground truth, making them inappropriate for training and evaluation. Enhancements of realistic details, such as clothing and facial expressions, can lead to subtle yet significant deviations from the ground truth, potentially misleading training models. We empirically verify that this misalignment causes the accuracy of HPS networks to decline when trained with generated images. To address this, we design a controllable synthesis method that effectively balances image realism with precise ground truth. We use this to create the Generative BEDLAM (Gen-B) dataset, which improves the realism of the existing synthetic BEDLAM dataset while preserving ground truth accuracy. We perform extensive experiments, with various noise-conditioning strategies, to evaluate the tradeoff between visual realism and HPS accuracy. We show, for the first time, that generative image models can be controlled by traditional graphics methods to produce training data that increases the accuracy of HPS methods.

Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling using GPs, the computational complexity associated with GPs demanding immense resources make their practical usage highly challenging. In this article, we develop a quantum version of multi-output Gaussian Process (QGP) by implementing a well-known quantum algorithm called HHL, to perform the Kernel matrix inversion within the Gaussian Process. To reduce the large circuit depth of HHL a circuit optimization technique called Approximate Quantum Compiling (AQC) has been implemented. We further showcase the application of QGP for a real-world problem to estimate line parameters of an electrical grid. Using AQC, up to 13-qubit HHL circuit has been implemented for a 32x32 kernel matrix inversion on IBM Quantum hardware for demonstrating QGP based line parameter estimation experimentally. Finally, we compare its performance against noise-less quantum simulators and classical computation results., Comment: 9 pages, 7 figures

We address the challenge of accurate 3D human pose and shape estimation from monocular images. The key to accuracy and robustness lies in high-quality training data. Existing training datasets containing real images with pseudo ground truth (pGT) use SMPLify to fit SMPL to sparse 2D joint locations, assuming a simplified camera with default intrinsics. We make two contributions that improve pGT accuracy. First, to estimate camera intrinsics, we develop a field-of-view prediction model (HumanFoV) trained on a dataset of images containing people. We use the estimated intrinsics to enhance the 4D-Humans dataset by incorporating a full perspective camera model during SMPLify fitting. Second, 2D joints provide limited constraints on 3D body shape, resulting in average-looking bodies. To address this, we use the BEDLAM dataset to train a dense surface keypoint detector. We apply this detector to the 4D-Humans dataset and modify SMPLify to fit the detected keypoints, resulting in significantly more realistic body shapes. Finally, we upgrade the HMR2.0 architecture to include the estimated camera parameters. We iterate model training and SMPLify fitting initialized with the previously trained model. This leads to more accurate pGT and a new model, CameraHMR, with state-of-the-art accuracy. Code and pGT are available for research purposes., Comment: 3DV 2025

We extend the Standard Model with the general effective Hamiltonian for the quark level transition $c \to s \ell \nu$ with a complete set of four fermion operators including right-handed neutrinos. The current experimental measurements in charm decays are compatible with the Standard Model predictions and are used to constrain the new physics. With the available experimental data, we fit a $\chi^2$ function to get the best-fit values of the NP WCs. We investigate the impact of allowed new physics in the observables such as differential branching fraction, forward-backward asymmetry, lepton polarization asymmetry, and convexity parameter in the semileptonic decay $B_c^+ \to B_s \mu^+ \nu_{\mu}$. The different types of new physics scenarios have significant effects on these considered observables. The future experimental information of these observables can help to disentangle the structure of new physics., Comment: 30 pages, 11 figures, 5 tables

Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the high number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys can potentially cause significant bias in the angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure the 2PCF from the newly published KiDS-Legacy sample. Given an $r$-band $5\sigma$ magnitude limit of $24.8$ and survey footprint of $1347$ deg$^2$, it achieves an excellent combination of sky coverage and depth for such a measurement. We find that complex selection effects, primarily induced by varying seeing, introduce over-estimation of the 2PCF by approximately an order of magnitude. To correct for such effects, we apply a machine learning-based method to recover an organised random (OR) that presents the same selection pattern as the galaxy sample. The basic idea is to find the selection-induced clustering of galaxies using a combination of self-organising maps (SOMs) and hierarchical clustering (HC). This unsupervised machine learning method is able to recover complicated selection effects without specifying their functional forms. We validate this SOM+HC method on mock deep galaxy samples with realistic systematics and selections derived from the KiDS-Legacy catalogue. Using mock data, we demonstrate that the OR delivers unbiased 2PCF cosmological parameter constraints, removing the $27\sigma$ offset in the galaxy bias parameter that is recovered when adopting uniform randoms. Blinded measurements on the real KiDS-Legacy data show that the corrected 2PCF is robust to the SOM+HC configuration near the optimal set-up suggested by the mock tests., Comment: 29 pages, 27 figures, 4 tables; Accepted for publication on Astronomy & Astrophysics; The code used for this work is published on https://github.com/yanzastro/tiaogeng

Session-based recommendation (SR) models aim to recommend top-K items to a user, based on the user's behaviour during the current session. Several SR models are proposed in the literature, however,concerns have been raised about their susceptibility to inherent biases in the training data (observed data) such as popularity bias. SR models when trained on the biased training data may encounter performance challenges on out-of-distribution data in real-world scenarios. One way to mitigate popularity bias is counterfactual data augmentation. Compared to prior works that rely on generating data using SR models, we focus on utilizing the capabilities of state-of-the art diffusion models for generating counterfactual data. We propose a guided diffusion-based counterfactual augmentation framework for SR. Through a combination of offline and online experiments on a real-world and simulated dataset, respectively, we show that our approach performs significantly better than the baseline SR models and other state-of-the art augmentation frameworks. More importantly, our framework shows significant improvement on less popular target items, by achieving up to 20% gain in Recall and 13% gain in CTR on real-world and simulated datasets,respectively.

Federated learning (FL) is a collaborative approach where multiple clients, coordinated by a parameter server (PS), train a unified machine-learning model. The approach, however, suffers from two key challenges: data heterogeneity and communication overhead. Data heterogeneity refers to inconsistencies in model training arising from heterogeneous data at different clients. Communication overhead arises from the large volumes of parameter updates exchanged between the PS and clients. Existing solutions typically address these challenges separately. This paper introduces a new communication-efficient algorithm that uses the age of information metric to simultaneously tackle both limitations of FL. We introduce age vectors at the PS, which keep track of how often the different model parameters are updated from the clients. The PS uses this to selectively request updates for specific gradient indices from each client. Further, the PS employs age vectors to identify clients with statistically similar data and group them into clusters. The PS combines the age vectors of the clustered clients to efficiently coordinate gradient index updates among clients within a cluster. We evaluate our approach using the MNIST and CIFAR10 datasets in highly non-i.i.d. settings. The experimental results show that our proposed method can expedite training, surpassing other communication-efficient strategies in efficiency.

Semiconducting CrSBr is a layered A-type antiferromagnet, with individual layers antiferromagnetically coupled along the stacking direction. Due to its unique orthorhombic crystal structure, CrSBr exhibits highly anisotropic mechanical and optoelectronic properties acting itself as a quasi-1D material. CrSBr demonstrates complex coupling phenomena involving phonons, excitons, magnons, and polaritons. Here we show through polarization-resolved resonant Raman scattering the intricate interaction between the vibrational and electronic properties of CrSBr. For samples spanning from few-layer to bulk thickness, we observe that the polarization of the A$_g^2$ Raman mode can be rotated by 90 degrees, shifting from alignment with the crystallographic a (intermediate magnetic) axis to the b (easy magnetic) axis, depending on the excitation energy. In contrast, the A$_g^1$ and A$_g^3$ modes consistently remain polarized along the b axis, regardless of the laser energy used. We access real and imaginary parts of the Raman tensor in our analysis, uncovering resonant electron-phonon coupling., Comment: Main text + Supplementary Information

Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping by alleviating task-specific engineering efforts and by providing users with interactive conversations. Despite the potential, LLMs face unique challenges in online shopping, such as domain-specific concepts, implicit knowledge, and heterogeneous user behaviors. Motivated by the potential and challenges, we propose Shopping MMLU, a diverse multi-task online shopping benchmark derived from real-world Amazon data. Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants. With Shopping MMLU, we benchmark over 20 existing LLMs and uncover valuable insights about practices and prospects of building versatile LLM-based shop assistants. Shopping MMLU can be publicly accessed at https://github.com/KL4805/ShoppingMMLU. In addition, with Shopping MMLU, we host a competition in KDD Cup 2024 with over 500 participating teams. The winning solutions and the associated workshop can be accessed at our website https://amazon-kddcup24.github.io/., Comment: NeurIPS 2024 Datasets and Benchmarks Track Accepted. Modified typos in Figure 9

In this article, we propose a new way to handle interactions between two scalar fields in the cosmological backdrop where one scalar field oscillates rapidly in the cosmological time scale while the other does not show any periodic behavior in the same time scale. We have interpreted the rapidly oscillating scalar field as the dark matter candidate while the other scalar field is supposed to be the canonical quintessence field or the non-canonical phantom field. A model of a generalized top-hat-like collapse is developed where the dark sector is composed of the aforementioned scalar fields. We show how the non-minimal coupling in the dark sector affects the gravitational collapse of a slightly overdense spherical patch of the universe. The results show that one can have both unclustered and clustered dark energy in such collapses, the result depends upon the magnitude of the non-minimal coupling strength., Comment: 17 pages, 8 figures

In recent years, learned image compression methods have demonstrated superior rate-distortion performance compared to traditional image compression methods. Recent methods utilize convolutional neural networks (CNN), variational autoencoders (VAE), invertible neural networks (INN), and transformers. Despite their significant contributions, a main drawback of these models is their poor performance in capturing local redundancy. Therefore, to leverage global features along with local redundancy, we propose a CNN-based solution integrated with a feature encoding module. The feature encoding module encodes important features before feeding them to the CNN and then utilizes cross-scale window-based attention, which further captures local redundancy. Cross-scale window-based attention is inspired by the attention mechanism in transformers and effectively enlarges the receptive field. Both the feature encoding module and the cross-scale window-based attention module in our architecture are flexible and can be incorporated into any other network architecture. We evaluate our method on the Kodak and CLIC datasets and demonstrate that our approach is effective and on par with state-of-the-art methods. Our code is publicly available at https://github.com/prmudgal/CWAM_IC_ISVC. ., Comment: Paper accepted and presented in ISVC'24. Copyrights stay with ISVC Our code is available at: https://github.com/prmudgal/CWAM_IC_ISVC

Unequal exposure to air pollution by race and socioeconomic status is well-documented in the U.S. However, there has been relatively little research on inequities in the collection of PM2.5 data, creating a critical gap in understanding which neighborhood exposures are represented in these datasets. In this study we use multilevel models with random intercepts by county and state, stratified by urbanicity to investigate the association between six key environmental justice (EJ) attributes (%AIAN, %Asian %Black, %Hispanic, %White, %Poverty) and proximity to the nearest regulatory monitor at the census tract-level across the contiguous 48 states. We also separately stratify our models by EPA region. Our results show that most EJ attributes exhibit weak or statistically insignificant associations with monitor proximity, except in rural areas where higher poverty levels are significantly linked to greater monitor distances ($\beta$ = 0.6, 95%CI = [0.49, 0.71]). While the US EPA's siting criteria may be effective in ensuring equitable monitor distribution in some contexts, the low density of monitors in rural areas may impact the accuracy of national-level air pollution monitoring., Comment: 53 pages, 7 figures

Differentiating between Intestinal Tuberculosis (ITB) and Crohn's Disease (CD) poses a significant clinical challenge due to their similar symptoms, clinical presentations, and imaging features. This study leverages Computed Tomography Enterography (CTE) scans, deep learning, and traditional computer vision to address this diagnostic dilemma. A consensus among radiologists from renowned institutions has recognized the visceral-to-subcutaneous fat (VF/SF) ratio as a surrogate biomarker for differentiating between ITB and CD. Previously done manually, we propose a novel 2D image computer vision algorithm for auto-segmenting subcutaneous fat to automate this ratio calculation, enhancing diagnostic efficiency and objectivity. As a benchmark, we compare the results to those obtained using the TotalSegmentator tool, a popular deep learning-based software for automatic segmentation of anatomical structures, and manual calculations by radiologists. We also demonstrated the performance on 3D CT volumes using a slicing method and provided a benchmark comparison of the algorithm with the TotalSegmentator tool. Additionally, we propose a scoring approach to integrate scores from radiological features, such as the fat ratio and pulmonary TB probability, into a single score for diagnosis. We trained a ResNet10 model on a dataset of CTE scans with samples from ITB, CD, and normal patients, achieving an accuracy of 75%. To enhance interpretability and gain clinical trust, we integrated the explainable AI technique Grad-CAM with ResNet10 to explain the model's predictions. Due to the small dataset size (100 total cases), the feature-based scoring system is considered more reliable and trusted by radiologists compared to the deep learning model for disease diagnosis., Comment: 9 pages, 3 figures, 3 algorithms

The baryonic physics shaping galaxy formation and evolution are complex, spanning a vast range of scales and making them challenging to model. Cosmological simulations rely on subgrid models that produce significantly different predictions. Understanding how models of stellar and active galactic nuclei (AGN) feedback affect baryon behavior across different halo masses and redshifts is essential. Using the SIMBA and IllustrisTNG suites from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, we explore the effect of parameters governing the subgrid implementation of stellar and AGN feedback. We find that while IllustrisTNG shows higher cumulative feedback energy across all halos, SIMBA demonstrates a greater spread of baryons, quantified by the closure radius and circumgalactic medium (CGM) gas fraction. This suggests that feedback in SIMBA couples more effectively to baryons and drives them more efficiently within the host halo. There is evidence that different feedback modes are highly interrelated in these subgrid models. Parameters controlling stellar feedback efficiency significantly impact AGN feedback, as seen in the suppression of black hole mass growth and delayed activation of AGN feedback to higher mass halos with increasing stellar feedback efficiency in both simulations. Additionally, AGN feedback efficiency parameters affect the CGM gas fraction at low halo masses in SIMBA, hinting at complex, non-linear interactions between AGN and SNe feedback modes. Overall, we demonstrate that stellar and AGN feedback are intimately interwoven, especially at low redshift, due to subgrid implementation, resulting in halo property effects that might initially seem counterintuitive., Comment: 29 pages, 8 figures, accepted to ApJ

In this paper, we study a Markov chain-based stochastic gradient algorithm in general Hilbert spaces, aiming to approximate the optimal solution of a quadratic loss function. We establish probabilistic upper bounds on its convergence. We further extend these results to an online regularized learning algorithm in reproducing kernel Hilbert spaces, where the samples are drawn along a Markov chain trajectory hence the samples are of the non i.i.d. type.