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The type IIB matrix model is conjectured to describe superstring theory nonperturbatively in terms of ten $N \times N$ bosonic traceless Hermitian matrices $A_\mu$ ($\mu=0, \ldots , 9$), whose eigenvalues correspond to $(9+1)$-dimensional space-time. Quite often, this model has been investigated in its Euclidean version, which is well defined although the ${\rm SO}(9,1)$ Lorentz symmetry of the original model is replaced by the ${\rm SO}(10)$ rotational symmetry. Recently, a well-defined model respecting the Lorentz symmetry has been proposed by gauge-fixing the Lorentz symmetry nonperturbatively using the Faddeev-Popov procedure. Here we investigate the two models by Monte Carlo simulations, overcoming the severe sign problem by the Lefschetz thimble method, in the case of matrix size $N=2$ omitting fermionic contributions. We add a quadratic term $\gamma \, \mathrm{tr} (A_\mu A^\mu)$ in the action and calculate the expectation values of rotationally symmetric (or Lorentz symmetric) observables as a function of the coefficient $\gamma$. Our results exhibit striking differences between the two models around $\gamma=0$ and in the $\gamma>0$ region, associated with the appearance of different saddle points, clearly demonstrating their inequivalence against naive expectations from quantum field theory., Comment: 6 pages, 3 figures

Nonreciprocal optics enables asymmetric transmission of light when its sources and detectors are exchanged. A canonical example -- optical isolator -- enables light propagation in only one direction, similar to how electrical diodes enable unidirectional flow of electric current. Nonreciprocal optics today, unlike nonreciprocal electronics, remains bulky. Recently, nonlinear metasurfaces opened up a pathway to strong optical nonreciprocity at the nanoscale. However, demonstrations to date were based on optically slow nonlinearities involving thermal effects or phase transition materials. In this work, we demonstrate a nonreciprocal metasurface with an ultra-fast optical response based on indium tin oxide in its epsilon-near-zero regime. It operates in the spectral range of 1200-1300 nm with incident power densities of 40-70 GW/cm$^2$. Furthermore, the nonreciprocity of the metasurface extends to both amplitude and phase of the forward/backward transmission opening a pathway to nonreciprocal wavefront control at the nanoscale., Comment: 8 pages 3 figures

Approximating the ground state of many-body systems is a key computational bottleneck underlying important applications in physics and chemistry. It has long been viewed as a promising application for quantum computers. The most widely known quantum algorithm for ground state approximation, quantum phase estimation, is out of reach of current quantum processors due to its high circuit-depths. Quantum diagonalization algorithms based on subspaces represent alternatives to phase estimation, which are feasible for pre-fault-tolerant and early-fault-tolerant quantum computers. Here, we introduce a quantum diagonalization algorithm which combines two key ideas on quantum subspaces: a classical diagonalization based on quantum samples, and subspaces constructed with quantum Krylov states. We prove that our algorithm converges in polynomial time under the working assumptions of Krylov quantum diagonalization and sparseness of the ground state. We then show numerical investigations of lattice Hamiltonians, which indicate that our method can outperform existing Krylov quantum diagonalization in the presence of shot noise, making our approach well-suited for near-term quantum devices. Finally, we carry out the largest ground-state quantum simulation of the single-impurity Anderson model on a system with $41$ bath sites, using $85$ qubits and up to $6 \cdot 10^3$ two-qubit gates on a Heron quantum processor, showing excellent agreement with density matrix renormalization group calculations., Comment: 22 pages, 6 figures

Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This survey provides a comprehensive analysis of Quantum Machine Learning, detailing foundational concepts, algorithmic advancements, and their applications across domains such as healthcare, finance, and quantum chemistry. Key techniques, including Quantum Support Vector Machine, Quantum Neural Network, Quantum Decision Trees, and hybrid quantum-classical models, are explored with a focus on their theoretical foundations, computational benefits, and comparative performance against classical counterparts. While the potential for exponential speedups and enhanced efficiency is evident, the field faces significant challenges, including hardware constraints, noise, and limited qubit coherence in the current era of Noisy Intermediate-Scale Quantum devices. Emerging solutions, such as error mitigation techniques, hybrid frameworks, and advancements in quantum hardware, are discussed as critical enablers for scalable and fault-tolerant Quantum Machine Learning systems. By synthesizing state-of-the-art developments and identifying research gaps, this survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications in tackling computationally intensive problems.

Extensive research in pedestrian dynamics has primarily focused on crowded conditions and associated phenomena, such as lane formation, evacuation, etc. Several force-based models have been developed to predict the behavior in these situations. In contrast, there is a notable gap in terms of investigations of the moderate-to-low density situations. These scenarios are extremely commonplace across the world, including the highly populated nations like India. Additionally, the details of force-based models are expected to show significant effects at these densities, whereas the crowded, nearly packed, conditions may be expected to be governed largely by contact forces. In this study, we address this gap and comprehensively evaluate the performance of different force-based models in some common scenarios. Towards this, we perform controlled experiments in four situations: avoiding a stationary obstacle, position-swapping by walking toward each other, overtaking to reach a common goal, and navigating through a maze of obstacles. The performance evaluation consists of two stages and six evaluating parameters - successful trajectories, overlapping proportion, oscillation strength, path smoothness, speed deviation, and travel time. Firstly, models must meet an eligibility criterion of at least 80\% successful trajectories and secondly, the models are scored based on the cutoff values established from the experimental data. We evaluated five force-based models where the best one scored 57.14\%. Thus, our findings reveal significant shortcomings in the ability of these models to yield accurate predictions of pedestrian dynamics in these common situations.

In the existing literature, pedestrian dynamics models have successfully captured various complex scenarios such as lane formation, evacuation, bottlenecks, crowd intersections, etc. However, many models, particularly force-based models, struggle to replicate simple yet real-life scenarios due to the absence of human intelligence during the processes. In this study, we present a novel Variable Goal Approach (VGA), which incorporates human intelligence into pedestrian dynamics models, significantly enhancing their efficiency and realism. VGA introduces the concept of multiple intermediate goals, termed variable goals, which guide pedestrians toward their final destination while minimizing interactions and disruptions. These variable goals act as a guidance system, allowing smoother transitions and adaptive navigation. By strategically positioning variable goals, VGA introduces an element of stochasticity. This allows the model to simulate varied pedestrian paths under identical conditions, reflecting the diversity in human decision-making. In addition to its effectiveness in simple scenarios, VGA demonstrates strong performance in replicating high-density scenarios, such as lane formation, providing results that closely match real-world data.

Egocentric videos capture scenes from a wearer's viewpoint, resulting in dynamic backgrounds, frequent motion, and occlusions, posing challenges to accurate keystep recognition. We propose a flexible graph-learning framework for fine-grained keystep recognition that is able to effectively leverage long-term dependencies in egocentric videos, and leverage alignment between egocentric and exocentric videos during training for improved inference on egocentric videos. Our approach consists of constructing a graph where each video clip of the egocentric video corresponds to a node. During training, we consider each clip of each exocentric video (if available) as additional nodes. We examine several strategies to define connections across these nodes and pose keystep recognition as a node classification task on the constructed graphs. We perform extensive experiments on the Ego-Exo4D dataset and show that our proposed flexible graph-based framework notably outperforms existing methods by more than 12 points in accuracy. Furthermore, the constructed graphs are sparse and compute efficient. We also present a study examining on harnessing several multimodal features, including narrations, depth, and object class labels, on a heterogeneous graph and discuss their corresponding contribution to the keystep recognition performance., Comment: 9 pages, 6 figures

Pronoun translation is a longstanding challenge in neural machine translation (NMT), often requiring inter-sentential context to ensure linguistic accuracy. To address this, we introduce ProNMT, a novel framework designed to enhance pronoun and overall translation quality in context-aware machine translation systems. ProNMT leverages Quality Estimation (QE) models and a unique Pronoun Generation Likelihood-Based Feedback mechanism to iteratively fine-tune pre-trained NMT models without relying on extensive human annotations. The framework combines QE scores with pronoun-specific rewards to guide training, ensuring improved handling of linguistic nuances. Extensive experiments demonstrate significant gains in pronoun translation accuracy and general translation quality across multiple metrics. ProNMT offers an efficient, scalable, and context-aware approach to improving NMT systems, particularly in translating context-dependent elements like pronouns.

The Solar Ultraviolet Imaging Telescope (SUIT) is an instrument on the Aditya-L1 mission of the Indian Space Research Organization (ISRO) launched on September 02, 2023. SUIT continuously provides, near-simultaneous full-disk and region-of-interest images of the Sun, slicing through the photosphere and chromosphere and covering a field of view up to 1.5 solar radii. For this purpose, SUIT uses 11 filters tuned at different wavelengths in the 200{--}400~nm range, including the Mg~{\sc ii} h~and~k and Ca~{\sc ii}~H spectral lines. The observations made by SUIT help us understand the magnetic coupling of the lower and middle solar atmosphere. In addition, for the first time, it allows the measurements of spatially resolved solar broad-band radiation in the near and mid ultraviolet, which will help constrain the variability of the solar ultraviolet irradiance in a wavelength range that is central for the chemistry of the Earth's atmosphere. This paper discusses the details of the instrument and data products., Comment: 37 pages, Accepted for Publication in Solar Physics

The masked projection techniques are popular in the area of non-linear model reduction. Quantifying and minimizing the error in model reduction, particularly from masked projections, is important. The exact error expressions are often infeasible. This leads to the use of error-bound expressions in the literature. In this paper, we derive two generalized error bounds using cosine-sine decomposition for uniquely determined masked projection techniques. Generally, the masked projection technique is employed to efficiently approximate non-linear functions in the model reduction of dynamical systems. The discrete empirical interpolation method (DEIM) is also a masked projection technique; therefore, the proposed error bounds apply to DEIM projection errors. Furthermore, the proposed error bounds are shown tighter than those currently available in the literature.

Automatic speech recognition has recently seen a significant advancement with large foundational models such as Whisper. However, these models often struggle to perform well in low-resource languages, such as Indian languages. This paper explores two novel approaches to enhance Whisper's multilingual speech recognition performance in Indian languages. First, we propose prompt-tuning with language family information, which enhances Whisper's accuracy in linguistically similar languages. Second, we introduce a novel tokenizer that reduces the number of generated tokens, thereby accelerating Whisper's inference speed. Our extensive experiments demonstrate that the tokenizer significantly reduces inference time, while prompt-tuning enhances accuracy across various Whisper model sizes, including Small, Medium, and Large. Together, these techniques achieve a balance between optimal WER and inference speed., Comment: Accepted at ICASSP 2025, 5 pages, 1 figures, 5 tables

We perform the Becchi-Rouet-Stora-Tyutin BRST) quantization of a (0 + 1)-dimensional cosmological Friedmann-Robertson-Walker (FRW) model. This quantization leverages the classical infinitesimal and continuous re-parameterization symmetry transformations of the system. To derive the nilpotent re-parameterization invariant BRST-anti-BRST symmetry transformations for the scale factor and corresponding momentum variables present in the cosmological FRW model, we employ the modified Bonora-Tonin supervariable approach (MBTSA) to BRST formalism. Through this approach, we also establish the BRST-anti-BRST invariant Curci-Ferrari (CF)-type restriction for this cosmological re-parameterization invariant model. Further, we obtain the off-shell nilpotent quantum BRST-anti-BRST symmetry transformations for other variables within the model using the (anti-)chiral supervariable approach (ACSA) to BRST formalism. Within the framework of ACSA, the CF-type restriction is demonstrated through two key aspects: (i) the invariance of the coupled Lagrangians under symmetry transformations, and (ii) the absolute anti-commutativity of the conserved BRST-anti-BRST charges. Notably, applying the MBTSA to a physical cosmological system, specifically a one-dimensional one, constitutes a novel contribution to this work. Additionally, in the application of ACSA, we restrict our analysis to (anti-)chiral super expansions of supervariables, leading to the unique observation of the absolute anti-commutativity of the conserved BRST-anti-BRST charges. Moreover, we highlight that the CF-type restriction demonstrates a universal nature, remaining consistent across any re-parameterization invariant models in general D-dimensional spacetime., Comment: LaTeX file, 27 pages, no figures

Logarithmic and inverse logarithmic coefficients play a crucial role in the theory of univalent functions. In this study, we focus on the class of starlike functions \(\mathcal{S}^*_\rho\), defined as \[ \mathcal{S}^*_\rho = \left\{ f \in \mathcal{A} : \frac{z f'(z)}{f(z)} \prec \rho(z), \; z \in \mathbb{D} \right\}, \] where \(\rho(z) := 1 + \sinh^{-1}(z)\), which maps the unit disk \(\mathbb{D}\) onto a petal-shaped domain. This investigation aims to establish bounds for the second Hankel and Toeplitz determinants, with their entries determined by the logarithmic coefficients of \(f\) and its inverse \(f^{-1}\), for functions \(f \in \mathcal{S}^*_\rho\).

In many problem settings that require spatio-temporal forecasting, the values in the time-series not only exhibit spatio-temporal correlations but are also influenced by spatial diffusion across locations. One such example is forecasting the concentration of fine particulate matter (PM2.5) in the atmosphere which is influenced by many complex factors, the most important ones being diffusion due to meteorological factors as well as transport across vast distances over a period of time. We present a novel Spatio-Temporal Graph Neural Network architecture, that specifically captures these dependencies to forecast the PM2.5 concentration. Our model is based on an encoder-decoder architecture where the encoder and decoder parts leverage gated recurrent units (GRU) augmented with a graph neural network (TransformerConv) to account for spatial diffusion. Our model can also be seen as a generalization of various existing models for time-series or spatio-temporal forecasting. We demonstrate the model's effectiveness on two real-world PM2.5 datasets: (1) data collected by us using a recently deployed network of low-cost PM$_{2.5}$ sensors from 511 locations spanning the entirety of the Indian state of Bihar over a period of one year, and (2) another publicly available dataset that covers severely polluted regions from China for a period of 4 years. Our experimental results show our model's impressive ability to account for both spatial as well as temporal dependencies precisely., Comment: 9 pages, 4 figures, International Conference on Data Science and Management of Data (CODS-COMAD), IIT Jodhpur, 2024

The Solar Ultraviolet Imaging Telescope (SUIT) on board the Aditya-L1 mission is designed to observe the Sun across 200-400 nm wavelength. The telescope used 16 dichroic filters tuned at specific wavelengths in various combinations to achieve its science goals. For accurate measurements and interpretation, it is important to characterize these filters for spectral variations as a function of spatial location and tilt angle. Moreover, we also measured out-of-band and in-band transmission characteristics with respect to the inband transmissions. In this paper, we present the experimental setup, test methodology, and the analyzed results. Our findings reveal that the transmission properties of all filters meet the expected performance for spatial variation of transmission and the transmission band at a specific tilt angle. The out-of-band transmission for all filters is below 1% with respect to in-band, except for filters BB01 and NB01. These results confirm the capabilities of SUIT to effectively capture critical solar features in the anticipated layer of the solar atmosphere., Comment: 23 Pages, 10 Figures, 7 Tables

We examine lengthy radio light curves of the flat spectrum radio galaxy 3C 454.3 for possible quasi-periodic oscillations (QPOs). The data used in this work were collected at five radio frequencies, 4.8, 8.0, 14.5, 22.0, and 37.0 GHz between 1979--2013 as observed at the University of Michigan Radio Astronomical Observatory, Crimean Astrophysical Observatory, and Aalto University Mets{\"a}hovi Radio Observatory. We employ generalized Lomb-Scargle periodogram and weighted wavelet transform analyses to search for periodicities in these light curves. We confirm a QPO period of $\sim$ 2000 day to be at least 4$\sigma$ significant using both methods at all five radio frequencies between 1979 and 2007, after which a strong flare changed the character of the light curve. We also find a $\sim$~600 day period which is at least 4$\sigma$ significant, but only in the 22.0 and 37.0 GHz light curves. We briefly discuss physical mechanisms capable of producing such variations., Comment: 19 pages, 11 figures, Published in the Astrophysical Journal

The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehensive understanding of the plasma and thermodynamic properties of chromospheric and photospheric morphological structures requires a large sample statistical study, necessitating the development of automatic feature detection methods. To this end, we develop the feature detection algorithm SPACE-SUIT: Solar Phenomena Analysis and Classification using Enhanced vision techniques for SUIT, to detect and classify the solar chromospheric features to be observed from SUIT's Mg II k filter. Specifically, we target plage regions, sunspots, filaments, and off-limb structures. SPACE uses You Only Look Once(YOLO), a neural network-based model to identify regions of interest. We train and validate SPACE using mock-SUIT images developed from Interface Region Imaging Spectrometer(IRIS) full-disk mosaic images in Mg II k line, while we also perform detection on Level-1 SUIT data. SPACE achieves an approximate precision of 0.788, recall 0.863 and MAP of 0.874 on the validation mock SUIT FITS dataset. Given the manual labeling of our dataset, we perform "self-validation" by applying statistical measures and Tamura features on the ground truth and predicted bounding boxes. We find the distributions of entropy, contrast, dissimilarity, and energy to show differences in the features. These differences are qualitatively captured by the detected regions predicted by SPACE and validated with the observed SUIT images, even in the absence of labeled ground truth. This work not only develops a chromospheric feature extractor but also demonstrates the effectiveness of statistical metrics and Tamura features for distinguishing chromospheric features, offering independent validation for future detection schemes.

Shocked POst-starburst Galaxies (SPOGs) exhibit both emission lines suggestive of shock-heated gas and post-starburst-like stellar absorption, resulting in a unique subset for galaxy evolution studies. We have observed 77 galaxies that fulfilled the SPOGs criteria selection using the DeVeny Spectrograph on the Lowell Discovery Telescope. Our long-slit minor axis spectra detect H$\alpha$ and [O III] in some SPOGs out to 6 kpc above the galactic plane. We find extraplanar ionized gas in 31 targets of our sample overall. Using their internal and external kinematics, we argue that 22 galaxies host outflows with ionized gas masses ranging from $10^2 M_{\odot}$ to $10^5 M_{\odot}$. The rest are likely extended diffuse ionized gas. A positive correlation exists between AGN luminosity and the extraplanar gas extent, velocity dispersion, and mass$\unicode{x2013}\unicode{x2013}$suggesting that the AGN may indeed drive the outflows detected in AGN hosts. The low masses of the extraplanar gas suggest that these outflows are not depleting each galaxy's gas reserves. The outflows, therefore, are not likely a significant quenching mechanism in these SPOGs., Comment: 27 pages, 11 figures, ApJ accepted

We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges between 0.001 and 0.02 of the electron charge e. With an exposure of 60 live days and a 5.5 tonne fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

We investigate accretion onto an isolated black hole from uniform winds. If the winds are directed towards the black hole, then the accretion process can be well described by the classical Bondi-Hoyle Lyttleton or BHL accretion. If the wind is not directed towards the black hole and flows past it, then a smaller fraction of the flow can be attracted by the black hole, and this type of accretion cannot be described by the classical BHL, and we coin the second kind as the lateral BHL. We show that the classical BHL cannot form an accretion disk, while lateral BHL can form transient accretion disks. To describe the thermodynamics of the flow, we have used a variable adiabatic index equation of state which depends on the temperature of the flow as well as the composition of the gas. We show that the electron-proton gas forms an accretion disk, which disappears and forms a shock cone, only to form the disk again at a later time, while for flows with less protons, the accretion disk, once lost, does not reappear again. Only when the flow is pair-dominated does it form a persistent accretion disk. We also show that a shock cone is less luminous than the accretion disk., Comment: Accepted for publication in ApJ. 15 pages, 12 figures, one table

Federated Learning (FL) marks a transformative approach to distributed model training by combining locally optimized models from various clients into a unified global model. While FL preserves data privacy by eliminating centralized storage, it encounters significant challenges such as performance degradation, slower convergence, and reduced robustness of the global model due to the heterogeneity in client data distributions. Among the various forms of data heterogeneity, label skew emerges as a particularly formidable and prevalent issue, especially in domains such as image classification. To address these challenges, we begin with comprehensive experiments to pinpoint the underlying issues in the FL training process. Based on our findings, we then introduce an innovative dual-strategy approach designed to effectively resolve these issues. First, we introduce an adaptive loss function for client-side training, meticulously crafted to preserve previously acquired knowledge while maintaining an optimal equilibrium between local optimization and global model coherence. Secondly, we develop a dynamic aggregation strategy for aggregating client models at the server. This approach adapts to each client's unique learning patterns, effectively addressing the challenges of diverse data across the network. Our comprehensive evaluation, conducted across three diverse real-world datasets, coupled with theoretical convergence guarantees, demonstrates the superior efficacy of our method compared to several established state-of-the-art approaches.

This paper presents ClinicSum, a novel framework designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architecture: a retrieval-based filtering module that extracts Subjective, Objective, Assessment, and Plan (SOAP) information from conversation transcripts, and an inference module powered by fine-tuned Pre-trained Language Models (PLMs), which leverage the extracted SOAP data to generate abstracted clinical summaries. To fine-tune the PLM, we created a training dataset of consisting 1,473 conversations-summaries pair by consolidating two publicly available datasets, FigShare and MTS-Dialog, with ground truth summaries validated by Subject Matter Experts (SMEs). ClinicSum's effectiveness is evaluated through both automatic metrics (e.g., ROUGE, BERTScore) and expert human assessments. Results show that ClinicSum outperforms state-of-the-art PLMs, demonstrating superior precision, recall, and F-1 scores in automatic evaluations and receiving high preference from SMEs in human assessment, making it a robust solution for automated clinical summarization., Comment: accepted at the the 2024 IEEE International Conference on Big Data workshop Workshop on Big Data and AI for Healthcare

We use a quasilift type construction to obtain some bounds on the depth of the tensor product of certain modules over a local $\mathcal{TE}$ ring. We recover a result of Celikbas, Sadeghi, and Takahashi for local complete intersection rings. Some general conditions for lifting hulls and approximations are also studied.

In this paper, we study the multiplicity of nonnegative solutions for mixed local and non-local problem involving critical nonlinearity with sign changing weight. Using Nehari manifold method and fibering map analysis, we have shown existence of two solutions.

The newly introduced Visual State Space Model (VMamba), which employs \textit{State Space Mechanisms} (SSM) to interpret images as sequences of patches, has shown exceptional performance compared to Vision Transformers (ViT) across various computer vision tasks. However, recent studies have highlighted that deep models are susceptible to adversarial attacks. One common approach is to embed a trigger in the training data to retrain the model, causing it to misclassify data samples into a target class, a phenomenon known as a backdoor attack. In this paper, we first evaluate the robustness of the VMamba model against existing backdoor attacks. Based on this evaluation, we introduce a novel architectural backdoor attack, termed BadScan, designed to deceive the VMamba model. This attack utilizes bit plane slicing to create visually imperceptible backdoored images. During testing, if a trigger is detected by performing XOR operations between the $k^{th}$ bit planes of the modified triggered patches, the traditional 2D selective scan (SS2D) mechanism in the visual state space (VSS) block of VMamba is replaced with our newly designed BadScan block, which incorporates four newly developed scanning patterns. We demonstrate that the BadScan backdoor attack represents a significant threat to visual state space models and remains effective even after complete retraining from scratch. Experimental results on two widely used image classification datasets, CIFAR-10, and ImageNet-1K, reveal that while visual state space models generally exhibit robustness against current backdoor attacks, the BadScan attack is particularly effective, achieving a higher Triggered Accuracy Ratio (TAR) in misleading the VMamba model and its variants.

Thermal Hall transport has emerged as a valuable tool for probing the fractionalized excitations in chiral quantum spin liquids. Observing quantized thermal Hall response, expected at temperatures below the spectral gap, has been challenging and controversial. The finite temperature behavior, especially in the quantum critical regime above the spectral gap, can provide useful signatures of the underlying topological order. In this context, we study the spin-$1/2$ Heisenberg antiferromagnet on a kagome lattice that is believed to be a U$(1)$ Dirac spin liquid over a wide intermediate energy range. Scalar spin chirality perturbations turn this into a gapped abelian chiral spin liquid (CSL) with semionic topological order. Using a recently developed large-$N$ technique [Guo et al., Phys. Rev. B 101, 195126 (2020)], we obtain explicit expressions for the thermal Hall conductivity $\kappa_{xy}$ at finite temperatures taking into account both matter and gauge fluctuations. At low temperatures below the spectral gap, the quantized thermal Hall response agrees with that expected from conformal field theory and gravitational anomaly arguments. Our main finding is that in a large temperature window spanning the spectral gap and the Curie temperature scales where quantum critical fluctuations dominate, $\kappa_{xy}/T$ obeys a power-law with logarithmic corrections. Our analysis also provides a route to understanding the thermal Hall response at temperatures in the quantum critical regime of other spin liquid phases., Comment: 29 pages, 12 figures

Invoices and receipts submitted by employees are visually rich documents (VRDs) with textual, visual and layout information. To protect against the risk of fraud and abuse, it is crucial for organizations to efficiently extract desired information from submitted receipts. This helps in the assessment of key factors such as appropriateness of the expense claim, adherence to spending and transaction policies, the validity of the receipt, as well as downstream anomaly detection at various levels. These documents are heterogeneous, with multiple formats and languages, uploaded with different image qualities, and often do not contain ground truth labels for the efficient training of models. In this paper we propose Task Aware Instruction-based Labelling (TAIL), a method for synthetic label generation in VRD corpuses without labels, and fine-tune a multimodal Visually Rich Document Understanding Model (VRDU) on TAIL labels using response-based knowledge distillation without using the teacher model's weights or training dataset to conditionally generate annotations in the appropriate format. Using a benchmark external dataset where ground truth labels are available, we demonstrate conditions under which our approach performs at par with Claude 3 Sonnet through empirical studies. We then show that the resulting model performs at par or better on the internal expense documents of a large multinational organization than state-of-the-art LMM (large multimodal model) Claude 3 Sonnet while being 85% less costly and ~5X faster, and outperforms layout-aware baselines by more than 10% in Average Normalized Levenshtein Similarity (ANLS) scores due to its ability to reason and extract information from rare formats. Finally, we illustrate the usage of our approach in overpayment prevention., Comment: Accepted to WACV 2025

Calculation of the coherent nonlinear response of a system is essential to correctly interpret results from advanced techniques such as two-dimensional coherent spectroscopy (2DCS). Usually, even for the simplest systems, such calculations are either performed for low-intensity excitations where perturbative methods are valid and/or by assuming a simplified pulse envelope, such as a {\delta}-function in time. We present exact calculations using the phase-cycling method without making the aforementioned approximations. We introduce a generalized version of the phase-cycling method to isolate an arbitrary N-wave mixing signal. We then apply this method to model the saturation of the nonlinear signal from excitons in semiconductor quantum wells, which is consistent with 2DCS experiments. We also present simulation results that replicate previously-reported experiments with high-intensity excitation of semiconductor quantum dots. By accurately reproducing a variety of phenomena such as higher-order contributions, switching of coherent signal, and changes in photon-echo transients, we prove the efficacy of the phase-cycling method to calculate the coherent nonlinear signal for high-intensity excitation. This method would be particularly useful for systems with multiple, well-separated peaks and/or large inhomogeneity.

Infrared singularities in perturbative Quantum Chromodynamics (QCD) are captured by the Soft function, which can be calculated efficiently using Feynman diagrams known as webs. The starting point for calculating Soft function using webs is to compute the web mixing matrices using a well known replica trick algorithm. We present a package implemented in Mathematica to calculate these mixing matrices. Along with the package, we provide several state-of-the art computations., Comment: 19 pages, 5 figures, 2 tables

Misinformation about vaccination poses a significant public health threat by reducing vaccination rates and increasing disease burden. Understanding population heterogeneity can aid in recognizing and mitigating the effects of such misinformation, especially when vaccine effectiveness is low. Our research quantifies the impact of misinformation on vaccination uptake and explores its effects in heterogeneous versus homogeneous populations. We employed a dual approach combining ordinary differential equations (ODE) and complex network models to analyze how different epidemiological parameters influence disease spread and vaccination behaviour. Our results indicate that misinformation significantly lowers vaccination rates, particularly in homogeneous populations, while heterogeneous populations demonstrate greater resilience. Among network topologies, small-world networks achieve higher vaccination rates under varying vaccine efficacies, while scale-free networks experience reduced vaccine coverage with higher misinformation amplification. Notably, cumulative infection remains independent of disease transmission rate when the vaccine is partially effective. In small-world networks, cumulative infection shows high stochasticity across vaccination rates and misinformation parameters, while cumulative vaccination is highest with higher and lower misinformation. To control disease spread, public health efforts should address misinformation, particularly in homogeneous populations and scale-free networks. Building resilience by promoting reliable vaccine information can boost vaccination rates. Focusing campaigns on small-world networks can result in higher vaccine uptake.

We review and clarify several recent theoretical approaches to transverse orbital angular momentum (tOAM). We then apply our tOAM theory [Phys. Rev. Lett. 127, 193901 (2021)] to calculate the spatiotemporal torque applied to a light pulse by a pure amplitude perturbation. The theoretical results and associated simulations are in excellent agreement with experiments measuring the change in tOAM per photon, delta_Ly. The crucial factor in determining delta_Ly is the spatiotemporal distribution of tOAM density in the pulse. We show that even Gaussian pulses with zero total tOAM can have net tOAM induced by an amplitude perturbation stationary in the lab frame.

For a set $A$ of positive integers with $\gcd(A)=1$, let $\langle A \rangle$ denote the set of all finite linear combinations of elements of $A$ over the non-negative integers. The it is well known that only finitely many positive integers do not belong to $\langle A \rangle$. The Frobenius number and the genus associated with the set $A$ is the largest number and the cardinality of the set of integers non-representable by $A$. By a generalized Fibonacci sequence $\{V_n\}_{n \ge 1}$ we mean any sequence of positive integers satisfying the recurrence $V_n=V_{n-1}+V_{n-2}$ for $n \ge 3$. We study the problem of determining the Frobenius number and genus for sets $A=\{V_n,V_{n+d},V_{n+2d},\ldots\}$ for arbitrary $n$, where $d$ odd or $d=2$., Comment: 18 pages, 9 references

A graph $G$ is cordial if there exists a function $f$ from the vertices of $G$ to $\{0,1\}$ such that the number of vertices labelled $0$ and the number of vertices labelled $1$ differ by at most $1$, and if we assign to each edge $xy$ the label $|f(x)-f(y)|$, the number of edges labelled $0$ and the number of edges labelled $1$ also differ at most by $1$. We introduce two measures of how close a graph is to being cordial, and compute these measures for a variety of classes of graphs., Comment: 13 pages, 3 tables, 11 references

Using optical technology for current injection and electromagnetic emission simplifies the comparison between materials. Here, we inject current into monolayer graphene and bulk gallium arsenide (GaAs) using two-color quantum interference and detect the emitted electric field by electro-optic sampling. We find the amplitude of emitted terahertz (THz) radiation scales in the same way for both materials even though they differ in dimension, band gap, atomic composition, symmetry and lattice structure. In addition, we observe the same mapping of the current direction to the light characteristics. With no electrodes for injection or detection, our approach will allow electron scattering timescales to be directly measured. We envisage that it will enable exploration of new materials suitable for generating terahertz magnetic fields., Comment: 4 figures

A non-uniform implicit-explicit L1 mixed finite element method (IMEX-L1-MFEM) is investigated for a class of time-fractional partial integro-differential equations (PIDEs) with space-time dependent coefficients and non-self-adjoint elliptic part. The proposed fully discrete method combines an IMEX-L1 method on a graded mesh in the temporal variable with a mixed finite element method in spatial variables. The focus of the study is to analyze stability results and to establish optimal error estimates, up to a logarithmic factor, for both the solution and the flux in $L^2$-norm when the initial data $u_0\in H_0^1(\Omega)\cap H^2(\Omega)$. Additionally, an error estimate in $L^\infty$-norm is derived for 2D problems. All the derived estimates and bounds in this article remain valid as $\alpha\to 1^{-}$, where $\alpha$ is the order of the Caputo fractional derivative. Finally, the results of several numerical experiments conducted at the end of this paper are confirming our theoretical findings.

In ocean colour remote sensing, radiance at the sensor level can be modeled using molecular scattering and particle scattering based on existing mathematical models and gaseous absorption in the atmosphere. The modulation of light field by optical constituents within the seawater waters results in the spectral variation of water leaving radiances that can be related to phytoplankton pigment concentration, total suspended matter, vertical diffuse attenuation coefficients etc. Atmospheric correction works very well over open ocean using NIR channels of ocean colour sensors to retrieve geophysical products with reasonable accuracy while it fails over sediment laden and/or optically complex waters. To resolve this issue, a combination of SWIR channels or NIR-SWIR channels are configured in some ocean colour sensors such as Sentinel- OLCI, EOS- 06 OCM etc. Ocean Colour Monitor (OCM)-3 on board EOS -06 was launched on Nov 26, 2022. It has 13 bands in VNIR (400-1010 nm range) with ~1500 km swath for ocean colour monitoring. Arabian Sea near Gujarat coast is chosen as our study site to showcase the geophysical products derived using OCM-3 onboard EOS-06., Comment: https://bhoonidhi.nrsc.gov.in/bhoonidhi_resources/help/docs/EOS06-ATBD-OCM3_Ocean_Products-V1.pdf

In general, matrix or tensor-valued functions are approximated using the method developed for vector-valued functions by transforming the matrix-valued function into vector form. This paper proposes a tensor-based interpolation method to approximate a matrix-valued function without transforming it into the vector form. The tensor-based technique has the advantage of reducing offline and online computation without sacrificing much accuracy. The proposed method is an extension of the empirical interpolation method (EIM) for tensor bases. This paper presents a necessary theoretical framework to understand the method's functioning and limitations. Our mathematical analysis establishes a key characteristic of the proposed method: it consistently generates interpolation points in the form of a rectangular grid. This observation underscores a fundamental limitation that applies to any matrix-based approach relying on widely used techniques like EIM or DEIM method. It has also been theoretically shown that the proposed method is equivalent to the DEIM method applied in each direction due to the rectangular grid structure of the interpolation points. The application of the proposed method is shown in the model reduction of the semi-linear matrix differential equation. We have compared the approximation result of our proposed method with the DEIM method used to approximate a vector-valued function. The comparison result shows that the proposed method takes less time, albeit with a minor compromise with accuracy.

The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results., Comment: 8 pages, 9 figures, Accepted in ACM Wintech 2024

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

As machine learning models scale in size and complexity, their computational requirements become a significant barrier. Mixture-of-Experts (MoE) models alleviate this issue by selectively activating relevant experts. Despite this, MoE models are hindered by high communication overhead from all-to-all operations, low GPU utilization due to the synchronous communication constraint, and complications from heterogeneous GPU environments. This paper presents Aurora, which optimizes both model deployment and all-to-all communication scheduling to address these challenges in MoE inference. Aurora achieves minimal communication times by strategically ordering token transmissions in all-to-all communications. It improves GPU utilization by colocating experts from different models on the same device, avoiding the limitations of synchronous all-to-all communication. We analyze Aurora's optimization strategies theoretically across four common GPU cluster settings: exclusive vs. colocated models on GPUs, and homogeneous vs. heterogeneous GPUs. Aurora provides optimal solutions for three cases, and for the remaining NP-hard scenario, it offers a polynomial-time sub-optimal solution with only a 1.07x degradation from the optimal. Aurora is the first approach to minimize MoE inference time via optimal model deployment and communication scheduling across various scenarios. Evaluations demonstrate that Aurora significantly accelerates inference, achieving speedups of up to 2.38x in homogeneous clusters and 3.54x in heterogeneous environments. Moreover, Aurora enhances GPU utilization by up to 1.5x compared to existing methods.

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of $4.2\pm0.1$ tonne-years from 280 live days of LZ operation, of which $3.3\pm0.1$ tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from $^{214}$Pb $\beta$ decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of $^{124}$Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the data set to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses $\geq$9 GeV/$c^2$. The strongest SI exclusion set is $2.1\times10^{-48}$ cm$^{2}$ at the 90% confidence level at a mass of 36 GeV/$c^2$, and the best SI median sensitivity achieved is $5.0\times10^{-48}$ cm$^{2}$ for a mass of 40 GeV/$c^2$., Comment: 9 pages, 7 figures. See https://www.hepdata.net/record/155182 for a data release related to this paper

Matter falling onto black holes, {also called} accretion discs, emit intense high-energy radiation. Accretion discs during {hard to hard intermediate} spectral states also emit bipolar outflows. Radiation drag was supposed to impose the upper limit on the terminal speed. It was later shown that a radiation field around an advective accretion disc imposes no upper limit on speed, about a few hundred of Schwarzschild radius from the disc surface. We {study radiatively driven electron-proton and electron-positron jets, for gemeotrically thick and slim transonic discs} by using numerical simulation. We show that pair-dominated jets can reach ultra-relativistic speeds by radiation driving. We also discuss at what limits radiative acceleration may fail., Comment: 10 pages, 6 figures. Refereed conference procedings of International Symposium on Recent Developments i Relativistic Astrophysics (ISRA 2023), held in SRM University Sikkim

Let $a_1,\ldots,a_m$ be nonzero integers, $c \in \mathbb Z$ and $r \ge 2$. The Rado number for the equation \[ \sum_{i=1}^m a_ix_i = c \] in $r$ colours is the least positive integer $N$ such that any $r$-colouring of the integers in the interval $[1,N]$ admits a monochromatic solution to the given equation. We introduce the concept of $t$-distributability of sets of positive integers, and determine exact values whenever possible, and upper and lower bounds otherwise, for the Rado numbers when the set $\{a_1,\ldots,a_{m-1}\}$ is $2$-distributable or $3$-distributable, $a_m=-1$, and $r=2$. This generalizes previous works by several authors., Comment: One table of summary, 25 referenecs, 17 pages

Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI's ability to process vast and intricate data sets has led to more sophisticated forecasts. However, achieving consistently high accuracy in stock price forecasting remains elusive. In this paper, we leverage 30 years of historical data from national banks in India, sourced from the National Stock Exchange, to forecast stock prices. Our approach utilizes state-of-the-art deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM optimized through Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We further integrate sentiment analysis from tweets and reliable financial sources such as Business Standard and Reuters, acknowledging their crucial influence on stock price fluctuations., Comment: 7 pages, 9 figures, 1 table

In a seminal paper in 1959, Marcus and Ree proved that every $n\times n$ bistochastic matrix $A$ satisfies $\|A\|_{\operatorname{F}}^2\leq \max_{\sigma\in S_n}A_{i,\sigma(i)}$ where $S_n$ is the symmetric group on $\{1, \ldots, n\}$. Erd\H{o}s asked to characterize the bistochastic matrices for which the equality holds in the Marcus--Ree inequality. We refer to such matrices as Erd\H{o}s matrices. While this problem is trivial in dimension $n=2$, the case of dimension $n=3$ was only resolved recently in~\cite{bouthat2024question} in 2023. We prove that for every $n$, there are only finitely many $n\times n$ Erd\H{o}s matrices. We also give a characterization of Erd\H{o}s matrices that yields an algorithm to generate all Erd\H{o}s matrices in any given dimension. We also prove that Erd\H{o}s matrices can have only rational entries. This answers a question of~\cite{bouthat2024question}., Comment: 15 pages; Fixed a small mistake in the main result; This is the accepted version in Linear algebra and its applications

This research introduces a comprehensive Bahasa text-to-speech (TTS) dataset and a novel TTS model, EnGen-TTS, designed to enhance the quality and versatility of synthetic speech in the Bahasa language. The dataset, spanning \textasciitilde55.0 hours and 52K audio recordings, integrates diverse textual sources, ensuring linguistic richness. A meticulous recording setup captures the nuances of Bahasa phonetics, employing professional equipment to ensure high-fidelity audio samples. Statistical analysis reveals the dataset's scale and diversity, laying the foundation for model training and evaluation. The proposed EnGen-TTS model performs better than established baselines, achieving a Mean Opinion Score (MOS) of 4.45 $\pm$ 0.13. Additionally, our investigation on real-time factor and model size highlights EnGen-TTS as a compelling choice, with efficient performance. This research marks a significant advancement in Bahasa TTS technology, with implications for diverse language applications. Link to Generated Samples: \url{https://bahasa-harmony-comp.vercel.app/}

Accurately determining the origin of radio emissions is essential for numerous scientific experiments, particularly in radio astronomy. Conventional techniques, such as the use of antenna arrays encounter significant challenges, specially at very low frequencies, due to factors like the substantial size of the antennas and ionospheric interference. To address these challenges, we employ a space-based single-telescope that utilizes co-located antennas, complemented by goniopolarimetric techniques for precise source localization. This study explores a novel and elementary machine learning (ML) technique as a way to improve and estimate Direction of Arrival (DoA), leveraging a tri-axial antenna arrangement for radio source localization. Employing a simplistic emission and receiving antenna model, our study involves training an artificial neural network (ANN) using synthetic radio signals. These synthetic signals can originate from any location in the sky and cover an incoherent frequency range of 0.3 to 30 MHz, with a signal-to-noise ratio (SNR) between 0 and 60 dB. Then, a large data set was generated to train the ANN model catering to the possible signal configurations and variations. After training, the developed ANN model demonstrated exceptional performance, achieving loss levels in the training ($\sim0.02$), validation ($\sim0.23\%$), and testing ($\sim0.21\%$) phases. The machine learning-based approach remarkably, exhibits substantially quicker inference times ($\sim5$ ms) in contrast to analytically derived Direction of Arrival (DoA) methods, which typically range from 100 ms to a few seconds. This underscores its practicality for real-time applications in radio source localization, particularly in scenarios with limited number of sensors., Comment: Accepted for publication in JoAA, 11 pages, 5 figures

Coherent radio emission with properties similar to planetary auroral signals has been reported from GJ 1151, a quiescent, slow-rotating mid-M star, by the LOFAR Two-metre (120-170 MHz) Sky Survey (LoTSS). The observed {LOFAR} emission is fairly bright at 0.89 mJy with 64% circular polarization, and the emission characteristics are consistent with the interaction between an Earth-sized planet with an orbital period of 1-5 days and the magnetic field of the host star. However, no short-period planet has been detected around GJ 1151. To confirm the reported radio emission caused by the putative planet around GJ 1151 and to investigate the nature of this emission, we carried out uGMRT observations of GJ 1151 at 150, 218, and 400 MHz over 33 hours across ten epochs. No emission was detected at any frequency. While at 150 MHz and 218 MHz, non-detection could be due to the low sensitivity of our observations, at 400 MHz, the rms sensitivities achieved were sufficient to detect the emission observed with LOFAR at $\sim$ 20$\sigma$ level. Our findings suggest that the radio emission is highly time-variable, likely influenced by the star-planet system's phase and the host star's magnetic field. Additional observations below 170 MHz, at more frequent epochs (as the periodicity of the emission is unknown), especially during periods of high stellar magnetic field strength, are needed to confirm the emission., Comment: Accepted at AJ, 8 pages, 2 figures