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India has made rapid progress towards the universalization of school education, hand in hand with a decline in child labour. Despite progress, child labour persists in the country, just as school attendance and completion rates reveal gaps in educational attainment. This report addresses a timely need for new research to help decision-makers and practitioners build an improved understanding of how to?strengthen the role of education in the elimination of child labour across India. Drawing on quantitative secondary data analysis and qualitative primary data analysis, it offers an accessible and rigorous overview of recent patterns in child labour in India and their linkages with children's school participation. The report also explores children's perspectives of their labour and schooling experiences, including in migration contexts as well as during the COVID-19 pandemic and resulting school closures. The report concludes with programmatic and research recommendations for further action.

Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify H$\alpha$-excess point sources in the Southern Sky, combining photometric data from 12 S-PLUS filters with machine learning to improve classification of H$\alpha$-related phenomena. Aims. Our goal is to classify H$\alpha$-excess point sources by distinguishing Galactic and extragalactic objects, particularly those with redshifted emission lines, and identifying variability phenomena like RR Lyrae stars. Methods. We selected H$\alpha$-excess candidates using the ($r - J0660$) vs. ($r - i$) colour-colour diagram from the S-PLUS main survey (MS) and Galactic Disk Survey (GDS). UMAP for dimensionality reduction and HDBSCAN clustering were used to separate source types. Infrared data was incorporated, and a Random Forest model was trained on clustering results to identify key colour features. New colour-colour diagrams from S-PLUS MS and infrared data offer a preliminary classification. Results. Combining multiwavelength data with machine learning significantly improved H$\alpha$-excess source classification. We identified 6956 sources with excess in the $J0660$ filter. Cross-matching with SIMBAD explored object types, including emission-line stars, young stellar objects, nebulae, stellar binaries, cataclysmic variables, QSOs, AGNs, and galaxies. Using S-PLUS colours and machine learning, we separated RR Lyrae stars from other sources. The separation of Galactic and extragalactic sources was clearer, but distinguishing cataclysmic variables from QSOs at certain redshifts remained challenging. Infrared data refined the classification, and the Random Forest model highlighted key colour features for future follow-up spectroscopy., Comment: 23 pages, 20 figures, 3 tables, accepted to be published in A&A

Increased electrification of energy end-usage can lead to network congestion during periods of high consumption. Flexibility of loads, such as aggregate smart charging of Electric Vehicles (EVs), is increasingly leveraged to manage grid congestion through various market-based mechanisms. Under such an arrangement, this paper quantifies the effect of lead time on the aggregate flexibility of EV fleets. Simulations using real-world charging transactions spanning over different categories of charging stations are performed for two flexibility products (redispatch and capacity limitations) when offered along with different business-as-usual (BAU) schedules. Results show that the variation of tradable flexibility depends mainly on the BAU schedules, the duration of the requested flexibility, and its start time. Further, the implication of these flexibility products on the average energy costs and emissions is also studied for different cases. Simulations show that bidirectional (V2G) charging outperforms unidirectional smart charging in all cases., Comment: 6 pages, 6 figures

Studying the interstellar medium in nearby starbursts is essential for understanding the physical mechanisms driving these objects, thought to resemble young star-forming galaxies. This study aims to analyze the physical properties of the first spatially-resolved multi-wavelength SED of an extragalactic source, spanning six decades in frequency (from near-UV to cm wavelengths) at an angular resolution of 3$^{\prime\prime}$ (51 pc at the distance of NGC,253). We focus on the central molecular zone (CMZ) of NGC,253, which contains giant molecular clouds (GMCs) responsible for half of the galaxy's star formation. We use archival data, spanning optical to centimeter wavelengths, to compute SEDs with the GalaPy and CIGALE codes for validation, and analyze stellar optical spectra with the \textsc{starlight} code. Our results show significant differences between central and external GMCs in terms of stellar and dust masses, star formation rates (SFRs), and bolometric luminosities. We identify the best SFR tracers as radio continuum bands at 33 GHz, radio recombination lines, and the total infrared luminosity (L$_{\rm IR}$; 8-1000$\mu$m), as well as 60$\mu$m IR emission. BPT and WHAN diagrams indicate shock signatures in NGC~253's nuclear region, associating it with AGN/star-forming hybrids, though the AGN fraction is negligible ($\leq$7.5%). Our findings show significant heterogeneity in the CMZ, with central GMCs exhibiting higher densities, SFRs, and dust masses compared to external GMCs. We confirm that certain centimeter photometric bands can reliably estimate global SFR at GMC scales., Comment: Submitted to A&A. 33 pages (20 main text), 20 figures (13 main text)

Parameter-Efficient Fine-Tuning (PEFT) is an efficient alternative to full scale fine-tuning, gaining popularity recently. With pre-trained model sizes growing exponentially, PEFT can be effectively utilized to fine-tune compact modules, Parameter-Efficient Modules (PEMs), trained to be domain experts over diverse domains. In this project, we explore composing such individually fine-tuned PEMs for distribution generalization over the composite domain. To compose PEMs, simple composing functions are used that operate purely on the weight space of the individually fine-tuned PEMs, without requiring any additional fine-tuning. The proposed method is applied to the task of representing the 16 Myers-Briggs Type Indicator (MBTI) composite personalities via 4 building block dichotomies, comprising of 8 individual traits which can be merged (composed) to yield a unique personality. We evaluate the individual trait PEMs and the composed personality PEMs via an online MBTI personality quiz questionnaire, validating the efficacy of PEFT to fine-tune PEMs and merging PEMs without further fine-tuning for domain composition., Comment: 6 pages, 3 figures, 2 tables

Let $\Gamma$ be an undirected and simple graph. A set $ S $ of vertices in $\Gamma$ is called a {cyclic vertex cutset} of $\Gamma$ if $\Gamma - S$ is disconnected and has at least two components containing cycles. If $\Gamma$ has a cyclic vertex cutset, then it is said to be {cyclically separable}. The {cyclic vertex connectivity} of $\Gamma$ is the minimum of cardinalities of the cyclic vertex cutsets of $\Gamma$. For any finite group $G$, the order supergraph $\mathcal{S}(G)$ is the simple and undirected graph whose vertices are elements of $G$, and two vertices are adjacent if the order of one divides that of the other. In this paper, we characterize the finite nilpotent groups and various non-nilpotent groups whose order super graphs are cyclically separable.

For the first time, we analyze the impact of dark matter (DM) on the curvature properties of quarkyonic neutron stars (NS) using a hybrid model based on quarkyonic-effective field theory within the relativistic mean-field (E-RMF) framework. This study examines the radial variation of curvature components, including the Ricci scalar ($\cal{R}$), Ricci tensor ($\cal{J}$), Kretschmann scalar ($\cal{K}$), and Weyl tensor ($\cal{W}$), under different DM admixtures. These components offer critical insights into the spacetime geometry and gravitational field strength within the star. The analysis spans canonical mass (1.4 $M_{\odot}$) and maximum mass configurations, varying key parameters such as the transition density ($n_t$) and QCD confinement scale ($\Lambda_{\rm cs}$), which influence matter transitions and quark confinement. Our results reveal that DM and quarkyonic matter (QM) significantly affect the star's curvature. Central curvature values, particularly $\cal{R}$, $\cal{J}$, and $\cal{K}$, increase with DM due to higher central densities but decrease with stronger QM effects. Stiffer EOSs yield smoother curvature profiles, while softer EOSs influenced by DM redistribute curvature more dynamically. DM softens the EOS, reducing central pressure and compactness, whereas higher $n_t$ values enhance compactness and central pressures. These findings show that dark matter plays a key role in shaping the curvature of quarkyonic neutron stars, offering new insights into compact objects with exotic matter., Comment: 5 figures

In this study, we investigate the impact of electromagnetic fields, highlighting the dominant effect of electric fields on the splitting of elliptic flow, \( \Delta v_2 \) with transverse momentum ($p_T$). The velocity and temperature profiles of quark-gluon plasma (QGP) is described through thermal model calculations. The electromagnetic field evolution is however determined from the solutions of Maxwell's equations, assuming constant electric and chiral conductivities. We find that the slower decay of the electric fields compared to the magnetic fields makes its impact on the splitting of the elliptic flow more dominant. We further estimated that the maximum value of \( |\langle eF \rangle| \), evaluated by averaging the field values over all spatial points on the hypersurface and across all field components, is approximately \( (0.010003 \pm 0.000195) \, m_{\pi}^2 \) for \( \sqrt{s_{\text{NN}}} = 7.7 \, \text{GeV} \), which could describe the splitting of elliptic flow data within the current experimental uncertainty reasonably well., Comment: 12 pages, 7 figures

Public distrust of self-driving cars is growing. Studies emphasize the need for interpreting the behavior of these vehicles to passengers to promote trust in autonomous systems. Interpreters can enhance trust by improving transparency and reducing perceived risk. However, current solutions often lack a human-centric approach to integrating multimodal interpretations. This paper introduces a novel Human-centered Multimodal Interpreter (HMI) system that leverages human preferences to provide visual, textual, and auditory feedback. The system combines a visual interface with Bird's Eye View (BEV), map, and text display, along with voice interaction using a fine-tuned large language model (LLM). Our user study, involving diverse participants, demonstrated that the HMI system significantly boosts passenger trust in AVs, increasing average trust levels by over 8%, with trust in ordinary environments rising by up to 30%. These results underscore the potential of the HMI system to improve the acceptance and reliability of autonomous vehicles by providing clear, real-time, and context-sensitive explanations of vehicle actions., Comment: This paper has been accepted for presentation at WACV Workshop HAVI 2025

Scaling up Large Language Model(LLM) training involves fitting a tremendous amount of training parameters across a limited number of workers. However, methods like ZeRO-3 that drastically reduce GPU memory pressure often incur heavy communication to ensure global synchronization and consistency. Established efforts such as ZeRO++ use secondary partitions to avoid inter-node communications, given that intra-node GPU-GPU transfer generally has more bandwidth and lower latency than inter-node connections. However, as more capable infrastructure like Frontier, equipped with AMD GPUs, emerged with impressive computing capability, there is a need for investigations on the hardware topology and to develop targeted strategies to improve training efficiency. In this work, we propose a collection of communication and optimization strategies for ZeRO++ to reduce communication costs and improve memory utilization. In this paper, we propose a 3-level hierarchical partitioning specifically for the current Top-1 supercomputing cluster, Frontier, which aims at leveraging various bandwidths across layers of communications (GCD-GCD, GPU-GPU, and inter-node) to reduce communication overhead. For a 20B GPT model, we observe a 1.71x increase in TFLOPS per GPU when compared with ZeRO++ up to 384 GCDs and a scaling efficiency of 0.94 for up to 384 GCDs. To the best of our knowledge, our work is also the first effort to efficiently optimize LLM workloads on Frontier AMD GPUs.

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

An adaptive multiexpert mixture of feedback causal models can approximate missing or phantom nodes in large-scale causal models. The result gives a scalable form of \emph{big knowledge}. The mixed model approximates a sampled dynamical system by approximating its main limit-cycle equilibria. Each expert first draws a fuzzy cognitive map (FCM) with at least one missing causal node or variable. FCMs are directed signed partial-causality cyclic graphs. They mix naturally through convex combination to produce a new causal feedback FCM. Supervised learning helps each expert FCM estimate its phantom node by comparing the FCM's partial equilibrium with the complete multi-node equilibrium. Such phantom-node estimation allows partial control over these causal hallucinations and helps approximate the future trajectory of the dynamical system. But the approximation can be computationally heavy. Mixing the tuned expert FCMs gives a practical way to find several phantom nodes and thereby better approximate the feedback system's true equilibrium behavior., Comment: 17 pages, 9 figures, The Ninth International Conference on Data Mining and Big Data 2024 (DMBD 2024), 13 December 2024

Multimodal Large Language Models (MLLMs) have driven major advances in video understanding, yet their vulnerability to adversarial tampering and manipulations remains underexplored. To address this gap, we introduce MVTamperBench, a benchmark that systematically evaluates MLLM robustness against five prevalent tampering techniques: rotation, masking, substitution, repetition, and dropping. Built from 3.4K original videos-expanded to over 17K tampered clips spanning 19 video tasks. MVTamperBench challenges models to detect manipulations in spatial and temporal coherence. We evaluate 45 recent MLLMs from 15+ model families, revealing substantial variability in resilience across tampering types and showing that larger parameter counts do not necessarily guarantee robustness. MVTamperBench sets a new benchmark for developing tamper-resilient MLLM in safety-critical applications, including detecting clickbait, preventing harmful content distribution, and enforcing policies on media platforms. We release all code and data to foster open research in trustworthy video understanding. Code: https://amitbcp.github.io/MVTamperBench/ Data: https://huggingface.co/datasets/Srikant86/MVTamperBench

Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems by integrating and analyzing diverse types of data, including text, images, audio, and video. Inspired by the human ability to assimilate information through many senses, this method enables applications such as text-to-video conversion, visual question answering, and image captioning. Recent developments in datasets that support multimodal language models (MLLMs) are highlighted in this overview. Large-scale multimodal datasets are essential because they allow for thorough testing and training of these models. With an emphasis on their contributions to the discipline, the study examines a variety of datasets, including those for training, domain-specific tasks, and real-world applications. It also emphasizes how crucial benchmark datasets are for assessing models' performance in a range of scenarios, scalability, and applicability. Since multimodal learning is always changing, overcoming these obstacles will help AI research and applications reach new heights.

Parametric oscillations of an interface separating two fluid phases create nonlinear surface waves, called Faraday waves, which organise into simple patterns, like squares and hexagons, as well as complex structures, such as double hexagonal and superlattice patterns. In this work, we study the influence of surfactant-induced Marangoni stresses on the formation and transition of Faraday wave patterns. We use a quantity $B$, that assesses the relative importance of Marangoni stresses as compared to the the surface wave dynamics. Our results show that the threshold acceleration required to destabilise a surfactant-covered interface through vibration increases with increasing $B$. For a surfactant-free interface, a square wave pattern is observed. As $B$ is incremented, we report transitions from squares to asymmetric squares, weakly wavy stripes, and ultimately to ridges and hills. These hills are a consequence of the bi-directional Marangoni stresses at the neck of the ridges. The mechanisms underlying the pattern transitions and the formation of exotic ridges and hills are discussed., Comment: 10 pages, 4 figures

Avatars are edging into mainstream videoconferencing, but evaluation of how avatar animation modalities contribute to work meeting outcomes has been limited. We report a within-group videoconferencing experiment in which 68 employees of a global technology company, in 16 groups, used the same stylized avatars in three modalities (static picture, audio-animation, and webcam-animation) to complete collaborative decision-making tasks. Quantitatively, for meeting outcomes, webcam-animated avatars improved meeting effectiveness over the picture modality and were also reported to be more comfortable and inclusive than both other modalities. In terms of avatar satisfaction, there was a similar preference for webcam animation as compared to both other modalities. Our qualitative analysis shows participants expressing a preference for the holistic motion of webcam animation, and that meaningful movement outweighs realism for meeting outcomes, as evidenced through a systematic overview of ten thematic factors. We discuss implications for research and commercial deployment and conclude that webcam-animated avatars are a plausible alternative to video in work meetings., Comment: to be published in PACM HCI

The second Hot QCD Matter 2024 conference at IIT Mandi focused on various ongoing topics in high-energy heavy-ion collisions, encompassing theoretical and experimental perspectives. This proceedings volume includes 19 contributions that collectively explore diverse aspects of the bulk properties of hot QCD matter. The topics encompass the dynamics of electromagnetic fields, transport properties, hadronic matter, spin hydrodynamics, and the role of conserved charges in high-energy environments. These studies significantly enhance our understanding of the complex dynamics of hot QCD matter, the quark-gluon plasma (QGP) formed in high-energy nuclear collisions. Advances in theoretical frameworks, including hydrodynamics, spin dynamics, and fluctuation studies, aim to improve theoretical calculations and refine our knowledge of the thermodynamic properties of strongly interacting matter. Experimental efforts, such as those conducted by the ALICE and STAR collaborations, play a vital role in validating these theoretical predictions and deepening our insight into the QCD phase diagram, collectivity in small systems, and the early-stage behavior of strongly interacting matter. Combining theoretical models with experimental observations offers a comprehensive understanding of the extreme conditions encountered in relativistic heavy-ion and proton-proton collisions., Comment: Compilation of the 19 contributions in Bulk Matter presented at the second 'Hot QCD Matter 2024 Conference' held from July 1-3, 2024, organized by IIT Mandi, Himachal Pradesh, India

We demonstrate a magnonic diode based on a bilayer structure of Yttrium Iron Garnet (YIG) and Cobalt Iron Boron (CoFeB). The bilayer exhibits pronounced non-reciprocal spin-wave propagation, enabled by dipolar coupling and the magnetic properties of the two layers. The YIG layer provides low damping and efficient spin-wave propagation, while the CoFeB layer introduces strong magnetic anisotropy, critical for achieving diode functionality. Experimental results, supported by numerical simulations, show unidirectional propagation of Magnetostatic Surface Spin Waves (MSSW), significantly suppressing backscattered waves. This behavior was confirmed through wavevector-resolved and micro-focused Brillouin Light Scattering measurements and is supported by numerical simulations. The proposed YIG/SiO$_2$/CoFeB bilayer magnonic diode demonstrates the feasibility of leveraging non-reciprocal spin-wave dynamics for functional magnonic devices, paving the way for energy-efficient, wave-based signal processing technologies.

A key component of building safe and reliable language models is enabling the models to appropriately refuse to follow certain instructions or answer certain questions. We may want models to output refusal messages for various categories of user queries, for example, ill-posed questions, instructions for committing illegal acts, or queries which require information past the model's knowledge horizon. Engineering models that refuse to answer such questions is complicated by the fact that an individual may want their model to exhibit varying levels of sensitivity for refusing queries of various categories, and different users may want different refusal rates. The current default approach involves training multiple models with varying proportions of refusal messages from each category to achieve the desired refusal rates, which is computationally expensive and may require training a new model to accommodate each user's desired preference over refusal rates. To address these challenges, we propose refusal tokens, one such token for each refusal category or a single refusal token, which are prepended to the model's responses during training. We then show how to increase or decrease the probability of generating the refusal token for each category during inference to steer the model's refusal behavior. Refusal tokens enable controlling a single model's refusal rates without the need of any further fine-tuning, but only by selectively intervening during generation., Comment: 19 pages

We systematically investigate $f-$mode oscillations ($\ell$ = 2) in quarkyonic neutron stars with dark matter, employing the Cowling approximation within the framework of linearized general relativity. The relativistic mean-field approach is used to compute various macroscopic properties of neutron stars. The analysis focuses on three key free parameters in the model: transition density, QCD confinement scale, and dark matter (DM) Fermi momentum, all of which significantly affect the properties of $f-$mode oscillations. The inclusion of dark matter in quarkyonic equations of state leads to notable variations in $f-$mode frequencies. Despite these changes, several universal relations among the oscillation properties are found to hold, demonstrating their robustness in the presence of dark matter., Comment: 11 pages, 8 figures

In this work, we explore the possibility of probing the mass ordering sensitivity as a function of supernova distance in the context of the ongoing neutrino experiment NO$\nu$A. We provide a detailed study of the active-active and active-sterile mixing frameworks, illustrating how supernova neutrinos can be used to realize the existence of sterile neutrinos. Interestingly, we infer that observation of the NC channel alone can differentiate between the presence and absence of sterile neutrinos. Our results indicate that the primary channel of NO$\nu$A can distinguish normal mass hierarchy from inverted mass hierarchy at $5 \sigma$ confidence level for a supernova explosion occurring at a distance of 5 kpc. Additionally, we examine the impact of systematic uncertainties on mass hierarchy sensitivity, showing that higher levels of systematics lead to a reduction in sensitivity. Similarly, the inclusion of energy smearing significantly diminishes hierarchy sensitivity., Comment: 25 pages, 9 figures, 5 tables

We present Measure Anything, a comprehensive vision-based framework for dimensional measurement of objects with circular cross-sections, leveraging the Segment Anything Model (SAM). Our approach estimates key geometric features -- including diameter, length, and volume -- for rod-like geometries with varying curvature and general objects with constant skeleton slope. The framework integrates segmentation, mask processing, skeleton construction, and 2D-3D transformation, packaged in a user-friendly interface. We validate our framework by estimating the diameters of Canola stems -- collected from agricultural fields in North Dakota -- which are thin and non-uniform, posing challenges for existing methods. Measuring its diameters is critical, as it is a phenotypic traits that correlates with the health and yield of Canola crops. This application also exemplifies the potential of Measure Anything, where integrating intelligent models -- such as keypoint detection -- extends its scalability to fully automate the measurement process for high-throughput applications. Furthermore, we showcase its versatility in robotic grasping, leveraging extracted geometric features to identify optimal grasp points.

We include the $\Delta$-isobars in the equation of state (EOS) of neutron star (NS) and study its effects with various parameter sets of the RMF model. We compare our results with the NS's constraints from the mass-radius measurement of PSR J0348+0432, PSR J1614-2230, PSR J0030+0451, PSR J0740+6620, PSR J0952-0607, and tidal deformability of GW170817. We calculate the mass-radius profile and tidal deformabilities of the NS using 21 parameter sets of the RMF model.Analyzing the result with various parameters, it is clear that only few parameter sets can satisfy simultaneously the constraints from NICER and GW170817. NLD parameter set satisfy all the constraints of NICER and GW170817. For its strong predictive power for the bulk properties of the neutron star, we take NLD parameter set as a representative for the detailed calculation of effect of $\Delta$-isobar on neutron star properties. We demonstrate that it is possible that $\Delta$-isobar can produce at 2-3 times the saturation density by adjusting the coupling constants $X_{\sigma\Delta}$, $X_{\rho\Delta}$ and $X_{\omega\Delta}$ in an appropriate range. Bulk properties of the NS like mass-radius profile and tidal deformability is strongly affected by the interaction strength of $\Delta$-isobar. Our calculation shows that it is also possible that by choosing $X_{\sigma\Delta}$, $X_{\rho\Delta}$ and $X_{\omega\Delta}$ to a suitable range the threshold density of $\Delta^-$-isobar become lower than $\Lambda^0$ hyperon. For a particular value of $\Delta$-coupling constants, the $R_{1.4}$ decrease by 1.7 km. This manuscipt give an argumentative justification for allowing $\Delta$-isobar degrees of freedom in the calculation of the NS properties., Comment: 9 pages, 10 figures

The flavor-changing neutral current decays of heavy bottom quark, alongside the flavor-changing charged current processes mediated by $b \to (c, u)$ in semileptonic $B$ decays are emerged as powerful tools for exploring physics beyond the Standard Model. In this work, we focus on the feasibility of interpreting the processes mediated by $b \to c \tau \nu$ transitions, in particular, the semileptonic $b$-baryonic decay modes $\Sigma_b \to \Sigma_c^{(*)} \tau^-\bar{\nu}_\tau$ and $\Xi_b \to \Xi_c \tau^-\bar{\nu}_\tau$ in the context of SMEFT approach. We perform a detailed analysis of the sensitivity of new physics operators on various observables such as branching ratio, forward-backward asymmetry parameter, lepton non-universal observable and the longitudinal polarization fraction of the $b$-baryonic decay channels., Comment: 20 pages, 13 figures

The development of robust Document AI models has been constrained by limited access to high-quality, labeled datasets, primarily due to data privacy concerns, scarcity, and the high cost of manual annotation. Traditional methods of synthetic data generation, such as text and image augmentation, have proven effective for increasing data diversity but often fail to capture the complex layout structures present in real world documents. This paper proposes a novel approach to synthetic document layout generation using Graph Neural Networks (GNNs). By representing document elements (e.g., text blocks, images, tables) as nodes in a graph and their spatial relationships as edges, GNNs are trained to generate realistic and diverse document layouts. This method leverages graph-based learning to ensure structural coherence and semantic consistency, addressing the limitations of traditional augmentation techniques. The proposed framework is evaluated on tasks such as document classification, named entity recognition (NER), and information extraction, demonstrating significant performance improvements. Furthermore, we address the computational challenges of GNN based synthetic data generation and propose solutions to mitigate domain adaptation issues between synthetic and real-world datasets. Our experimental results show that graph-augmented document layouts outperform existing augmentation techniques, offering a scalable and flexible solution for training Document AI models., Comment: Published in IJERT, Volume 13, Issue 10 (October 2024)

We study the differences in physical properties of quasar-host galaxies using an optically selected sample of radio loud (RL) and radio quiet (RQ) quasars (in the redshift range 0.15 < z < 1.9) which we have further cross-matched with the VLA-FIRST survey catalog. The sources in our sample have broad Hbeta and MgII emission lines (1000 km/s < FWHM < 15000 km/s) with a subsample of high broad line quasars (FWHM > 15000 km/s). We construct the broadband spectral energy distribution (SED) of our broad line quasars using multi-wavelength archival data and targeted observations with the AstroSat telescope. We use the state-of-the-art SED modeling code CIGALE v2022.0 to model the SEDs and determine the best-fit physical parameters of the quasar host galaxies namely their star-formation rate (SFR), main-sequence stellar mass, luminosity absorbed by dust, e-folding time and stellar population age. We find that the emission from the host galaxy of our sources is between 20%-35% of the total luminosity, as they are mostly dominated by the central quasars. Using the best-fit estimates, we reconstruct the optical spectra of our quasars which show remarkable agreement in reproducing the observed SDSS spectra of the same sources. We plot the main-sequence relation for our quasars and note that they are significantly away from the main sequence of star-forming galaxies. Further, the main sequence relation shows a bimodality for our RL quasars indicating populations segregated by Eddington ratios. We conclude that RL quasars in our sample with lower Eddington ratios tend to have substantially lower star-formation rates for similar stellar mass. Our analyses, thus, provide a completely independent route in studying the host galaxies of quasars and addressing the radio dichotomy problem from the host galaxy perspective., Comment: Accepted in Astronomy & Astrophysics journal

The cervix is the narrow end of the uterus that connects to the vagina in the female reproductive system. Abnormal cell growth in the squamous epithelial lining of the cervix leads to cervical cancer in females. A Pap smear is a diagnostic procedure used to detect cervical cancer by gently collecting cells from the surface of the cervix with a small brush and analyzing their changes under a microscope. For population-based cervical cancer screening, visual inspection with acetic acid is a cost-effective method with high sensitivity. However, Pap smears are also suitable for mass screening due to their higher specificity. The current Pap smear analysis method is manual, time-consuming, labor-intensive, and prone to human error. Therefore, an artificial intelligence (AI)-based approach for automatic cell classification is needed. In this study, we aimed to classify cells in Pap smear images into five categories: superficial-intermediate, parabasal, koilocytes, dyskeratotic, and metaplastic. Various machine learning (ML) algorithms, including Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor, as well as deep learning (DL) approaches like ResNet-50, were employed for this classification task. The ML models demonstrated high classification accuracy; however, ResNet-50 outperformed the others, achieving a classification accuracy of 93.06%. This study highlights the efficiency of DL models for cell-level classification and their potential to aid in the early diagnosis of cervical cancer from Pap smear images., Comment: 15 pages, 4 figures

To safeguard sensitive user data, web developers typically rely on implicit access-control policies, which they implement using access checks and query filters. This ad hoc approach is error-prone as these scattered checks and filters are easy to misplace or misspecify, and the lack of an explicit policy precludes external access-control enforcement. More critically, it is difficult for humans to discern what policy is embedded in application code and what data the application may access -- an issue that worsens as development teams evolve. This paper tackles policy extraction: the task of extracting the access-control policy embedded in an application by summarizing its data queries. An extracted policy, once vetted for errors, can stand alone as a specification for the application's data access, and can be enforced to ensure compliance as code changes over time. We introduce Ote, a policy extractor for Ruby-on-Rails web applications. Ote uses concolic execution to explore execution paths through the application, generating traces of SQL queries and conditions that trigger them. It then merges and simplifies these traces into a final policy that aligns with the observed behaviors. We applied Ote to three real-world applications and compared extracted policies to handwritten ones, revealing several errors in the latter.

Physics-Informed Neural Networks (PINNs) have emerged as an influential technology, merging the swift and automated capabilities of machine learning with the precision and dependability of simulations grounded in theoretical physics. PINNs are often employed to solve algebraic or differential equations to replace some or even all steps of multi-stage computational workflows, leading to their significant speed-up. However, wide adoption of PINNs is still hindered by reliability issues, particularly at extreme ends of the input parameter ranges. In this study, we demonstrate this in the context of a system of coupled non-linear differential reaction-diffusion and heat transfer equations related to Fischer-Tropsch synthesis, which are solved by a finite-difference method with a PINN used in evaluating their source terms. It is shown that the testing strategies traditionally used to assess the accuracy of neural networks as function approximators can overlook the peculiarities which ultimately cause instabilities of the finite-difference solver. We propose a domain knowledge-based modifications to the PINN architecture ensuring its correct asymptotic behavior. When combined with an improved numerical scheme employed as an initial guess generator, the proposed modifications are shown to recover the overall stability of the simulations, while preserving the speed-up brought by PINN as the workflow component. We discuss the possible applications of the proposed hybrid transport equation solver in context of chemical reactors simulations.

In this study, we explore two intriguing new physics scenarios: the theory of Large Extra Dimensions (LED) and the theory of neutrino decay. We analyze the impact of LED on neutrino oscillations in the contexts of P2SO, DUNE, and T2HK, with a particular emphasis on P2SO. In contrast, the effects of neutrino decay are examined exclusively in the context of P2SO. For the LED scenario, we find that combining data from P2SO, DUNE, and T2HK can yield tighter constraints than current bounds, but only if all oscillation parameters are measured with high precision. In the case of neutrino decay, P2SO can achieve slightly better bounds compared to ESSnuSB and MOMENT, although its bounds remain weaker than those provided by DUNE and T2HK. Regarding sensitivities to unresolved oscillation parameters, the existence of LED has a minimal impact on the determination of CP violation, mass ordering and octant. However, neutrino decay can significantly influence the sensitivities related to CP violation and octant in a non-trivial manner., Comment: 27 pages, 10 figures, 5 tables

Thought experiments are considered valuable tools in science, enabling the exploration of hypotheses and the examination of complex ideas in a conceptual, non-empirical framework. These thought experiments can be useful in design fiction for speculating future possibilities, examining existing and alternate scenarios in new ways or challenging current paradigms. In visualization, speculating future possibilities or exploring new ways of interpreting existing scenarios can provoke critical reflection and envision novel approaches. In this paper we present such thought experiments for visualization. We conceptualize and define a thought experiment to consist of a situation, a story, and a scenario. Situations are derived from different tools of thought experiments and visualization practice; a story is an AI-generated fiction based on the situation and the scenario is the grounding of the situation and story in visualization research. We present ten such thought experiments and demonstrate their utility in visualization by deriving critiques from them.

Query determinacy is decidable for project-select views and a project-select-join query with no self joins, as long as the selection predicates are in a first-order theory for which satisfiability is decidable.

Incorporating nanomaterials into hydrogels allows for the creation of versatile materials with properties that can be precisely tailored by manipulating their nanoscale structures, leading to a wide range of bulk properties. Investigating the structural and property characteristics of composite hydrogels is crucial in tailoring their performance for specific applications. This study focuses on investigating the correlation between the structural arrangement and properties of a composite hydrogel of thermoresponsive polymer, gelatin, and light-responsive antimicrobial porous gold nanorods, $PAuNR$. The rheo-mechanical properties of the composite hydrogels are correlated with their nanoscale structural characteristics, investigated using small-angle neutron scattering ($SANS$). Analysis of $SANS$ data reveals a decrease in the fractal dimension of $PAuNRs$ incorporated hydrogel matrix, as compared to pure gelatin. Incorporating $PAuNRs$ results in formation of softer composite hydrogel as evident from decrease in viscoelastic moduli, critical yield strain, denaturation temperature and swelling ratio. Our results demonstrates that the structural modulation at the nanoscale can be precisely controlled through adjusting $PAuNRs$ concentration and temperature providing an fabrication mechanism for hydrogels with desired elastic properties. The reduced elasticity of the composite hydrogel and light sensitive/antimicrobial property of the $PAuNRs$ makes this system suitable for specific biomedical applications, such as tissue engineering, device fabrication and stimuli based controlled drug delivery devices respectively., Comment: Under review: The Journal of Chemical Physics (JCP24-AR-04120)

We present the results of a nearly decade-long photometric reverberation mapping (PRM) survey of the H$\alpha$ emission line in nearby ($0.01\lesssim z \lesssim0.05$) Seyfert-Galaxies using small ($15\,\mathrm{cm}-40\,\mathrm{cm}$) telescopes. Broad-band filters were used to trace the continuum emission, while narrow-band filters tracked the H$\alpha$-line signal. We introduce a new PRM formalism to determine the time delay between continuum and line emission using combinations of auto- and cross-correlation functions. We obtain robust delays for 33/80 objects, allowing us to estimate the broad-line region (BLR) size. Additionally, we measure multi-epoch delays for 6 objects whose scatter per source is smaller than the scatter in the BLR size-luminosity relation. Our study enhances the existing H$\alpha$ size-luminosity relation by adding high-quality results for 31 objects, whose nuclear luminosities were estimated using the flux-variation gradient method, resulting in a scatter of 0.26dex within our sample. The scatter reduces to 0.17dex when the 6 lowest luminosity sources are discarded, which is comparable to that found for the H$\beta$ line. Single-epoch spectra enable us to estimate black hole masses using the H$\alpha$ line and derive mass accretion rates from the iron-blend feature adjacent to H$\beta$. A similar trend, as previously reported for the H$\beta$ line, is implied whereby highly accreting objects tend to lie below the size-luminosity relation of the general population. Our work demonstrates the effectiveness of small telescopes in conducting high-fidelity PRM campaigns of prominent emission lines in bright active galactic nuclei.

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

Multi-modal image fusion (MMIF) enhances the information content of the fused image by combining the unique as well as common features obtained from different modality sensor images, improving visualization, object detection, and many more tasks. In this work, we introduce an interpretable network for the MMIF task, named FNet, based on an l0-regularized multi-modal convolutional sparse coding (MCSC) model. Specifically, for solving the l0-regularized CSC problem, we develop an algorithm unrolling-based l0-regularized sparse coding (LZSC) block. Given different modality source images, FNet first separates the unique and common features from them using the LZSC block and then these features are combined to generate the final fused image. Additionally, we propose an l0-regularized MCSC model for the inverse fusion process. Based on this model, we introduce an interpretable inverse fusion network named IFNet, which is utilized during FNet's training. Extensive experiments show that FNet achieves high-quality fusion results across five different MMIF tasks. Furthermore, we show that FNet enhances downstream object detection in visible-thermal image pairs. We have also visualized the intermediate results of FNet, which demonstrates the good interpretability of our network.

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

The highly energetic particle medium formed in the ultrarelativistic heavy ion collision displays a notable difference in temperatures between its central and peripheral regions. This temperature gradient can generate an electric field within the medium, a phenomenon referred to as the Seebeck effect. We have estimated the Seebeck coefficient for a dense quark-gluon plasma medium by using the relativistic Boltzmann transport equation in the recently developed novel relaxation time approximation (RTA) model within the kinetic theory framework. This study explores the Seebeck coefficient of individual quark flavors as well as the entire partonic medium, with the emphasis on its dependence on the temperature and the chemical potential. Our observation indicates that, for given current quark masses, the magnitude of the Seebeck coefficient for each quark flavor as well as for the partonic medium decreases as the temperature rises and increases as the chemical potential increases. Furthermore, we have investigated the Seebeck effect by considering the partonic interactions described in perturbative thermal QCD within the quasiparticle model. In addition, we have presented a comparison between our findings and the results of the standard RTA model., Comment: 22 pages, 10 figures

Gigahertz Peaked Spectrum (GPS) and Compact Steep Spectrum (CSS) sources are compact radio galaxies (RGs), with jets extending up to 20 kpc and ages <10^3 years. They are considered to evolve to Fanaroff-Riley RGs, but the real scenario to explain the compact sources remains unsolved. The young compact jets make GPS/CSS ideal for studying feedback in the nuclear region of AGNs because the jets are just starting to leave this region. Numerical simulations and jet power estimates suggest that compact sources can drive outflows on scales several times larger than the radio source itself, but the lack of suitable data limits comparisons between theory and observation. We carried out an optical spectroscopic study of 82 CSS/GPS with SDSS-DR12 data to investigate the influence of compact jets in the gas. We found outflowing gas components in the [OIII]\lambda5007 emission lines in half of our sample. The kinetic energy of the outflowing gas in compact sources is comparable to that observed in extended RGs, indicating that the compact jets can drive powerful outflows similar to those in FR RGs. The observed anti-correlation between the kinetic power of the outflow and the radio luminosity suggests an interaction between the young jet and the interstellar medium (ISM). This finding provides significant observational support for previous simulations of jet-ISM interactions and supports the evolutionary scenario for RGs. However, the lack of sources with high kinetic efficiency indicates that some compact galaxies may be frustrated sources., Comment: Accepted for publication in ApJ, 49 pages, 37 figures. Some typos corrected

A crucial question in active patient care is determining if a treatment is having the desired effect, especially when changes are subtle over short periods. We propose using inter-patient data to train models that can learn to detect these fine-grained changes within a single patient. Specifically, can a model trained on multi-patient scans predict subtle changes in an individual patient's scans? Recent years have seen increasing use of deep learning (DL) in predicting diseases using biomedical imaging, such as predicting COVID-19 severity using lung ultrasound (LUS) data. While extensive literature exists on successful applications of DL systems when well-annotated large-scale datasets are available, it is quite difficult to collect a large corpus of personalized datasets for an individual. In this work, we investigate the ability of recent computer vision models to learn fine-grained differences while being trained on data showing larger differences. We evaluate on an in-house LUS dataset and a public ADNI brain MRI dataset. We find that models pre-trained on clips from multiple patients can better predict fine-grained differences in scans from a single patient by employing contrastive learning., Comment: Under review at ISBI 2025 conference

Large language models (LLMs) have revolutionized natural language processing, albeit at the cost of immense memory and computation requirements. Post-training quantization (PTQ) is becoming the de facto method to reduce the memory footprint and improve the inference throughput of LLMs. In this work, we aim to push the upper limit of LLM PTQ by optimizing the weight rounding parameters with the block reconstruction technique, a predominant method in previous vision models. We propose TesseraQ, a new state-of-the-art PTQ technique, to quantize the weights of LLMs to ultra-low bits. To effectively optimize the rounding in LLMs and stabilize the reconstruction process, we introduce progressive adaptive rounding. This approach iteratively transits the soft rounding variables to hard variables during the reconstruction process. Additionally, we optimize the dequantization scale parameters to fully leverage the block reconstruction technique. We demonstrate that TesseraQ can be seamlessly integrated with existing scaling or clipping-based PTQ algorithms such as AWQ and OmniQuant, significantly enhancing their performance and establishing a new state-of-the-art. For instance, when compared to AWQ, TesseraQ improves the wikitext2 perplexity from 14.65 to 6.82 and average downstream accuracy from 50.52 to 59.27 with 2-bit weight-only quantization of LLaMA-2-7B. Across a range of quantization schemes, including W2A16, W3A16, W3A3, and W4A4, TesseraQ consistently exhibits superior performance.

The self-attention mechanism traditionally relies on the softmax operator, necessitating positional embeddings like RoPE, or position biases to account for token order. But current methods using still face length generalisation challenges. We propose an alternative attention mechanism based on the stick-breaking process: For each token before the current, we determine a break point $\beta_{i,j}$, which represents the proportion of the remaining stick to allocate to the current token. We repeat the process until the stick is fully allocated, resulting in a sequence of attention weights. This process naturally incorporates recency bias, which has linguistic motivations for grammar parsing (Shen et. al., 2017). We study the implications of replacing the conventional softmax-based attention mechanism with stick-breaking attention. We then discuss implementation of numerically stable stick-breaking attention and adapt Flash Attention to accommodate this mechanism. When used as a drop-in replacement for current softmax+RoPE attention systems, we find that stick-breaking attention performs competitively with current methods on length generalisation and downstream tasks. Stick-breaking also performs well at length generalisation, allowing a model trained with $2^{11}$ context window to perform well at $2^{14}$ with perplexity improvements.

We present a novel way to constrain the ghost field with respect to other physical fields present in a given theory such that the theory becomes quantizable. This is achieved by imposing positivity of the total kinetic energy of the system and performing Lorentz transformations in the field space manifold to arrive at an effective Lagrangian containing only physical degrees of freedom. Since models containing ghost fields such as quintom models are relevant in the cosmological context, this method can help ensure that such theories don't violate unitarity and can be treated as realistic candidates without the need to completely eliminate ghost(s).

The rise of non-magnetic topological semimetals, which provide a promising platform for observing and controlling various spin-orbit effects, has led to significant advancements in the field of topological spintronics. RhSi exists in two distinct polymorphs: cubic and orthorhombic crystal structures. The noncentrosymmetric B20 cubic structure has been extensively studied for hosting unconventional multifold fermions. In contrast, the orthorhombic structure, which crystallizes in the Pnma space group (No. 62), remains less explored and belongs to the family of topological Dirac semimetals. In this work, we investigate the structural, magnetic, and electrical properties of RhSi textured-epitaxial films grown on Si(111) substrates, which crystallize in the orthorhombic structure. We investigate the efficiency of pure spin current transport across RhSi/permalloy interfaces and the subsequent spin-to-charge current conversion via inverse spin Hall effect measurements. The xperimentally determined spin Hall conductivity in orthorhombic RhSi reaches a maximum value of 126 ($\hbar$/e)($\Omega$.cm)$^{-1}$ at 10 K, which aligns reasonably well with first-principles calculations that attribute the spin Hall effect in RhSi to the spin Berry curvature mechanism. Additionally, we demonstrate the ability to achieve a sizable spin-mixing conductance (34.7 nm$^{-2}$) and an exceptionally high interfacial spin transparency of 88$%$ in this heterostructure, underlining its potential for spin-orbit torque switching applications. Overall, this study broadens the scope of topological spintronics, emphasizing the controlled interfacial spin-transport processes and subsequent spin-to-charge conversion in a previously unexplored topological Dirac semimetal RhSi/ferromagnet heterostructure.

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

Quantum states exhibiting single-particle entanglement (SPE) can encode and process quantum information more robustly than their multi-particle analogs. Understanding the vulnerability and resilience of SPE to disorder is therefore crucial. This letter investigates phase, coin, and position disorder via discrete-time quantum walks on odd and even cyclic graphs to study their effect on SPE. The reduction in SPE is insignificant for low levels of phase or coin disorder, showing the resilience of SPE to minor perturbations. However, SPE is seen to be more vulnerable to position disorder. We analytically prove that maximally entangled single-particle states (MESPS) at time step $t=1$ are impervious to phase disorder regardless of the choice of the initial state. Further, MESPS at timestep $t=1$ is also wholly immune to coin disorder for phase-symmetric initial states. Position disorder breaks odd-even parity and distorts the physical time cone of the quantum walker, unlike phase or coin disorder. SPE saturates towards a fixed value for position disorder, irrespective of the disorder strength at large timestep $t$. Furthermore, SPE can be enhanced with moderate to significant phase or coin disorder strengths at specific time steps. Interestingly, disorder can revive single-particle entanglement from absolute zero in some instances, too. These results are crucial in understanding single-particle entanglement evolution and dynamics in a lab setting., Comment: 17 pages, 14 figures

As generative AI systems, including large language models (LLMs) and diffusion models, advance rapidly, their growing adoption has led to new and complex security risks often overlooked in traditional AI risk assessment frameworks. This paper introduces a novel formal framework for categorizing and mitigating these emergent security risks by integrating adaptive, real-time monitoring, and dynamic risk mitigation strategies tailored to generative models' unique vulnerabilities. We identify previously under-explored risks, including latent space exploitation, multi-modal cross-attack vectors, and feedback-loop-induced model degradation. Our framework employs a layered approach, incorporating anomaly detection, continuous red-teaming, and real-time adversarial simulation to mitigate these risks. We focus on formal verification methods to ensure model robustness and scalability in the face of evolving threats. Though theoretical, this work sets the stage for future empirical validation by establishing a detailed methodology and metrics for evaluating the performance of risk mitigation strategies in generative AI systems. This framework addresses existing gaps in AI safety, offering a comprehensive road map for future research and implementation., Comment: This paper was accepted in NeurIPS 2024 workshop on Red Teaming GenAI: What can we learn with Adversaries?