arXiv subjects

Aman Agarwal

Publications and source records attributed to Aman Agarwal.

At least 19 recordsLinked to original sources

arXiv2026

VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation

While recent video diffusion models (VDMs) produce visually impressive results, they fundamentally struggle to maintain 3D structural consistency, often resulting in object deformation or spatial drift. We hypothesize that these failures arise because standard denoising objectives lack explicit incentives for geometric coherence. To address this, we introduce VideoGPA (Video Geometric Preference Alignment), a data-efficient self-supervised framework that leverages a geometry foundation model to automatically derive dense preference signals that guide VDMs via Direct Preference Optimization (DPO). This approach effectively steers the generative distribution toward inherent 3D consistency without requiring human annotations. VideoGPA significantly enhances temporal stability, geometric plausibility, and motion coherence using minimal preference pairs, consistently outperforming state-of-the-art baselines in extensive experiments.

cs.CV↗

arXiv2026

Inferring the role of binary neutron star mergers in r-process nucleosynthesis with multi-messenger observations using Cosmic Explorer and Einstein Telescope

Identifying the cosmic origin of rapid neutron-capture (r-process) elements remains an open problem. Binary neutron-star (BNS) mergers and rare classes of core-collapse supernovae (CCSNe) represent the main contenders as major r-process production sites. Although BNS mergers could exclusively account for r-process nucleosynthesis, results from chemical evolution studies taking into account their delays with respect to star formation, observed BNS rates by gravitational-wave (GW) detectors, as well as issues with retention in low-mass halos suggest otherwise. Here, we propose a method to measure the contribution of BNS mergers to cosmic r-process nucleosynthesis with the third-generation GW detectors Cosmic Explorer and Einstein Telescope. It exploits the redshift-dependent correlation between the total number of BNS GW events and the average r-process abundances at redshifts $z \lesssim 1$. We apply this correlation technique to mock GW and abundance data, accounting for expected observational uncertainties in two limiting scenarios: GW events with electromagnetic counterpart (multi-messenger 'bright-sirens') and without ('dark-sirens'). Using Fisher forecasts, we demonstrate that the fractional cumulative contribution of BNS mergers to the total cosmic r-process $F_{\rm{BNS,z0}}$ can be estimated to the $\lesssim 5-6\%$ precision level for both scenarios at $1σ$ for fiducial astrophysical scenarios with $F_{\rm{BNS,z0}} \gtrsim 0.1-1$. Furthermore, the method also yields estimates of the BNS delay-time distribution parameters comparable to other approaches. Although cosmic r-process abundances may be reconstructed from local observations at low metallicity, this method also provides a science case to identify signatures of neutron-capture elements beyond the local Universe.

astro-ph.HE↗

arXiv2025

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results

Face images play a crucial role in numerous applications; however, real-world conditions frequently introduce degradations such as noise, blur, and compression artifacts, affecting overall image quality and hindering subsequent tasks. To address this challenge, we organized the VQualA 2025 Challenge on Face Image Quality Assessment (FIQA) as part of the ICCV 2025 Workshops. Participants created lightweight and efficient models (limited to 0.5 GFLOPs and 5 million parameters) for the prediction of Mean Opinion Scores (MOS) on face images with arbitrary resolutions and realistic degradations. Submissions underwent comprehensive evaluations through correlation metrics on a dataset of in-the-wild face images. This challenge attracted 127 participants, with 1519 final submissions. This report summarizes the methodologies and findings for advancing the development of practical FIQA approaches.

cs.CV↗

arXiv2025

Blinded Mock Data Challenge for Gravitational-Wave Cosmology-I: Assessing the Robustness of Methods Using Binary Black Holes Mass Spectrum

Gravitational Wave (GW) sources are standard sirens that provide an independent way to map the cosmic expansion history by combining with an independent redshift measurement either from an electromagnetic counterpart for a bright siren or using different statistical techniques for dark sirens. In this analysis, we perform the first Blinded Mock Data Challenge (\texttt{Blinded-MDC}) to test the robustness in inferring the value of Hubble constant $H_0$ for a dark siren technique which depends on astrophysical mass distribution of Binary Black Holes (BBHs). We have considered different analysis setups for the \texttt{Blinded-MDC} to test both statistical and systematic uncertainties and demonstrate the capabilities in inferring $H_0$ with detector sensitivity as per the fourth observation run of LIGO-Virgo-KAGRA. We find that when the astrophysical population of BBHs matches with the underlying assumption of the model, a cosmological pipeline can recover the injected parameters using the observed mass distribution. However, when the mock mass distribution of the astrophysical population depends slightly on redshift and one is ignorant about it in analyzing the data, it can cause a systematic discrepancy in the inferred value of $H_0$ by about $1.5σ$, above the statistical fluctuations due to noise and a limited number of events. In the future, elaborate studies will be required to mitigate systematic uncertainties due to unknown astrophysical complexities. This MDC framework sets the road map for inspecting the precision and accuracy of standard siren cosmology and provides the first insight into the robustness of the population-dependent cosmology inference in a blinded analysis setup.

astro-ph.CO↗

arXiv2025

Fighting Fires from Space: Leveraging Vision Transformers for Enhanced Wildfire Detection and Characterization

Wildfires are increasing in intensity, frequency, and duration across large parts of the world as a result of anthropogenic climate change. Modern hazard detection and response systems that deal with wildfires are under-equipped for sustained wildfire seasons. Recent work has proved automated wildfire detection using Convolutional Neural Networks (CNNs) trained on satellite imagery are capable of high-accuracy results. However, CNNs are computationally expensive to train and only incorporate local image context. Recently, Vision Transformers (ViTs) have gained popularity for their efficient training and their ability to include both local and global contextual information. In this work, we show that ViT can outperform well-trained and specialized CNNs to detect wildfires on a previously published dataset of LandSat-8 imagery. One of our ViTs outperforms the baseline CNN comparison by 0.92%. However, we find our own implementation of CNN-based UNet to perform best in every category, showing their sustained utility in image tasks. Overall, ViTs are comparably capable in detecting wildfires as CNNs, though well-tuned CNNs are still the best technique for detecting wildfire with our UNet providing an IoU of 93.58%, better than the baseline UNet by some 4.58%.

cs.CV↗

arXiv2025

Ignition of weak interactions and r-process outflows in super-collapsar accretion disks

The collapse of rotating massive (~$10 M_\odot$) stars resulting in hyperaccreting black holes (BHs; "collapsars") is a leading model for the central engines of long-duration gamma-ray bursts (GRBs) and a promising source of rapid neutron capture ("r-process") elements. R-process nucleosynthesis in disk outflows requires the accretion flow to self-neutronize. This occurs because of Pauli-blocking at finite electron degeneracy, associated with a critical accretion rate $\dot M > \dot{M}_{\rm ign}$. We analytically examine the assumptions underlying this "ignition threshold" and its possible breakdown with increasing BH mass $M$. Employing three-dimensional general-relativistic magnetohydrodynamic simulations with weak interactions, we explore the physical conditions of collapsar accretion disks with $M$ ~ 80-3000 $M_\odot$ over more than a viscous timescale as they transition through the threshold. There is remarkable agreement between our simulations and the analytic result $\dot{M}_{\rm ign}\propto α^{5/3}M^{4/3}$ for $M$ ~ 3-3000 $M_\odot$. Simulations and analytic analyses consistently show that the largest BHs leading to r-process nucleosynthesis at $\dot{M}_{\rm ign}$ are $\approx 3000 M_\odot$, beyond which self-neutronization ceases, since the disk temperature $T\propto M^{-1/6}$ decreases below the neutron-proton mass difference (~MeV), suppressing the conversion of protons into neutrons. We show that stellar models of ~$250-10^5M_\odot$ can give rise to BHs of $M$ ~30-1000 $M_\odot$ accreting at $\dot M\gtrsim \dot{M}_{\rm ign}$, yielding ~$10-100 M_\odot$ of light and heavy r-process elements per event. These rare but prolific r-process sources in low-metallicity environments are associated with super-kilonovae and likely extremely energetic GRBs. Such signatures may be used to probe Population III stars.

astro-ph.HE↗

arXiv2023

Caustics in the sine-Gordon model from quenches in coupled 1D Bose gases

Caustics are singularities that occur naturally in optical, hydrodynamic and quantum waves, giving rise to high amplitude patterns that can be described using catastrophe theory. In this paper we study caustics in a statistical field theory setting in the form of the sine-Gordon model that describes a variety of physical systems including coupled 1D superfluids. Specifically, we use classical field simulations to study the dynamics of two ultracold 1D Bose gases (quasi-condensates) that are suddenly coupled to each other and find that the resulting non-equilibrium dynamics are dominated by caustics. Thermal noise is included by sampling the initial states from a Boltzmann distribution for phononic excitations. We find that caustics pile up over time in both the number and phase difference observables leading to a characteristic non-thermal `circus tent' shaped probability distribution at long times.

cond-mat.quant-gas↗

arXiv2022

Explain to me like I am five -- Sentence Simplification Using Transformers

Sentence simplification aims at making the structure of text easier to read and understand while maintaining its original meaning. This can be helpful for people with disabilities, new language learners, or those with low literacy. Simplification often involves removing difficult words and rephrasing the sentence. Previous research have focused on tackling this task by either using external linguistic databases for simplification or by using control tokens for desired fine-tuning of sentences. However, in this paper we purely use pre-trained transformer models. We experiment with a combination of GPT-2 and BERT models, achieving the best SARI score of 46.80 on the Mechanical Turk dataset, which is significantly better than previous state-of-the-art results. The code can be found at https://github.com/amanbasu/sentence-simplification.

cs.CL↗

arXiv2021

"Super-Kilonovae" from Massive Collapsars as Signatures of Black-Hole Birth in the Pair-instability Mass Gap

The core collapse of rapidly rotating massive ~10 Msun stars ("collapsars"), and resulting formation of hyper-accreting black holes, are a leading model for the central engines of long-duration gamma-ray bursts (GRB) and promising sources of r-process nucleosynthesis. Here, we explore the signatures of collapsars from progenitors with extremely massive helium cores >130 Msun above the pair-instability mass gap. While rapid collapse to a black hole likely precludes a prompt explosion in these systems, we demonstrate that disk outflows can generate a large quantity (up to >50 Msun) of ejecta, comprised of >5-10 Msun in r-process elements and ~0.1-1 Msun of $^{56}$Ni, expanding at velocities ~0.1c. Radioactive heating of the disk-wind ejecta powers an optical/infrared transient, with a characteristic luminosity $\sim 10^{42}$ erg s$^{-1}$ and spectral peak in the near-infrared (due to the high optical/UV opacities of lanthanide elements) similar to kilonovae from neutron star mergers, but with longer durations $\gtrsim$ 1 month. These "super-kilonovae" (superKNe) herald the birth of massive black holes >60 Msun, which, as a result of disk wind mass-loss, can populate the pair-instability mass gap 'from above' and could potentially create the binary components of GW190521. SuperKNe could be discovered via wide-field surveys such as those planned with the Roman Space Telescope or via late-time infrared follow-up observations of extremely energetic GRBs. Gravitational waves of frequency ~0.1-50 Hz from non-axisymmetric instabilities in self-gravitating massive collapsar disks are potentially detectable by proposed third-generation intermediate and high-frequency observatories at distances up to hundreds of Mpc; in contrast to the "chirp" from binary mergers, the collapsar gravitational-wave signal decreases in frequency as the disk radius grows ("sad trombone").

astro-ph.HE↗

arXiv2021

Multi-qubit quantum computing using discrete-time quantum walks on closed graphs

Universal quantum computation can be realised using both continuous-time and discrete-time quantum walks. We present a version based on single particle discrete-time quantum walk to realize multi-qubit computation tasks. The scalability of the scheme is demonstrated by using a set of walk operations on a closed lattice form to implement the universal set of quantum gates on multi-qubit system. We also present a set of experimentally realizable walk operations that can implement Grover's algorithm, quantum Fourier transformation and quantum phase estimation algorithms. An elementary implementation of error detection and correction is also presented. Analysis of space and time complexity of the scheme highlights the advantages of quantum walk based model for quantum computation on systems where implementation of quantum walk evolution operations is an inherent feature of the system.

quant-ph↗

arXiv2020

Using LSTM for the Prediction of Disruption in ADITYA Tokamak

Major disruptions in tokamak pose a serious threat to the vessel and its surrounding pieces of equipment. The ability of the systems to detect any behavior that can lead to disruption can help in alerting the system beforehand and prevent its harmful effects. Many machine learning techniques have already been in use at large tokamaks like JET and ASDEX, but are not suitable for ADITYA, which is comparatively small. Through this work, we discuss a new real-time approach to predict the time of disruption in ADITYA tokamak and validate the results on an experimental dataset. The system uses selected diagnostics from the tokamak and after some pre-processing steps, sends them to a time-sequence Long Short-Term Memory (LSTM) network. The model can make the predictions 12 ms in advance at less computation cost that is quick enough to be deployed in real-time applications.

cs.LG↗

arXiv2020

Functionally Effective Conscious AI Without Suffering

Insofar as consciousness has a functional role in facilitating learning and behavioral control, the builders of autonomous AI systems are likely to attempt to incorporate it into their designs. The extensive literature on the ethics of AI is concerned with ensuring that AI systems, and especially autonomous conscious ones, behave ethically. In contrast, our focus here is on the rarely discussed complementary aspect of engineering conscious AI: how to avoid condemning such systems, for whose creation we would be solely responsible, to unavoidable suffering brought about by phenomenal self-consciousness. We outline two complementary approaches to this problem, one motivated by a philosophical analysis of the phenomenal self, and the other by certain computational concepts in reinforcement learning.

cs.CY↗

arXiv2019

Characterizing behavioral trends in a community driven discussion platform

This article presents a systematic analysis of the patterns of behavior of individuals as well as groups observed in community-driven platforms for discussion like Reddit, where users usually exchange information and viewpoints on their topics of interest. We perform a statistical analysis of the behavior of posts and model the users' interactions around them. A platform like Reddit which has grown exponentially, starting from a very small community to one of the largest social networks, with its large user base and popularity harboring a variety of behavior of users in terms of their activity. Our work provides interesting insights about a huge number of inactive posts which fail to attract attention despite their authors exhibiting Cyborg-like behavior to attract attention. We also observe short-lived yet extremely active posts emulate a phenomenon like Mayfly Buzz. A method is presented, to study the activity around posts which are highly active, to determine the presence of Limelight hogging activity. We also present a systematic analysis to study the presence of controversies in posts. We analyzed data from two periods of one-year duration but separated by few years in time, to understand how social media has evolved through the years.

cs.SI↗

arXiv2019

A General Framework for Counterfactual Learning-to-Rank

Implicit feedback (e.g., click, dwell time) is an attractive source of training data for Learning-to-Rank, but its naive use leads to learning results that are distorted by presentation bias. For the special case of optimizing average rank for linear ranking functions, however, the recently developed SVM-PropRank method has shown that counterfactual inference techniques can be used to provably overcome the distorting effect of presentation bias. Going beyond this special case, this paper provides a general and theoretically rigorous framework for counterfactual learning-to-rank that enables unbiased training for a broad class of additive ranking metrics (e.g., Discounted Cumulative Gain (DCG)) as well as a broad class of models (e.g., deep networks). Specifically, we derive a relaxation for propensity-weighted rank-based metrics which is subdifferentiable and thus suitable for gradient-based optimization. We demonstrate the effectiveness of this general approach by instantiating two new learning methods. One is a new type of unbiased SVM that optimizes DCG -- called SVM PropDCG --, and we show how the resulting optimization problem can be solved via the Convex Concave Procedure (CCP). The other is Deep PropDCG, where the ranking function can be an arbitrary deep network. In addition to the theoretical support, we empirically find that SVM PropDCG significantly outperforms existing linear rankers in terms of DCG. Moreover, the ability to train non-linear ranking functions via Deep PropDCG further improves performance.

cs.IR↗

arXiv2019

Intervention Harvesting for Context-Dependent Examination-Bias Estimation

Accurate estimates of examination bias are crucial for unbiased learning-to-rank from implicit feedback in search engines and recommender systems, since they enable the use of Inverse Propensity Score (IPS) weighting techniques to address selection biases and missing data. Unfortunately, existing examination-bias estimators are limited to the Position-Based Model (PBM), where the examination bias may only depend on the rank of the document. To overcome this limitation, we propose a Contextual Position-Based Model (CPBM) where the examination bias may also depend on a context vector describing the query and the user. Furthermore, we propose an effective estimator for the CPBM based on intervention harvesting. A key feature of the estimator is that it does not require disruptive interventions but merely exploits natural variation resulting from the use of multiple historic ranking functions. Real-world experiments on the ArXiv search engine and semi-synthetic experiments on the Yahoo Learning-To-Rank dataset demonstrate the superior effectiveness and robustness of the new approach.

cs.IR↗

arXiv2018

Estimating Position Bias without Intrusive Interventions

Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counterfactual learning-to-rank (LTR) approaches \cite{Joachims/etal/17a} can provably overcome presentation bias when observation propensities are known, it remains to show how to effectively estimate these propensities. In this paper, we propose the first method for producing consistent propensity estimates without manual relevance judgments, disruptive interventions, or restrictive relevance modeling assumptions. First, we show how to harvest a specific type of intervention data from historic feedback logs of multiple different ranking functions, and show that this data is sufficient for consistent propensity estimation in the position-based model. Second, we propose a new extremum estimator that makes effective use of this data. In an empirical evaluation, we find that the new estimator provides superior propensity estimates in two real-world systems -- Arxiv Full-text Search and Google Drive Search. Beyond these two points, we find that the method is robust to a wide range of settings in simulation studies.

cs.IR↗

arXiv2018

Offline Comparison of Ranking Functions using Randomized Data

Ranking functions return ranked lists of items, and users often interact with these items. How to evaluate ranking functions using historical interaction logs, also known as off-policy evaluation, is an important but challenging problem. The commonly used Inverse Propensity Scores (IPS) approaches work better for the single item case, but suffer from extremely low data efficiency for the ranked list case. In this paper, we study how to improve the data efficiency of IPS approaches in the offline comparison setting. We propose two approaches Trunc-match and Rand-interleaving for offline comparison using uniformly randomized data. We show that these methods can improve the data efficiency and also the comparison sensitivity based on one of the largest email search engines.

cs.IR↗

arXiv2018

Analyzing behavioral trends in community driven discussion platforms like Reddit

The aim of this paper is to present methods to systematically analyze individual and group behavioral patterns observed in community driven discussion platforms like Reddit where users exchange information and views on various topics of current interest. We conduct this study by analyzing the statistical behavior of posts and modeling user interactions around them. We have chosen Reddit as an example, since it has grown exponentially from a small community to one of the biggest social network platforms in the recent times. Due to its large user base and popularity, a variety of behavior is present among users in terms of their activity. Our study provides interesting insights about a large number of inactive posts which fail to gather attention despite their authors exhibiting Cyborg-like behavior to draw attention. We also present interesting insights about short-lived but extremely active posts emulating a phenomenon like Mayfly Buzz. Further, we present methods to find the nature of activity around highly active posts to determine the presence of Limelight hogging activity, if any. We analyzed over $2$ million posts and more than $7$ million user responses to them during entire 2008 and over $63$ million posts and over $608$ million user responses to them from August 2014 to July 2015 amounting to two one-year periods, in order to understand how social media space has evolved over the years.

cs.SI↗