arXiv subjects

Amit Kumar

Publications and source records attributed to Amit Kumar.

At least 109 records · Page 6Linked to original sources

arXiv2024

EnterpriseEM: Fine-tuned Embeddings for Enterprise Semantic Search

Enterprises grapple with the significant challenge of managing proprietary unstructured data, hindering efficient information retrieval. This has led to the emergence of AI-driven information retrieval solutions, designed to adeptly extract relevant insights to address employee inquiries. These solutions often leverage pre-trained embedding models and generative models as foundational components. While pre-trained embeddings may exhibit proximity or disparity based on their original training objectives, they might not fully align with the unique characteristics of enterprise-specific data, leading to suboptimal alignment with the retrieval goals of enterprise environments. In this paper, we propose a comprehensive methodology for contextualizing pre-trained embedding models to enterprise environments, covering the entire process from data preparation to model fine-tuning and evaluation. By adapting the embeddings to better suit the retrieval tasks prevalent in enterprises, we aim to enhance the performance of information retrieval solutions. We discuss the process of fine-tuning, its effect on retrieval accuracy, and the potential benefits for enterprise information management. Our findings demonstrate the efficacy of fine-tuned embedding models in improving the precision and relevance of search results in enterprise settings.

cs.IR↗

arXiv2024

Shock propagation following an intense explosion in an inhomogeneous gas: core scaling and hydrodynamics

We study the shock propagation in a spatially inhomogeneous gas following an intense explosion. We generalize the exact solution of the Euler equation for the spatio-temporal variation of density, velocity, and temperature to arbitrary dimensions. From the asymptotic behavior of the solution near the shock center, we argue that only for a critical dimension dependent initial density distribution will the Euler equation provide a correct description of the problem. For general initial density distributions, we use event-driven molecular dynamics simulations in one dimension to demonstrate that the Euler equation fails to capture the behavior near the shock center. However, the Navier-Stokes equation successfully resolves this issue. The crossover length scale below which the dissipation terms are relevant and the core scaling for the data near the shock center are derived and confirmed in EDMD simulations.

cond-mat.stat-mech↗

arXiv2024

TalkinNeRF: Animatable Neural Fields for Full-Body Talking Humans

We introduce a novel framework that learns a dynamic neural radiance field (NeRF) for full-body talking humans from monocular videos. Prior work represents only the body pose or the face. However, humans communicate with their full body, combining body pose, hand gestures, as well as facial expressions. In this work, we propose TalkinNeRF, a unified NeRF-based network that represents the holistic 4D human motion. Given a monocular video of a subject, we learn corresponding modules for the body, face, and hands, that are combined together to generate the final result. To capture complex finger articulation, we learn an additional deformation field for the hands. Our multi-identity representation enables simultaneous training for multiple subjects, as well as robust animation under completely unseen poses. It can also generalize to novel identities, given only a short video as input. We demonstrate state-of-the-art performance for animating full-body talking humans, with fine-grained hand articulation and facial expressions.

cs.CV↗

arXiv2024

Multiplicative Lie algebra structure on nilpotent group of class $2$

This paper explores the properties of multiplicative Lie algebra structures on a nilpotent group of class $2$. We also present a method for determining a multiplicative Lie algebra structure on a group that serves as an extension of one Lie ring by another Lie ring such as a metacyclic group and a nilpotent group of class $2$.

math.GR↗

arXiv2024

Environmental dependence on galaxy-halo connections for satellites using HSC weak lensing

We present the luminosity-halo mass relations of satellite (sLHMRs) galaxies in the SDSS redMaPPer cluster catalogue and the effects of the dense cluster environment on subhalo mass evolution. We use data from the Subaru Hyper Suprime-Cam survey Year-3 catalogue of galaxy shapes to measure the weak lensing signal around these satellites. This signal serves as a probe of the matter distribution around the satellites, thereby providing the masses of their associated subhalos. We bin our satellites based on physical observable quantities such as their luminosity or the host cluster's richness, combined with their cluster-centric radial separations. Our results indicate that although more luminous satellites tend to reside in more massive halos, the sLHMRs depend on the distance of the satellite from the cluster centre. Subhalos near the cluster centre (within $<0.3 h^{-1}Mpc$) are stripped of mass. Consequently, the ratio of subhalo mass to luminosity decreases near the cluster centre. For low luminosity galaxies ($L < 10^{10} h^{-2}L_{\odot}$), the lack of evidence of increasing subhalo masses with luminosity shows the impact of tidal stripping. We also present stellar-to-subhalo mass relations (sSHMRs) for our satellite sample evolving at different cluster-centric separations. Inferred sSHMRs in the outer radial bin appear to match that observed for the field galaxies. We show that the sSHMRs from the mock-redMaPPer run on galaxy catalogues generated by the empirical UniverseMachine galaxy formation model are in good agreement with our observational results. Satellites, when binned based on the host cluster's richness, show very little dependence of the subhalo mass on the richness.

astro-ph.CO↗

arXiv2024

Centralized Selection with Preferences in the Presence of Biases

This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the institutions, and the goal is to select candidates for each institution in a way that maximizes utility while also considering the candidates' preferences. The paper focuses on the setting in which candidates are divided into multiple groups and the observed utilities of candidates in some groups are biased--systematically lower than their true utilities. The first result is that, in these biased settings, prior algorithms can lead to selections with sub-optimal true utility and significant discrepancies in the fraction of candidates from each group that get their preferred choices. Subsequently, an algorithm is presented along with proof that it produces selections that achieve near-optimal group fairness with respect to preferences while also nearly maximizing the true utility under distributional assumptions. Further, extensive empirical validation of these results in real-world and synthetic settings, in which the distributional assumptions may not hold, are presented.

cs.DS↗

arXiv2024

Dogs on forest trails; Understanding ecology of Striped Hyena and wild Canids in the presence of free-ranging dogs in Udanti-Sitanadi Tiger Reserve, Central India using Joint Distribution and Deep Neural Networks

This study uses Joint Species Distribution Models (JSDMs) and Deep Neural Networks (DNNs) to explore how wild carnivores and free-ranging dogs interact in the Udanti-Sitanadi Tiger Reserve (USTR) in Central India. The research focuses on key species like the Striped Hyena, Grey Wolf, Golden Jackal, and Indian Fox, revealing significant overlaps in habitat with free-ranging dogs, especially in densely populated areas like the Sitanadi region of the tiger reserve. These overlaps pose serious risks to wildlife through competition for resources, predation, and the spread of diseases. The study shows that the Striped Hyena prefers gentle slopes and forested areas, while the Grey Wolf tends to avoid cropland and thrives in regions with higher rainfall that supports a stable prey base. The Golden Jackal, more adaptable than the others, favors west-facing slopes and stable temperatures, whereas the Indian Fox is mainly found in the less disturbed, mountainous Kuladighat region. Additionally, the study highlights the potential impacts of climate change, predicting that the Grey Wolf could face habitat extinction under more severe scenarios. These findings underscore the urgent need for conservation strategies tailored to address both dog wild carnivore interactions and the growing challenges posed by climate change, focusing on protecting the critical habitats of vulnerable species like the Striped Hyena and Grey Wolf.

q-bio.PE↗

arXiv2024

StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction

The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways to improve these predictions. While recent financial large language models (FinLLMs) have shown considerable progress in financial NLP tasks compared to smaller pre-trained language models (PLMs), challenges persist in stock price forecasting. Firstly, effectively integrating the modalities of time series data and natural language to fully leverage these capabilities remains complex. Secondly, FinLLMs focus more on analysis and interpretability, which can overlook the essential features of time series data. Moreover, due to the abundance of false and redundant information in financial markets, models often produce less accurate predictions when faced with such input data. In this paper, we introduce StockTime, a novel LLM-based architecture designed specifically for stock price data. Unlike recent FinLLMs, StockTime is specifically designed for stock price time series data. It leverages the natural ability of LLMs to predict the next token by treating stock prices as consecutive tokens, extracting textual information such as stock correlations, statistical trends and timestamps directly from these stock prices. StockTime then integrates both textual and time series data into the embedding space. By fusing this multimodal data, StockTime effectively predicts stock prices across arbitrary look-back periods. Our experiments demonstrate that StockTime outperforms recent LLMs, as it gives more accurate predictions while reducing memory usage and runtime costs.

q-fin.ST↗

arXiv2024

Deep Learning-based Classification of Dementia using Image Representation of Subcortical Signals

Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and Frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI). Previous studies have utilized various EEG features, such as subband power and connectivity patterns to differentiate these conditions. However, artifacts in EEG signals can obscure crucial information, necessitating advanced signal processing techniques. This study aims to develop a deep learning-based classification system for dementia by analyzing scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. The study utilizes scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to check for the efficacy of the proposed method: the online BrainLat dataset (comprising AD, FTD, and healthy controls (HC)) and the in-house IITD-AIIA dataset (including subjects with AD, MCI, and HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes on both datasets. The best results were achieved by using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yields accuracies of 94.17$\%$ and 77.72$\%$ on the BrainLat and IITD-AIIA datasets, respectively. This highlights the potential of this approach for early and accurate differentiation of neurodegenerative disorders.

eess.SP↗

arXiv2024

GERry: A Code to Optimise the Hunt for the Electromagnetic Counter-parts to Gravitational Wave Events

The search for the electromagnetic counterparts to gravitational wave (GW) events has been rapidly gathering pace in recent years thanks to the increasing number and capabilities of both gravitational wave detectors and wide field survey telescopes. Difficulties remain, however, in detecting these counterparts due to their inherent scarcity, faintness and rapidly evolving nature. To find these counterparts, it is important that one optimises the observing strategy for their recovery. This can be difficult due to the large number of potential variables at play. Such follow-up campaigns are also capable of detecting hundreds or potentially thousands of unrelated transients, particularly for GW events with poor localisation. Even if the observations are capable of detecting a counterpart, finding it among the numerous contaminants can prove challenging. Here we present the Gravitational wave Electromagnetic RecovRY code (GERry) to perform detailed analysis and survey-agnostic quantification of observing campaigns attempting to recover electromagnetic counterparts. GERry considers the campaign's spatial, temporal and wavelength coverage, in addition to Galactic extinction and the expected counterpart light curve evolution from the GW 3D localisation volume. It returns quantified statistics that can be used to: determine the probability of having detected the counterpart, identify the most promising sources, and assess and refine strategy. Here we demonstrate the code to look at the performance and parameter space probed by current and upcoming wide-field surveys such as GOTO & VRO.

astro-ph.IM↗

arXiv2024

The Gravitational-wave Optical Transient Observer (GOTO)

The Gravitational-wave Optical Transient Observer (GOTO) is a project dedicated to identifying optical counter-parts to gravitational-wave detections using a network of dedicated, wide-field telescopes. After almost a decade of design, construction, and commissioning work, the GOTO network is now fully operational with two antipodal sites: La Palma in the Canary Islands and Siding Spring in Australia. Both sites host two independent robotic mounts, each with a field-of-view of 44 square degrees formed by an array of eight 40 cm telescopes, resulting in an instantaneous 88 square degree field-of-view per site. All four telescopes operate as a single integrated network, with the ultimate aim of surveying the entire sky every 2-3 days and allowing near-24-hour response to transient events within a minute of their detection. In the modern era of transient astronomy, automated telescopes like GOTO form a vital link between multi-messenger discovery facilities and in-depth follow-up by larger telescopes. GOTO is already producing a wide range of scientific results, assisted by an efficient discovery pipeline and a successful citizen science project: Kilonova Seekers.

astro-ph.IM↗

arXiv2024

Tensor square and isoclinic extensions of multiplicative Lie algebras

In this paper, we discuss the capable and isoclinic properties of the tensor square in the context of multiplicative Lie algebras. We also developed the concept of isoclinic extensions and proved several results for multiplicative Lie algebras. Consequently, we demonstrate that covers of a multiplicative Lie algebra are mutually isoclinic.

math.GR↗

arXiv2024

Legendrian knots and multi-crossings

It was shown in arXiv:1208.5742 that any smooth knot can be represented by an übercrossing projection, i.e. a knot projection with no crossings aside from a single multi-crossing. We extend this idea to Legendrian knots and investigate übercrossing and petal projections in the front and Lagrangian projections. We show that any Legendrian knot with an übercrossing projection in the front projection is smoothly isotopic to the unknot and we demonstrate how to compute the $tb$ and rotation numbers for petal projections in the Lagrangian projection.

math.GT↗

arXiv2024

A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks

Regulatory networks depict promoting or inhibiting interactions between molecules in a biochemical system. We introduce a category-theoretic formalism for regulatory networks, using signed graphs to model the networks and signed functors to describe occurrences of one network in another, especially occurrences of network motifs. With this foundation, we establish functorial mappings between regulatory networks and other mathematical models in biochemistry. We construct a functor from reaction networks, modeled as Petri nets with signed links, to regulatory networks, enabling us to precisely define when a reaction network could be a physical mechanism underlying a regulatory network. Turning to quantitative models, we associate a regulatory network with a Lotka-Volterra system of differential equations, defining a functor from the category of signed graphs to a category of parameterized dynamical systems. We extend this result from closed to open systems, demonstrating that Lotka-Volterra dynamics respects not only inclusions and collapsings of regulatory networks, but also the process of building up complex regulatory networks by gluing together simpler pieces. Formally, we use the theory of structured cospans to produce a lax double functor from the double category of open signed graphs to that of open parameterized dynamical systems. Throughout the paper, we ground the categorical formalism in examples inspired by systems biology.

q-bio.MN↗

arXiv2024

Subaru HSC weak lensing of SDSS redMaPPer cluster satellite galaxies: Empirical upper limit on orphan fractions

Weak gravitational lensing directly probes the matter distribution surrounding satellite galaxies in galaxy clusters. We measure the weak lensing signal induced on the shapes of background galaxies around SDSS redMaPPer cluster satellite galaxies, which have their central galaxies assigned with a probability $P_{\rm cen}>0.95$ in the redshift range, $0.1\leq z\leq 0.33$. We use the galaxy shapes from the Subaru Hyper Suprime-Cam (HSC) survey for this purpose. We bin satellite galaxies by their distance from the cluster centre and compare it to the signal around a control sample of galaxies which do not reside in clusters but have similar colours and magnitudes. We explore the effect of environmental processes on the dark matter mass around satellites. We see hints of a difference in the mass of the subhalo of the satellite compared to the halo masses of galaxies in our control sample, especially in the innermost cluster-centric radial bin ($0.1<r<0.3$ [$h^{-1}\rm Mpc$]). For the first time, we put an upper limit on the prevalence of orphan galaxies which have entirely lost their dark matter halos with cluster-centric distances with the help of our measurements. However, these upper limits could be relaxed if there is substantial contamination in the satellite galaxy sample.

astro-ph.CO↗

arXiv2024

Numerical modelling of flame spread over thin circular ducts

This paper presents a numerical investigation into the phenomenon of flame spread over thin circular ducts in normal gravity and microgravity environments. Flame spread over such geometry is of significant interest due to its relevance in various practical applications, including tubes for flow purpose in medical system, fire safety in spacecrafts, ducts as well as wiring tubes. This study comprises of a comprehensive investigation of key parameters affecting flame spread rate, including fuel radius and opposed flow speed in normal gravity and microgravity environments. A 2-D axisymmetric flame spread model accounted for char and numerical simulations were performed which revealed valuable insights into the underlying mechanisms governing flame spread over such geometry. The results computed from the numerical model is compared with the experimentally observed flame spread rate to validate the numerical model which can be used to gain a comprehensive understanding of the underlying physical phenomena. As the radius of circular duct increases the flame spread rate increases both in normal gravity and microgravity environments. The conduction heat feedback and radiation heat gain coming from hot char through gas phase at inner core region are the two major mechanisms which controls the flame spread phenomena over the circular duct fuels. The flame spread rate at different flow ranging from quiescent (0 cm/s) to 30 cm/s is also evaluated and 21 % oxygen and found a non-monotonic increasing decreasing trend of flame spread rate at different opposed flow speed in both normal gravity and microgravity environments.

physics.flu-dyn↗

arXiv2024

Magnetars as Powering Sources of Gamma-Ray Burst Associated Supernovae, and Unsupervised Clustering of Cosmic Explosions

We present the semi-analytical light curve modelling of 13 supernovae associated with gamma-ray bursts (GRB-SNe) along with two relativistic broad-lined (Ic-BL) SNe without GRBs association (SNe 2009bb and 2012ap), considering millisecond magnetars as central-engine-based power sources for these events. The bolometric light curves of all 15 SNe in our sample are well-regenerated utilising a $χ^2-$minimisation code, $\texttt{MINIM}$, and numerous parameters are constrained. The median values of ejecta mass ($M_{\textrm{ej}}$), magnetar's initial spin period ($P_\textrm{i}$) and magnetic field ($B$) for GRB-SNe are determined to be $\approx$ 5.2 M$_\odot$, 20.5 ms and 20.1 $\times$ 10$^{14}$ G, respectively. We leverage machine learning (ML) algorithms to comprehensively compare the 3-dimensional parameter space encompassing $M_{\textrm{ej}}$, $P_\textrm{i}$, and $B$ for GRB-SNe determined herein to those of H-deficient superluminous SNe (SLSNe-I), fast blue optical transients (FBOTs), long GRBs (LGRBs), and short GRBs (SGRBs) obtained from the literature. The application of unsupervised ML clustering algorithms on the parameters $M_{\textrm{ej}}$, $P_\textrm{i}$, and $B$ for GRB-SNe, SLSNe-I, and FBOTs yields a classification accuracy of $\sim$95%. Extending these methods to classify GRB-SNe, SLSNe-I, LGRBs, and SGRBs based on $P_\textrm{i}$ and $B$ values results in an accuracy of $\sim$84%. Our investigations show that GRB-SNe and relativistic Ic-BL SNe presented in this study occupy different parameter spaces for $M_{\textrm{ej}}$, $P_\textrm{i}$, and $B$ than those of SLSNe-I, FBOTs, LGRBs and SGRBs. This indicates that magnetars with different $P_\textrm{i}$ and $B$ can give birth to distinct types of transients.

astro-ph.HE↗

arXiv2024

Calibration of VELC detectors on-board Aditya-L1 mission

Aditya-L1 is the first Indian space mission to explore the Sun and solar atmosphere with seven multi-wavelength payloads, with Visible Emission Line Coronagraph (VELC) being the prime payload. It is an internally occulted coronagraph with four channels to image the Sun at 5000 Å~ in the field of view 1.05 - 3 \rsun, and to pursue spectroscopy at 5303 Å, 7892 Å~ and 10747 Å~ channels in the FOV (1.05 - 1.5 \rsun). In addition, spectropolarimetry is planned at 10747 Å~ channel. Therefore, VELC has three sCMOS detectors and one InGaAs detector. In this article, we aim to describe the technical details and specifications of the detectors achieved by way of thermo-vacuum calibration at the CREST campus of the Indian Institute of Astrophysics, Bangalore, India. Furthermore, we report the estimated conversion gain, full-well capacity, and readout noise at different temperatures. Based on the numbers, it is thus concluded that it is essential to operate the sCMOS detectors and InGaAs detectors at $-5^{\circ}$ and $-17^{\circ}$ C, respectively, at the spacecraft level.

astro-ph.SR↗