arXiv subjects

Shivam Gupta

Publications and source records attributed to Shivam Gupta.

At least 37 records · Page 2Linked to original sources

arXiv2025

Signatures of extreme events in the cumulative entropic spectrum

In this study, the cumulative effect of the empirical probability distribution of a random variable is identified as a factor that amplifies the occurrence of extreme events in datasets. To quantify this observation, a corresponding information measure is introduced, drawing upon Shannon entropy for joint probabilities. The proposed approach is validated using selected market data as case studies, encompassing various instances of extreme events. In particular, the results indicate that the introduced cumulative measure exhibits distinctive signatures of such events, even when the data is relatively noisy. These findings highlight the potential of the discussed concept for developing a new class of related indicators or classifiers.

physics.data-an↗

arXiv2025

Entropy-Assisted Quality Pattern Identification in Finance

Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain. Patterns that lead to high one-sided movements in historical data, yet retain low local entropy, are more informative in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMM), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies.

q-fin.TR↗

arXiv2025

FLIGHT: Facility Location Integrating Generalized, Holistic Theory of Welfare

The Facility Location Problem (FLP) is a well-studied optimization problem with applications in many real-world scenarios. Past literature has explored the solutions from different perspectives to tackle FLPs. These include investigating FLPs under objective functions such as utilitarian, egalitarian, Nash welfare, etc. Also, there is no treatment for asymmetric welfare functions around the facility. We propose a unified framework, FLIGHT, to accommodate a broad class of welfare notions. The framework undergoes rigorous theoretical analysis, and we prove some structural properties of the solution to FLP. Additionally, we provide approximation bounds, which provide insight into an interesting fact: as the number of agents arbitrarily increases, the choice of welfare notion is irrelevant. Furthermore, the paper also includes results around concentration bounds under certain distributional assumptions over the preferred locations of agents.

cs.GT↗

arXiv2024

Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design

Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of precise and regulation-compliant diagrams remains a significant challenge, particularly in scaling breakthroughs from material discovery to industrial production in an era of automation and digitalization. This paper introduces an autonomous agentic framework to address these challenges through a twostage approach involving knowledge acquisition and generation. The framework integrates specialized sub-agents for retrieving and synthesizing multimodal data from publicly available online sources and constructs ontological knowledge graphs using a Graph Retrieval-Augmented Generation (Graph RAG) paradigm. These capabilities enable the automation of diagram generation and open-domain question answering (ODQA) tasks with high contextual accuracy. Extensive empirical experiments demonstrate the frameworks ability to deliver regulation-compliant diagrams with minimal expert intervention, highlighting its practical utility for industrial applications.

cs.LG↗

arXiv2024

Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel

Sampling algorithms play an important role in controlling the quality and runtime of diffusion model inference. In recent years, a number of works~\cite{chen2023sampling,chen2023ode,benton2023error,lee2022convergence} have proposed schemes for diffusion sampling with provable guarantees; these works show that for essentially any data distribution, one can approximately sample in polynomial time given a sufficiently accurate estimate of its score functions at different noise levels. In this work, we propose a new scheme inspired by Shen and Lee's randomized midpoint method for log-concave sampling~\cite{ShenL19}. We prove that this approach achieves the best known dimension dependence for sampling from arbitrary smooth distributions in total variation distance ($\widetilde O(d^{5/12})$ compared to $\widetilde O(\sqrt{d})$ from prior work). We also show that our algorithm can be parallelized to run in only $\widetilde O(\log^2 d)$ parallel rounds, constituting the first provable guarantees for parallel sampling with diffusion models. As a byproduct of our methods, for the well-studied problem of log-concave sampling in total variation distance, we give an algorithm and simple analysis achieving dimension dependence $\widetilde O(d^{5/12})$ compared to $\widetilde O(\sqrt{d})$ from prior work.

cs.LG↗

arXiv2024

Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series Representation Learning

Accurately predicting the behavior of complex dynamical systems, characterized by high-dimensional multivariate time series(MTS) in interconnected sensor networks, is crucial for informed decision-making in various applications to minimize risk. While graph forecasting networks(GFNs) are ideal for forecasting MTS data that exhibit spatio-temporal dependencies, prior works rely solely on the domain-specific knowledge of time-series variables inter-relationships to model the nonlinear dynamics, neglecting inherent relational structural dependencies among the variables within the MTS data. In contrast, contemporary works infer relational structures from MTS data but neglect domain-specific knowledge. The proposed hybrid architecture addresses these limitations by combining both domain-specific knowledge and implicit knowledge of the relational structure underlying the MTS data using Knowledge-Based Compositional Generalization. The hybrid architecture shows promising results on multiple benchmark datasets, outperforming state-of-the-art forecasting methods. Additionally, the architecture models the time varying uncertainty of multi-horizon forecasts.

cs.LG↗

arXiv2024

Multi-Knowledge Fusion Network for Time Series Representation Learning

Forecasting the behaviour of complex dynamical systems such as interconnected sensor networks characterized by high-dimensional multivariate time series(MTS) is of paramount importance for making informed decisions and planning for the future in a broad spectrum of applications. Graph forecasting networks(GFNs) are well-suited for forecasting MTS data that exhibit spatio-temporal dependencies. However, most prior works of GFN-based methods on MTS forecasting rely on domain-expertise to model the nonlinear dynamics of the system, but neglect the potential to leverage the inherent relational-structural dependencies among time series variables underlying MTS data. On the other hand, contemporary works attempt to infer the relational structure of the complex dependencies between the variables and simultaneously learn the nonlinear dynamics of the interconnected system but neglect the possibility of incorporating domain-specific prior knowledge to improve forecast accuracy. To this end, we propose a hybrid architecture that combines explicit prior knowledge with implicit knowledge of the relational structure within the MTS data. It jointly learns intra-series temporal dependencies and inter-series spatial dependencies by encoding time-conditioned structural spatio-temporal inductive biases to provide more accurate and reliable forecasts. It also models the time-varying uncertainty of the multi-horizon forecasts to support decision-making by providing estimates of prediction uncertainty. The proposed architecture has shown promising results on multiple benchmark datasets and outperforms state-of-the-art forecasting methods by a significant margin. We report and discuss the ablation studies to validate our forecasting architecture.

cs.LG↗

arXiv2024

Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology

Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. This advanced technology offers a range of intelligent functionalities, such as modeling, simulation, and data-driven decision-making, that facilitate design optimization, performance estimation, and monitoring operations. Forecasting plays a pivotal role in Digital Twin technology, as it enables the prediction of future outcomes, supports informed decision-making, minimizes risks, driving improvements in efficiency, productivity, and cost reduction. Recently, Digital Twin technology has leveraged Graph forecasting techniques in large-scale complex sensor networks to enable accurate forecasting and simulation of diverse scenarios, fostering proactive and data-driven decision making. However, existing Graph forecasting techniques lack scalability for many real-world applications. They have limited ability to adapt to non-stationary environments, retain past knowledge, lack a mechanism to capture the higher order spatio-temporal dynamics, and estimate uncertainty in model predictions. To surmount the challenges, we introduce a hybrid architecture that enhances the hypergraph representation learning backbone by incorporating fast adaptation to new patterns and memory-based retrieval of past knowledge. This balance aims to improve the slowly-learned backbone and achieve better performance in adapting to recent changes. In addition, it models the time-varying uncertainty of multi-horizon forecasts, providing estimates of prediction uncertainty. Our forecasting architecture has been validated through ablation studies and has demonstrated promising results across multiple benchmark datasets, surpassing state-ofthe-art forecasting methods by a significant margin.

cs.LG↗

arXiv2024

Genetic Algorithm to Optimize Design of Micro-Surgical Scissors

Microrobotics is an attractive area of research as small-scale robots have the potential to improve the precision and dexterity offered by minimally invasive surgeries. One example of such a tool is a pair of micro-surgical scissors that was developed for cutting of tumors or cancerous tissues present deep inside the body such as in the brain. This task is often deemed difficult or impossible with conventional robotic tools due to their size and dexterity. The scissors are designed with two magnets placed a specific distance apart to maximize deflection and generate cutting forces. However, remote actuation and size requirements of the micro-surgical scissors limits the force that can be generated to puncture the tissue. To address the limitation of small output forces, we use an evolutionary algorithm to further optimize the performance of the scissors. In this study, the design of the previously developed untethered micro-surgical scissors has been modified and their performance is enhanced by determining the optimal position of the magnets as well as the direction of each magnetic moment. The developed algorithm is successfully applied to a 4-magnet configuration which results in increased net torque. This improvement in net torque is directly translated into higher cutting forces. The new configuration generates a cutting force of 58 mN from 80 generations of the evolutionary algorithm which is a 1.65 times improvement from the original design. Furthermore, the developed algorithm has the advantage that it can be deployed with minor modifications to other microrobotic tools and systems, opening up new possibilities for various medical procedures and applications.

cs.RO↗

arXiv2024

Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributions

The rapid growth of data from edge devices has catalyzed the performance of machine learning algorithms. However, the data generated resides at client devices thus there are majorly two challenge faced by traditional machine learning paradigms - centralization of data for training and secondly for most the generated data the class labels are missing and there is very poor incentives to clients to manually label their data owing to high cost and lack of expertise. To overcome these issues, there have been initial attempts to handle unlabelled data in a privacy preserving distributed manner using unsupervised federated data clustering. The goal is partition the data available on clients into $k$ partitions (called clusters) without actual exchange of data. Most of the existing algorithms are highly dependent on data distribution patterns across clients or are computationally expensive. Furthermore, due to presence of skewed nature of data across clients in most of practical scenarios existing models might result in clients suffering high clustering cost making them reluctant to participate in federated process. To this, we are first to introduce the idea of personalization in federated clustering. The goal is achieve balance between achieving lower clustering cost and at same time achieving uniform cost across clients. We propose p-FClus that addresses these goal in a single round of communication between server and clients. We validate the efficacy of p-FClus against variety of federated datasets showcasing it's data independence nature, applicability to any finite $\ell$-norm, while simultaneously achieving lower cost and variance.

cs.LG↗

arXiv2024

Python-based DSL for generating Verilog model of Synchronous Digital Circuits

We have designed a Python-based Domain Specific Language (DSL) for modeling synchronous digital circuits. In this DSL, hardware is modeled as a collection of transactions -- running in series, parallel, and loops. When the model is executed by a Python interpreter, synthesizable and behavioural Verilog is generated as output, which can be integrated with other RTL designs or directly used for FPGA and ASIC flows. In this paper, we describe - 1) the language (DSL), which allows users to express computation in series/parallel/loop constructs, with explicit cycle boundaries, 2) the internals of a simple Python implementation to produce synthesizable Verilog, and 3) several design examples and case studies for applications in post-quantum cryptography, stereo-vision, digital signal processing and optimization techniques. In the end, we list ideas to extend this framework.

cs.AR↗

arXiv2024

Beyond Catoni: Sharper Rates for Heavy-Tailed and Robust Mean Estimation

We study the fundamental problem of estimating the mean of a $d$-dimensional distribution with covariance $Σ\preccurlyeq σ^2 I_d$ given $n$ samples. When $d = 1$, \cite{catoni} showed an estimator with error $(1+o(1)) \cdot σ\sqrt{\frac{2 \log \frac{1}δ}{n}}$, with probability $1 - δ$, matching the Gaussian error rate. For $d>1$, a natural estimator outputs the center of the minimum enclosing ball of one-dimensional confidence intervals to achieve a $1-δ$ confidence radius of $\sqrt{\frac{2 d}{d+1}} \cdot σ\left(\sqrt{\frac{d}{n}} + \sqrt{\frac{2 \log \frac{1}δ}{n}}\right)$, incurring a $\sqrt{\frac{2d}{d+1}}$-factor loss over the Gaussian rate. When the $\sqrt{\frac{d}{n}}$ term dominates by a $\sqrt{\log \frac{1}δ}$ factor, \cite{lee2022optimal-highdim} showed an improved estimator matching the Gaussian rate. This raises a natural question: Is the $\sqrt{\frac{2 d}{d+1}}$ loss \emph{necessary} when the $\sqrt{\frac{2 \log \frac{1}δ}{n}}$ term dominates? We show that the answer is \emph{no} -- we construct an estimator that improves over the above naive estimator by a constant factor. We also consider robust estimation, where an adversary is allowed to corrupt an $ε$-fraction of samples arbitrarily: in this case, we show that the above strategy of combining one-dimensional estimates and incurring the $\sqrt{\frac{2d}{d+1}}$-factor \emph{is} optimal in the infinite-sample limit.

math.ST↗

arXiv2023

Finite-Sample Symmetric Mean Estimation with Fisher Information Rate

The mean of an unknown variance-$σ^2$ distribution $f$ can be estimated from $n$ samples with variance $\frac{σ^2}{n}$ and nearly corresponding subgaussian rate. When $f$ is known up to translation, this can be improved asymptotically to $\frac{1}{n\mathcal I}$, where $\mathcal I$ is the Fisher information of the distribution. Such an improvement is not possible for general unknown $f$, but [Stone, 1975] showed that this asymptotic convergence $\textit{is}$ possible if $f$ is $\textit{symmetric}$ about its mean. Stone's bound is asymptotic, however: the $n$ required for convergence depends in an unspecified way on the distribution $f$ and failure probability $δ$. In this paper we give finite-sample guarantees for symmetric mean estimation in terms of Fisher information. For every $f, n, δ$ with $n > \log \frac{1}δ$, we get convergence close to a subgaussian with variance $\frac{1}{n \mathcal I_r}$, where $\mathcal I_r$ is the $r$-$\textit{smoothed}$ Fisher information with smoothing radius $r$ that decays polynomially in $n$. Such a bound essentially matches the finite-sample guarantees in the known-$f$ setting.

math.ST↗

arXiv2023

High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors

In location estimation, we are given $n$ samples from a known distribution $f$ shifted by an unknown translation $λ$, and want to estimate $λ$ as precisely as possible. Asymptotically, the maximum likelihood estimate achieves the Cramér-Rao bound of error $\mathcal N(0, \frac{1}{n\mathcal I})$, where $\mathcal I$ is the Fisher information of $f$. However, the $n$ required for convergence depends on $f$, and may be arbitrarily large. We build on the theory using \emph{smoothed} estimators to bound the error for finite $n$ in terms of $\mathcal I_r$, the Fisher information of the $r$-smoothed distribution. As $n \to \infty$, $r \to 0$ at an explicit rate and this converges to the Cramér-Rao bound. We (1) improve the prior work for 1-dimensional $f$ to converge for constant failure probability in addition to high probability, and (2) extend the theory to high-dimensional distributions. In the process, we prove a new bound on the norm of a high-dimensional random variable whose 1-dimensional projections are subgamma, which may be of independent interest.

math.ST↗

arXiv2023

Effect of externally deposited nanoscale heterogeneities in thin polymer films on their adhesion behavior

Adhesion between two surfaces depend on the chemical and the mechanical properties of both the materials. However, heterogeneities (surface or bulk) affect the adhesion between the two surfaces tremendously. In this work, we study the role of externally deposited nanoscale heterogeneities on their adhesion behavior. Silica nanoparticles in the bulk polydimethylsiloxane (PDMS) polymer matrix act as external heterogeneities, which subsequently affect their adhesion. The nanoscale heterogeneities change the polymer environment locally, which subsequently modify its mechanical properties, e.g. elastic modulus, as a result, it affects its adhesion behavior. We observe that the adhesion behavior vary in nonlinear manner with increasing nanoparticle concentration. Therefore, probing these heterogeneities may allow us to understand the emergence of processing-induced deviations and the role of external defects in the macroscopic properties of a polymer.

cond-mat.soft↗

arXiv2022

Outlier-Robust Sparse Estimation via Non-Convex Optimization

We explore the connection between outlier-robust high-dimensional statistics and non-convex optimization in the presence of sparsity constraints, with a focus on the fundamental tasks of robust sparse mean estimation and robust sparse PCA. We develop novel and simple optimization formulations for these problems such that any approximate stationary point of the associated optimization problem yields a near-optimal solution for the underlying robust estimation task. As a corollary, we obtain that any first-order method that efficiently converges to stationarity yields an efficient algorithm for these tasks. The obtained algorithms are simple, practical, and succeed under broader distributional assumptions compared to prior work.

cs.LG↗

arXiv2022

Numerical and Experimental Investigation of Static Wetting Morphologies of Aqueous Drops on Lubricated Slippery Surfaces Using a Quasi-Static Approach

Due to the slow dynamics of the wetting ridge, it is challenging to predict the wetting morphology of liquid drops on thin lubricant coated surfaces. It is hypothesized that when a drop sinks on a lubricated surface, quasi-static wetting morphology can be numerically computed only from the knowledge of interfacial energies, lubricant thickness, and drop volume. We used Surface Evolver software for the numerical computation of the interface profiles for a four-phase system. For the experiments, we used drops of 80 wt% formamide on silicone oil coated substrates with varying lubricant thickness, substrate wettability and drop volume. Optical images of drops were used to compare the experimental interfacial profiles and apparent contact angles with the numerically computed ones. We found good agreement between the experiments and the simulations for the interfacial profiles and apparent contact angles as a function of various systems parameters except for very thin lubricating films. Apparent contact angles varied non-linearly as a function of substrate wettability and lubricant thickness, however, were found constant with the drop volume.

cond-mat.soft↗

arXiv2022

AI-based Monitoring and Response System for Hospital Preparedness towards COVID-19 in Southeast Asia

This research paper proposes a COVID-19 monitoring and response system to identify the surge in the volume of patients at hospitals and shortage of critical equipment like ventilators in South-east Asian countries, to understand the burden on health facilities. This can help authorities in these regions with resource planning measures to redirect resources to the regions identified by the model. Due to the lack of publicly available data on the influx of patients in hospitals, or the shortage of equipment, ICU units or hospital beds that regions in these countries might be facing, we leverage Twitter data for gleaning this information. The approach has yielded accurate results for states in India, and we are working on validating the model for the remaining countries so that it can serve as a reliable tool for authorities to monitor the burden on hospitals.

cs.CY↗