arXiv subjects

Arnab Chatterjee

Publications and source records attributed to Arnab Chatterjee.

At least 19 recordsLinked to original sources

arXiv2026

A Method for Securely Transmitting Large Video Files Using Chaotic Compression and Encryption

Conventional techniques for compression and encryption are frequently laborious and resource-intensive, rendering them inappropriate for real-time applications. A plethora of research has been presented in the current literature to address these difficulties together; yet, it fails to propose any suitable strategy. Therefore, this study introduces an innovative simultaneous data compression and encryption (SDCE) system specifically designed for large video files. The methodology amalgamates chaotic map-based encryption with Huffman encoding for lossless compression into a cohesive framework, markedly diminishing computational overhead and processing duration while augmenting data security. The logistic map is utilized to produce a pseudo-random chaotic sequence for XOR-based encryption, guaranteeing robust security against unwanted access. The research findings demonstrate its efficacy in enhancing data privacy compared to other existing and related strategies, particularly in terms of generating greater entropy and avalanche effects. It produces superior throughput, compression ratio, peak signal-to-noise ratio (PSNR), and reduced bits per rate (BPC), along with a smaller percentage of data loss, which further supports its ability to provide enhanced data integrity compared to other existing methods.

cs.CR↗

arXiv2026

Towards socio-techno-economic power systems with demand-side flexibility

Harnessing the demand-side flexibility in building and mobility sectors can help to better integrate renewable energy into power systems and reduce global CO2 emissions. Enabling this sector coupling can be achieved with advances in energy management, business models, control technologies, and power grids. The study of demand-side flexibility extends beyond engineering, spanning social science, economics, and power and control systems, which present both challenges and opportunities to researchers and engineers in these fields. This Review outlines recent trends and studies in social, economic, and technological advancements in power systems that leverage demand-side flexibility. We first provide a concept of a socio-techno-economic system with an abstraction of end-users, building and mobility sectors, control systems, electricity markets, and power grids. We discuss the interconnections between these elements, highlighting the importance of bidirectional flows of information and coordinated decision-making. We then emphasize that fully realizing demand-side flexibility necessitates deep integration across stakeholders and systems, moving beyond siloed approaches. Finally, we discuss the future directions in renewable-based power systems and control engineering to address key challenges from both research and practitioners' perspectives. A holistic approach for identifying, measuring, and utilizing demand-side flexibility is key to successfully maximizing its multi-stakeholder benefits but requires further transdisciplinary collaboration and commercially viable solutions for broader implementation.

eess.SY↗

arXiv2026

A methodology for analyzing financial needs hierarchy from social discussions using LLM

This study examines the hierarchical structure of financial needs as articulated in social media discourse, employing generative AI techniques to analyze large-scale textual data. While human needs encompass a broad spectrum from fundamental survival to psychological fulfillment financial needs are particularly critical, influencing both individual well-being and day-to-day decision-making. Our research advances the understanding of financial behavior by utilizing large language models (LLMs) to extract and analyze expressions of financial needs from social media posts. We hypothesize that financial needs are organized hierarchically, progressing from short-term essentials to long-term aspirations, consistent with theoretical frameworks established in the behavioral sciences. Through computational analysis, we demonstrate the feasibility of identifying these needs and validate the presence of a hierarchical structure within them. In addition to confirming this structure, our findings provide novel insights into the content and themes of financial discussions online. By inferring underlying needs from naturally occurring language, this approach offers a scalable and data-driven alternative to conventional survey methodologies, enabling a more dynamic and nuanced understanding of financial behavior in real-world contexts.

cs.SI↗

arXiv2025

Symmetric Rule-Based Achlioptas Processes for Random $k$-SAT

Inspired by the "power-of-two-choices" model from random graphs, we investigate the possibility of limited choices of online clause choices that could shift the satisfiability threshold in random $k$-SAT.Here, we introduce an assignment symmetric, non-adaptive, topology-oblivious online rule called \emph{MIDDLE-HEAVY}, that prioritizes balanced sign profile clauses.Upon applying a biased $2$-SAT projection and a two-type branching process certificate, we derive closed-form expressions for the shifted thresholds $\alpha_{\textbf{SYM}}(k,\ell)$ for this algorithm.We show that minimal choices $\ell=5$ for $k=4$, $\ell=4$ for $k=5$, and $\ell=3$ for $k\ge 6$ suffice to exceed the asymptotic first-moment upper bound $\sim 2^k \ln 2$ for random $k$-SAT.Moreover, to bridge the gap with biased assignment rules used in maximum of the previous works in this context, we propose a hybrid symmetric biased rule that achieves thresholds comparable to prior work while maintaining symmetry.Our results advance the understanding of Achlioptas processes in random CSPs beyond classical graph-theoretic settings.

cs.DM↗

arXiv2025

MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM

Mental health forums offer valuable insights into psychological issues, stressors, and potential solutions. We propose MHINDR, a large language model (LLM) based framework integrated with DSM-5 criteria to analyze user-generated text, dignose mental health conditions, and generate personalized interventions and insights for mental health practitioners. Our approach emphasizes on the extraction of temporal information for accurate diagnosis and symptom progression tracking, together with psychological features to create comprehensive mental health summaries of users. The framework delivers scalable, customizable, and data-driven therapeutic recommendations, adaptable to diverse clinical contexts, patient needs, and workplace well-being programs.

cs.SI↗

arXiv2025

Asymptotic size of the Karp-Sipser Core in Configuration Model

We study the asymptotic size of the Karp-Sipser core in the configuration model with arbitrary degree distributions. The Karp-Sipser core is the induced subgraph obtained by iteratively removing all leaves and their neighbors through the leaf-removal process, and finally discarding any isolated vertices \cite{BCC}. Our main result establishes the convergence of the Karp-Sipser core size to an explicit fixed-point equation under general degree assumptions.The approach is based on analyzing the corresponding local weak limit of the configuration model - a unimodular Galton-Watson tree and tracing the evolution process of all vertex states under leaf-removal dynamics by use of the working mechanism of an enhanced version of Warning Propagation along with Node Labeling Propagation.

math.CO↗

arXiv2025

The random $k$-SAT Gibbs uniqueness threshold revisited

We prove that for any $k\geq3$ for clause/variable ratios up to the Gibbs uniqueness threshold of the corresponding Galton-Watson tree, the number of satisfying assignments of random $k$-SAT formulas is given by the `replica symmetric solution' predicted by physics methods [Monasson, Zecchina: Phys. Rev. Lett. (1996)]. Furthermore, while the Gibbs uniqueness threshold is still not known precisely for any $k\geq3$, we derive new lower bounds on this threshold that improve over prior work [Montanari and Shah: SODA (2007)].The improvement is significant particularly for small $k$.

cs.DM↗

arXiv2025

Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions

Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for help and others sympathize and offer support. We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders, detect their severity, and create recommendations for behavior change and therapeutic interventions based on users' posts on Reddit. To classify the disorders, we use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data. We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy. Our hybrid approach combines the generalization capabilities of pre-trained models with the domain-specific insights captured by LLMs, providing an improved understanding of mental health discourse. Our findings highlight the strengths and limitations of each model, offering valuable insights into their practical applicability. This research potentially facilitates early detection and personalized care to aid practitioners and aims to facilitate timely interventions and improve overall well-being, thereby contributing to the broader field of mental health surveillance and digital health analytics.

cs.SI↗

arXiv2025

Belief Propagation Guided Decimation on Random k-XORSAT

We analyse the performance of Belief Propagation Guided Decimation, a physics-inspired message passing algorithm, on the random $k$-XORSAT problem. Specifically, we derive an explicit threshold up to which the algorithm succeeds with a strictly positive probability $\Omega(1)$ that we compute explicitly, but beyond which the algorithm with high probability fails to find a satisfying assignment. In addition, we analyse a thought experiment called the decimation process for which we identify a (non-) reconstruction and a condensation phase transition. The main results of the present work confirm physics predictions from [RTS: J. Stat. Mech. 2009] that link the phase transitions of the decimation process with the performance of the algorithm, and improve over partial results from a recent article [Yung: Proc. ICALP 2024].

math.CO↗

arXiv2024

The number of random 2-SAT solutions is asymptotically log-normal

We prove that throughout the satisfiable phase, the logarithm of the number of satisfying assignments of a random 2-SAT formula satisfies a central limit theorem. This implies that the log of the number of satisfying assignments exhibits fluctuations of order $\sqrt n$, with $n$ the number of variables. The formula for the variance can be evaluated effectively. By contrast, for numerous other random constraint satisfaction problems the typical fluctuations of the logarithm of the number of solutions are {\em bounded} throughout all or most of the satisfiable regime.

cs.DM↗

arXiv2024

Understanding how social discussion platforms like Reddit are influencing financial behavior

This study proposes content and interaction analysis techniques for a large repository created from social media content. Though we have presented our study for a large platform dedicated to discussions around financial topics, the proposed methods are generic and applicable to all platforms. Along with an extension of topic extraction method using Latent Dirichlet Allocation, we propose a few measures to assess user participation, influence and topic affinities specifically. Our study also maps user-generated content to components of behavioral finance. While these types of information are usually gathered through surveys, it is obvious that large scale data analysis from social media can reveal many potentially unknown or rare insights. Characterising users based on their platform behavior to provide critical insights about how communities are formed and trust is established in these platforms using graphical analysis is also studied.

cs.SI↗

arXiv2024

Generating insights about financial asks from Reddit posts and user interactions

As an increasingly large number of people turn to platforms like Reddit, YouTube, Twitter, Instagram, etc. for financial advice, generating insights about the content generated and interactions taking place within these platforms have become a key research question. This study proposes content and interaction analysis techniques for a large repository created from social media content, where people interactions are centered around financial information exchange. We propose methods for content analysis that can generate human-interpretable insights using topic-centered clustering and multi-document abstractive summarization. We share details of insights generated from our experiments with a large repository of data gathered from subreddit for personal finance. We have also explored the use of ChatGPT and Vicuna for generating responses to queries and compared them with human responses. The methods proposed in this work are generic and applicable to all large social media platforms.

cs.SI↗

arXiv2023

Social dynamics through kinetic exchange: The BChS model

This review presents an overview of the current research in kinetic exchange models for opinion formation in a society. The review begins with a brief introduction to previous models and subsequently provides an in-depth discussion of the progress achieved in the Biswas-Chatterjee-Sen model proposed in 2012, also known as the BChS model in some later research publications. The unique feature of the model is its inclusion of negative interaction between agents. The review covers various topics, including phase transitions between different opinion states, critical behavior dependent on various parameters, and applications in realistic scenarios such as the United States presidential election and Brexit.

physics.soc-ph↗

arXiv2021

MultiMBNN: Matched and Balanced Causal Inference with Neural Networks

Causal inference (CI) in observational studies has received a lot of attention in healthcare, education, ad attribution, policy evaluation, etc. Confounding is a typical hazard, where the context affects both, the treatment assignment and response. In a multiple treatment scenario, we propose the neural network based MultiMBNN, where we overcome confounding by employing generalized propensity score based matching, and learning balanced representations. We benchmark the performance on synthetic and real-world datasets using PEHE, and mean absolute percentage error over ATE as metrics. MultiMBNN outperforms the state-of-the-art algorithms for CI such as TARNet and Perfect Match (PM).

stat.ME↗

arXiv2021

Hi-CI: Deep Causal Inference in High Dimensions

We address the problem of counterfactual regression using causal inference (CI) in observational studies consisting of high dimensional covariates and high cardinality treatments. Confounding bias, which leads to inaccurate treatment effect estimation, is attributed to covariates that affect both treatments and outcome. The presence of high-dimensional co-variates exacerbates the impact of bias as it is harder to isolate and measure the impact of these confounders. In the presence of high-cardinality treatment variables, CI is rendered ill-posed due to the increase in the number of counterfactual outcomes to be predicted. We propose Hi-CI, a deep neural network (DNN) based framework for estimating causal effects in the presence of large number of covariates, and high-cardinal and continuous treatment variables. The proposed architecture comprises of a decorrelation network and an outcome prediction network. In the decorrelation network, we learn a data representation in lower dimensions as compared to the original covariates and addresses confounding bias alongside. Subsequently, in the outcome prediction network, we learn an embedding of high-cardinality and continuous treatments, jointly with the data representation. We demonstrate the efficacy of causal effect prediction of the proposed Hi-CI network using synthetic and real-world NEWS datasets.

stat.ME↗

arXiv2021

MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population

Performing inference on data obtained through observational studies is becoming extremely relevant due to the widespread availability of data in fields such as healthcare, education, retail, etc. Furthermore, this data is accrued from multiple homogeneous subgroups of a heterogeneous population, and hence, generalizing the inference mechanism over such data is essential. We propose the MetaCI framework with the goal of answering counterfactual questions in the context of causal inference (CI), where the factual observations are obtained from several homogeneous subgroups. While the CI network is designed to generalize from factual to counterfactual distribution in order to tackle covariate shift, MetaCI employs the meta-learning paradigm to tackle the shift in data distributions between training and test phase due to the presence of heterogeneity in the population, and due to drifts in the target distribution, also known as concept shift. We benchmark the performance of the MetaCI algorithm using the mean absolute percentage error over the average treatment effect as the metric, and demonstrate that meta initialization has significant gains compared to randomly initialized networks, and other methods.

cs.LG↗

arXiv2021

CAMTA: Causal Attention Model for Multi-touch Attribution

Advertising channels have evolved from conventional print media, billboards and radio advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advertisers revisit the design of ad campaigns to concurrently serve the requirements emerging out of new ad channels, it is also critical for advertisers to estimate the contribution from touch-points (view, clicks, converts) on different channels, based on the sequence of customer actions. This process of contribution measurement is often referred to as multi-touch attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent neural network architecture which is a casual attribution mechanism for user-personalised MTA in the context of observational data. CAMTA minimizes the selection bias in channel assignment across time-steps and touchpoints. Furthermore, it utilizes the users' pre-conversion actions in a principled way in order to predict pre-channel attribution. To quantitatively benchmark the proposed MTA model, we employ the real world Criteo dataset and demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines. In addition, we provide results for budget allocation and user-behaviour modelling on the predicted channel attribution.

cs.LG↗

arXiv2020

The Ising universality class of kinetic exchange models of opinion dynamics

We show using scaling arguments and Monte Carlo simulations that a class of binary interacting models of opinion evolution belong to the Ising universality class in presence of an annealed noise term of finite amplitude. While the zero noise limit is known to show an active-absorbing transition, addition of annealed noise induces a continuous order-disorder transition with Ising universality class in the infinite-range (mean field) limit of the models.

physics.soc-ph↗