arXiv subjects

Sourav Mukherjee

Publications and source records attributed to Sourav Mukherjee.

17 recordsLinked to original sources

arXiv2026

Surface-mediated frequency aging beyond quality-factor saturation in an AlScN-on-silicon resonator

Vacuum package integrity in micro- and nanoelectromechanical resonators is commonly assessed through the quality factor Q, although Q probes residual-gas damping rather than the surface-state evolution that can govern frequency aging. Here, we disentangle the pressure responses of Q and the resonance frequency f0 in a 64.21 MHz Al0.7Sc0.3N-on-silicon cross-sectional Lam\'e-mode resonator between 0.01 to 760 Torr. Measurements at 25 {\deg}C and at the 68.8{\deg}C frequency turnover, where first-order thermal sensitivity is suppressed, reveal widely separated equilibration timescales. Following each pressure step, Q reaches a reversible, history-independent steady value on the pressure-control timescale and approaches a fitted pressure-independent ceiling of 8.5e4 below approximately 1 Torr. By contrast, f0 responds measurably down to 1e-5 Torr and remains history-dependent, relaxing for hours at fixed pressure. The transients follow stretched-exponential kinetics, consistent with a broad distribution of surface relaxation rates, and individual pressure steps produce fractional frequency shifts as large as 1.9 ppm. Ten-hour phase-locked measurements show a common short-term time-deviation floor near 1e-10 s from 0.01 to 100 Torr, whereas residual deterministic relaxation dominates at long averaging times; at 760 Torr, gas damping degrades short-term tracking through the reduced Q. These results establish gas-damping equilibrium and frequency equilibrium as distinct states. Quality-factor saturation alone is therefore insufficient to qualify vacuum packaging for precision mechanical frequency references; package specifications must also constrain surface-mediated frequency aging.

cond-mat.mes-hall↗

arXiv2026

Dynamic FDD for Spectrum Sharing in Non-Terrestrial Networks

Future 6G networks are envisioned to integrate low Earth orbit satellite mega-constellations to enable seamless global connectivity, particularly in underserved and remote areas. However, the deployment of dense mega-constellations introduces interference among satellites operating over shared frequency bands. This represents a rather new setup for studying spectrum sharing, which exacerbates the limited flexibility of conventional FDD systems based on fixed bands for downlink and uplink transmissions. We address this spectrum-sharing problem and propose dynamic re-assignment of FDD bands for improved interference management in dense deployments, as well as evaluate the performance gain of this approach. To this end, we formulate a joint optimization problem that incorporates dynamic band assignment, user scheduling, and power allocation in both directions. This non-convex mixed integer problem is solved using a combination of equivalence transforms, alternating optimization, and state-of-the-art industrial-grade mixed integer solvers. Numerical results demonstrate that the proposed approach of dynamic FDD band assignment significantly enhances system performance over conventional FDD, achieving up to 30\% improvement in throughput in dense deployments.

cs.IT↗

arXiv2025

Dynamic Downlink-Uplink Spectrum Sharing between Terrestrial and Non-Terrestrial Networks

6G networks are expected to integrate low Earth orbit satellites to ensure global connectivity by extending coverage to underserved and remote regions. However, the deployment of dense mega-constellations introduces severe interference among satellites operating over shared frequency bands. This is, in part, due to the limited flexibility of conventional frequency division duplex (FDD) systems, where fixed bands for downlink (DL) and uplink (UL) transmissions are employed. In this work, we propose dynamic re-assignment of FDD bands for improved interference management in dense deployments and evaluate the performance gain of this approach. To this end, we formulate a joint optimization problem that incorporates dynamic band assignment, user scheduling, and power allocation in both directions. This non-convex mixed integer problem is solved using a combination of equivalence transforms, alternating optimization, and state-of-the-art industrial-grade mixed integer solvers. Numerical results demonstrate that the proposed approach of dynamic FDD band assignment significantly enhances system performance over conventional FDD, achieving up to 94\% improvement in throughput in dense deployments.

cs.IT↗

arXiv2025

Escape-Induced Temporally Correlated Noise Driven Universality Crossover

Universal behavior in far-from-equilibrium systems is driven by interactions between transport processes and noise structure. The Kardar-Parisi-Zhang (KPZ) framework predicts that extensions incorporating conserved currents or temporally correlated noise give rise to distinct growth morphologies and universality classes, yet direct experimental realization has remained elusive. Here, we report atomically resolved Sn thin-film growth on Sb-doped MnBi$_2$Te$_4$, revealing a sharp dynamical crossover between two fundamentally different regimes. Early stage growth follows conserved KPZ scaling, forming two-dimensional islands and stanene layers. Beyond a critical deposition time, temporally correlated noise dominates, driving the nucleation of $\alpha$ -Sn clusters, their evolution into faceted grains, and coexistence with faceted $\beta$-Sn. Molecular dynamics simulation and Auger electron spectroscopy show adatom escape as the microscopic origin of temporally correlated noise, providing a microscopic mechanism for the universality crossover. These findings establish, for the first time, that temporal noise correlations can fundamentally alter the scaling class of a growing interface, linking atomistic kinetics to emergent universal behavior.

cond-mat.mtrl-sci↗

arXiv2025

Sparse Incremental Aggregation in Satellite Federated Learning

This paper studies Federated Learning (FL) in low Earth orbit (LEO) satellite constellations, where satellites are connected via intra-orbit inter-satellite links (ISLs) to their neighboring satellites. During the FL training process, satellites in each orbit forward gradients from nearby satellites, which are eventually transferred to the parameter server (PS). To enhance the efficiency of the FL training process, satellites apply in-network aggregation, referred to as incremental aggregation. In this work, the gradient sparsification methods from [1] are applied to satellite scenarios to improve bandwidth efficiency during incremental aggregation. The numerical results highlight an increase of over 4 x in bandwidth efficiency as the number of satellites in the orbital plane increases.

eess.SP↗

arXiv2024

Sparse Incremental Aggregation in Multi-Hop Federated Learning

This paper investigates federated learning (FL) in a multi-hop communication setup, such as in constellations with inter-satellite links. In this setup, part of the FL clients are responsible for forwarding other client's results to the parameter server. Instead of using conventional routing, the communication efficiency can be improved significantly by using in-network model aggregation at each intermediate hop, known as incremental aggregation (IA). Prior works [1] have indicated diminishing gains for IA under gradient sparsification. Here we study this issue and propose several novel correlated sparsification methods for IA. Numerical results show that, for some of these algorithms, the full potential of IA is still available under sparsification without impairing convergence. We demonstrate a 15x improvement in communication efficiency over conventional routing and a 11x improvement over state-of-the-art (SoA) sparse IA.

cs.DC↗

arXiv2024

Active particles knead three-dimensional gels into open crumbs

Colloidal gels are prime examples of functional materials exhibiting disordered, amorphous, yet meta-stable forms. They maintain stability through short-range attractive forces and their material properties are tunable by external forces. Combining persistent homology analyses and simulations of three-dimensional colloidal gels doped with active particles, we reveal novel dynamically evolving structures of colloidal gels. Specifically, we show that the local injection of energy by active dopants can lead to highly porous, yet compact gel structures that can significantly affect the transport of active particles within the modified colloidal gel. We further show the substantially distinct structural behaviour between active doping of 2D and 3D systems by revealing how passive interfaces play a topologically different role in interacting with active particles in two and three dimensions. The results open the door to an unexplored prospect of forming a wide variety of compact but highly heterogeneous and percolated porous media through active doping of 3D passive matter, with diverse implications in designing new functional materials to active ground remediation.

cond-mat.soft↗

arXiv2023

A semi-parametric approach for estimating consumer valuation distributions using second price auctions

We focus on online second price auctions, where bids are made sequentially, and the winning bidder pays the maximum of the second-highest bid and a seller specified starting price. For many such auctions, the seller does not see all the bids or the total number of bidders accessing the auction, and only observes the current selling prices throughout the course of the auction. We develop a novel semi-parametric approach to estimate the underlying consumer valuation distribution based on this data. Previous semi-parametric or non-parametric approaches in the literature only use the final selling price and assume knowledge of the total number of bidders. The resulting estimate, in particular, can be used by the seller to compute the optimal profit-maximizing price for the product. Our approach is free of tuning parameters, and we demonstrate its computational and statistical efficiency in a variety of simulation settings, and also on an Xbox 7-day auction dataset on eBay.

stat.ME↗

arXiv2022

Efficient Estimation in Tensor Ising Models

The tensor Ising model is a discrete exponential family used for modeling binary data on networks with not just pairwise, but higher-order dependencies. A particularly important class of tensor Ising models are the tensor Curie-Weiss models, where all tuples of nodes of a particular order interact with the same intensity. The maximum likelihood estimator (MLE) is not explicit in this model, due to the presence of an intractable normalizing constant in the likelihood, and a computationally efficient alternative is to use the maximum pseudolikelihood estimator (MPLE). In this paper, we show that the MPLE is in fact as efficient as the MLE (in the Bahadur sense) in the $2$-spin model, and for all values of the null parameter above $\log 2$ in higher-order tensor models. Even if the null parameter happens to lie within the very small window between the threshold and $\log 2$, they are equally efficient unless the alternative parameter is large. Therefore, not only is the MPLE computationally preferable to the MLE, but also theoretically as efficient as the MLE over most of the parameter space. Our results extend to the more general class of Erdős-Rényi hypergraph Ising models, under slight sparsities too.

math.ST↗

arXiv2021

Convergence properties of data augmentation algorithms for high-dimensional robit regression

The logistic and probit link functions are the most common choices for regression models with a binary response. However, these choices are not robust to the presence of outliers/unexpected observations. The robit link function, which is equal to the inverse CDF of the Student's $t$-distribution, provides a robust alternative to the probit and logistic link functions. A multivariate normal prior for the regression coefficients is the standard choice for Bayesian inference in robit regression models. The resulting posterior density is intractable and a Data Augmentation (DA) Markov chain is used to generate approximate samples from the desired posterior distribution. Establishing geometric ergodicity for this DA Markov chain is important as it provides theoretical guarantees for asymptotic validity of MCMC standard errors for desired posterior expectations/quantiles. Previous work [Roy(2012)] established geometric ergodicity of this robit DA Markov chain assuming (i) the sample size $n$ dominates the number of predictors $p$, and (ii) an additional constraint which requires the sample size to be bounded above by a fixed constant which depends on the design matrix $X$. In particular, modern high-dimensional settings where $n < p$ are not considered. In this work, we show that the robit DA Markov chain is trace-class (i.e., the eigenvalues of the corresponding Markov operator are summable) for arbitrary choices of the sample size $n$, the number of predictors $p$, the design matrix $X$, and the prior mean and variance parameters. The trace-class property implies geometric ergodicity. Moreover, this property allows us to conclude that the sandwich robit chain (obtained by inserting an inexpensive extra step in between the two steps of the DA chain) is strictly better than the robit DA chain in an appropriate sense.

math.ST↗

arXiv2021

Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions

Accurate specification of standard occupational classification (SOC) code is critical to the success of many U.S. work visa applications. Determination of correct SOC code relies on careful study of job requirements and comparison to definitions given by the U.S. Bureau of Labor Statistics, which is often a tedious activity. In this paper, we apply methods from natural language processing (NLP) to computationally determine SOC code based on job description. We implement and empirically evaluate a broad variety of predictive models with respect to quality of prediction and training time, and identify models best suited for this task.

cs.LG↗

arXiv2020

Immigration Document Classification and Automated Response Generation

In this paper, we consider the problem of organizing supporting documents vital to U.S. work visa petitions, as well as responding to Requests For Evidence (RFE) issued by the U.S.~Citizenship and Immigration Services (USCIS). Typically, both processes require a significant amount of repetitive manual effort. To reduce the burden of mechanical work, we apply machine learning methods to automate these processes, with humans in the loop to review and edit output for submission. In particular, we use an ensemble of image and text classifiers to categorize supporting documents. We also use a text classifier to automatically identify the types of evidence being requested in an RFE, and used the identified types in conjunction with response templates and extracted fields to assemble draft responses. Empirical results suggest that our approach achieves considerable accuracy while significantly reducing processing time.

cs.LG↗

arXiv2019

Graph Node Embeddings using Domain-Aware Biased Random Walks

The recent proliferation of publicly available graph-structured data has sparked an interest in machine learning algorithms for graph data. Since most traditional machine learning algorithms assume data to be tabular, embedding algorithms for mapping graph data to real-valued vector spaces has become an active area of research. Existing graph embedding approaches are based purely on structural information and ignore any semantic information from the underlying domain. In this paper, we demonstrate that semantic information can play a useful role in computing graph embeddings. Specifically, we present a framework for devising embedding strategies aware of domain-specific interpretations of graph nodes and edges, and use knowledge of downstream machine learning tasks to identify relevant graph substructures. Using two real-life domains, we show that our framework yields embeddings that are simple to implement and yet achieve equal or greater accuracy in machine learning tasks compared to domain independent approaches.

cs.LG↗

arXiv2019

Indexes in Microsoft SQL Server

Indexes are the best apposite choice for quickly retrieving the records. This is nothing but cutting down the number of Disk IO. Instead of scanning the complete table for the results, we can decrease the number of IO's or page fetches using index structures such as B-Trees or Hash Indexes to retrieve the data faster. The most convenient way to consider an index is to think like a dictionary. It has words and its corresponding definitions against those words. The dictionary will have an index on "word" because when we open a dictionary and we want to fetch its corresponding word quickly, then find its definition. The dictionary generally contains just a single index - an index ordered by word. When we modify any record and change the corresponding value of an indexed column in a clustered index, the database might require moving the entire row into a separately new position to maintain the rows in the sorted order. This action is essentially turned into an update query into a DELETE followed by an INSERT, and it decreases the performance of the query. The clustered index in the table can often be available on the primary key or a foreign key column because key values usually do not modify once a record is injected into the database.

cs.DB↗

arXiv2019

Benefits of AWS in Modern Cloud

This article gives an overview of the benefits of AWS in the modern cloud. Cloud computing is performing well in todays World and boosting the ability to use the internet more than ever. Cloud computing gradually developed a method to use the benefits of it in most of the organizations. It is very demanding in all businesses tasked with improving the quality of service reducing costs as the organization pays for the service only what they consume based on the incoming and outgoing traffic.

cs.CY↗

arXiv2019

How IT allows E-Participation in Policy-Making Process

With the art and practice of government policy-making, public work, and citizen participation, many governments adopt information and communication technologies (ICT) as a vehicle to facilitate their relationship with citizens. This participation process is widely known as E-Participation or Electronic Participation. This article focuses on different performance indicators and the relevant tools for each level. Despite the growing scientific and pragmatic significance of e-participation, that area still was not able to grow as it was expected. Our diverse set of knowledge and e-participation policies and its implementation is very limited. This is the key reason why e-participation initiatives in practice often fall short of expectations. This study collects the existing perceptions from the various interdisciplinary scientific literature to determine a unifying definition and demonstrates the strong abilities of e-participation and other related components which have great potential in the coming years.

cs.CY↗

arXiv2019

Popular SQL Server Database Encryption Choices

This article gives an overview of different database encryption choices in SQL Server. Which one works best in which situation. In today's world Data is more crucial than the expensive hardware cost. No one wants their personal data to be comprised. Same for business houses as well and they also do not want their data to be inappropriately handled to go out of the business. To help protect the public rights and safety, recently this year, the European Union had come up with strict rules and regulation of GDPR (General Data Protection Regulation).

cs.DB↗