S. N. Lahiri

S. N. Lahiri
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S. N. Lahiri

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Physics - Statistical Mechanics (15)
Mathematics - Statistics (10)
Statistics - Theory (10)
Computer Science - Computation and Language (7)
High Energy Physics - Theory (7)
Statistics - Machine Learning (4)
Statistics - Methodology (4)
General Relativity and Quantum Cosmology (3)
High Energy Physics - Experiment (3)
Physics - Instrumentation and Detectors (3)
Quantitative Biology - Neurons and Cognition (3)
Physics - Disordered Systems and Neural Networks (2)
Computer Science - Learning (2)
High Energy Physics - Phenomenology (1)
Physics - Physics and Society (1)
Nuclear Experiment (1)
Statistics - Applications (1)
Physics - Atomic Physics (1)
Computer Science - Information Theory (1)
Computer Science - Digital Libraries (1)
Computer Science - Software Engineering (1)
Mathematics - Information Theory (1)
Physics - Biological Physics (1)
Mathematics - Differential Geometry (1)
Computer Science - Information Retrieval (1)

Publications Authored By S. N. Lahiri

In presence of Gauss-Bonnet corrections, we study anisotropic inflation aided by a massless $SU(2)$ gauge field where both the gauge field and the Gauss-Bonnet term are non-minimally coupled to the inflaton. In this scenario, under slow-roll approximations, the anisotropic inflation is realized as an attractor solution with quadratic forms of inflaton potential and Gauss-Bonnet coupling function. We show that the degree of anisotropy is proportional to the additive combination of two slow-roll parameters of the theory. Read More

The Fluctuation Theorems are a group of exact relations that remain valid irrespective of how far the system has been driven away from equilibrium. Other than having practical applications, like determination of equilibrium free energy change from nonequilibrium processes, they help in our understanding of the Second Law and the emergence of irreversibility from time-reversible equations of motion at microscopic level. A vast number of such theorems have been proposed in literature, ranging from Hamiltonian to stochastic systems, from systems in steady state to those in transient regime, and for both open and closed quantum systems. Read More

Change-impact analysis (CIA) is the task of determining the set of program elements impacted by a program change. Precise CIA has great potential to avoid expensive testing and code reviews for (parts of) changes that are refactorings (semantics-preserving). Existing CIA is imprecise because it is coarse-grained, deals with only few refactoring patterns, or is unaware of the change semantics. Read More

It has recently been shown that the Jarzynski equality gets modified, when there are experimental errors in computing work. This modified result also holds good in presence of feedback. In this work, we use a simple toy model, that of a Szilard engine, to prove these results both in the presence and in absence of feedback. Read More

We investigate the statistics of heat exchange between a finite system coupled to reservoir(s). We have obtained analytical results for heat fluctuation theorem in the transient regime considering the Hamiltonian dynamics of the composite system consisting of the system of interest and the heat bath(s). The system of interest is driven by an external protocol. Read More

Interesting data often concentrate on low dimensional smooth manifolds inside a high dimensional ambient space. Random projections are a simple, powerful tool for dimensionality reduction of such data. Previous works have studied bounds on how many projections are needed to accurately preserve the geometry of these manifolds, given their intrinsic dimensionality, volume and curvature. Read More

Authorship Attribution is a long-standing problem in Natural Language Processing. Several statistical and computational methods have been used to find a solution to this problem. In this paper, we have proposed methods to deal with the authorship attribution problem in Bengali. Read More

In this paper we have proposed methods to analyze the readability of Bengali language texts. We have got some exceptionally good results out of the experiments. Read More

We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights. Our results reveal an order-to-chaos expressivity phase transition, with networks in the chaotic phase computing nonlinear functions whose global curvature grows exponentially with depth but not width. We prove this generic class of deep random functions cannot be efficiently computed by any shallow network, going beyond prior work restricted to the analysis of single functions. Read More

In this paper we explore the problem of document summarization in Persian language from two distinct angles. In our first approach, we modify a popular and widely cited Persian document summarization framework to see how it works on a realistic corpus of news articles. Human evaluation on generated summaries shows that graph-based methods perform better than the modified systems. Read More

We study anisotropic inflation with Gauss-Bonnet correction in presence of a massless vector field. In this scenario, exact anisotropic power-law inflation is realized when the inflaton potential, gauge coupling function and the Gauss-Bonnet coupling are exponential functions. We show that anisotropy becomes proportional to two slow-roll parameters of the theory and hence gets enhanced in presence of quadratic curvature corrections. Read More

This paper provides an alternative to penalized estimators for estimation and vari- able selection in high dimensional linear regression models with measurement error or missing covariates. We propose estimation via bias corrected least squares after model selection. We show that by separating model selection and estimation, it is possible to achieve an improved rate of convergence of the L2 estimation error compared to the rate sqrt{s log p/n} achieved by simultaneous estimation and variable selection methods such as L1 penalized corrected least squares. Read More

This paper considers inference in a partially identified moment (in)equality model with many moment inequalities. We propose a novel two-step inference procedure that combines the methods proposed by Chernozhukov, Chetverikov and Kato (2014c) (CCK14, hereafter) with a first-step moment inequality selection based on the Lasso. Our method controls size uniformly, both in underlying parameter and data distribution. Read More

Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynamics do not provide general limits on power, precision and speed. Read More

Equilibrium is characterized by its fundamental properties such as the fluctuation-dissipation theorem, the detailed balance, and no heat dissipation. Based on the stochastic thermodynamics, we show that these three properties are equivalent to each other in conventional Langevin systems with microscopic reversibility. In the presence of velocity-dependent forces breaking the microscopic reversibility, we prove that the fluctuation-dissipation theorem and the detailed balance mutually exclude each other and no equivalence relation is possible between any two of the three properties. Read More

Central limit theorems are established for the sum, over a spatial region, of observations from a linear process on a $d$-dimensional lattice. This region need not be rectangular, but can be irregularly-shaped. Separate results are established for the cases of positive strong dependence, short range dependence, and negative dependence. Read More

Fluctuation theorems have become an important tool in single molecule biophysics to measure free energy differences from non-equilibrium experiments. When significant coarse-graining or noise affect the measurements, the determination of the free energies becomes challenging. In order to address this thermodynamic inference problem, we propose improved estimators of free energy differences based on fluctuation theorems, which we test on a number of examples. Read More

Motivated by a recent work on the metabolism of carbohydrates in bacteria, we study the kinetics and thermodynamics of two classic models for reversible polymerization, one preserving the total polymer concentration and the other one not. The chemical kinetics is described by rate equations following the mass-action law. We consider a closed system and nonequilibrium initial conditions and show that the system dynamically evolves towards equilibrium where detailed balance is satisfied. Read More

We introduce a corpus of 7,032 sentences rated by human annotators for formality, informativeness, and implicature on a 1-7 scale. The corpus was annotated using Amazon Mechanical Turk. Reliability in the obtained judgments was examined by comparing mean ratings across two MTurk experiments, and correlation with pilot annotations (on sentence formality) conducted in a more controlled setting. Read More

We explore the existence of Lorentzian wormholes in the context of an effective on-brane, scalar-tensor theory of gravity. In such theories, the timelike convergence condition, which is always violated for wormholes, has contributions, via the field equations,from on-brane matter as well as from an effective geometric stress energy generated by a bulk-induced radion field. It is shown that, for a class of wormholes, the required on-brane matter, as seen by an on-brane observer in the Jordan frame, is not exotic and does not violate the Weak Energy Condition. Read More

We derive the extended fluctuation theorems in presence of multiple measurements and feedback, when the system is governed by Hamiltonian dynamics. We use only the forward phase space trajectories in the derivation. However, to obtain an expression for the efficacy parameter, we must necessarily use the notion of reverse trajectory. Read More

This paper develops empirical likelihood methodology for irregularly spaced spatial data in the frequency domain. Unlike the frequency domain empirical likelihood (FDEL) methodology for time series (on a regular grid), the formulation of the spatial FDEL needs special care due to lack of the usual orthogonality properties of the discrete Fourier transform for irregularly spaced data and due to presence of nontrivial bias in the periodogram under different spatial asymptotic structures. A spatial FDEL is formulated in the paper taking into account the effects of these factors. Read More

Spherically symmetric, static on-brane geometries in the Kanno-Soda (KS) effective scalar-tensor theory of on-brane gravity are discussed. In order to avoid brane collisions and/or an infinite inter-brane distance, at finite values of the brane coordinates, it is necessary that the radion scalar be everywhere finite and non-zero. This requirement constrains the viability of the standard, well-known solutions in General Relativity (GR), in the context of the KS effective theory. Read More

The square root velocity function (SRVF), introduced by Srivastava et al, has proved to be an effective way to compare absolutely continuous curves in $R^N$ modulo reparametrization. Several computational papers have been published based on this method. In this paper, we carefully establish the theoretical foundations of the SRVF method. Read More

As a follow-up to our previous paper arxiv: 1309.4244[hep-th], we determine radion induced spherically symmetric solution using the gradient approximation scheme, when two warped $3$-branes are slant with respect to each other such that the radion field in this case is a radial co-ordinate varying function. The slanting between the branes is assumed to be small. Read More

An inter-rater agreement study is performed for readability assessment in Bengali. A 1-7 rating scale was used to indicate different levels of readability. We obtained moderate to fair agreement among seven independent annotators on 30 text passages written by four eminent Bengali authors. Read More

We investigate the accuracy of two general non-parametric methods for estimating optimal block lengths for block bootstraps with time series - the first proposed in the seminal paper of Hall, Horowitz and Jing (Biometrika 82 (1995) 561-574) and the second from Lahiri et al. (Stat. Methodol. Read More

The detailed fluctuation theorems of the exact form $P(A)/P(-A)=e^A$ exist only for a handful of variables $A$, namely for work (Crooks theorem), for total entropy change (Seifert's theorem), etc. However, the so-called modified detailed fluctuation theorems can be formulated for several other thermodynamic variables as well. The difference is that the modified relations contain an extra factor, which is dependent on $A$. Read More

Keyword and keyphrase extraction is an important problem in natural language processing, with applications ranging from summarization to semantic search to document clustering. Graph-based approaches to keyword and keyphrase extraction avoid the problem of acquiring a large in-domain training corpus by applying variants of PageRank algorithm on a network of words. Although graph-based approaches are knowledge-lean and easily adoptable in online systems, it remains largely open whether they can benefit from centrality measures other than PageRank. Read More

Standard blockwise empirical likelihood (BEL) for stationary, weakly dependent time series requires specifying a fixed block length as a tuning parameter for setting confidence regions. This aspect can be difficult and impacts coverage accuracy. As an alternative, this paper proposes a new version of BEL based on a simple, though nonstandard, data-blocking rule which uses a data block of every possible length. Read More

The exchange fluctuation theorem for heat exchanged between two systems at different temperatures, when kept in direct contact, has been investigated by C. Jarzynski and D. K. Read More

We present a simple derivation of the integral fluctuation theorems for excess housekeeping heat for an underdamped Langevin system, without using the concept of dual dynamics. In conformity with the earlier results, we find that the fluctuation theorem for housekeeping heat holds when the steady state distributions are symmetric in velocity, whereas there is no such requirement for the excess heat. We first prove the integral fluctuation theorem for the excess heat, and then show that it naturally leads to the integral fluctuation theorem for housekeeping heat. Read More

In this paper, we explore a set of novel features for authorship attribution of documents. These features are derived from a word network representation of natural language text. As has been noted in previous studies, natural language tends to show complex network structure at word level, with low degrees of separation and scale-free (power law) degree distribution. Read More

In this paper, we explore complex network properties of word collocation networks (Ferret, 2002) from four different genres. Each document of a particular genre was converted into a network of words with word collocations as edges. We analyzed graphically and statistically how the global properties of these networks varied across different genres, and among different network types within the same genre. Read More

The third Workshop of the NuMass series ("The Future of Neutrino Mass Measurements: Terrestrial, Astrophysical, and Cosmological Measurements in the Next Decade: NuMass 2013") was held at Dipartimento di Fisica "G. Occhialini, University of Milano-Bicocca in Milano, Italy, on 4-7 February 2013. The goal of this international workshop was to review the status and future of direct and indirect neutrino mass measurements in the laboratory as well as from astrophysical and cosmological observations. Read More

The determination of the absolute scale of the neutrino masses is one of the most challenging present questions in particle physics. The most stringent limit, $m(\bar{\nu}_{\mathrm{e}})<2$eV, was achieved for the electron anti-neutrino mass \cite{numass}. Different approaches are followed to achieve a sensitivity on neutrino masses in the sub-eV range. Read More

About a decade ago, using a specific expansion scheme, effective, on-brane scalar tensor theories of gravity were proposed by Kanno and Soda (Phys.Rev. {\bf D 66} 083506 ,(2002)) in the context of the warped two brane model of Randall--Sundrum. Read More

The determination of the absolute scale of the neutrino masses is one of the most challenging questions in particle physics. Different approaches are followed to achieve a sensitivity on neutrino masses in the sub-eV range. Among them, experiments exploring the beta decay and electron capture processes of suitable nuclides can provide necessary information on the electron neutrino mass value. Read More

We compare the fluctuation relations for work and entropy in underdamped and overdamped systems, when the friction coefficient of the medium is space-dependent. We find that these relations remain unaffected in both cases. However, for the overdamped system, the analysis is more involved, and a blind application of normal rules of calculus would lead to inconsistent results. Read More

This paper formulates a penalized empirical likelihood (PEL) method for inference on the population mean when the dimension of the observations may grow faster than the sample size. Asymptotic distributions of the PEL ratio statistic is derived under different component-wise dependence structures of the observations, namely, (i) non-Ergodic, (ii) long-range dependence and (iii) short-range dependence. It follows that the limit distribution of the proposed PEL ratio statistic can vary widely depending on the correlation structure, and it is typically different from the usual chi-squared limit of the empirical likelihood ratio statistic in the fixed and finite dimensional case. Read More

Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? And second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. Read More

In this paper we describe two bootstrap methods for massive data sets. Naive applications of common resampling methodology are often impractical for massive data sets due to computational burden and due to complex patterns of inhomogeneity. In contrast, the proposed methods exploit certain structural properties of a large class of massive data sets to break up the original problem into a set of simpler subproblems, solve each subproblem separately where the data exhibit approximate uniformity and where computational complexity can be reduced to a manageable level, and then combine the results through certain analytical considerations. Read More

A fluctuation relation for heat engines (FRHE) has been derived recently. In the beginning, the system is in contact with the cooler bath. The system is then coupled to the hotter bath and external parameters are changed cyclically, eventually bringing the system back to its initial state, once the coupling with the hot bath is switched off. Read More

The fluctuation theorems have remained one of the cornerstones in the study of systems that are driven far out of equilibrium, and they provide strong constraints on the fraction of trajectories that behave atypically in light of the second law. They have mainly been derived for a predetermined external drive applied to the system. However, to improve the efficiency of a process, one needs to incorporate protocols that are modified by receiving feedbacks about the recent state of the system, during its evolution. Read More

This paper develops goodness of fit statistics that can be used to formally assess Markov random field models for spatial data, when the model distributions are discrete or continuous and potentially parametric. Test statistics are formed from generalized spatial residuals which are collected over groups of nonneighboring spatial observations, called concliques. Under a hypothesized Markov model structure, spatial residuals within each conclique are shown to be independent and identically distributed as uniform variables. Read More

In the backdrop of generalised Randall-Sundrum braneworld scenario, we look for the possible origin of an effective four dimensional cosmological constant ($\Omega_{vis}$) on the visible 3-brane due to the effects of bulk curvature and the modulus field that can either be a constant or dependent on extra dimensional co-ordinate $y$ or a time dependent quantity. In case of constant or $y$ dependent modulus field, the induced $\Omega_{vis}$ leads to an exponentially expanding Universe. For such modulus fields the presence of vacuum energy densities on either of the 3-branes as well as a non-vanishing bulk curvature $l$ ($l \sim {\Lambda_5}^{-1}$) are essential to generate an effective $\Omega_{vis}$. Read More