arXiv subjects

Ashish Gupta

Publications and source records attributed to Ashish Gupta.

At least 37 records · Page 2Linked to original sources

arXiv2022

Systematic investigation of channel coupling effects on elastic, inelastic and neutron transfer channels in $^6$Li+$^{159}$Tb

Elastic scattering angular distribution for weakly bound nucleus $^{6}$Li on the deformed rare earth $^{159}$Tb target nucleus has been measured at energies around the Coulomb barrier. The elastic scattering cross sections for this reaction consist of inelastic contributions from low lying excited states of $^{159}$Tb. The pure elastic cross-sections have been extracted from the admixture of elastic and inelastic data. The optical model potential parameters for the system have been obtained from the extracted pure elastic scattering cross sections. Coupled channel calculations have been performed with this set of potential parameters, to compare the theoretical and experimental inelastic scattering cross sections. The work has been extended to obtain the spectroscopic factor for $^{158}$Tb+n configuration from the experimental 1n-pickup data.

nucl-ex↗

arXiv2022

Securing Federated Learning against Overwhelming Collusive Attackers

In the era of a data-driven society with the ubiquity of Internet of Things (IoT) devices storing large amounts of data localized at different places, distributed learning has gained a lot of traction, however, assuming independent and identically distributed data (iid) across the devices. While relaxing this assumption that anyway does not hold in reality due to the heterogeneous nature of devices, federated learning (FL) has emerged as a privacy-preserving solution to train a collaborative model over non-iid data distributed across a massive number of devices. However, the appearance of malicious devices (attackers), who intend to corrupt the FL model, is inevitable due to unrestricted participation. In this work, we aim to identify such attackers and mitigate their impact on the model, essentially under a setting of bidirectional label flipping attacks with collusion. We propose two graph theoretic algorithms, based on Minimum Spanning Tree and k-Densest graph, by leveraging correlations between local models. Our FL model can nullify the influence of attackers even when they are up to 70% of all the clients whereas prior works could not afford more than 50% of clients as attackers. The effectiveness of our algorithms is ascertained through experiments on two benchmark datasets, namely MNIST and Fashion-MNIST, with overwhelming attackers. We establish the superiority of our algorithms over the existing ones using accuracy, attack success rate, and early detection round.

cs.LG↗

arXiv2022

A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit

The phenomenon of compounding is ubiquitous in Sanskrit. It serves for achieving brevity in expressing thoughts, while simultaneously enriching the lexical and structural formation of the language. In this work, we focus on the Sanskrit Compound Type Identification (SaCTI) task, where we consider the problem of identifying semantic relations between the components of a compound word. Earlier approaches solely rely on the lexical information obtained from the components and ignore the most crucial contextual and syntactic information useful for SaCTI. However, the SaCTI task is challenging primarily due to the implicitly encoded context-sensitive semantic relation between the compound components. Thus, we propose a novel multi-task learning architecture which incorporates the contextual information and enriches the complementary syntactic information using morphological tagging and dependency parsing as two auxiliary tasks. Experiments on the benchmark datasets for SaCTI show 6.1 points (Accuracy) and 7.7 points (F1-score) absolute gain compared to the state-of-the-art system. Further, our multi-lingual experiments demonstrate the efficacy of the proposed architecture in English and Marathi languages.The code and datasets are publicly available at https://github.com/ashishgupta2598/SaCTI

cs.CL↗

arXiv2022

FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Buildings

With the enhancement of people's living standards and rapid growth of communication technologies, residential environments are becoming smart and well-connected, increasing overall energy consumption substantially. As household appliances are the primary energy consumers, their recognition becomes crucial to avoid unattended usage, thereby conserving energy and making smart environments more sustainable. An appliance recognition model is traditionally trained at a central server (service provider) by collecting electricity consumption data, recorded via smart plugs, from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels that may appear when an appliance gets connected to a non-designated smart plug. While addressing these issues jointly, we propose a novel federated learning approach to appliance recognition, called FedAR+, enabling decentralized model training across clients in a privacy preserving way even with mislabeled training data. FedAR+ introduces an adaptive noise handling method, essentially a joint loss function incorporating weights and label distribution, to empower the appliance recognition model against noisy labels. By deploying smart plugs in an apartment complex, we collect a labeled dataset that, along with two existing datasets, are utilized to evaluate the performance of FedAR+. Experimental results show that our approach can effectively handle up to $30\%$ concentration of noisy labels while outperforming the prior solutions by a large margin on accuracy.

cs.LG↗

arXiv2022

Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning

Federated learning offers a framework of training a machine learning model in a distributed fashion while preserving privacy of the participants. As the server cannot govern the clients' actions, nefarious clients may attack the global model by sending malicious local gradients. In the meantime, there could also be unreliable clients who are benign but each has a portion of low-quality training data (e.g., blur or low-resolution images), thus may appearing similar as malicious clients. Therefore, a defense mechanism will need to perform a three-fold differentiation which is much more challenging than the conventional (two-fold) case. This paper introduces MUD-HoG, a novel defense algorithm that addresses this challenge in federated learning using long-short history of gradients, and treats the detected malicious and unreliable clients differently. Not only this, but we can also distinguish between targeted and untargeted attacks among malicious clients, unlike most prior works which only consider one type of the attacks. Specifically, we take into account sign-flipping, additive-noise, label-flipping, and multi-label-flipping attacks, under a non-IID setting. We evaluate MUD-HoG with six state-of-the-art methods on two datasets. The results show that MUD-HoG outperforms all of them in terms of accuracy as well as precision and recall, in the presence of a mixture of multiple (four) types of attackers as well as unreliable clients. Moreover, unlike most prior works which can only tolerate a low population of harmful users, MUD-HoG can work with and successfully detect a wide range of malicious and unreliable clients - up to 47.5% and 10%, respectively, of the total population. Our code is open-sourced at https://github.com/LabSAINT/MUD-HoG_Federated_Learning.

cs.CR↗

arXiv2021

Introducing Services and Protocols for Inter-Hub Transportation in the Physical Internet

The Physical Internet (PI) puts high emphasis on enabling logistics to reliably perform at the speed mandated by and promised to customers, and to do so efficiently and sustainably. To do so, goods to be moved are encapsulated in modular containers and these are flowed from hub to hub in relay mode. At each hub, PI enables fast and efficient dynamic consolidation of sets of containers to be shipped together to next hubs. Each consolidated set is assigned to an appropriate vehicle so to enact the targeted transport. In this paper, we address the case where transportation service providers are available to provide vehicles and trailers of distinct dimensions on demand according to openly agreed and/or contracted terms. We describe the essence of such terms, notably relative to expected frequency distribution of transport requests, and expectations about time between request and arrival at hub. In such a context, we introduce rigorous generic protocols that can be applied at each hub so as to dynamically generate consolidation sets of modular containers and requests for on-demand transportation services, in an efficient, resilient, and sustainable way ensuring reliable pickup and delivery within the promised time windows. We demonstrate the performance of such protocols using a simulation-based experiment for a national intercity express parcel logistic network. We finally provide conclusive remarks and promising avenues for field implementation and further research.

math.OC↗

arXiv2021

Fusion of 16O+165Ho at deep sub-barrier energies

Fusion cross-sections have been measured for the asymmetric system 16O+165Ho at energies near and deep below the Coulomb barrier with an aim to investigate the occurrence of fusion hindrance for the system. Fusion cross sections down to ~ 700 nb have been measured using the off-beam gamma-ray technique. The fusion cross sections have been compared with the coupled channel calculations. Although the onset of fusion hindrance could not be observed experimentally, an indication of a small deviation of the experimental fusion cross-sections with respect to the calculated cross-sections could be observed at the lowest energy measured. However, the energy onset of fusion hindrance has been obtained from the extrapolation technique and is found to be about 2 MeV below the lowest energy of the present measurement.

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arXiv2021

Automorphisms of Quantum Polynomials

An important step in the determination of the automorphism group of the quantum torus of rank $n$ (or twisted group algebra of $\mathbb Z^n$) is the determination of its so-called non-scalar automorphisms. We present a new algorithimic approach towards this problem based on the bivector representation $\wedge^2 : \mathrm{GL}(n, \mathbb Z) \rightarrow \mathrm{GL}(\binom{n}{2}, \mathbb Z)$ of $\mathrm{GL}(n, \mathbb Z)$ and thus compute the non-scalar automorphism group $\mathrm{Aut}(\mathbb Z^n, λ)$ in several new cases. As an application of our ideas we show that the quantum polynomial algebra (multiparameter quantum affine space of rank $n$) has only scalar (or toric) automorphisms provided that the torsion-free rank of the subgroup generated by the defining multiparameters is no less than $\binom{n - 1}{2} + 1$ thus improving an earlier result. We also investigate the question: when is a multiparameter quantum affine space free of so-called linear automorphisms other than those arising from the action of the $n$-torus ${(\mathbb F^\ast)}^n$.

math.RA↗

arXiv2020

Hyperparameter optimization with REINFORCE and Transformers

Reinforcement Learning has yielded promising results for Neural Architecture Search (NAS). In this paper, we demonstrate how its performance can be improved by using a simplified Transformer block to model the policy network. The simplified Transformer uses a 2-stream attention-based mechanism to model hyper-parameter dependencies while avoiding layer normalization and position encoding. We posit that this parsimonious design balances model complexity against expressiveness, making it suitable for discovering optimal architectures in high-dimensional search spaces with limited exploration budgets. We demonstrate how the algorithm's performance can be further improved by a) using an actor-critic style algorithm instead of plain vanilla policy gradient and b) ensembling Transformer blocks with shared parameters, each block conditioned on a different auto-regressive factorization order. Our algorithm works well as both a NAS and generic hyper-parameter optimization (HPO) algorithm: it outperformed most algorithms on NAS-Bench-101, a public data-set for benchmarking NAS algorithms. In particular, it outperformed RL based methods that use alternate architectures to model the policy network, underlining the value of using attention-based networks in this setting. As a generic HPO algorithm, it outperformed Random Search in discovering more accurate multi-layer perceptron model architectures across 2 regression tasks. We have adhered to guidelines listed in Lindauer and Hutter while designing experiments and reporting results.

cs.LG↗

arXiv2020

Approaches and Applications of Early Classification of Time Series: A Review

Early classification of time series has been extensively studied for minimizing class prediction delay in time-sensitive applications such as healthcare and finance. A primary task of an early classification approach is to classify an incomplete time series as soon as possible with some desired level of accuracy. Recent years have witnessed several approaches for early classification of time series. As most of the approaches have solved the early classification problem with different aspects, it becomes very important to make a thorough review of the existing solutions to know the current status of the area. These solutions have demonstrated reasonable performance in a wide range of applications including human activity recognition, gene expression based health diagnostic, industrial monitoring, and so on. In this paper, we present a systematic review of current literature on early classification approaches for both univariate and multivariate time series. We divide various existing approaches into four exclusive categories based on their proposed solution strategies. The four categories include prefix based, shapelet based, model based, and miscellaneous approaches. The authors also discuss the applications of early classification in many areas including industrial monitoring, intelligent transportation, and medical. Finally, we provide a quick summary of the current literature with future research directions.

cs.LG↗

arXiv2019

Gelfand-Kirillov dimensions of simple modules over twisted group algebras $k \ast A$

For the $n$-dimensional multiparameter quantum torus algebra $Λ_{\mathfrak q}$ over a field $k$ defined by a multiplicatively antisymmetric matrix $\mathfrak q = (q_{ij})$ we show that in the case when the torsion-free rank of the subgroup of $k^\times$ generated by the $q_{ij}$ is large enough there is a characteristic set of values (possibly with gaps) from $0$ to $n$ that can occur as the Gelfand--Kirillov dimension of simple modules. The special case when $\mathrm{K}.\dim(Λ_{\mathfrak q}) = n - 1$ and $Λ_{\mathfrak q}$ is simple studied in A.~Gupta, {\it $\uppercase{\mbox{GK}}$-dimensions of simple modules over $K[X^{\pm 1}, σ]$}, Comm. Algebra, {\bf 41(7)} (2013), 2593--2597 is considered without assuming simplicity and it is shown that a dichotomy still holds for the GK dimension of simple modules.

math.RA↗

arXiv2016

A Dichotomy for GK dimensions of simple modules over simple differential rings

The Gelfand--Kirillov dimension has gained importance since its introduction as an tool in the study of non-commutative infinite dimensional algebras and their modules. In this paper we show a dichotomy for the Gelfand--Kirillov dimension of simple modules over certain simple rings of differential operators.

math.RA↗

arXiv2016

Maximal abelian normal subgroups in Nilpotent groups of class 2

A maximal abelian normal subgroup A in a nilpotent group N is self-centralizing. This makes their role an important one in determining the structure of the nilpotent group. For example if A is finite then N is also finite. In the free nilpotent group of class 2 such a group is always a cyclic extension of the center. However this need not be true in an arbitrary finitely generated class two nilpotent group. We show that three generator nilpotent groups violating this condition can be easily found. However in the case when there are four or more generators certain conditions must hold.

math.GR↗

arXiv2015

Representations of the $n$ dimensional quantum torus

The $n$-dimensional quantum torus $\mathcal O_{\mathbf q}((F^\times)^n)$ is defined as the associative $F$-algebra generated by $x_1, \cdots, x_n$ together with their inverses satisfying the relations $x_ix_j = q_{ij}x_jx_i$, where $\mathbf q = (q_{ij})$. We show that the modules that are finitely generated over certain commutative sub-algebras $\mathscr B$ are $\mathscr B$-torsion-free and have finite length. We determine the Gelfand-Kirillov dimensions of simple modules in the case when \[ \Kdim(\mathcal O_{\mathbf q}((F^\times)^n)) = n - 1, \] where $\Kdim$ stands for the Krull dimension. In this case if $M$ is a simple $\mathcal O_{\mathbf q}((F^\times)^n)$-module then $ \gk(M) = 1$ or \[ \gk(M) \ge \gk(\mathcal O_{\mathbf q}((F^\times)^n)) - \gk(\mathcal Z(\mathcal O_{\mathbf q}((F^\times)^n))) - 1,\] where $\mathcal Z(C)$ stands for the center of an algebra $C$. We also show that there always exists a simple $F \s A$-module satisfying the above inequality.

math.QA↗

arXiv2014

A note on modules over the quantum torus

The $n$-dimensional quantum torus $Λ$ is defined to be the $F$-algebra generated by variables $y_1, \cdots, y_n$ with the relations $y_iy_j = q_{ij}y_jy_i$ where $q_{ij}$ are suitable scalars from the base field. This algebra is also the twisted group algebra of the free abelian group $A$ on $n$ generators. Each subgroup of corresponds to a sub-algebra of the quanutm torus. $A$ may contain non-trivial subgroups $B$ so that the corresponding sub-algebra is commutative. In this paper we show that whenever the quantum torus $Λ$ has center $F$, a $Λ$ module $M$ that is finitely generated over such a commutative sub-algebra $U$ is necessarily torsion-free over $U$ and has finite length. We also show that $M$ has finite length. We also apply tbis result to modules over infinite nilpotent groups of class 2.

math.RA↗

arXiv2014

The krull and global dimension of the tensor product of n-dimensional quantum tori

The n-dimensional quantum torus is defined as the $F$-algebra generated by variables $x_1, \cdots, x_n$ together with their inverses satisfying the relations $x_ix_j = q_{ij}x_jx_i$, where $q_{ij} \in F$. The Krull and global dimensions of this algebra are known to coincide and the common value is equal to the supremum of the rank of certain subgroups of $\langle x_1, \cdots, x_n \rangle$ that can be associated with this algebra. In this paper we study how these dimensions behave with respect to taking tensor products of quantum tori %over the base field. We derive a best possible upper bound for the dimension of such a tensor product and %deduce from this special cases in which the dimension is additive with respect to tensoring.

math.RA↗

arXiv2012

Minimally Infrequent Itemset Mining using Pattern-Growth Paradigm and Residual Trees

Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth paradigm to find minimally infrequent itemsets. A minimally infrequent itemset has no subset which is also infrequent. We also introduce the novel concept of residual trees. We further utilize the residual trees to mine multiple level minimum support itemsets where different thresholds are used for finding frequent itemsets for different lengths of the itemset. Finally, we analyze the behavior of our algorithm with respect to different parameters and show through experiments that it outperforms the competing ones.

cs.DB↗

arXiv2011

Modules over quantum Laurent polynomials I

It is shown that the Gelfand--Kirillov dimension for modules over quantum Laurent polynomials is tensor-minimal. The Brookes--Groves invariant associated with a tensor product of modules is determined. It is also shown that there can be nonholonmic simple modules.

math.RA↗