arXiv subjects

Dipankar Das

Publications and source records attributed to Dipankar Das.

At least 37 records · Page 2Linked to original sources

arXiv2023

A Model of Competitive Assortment Planning Algorithm

With a novel search algorithm or assortment planning or assortment optimization algorithm that takes into account a Bayesian approach to information updating and two-stage assortment optimization techniques, the current research provides a novel concept of competitiveness in the digital marketplace. Via the search algorithm, there is competition between the platform, vendors, and private brands of the platform. The current paper suggests a model and discusses how competition and collusion arise in the digital marketplace through assortment planning or assortment optimization algorithm. Furthermore, it suggests a model of an assortment algorithm free from collusion between the platform and the large vendors. The paper's major conclusions are that collusive assortment may raise a product's purchase likelihood but fail to maximize expected revenue. The proposed assortment planning, on the other hand, maintains competitiveness while maximizing expected revenue.

econ.TH↗

arXiv2023

Fingerprinting the Type-Z three Higgs doublet models

There has been great interest in a model with three Higgs doublets in which fermions with a particular charge couple to a single and distinct Higgs field. We study the phenomenological differences between the two common incarnations of this so-called Type-Z 3HDM. We point out that the differences between the two models arise from the scalar potential only. Thus we focus on observables that involve the scalar self-couplings. We find it difficult to uncover features that can uniquely set apart the $Z_3$ variant of the model. However, by studying the dependence of the trilinear Higgs couplings on the nonstandard masses, we have been able to isolate some of the exclusive indicators for the $Z_2\times Z_2$ version of the Type-Z 3HDM. This highlights the importance of precision measurements of the trilinear Higgs couplings.

hep-ph↗

arXiv2023

Complementarity in Demand-side Variables and Educational Participation

Decision to participate in education depends on the circumstances individual inherits and on the returns to education she expects as well. If one person from any socio-economically disadvantaged social group inherits poor circumstances measured in terms of family background, then she is having poor opportunities vis-à-vis her capability set becomes confined. Accordingly, her freedom to choose the best alternative from many is also less, and she fails to expect the potential returns from educational participation. Consequently, a complementary relationship between the circumstances one inherits and the returns to education she expects can be observed. This paper is an attempt to look at this complementarity on the basis of theoretical logic and empirical investigation, which enables us to unearth the origin of inter-group disparity in educational participation, as is existed across the groups defined by taking caste and gender together in Indian society. Furthermore, in the second piece of analysis, we assess the discrimination in the likelihood of educational participation by invoking the method of decomposition of disparity in the likelihood of educational participation applicable in the logistic regression models, which enables us to re-establish the earlier mentioned complementary relationship.

econ.TH↗

arXiv2023

Democratic three Higgs-doublet models: the custodial limit and wrong-sign Yukawa

We study two novel aspects of democratic 3HDMs -- the custodial limit and the possibility of wrong-sign Yukawa couplings. In the custodial limit, the democratic 3HDMs can easily negotiate the constraints from the electroweak $T$-parameter. We also uncover the possibility of having wrong-sign Yukawa couplings in democratic 3HDMs, as in the case of 2HDMs. We show that a democratic 3HDM encompasses all the wrong-sign possibilities entertained by 2HDMs, and has considerably more leeway in the wrong-sign limit as compared to the 2HDM case. Our study underscores the importance of reporting analysis in the kappa-formalism without any implicit assumptions on the signs of the kappas.

hep-ph↗

arXiv2022

JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text

In this paper we describe a system submitted to the INLG 2022 Generation Challenge (GenChal) on Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text. We implement a Bi-LSTM-based neural network model to predict the Average rating score and Disagreement score of the synthetic Hinglish dataset. In our models, we used word embeddings for English and Hindi data, and one hot encodings for Hinglish data. We achieved a F1 score of 0.11, and mean squared error of 6.0 in the average rating score prediction task. In the task of Disagreement score prediction, we achieve a F1 score of 0.18, and mean squared error of 5.0.

cs.CL↗

arXiv2022

Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?

Identifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining. The intrinsic complexity of these tasks demands powerful learning models. While pretrained Transformer-based Language Models (LM) have been shown to provide state-of-the-art results over different NLP tasks, the scarcity of manually annotated data and the highly domain-dependent nature of argumentation restrict the capabilities of such models. In this work, we propose a novel transfer learning strategy to overcome these challenges. We utilize argumentation-rich social discussions from the ChangeMyView subreddit as a source of unsupervised, argumentative discourse-aware knowledge by finetuning pretrained LMs on a selectively masked language modeling task. Furthermore, we introduce a novel prompt-based strategy for inter-component relation prediction that compliments our proposed finetuning method while leveraging on the discourse context. Exhaustive experiments show the generalization capability of our method on these two tasks over within-domain as well as out-of-domain datasets, outperforming several existing and employed strong baselines.

cs.CL↗

arXiv2022

Measuring frequency and period separations in red-giant stars using machine learning

Asteroseismology is used to infer the interior physics of stars. The \textit{Kepler} and TESS space missions have provided a vast data set of red-giant light curves, which may be used for asteroseismic analysis. These data sets are expected to significantly grow with future missions such as \textit{PLATO}, and efficient methods are therefore required to analyze these data rapidly. Here, we describe a machine learning algorithm that identifies red giants from the raw oscillation spectra and captures \textit{p} and \textit{mixed} mode parameters from the red-giant power spectra. We report algorithmic inferences for large frequency separation ($Δν$), frequency at maximum amplitude ($ν_{max}$), and period separation ($ΔΠ$) for an ensemble of stars. In addition, we have discovered $\sim$25 new probable red giants among 151,000 \textit{Kepler} long-cadence stellar-oscillation spectra analyzed by the method, among which four are binary candidates which appear to possess red-giant counterparts. To validate the results of this method, we selected $\sim$ 3,000 \textit{Kepler} stars, at various evolutionary stages ranging from subgiants to red clumps, and compare inferences of $Δν$, $ΔΠ$, and $ν_{max}$ with estimates obtained using other techniques. The power of the machine-learning algorithm lies in its speed: it is able to accurately extract seismic parameters from 1,000 spectra in $\sim$5 seconds on a modern computer (single core of the Intel Xeon Platinum 8280 CPU).

astro-ph.SR↗

arXiv2022

Diluting quark flavor hierarchies using dihedral symmetry

We present a $D_4$ flavored extension of the SM which provides an intuitive reasoning for the masses and mixing patterns in the quark sector. In our model, the Cabibbo mixing angle stems purely from the scalar sector dynamics. In fact, the orders of magnitude of the CKM matrix elements are readily obtained from the hierarchical nature of the vacuum expectation values. Moreover, we also show that the smallness of the off-Cabibbo elements in the CKM matrix is strongly connected to the heaviness of the third generation of quarks.

hep-ph↗

arXiv2021

AdvCodeMix: Adversarial Attack on Code-Mixed Data

Research on adversarial attacks are becoming widely popular in the recent years. One of the unexplored areas where prior research is lacking is the effect of adversarial attacks on code-mixed data. Therefore, in the present work, we have explained the first generalized framework on text perturbation to attack code-mixed classification models in a black-box setting. We rely on various perturbation techniques that preserve the semantic structures of the sentences and also obscure the attacks from the perception of a human user. The present methodology leverages the importance of a token to decide where to attack by employing various perturbation strategies. We test our strategies on various sentiment classification models trained on Bengali-English and Hindi-English code-mixed datasets, and reduce their F1-scores by nearly 51 % and 53 % respectively, which can be further reduced if a larger number of tokens are perturbed in a given sentence.

cs.CL↗

arXiv2021

Prospects of light charged scalars in a three Higgs doublet model with $Z_3$ symmetry

The stringent constraints from the direct searches for exotic scalars at the LHC as well as indirect bounds from flavor physics measurements have imposed severe restrictions on the parameter space of new physics models featuring extended Higgs sectors. In the Type-II 2HDM, this implies a lower bound on the charged Higgs masses of $\cal O$(600 GeV). In this work we analyze the phenomenology of a Z3HDM in the alignment limit focusing on the impact of flavor physics constraints on its parameter space. We show that the couplings of the two charged Higgs bosons in this model feature an additional suppression factor compared to Type-II 2HDM. This gives rise to a significant relaxation of the flavor physics constraints in this model, allowing the charged Higgs masses to be as low as $\cal O$(200 GeV). We also consider the constraints coming from precision electroweak observables and the observed diphoton decay rate of the 125 GeV Higgs boson at the LHC. The bounds coming from the direct searches of nonstandard Higgs bosons at the LHC, particularly those from resonance searches in the ditau channel, prove to be very effective in constraining this scenario further.

hep-ph↗

arXiv2021

A three Higgs doublet model with symmetry-suppressed flavour changing neutral currents

We construct a three-Higgs doublet model with a flavour non-universal ${\rm U}(1)\times \mathbb{Z}_2$ symmetry. That symmetry induces suppressed flavour-changing interactions mediated by neutral scalars. New scalars with masses below the TeV scale can still successfully negotiate the constraints arising from flavour data. Such a model can thus encourage direct searches for extra Higgs bosons in the future collider experiments, and includes a non-trivial flavour structure.

hep-ph↗

arXiv2021

Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals

The behaviour of information cascades (such as retweets) has been modelled extensively. While point process-based generative models have long been in use for estimating cascade growths, deep learning has greatly enhanced diverse feature integration. We observe two significant temporal signals in cascade data that have not been emphasized or reported to our knowledge. First, the popularity of the cascade root is known to influence cascade size strongly; but the effect falls off rapidly with time. Second, there is a measurable positive correlation between the novelty of the root content (with respect to a streaming external corpus) and the relative size of the resulting cascade. Responding to these observations, we propose GammaCas, a new cascade growth model as a parametric function of time, which combines deep influence signals from content (e.g., tweet text), network features (e.g., followers of the root user), and exogenous event sources (e.g., online news). Specifically, our model processes these signals through a customized recurrent network, whose states then provide the parameters of the cascade rate function, which is integrated over time to predict the cascade size. The network parameters are trained end-to-end using observed cascades. GammaCas outperforms seven recent and diverse baselines significantly on a large-scale dataset of retweet cascades coupled with time-aligned online news -- it beats the best baseline with an 18.98% increase in terms of Kendall's $τ$ correlation and $35.63$ reduction in Mean Absolute Percentage Error. Extensive ablation and case studies unearth interesting insights regarding retweet cascade dynamics.

cs.SI↗

arXiv2021

GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks

Graph Neural Networks (GNNs) use a fully-connected layer to extract features from the nodes of a graph and aggregate these features using message passing between nodes, combining two distinct computational patterns: dense, regular computations and sparse, irregular computations. To address this challenge, we propose GNNerator, an accelerator with heterogeneous compute engines optimized for these two patterns. Further, GNNerator implements feature-blocking, a novel GNN dataflow that beneficially trades off irregular memory accesses during aggregation for regular memory accesses during feature extraction. We show GNNerator achieves speedups of 5.7-37x over an NVIDIA RTX 2080-Ti, and 2.3x-3.8x over HyGCN, a state-of-the-art GNN accelerator.

cs.AR↗

arXiv2021

Extending Sparse Tensor Accelerators to Support Multiple Compression Formats

Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential memory and compute savings. These applications use data stored in a variety of compression formats. We demonstrate that both the compactness of different compression formats and the compute efficiency of the algorithms enabled by them vary across tensor dimensions and amount of sparsity. Since DL and scientific workloads span across all sparsity regions, there can be numerous format combinations for optimizing memory and compute efficiency. Unfortunately, many proposed accelerators operate on one or two fixed format combinations. This work proposes hardware extensions to accelerators for supporting numerous format combinations seamlessly and demonstrates ~4X speedup over performing format conversions in software.

cs.DC↗

arXiv2020

JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags

Sentiment analysis has been an active area of research in the past two decades and recently, with the advent of social media, there has been an increasing demand for sentiment analysis on social media texts. Since the social media texts are not in one language and are largely code-mixed in nature, the traditional sentiment classification models fail to produce acceptable results. This paper tries to solve this very research problem and uses bi-directional LSTMs along with language tagging, to facilitate sentiment tagging of code-mixed Tamil texts that have been extracted from social media. The presented algorithm, when evaluated on the test data, garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.

cs.CL↗

arXiv2020

JUNLP@SemEval-2020 Task 9:Sentiment Analysis of Hindi-English code mixed data using Grid Search Cross Validation

Code-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic typing and the insertion of anglicisms in their main language. This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few. In this work, we focus on working out a plausible solution to the domain of Code-Mixed Sentiment Analysis. This work was done as participation in the SemEval-2020 Sentimix Task, where we focused on the sentiment analysis of English-Hindi code-mixed sentences. our username for the submission was "sainik.mahata" and team name was "JUNLP". We used feature extraction algorithms in conjunction with traditional machine learning algorithms such as SVR and Grid Search in an attempt to solve the task. Our approach garnered an f1-score of 66.2\% when tested using metrics prepared by the organizers of the task.

cs.CL↗

arXiv2020

Development of POS tagger for English-Bengali Code-Mixed data

Code-mixed texts are widespread nowadays due to the advent of social media. Since these texts combine two languages to formulate a sentence, it gives rise to various research problems related to Natural Language Processing. In this paper, we try to excavate one such problem, namely, Parts of Speech tagging of code-mixed texts. We have built a system that can POS tag English-Bengali code-mixed data where the Bengali words were written in Roman script. Our approach initially involves the collection and cleaning of English-Bengali code-mixed tweets. These tweets were used as a development dataset for building our system. The proposed system is a modular approach that starts by tagging individual tokens with their respective languages and then passes them to different POS taggers, designed for different languages (English and Bengali, in our case). Tags given by the two systems are later joined together and the final result is then mapped to a universal POS tag set. Our system was checked using 100 manually POS tagged code-mixed sentences and it returned an accuracy of 75.29%

cs.CL↗

arXiv2020

Preparation of Sentiment tagged Parallel Corpus and Testing its effect on Machine Translation

In the current work, we explore the enrichment in the machine translation output when the training parallel corpus is augmented with the introduction of sentiment analysis. The paper discusses the preparation of the same sentiment tagged English-Bengali parallel corpus. The preparation of raw parallel corpus, sentiment analysis of the sentences and the training of a Character Based Neural Machine Translation model using the same has been discussed extensively in this paper. The output of the translation model has been compared with a base-line translation model using automated metrics such as BLEU and TER as well as manually.

cs.CL↗