arXiv subjects

Rashidul Islam

Publications and source records attributed to Rashidul Islam.

At least 19 recordsLinked to original sources

arXiv2025

Probing the Phenomenology of Dark Matter from Decoupled Freeze-Out

We consider a model of dark matter where the mediator corresponds to a superposition of a scalar and pseudoscalar, and the scenario where, after reheating, the number densities of the dark sector particles, i.e. the dark matter and the mediators, are negligible. If the coupling of the mediators to the Standard Model is feeble, but the coupling to the dark matter is large enough, the dark sector may reach equilibrium at a temperature distinct from that of the thermal bath. The relic density is then said to be obtained via decoupled freeze out (DFO). We focus on the $s$-wave annihilation scenario, which particularly benefits from the DFO mechanism by evading standard CMB limits while still yielding indirect detection signals. We calculate the relic density by solving a set of four coupled Boltzmann equations for the number densities of the dark sector particles and the energy transfer from the light to dark sector. We finally perform a thorough analysis of experimental bounds on this scenario, namely from indirect detection and the CMB, as well as from BBN, and find that, while there are considerable constraints on the parameter space where the correct relic density is obtained, a viable region remains to be explored.

hep-ph↗

arXiv2025

Model Independent Effective Dimension Six Operators And Scattering Unitarity

The effective field theory containing higher dimensional operators violates the unitarity of the $2 \rightarrow 2$ scattering processes in the Standard Model. This unitarity violation depends on the values of the Wilson coefficients corresponding to a higher dimensional operator. We showed that even a small values of some of the Wilson coefficients lead to the unitarity violation at the LHC centre of mass energies. Considering the final states as the gauge bosons in the longitudinal mode, the scalars, and the $t\overline{t}$, we showed that the unitarity violation in $WW \rightarrow WW$, $WW \rightarrow ZZ$, and $ZZ \rightarrow hh$ scattering amplitudes sets bounds on the Wilson coefficients of the dimension six effective operators.

hep-ph↗

arXiv2025

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization

To deal with distribution shifts in graph data, various graph out-of-distribution (OOD) generalization techniques have been recently proposed. These methods often employ a two-step strategy that first creates augmented environments and subsequently identifies invariant subgraphs to improve generalizability. Nevertheless, this approach could be suboptimal from the perspective of consistency. First, the process of augmenting environments by altering the graphs while preserving labels may lead to graphs that are not realistic or meaningfully related to the origin distribution, thus lacking distribution consistency. Second, the extracted subgraphs are obtained from directly modifying graphs, and may not necessarily maintain a consistent predictive relationship with their labels, thereby impacting label consistency. In response to these challenges, we introduce an innovative approach that aims to enhance these two types of consistency for graph OOD generalization. We propose a modifier to obtain both augmented and invariant graphs in a unified manner. With the augmented graphs, we enrich the training data without compromising the integrity of label-graph relationships. The label consistency enhancement in our framework further preserves the supervision information in the invariant graph. We conduct extensive experiments on real-world datasets to demonstrate the superiority of our framework over other state-of-the-art baselines.

cs.LG↗

arXiv2024

Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness

Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests. However, the complexity and nuanced nature of users' interests, which span a wide range of diversity, pose a significant challenge in delivering fair recommendations. In practice, user preferences vary significantly; some users show a clear preference toward certain item categories, while others have a broad interest in diverse ones. Even though it is expected that all users should receive high-quality recommendations, the effectiveness of RSs in catering to this disparate interest diversity remains under-explored. In this work, we investigate whether users with varied levels of interest diversity are treated fairly. Our empirical experiments reveal an inherent disparity: users with broader interests often receive lower-quality recommendations. To mitigate this, we propose a multi-interest framework that uses multiple (virtual) interest embeddings rather than single ones to represent users. Specifically, the framework consists of stacked multi-interest representation layers, which include an interest embedding generator that derives virtual interests from shared parameters, and a center embedding aggregator that facilitates multi-hop aggregation. Experiments demonstrate the effectiveness of the framework in achieving better trade-off between fairness and utility across various datasets and backbones.

cs.IR↗

arXiv2022

Fair Inference for Discrete Latent Variable Models

It is now well understood that machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations. Traditional algorithmic fairness research has mainly focused on supervised learning tasks, particularly classification. While fairness in unsupervised learning has received some attention, the literature has primarily addressed fair representation learning of continuous embeddings. In this paper, we conversely focus on unsupervised learning using probabilistic graphical models with discrete latent variables. We develop a fair stochastic variational inference technique for the discrete latent variables, which is accomplished by including a fairness penalty on the variational distribution that aims to respect the principles of intersectionality, a critical lens on fairness from the legal, social science, and humanities literature, and then optimizing the variational parameters under this penalty. We first show the utility of our method in improving equity and fairness for clustering using na\"ive Bayes and Gaussian mixture models on benchmark datasets. To demonstrate the generality of our approach and its potential for real-world impact, we then develop a special-purpose graphical model for criminal justice risk assessments, and use our fairness approach to prevent the inferences from encoding unfair societal biases.

cs.LG↗

arXiv2022

Framework for Behavioral Disorder Detection Using Machine Learning and Application of Virtual Cognitive Behavioral Therapy in COVID-19 Pandemic

In this modern world, people are becoming more self-centered and unsocial. On the other hand, people are stressed, becoming more anxious during COVID-19 pandemic situation and exhibits symptoms of behavioral disorder. To measure the symptoms of behavioral disorder, usually psychiatrist use long hour sessions and inputs from specific questionnaire. This process is time consuming and sometime is ineffective to detect the right behavioral disorder. Also, reserved people sometime hesitate to follow this process. We have created a digital framework which can detect behavioral disorder and prescribe virtual Cognitive Behavioral Therapy (vCBT) for recovery. By using this framework people can input required data that are highly responsible for the three behavioral disorders namely depression, anxiety and internet addiction. We have applied machine learning technique to detect specific behavioral disorder from samples. This system guides the user with basic understanding and treatment through vCBT from anywhere any time which would potentially be the steppingstone for the user to be conscious and pursue right treatment.

cs.HC↗

arXiv2021

User Acceptance of Gender Stereotypes in Automated Career Recommendations

Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g. along lines of gender, age, and race. While most research in this domain focuses on developing fair AI algorithms, in this work, we show that a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using career recommendation as a case study, we build a fair AI career recommender by employing gender debiasing machine learning techniques. Our offline evaluation showed that the debiased recommender makes fairer career recommendations without sacrificing its accuracy. Nevertheless, an online user study of more than 200 college students revealed that participants on average prefer the original biased system over the debiased system. Specifically, we found that perceived gender disparity is a determining factor for the acceptance of a recommendation. In other words, our results demonstrate we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans.

cs.CY↗

arXiv2021

Fair Representation Learning for Heterogeneous Information Networks

Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness. A growing body of research has identified unfair AI systems and proposed methods to debias them, yet many challenges remain. Representation learning for Heterogeneous Information Networks (HINs), a fundamental building block used in complex network mining, has socially consequential applications such as automated career counseling, but there have been few attempts to ensure that it will not encode or amplify harmful biases, e.g. sexism in the job market. To address this gap, in this paper we propose a comprehensive set of de-biasing methods for fair HINs representation learning, including sampling-based, projection-based, and graph neural networks (GNNs)-based techniques. We systematically study the behavior of these algorithms, especially their capability in balancing the trade-off between fairness and prediction accuracy. We evaluate the performance of the proposed methods in an automated career counseling application where we mitigate gender bias in career recommendation. Based on the evaluation results on two datasets, we identify the most effective fair HINs representation learning techniques under different conditions.

cs.AI↗

arXiv2020

Equitable Allocation of Healthcare Resources with Fair Cox Models

Healthcare programs such as Medicaid provide crucial services to vulnerable populations, but due to limited resources, many of the individuals who need these services the most languish on waiting lists. Survival models, e.g. the Cox proportional hazards model, can potentially improve this situation by predicting individuals' levels of need, which can then be used to prioritize the waiting lists. Providing care to those in need can prevent institutionalization for those individuals, which both improves quality of life and reduces overall costs. While the benefits of such an approach are clear, care must be taken to ensure that the prioritization process is fair or independent of demographic information-based harmful stereotypes. In this work, we develop multiple fairness definitions for survival models and corresponding fair Cox proportional hazards models to ensure equitable allocation of healthcare resources. We demonstrate the utility of our methods in terms of fairness and predictive accuracy on two publicly available survival datasets.

cs.LG↗

arXiv2020

Neural Fair Collaborative Filtering

A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical framework for mitigating gender bias in recommending sensitive items (e.g. jobs, academic concentrations, or courses of study) using a pre-training and fine-tuning approach to neural collaborative filtering, augmented with bias correction techniques. We show the utility of our methods for gender de-biased career and college major recommendations on the MovieLens dataset and a Facebook dataset, respectively, and achieve better performance and fairer behavior than several state-of-the-art models.

cs.IR↗

arXiv2020

Implications of dark sector mixing on leptophilic scalar dark matter

We propose a new viable outlook to the mixing between a singlet and a doublet leptonic dark sector fields. This choice relaxes the dark matter (DM) search constraints on the quintessential scalar singlet DM as well as presents new opportunities for its detection in the lab. The mixing produces an arbitrary mass difference between the two components of the extra doublet in a gauge-invariant way, without introducing any new scale of electroweak symmetry breaking in the theory. It also provides a useful handle to distinguish between the dark sector particles of different isospins, which is a challenging task otherwise. As the dark leptons coannihilate non-trivially, the mixing effectively enhances the viable parameter space for the relic density constraint. In low DM mass regime, our analysis shows that with a non-zero mixing, it is possible to relax the existing indirect search bounds on the upper limit of the DM-Standard Model coupling. From the analysis of the $3\tau + E^{miss}_T$ and $\ell\,\tau + E^{miss}_T$ channels for LHC at $\sqrt{s} = 13$ TeV, we show that one ensures the presence of the mixing parameter between the dark sector particles of the theory by looking at the peak and tail positions of the kinematic distributions. Even with a tweak in the values of other free parameters within the viable parameter region, the distinct peak and tail positions of the kinematic distributions remains a constant feature of the model. While both the channels present us the opportunity to detect the mixing signature at the LHC/HL-LHC, the former gives better results in terms of a larger region of mixing parameter. From the fiducial cross section, the projected statistical significance for the integrated luminosity, ${\mathscr L} = 3~\text{ab}^{-1}$, are shown for a combined parameter region obeying all the existing constraints, where there is the best possibility to detect such a signature.

hep-ph↗

arXiv2020

Multilepton signatures for scalar dark matter searches in coannihilation scenario

We revisit the scalar singlet dark matter (DM) scenario with a pair of dark lepton partners which form a vectorlike Dirac fermionic doublet. The extra doublet couples with the Standard Model (SM) leptonic doublet and the scalar singlet via a non-SM-like Yukawa structure. As a result, (i) since the extra fermionic states interact with other dark sector particles as well as the SM via gauge and Yukawa interactions, it gives rise to new DM annihilation processes including pair annihilation as well as coannihilation channels, and (ii) such a Yukawa structure opens up new production channels for leptonic final states giving much enhancement in cross sections to search for dark matter in the LHC. Using suitable kinematic observables, we train a {\em boosted decision tree} (BDT) classifier to separate enhanced but still feeble light leptonic signals from the background in an effective manner. On the other hand, the same technique is applied to study $τ$-tagged jets in the search for DM signals.

hep-ph↗

arXiv2019

Dark matter in the Randall-Sundrum model with non-universal coupling

We consider simplified dark matter models (DM) interacting gravitationally with the standard model (SM) particles in a Randall-Sundrum (RS) framework. In this framework, the DM particles interact through the exchange of spin-2 Kaluza-Klein (KK) gravitons in the $s$-channel with the SM particles. The parameter space of the RS model with universal couplings to SM particles is known to be strongly constrained from the LHC data. We are thus led to consider models with non-universal couplings. The first model we consider in this study is a top-philic graviton model in which only the right-handed top quarks are taken to interact strongly with the gravitons. In the second, the lepto-philic model, we assume that only the right-handed charged leptons interact strongly with the gravitons. We extend the study to include not only the scalar, vector and spin-1/2 fermions but also spin-3/2 fermionic dark matter. We find that there is a large parameter space in these benchmark models where it is possible to achieve the observed relic density consistent with the direct and indirect searches and yet not to be constrained from the LHC data.

hep-ph↗

arXiv2019

An Intersectional Definition of Fairness

We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens arising from the Humanities literature which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including gender, race, sexual orientation, class, and disability. We show that our criteria behave sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. We provide a learning algorithm which respects our intersectional fairness criteria. Case studies on census data and the COMPAS criminal recidivism dataset demonstrate the utility of our methods.

cs.LG↗

arXiv2019

Bayesian Modeling of Intersectional Fairness: The Variance of Bias

Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. Intersectionality theory therefore implies it is important that fairness in artificial intelligence systems be protected with regard to multi-dimensional protected attributes. However, the measurement of fairness becomes statistically challenging in the multi-dimensional setting due to data sparsity, which increases rapidly in the number of dimensions, and in the values per dimension. We present a Bayesian probabilistic modeling approach for the reliable, data-efficient estimation of fairness with multi-dimensional protected attributes, which we apply to two existing intersectional fairness metrics. Experimental results on census data and the COMPAS criminal justice recidivism dataset demonstrate the utility of our methodology, and show that Bayesian methods are valuable for the modeling and measurement of fairness in an intersectional context.

cs.LG↗

arXiv2019

Dark matter in the Randall-Sundrum model

In Ref. \cite{Goyal:2019vsw}, we had considered simplified dark matter models interacting gravitationally with the Standard Model particles in a Randall-Sundrum frame work where models are considered with non-universal couplings. In the top-philic graviton model the right-handed top quarks are taken to interact strongly with the gravitons and in the lepton-philic model, we assume that only the right-handed charged leptons interact strongly with the gravitons. We extend the study to include not only the scalar, vector and spin-1/2 fermions but also spin-3/2 fermionic dark matter. We find that there is a large parameter space in these benchmark models where it is possible to achieve the observed relic density consistent with the direct and indirect searches, which are also consistent with the data from Large Hadron Collider.

hep-ph↗

arXiv2019

Probing Multicomponent Extension of Inert Doublet Model with a Vector Dark Matter

Although theoretically well-motivated as a minimal consistent Dark Matter (DM) model, the Inert Doublet Model (IDM) fell short in explaining the existence of DM in the intermediate mass region ($100-500$ GeV). We try to address this problem by introducing an additional vector dark matter. We show that the relic density of inert dark matter candidate can be enhanced significantly with new interactions in the intermediate mass region $100-500$ GeV in the multicomponent dark matter model when compared with the usual single component inert doublet model. We also show that we can get a reasonable excess in the $\ /\!\!\! E_T$ distribution if we do not apply a very hard $\ /\!\!\! E_T$ cut on it as is customary in any dark matter search at the LHC.

hep-ph↗

arXiv2019

Fingerprinting the Top quark FCNC via anomalous $Ztq$ couplings at the LHeC

A study of the top quark \emph{Flavour Changing Neutral Current} (FCNC) through $Z$-boson has been performed in the proposed future $e^-p$ collider for the energy, $E_{e(p)} = 60~(7000)$~GeV. We considered an effective theory where the anomalous FCNC couplings are of vector and tensor nature. The effect of these couplings is probed in the single top production along with the scattered electron. The polar angle $θ$ of the electrons coming out of the primary vertex in association with the top quark polarization asymmetries constructed from the angular distribution of the secondary lepton arising from the top decay, allow to distinguish the Lorentz structure of the coupling. From a multi-parameter analysis, we obtain a reach of ${\cal O} (10^{-2})$ in the case of $Ztu$ and $Ztc$ couplings at an integrated luminosity of 2~ab$^{-1}$ at 95\% C.L.

hep-ph↗