arXiv subjects
Publications and source records attributed to Mehedi Masud.
This paper presents a structured analysis of security challenges in long-horizon agentic AI systems. The study reviews existing threats, evaluation approaches, attack propagation mechanisms, and security frameworks. A taxonomy of security threats and a framework for analyzing attack propagation are proposed to support future research in agentic AI security
The Electron-Ion Collider (EIC) is a next-generation accelerator primarily designed to study the internal structure of nucleons through high-precision electron-hadron collisions. In this work, we explore the feasibility of employing a 1 MW fraction of the EIC proton beam to generate a high-intensity GeV-scale neutrino beam for long-baseline oscillation studies. We have simulated proton-target interactions and optimize the resulting neutrino fluxes for water-based liquid scintillator (WbLS) detectors located at distinct baselines of 900 km and 2900 km. Oscillation analyses performed with GLoBES show that extended baselines allow access to multiple oscillation maxima, significantly enhancing sensitivity to leptonic CP violation. The study also examines the interplay between matter effects and the intrinsic CP violating phase in shaping observable asymmetries. We note that simplified systematics and no backgrounds are used in this analysis to establish the baseline physics potential. These results suggest that the EIC proton beam could provide a novel and complementary source for precision neutrino physics, extending the scientific reach of the EIC program.
We present a comprehensive comparison of the physics sensitivities of a Liquid Argon Time Projection Chamber (LArTPC) and a Water-based Liquid Scintillator (WbLS) detector, considering their potential deployment as the fourth far detector module in the DUNE facility. Using GLoBES-based simulations, we evaluate their performance in measuring standard neutrino oscillation parameters ($\theta_{23}, \delta_{13}$ and $\Delta m^{2}_{31}$), both in standard 3-neutrino case, as well as in presence of new physics scenarios involving light sterile neutrinos and neutral-current non-standard interactions (NC NSI). Our findings show that THEIA (a WbLS-based detector) significantly outperforms LArTPC in resolving the CP phase $\delta_{13}$,- especially near maximal CP violation, and in lifting the octant degeneracy of $\theta_{23}$ due to its superior energy resolution and ability to clearly identify the second oscillation maximum. Furthermore, THEIA offers competitive reconstruction precision even with relatively moderate energy resolutions ($7-10\%/\sqrt{E}$) and demonstrates enhanced robustness under new physics scenarios. These results support the physics-driven case for a hybrid DUNE configuration utilizing both LArTPC and WbLS technologies for optimized sensitivity across the full spectrum of neutrino oscillation and physics beyond the standard model.
Since the discovery of the Higgs boson, the long-standing task at hand in particle physics is the search for new physics beyond the Standard Model, which accounts for only about 5\% of the Universe. In light of this situation, the neutrino sector has drawn significant attention due to neutrino oscillations, which require physics beyond the Standard Model and have prompted a wide array of active and planned experimental programs. Notably, neutrino facilities offer substantial potential to search for new physics beyond neutrino oscillations, owing to their precision measurement capabilities, diverse experimental configurations, and various neutrino sources. This white paper summarizes the landscape of new physics that can be probed at current and future neutrino experiments, categorized into laboratory-produced and cosmogenic signals. We discuss recent experimental results interpreted through the lens of new physics, as well as detailed plans and projected sensitivities of next-generation facilities. This summary is based on presentations from the 4th Workshop on New Physics Opportunities in Neutrino Facilities (NPN 2024), held at IBS in Daejeon, Korea, on June 3-5, 2024. Particular emphasis is placed on accelerator-based neutrino experiments and a range of neutrino programs in East Asia. We also outline key tasks necessary to realize the promising new physics opportunities ahead.
Traditional machine learning models often prioritize predictive accuracy, often at the expense of model transparency and interpretability. The lack of transparency makes it difficult for organizations to comply with regulatory requirements and gain stakeholders trust. In this research, we propose a fraud detection framework that combines a stacking ensemble of well-known gradient boosting models: XGBoost, LightGBM, and CatBoost. In addition, explainable artificial intelligence (XAI) techniques are used to enhance the transparency and interpretability of the model's decisions. We used SHAP (SHapley Additive Explanations) for feature selection to identify the most important features. Further efforts were made to explain the model's predictions using Local Interpretable Model-Agnostic Explanation (LIME), Partial Dependence Plots (PDP), and Permutation Feature Importance (PFI). The IEEE-CIS Fraud Detection dataset, which includes more than 590,000 real transaction records, was used to evaluate the proposed model. The model achieved a high performance with an accuracy of 99% and an AUC-ROC score of 0.99, outperforming several recent related approaches. These results indicate that combining high prediction accuracy with transparent interpretability is possible and could lead to a more ethical and trustworthy solution in financial fraud detection.
We investigate the physics potential of the upcoming Deep Underground Neutrino Experiment (DUNE) in probing active-sterile mixing. We present analytic expressions for relevant oscillation probabilities for three active and one sterile neutrino of eV-scale mass and highlight essential parameters impacting the oscillation signals at DUNE. We then explore the space of sterile parameters as well as study their correlations among themselves and with parameters appearing in the standard framework ($\delta_{13}$ and $\theta_{23}$). We perform a combined fit for the near and far detector at DUNE using GLoBES. We consider alternative beam tune (low energy and medium energy) and runtime combinations for constraining the sterile parameter space. We show that charged current and neutral current interactions over the near and far detector at DUNE allow for an improved sensitivity for a wide range of sterile neutrino mass splittings.
The Deep Underground Neutrino Experiment (DUNE) is a leading experiment in neutrino physics which is presently under construction. DUNE aims to measure the yet unknown parameters in the three flavor oscillation case which includes discovery of leptonic CP violation, determination of the neutrino mass hierarchy and measuring the octant of $\theta_{23}$. Additionally, the ancillary goals of DUNE include probing the subdominant effects induced by possible physics beyond the Standard Model (BSM). One such new physics scenario is the possible presence of Large Extra Dimension (LED) which can naturally give rise to tiny neutrino masses. LED impacts neutrino oscillation through two new parameters, - namely the lightest Dirac mass $m_{0}$ and the radius of the extra dimension $R_{\text{ED}}$ ($< 2$ $\mu$m). At the DUNE baseline of 1300 km, the probability seems to be modified more at the higher energy ($\gtrsim 4-5$ GeV) in presence of LED. In this work, we attempt to constrain the parameter space of $m_{0}$ and $R_{\text{ED}}$ by performing a statistical analysis of neutrino data simulated at DUNE far detector (FD). We illustrate how a combination of the standard low energy (LE) neutrino beam and a medium energy (ME) neutrino beam can take advantage of the relatively large impact of LED at higher energy and improve the constraints. In the analysis we also show the role of the individual oscillation channels ($\nu_{\mu} \to \nu_{e}, \nu_{\mu} \to \nu_{\mu}, \nu_{\mu} \to \nu_{\tau}$), as well as the two neutrino mass hierarchies.
Light hypothetical particles with masses up to $\mathcal{O}(100)\ {\rm MeV}$ can be produced in the core of supernovae. Their subsequent decays to neutrinos can produce a flux component with higher energies than the standard flux. We study the impact of heavy neutral leptons, $Z'$ bosons, in particular ${\rm U(1)}_{L_μ-L_τ}$ and ${\rm U(1)}_{B-L}$ gauge bosons, and majorons coupled to neutrinos flavor-dependently. We obtain new strong limits on these particles from no events of high-energy SN 1987A neutrinos and their future sensitivities from observations of galactic supernova neutrinos.
Viruses are submicroscopic agents that can infect all kinds of lifeforms and use their hosts' living cells to replicate themselves. Despite having some of the simplest genetic structures among all living beings, viruses are highly adaptable, resilient, and given the right conditions, are capable of causing unforeseen complications in their hosts' bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses are the biggest biological threat faced by animal and plant species. It is often challenging to promptly detect the presence of a virus in a possible host's body and accurately determine its type using manual examination techniques; however, it can be done using computer-based automatic diagnosis methods. Most notably, the analysis of Transmission Electron Microscopy (TEM) images has been proven to be quite successful in instant virus identification. Using TEM images collected from a recently published dataset, this article proposes a deep learning-based classification model to identify the type of virus within those images correctly. The methodology of this study includes two coherent image processing techniques to reduce the noise present in the raw microscopy images. Experimental results show that it can differentiate among the 14 types of viruses present in the dataset with a maximum of 97.44% classification accuracy and F1-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable way of virus identification subsidiary to the thorough diagnostic procedures.
The environment, especially water, gets polluted due to industrialization and urbanization. Pollution due to industrialization and urbanization has harmful effects on both the environment and the lives on Earth. This polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious health complications. In a developing country like Bangladesh, where ready-made garments sector is one of the major sources of the total Gross Domestic Product (GDP), most of the wastes released from the garment factories are dumped into the nearest rivers or canals. Hence, the quality of the water of these bodies become very incompatible for the living beings, and so, it has become one of the major threats to the environment and human health. In addition, the amount of fish in the rivers and canals in Bangladesh is decreasing day by day as a result of water pollution. Therefore, to save fish and other water animals and the environment, we need to monitor the quality of the water and find out the reasons for the pollution. Real-time monitoring of the quality of water is vital for controlling water pollution. Most of the approaches for controlling water pollution are mainly biological and lab-based, which takes a lot of time and resources. To address this issue, we developed an Internet of Things (IoT)-based real-time water quality monitoring system, integrated with a mobile application. The proposed system in this research measures some of the most important indexes of water, including the potential of hydrogen (pH), total dissolved solids (TDS), and turbidity, and temperature of water. The proposed system results will be very helpful in saving the environment, and thus, improving the health of living creatures on Earth.
One of the basic propositions of quantum field theory is Lorentz invariance. The spontaneous breaking of Lorentz symmetry at a high energy scale can be studied at low energy extensions like the Standard model in a model-independent way through effective field theory (EFT). The present and future Long-baseline neutrino experiments can give a scope to observe such a Planck-suppressed physics of Lorentz invariance violation (LIV). A proposed long baseline experiment, Protvino to ORCA (dubbed "P2O") with a baseline of 2595 km, is expected to provide good sensitivities to unresolved issues, especially neutrino mass ordering. P2O can offer good statistics even with a moderate beam power and runtime, owing to the very large ($\sim 6$ Mt) detector volume at KM3NeT/ ORCA. Here we discuss in detail, how the individual LIV parameters affect neutrino oscillations at P2O and DUNE baselines at the level of probability and derive analytical expressions to understand interesting degeneracies and other features. We estimate $Δχ^{2}$ sensitivities to the LIV parameters, analyzing their correlations among each other, and also with the standard oscillation parameters. We calculate these results for P2O alone and also carry out a combined analysis of P2O with DUNE. We point out crucial features in the sensitivity contours and explain them qualitatively with the help of the relevant probability expressions derived here. Finally, we estimate constraints on the individual LIV parameters at $95\%$ confidence level (C.L.) intervals stemming from the combined analysis of P2O and DUNE datasets and highlight the improvement over the existing constraints. We also find out that the additional degeneracy induced by the LIV parameter $a_{ee}$ around $-22 \times 10^{-23}$ GeV is lifted by the combined analysis at $95\%$ C.L.
Non-standard neutrino interactions with a massive boson can produce the bosons in the core of core-collapse supernovae (SNe). After the emission of the bosons from the SN core, their subsequent decays into neutrinos can modify the SN neutrino flux. We show future observations of neutrinos from a next galactic SN in Super-Kamiokande (SK) and Hyper-Kamiokande (HK) can probe flavor-universal non-standard neutrino couplings to a light boson, improving the previous limit from the SN 1987A neutrino burst by several orders of magnitude. We also discuss sensitivity of the flavor-universal non-standard neutrino interactions in future observations of diffuse neutrinos from all the past SNe, known as the diffuse supernova neutrino background (DSNB). According to our analysis, observations of the DSNB in HK, JUNO and DUNE experiments can probe such couplings by a factor of $\sim 2$ beyond the SN 1987A constraint. However, our result is also subject to a large uncertainty concerning the precise estimation of the DSNB.
This COVID-19 pandemic is so dreadful that it leads to severe anxiety, phobias, and complicated feelings or emotions. Even after vaccination against Coronavirus has been initiated, people feelings have become more diverse and complex, and our goal is to understand and unravel their sentiments in this research using some Deep Learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of it, specifically Twitter, one can have a better idea of what is trending and what is going on in people minds. Our motivation for this research is to understand the sentiment of people regarding the vaccination process, and their diverse thoughts regarding this. In this research, the timeline of the collected tweets was from December 21 to July 21, and contained tweets about the most common vaccines available recently from all across the world. The sentiments of people regarding vaccines of all sorts were assessed by using a Natural Language Processing (NLP) tool named Valence Aware Dictionary for sEntiment Reasoner (VADER). By initializing the sentiment polarities into 3 groups (positive, negative and neutral), the overall scenario was visualized here and our findings came out as 33.96% positive, 17.55% negative and 48.49% neutral responses. Recurrent Neural Network (RNN) oriented architecture such as Long Short-Term Memory (LSTM and Bi-LSTM) is used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving an accuracy of 90.83%. Other performance metrics such as Precision, Recall, F-1 score, and Confusion matrix were also shown to validate our models and findings more effectively. This study will help everyone understand public opinion on the COVID-19 vaccines and impact the aim of eradicating the Coronavirus from our beautiful world.
The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive review of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough analysis of 75 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. We study articles published between 1999 to 2021, and 50% of the papers were published after 2015. Furthermore, we discuss Classical, Machine Learning and Deep Learning approaches with different datasets while addressing the limitations and current and future trends of the BNLP.
The various global analyses of available neutrino oscillation data indicate the presence of the standard $3+0$ neutrino oscillation picture. However, there are a few short baseline anomalies that point to the possible existence of a fourth neutrino (with mass in the eV-scale), essentially sterile in nature. Should sterile neutrino exist in nature and its presence is not taken into consideration properly in the analyses of neutrino data, the interference terms arising due to the additional CP phases in presence of a sterile neutrino can severely impact the physics searches in long baseline (LBL) neutrino oscillation experiments. In the current work we consider one light (eV-scale) sterile neutrino and probe all the three CP phases ($δ_{13}$, $δ_{24}$, $δ_{34}$) in the context of the upcoming Deep Underground Neutrino Experiment (DUNE) and also estimate how the results improve when data from NOvA, T2K and T2HK are added in the analysis. We illustrate the $Δχ^2$ correlations of the CP phases among each other, and also with the three active-sterile mixing angles. Finally, we briefly illustrate how the relevant parameter spaces in the context of neutrinoless double beta decay get modified in light of the bounds in presence of a light sterile neutrino.
We formulate an alternative approach based on unitarity triangles to describe neutrino oscillations in presence of non-standard interactions (NSI). Using perturbation theory, we derive the expression for the oscillation probability in case of NSI and cast it in terms of the three independent parameters of the leptonic unitarity triangle (LUT). The form invariance of the probability expression (even in presence of new physics scenario as long as the mixing matrix is unitary) facilitates a neat geometric view of neutrino oscillations in terms of LUT. We examine the regime of validity of perturbative expansions in the NSI case and make comparisons with approximate expressions existing in literature. We uncover some interesting dependencies on NSI terms while studying the evolution of LUT parameters and the Jarlskog invariant. Interestingly, the geometric approach based on LUT allows us to express the oscillation probabilities for a given pair of neutrino flavours in terms of only three (and not four) degrees of freedom which are related to the geometric properties (sides and angles) of the triangle. Moreover, the LUT parameters are invariant under rephasing transformations and independent of the parameterization adopted.
We explore the role of matter effect in the evolution of neutrino oscillation parameters in the presence of lepton-flavor-conserving and lepton-flavor-violating neutral-current non-standard interactions (NSI) of the neutrino. We derive simple approximate analytical expressions showing the evolution/running of mass-mixing parameters in matter with energy in the presence of standard interactions (SI) and SI+NSI (considering both positive and negative values of real NSI parameters). We observe that only the NSI parameters in the (2,3) block, namely $\varepsilon_{μτ}$ and $(γ- β) \equiv (\varepsilon_{ττ} - \varepsilon_{μμ})$ affect the running of $θ_{23}$. Though all the NSI parameters influence the evolution of $θ_{13}$, $\varepsilon_{eμ}$ and $\varepsilon_{eτ}$ show a stronger impact at the energies relevant for DUNE. $θ_{12}$ quickly approaches to $\sim$ $90^{\circ}$ with increasing energy in both SI and SI+NSI cases. The change in $Δm^2_{21,m}$ is quite significant as compared to $Δm^2_{31,m}$ both in SI and SI+NSI frameworks. Flipping the signs of the NSI parameters alters the way in which mass-mixing parameters run with energy. We demonstrate the utility of our approach in addressing several important features related to neutrino oscillation such as: a) unraveling interesting degeneracies between $θ_{23}$ and NSI parameters, b) estimating the resonance energy in presence of NSI when $θ_{13}$ in matter becomes maximal, c) figuring out the required baselines and energies to have maximal matter effect in $ν_μ$ $\rightarrow$ $ν_{e}$ transition in the presence of different NSI parameters, and d) studying the impact of NSI parameters $\varepsilon_{μτ}$ and $(γ- β)$ on the $ν_μ \to ν_μ$ survival probability.
We examine the impact of Lorentz Invariance Violation (LIV) in measuring the octant of $θ_{23}$ and CP phases in the context of the Deep Underground Neutrino Experiment (DUNE). We consider the CPT-violating LIV parameters involving $e - μ$ ($a_{eμ}$) and $e - τ$ ($a_{eτ}$) flavors, which induce an additional interference term in neutrino and antineutrino appearance probabilities. This new interference term depends on both the standard CP phase $δ$ and the new dynamical CP phase $φ_{eμ}$/$φ_{eτ}$, giving rise to new degeneracies among ($θ_{23}$, $δ$, $φ$). Taking one LIV parameter at-a-time and considering a small value of $|a_{eμ}| = |a_{eτ}| = 5 \times 10^{-24}$ GeV, we find that the octant discovery potential of DUNE gets substantially deteriorated for unfavorable combinations of $δ$ and $φ_{eμ}$/$φ_{eτ}$. The octant of $θ_{23}$ can only be resolved at $3σ$ if the true value of $\sin^2θ_{23} \lesssim 0.42$ or $\gtrsim 0.62$ for any choices of $δ$ and $φ$. Interestingly, we also observe that when both the LIV parameters $a_{eμ}$ and $a_{eτ}$ are present together, they cancel out the impact of each other to a significant extent, allowing DUNE to largely regain its octant resolution capability. We also reconstruct the CP phases $δ$ and $φ_{eμ}$/$φ_{eτ}$. The typical $1σ$ uncertainty on $δ$ is $10^{\circ}$ to $15^{\circ}$ and the same on $φ_{eμ}$/$φ_{eτ}$ is $25^{\circ}$ to $30^{\circ}$ depending on the choices of their true values.