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Edge reconstruction of gate-tunable compressible quantum Hall fluids in the filling fraction range 1/3 to 2/3 is studied by measuring transmitted conductance of two individually excited fractional $e^2/3h$ edge modes of bulk 2/3 fractional quantum Hall fluid. Our findings reveal that the measured transmitted conductance deviates from the fully equilibrated value for the filling fraction range 1/3 to 2/3 of the gate-tunable compressible quantum Hall fluids at higher magnetic fields. This observation suggests that at the boundary of the compressible fluid a reconstructed $e^2/3h$ fractional edge mode is present and the mode does not completely equilibrate with the inner dissipative bulk region. Consequently, this outer reconstructed edge mode supports adiabatic charge transport, allowing non-equilibrated current transport through the compressible region. These studies open new avenues for achieving robust fractional edge modes even in compressible quantum Hall fluids under strong magnetic fields, enhancing our understanding of edge state dynamics in these complex systems., Comment: 9 pages, 5 figures

The sheath plasma resonance (SPR) in an inverted fireball (IFB) system is semi-analytically investigated using a generalized hydrodynamic isothermal model formalism. It incorporates the constitutive ionic fluid viscosity, inter-species collisions, and geometric curvature effects. The SPR stability is studied for an anodic (hollow, meshed) IFB for the first time against the traditional cathode-plasma arrangements of regular electrode (solid, smooth) fireballs. The SPR develops near a spherical electrode enclosed by a plasma sheath amid a given electric potential. A generalized linear quartic dispersion relation (DR) with diverse plasma multi-parametric coefficients is methodically derived using a standard normal mode analysis. The mathematical construction of the obtained DR roots confirms that only one feasible nonzero frequency mode exists (emerging in the IFB). This root existence is confirmed both analytically and numerically. This consequent SPR creates trapped acoustic fluctuations in the IFB plasmas because of the internal reflections at the sheath plasma boundary. Also, sensible parametric changes in the SPR features, with both plasma density and viscosity, are seen. A local condition for the SPR excitation and its subsequent transition to collective standing wave-like patterns in the IFBs is illustrated. A fair corroboration of our results with the earlier SPR experimental observations of standing wave-like eigenmode patterns (evanescent) strengthens the reliability of our study alongside new applicability., Comment: This original article contains 24 pages, 15 figures, etc. It has potential applications in the areas if accepted. It is for your kind consideration and needful action

We investigate the effects of random pinning, where we freeze the relaxation degrees of freedom for a fraction of randomly selected particles, on the yielding transition under oscillatory shear through extensive computer simulations. Using Kob-Anderson model as our model glass former, we pin a fraction of the particles. These pinned particles can move affinely under the imposed oscillatory shear deformation but are not allowed to relax through subsequent rearrangements due to plastic events. As the fraction of pinned particles increases, the system transitions from being a fragile glass former to a strong glass former. This gives us opportunity to examine how changes in fragility impact the yielding transition. Our results demonstrate notable differences in the yielding transition between strong and fragile glass formers under random pinning. This aligns with previous observations where fragility was altered by changing the packing fraction in soft sphere model albeit with a main difference that random pinning significantly suppresses the formation of shear bands, even in well-annealed glass samples.

Digital signature schemes based on multivariate- and code-based hard problems are promising alternatives for lattice-based signature schemes due to their smaller signature size. Hence, several candidates in the ongoing additional standardization for quantum secure digital signature (DS) schemes by the NIST rely on such alternate hard problems. Gaussian Elimination (GE) is a critical component in the signing procedure of these schemes. In this paper, we provide a masking scheme for GE with back substitution to defend against first- and higher-order attacks. To the best of our knowledge, this work is the first to analyze and propose masking techniques for multivariate- or code-based DS algorithms. We propose a masked algorithm for transforming a system of linear equations into row-echelon form. This is realized by introducing techniques for efficiently making leading (pivot) elements one while avoiding costly conversions between Boolean and multiplicative masking at all orders. We also propose a technique for efficient masked back substitution, which eventually enables a secure unmasking of the public output. We evaluate the overhead of our countermeasure for several post-quantum candidates and their different security levels at first-, second-, and third-order, including UOV, MAYO, SNOVA, QR-UOV, and MQ-Sign. Notably, the operational cost of first-, second-, and third-order masked GE is 2.3x higher, and the randomness cost is 1.2x higher in MAYO compared to UOV for security levels III and V. We also show detailed performance results for masked GE implementations for all three security versions of UOV on the Arm Cortex-M4 and compare them with unmasked results. Our first-order implementations targeting UOV parameters have overheads of factor 6.5x, 5.9x, and 5.7x compared to the unprotected implementation for NIST security level I, III, and V., Comment: 30 pages, 9 figures, 3 tables

The complex interaction between the intrinsic and extrinsic state variables of strongly correlated insulator thin films is drawing interest as it shows memristive behavior that may be used in neuromorphic computing. In this study, we have observed that laser irradiation in cerium oxide thin film helps to make transition from metastable state. We study the transport properties of cerium oxide thin film which shows the metal-insulator transition. As for ceria the reduction of oxidation states depends upon oxygen vacancy defects which can be controlled by external ways due to which the transport property can vary. In this study, a decrease in the value of transition temperature under dark conditions was observed as laser-induced and we have explained this result by the generation of energy states observed from temperature-dependent photoluminescence (PL) study, oxygen vacancy defects, and polaron hopping within ceria.

One of the main objectives of the current paper is to revisit the well known Laurent series expansions of the Riemann zeta function $\zeta(s)$, Hurwitz zeta function $\zeta(s,a)$ and Dirichlet $L$-function $L(s,\chi)$ at $s=1$. Moreover, we also present a new Laurent series expansion of $L$-functions associated to cusp forms over the full modular group., Comment: 14 pages, comments are welcome!

We consider a class of non-standard, two-dimensional (2D) Hamiltonian models that may show features of active particle dynamics, and therefore, we refer to these models as active Hamiltonian (AH) systems. The idea is to consider a spin fluid where -- on top of spin-spin and particle-particle interactions -- spins are coupled to the particle's velocities via a vector potential. Continuous spin variables interact with each other as in a standard $XY$ model. Typically, the AH models exhibit non-standard thermodynamic properties (e.g., for temperature and pressure) and equations of motion with non-standard forces. This implies that the derivation of symplectic algorithms to solve Hamilton's equations of motion numerically, as well as the thermostatting for these systems, is not straightforward. Here, we derive a symplectic integration scheme and propose a Nos\'e-Poincar\'e thermostat, providing a correct sampling in the canonical ensemble. The expressions for AH systems that we find for temperature and pressure might have parallels with the ongoing debate about the definition of pressure and the equation of state in active matter systems. For a specific AH model, recently proposed by Casiulis et al. [Phys. Rev. Lett. {\bf 124}, 198001 (2020)], we rationalize the symplectic algorithm and the proposed thermostatting, and investigate the transition from a fluid at high temperature to a cluster phase at low temperature where, due to the coupling of velocities and spins, the cluster phase shows a collective motion that is reminiscent to that observed in a variety of active systems.

The stability of plane Poiseuille flow of a viscous Newtonian fluid in a multilayer channel with anisotropic porous walls is analyzed using the classical modal analysis, the energy method, and the non-modal analysis. The influence of porous wall parameters such as depth ratio (ratio of porous layer thickness to fluid layer thickness) and anisotropic permeability (in terms of meanpermeability and anisotropy parameter) on flow instability are investigated. The modal stability analysis and energy method show that the anisotropy parameter can stabilize the flow, whereas the depth ratio and mean permeability effects can cause destabilization. Furthermore, the energy budget analysis reveals that the energy production term transfers energy to the disturbance from the base flow through the Reynolds stress, amplifying the kinetic energies in all layers and, hence, enhancing the growth rates of the unstable modes. A significant disparity is observed between the critical Reynolds number obtained through modal analysis and the one determined by the energy method, which confirms the growth of transient perturbation kinetic energy. Specifically, transient growth and response functions are examined to understand the flow response to initial conditions and external excitation (receptivity analysis). It turns out that there is substantial transient growth at a sub-critical Reynolds number. These transient growths are greatly enhanced by increasing the mean permeability or the depth ratio and reducing the anisotropy parameter. The optimal perturbations leading to the maximum transient amplification are determined for various parameters, including counter-rotating vortices (rolls) and streaks.

Machine learning models have been progressively used for predicting materials properties. These models can be built using pre-existing data and are useful for rapidly screening the physicochemical space of a material, which is astronomically large. However, ML models are inherently interpolative, and their efficacy for searching candidates outside a material's known range of property is unresolved. Moreover, the performance of an ML model is intricately connected to its learning strategy and the volume of training data. Here, we determine the relationship between the extrapolation ability of an ML model, the size and range of its training dataset, and its learning approach. We focus on a canonical problem of predicting the properties of a copolymer as a function of the sequence of its monomers. Tree search algorithms, which learn the similarity between polymer structures, are found to be inefficient for extrapolation. Conversely, the extrapolation capability of neural networks and XGBoost models, which attempt to learn the underlying functional correlation between the structure and property of polymers, show strong correlations with the volume and range of training data. These findings have important implications on ML-based new material development.

Recently, the construction of cryptographic schemes based on hard lattice problems has gained immense popularity. Apart from being quantum resistant, lattice-based cryptography allows a wide range of variations in the underlying hard problem. As cryptographic schemes can work in different environments under different operational constraints such as memory footprint, silicon area, efficiency, power requirement, etc., such variations in the underlying hard problem are very useful for designers to construct different cryptographic schemes. In this work, we explore various design choices of lattice-based cryptography and their impact on performance in the real world. In particular, we propose a suite of key-encapsulation mechanisms based on the learning with rounding problem with a focus on improving different performance aspects of lattice-based cryptography. Our suite consists of three schemes. Our first scheme is Florete, which is designed for efficiency. The second scheme is Espada, which is aimed at improving parallelization, flexibility, and memory footprint. The last scheme is Sable, which can be considered an improved version in terms of key sizes and parameters of the Saber key-encapsulation mechanism, one of the finalists in the National Institute of Standards and Technology's post-quantum standardization procedure. In this work, we have described our design rationale behind each scheme. Further, to demonstrate the justification of our design decisions, we have provided software and hardware implementations. Our results show Florete is faster than most state-of-the-art KEMs on software and hardware platforms. The scheme Espada requires less memory and area than the implementation of most state-of-the-art schemes. The implementations of Sable maintain a trade-off between Florete and Espada regarding performance and memory requirements on the hardware and software platform.

The theory and computation of tensors with different tensor products play increasingly important roles in scientific computing and machine learning. Different products aim to preserve different algebraic properties from the matrix algebra, and the choice of tensor product determines the algorithms that can be directly applied. This study introduced a novel full-rank decomposition and $M$-$\mc{QDR}$ decomposition for third-order tensors based on $M$-product. Then, we designed algorithms for computing these two decompositions along with the Moore-Penrose inverse, and outer inverse of the tensors. In support of these theoretical results, a few numerical examples were discussed. In addition, we derive exact expressions for the outer inverses of tensors using symbolic tensor (tensors with polynomial entries) computation. We designed efficient algorithms to compute the Moore-Penrose inverse of symbolic tensors. The prowess of the proposed $M$-$\mc{QDR}$ decomposition for third-order tensors is applied to compress lossy color images., Comment: 23

Computationally hard problems based on coding theory, such as the syndrome decoding problem, have been used for constructing secure cryptographic schemes for a long time. Schemes based on these problems are also assumed to be secure against quantum computers. However, these schemes are often considered impractical for real-world deployment due to large key sizes and inefficient computation time. In the recent call for standardization of additional post-quantum digital signatures by the National Institute of Standards and Technology, several code-based candidates have been proposed, including LESS, CROSS, and MEDS. These schemes are designed on the relatively new zero-knowledge framework. Although several works analyze the hardness of these schemes, there is hardly any work that examines the security of these schemes in the presence of physical attacks. In this work, we analyze these signature schemes from the perspective of fault attacks. All these schemes use a similar tree-based construction to compress the signature size. We attack this component of these schemes. Therefore, our attack is applicable to all of these schemes. In this work, we first analyze the LESS signature scheme and devise our attack. Furthermore, we showed how this attack can be extended to the CROSS signature scheme. Our attacks are built on very simple fault assumptions. Our results show that we can recover the entire secret key of LESS and CROSS using as little as a single fault. Finally, we propose various countermeasures to prevent these kinds of attacks and discuss their efficiency and shortcomings., Comment: 35 pages including appendix and bibliography

Herein, we report a case study in which we saw the spontaneous conversion of commercial bulk graphite into LaB6 decorated carbon nanotubes (CNTs) under normal atmospheric conditions. The feedstock graphite was used as a hollow cylindrical anode filled with LaB6 powder and partially eroded in a DC electric-arc plasma reactor in pure nitrogen atmosphere. An unusual and spontaneous deformation of the plasma-treated residual anode into a fluffy powder was seen to continue for months when left to ambient atmospheric conditions. The existence of LaB6 decorated multi-walled CNTs at large quantity was confirmed in the as-generated powder by using electron microscopy, Raman spectroscopy and x-ray diffraction. The as-synthesized CNT-based large-area field emitter showed promising field-emitting properties with a low turn-on electric field of ~1.5 V per micrometer, and a current density of ~1.17 mA per square cm at an applied electric field of 3.24 V per micrometer., Comment: 6 pages, 5figures

We present a Fair Housing and Fair Lending dataset (FairHome): A dataset with around 75,000 examples across 9 protected categories. To the best of our knowledge, FairHome is the first publicly available dataset labeled with binary labels for compliance risk in the housing domain. We demonstrate the usefulness and effectiveness of such a dataset by training a classifier and using it to detect potential violations when using a large language model (LLM) in the context of real-estate transactions. We benchmark the trained classifier against state-of-the-art LLMs including GPT-3.5, GPT-4, LLaMA-3, and Mistral Large in both zero-shot and few-shot contexts. Our classifier outperformed with an F1-score of 0.91, underscoring the effectiveness of our dataset., Comment: 14 pages, 5 figures

The problem of $\mathcal{L}_2$ stabilization of a state feedback stochastic control loop is investigated under different constraints. The discrete time linear time invariant (LTI) open loop plant is chosen to be unstable. The additive white Gaussian noise is assumed to be stationary. The link between the plant and the controller is assumed to be a finite capacity stationary channel, which puts a constraint on the bit rate of the transmission. Moreover, the state of the plant is observed only intermittently keeping the loop open some of the time. In this manuscript both scalar and vector plants under Bernoulli and Markov intermittence models are investigated. Novel bounds on intermittence parameters are obtained to ensure $\mathcal{L}_2$ stability. Moreover, novel recursive quantization algorithms are developed to implement the stabilization scheme under all the constraints. Suitable illustrative examples are provided to elucidate the main results.

The $SU(2)_L\times U(1)_Y$ invariance of the Standard Model Effective Field Theory (SMEFT) imposes relations among different low-energy effective field theory Wilson coefficients (WCs), any deviations from which would signal the presence of physics beyond SMEFT. In this work, we investigate two such relations ($C_S = -C_P$, $C_S^\prime = C_P^\prime$) among the scalar and the pseudoscalar new-physics WCs that can contribute to $b\to s \,\tau \tau $ processes. We show that, even when new physics violating these relations would not be measurable in the branching ratios of $B_s \to \tau^+ \tau^-$ and $B\to K^{(*)}\,\tau^+ \tau^-$ at HL-LHC and FCC-ee, it can still manifest itself in the angular distribution of $B\to K^{*0}\,\tau^+ \tau^-$. We identify the combinations of angular observables in this decay channel that are sensitive to scenarios beyond SMEFT. We find that the two observables $S_6^c$ and $A_7$, and their combination, have the potential to identify physics beyond SMEFT., Comment: 10 pages, 9 figures

Rotating black holes are well known to amplify the perturbing bosonic fields in certain parameter spaces. This phenomenon is popularly known as superradiance. In addition to rotation in the spacetime, charge plays a crucial role in the amplification process. In the present study, we have considered the spacetime of a magnetically charged rotating black hole emerging from the coupling of nonlinear electromagnetic field configuration to gravity. Due to this black hole spacetime, we have studied the scattering states of a massive scalar field and investigated the superradiant amplification process. We find that the magnetic charge of the spacetime affects the magnitude of the amplification and also significantly enlarges the allowed frequency ranges for which superradiance happens. In comparison, our analysis reveals that some of the behaviours are quite similar to what has been found in the case of the Kerr-Newmann black hole. Moreover, in specific parameter spaces, we observe exact similarities, which could imply a probable correspondence between the magnetically charged black hole and the Kerr-Newman black hole in certain scenarios. In addition to this, we have investigated the superradiant instability regime due to the massive potential barrier and also compared our results with that of the Kerr-Newmann black hole.

Changing public perceptions and government regulations have led to the widespread use of low-cost air quality monitors in modern indoor spaces. Typically, these monitors detect air pollutants to augment the end user's understanding of her indoor environment. Studies have shown that having access to one's air quality context reinforces the user's urge to take necessary actions to improve the air over time. Thus, user's activities significantly influence the indoor air quality. Such correlation can be exploited to get hold of sensitive indoor activities from the side-channel air quality fluctuations. This study explores the odds of identifying eight indoor activities (i.e., enter, exit, fan on, fan off, AC on, AC off, gathering, eating) in a research lab with an in-house low-cost air quality monitoring platform named DALTON. Our extensive data collection and analysis over three months shows 97.7% classification accuracy in our dataset., Comment: 6 pages, 7 figures, accepted as Late Breaking Work in ACM MobileHCI 2024

In this article, we report our study on the absorption cross section for the electromagnetic field due to static spherically symmetric regular black holes of two distinct categories: the Fan-Wang generic class and the Simpson-Visser class. Existing studies on these two categories have relied on the WKB method, which applies to the low-frequency regime. In our analysis, we have numerically evaluated the absorption cross section for the electromagnetic field across all frequency ranges. To compute the absorption cross section, we followed a gauge-invariant approach, which avoids the need for gauge fixing for the electromagnetic field, thereby simplifying the methodology further. To emphasize the usefulness of this gauge invariant approach we have performed the same analysis for Simpson-Visser class black holes, which is more general in terms of the structure of the metric. For both types of regular black holes, the influence of regularity parameters on the absorption cross section reveals fundamental properties of their spacetime and their interactions with the surrounding astrophysical environment. Moreover, certain similarities observed between the Reissner-Nordstr\"om black hole and regular black holes of the Fan-Wang generic class indicate potential scenarios of correspondence between linear and nonlinear electrodynamics when coupled with gravity.

DNA is now firmly established as a versatile and robust platform for achieving synthetic nanostructures. While the folding of single molecules into complex structures is routinely achieved through engineering basepair sequences, much less is known about the emergence of structure on larger scales in DNA fluids. The fact that polymeric DNA fluids can undergo phase separation into dense fluid and dilute gas opens avenues to design hierachical and multifarious assemblies. Here we investigate to which extent the phase behavior of single-stranded DNA fluids is captured by a minimal model of semiflexible charged homopolymers while neglecting specific hybridization interactions. We first characterize the single-polymer behavior and then perform direct coexistence simulations to test the model against experimental data. We conclude that counterions not only determine the effective range of direct electrostatic interactions but also the effective attractions.

Statistical inference for time series such as curve estimation for time-varying models or testing for existence of change-point have garnered significant attention. However, these works are generally restricted to the assumption of independence and/or stationarity at its best. The main obstacle is that the existing Gaussian approximation results for non-stationary processes only provide an existential proof and thus they are difficult to apply. In this paper, we provide two clear paths to construct such a Gaussian approximation for non-stationary series. While the first one is theoretically more natural, the second one is practically implementable. Our Gaussian approximation results are applicable for a very large class of non-stationary time series, obtain optimal rates and yet have good applicability. Building on such approximations, we also show theoretical results for change-point detection and simultaneous inference in presence of non-stationary errors. Finally we substantiate our theoretical results with simulation studies and real data analysis.

Heart disease is a serious global health issue that claims millions of lives every year. Early detection and precise prediction are critical to the prevention and successful treatment of heart related issues. A lot of research utilizes machine learning (ML) models to forecast cardiac disease and obtain early detection. In order to do predictive analysis on "Heart disease health indicators " dataset. We employed five machine learning methods in this paper: Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis, Extra Tree Classifier, and AdaBoost. The model is further examined using various feature selection (FS) techniques. To enhance the baseline model, we have separately applied four FS techniques: Sequential Forward FS, Sequential Backward FS, Correlation Matrix, and Chi2. Lastly, K means SMOTE oversampling is applied to the models to enable additional analysis. The findings show that when it came to predicting heart disease, ensemble approaches in particular, random forests performed better than individual classifiers. The presence of smoking, blood pressure, cholesterol, and physical inactivity were among the major predictors that were found. The accuracy of the Random Forest and Decision Tree model was 99.83%. This paper demonstrates how machine learning models can improve the accuracy of heart disease prediction, especially when using ensemble methodologies. The models provide a more accurate risk assessment than traditional methods since they incorporate a large number of factors and complex algorithms.

Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have been used traditionally, these models often do not consider variations in lifestyles, socioeconomic conditions, or genetic predispositions. Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models. We used data from the UK Biobank study, which included over 500,000 middle-aged participants from different primary healthcare centers in the UK. Data collected at baseline (2006--2010) and during imaging visits after 2014 were used in this study. Baseline characteristics, including sex, age, and the Townsend Deprivation Index, were included. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram and echocardiography data, including left ventricular size and function, cardiac output, and stroke volume, were also used. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are explainable and easily interpretable. We reported the accuracy, precision, recall, and F-1 scores; confusion matrices; and area under the curve (AUC) curves., Comment: 19 pages, 3 figures

In recent years, indoor air pollution has posed a significant threat to our society, claiming over 3.2 million lives annually. Developing nations, such as India, are most affected since lack of knowledge, inadequate regulation, and outdoor air pollution lead to severe daily exposure to pollutants. However, only a limited number of studies have attempted to understand how indoor air pollution affects developing countries like India. To address this gap, we present spatiotemporal measurements of air quality from 30 indoor sites over six months during summer and winter seasons. The sites are geographically located across four regions of type: rural, suburban, and urban, covering the typical low to middle-income population in India. The dataset contains various types of indoor environments (e.g., studio apartments, classrooms, research laboratories, food canteens, and residential households), and can provide the basis for data-driven learning model research aimed at coping with unique pollution patterns in developing countries. This unique dataset demands advanced data cleaning and imputation techniques for handling missing data due to power failure or network outages during data collection. Furthermore, through a simple speech-to-text application, we provide real-time indoor activity labels annotated by occupants. Therefore, environmentalists and ML enthusiasts can utilize this dataset to understand the complex patterns of the pollutants under different indoor activities, identify recurring sources of pollution, forecast exposure, improve floor plans and room structures of modern indoor designs, develop pollution-aware recommender systems, etc., Comment: 23 pages, 15 figures

Indoor air pollution is a major issue in developing countries such as India and Bangladesh, exacerbated by factors like traditional cooking methods, insufficient ventilation, and cramped living conditions, all of which elevate the risk of health issues like lung infections and cardiovascular diseases. With the World Health Organization associating around 3.2 million annual deaths globally to household air pollution, the gravity of the problem is clear. Yet, extensive empirical studies exploring these unique patterns and indoor pollutions extent are missing. To fill this gap, we carried out a six months long field study involving over 30 households, uncovering the complexity of indoor air pollution in developing countries, such as the longer lingering time of VOCs in the air or the significant influence of air circulation on the spatiotemporal distribution of pollutants. We introduced an innovative IoT air quality sensing platform, the Distributed Air QuaLiTy MONitor (DALTON ), explicitly designed to meet the needs of these nations, considering factors like cost, sensor type, accuracy, network connectivity, power, and usability. As a result of a multi-device deployment, the platform identifies pollution hot-spots in low and middle-income households in developing nations. It identifies best practices to minimize daily indoor pollution exposure. Our extensive qualitative survey estimates an overall system usability score of 2.04, indicating an efficient system for air quality monitoring., Comment: 33 pages, 22 figures, accepted in ACM Journal on Computing and Sustainable Societies

Controlling excitons and their transport in two-dimensional (2D) transition metal dichalcogenides (TMDs) heterostructures is central to advancing photonics and electronics on-chip integration. We investigate the controlled generation and manipulation of excitons and their complexes in monolayer (1L) MoSe2-WSe2 lateral heterostructure (LHS), directly grown via water-assisted chemical vapor deposition. Using a field-effect transistor design by incorporating a few-layer graphene back gate, single-layer graphene edge contact and encapsulation with few-layer hexagonal boron nitride, we achieve precise electrical tuning of exciton complexes and their transfer across 1D interfaces. At cryogenic temperatures (4 K), photoluminescence and photocurrent maps reveal the synergistic effect of local electric field and interface phenomena in the modulation of excitons, trions, and free carriers. We observe spatial variations in exciton and trion densities driven by exciton-trion conversion under electrical manipulation. The first-principle density functional theory calculation reveals significant band modification at the lateral interfaces and graphene-TMDs contact region. Furthermore, we demonstrate the versatility of 2D TMDS LHS in hosting and manipulating quantum emitters, achieving precise control over narrow-band emissions through modulating carrier injection and electrical biasing. This work extends the boundary of the present understanding of excitonic behaviour within lateral heterojunctions, highlighting the potential for controlled exciton manipulation across 1D interfaces and paving the way for next-generation electro-optical quantum devices., Comment: 5 Figs

Sentiment analysis (SA), is an approach of natural language processing (NLP) for determining a text's emotional tone by analyzing subjective information such as views, feelings, and attitudes toward specific topics, products, services, events, or experiences. This study attempts to develop an advanced deep learning (DL) model for SA to understand global audience emotions through tweets in the context of the Olympic Games. The findings represent global attitudes around the Olympics and contribute to advancing the SA models. We have used NLP for tweet pre-processing and sophisticated DL models for arguing with SA, this research enhances the reliability and accuracy of sentiment classification. The study focuses on data selection, preprocessing, visualization, feature extraction, and model building, featuring a baseline Na\"ive Bayes (NB) model and three advanced DL models: Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the BERT model can efficiently classify sentiments related to the Olympics, achieving the highest accuracy of 99.23%.

We consider the log-perturbed Br\'ezis-Nirenberg problem on the hyperbolic space \begin{align*} \Delta_{\mathbb{B}^N}u+\lambda u +|u|^{p-1}u+\theta u \ln u^2 =0, \ \ \ \ u \in H^1(\mathbb{B}^N), \ u > 0 \ \text{in} \ \mathbb{B}^N. \end{align*} and study the existence vs non-existence results. We show that whenever $\theta >0,$ there exists an $H^1$-solution, while for $\theta <0$, there does not exist a positive solution in a reasonably general class. Since the perturbation $ u \ln u^2$ changes sign, Pohozaev type identities do not yield any non-existence results. The main contribution of this article is obtaining an ``almost" precise lower asymptotic decay estimate on the positive solutions for $\theta <0,$ culminating in proving their non-existence assertion., Comment: 28 pages, Suggestions are welcomed

This study investigates the critical role of buffer layers in enhancing the efficiency of thin-film CdTe solar cells. We explore non-toxic buffer materials, specifically ZnSe, ZnMgO, 3C-SiC, and WS2, as potential replacements for the traditional Cd-based CdS buffer layer. Our analysis includes a thorough evaluation of the electrical and optical performance of these buffer materials in conjunction with CdTe absorbers. We optimize the thickness of both the buffer and absorber layers to achieve the best performance. Additionally, we examine the impact of defect density variations in the buffer materials and their corresponding temperature effects. Among the tested materials, ZnSe and ZnMgO demonstrate the highest potential, achieving power conversion efficiencies of 20.74% and 20.73%, respectively., Comment: 19 pages, 7 Figures, 3 Tables

It is well-known that the cohomology of symmetric quandles generates robust cocycle invariants for unoriented classical and surface links. Expanding on the recently introduced module-theoretic generalized cohomology for symmetric quandles, we derive a four-term exact sequence that relates 1-cocycles, second cohomology, and a specific group of automorphisms associated with the extensions of symmetric quandles. This exact sequence shows that the obstruction to lifting and extending automorphisms is found in the second symmetric quandle cohomology. Additionally, some general aspects of dynamical cocycles and extensions are discussed., Comment: 18 pages

We derive novel black hole solutions in a modified gravity theory, namely the Hu-Sawicki model of $f(R)$ gravity. After obtaining the black hole solution, we study the horizon radius of the black hole from the metric and then analyse the dependence of the model parameters on the horizon. We then use the 6th-order WKB method to study the quasinormal modes of oscillations (QNMs) of the black hole perturbed by a scalar field. The dependence of the amplitude and damping part of the QNMs are analysed with respect to variations in model parameters and the errors associated with the QNMs are also computed. After that, we study some thermodynamic properties associated with the black hole such as its thermodynamic temperature as well as greybody factors. It is found that the black hole has the possibility of showcasing negative temperatures. We also analyse the geodesics and derive the photon sphere radius as well as the shadow radius of the black hole. The photon radius is independent of the model parameters while the shadow radius showed a fair amount of dependence on the model parameters. We tried to constrain the parameters with the help of Keck and VLTI observational data and obtained some bounds on $m$ and $c_{2}$ parameters., Comment: 14 pages, 9 figures; published version

The pulsational mode of gravitational collapse (PMGC) originating from the combined gravito-electrostatic interaction in complex dust molecular clouds (DMCs) is a canonical mechanism leading to the onset of astronomical structure formation dynamics. A generalized semi-analytic model is formulated to explore the effects of the Eddington-inspired Born-Infeld (EiBI) gravity, non-thermal (r, q)-distributed electrons, and dust-polarization force on the PMGC stability concurrently. The thermal ions are treated thermo-statistically with the Maxwellian distribution law and the non-thermal electrons with the (r, q)-distribution law. The constitutive partially ionized dust grains are modeled in the fluid fabric. A spherical normal mode analysis yields a generalized linear PMGC dispersion relation. Its oscillatory and propagation characteristics are investigated in a reasonable numerical platform. It is found that an increase in the polarization force and positive EiBI parameter significantly enhances the instability, causing the DMC collapse and vice versa. The electron non-thermality spectral parameters play as vital stabilizing factors, and so on. Its reliability and applicability are finally outlined in light of astronomical predictions previously reported in the literature.

We propose a new realisation of light Dirac neutrino mass and dark matter (DM) within the framework of a non-Abelian discrete flavour symmetry based on $A_4$ group. In addition to $A_4$, we also consider a $Z_2$ and an unbroken global lepton number symmetry $U(1)_L$ to keep unwanted terms away while guaranteeing the Dirac nature of light neutrinos. The field content, their transformations and flavon vacuum alignments are chosen in such a way that the type-I Dirac seesaw generates only one light Dirac neutrino mass while the other two masses arise from scotogenic contributions at one-loop. This leads to the Dirac scoto-seesaw framework, a generalisation of the widely studied scoto-seesaw model to Dirac neutrinos. The symmetry breaking of $A_4$ leaves a remnant $\mathcal{Z}_2$ symmetry responsible for stabilising DM. Dirac nature of light neutrinos introduces additional relativistic degrees of freedom $\Delta N_{\rm eff}$ within reach of cosmic microwave background experiments., Comment: version 2: Added references, corrected typos and improved discussions; 36 pages, 11 captioned figures

In 2009, T. Banica and R. Speicher introduced the orthogonal easy quantum group which can be completely determined using the combinatorics of set partitions. Later, P. Tarrago and M. Weber extended this formulation to another special class of compact matrix quantum groups, known as unitary easy quantum groups, which are quantum subgroups of the free unitary quantum group ($U_n^+$) containing $S_n$. On the other hand, the quantum symmetry of graph $C^{*}$-algebras has been explored by several mathematicians within different categories in the past few years. In this article, we establish that there are exactly three families of (identically) unitary easy quantum groups that can be achieved as the quantum symmetries of graph $C^*$-algebras $C^*(\Gamma)$ associated with some finite, connected, directed graphs $\Gamma$ in the category introduced by Joardar and Mandal. Moreover, we demonstrate that there does not exist any graph $C^*$-algebra associated with a finite, connected, directed graph $\Gamma$ having $A_{U^t}(F^{\Gamma})$ as the quantum automorphism group of $C^*(\Gamma)$ for non-scalar matrix $F^{\Gamma}$., Comment: Statements of some lemmas, propositions and theorems have been modified in the 3rd section. Some prerequisites and definitions have been added in the 2nd section and Proposion 3.1, Remark 3.16 have been added in the 3rd section. A few stylistic changes have been made. Comments are welcome

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

This tutorial review focuses on the basic theoretical backgrounds, their working principles, and implementation of impedance spectroscopy in both electroceramics and electrochemical research and technological applications. Various contributions to the impedance, admittance, dielectric, and conductivity characteristics of electroceramic materials can be disentangled and independently characterized with the help of impedance spectroscopy as a function of frequency and temperature. In polycrystalline materials, the impedance, charge transport/ conduction mechanism, and the macroscopic dielectric properties i.e., dielectric constant and loss are typically composed of many contributions, including the bulk or grain resistance/capacitance, grain boundary, and sample-electrode interface effect. Similarly, electrochemical impedance spectroscopy (EIS) endeavors to the charging kinetics, diffusion, and mechanical impact of various electrochemical systems widely used in energy storage (i.e., supercapacitor, battery), corrosion resistance, chemical and bio-sensing, diagnostics, etc. in electrolytes as a function of frequency. The understanding of various contributions in the EIS spectra i.e., kinetic control, mass control, and diffusion control is essential for their practical implications. It is demonstrated that electrochemical and electroceramics impedance spectroscopy is an effective method to explain and simulate such behavior. Deconvolute these contributions to obtain a detailed understanding of the functionality of polycrystalline electroceramic materials. This short review aims to endow the expertise of senior researchers in many fields where both EIS (electrochemical and ceramics) are involved, as well as to provide the necessary background information for junior researchers working in these fields.

Let $L\subseteq \mathbb{R}^{n}$ an even lattice and $T_{L}=\mathbb{R}^{n}/L$ the associated torus. Associated with $L$ we construct $T_{L}$--kernel on a hyperfinite factor type $\mathcal{A}_{L}$, i.e. a monomorphism $T_{L}\to\mathsf{Out}(\mathcal{A}_{L})$, and compute Sutherland's obstruction class in $H^{3}_{\mathrm{Borel}}(T_{L},\mathbb{T})\cong H^{4}(BT_{L} ,\mathbb{Z})$, which is an invariant of the $T_{L}$--kernel and an obstruction to the existence of a twisted crossed product by $T_{L}$. As a Corollary, we obtain that for any $n$-torus $T$ any class in $H^{3}_{\mathrm{Borel}}(T,\mathbb{T})$ arises as an obstruction for a $T$-kernel on the hyperfinite type III${}_{1}$ factor $R$. The construction is an analogue of the construction of Vaughan Jones for finite groups on the hyperfinite type II${}_{1}$ factor but is also motivated by and has applications to conformal nets. Namely, there is an associated local extension $\mathcal{A}_{L}\supseteq \mathcal{A}_{\mathbb{R}^n}$ of conformal nets and the $T_{L}$--kernel corresponds to a family of $T_{L^\ast}$--twisted sectors representations whose anomaly (obstruction) can be identified with the inner product on $L$ seen as a class in $H^{4}(BT_{L},\mathbb{Z})\cong \operatorname{Sym}^{2}(L,\mathbb{Z})$., Comment: Comments are welcome!

Consider a rooted Galton-Watson tree $T$, to each of whose edges we assign, independently, a weight that equals $+1$ with probability $p_{1}$, $0$ with probability $p_{0}$ and $-1$ with probability $p_{-1}=1-p_{1}-p_{0}$. We play a game on this rooted, edge-weighted Galton-Watson tree, involving two players and a token. The token is allowed to be moved from where it is currently located, say a vertex $u$ of $T$, to any child $v$ of $u$. The players begin with initial capitals that amount to $i$ and $j$ units respectively, and a player wins if either she is the first to amass a capital worth $\kappa$ units, where $\kappa$ is a pre-specified positive integer, or her opponent is the first to have her capital dwindle to $0$, or she is able to move the token to a leaf vertex, from where her opponent cannot move it any farther. This paper is concerned with studying the probabilities of the three possible outcomes (i.e. win for the first player, loss for the first player, and draw for both players) of this game, as well as finding conditions under which the expected duration of this game is finite. The theory we develop in this paper for the analysis of this game is further supported by observations obtained via computer simulations, and these observations provide a deeper insight into how the above-mentioned probabilities behave as the underlying parameters and / or offspring distributions are allowed to vary. We conclude the paper with a couple of conjectures, one of which suggests the occurrence of a phase transition phenomenon whereby the probability of draw in this game goes from being $0$ to being strictly positive as the parameter-pair $(p_{0},p_{1})$ is varied suitably while keeping the underlying offspring distribution of $T$ fixed., Comment: 4 tables included

Experimental materials science is experiencing significant growth due to automated experimentation and AI techniques. Integrated autonomous platforms are emerging, combining generative models, robotics, simulations, and automated systems for material synthesis. However, two major challenges remain: democratizing access to these technologies and creating accessible infrastructure for under-resourced scientists. This paper introduces the Quantum Data Hub (QDH), a community-accessible research infrastructure aimed at researchers working with quantum materials. QDH integrates with the National Data Platform, adhering to FAIR principles while proposing additional UNIT principles for usability, navigability, interpretability, and timeliness. The QDH facilitates collaboration and extensibility, allowing seamless integration of new researchers, instruments, and data into the system.

We discuss the modeling of the hadronic phase of QCD at finite magnetic field in the framework of hadron resonance gas (HRG). We focus on the statistical description of particle yields that include contribution from resonance decays. We demonstrate that the swift increase in the number of protons with magnetic field predicted in the HRG is due to the ill-defined description of higher-spin states. We discuss fluctuations of conserved charges and show that at present the qualitative comparison of the model predictions with the Lattice QCD data should be treated with care. We also discuss the principle of detailed balance which allows to study the magnetic field dependence of neutral resonances.

We consider bond percolation games on the $2$-dimensional square lattice in which each edge (that is either between the sites $(x,y)$ and $(x+1,y)$ or between the sites $(x,y)$ and $(x,y+1)$, for all $(x,y) \in \mathbb{Z}^{2}$) has been assigned, independently, a label that reads "trap" with probability $p$, "target" with probability $q$, and "open" with probability $1-p-q$. Once a realization of this labeling is generated, it is revealed in its entirety to the players before the game starts. The game involves a single token, initially placed at the origin, and two players who take turns to make moves. A move involves relocating the token from where it is currently located, say the site $(x,y)$, to any one of $(x+1,y)$ and $(x,y+1)$. A player wins if she is able to move the token along an edge labeled a target, or if she is able to force her opponent to move the token along an edge labeled a trap. The game is said to result in a draw if it continues indefinitely (i.e.\ with the token always being moved along open edges). We ask the question: for what values of $p$ and $q$ is the probability of draw equal to $0$? By establishing a close connection between the event of draw and the ergodicity of a suitably defined probabilistic cellualar automaton, we are able to show that the probability of draw is $0$ when $p > 0.157175$ and $q=0$, and when $p=q \geqslant 0.10883$., Comment: Main body of the paper: 21 pages. Appendix: from page 21 till page 43. No. of figures: 4

The success of autonomous navigation relies on robust and precise vehicle recognition, hindered by the scarcity of region-specific vehicle detection datasets, impeding the development of context-aware systems. To advance terrestrial object detection research, this paper proposes a native vehicle detection dataset for the most commonly appeared vehicle classes in Bangladesh. 17 distinct vehicle classes have been taken into account, with fully annotated 81542 instances of 17326 images. Each image width is set to at least 1280px. The dataset's average vehicle bounding box-to-image ratio is 4.7036. This Bangladesh Native Vehicle Dataset (BNVD) has accounted for several geographical, illumination, variety of vehicle sizes, and orientations to be more robust on surprised scenarios. In the context of examining the BNVD dataset, this work provides a thorough assessment with four successive You Only Look Once (YOLO) models, namely YOLO v5, v6, v7, and v8. These dataset's effectiveness is methodically evaluated and contrasted with other vehicle datasets already in use. The BNVD dataset exhibits mean average precision(mAP) at 50% intersection over union (IoU) is 0.848 corresponding precision and recall values of 0.841 and 0.774. The research findings indicate a mAP of 0.643 at an IoU range of 0.5 to 0.95. The experiments show that the BNVD dataset serves as a reliable representation of vehicle distribution and presents considerable complexities., Comment: 13 pages, 8 figures

We propose a neutrino mass matrix texture bearing a suitable correlation $m_{22}=-2\,m_{13}$ and study its phenomenological implications. In light of both normal and inverted hierarchies, the texture imposes specific bounds on some observational parameters. As a potential application, the prediction of effective Majorana neutrino mass $m_{\beta\beta}$ is visualized for both hierarchies. To understand the proposed texture from the first principle, we incorporate the type-I+II seesaw mechanism in association with $A_4 \times Z_{10} \times Z_2$ group.

The patch dynamics scheme in equation-free multiscale modelling can efficiently predict the macroscopic behaviours by simulating the microscale problem in a fraction of the space-time domain. The patch dynamics schemes developed so far, are mainly on rectangular domains with uniform grids and uniform rectangular patches. In real-life problems where the geometry of the domain is not regular or simple, rectangular and uniform grids or patches may not be useful. To address this kind of complexity, the concept of a generalized curvilinear coordinate system is used. An explicit representation of a patch dynamics scheme on a generalized curvilinear coordinate system in a two-dimensional domain is proposed for evolution equations. It has been applied to unsteady convection-diffusion-reaction (CDR) problems. The robustness of the scheme on the generalized curvilinear coordinate system is assessed through numerical test cases. Firstly, a convection-dominated CDR equation is considered, featuring high gradient regions in some part of the domain, for which stretched grids with non-uniform patch sizes are employed. Secondly, a non-axisymmetric diffusion equation is examined in an annulus region, where the patches have non-rectangular shapes. The results obtained demonstrate excellent agreement with the analytical solution or existing numerical solutions., Comment: 21 pages, 8 figures, 4 tables

We present the first limit on $g_{A\gamma}$ coupling constant using the Bragg-Primakoff conversion based on an exposure of 1107.5 kg days of data from the CDEX-1B experiment at the China Jinping Underground Laboratory. The data are consistent with the null signal hypothesis, and no excess signals are observed. Limits of the coupling $g_{A\gamma}<2.08\times10^{-9}$ GeV$^{-1}$ (95\% C.L.) are derived for axions with mass up to 100 eV/$c^2$. Within the hadronic model of KSVZ, our results exclude axion mass $>5.3~\rm{eV}/c^2$ at 95\% C.L., Comment: 7 pages, 5 figures

This paper explores the application of FilmFrenzy, a python based ticket booking web application, in the revival of traditional Indian theatres. Additionally, this research paper explores how NLP can be implemented to improve user experience. Through clarifying audience views and pinpointing opportunities for development, FilmFrenzy aims to promote involvement and rejuvenation in India's conventional theatre scene. The platform seeks to maintain the relevance and vitality of conventional theatres by bridging the gap between audiences and them through the incorporation of contemporary technologies, especially NLP. This research envisions a future in which technology plays a crucial part in maintaining India's rich theatrical traditions, thereby contributing to the preservation and development of cultural heritage. With sentiment analysis and natural language processing (NLP) as essential instruments for improving stagecraft, the research envisions a period when traditional theatre will still be vibrant.

Online commerce relies heavily on user generated reviews to provide unbiased information about products that they have not physically seen. The importance of reviews has attracted multiple exploitative online behaviours and requires methods for monitoring and detecting reviews. We present a machine learning methodology for review detection and extraction, and demonstrate that it generalises for use across websites that were not contained in the training data. This method promises to drive applications for automatic detection and evaluation of reviews, regardless of their source. Furthermore, we showcase the versatility of our method by implementing and discussing three key applications for analysing reviews: Sentiment Inconsistency Analysis, which detects and filters out unreliable reviews based on inconsistencies between ratings and comments; Multi-language support, enabling the extraction and translation of reviews from various languages without relying on HTML scraping; and Fake review detection, achieved by integrating a trained NLP model to identify and distinguish between genuine and fake reviews.

We introduce the DREAMS project, an innovative approach to understanding the astrophysical implications of alternative dark matter models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over dark matter physics, astrophysics, and cosmology in modeling a range of systems -- from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of Warm Dark Matter, each comprised of 1024 simulations generated using the Arepo code. One suite consists of uniform-box simulations covering a $(25~h^{-1}~{\rm M}_\odot)^3$ volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the Warm Dark Matter particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and Convolutional Neural Networks, to demonstrate how such simulation suites can be used to disentangle the effects of dark matter and baryonic physics on galactic properties. The DREAMS project can be extended further to include different dark matter models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of dark matter on these scales., Comment: 28 pages, 8 figures, DREAMS website: https://www.dreams-project.org