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This article reports the development of a versatile high-speed reciprocating drive system (HRDS) with interchangeable probe heads to characterize the edge plasma region of ADITYA-U tokamak. This reciprocating probe drive system consisting of Langmuir and magnetic probe heads, is designed, fabricated, installed, and operated for studying the extent of fuel/impurity gas propagation and its influence on plasma dynamics in the far-edge region inside the last closed magnetic flux surface (LCFS). The HRDS is driven by a highly accurate, easy-to-control, dynamic, brushless, permanently excited synchronous servo motor operated by a PXI-commanded controller. The system is remotely operated and allows for precise control of the speed, acceleration, and distance traveled of the probe head on a shot-to-shot basis, facilitating seamless control of operations according to experimental requirements. Using this system, consisting of a linear array of Langmuir probes, measurements of plasma density, temperature, potential, and their fluctuations revealed that the fuel gas-puff impact these mean and fluctuating parameters up to three to four cm inside the LCFS. Attaching an array of magnetic probes to this system led to measurements of magnetic fluctuations inside the LCFS. The HRDS system is fully operational and serves as an important diagnostic tool for ADITYA-U tokamak.

Experiments on ADITYA-U tokamak show a marked enhancement in the sawtooth period by application of short gas puffs of fuel that cause a modification of the radial density profile. A consequent suppression of the trapped electron modes (TEMs) then leads to an increase in the core electron temperature. This slows down the heat propagation following a sawtooth crash, causing a delay in achieving the critical temperature gradient inside the q = 1 surface required for the next sawtooth crash to happen. The overall scenario has strong similarities with the behavior of sawtooth under electron cyclotron resonance heating (ECRH). Our findings suggest an alternate, simpler technique for sawtooth control that may be usefully employed in small/medium-sized tokamaks that do not have an ECRH or any other auxiliary heating facility.

The interaction of TeV blazars emitted gamma-rays with the extragalactic background photons gives rise to a relativistic beam of electron-positron ($e^- e^+$) pairs propagating through the intergalactic medium, producing a cascade through up-scattering low-energy photons. Plasma instability is considered one of the underlying energy-loss processes of the beams. We employ particle-in-cell (PIC) simulations to study the plasma instabilities of ultra-relativistic pair beams propagating in a denser background plasma, using the parameters designed to replicate astrophysical jets under laboratory conditions. In an astrophysical scenario with a broad, dilute beam, electromagnetic instability can be disregarded because its growth rate is slower than that of electrostatic instability, indicating the electromagnetic modes are suppressed. We calculate the physical limit of density contrast at which a warm beam achieves suppression of electromagnetic instabilities in laboratory experiments, consistent with the physically relevant conditions for Blazar-induced beams. We have used a composite Cauchy distribution for the beam particles, which is more realistic in representing the non-Maxwellian nature of pair beams, improving upon previous studies. We investigate the interplay between the magnetic field forming from localized currents and transverse beam momentum spread. We extrapolate to the non-linear feedback of instability where the beam is energetically broadened. We observe that the instability generates a negligible angular broadening for Blazar-Induced beams., Comment: 13 pages, 12 figures

Estimating rare event kinetics from molecular dynamics simulations is a non-trivial task despite the great advances in enhanced sampling methods. Weighted Ensemble (WE) simulation, a special class of enhanced sampling techniques, offers a way to directly calculate kinetic rate constants from biased trajectories without the need to modify the underlying energy landscape using bias potentials. Conventional WE algorithms use different binning schemes to partition the collective variable (CV) space separating the two metastable states of interest. In this work, we have developed a new "binless" WE simulation algorithm to bypass the hurdles of optimizing binning procedures. Our proposed protocol (WeTICA) uses a low-dimensional CV space to drive the WE simulation toward the specified target state. We have applied this new algorithm to recover the unfolding kinetics of three proteins: (A) TC5b Trp-cage mutant, (B) TC10b Trp-cage mutant, and (C) Protein G, with unfolding times spanning the range between 3 and 40 {\mu}s using projections along predefined fixed Time-lagged Independent Component Analysis (TICA) eigenvectors as CVs. Calculated unfolding times converge to the reported values with good accuracy with more than one order of magnitude less cumulative WE simulation time than the unfolding time scales with or without a priori knowledge of the CVs that can capture unfolding. Our algorithm can be used with other linear CVs, not limited to TICA. Moreover, the new walker selection criteria for resampling employed in this algorithm can be used on more sophisticated nonlinear CV space for further improvements of binless WE methods., Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in The Journal of Chemical Physics (J. Chem. Phys. 162, 034106 (2025)) and may be found at https://doi.org/10.1063/5.0239713

In this paper, we introduce a reduced order model-based reinforcement learning (MBRL) approach, utilizing the Iterative Linear Quadratic Regulator (ILQR) algorithm for the optimal control of nonlinear partial differential equations (PDEs). The approach proposes a novel modification of the ILQR technique: it uses the Method of Snapshots to identify a reduced order Linear Time Varying (LTV) approximation of the nonlinear PDE dynamics around a current estimate of the optimal trajectory, utilizes the identified LTV model to solve a time-varying reduced order LQR problem to obtain an improved estimate of the optimal trajectory along with a new reduced basis, and iterates till convergence. The convergence behavior of the reduced order approach is analyzed and the algorithm is shown to converge to a limit set that is dependent on the truncation error in the reduction. The proposed approach is tested on the viscous Burger's equation and two phase-field models for microstructure evolution in materials, and the results show that there is a significant reduction in the computational burden over the standard ILQR approach, without significantly sacrificing performance.

Systems brought out of equilibrium through a rapid quench from a disordered initial state into an ordered phase undergo physical aging in the form of phase-ordering kinetics, with characteristic dynamical scaling. In many systems, notably glasses, dynamical scaling is often described through sub-aging, where a phenomenological sub-aging exponent $0<\mu< 1$ is empirically chosen to achieve the best possible data collapse. Here it is shown that finite-size effects modify the dynamical scaling behavior, away from simple aging with $\mu=1$ towards $\mu<1$, such that phenomenologically it would appear as sub-aging. This is exemplified for the exactly solved dynamical spherical model in dimensions $2

Despite being the most common types of stars in the Galaxy, the physical properties of late M dwarfs are often poorly constrained. A trend of radius inflation compared to evolutionary models has been observed for earlier type M dwarfs in eclipsing binaries, possibly caused by magnetic activity. It is currently unclear whether this trend also extends to later type M dwarfs below the convective boundary. This makes the discovery of lower-mass, fully convective, M dwarfs in eclipsing binaries valuable for testing evolutionary models especially in longer-period binaries where tidal interaction between the primary and secondary is negligible. With this context, we present the discovery of the NGTS-EB-7 AB system, an eclipsing binary containing a late M dwarf secondary and an evolved G-type primary star. The secondary star has a radius of $0.125 \pm 0.006 R_\odot$ , a mass of $0.096 \pm 0.004 M_\odot$ and follows a highly eccentric $(e=0.71436 \pm 0.00085)$ orbit every $193.35875 \pm 0.00034$ days. This makes NGTS-EB-7 AB the third longest-period eclipsing binary system with a secondary smaller than $200 M_J$ with the mass and radius constrained to better than $5 \%$. In addition, NGTS-EB-7 is situated near the centre of the proposed LOPS2 southern field of the upcoming PLATO mission, allowing for detection of the secondary eclipse and measurement of the companion`s temperature. With its long-period and well-constrained physical properties - NGTS-EB-7 B will make a valuable addition to the sample of M dwarfs in eclipsing binaries and help in determining accurate empirical mass/radius relations for later M dwarf stars., Comment: Main body: 14 pages, 6 figures, 5 tables. Appendices: 7 pages, 5 figures, 5 tables

This paper investigates the resilience of a ResNet-50 image classification model under two prominent security threats: Fast Gradient Sign Method (FGSM) adversarial attacks and malicious payload injection. Initially, the model attains a 53.33% accuracy on clean images. When subjected to FGSM perturbations, its overall accuracy remains unchanged; however, the model's confidence in incorrect predictions notably increases. Concurrently, a payload injection scheme is successfully executed in 93.33% of the tested samples, revealing how stealthy attacks can manipulate model predictions without degrading visual quality. These findings underscore the vulnerability of even high-performing neural networks and highlight the urgency of developing more robust defense mechanisms for security-critical applications.

Given an analytic function $f=u+iv$ in the unit disk $\mathbb{D}$, Zygmund's theorem gives the minimal growth restriction on $u$ which ensures that $v$ is in the Hardy space $h^1$. This need not be true if $f$ is a complex-valued harmonic function. However, we prove that Zygmund's theorem holds if $f$ is a harmonic $K$-quasiregular mapping in $\ID$. Our work makes further progress on the recent Riesz-type theorem of Liu and Zhu (Adv. Math., 2023), and the Kolmogorov-type theorem of Kalaj (J. Math. Anal. Appl., 2025), for harmonic quasiregular mappings. We also obtain a partial converse, thus showing that the proposed growth condition is the best possible. Furthermore, as an application of the classical conjugate function theorems, we establish a harmonic analogue of a well-known result of Hardy and Littlewood., Comment: 12 pages

Thermoelectric materials provide a sustainable way to convert waste heat into electricity. However, data-driven discovery and optimization of these materials are challenging because of a lack of a reliable database. Here we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.

Drone fleets with onboard cameras coupled with computer vision and DNN inferencing models can support diverse applications. One such novel domain is for one or more buddy drones to assist Visually Impaired People (VIPs) lead an active lifestyle. Video inferencing tasks from such drones can help both navigate the drone and provide situation awareness to the VIP, and hence have strict execution deadlines. We propose a deadline-driven heuristic, DEMS-A, to schedule diverse DNN tasks generated continuously to perform inferencing over video segments generated by multiple drones linked to an edge, with the option to execute on the cloud. We use strategies like task dropping, work stealing and migration, and dynamic adaptation to cloud variability, to guarantee a Quality of Service (QoS), i.e. maximize the utility and the number of tasks completed. We also introduce an additional Quality of Experience (QoE) metric useful to the assistive drone domain, which values the frequency of success for task types to ensure the responsiveness and reliability of the VIP application. We extend our DEMS solution to GEMS to solve this. We evaluate these strategies, using (i) an emulated setup of a fleet of over 80 drones supporting over 25 VIPs, with real DNN models executing on pre-recorded drone video streams, using Jetson Nano edges and AWS Lambda cloud functions, and (ii) a real-world setup of a Tello drone and a Jetson Orin Nano edge generating drone commands to follow a VIP in real-time. Our strategies present a task completion rate of up to 88%, up to 2.7x higher QoS utility compared to the baselines, a further 16% higher QoS utility while adapting to network variability, and up to 75% higher QoE utility. Our practical validation exhibits task completion of up to 87% for GEMS and 33% higher total utility of GEMS compared to edge-only., Comment: 18 pages

With the increasing use of plastic, the challenges associated with managing plastic waste have become more challenging, emphasizing the need of effective solutions for classification and recycling. This study explores the potential of deep learning, focusing on convolutional neural networks (CNNs) and object detection models like YOLO (You Only Look Once), to tackle this issue using the WaDaBa dataset. The study shows that YOLO- 11m achieved highest accuracy (98.03%) and mAP50 (0.990), with YOLO-11n performing similarly but highest mAP50(0.992). Lightweight models like YOLO-10n trained faster but with lower accuracy, whereas MobileNet V2 showed impressive performance (97.12% accuracy) but fell short in object detection. Our study highlights the potential of deep learning models in transforming how we classify plastic waste, with YOLO models proving to be the most effective. By balancing accuracy and computational efficiency, these models can help to create scalable, impactful solutions in waste management and recycling., Comment: 18 pages, 12 figures

Graph games are fundamental in strategic reasoning of multi-agent systems and their environments. We study a new family of graph games which combine stochastic environmental uncertainties and auction-based interactions among the agents, formalized as bidding games on (finite) Markov decision processes (MDP). Normally, on MDPs, a single decision-maker chooses a sequence of actions, producing a probability distribution over infinite paths. In bidding games on MDPs, two players -- called the reachability and safety players -- bid for the privilege of choosing the next action at each step. The reachability player's goal is to maximize the probability of reaching a target vertex, whereas the safety player's goal is to minimize it. These games generalize traditional bidding games on graphs, and the existing analysis techniques do not extend. For instance, the central property of traditional bidding games is the existence of a threshold budget, which is a necessary and sufficient budget to guarantee winning for the reachability player. For MDPs, the threshold becomes a relation between the budgets and probabilities of reaching the target. We devise value-iteration algorithms that approximate thresholds and optimal policies for general MDPs, and compute the exact solutions for acyclic MDPs, and show that finding thresholds is at least as hard as solving simple-stochastic games., Comment: To appear in AAMAS 2025

Synchronization is an emergent phenomenon in coupled dynamical networks. The Master Stability Function (MSF) is a highly elegant and powerful tool for characterizing the stability of synchronization states. However, a significant challenge lies in determining the MSF for complex dynamical networks driven by nonlinear interaction mechanisms. These mechanisms introduce additional complexity through the intricate connectivity of interacting elements within the network and the intrinsic dynamics, which are governed by nonlinear processes with diverse parameters and higher dimensionality of systems. Over the past 25 years, extensive research has focused on determining the MSF for pairwise coupled identical systems with diffusive coupling. Our literature survey highlights two significant advancements in recent years: the consideration of multilayer networks instead of single-layer networks and the extension of MSF analysis to incorporate higher-order interactions alongside pairwise interactions. In this review article, we revisit the analysis of the MSF for diffusively pairwise coupled dynamical systems and extend this framework to more general coupling schemes. Furthermore, we systematically derive the MSF for multilayer dynamical networks and single-layer coupled systems by incorporating higher-order interactions alongside pairwise interactions. The primary focus of our review is on the analytical derivation and numerical computation of the MSF for complex dynamical networks. Finally, we demonstrate the application of the MSF in data science, emphasizing its relevance and potential in this rapidly evolving field., Comment: 38 pages, 1 figure

We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, our model has precise control over object dynamics, ego-agent motion and human poses. GEM generates paired RGB and depth outputs for richer spatial understanding. We introduce autoregressive noise schedules to enable stable long-horizon generations. Our dataset is comprised of 4000+ hours of multimodal data across domains like autonomous driving, egocentric human activities, and drone flights. Pseudo-labels are used to get depth maps, ego-trajectories, and human poses. We use a comprehensive evaluation framework, including a new Control of Object Manipulation (COM) metric, to assess controllability. Experiments show GEM excels at generating diverse, controllable scenarios and temporal consistency over long generations. Code, models, and datasets are fully open-sourced.

Structured optical fields have led to several ground-breaking techniques in classical imaging and microscopy. At the same time, in the quantum domain, position-momentum entangled photon fields have been shown to have several unique features that can lead to beyond-classical imaging and microscopy capabilities. Therefore, it is natural to expect that position-momentum entangled two-photon fields that are structured can push the boundaries of quantum imaging and microscopy even further beyond. Nonetheless, the existing experimental schemes are able to produce either structured two-photon fields without position-momentum entanglement, or position-momentum entangled two-photon fields without structures. In this article, by manipulating the phase-matching condition of the spontaneous parametric down-conversion process, we report experimental generation of two-photon fields with various structures in their spatial correlations. We experimentally measure the minimum bound on the entanglement of formation and thereby verify the position-momentum entanglement of the structured two-photon field. We expect this work to have important implications for quantum technologies related to imaging and sensing., Comment: 9 pages, 6 figures

A comprehensive study on persistent and thermonuclear burst emission of 4U 1728-34, commonly known as 'Slow Burster' is performed using seven archival observations of AstroSat spanning from 2016-2019. The burst-free persistent spectra can be well fitted with a blackbody (bbody) and a powerlaw (powerlaw) components, with a powerlaw photon index (Gamma) was found to be ~2 indicating the source was in "high/soft" bananna state or intermediate state. The time averaged power density spectrum reveals the presence of twin kilohertz Quasi Periodic Oscillations (kHz QPOs) with centroid frequencies 619+/-10 Hz and 965+/-6 Hz with a maximum fractional root mean squared amplitude of 6.24+/-1.31% at ~16 keV. From the upper kHz QPO, we infer the magnetospheric disk radius to be ~17 km, corresponding to a magnetic field strength of 0.35-1.27 * 10^7 G. The burst spectral evolution indicates Photospheric Radius Expansion (PRE) in five bursts, yeilding a touchdown radius of 3.1-5.47 km. These bursts reached near-Eddington luminosities, through which the distance of the source was calculated to be 5.18-5.21 kpc. Two of the bursts show coherent oscillations at 362.81-363.93 Hz. The presence of twin kHz QPOs and coherent Burst Oscillations allows us to provide two different estimates for the spin frequency of the Neutron Star in the system, for the first time using AstroSat., Comment: 18 pages, 13 Figures

The paper explores the concept of the rank of a bicomplex matrix, delving into four distinct types of ranks and investigating conditions under which these ranks are equivalent. It also defines and analyzes the concept of idempotent row space and idempotent column space of a bicomplex matrix. Some examples and counter examples have been presented to substantiate the study.

In integrated circuit design, the analysis of wafer map patterns is critical to improve yield and detect manufacturing issues. We develop Wafer2Spike, an architecture for wafer map pattern classification using a spiking neural network (SNN), and demonstrate that a well-trained SNN achieves superior performance compared to deep neural network-based solutions. Wafer2Spike achieves an average classification accuracy of 98\% on the WM-811k wafer benchmark dataset. It is also superior to existing approaches for classifying defect patterns that are underrepresented in the original dataset. Wafer2Spike achieves this improved precision with great computational efficiency.

Smart grids are designed to efficiently handle variable power demands, especially for large loads, by real-time monitoring, distributed generation and distribution of electricity. However, the grid's distributed nature and the internet connectivity of large loads like Heating Ventilation, and Air Conditioning (HVAC) systems introduce vulnerabilities in the system that cyber-attackers can exploit, potentially leading to grid instability and blackouts. Traditional protection strategies, primarily designed to handle transmission line faults are often inadequate against such threats, emphasising the need for enhanced grid security. In this work, we propose a Deep Reinforcement Learning (DRL)-based protection system that learns to differentiate any stealthy load alterations from normal grid operations and adaptively adjusts activation thresholds of the protection schemes. We train this adaptive protection scheme against an optimal and stealthy load alteration attack model that manipulates the power demands of HVACs at the most unstable grid buses to induce blackouts. We theoretically prove that the adaptive protection system trained in this competitive game setting can effectively mitigate any stealthy load alteration-based attack. To corroborate this, we also demonstrate the method's success in several real-world grid scenarios by implementing it in a hardware-in-loop setup.

We consider a one-dimensional system comprising of $N$ run-and-tumble particles confined in a harmonic trap interacting via a repulsive inverse-square power-law interaction. This is the ``active" version of the Calogero-Moser system where the particles are associated with telegraphic noise with two possible states $\pm v_0$. We numerically compute the global density profile in the steady state which shows interesting crossovers between three different regimes: as the activity increases, we observe a change from a density with sharp peaks characteristic of a crystal region to a smooth bell-shaped density profile, passing through the intermediate stage of a smooth Wigner semi-circle characteristic of a liquid phase. We also investigate analytically the crossover between the crystal and the liquid regions by computing the covariance of the positions of these particles in the steady state in the weak noise limit. It is achieved by using the method introduced in Touzo {\it et al.} [Phys. Rev. E {\bf 109}, 014136 (2024)] to study the active Dyson Brownian motion. Our analytical results are corroborated by thorough numerical simulations.

Null-trajectories, deflection angle, and gravitational lensing are studied in the background spacetime of the generalised Ellis-Bronnikov wormholes and it's five dimensional version embedded in a warped background. We show that these wormholes can be distinguished through the study of gravitational lensing. In particular, the difference in deflection angles, radius of the Einstein rings, and the image positions are quantified by the wormhole parameters -- the steepness constant ($m\geq 2$), the throat radius, and `$\delta$' that characterizes the warping in extra dimension. We demonstrate our results analytically with $m=2$ cases and graphically otherwise., Comment: 25 pages, 2 figures

In this work, we have studied the medium effects in strange quark matter in the framework of a grand-canonical ensemble using the phenomenological quasi-particle model. This model is studied with proper self-consistent thermodynamical treatment by incorporating chemical potential-dependent quark mass. We have also included the vector interaction in a self-consistent way. The main aim of this work is to explore the proper thermodynamic treatment in addressing the medium effects at both zero and finite temperatures. In the case of the finite temperature, we explore the study of self-consistent thermodynamics in the isothermal as well as the isentropic processes. The effect of finite temperature and lepton fraction have been studied on the equation of state, speed of sound, and particle fraction. The $M-R$ and $M-\Lambda$ diagrams are found to be consistent with the observational constraints.

Based on previous works, in this article we systematically analyze the implications of the explicit normal modes of a probe scalar sector in a BTZ background with a Dirichlet wall, in an asymptotically AdS-background. This is a Fuzzball-inspired geometric model, at least in an effective sense. We demonstrate explicitly that in the limit when the Dirichlet wall approaches the event horizon, the normal modes condense fast to yield an effective branch cut along the real line in the complex frequency plane. In turn, in this approximation, quasi-normal modes associated to the BTZ black hole emerge and the corresponding two-point function is described by a thermal correlator, associated with the Hawking temperature in the general case and with the right-moving temperature in the extremal limit. We further show, analytically, that the presence of a non-vanishing angular momentum non-perturbatively enhances this condensation. The consequences are manifold: {\it e.g.}~there is an emergent {\it strong thermalization} due to these modes, adding further support to a quantum chaotic nature associated to the spectral form factor. We explicitly demonstrate, by considering a classical collapsing geometry, that the one-loop scalar determinant naturally inherits a Dirichlet boundary condition, as the shell approaches the scale of the event horizon. This provides a plausible dynamical mechanism in the dual CFT through a global quench, that can create an emergent Dirichlet boundary close to the horizon-scale. We offer comments on how this simple model can describe salient features of Fuzzball-geometries, as well as of extremely compact objects. This also provides an explicit realization of how an effective thermal physics emerges from a non-thermal microscopic description, within a semi-classical account of gravity, augmented with an appropriate boundary condition., Comment: 69 pages, multiple figures, improved discussions

Two-dimensional (2D) transition metal di-chalcogenide layers with high electrical conductivity and spin-orbit coupling (SOC) can find huge potential in spintronic devices. With limited success of 2D spin Hall material development, we demonstrate vanadium (V) substitutionally doped monolayer MoS2 (VMS) as a potential spin Hall material having tunable electrical conductivity, SOC strength, and room temperature magnetism. Systematic enhancement in the electrical conductivity is observed with the extent of V doping, where it is enhanced from ~0.3 S/m of MoS2 to ~100000 S/m upon doping to the level of 9 atomic%. Ferromagnetic resonance (FMR) based spin-pumping experiments indicate the spin transport across the junction of permalloy (Py) and VMS. Spin-torque FMR measurements demonstrate the suggesting latter's potential as a spin-orbit torque layer in 2D spintronic devices.

In the last few decades planet search surveys have been focusing on solar type stars, and only recently the high-mass regimes. This is mostly due to challenges arising from the lack of instrumental precision, and more importantly, the inherent active nature of fast rotating massive stars. Here we report NGTS-33b (TOI-6442b), a super-Jupiter planet with mass, radius and orbital period of 3.6 $\pm$ 0.3 M$_{\rm jup}$, 1.64 $\pm$ 0.07 R$_{\rm jup}$ and $2.827972 \pm 0.000001$ days, respectively. The host is a fast rotating ($0.6654 \pm 0.0006$ day) and hot (T$_{\rm eff}$ = 7437 $\pm$ 72 K) A9V type star, with a mass and radius of 1.60 $\pm$ 0.11 M$_{\odot}$ and 1.47 $\pm$ 0.06 R$_{\odot}$, respectively. Planet structure and Gyrochronology models shows that NGTS-33 is also very young with age limits of 10-50 Myr. In addition, membership analysis points towards the star being part of the Vela OB2 association, which has an age of $\sim$ 20-35 Myr, thus providing further evidences about the young nature of NGTS-33. Its low bulk density of 0.19$\pm$0.03 g cm$^{-3}$ is 13$\%$ smaller than expected when compared to transiting hot Jupiters with similar masses. Such cannot be solely explained by its age, where an up to 15$\%$ inflated atmosphere is expected from planet structure models. Finally, we found that its emission spectroscopy metric is similar to JWST community targets, making the planet an interesting target for atmospheric follow-up. Therefore, NGTS-33b's discovery will not only add to the scarce population of young, massive and hot Jupiters, but will also help place further strong constraints on current formation and evolution models for such planetary systems., Comment: 19 pages, 17 figures, 7 tables, accepted for publication in MNRAS

Distributed microgrids are conventionally dependent on communication networks to achieve secondary control objectives. This dependence makes them vulnerable to stealth data integrity attacks (DIAs) where adversaries may perform manipulations via infected transmitters and repeaters to jeopardize stability. This paper presents a physics-guided, supervised Artificial Neural Network (ANN)-based framework that identifies communication-level cyberattacks in microgrids by analyzing whether incoming measurements will cause abnormal behavior of the secondary control layer. If abnormalities are detected, an iteration through possible spanning tree graph topologies that can be used to fulfill secondary control objectives is done. Then, a communication network topology that would not create secondary control abnormalities is identified and enforced for maximum stability. By altering the communication graph topology, the framework eliminates the dependence of the secondary control layer on inputs from compromised cyber devices helping it achieve resilience without instability. Several case studies are provided showcasing the robustness of the framework against False Data Injections and repeater-level Man-in-the-Middle attacks. To understand practical feasibility, robustness is also verified against larger microgrid sizes and in the presence of varying noise levels. Our findings indicate that performance can be affected when attempting scalability in the presence of noise. However, the framework operates robustly in low-noise settings., Comment: Accepted in IEEE Design Methodologies Conference (DMC) 2024

In recent years, the development of the Lunar Gateway and Artemis missions has renewed interest in lunar exploration, including both manned and unmanned missions. This interest necessitates accurate initial orbit determination (IOD) and orbit prediction (OP) in this domain, which faces significant challenges such as severe nonlinearity, sensitivity to initial conditions, large state-space volume, and sparse, faint, and unreliable measurements. This paper explores the capability of data-driven Koopman operator-based approximations for OP in these scenarios. Three stable periodic trajectories from distinct cislunar families are analyzed. The analysis includes theoretical justification for using a linear time-invariant system as the data-driven surrogate. This theoretical framework is supported by experimental validation. Furthermore, the accuracy is assessed by comparing the spectral content captured to period estimates derived from the fast Fourier transform (FFT) and Poincare-like sections., Comment: arXiv admin note: text overlap with arXiv:2401.13784

Dose prediction is an area of ongoing research that facilitates radiotherapy planning. Most commercial models utilise imaging data and intense computing resources. This study aimed to predict the dose-volume of rectum and bladder from volumes of target, at-risk structure organs and their overlap regions using machine learning. Dose-volume information of 94 patients with prostate cancer planned for 6000cGy in 20 fractions was exported from the treatment planning system as text files and mined to create a training dataset. Several statistical modelling, machine learning methods, and a new fuzzy rule-based prediction (FRBP) model were explored and validated on an independent dataset of 39 patients. The median absolute error was 2.0%-3.7% for bladder and 1.7-2.4% for rectum in the 4000-6420cGy range. For 5300cGy, 5600cGy and 6000cGy, the median difference was less than 2.5% for rectum and 3.8% for bladder. The FRBP model produced errors of 1.2%, 1.3%, 0.9% and 1.6%, 1.2%, 0.1% for the rectum and bladder respectively at these dose levels. These findings indicate feasibility of obtaining accurate predictions of the clinically important dose-volume parameters for rectum and bladder using just the volumes of these structures.

The interaction between macroscopic fluid flow and nanoscale forces has resulted in the formation of long-range assemblies through evaporation-induced self-assembly. Anisotropic gold nanorods (AuNR) can form disordered, smectic, or vertically ordered long-range structures, but controlling their assembly remains a challenge and requires a deeper understanding of fundamental interaction mechanisms. In this work, we established a correlation between the $in~situ$ drying profiles, measured using an optical tensiometer, and deposit pattern, imaged $ex~situ$ using electron microscopy. Increasing particle concentration induced a transition from coffee-ring to uniform deposition at the microscale, while nanoscale structures shifted from isotropic/smectic to vertically aligned crystalline AuNRs. The interplay of capillary and Marangoni flow influences assembly at both macro and nanoscales, with the deposition process and nanoparticle ordering being highly sensitive to interparticle and nanoparticle-substrate interactions. By systematically studying key parameters, we aim to develop a comprehensive framework for the rational design and fabrication of nanomaterials with precisely controlled structure and properties., Comment: 35 pages including SI, 13 figures including SI, corresponding author: Sunita Srivastava (sunita.srivastava@iitb.ac.in)

Automatic Speech Translation (AST) datasets for Indian languages remain critically scarce, with public resources covering fewer than 10 of the 22 official languages. This scarcity has resulted in AST systems for Indian languages lagging far behind those available for high-resource languages like English. In this paper, we first evaluate the performance of widely-used AST systems on Indian languages, identifying notable performance gaps and challenges. Our findings show that while these systems perform adequately on read speech, they struggle significantly with spontaneous speech, including disfluencies like pauses and hesitations. Additionally, there is a striking absence of systems capable of accurately translating colloquial and informal language, a key aspect of everyday communication. To this end, we introduce BhasaAnuvaad, the largest publicly available dataset for AST involving 13 out of 22 scheduled Indian languages and English spanning over 44,400 hours and 17M text segments. BhasaAnuvaad contains data for English speech to Indic text, as well as Indic speech to English text. This dataset comprises three key categories: (1) Curated datasets from existing resources, (2) Large-scale web mining, and (3) Synthetic data generation. By offering this diverse and expansive dataset, we aim to bridge the resource gap and promote advancements in AST for Indian languages., Comment: Work in Progress

Multimodal device operations are essential to advancing the integration of 2D semiconductors in electronics, photonics, information and quantum technology. Precise control over carrier dynamics, particularly exciton generation and transport, is crucial for finetuning the functionality of optoelectronic devices based on 2D semiconductor heterostructure. However, the traditional exciton engineering methods in 2D semiconductors are mainly restricted to the artificially assembled vertical pn heterostructures with electrical or strain induced confinements. In this study, we utilized bilayer 2D lateral npn multijunction heterostructures with intrinsically spatially separated energy landscapes to achieve preferential exciton generation and manipulation without external confinement. In lateral npn FET geometry, we uncover unique and nontrivial properties, including dynamic tuning of channel photoresponsivity from positive to negative. The multimodal operation of these 2D FETs is achieved by carefully adjusting electrical bias and the impinging photon energy, enabling precise control over the trions generation and transport. Cryogenic photoluminescence measurement revealed the presence of trions in bilayer MoSe2 and intrinsic trap states in WSe2. Measurements in different FET device geometries show the multifunctionality of 2D lateral heterostructure phototransistors for efficient tuning and electrical manipulation of excitonic characteristics. Our findings pave the way for developing practical exciton-based transistors, sensors, multimodal optoelectronic and quantum technologies

The structural and magnetic effects of non-magnetic vanadium (V) doping in helimagnetic SrFeO$_{3-\delta}$ (SFO) are investigated, focusing on up to 3% substitution at the Fe site. Structural analysis from X-ray diffraction (XRD) and Raman spectroscopy, supported by phonon mode calculations, reveals that pure SFO exists as a mixed tetragonal-orthorhombic phase, while V-doped samples exhibit an emerging cubic phase alongside tetragonal symmetry. Magnetic hysteresis (M-H) loops show notable ferromagnetic behavior within the antiferromagnetic matrix, persisting even at room temperature. Temperature-dependent magnetization measurements indicate a Neel temperature (TN ) shift from 70K to 55K, along with increased magnetization differences in field-cooled (FC) and zero field-cooled (ZFC) data, reflecting heightened magnetic frustration due to competing FM/AFM exchange interactions. X-ray photoelectron spectroscopy (XPS) and X-ray absorption near-edge structure (XANES) analyses reveal a rise in Fe$^{3+}$ and V$^{5+}$ states, affecting oxygen vacancy distributions and corresponding structural shifts seen in XRD and Raman results. The multivalent Fe$^{3+}$/Fe$^{4+}$ and V$^{4+}$/V$^{5+}$ states enhance double-exchange (DE) and super-exchange (SE) interactions (Fe$^{3+}$-O-Fe$^{4+}$ and Fe$^{3+}$-O-V$^{5+}$), promoting ferromagnetism. Frequency-dependent magnetization studies display a subtle susceptibility peak shift, indicating spin-glass-like behavior in V-doped samples.

We present results of a comprehensive analysis of the polarization characteristics of GRB 200503A and GRB 201009A observed with the Cadmium Zinc Telluride Imager (CZTI) on board AstroSat. Despite these GRBs being reasonably bright, they were missed by several spacecraft and had thus far not been localized well, hindering polarization analysis. We present positions of these bursts obtained from the Inter-Planetary Network (IPN) and the newly developed CZTI localization pipeline. We then undertook polarization analyses using the standard CZTI pipeline. We cannot constrain the polarization properties for GRB 200503A, but find that GRB 201009A has a high degree of polarization.

Materials with magnetoelectric coupling (MEC) between ferroic orders at room temperature are extremely important in modern technology and physics. BaTiO$_3$ is a robust ferroelectric in which several doping have led to MEC. However, often MEC depends on the amount of substitution. In a series of (1-x)BaTiO$_{3.}$(x)CaMnO$_3$ (x=0,0.03, 0.06, 0.09), MEC was only observed in (0.97)BaTiO$_{3.}$(0.03)CaMnO$_3$. The structural modifications with changing substitution reveal a reduced Ti-O-Ti angle for this sample which is the most ferromagnetic in nature. A mixed phase of tetragonal P4mm and hexagonal P6$_3$/mmc space groups of BaTiO$_3$ is observed in the substituted samples, with nominal contribution of the hexagonal phase for x=0.03. A valence state study using XPS and XANES reveals the presence of enhanced proportion of Mn3+ ions in the sample which support a Jahn-Teller distortion, thereby supporting the ferroelectricity for x=0.03. Indirect and direct evidences of MEC was obtained from magnetodielectric and magnetoelectric measurements. A magnetocapacitance of 0.4% was observed with a dc magnetic field of 4kOe at 10kHz, while magnetoelectric coupling coefficient, $\alpha_{\rm ME} \sim 88$ mVcm$^{-1}$Oe$^{-1}$ was obtained for dc magnetic field of 600 Oe and a 10Hz ac field of 40 Oe. Such MEC was not observed for higher substitution which emphasizes the sensitivity of the structural properties on substitution.

Low-mass emission-line stars belong to various evolutionary stages, from pre-main-sequence young stars to evolved stars. In this work, we present a catalog of late-type (F0 to M9) emission-line stars from the LAMOST Data Release 6. Using the scipy package, we created a Python code that finds the emission peak at H-alpha in all late-type stellar spectra. A dataset of 38,152 late-type emission-line stars was obtained after a rigorous examination of the photometric quality flags and the signal-to-noise ratio of the spectra. Adopting well-known photometric and spectroscopic methods, we classified our sample into 438 infrared excess sources, 4,669 post-main-sequence candidates, 9,718 Fe/Ge/Ke sources, and 23,264 dMe sources. From a cross-match with known databases, we found that 29,222 sources, comprising 65 IR excess sources, 7,899 Fe/Ge/Ke stars, 17,533 dMe stars, and 3,725 PtMS candidates, are new detections. We measured the equivalent width of the major emission lines observed in the spectra of our sample of emission-line stars. Furthermore, the trend observed in the line strengths of major emission lines over the entire late-type spectral range is analyzed. We further classified the sample into 4 groups based on the presence of Hydrogen and Calcium emission lines. This work presents a large dataset of late-type emission-line stars, which can be used to study active phenomena in late-type stars., Comment: Accepted for publication in RAA, 24 pages, 12 figures, 2 tables

We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accounting for confounding factors. This problem commonly arises in various applications, such as domain adaptation and algorithmic fairness, where comparing two groups is essential while controlling for confounding variables. We begin by establishing a hardness result for conditional two-sample testing, demonstrating that no valid test can have significant power against any single alternative without proper assumptions. We then introduce two general frameworks that implicitly or explicitly target specific classes of distributions for their validity and power. Our first framework allows us to convert any conditional independence test into a conditional two-sample test in a black-box manner, while preserving the asymptotic properties of the original conditional independence test. The second framework transforms the problem into comparing marginal distributions with estimated density ratios, which allows us to leverage existing methods for marginal two-sample testing. We demonstrate this idea in a concrete manner with classification and kernel-based methods. Finally, simulation studies are conducted to illustrate the proposed frameworks in finite-sample scenarios., Comment: 39 pages, 6 figures

Weak-line T Tauri stars (WTTS) exhibit X-ray flares, likely resulting from magnetic reconnection that heats the stellar plasma to very high temperatures. These flares are difficult to identify through targeted observations. Here, we report the serendipitous detection of the brightest X-ray flaring state of KM Ori in the eROSITA DR1 survey. Observations from SRG/eROSITA, Chandra X-ray Observatory, and XMM-Newton are analysed to assess the X-ray properties of KM Ori, thereby establishing its flaring state at the eROSITA epoch. The long-term (1999-2020) X-ray light curve generated for the Chandra observations confirmed that eROSITA captured the source at its highest X-ray flaring state recorded to date. Multi-instrument observations support the X-ray flaring state of the source, with time-averaged X-ray luminosity ($L_{0.2-5keV}$) reaching $\sim 1.9\times10^{32}{erg~s^{-1}}$ at the eROSITA epoch, marking it the brightest and possibly the longest flare observed to date. Such intense X-ray flares have been detected only in a few WTTS. The X-ray spectral analysis unveils the presence of multiple thermal plasma components at all epochs. The notably high luminosity ($L_{0.5-8~keV}\sim10^{32}{erg~s}^{-1}$), energy ($E_{0.5-8~keV}\sim10^{37}$erg), and the elevated emission measures of the thermal components in the eROSITA epoch indicate a superflare/megaflare state of KM Ori. Additionally, the H$\alpha$ line equivalent width of $\sim$-5\AA from our optical spectral analysis, combined with the lack of infrared excess in the spectral energy distribution, were used to re-confirm the WTTS (thin disk/disk-less) classification of the source. The long-duration flare of KM Ori observed by eROSITA indicates the possibility of a slow-rise top-flat flare. The detection demonstrates the potential of eROSITA to uncover such rare, transient events, thereby providing new insights into the X-ray activity of WTTS., Comment: 11 pages, 6 figures, 2 tables. This paper has been accepted for publication in PASA

This systematic review examines the synergistic and individual influences of resistance exercise, dietary protein supplementation, and sleep/recovery on muscle protein synthesis (MPS). Electronic databases such as Scopus, Google Scholar, and Web of Science were extensively used. Studies were selected based on relevance to the criteria and were ensured to be directly applicable to the objectives. Research indicates that a protein dose of 20 to 25 grams maximally stimulates MPS post-resistance training. It is observed that physically frail individuals aged 76 to 92 and middle-aged adults aged 62 to 74 have lower mixed muscle protein synthetic rates than individuals aged 20 to 32. High-whey protein and leucine-enriched supplements enhance MPS more efficiently than standard dairy products in older adults engaged in resistance programs. Similarly, protein intake before sleep boosts overnight MPS rates, which helps prevent muscle loss associated with sleep debt, exercise-induced damage, and muscle-wasting conditions like sarcopenia and cachexia. Resistance exercise is a functional intervention to achieve muscular adaptation and improve function. Future research should focus on variables such as fluctuating fitness levels, age groups, genetics, and lifestyle factors to generate more accurate and beneficial results.

Artificial Neural Networks of varying architectures are generally paired with affine transformation at the core. However, we find dot product neurons with global influence less interpretable as compared to local influence of euclidean distance (as used in Radial Basis Function Network). In this work, we explore the generalization of dot product neurons to $l^p$-norm, metrics, and beyond. We find that metrics as transform performs similarly to affine transform when used in MultiLayer Perceptron or Convolutional Neural Network. Moreover, we explore various properties of Metrics, compare it with Affine, and present multiple cases where metrics seem to provide better interpretability. We develop an interpretable local dictionary based Neural Networks and use it to understand and reject adversarial examples., Comment: 22 pages, 20 figures, 3 tables

We know that if there are $k$ distinct prime factors of $n \in \mathbb{N}$, then the ring $\mathbb{Z}_n$ of integers modulo $n$ has exactly $2^k$ idempotent elements. In this article, we try to describe all the idempotents of $\mathbb{Z}_n$ for any given $n \in \mathbb{N}$., Comment: 13 pages

Our proposal is on a new stochastic optimizer for non-convex and possibly non-smooth objective functions typically defined over large dimensional design spaces. Towards this, we have tried to bridge noise-assisted global search and faster local convergence, the latter being the characteristic feature of a Newton-like search. Our specific scheme -- acronymed FINDER (Filtering Informed Newton-like and Derivative-free Evolutionary Recursion), exploits the nonlinear stochastic filtering equations to arrive at a derivative-free update that has resemblance with the Newton search employing the inverse Hessian of the objective function. Following certain simplifications of the update to enable a linear scaling with dimension and a few other enhancements, we apply FINDER to a range of problems, starting with some IEEE benchmark objective functions to a couple of archetypal data-driven problems in deep networks to certain cases of physics-informed deep networks. The performance of the new method vis-\'a-vis the well-known Adam and a few others bears evidence to its promise and potentialities for large dimensional optimization problems of practical interest., Comment: 19 pages, 12 figures, 3 tables

The concept of evolutionarily stability and its relation with the fixed points of the replicator equation are important aspects of evolutionary game dynamics. In the light of the fact that oscillating state of a population and individuals (or players) of different roles are quite natural occurrences, we ask the question how the concept of evolutionarily stability can be generalized so as to associate game-theoretic meaning to oscillatory behaviours of players asymmetrically interacting, i.e., if there are both intraspecific and interspecific interactions between two subpopulations in the population. We guide our scheme of generalization such that the evolutionary stability is related to the dynamic stability of the corresponding periodic orbits of a time-discrete replicator dynamics. We name the generalization of evolutionarily stable state as two-species heterogeneity stable orbit. Furthermore, we invoke the principle of decrease of relative entropy in order to associate the generalization of evolutionary stability with an information-theoretic meaning. This particular generalization is aptly termed as two-species information stable orbit.

Missing frequency channels pose a problem for estimating $P(k_\perp,k_\parallel)$ the redshifted 21-cm power spectrum (PS) from radio-interferometric visibility data. This is particularly severe for the Murchison Widefield Array (MWA), which has a periodic pattern of missing channels that introduce spikes along $k_\parallel$. The Tracking Tapered Gridded Estimator (TTGE) overcomes this by first correlating the visibilities in the frequency domain to estimate the multi-frequency angular power spectrum (MAPS) $C_\ell(\Delta\nu)$ that has no missing frequency separation $\Delta\nu$. We perform a Fourier transform along $\Delta\nu$ to estimate $P(k_\perp,k_\parallel)$. Considering our earlier work, simulations demonstrate that the TTGE can estimate $P(k_\perp,k_\parallel)$ without any artifacts due to the missing channels. However, the spikes were still found to persist for the actual data, which is foreground-dominated. The current work presents a detailed investigation considering both simulations and actual data. We find that the spikes arise due to a combination of the missing channels and the strong spectral dependence of the foregrounds. Based on this, we propose and demonstrate a technique to mitigate the spikes. Applying this, we find the values of $P(k_\perp,k_\parallel)$ in the region $0.004 \leq k_\perp \leq 0.048\,{\rm Mpc^{-1}}$ and $k_\parallel > 0.35 \,{\rm Mpc^{-1}}$ to be consistent with zero within the expected statistical fluctuations. We obtain the $2\sigma$ upper limit of $\Delta_{\rm UL}^2(k)=(918.17)^2\,{\rm mK^2}$ at $k=0.404\,{\rm Mpc^{-1}}$ for the mean squared brightness temperature fluctuations of the $z=8.2$ epoch of reionization (EoR) 21-cm signal. This upper limit is from just $\sim 17$ minutes of observation for a single pointing direction. We expect tighter constraints when we combine all $162$ different pointing directions of the drift scan observation., Comment: 11 pages, 16 figures, comments are welcome

Event cameras are novel bio-inspired vision sensors that measure pixel-wise brightness changes asynchronously instead of images at a given frame rate. They offer promising advantages, namely a high dynamic range, low latency, and minimal motion blur. Modern computer vision algorithms often rely on artificial neural network approaches, which require image-like representations of the data and cannot fully exploit the characteristics of event data. We propose approaches to action recognition based on the Fourier Transform. The approaches are intended to recognize oscillating motion patterns commonly present in nature. In particular, we apply our approaches to a recent dataset of breeding penguins annotated for "ecstatic display", a behavior where the observed penguins flap their wings at a certain frequency. We find that our approaches are both simple and effective, producing slightly lower results than a deep neural network (DNN) while relying just on a tiny fraction of the parameters compared to the DNN (five orders of magnitude fewer parameters). They work well despite the uncontrolled, diverse data present in the dataset. We hope this work opens a new perspective on event-based processing and action recognition., Comment: 11 pages, 10 figures, 7 tables

Ground motion prediction equations (GMPEs) play a key role in seismic hazard assessment (SHA). Considering the seismo-tectonic, geophysical and geotectonic characteristics of a target region, all the GMPEs may not be suitable in predicting the observed ground motion effectively. With a fairly large number of published GMPEs, the selection and ranking of suitable GMPEs for the design of logic trees in SHA for a particular target region has become a necessity of late. This paper presents a detailed quantitative evaluation of performance of 16 GMPEs against recorded ground motion data in two target regions, characterized by distinct seismo-tectonic, geophysical and geotectonical nature. The data set comprises of 465 three-component spectral accelerograms corresponding to 122 earthquake events. The suitability of a GMPE is tested by two widely accepted data-driven statistical methods, namely, likelihood (LH) and log-likelihood (LLH) method. Different suites of GMPEs are shown suitable for different periods of interest. The results will be useful to scientists and engineers for microzonation and estimation of seismic design parameters for the design of earthquake-resistant structures in these regions., Comment: 24 pages, 5 figures

In this study, we investigate strange quark stars within the framework of modified $f(R,T)$ gravity, where $R$ represents the Ricci scalar and $T$ denotes the trace of the energy-momentum tensor, specifically defined as $ f(R,T) = R + 2\chi T $. The equation of state is obtained with the different forms of the MIT bag model and quark mass model with medium effects and self-consistent thermodynamical treatment. We find that negative values of $\chi$ significantly increase both the mass and radius of the quark star. The inclusion of $\chi$ helps to satisfy recent the astrophysical constraints on the mass-radius relationship. We have also constrained the values of $\chi$ for each EoS, based on the observed maximum mass and corresponding radius, demonstrating that the inclusion of this parameter helps to address the challenges posed by both the GW190814 event and NICER observations of PSR J0030+0451. We also observe that the inclusion of $f(R,T)$ gravity leads to an increase in both the maximum mass, by about $ (0.23- 0.27)~M_{\odot}$, and the corresponding radius, by approximately (1.5-2.0)~\text{km}, depending on the chosen equation of state.

Deploying large language models (LLMs) locally on mobile devices is advantageous in scenarios where transmitting data to remote cloud servers is either undesirable due to privacy concerns or impractical due to network connection. Recent advancements (MLC, 2023a; Gerganov, 2023) have facilitated the local deployment of LLMs. However, local deployment also presents challenges, particularly in balancing quality (generative performance), latency, and throughput within the hardware constraints of mobile devices. In this paper, we introduce our lightweight, all-in-one automated benchmarking framework that allows users to evaluate LLMs on mobile devices. We provide a comprehensive benchmark of various popular LLMs with different quantization configurations (both weights and activations) across multiple mobile platforms with varying hardware capabilities. Unlike traditional benchmarks that assess full-scale models on high-end GPU clusters, we focus on evaluating resource efficiency (memory and power consumption) and harmful output for compressed models on mobile devices. Our key observations include i) differences in energy efficiency and throughput across mobile platforms; ii) the impact of quantization on memory usage, GPU execution time, and power consumption; and iii) accuracy and performance degradation of quantized models compared to their non-quantized counterparts; and iv) the frequency of hallucinations and toxic content generated by compressed LLMs on mobile devices., Comment: 10 pages