arXiv subjects

Samarjit Chakraborty

Publications and source records attributed to Samarjit Chakraborty.

At least 19 recordsLinked to original sources

arXiv2026

Temperature of free gravitational field: A geometrical perspective

In this paper, using a novel geometrical approach, we relate the concept of the thermodynamic temperature of the free gravitational field, to the non-affinity of real null geodesics in a Newman Penrose tetrad. This naturally links various temperature functions like Clifton, Ellis and Tavakol temperature, Hawking temperature, Unruh temperature etc., in their respective proper limits. Although our analysis is done within the realm of local rotational symmetry, we show that the result can be extended to other Petrov type D geometries, like the Kerr spacetime. We also obtain the geometrical and causal transport equations for this temperature function, in the form of a hyperbolic wave equation with a forcing term, sourced by Weyl curvature and matter. Finally, as a possible physical interpretation of the non-affinity, we relate the geometrical temperature with the gravitational red/blue shift of light rays travelling along null geodesics.

gr-qc↗

arXiv2026

Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certification

The design of embedded safety-critical systems such as those used in next-generation automotive and autonomous platforms, is increasingly challenged by escalating system complexity, hardware-software heterogeneity, and the integration of intelligent, data-driven components. Ensuring dependability in such systems requires a holistic approach that spans multiple abstraction layers and encompasses both design- and run-time assurance. Traditional methods for reliability, safety, and security management often fall short in addressing the dynamic and uncertain behaviors introduced by Artificial Intelligence (AI) and Machine Learning (ML) components, especially under stringent real-time, power, and safety constraints. While AI and ML offer powerful predictive, adaptive, and self-optimizing capabilities that can enhance system dependability, their inherent non-determinism, data-dependence, and lack of formal guarantees introduce new challenges for verification, validation, and certification. This paper explores emerging methodologies, architectures, and frameworks for designing dependable autonomous and embedded systems in the era of AI. It highlight advances in reliability modeling, secure system design, and certification approaches that account for imperfect, learning-enabled components, aiming to bridge the gap between AI innovation and certifiable system-level dependability.

cs.AI↗

arXiv2026

Arrow of time problem in gravitational collapse

We investigate the arrow of time problem in the context of gravitational collapse of radiating stars in higher dimensions for both neutral and charged matter. The interior spacetime is described by a shear-free spherically symmetric metric filled with a dissipative fluid. The exterior spacetime of the radiating star is taken as the higher dimensional Vaidya metric. We establish that the arrow of time associated with gravitational entropy is opposite to the thermodynamic arrow of time for all dimensions. The physical consequences of our results are considered. Our result conforms with previous studies on shear-free spherical collapse, which suggests, avoidance of the naked singularity as the end state results in a wrong arrow of time, indicating a fundamental problem with the local application of the Weyl curvature hypothesis.

gr-qc↗

arXiv2026

Complexity and structure scalars of Type II matter fields

A general semi-tetrad covariant approach is adopted to analyse the structure scalars of a Type II fluid in generalized Vaidya spacetime. The relationship between the $1+1+2$ covariant quantities and the structure scalars are obtained. We calculate the complexity factor in terms of the Misner-Sharp mass and the matter variables to obtain a non-trivial class of spacetimes with vanishing complexity. Also the Vaidya spacetime with pure Type II matter field has negative complexity. The differences between the complexity of Type I and Type II matter fields are highlighted. We compute the propagation and evolution equations of the structure scalars, showcasing their interdependency through the kinematical variables. The causal wave equation of the Gaussian curvature of the 2-shell and its dependence on the structure scalars are also studied.

gr-qc↗

arXiv2026

Ricci-Weyl curvature balance in viscous dissipative collapse: A covariant analysis of singularity censorship

We investigate the cosmic censorship conjecture in a spherically symmetric collapse with shear and bulk viscosity, heat flux, and pressure anisotropy, imposing physically reasonable energy conditions. Using the semi-tetrad covariant formalism, we derive the dynamics of the collapsing fluid, including a master equation for the evolution of the Weyl curvature, to examine the role of viscosity. The analysis of null geodesic geometry uncovers a novel curvature-balance mechanism between Ricci (matter) and Weyl (free gravitational field) curvature on the apparent horizon; this balance determines the causal nature of the horizon and thereby governs the visibility of the singularity. We then derive necessary and sufficient covariant conditions for the central singularity to be locally naked. Our findings support a weaker form of cosmic censorship and extend the covariant censorship analysis to realistic dissipative, viscous collapse.

gr-qc↗

arXiv2026

Bandit Algorithms for Deep Brain Stimulation

Deep Brain Stimulation (DBS) is an effective treatment for Parkinson's disease, but conventional fixed-parameter stimulation can reduce battery life and cause side effects while failing to adapt to changing neural dynamics. Recent reinforcement learning approaches improve adaptability, yet most rely on deep neural networks that require offline training and are computationally too expensive for implantable hardware. This paper presents a resource-conscious adaptive DBS framework based on a Time- and Threshold-Triggered Pruned Multi-Armed Bandit (T3P MAB) algorithm. The proposed method jointly tunes stimulation frequency and amplitude, avoids prior training, and remains transparent enough to support clinician-guided adjustment. Using a computational basal ganglia-thalamic model, we show that T3P converges faster than competing MAB methods and outperforms deep-RL baselines in suppressing pathological beta-band activity while reducing stimulation power. We implemented it on different microcontrollers and report detailed energy measurements, showing convergence in under two minutes and suitability for resource-constrained implantable systems. These results support lightweight bandit-based control as a practical path toward personalized, energy-efficient DBS.

cs.LG↗

arXiv2025

In-Vivo Training for Deep Brain Stimulation

Deep Brain Stimulation (DBS) is a highly effective treatment for Parkinson's Disease (PD). Recent research uses reinforcement learning (RL) for DBS, with RL agents modulating the stimulation frequency and amplitude. But, these models rely on biomarkers that are not measurable in patients and are only present in brain-on-chip (BoC) simulations. In this work, we present an RL-based DBS approach that adapts these stimulation parameters according to brain activity measurable in vivo. Using a TD3 based RL agent trained on a model of the basal ganglia region of the brain, we see a greater suppression of biomarkers correlated with PD severity compared to modern clinical DBS implementations. Our agent outperforms the standard clinical approaches in suppressing PD biomarkers while relying on information that can be measured in a real world environment, thereby opening up the possibility of training personalized RL agents specific to individual patient needs.

cs.LG↗

arXiv2024

A shallow dive into the depths of non-termination checking for C programs

Checking for Non-Termination (NT) of a given program P, i.e., determining if P has at least one non-terminating run, is an undecidable problem that continues to garner significant research attention. While unintended NT is common in real-world software development, even the best-performing tools for NT checking are often ineffective on real-world programs and sometimes incorrect due to unrealistic assumptions such as absence of overflows. To address this, we propose a sound and efficient technique for NT checking that is also effective on realworld software. Given P, we encode the NT property as an assertion inside each loop of P to check for recurrent states in that loop, up to a fixed unwinding depth, using a Bounded Model Checker. The unwinding depth is increased iteratively until either NT is found or a predefined limit is reached. Our experiments on wide ranging software benchmarks show that the technique outperforms state-of-the-art NT checkers

cs.SE↗

arXiv2024

Enhancing Split Computing and Early Exit Applications through Predefined Sparsity

In the past decade, Deep Neural Networks (DNNs) achieved state-of-the-art performance in a broad range of problems, spanning from object classification and action recognition to smart building and healthcare. The flexibility that makes DNNs such a pervasive technology comes at a price: the computational requirements preclude their deployment on most of the resource-constrained edge devices available today to solve real-time and real-world tasks. This paper introduces a novel approach to address this challenge by combining the concept of predefined sparsity with Split Computing (SC) and Early Exit (EE). In particular, SC aims at splitting a DNN with a part of it deployed on an edge device and the rest on a remote server. Instead, EE allows the system to stop using the remote server and rely solely on the edge device's computation if the answer is already good enough. Specifically, how to apply such a predefined sparsity to a SC and EE paradigm has never been studied. This paper studies this problem and shows how predefined sparsity significantly reduces the computational, storage, and energy burdens during the training and inference phases, regardless of the hardware platform. This makes it a valuable approach for enhancing the performance of SC and EE applications. Experimental results showcase reductions exceeding 4x in storage and computational complexity without compromising performance. The source code is available at https://github.com/intelligolabs/sparsity_sc_ee.

cs.LG↗

arXiv2024

MTL-Split: Multi-Task Learning for Edge Devices using Split Computing

Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promising approach. It allows the power of DNNs to be leveraged for latency-sensitive applications that do not allow the entire DNN to be deployed remotely, while not having sufficient computation bandwidth available locally. In many such embedded systems scenarios, such as those in the automotive domain, computational resource constraints also necessitate Multi-Task Learning (MTL), where the same DNN is used for multiple inference tasks instead of having dedicated DNNs for each task, which would need more computing bandwidth. However, how to partition such a multi-tasking DNN to be deployed within a SC framework has not been sufficiently studied. This paper studies this problem, and MTL-Split, our novel proposed architecture, shows encouraging results on both synthetic and real-world data. The source code is available at https://github.com/intelligolabs/MTL-Split.

cs.LG↗

arXiv2024

Arrow of time and gravitational entropy in collapse

We investigate the status of the gravitational arrow of time in the case of a spherical collapse of a fluid that conducts heat and radiates energy. In particular, we examine the results obtained by W. B. Bonnor in his 1985 paper where he found that the gravitational arrow of time was opposite to the thermodynamic arrow of time. The measure of gravitational epoch function $P$ used by Bonnor was given by the ratio of the Weyl square to the Ricci square. In this paper, we have assumed the measure of gravitational entropy $P_{1}$ to be given by the ratio of the Weyl scalar to the Kretschmann scalar. Our analysis indicates that Bonnor's result seems to be validated, i.e., the gravitational arrow and the thermodynamic arrow of time point in opposite directions. This strengthens the opinion that the Weyl proposal of gravitational entropy applies only to the universe as a whole (provided that we exclude the white holes).

gr-qc↗

arXiv2024

On the thermodynamic aspects of gravity

Here the Weyl curvature hypothesis is examined using the gravitational entropy (GE). We have considered the family of C-metric accelerating black holes and evaluated their corresponding gravitational entropy. Then we studied the GE in some isotropic and anisotropic cosmologies utilizing the definition proposed by Clifton, Ellis, and Tavakol, where the Bel-Robinson tensor is used to determine the energy-momentum tensor of the free gravitational field. We checked whether, in the vicinity of the initial cosmic singularity, the ratio of the energy density of free gravity to that of matter density goes to zero or not. We showed that whenever this is true, the gravitational entropy increases monotonically with the structure formation of the universe and discussed the conditions of validity for the Weyl curvature hypothesis. Subsequently, the next part of the thesis deals with the validity of two different proposals of gravitational entropy (GE) in traversable wormhole systems. We found that the GE proposals do provide us with a consistent measure of GE in several wormhole solutions. In the later portion of the thesis, we examined the validity of the generalized second law of thermodynamics (GSLT) in an expanding FRW universe filled with different variants of the Chaplygin gas. Lastly, we studied the evolution of the FRW universe in the presence of variable modified Chaplygin gas and obtained its temperature and other parameters as a function of the redshift. Finally, the thesis is concluded.

gr-qc↗

arXiv2024

To Balance or to Not? Battery Aging-Aware Active Cell Balancing for Electric Vehicles

Due to manufacturing variabilities and temperature gradients within an electric vehicle's battery pack, the capacities of cells in it decrease differently over time. This reduces the usable capacity of the battery - the charge levels of one or more cells might be at the minimum threshold while most of the other cells have residual charge. Active cell balancing (i.e., transferring charge among cells) can equalize their charge levels, thereby increasing the battery pack's usable capacity. But performing balancing means additional charge transfer, which can result in energy loss and cell aging, akin to memory aging in storage technologies due to writing. This paper studies when cell balancing should be optimally triggered to minimize aging while maintaining the necessary driving capability. In particular, we propose optimization strategies for cell balancing while minimizing their impact on aging. By borrowing terminology from the storage domain, we refer to this as "wear leveling-aware" active balancing.

eess.SY↗

arXiv2022

On the propagation of gravitational waves in matter-filled Bianchi I universe

In this paper we apply the Regge-Wheeler formalism to study the propagation of axial and polar gravitational waves in matter-filled Bianchi I universe. Assuming that the expansion scalar $ \Theta $, of the background space-time, is proportional to the shear scalar $ \sigma $, we solved the background field equations in the presence of matter (found to behave like a stiff fluid). We then derive the linearised perturbation equations for both the axial and polar modes. The analytical solutions in vacuum spacetime could be determined in an earlier paper \cite{GD1} in a relatively straightforward manner. However, here we find that in the presence of matter, they require more assumptions for their solution, and bear more involved forms. As compared to the axial modes, the polar perturbation equations contain far more complicated couplings among the perturbing terms. Thus we have to apply suitable assumptions to derive the analytical solutions for some of the cases of polar perturbations. In both the axial and polar cases, the radial and temporal solutions for the perturbations separate out as products. We find that the axial waves are damped owing to the background anisotropy, and can deform only the azimuthal velocity of the fluid. In contrast, the polar waves must trigger perturbations in the energy density, the pressure as well as in the non-azimuthal components of the fluid velocity. Similar behaviour is exhibited by axial and polar gravitational waves propagating in the Kantowski-Sachs universe \cite{GD2}. Our work is in contrast to the work done in \cite{SYK}, where the authors analysed anisotropic universes modelled by Kasner spacetime and Rindler wedges using the method of gauge-invariant perturbations in the RW gauge.

gr-qc↗

arXiv2022

WiFiEye -- Seeing over WiFi Made Accessible

While commonly used for communication purposes, an increasing number of recent studies consider WiFi for sensing. In particular, wireless signals are altered (e.g., reflected and attenuated) by the human body and objects in the environment. This can be perceived by an observer to infer information on human activities or changes in the environment and, hence, to "see" over WiFi. Until now, works on WiFi-based sensing have resulted in a set of custom software tools - each designed for a specific purpose. Moreover, given how scattered the literature is, it is difficult to even identify all steps/functions necessary to build a basic system for WiFi-based sensing. This has led to a high entry barrier, hindering further research in this area. There has been no effort to integrate these tools or to build a general software framework that can serve as the basis for further research, e.g., on using machine learning to interpret the altered WiFi signals. To address this issue, in this paper, we propose WiFiEye - a generic software framework that makes all necessary steps/functions available "out of the box". This way, WiFiEye allows researchers to easily bootstrap new WiFi-based sensing applications, thereby, focusing on research rather than on implementation aspects. To illustrate WiFiEye's workflow, we present a case study on WiFi-based human activity recognition.

cs.NI↗

arXiv2022

How appropriate are the gravitational entropy proposals for traversable wormholes?

In this paper we have examined the validity of some proposed definitions of gravitational entropy (GE) in the context of traversable wormhole solutions of the Einstein field equations. Here we have adopted two different proposals of GE and checked for their applicability in the case of these wormholes. The first one is the phenomenological approach proposed by Rudjord et al \cite{entropy1} and expanded by Romero et al in \cite{entropy2}, which is a purely geometric method of measuring gravitational entropy. The latter one is the Clifton-Ellis-Tavakol (CET) proposal \cite{CET} for the gravitational entropy which arises in relativistic thermodynamics and is based on the Bel-Robinson tensor, which represents the effective super-energy-momentum tensor of free gravitational fields. Considering some of the Lorentzian traversable wormholes along with the Brill solution for NUT wormholes and the AdS wormholes, we have evaluated the gravitational entropy for these systems. Incidentally, the application of the CET proposal can provide unique gravitational entropies for spacetimes of Petrov type D and N only, whereas the geometric method can be applied to almost every kind of spacetime, although it has no relation with thermodynamics. For any traversable wormhole to be physically realistic, it should have a viable GE. We found that the GE proposals do give us a consistent measure of GE in several of them. This means that the existence of a viable gravitational entropy strictly depends on its definition.

gr-qc↗

arXiv2021

Density perturbation and cosmological evolution in the presence of magnetic field in $f(R)$ gravity models

In this paper, we have investigated the density perturbations and cosmological evolution in the FLRW universe in presence of a cosmic magnetic field, which may be assumed to mimic primordial magnetic fields. Such magnetic fields have sufficient strength to influence galaxy formation and cluster dynamics, thereby leaving an imprint on the CMB anisotropies. We have considered the FLRW universe as a representative of the isotropic cosmological model in the 1+3 covariant formalism for $f(R)$ gravity. The propagation equations have been determined and analyzed, where we have assumed that the magnetic field is aligned uniformly along the $x$-direction, resulting in a diagonal shear tensor. Subsequently,the density perturbation evolution equations have been studied and the results have been interpreted. We have also indicated how these results change in the general relativistic case and briefly mentioned the expected change in higher-order gravity theories.

hep-th↗

arXiv2021

Performance Limits of Neighbor Discovery in Wireless Networks

Neighbor Discovery (ND) is the process employed by two wireless devices to discover each other. There are many different ND protocols, both in the scientific literature and also those employed in practice. All ND protocols involve devices sending beacons, and also listening for them. Protocols differ in terms of how the beacon transmissions and reception windows are scheduled, and the device sleeps in between consecutive transmissions and reception windows in order to save energy. A successful discovery constitutes a sending device's beacon overlapping with a receiving device's reception window. The goal of all ND protocols is to minimize the discovery latency. In spite of the ubiquity of ND protocols and active research on this topic for over two decades, the basic question "Given an energy budget, what is the minimum guaranteed ND latency?", however, still remains unanswered. Given the different kinds of protocols that exist, there has also been no standard way of comparing them and their performance. This paper, for the first time, answers the question on the best-achievable ND latency for a given energy budget. We derive discovery latencies for different scenarios, e.g., when both devices have the same energy budgets, and both devices have different energy budgets. We also show that some existing protocols can be parametrized such that they perform optimally. The fact that the parametrizations of some other protocols were optimal was not known before, and can now be established using our technique. Our results are restricted to the case when a few devices discover each other at a time, as is the case in most real-life scenarios. When many devices need to discover each other simultaneously, packet collisions play a dominant role in the discovery latency and how to analyze such scenarios need further study.

cs.NI↗