Search

Sequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this challenge in the traditional offline RL setting, where an agent must learn how to make decisions based on data collected through a stochastic behavior policy. We present \textit{Latent Macro Action Planner} (L-MAP), which addresses this challenge by learning a set of temporally extended macro-actions through a state-conditional Vector Quantized Variational Autoencoder (VQ-VAE), effectively reducing action dimensionality. L-MAP employs a (separate) learned prior model that acts as a latent transition model and allows efficient sampling of plausible actions. During planning, our approach accounts for stochasticity in both the environment and the behavior policy by using Monte Carlo tree search (MCTS). In offline RL settings, including stochastic continuous control tasks, L-MAP efficiently searches over discrete latent actions to yield high expected returns. Empirical results demonstrate that L-MAP maintains low decision latency despite increased action dimensionality. Notably, across tasks ranging from continuous control with inherently stochastic dynamics to high-dimensional robotic hand manipulation, L-MAP significantly outperforms existing model-based methods and performs on-par with strong model-free actor-critic baselines, highlighting the effectiveness of the proposed approach in planning in complex and stochastic environments with high-dimensional action spaces., Comment: Accepted by ICLR2025. Code would be available at \href{https://github.com/BaitingLuo/L-MAP.git}{this https URL}

Strategic aggregation of electric vehicle batteries as energy reservoirs can optimize power grid demand, benefiting smart and connected communities, especially large office buildings that offer workplace charging. This involves optimizing charging and discharging to reduce peak energy costs and net peak demand, monitored over extended periods (e.g., a month), which involves making sequential decisions under uncertainty and delayed and sparse rewards, a continuous action space, and the complexity of ensuring generalization across diverse conditions. Existing algorithmic approaches, e.g., heuristic-based strategies, fall short in addressing real-time decision-making under dynamic conditions, and traditional reinforcement learning (RL) models struggle with large state-action spaces, multi-agent settings, and the need for long-term reward optimization. To address these challenges, we introduce a novel RL framework that combines the Deep Deterministic Policy Gradient approach (DDPG) with action masking and efficient MILP-driven policy guidance. Our approach balances the exploration of continuous action spaces to meet user charging demands. Using real-world data from a major electric vehicle manufacturer, we show that our approach comprehensively outperforms many well-established baselines and several scalable heuristic approaches, achieving significant cost savings while meeting all charging requirements. Our results show that the proposed approach is one of the first scalable and general approaches to solving the V2B energy management challenge.

Microbes and pathogens play a detrimental role in healing wounds, causing infections like impetigo through bodily fluids and skin and entering the bloodstream through the wounds, thereby hindering the healing process and tissue regeneration. Clay, known for its long history of natural therapeutic use, has emerged as one of the most promising candidates for biomedical applications due to its non-toxic nature, porosity, high surface area, ubiquity, and excellent cation exchange capacity. This study demonstrates an innovative approach to engineering an organo-functionalized, infection-resistant, easy-to-use bandage material from clay, an environmentally benign and sustainable material. The hybrid membranes have been developed using clays, zwitterions, silver ions, and terbinafine hydrochloride (TBH) to impart antibacterial and antifungal efficacy. A critical aspect of this study is embedding organic molecules and metal ions with the clays and releasing them to resist the growth and kill the pathogens. The antimicrobial efficacy of the membranes has been tested using a zone of inhibition study against the most common microbes in skin wounds, viz. S. aureus, E. coli, and C. albicans. Results from our studies not only demonstrate the potential of these hybrid clay membranes as a cost-effective, scalable, and effective solution for treating microbial infections but also instill newer avenues for point-of-care wound-healing treatments, offering hope for improved patient outcomes.

It is known that non-unital noise such as the amplitude damping can sometimes increase quantum correlations, while unital noise such as the dephasing usually decreases quantum correlations. It is, therefore, important to delineate the conditions, when noise can enhance the quantumness of the system. Here, we show that if the noise acting on the system is unital (non-unital), then the noise acting on the environment must also be unital (non-unital), for the evolution to be unitary in the joint system-environment space. For example, if the first two qubits are treated as system and the third qubit is treated as environment, then both the system and the environment evolve unitally in case of a three-qubit GHZ state, and both of them evolve non-unitally in case of a three-qubit W state. Our result may be of interest in quantum information, and we anticipate it to be useful in various contexts, such as to better tackle noise in quantum computing and quantum information processing., Comment: 6 pages, 1 figure

In this paper, we report on a study of the road networks of the provinces of Canada as complex networks. A number of statistical features have been analyzed and compared with two random models. In addition, we have also studied the resilience of these road networks under random failures and targeted attacks., Comment: 18 pages, 11 figures

The inverse diffusion flame (IDF) can experience thermoacoustic instability due to variations in power input or flow conditions. However, the dynamical transitions in IDF that lead to this instability when altering control parameters have not been thoroughly investigated. In this study, we explore the control parameters through two different approaches and employ multifractal detrended fluctuation analysis to characterize the transitions observed prior to the onset of thermoacoustic instability in the inverse diffusion flame. Our findings reveal a loss of multifractality near the region associated with thermoacoustic instability, which suggests a more ordered behavior. We determine that the singularity exponent, the width of the multifractal spectrum, and the Hurst exponent are reliable indicators of thermoacoustic instability and serve as effective classifiers of dynamical states in inverse diffusion flames., Comment: 17 pages, 10 figures

In view of their advantages for memory and storage applications, the quest to find suitable magnetic thin film heterostructures that can exhibit strong spin pumping effect persists in the scientific community. Here, the spin pumping phenomenon is investigated in Ta/Galfenol (FeGa) thin film heterostructures by systematically varying the thickness of the heavy metallic Ta underlayer (UL). The films exhibit soft magnetic property with a bcc-phase with a notably low Gilbert damping obtained for FeGa on Si (100). The precessional magnetization dynamics of Ta/FeGa films are explored using time-resolved magneto-optical Kerr effect magnetometry, revealing the presence of a resonant Kittel mode and additional strain-induced modes. The lowest value of effective Gilbert damping in Ta/FeGa is obtained as ~ 0.015, which rises by ~ 65 % as the thickness of UL increases. Spin pumping and two-magnon scattering mechanisms are validated using a ballistic spin transport model. An overall effective spin mixing conductance value of ~ 5.48 $\times$ 10$^{15}$ cm$^{-2}$ is found, which is the highest value ever reported in magnetostrictive Galfenol films. Additionally, micromagnetic simulations are performed to understand the effect of tilted magnetic anisotropy on the formation of magnetic modes in these films. These findings in FeGa films establish it as an effective spin source material and offer innovative ideas to control spin-wave propagation and diverse applications in straintronics.

We investigated theoretically the transmission properties of Dirac Fermions tunneling through a periodically (sinusoidal and rectangular) driven electrostatic barrier in Monolayer graphene. For the time harmonic potential with moderate to high alpha (=amplitude/frequency) the central Floquet band is found to be almost cloaked for the Klein transmitted electron in contrast to electron at higher grazing incidences. As a time periodic drive, we mainly focused on the use of rectangular wave electric signal to modulate the transparency of the barrier. It is noted that the asymmetric Fano resonance, a characteristic feature of photon assisted tunneling, is more likely to occur for rectangular drive in contrast to the harmonic one. The height of the modulating potential is particularly responsible for the dressing effect of the barrier. The position and nature of the FR can be tailored by changing the height and frequency of the rectangular drive. Moreover, the duty cycle of the driving potential turns out to be an important controlling parameter for the transmission process. Thus, the rectangular modulation plays an important role for the occurrence and detection of the Fano resonances which is vital for the use of graphene nanostructure in the field of detectors, sensors, modulators etc. The present findings attempt for the first time, to realize the effect of duty cycle on the quantum interference in semiconductor nanostructures., Comment: 23 pages with 5 figures

The ferrimagnetic nodal-line semiconductor Mn$_3$Si$_2$Te$_6$ exhibits colossal magnetoresistance (CMR) owing to the chiral orbital currents (COC). The COC is developed due to spin-orbit interaction (SOI) attributed to the tellurium (Te) atoms. Here, we observe that on chemical substitution of the Te atoms with selenium (Se), the COC, which runs along the Te-Te edges of the MnTe$_6$ octahedra, becomes weaker and thus affects the angular magnetoresistance (MR) of Mn$_3$Si$_2$Te$_6$. We find that the application of magnetic field along the easy axis leads to a considerable drop in resistance in substituted crystals, which otherwise exhibits weak MR. On the other hand, the CMR effect along the partially polarized magnetization direction is found to be only marginally affected due to the substitution and persists even for a significantly high concentration of Se.

Combining transport and magnetization measurements, we discover the emergence of a tricritical point connecting the antiferromagnetic, metamagnetic and paramagnetic regions in MnBi$_2$Te$_4$, a magnetic topological insulator candidate which can potentially exhibit axion electrodynamics. We observe a unique magnetic field-driven phase diagram where the second-order antiferromagnetic to paramagnetic phase boundary bifurcates into two first-order lines at the tricritical point. The two first-order lines enclosing the metamagnetic phase eventually terminate at the quantum critical endpoints with the application of magnetic field.

Purpose: Foundation models, trained on multitudes of public datasets, often require additional fine-tuning or re-prompting mechanisms to be applied to visually distinct target domains such as surgical videos. Further, without domain knowledge, they cannot model the specific semantics of the target domain. Hence, when applied to surgical video segmentation, they fail to generalise to sections where previously tracked objects leave the scene or new objects enter. Methods: We propose SASVi, a novel re-prompting mechanism based on a frame-wise Mask R-CNN Overseer model, which is trained on a minimal amount of scarcely available annotations for the target domain. This model automatically re-prompts the foundation model SAM2 when the scene constellation changes, allowing for temporally smooth and complete segmentation of full surgical videos. Results: Re-prompting based on our Overseer model significantly improves the temporal consistency of surgical video segmentation compared to similar prompting techniques and especially frame-wise segmentation, which neglects temporal information, by at least 1.5%. Our proposed approach allows us to successfully deploy SAM2 to surgical videos, which we quantitatively and qualitatively demonstrate for three different cholecystectomy and cataract surgery datasets. Conclusion: SASVi can serve as a new baseline for smooth and temporally consistent segmentation of surgical videos with scarcely available annotation data. Our method allows us to leverage scarce annotations and obtain complete annotations for full videos of the large-scale counterpart datasets. We make those annotations publicly available, providing extensive annotation data for the future development of surgical data science models.

We study polynomial-time approximation algorithms for two closely-related problems, namely computing shortcuts and transitive-closure spanners (TC spanner). For a directed unweighted graph $G=(V, E)$ and an integer $d$, a set of edges $E'\subseteq V\times V$ is called a $d$-TC spanner of $G$ if the graph $H:=(V, E')$ has (i) the same transitive-closure as $G$ and (ii) diameter at most $d.$ The set $E''\subseteq V\times V$ is a $d$-shortcut of $G$ if $E\cup E''$ is a $d$-TC spanner of $G$. Our focus is on the following $(\alpha_D, \alpha_S)$-approximation algorithm: given a directed graph $G$ and integers $d$ and $s$ such that $G$ admits a $d$-shortcut (respectively $d$-TC spanner) of size $s$, find a $(d\alpha_D)$-shortcut (resp. $(d\alpha_D)$-TC spanner) with $s\alpha_S$ edges, for as small $\alpha_S$ and $\alpha_D$ as possible. As our main result, we show that, under the Projection Game Conjecture (PGC), there exists a small constant $\epsilon>0$, such that no polynomial-time $(n^{\epsilon},n^{\epsilon})$-approximation algorithm exists for finding $d$-shortcuts as well as $d$-TC spanners of size $s$. Previously, super-constant lower bounds were known only for $d$-TC spanners with constant $d$ and ${\alpha_D}=1$ [Bhattacharyya, Grigorescu, Jung, Raskhodnikova, Woodruff 2009]. Similar lower bounds for super-constant $d$ were previously known only for a more general case of directed spanners [Elkin, Peleg 2000]. No hardness of approximation result was known for shortcuts prior to our result. As a side contribution, we complement the above with an upper bound of the form $(n^{\gamma_D}, n^{\gamma_S})$-approximation which holds for $3\gamma_D + 2\gamma_S > 1$ (e.g., $(n^{1/5+o(1)}, n^{1/5+o(1)})$-approximation).

Surgical simulation offers a promising addition to conventional surgical training. However, available simulation tools lack photorealism and rely on hardcoded behaviour. Denoising Diffusion Models are a promising alternative for high-fidelity image synthesis, but existing state-of-the-art conditioning methods fall short in providing precise control or interactivity over the generated scenes. We introduce SurGrID, a Scene Graph to Image Diffusion Model, allowing for controllable surgical scene synthesis by leveraging Scene Graphs. These graphs encode a surgical scene's components' spatial and semantic information, which are then translated into an intermediate representation using our novel pre-training step that explicitly captures local and global information. Our proposed method improves the fidelity of generated images and their coherence with the graph input over the state-of-the-art. Further, we demonstrate the simulation's realism and controllability in a user assessment study involving clinical experts. Scene Graphs can be effectively used for precise and interactive conditioning of Denoising Diffusion Models for simulating surgical scenes, enabling high fidelity and interactive control over the generated content.

We present a comprehensive study of dielectric properties including complex permittivity, loss, and leakage of high-ScN-fraction ScAlN thin films grown using molecular beam epitaxy (MBE). Dielectric spectroscopy is carried out on high-ScN-fraction (30%-40% ScN fraction) samples from 20 Hz to 10 GHz. We find that real permittivity {\epsilon}' increases significantly with increasing ScN fraction; a trend confirmed by density functional theory. Further, {\epsilon}' is strongly dispersive with frequency and increasing ScN fraction, with values for Sc0.4Al0.6N varying from 150 down to 60 with increasing frequency. Loss, dispersion, and DC leakage current correspondingly increase with ScN fraction. The high {\epsilon}' and strongly dispersive behavior in MBE ScAlN are not observed in a sputter-deposited ScAlN control with a similar ScN fraction, highlighting fundamental differences between films produced by the two deposition methods. Microscopy and spectroscopy analyses are carried out on MBE- and sputter-deposited samples to compare microstructure, alloy, and dopant concentration.

General relativistic magnetohydrodynamic (GRMHD) simulations have been instrumental in our understanding of high energy astrophysical phenomena over the past two decades. Their robustness and modularity make them a great tool for understanding the dynamics of various astrophysical objects. In this paper we have used GRMHD simulations to understand the accretion flows of ultraluminous X-ray sources (ULXs) and blazars. ULXs are enigmatic sources which exhibit very high luminosities (super-Eddington for stellar mass black holes) even in their low-hard state. Numerical steady state calculations have shown that this behaviour can be explained by considering ULXs to be highly magnetised advective accretion sources around stellar-mass black holes. Our simulation confirms that such an accretion flow can indeed produce the high luminosities observed in ULXs. Further to continue towards the supermassive black holes, we have also modeled blazars and have used our simulation results to explain the apparent dichotomy in the two blazar classes: flat spectrum radio quasars (FSRQs) and BL Lacertae (BL Lacs). Our results show that FSRQ and BL Lacs show different spectral characteristics due to a difference in their magnetic field characteristics. The different categories of FSRQs and BL Lacs have also been explained by the interplay between the spin, magnetic field and accretion rate of the central supermassive black hole., Comment: 14 pages including 11 figures (19 png files); accepted for publication in ApJ

Autonomous edge computing in robotics, smart cities, and autonomous vehicles relies on the seamless integration of sensing, processing, and actuation for real-time decision-making in dynamic environments. At its core is the sensing-to-action loop, which iteratively aligns sensor inputs with computational models to drive adaptive control strategies. These loops can adapt to hyper-local conditions, enhancing resource efficiency and responsiveness, but also face challenges such as resource constraints, synchronization delays in multi-modal data fusion, and the risk of cascading errors in feedback loops. This article explores how proactive, context-aware sensing-to-action and action-to-sensing adaptations can enhance efficiency by dynamically adjusting sensing and computation based on task demands, such as sensing a very limited part of the environment and predicting the rest. By guiding sensing through control actions, action-to-sensing pathways can improve task relevance and resource use, but they also require robust monitoring to prevent cascading errors and maintain reliability. Multi-agent sensing-action loops further extend these capabilities through coordinated sensing and actions across distributed agents, optimizing resource use via collaboration. Additionally, neuromorphic computing, inspired by biological systems, provides an efficient framework for spike-based, event-driven processing that conserves energy, reduces latency, and supports hierarchical control--making it ideal for multi-agent optimization. This article highlights the importance of end-to-end co-design strategies that align algorithmic models with hardware and environmental dynamics and improve cross-layer interdependencies to improve throughput, precision, and adaptability for energy-efficient edge autonomy in complex environments.

Iron-based van der Waals (vdW) ferromagnets with relatively high ordering temperatures are a current research focus due to their significance in fundamental physics and potential applications in spintronics. Competing magnetic interactions and anisotropies can give rise to nontrivial spin textures in these materials, resulting in novel topological features. Fe4GeTe2 (F4GT) is a nearly room-temperature vdW ferromagnet, well known for hosting a spin-reorientation transition (SRT) arising from the interplay of perpendicular magnetic anisotropy (PMA) and shape anisotropy. In this work, we investigate the angle-dependent magneto-transport properties of F4GT single crystals. We report a large topological Hall effect (THE) in a multi-layer F4GT originating from the SRT-driven non-coplanar spin textures. The THE appears at the in-plane orientation of the external magnetic field and persists over a wide range of temperatures around SRT. Additionally, we find a thickness-sensitive THE signal for the c axis orientation of the magnetic field at a low-temperature regime which is associated with a reentrant Lifshitz transition.

Global-scale inertial modes of oscillations have been recently observed on the Sun. They might play an important dynamic and diagnostic role for the Sun. This work aims to assess the validity of simplifying assumptions in the equation of continuity, which have often been used in the linear models of solar inertial modes. We compute the linear eigenmodes of the Sun's convection zone in the inertial frequency range using the Dedalus code. This single framework enables us to compare the sensitivity of the modes to different model setups, such as the compressible setup and the Boussinesq and anelastic approximations. We consider both the cases of uniform rotation and solar differential rotation (as given by helioseismology). We find that the compressible and anelastic models have almost identical eigenmodes under uniform rotation and solar differential rotation. On the other hand, the absence of density stratification in the Boussinesq model results in significantly different eigenmodes under these setups. The differences are most prominent for the non-toroidal modes with significant radial motions mainly due to the absence of the compressional $\beta$-effect. The anelastic approximation simplifies the calculations and reduces the numerical cost without affecting the solar inertial modes. The Boussinesq or incompressible approximations cannot be used to model the solar inertial modes accurately. Since the effects of differential rotation on the eigenmodes are very significant, an acceptable setup is to use the anelastic approximation together with the solar differential rotation., Comment: Accepted for publication in Astronomy & Astrophysics, 10 pages, 8 figures + Appendix

Augmentation by generative modelling yields a promising alternative to the accumulation of surgical data, where ethical, organisational and regulatory aspects must be considered. Yet, the joint synthesis of (image, mask) pairs for segmentation, a major application in surgery, is rather unexplored. We propose to learn semantically comprehensive yet compact latent representations of the (image, mask) space, which we jointly model with a Latent Diffusion Model. We show that our approach can effectively synthesise unseen high-quality paired segmentation data of remarkable semantic coherence. Generative augmentation is typically applied pre-training by synthesising a fixed number of additional training samples to improve downstream task models. To enhance this approach, we further propose Generative Adaptive Uncertainty-guided Diffusion-based Augmentation (GAUDA), leveraging the epistemic uncertainty of a Bayesian downstream model for targeted online synthesis. We condition the generative model on classes with high estimated uncertainty during training to produce additional unseen samples for these classes. By adaptively utilising the generative model online, we can minimise the number of additional training samples and centre them around the currently most uncertain parts of the data distribution. GAUDA effectively improves downstream segmentation results over comparable methods by an average absolute IoU of 1.6% on CaDISv2 and 1.5% on CholecSeg8k, two prominent surgical datasets for semantic segmentation.

In many real-world applications, agents must make sequential decisions in environments where conditions are subject to change due to various exogenous factors. These non-stationary environments pose significant challenges to traditional decision-making models, which typically assume stationary dynamics. Non-stationary Markov decision processes (NS-MDPs) offer a framework to model and solve decision problems under such changing conditions. However, the lack of standardized benchmarks and simulation tools has hindered systematic evaluation and advance in this field. We present NS-Gym, the first simulation toolkit designed explicitly for NS-MDPs, integrated within the popular Gymnasium framework. In NS-Gym, we segregate the evolution of the environmental parameters that characterize non-stationarity from the agent's decision-making module, allowing for modular and flexible adaptations to dynamic environments. We review prior work in this domain and present a toolkit encapsulating key problem characteristics and types in NS-MDPs. This toolkit is the first effort to develop a set of standardized interfaces and benchmark problems to enable consistent and reproducible evaluation of algorithms under non-stationary conditions. We also benchmark six algorithmic approaches from prior work on NS-MDPs using NS-Gym. Our vision is that NS-Gym will enable researchers to assess the adaptability and robustness of their decision-making algorithms to non-stationary conditions., Comment: 23 pages, 17 figures

Federated- and Continual Learning have been established as approaches to enable privacy-aware learning on continuously changing data, as required for deploying AI systems in histopathology images. However, data shifts can occur in a dynamic world, spatially between institutions and temporally, due to changing data over time. This leads to two issues: Client Drift, where the central model degrades from aggregating data from clients trained on shifted data, and Catastrophic Forgetting, from temporal shifts such as changes in patient populations. Both tend to degrade the model's performance of previously seen data or spatially distributed training. Despite both problems arising from the same underlying problem of data shifts, existing research addresses them only individually. In this work, we introduce a method that can jointly alleviate Client Drift and Catastrophic Forgetting by using our proposed Dynamic Barlow Continuity that evaluates client updates on a public reference dataset and uses this to guide the training process to a spatially and temporally shift-invariant model. We evaluate our approach on the histopathology datasets BCSS and Semicol and prove our method to be highly effective by jointly improving the dice score as much as from 15.8% to 71.6% in Client Drift and from 42.5% to 62.8% in Catastrophic Forgetting. This enables Dynamic Learning by establishing spatio-temporal shift-invariance.

Contributions from the Indian gravity community have played a significant role in shaping several branches of astronomy and astrophysics. This document reviews some of the most important contributions and presents a vision for gravity research in the context of astronomy \& astrophysics in India. This is an expanded version of one of the chapters in the recently released Vision Document of the Astronomical Society of India., Comment: Published in JoAA special issue "Indian Astronomy in the Global Context: A compendium of white papers submitted towards the Astronomical Society of India Vision Document (2024"

Per- and polyfluoroalkyl substances (PFAS), often referred as "forever chemicals," are pervasive environmental pollutants due to their resistance to degradation. Among these, perfluorooctanoic acid (PFOA) poses significant threats to human health, contaminating water sources globally. Here, we have demonstrated the potential of a novel electrochemical sensor based on two-dimensional (2D) aluminium-based multicomponent quasicrystals (2D-Al QC) for the ultrasensitive sub-picomolar level detection of PFOA. The 2D-Al QC-inked electrode was employed here to detect PFOA by differential pulse voltammetry (DPV). The limit of detection (LoD) achieved is 0.59 +/- 0.05 pM. The sensor was evaluated for selectivity with other interfering compounds, repeatability of cycles, and reproducibility for five similar electrodes with a deviation of 0.8 %. The stability of the sensor has also been analysed after ninety days ,which shows a minimal variation of 15%. Spectroscopic techniques and theoretical calculations were further utilized to understand the interaction between the 2D-Al QC and PFOA. The results demonstrate that the 2D-Al QC offers a promising platform for the rapid and sensitive detection of PFOA, potentially addressing current environmental monitoring challenges.

The graph based approach to multiple testing is an intuitive method that enables a study team to represent clearly, through a directed graph, its priorities for hierarchical testing of multiple hypotheses, and for propagating the available type-1 error from rejected or dropped hypotheses to hypotheses yet to be tested. Although originally developed for single stage non-adaptive designs, we show how it may be extended to two-stage designs that permit early identification of efficacious treatments, adaptive sample size re-estimation, dropping of hypotheses, and changes in the hierarchical testing strategy at the end of stage one. Two approaches are available for preserving the family wise error rate in the presence of these adaptive changes; the p-value combination method, and the conditional error rate method. In this investigation we will present the statistical methodology underlying each approach and will compare the operating characteristics of the two methods in a large simulation experiment.

Denoising Diffusion Models (DDMs) are widely used for high-quality image generation and medical image segmentation but often rely on Unet-based architectures, leading to high computational overhead, especially with high-resolution images. This work proposes three NCA-based improvements for diffusion-based medical image segmentation. First, Multi-MedSegDiffNCA uses a multilevel NCA framework to refine rough noise estimates generated by lower level NCA models. Second, CBAM-MedSegDiffNCA incorporates channel and spatial attention for improved segmentation. Third, MultiCBAM-MedSegDiffNCA combines these methods with a new RGB channel loss for semantic guidance. Evaluations on Lesion segmentation show that MultiCBAM-MedSegDiffNCA matches Unet-based model performance with dice score of 87.84% while using 60-110 times fewer parameters, offering a more efficient solution for low resource medical settings., Comment: 5 pages, 3 figures

We consider the problem of \textit{online sparse linear approximation}, where one predicts the best sparse approximation of a sequence of measurements in terms of linear combination of columns of a given measurement matrix. Such online prediction problems are ubiquitous, ranging from medical trials to web caching to resource allocation. The inherent difficulty of offline recovery also makes the online problem challenging. In this letter, we propose Follow-The-Approximate-Sparse-Leader, an efficient online meta-policy to address this online problem. Through a detailed theoretical analysis, we prove that under certain assumptions on the measurement sequence, the proposed policy enjoys a data-dependent sublinear upper bound on the static regret, which can range from logarithmic to square-root. Numerical simulations are performed to corroborate the theoretical findings and demonstrate the efficacy of the proposed online policy., Comment: 12 pages, 5 figures, corrected title, added proof of a lemma in appendix

We investigate the nonlinear dynamics of spin wave active ring oscillators (SWAROs) injected with GHz drive signal. The injected signal frequency was swept over a 5 MHz wide frequency range across a magnetostatic surface spin wave (MSSW) dipole gap, with the drive power varying from -10 to 10 dBm. We measured the output power spectra from the SWARO at different gains for each drive power and frequency combination. Near the drive frequency, we observe the formation of sidebands, which are the products of the nonlinear scattering processes. Furthermore, at higher drive amplitudes, the spin wave nonlinearity in the ring oscillator is suppressed, and the SWARO spectrum is pulled toward the drive frequency., Comment: 13 pages, 11 figures

Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such variations, deep learning models cannot. In fact, such models are highly susceptible to even slight variations in image characteristics. Consequently, manufacturers must conduct regular updates to ensure that they remain safe and effective. Performing such updates in the United States and European Union required, until recently, obtaining re-approval. Given the time and financial burdens associated with these processes, updates were infrequent, and obsolete systems remained in operation for too long. During 2024, several regulatory developments promised to streamline the safe rollout of model updates: The European Artificial Intelligence Act came into effect last August, and the Food and Drug Administration (FDA) issued final marketing submission recommendations for a Predetermined Change Control Plan (PCCP) in December. We provide an overview of these developments and outline the key building blocks necessary for successfully deploying dynamic systems. At the heart of these regulations - and as prerequisites for manufacturers to conduct model updates without re-approval - are clear descriptions of data collection and re-training processes, coupled with robust real-world quality monitoring mechanisms.

We relate the recently defined spectral torsion with the algebraic torsion of noncommutative differential calculi on the example of the almost-commutative geometry of the product of a closed oriented Riemannian spin manifold $M$ with the two-point space $\mathcal Z_2$., Comment: 25 pages

Magnetic fields are ubiquitous across different physical systems of current interest; from the early Universe, compact astrophysical objects and heavy-ion collisions to condensed matter systems. A proper treatment of the effects produced by magnetic fields during the dynamical evolution of these systems, can help to understand observables that otherwise show a puzzling behavior. Furthermore, when these fields are comparable to or stronger than \Lambda_QCD, they serve as excellent probes to help elucidate the physics of strongly interacting matter under extreme conditions of temperature and density. In this work we provide a comprehensive review of recent developments on the description of QED and QCD systems where magnetic field driven effects are important. These include the modification of meson static properties such as masses and form factors, the chiral magnetic effect, the description of anomalous transport coefficients, superconductivity in extreme magnetic fields, the properties of neutron stars, the evolution of heavy-ion collisions, as well as effects on the QCD phase diagram. We describe recent theory and phenomenological developments using effective models as well as LQCD methods. The work represents a state-of-the-art review of the field, motivated by presentations and discussions during the "Workshop on Strongly Interacting Matter in Strong Electromagnetic Fields" that took place in the European Centre for Theoretical Studies in Nuclear Physics and Related Areas (ECT*) in the city of Trento, Italy, September 25-29, 2023., Comment: 325 pages-long review of recent topics of interest in the field of magnetic field effects on QED and QCD matter. To be susbmitted to PNPP

Reinforcement learning (RL) can be formulated as a sequence modeling problem, where models predict future actions based on historical state-action-reward sequences. Current approaches typically require long trajectory sequences to model the environment in offline RL settings. However, these models tend to over-rely on memorizing long-term representations, which impairs their ability to effectively attribute importance to trajectories and learned representations based on task-specific relevance. In this work, we introduce AdaCred, a novel approach that represents trajectories as causal graphs built from short-term action-reward-state sequences. Our model adaptively learns control policy by crediting and pruning low-importance representations, retaining only those most relevant for the downstream task. Our experiments demonstrate that AdaCred-based policies require shorter trajectory sequences and consistently outperform conventional methods in both offline reinforcement learning and imitation learning environments., Comment: Accepted to 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025)

Utilizing quantum information theory, it has been shown that irreversible entropy production is bounded from both below and above in physical processes. Both these bounds are positive and generalize the Clausius inequality. Such bounds are, however, obtained from distance measures in the space of states, which are hard to define and compute in quantum field theories. We show that the quantum null energy condition (QNEC) can be utilized to obtain both lower and upper bounds on irreversible entropy production for quenches leading to transitions between thermal states carrying uniform momentum density in two dimensional holographic conformal field theories. We achieve this by refining earlier methods and developing an algebraic procedure for determining HRT surfaces in arbitrary Ba\~nados-Vaidya geometries which are dual to quenches involving transitions between general quantum equilibrium states (e.g. thermal states) where the QNEC is saturated. We also discuss results for the growth and thermalization of entanglement entropy for arbitrary initial and final temperatures and momentum densities. The rate of quadratic growth of entanglement just after the quench depends only on the change in the energy density and is independent of the entangling length. For sufficiently large entangling lengths, the entanglement tsunami phenomenon can be established. Finally, we study recovery of the initial state from the evolving entanglement entropy and argue that the Renyi entropies should give us a refined understanding of scrambling of quantum information., Comment: 50 pages, 8 figures

This paper aims to study a newly proposed fluid description of dark energy in the context of late-time accelerated expansion of the universe. We examine the probable origin of the proposed equation of state in correspondence with some vastly discussed scalar field models of dark energy and reconstruct the field parameters like scalar field $\phi$ and scalar potential $V(\phi)$, analyzing their behavior in the evolution of the universe. The study also incorporates an analysis of fundamental energy conditions: Null Energy Condition (NEC), Dominant Energy Condition (DEC), and Strong Energy Condition (SEC), to assess the physical consistency and cosmological implications of the model. We perform a detailed stability analysis and investigate the evolutionary dynamics of the proposed fluid model from a thermodynamic perspective. Additionally, the model is analyzed using some of the latest observational datasets, such as Cosmic Chronometers (CC), Baryon Acoustic Oscillation (BAO), and Supernova Type-Ia (using Pantheon+SH0ES compilation and Union 2.1), to determine its viability and consistency with observations. The results suggest that the model offers a robust description of dark energy dynamics while maintaining agreement with current observational data., Comment: 49 pages, 37 figures, All comments are welcome

Oversmoothing in Graph Neural Networks (GNNs) poses a significant challenge as network depth increases, leading to homogenized node representations and a loss of expressiveness. In this work, we approach the oversmoothing problem from a dynamical systems perspective, providing a deeper understanding of the stability and convergence behavior of GNNs. Leveraging insights from dynamical systems theory, we identify the root causes of oversmoothing and propose \textbf{\textit{DYNAMO-GAT}}. This approach utilizes noise-driven covariance analysis and Anti-Hebbian principles to selectively prune redundant attention weights, dynamically adjusting the network's behavior to maintain node feature diversity and stability. Our theoretical analysis reveals how DYNAMO-GAT disrupts the convergence to oversmoothed states, while experimental results on benchmark datasets demonstrate its superior performance and efficiency compared to traditional and state-of-the-art methods. DYNAMO-GAT not only advances the theoretical understanding of oversmoothing through the lens of dynamical systems but also provides a practical and effective solution for improving the stability and expressiveness of deep GNNs., Comment: 26 pages

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets containing numerical simulations of a wide variety of spatiotemporal physical systems. The Well draws from domain experts and numerical software developers to provide 15TB of data across 16 datasets covering diverse domains such as biological systems, fluid dynamics, acoustic scattering, as well as magneto-hydrodynamic simulations of extra-galactic fluids or supernova explosions. These datasets can be used individually or as part of a broader benchmark suite. To facilitate usage of the Well, we provide a unified PyTorch interface for training and evaluating models. We demonstrate the function of this library by introducing example baselines that highlight the new challenges posed by the complex dynamics of the Well. The code and data is available at https://github.com/PolymathicAI/the_well., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks

The exact theory of gravity in the strong field regime is still under debate. There are observations implying the need for modification to Einstein's gravity. On the other hand, the exact constituents of dark matter are also a big puzzle, where primordial black holes (PBHs) are argued to be a potential candidate. We explore Hawking radiation in a modified gravity and find that PBHs evaporate faster in a scalar-tensor theory based modified gravity. Subsequently, all the nonrotating BHs of mass $\sim 10^{15}$ g or less should have been evaporated by today, which is an order of magnitude heavier than what the Einstein gravity predicts. This has many consequences including a strict constraint on contributing PBHs to dark matter, widening the debate of dark matter origin., Comment: 9 pages, 4 figures; To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh & A R Rao, Springer Nature

The existence of massive white dwarfs (WDs) containing more than Chandrasekhar's maximum mass has been suggested via the detection of peculiar type Ia Supernovae. It had been crucial to directly detect those 'super' (more massive)-Chandrasekhar WDs to confirm their existence. The WD's small size and cold internal environment have been a great disadvantage for detecting them via electromagnetic surveys like GAIA and SDSS. Although many WDs are detected in optical observations, none of the super-Chandrasekhar WDs was observed directly. Mukhopadhyay and his group proposed a decade ago that one of the possibilities to explain the high mass of super-Chandrasekhar WD is to exercise the existence of a strong magnetic field in the WD interior. The group also recently proposed that a high magnetic field, in turn, can nontrivially deform the star from a spherical shape. In the presence of rotation and obliquity angle, such WDs can radiate continuous gravitational waves (CGW). This opens up the exciting possibility of detecting the super-Chandrasekhar WDs with the upcoming detector LISA. However, the gravitational wave (GW) amplitude will decay with time due to electromagnetic radiation and quadrupolar radiation. Altogether, those decay mechanisms will set a timescale for detecting WDs via CGW. Even with a timescale bound, we can expect to detect a few super-Chandrasekhar WDs with a couple of years of cumulative detection, leading to the direct detection of super-Chandrasekhar WDs.

The expanding influence of social media platforms over the past decade has impacted the way people communicate. The level of obscurity provided by social media and easy accessibility of the internet has facilitated the spread of hate speech. The terms and expressions related to hate speech gets updated with changing times which poses an obstacle to policy-makers and researchers in case of hate speech identification. With growing number of individuals using their native languages to communicate with each other, hate speech in these low-resource languages are also growing. Although, there is awareness about the English-related approaches, much attention have not been provided to these low-resource languages due to lack of datasets and online available data. This article provides a detailed survey of hate speech detection in low-resource languages around the world with details of available datasets, features utilized and techniques used. This survey further discusses the prevailing surveys, overlapping concepts related to hate speech, research challenges and opportunities., Comment: 34 pages, 12 figures

Displacement plays a crucial role in structural health monitoring (SHM) and damage detection of structural systems subjected to dynamic loads. However, due to the inconvenience associated with the direct measurement of displacement during dynamic loading and the high cost of displacement sensors, the use of displacement measurements often gets restricted. In recent years, indirect estimation of displacement from acceleration and strain data has gained popularity. Several researchers have developed data fusion techniques to estimate displacement from acceleration and strain data. However, existing data fusion techniques mostly rely on system properties like mode shapes or finite element models and require accurate knowledge about the system for successful implementation. Hence, they have the inherent limitation of their applicability being restricted to relatively simple structures where such information is easily available. In this article, B-spline basis functions have been used to formulate a Kalman filter-based algorithm for acceleration and strain data fusion using only elementary information about the system, such as the geometry and boundary conditions, which is the major advantage of this method. Also, the proposed algorithm enables us to monitor the full-field displacement of the system online with only a limited number of sensors. The method has been validated on a numerically generated dataset from the finite element model of a tapered beam subjected to dynamic excitation. Later, the proposed data fusion technique was applied to an experimental benchmark test of a wind turbine blade under dynamic load to estimate the displacement time history. In both cases, the reconstructed displacement from strain and acceleration was found to match well with the response from the FE model., Comment: 20 pages, 17 figures

The theory of general relativity is often considered under the framework of modified Einstein gravity to explain different phenomena under strong curvature. The strong curvature effect plays a main role near black holes, where the gravitational field is strongest. The idea of black hole thermodynamics is to describe the strong field curvature properties of a black hole in the effective thermodynamical framework, e.g. entropy, temperature, heat capacity etc. In this paper, our aim is to explore how the effect of modified gravity changes the thermodynamic properties of black hole. We show that even a small modification to Einstein gravity affects the thermodynamical properties of a black hole., Comment: 8 pages,5 figures;To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh & A R Rao, Springer Nature

Over the last few decades, there has been considerable interest in the violation of the sacred "Chandrasekhar" mass limit of white dwarfs (WDs). Peculiar over-luminous type Ia supernovae (such as SNLS-03D3bb) lend observational support to the idea that these super-Chandrasekhar WDs exist. Our group, for more than a decade, has been actively working on the theoretical possibility of these objects through the presence of the star's magnetic field. The magnetic field greatly contributes to the existence of these massive WDs, both through classical and quantum effects. In this work, we explore super-Chandrasekhar WDs, formed via evolution from a main sequence star, as a result of the classical effects of the star's magnetic field. We obtain super-Chandrasekhar WDs and new mass limit(s), depending on the magnetic field geometry. We explore the full evolution and stability of these objects from the main sequence stage through the one-dimensional stellar evolution code STARS. In order to do so, we have appropriately modified the given codes by introducing magnetic effect and cooling. Our simulation confirms that massive WDs are possible in the presence of a magnetic field satisfying underlying stability., Comment: 12 pages, 4 figures; To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh & A R Rao, Springer Nature

Quantum speed limit (QSL) defines the theoretical upper bound on how fast a quantum system can evolve between states. It imposes a fundamental constraint on the rate of quantum information processing. For a relativistic spin-up electron in a uniform magnetic field, QSL increased with the magnetic field strength till around $10^{15}$ Gauss, before saturating at a saturated QSL (SQSL) of 0.2407c, where c is the speed of light. We show that by using variable magnetic fields, it is possible to surpass this limit, achieving SQSL upto 0.4-0.6c. To attain this quantum phenomenon, we solve the evolution equation of relativistic electron in spatially varying magnetic fields and find that the energies of various electron states become non-degenerate as opposed to the constant magnetic field case. This redistribution of energy is the key ingredient to accomplish higher QSL and, thus, a high information processing speed. We further explore how QSL can serve as a bridge between relativistic and non-relativistic quantum dynamics, providing insights via the Bremermann-Bekenstein bound, a quantity which constrains the maximal rate of information production. We also propose a practical experimental setup to realize these advancements. These results hold immense potential for propelling fields of quantum computation, thermodynamics and metrology., Comment: To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh and A R Rao, Springer Nature

The differentiation between chaotic and stochastic systems has long been scrutinized, particularly in observations where data is often noise-contaminated and finite. Our research examines the dual nature of the black hole X-ray binary IGR J17091-3624, an object whose behavior has been closely studied in parallel to GRS 1915+105. Remarkable similarities in the temporal classes of these two objects are explored in literature. However, this was not the case with their non-linear dynamics: GRS 1915+105 shows signs of determinism, while IGR J17091-3624 was found to be stochastic. In this study, we confront the inherent challenge of noise contamination, as in IGR J17091-3624, faced by previous studies, particularly Poisson noise, which adversely impacts the reliability of non-linear results. We employ several denoising techniques to mitigate noise effects and employ methods like Autoencoder, Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Correlation Integral (CI) to isolate the deterministic signatures. We have found signs of determinism in IGR J17091-3624 after denoising, thus supporting the hypothesis of it being similar to GRS 1915+105, even as a dynamical system., Comment: To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary, Editors: S Ghosh & A R Rao, Springer Nature

We investigate the quantum speed limit (QSL) during the time evolution of neutrino-antineutrino system under the influence of the gravitational field of a spinning primordial black hole (PBH). We derive an analytical expression for the four-vector gravitational potential in the underlying Hermitian Dirac Hamiltonian using the Boyer-Lindquist (BL) coordinates. This gravitational potential leads to an axial vector term in the Dirac equation in curved spacetime, contributing to the effective mass matrix of the neutrino-antineutrino systems. Our findings indicate that the gravitational field, expressed in BL coordinates, significantly influences the transition probabilities in two-flavor oscillations of the neutrino-antineutrino system. We then apply the expression for transition probabilities between states to analyze the Bures angle, which quantifies the closeness between the initial and final states of the time-evolved flavor state. We use this concept to probe the QSL for the time evolution of the initial flavor neutrino state., Comment: 16 pages, 12 figures, Accepted for publication in the Proceedings of the 17th Marcel Grossman Meeting (MG17), Pescara, Italy, 7-12 July 2024. Based on the talk presented in the parallel session "Unveiling neutrino secrets through cosmology: current status and future developments (NU2)"

An optically thin advective accretion disk is crucial for explaining the hard state of black hole sources. Using general relativistic magnetohydrodynamic (GRMHD) simulations, we investigate how a large-scale, strong magnetic field influences accretion and outflows/jets, depending on the field geometry, magnetic field strength, and the spin parameter of the black hole. We simulate a sub-Eddington, advective disk-outflow system in the presence of a strong magnetic field, which likely remains in the hard state. The model simulations based on HARMPI successfully explain ultra-luminous X-ray sources (ULXs) in the hard state, typically observed with luminosities ranging from $10^{39}$ - $10^{40}$ ergs s$^{-1}$. Our simulations generally describe the bright, hard state of stellar-mass black hole sources without requiring a super-Eddington accretion rate. This work explores the characteristics of ULXs without invoking intermediate-mass black holes. The observed high luminosity is attributed to the energy stored in the strong magnetic fields, which can generate super-Eddington luminosity. The combined energy of the matter and magnetic field leads to such significant luminosity., Comment: To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh \& A R Rao, Springer Nature

The quantum speed limits (QSLs) determine the minimal amount of time required for a quantum system to evolve from an initial to a final state. We investigate QSLs for the unitary evolution of the neutrino-antineutrino system in the presence of a gravitational field. It is known that the transition probabilities between neutrino and antineutrino in the framework of one and two flavors depend on the strength of the gravitational field. The behavior of the QSL time in the two-flavor system indicates fast flavor transitions as the gravitational field strength increases. Subsequently, we observe quick suppression of entanglement by exploring the speed limit for entanglement entropy of two-flavor oscillations in the neutrino-antineutrino system in the proximity of a spinning primordial black hole., Comment: 12 pages, 5 figures, To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh & A R Rao, Springer Nature

Ultraluminous X-ray sources (ULXs) have been objects of great interest for the past few decades due to their unusually high luminosities and spectral properties. A few of these sources exhibit super-Eddington luminosities assuming them to be centering around stellar mass objects, even in their hard state. It has been shown via numerical steady state calculations that ULXs in hard state can be interpreted as highly magnetised advective accretion sources around stellar mass black holes. We use general relativistic magnetohydrodynamic (GRMHD) framework to simulate highly magnetised advective accretion flows around a black hole and show that such systems can indeed produce high luminosities like ULXs. We also verify that the magnetic fields required for such high emissions is around $10^7$ G, in accordance with previous numerical steady state calculations. We further present power profiles for zero angular momentum observer (ZAMO) frame. These profiles show interesting features which can be interpreted as effects of emission due to the Blandford-Znajek and Blandford-Payne mechanisms., Comment: 13 pages, 8 figures; To be published in Astrophysics and Space Science Proceedings, titled "The Relativistic Universe: From Classical to Quantum, Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics", Gangtok, December 11-13, 2023: to felicitate Prof. Banibrata Mukhopadhyay on his 50th Birth Anniversary", Editors: S Ghosh & A R Rao, Springer Nature

Understanding nonlinear properties in accreting systems, particularly for black holes, from observation is illuminating as they are expected to be general relativistic magnetohydrodynamic flows that are nonlinear. Two features associated with nonlinear systems, used commonly, are chaos, which is deterministic, and random, which is stochastic. The differentiation between chaotic and stochastic systems is often considered to quantify the nonlinear properties of an astrophysical system. The particular emphasis is that data is often noise-contaminated and finite. We examine the dual nature of the black hole X-ray binary IGR J17091-3624, whose behavior has been closely studied in parallel to GRS 1915+105. Certain similarities in the temporal classes of these two objects are explored in literature. However, this has not been the case with their non-linear dynamics: GRS 1915+105 shows signs of determinism and stochasticity both, while IGR J17091-3624 was found to be predominantly stochastic. Here, we confront the inherent challenge of noise contamination faced by previous studies, particularly Poisson noise, which adversely impacts the reliability of non-linear results. We employ several denoising techniques to mitigate noise effects and employ methods like Principal Component Analysis, Singular Value Decomposition, and Correlation Integral to isolate the deterministic signatures. We have found signs of determinism in IGR J17091-3624, thus supporting the hypothesis of it being similar to GRS 1915+105, even as a dynamical system. Our findings not only shed light on the complex nature of IGR J17091-3624 but also pave the way for future research employing noise-reduction techniques to analyze non-linearity in observed dynamical systems., Comment: 23 pages including 12 figures (15 png files) and 7 tables; Version accepted for publication in ApJ

We introduce MecQaBot, an open-source, affordable, and modular autonomous mobile robotics framework developed for education and research at Macquarie University, School of Engineering, since 2019. This platform aims to provide students and researchers with an accessible means for exploring autonomous robotics and fostering hands-on learning and innovation. Over the five years, the platform has engaged more than 240 undergraduate and postgraduate students across various engineering disciplines. The framework addresses the growing need for practical robotics training in response to the expanding robotics field and its increasing relevance in industry and academia. The platform facilitates teaching critical concepts in sensing, programming, hardware-software integration, and autonomy within real-world contexts, igniting student interest and engagement. We describe the design and evolution of the MecQaBot framework and the underlying principles of scalability and flexibility, which are keys to its success. Complete documentation: https://github.com/AliceJames-1/MecQaBot, Comment: 6 pages, 7 figures. Github: https://github.com/AliceJames-1/MecQaBot [This paper was submitted to the 2024 International Conference on Sensing Technology (ICST 2024)]

Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban environments. We present a comprehensive approach to optimize UAV-based search and rescue operations in neighborhood areas, utilizing both a 3D AirSim-ROS2 simulator and a 2D simulator. The path planning problem is formulated as a partially observable Markov decision process (POMDP), and we propose a novel ``Shrinking POMCP'' approach to address time constraints. In the AirSim environment, we integrate our approach with a probabilistic world model for belief maintenance and a neurosymbolic navigator for obstacle avoidance. The 2D simulator employs surrogate ROS2 nodes with equivalent functionality. We compare trajectories generated by different approaches in the 2D simulator and evaluate performance across various belief types in the 3D AirSim-ROS simulator. Experimental results from both simulators demonstrate that our proposed shrinking POMCP solution achieves significant improvements in search times compared to alternative methods, showcasing its potential for enhancing the efficiency of UAV-assisted search and rescue operations., Comment: Accepted to the The 3rd International Conference on Assured Autonomy