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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, 12 figures (15 png files), 7 tables; Comments welcome

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

This paper proposes a novel problem: vision-based perception to learn and predict the collective dynamics of multi-agent systems, specifically focusing on interaction strength and convergence time. Multi-agent systems are defined as collections of more than ten interacting agents that exhibit complex group behaviors. Unlike prior studies that assume knowledge of agent positions, we focus on deep learning models to directly predict collective dynamics from visual data, captured as frames or events. Due to the lack of relevant datasets, we create a simulated dataset using a state-of-the-art flocking simulator, coupled with a vision-to-event conversion framework. We empirically demonstrate the effectiveness of event-based representation over traditional frame-based methods in predicting these collective behaviors. Based on our analysis, we present event-based vision for Multi-Agent dynamic Prediction (evMAP), a deep learning architecture designed for real-time, accurate understanding of interaction strength and collective behavior emergence in multi-agent systems.

Quantum sensors are now universally acknowledged as one of the most promising near-term quantum technologies. The traditional formulation of quantum sensing introduces a concrete bound on ultimate precision through the so-called local sensing framework, in which a significant knowledge of prior information about the unknown parameter value is implicitly assumed. Moreover, the framework provides a systematic approach for optimizing the sensing protocol. In contrast, the paradigm of global sensing aims to find a precision bound for parameter estimation in the absence of such prior information. In recent years, vigorous research has been pursued to describe the contours of global quantum estimation. Here, we review some of these emerging developments. These developments are both in the realm of finding ultimate precision bounds with respect to appropriate figures of merit in the global sensing paradigm, as well as in the search for algorithms that achieve these bounds. We categorize these developments into two largely mutually exclusive camps; one employing Bayesian updating and the other seeking to generalize the frequentist picture of local sensing towards the global paradigm. In the first approach, in order to achieve the best performance, one has to optimize the measurement settings adaptively. In the second approach, the measurement setting is fixed, however the challenge is to identify this fixed measurement optimally., Comment: Review article, 12 pages, feedback is very welcome

Generalized Category Discovery (GCD) aims to cluster unlabeled images into known and novel categories using labeled images from known classes. To address the challenge of transferring features from known to unknown classes while mitigating model bias, we introduce GraphVL, a novel approach for vision-language modeling in GCD, leveraging CLIP. Our method integrates a graph convolutional network (GCN) with CLIP's text encoder to preserve class neighborhood structure. We also employ a lightweight visual projector for image data, ensuring discriminative features through margin-based contrastive losses for image-text mapping. This neighborhood preservation criterion effectively regulates the semantic space, making it less sensitive to known classes. Additionally, we learn textual prompts from known classes and align them to create a more contextually meaningful semantic feature space for the GCN layer using a contextual similarity loss. Finally, we represent unlabeled samples based on their semantic distance to class prompts from the GCN, enabling semi-supervised clustering for class discovery and minimizing errors. Our experiments on seven benchmark datasets consistently demonstrate the superiority of GraphVL when integrated with the CLIP backbone., Comment: Accepted in ACM ICVGIP 2024

We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using streaming data. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point, thereby allowing it to adapt to changing environments in real-time. A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning rate technique called AdaRelation, which adjusts based on the historical sensitivity of the decoder to changes in the interaction graph. Additionally, a data augmentation method named Trajectory Mirror (TM) is introduced to improve generalization by exposing the model to varied trajectory patterns. Experimental results on both synthetic datasets and real-world data (CMU MoCap for human motion) demonstrate that ORI significantly improves the accuracy and adaptability of relational inference in dynamic settings compared to existing methods. This approach is model-agnostic, enabling seamless integration with various neural relational inference (NRI) architectures, and offers a robust solution for real-time applications in complex, evolving systems., Comment: Accepted at NeurIPS 2024

Intracranial Hemorrhage is a potentially lethal condition whose manifestation is vastly diverse and shifts across clinical centers worldwide. Deep-learning-based solutions are starting to model complex relations between brain structures, but still struggle to generalize. While gathering more diverse data is the most natural approach, privacy regulations often limit the sharing of medical data. We propose the first application of Federated Scene Graph Generation. We show that our models can leverage the increased training data diversity. For Scene Graph Generation, they can recall up to 20% more clinically relevant relations across datasets compared to models trained on a single centralized dataset. Learning structured data representation in a federated setting can open the way to the development of new methods that can leverage this finer information to regularize across clients more effectively.

Continual learning (CL) in medical imaging presents a unique challenge, requiring models to adapt to new domains while retaining previously acquired knowledge. We introduce NCAdapt, a Neural Cellular Automata (NCA) based method designed to address this challenge. NCAdapt features a domain-specific multi-head structure, integrating adaptable convolutional layers into the NCA backbone for each new domain encountered. After initial training, the NCA backbone is frozen, and only the newly added adaptable convolutional layers, consisting of 384 parameters, are trained along with domain-specific NCA convolutions. We evaluate NCAdapt on hippocampus segmentation tasks, benchmarking its performance against Lifelong nnU-Net and U-Net models with state-of-the-art (SOTA) CL methods. Our lightweight approach achieves SOTA performance, underscoring its effectiveness in addressing CL challenges in medical imaging. Upon acceptance, we will make our code base publicly accessible to support reproducibility and foster further advancements in medical CL.

Medical image registration is a critical process that aligns various patient scans, facilitating tasks like diagnosis, surgical planning, and tracking. Traditional optimization based methods are slow, prompting the use of Deep Learning (DL) techniques, such as VoxelMorph and Transformer-based strategies, for faster results. However, these DL methods often impose significant resource demands. In response to these challenges, we present NCA-Morph, an innovative approach that seamlessly blends DL with a bio-inspired communication and networking approach, enabled by Neural Cellular Automata (NCAs). NCA-Morph not only harnesses the power of DL for efficient image registration but also builds a network of local communications between cells and respective voxels over time, mimicking the interaction observed in living systems. In our extensive experiments, we subject NCA-Morph to evaluations across three distinct 3D registration tasks, encompassing Brain, Prostate and Hippocampus images from both healthy and diseased patients. The results showcase NCA-Morph's ability to achieve state-of-the-art performance. Notably, NCA-Morph distinguishes itself as a lightweight architecture with significantly fewer parameters; 60% and 99.7% less than VoxelMorph and TransMorph. This characteristic positions NCA-Morph as an ideal solution for resource-constrained medical applications, such as primary care settings and operating rooms.

In this paper, we address the challenge of navigating through unknown indoor environments using autonomous aerial robots within confined spaces. The core of our system involves the integration of key sensor technologies, including depth sensing from the ZED 2i camera, IMU data, and LiDAR measurements, facilitated by the Robot Operating System (ROS) and RTAB-Map. Through custom designed experiments, we demonstrate the robustness and effectiveness of this approach. Our results showcase a promising navigation accuracy, with errors as low as 0.4 meters, and mapping quality characterized by a Root Mean Square Error (RMSE) of just 0.13 m. Notably, this performance is achieved while maintaining energy efficiency and balanced resource allocation, addressing a crucial concern in UAV applications. Flight tests further underscore the precision of our system in maintaining desired flight orientations, with a remarkable error rate of only 0.1%. This work represents a significant stride in the development of autonomous indoor UAV navigation systems, with potential applications in search and rescue, facility inspection, and environmental monitoring within GPS-denied indoor environments., Comment: 6 pages, 8 figures

In an era of rapid urbanization and e-commerce growth, efficient parcel delivery methods are crucial. This paper presents a detailed study of the aerodynamics and sensing analysis of drones for parcel delivery. Utilizing Computational Fluid Dynamics (CFD), the study offers a comprehensive airflow analysis, revealing the aerodynamic forces affecting drone stability due to payload capacity. A multidisciplinary approach is employed, integrating mechanical design, control theory, and sensing systems to address the complex issue of parcel positioning. The experimental validation section rigorously tests different size payloads and their positions and impact on drones with maximum thrusts of 2000 gf. The findings prove the drone's capacity to lift a large payload that covers up to 50 percent of the propeller, thereby contributing to optimizing drone designs and sustainable parcel delivery systems. It has been observed that the drone can lift a large payload smoothly when placed above the drone, with an error rate as low as 0.1 percent for roll, pitch, and yaw. This work paved the way for more versatile, real-world applications of drone technology, setting a new standard in the field., Comment: 6 pages, 9 figures

We present a comprehensive investigation exploring the theoretical framework of Einstein-Aether gravity theory when combined with two novel cosmological paradigms: the Barrow Agegraphic Dark Energy (BADE) and its newer variant, the New Barrow Agegraphic Dark Energy (NBADE). Our study focuses on deriving the functional relationships within Einstein-Aether gravity as they emerge from these dark energy formulations. The parameter space of our theoretical models is rigorously constrained through statistical analysis employing the Markov Chain Monte Carlo (MCMC) methodology, utilizing multiple observational datasets, incorporating measurements from cosmic chronometers (CC), Baryon Acoustic Oscillations (BAO), and the combined Pantheon+SH0ES compilation. Based on our optimized parameter sets, we conduct an extensive analysis of fundamental cosmological indicators, including cosmographic parameter evolution, dark energy equation of state parameter ($\omega_{DE}$), evolution of the density parameter $\Omega(z)$, dynamical characteristics in the $\omega'_{DE}-\omega_{DE}$ space, behavior of statefinder diagnostic pairs $(r,s^*)$ and $(r,q)$, and Om(z) diagnostic trajectories. Our analysis demonstrates that the current cosmic expansion exhibits accelerated behavior, with the dark energy component manifesting quintessence-like properties in the present epoch while trending toward phantom behavior in future evolution. We additionally evaluate the viability of both BADE and NBADE frameworks through an examination of the squared sound speed ($v_s^2$) stability criterion. The cumulative evidence suggests that these models effectively characterize contemporary cosmic evolution while offering novel perspectives on dark energy phenomenology., Comment: 32 pages, 32 figures. All comments are welcome

More than 3 kV breakdown voltage was demonstrated in monolithic bidirectional GaN HEMTs for the first time having potential applications in 1200V or 1700V-class novel power converters. The on resistance of the fabricated transistors was ~20 ohm.mm or ~11 mili ohm.cm^2. Breakdown voltage was optimized by utilizing two field plates in either side of the transistor and optimizing their geometry. Shorter first field plate lengths (less than 2 micron) resulted in higher breakdown voltage and the possible reason for this was discussed. The transistors had a steep subthreshold swing of 92 mV / dec. The on/off ratio was greater than 10^5 and it was limited by the tool capacity. The fabricated 3 kV transistor was benchmarked against the state-of-the-art monolithic bidirectional GaN HEMTs in the performance matrices of breakdown voltage and on resistance, that showed crucial progress., Comment: 4 pages, 7 figures

In this note, we consider the problem of generating $k$-factorable graphic sequences with connected (resp. no connected) $k$-factors., Comment: 14 pages; 16 figures

Recent years have witnessed significant developments in the study of nonlinear properties of various optical materials at the nanoscale. However, in most cases experimental results on harmonic generation from nanostructured materials are reported without the benefit of suitable theoretical models and appropriate comparisons to assess enhancement of conversion efficiencies compared to the intrinsic properties of a given material. Here, we report experimental observations of even and odd optical harmonics generated from a suspended subwavelength silicon film, a dielectric membrane, up to the 7th harmonic tuned deep in the UV range at 210nm, which is the current limit of our detection system, using peak power densities of order 3TW/cm^2. We explain the experimental data with a time domain, hydrodynamic-Maxwell approach broadly applicable to most materials. Our approach accounts simultaneously for surface and magnetic nonlinearities that generate even optical harmonics, as well as linear and nonlinear material dispersions beyond the third order to account for odd optical harmonics, plasma formation, and a phase locking mechanism that makes the generation of high harmonics possible deep into the UV range, where semiconductors like silicon start operating in a metallic regime.

Conventional formulation of quantum sensing has been mostly developed in the context of local estimation, where the unknown parameter is roughly known. In contrast, global sensing, where the prior information is incomplete and the unknown parameter is only known to lie within a broad interval, is practically more engaging but has received far less theoretical attention. Available formulations of global sensing rely on adaptive Bayesian strategies or minimizing average uncertainty. These methods either rely on challenging adaptive measurements or provide unsaturable bounds. Here, we provide an operationally motivated approach to global sensing in the frequentist picture. Our scheme yields a saturable bound on average uncertainty and allows for optimizing the measurement as well as the probe preparation simultaneously. We illustrate the implications for Gaussian single-mode sensing tasks like thermometry and phase estimation by showing that the optimal measurement indeed changes from homodyne, for local sensing, towards heterodyne, for global sensing. Depending on the task, this transformation can be gradual or sudden., Comment: 4.5 pages + 3 figs, Comments/suggestions welcome

This paper explores the thermodynamic properties and stability of two newly introduced gas models, namely the Modified Chaplygin Jacobi gas and the Modified Chaplygin Abel gas. To achieve this, we examine the behavior of relevant physical parameters to gain in depth information about the evolution of the universe. The specific heat formalism is employed to verify the applicability of the third law of thermodynamics. Furthermore, the equation of state for the thermal system is obtained by applying thermodynamic variables. The stability of the gas models is investigated within the framework of classical thermodynamics, focusing on adiabatic processes, specific heat capacities, and isothermal conditions. It is inferred that the proposed fluid configurations exhibit thermodynamic stability and undergo adiabatic expansion for suitable parameter choices. We then perform observational analysis using CC+BAO and Pantheon+SH0ES datasets to impose constraints on our model parameters using the Markov Chain Monte Carlo (MCMC) process., Comment: 37 pages, 30 figures. All comments are welcome

In this paper, we analyze the performance of one- and two-sided amplitude shift keying (ASK) modulations in single-input single-output wireless communication aided by a reconfigurable intelligent surface (RIS). Two scenarios are considered for the channel conditions: a blocked direct channel between the transmitter and the receiver, and an unblocked one. For the receiver, a noncoherent maximum likelihood detector is proposed, which detects the transmitted data signal based on statistical knowledge of the channel. The system's performance is then evaluated by deriving the symbol error probability (SEP) for both scenarios using the proposed noncoherent receiver structures. We also present a novel optimization framework to obtain the optimal one- and two-sided ASK modulation schemes that minimize the SEP under constraints on the available average transmit power for both the blocked and unblocked direct channel scenarios. Our extensive numerical investigations showcase that the considered RIS-aided communication system achieves superior error performance with both derived SEP-optimal ASK modulation schemes as compared to respective traditional ASK modulation. It is also demonstrated that, between the two proposed modulation schemes, the two-sided one yields the best SEP. The error performance is further analyzed for different system parameters, providing a comprehensive performance investigation of RIS-assisted noncoherent wireless communication systems., Comment: 13 pages, 6 figures

The safety of medical products continues to be a significant health concern worldwide. Spontaneous reporting systems (SRS) and pharmacovigilance databases are essential tools for postmarketing surveillance of medical products. Various SRS are employed globally, such as the Food and Drug Administration Adverse Event Reporting System (FAERS), EudraVigilance, and VigiBase. In the pharmacovigilance literature, numerous methods have been proposed to assess product - adverse event pairs for potential signals. In this paper, we introduce an R and Python package that implements a novel pattern discovery method for postmarketing adverse event identification, named Modified Detecting Deviating Cells (MDDC). The package also includes a data generation function that considers adverse events as groups, as well as additional utility functions. We illustrate the usage of the package through the analysis of real datasets derived from the FAERS database.

There is a significant uncertainty in the prediction of atmospheric muon neutrino and anti-neutrino flux ratio using different flux models, especially at higher energies. We study the prospects of experimentally measuring this flux ratio as a function of energy with the ATLAS detector at the LHC. To this end, we compute the contained-vertex and external upward going charged current event rates induced by atmospheric muon (anti-)neutrinos through deep inelastic scattering at the 4 kiloton hadron calorimeter (HCAL) component of ATLAS. We illustrate the event selection criteria necessary to eliminate the cosmic ray muon background for the above event classes. While the contained vertex events have a striking topology with a muon being created inside the HCAL and then travelling to the muon chamber possibly through the tracker, for muons with energy larger than 3 GeV, a much higher statistics is obtained for the external upward going events created in the rock column below the detector. Our estimates show that the energy dependence of the ratio of negative and positively charged muons induced by atmospheric muon neutrino and anti-neutrino fluxes can be measured by ATLAS upto a muon energy of 100 GeV, with 1000-live days of neutrino physics exposure over a period of several years, considering only the period with the LHC beams not in circulation, but the detector and magnetic fields of ATLAS in operation. With this exposure, we obtain $63~\mu^-$ and $32~\mu^+$ contained vertex events, and $630~\mu^-$ and $307~\mu^+$ external upward-going events, after imposing the necessary selection criteria., Comment: 15 pages, 7 figures, 2 tables

The ion-lipid interface in Langmuir monolayers of Dipalmitoylphosphatidylcholine (DPPC) on pure water and 10 mM solutions of Na+ and K+ at different [K+]/[Na+] (a), atom/atom ratios, were studied initially by Surface Pressure (p) versus Specific Molecular Area (A) isotherms. The values of a were chosen as 0 (no K+), 0.43 ([K+]:[Na+] = 30:70) and 1.0 ([K+]:[Na+] = 50:50) These monolayers were studied through X-Ray Reflectivity (XRR) and Near Edge X-ray Absorption Fine Structure (NEXAFS) spectroscopy at the O K-edge. The two-dimensional rigidity of the monolayer was found to increase with Na+ ions with respect to the pristine monolayer but fall drastically and non-linearly below the pristine value with introduction of the K+ ions, as a was increased. Analysis of the XRR profiles provided the thickness, average electron density (aed) and the interfacial roughness of the phosphatidylcholine head group and the two hydrocarbon tails of the monolayers on Si (001), from which the angle (f) between the head and the tails was determined. This was also follow the same as former one. From NEXAFS, it was found that a linear increase in the cation ratio towards K led to a nonlinear variation in the P=O bond energy and a weakening of the P-O bond energy, the latter becoming more pronounced with K ions, consistent with Fajans rule. Also a split in the C=O p-bond peak was observed at a = 1.0. These results cannot be explained with the model of a uniform electric field due to the cations, which would fall linearly with increase in the K+ proportion, and rather suggest a structured field due a spatial variation in charge density in an interfacial layer of high ion concentration assembled by the counterionic attraction of the phosphatidylcholine head groups. Our results have important implications for the cell membrane, where such mixtures at high concentrations constitute the norm.

A numerical model based on hydrodynamic approach has been developed to emulate the device dynamics of active target Time Projection Chamber which is utilized for studying nuclear reaction through three dimensional tracking of concerned low energy particles. The proposed model has been used to investigate the performance of a prototype active target Time Projection Chamber, namely SAT-TPC, to be fabricated at Saha Institute of Nuclear Physics, for its application in nuclear physics experiments. A case study of non-relativistic elastic scattering $^4He+^{12}C$ with beam energy $25~MeV$ and current $2.3~pA$ has been opted for this purpose. The effect of beam induced space charge on the tracking performance the SAT-TPC prototype has been studied to optimize the beam current and scheme of the anode readout segmentation. The model has been validated by comparing its results to that of a particle model used to explain observed distortion in scattered particle tracks in a low energy nuclear physics experiment.

We obtain an action for a generalized conformal mechanics (GCM) coupled to Jackiw-Teitelboim (JT) gravity from a double scaling limit of the motion of a charged massive particle in the near-horizon geometry of a near-extremal spherical black hole. When JT gravity is treated in the classical approximation, the backreaction of the particle's wavefunction on the time-reparametrization mode (and therefore the bulk metric) vanishes while the conformal symmetry in GCM is reparametrized in a state-dependent way. We also construct the semi-classical Hilbert space of the full theory by explicitly solving the general time-dependent normalizable solutions of the Schr\"{o}dinger equation for GCM, and show that the time-reparametrization mode can be inferred from the measurement of suitable observables. Since the full theory of the GCM coupled to JT gravity is amenable to quantization, it can lead to a solvable model for a detector coupled to quantum gravity., Comment: 35 pages

Small businesses need vulnerability assessments to identify and mitigate cyber risks. Cybersecurity clinics provide a solution by offering students hands-on experience while delivering free vulnerability assessments to local organizations. To scale this model, we propose an Open Source Intelligence (OSINT) clinic where students conduct assessments using only publicly available data. We enhance the quality of investigations in the OSINT clinic by addressing the technical and collaborative challenges. Over the duration of the 2023-24 academic year, we conducted a three-phase co-design study with six students. Our study identified key challenges in the OSINT investigations and explored how generative AI could address these performance gaps. We developed design ideas for effective AI integration based on the use of AI probes and collaboration platform features. A pilot with three small businesses highlighted both the practical benefits of AI in streamlining investigations, and limitations, including privacy concerns and difficulty in monitoring progress.

Emotion and fairness play a key role in mediating socioeconomic decisions in humans; however, the underlying neurocognitive mechanism remains largely unknown. In this study, we explored the interplay between proposers' emotions and fairness of offer magnitudes in rational decision-making. Employing a time-bound UG paradigm, 40 (male, age: 18-20) participants were exposed to three distinct proposers' emotions (Happy, Neutral, and Disgusted) followed by one of the three offer ranges (Low, Intermediate, Maximum). Our findings show a robust influence of fairness of offer on acceptance rates, with the impact of emotions obtained only within the low offer range. The increment of the offer amount resulted in shorter reaction times, while emotional stimuli resulted in prolonged reaction times. A multilevel generalized linear model showed offer as the dominant predictor of trial-specific responses. Subsequent agglomerative clustering grouped participants into five primary clusters based on responses modulated by emotions/offers. The Drift Diffusion Model based on the clustering further corroborated our findings. Emotion-sensitive markers, including N170 and LPP, demonstrated the participants' effect on facial expressions; however, facial emotions had minimal effect on subsequent socioeconomic decisions. Our study suggests that, in general, participants gave more preference to the fairness of the offer with a slight effect of emotions in decision-making. We show that though emotion is perceived and has an effect on decision-making time, people mostly prioritise financial gain and fairness of offer. Moreover, it establishes a connection between reaction time and responses and further dives deep into individualistic decision-making processes revealing different cognitive strategies.

We give an asymptotic formula for the number of $\mathbb{F}_{q}$-rational points over a fixed determinant moduli space of stable vector bundles of rank $r$ and degree $d$ over a smooth, projective curve $X$ of genus $g \geq 2$ defined over $\mathbb{F}_{q}.$ Further, we study the distribution of the error term when $X$ varies over a family of hyperelliptic curves. We then extend the results to the Seshadri desingularisation of the moduli space of semi-stable vector bundles of rank $2$ with trivial determinant, and also to the moduli space of rank $2$ stable Higgs bundles., Comment: This is a corrected and vastly extended version of our previous submission "Statistics of Moduli Spaces of vector bundles II". In particular, the results on the Higgs bundles are new additions. arXiv admin note: substantial text overlap with arXiv:2309.15085

Analogous to Andrews' and Newman's discovery and work on the minimal excludant or "mex" of partitions, we define four new classes of minimal excludants for overpartitions and unearth relations to certain functions due to Ramanujan., Comment: 14 pages. Comments are welcome!

Modeling and controlling complex spatiotemporal dynamical systems driven by partial differential equations (PDEs) often necessitate dimensionality reduction techniques to construct lower-order models for computational efficiency. This paper explores a deep autoencoding learning method for reduced-order modeling and control of dynamical systems governed by spatiotemporal PDEs. We first analytically show that an optimization objective for learning a linear autoencoding reduced-order model can be formulated to yield a solution closely resembling the result obtained through the dynamic mode decomposition with control algorithm. We then extend this linear autoencoding architecture to a deep autoencoding framework, enabling the development of a nonlinear reduced-order model. Furthermore, we leverage the learned reduced-order model to design controllers using stability-constrained deep neural networks. Numerical experiments are presented to validate the efficacy of our approach in both modeling and control using the example of a reaction-diffusion system., Comment: 8 pages, 5 figures. Accepted to IEEE Conference on Decision and Control (CDC 2024)

This paper presents an approach for the economic statistical design of the Cumulative Sum (CUSUM) control chart in a multi-objective optimization framework. The proposed methodology integrates economic considerations with statistical aspects to optimize the design parameters like the sample size ($n$), sampling interval ($h$), and decision interval ($H$) of the CUSUM chart. The Non-dominated Sorting Genetic Algorithm II (NSGA II) is employed to solve the multi-objective optimization problem, aiming to minimize both the average cost per cycle ($C_E$) and the out-of-control Average Run Length ($ARL_\delta$) simultaneously. The effectiveness of the proposed approach is demonstrated through a numerical example by determining the optimized CUSUM chart parameters using NSGA II. Additionally, sensitivity analysis is conducted to assess the impact of variations in input parameters. The corresponding results indicate that the proposed methodology significantly reduces the expected cost per cycle by about 43% when compared to the findings of the article by M. Lee in the year 2011. A more extensive comparison with respect to both $C_E$ and $ARL_\delta$ has also been provided for justifying the methodology proposed in this article. This highlights the practical relevance and potential of this study for the right application of the technique of the CUSUM chart for process control purposes in industries.

Developing agents that can perform complex control tasks from high-dimensional observations is a core ability of autonomous agents that requires underlying robust task control policies and adapting the underlying visual representations to the task. Most existing policies need a lot of training samples and treat this problem from the lens of two-stage learning with a controller learned on top of pre-trained vision models. We approach this problem from the lens of Koopman theory and learn visual representations from robotic agents conditioned on specific downstream tasks in the context of learning stabilizing control for the agent. We introduce a Contrastive Spectral Koopman Embedding network that allows us to learn efficient linearized visual representations from the agent's visual data in a high dimensional latent space and utilizes reinforcement learning to perform off-policy control on top of the extracted representations with a linear controller. Our method enhances stability and control in gradient dynamics over time, significantly outperforming existing approaches by improving efficiency and accuracy in learning task policies over extended horizons., Comment: Accepted to the $8^{th}$ Conference on Robot Learning (CoRL 2024)

Semi-holography, originally proposed as a model for conducting lattice electrons coupled to a holographic critical sector, leads to an effective theory of non-Fermi liquids with only a few relevant interactions on the Fermi surface in the large $N$ limit. A refined version of such theories has only two effective couplings which give holographic and Fermi-liquid-like contributions to the self-energy, respectively. We show that a low co-dimension sub-manifold exists in the space of refined semi-holographic theories in which strange metallic behavior is manifested, and which can be obtained just by tuning the ratio of the two couplings. On this sub-manifold, the product of the spectral function and the temperature is approximately independent of the critical exponent, the Fermi energy, and the temperature at all frequencies and near the Fermi surface when expressed in terms of suitably scaled momentum and frequency variables. This quasi-universal behavior leads to linear-in-$T$ dc resistivity and Planckian dissipation over a large range of temperatures, and we also obtain $T^{-3}$ scaling of the Hall conductivity at higher temperatures. The quasi-universal spectral function also fits well with photoemission spectroscopic data without varying the critical exponent with the doping. Combining with the results for optical conductivity, we construct a generalized version of Drude phenomenology for strange-metallic behavior which satisfies non-trivial consistency tests. Finally, we discuss a possible dynamical mechanism for the fine-tuning of the ratio of the two couplings necessary to realize the strange metallic behavior in a typical state., Comment: 53 pages, 18 figures; version to appear in JHEP

Vision-language models (VLMs) excel at tasks requiring joint understanding of visual and linguistic information. A particularly promising yet under-explored application for these models lies in answering questions based on various kinds of maps. This study investigates the efficacy of VLMs in answering questions based on choropleth maps, which are widely used for data analysis and representation. To facilitate and encourage research in this area, we introduce a novel map-based question-answering benchmark, consisting of maps from three geographical regions (United States, India, China), each containing 1000 questions. Our benchmark incorporates 43 diverse question templates, requiring nuanced understanding of relative spatial relationships, intricate map features, and complex reasoning. It also includes maps with discrete and continuous values, encompassing variations in color-mapping, category ordering, and stylistic patterns, enabling comprehensive analysis. We evaluate the performance of multiple VLMs on this benchmark, highlighting gaps in their abilities and providing insights for improving such models., Comment: 30 Pages, 46 Tables, 6 Figure

Quantum algorithms claim significant speedup over their classical counterparts for solving many problems. An important aspect of many of these algorithms is the existence of a quantum oracle, which needs to be implemented efficiently in order to realize the claimed advantages. A quantum random access memory (QRAM) is a promising architecture for realizing these oracles. In this paper we develop a new design for QRAM and implement it with Clifford+T circuit. We focus on optimizing the T-count and T-depth since non-Clifford gates are the most expensive to implement fault-tolerantly. Integral to our design is a polynomial encoding of bit strings and so we refer to this design as $\text{QRAM}_{poly}$. Compared to the previous state-of-the-art bucket brigade architecture for QRAM, we achieve an exponential improvement in T-depth, while reducing T-count and keeping the qubit count same. Specifically, if $N$ is the number of memory locations, then $\text{QRAM}_{poly}$ has T-depth $O(\log\log N)$, T-count $O(N-\log N)$ and qubit count $O(N)$, while the bucket brigade circuit has T-depth $O(\log N)$, T-count $O(N)$ and qubit count $O(N)$. Combining two $\text{QRAM}_{poly}$ we design a quantum look-up-table, $\text{qLUT}_{poly}$, that has T-depth $O(\log\log N)$, T-count $O(\sqrt{N})$ and qubit count $O(\sqrt{N})$. A qLUT or quantum read-only memory (QROM) has restricted functionality than a QRAM and needs to be compiled each time the contents of the memory change. The previous state-of-the-art CSWAP architecture has T-depth $O(\sqrt{N})$, T-count $O(\sqrt{N})$ and qubit count $O(\sqrt{N})$. Thus we achieve a double exponential improvement in T-depth while keeping the T-count and qubit-count asymptotically same. Additionally, with our polynomial encoding of bit strings, we develop a method to optimize the Toffoli-count of circuits, specially those consisting of multi-controlled-NOT gates., Comment: 26 pages, 4 figures