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Digital signatures are one of the simplest cryptographic building blocks that provide appealing security characteristics such as authenticity, unforgeability, and undeniability. In 1984, Shamir developed the first Identity-based signature (IBS) to simplify public key infrastructure and circumvent the need for certificates. It makes the process uncomplicated by enabling users to verify digital signatures using only the identifiers of signers, such as email, phone number, etc. Nearly all existing IBS protocols rely on several theoretical assumption-based hard problems. Unfortunately, these hard problems are unsafe and pose a hazard in the quantum realm. Thus, designing IBS algorithms that can withstand quantum attacks and ensure long-term security is an important direction for future research. Quantum cryptography (QC) is one such approach. In this paper, we propose an IBS based on QC. Our scheme's security is based on the laws of quantum mechanics. It thereby achieves long-term security and provides resistance against quantum attacks. We verify the proposed design's correctness and feasibility by simulating it in a prototype quantum device and the IBM Qiskit quantum simulator. The implementation code in qiskit with Jupyternotebook is provided in the Annexure. Moreover, we discuss the application of our design in secure email communication.

This paper proposes an Emergency Battery Service (EBS) for drones in which an EBS drone flies to a drone in the field with a depleted battery and transfers a fresh battery to the exhausted drone. The authors present a unique battery transfer mechanism and drone localization that uses the Cross Marker Position (CMP) method. The main challenges include a stable and balanced transfer that precisely localizes the receiver drone. The proposed EBS drone mitigates the effects of downwash due to the vertical proximity between the drones by implementing diagonal alignment with the receiver, reducing the distance to 0.5 m between the two drones. CFD analysis shows that diagonal instead of perpendicular alignment minimizes turbulence, and the authors verify the actual system for change in output airflow and thrust measurements. The CMP marker-based localization method enables position lock for the EBS drone with up to 0.9 cm accuracy. The performance of the transfer mechanism is validated experimentally by successful mid-air transfer in 5 seconds, where the EBS drone is within 0.5 m vertical distance from the receiver drone, wherein 4m/s turbulence does not affect the transfer process.

Recent theoretical and practical research has focused on multi-component High Entropy Alloys (HEAs), which have superior mechanical and functional properties than standard alloys based on a single major element, thereby establishing a new field. A multi-component HEA contains five or more primary elements at concentrations ranging from 5 to 35 atomic percent. We examined the microstructure and mechanical properties of TiVCoNiMn2 HEA. The mixing enthalpy and other thermodynamic parameters were determined using Meidma's model. TiVCoNiMn2 exhibits a mixing enthalpy of -15.6 kJ/mol and an atomic radius mismatch of approximately 10.03%. HEA is derived from both hydride and non-hydride-producing elements. This could be a useful hydrogen storage material. The hydrogen absorption/desorption capabilities of these HEAs are promising., Comment: 5 pages, 4 Figures

We present the observational mass-radius (M-R) relation for a sample of 47 magnetized white dwarfs (WDs) with the magnetic field strength (B) ranging from 1 to 773 MG, identified from the SDSS data release 7 (DR7). We derive their effective temperature, surface gravity (log g), luminosity, radius, and mass. While atmospheric parameters are derived using a Virtual Observatory Spectral Energy Distribution Analyzer (VOSA), the mass is derived using their location in the HR diagram in comparison with the evolutionary tracks of different masses. We implement this mass measurement instead of a more traditional method of deriving masses from log g, which is unreliable as is based on SED and generates errors from other physical parameters involved. The main disadvantage of this method is that we need to assume a core composition of WDs. As it is complicated to identify the exact composition of these WDs from low-resolution spectra, we use tracks for the masses 0.2 to 0.4 solar mass assuming a He-core, 0.5 to 1.0 solar mass assuming CO core, and above solar mass assuming O-Ne-Mg core. We compare the observed M-R relation with those predicted by the finite temperature model by considering different B, which are well in agreement considering their relatively low surface fields, less than or of the order of 10^9 G. Currently, there is no direct observational detection of magnetized WDs with B > 10^9 G. We propose that our model can be further extrapolated to higher B, which may indicate the existence of super-Chandrasekhar mass (M > 1.4 solar mass) WDs at higher B., Comment: 9 latex pages including 10 figures (11 pdf figures); accepted for publication in MNRAS

Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and artificial intelligence, providing brain-inspired computation. Recent advances in literature have studied the network representations of deep neural networks. However, there has been little work that studies representations learned by SNNs, especially using unsupervised local learning methods like spike-timing dependent plasticity (STDP). Recent work by \cite{barannikov2021representation} has introduced a novel method to compare topological mappings of learned representations called Representation Topology Divergence (RTD). Though useful, this method is engineered particularly for feedforward deep neural networks and cannot be used for recurrent networks like Recurrent SNNs (RSNNs). This paper introduces a novel methodology to use RTD to measure the difference between distributed representations of RSNN models with different learning methods. We propose a novel reformulation of RSNNs using feedforward autoencoder networks with skip connections to help us compute the RTD for recurrent networks. Thus, we investigate the learning capabilities of RSNN trained using STDP and the role of heterogeneity in the synaptic dynamics in learning such representations. We demonstrate that heterogeneous STDP in RSNNs yield distinct representations than their homogeneous and surrogate gradient-based supervised learning counterparts. Our results provide insights into the potential of heterogeneous SNN models, aiding the development of more efficient and biologically plausible hybrid artificial intelligence systems., Comment: Accepted in IEEE World Congress on Computational Intelligence (IEEE WCCI) 2024

By uncovering novel aspects of second harmonic generation in aluminum we show that there are unusual and remarkable consequences of resonant absorption, namely an unexpectedly critical role that bound electrons play for light-matter interactions across the optical spectrum, suggesting that a different basic approach is required to fully explain the physics of surfaces. We tackle an issue that is never under consideration given the generic hostile conditions to the propagation of light under resonant absorption. Unlike most noble metals, aluminum displays Lorentz-like behavior and interband transitions centered near 810 nm, thus splitting the plasmonic range in an atypical manner and setting its linear and nonlinear optical properties apart. Studies of aluminum nanostructures having complex topologies abound, as do reported inconsistencies in the linear spectral response of surface plasmons and harmonic generation. Our experimental observations of second harmonic generation from aluminum nanolayers show that bound electrons are responsible for a unique signature neither predicted nor observed previously: a hole in the second harmonic spectrum. A hydrodynamic-Maxwell theory explains these findings exceptionally well and becomes the basis for renewed studies of surface physics., Comment: 8 pages, 5 figures

Water collection from natural and industrial fogs has recently been viewed as a viable freshwater source. An interesting outgrowth of the relevant research as focused on arresting of the drift losses (un-evaporated and re-condensed water droplets present in the exhaust plume from industrial cooling towers. Such exploits in fog collection have implemented metal and polyester meshes as fog water collectors (FWC). Fog droplets impinge and deposit on mesh fibers. They coalesce with previously deposited liquid to evolve as larger drops before detaching from the fibers under their own weight, an event largely dependent on the mesh fiber wettability, diameter and its arrangement relative to the fog flow. To better estimate drainage and hence collection from these fibers, the study, focuses on droplet detachment from differently wetted horizontally positioned cylindrical fibers of various diameters, placed orthogonally in the path of an oncoming fog. Droplet detachment volume is found to increase with fiber diameter and fiber surface wettability. Interestingly, in a typical fogging condition, the detachment volume is also found to exhibit a time-dependent behaviour, altering the droplet detachment criteria otherwise predicted from emulation. Our current study sheds light on this unexplored phenomenon., Comment: Presented at the Fluid Mechanics and Fluid Power Conference, India (2022). Corresponding author: ranjan.ganguly@jadavpuruniversity.in . Lecture Notes in Mechanical Engineering. Springer, Singapore (2024)

Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disruptions lead to delays and overcrowding, which are detrimental to the passengers' experience and to the overall performance of the transit service. To proactively mitigate such events, many transit agencies station substitute (reserve) vehicles throughout their service areas, which they can dispatch to augment or replace vehicles on routes that suffer overcrowding or disruption. However, determining the optimal locations where substitute vehicles should be stationed is a challenging problem due to the inherent randomness of disruptions and due to the combinatorial nature of selecting locations across a city. In collaboration with the transit agency of Nashville, TN, we address this problem by introducing data-driven statistical and machine-learning models for forecasting disruptions and an effective randomized local-search algorithm for selecting locations where substitute vehicles are to be stationed. Our research demonstrates promising results in proactive disruption management, offering a practical and easily implementable solution for transit agencies to enhance the reliability of their services. Our results resonate beyond mere operational efficiency: by advancing proactive strategies, our approach fosters more resilient and accessible public transportation, contributing to equitable urban mobility and ultimately benefiting the communities that rely on public transportation the most.

Aging and stability of gaseous ionization detectors are intricately related to charging up, accumulation of space charge and formation of discharges. All these phenomena, in their turn, depend on the dynamics of charged particles within the device. Because of the large number of particles involved and their complex interactions, the dynamic processes of generation and loss of charged particles, and their transport within the detector volume are extremely expensive to simulate numerically. In this work, we propose and evaluate possible algorithms / approaches that show some promise in relation to the above-mentioned problems. Several important ionization detectors having parallel plate configurations, such as GEM, Micromegas, RPCs and THGEMs, are considered for this purpose. Information related to primary ionization is obtained from HEED, while all the transport properties are evaluated using MAGBOLTZ. The transport dynamics have been followed using two different approaches. In one, particle description using neBEM-Garfield++ combination has been used. For this purpose, the neBEM solver has been significantly improved such that perturbations due to the charged particles present within the device are considered while estimating electric field. In the other approach, the transport is simulated following hydrodynamic model using COMSOL during which the electric field is also provided by COMSOL where it is easy to set up space charge effects. A comparison between these possible approaches will be presented. Effect of different simulation parameters will also be demonstrated using simple examples.

Recurrent Spiking Neural Networks (RSNNs) have emerged as a computationally efficient and brain-inspired learning model. The design of sparse RSNNs with fewer neurons and synapses helps reduce the computational complexity of RSNNs. Traditionally, sparse SNNs are obtained by first training a dense and complex SNN for a target task, and, then, pruning neurons with low activity (activity-based pruning) while maintaining task performance. In contrast, this paper presents a task-agnostic methodology for designing sparse RSNNs by pruning a large randomly initialized model. We introduce a novel Lyapunov Noise Pruning (LNP) algorithm that uses graph sparsification methods and utilizes Lyapunov exponents to design a stable sparse RSNN from a randomly initialized RSNN. We show that the LNP can leverage diversity in neuronal timescales to design a sparse Heterogeneous RSNN (HRSNN). Further, we show that the same sparse HRSNN model can be trained for different tasks, such as image classification and temporal prediction. We experimentally show that, in spite of being task-agnostic, LNP increases computational efficiency (fewer neurons and synapses) and prediction performance of RSNNs compared to traditional activity-based pruning of trained dense models., Comment: Published as a conference paper at ICLR 2024

Public bus transit systems provide critical transportation services for large sections of modern communities. On-time performance and maintaining the reliable quality of service is therefore very important. Unfortunately, disruptions caused by overcrowding, vehicular failures, and road accidents often lead to service performance degradation. Though transit agencies keep a limited number of vehicles in reserve and dispatch them to relieve the affected routes during disruptions, the procedure is often ad-hoc and has to rely on human experience and intuition to allocate resources (vehicles) to affected trips under uncertainty. In this paper, we describe a principled approach using non-myopic sequential decision procedures to solve the problem and decide (a) if it is advantageous to anticipate problems and proactively station transit buses near areas with high-likelihood of disruptions and (b) decide if and which vehicle to dispatch to a particular problem. Our approach was developed in partnership with the Metropolitan Transportation Authority for a mid-sized city in the USA and models the system as a semi-Markov decision problem (solved as a Monte-Carlo tree search procedure) and shows that it is possible to obtain an answer to these two coupled decision problems in a way that maximizes the overall reward (number of people served). We sample many possible futures from generative models, each is assigned to a tree and processed using root parallelization. We validate our approach using 3 years of data from our partner agency. Our experiments show that the proposed framework serves 2% more passengers while reducing deadhead miles by 40%.

In this paper, we introduce a novel approach to address the task of synthesizing speech from silent videos of any in-the-wild speaker solely based on lip movements. The traditional approach of directly generating speech from lip videos faces the challenge of not being able to learn a robust language model from speech alone, resulting in unsatisfactory outcomes. To overcome this issue, we propose incorporating noisy text supervision using a state-of-the-art lip-to-text network that instills language information into our model. The noisy text is generated using a pre-trained lip-to-text model, enabling our approach to work without text annotations during inference. We design a visual text-to-speech network that utilizes the visual stream to generate accurate speech, which is in-sync with the silent input video. We perform extensive experiments and ablation studies, demonstrating our approach's superiority over the current state-of-the-art methods on various benchmark datasets. Further, we demonstrate an essential practical application of our method in assistive technology by generating speech for an ALS patient who has lost the voice but can make mouth movements. Our demo video, code, and additional details can be found at \url{http://cvit.iiit.ac.in/research/projects/cvit-projects/ms-l2s-itw}., Comment: 8 pages of content, 1 page of references and 4 figures

This paper presents the first systematic study of evalution of Deep Neural Network (DNN) designed and trained to predict the evolution of a stochastic dynamical system, using wildfire prediction as a case study. We show that traditional evaluation methods based on threshold based classification metrics and error-based scoring rules assess a DNN's ability to replicate the observed ground truth (GT), but do not measure the fidelity of the DNN's learning of the underlying stochastic process. To address this gap, we propose a new system property: Statistic-GT, representing the GT of the stochastic process, and an evaluation metric that exclusively assesses fidelity to Statistic-GT. Utilizing a synthetic dataset, we introduce a stochastic framework to characterize this property and establish criteria for a metric to be a valid measure of the proposed property. We formally show that Expected Calibration Error (ECE) tests the necessary condition for fidelity to Statistic-GT. We perform empirical experiments, differentiating ECE's behavior from conventional metrics and demonstrate that ECE exclusively measures fidelity to the stochastic process. Extending our analysis to real-world wildfire data, we highlight the limitations of traditional evaluation methods and discuss the utility of evaluating fidelity to the stochastic process alongside existing metrics., Comment: Under peer review

Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with limited context and training data. To address this challenge, we propose an "Inductive Learning" approach utilizing a distributed network of SLMs. This network leverages error-based learning and hint incorporation to refine the reasoning capabilities of SLMs. Our goal is to provide a framework that empowers SLMs to approach the level of logic-based applications achieved by high-parameter models, potentially benefiting any language model. Ultimately, this novel concept paves the way for bridging the logical gap between humans and LLMs across various fields., Comment: 8 pages

Machine learning, with its myriad applications, has become an integral component of numerous technological systems. A common practice in this domain is the use of transfer learning, where a pre-trained model's architecture, readily available to the public, is fine-tuned to suit specific tasks. As Machine Learning as a Service (MLaaS) platforms increasingly use pre-trained models in their backends, it's crucial to safeguard these architectures and understand their vulnerabilities. In this work, we present an approach based on the observation that the classification patterns of adversarial images can be used as a means to steal the models. Furthermore, the adversarial image classifications in conjunction with timing side channels can lead to a model stealing method. Our approach, designed for typical user-level access in remote MLaaS environments exploits varying misclassifications of adversarial images across different models to fingerprint several renowned Convolutional Neural Network (CNN) and Vision Transformer (ViT) architectures. We utilize the profiling of remote model inference times to reduce the necessary adversarial images, subsequently decreasing the number of queries required. We have presented our results over 27 pre-trained models of different CNN and ViT architectures using CIFAR-10 dataset and demonstrate a high accuracy of 88.8% while keeping the query budget under 20.

We introduce a new model of \emph{step} Chemical Reaction Networks (step CRNs), motivated by the step-wise addition of materials in standard lab procedures. Step CRNs have ordered reactants that transform into products via reaction rules over a series of steps. We study an important subset of weak reaction rules, \emph{void} rules, in which chemical species may only be deleted but never changed. We demonstrate the capabilities of these simple limited systems to simulate threshold circuits and compute functions using various configurations of rule sizes and step constructions, and prove that without steps, void rules are incapable of these computations, which further motivates the step model. Additionally, we prove the coNP-completeness of verifying if a given step CRN computes a function, holding even for $O(1)$ step systems.

The COVID-19 pandemic has profoundly reshaped our lives, prompting a search for solutions to its far-reaching effects. Vaccines emerged as a beacon of hope, yet reaching remote areas faces last-mile hurdles and cost issues due to loss of vaccine potency due to poor temperature regulation of the storage units and unanticipated vaccine wastage en route, a common occurrence in conventional vaccine transportation methods. We introduce ALIVE, a low-cost Interactive Vaccine Storage Environment module. ALIVE provides an off-grid, self-sufficient solution for vaccine storage and transport, enabled by active cooling technology. ALIVE's innovation lies in its integration with the Internet of Things (IoT), allowing real-time monitoring and control. This IoT-enabled Application Programming Interface (API) features a data acquisition and environment parameter control system, managing oversight and decision-making. ALIVE's compact, lightweight design makes it adaptable to various logistical scenarios, while its versatility enables it to maintain both time-invariant and time-dependent thermophysical and spatial parameters. Operationalized through a PID algorithm, ALIVE ensures precise temperature control within the vaccine chamber. Its dynamic features, such as remote actuation and data sharing, demonstrate its adaptability and potential applications. Despite the frugal nature of development, the system promises significant benefits, including reduced vaccine loss and remote monitoring advantages. Collaborations with healthcare partners seek to further enhance ALIVE's readiness and expand its impact. ALIVE revolutionizes vaccine logistics, offering scalable, cost-effective solutions for bridging accessibility gaps in challenging distribution scenarios. Its adaptability positions it for widespread application, from last-mile vaccine delivery to environment-controlled supply chains and beyond., Comment: Presented at the International Conference on Robotics, Control, Automation, and Artificial Intelligence (RCAAI 2023). Corresponding: arkadeepdatta@gmail.com

The masses and radii of strongly magnetized anisotropic deformed white dwarf stars are investigated using the stellar structure equations in the parameterized $\gamma$-metric formalism. The Equation of State (EoS) of a completely degenerate relativistic electron gas in strong quantizing density-dependent magnetic field is developed. The fluid and field pressure anisotropy among the parallel and perpendicular components to the magnetic field is taken into consideration. This anisotropy in the EoS causes axisymmetric deformation of the star. We found stable solutions of deformed super-Chandrasekhar ultramassive white dwarfs. The masses of anisotropic magnetized white dwarfs at the same central density decrease monotonically with the increase in the strength of the central magnetic field, while the equatorial radii increase monotonically. This is in sharp contrast to the isotropic case where both the mass and radius increase monotonically. High magnetic field increases anisotropy and oblateness. We also see that the maximum mass and its corresponding equatorial radius both decrease as central magnetic field strength increases. We also notice that the maximum mass occurs at higher central density as the magnetic field increases. This shows that increasing magnetic field (hence increasing anisotropy) softens the EoS and makes the star more compact.

We introduce the QuadratiK package that incorporates innovative data analysis methodologies. The presented software, implemented in both R and Python, offers a comprehensive set of goodness-of-fit tests and clustering techniques using kernel-based quadratic distances, thereby bridging the gap between the statistical and machine learning literatures. Our software implements one, two and k-sample tests for goodness of fit, providing an efficient and mathematically sound way to assess the fit of probability distributions. Expanded capabilities of our software include supporting tests for uniformity on the d-dimensional Sphere based on Poisson kernel densities. Particularly noteworthy is the incorporation of a unique clustering algorithm specifically tailored for spherical data that leverages a mixture of Poisson kernel-based densities on the sphere. Alongside this, our software includes additional graphical functions, aiding the users in validating, as well as visualizing and representing clustering results. This enhances interpretability and usability of the analysis. In summary, our R and Python packages serve as a powerful suite of tools, offering researchers and practitioners the means to delve deeper into their data, draw robust inference, and conduct potentially impactful analyses and inference across a wide array of disciplines., Comment: 36 pages, 9 figures

Post-quantum cryptographic (PQC) algorithms, especially those based on the learning with errors (LWE) problem, have been subjected to several physical attacks in the recent past. Although the attacks broadly belong to two classes - passive side-channel attacks and active fault attacks, the attack strategies vary significantly due to the inherent complexities of such algorithms. Exploring further attack surfaces is, therefore, an important step for eventually securing the deployment of these algorithms. Also, it is important to test the robustness of the already proposed countermeasures in this regard. In this work, we propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms (KEMs) exploiting fault propagation. The attack typically originates due to an algorithmic modification widely used to enable masking, namely the Arithmetic-to-Boolean (A2B) conversion. We exploit the data dependency of the adder carry chain in A2B and extract sensitive information, albeit masking (of arbitrary order) being present. As a practical demonstration of the exploitability of this information leakage, we show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber. The attack on Kyber targets the decapsulation module and utilizes Belief Propagation (BP) for key recovery. To the best of our knowledge, it is the first attack exploiting an algorithmic component introduced to ease masking rather than only exploiting the randomness introduced by masking to obtain desired faults (as done by Delvaux). Finally, we performed both simulated and electromagnetic (EM) fault-based practical validation of the attack for an open-source first-order secure Kyber implementation running on an STM32 platform.

Learning accurate, data-driven predictive models for multiple interacting agents following unknown dynamics is crucial in many real-world physical and social systems. In many scenarios, dynamics prediction must be performed under incomplete observations, i.e., only a subset of agents are known and observable from a larger topological system while the behaviors of the unobserved agents and their interactions with the observed agents are not known. When only incomplete observations of a dynamical system are available, so that some states remain hidden, it is generally not possible to learn a closed-form model in these variables using either analytic or data-driven techniques. In this work, we propose STEMFold, a spatiotemporal attention-based generative model, to learn a stochastic manifold to predict the underlying unmeasured dynamics of the multi-agent system from observations of only visible agents. Our analytical results motivate STEMFold design using a spatiotemporal graph with time anchors to effectively map the observations of visible agents to a stochastic manifold with no prior information about interaction graph topology. We empirically evaluated our method on two simulations and two real-world datasets, where it outperformed existing networks in predicting complex multiagent interactions, even with many unobserved agents., Comment: Accepted as a conference paper at $6^{th}$ Annual Learning for Dynamics & Control Conference 2024

Early observations with the James Webb Space Telescope (JWST) have revealed the existence of an unexpectedly large abundance of extremely massive galaxies at redshifts $z \gtrsim 5$: these observations are in tension with the predictions not only of the standard $\Lambda$CDM cosmology, but also with those of a wide class of dynamical dark energy (DE) models, and are generally in better agreement with models characterized by a phantom behaviour. Here we consider a model, inspired by string theory and the ubiquity of anti-de Sitter vacua therein, featuring an evolving DE component with positive energy density on top of a negative cosmological constant, argued in an earlier exploratory analysis to potentially be able to explain the JWST observations. We perform a robust comparison of this model against JWST data, considering both photometric observations from the CEERS program, and spectroscopic observations from the FRESCO survey. We show that the model is able to accommodate the JWST observations, with a consistency probability of up to $98\%$, even in the presence of an evolving component with a quintessence-like behaviour (easier to accommodate theoretically compared to phantom DE), while remaining consistent with standard low-redshift probes. Our results showcase the tremendous potential of measurements of high-redshift galaxy abundances in tests of fundamental physics, and their valuable complementarity with standard cosmological probes., Comment: 32 pages, 3 figures. v2: references added, minor edits to figures, clarified certain aspects of the analysis. Version accepted for publication in JCAP

In a modern DBMS, working memory is frequently the limiting factor when processing in-memory analytic query operations such as joins, sorting, and aggregation. Existing resource estimation approaches for a DBMS estimate the resource consumption of a query by computing an estimate of each individual database operator in the query execution plan. Such an approach is slow and error-prone as it relies upon simplifying assumptions, such as uniformity and independence of the underlying data. Additionally, the existing approach focuses on individual queries separately and does not factor in other queries in the workload that may be executed concurrently. In this research, we are interested in query performance optimization under concurrent execution of a batch of queries (a workload). Specifically, we focus on predicting the memory demand for a workload rather than providing separate estimates for each query within it. We introduce the problem of workload memory prediction and formalize it as a distribution regression problem. We propose Learned Workload Memory Prediction (LearnedWMP) to improve and simplify estimating the working memory demands of workloads. Through a comprehensive experimental evaluation, we show that LearnedWMP reduces the memory estimation error of the state-of-the-practice method by up to 47.6%. Compared to an alternative single-query model, during training and inferencing, the LearnedWMP model and its variants were 3x to 10x faster. Moreover, LearnedWMP-based models were at least 50% smaller in most cases. Overall, the results demonstrate the advantages of the LearnedWMP approach and its potential for a broader impact on query performance optimization.

Despite considerable success, large Denoising Diffusion Models (DDMs) with UNet backbone pose practical challenges, particularly on limited hardware and in processing gigapixel images. To address these limitations, we introduce two Neural Cellular Automata (NCA)-based DDMs: Diff-NCA and FourierDiff-NCA. Capitalizing on the local communication capabilities of NCA, Diff-NCA significantly reduces the parameter counts of NCA-based DDMs. Integrating Fourier-based diffusion enables global communication early in the diffusion process. This feature is particularly valuable in synthesizing complex images with important global features, such as the CelebA dataset. We demonstrate that even a 331k parameter Diff-NCA can generate 512x512 pathology slices, while FourierDiff-NCA (1.1m parameters) reaches a three times lower FID score of 43.86, compared to the four times bigger UNet (3.94m parameters) with a score of 128.2. Additionally, FourierDiff-NCA can perform diverse tasks such as super-resolution, out-of-distribution image synthesis, and inpainting without explicit training.

Fog harvesting relies on intercepting atmospheric or industrial fog by placing a porous obstacle, e.g., a mesh and collecting the deposited water. In the face of global water scarcity, such fog harvesting has emerged as a viable alternative source of potable water. Typical fog harvesting meshes suffer from poor collection efficiency due to aerodynamic bypassing of the oncoming fog stream and poor collection of the deposited water from the mesh. One pestering challenge in this context is the frequent clogging up of mesh pores by the deposited fog water, which not only yields low drainage efficiency but also generates high aerodynamic resistance to the oncoming fog stream, thereby negatively impacting the fog collection efficiency. Minimizing the clogging is possible by rendering the mesh fiber superhydrophobic, but that entails other detrimental effects like premature dripping and flow-induced re-entrainment of water droplets into the fog stream from the mesh fiber. Herein, we improvise on the traditional interweaved metal mesh designs by defining critical parameters, viz., mesh pitch, shade coefficient, and fiber wettability, and deduce their optimal values from numerically and experimentally observed morphology of collected fog-water droplets under various operating scenarios. We extend our investigations over a varying range of mesh-wettability, including superhydrophilic and hydrophobic fibers, and go on to find optimal shade coefficients which would theoretically render clog-proof fog harvesting meshes. The aerodynamic, deposition, and overall collection efficiencies are characterized. Hydrophobic meshes with square pores, having fiber diameters smaller than the capillary length scale of water, and an optimal shade coefficient, are found to be the most effective design of such clog-proof meshes., Comment: Arani and Arkadeep contributed equally. Corresponding author: Prof. Ranjan Ganguly (Email: ranjan.ganguly@jadavpuruniversity.in). All work carried out in the Advanced Materials Research and Applications (AMRA) Laboratory, India

We present a four-component relativistic unitary coupled cluster method for molecules. We have used commutator-based non-perturbative approximation using the ''Bernoulli expansion'' to derive an approximation to the relativistic unitary coupled cluster method. The performance of the full quadratic unitary coupled-cluster singles and doubles method \left ( qUCCSD \right ), as well as a perturbative approximation variant \left ( UCC3 \right ), has been reported for both energies and properties. It can be seen that both methods give results comparable to those of the standard relativistic coupled cluster method. The qUCCSD method shows better agreement with experimental results due to better inclusion of the relaxation effects. A natural spinor-based scheme to reduce the computation cost of relativistic UCC3 and qUCCSD methods has been discussed., Comment: 34 pages, 10 figures, 4 tables

This article aims to study efficient/trace optimal designs for crossover trials with multiple responses recorded from each subject in the time periods. A multivariate fixed effects model is proposed with direct and carryover effects corresponding to the multiple responses. The corresponding error dispersion matrix is chosen to be either of the proportional or the generalized Markov covariance type, permitting the existence of direct and cross-correlations within and between the multiple responses. The corresponding information matrices for direct effects under the two types of dispersions are used to determine efficient designs. The efficiency of orthogonal array designs of Type $I$ and strength $2$ is investigated for a wide choice of covariance functions, namely, Mat($0.5$), Mat($1.5$) and Mat($\infty$). To motivate these multivariate crossover designs, a gene expression dataset in a $3 \times 3$ framework is utilized.

Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (e.g., Monte Carlo tree search). While the former learns a policy by interacting with the environment (typically done before execution), the latter uses a generative model of the environment to sample promising action trajectories at decision time. Decision-making is particularly challenging in non-stationary environments, where the environment in which an agent operates can change over time. Both approaches have shortcomings in such settings -- on the one hand, policies learned before execution become stale when the environment changes and relearning takes both time and computational effort. Online search, on the other hand, can return sub-optimal actions when there are limitations on allowed runtime. In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment. We prove theoretical results showing conditions under which PA-MCTS selects the one-step optimal action and also bound the error accrued while following PA-MCTS as a policy. We compare and contrast our approach with AlphaZero, another hybrid planning approach, and Deep Q Learning on several OpenAI Gym environments. Through extensive experiments, we show that under non-stationary settings with limited time constraints, PA-MCTS outperforms these baselines., Comment: Extended Abstract accepted for presentation at AAMAS 2024

A fundamental (and largely open) challenge in sequential decision-making is dealing with non-stationary environments, where exogenous environmental conditions change over time. Such problems are traditionally modeled as non-stationary Markov decision processes (NSMDP). However, existing approaches for decision-making in NSMDPs have two major shortcomings: first, they assume that the updated environmental dynamics at the current time are known (although future dynamics can change); and second, planning is largely pessimistic, i.e., the agent acts ``safely'' to account for the non-stationary evolution of the environment. We argue that both these assumptions are invalid in practice -- updated environmental conditions are rarely known, and as the agent interacts with the environment, it can learn about the updated dynamics and avoid being pessimistic, at least in states whose dynamics it is confident about. We present a heuristic search algorithm called \textit{Adaptive Monte Carlo Tree Search (ADA-MCTS)} that addresses these challenges. We show that the agent can learn the updated dynamics of the environment over time and then act as it learns, i.e., if the agent is in a region of the state space about which it has updated knowledge, it can avoid being pessimistic. To quantify ``updated knowledge,'' we disintegrate the aleatoric and epistemic uncertainty in the agent's updated belief and show how the agent can use these estimates for decision-making. We compare the proposed approach with the multiple state-of-the-art approaches in decision-making across multiple well-established open-source problems and empirically show that our approach is faster and highly adaptive without sacrificing safety., Comment: Accepted for publication at the International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 2024

Several works related to spatial crowdsourcing have been proposed in the direction where the task executers are to perform the tasks within the stipulated deadlines. Though the deadlines are set, it may be a practical scenario that majority of the task executers submit the tasks as late as possible. This situation where the task executers may delay their task submission is termed as procrastination in behavioural economics. In many applications, these late submission of tasks may be problematic for task providers. So here, the participating agents (both task providers and task executers) are articulated with the procrastination issue. In literature, how to prevent this procrastination within the deadline is not addressed in spatial crowdsourcing scenario. However, in a bipartite graph setting one procrastination aware scheduling is proposed but balanced job (task and job will synonymously be used) distribution in different slots (also termed as schedules) is not considered there. In this paper, a procrastination aware scheduling of jobs is proliferated by proposing an (randomized) algorithm in spatial crowdsourcing scenario. Our algorithm ensures that balancing of jobs in different schedules are maintained. Our scheme is compared with the existing algorithm through extensive simulation and in terms of balancing effect, our proposed algorithm outperforms the existing one. Analytically it is shown that our proposed algorithm maintains the balanced distribution.

Open Source Intelligence (OSINT) investigations, which rely entirely on publicly available data such as social media, play an increasingly important role in solving crimes and holding governments accountable. The growing volume of data and complex nature of tasks, however, means there is a pressing need to scale and speed up OSINT investigations. Expert-led crowdsourcing approaches show promise but tend to either focus on narrow tasks or domains or require resource-intense, long-term relationships between expert investigators and crowds. We address this gap by providing a flexible framework that enables investigators across domains to enlist crowdsourced support for the discovery and verification of OSINT. We use a design-based research (DBR) approach to develop OSINT Research Studios (ORS), a sociotechnical system in which novice crowds are trained to support professional investigators with complex OSINT investigations. Through our qualitative evaluation, we found that ORS facilitates ethical and effective OSINT investigations across multiple domains. We also discuss broader implications of expert-crowd collaboration and opportunities for future work., Comment: To be published in CSCW 2024

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in underdeveloped countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. In particular, low vaccination uptake in Nigeria is a major driver of more than 2,000 daily deaths of children under the age of five years. In this paper, we describe our collaboration with government partners in Nigeria to deploy ADVISER: AI-Driven Vaccination Intervention Optimiser. The framework, based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination, is the first successful deployment of an AI-enabled toolchain for optimizing the allocation of health interventions in Nigeria. In this paper, we provide a background of the ADVISER framework and present results, lessons, and success stories of deploying ADVISER to more than 13,000 families in the state of Oyo, Nigeria., Comment: Accepted for publication at the AAAI Conference on Artificial Intelligence (AAAI-24)

When a star undergoes core collapse, a vast amount of energy is released in a ~10 s long burst of neutrinos of all species. Inverse beta decay in the star's hydrogen envelope causes an electromagnetic cascade which ultimately results in a flare of gamma rays - an "echo" of the neutrino burst - at the characteristic energy of 0.511 MeV. We study the phenomenology and detectability of this flare. Its luminosity curve is characterized by a fast, seconds-long, rise and an equally fast decline, with a minute- or hour-long plateau in between. For a near-Earth star (distance D<1 kpc) the echo will be observable at near future gamma ray telescopes with an effective area of 10^3 cm^2 or larger. Its observation will inform us on the envelope size and composition. In conjunction with the direct detection of the neutrino burst, it will also give information on the neutrino emission away from the line of sight and will enable tests of neutrino propagation effects between the stellar surface and Earth., Comment: 10 pages, 5 figures and 1 table. Description of the microphysics has been expanded; minor edits were made for clarity. This version matches the one to be published in the Astrophysical Journal

The initial density of dark matter (DM) particles, otherwise secluded from the standard model (SM), may be generated at reheating, with an initial temperature ratio for internal thermalizations, $\xi_i=T_{\rm DM,i}/T_{\rm SM,i}$. This scenario necessarily implies inflaton-mediated scatterings between DM and SM after reheating, with a rate fixed by the relic abundance of DM and the reheat temperature. These scatterings can be important for an inflaton mass and reheat temperature as high as $\mathcal{O}(10^7 {~\rm GeV})$ and $\mathcal{O}(10^9{~\rm GeV})$, respectively, since the thermally averaged collision terms become approximately independent of the inflaton mass when the bath temperature is larger than the mass. The impact of these scatterings on DM cosmology is studied modeling the perturbative reheating physics by a gauge-invariant set of inflaton interactions upto dimension-5 with the SM gauge bosons, fermions and the Higgs fields. It is observed that an initially lower (higher) DM temperature will rapidly increase (decrease), even with very small couplings to the inflaton. There is a sharp lower bound on the DM mass below which the relic abundance cannot be satisfied due to faster back-scatterings depleting DM quanta to SM particles. For low DM masses, the CMB constraints become stronger due to the collisions for $\xi_i<1$, probing values as small as $\mathcal{O}(10^{-4})$, and weaker for $\xi_i>1$. The BBN constraints become stronger due to the collisions for lower DM masses, probing $\xi_i$ as small as $\mathcal{O}(0.1)$, and weaker for higher DM mass. Thus inflaton-mediated collisions with predictable rates, relevant even for high-scale inflation models, can significantly impact the cosmology of light DM., Comment: 19 pages, 10 figures; v2: comments added, version as published in Physical Review D

The Join-the-Shortest-Queue (JSQ) load balancing scheme is widely acknowledged for its effectiveness in minimizing the average response time for jobs in systems with identical servers. However, when applied to a heterogeneous server system with servers of different processing speeds, the JSQ scheme exhibits suboptimal performance. Recently, a variation of JSQ called the Speed-Aware-Join-the-Shortest-Queue (SA-JSQ) scheme has been shown to attain fluid limit optimality for systems with heterogeneous servers. In this paper, we examine the SA-JSQ scheme for heterogeneous server systems under the Halfin-Whitt regime. Our analysis begins by establishing that the scaled and centered version of the system state weakly converges to a diffusion process characterized by stochastic integral equations. Furthermore, we prove that the diffusion process is positive recurrent and the sequence of stationary measures for the scaled and centered queue length processes converge to the stationary measure for the limiting diffusion process. To achieve this result, we employ Stein's method with a generator expansion approach.

We apply machine learning to understand fundamental aspects of holographic duality, specifically the entropies obtained from the apparent and event horizon areas. We show that simple features of only the time series of the pressure anisotropy, namely the values and half-widths of the maxima and minima, the times these are attained, and the times of the first zeroes can predict the areas of the apparent and event horizons in the dual bulk geometry at all times with a fixed maximum length (30) of the input vector. Given that simple Vaidya-type metrics constructed just from the apparent and event horizon areas can be used to approximately obtain unequal time correlation functions, we argue that the corresponding entropy functions are the measures of information that need to be extracted from simple one-point functions to reconstruct specific aspects of correlation functions of the dual state with the best possible approximations., Comment: 10+10 pages, 1 Figure; Discussion improved, k-fold cross validation added

We present a systematic investigation of the quantum games, constructed using a novel repeated game protocol, when played repeatedly ad infinitum. We focus on establishing that such repeated games -- by virtue of inherent quantum-mechanical randomness -- can be mapped to the paradigm of stochastic games. Subsequently, using the setup of two-player--two-action games, we explore the pure reactive strategies belonging to the set of reactive strategies, whose support in the quantum games is no longer countably finite but rather non-denumerably infinite. We find that how two pure strategies fare against each other is crucially dependent on the discount factor (the probability of occurrence of every subsequent round) and how much entangled the quantum states of the players are. We contrast the results obtained with the corresponding results in the classical setup and find fundamental differences between them: e.g, when the underlying game is the prisoner's dilemma, in the quantum game setup, always-defect strategy can be beaten by the tit-for-tat strategy for high enough discount factor.

The origin of the solar system abundances of several proton-rich isotopes, especially $^{92,94}$Mo and $^{96,98}$Ru, has been an enduring mystery in nuclear astrophysics. An attractive proposal to solve this problem is the $\nu p$-process, which can operate in neutrino-driven outflows in a core-collapse supernova after the shock is launched. Years of detailed studies, however, have cast doubt over the ability of this process to generate sufficiently high absolute and relative amounts of various $p$-nuclei. The $\nu p$-process is also thought to be excluded by arguments based on the long-lived radionuclide $^{92}$Nb. Here, we present explicit calculations, in which both the abundance ratios and the absolute yields of the $p$-nuclei up to $A\lesssim105$ are successfully reproduced, even when using the modern (medium enhanced) triple-$\alpha$ reaction rates. The process is also shown to produce the necessary amounts of $^{92}$Nb. The models are characterized by subsonic outflows and by the protoneutron star masses in the {$\gtrsim1.7 M_\odot$ range}. This suggests that the Mo and Ru $p$-nuclides observed in the Solar System were made in CCSN explosions characterized by an extended accretion stage., Comment: 13 pages, 7 figures