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This paper presents a projection-based reduced order modelling (ROM) framework for unsteady parametrized optimal control problems (OCP$_{(\mu)}$s) arising from cardiovascular (CV) applications. In real-life scenarios, accurately defining outflow boundary conditions in patient-specific models poses significant challenges due to complex vascular morphologies, physiological conditions, and high computational demands. These challenges make it difficult to compute realistic and reliable CV hemodynamics by incorporating clinical data such as 4D magnetic resonance imaging. To address these challenges, we focus on controlling the outflow boundary conditions to optimize CV flow dynamics and minimize the discrepancy between target and computed flow velocity profiles. The fluid flow is governed by unsteady Navier--Stokes equations with physical parametric dependence, i.e. the Reynolds number. Numerical solutions of OCP$_{(\mu)}$s require substantial computational resources, highlighting the need for robust and efficient ROMs to perform real-time and many-query simulations. Here, we aim at investigating the performance of a projection-based reduction technique that relies on the offline-online paradigm, enabling significant computational cost savings. The Galerkin finite element method is used to compute the high-fidelity solutions in the offline phase. We implemented a nested-proper orthogonal decomposition (nested-POD) for fast simulation of OCP$_{(\mu)}$s that encompasses two stages: temporal compression for reducing dimensionality in time, followed by parametric-space compression on the precomputed POD modes. We tested the efficacy of the methodology on vascular models, namely an idealized bifurcation geometry and a patient-specific coronary artery bypass graft, incorporating stress control at the outflow boundary, observing consistent speed-up with respect to high-fidelity strategies.

We present new measurements of the two-dimensional (2-D) geometry of the LMC's stellar bar with precise astrometric observations of red clump stars in Gaia DR3. We develop a novel solution to tackle crowding induced incompleteness in Gaia datasets with the Gaia BP-RP color excess. Utilizing the color excess information, we derive a 2-D completeness map of the LMC's disk. We find that incompleteness biases the bar measurements and induces large uncertainties. With the completeness-corrected 2-D red clump map, we precisely measure the LMC bar's properties for the first time using Fourier decomposition. The bar radius is $R_{bar} = 2.13^{+0.03}_{-0.04}$ kpc, and its position angle is $121.26^{\circ} \pm 0.21^{\circ}$. The bar's strength as quantified by the Fourier bi-symmetric amplitude is $S_{bar} = 0.27$, indicating that the LMC has a significant bar perturbation. We find the bar has an axis ratio of $0.54 \pm 0.03$, and is offset with respect to the center of the outer disk isophote at R $\approx$ 5 kpc by $0.76 \pm 0.01$ kpc. These LMC bar properties agree with a hydrodynamic model where the SMC has undergone a recent direct collision with the LMC. We compare the LMC's bar properties with other barred galaxies in the local universe, and discover that the LMC is similar to other barred galaxies in terms of bar-galaxy scaling relations. We discuss how our completeness correction framework can be applied to other systems in the Local Group., Comment: 21 pages, submitted to ApJ, comments welcome

Cerebral microbleeds, markers of brain damage from vascular and amyloid pathologies, are linked to cognitive decline in aging, but their role in Alzheimer's disease (AD) onset and progression remains unclear. This study aimed to explore whether the presence and location of lobar microbleeds are associated with amyloid-$\beta$ (A$\beta$)-PET, tau tangle formation (tau-PET), and longitudinal cognitive decline. We analyzed 1,573 ADNI participants with MR imaging data and information on the number and location of microbleeds. Associations between lobar microbleeds and pathology, cerebrospinal fluid (CSF), genetics, and cognition were examined, focusing on regional microbleeds and domain-specific cognitive decline using ordinary least-squares regression while adjusting for covariates. Cognitive decline was assessed with ADAS-Cog11 and its domain-specific sub-scores. Participants underwent neuropsychological testing at least twice, with a minimum two-year interval between assessments. Among the 1,573 participants (692 women, mean age 71.23 years), 373 participants had microbleeds. The presence of microbleeds was linked to cognitive decline, particularly in the semantic, language, and praxis domains for those with temporal lobe microbleeds. Microbleeds in the overall cortex were associated with language decline. Pathologically, temporal lobe microbleeds were associated with increased tau in the overall cortex, while cortical microbleeds were linked to elevated A$\beta$ in the temporal, parietal, and frontal regions. In this mixed population, microbleeds were connected to longitudinal cognitive decline, especially in semantic and language domains, and were associated with higher baseline A$\beta$ and tau pathology. These findings suggest that lobar microbleeds should be included in AD diagnostic and prognostic evaluations., Comment: 11 pages, 2 figures

The Josephson diode effect (JDE), characterized by a unidirectional supercurrent across a Josephson junction, arises from non-reciprocal critical currents typically controlled by magnetic fields. In this study, we demonstrate JDE controlled by local electric fields, achieving complete polarity reversal through gate voltages on epitaxial Al-InAs Josephson junctions. Our observations reveal intricate effects of in-plane magnetic fields and gate voltages on the JDE. We develop a theoretical model that considers the coherent interplay between finite Cooper-pair momentum (fCPM) and anisotropic spin-orbit coupling (SOC), identifying that fCPM dominates JDE at low in-plane magnetic fields, while SOC governs its polarity reversal at high fields. This model aligns well with experimental data, illustrating precise electric control of JDE polarity through anisotropic SOC. These findings introduce a novel approach to controlling superconducting circuits, paving a route toward advanced applications in superconducting quantum devices., Comment: 42 pages, 19 figures, 1 table

Estimating the location and intensity of tropical cyclones holds crucial significance for predicting catastrophic weather events. In this study, we approach this task as a detection and regression challenge, specifically over the North Indian Ocean (NIO) region where best tracks location and wind speed information serve as the labels. The current process for cyclone detection and intensity estimation involves physics-based simulation studies which are time-consuming, only using image features will automate the process for significantly faster and more accurate predictions. While conventional methods typically necessitate substantial prior knowledge for training, we are exploring alternative approaches to enhance efficiency. This research aims to focus specifically on cyclone detection, intensity estimation and related aspects using only image input and data-driven approaches and will lead to faster inference time and automate the process as opposed to current NWP models being utilized at SAC. In context to algorithm development, a novel two stage detection and intensity estimation module is proposed. In the first level detection we try to localize the cyclone over an entire image as captured by INSAT3D over the NIO (North Indian Ocean). For the intensity estimation task, we propose a CNN-LSTM network, which works on the cyclone centered images, utilizing a ResNet-18 backbone, by which we are able to capture both temporal and spatial characteristics., Comment: 10 pages, 22 figures

$\text{RuO}_2$, a transition metal oxide, is attracting attention in spintronics for its unique altermagnetic properties, which influence spin currents. Its ability to produce large spin-orbit torques and spin Hall effects is key for energy-efficient magnetic memory and logic devices. Additionally, the tunable thickness and crystallinity of $\text{RuO}_2$ thin films optimize torque efficiency for low-power switching. Spin pumping, a versatile method for investigating spin dynamics in $\text{RuO}_2$ thin films, has garnered considerable interest because of its straightforward, non-invasive and uncomplicated approach to addressing impedance mismatch and direct measurement of spintronic parameters. Here we present a systematic and detailed analysis on the efficient spin to charge conversion in (110)-oriented $\text{RuO}_2$ films with amorphous CoFeB as spin source. The spin Hall angle, and spin diffusion length were estimated to be 0.14 $\pm$ 0.01 and 4.58 $\pm$ 0.40 nm, respectively. The spin Hall conductivity of 998.89 $\pm$ 58.23 $\hbar \cdot \frac{\Omega^{-1} \, \text{cm}^{-1}}{e}$ has been estimated which is theoretically predicted to be of the similar order. The interfacial spin transparency has been achieved to be 90%. We have shown that the spin memory loss at the $\text{RuO}_2$/CoFeB interface is 15%, which is very small.

This work presents the design and development of IIT Bombay Racing's Formula Student style autonomous racecar algorithm capable of running at the racing events of Formula Student-AI, held in the UK. The car employs a cutting-edge sensor suite of the compute unit NVIDIA Jetson Orin AGX, 2 ZED2i stereo cameras, 1 Velodyne Puck VLP16 LiDAR and SBG Systems Ellipse N GNSS/INS IMU. It features deep learning algorithms and control systems to navigate complex tracks and execute maneuvers without any human intervention. The design process involved extensive simulations and testing to optimize the vehicle's performance and ensure its safety. The algorithms have been tested on a small scale, in-house manufactured 4-wheeled robot and on simulation software. The results obtained for testing various algorithms in perception, simultaneous localization and mapping, path planning and controls have been detailed., Comment: 8 pages, 19 figures

Topology and superconductivity, two distinct phenomena offer unique insight into quantum properties and their applications in quantum technologies, spintronics, and sustainable energy technologies if system can be found where they coexist. Tin (Sn) plays a pivotal role here as an element due to its two structural phases, $\alpha$-Sn and $\beta$-Sn, exhibiting topological characteristics ($\alpha$-Sn) and superconductivity ($\beta$-Sn). In this study we show how precise control of $\alpha$ and $\beta$ phases of Sn thin films can be achieved by using molecular beam epitaxy grown buffer layers with systematic control over the lattice parameter. The resulting Sn films showed either $\beta$-Sn or $\alpha$-Sn phases as the lattice constant of the buffer layer was varied from 6.10 A to 6.48 A, covering the range between GaSb (closely matched to InAs) and InSb. The crystal structures of the $\alpha$- and $\beta$-Sn films were characterized by x-ray diffraction and confirmed by Raman spectroscopy and scanning transmission electron microscopy. The smooth and continuous surface morphology of the Sn films was validated using atomic force microscopy. The characteristics of $\alpha$- and $\beta$-Sn phases were further verified using electrical transport measurements by observing resistance drop near 3.7 K for superconductivity of the $\beta$-Sn phase and Shubnikov-de Haas oscillations for the $\alpha$-Sn phase. Density functional theory calculations showed that the stability of the Sn phases is highly dependent on lattice strain, with $\alpha$-Sn being more stable under tensile strain and $\beta$-Sn becoming favorable under compressive strain, which is in good agreement with experimental observations. Hence, this study sheds light on controlling Sn phases through lattice engineering, enabling innovative applications in quantum technologies and beyond.

Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems that range from computational chemistry to health analytics, with a particular interest due to its starring role in Gaussian process regression. However, it is challenging to scale KRR solvers to large datasets: with $n$ training points, a direct solver (i.e., Cholesky decomposition) uses $O(n^2)$ storage and $O(n^3)$ flops. Iterative methods for KRR, such as preconditioned conjugate gradient (PCG), avoid the cubic scaling of direct solvers and often use low-rank preconditioners; a rank $r$ preconditioner uses $O(rn)$ storage and each iteration requires $O(n^2)$ flops. To reduce the storage and iteration complexity of iterative solvers for KRR, we propose ASkotch ($\textbf{A}$ccelerated $\textbf{s}$calable $\textbf{k}$ernel $\textbf{o}$p$\textbf{t}$imization using block $\textbf{c}$oordinate descent with $\textbf{H}$essian preconditioning). For a given block size $|b| << n$, each iteration of ASkotch uses $O(r|b| + n)$ storage and $O(n|b|)$ flops, so ASkotch scales better than Cholesky decomposition and PCG. We prove that ASkotch obtains linear convergence to the optimum, with the convergence rate depending on the square roots of the $\textit{preconditioned}$ block condition numbers. Furthermore, we solve KRR problems that were considered to be impossibly large while using limited computational resources: we show that ASkotch outperforms PCG methods with respect to generalization error on large-scale KRR (up to $n = 10^8$) and KRR classification tasks (up to $n = 10^7$) while running each of our experiments on $\textit{a single 12 GB Titan V GPU}$. Our work opens up the possibility of as-yet-unimagined applications of KRR across a wide range of disciplines., Comment: 40 pages (including appendices), 14 figures, 3 tables

The application of quantum algorithms to classical problems is generally accompanied by significant bottlenecks when transferring data between quantum and classical states, often negating any intrinsic quantum advantage. Here we address this challenge for a well-known algorithm for linear systems of equations, originally proposed by Harrow, Hassidim and Lloyd (HHL), by adapting it into a predictor-corrector instead of a direct solver. Rather than seeking the solution at the next time step, the goal now becomes determining the change between time steps. This strategy enables the intelligent omission of computationally costly steps commonly found in many classical algorithms, while simultaneously mitigating the notorious readout problems associated with extracting solutions from a quantum state. Random or regularly performed skips instead lead to simulation failure. We demonstrate that our methodology secures a useful polynomial advantage over a conventional application of the HHL algorithm. The practicality and versatility of the approach are illustrated through applications in various fields such as smoothed particle hydrodynamics, plasma simulations, and reactive flow configurations. Moreover, the proposed algorithm is well suited to run asynchronously on future heterogeneous hardware infrastructures and can effectively leverage the synergistic strengths of classical as well as quantum compute resources.

Recently, very large language models (LLMs) have shown exceptional performance on several English NLP tasks with just in-context learning (ICL), but their utility in other languages is still underexplored. We investigate their effectiveness for NLP tasks in low-resource languages (LRLs), especially in the setting of zero-labelled cross-lingual transfer (0-CLT), where no labelled training data for the target language is available -- however training data from one or more related medium-resource languages (MRLs) is utilized, alongside the available unlabeled test data for a target language. We introduce Self-Supervised Prompting (SSP), a novel ICL approach tailored for the 0-CLT setting. SSP is based on the key observation that LLMs output more accurate labels if in-context exemplars are from the target language (even if their labels are slightly noisy). To operationalize this, since target language training data is not available in 0-CLT, SSP operates in two stages. In Stage I, using source MRL training data, target language's test data is noisily labeled. In Stage II, these noisy test data points are used as exemplars in ICL for further improved labelling. Additionally, our implementation of SSP uses a novel Integer Linear Programming (ILP)-based exemplar selection that balances similarity, prediction confidence (when available) and label coverage. Experiments on three tasks and eleven LRLs (from three regions) demonstrate that SSP strongly outperforms existing SOTA fine-tuned and prompting-based baselines in 0-CLT setup.

This study explores a green pathway for urea synthesis using plasma-ice interaction with gas mixtures of N2 + CO2 and NH3 + CO2. Electrical and optical emission spectroscopy were employed to characterize the plasmas, revealing that urea formation involves complex reactions driven by high-energy species, producing reactive nitrogen and carbon intermediates that further react to form urea. Physicochemical analyses of plasma-treated ice showed increased pH, electrical conductivity (EC), and reduced oxidation-reduction potential (ORP). Optimization of plasma process parameters (gas pressure, applied voltage, and treatment time) was performed to enhance urea formation. Among these parameters, plasma treatment time had the most substantial influence. Increasing treatment time from 20 to 60 minutes significantly impacted physicochemical properties: for N2 + CO2 plasma, pH increased by 21.05%, EC by 184.7%, and ORP decreased by 27.48%; for NH3 + CO2 plasma, pH increased by 27.37%, EC by 239.05%, and ORP decreased by 72.67%, respectively. The study shows that NH3 + CO2 plasma produces a significantly higher concentration of urea (7.7 mg L-1) compared to N2 + CO2 plasma (0.55 mg L-1). This is attributed to the direct availability and reactivity of ammonia, which simplifies reaction pathways and enhances intermediate formation. These findings highlight the potential of plasma-ice interaction as an energy-efficient and environmentally friendly method for urea synthesis, offering a sustainable alternative to conventional processes.

In this brief note we calculate the entanglement entropy in $M^{\otimes N}/S_N$ symmetric orbifold CFTs in the presence of topological defects, which were recently constructed in \cite{Gutperle:2024vyp,Knighton:2024noc}. We consider both universal defects which realize $Rep(S_N)$ non-invertible symmetry and non-universal defects. We calculate the sub-leading defect entropy/g-factor for defects at the boundary entangling surface as well as inside it., Comment: 16 pages, 5 figures

In this work, we delve into the EEG classification task in the domain of visual brain decoding via two frameworks, involving two different learning paradigms. Considering the spatio-temporal nature of EEG data, one of our frameworks is based on a CNN-BiLSTM model. The other involves a CNN-Transformer architecture which inherently involves the more versatile attention based learning paradigm. In both cases, a special 1D-CNN feature extraction module is used to generate the initial embeddings with 1D convolutions in the time and the EEG channel domains. Considering the EEG signals are noisy, non stationary and the discriminative features are even less clear (than in semantically structured data such as text or image), we also follow a window-based classification followed by majority voting during inference, to yield labels at a signal level. To illustrate how brain patterns correlate with different image classes, we visualize t-SNE plots of the BiLSTM embeddings alongside brain activation maps for the top 10 classes. These visualizations provide insightful revelations into the distinct neural signatures associated with each visual category, showcasing the BiLSTM's capability to capture and represent the discriminative brain activity linked to visual stimuli. We demonstrate the performance of our approach on the updated EEG-Imagenet dataset with positive comparisons with state-of-the-art methods., Comment: The paper has been submitted at ICPR 2024. It contains 17 pages with 9 images

We study non-invertible defects in two-dimensional $S_N$ orbifold CFTs. We construct universal defects which do not depend on the details of the seed CFT and hence exist in any orbifold CFT. Additionally, we investigate non-universal defects arising from the topological defects of the seed CFT. We argue that there exist universal defects that are non-trivial in the large-$N$ limit, making them relevant for the AdS$_3$/CFT$_2$ correspondence. We then focus on AdS$_3\times$S$^3\times \mathcal M_4$ with one unit of NS-NS flux and propose an explicit realization of these defects on the worldsheet.

This study presents a novel and ecofriendly method for synthesizing ammonium nitrate using activated prepared through air and ammonia plasma treatments. Initially, PAW containing nitrate ions is produced by treating water with air plasma. This PAW air is then frozen and exposed to low pressure NH$_3$ plasma, introducing ammonium ions to from NH$_4$NO$_3$. We systematically investigate the voltage current characteristics of the air and NH$_3$ plasma, analyze the generated species and radicals to understand the mechanism of NH$_4$NO$_3$ formation, and evaluate the effects of process parameters such as NH$_3$ gas pressure, applied voltage, and treatment time on the properties of PAW. Our results indicate that all examined process parameters positively influence the properties of PAW. Among these parameters, the duration of NH$_3$ plasma treatment of PAW ice exerts the most significant effect. Specifically, the concentration of NH4 ions increased by 134.2 percent when the NH$_3$ treatment time was extended from 0.5 h to 1 h, compared to 12.7 and 33.3 percent increases for NH$_3$ pressure, ranging from 0.25 to 0.55 mbar, and applied voltage, ranging from 500 to 700 V, respectively. Similarly, variations in pH, oxidation reduction potential, and electrical conductivity were substantially higher with increased treatment time than with changes in gas pressure and applied voltage. The PAW exhibited a neutral to slightly basic pH, making it ideal for soil applications, thereby addressing the existing issue of the high acidity of PAW and its use in agriculture.

The study investigates the potential of Plasma-Activated Water (PAW) as a nitrogen supplement in hydroponic cultivation (HS-N+PAW), specifically focusing on radish seed germination and plant growth. PAW, produced using a dielectric barrier discharge pencil plasma jet using air as plasma forming gas, is compared against conventional hydroponic solution (HS) and hydroponic solution without nitrogen (HS-N). PAW treatment completely eliminates microbial growth in seeds. Radish plants cultivated with HS-N+PAW display approximately 30% and 3% longer roots compared to those grown with HS-N and HS, respectively, with shoot length increasing by ~16.5% (HS-N) and <1% (HS). Root weight sees a substantial increase of ~51% with HS-N+PAW compared to HS-N, while the increase with HS is not significant. Similarly, shoot fresh weight sees a notable increase of 50% (HS-N) and 10% (HS). In terms of biochemical composition, radish roots show a significant increase of approximately 15.3% in soluble sugar concentration with HS-N+PAW compared to HS-N. Protein concentration in radish leaves increases by ~5.1% and ~19.0% with HS-N+PAW compared to HS-N and HS, respectively. Heightened soluble sugar and protein concentrations in HS-N+PAW-grown plants, indicating enhanced metabolic activity and nutrient uptake. However, variations in chlorophyll and carotenoid concentrations in leaves among different growth media are statistically insignificant. H2O2 concentration root and shoot remains consistent across growth media, electrolytic and phenolic leakage, along with antioxidant enzyme activities, exhibit differential responses, underscoring the impact of growth conditions on plant stress responses.

The Handwritten Text Recognition problem has been a challenge for researchers for the last few decades, especially in the domain of computer vision, a subdomain of pattern recognition. Variability of texts amongst writers, cursiveness, and different font styles of handwritten texts with degradation of historical text images make it a challenging problem. Recognizing scanned document images in neural network-based systems typically involves a two-step approach: segmentation and recognition. However, this method has several drawbacks. These shortcomings encompass challenges in identifying text regions, analyzing layout diversity within pages, and establishing accurate ground truth segmentation. Consequently, these processes are prone to errors, leading to bottlenecks in achieving high recognition accuracies. Thus, in this study, we present an end-to-end paragraph recognition system that incorporates internal line segmentation and gated convolutional layers based encoder. The gating is a mechanism that controls the flow of information and allows to adaptively selection of the more relevant features in handwritten text recognition models. The attention module plays an important role in performing internal line segmentation, allowing the page to be processed line-by-line. During the decoding step, we have integrated a connectionist temporal classification-based word beam search decoder as a post-processing step. In this work, we have extended existing LexiconNet by carefully applying and utilizing gated convolutional layers in the existing deep neural network. Our results at line and page levels also favour our new GatedLexiconNet. This study reported character error rates of 2.27% on IAM, 0.9% on RIMES, and 2.13% on READ-16, and word error rates of 5.73% on IAM, 2.76% on RIMES, and 6.52% on READ-2016 datasets.

This study investigated the use of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis for rapid soil fertility assessment, with a focus on key indicators such as available boron (B), organic carbon (OC), available manganese (Mn), available sulfur (S), and the sulfur availability index (SAI). A total of 1,133 soil samples from diverse agro-climatic zones in Eastern India were analyzed. The research integrated color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model. Results showed that combining image features (IFs) with AVs significantly improved prediction accuracy for available B (R2 = 0.80) and OC (R2 = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data, further enhanced predictions for available Mn and SAI, with R2 values of 0.72 and 0.70, respectively. The study highlights the potential of integrating these technologies to offer rapid, cost-effective soil testing methods, paving the way for more advanced predictive models and a deeper understanding of soil fertility. Future work should explore the application of deep learning models on a larger dataset, incorporating soils from a wider range of agro-climatic zones under field conditions., Comment: Published in 'Soil Advances'

The paper presents a novel Auto Language Prediction Dictionary Capsule (ALPDC) framework for language prediction and machine translation. The model uses a combination of neural networks and symbolic representations to predict the language of a given input text and then translate it to a target language using pre-built dictionaries. This research work also aims to translate the text of various languages to its literal meaning in English. The proposed model achieves state-of-the-art results on several benchmark datasets and significantly improves translation accuracy compared to existing methods. The results show the potential of the proposed method for practical use in multilingual communication and natural language processing tasks., Comment: 21 Pages

With the advent of exascale computing, effective load balancing in massively parallel software applications is critically important for leveraging the full potential of high performance computing systems. Load balancing is the distribution of computational work between available processors. Here, we investigate the application of quantum annealing to load balance two paradigmatic algorithms in high performance computing. Namely, adaptive mesh refinement and smoothed particle hydrodynamics are chosen as representative grid and off-grid target applications. While the methodology for obtaining real simulation data to partition is application specific, the proposed balancing protocol itself remains completely general. In a grid based context, quantum annealing is found to outperform classical methods such as the round robin protocol but lacks a decisive advantage over more advanced methods such as steepest descent or simulated annealing despite remaining competitive. The primary obstacle to scalability is found to be limited coupling on current quantum annealing hardware. However, for the more complex particle formulation, approached as a multi-objective optimization, quantum annealing solutions are demonstrably Pareto dominant to state of the art classical methods across both objectives. This signals a noteworthy advancement in solution quality which can have a large impact on effective CPU usage.