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Cloud Operations (CloudOps) is a rapidly growing field focused on the automated management and optimization of cloud infrastructure which is essential for organizations navigating increasingly complex cloud environments. MontyCloud Inc. is one of the major companies in the CloudOps domain that leverages autonomous bots to manage cloud compliance, security, and continuous operations. To make the platform more accessible and effective to the customers, we leveraged the use of GenAI. Developing a GenAI-based solution for autonomous CloudOps for the existing MontyCloud system presented us with various challenges such as i) diverse data sources; ii) orchestration of multiple processes; and iii) handling complex workflows to automate routine tasks. To this end, we developed MOYA, a multi-agent framework that leverages GenAI and balances autonomy with the necessary human control. This framework integrates various internal and external systems and is optimized for factors like task orchestration, security, and error mitigation while producing accurate, reliable, and relevant insights by utilizing Retrieval Augmented Generation (RAG). Evaluations of our multi-agent system with the help of practitioners as well as using automated checks demonstrate enhanced accuracy, responsiveness, and effectiveness over non-agentic approaches across complex workflows., Comment: The paper has been accepted as full paper to CAIN 2025 (https://conf.researchr.org/home/cain-2025), co-located with ICSE 2025 (https://conf.researchr.org/home/icse-2025). The paper was submitted to CAIN for review on 9 November 2024

The translation and shape deformations of a passive viscous Newtonian droplet immersed in an active nematic liquid crystal under circular confinement are analyzed using a linear stability analysis. We focus on the case of a sharply aligned active nematic in the limit of strong elastic relaxation in two dimensions. Using an active liquid crystal model, we employ the Lorentz reciprocal theorem for Stokes flow to study the growth of interfacial perturbations as a result of both active and elastic stresses. Instabilities are uncovered in both extensile and contractile systems, for which growth rates are calculated and presented in terms of the dimensionless ratios of active, elastic, and capillary stresses, as well as the viscosity ratio between the two fluids. We also extend our theory to analyze the inverse scenario, namely, the stability of an active nematic droplet surrounded by a passive viscous layer. Our results highlight the subtle interplay of capillary, active, elastic, and viscous stresses in governing droplet stability. The instabilities uncovered here may be relevant to a plethora of biological active systems, from the dynamics of passive droplets in bacterial suspensions to the organization of subcellular compartments inside the cell and cell nucleus.

Anyons are low-dimensional quasiparticles that obey fractional statistics, hence interpolating between bosons and fermions. In two dimensions, they exist as elementary excitations of fractional quantum Hall states and they are believed to enable topological quantum computing. One-dimensional (1D) anyons have been theoretically proposed, but their experimental realization has proven to be difficult. Here, we observe anyonic correlations, which emerge through the phenomenon of spin-charge separation, in a 1D strongly-interacting quantum gas. The required spin degree of freedom is provided by a mobile impurity, whose effective anyonic correlations are associated with an experimentally tunable statistical angle. These anyonic correlations are measured by monitoring the impurity momentum distribution, whose asymmetric feature demonstrates the transmutation of bosons via anyons to fermions. Going beyond equilibrium conditions, we study the dynamical properties of the anyonic correlations via dynamical fermionization of the anyons. Our work opens up the door to the exploration of non-equilibrium anyonic phenomena in a highly controllable setting.

We obtain new bounds for (a variant of) the Furstenberg set problem for high dimensional flats over $\mathbb{R}^n$. In particular, let $F\subset \mathbb{R}^n$, $1\leq k \leq n-1$, $s\in (0,k]$, and $t\in (0,k(n-k)]$. We say that $F$ is a $(s,t;k)$-spread Furstenberg set if there exists a $t$-dimensional set of subspaces $\mathcal P \subset \mathcal G(n,k)$ such that for all $P\in \mathcal P$, there exists a translation vector $a_P \in \mathbb{R}^n$ such that $\dim(F\cap (P + a_P)) \geq s$. We show that given $k \geq k_0 +1$ (where $k_0:= k_0(n)$ is sufficiently large) and $s>k_0$, every $(s,t;k)$-spread Furstenberg set $F$ in $\mathbb{R}^n$ satisfies \[ \dim F \geq n-k + s - \frac{k(n-k) - t}{\lceil s\rceil - k_0 +1 }. \] Our methodology is motivated by the work of the second author, Dvir, and Lund over finite fields., Comment: 20 pages, no figures

In this work, we have implemented an accurate machine-learning approach for predicting various key analog and RF parameters of Negative Capacitance Field-Effect Transistors (NCFETs). Visual TCAD simulator and the Python high-level language were employed for the entire simulation process. However, the computational cost was found to be excessively high. The machine learning approach represents a novel method for predicting the effects of different sources on NCFETs while also reducing computational costs. The algorithm of an artificial neural network can effectively predict multi-input to single-output relationships and enhance existing techniques. The analog parameters of Double Metal Double Gate Negative Capacitance FETs (D2GNCFETs) are demonstrated across various temperatures ($T$), oxide thicknesses ($T_{ox}$), substrate thicknesses ($T_{sub}$), and ferroelectric thicknesses ($T_{Fe}$). Notably, at $T=300K$, the switching ratio is higher and the leakage current is $84$ times lower compared to $T=500K$. Similarly, at ferroelectric thicknesses $T_{Fe}=4nm$, the switching ratio improves by $5.4$ times compared to $T_{Fe}=8nm$. Furthermore, at substrate thicknesses $T_{sub}=3nm$, switching ratio increases by $81\%$ from $T_{sub}=7nm$. For oxide thicknesses at $T_{ox}=0.8nm$, the ratio increases by $41\%$ compared to $T_{ox}=0.4nm$. The analysis reveals that $T_{Fe}=4nm$, $T=300K$, $T_{ox}=0.8nm$, and $T_{sub}=3nm$ represent the optimal settings for D2GNCFETs, resulting in significantly improved performance. These findings can inform various applications in nanoelectronic devices and integrated circuit (IC) design., Comment: 7 pages, 8 figures

Deploying local AI models, such as Large Language Models (LLMs), to edge devices can substantially enhance devices' independent capabilities, alleviate the server's burden, and lower the response time. Owing to these tremendous potentials, many big tech companies have released several lightweight Small Language Models (SLMs) to bridge this gap. However, we still have huge motivations to deploy more powerful (LLMs) AI models on edge devices and enhance their smartness level. Unlike the conventional approaches for AI model compression, we investigate activation sparsity. The activation sparsity method is orthogonal and combinable with existing techniques to maximize compression rate while maintaining great accuracy. LLMs' Feed-Forward Network (FFN) components, which typically comprise a large proportion of parameters (around 3/2), ensure that our FFN optimizations would have a better chance of achieving effective compression. Moreover, our findings are beneficial to general LLMs and are not restricted to ReLU-based models. This work systematically investigates the tradeoff between enforcing activation sparsity and perplexity (accuracy) on state-of-the-art LLMs. Our empirical analysis demonstrates that we can obtain around 50% of main memory and computing reductions for critical FFN components with negligible accuracy degradation. This extra 50% sparsity does not naturally exist in the current LLMs, which require tuning LLMs' activation outputs by injecting zero-enforcing thresholds. To obtain the benefits of activation sparsity, we provide a guideline for the system architect for LLM prediction and prefetching. The success prediction allows the system to prefetch the necessary weights while omitting the inactive ones and their successors, therefore lowering cache and memory pollution and reducing LLM execution time on resource-constrained edge devices., Comment: Conference submission for IPCCC 2024

Multimodal fusion learning has shown significant promise in classifying various diseases such as skin cancer and brain tumors. However, existing methods face three key limitations. First, they often lack generalizability to other diagnosis tasks due to their focus on a particular disease. Second, they do not fully leverage multiple health records from diverse modalities to learn robust complementary information. And finally, they typically rely on a single attention mechanism, missing the benefits of multiple attention strategies within and across various modalities. To address these issues, this paper proposes a dual robust information fusion attention mechanism (DRIFA) that leverages two attention modules, i.e. multi-branch fusion attention module and the multimodal information fusion attention module. DRIFA can be integrated with any deep neural network, forming a multimodal fusion learning framework denoted as DRIFA-Net. We show that the multi-branch fusion attention of DRIFA learns enhanced representations for each modality, such as dermoscopy, pap smear, MRI, and CT-scan, whereas multimodal information fusion attention module learns more refined multimodal shared representations, improving the network's generalization across multiple tasks and enhancing overall performance. Additionally, to estimate the uncertainty of DRIFA-Net predictions, we have employed an ensemble Monte Carlo dropout strategy. Extensive experiments on five publicly available datasets with diverse modalities demonstrate that our approach consistently outperforms state-of-the-art methods. The code is available at https://github.com/misti1203/DRIFA-Net., Comment: 10 pages

Robotic Wire Arc Additive Manufacturing (WAAM) is a metal additive manufacturing technology, offering flexible 3D printing while ensuring high quality near-net-shape final parts. However, WAAM also suffers from geometric imprecision, especially for low-melting-point metal such as aluminum alloys. In this paper, we present a multi-robot framework for WAAM process monitoring and control. We consider a three-robot setup: a 6-dof welding robot, a 2-dof trunnion platform, and a 6-dof sensing robot with a wrist-mounted laser line scanner measuring the printed part height profile. The welding parameters, including the wire feed rate, are held constant based on the materials used, so the control input is the robot path speed. The measured output is the part height profile. The planning phase decomposes the target shape into slices of uniform height. During runtime, the sensing robot scans each printed layer, and the robot path speed for the next layer is adjusted based on the deviation from the desired profile. The adjustment is based on an identified model correlating the path speed to change in height. The control architecture coordinates the synchronous motion and data acquisition between all robots and sensors. Using a three-robot WAAM testbed, we demonstrate significant improvements of the closed loop scan-n-print approach over the current open loop result on both a flat wall and a more complex turbine blade shape.

In this report, the optical properties of Ta doped SrTiO3 (STO) due to its potential in transparent conducting oxides (TCOs) is explored by a combination of theoretical studies based on density functional theory and spectroscopic ellipsometry. To achieve this theoretically, we vary the concentration of Ta from 0 - 12.5% in SrTi1-xTaxO3 system by substitutional doping and report its effect on the resulting structural, chemical, electronic, chemical, and optical properties. Additionally, we perform band unfolding to shed light on the true nature of optical transitions due to Ta doping. We verify these results experimentally by fabricating epitaxial SrTi1-xTaxO3 thin films ( x = 0 - 5%) by pulsed laser deposition and obtain the optical dielectric properties of the system with the help of spectroscopic ellipsometry. By combining theoretical and experimental studies, we provide evidence that the band gap of STO increases due to Ta doping while also enhancing its electronic properties. The findings of our study offer an extensive understanding of the intricacies associated with elemental doping in perovskite oxides and propose strategies for addressing obstacles associated with TCOs., Comment: 36 pages, 14 figures including supplementary figures

We consider a one-dimensional system comprising of $N$ run-and-tumble particles confined in a harmonic trap interacting via a repulsive inverse-square power-law interaction. This is the ``active" version of the Calogero-Moser system where the particles are associated with telegraphic noise with two possible states $\pm v_0$. We numerically compute the global density profile in the steady state which shows interesting crossovers between three different regimes: as the activity increases, we observe a change from a density with sharp peaks characteristic of a crystal region to a smooth bell-shaped density profile, passing through the intermediate stage of a smooth Wigner semi-circle characteristic of a liquid phase. We also investigate analytically the crossover between the crystal and the liquid regions by computing the covariance of the positions of these particles in the steady state in the weak noise limit. It is achieved by using the method introduced in Touzo {\it et al.} [Phys. Rev. E {\bf 109}, 014136 (2024)] to study the active Dyson Brownian motion. Our analytical results are corroborated by thorough numerical simulations.

We consider the dynamics of a continuously monitored qubit in the limit of strong measurement rate where the quantum trajectory is described by a stochastic master equation with Poisson noise. Such limits are expected to give rise to quantum jumps between the pointer states associated with the non-demolition measurement. A surprising discovery in earlier work [Tilloy et al., Phys. Rev. A 92, 052111 (2015)] on quantum trajectories with Brownian noise was the phenomena of spikes observed in between the quantum jumps. Here, we show that spikes are observed also for Poisson noise. We consider three cases where the non-demolition is broken by adding, to the basic strong measurement dynamics, either unitary evolution or thermal noise or additional measurements. We present a complete analysis of the spike and jump statistics for all three cases using the fact that the dynamics effectively corresponds to that of stochastic resetting. We provide numerical results to support our analytic results., Comment: 23 pages, 11 figures

Let $p$ be a prime and let $\mathbb{Q}_p$ be the field of $p$-adic numbers. It is known that the finite extensions of $\mathbb{Q}_p$ of a given degree are finite up to isomorphism. Given a cubic field extension $L$ of $\mathbb{Q}_p$ generated by the root of an irreducible polynomial $h$, we present a practical (closed-form) method to determine the isomorphism class in which $L$ lives, based on the coefficients of $h$. We discuss the subtleties of the wildly ramified case, when the degree of the extension coincides with $p$, the characteristic of the residue field. We also present a method for tamely ramified extensions of arbitrary prime degree., Comment: 20 pages, 2 figures

The radio galaxy NGC 1275, located at the central region of Perseus cluster, is a well-known very high-energy (VHE) gamma-ray emitter. The Major Atmospheric Cherenkov Experiment Telescope has detected two distinct episodes of VHE (E > 80 GeV) gamma-ray emission from NGC 1275 during 2022 December and 2023 January. The second outburst, observed on 2023 January 10, was the more intense of the two, with flux reaching 58$\%$ of the Crab Nebula flux above 80 GeV. The differential energy spectrum measured between 80 GeV and 1.5 TeV can be described by a power law with a spectral index of $\Gamma = - 2.90 \pm 0.16_{stat}$ for both flaring events. The broadband spectral energy distribution derived from these flares, along with quasisimultaneous low-energy counterparts, suggests that the observed gamma-ray emission can be explained using a homogeneous single-zone synchrotron self-Compton model. The physical parameters derived from this model for both flaring states are similar. The intermediate state observed between two flaring episodes is explained by a lower Doppler factor or magnetic field, which subsequently returned to its previous value during the high-activity state observed on 2023 January 10., Comment: 7 Pages, 5 Figures, and 1 Table

Hyperspectral imaging is a powerful tool for Earth exploration, allowing for detailed analysis of spectral features. India has launched a dedicated hyperspectral Earth observation satellite capable of capturing data across a wide electromagnetic spectrum from 400 nm to 2500 nm, with an impressive spatial resolution of 30 meters. This paper details the comprehensive calibration procedures performed before launch to ensure data accuracy and instrument optimization for Hysis payload. Performance metrics demonstrate the satellite's ability to deliver high-quality imagery of the Earth's surface. In-orbit radiometric and geometric calibration further validated the satellite's functionality, while observations and anomalies encountered during this phase led to the development of specialized algorithms for operational enhancement. The paper outlines the operational data processing pipeline, covering stages from raw data acquisition to final image generation. A thorough assessment of the satellite's radiometric and geometric quality reinforces its reliability and effectiveness, highlighting its contribution to advancing Earth exploration through hyperspectral imaging technology., Comment: Preprint

Resourcesat-3S/3SA, an upcoming Indian satellite, is designed with Aft and Fore payloads capturing stereo images at look angles of -5deg and 26deg, respectively. Operating at 632.6 km altitude, it features a panchromatic (PAN) band offering a Ground Sampling Distance (GSD) of 1.25 meters and a 60 km swath. To balance swath width and resolution, an Instantaneous Geometric Field of View (IGFOV) of 2.5 meters is maintained while ensuring a 1.25-meter GSD both along and across track. Along-track sampling is achieved through precise timing, while across-track accuracy is ensured by using two staggered pixel arrays. Signal-to-Noise Ratio (SNR) is enhanced through Time Delay and Integration (TDI), employing two five-stage subarrays spaced 80 {\mu}m apart along the track, with a 4 {\mu}m (0.5 pixel) stagger in the across-track direction to achieve 1.25-meter resolution. To further boost resolution, the satellite employs super-resolution (SR), combining multiple low-resolution captures using sub-pixel shifts to produce high-resolution images. This technique, effective when images contain aliased high-frequency details, reconstructs full-resolution imagery using phase information from multiple observations, and has been successfully applied in remote sensing missions like SPOT-5, SkySat, and DubaiSat-1. A Monte Carlo simulation explores the factors influencing the resolution in Resourcesat-3S/3SA, with sensitivity analysis highlighting key impacts. The simulation methodology is broadly applicable to other remote sensing missions, optimizing SR for enhanced image clarity and resolution in future satellite systems., Comment: Preprint

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

The Geo High Resolution Camera (GHRC) aboard ISRO GSAT-29 satellite is a state-of-the-art 6-band Visible and Near Infrared (VNIR) imager in geostationary orbit at 55degE longitude. It provides a ground sampling distance of 55 meters at nadir, covering 110x110 km at a time, and can image the entire Earth disk using a scan mirror mechanism. To cover India, GHRC uses a two-dimensional raster scanning technique, resulting in over 1,000 scenes that must be stitched into a seamless mosaic. This paper presents the geolocation model and examines potential sources of targeting error, with an assessment of location accuracy. Challenges in inter-band registration and inter-frame mosaicing are addressed through algorithms for geometric correction, band-to-band registration, and seamless mosaic generation. In-flight geometric calibration, including adjustments to the instrument interior alignment angles using ground reference images, has improved pointing and location accuracy. A backtracking algorithm has been developed to correct frame-to-frame mosaicing errors for large-scale mosaics, leveraging geometric models, image processing, and space resection techniques. These advancements now enable the operational generation of full India mosaics with 100-meter resolution and high geometric fidelity, enhancing the GHRC capabilities for Earth observation and monitoring applications., Comment: Preprint

Hyperspectral images enable precise identification of ground objects by capturing their spectral signatures with fine spectral resolution.While high spatial resolution further enhances this capability, increasing spatial resolution through hardware like larger telescopes is costly and inefficient. A more optimal solution is using ground processing techniques, such as hypersharpening, to merge high spectral and spatial resolution data. However, this method works best when datasets are captured under similar conditions, which is difficult when using data from different times. In this work, we propose a superresolution approach to enhance hyperspectral data's spatial resolution without auxiliary input. Our method estimates the high-resolution point spread function (PSF) using blind deconvolution and corrects for sampling-related blur using a model-based superresolution framework. This differs from previous approaches by not assuming a known highresolution blur. We also introduce an adaptive prior that improves performance compared to existing methods. Applied to the visible and near-infrared (VNIR) spectrometer of HySIS, ISRO hyperspectral sensor, our algorithm removes aliasing and boosts resolution by approximately 1.3 times. It is versatile and can be applied to similar systems., Comment: Preprint

The Ocean Color Monitor-3 (OCM-3) sensor is instrumental in Earth observation, achieving a critical balance between high-resolution imaging and broad coverage. This paper explores innovative imaging methods employed in OCM-3 and the transformative potential of super-resolution techniques to enhance image quality. The super-resolution model for OCM-3 (SOCM-3) addresses the challenges of contemporary satellite imaging by effectively navigating the trade-off between image clarity and swath width. With resolutions below 240 meters in Local Area Coverage (LAC) mode and below 750 meters in Global Area Coverage (GAC) mode, coupled with a wide 1550-kilometer swath and a 2-day revisit time, SOCM-3 emerges as a leading asset in remote sensing. The paper details the intricate interplay of atmospheric, motion, optical, and detector effects that impact image quality, emphasizing the necessity for advanced computational techniques and sophisticated algorithms for effective image reconstruction. Evaluation methods are thoroughly discussed, incorporating visual assessments using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metric and computational metrics such as Line Spread Function (LSF), Full Width at Half Maximum (FWHM), and Super-Resolution (SR) ratio. Additionally, statistical analyses, including power spectrum evaluations and target-wise spectral signatures, are employed to gauge the efficacy of super-resolution techniques. By enhancing both spatial resolution and revisit frequency, this study highlights significant advancements in remote sensing capabilities, providing valuable insights for applications across cryospheric, vegetation, oceanic, coastal, and domains. Ultimately, the findings underscore the potential of SOCM-3 to contribute meaningfully to our understanding of finescale oceanic phenomena and environmental monitoring., Comment: Preprint

The Ocean Color Monitor-3, launched aboard Oceansat-3, represents a significant advancement in ocean observation technology, building upon the capabilities of its predecessors. With thirteen spectral bands, OCM-3 enhances feature identification and atmospheric correction, enabling precise data collection from a sun-synchronous orbit. With thirteen spectral bands, OCM-3 enhances feature identification and atmospheric correction, enabling precise data collection from a sunsynchronous orbit. Operating at an altitude of 732.5 km, the satellite achieves high signal-to-noise ratios (SNR) through sophisticated onboard and ground processing techniques, including advanced geometric modeling for pixel registration.The OCM-3 processing pipeline, consisting of multiple levels, ensures rigorous calibration and correction of radiometric and geometric data. This paper presents key methodologies such as dark data modeling, photo response non-uniformity correction, and smear correction, are employed to enhance data quality. The effective implementation of ground time delay integration (TDI) allows for the refinement of SNR, with evaluations demonstrating that performance specifications were exceeded. Geometric calibration procedures, including band-to-band registration and geolocation accuracy assessments, which further optimize data reliability are presented in the paper. Advanced image registration techniques leveraging Ground Control Points (GCPs) and residual error analysis significantly reduce geolocation errors, achieving precision within specified thresholds. Overall, OCM-3 comprehensive calibration and processing strategies ensure high-quality, reliable data crucial for ocean monitoring and change detection applications, facilitating improved understanding of ocean dynamics and environmental changes., Comment: Preprint

In this paper, we study collision-free graph exploration in an anonymous pot labeled network. Two identical mobile agents, starting from different nodes in $G$ have to explore the nodes of $G$ in such a way that for every node $v$ in $G$, at least one mobile agent visits $v$ and no two agents are in the same node in any round and stop. The agents know the size of the graph but do not know its topology. If an agent arrives in the one-hop neighborhood of the other agent, both agents can detect the presence of the other agent but have no idea at which neighboring node the other agent resides. The agents may wake up in different rounds An agent, after waking up, has no knowledge about the wake-up time of the other agent. We study the problem of collision-free exploration where some pebbles are placed by an Oracle at the nodes of the graph to assist the agents in achieving collision-free exploration. The Oracle knows the graph, the starting positions of the agents, and their wake-up schedule, and it places some pebbles that may be of different colors, at most one at each node. The number of different colors of the pebbles placed by the Oracle is called the {\it color index} of the corresponding pebble placement algorithm. The central question we study is as follows: "What is the minimum number $z$ such that there exists a collision-free exploration of a given graph with pebble placement of color index $z$?" For general graphs, we show that it is impossible to design an algorithm that achieves collision-free exploration with color index 1. We propose an exploration algorithm with color index 3. We also proposed a polynomial exploration algorithm for bipartite graphs with color index 2.

In order to enable the sequential implementation of quantum information theoretic protocols in the continuous variable framework, we propose two schemes for resource reusability, resource-splitting protocol and unsharp homodyne measurements. We demonstrate the advantage offered by the first scheme in implementing sequential attempts at continuous variable teleportation when the protocol fails in the previous round. On the other hand, unsharp quadrature measurements are employed to implement the detection of entanglement between several pairs of parties. We exhibit that, under specific conditions, it is possible to witness the entanglement of a state an arbitrary number of times via a scheme that differs significantly from any protocol proposed for finite dimensional systems., Comment: 12 pages, 3 figures

In recent years, various materials have been developed to reduce the reliance of industries on Indium, a primary component of transparent conducting oxides (TCOs) used in the current generation of devices. The leading candidates for indium free TCOs are strontium vanadates, niobates and molybdates -- strongly correlated perovskite systems that exhibit high intrinsic electrical conductivity and optimal transparency. In this work, we focus on the strontium niobate thin films and manipulate its optical conductivity by Ti doping, which shifts the plasma frequency and reduces electronic correlations. This allows us to achieve a low resistance for Ti doped SNO thin films, while maintaining a high transparency in the visible spectrum. We obtain the optimal figure-of-merit (FOM) of 10.3 ($10^{-3}\Omega^{-1}$) for $x = 0.3$. This FOM significantly outperforms the optoelectronic capabilities of Tin-doped Indium oxide (ITO) and several other proposed transparent conductor materials. Our research paves the way for designing the next generation of transparent conductors, guided by insights from density-functional theory (DFT) and dynamical mean-field theory (DMFT)., Comment: 35 pages, 14 figure including supplementary figures

Knowledge claims are abundant in the literature on large language models (LLMs); but can we say that GPT-4 truly "knows" the Earth is round? To address this question, we review standard definitions of knowledge in epistemology and we formalize interpretations applicable to LLMs. In doing so, we identify inconsistencies and gaps in how current NLP research conceptualizes knowledge with respect to epistemological frameworks. Additionally, we conduct a survey of 100 professional philosophers and computer scientists to compare their preferences in knowledge definitions and their views on whether LLMs can really be said to know. Finally, we suggest evaluation protocols for testing knowledge in accordance to the most relevant definitions., Comment: EMNLP 2024

The randomized online-LOCAL model captures a number of models of computing; it is at least as strong as all of these models: - the classical LOCAL model of distributed graph algorithms, - the quantum version of the LOCAL model, - finitely dependent distributions [e.g. Holroyd 2016], - any model that does not violate physical causality [Gavoille, Kosowski, Markiewicz, DISC 2009], - the SLOCAL model [Ghaffari, Kuhn, Maus, STOC 2017], and - the dynamic-LOCAL and online-LOCAL models [Akbari et al., ICALP 2023]. In general, the online-LOCAL model can be much stronger than the LOCAL model. For example, there are locally checkable labeling problems (LCLs) that can be solved with logarithmic locality in the online-LOCAL model but that require polynomial locality in the LOCAL model. However, in this work we show that in trees, many classes of LCL problems have the same locality in deterministic LOCAL and randomized online-LOCAL (and as a corollary across all the above-mentioned models). In particular, these classes of problems do not admit any distributed quantum advantage. We present a near-complete classification for the case of rooted regular trees. We also fully classify the super-logarithmic region in unrooted regular trees. Finally, we show that in general trees (rooted or unrooted, possibly irregular, possibly with input labels) problems that are global in deterministic LOCAL remain global also in the randomized online-LOCAL model., Comment: 39 pages, 8 figures

Credit card is one of the most extensive methods of instalment for both online and offline mode of payment for electronic transactions in recent times. credit cards invention has provided significant ease in electronic transactions. However, it has also provided new fraud opportunities for criminals, which results in increased fraud rates. Substantial amount of money has been lost by many institutions and individuals due to fraudulent credit card transactions. Adapting improved and dynamic fraud recognition frameworks thus became essential for all credit card distributing banks to mitigate their losses. In fact, the problem of fraudulent credit card transactions implicates a number of relevant real-time challenges, namely: Concept drift, Class imbalance, and Verification latency. However, the vast majority of current systems are based on artificial intelligence (AI), Fuzzy logic, Machine Learning, Data mining, Genetic Algorithms, and so on, rely on assumptions that hardly address all the relevant challenges of fraud-detection system (FDS). This paper aims to understand & implement Deep Learning algorithms in order to obtain a high fraud coverage with very low false positive rate. Also, it aims to implement an auto-encoder as an unsupervised (semi-supervised) method of learning common patterns. Keywords: Credit card fraud, Fraud-detection system (FDS), Electronic transactions, Concept drift, Class imbalance, Verification latency, Machine Learning, Deep Learning, Comment: Part of the M.Tech. thesis. Sourav Verma, ABV-Indian Institute of Information Technology, Gwalior 2013-18

We investigate the potential of epitaxial (001)p-NiO/(0001)n-ZnO heterostructures grown on (0001)sapphire substrates by pulsed laser deposition technique for ultraviolet photodetector application. Our study reveals that in the self-powered mode, these devices can serve as effective photodetectors for the UV-A band (320-400 nm) with response time as short as 400 microseconds. Peak responsivity as high as 5mA/W at zero bias condition have been achieved. These devices also show a very high level of stability under repeated on/off illumination cycles over a long period of time. Furthermore, we find that the response time of these detectors can be controlled from several microseconds to thousands of seconds by applying bias both in the forward and the reverse directions. This persistent photoconductivity effect has been explained in terms of the field induced change in the capture barrier height associated with certain traps located at the junction., Comment: 10 Pages

Bilayer (2L) transition metal dichalcogenides (TMD) have the ability to host interlayer excitons, where electron and hole parts are spatially separated that leads to much longer lifetime as compared to direct excitons. This property can be utilized for the development of exciton-based logic devices, which are supposed to be superior in terms of energy efficiency and optical communication compatibility as compared to their electronic counterparts. However, obtaining uniformly thick bilayer epitaxial films with large area coverage is challenging. Here, we have engineered the flow pattern of the precursors over the substrate surface to obtain large area (mm2) covered strictly bilayer MoS$_2$ films on SiO$_2$ by chemical vapour deposition (CVD) technique without any plasma treatment of the substrate prior to the growth. Bilayer nature of these films is confirmed by Raman, low-frequency Raman, atomic force microscopy (AFM) and photoluminescence (PL) studies. The uniformity of the film has been checked by Raman peak separation and PL intensity map. High resolution transmission electron microscopy (HRTEM) reveals that crystalline and twisted bilayer islands coexist within the layer. Back gated field-effect transistor (FET) structures fabricated on the bilayers show on/off ratio of 10^6 and subthreshold swings (SS) of 2.5 V/Decade., Comment: 9 pages, 6 figures (main manuscript) and 3 pages, 2 figures (supplementary materials)

Inspired by Andrews' and Newman's work on the minimal excludant or "mex" of partitions, we define four new classes of minimal excludants for overpartitions and establish relations to certain functions due to Ramanujan., Comment: To appear in the Bulletin of the Australian Mathematical Society

NiO/ZnO heterostructures are grown on c-sapphire substrates using pulsed laser deposition (PLD) technique. X-ray diffraction study shows that the ZnO layer epitaxially grows along [0001]-direction on (0001)sapphire surface as expected. While, the epitaxial NiO film is found to be deposited along [001]-direction on the (0001)ZnO surface. Moreover, the presence of three (001)NiO domains laterally rotated by 30{\deg} with respect to each other, has also been observed in our NiO films. The study reveals the continuous nature of the NiO film, which also possesses a very smooth surface morphology. In a sharp contrast, ZnO films are found to grow along [0001]-direction when deposited on (111)NiO layers. These films also show columnar morphology. (001)NiO/(0001)ZnO layers exhibit the rectifying current-voltage characteristics that suggests the existence of p-n junction in these devices. However, the behavior could not be observed in (0001)ZnO/(111)NiO heterojunctions. The reason could be the columnar morphology of the ZnO layer. Such a morphology can facilitate the propagation of the metal ions from the contact pads to the underlying NiO layer and suppress the p-n junction effect., Comment: 6 pages and 6 figures (main manuscript), 6 pages and 6 figures (supplemental material)

The fractal and phase transitional properties of each type of pions (i.e. $\pi^{\pm,0}$) through one-dimensional $\eta-$space, at an energy of $\sqrt{s}=13~$TeV, have been studied with the help of the Scaled Factorial Moment (SFM) framework. To generate simulated data sets for $pp$ collisions under the minimum bias (MB) condition at $\sqrt{s}=13~$TeV, we have employed the Monte Carlo-based event simulator PYTHIA. Various parameters such as the Levy index $(\mu)$, degree of multifractality $(r)$, anomalous fractal dimension $(d_q)$, multifractal specific heat $(c)$ and critical exponent $(\nu)$ have been calculated. To study the Bose Einstein(BE) effect due to identical particles (here pions) we have also derived these parameters for mixed pion pairs (i.e. $\{\pi^{+},\pi^{-}\}$, $\{\pi^{+},\pi^{0}\}$ and $\{\pi^{-},\pi^{0}\}$) and we find that the effects of identical particles weakened for the mixture with respect to the individual distributions. The quest for the quark-hadron phase transition has also been conducted within the framework of the Ginzburg-Landau (GL) theory of second-order phase transition. Analysis revealed that for PYTHIA-generated MB events, there is a clear indication of the quark-hadron phase transition according to the GL theory. Furthermore, the values of the multifractal specific heat ($c$) for each $\pi^{+}, \pi^{-}, \pi^{0}$ and the mixture pair data sets of pions generated by PYTHIA model at MB condition, indicate a transition from multifractality to monofractality in $pp$ collisions at $\sqrt{s}=13~$TeV., Comment: 19 pages, 10 Figures ( Total 18 Figures with sub-figures)

The time-to-recovery or time-to-death for various infectious diseases can vary significantly among individuals, influenced by several factors such as demographic differences, immune strength, medical history, age, pre-existing conditions, and infection severity. To capture these variations, time-since-infection dependent recovery and death rates offer a detailed description of the epidemic. However, obtaining individual-level data to estimate these rates is challenging, while aggregate epidemiological data (such as the number of new infections, number of active cases, number of new recoveries, and number of new deaths) are more readily available. In this article, a new methodology is proposed to estimate time-since-infection dependent recovery and death rates using easily available data sources, accommodating irregular data collection timings reflective of real-world reporting practices. The Nadaraya-Watson estimator is utilized to derive the number of new infections. This model improves the accuracy of epidemic progression descriptions and provides clear insights into recovery and death distributions. The proposed methodology is validated using COVID-19 data and its general applicability is demonstrated by applying it to some other diseases like measles and typhoid.

Spin ensembles are promising quantum technological platforms, but their utility relies on the ability to perform quantum error correction (QEC) for the specific decoherence in these systems. Typical QEC for ensembles requires addressing individually resolved qubits, but this is practically challenging in most realistic architectures. Here, we propose QEC schemes for unresolvable spin ensembles. By using degenerate superpositions of excited states, which are fundamentally mixed, we find codes that can protect against both individual and collective errors, including dephasing, decay, and pumping. We show how information recovery can be achieved with only collective measurement and control, and illustrate its applications in extending memory lifetime and loss-tolerant sensing., Comment: 7 pages, 3 figures

Wire Arc Additive Manufacturing (WAAM) is a metal 3D printing technology that deposits molten metal wire on a substrate to form desired geometries. Articulated robot arms are commonly used in WAAM to produce complex geometric shapes. However, they mostly rely on proprietary robot and weld control software that limits process tuning and customization, incorporation of third-party sensors, implementation on robots and weld controllers from multiple vendors, and customizable user programming. This paper presents a general open-source software architecture for WAAM that addresses these limitations. The foundation of this architecture is Robot Raconteur, an open-source control and communication framework that serves as the middleware for integrating robots and sensors from different vendors. Based on this architecture, we developed an end-to-end robotic WAAM implementation that takes a CAD file to a printed WAAM part and evaluates the accuracy of the result. The major components in the architecture include part slicing, robot motion planning, part metrology, in-process sensing, and process tuning. The current implementation is based on Motoman robots and Fronius weld controller, but the approach is applicable to other industrial robots and weld controllers. The capability of the WAAM tested is demonstrated through the printing of parts of various geometries and acquisition of in-process sensor data for motion adjustment.

This article introduces operator on operator regression in quantum probability. Here in the regression model, the response and the independent variables are certain operator valued observables, and they are linearly associated with unknown scalar coefficient (denoted by $\beta$), and the error is a random operator. In the course of this study, we propose a quantum version of a class of estimators (denoted by $M$ estimator) of $\beta$, and the large sample behaviour of those quantum version of the estimators are derived, given the fact that the true model is also linear and the samples are observed eigenvalue pairs of the operator valued observables.

In this article, we study whether the slope functions of two functional regression models in two samples are associated with any arbitrary transformation (barring constant and linear transformation) or not along the vertical axis. In order to address this issue, a statistical testing of the hypothesis problem is formalized, and the test statistic is formed based on the estimated second derivative of the unknown transformation. The asymptotic properties of the test statistics are investigated using some advanced techniques related to the empirical process. Moreover, to implement the test for small sample size data, a Bootstrap algorithm is proposed, and it is shown that the Bootstrap version of the test is as good as the original test for sufficiently large sample size. Furthermore, the utility of the proposed methodology is shown for simulated data sets, and DTI data is analyzed using this methodology., Comment: Some typos have been fixed

Large language models (LLMs) are very performant connectionist systems, but do they exhibit more compositionality? More importantly, is that part of why they perform so well? We present empirical analyses across four LLM families (12 models) and three task categories, including a novel task introduced below. Our findings reveal a nuanced relationship in learning of compositional strategies by LLMs -- while scaling enhances compositional abilities, instruction tuning often has a reverse effect. Such disparity brings forth some open issues regarding the development and improvement of large language models in alignment with human cognitive capacities., Comment: Accepted to ICML 2024 Workshop on LLMs & Cognition

We report an extensive computational and experimental investigation of droplet impact and subsequent splitting hydrodynamics on superhydrophobic wedges. 2D and necessary 3D simulations using the volume of fluid method, backed with experimentations, have been performed to predict the droplet impact, spreading, split up, retraction against sliding, and daughter droplet lift off events from the SH wedge. In particular, we examine how the wedge angle , wedge asymmetry , Weber number and normalized Bond number influence the post-impact dynamics. We observe that for symmetric wedges, the maximum spread factor of the droplet decreases with an increase in wedge angle at a fixed We. At high wedge angles, the sharp steepness of the wedge causes less contact area for the droplet to spread. For the asymmetric wedges, it has been noted that beta max increases with an increase in the We due to the higher inertial forces of the droplet against sliding. Furthermore, the increases with an increase in Bo at a fixed We due to the dominance of the gravitational force over the capillary force of the droplet. It has been also found that at the same Bo, the increases with an increase in We due to the dominance of inertial forces over the capillary forces. The split volume of daughter droplets during the split up stage for different symmetric and asymmetric wedge angles has been discussed. In general, our 2D simulations agree well with the experiments for a major part of the droplet lifetime. Further, we have conducted a detailed 3D simulation based energy budget analysis to estimate the temporal evolution of the various energy components at different post impact hydrodynamic regimes.

Cloud workloads have dominated generative AI based on large language models (LLM). Specialized hardware accelerators, such as GPUs, NPUs, and TPUs, play a key role in AI adoption due to their superior performance over general-purpose CPUs. The AI models and the data are often highly sensitive and come from mutually distrusting parties. Existing CPU-based TEEs such as Intel SGX or AMD SEV do not provide sufficient protection. Device-centric TEEs like Nvidia-CC only address tightly coupled CPU-GPU systems with a proprietary solution requiring TEE on the host CPU side. On the other hand, existing academic proposals are tailored toward specific CPU-TEE platforms. To address this gap, we propose Ascend-CC, a confidential computing architecture based on discrete NPU devices that requires no trust in the host system. Ascend-CC provides strong security by ensuring data and model encryption that protects not only the data but also the model parameters and operator binaries. Ascend-CC uses delegation-based memory semantics to ensure isolation from the host software stack, and task attestation provides strong model integrity guarantees. Our Ascend-CC implementation and evaluation with state-of-the-art LLMs such as Llama2 and Llama3 shows that Ascend-CC introduces minimal overhead with no changes in the AI software stack.

Intercalation of magnetic atoms into the van der Waals gaps of layered transition metal dichalcogenides offers an excellent platform to produce exotic physical properties. Here, we report a detailed study of magnetic and electrical transport properties of Co$_{0.28}$TaS$_2$. The temperature dependent resistivity measurements display anomalous upturn below 11 K, which persists in presence of magnetic field even up to 14 T. In the low temperature region, the resistivity upturn exhibits a unique $T^{1/2}$ scaling behavior, which remains unchanged when an external magnetic field is applied. The $T^{1/2}$ dependence of resistivity upturn is the hallmark of non-Fermi liquid state in orbital two-channel Kondo effect(2CK). This anomalous resistivity upturn in Co$_{0.28}$TaS$_2$ can be attributed to the orbital two-channel Kondo mechanism., Comment: 7 pages, 5 figures

Motivated by recent research of Krattenthaler and Wang, we propose five new "Borwein-type" conjectures modulo $3$ and two new "Borwein-type" conjectures modulo $5$., Comment: 6 pages. Comments are welcome!

Let $P$ be an orthogonal polygon of $n$ vertices, without holes. The Orthogonal Polygon Covering with Squares (OPCS) problem takes as input such an orthogonal polygon $P$ with integral vertex coordinates, and asks to find the minimum number of axis-parallel squares whose union is $P$ itself. [Aupperle et. al, 1988] provide an $\mathcal O(N^{1.5})$-time algorithm for OPCS, where $N$ is the number of integral lattice points lying in $P$. In their paper, designing algorithms for OPCS with a running time polynomial in $n$, was stated as an open question; $N$ can be arbitrarily larger than $n$. Output sensitive algorithms were known due to [Bar-Yehuda and Ben-Chanoch, 1994], but these fail to address the open question, as the output can be arbitrarily larger than $n$. We address this open question by designing a polynomial-time exact algorithm for OPCS with a worst-case running time of $\mathcal O(n^{10})$. We also consider the following structural parameterized version of the problem. Let a knob be a polygon edge whose both endpoints are convex polygon vertices. Given an input orthogonal polygon without holes that has $n$ vertices and at most $k$ knobs, we design an algorithm for OPCS with a worst-case running time $\mathcal O(n^2 + k^{10} \cdot n)$. This algorithm is more efficient than the former, whenever $k = o(n^{9/10})$. The problem of Orthogonal Polygon with Holes Covering with Squares (OPCSH) is also studied by [Aupperle et. al, 1988], where the input polygon could have holes. They claim a proof that OPCSH is NP-complete even when the input is the $N$ lattice points inside the polygon. We think there is an error in their proof, where an incorrect reduction from Planar 3-CNF is shown. We provide a correct reduction with a novel construction of one of the gadgets, and show how this leads to a correct proof of NP-completeness of OPCSH.

In this paper we will be classifying some regular upper-triangular subalgebras of $\mathfrak{sl}(n, \mathbb C)$ up to conjugacy by matrices in $SL(n, \mathbb C)$. We do so for dimension 2, codimension 1, and codimension 2 subalgebras. We prove some general results for codimension $k$. The approach we use reduces an abstract classification problem to a combinatorial one, which we solve through a mixture of inductive and computational approaches.

Identifying individual tissues, so-called tissue segmentation, in diabetic foot ulcer (DFU) images is a challenging task and little work has been published, largely due to the limited availability of a clinical image dataset. To address this gap, we have created a DFUTissue dataset for the research community to evaluate wound tissue segmentation algorithms. The dataset contains 110 images with tissues labeled by wound experts and 600 unlabeled images. Additionally, we conducted a pilot study on segmenting wound characteristics including fibrin, granulation, and callus using deep learning. Due to the limited amount of annotated data, our framework consists of both supervised learning (SL) and semi-supervised learning (SSL) phases. In the SL phase, we propose a hybrid model featuring a Mix Transformer (MiT-b3) in the encoder and a CNN in the decoder, enhanced by the integration of a parallel spatial and channel squeeze-and-excitation (P-scSE) module known for its efficacy in improving boundary accuracy. The SSL phase employs a pseudo-labeling-based approach, iteratively identifying and incorporating valuable unlabeled images to enhance overall segmentation performance. Comparative evaluations with state-of-the-art methods are conducted for both SL and SSL phases. The SL achieves a Dice Similarity Coefficient (DSC) of 84.89%, which has been improved to 87.64% in the SSL phase. Furthermore, the results are benchmarked against two widely used SSL approaches: Generative Adversarial Networks and Cross-Consistency Training. Additionally, our hybrid model outperforms the state-of-the-art methods with a 92.99% DSC in performing binary segmentation of DFU wound areas when tested on the Chronic Wound dataset. Codes and data are available at https://github.com/uwm-bigdata/DFUTissueSegNet.

Computing an optimal cycle in a given homology class, also referred to as the homology localization problem, is known to be an NP-hard problem in general. Furthermore, there is currently no known optimality criterion that localizes classes geometrically and admits a stability property under the setting of persistent homology. We present a geometric optimization of the cycles that is computable in polynomial time and is stable in an approximate sense. Tailoring our search criterion to different settings, we obtain various optimization problems like optimal homologous cycle, minimum homology basis, and minimum persistent homology basis. In practice, the (trivial) exact algorithm is computationally expensive despite having a worst case polynomial runtime. Therefore, we design approximation algorithms for the above problems and study their performance experimentally. These algorithms have reasonable runtimes for moderate sized datasets and the cycles computed by these algorithms are consistently of high quality as demonstrated via experiments on multiple datasets., Comment: To Appear in CCCG 2024 : Proc. 36th Canadian Conference on Computational Geometry

Learning robust and effective representations of visual data is a fundamental task in computer vision. Traditionally, this is achieved by training models with labeled data which can be expensive to obtain. Self-supervised learning attempts to circumvent the requirement for labeled data by learning representations from raw unlabeled visual data alone. However, unlike humans who obtain rich 3D information from their binocular vision and through motion, the majority of current self-supervised methods are tasked with learning from monocular 2D image collections. This is noteworthy as it has been demonstrated that shape-centric visual processing is more robust compared to texture-biased automated methods. Inspired by this, we propose a new approach for strengthening existing self-supervised methods by explicitly enforcing a strong 3D structural prior directly into the model during training. Through experiments, across a range of datasets, we demonstrate that our 3D aware representations are more robust compared to conventional self-supervised baselines.