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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

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, DICS 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)

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

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!

The Orthogonal Polygon Covering with Squares (OPCS) problem takes as input an orthogonal polygon $P$ without holes with $n$ vertices, where vertices have integral coordinates. The aim is to find a minimum number of axis-parallel, possibly overlapping squares which lie completely inside $P$, such that their union covers the entire region inside $P$. Aupperle et. al~\cite{aupperle1988covering} provide an $\mathcal O(N^{1.5})$-time algorithm to solve OPCS for orthogonal polygons without holes, where $N$ is the number of integral lattice points lying in the interior or on the boundary of $P$. Designing algorithms for OPCS with a running time polynomial in $n$ (the number of vertices of $P$) was discussed as an open question in \cite{aupperle1988covering}, since $N$ can be exponentially larger than $n$. In this paper we design a polynomial-time exact algorithm for OPCS with a running time of $\mathcal O(n^{14})$. We also consider the following structural parameterized version of the problem. A knob in an orthogonal polygon is a polygon edge whose both endpoints are convex polygon vertices. Given an input orthogonal polygon with $n$ vertices and $k$ knobs, we design an algorithm for OPCS with running time $\mathcal O(n^2 + k^{14} \cdot n)$. In \cite{aupperle1988covering}, the Orthogonal Polygon with Holes Covering with Squares (OPCSH) problem is also studied where orthogonal polygon could have holes, and the objective is to find a minimum square covering of the input polygon. This is shown to be NP-complete. We think there is an error in the existing proof in \cite{aupperle1988covering}, where a reduction from Planar 3-CNF is shown. We fix this error in the proof with an alternate construction of one of the gadgets used in the reduction, hence completing the 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.

We present a fluctuating hydrodynamic description of a non-polar active lattice gas model with excluded volume interactions that exhibits motility-induced phase separation under appropriate conditions. For quasi-one dimension and higher, stability analysis of the noiseless hydrodynamics gives quantitative bounds on the phase boundary of the motility-induced phase separation in terms of spinodal and binodal. Inclusion of the multiplicative noise in the fluctuating hydrodynamics describes the exponentially decaying two-point correlations in the stationary-state homogeneous phase. Our hydrodynamic description and theoretical predictions based on it are in excellent agreement with our Monte-Carlo simulations and pseudo-spectral iteration of the hydrodynamics equations. Our construction of hydrodynamics for this model is not suitable in strictly one-dimension with single-file constraints, and we argue that this breakdown is associated with micro-phase separation., Comment: 6 pages, 6 figures, + 16 supplemental pages

In this article, we propose L-estimators of the unknown parameters in the instrumental variables regression in the presence of censored data under endogeneity. We allow the random errors involved in the model to be dependent. The proposed estimation procedure is a two-stage procedure, and the large sample properties of the proposed estimators are established. The utility of the proposed methodology is demonstrated for various simulated data and a benchmark real data set., Comment: For better understanding, in the Introduction, the merits of the work is more elaborated along with more explanation of the results in real data analysis. Slight modifications have been made in the few assumptions to make it easy to read the articles

We study the evolution of a classical harmonic chain with nearest-neighbor interactions starting from domain wall initial conditions. The initial state is taken to be either a product of two Gibbs Ensembles (GEs) with unequal temperatures on the two halves of the chain or a product of two Generalized Gibbs Ensembles (GGEs) with different parameters in the two halves. For this system, we construct the Wigner function and demonstrate that its evolution defines the Generalized Hydrodynamics (GHD) describing the evolution of the conserved quantities. We solve the GHD for both finite and infinite chains and compute the evolution of conserved densities and currents. For a finite chain with fixed boundaries, we show that these quantities relax as $\sim 1/\sqrt{t}$ to their respective steady-state values given by the final expected GE or GGE state, depending on the initial conditions. Exact expressions for the Lagrange multipliers of the final expected GGE state are obtained in terms of the steady state densities. In the case of an infinite chain, we find that the conserved densities and currents at any finite time exhibit ballistic scaling while, at infinite time, any finite segment of the system can be described by a current-carrying non-equilibrium steady state (NESS). We compute the scaling functions analytically and show that the relaxation to the NESS occurs as $\sim 1/t$ for the densities and as $\sim 1/t^2$ for the currents. We compare the analytic results from hydrodynamics with those from exact microscopic numerics and find excellent agreement., Comment: 22 pages, 11 figures

Plastic strain-induced phase transformations (PTs) and chemical reactions under high pressure are broadly spread in modern technologies, friction and wear, geophysics, and astrogeology. However, because of very heterogeneous fields of plastic strain $\mathbf{E}^{p}$ and stress $\mathbf{\sigma}$ tensors and volume fraction $c$ of phases in a sample compressed in a diamond anvil cell (DAC) and impossibility of measurements of $\mathbf{\sigma}$ and $\mathbf{E}^{p}$, there are no strict kinetic equations for them. Here, we develop combined experimental-computational approaches to determine all fields in strongly plastically predeformed Zr and kinetic equation for $\alpha$-$\omega$ PT consistent with experimental data for the entire sample. Kinetic equation depends on accumulated plastic strain (instead of time) and pressure and is independent of plastic strain and deviatoric stress tensors, i.e., it can be applied for various above processes. Our results initiate kinetic studies of strain-induced PTs and provide efforts toward more comprehensive understanding of material behavior in extreme conditions., Comment: 28 pages, 11 figures

In 1797, Pfaff gave a simple proof of a ${}_3F_2$ hypergeometric series summation formula which was much later reproved by Andrews in 1996. In the same paper, Andrews also proved other well-known hypergeometric identities using Pfaff's method. In this paper, we prove a number of terminating $q$-hypergeometric series-product identities using Pfaff's method thereby providing a detailed account of its wide applicability., Comment: 17 pages. Comments are welcome!

In this paper, the authors propose the utilization of Fibonacci Neural Networks (FNN) for solving arbitrary order differential equations. The FNN architecture comprises input, middle, and output layers, with various degrees of Fibonacci polynomials serving as activation functions in the middle layer. The trial solution of the differential equation is treated as the output of the FNN, which involves adjustable parameters (weights). These weights are iteratively updated during the training of the Fibonacci neural network using backpropagation. The efficacy of the proposed method is evaluated by solving five differential problems with known exact solutions, allowing for an assessment of its accuracy. Comparative analyses are conducted against previously established techniques, demonstrating superior accuracy and efficacy in solving the addressed problems.

We establish a connection between problems studied in rigidity theory and matroids arising from linear algebraic constructions like tensor products and symmetric products. A special case of this correspondence identifies the problem of giving a description of the correctable erasure patterns in a maximally recoverable tensor code with the problem of describing bipartite rigid graphs or low-rank completable matrix patterns. Additionally, we relate dependencies among symmetric products of generic vectors to graph rigidity and symmetric matrix completion. With an eye toward applications to computer science, we study the dependency of these matroids on the characteristic by giving new combinatorial descriptions in several cases, including the first description of the correctable patterns in an (m, n, a=2, b=2) maximally recoverable tensor code.

This paper is devoted to find the numerical solutions of one dimensional general nonlinear system of third-order boundary value problems (BVPs) for the pair of functions using Galerkin weighted residual method. We derive mathematical formulations in matrix form, in details, by exploiting Bernstein polynomials as basis functions. A reasonable accuracy is found when the proposed method is used on few examples. At the end of the study, a comparison is made between the approximate and exact solutions, and also with the solutions of the existing methods. Our results converge monotonically to the exact solutions. In addition, we show that the the derived formulations may be applicable by reducing higher order complicated BVP into a lower order system of BVPs, and the performance of the numerical solutions is satisfactory.

(111)NiO epitaxial films are grown on c-sapphire substrates at various growth temperatures ranging from room-temperature to 600C using pulsed laser deposition (PLD) technique. Two series of samples, where different laser fluences are used to ablate the target, are studied here. Films grown with higher laser fluence, are found to be embedded with Ni-clusters crystallographically aligned with the (111)NiO matrix. While the layers grown with lower laser energy density exhibit p-type conductivity specially at low growth temperatures. X-ray diffraction study shows the coexistence of biaxial compressive and tensile hydrostatic strains in these samples, which results in an expansion of the lattice primarily along the growth direction. This effective uniaxial expansion {epsilon}_perpendicular increases with the reduction of the growth temperature. Band gap of these samples is found to decrease linearly with {epsilon}_perpendicular. This result is validated by density functional theory (DFT) calculations. Experimental findings and the theoretical study further indicate that V_Ni + O_I and V_O + Ni_I complexes exist as the dominant native defects in samples grown with Ni-deficient (low laser fluence) and Ni-rich (high laser fluence) conditions, respectively. P-type conductivity observed in the samples grown in Ni-deficient condition is more likely to be resulting from V_Ni + O_I defects than Ni-vacancies (V_Ni)., Comment: 9 pages and 9 figures (Main manuscript), 4 pages and 4 figures (supplemental material)

Advances in neural sensing technology are making it possible to observe the olfactory process in great detail. In this paper, we conceptualize smell from a Data Science and AI perspective, that relates the properties of odorants to how they are sensed and analyzed in the olfactory system from the nose to the brain. Drawing distinctions to color vision, we argue that smell presents unique measurement challenges, including the complexity of stimuli, the high dimensionality of the sensory apparatus, as well as what constitutes ground truth. In the face of these challenges, we argue for the centrality of odorant-receptor interactions in developing a theory of olfaction. Such a theory is likely to find widespread industrial applications, and enhance our understanding of smell, and in the longer-term, how it relates to other senses and language. As an initial use case of the data, we present results using machine learning-based classification of neural responses to odors as they are recorded in the mouse olfactory bulb with calcium imaging., Comment: 20 pages, 10 Figures, 2 Appendix, 1 Table

The Major Atmospheric Cherenkov Experiment (MACE) is a large size (21m) Imaging Atmospheric Cherenkov Telescope (IACT) installed at an altitude of 4270m above sea level at Hanle, Ladakh in northern India. Here we report the detection of Very High Energy (VHE) gamma-ray emission from Crab Nebula above 80 GeV. We analysed ~15 hours of data collected at low zenith angle between November 2022 and February 2023. The energy spectrum is well described by a log-parabola function with a flux of ~(3.46 +/- 0.26stat) x 10-10 TeV-1 cm-2 s-1, at 400 GeV with spectral index of 2.09 +/- 0.06stat and a curvature parameter of 0.08 +/- 0.07stat. The gamma-rays are detected in an energy range spanning from 80 GeV to ~5 TeV. The energy resolution improves from ~34% at an analysis energy threshold of 80 GeV to ~21% above 1 TeV. The daily light curve and the spectral energy distribution obtained for the Crab Nebula is in agreement with previous measurements, considering statistical and systematic uncertainties., Comment: Accepted for publication in Astroparticle Physics

Predicting residual stresses has always been a topic of significance due to its implications in the development of enhanced materials and better processing conditions. In this work, an analytical model for prediction of residual stresses is developed for orthogonal machining. It consists of three component models for force, temperature and stress computation. The Oxley force model and Waldorf's slip-line model are employed for obtaining cutting force, thrust force, and temperatures at the shear zone and tool-chip interface for the given parameters. The Komanduri-Hou two heat source model is used for obtaining the temperature distribution in the workpiece. The effect of coolant with differing mass flow rates has also been incorporated. The residual stresses are obtained by combining the mechanical and thermal components, followed by the loading and relaxation of the stresses. Optimal values for unknown parameters are predicted by leveraging a cost function. The residual stress distributions obtained give a tensile region near the surface for Inconel 718, and a compressive region for Ti6Al4V, which are in line with experimental results found in literature.

We introduce a one-dimensional correlated-hopping model of spinless fermions in which a particle can hop between two neighboring sites only if the sites to the left and right of those two sites have different particle numbers. Using a bond-to-site mapping, this model involving four-site terms can be mapped to an assisted pair-flipping model involving only three-site terms. This model shows strong Hilbert space fragmentation (HSF). We define irreducible strings (IS) to label the different fragments, determine the number of fragments, and the sizes of fragments corresponding to some special IS. In some classes of fragments, the Hamiltonian can be diagonalized completely, and in others it can be seen to have a structure characteristic of models which are not fully integrable. In the largest fragment in our model, the number of states grows exponentially with the system size, but the ratio of this number to the total Hilbert space size tends to zero exponentially in the thermodynamic limit. Within this fragment, we provide numerical evidence that only a weak version of the eigenstate thermalization hypothesis (ETH) remains valid; we call this subspace-restricted ETH. To understand the out-of-equilibrium dynamics of the model, we study the infinite-temperature time-dependent autocorrelation functions starting from a random initial state; we find that these exhibit a different behavior near the boundary compared to the bulk. Finally we propose an experimental setup to realize our correlated-hopping model., Comment: 22 pages, 17 figures; added discussion of typical and atypical fragments, ETH properties of additional typical fragments, and zero energy states; added some references

The dynamic yield stress associated with the flow cessation of cement pastes is measured using a rheometer equipped with various shear geometries such as vane, helical, sandblasted co-axial cylinders, and serrated parallel plates, as well as with the mini-cone spread test. Discrepancies in yield stress values are observed for cement pastes at various volume fractions, with one to two orders of magnitude difference between vane, helical and mini-cone spread measurements on the one hand, and co-axial cylinder and parallel plate measurements on the other hand. To understand this discrepancy, the flow profile of a cement paste in the parallel-plate geometry is investigated with a high-speed camera, revealing the rapid formation of an un-sheared band near the static bottom plate. The width of this band depends upon the rotational velocity of the top plate, and upon the shear time. Recalculation of shear stress shows that the reduced sheared gap alone cannot explain the low measured yield stress. Further exploration suggests the formation of zones with lower particle content, possibly linked to cement particle sedimentation. Here, we argue that the complex nature of cement pastes, composed of negatively buoyant non-Brownian particles with attractive interactions due to highly charged nano-size hydration products, accounts for their complex rheological behavior., Comment: Submitted to Journal Rheologica Acta for the peer review process on 13-March-2024, 11 figures. Also includes supplementary information with 5 figures

Architectural Knowledge Management (AKM) involves the organized handling of information related to architectural decisions and design within a project or organization. An essential artifact of AKM is the Architecture Decision Records (ADR), which documents key design decisions. ADRs are documents that capture decision context, decision made and various aspects related to a design decision, thereby promoting transparency, collaboration, and understanding. Despite their benefits, ADR adoption in software development has been slow due to challenges like time constraints and inconsistent uptake. Recent advancements in Large Language Models (LLMs) may help bridge this adoption gap by facilitating ADR generation. However, the effectiveness of LLM for ADR generation or understanding is something that has not been explored. To this end, in this work, we perform an exploratory study that aims to investigate the feasibility of using LLM for the generation of ADRs given the decision context. In our exploratory study, we utilize GPT and T5-based models with 0-shot, few-shot, and fine-tuning approaches to generate the Decision of an ADR given its Context. Our results indicate that in a 0-shot setting, state-of-the-art models such as GPT-4 generate relevant and accurate Design Decisions, although they fall short of human-level performance. Additionally, we observe that more cost-effective models like GPT-3.5 can achieve similar outcomes in a few-shot setting, and smaller models such as Flan-T5 can yield comparable results after fine-tuning. To conclude, this exploratory study suggests that LLM can generate Design Decisions, but further research is required to attain human-level generation and establish standardized widespread adoption., Comment: This paper has been accepted to IEEE ICSA 2024 (Main Track - Research Track)