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Quality of health is greatly impacted by the quality of measurement, hence active monitoring of patient's vitals status greatly affects recovery and improves prognosis. In this paper, we proposed an IoT health based monitoring system which comprises of a wearable wristband mounted on the patient wrist to eliminate the concept of patient's being static. The wearable wristband composed of a digital temperature sensor and a pulse rate sensor can remotely monitor health status and utilizing Thingspeak result viewed via a web app or delivered on a smartphone to a specialist who is in a remote location. In order to achieve high accuracy in readings, the signal measured from the pulse rate sensor was passed through a two stage Hp-Lp circuit to eliminate DC offset and noise. In addition, a scaling factor of 10.41909 was formulated and used to convert the digital output from the pulse rate sensor into a more accurate representation of the patient heat rate readings. The results displayed by the proposed system proved the system to be well suited and reliable for healthcare monitoring., Comment: Online: ISSN 2645-2960

With the ubiquitous use of document corpora for question answering, one important aspect which is especially relevant for technical documents is the ability to extract information from tables which are interspersed with text. The major challenge in this is that unlike free-flow text or isolated set of tables, the representation of a table in terms of what is a relevant chunk is not obvious. We conduct a series of experiments examining various representations of tabular data interspersed with text to understand the relative benefits of different representations. We choose a corpus of $3^{rd}$ Generation Partnership Project (3GPP) documents since they are heavily interspersed with tables. We create expert curated dataset of question answers to evaluate our approach. We conclude that row level representations with corresponding table header information being included in every cell improves the performance of the retrieval, thus leveraging the structural information present in the tabular data., Comment: 10 pages, 4 figures, 2 tables

Let $A$ be an $m\times m$ complex matrix and let $\lambda _1, \lambda _2, \ldots , \lambda _m$ be the eigenvalues of $A$ arranged such that $|\lambda _1|\geq |\lambda _2|\geq \cdots \geq |\lambda _m|$ and for $n\geq 1,$ let $s^{(n)}_1\geq s^{(n)}_2\geq \cdots \geq s^{(n)}_m$ be the singular values of $A^n$. Then a famous theorem of Yamamoto (1967) states that $$\lim _{n\to \infty}(s^{(n)}_j )^{\frac{1}{n}}= |\lambda _j|, ~~\forall \,1\leq j\leq m.$$ Recently S. Nayak strengthened this result very significantly by showing that the sequence of matrices $|A^n|^{\frac{1}{n}}$ itself converges to a positive matrix $B$ whose eigenvalues are $|\lambda _1|,|\lambda _2|,$ $\ldots , |\lambda _m|.$ Here this theorem has been extended to arbitrary compact operators on infinite dimensional complex separable Hilbert spaces. The proof makes use of Nayak's theorem, Stone-Weirstrass theorem, Borel-Caratheodory theorem and some technical results of Anselone and Palmer on collectively compact operators. Simple examples show that the result does not hold for general bounded operators., Comment: 14 Pages

We prove a Hankel-variant commutant lifting theorem. This also uncovers the complete structure of the Beurling-type reducing and invariant subspaces of Hankel operators. Kernel spaces of Hankel operators play a key role in the analysis., Comment: 18 pages. Revised

Electron backscatter diffraction (EBSD) patterns can exhibit Kikuchi bands with inverted contrast due to anomalous absorption. This can be observed, for example, on samples with nanoscale topography, in case of a low tilt backscattering geometry, or for transmission Kikuchi diffraction (TKD) on thicker samples. Three examples are discussed where contrast-inverted physics-based simulated master patterns have been applied to find the correct crystal orientation. As the first EBSD example, self-assembled gold nanostructures made of Au fcc and Au hcp phases on single-crystal germanium were investigated. Gold covered about 12% of the mapped area, with only two-thirds being successfully interpreted using standard Hough-based indexing. The remaining third was solved by brute force indexing using a contrast-inverted master pattern. The second EBSD example deals with maps collected at a non-tilted surface instead of the commonly used 70 degree tilted one. As TKD example, a jet-polished foil made of duplex stainless steel 2205 was examined. The thin part close to the hole edge producing normal-contrast patterns were standard indexed. The areas of the foil that become thicker with increasing distance from the edge of the hole produce contrast-inverted patterns. They covered three times the evaluable area and were successfully processed using the contrast-inverted master pattern. In the last example, inverted patterns collected at a non-tiled sample were mathematically inverted to normal contrast, and Hough/Radon-based indexing was successfully applied.

Graphical models have found widespread applications in many areas of modern statistics and machine learning. Iterative Proportional Fitting (IPF) and its variants have become the default method for undirected graphical model estimation, and are thus ubiquitous in the field. As the IPF is an iterative approach, it is not always readily scalable to modern high-dimensional data regimes. In this paper we propose a novel and fast non-iterative method for positive definite graphical model estimation in high dimensions, one that directly addresses the shortcomings of IPF and its variants. In addition, the proposed method has a number of other attractive properties. First, we show formally that as the dimension p grows, the proportion of graphs for which the proposed method will outperform the state-of-the-art in terms of computational complexity and performance tends to 1, affirming its efficacy in modern settings. Second, the proposed approach can be readily combined with scalable non-iterative thresholding-based methods for high-dimensional sparsity selection. Third, the proposed method has high-dimensional statistical guarantees. Moreover, our numerical experiments also show that the proposed method achieves scalability without compromising on statistical precision. Fourth, unlike the IPF, which depends on the Gaussian likelihood, the proposed method is much more robust.

Advancements in high-computing devices increase the necessity for improved and new understanding and development of smart manufacturing factories. Discrete-event models with simulators have been shown to be critical to architect, designing, building, and operating the manufacturing of semiconductor chips. The diffusion, implantation, and lithography machines have intricate processes due to their feedforward and feedback connectivity. The dataset collected from simulations of the factory models holds the promise of generating valuable machine-learning models. As surrogate data-based models, their executions are highly efficient compared to the physics-based counterpart models. For the development of surrogate models, it is beneficial to have publicly available benchmark simulation models that are grounded in factory models that have concise structures and accurate behaviors. Hence, in this research, a dataset is devised and constructed based on a benchmark model of an Intel semiconductor fabrication factory. The model is formalized using the Parallel Discrete-Event System Specification and executed using the DEVS-Suite simulator. The time series dataset is constructed using discrete-event time trajectories. This dataset is further analyzed and used to develop baseline univariate and multivariate machine learning models. The dataset can also be utilized in the machine learning community for behavioral analysis based on formalized and scalable component-based discrete-event models and simulations.

It is essential to precisely determine the evolving concentrations of radioactive nuclides within transmutation problems. It is also a crucial aspect of nuclear physics with widespread applications in nuclear waste management and energy production. This paper introduces CNUCTRAN, a novel computer program that employs a probabilistic approach to estimate nuclide concentrations in transmutation problems. CNUCTRAN directly simulates nuclei transformations arising from various nuclear reactions, diverging from the traditional deterministic methods that solve the Bateman equation using matrix exponential approximation. This approach effectively addresses numerical challenges associated with solving the Bateman equations, therefore, circumventing the need for matrix exponential approximations that risk producing nonphysical concentrations. Our sample calculations using CNUCTRAN shows that the concentration predictions of CNUCTRAN have a relative error of less than 0.001% compared to the state-of-the-art method, CRAM, in different test cases. This makes CNUCTRAN a valuable alternative tool for transmutation analysis

The safety of Large Language Models (LLMs) remains a critical concern due to a lack of adequate benchmarks for systematically evaluating their ability to resist generating harmful content. Previous efforts towards automated red teaming involve static or templated sets of illicit requests and adversarial prompts which have limited utility given jailbreak attacks' evolving and composable nature. We propose a novel dynamic benchmark of composable jailbreak attacks to move beyond static datasets and taxonomies of attacks and harms. Our approach consists of three components collectively called h4rm3l: (1) a domain-specific language that formally expresses jailbreak attacks as compositions of parameterized prompt transformation primitives, (2) bandit-based few-shot program synthesis algorithms that generate novel attacks optimized to penetrate the safety filters of a target black box LLM, and (3) open-source automated red-teaming software employing the previous two components. We use h4rm3l to generate a dataset of 2656 successful novel jailbreak attacks targeting 6 state-of-the-art (SOTA) open-source and proprietary LLMs. Several of our synthesized attacks are more effective than previously reported ones, with Attack Success Rates exceeding 90% on SOTA closed language models such as claude-3-haiku and GPT4-o. By generating datasets of jailbreak attacks in a unified formal representation, h4rm3l enables reproducible benchmarking and automated red-teaming, contributes to understanding LLM safety limitations, and supports the development of robust defenses in an increasingly LLM-integrated world. Warning: This paper and related research artifacts contain offensive and potentially disturbing prompts and model-generated content.

Teaching Computer Science (CS) by having students write programs by hand on paper has key pedagogical advantages: It allows focused learning and requires careful thinking compared to the use of Integrated Development Environments (IDEs) with intelligent support tools or "just trying things out". The familiar environment of pens and paper also lessens the cognitive load of students with no prior experience with computers, for whom the mere basic usage of computers can be intimidating. Finally, this teaching approach opens learning opportunities to students with limited access to computers. However, a key obstacle is the current lack of teaching methods and support software for working with and running handwritten programs. Optical character recognition (OCR) of handwritten code is challenging: Minor OCR errors, perhaps due to varied handwriting styles, easily make code not run, and recognizing indentation is crucial for languages like Python but is difficult to do due to inconsistent horizontal spacing in handwriting. Our approach integrates two innovative methods. The first combines OCR with an indentation recognition module and a language model designed for post-OCR error correction without introducing hallucinations. This method, to our knowledge, surpasses all existing systems in handwritten code recognition. It reduces error from 30\% in the state of the art to 5\% with minimal hallucination of logical fixes to student programs. The second method leverages a multimodal language model to recognize handwritten programs in an end-to-end fashion. We hope this contribution can stimulate further pedagogical research and contribute to the goal of making CS education universally accessible. We release a dataset of handwritten programs and code to support future research at https://github.com/mdoumbouya/codeocr

Business process management (BPM) has been widely used to discover, model, analyze, and optimize organizational processes. BPM looks at these processes with analysis techniques that assume a clearly defined start and end. However, not all processes adhere to this logic, with the consequence that their behavior cannot be appropriately captured by BPM analysis techniques. This paper addresses this research problem at a conceptual level. More specifically, we introduce the notion of vitalizing business processes that target the lifecycle process of one or more entities. We show the existence of lifecycle processes in many industries and that their appropriate conceptualizations pave the way for the definition of suitable modeling and analysis techniques. This paper provides a set of requirements for their analysis, and a conceptualization of lifecycle and vitalizing processes.

Precise phase control of microwaves and millimeter waves is critical for today's wireless communication and signal processing electronics. For decades, achieving even modest phase-shifting resolution has demanded a complex mix of transistor switches and passive electromagnetic structures. These true-phase delay, true-time-delay or quasi-true-time-delay circuits operate near resonances that heavily attenuate and distort signals, limiting transmission bandwidth. Despite numerous attempts, passive phase shifters have remained lossy and incompatible with high-channel-capacity beamforming, irrespective of the semiconductor fabrication process. In this article, we introduce a mechanism whereby waveguide reflectors based on coupled resonances can be reprogrammed to evade loss. Central to their operation is that loss from internal resonances is confined to low frequencies, while at high frequencies, their reflectivity is maximized and broadband phase variations, induced by those resonances, still persist. Since performance in the low-loss post-resonance spectrum is largely agnostic to the number of switches, ultra-fine digital tuning is possible. This breaks the historical tradeoff between loss and precision to achieve resolution surpassing state-of-the-art integrated circuits by over three orders (bits) of magnitude. The device does not distort the transmitted signal and consumes no power. Moreover, the chip occupies a sub-wavelength footprint in a Complementary Metal Oxide Semiconductor platform. This makes it an optimal candidate for seamless integration in on-chip multi-gigabit data links, radio astronomy transceivers and control hardware for millimeter-wave qubits., Comment: 12 main article pages, 4 main figures, 4 extended figures, 15 Supplementary information pages

Large language models (LLMs) are playing a pivotal role in deploying strategic use cases across a range of organizations, from large pan-continental companies to emerging startups. The issues and challenges involved in the successful utilization of LLMs can vary significantly depending on the size of the organization. It is important to study and discuss these pertinent issues of LLM adaptation with a focus on the scale of the industrial concerns and brainstorm possible solutions and prospective directions. Such a study has not been prominently featured in the current research literature. In this study, we adopt a threefold strategy: first, we conduct a case study with industry practitioners to formulate the key research questions; second, we examine existing industrial publications to address these questions; and finally, we provide a practical guide for industries to utilize LLMs more efficiently., Comment: 17 pages, 3 figures

Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models (LLMs) perform Question Answering (QA) tasks in various domains. However, RAG based on open-source LLM for specialized domains has challenges of evaluating generated responses. A popular framework in the literature is the RAG Assessment (RAGAS), a publicly available library which uses LLMs for evaluation. One disadvantage of RAGAS is the lack of details of derivation of numerical value of the evaluation metrics. One of the outcomes of this work is a modified version of this package for few metrics (faithfulness, context relevance, answer relevance, answer correctness, answer similarity and factual correctness) through which we provide the intermediate outputs of the prompts by using any LLMs. Next, we analyse the expert evaluations of the output of the modified RAGAS package and observe the challenges of using it in the telecom domain. We also study the effect of the metrics under correct vs. wrong retrieval and observe that few of the metrics have higher values for correct retrieval. We also study for differences in metrics between base embeddings and those domain adapted via pre-training and fine-tuning. Finally, we comment on the suitability and challenges of using these metrics for in-the-wild telecom QA task., Comment: Accepted for publication in ICML 2024 Workshop on Foundation Models in the Wild

Magnetic switchbacks are of continuing interest to the scientific community due to the fact that the phenomenon has not been completely understood. Although most of the research into them in the Parker Solar Probe era has largely focused on creating a theoretical framework for causing the field reversal through magnetic interchange reconnection, reconnecting streams of plasma in the solar wind, or shear driven turbulence, it remains unclear to what extent these models may or may not represent the underlying physical reality of magnetic switchbacks. In this paper, we present the results of our study on the energetic ion composition of magnetic switchback events using statistical methods with the aim of obtaining new insights into the underlying physics. In doing so, we find consistent differences in arrival times of the peak flux of different energetic ion species in magnetic switchbacks which are indicative of switchback formed remotely in the solar corona and propagated outwards in the solar wind. We also find that while the data does not directly contradict a linear interchange reconnection model nor the flux rope driven interchange reconnection model, the structure of the plasma within the switchbacks themselves is reminiscent of reconnection produced plasmoids.

Although recent research suggests a link in support of a model of switchback formation in the solar corona via interchange reconnection that is propagated outward with the solar wind and similarities in their ion composition to plasma instability produced plasmoids, these plasma instabilities have yet to be observationally linked to magnetic switchbacks. In this paper we aim to use the theoretical framework of a twin coronal mass ejection event which is known to include interchange reconnection processes and compare this model with experimental observations using Parker Solar Probe FIELDS and $\isis$ instrumentationof an actual event containing two CMEs identified via the associated solar proton event in order to further test and refine the hypothesis of interchange reconnection as a possible physical origin for the magnetic switchback phenomenon. We also intend to introduce a plasma model for the formation of the switchbacks noted within the CME event.

This study modifies the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm for multi-modal optimization problems. The enhancements focus on addressing the challenges of multiple global minima, improving the algorithm's ability to maintain diversity and explore complex fitness landscapes. We incorporate niching strategies and dynamic adaptation mechanisms to refine the algorithm's performance in identifying and optimizing multiple global optima. The algorithm generates a population of candidate solutions by sampling from a multivariate normal distribution centered around the current mean vector, with the spread determined by the step size and covariance matrix. Each solution's fitness is evaluated as a weighted sum of its contributions to all global minima, maintaining population diversity and preventing premature convergence. We implemented the algorithm on 8 tunable composite functions for the GECCO 2024 Competition on Benchmarking Niching Methods for Multi-Modal Optimization (MMO), adhering to the competition's benchmarking framework. The results are presenting in many ways such as Peak Ratio, F1 score on various dimensions. They demonstrate the algorithm's robustness and effectiveness in handling both global optimization and MMO- specific challenges, providing a comprehensive solution for complex multi-modal optimization problems., Comment: 15 pages, 1 figure, 16 tables. Submitted for GECCO 2024 competition on Benchmarking Niching Methods for Multimodal Optimization

Widespread air source heat pump (ASHP) adoption faces several challenges that on-site thermal energy storage (TES), particularly thermochemical salt hydrate TES, can mitigate. No techno-economic analyses for salt-hydrate-based TES in residential applications exist. We quantify the residential space heating value of four salt hydrate TES materials - MgSO4, MgCl2, K2CO3, and SrBr2 - coupled with ASHPs across 4,800 representative households in 12 U.S. cities by embedding salt-hydrate-specific Ragone plots into a techno-economic model of coupled ASHP-TES operations. In Detroit, salt hydrate TES is projected to reduce household annual electricity costs by up to $\$$241 (8$\%$). Cost savings from TES can differ by over an order of magnitude between households and salt hydrates. We identify the most promising salt in this study, SrBr2, due to its high energy density and low humidification parasitic load. Break-even capital costs of SrBr2-based TES range from $\$$13/kWh to $\$$17/kWh, making it the only salt hydrate studied to reach and exceed the U.S. Department of Energy's $\$$15/kWh TES cost target. Sensitivities highlight the importance of variable TES sizing and efficiency losses in the value of TES.

A plethora of sentence embedding models makes it challenging to choose one, especially for domains such as telecom, rich with specialized vocabulary. We evaluate multiple embeddings obtained from publicly available models and their domain-adapted variants, on both point retrieval accuracies as well as their (95\%) confidence intervals. We establish a systematic method to obtain thresholds for similarity scores for different embeddings. We observe that fine-tuning improves mean bootstrapped accuracies as well as tightens confidence intervals. The pre-training combined with fine-tuning makes confidence intervals even tighter. To understand these variations, we analyse and report significant correlations between the distributional overlap between top-$K$, correct and random sentence similarities with retrieval accuracies and similarity thresholds. Following current literature, we analyze if retrieval accuracy variations can be attributed to isotropy of embeddings. Our conclusions are that isotropy of embeddings (as measured by two independent state-of-the-art isotropy metric definitions) cannot be attributed to better retrieval performance. However, domain adaptation which improves retrieval accuracies also improves isotropy. We establish that domain adaptation moves domain specific embeddings further away from general domain embeddings., Comment: 10 pages, 3 figures, 4 tables

In this article, the molecular permanent electric dipole moments and components of static dipole polarizabilities for the electronic ground state of singly charged aluminum monohalides are reported. The coupled-cluster method by considering single and double excitations (CCSD) together with relativistic Dyall basis sets have been used to carry out these molecular property calculations. The contribution from triple excitations are incorporated through perturbative triples (CCSD(T)). The results from a series of progressively larger basis sets are extrapolated to the complete basis set limit. Further, the role of correlation and relativistic effects, and also the effect of augmentation over the considered basis sets on the valence molecular properties are studied. Our results are compared with those available in the literature., Comment: 13 pages, 8 figures, 6 tables

The quantum-classical hybrid Variational Quantum Eigensolver (VQE) algorithm is recognized to be the method of choice to obtain ground state energies of quantum many-body systems in the noisy intermediate scale quantum (NISQ) era. This study not only extends the VQE algorithm to the relativistic regime, but also calculates a property other than energy, namely the molecular permanent electric dipole moment (PDM). We carry out 18-qubit quantum simulations to obtain ground state energies as well as PDMs of single-valence diatomic molecules, ranging from the light BeH to the heavy radioactive RaH molecule. We investigate the correlation trends in these systems as well as access the precision in our results. Furthermore, we measure the PDM of the moderately heavy SrH and SrF molecules on the optimized unitary coupled cluster state, using the state-of-the-art IonQ Aria-I quantum computer in an active space of 6 qubits. The associated quantum circuits for these computations were extensively optimized in view of limitations imposed by NISQ hardware. To that end, we employ an array of techniques, including the use of point group symmetries, integrating ZX-Calculus into our pipeline-based circuit optimization, and energy sort VQE procedure. Through these methods, we compress our 6-qubit quantum circuit from 280 two-qubit gates to 37 two-qubit gates (with a marginal trade-off of 0.33 and 0.31 percent in the PDM for SrH and SrF in their respective 6-spin orbital active spaces). We anticipate that our proof-of-concept demonstration lays the groundwork for future quantum hardware calculations involving heavy atoms and molecules.

Adapting electronics to perfectly conform to non-planar and rough surfaces, such as human skin, is a very challenging task which, if solved, could open up new applications in fields of high economic and scientific interest ranging from health to robotics, wearable electronics, human machine interface and Internet of Things. The key to success lies in defining a technology that can lead to the fabrication of ultra-thin devices while exploiting materials that are ultimately thin, with high mechanical flexibility and excellent electrical properties. Here, we report a hybrid approach for the definition of high-performance, ultra-thin and conformable electronic devices and circuits, based on the integration of ultimately thin semiconducting transition metal dichalcogenides (TMDC), i.e., MoS2, with organic gate dielectric material, i.e., polyvinyl formal (PVF) combined with the ink-jet printing of conductive PEDOT:PSS ink for electrodes definition. Through this cost-effective, fully bottom-up and solution-based approach, transistors and simple digital and analogue circuits are fabricated by a sequential stacking of ultrathin (nanometer) layers on a few micron thick polyimide substrate, which guarantees the high flexibility mandatory for the targeted applications.

This article introduces an enhanced particle swarm optimizer (PSO), termed Orthogonal PSO with Mutation (OPSO-m). Initially, it proposes an orthogonal array-based learning approach to cultivate an improved initial swarm for PSO, significantly boosting the adaptability of swarm-based optimization algorithms. The article further presents archive-based self-adaptive learning strategies, dividing the population into regular and elite subgroups. Each subgroup employs distinct learning mechanisms. The regular group utilizes efficient learning schemes derived from three unique archives, which categorize individuals based on their quality levels. Additionally, a mutation strategy is implemented to update the positions of elite individuals. Comparative studies are conducted to assess the effectiveness of these learning strategies in OPSO-m, evaluating its optimization capacity through exploration-exploitation dynamics and population diversity analysis. The proposed OPSO-m model is tested on real-parameter challenges from the CEC 2017 suite in 10, 30, 50, and 100-dimensional search spaces, with its results compared to contemporary state-of-the-art algorithms using a sensitivity metric. OPSO-m exhibits distinguished performance in the precision of solutions, rapidity of convergence, efficiency in search, and robust stability, thus highlighting its superior aptitude for resolving intricate optimization issues.

GW230529 is the first compact binary coalescence detected by the LIGO-Virgo-KAGRA collaboration with at least one component mass confidently in the lower mass-gap, corresponding to the range 3-5$M_{\odot}$. If interpreted as a neutron star-black hole merger, this event has the most symmetric mass ratio detected so far and therefore has a relatively high probability of producing electromagnetic (EM) emission. However, no EM counterpart has been reported. At the merger time $t_0$, Swift-BAT and Fermi-GBM together covered 100$\%$ of the sky. Performing a targeted search in a time window $[t_0-20 \text{s},t_0+20 \text{s}]$, we report no detection by the Swift-BAT and the Fermi-GBM instruments. Combining the position-dependent $\gamma-$ray flux upper limits and the gravitational-wave posterior distribution of luminosity distance, sky localization and inclination angle of the binary, we derive constraints on the characteristic luminosity and structure of the jet possibly launched during the merger. Assuming a top-hat jet structure, we exclude at 90$\%$ credibility the presence of a jet which has at the same time an on-axis isotropic luminosity $\gtrsim 10^{48}$ erg s$^{-1}$, in the bolometric band 1 keV-10 MeV, and a jet opening angle $\gtrsim 15$ deg. Similar constraints are derived testing other assumptions about the jet structure profile. Excluding GRB 170817A, the luminosity upper limits derived here are below the luminosity of any GRB observed so far., Comment: 18 pages, 1 table, 11 figures

Obtaining sufficient information in one's mother tongue is crucial for satisfying the information needs of the users. While high-resource languages have abundant online resources, the situation is less than ideal for very low-resource languages. Moreover, the insufficient reporting of vital national and international events continues to be a worry, especially in languages with scarce resources, like \textbf{Mizo}. In this paper, we conduct a study to investigate the effectiveness of a simple methodology designed to generate a holistic summary for Mizo news articles, which leverages English-language news to supplement and enhance the information related to the corresponding news events. Furthermore, we make available 500 Mizo news articles and corresponding enriched holistic summaries. Human evaluation confirms that our approach significantly enhances the information coverage of Mizo news articles. The mizo dataset and code can be accessed at \url{https://github.com/barvin04/mizo_enrichment, Comment: Accepted at LREC-COLING2024 WILDRE Workshop

Virtual try-on and product personalization have become increasingly important in modern online shopping, highlighting the need for accurate body measurement estimation. Although previous research has advanced in estimating 3D body shapes from RGB images, the task is inherently ambiguous as the observed scale of human subjects in the images depends on two unknown factors: capture distance and body dimensions. This ambiguity is particularly pronounced in partial-view scenarios. To address this challenge, we propose a modular and simple height normalization solution. This solution relocates the subject skeleton to the desired position, thereby normalizing the scale and disentangling the relationship between the two variables. Our experimental results demonstrate that integrating this technique into state-of-the-art human mesh reconstruction models significantly enhances partial body measurement estimation. Additionally, we illustrate the applicability of this approach to multi-view settings, showcasing its versatility., Comment: Accepted to CVPRW 2024 Computer Vision for Fashion, Art, and Design

Uncertainty quantification plays a vital role in facilitating the practical implementation of AI in radiology by addressing growing concerns around trustworthiness. Given the challenges associated with acquiring large, annotated datasets in this field, there is a need for methods that enable uncertainty quantification in small data AI approaches tailored to radiology images. In this study, we focused on uncertainty quantification within the context of the small data evolutionary strategies-based technique of deep neuroevolution (DNE). Specifically, we employed DNE to train a simple Convolutional Neural Network (CNN) with MRI images of the eyes for binary classification. The goal was to distinguish between normal eyes and those with metastatic tumors called choroidal metastases. The training set comprised 18 images with choroidal metastases and 18 without tumors, while the testing set contained a tumor-to-normal ratio of 15:15. We trained CNN model weights via DNE for approximately 40,000 episodes, ultimately reaching a convergence of 100% accuracy on the training set. We saved all models that achieved maximal training set accuracy. Then, by applying these models to the testing set, we established an ensemble method for uncertainty quantification.The saved set of models produced distributions for each testing set image between the two classes of normal and tumor-containing. The relative frequencies permitted uncertainty quantification of model predictions. Intriguingly, we found that subjective features appreciated by human radiologists explained images for which uncertainty was high, highlighting the significance of uncertainty quantification in AI-driven radiological analyses.

We define a new framework that unifies the filtration and mapper approaches from TDA, and present efficient algorithms to compute it. Termed the box filtration of a PCD, we grow boxes (hyperrectangles) that are not necessarily centered at each point (in place of balls centered at points). We grow the boxes non-uniformly and asymmetrically in different dimensions based on the distribution of points. We present two approaches to handle the boxes: a point cover where each point is assigned its own box at start, and a pixel cover that works with a pixelization of the space of the PCD. Any box cover in either setting automatically gives a mapper of the PCD. We show that the persistence diagrams generated by the box filtration using both point and pixel covers satisfy the classical stability based on the Gromov-Hausdorff distance. Using boxes also implies that the box filtration is identical for pairwise or higher order intersections whereas the VR and Cech filtration are not the same. Growth in each dimension is computed by solving a linear program (LP) that optimizes a cost functional balancing the cost of expansion and benefit of including more points in the box. The box filtration algorithm runs in $O(m|U(0)|\log(mn\pi)L(q))$ time, where $m$ is number of steps of increments considered for box growth, $|U(0)|$ is the number of boxes in the initial cover ($\leq$ number of points), $\pi$ is the step length for increasing each box dimension, each LP is solved in $O(L(q))$ time, $n$ is the PCD dimension, and $q = n \times |X|$. We demonstrate through multiple examples that the box filtration can produce more accurate results to summarize the topology of the PCD than VR and distance-to-measure (DTM) filtrations. Software for our implementation is available at https://github.com/pragup/Box-Filteration., Comment: 17 figures

We explore the application of machine learning algorithms to predict the suitability of Russet potato clones for advancement in breeding trials. Leveraging data from manually collected trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely the multi-layer perceptron classifier (MLPC), histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVC), demonstrate consistent and significant results. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs., Comment: 19 pages, 3 figures, submitted to Artificial Intelligence in Agriculture

The Large Language Models (LLMs) exhibit remarkable ability to generate fluent content across a wide spectrum of user queries. However, this capability has raised concerns regarding misinformation and personal information leakage. In this paper, we present our methods for the SemEval2024 Task8, aiming to detect machine-generated text across various domains in both mono-lingual and multi-lingual contexts. Our study comprehensively analyzes various methods to detect machine-generated text, including statistical, neural, and pre-trained model approaches. We also detail our experimental setup and perform a in-depth error analysis to evaluate the effectiveness of these methods. Our methods obtain an accuracy of 86.9\% on the test set of subtask-A mono and 83.7\% for subtask-B. Furthermore, we also highlight the challenges and essential factors for consideration in future studies., Comment: 8 pages, 1 Figure

Granular flows with solid particles play an important role in energy and catalysis applications. To predict thermal performance, this study develops a comprehensive discrete particle tracking flow model fully coupled with conductive and radiative heat transfer. Leveraging LIGGGHTS, our model uniquely integrates particle-particle and particle-wall thermal radiation for grey radiative surfaces up to nearly 10 times of particle size. Models include: (a) plug flows of particles with a near-constant streamwise velocity and (b) gravity-driven, dense, moving beds of particles to determine wall-to-particle heat-transfer coefficients with isothermal channel walls. The plug flow model is used to interrogate the interdependent effects of flow-regime-dependent solid volume fractions (0.02 -- 0.48), particle size (0.4 -- 1 mm), operating wall temperatures (700 -- 1300 K), and thermophysical particles properties including emissivity (0.1 -- 1) and thermal conductivity (0.16, 33 W/m/K). The overall and conductive heat-transfer coefficients increase with solid volume fraction, while radiation contribution decreases. For a fixed solid volume fraction, heat-transfer coefficients exhibit linear dependence on particle emissivity and cubic dependence on wall temperature, driven by enhanced thermal radiation. For a fixed mass flow rate, the dependence of radiative heat-transfer coefficient on particle size changes with solid volume fraction. To generalize the applicability of our results, heat-transfer coefficients are transformed into a dimensionless Nusselt number, and establish its dependence on solid volume fraction. These results reveal the high sensitivity of the overall Nusselt number to even small changes in solid volume fraction in the dense flow regime, particularly for the high-conductivity alumina particles. In contrast, the radiative Nusselt number asymptotes for large solid volume fractions.

Thermal photons produced in heavy-ion collision experiments are an important observable for understanding quark-gluon plasma (QGP). The thermal photon rate from the QGP at a given temperature can be calculated from the spectral function of the vector current correlator. Extraction of the spectral function from the lattice correlator is known to be an ill-conditioned problem, as there is no unique solution for a spectral function for a given lattice correlator with statistical errors. The vector current correlator, on the other hand, receives a large ultraviolet contribution from the vacuum, which makes the extraction of the thermal photon rate difficult from this channel. We therefore consider the difference between the transverse and longitudinal part of the spectral function, only capturing the thermal contribution to the current correlator, simplifying the reconstruction significantly. The lattice correlator is calculated for light quarks in quenched QCD at $T=470~$MeV ($\sim 1.5\, T_c$), as well as in 2+1 flavor QCD at $T=220~$MeV ($\sim 1.2 \, T_{pc}$) with $m_{\pi}=320$ MeV. In order to quantify the non-perturbative effects, the lattice correlator is compared with the corresponding $\text{NLO}+\text{LPM}^{\text{LO}}$ estimate of correlator. The reconstruction of the spectral function is performed in several different frameworks, ranging from physics-informed models of the spectral function to more general models in the Backus-Gilbert method and Gaussian Process regression. We find that the resulting photon rates agree within errors., Comment: 20 pages, 16 figures

We study the problem of comparing a pair of geometric networks that may not be similarly defined, i.e., when they do not have one-to-one correspondences between their nodes and edges. Our motivating application is to compare power distribution networks of a region. Due to the lack of openly available power network datasets, researchers synthesize realistic networks resembling their actual counterparts. But the synthetic digital twins may vary significantly from one another and from actual networks due to varying underlying assumptions and approaches. Hence the user wants to evaluate the quality of networks in terms of their structural similarity to actual power networks. But the lack of correspondence between the networks renders most standard approaches, e.g., subgraph isomorphism and edit distance, unsuitable. We propose an approach based on the multiscale flat norm, a notion of distance between objects defined in the field of geometric measure theory, to compute the distance between a pair of planar geometric networks. Using a triangulation of the domain containing the input networks, the flat norm distance between two networks at a given scale can be computed by solving a linear program. In addition, this computation automatically identifies the 2D regions (patches) that capture where the two networks are different. We demonstrate through 2D examples that the flat norm distance can capture the variations of inputs more accurately than the commonly used Hausdorff distance. As a notion of stability, we also derive upper bounds on the flat norm distance between a simple 1D curve and its perturbed version as a function of the radius of perturbation for a restricted class of perturbations. We demonstrate our approach on a set of actual power networks from a county in the USA., Comment: A shorter version (with subset of results) appeared in ICCS 2023

We propose a single-shot approach to determining 6-DoF pose of an object with available 3D computer-aided design (CAD) model from a single RGB image. Our method, dubbed MRC-Net, comprises two stages. The first performs pose classification and renders the 3D object in the classified pose. The second stage performs regression to predict fine-grained residual pose within class. Connecting the two stages is a novel multi-scale residual correlation (MRC) layer that captures high-and-low level correspondences between the input image and rendering from first stage. MRC-Net employs a Siamese network with shared weights between both stages to learn embeddings for input and rendered images. To mitigate ambiguity when predicting discrete pose class labels on symmetric objects, we use soft probabilistic labels to define pose class in the first stage. We demonstrate state-of-the-art accuracy, outperforming all competing RGB-based methods on four challenging BOP benchmark datasets: T-LESS, LM-O, YCB-V, and ITODD. Our method is non-iterative and requires no complex post-processing., Comment: Accepted to CVPR 2024

Given a bipartite graph $G(V= (A \cup B),E)$ with $n$ vertices and $m$ edges and a function $b \colon V \to \mathbb{Z}_+$, a $b$-matching is a subset of edges such that every vertex $v \in V$ is incident to at most $b(v)$ edges in the subset. When we are also given edge weights, the Max Weight $b$-Matching problem is to find a $b$-matching of maximum weight, which is a fundamental combinatorial optimization problem with many applications. Extending on the recent work of Zheng and Henzinger (IPCO, 2023) on standard bipartite matching problems, we develop a simple auction algorithm to approximately solve Max Weight $b$-Matching. Specifically, we present a multiplicative auction algorithm that gives a $(1 - \varepsilon)$-approximation in $O(m \varepsilon^{-1} \log \varepsilon^{-1} \log \beta)$ worst case time, where $\beta$ the maximum $b$-value. Although this is a $\log \beta$ factor greater than the current best approximation algorithm by Huang and Pettie (Algorithmica, 2022), it is considerably simpler to present, analyze, and implement., Comment: 14 pages; Accepted as a refereed paper in the 2024 INFORMS Optimization Society conference

In this note, we study the composition operators on Segal-Bargmann spaces, which attains its norm and we show that every composition operators on the classical Fock space over $\mathbb{ C}^n$ is norm attaining. Also, we establish a necessary and sufficient condition for a sum of two kernel functions to be an extremal function for the norm of composition operators., Comment: 12 Pages

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions., Comment: 25 pages, 7 figures

Layer 1 (L1) blockchains such as Ethereum are secured under an "honest supermajority of stake" assumption for a large pool of validators who verify each and every transaction on it. This high security comes at a scalability cost which not only effects the throughput of the blockchain but also results in high gas fees for executing transactions on chain. The most successful solution for this problem is provided by optimistic rollups, Layer 2 (L2) blockchains that execute transactions outside L1 but post the transaction data on L1. The security for such L2 chains is argued, informally, under the assumption that a set of nodes will check the transaction data posted on L1 and raise an alarm (a fraud proof) if faulty transactions are detected. However, all current deployments lack a proper incentive mechanism for ensuring that these nodes will do their job ``diligently'', and simply rely on a cursory incentive alignment argument for security. We solve this problem by introducing an incentivized watchtower network designed to serve as the first line of defense for rollups. Our main contribution is a ``Proof of Diligence'' protocol that requires watchtowers to continuously provide a proof that they have verified L2 assertions and get rewarded for the same. Proof of Diligence protocol includes a carefully-designed incentive mechanism that is provably secure when watchtowers are rational actors, under a mild rational independence assumption.

The Cadmium Zinc Telluride Imager (CZTI) aboard AstroSat has good sensitivity to Gamma Ray Bursts (GRBs), with close to 600 detections including about 50 discoveries undetected by other missions. However, CZTI was not designed to be a GRB monitor and lacks localisation capabilities. We introduce a new method of localising GRBs using "shadows" cast on the CZTI detector plane due to absorption and scattering by satellite components and instruments. Comparing the observed distribution of counts on the detector plane with simulated distributions with the AstroSat Mass Model, we can localise GRBs in the sky. Our localisation uncertainty is defined by a two-component model, with a narrow Gaussian component that has close to 50% probability of containing the source, and the remaining spread over a broader Gaussian component with an 11.3 times higher $\sigma$. The width ($\sigma$) of the Gaussian components scales inversely with source counts. We test this model by applying the method to GRBs with known positions and find good agreement between the model and observations. This new ability expands the utility of CZTI in the study of GRBs and other rapid high-energy transients., Comment: 9 pages, 5 figures

Numerous successes have been achieved in combating the COVID-19 pandemic, initially using various precautionary measures like lockdowns, social distancing, and the use of face masks. More recently, various vaccinations have been developed to aid in the prevention or reduction of the severity of the COVID-19 infection. Despite the effectiveness of the precautionary measures and the vaccines, there are several controversies that are massively shared on social media platforms like Twitter. In this paper, we explore the use of state-of-the-art transformer-based language models to study people's acceptance of vaccines in Nigeria. We developed a novel dataset by crawling multi-lingual tweets using relevant hashtags and keywords. Our analysis and visualizations revealed that most tweets expressed neutral sentiments about COVID-19 vaccines, with some individuals expressing positive views, and there was no strong preference for specific vaccine types, although Moderna received slightly more positive sentiment. We also found out that fine-tuning a pre-trained LLM with an appropriate dataset can yield competitive results, even if the LLM was not initially pre-trained on the specific language of that dataset.

The conversion of content from one language to another utilizing a computer system is known as Machine Translation (MT). Various techniques have come up to ensure effective translations that retain the contextual and lexical interpretation of the source language. End-to-end Neural Machine Translation (NMT) is a popular technique and it is now widely used in real-world MT systems. Massive amounts of parallel datasets (sentences in one language alongside translations in another) are required for MT systems. These datasets are crucial for an MT system to learn linguistic structures and patterns of both languages during the training phase. One such dataset is Samanantar, the largest publicly accessible parallel dataset for Indian languages (ILs). Since the corpus has been gathered from various sources, it contains many incorrect translations. Hence, the MT systems built using this dataset cannot perform to their usual potential. In this paper, we propose an algorithm to remove mistranslations from the training corpus and evaluate its performance and efficiency. Two Indic languages (ILs), namely, Hindi (HIN) and Odia (ODI) are chosen for the experiment. A baseline NMT system is built for these two ILs, and the effect of different dataset sizes is also investigated. The quality of the translations in the experiment is evaluated using standard metrics such as BLEU, METEOR, and RIBES. From the results, it is observed that removing the incorrect translation from the dataset makes the translation quality better. It is also noticed that, despite the fact that the ILs-English and English-ILs systems are trained using the same corpus, ILs-English works more effectively across all the evaluation metrics., Comment: 18 pages

In the standard von Neumann interaction used in Quantum measurements, the chosen observable to which the environment (apparatus) entangles is exactly reproduced in the state of the environment, thereby decohering the quantum system in the eigenbasis of the observable. We relax this by allowing for imperfect measurements whereby the environment evolves to a state that approximately, but not exactly, reflects the state of the system. In this scheme it is possible to attain approximate decoherence of conjugate quantities that resembles classical physics, which we demonstrate using an example., Comment: 12 pages, comments welcome

TlTaSe2 is a non-centrosymmetric quasi-2D crystal semi-metal hosting nodal-line topological features protected by mirror-reflection symmetry. Here, we investigated the superconducting properties of TlTaSe2 using the first-principles anisotropic Migdal-Eliashberg theory. The Fermi surface hosts well gapped multiband features contributed by the Ta 5d and Tl 6p orbitals. Moreso, anisotropic superconducting gaps were found to exist at 2.15 and 4.5 meV around the in-plane orbitals, coupling effectively with the in-plane phonons of the Ta and Tl atoms. Using the Allen-Dynes-modified McMillan formula, we found a superconducting transition temperature of 6.67 K, accompanied by a robust electron-phonon coupling constant {\lambda} of 0.970. This investigation provides valuable insights into the mechanisms underlying anisotropic superconductivity in TlTaSe2., Comment: 17 pages, 5 figures