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We study the fluctuations, particularly the inequality of fluctuations, in cryptocurrency prices over the last ten years. We calculate the inequality in the price fluctuations through different measures, such as the Gini and Kolkata indices, and also the $Q$ factor (given by the ratio between the highest value and the average value) of these fluctuations. We compare the results with the equivalent quantities in some of the more prominent national currencies and see that while the fluctuations (or inequalities in such fluctuations) for cryptocurrencies were initially significantly higher than national currencies, over time the fluctuation levels of cryptocurrencies tend towards the levels characteristic of national currencies. We also compare similar quantities for a few prominent stock prices., Comment: Invited contribution for Physica A Spl. Issue on "Crypto's Global Impact"

Effective question classification is crucial for AI-driven educational tools, enabling adaptive learning systems to categorize questions by skill area, difficulty level, and competence. This classification not only supports educational diagnostics and analytics but also enhances complex tasks like information retrieval and question answering by associating questions with relevant categories. Traditional methods, often based on word embeddings and conventional classifiers, struggle to capture the nuanced relationships in natural language, leading to suboptimal performance. To address this, we propose a novel approach leveraging graph convolutional networks (GCNs), named Phrase Question-Graph Convolutional Network (PQ-GCN) to better model the inherent structure of questions. By representing questions as graphs -- where nodes signify words or phrases and edges denote syntactic or semantic relationships -- our method allows GCNs to learn from the interconnected nature of language more effectively. Additionally, we explore the incorporation of phrase-based features to enhance classification accuracy, especially in low-resource settings. Our findings demonstrate that GCNs, augmented with these features, offer a promising solution for more accurate and context-aware question classification, bridging the gap between graph neural network research and practical educational applications.

Summarizing software artifacts is an important task that has been thoroughly researched. For evaluating software summarization approaches, human judgment is still the most trusted evaluation. However, it is time-consuming and fatiguing for evaluators, making it challenging to scale and reproduce. Large Language Models (LLMs) have demonstrated remarkable capabilities in various software engineering tasks, motivating us to explore their potential as automatic evaluators for approaches that aim to summarize software artifacts. In this study, we investigate whether LLMs can evaluate bug report summarization effectively. We conducted an experiment in which we presented the same set of bug summarization problems to humans and three LLMs (GPT-4o, LLaMA-3, and Gemini) for evaluation on two tasks: selecting the correct bug report title and bug report summary from a set of options. Our results show that LLMs performed generally well in evaluating bug report summaries, with GPT-4o outperforming the other LLMs. Additionally, both humans and LLMs showed consistent decision-making, but humans experienced fatigue, impacting their accuracy over time. Our results indicate that LLMs demonstrate potential for being considered as automated evaluators for bug report summarization, which could allow scaling up evaluations while reducing human evaluators effort and fatigue.

Wind flow can be highly unpredictable and can suffer substantial fluctuations in speed and direction due to the shape and height of hills, mountains, and valleys, making accurate wind speed (WS) forecasting essential in complex terrain. This paper presents a novel and adaptive model for short-term forecasting of WS. The paper's key contributions are as follows: (a) The Partial Auto Correlation Function (PACF) is utilised to minimise the dimension of the set of Intrinsic Mode Functions (IMF), hence reducing training time; (b) The sample entropy (SampEn) was used to calculate the complexity of the reduced set of IMFs. The proposed technique is adaptive since a specific Deep Learning (DL) model-feature combination was chosen based on complexity; (c) A novel bidirectional feature-LSTM framework for complicated IMFs has been suggested, resulting in improved forecasting accuracy; (d) The proposed model shows superior forecasting performance compared to the persistence, hybrid, Ensemble empirical mode decomposition (EEMD), and Variational Mode Decomposition (VMD)-based deep learning models. It has achieved the lowest variance in terms of forecasting accuracy between simple and complex terrain conditions 0.70%. Dimension reduction of IMF's and complexity-based model-feature selection helps reduce the training time by 68.77% and improve forecasting quality by 58.58% on average.

We introduce Contested Logistics Games, a variant of logistics problems that account for the presence of an adversary that can disrupt the movement of goods in selected areas. We model this as a large two-player zero-sum one-shot game played on a graph representation of the physical world, with the optimal logistics plans described by the (possibly randomized) Nash equilibria of this game. Our logistics model is fairly sophisticated, and is able to handle multiple modes of transport and goods, accounting for possible storage of goods in warehouses, as well as Leontief utilities based on demand satisfied. We prove computational hardness results related to equilibrium finding and propose a practical double-oracle solver based on solving a series of best-response mixed-integer linear programs. We experiment on both synthetic and real-world maps, demonstrating that our proposed method scales to reasonably large games. We also demonstrate the importance of explicitly modeling the capabilities of the adversary via ablation studies and comparisons with a naive logistics plan based on heuristics., Comment: GameSec '24

Existing methods for reconstructing objects and humans from a monocular image suffer from severe mesh collisions and performance limitations for interacting occluding objects. This paper introduces a method to obtain a globally consistent 3D reconstruction of interacting objects and people from a single image. Our contributions include: 1) an optimization framework, featuring a collision loss, tailored to handle human-object and human-human interactions, ensuring spatially coherent scene reconstruction; and 2) a novel technique to robustly estimate 6 degrees of freedom (DOF) poses, specifically for heavily occluded objects, exploiting image inpainting. Notably, our proposed method operates effectively on images from real-world scenarios, without necessitating scene or object-level 3D supervision. Extensive qualitative and quantitative evaluation against existing methods demonstrates a significant reduction in collisions in the final reconstructions of scenes with multiple interacting humans and objects and a more coherent scene reconstruction., Comment: Accepted at AI for 3D Generation, CVPR Workshop

We investigate the leading-twist gluonic distribution functions within the proton by formulating a light-front spectator model. The proton light-front wave functions (LFWFs) are adopted from the soft-wall AdS/QCD predictions, and the model parameters are determined using the gluon unpolarized parton distribution function from the NNPDF3.0nlo dataset. Furthermore, we demonstrated the model calculations for gluon transverse momentum distributions (TMDs) and generalized parton distributions (GPDs). We also predicted the gluon spin and orbital angular momentum (OAM) contributions to the total proton spin., Comment: Contribution to the 31st International Workshop on Deep Inelastic Scattering (DIS2024), 8-12 April 2024, Grenoble, France

In this note we develop a generalisation of the $\lambda$-Core solution for non-cooperative games in normal form. We show that this generalised $\lambda$-Core is non-empty for the class of separable games that admit a socially optimal Nash equilibrium. Examples are provided that indicate that non-emptiness of the generalised $\lambda$-Core cannot be expected for large classes of normal form games.

The accreting collapsed object GRO J1655-40 could contain the gravitomagnetic monopole (GMM), and it was shown to be better described by the Kerr-Taub-NUT (KTN) spacetime instead of the Kerr spacetime. The warped accretion disk has also been observed for the same collapsed object. Motivated by these, we study a tilted thin inner accretion disk around a slowly-spinning KTN black hole that contains a small GMM. Such a tilting could have a significant effect on the X-ray spectral and timing features via the Lense-Thirring effect. Taking into account the contribution from the inner accretion disk for the KTN black hole, here we calculate the radial profile of a tilt angle. Depending on the numerical values of the viscosity of the accreting material and Kerr parameter, we show that the GMM tends the angular momentum of the disk to align along the black hole's spin axis, or to make it more tilted. Our solution for the radial profile of the tilted disk around a KTN black hole could be useful to probe the strong gravity regime, and could also give indirect evidence for the existence of GMM in nature., Comment: 17 pages, 7 figures

Past research relates design creativity to 'divergent thinking,' i.e., how well the concept space is explored during the early phase of design. Researchers have argued that generating several concepts would increase the chances of producing better design solutions. 'Variety' is one of the parameters by which one can quantify the breadth of a concept space explored by the designers. It is useful to assess variety at the conceptual design stage because, at this stage, designers have the freedom to explore different solution principles so as to satisfy a design problem with substantially novel concepts. This article elaborates on and critically examines the existing variety metrics from the engineering design literature, discussing their limitations. A new distance-based variety metric is proposed, along with a prescriptive framework to support the assessment process. This framework uses the SAPPhIRE model of causality as a knowledge representation scheme to measure the real-valued distance between two design concepts. The proposed framework is implemented in a software tool called 'VariAnT.' Furthermore, the tool's application is demonstrated through an illustrative example.

Research has shown that quantum walks can accelerate certain quantum algorithms and act as a universal paradigm for quantum processing. The discrete-time quantum walk (DTQW) model, owing to its discrete nature, stands out as one of the most suitable choices for circuit implementation. Nevertheless, most current implementations are characterized by extensive, multi-layered quantum circuits, leading to higher computational expenses and a notable decrease in the number of confidently executable time steps on current quantum computers. Since quantum computers are not scalable enough in this NISQ era, we also must confine ourselves to the ancilla-free frontier zone. Therefore, in this paper, we have successfully cut down the circuit cost concerning gate count and circuit depth by half through our proposed methodology in qubit systems as compared to the state-of-the-art increment-decrement approach. Furthermore, for the engineering excellence of our proposed approach, we implement DTQW in any finite-dimensional quantum system with akin efficiency. To ensure an efficient implementation of quantum walks without requiring ancilla, we have incorporated an intermediate qudit technique for decomposing multi-qubit gates. Experimental outcomes hold significance far beyond the realm of just a few time steps, laying the groundwork for dependable implementation and utilization on quantum computers., Comment: 13 pages, 21 figures

Adaptive gradient-descent optimizers are the standard choice for training neural network models. Despite their faster convergence than gradient-descent and remarkable performance in practice, the adaptive optimizers are not as well understood as vanilla gradient-descent. A reason is that the dynamic update of the learning rate that helps in faster convergence of these methods also makes their analysis intricate. Particularly, the simple gradient-descent method converges at a linear rate for a class of optimization problems, whereas the practically faster adaptive gradient methods lack such a theoretical guarantee. The Polyak-{\L}ojasiewicz (PL) inequality is the weakest known class, for which linear convergence of gradient-descent and its momentum variants has been proved. Therefore, in this paper, we prove that AdaGrad and Adam, two well-known adaptive gradient methods, converge linearly when the cost function is smooth and satisfies the PL inequality. Our theoretical framework follows a simple and unified approach, applicable to both batch and stochastic gradients, which can potentially be utilized in analyzing linear convergence of other variants of Adam., Comment: Accepted for publication at the main track of 27th European Conference on Artificial Intelligence (ECAI-2024)

A language is made up of an infinite/finite number of sentences, which in turn is composed of a number of words. The Electrocardiogram (ECG) is the most popular noninvasive medical tool for studying heart function and diagnosing various irregular cardiac rhythms. Intuitive inspection of the ECG reveals a marked similarity between ECG signals and the spoken language. As a result, the ECG signal may be thought of as a series of heartbeats (similar to sentences in a spoken language), with each heartbeat consisting of a collection of waves (similar to words in a sentence) with varying morphologies. Just as natural language processing (NLP) is used to help computers comprehend and interpret human natural language, it is conceivable to create NLP-inspired algorithms to help computers comprehend the electrocardiogram data more efficiently. In this study, we propose a novel ECG analysis technique, based on embedding and self attention, to capture the spatial as well as the temporal dependencies of the ECG data. To generate the embedding, an encoder-decoder network was proposed to capture the temporal dependencies of the ECG signal and perform data compression. The compressed and encoded data was fed to the embedding layer as its weights. Finally, the proposed CNN-LSTM-Self Attention classifier works on the embedding layer and classifies the signal as normal or anomalous. The approach was tested using the PTB-xl dataset, which is severely imbalanced. Our emphasis was to appropriately recognise the disease classes present in minority numbers, in order to limit the detection of False Negative cases. An accuracy of 91% was achieved with a good F1-score for all the disease classes. Additionally, the the size of the model was reduced by 34% due to compression, making it suitable for deployment in real time applications

Based on the rate of change of its orbital period, PSR J2043+1711 has a substantial peculiar acceleration of 3.5 $\pm$ 0.8 mm/s/yr, which deviates from the acceleration predicted by equilibrium Milky Way models at a $4\sigma$ level. The magnitude of the peculiar acceleration is too large to be explained by disequilibrium effects of the Milky Way interacting with orbiting dwarf galaxies ($\sim$1 mm/s/yr), and too small to be caused by period variations due to the pulsar being a redback. We identify and examine two plausible causes for the anomalous acceleration: a stellar flyby, and a long-period orbital companion. We identify a main-sequence star in \textit{Gaia} DR3 and Pan-STARRS DR2 with the correct mass, distance, and on-sky position to potentially explain the observed peculiar acceleration. However, the star and the pulsar system have substantially different proper motions, indicating that they are not gravitationally bound. However, it is possible that this is an unrelated star that just happens to be located near J2043+1711 along our line of sight (chance probability of 1.6\%). Therefore, we also constrain possible orbital parameters for a circumbinary companion in a hierarchical triple system with J2043+1711; the changes in the spindown rate of the pulsar are consistent with an outer object that has an orbital period of 80 kyr, a companion mass of 0.3 $M_\odot$ (indicative of a white dwarf or low-mass star), and a semi-major axis of 2000 AU. Continued timing and/or future faint optical observations of J2043+1711 may eventually allow us to differentiate between these scenarios.

Using the variational principle, we systematically investigate the mass spectra and wave functions of both $1S$ and $2S$ state heavy pseudoscalar $(P)$ and vector $(V)$ mesons within the light-front quark model. This approach incorporates a Coulomb plus logarithmic confinement potential to accurately describe the constituent quark and antiquark dynamics. Additionally, spin hyperfine interactions are introduced perturbatively to compute the masses of pseudoscalar and vector mesons. The present analyses of the $1S$ and $2S$ states require the consideration of mixing between them to account for empirical constraints. These constraints include the mass gap $\Delta M_{P} > \Delta M_{V}$, where $\Delta M_{P(V)} = M^{2S}_{P(V)} - M^{1S}_{P(V)}$ and the hierarchy of the decay constants $f_{1S} > f_{2S}$. We find the optimal value of the mixing angle to be $\theta = 18^{\circ}$, significantly enhancing the consistency between our spectroscopic predictions and the experimental data compiled by the Particle Data Group (PDG). Furthermore, based on the predicted mass, the newly observed resonance $B_J(5840)$ could be assigned as a $2^1S_0$ state in the $B$ meson family. The study also reports various pertinent observables, including twist-2 distribution amplitudes, electromagnetic form factors, charge radii, $\xi$ moments, and transition form factors which are found to be consistent with both available lattice simulations and experimental data. In addition, our predicted branching ratios for the channels of $B^+ \rightarrow \tau^+ \nu_{\tau}$ as well as rare decays of $B^0$ and $B_s^0$ appear in accordance with experimental data., Comment: 20 pages, 9 figures

Representation of systems using the SAPPhIRE model of causality can be an inspirational stimulus in design. However, creating a SAPPhIRE model of a technical or a natural system requires sourcing technical knowledge from multiple technical documents regarding how the system works. This research investigates how to generate technical content accurately relevant to the SAPPhIRE model of causality using a Large Language Model, also called LLM. This paper, which is the first part of the two-part research, presents a method for hallucination suppression using Retrieval Augmented Generating with LLM to generate technical content supported by the scientific information relevant to a SAPPhIRE con-struct. The result from this research shows that the selection of reference knowledge used in providing context to the LLM for generating the technical content is very important. The outcome of this research is used to build a software support tool to generate the SAPPhIRE model of a given technical system.

Representing systems using the SAPPhIRE causality model is found useful in supporting design-by-analogy. However, creating a SAPPhIRE model of artificial or biological systems is an effort-intensive process that requires human experts to source technical knowledge from multiple technical documents regarding how the system works. This research investigates how to leverage Large Language Models (LLMs) in creating structured descriptions of systems using the SAPPhIRE model of causality. This paper, the second part of the two-part research, presents a new Retrieval-Augmented Generation (RAG) tool for generating information related to SAPPhIRE constructs of artificial systems and reports the results from a preliminary evaluation of the tool's success - focusing on the factual accuracy and reliability of outcomes.

Measurements of the accelerations of stars enabled by time-series extreme-precision spectroscopic observations, from pulsar timing, and from eclipsing binary stars in the Solar Neighborhood offer insights into the mass distribution of the Milky Way that do not rely on traditional equilibrium modeling. Given the measured accelerations, we can determine a total mass density, and from this, by accounting for the mass in stars, gas, and dust, we can infer the amount of dark matter. Leveraging the FIRE-2 simulations of Milky Way-mass galaxies, we compare vertical acceleration profiles between cold dark matter (CDM) and self-interacting dark matter (SIDM) with constant cross-section of 1 cm$^2$ g$^{-1}$ across three halos with diverse assembly histories. Notably, significant asymmetries in vertical acceleration profiles near the midplane at fixed radii are observed in both CDM and SIDM, particularly in halos recently affected by mergers with satellites of Sagittarius/SMC-like masses or greater. These asymmetries offer a unique window into exploring the merger history of a galaxy. We show that SIDM halos consistently exhibit higher local stellar and dark matter densities and steeper vertical acceleration gradients, up to 30% steeper near the Solar Neighborhood. SIDM halos also manifest a more oblate halo shape in the Solar Neighborhood. Furthermore, enhanced precision in acceleration measurements and larger datasets promise to provide better constraints on the local dark matter density, complementing our understanding from kinematic analysis of their distribution within galaxies., Comment: 14 pages, 5 figures, and 2 tables. Submitted to APJ

One of the principal objectives of Natural Language Processing (NLP) is to generate meaningful representations from text. Improving the informativeness of the representations has led to a tremendous rise in the dimensionality and the memory footprint. It leads to a cascading effect amplifying the complexity of the downstream model by increasing its parameters. The available techniques cannot be applied to cross-modal applications such as text-to-image. To ameliorate these issues, a novel Text-to-Image methodology for generating fixed-length representations through a self-supervised Variational Auto-Encoder (VAE) for semantic evaluation applying transformers (TexIm FAST) has been proposed in this paper. The pictorial representations allow oblivious inference while retaining the linguistic intricacies, and are potent in cross-modal applications. TexIm FAST deals with variable-length sequences and generates fixed-length representations with over 75% reduced memory footprint. It enhances the efficiency of the models for downstream tasks by reducing its parameters. The efficacy of TexIm FAST has been extensively analyzed for the task of Semantic Textual Similarity (STS) upon the MSRPC, CNN/ Daily Mail, and XSum data-sets. The results demonstrate 6% improvement in accuracy compared to the baseline and showcase its exceptional ability to compare disparate length sequences such as a text with its summary., Comment: 19 pages, 33 figures

We present a detailed investigation of photometric, spectroscopic, and polarimetric observations of the Type II SN 2023ixf. Earlier studies have provided compelling evidence for a delayed shock breakout from a confined dense circumstellar matter (CSM) enveloping the progenitor star. The temporal evolution of polarization in SN~2023ixf revealed three distinct peaks in polarization evolution at 1.4 d, 6.4 d, and 79.2 d, indicating an asymmetric dense CSM, an aspherical shock front and clumpiness in the low-density extended CSM, and an aspherical inner ejecta/He-core. SN 2023ixf displayed two dominant axes, one along the CSM-outer ejecta and the other along the inner ejecta/He-core, showcasing the independent origin of asymmetry in the early and late evolution. The argument for an aspherical shock front is further strengthened by the presence of a high-velocity broad absorption feature in the blue wing of the Balmer features in addition to the P-Cygni absorption post 16 d. Hydrodynamical light curve modeling indicated a progenitor of 10 solar mass with a radius of 470 solar radii and explosion energy of 2e51 erg, along with 0.06 solar mass of 56-Ni, though these properties are not unique due to modeling degeneracies. The modeling also indicated a two-zone CSM: a confined dense CSM extending up to 5e14 cm, with a mass-loss rate of 1e-2 solar mass per year, and an extended CSM spanning from 5e14 cm to at least 1e16cm with a mass-loss rate of 1e-4 solar mass per year, both assuming a wind-velocity of 10 km/s. The early nebular phase observations display an axisymmetric line profile of [OI], red-ward attenuation of the emission of Halpha post 125 days, and flattening in the Ks-band, marking the onset of dust formation., Comment: 32 pages, 15 figures, 1 Table, Accepted in the Astrophysical Journal

A quantitative theoretical study of the dissociative recombination of SH$^+$ with electrons has been carried out. Multireference, configuration interaction calculations were used to determine accurate potential energy curves for SH$^+$ and SH. The block diagonalization method was used to disentangle strongly interacting SH valence and Rydberg states and to construct a diabatic Hamiltonian whose diagonal matrix elements provide the diabatic potential energy curves. The off-diagonal elements are related to the electronic valence-Rydberg couplings. Cross sections and rate coefficients for the dissociative recombination reaction were calculated with a step-wise version of the multichannel quantum defect theory, using the molecular data provided by the block diagonalization method. The calculated rates are compared with the most recent measurements performed on the TSR ion storage ring in Heidelberg, Germany., Comment: 17 pages, 15 figures, 4 tables

Multichannel quantum defect theory is applied in the treatment of the dissociative recombination and vibrational excitation processes for the BeD$^+$ ion in the twenty four vibrational levels of its ground electronic state ($\textrm{X}\,{^{1}\Sigma^{+}},v_{i}^{+}=0\ldots 23$). Three electronic symmetries of BeD$^{**}$ states (\ensuremath{^{2}\Pi}, \ensuremath{^{2}\Sigma^{+}}, and \ensuremath{^{2}\Delta}), are considered in the calculation of cross sections and the corresponding rate coefficients. The incident electron energy range is $10^{-5}$--2.7 eV and the electron temperature range is 100--5000~K. The vibrational dependence of these collisional processes is highlighted. The resulting data are useful in magnetic confinement fusion edge plasma modelling and spectroscopy, in devices with beryllium based main chamber materials, such as ITER and JET, and operating with the deuterium-tritium fuel mix. An extensive rate coefficients database is presented in graphical form and also by analytic fit functions whose parameters are tabulated in the supplementary material., Comment: 7 pages, 5 figures, 3 tables

We study the non-equilibrium dynamics of a one-dimensional Bose gas with long-range interactions that decay as $(\frac{1}{r^{\alpha}})$ $(0.5 < \alpha <4.0$). We investigate the system when the interactions are suddenly switched from the Tonks-Girardeau (TG) to the super-Tonks-Girardeau (sTG) limit. We find that relaxation is achieved through a complex intermediate dynamics demonstrated by violent fragmentation and chaotic delocalization. We establish that the sTG gas exhibits classical gaseous characteristics and an asymptotic state associated with unbounded entropy production. The phase diagram shows an exponential boundary between the nonthermal (quantum) gas and the thermal (classical) gas. We show the universality of the dynamics by also presenting results for spinless fermions. Weaker quench protocols give a certain degree of control over the relaxation process and induce some intermediate quasi-prethermal states. Our study showcases the complex relaxation behavior of tunable long-range interacting systems that could be engineered in state-of-the-art experiments, e.g. in trapped ions or Rydberg atoms., Comment: 14 pages, 13 figures

Calculations are performed for electron collision with the methylene molecular ion CH$_2^+$ in its bent equilibrium geometry, with the goal to obtain cross sections for electron impact excitation and dissociation. The polyatomic version of the UK molecular R-matrix codes was used to perform an initial configuration-interaction calculation on the doublet and quartet states of the CH$_2^+$ ion. Subsequently, scattering calculations are performed to obtain electron impact electronic excitation and dissociation cross sections and, additionally, the bound states of the CH$_2$ molecule and Feshbach resonances in the $e$-CH$_2^+$ system., Comment: 6 pages, 6 figures and 3 tables

Pyrolysis techniques have served to be a groundbreaking technique for effectively utilising natural and man-made biomass products like plastics, wood, crop residue, fruit peels etc. Recent advancements have shown a greater yield of essential products like biochar, bio-oil and other non-condensable gases by blending different biomasses in a certain ratio. This synergy effect of combining two pyrolytic raw materials i.e co-pyrolysis of algae and wood biomass has been systematically studied and grouped into 3 phases in this research paper-kinetic analysis of co-pyrolysis, correlation among proximate and ultimate analysis with bio-char yield and lastly grouping of different weight ratios based on biochar yield up to a certain percentage. Different ML and DL algorithms have been utilized for regression and classification techniques to give a comprehensive overview of the effect of the synergy of two different biomass materials on biochar yield. For the first phase, the best prediction of biochar yield was obtained by using a decision tree regressor with a perfect MSE score of 0.00, followed by a gradient-boosting regressor. The second phase was analyzed using both ML and DL techniques. Within ML, SVR proved to be the most convenient model with an accuracy score of 0.972 with DNN employed for deep learning technique. Finally, for the third phase, binary classification was applied to biochar yield with and without heating rate for biochar yield percentage above and below 40%. The best technique for ML was Support Vector followed by Random forest while ANN was the most suitable Deep Learning Technique., Comment: 6 pages

Recent studies have focused on the relationship between charge density wave (CDW) collective electronic ground states and nontrivial topological states. Using angle-resolved photoemission and density functional theory, we establish that YTe$_3$ is a CDW-induced Kramers nodal line (KNL) metal, a newly proposed topological state of matter. YTe$_3$ is a non-magnetic quasi-2D chalcogenide with a CDW wave vector ($q_{\rm cdw}$) of 0.2907c$^*$. Scanning tunneling microscopy and low energy electron diffraction revealed two orthogonal CDW domains, each with a unidirectional CDW and similar YTe$_3$. The effective band structure (EBS) computations, using DFT-calculated folded bands, show excellent agreement with ARPES because a realistic x-ray crystal structure and twin domains are considered in the calculations. The Fermi surface and ARPES intensity plots show weak shadow bands displaced by $q_{\rm cdw}$ from the main bands. These are linked to CDW modulation, as the EBS calculation confirms. Bilayer split main and shadow bands suggest the existence of crossings, according to theory and experiment. DFT bands, including spin-orbit coupling, indicate a nodal line along the $\Sigma$ line from multiple band crossings perpendicular to the KNL. Additionally, doubly degenerate bands are only found along the KNL at all energies, with some bands dispersing through the Fermi level.

The advent of transformers with attention mechanisms and associated pre-trained models have revolutionized the field of Natural Language Processing (NLP). However, such models are resource-intensive due to highly complex architecture. This limits their application to resource-constrained environments. While choosing an appropriate NLP model, a major trade-off exists over choosing accuracy over efficiency and vice versa. This paper presents a commentary on the evolution of NLP and its applications with emphasis on their accuracy as-well-as efficiency. Following this, a survey of research contributions towards enhancing the efficiency of transformer-based models at various stages of model development along with hardware considerations has been conducted. The goal of this survey is to determine how current NLP techniques contribute towards a sustainable society and to establish a foundation for future research.

This paper considers the observer design problem for discrete-time nonlinear dynamical systems with sampled measurement data. Earlier, the recently proposed Iteratively Preconditioned Gradient-Descent (IPG) observer, a Newton-type observer, has been empirically shown to have improved robustness against measurement noise than the prominent nonlinear observers, a property that other Newton-type observers lack. However, no theoretical guarantees on the convergence of the IPG observer were provided. This paper presents a rigorous convergence analysis of the IPG observer for a class of nonlinear systems in deterministic settings, proving its local linear convergence to the actual trajectory. Our assumptions are standard in the existing literature of Newton-type observers, and the analysis further confirms the relation of the IPG observer with the Newton observer, which was only hypothesized earlier., Comment: 7 pages

Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collaborative learning. Motivated by the increasing success of recovering input data from the gradients of classical models, this study addresses a central question: How hard is it to recover the input data from the gradients of quantum machine learning models? Focusing on variational quantum circuits (VQC) as learning models, we uncover the crucial role played by the dynamical Lie algebra (DLA) of the VQC ansatz in determining privacy vulnerabilities. While the DLA has previously been linked to the classical simulatability and trainability of VQC models, this work, for the first time, establishes its connection to the privacy of VQC models. In particular, we show that properties conducive to the trainability of VQCs, such as a polynomial-sized DLA, also facilitate the extraction of detailed snapshots of the input. We term this a weak privacy breach, as the snapshots enable training VQC models for distinct learning tasks without direct access to the original input. Further, we investigate the conditions for a strong privacy breach where the original input data can be recovered from these snapshots by classical or quantum-assisted polynomial time methods. We establish conditions on the encoding map such as classical simulatability, overlap with DLA basis, and its Fourier frequency characteristics that enable such a privacy breach of VQC models. Our findings thus play a crucial role in detailing the prospects of quantum privacy advantage by guiding the requirements for designing quantum machine learning models that balance trainability with robust privacy protection., Comment: 28 pages, 8 figures, 1 table

The latest molecular data - potential energy curves and Rydberg$/$valence interactions - characterizing the super-excited electronic states of CO are reviewed, in order to provide inputs for the study of their fragmentation dynamics. Starting from this input, the main paths and mechanisms for CO$^+$ dissociative recombination are analyzed; its cross sections are computed using a method based on Multichannel Quantum Defect Theory. Convoluted cross sections, giving both isotropic and anisotropic Maxwellian rate-coefficients, are compared with merged-beam and storage-ring experimental results. The calculated cross sections underestimate the measured ones by a factor of $2$, but display a very similar resonant shape. These facts confirm the quality of our approach for the dynamics, and call for more accurate and more extensive molecular structure calculations., Comment: 12 pages, 8 figures 5 tables

The latest molecular data - potential energy curves and Rydberg-valence interactions - characterising the super-excited electronic states of BF are reviewed in order to provide the input for the study of their fragmentation dynamics. Starting from this input, the main paths and mechanisms of BF$^+$ dissociative recombination and vibrational excitation are analysed. Their cross sections are computed for the first time using a method based on the multichannel quantum defect theory (MQDT), and Maxwellian rate-coefficients are calculated and displayed in ready-to-be-used format for low temperature plasma kinetics simulations., Comment: 8 pages, 4 figures, 4 tables

In medical imaging, segmentation and localization of spinal tumors in three-dimensional (3D) space pose significant computational challenges, primarily stemming from limited data availability. In response, this study introduces a novel data augmentation technique, aimed at automating spine tumor segmentation and localization through AI approaches. Leveraging a fusion of fuzzy c-means clustering and Random Forest algorithms, the proposed method achieves successful spine tumor segmentation based on predefined masks initially delineated by domain experts in medical imaging. Subsequently, a Convolutional Neural Network (CNN) architecture is employed for tumor classification. Moreover, 3D vertebral segmentation and labeling techniques are used to help pinpoint the exact location of the tumors in the lumbar spine. Results indicate a remarkable performance, with 99% accuracy for tumor segmentation, 98% accuracy for tumor classification, and 99% accuracy for tumor localization achieved with the proposed approach. These metrics surpass the efficacy of existing state-of-the-art techniques, as evidenced by superior Dice Score, Class Accuracy, and Intersection over Union (IOU) on class accuracy metrics. This innovative methodology holds promise for enhancing the diagnostic capabilities in detecting and characterizing spinal tumors, thereby facilitating more effective clinical decision-making., Comment: 9 pages, 12 figures

We inspect the origin of the inverted singlet-triplet gap (INVEST) and slow change in the reverse intersystem crossing (rISC) rate with temperature, as recently observed. A Wigner phase space study reveals, that though INVEST is found at equilibrium geometry, variation in the exchange interaction and the doubles-excitation for other geometries in the harmonic region leads to non- INVEST behavior. This highlights the importance of nuclear degrees of freedom for the INVEST phenomenon and in this case, geometric puckering of the studied molecule determines INVEST and the associated rISC dynamics.

The reactive collisions of the CH$^+$ molecular ion with electrons is studied in the framework of the multichannel quantum defect theory, taking into account the contribution of the core-excited Rydberg states. In addition to the $X^1\Sigma^+$ ground state of the ion, we also consider the contribution to the dynamics of the $a^3\Pi$ and $A^1\Pi$ excited states of CH$^+$. Our results - in the case of the dissociative recombination in good agreement with the storage ring measurements - rely on decisive improvements - complete account of the ionisation channels and accurate evaluation of the reaction matrix - of a previously used model., Comment: 8 pages, 8 figures

We report a family of self-similar exact solutions in General Relativity. The solutions are found in a Painleve-Gullstrand coordinate system but can also be transformed smoothly into a diagonal form. The solutions represent a gravitational collapse leading to three possible outcomes, depending on the parameter space : (i) a collapse followed by a bounce and dispersal of the clustered matter distribution, (ii) a rapid collapse followed by a bounce and an eventual re-collapse, and (iii) a standard collapse leading to zero proper volume. Profiles of the energy conditions are studied for all of the scenarios, and it is noted that a bounce is usually associated with a violation of the Null Energy Condition. It is found that more than one null surfaces (apparent horizons) can develop during the collapse. We also discuss that for a general metric tensor having a conformal symmetry, some regions of the parameter space allows a formation of null throat, much like a wormhole. Matching the metric with a Schwarzschild metric in Painleve-Gullstrand form leads to the geodesic equation for a zero energy falling particle in the exterior., Comment: 11 pages, 9 figures, comments are welcome

Segmentation and labeling of vertebrae in MRI images of the spine are critical for the diagnosis of illnesses and abnormalities. These steps are indispensable as MRI technology provides detailed information about the tissue structure of the spine. Both supervised and unsupervised segmentation methods exist, yet acquiring sufficient data remains challenging for achieving high accuracy. In this study, we propose an enhancing approach based on modified attention U-Net architecture for panoptic segmentation of 3D sliced MRI data of the lumbar spine. Our method achieves an impressive accuracy of 99.5\% by incorporating novel masking logic, thus significantly advancing the state-of-the-art in vertebral segmentation and labeling. This contributes to more precise and reliable diagnosis and treatment planning., Comment: 9 pages, 10 figures

Collective plasmon excitations in solids that result from the process of photoemission are an important area of fundamental research. In this study, we identify a significant number ($n$) of multiple bulk plasmons ($n\omega_p$) in the hard x-ray photoelectron spectra of the core levels and valence bands (VBs) of two well-known, nearly free electron metals, aluminum (Al) and magnesium (Mg). On the basis of earlier theoretical works, we estimate the contributions of extrinsic, intrinsic, and interference processes to the intensities of 1$s$ to 2$s$ core level plasmons. The intrinsic contribution diminishes from 22% for 1$\omega_p$, to 4.4% for 2$\omega_p$, and becomes negligible thereafter (0.5% for 3$\omega_p$). The extrinsic and intrinsic plasmon contributions do not vary significantly across a broad range of photoelectron kinetic energies, and also between the two metals (Al and Mg). The interference contribution varies from negative to zero as $n$ increases. An asymmetric line shape is observed for the bulk plasmons, which is most pronounced for 1$\omega_p$. Signature of the surface plasmon is detected in normal emission, and it exhibits a significantly increased intensity in the grazing emission. The VB spectra of Al and Mg, which are dominated by $s$-like states, exhibit excellent agreement with the calculated VB based on density functional theory. The VB exhibits four multiple bulk plasmon peaks in the loss region, which are influenced by an intrinsic process in addition to the extrinsic process. On a completely oxidized aluminum surface, the relative intensity of the Al metal bulk plasmon remains nearly unaltered, while the surface plasmon is completely attenuated., Comment: Phys. Rev. B (in press)

We demonstrate a design for a high-performance $2 \times 2$ splitter meeting the essential requirements of broadband coupling, support for arbitrary coupling ratio, ultra low-loss, high fabrication tolerance, and a compact footprint. This is achieved based on a rigorous coupled mode theory analysis of the broadband response of the bent directional coupler (DC) and by demonstrating a full coupling model, with measured broadband values of 0.4, 0.5, 0.6, and 0.7. As a benchmark, we demonstrate a 0.5:0.5 splitter that significantly reduces coupling variation from 0.391 in the traditional DC to just 0.051 over an 80 nm wavelength span. This represents a remarkable 7.67 times reduction in coupling variation. Further, newly-invented low-loss bends were used in the proposed design leading to an ultra low-loss design with negligible excess loss ($\mathrm{0.003 \pm 0.013 \ dB}$). The proposed 0.5:0.5 silicon strip waveguide-based design is tolerant and shows consistently low coupling variation over a full 300 mm wafer showcasing a maximum cross coupling variation of 0.112 over 80 nm wavelength range, at the extreme edge of the wafer. Futhermore, we augmented the wafer mapping with a waveguide width fabrication tolerance study, confirming the tolerance of the device with a mere 0.061 maximum coupling variation with a waveguide width deviation of $\pm 20$ nm over 80 nm wavelength range. These specs make the proposed splitter an attractive component for practical applications with mass production.

A bright ($m_{\rm F150W,AB}$=24 mag), $z=1.95$ supernova (SN) candidate was discovered in JWST/NIRCam imaging acquired on 2023 November 17. The SN is quintuply-imaged as a result of strong gravitational lensing by a foreground galaxy cluster, detected in three locations, and remarkably is the second lensed SN found in the same host galaxy. The previous lensed SN was called "Requiem", and therefore the new SN is named "Encore". This makes the MACS J0138.0$-$2155 cluster the first known system to produce more than one multiply-imaged SN. Moreover, both SN Requiem and SN Encore are Type Ia SNe (SNe Ia), making this the most distant case of a galaxy hosting two SNe Ia. Using parametric host fitting, we determine the probability of detecting two SNe Ia in this host galaxy over a $\sim10$ year window to be $\approx3\%$. These observations have the potential to yield a Hubble Constant ($H_0$) measurement with $\sim10\%$ precision, only the third lensed SN capable of such a result, using the three visible images of the SN. Both SN Requiem and SN Encore have a fourth image that is expected to appear within a few years of $\sim2030$, providing an unprecedented baseline for time-delay cosmography., Comment: Accepted, ApJL

Starting from the computation of Symmetry Resolved Entanglement Entropy (SREE) for boosted intervals in a two dimensional Conformal Field Theory, we compute the same in various non-Lorentzian limits, viz, Galilean and Carrollian Conformal Field Theory in same number of dimensions. We approach the problem both from a limiting perspective and by using intrinsic symmetries of respective non-Lorentzian conformal algebras. In particular, we calculate the leading order terms, logarithmic terms, and the $\mathcal{O}(1)$ terms and explicitly show exact compliance with $\textit{equipartition of entanglement}$, even in the non-Lorentzian system. Keeping in mind the holographic origin of SREE for the Carrollian limit, we further compute SREE for BMS$_{3}$-Kac-Moody, which couples a $U(1)\times U(1)$ theory with bulk gravity., Comment: 38 pages, typos corrected, references updated, changes made in the section 4 and 6 regarding SREE for GCFT and BMS, conclusion remains unchanged, updated version to appear in JHEP

Supernova (SN) H0pe is a gravitationally lensed, triply-imaged, Type Ia SN (SN Ia) discovered in James Webb Space Telescope imaging of the PLCK G165.7+67.0 cluster of galaxies. Well-observed multiply-imaged SNe provide a rare opportunity to constrain the Hubble constant ($H_0$), by measuring the relative time delay between the images and modeling the foreground mass distribution. SN H0pe is located at $z=1.783$, and is the first SN Ia with sufficient light curve sampling and long enough time delays for an $H_0$ inference. Here we present photometric time-delay measurements and SN properties of SN H0pe. Using JWST/NIRCam photometry we measure time delays of $\Delta t_{ab}=-116.6^{+10.8}_{-9.3}$ and $\Delta t_{cb}=-48.6^{+3.6}_{-4.0}$ observer-frame days relative to the last image to arrive (image 2b; all uncertainties are $1\sigma$), which corresponds to a $\sim5.6\%$ uncertainty contribution for $H_0$ assuming $70 \rm{km s^{-1} Mpc^{-1}}$. We also constrain the absolute magnification of each image to $\mu_{a}=4.3^{+1.6}_{-1.8}$, $\mu_{b}=7.6^{+3.6}_{-2.6}$, $\mu_{c}=6.4^{+1.6}_{-1.5}$ by comparing the observed peak near-IR magnitude of SN H0pe to the non-lensed population of SNe Ia., Comment: Accepted in ApJ

The maximum coverage problem is to select $k$ sets from a collection of sets such that the cardinality of the union of the selected sets is maximized. We consider $(1-1/e-\epsilon)$-approximation algorithms for this NP-hard problem in three standard data stream models. 1. {\em Dynamic Model.} The stream consists of a sequence of sets being inserted and deleted. Our multi-pass algorithm uses $\epsilon^{-2} k \cdot \text{polylog}(n,m)$ space. The best previous result (Assadi and Khanna, SODA 2018) used $(n +\epsilon^{-4} k) \text{polylog}(n,m)$ space. While both algorithms use $O(\epsilon^{-1} \log n)$ passes, our analysis shows that when $\epsilon$ is a constant, it is possible to reduce the number of passes by a $1/\log \log n$ factor without incurring additional space. 2. {\em Random Order Model.} In this model, there are no deletions and the sets forming the instance are uniformly randomly permuted to form the input stream. We show that a single pass and $k \text{polylog}(n,m)$ space suffices for arbitrary small constant $\epsilon$. The best previous result, by Warneke et al.~(ESA 2023), used $k^2 \text{polylog}(n,m)$ space. 3. {\em Insert-Only Model.} Lastly, our results, along with numerous previous results, use a sub-sampling technique introduced by McGregor and Vu (ICDT 2017) to sparsify the input instance. We explain how this technique and others used in the paper can be implemented such that the amortized update time of our algorithm is polylogarithmic. This also implies an improvement of the state-of-the-art insert only algorithms in terms of the update time: $\text{polylog}(m,n)$ update time suffices whereas the best previous result by Jaud et al.~(SEA 2023) required update time that was linear in $k$.

Motivated by the notion that the mathematics of gravity can be reproduced from a statistical requirement of maximal entropy, we study the consequence of introducing an entropic source term in the Einstein-Hilbert action. For a spatially homogeneous cosmological system driven by this entropic source and enveloped by a time evolving apparent horizon, we formulate a modified version of the second law of thermodynamics. An explicit differential equation governing the internal entropy profile is found. Using a Hessian matrix analysis of the internal entropy we check the thermodynamic stability for a {\Lambda}CDM cosmology, a unified cosmic expanson and a non-singular ekpyrotic bounce. We find the mathematical condition for a second order phase transition during these evolutions from the divergence of specific heat at constant volume. The condition is purely kinematic and quadratic in nature, relating the deceleration parameter and the jerk parameter that chalks out an interesting curve on the parameter space. This condition is valid even without the entropic source term and may be a general property of any phase transition., Comment: 10 pages, 6 figures, comments are welcome

We have investigated the characteristics of shadows cast by the Kerr black hole in the presence of plasma and compared them to those of a rotating wormhole in a uniform plasma space-time for an observer at infinity. Interestingly, for the same uniform plasma density, the apparent shadow size of the rotating wormhole is always greater than that of the Kerr black hole. To further distinguish the two compact objects we studied the deflection angle and did a comparative study in the presence of the uniform and non-uniform plasma profiles. The goal of this whole exercise is to deepen our understanding of the observational phenomena of these astrophysical objects. The analysis reveals the importance of specific plasma distribution profiles, the impact of plasma on the shadow diameter, and the behavior of deflection angles in different plasma scenarios. We have calculated constraints on the plasma parameters by considering observational data and employing analytical formulations. Our work therefore provides valuable insights into the behavior of light rays near compact objects in plasma space-time.

We numerically study the expansion dynamics of initially localized dipolar bosons in a homogeneous 1D optical lattice for different initial states. Comparison is made to interacting bosons with contact interaction. For shallow lattices the expansion is unimodal and ballistic, while strong lattices suppress tunneling. However for intermediate lattice depths a strong interplay between dipolar interaction and lattice depth occurs. The expansion is found to be bimodal, the central cloud expansion can be distinguished from the outer halo structure. In the regime of strongly interactions dipolar bosons exhibit two time scales, with an initial diffusion and then arrested transport in the long time; while strongly interacting bosons in the fermionized limit exhibit ballistic expansion. Our study highlights how different lattice depths and initial states can be manipulated to control tunneling dynamics., Comment: 14 pages, 14 figures

We analyze the topological properties of a family of generalized Su-Schrieffer-Heeger (SSH) chains and mesh geometries. In both the geometries the usual staggering in the distribution of the two overlap integrals is delayed (in space) by the inclusion of a third (additional) hopping term. A tight-binding Hamiltonian is used to unravel the topological phases, characterized by a topological invariant. While in the linear chains, the topological invariant (the Zak phase) always appears to be quantized, in the quasi-one dimensional strip geometries and the generalized SSH mesh patterns the quantization of the Zak phase is sensitive to the strength of the additional interaction (the `extra' hopping integral). We study its influence thoroughly and explore the edge states and their robustness against disorder in the cross-linked generalized SSH mesh geometries. The systems considered here can be taken to model (though crudely) two-dimensional polymers where the cross-linking brings in non-trivial modification of the energy bands and transport properties. In addition to the topological features studied, we provide a prescription to unravel any flat, non-dispersive energy bands in the mesh geometries, along with the structure and distribution of the compact localized eigenstates. Our results are analytically exact.

Large Language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for high-resource languages (HRLs). A few of them have been trained on low-resource languages (LRLs) and give decent performance. Owing to the prohibitive costs of training LLMs, they are usually used as a network service, with the client charged by the count of input and output tokens. The number of tokens strongly depends on the script and language, as well as the LLM's subword vocabulary. We show that LRLs are at a pricing disadvantage, because the well-known LLMs produce more tokens for LRLs than HRLs. This is because most currently popular LLMs are optimized for HRL vocabularies. Our objective is to level the playing field: reduce the cost of processing LRLs in contemporary LLMs while ensuring that predictive and generative qualities are not compromised. As means to reduce the number of tokens processed by the LLM, we consider code-mixing, translation, and transliteration of LRLs to HRLs. We perform an extensive study using the IndicXTREME classification and six generative tasks dataset, covering 15 Indic and 3 other languages, while using GPT-4 (one of the costliest LLM services released so far) as a commercial LLM. We observe and analyze interesting patterns involving token count, cost, and quality across a multitude of languages and tasks. We show that choosing the best policy to interact with the LLM can reduce cost by 90% while giving better or comparable performance compared to communicating with the LLM in the original LRL.