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By tailoring geometrical properties of magnetic nanocap structures, new possibilities appear to control its magnetic properties, such as the dynamics of magnetic vortices. Here, we develop an approach to describe dynamics of magnetic vortices on a spherical cap. Analytic results for the gyrofrequency of the vortex state are in a good agreement with micromagnetic simulations., Comment: 9 pages, 5 figures

The state of health of solar photovoltaic (PV) systems is assessed by measuring the current-voltage (I-V) curves, which present a collection of three cardinal points: the short-circuit point, the open-circuit point, and the maximum power point. To understand the response of PV systems, the I-V curve is typically modeled using the well-known single-diode model (SDM), which involves five parameters. However, the SDM can be expressed as a function of one parameter when the information of the cardinal points is incorporated into the formulation. This paper presents a methodology to address the uncertainty of the cardinal points on the parameters of the single-diode model based on the mathematical theory. Utilizing the one-dimensional single-diode model as the basis, the study demonstrates that it is possible to include the uncertainty by solving a set of nonlinear equations. The results highlight the feasibility and effectiveness of this approach in accounting for uncertainties in the SDM parameters.

We consider mappings satisfying an upper bound for the distortion of families of curves. We establish lower bounds for the distortion of distances under such mappings. As applications, we obtain theorems on the discreteness of the limit mapping of a sequence of mappings converging locally uniformly. We separately consider cases when mappings are defined in Euclidean $n$-dimensional space and in a metric space.

We give a short and relatively elementary proof of the Hilton-Milner Theorem., Comment: 4 pages

We address the challenge of creating 3D assets for household articulated objects from a single image. Prior work on articulated object creation either requires multi-view multi-state input, or only allows coarse control over the generation process. These limitations hinder the scalability and practicality for articulated object modeling. In this work, we propose a method to generate articulated objects from a single image. Observing the object in resting state from an arbitrary view, our method generates an articulated object that is visually consistent with the input image. To capture the ambiguity in part shape and motion posed by a single view of the object, we design a diffusion model that learns the plausible variations of objects in terms of geometry and kinematics. To tackle the complexity of generating structured data with attributes in multiple domains, we design a pipeline that produces articulated objects from high-level structure to geometric details in a coarse-to-fine manner, where we use a part connectivity graph and part abstraction as proxies. Our experiments show that our method outperforms the state-of-the-art in articulated object creation by a large margin in terms of the generated object realism, resemblance to the input image, and reconstruction quality., Comment: Project page: https://3dlg-hcvc.github.io/singapo

In this paper, we undertake a comprehensive examination of quasinormal modes linked to Lee-Wick black holes, delving into scalar, electromagnetic, and gravitational perturbations using the spectral method. Such black holes can display a rich structure of horizons, and our analysis considers all the representative scenarios, including extremal and non-extremal situations. In particular, we show that purely imaginary quasinormal modes emerge for extremal and near-extremal configurations, suggesting a rapid return to equilibrium without oscillation., Comment: 25 pages, 1 Figure, 20 Tables. arXiv admin note: text overlap with arXiv:2406.03353

Fault-tolerant quantum computing is crucial for realizing large-scale quantum computation, and the interplay between hardware architecture and quantum error-correcting codes is a key consideration. We present a comparative study of two quantum error-correcting codes - the surface code and the honeycomb Floquet code - implemented on variants of the spin-optical quantum computing architecture, enabling a direct comparison of the codes using consistent noise models. Our results demonstrate that the honeycomb Floquet code significantly outperforms the surface code in this setting. Notably, we achieve a photon loss threshold of 6.4% for the honeycomb Floquet code implementation - to our knowledge the highest reported for photonic platforms to date without large-scale multiplexing. This finding is particularly significant given that photon loss is the primary source of errors in photon-mediated quantum computing., Comment: 15 pages, 6 figures

In this paper, we undertake a comprehensive examination of quasinormal modes (QNMs) linked to Morris-Thorne, also known as Bronnikov-Ellis wormholes, delving into scalar, electromagnetic, and gravitational perturbations using the spectral method. Our research corrects inaccuracies previously reported in the literature and addresses areas where the Wentzel--Kramers--Brillouin (WKB) approximation proves inadequate. Moreover, we introduce and evaluate a novel spectral technique designed to consolidate recent advancements in formulating QNM boundary conditions at both the wormhole throat and space-like infinity. This innovative approach bridges critical gaps in existing methodologies and enhances the accuracy and applicability of QNM analysis in the study of wormhole physics., Comment: 16 pages

In this paper, we introduce a new type of bionic AI that enhances decision-making unpredictability by incorporating responses from a living fly. Traditional AI systems, while reliable and predictable, lack nuanced and sometimes unseasoned decision-making seen in humans. Our approach uses a fly's varied reactions, to tune an AI agent in the game of Gobang. Through a study, we compare the performances of different strategies on altering AI agents and found a bionic AI agent to outperform human as well as conventional and white-noise enhanced AI agents. We contribute a new methodology for creating a bionic random function and strategies to enhance conventional AI agents ultimately improving unpredictability., Comment: iWOAR 2024 - 9th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence

Despite much progress in large 3D datasets there are currently few interactive 3D object datasets, and their scale is limited due to the manual effort required in their construction. We introduce the static to openable (S2O) task which creates interactive articulated 3D objects from static counterparts through openable part detection, motion prediction, and interior geometry completion. We formulate a unified framework to tackle this task, and curate a challenging dataset of openable 3D objects that serves as a test bed for systematic evaluation. Our experiments benchmark methods from prior work and simple yet effective heuristics for the S2O task. We find that turning static 3D objects into interactively openable counterparts is possible but that all methods struggle to generalize to realistic settings of the task, and we highlight promising future work directions.

Autonomous implantable bioelectronics rely on wireless connectivity, necessitating highly efficient electromagnetic (EM) radiation systems. However, limitations in power, safety, and data transmission currently impede the advancement of innovative wireless medical devices, such as tetherless neural interfaces, electroceuticals, and surgical microrobots. To overcome these challenges and ensure sufficient link and power budgets for wireless implantable systems, this study explores the mechanisms behind EM radiation and losses, offering strategies to enhance radiation efficiency in wireless implantable bioelectronics. Using analytical modeling, the EM waves emitted by the implant are expanded as a series of spherical harmonics, enabling a detailed analysis of the radiation mechanisms. This framework is then extended to approximate absorption losses caused by the lossy and dispersive properties of tissues through derived analytical expressions. The radiation efficiency and in-body path loss are quantified and compared in terms of three primary loss mechanisms. The impact of various parameters on the EM efficiency of implantable devices is analyzed and quantified, including operating frequency, implant size, body-air interface curvature, and implantation location. Additionally, a rapid estimation technique is introduced to determine the optimal operating frequency for specific scenarios, along with a set of design principles aimed at improving radiation performance. The design strategies derived in this work - validated through numerical and experimental demonstrations on realistic implants - reveal a potential improvement in implant radiation efficiency or gain by a factor of five to ten, leading to a corresponding increase in overall link efficiency compared to conventional designs., Comment: 14 pages, 13 figures

Data missingness is a practical challenge of sustained interest to the scientific community. In this paper, we present Shades-of-Null, an evaluation suite for responsible missing value imputation. Our work is novel in two ways (i) we model realistic and socially-salient missingness scenarios that go beyond Rubin's classic Missing Completely at Random (MCAR), Missing At Random (MAR) and Missing Not At Random (MNAR) settings, to include multi-mechanism missingness (when different missingness patterns co-exist in the data) and missingness shift (when the missingness mechanism changes between training and test) (ii) we evaluate imputers holistically, based on imputation quality, as well as on the predictive performance, fairness and stability of the models that are trained and tested on the data post-imputation. We use Shades-of-Null to conduct a large-scale empirical study involving 23,940 experimental pipelines, and find that while there is no single best-performing imputation approach for all missingness types, interesting trade-offs arise between predictive performance, fairness and stability, based on the combination of missingness scenario, imputer choice, and the architecture of the predictive model. We make Shades-of-Null publicly available, to enable researchers to rigorously evaluate missing value imputation methods on a wide range of metrics in plausible and socially meaningful scenarios.

A surprising result from the theory of quantum control is the degree to which the properties of a physical system can be manipulated. Both atomic and many-body solid state models admit the possibility of creating a 'driven imposter', in which the optical response of one material mimics that of a dynamically distinct system. Here we apply these techniques to polarons in polar liquids. Such quasiparticles describe solvated electrons interacting with many-body degrees of freedom of their environment. The polaron frequency, which depends on the electron concentration in the liquid, is controlled with a pump field, rendering the polaron frequency of three different liquids identical. The experiments demonstrate the feasibility of 'polar impostorons', a so far purely theoretical phenomenon., Comment: 4 pages, 3 figures

Laser-induced water ablation triggers various physical effects, including atom ionization, optical breakdown of the liquid, phase explosion, cavitation, and shockwave propagation. These effects can be further amplified in heavy water by deuterium-deuterium fusion reactions, which require extremely high energy levels. Laser pulses can be grouped in bursts to achieve the necessary energy within the ablation plasma plume. This study aims to compare the ablation plasma glow and thermal effects in light and heavy water under both single-pulse and burst-mode ultrashort laser irradiation. Notably, this research introduces the novel application of burst laser ablation in heavy water for the first time. The ablation was conducted beneath the water surface along a circular, laser-scanned trajectory, with two distinct ablation regimes: burst mode and single-pulse mode, utilizing lenses with varying focal lengths and different pulse durations. Absorption processes and plasma glow were monitored using visible and infrared detectors, a fast silicon detector, and a thermocouple. The study revealed that the burst regime in heavy water produced the most intense plasma glow when 1 ps laser pulses were used, with shorter pulses yielding less intense glow and the longest pulses yielding the least. Surprisingly, plasma glow at a lower initial power density of 2.6e13 W/cm2 was four times higher than at a higher power density of 8e13 W/cm2. These findings were compared with existing theories on plasma formation in water by ultrashort laser pulses. The observed increase in pulse-to-pulse plasma glow in burst mode was attributed to thermo-electron accumulation effects. The density of excited and hydrated electrons was calculated using both strong-field ionization and avalanche ionization models. Additionally, the influence of pulse parity on burst ablation glow in heavy water was discussed., Comment: 15 pages, 9 Figures, Scientific article (material presented on conference LANE2024)

In this study, we introduce a refined method for ascertaining error estimations in numerical simulations of dynamical systems via an innovative application of composition techniques. Our approach involves a dual application of a basic one-step numerical method of order p in this part, and for the class of Backward Difference Formulas schemes in the second part [Deeb A., Dutykh D. and AL Zohbi M. Error estimation for numerical approximations of ODEs via composition techniques. Part II: BDF methods, Submitted, 2024]. This dual application uses complex coefficients, resulting outputs in the complex plane. The methods innovation lies in the demonstration that the real parts of these outputs correspond to approximations of the solutions with an enhanced order of p + 1, while the imaginary parts serve as error estimations of the same order, a novel proof presented herein using Taylor expansion and perturbation technique. The linear stability of the resulted scheme is enhanced compared to the basic one. The performance of the composition in computing the approximation is also compared. Results show that the proposed technique provide higher accuracy with less computational time. This dual composition technique has been rigorously applied to a variety of dynamical problems, showcasing its efficacy in adapting the time step,particularly in situations where numerical schemes do not have theoretical error estimation. Consequently, the technique holds potential for advancing adaptive time-stepping strategies in numerical simulations, an area where accurate local error estimation is crucial yet often challenging to obtain., Comment: First part of series of two papers, 44 pages

Geometric effects in curvilinear nanomagnets can enable chiral, anisotropic and even magnetoelectric responses. Here, we study the effects of magnetic frustration in curvilinear (quasi-)1D magnets represented by spin chains arranged along closed space curves of constant torsion. Considering the cases of easy- and hard-axis anisotropy in ferro- and antiferromagnetic samples, we determine their ground states and analyze the related magnetoelectric multipoles. A constant torsion along the chain results in alternating regions of high and low curvature, facilitating the spin spiral state perturbed by the (anti)periodic boundary conditions on the magnetic order parameter. While easy-axis ferromagnetic chains develop a purely toroidal configuration with the magnetic toroidal moment oriented along the geometry symmetry axis, hard-axis antiferromagnetic chains support multiple magnetic toroidal domains. Our findings suggest that tailoring curvature and torsion of a spin chain enables a new physical mechanism for magnetic frustration, which can be observed in the inhomogeneity of the magnetic order parameter and in the local ferrotoroidic responses.

The route to chaos and phase dynamics in a rotating shallow-water model were rigorously examined using a five-mode Galerkin truncated system with complex variables. This system is valuable for investigating how large/meso-scales destabilize and evolve into chaos. Two distinct transitions into chaotic behaviour were identified as energy levels increased. The initial transition occurs through bifurcations following the Feigenbaum sequence. The subsequent transition, at higher energy levels, shows a shift from quasi-periodic states to chaotic regimes. The first chaotic state is mainly due to inertial forces governing nonlinear interactions. The second chaotic state arises from the increased significance of free surface elevation in the dynamics. A novel reformulation using phase and amplitude representations for each truncated variable revealed that phase components exhibit a temporal piece-wise locking behaviour, maintaining a constant value for a prolonged interval before an abrupt transition of $\pm\pi$, while amplitudes remain chaotic. It was observed that phase stability duration decreases with increased energy, leading to chaos in phase components at high energy levels. This is attributed to the nonlinear term in the equations, where phase components are introduced through linear combinations of triads with different modes. When locking durations vary across modes, the dynamics result in a stochastic interplay of multiple $\pi$ phase shifts, creating a stochastic dynamic within the coupled phase triads, observable even at minimal energy injections., Comment: 31 pages, 13 figures, 55 references, 2 appendices

Physics-informed neural networks have proven to be a powerful tool for solving differential equations, leveraging the principles of physics to inform the learning process. However, traditional deep neural networks often face challenges in achieving high accuracy without incurring significant computational costs. In this work, we implement the Physics-Informed Kolmogorov-Arnold Neural Networks (PIKAN) through efficient-KAN and WAV-KAN, which utilize the Kolmogorov-Arnold representation theorem. PIKAN demonstrates superior performance compared to conventional deep neural networks, achieving the same level of accuracy with fewer layers and reduced computational overhead. We explore both B-spline and wavelet-based implementations of PIKAN and benchmark their performance across various ordinary and partial differential equations using unsupervised (data-free) and supervised (data-driven) techniques. For certain differential equations, the data-free approach suffices to find accurate solutions, while in more complex scenarios, the data-driven method enhances the PIKAN's ability to converge to the correct solution. We validate our results against numerical solutions and achieve $99 \%$ accuracy in most scenarios.

Wearable on-body millimeter-wave (mmWave) radars can provide obstacle detection and guidance for visually impaired people. However, their everyday performance is hindered by the rigid form factor and limited scanning range. In this article, we propose a low-profile, fast-scanning leaky-wave antenna (LWA) operating in the unlicensed V-band (57-64 GHz) to be integrated for on-body applications such as lightweight portable frequency-modulated continuous wave (FMCW) radars. The proposed LWA consists of meandering microstrips that can conform to the human body curvatures while maintaining beam-forming and beam-scanning properties. Experimental results demonstrate that the planar LWA achieves a realized gain above 10 dB with a fan-beam steering range in the H-plane from -40{\deg} to 43{\deg} over the operating frequency band while the half power beam-width (HPBW) is within 20{\deg}. Since for the foreseen application the antenna is supposed to conform to the user's body, the performance is also analyzed for a bent condition. The beam steering range changes to -32{\deg} to 50{\deg} when placed on the knee (corresponding to 80 mm radius). Under bending conditions, the LWA exhibits a maximum degradation of 1.75 dB, while the HPBW increases to 25{\deg}. This shows that due to the small size of the antenna, the impact of bending is low and the beam-forming and beam-scanning property of the designed LWA remain intact. Furthermore, we enable 2-D spatial scanning by employing an array of twelve LWAs with phased excitation, extending the scanning range in the E-plane from -40{\deg} to 40{\deg}, while the HPBW remains below 20{\deg} across the operational frequency range.

Trustworthiness and interpretability are inextricably linked concepts for LLMs. The more interpretable an LLM is, the more trustworthy it becomes. However, current techniques for interpreting LLMs when applied to code-related tasks largely focus on accuracy measurements, measures of how models react to change, or individual task performance instead of the fine-grained explanations needed at prediction time for greater interpretability, and hence trust. To improve upon this status quo, this paper introduces ASTrust, an interpretability method for LLMs of code that generates explanations grounded in the relationship between model confidence and syntactic structures of programming languages. ASTrust explains generated code in the context of syntax categories based on Abstract Syntax Trees and aids practitioners in understanding model predictions at both local (individual code snippets) and global (larger datasets of code) levels. By distributing and assigning model confidence scores to well-known syntactic structures that exist within ASTs, our approach moves beyond prior techniques that perform token-level confidence mapping by offering a view of model confidence that directly aligns with programming language concepts with which developers are familiar. To put ASTrust into practice, we developed an automated visualization that illustrates the aggregated model confidence scores superimposed on sequence, heat-map, and graph-based visuals of syntactic structures from ASTs. We examine both the practical benefit that ASTrust can provide through a data science study on 12 popular LLMs on a curated set of GitHub repos and the usefulness of ASTrust through a human study., Comment: Under Review to appear in ACM Transactions on Software Engineering and Methodology (TOSEM)

Video-based bug reports are increasingly being used to document bugs for programs centered around a graphical user interface (GUI). However, developing automated techniques to manage video-based reports is challenging as it requires identifying and understanding often nuanced visual patterns that capture key information about a reported bug. In this paper, we aim to overcome these challenges by advancing the bug report management task of duplicate detection for video-based reports. To this end, we introduce a new approach, called JANUS, that adapts the scene-learning capabilities of vision transformers to capture subtle visual and textual patterns that manifest on app UI screens - which is key to differentiating between similar screens for accurate duplicate report detection. JANUS also makes use of a video alignment technique capable of adaptive weighting of video frames to account for typical bug manifestation patterns. In a comprehensive evaluation on a benchmark containing 7,290 duplicate detection tasks derived from 270 video-based bug reports from 90 Android app bugs, the best configuration of our approach achieves an overall mRR/mAP of 89.8%/84.7%, and for the large majority of duplicate detection tasks, outperforms prior work by around 9% to a statistically significant degree. Finally, we qualitatively illustrate how the scene-learning capabilities provided by Janus benefits its performance., Comment: 13 pages, accepted to 46th International Conference on Software Engineering (ICSE 2024)

Ingestible sensor devices, which are increasingly used for internal health monitoring, rely on antennas to perform sensing functions and simultaneously to communicate with external devices. Despite the development of various ingestible antennas, there has been no comprehensive comparison of their performance as biosensors. This paper addresses this gap by examining and comparing the suitability of three common types of ingestible antennas -- dipole, patch, and loop -- as biosensors for distinguishing gastrointestinal tissues (stomach, small intestine, and large intestine) based on their electromagnetic properties. The antennas studied in this work conform to the inner surface of biocompatible polylactic acid capsules with varying shell thicknesses and operate in the 433 MHz Industrial, Scientific, and Medical band. The comparison is performed in gastrointestinal tissues using several antenna parameters: 1) Sensing Capability: Changes in the phase of the reflection coefficient in the tissues are selected as the sensing parameter. 2) Robustness: The frequency interval (f_i) in which the antennas are matched (|S11| < -10 dB) in all the tissues and the maximum change in the center frequency (f_c) in different tissues are examined. 3) Radiation Performance: The gain and radiation efficiency of the antennas are examined. The effect of shell thickness on gain and radiation efficiency at 434 MHz is presented. Additionally, the radiation efficiency at various frequencies allocated for medical communications is compared with the theoretical maximum achievable efficiencies. These comprehensive data provide valuable information for making engineering decisions when designing multiplexed biosensor antennas for ingestible applications.

Magnetism of oxide antiferromagnets (AFMs) has been studied in single crystals and extended thin films. The properties of AFM nanostructures still remain underexplored. Here, we report on the fabrication and magnetic imaging of granular 100-nm-thick magnetoelectric Cr$_2$O$_3$ films patterned in circular bits with diameters ranging from 500 down to 100 nm. With the change of the lateral size, the domain structure evolves from a multidomain state for larger bits to a single domain state for the smallest bits. Based on spin-lattice simulations, we show that the physics of the domain pattern formation in granular AFM bits is primarily determined by the energy dissipation upon cooling, which results in motion and expelling of AFM domain walls of the bit. Our results provide a way towards the fabrication of single domain AFM-bit-patterned memory devices and the exploration of the interplay between AFM nanostructures and their geometric shape.

Recent work of Scott and Wilmer and of Woodroofe extends the Erd\H{o}s-Ko-Rado theorem from set systems to subspaces of k-forms in an exterior algebra. We prove an extension of the Hilton-Milner theorem to the exterior algebra setting, answering in a strong way a question asked by these authors., Comment: 15 pages

The current-voltage (I-V) curves of solar photovoltaic (PV) systems have been widely used as a tool to determine their electrical operation. Usually, I-V curves are described employing three cardinal points: the short-circuit point $\left(0,I_\mathrm{sc}\right)$, the open-circuit point $\left(V_\mathrm{oc},0\right)$, and the maximum power point $\left(V_\mathrm{mp},I_\mathrm{mp} \right)$. In this context, to study the I-V curves, the single-diode model (SDM) is often used. In this manuscript, we present a practical approach for determining the parameters domain of the SDM given a set of cardinal points. An in-depth mathematical analysis showed that the SDM must satisfy the following inequalities $V_\mathrm{oc}/2

We computationally completely enumerate a number of types of row-column designs up to isotopism, including double, sesqui and triple arrays as known from the literature, and two newly introduced types that we call mono arrays and AO-arrays. We calculate autotopism group sizes for the designs we generate. For larger parameter values, where complete enumeration is not feasible, we generate examples of some of the designs, and generate exhaustive lists of admissible parameters. For some admissible parameter sets, we prove non-existence results. We also give some explicit constructions of sesqui arrays, mono arrays and AO-arrays, and investigate connections to Youden rectangles and binary pseud Youden designs.

This article explores the exceptional integrability property of a family of higher-order Benjamin-Bona-Mahony (BBM)-type nonlinear dispersive equations. Here, we highlight its deep relationship with a generalized infinite hierarchy of the integrable Riemann-type hydrodynamic equations. A previous Lie symmetry analysis revealed a particular case which was conjectured to be integrable. Namely, a Lie-Baecklund symmetry exists, thus highlighting another associated dynamical system. Here, we investigate these two equations using the gradient-holonomic integrability scheme. Moreover, we construct their infinite hierarchy of conservation laws analytically, using three compatible Poisson structures to prove the complete integrability of both dynamical systems. We investigate these two equations using the so-called gradient-holonomic integrability scheme. Based on this scheme, applied to the equation under consideration, we have analytically constructed its infinite hierarchy of conservation laws, derived two compatible Poisson structures and proved its complete integrability., Comment: 21 pages, 4 figures, 33 references

We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open Model License Agreement, a permissive model license that allows distribution, modification, and use of the models and its outputs. These models perform competitively to open access models on a wide range of evaluation benchmarks, and were sized to fit on a single DGX H100 with 8 GPUs when deployed in FP8 precision. We believe that the community can benefit from these models in various research studies and commercial applications, especially for generating synthetic data to train smaller language models. Notably, over 98% of data used in our model alignment process is synthetically generated, showcasing the effectiveness of these models in generating synthetic data. To further support open research and facilitate model development, we are also open-sourcing the synthetic data generation pipeline used in our model alignment process.

We investigate the out-of-domain generalization of random feature (RF) models and Transformers. We first prove that in the `generalization on the unseen (GOTU)' setting, where training data is fully seen in some part of the domain but testing is made on another part, and for RF models in the small feature regime, the convergence takes place to interpolators of minimal degree as in the Boolean case (Abbe et al., 2023). We then consider the sparse target regime and explain how this regime relates to the small feature regime, but with a different regularization term that can alter the picture in the non-Boolean case. We show two different outcomes for the sparse regime with q-ary data tokens: (1) if the data is embedded with roots of unities, then a min-degree interpolator is learned like in the Boolean case for RF models, (2) if the data is not embedded as such, e.g., simply as integers, then RF models and Transformers may not learn minimal degree interpolators. This shows that the Boolean setting and its roots of unities generalization are special cases where the minimal degree interpolator offers a rare characterization of how learning takes place. For more general integer and real-valued settings, a more nuanced picture remains to be fully characterized., Comment: 9 pages of main body, 24 pages in total. 7 figures Proceedings of the 41-st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

We present a comprehensive analysis of quasinormal modes (QNMs) for noncommutative geometry-inspired Schwarzschild black holes, encompassing both non-extreme and extreme cases. By employing a spectral method, we calculate the QNMs in the context of scalar, electromagnetic, and gravitational perturbations. Our findings not only challenge previous claims in the literature regarding the instability of these black holes but also reveal remarkable stability for both non-extreme and extreme Schwarzschild black holes under various perturbations., Comment: 18 pages, 1 figure, 11 tables, one reference corrected

HESS J1702-420 is a peculiar TeV complex with a morphology changing from a diffuse (HESS J1702-420B source) at $\lesssim 2$ TeV to point-like (HESS J1702-420A) at $\gtrsim 10$ TeV energies. The morphology and the spectral properties of HESS J1702-420 could be understood in terms of a (diffusive) hadronic or leptonic models in which the observed TeV emission arises correpondingly from proton-proton or IC-radiation of relativistic particles present in the region. In this work we perform searches of the X-ray counterpart of HESS J1702-420B source originated from the synchrotron emission of the primary or secondary relativistic electrons produced within leptonic or hadronic models. Such an emission can be extended and remain beyond the detection capabilities of a narrow-FoV instruments such as XMM-Newton. We utilise the publicly available first 6-months eROSITA dataset (DR1) fully covering selected for the analysis region of $> 5^\circ$-radius around HESS J1702-420. We discuss biases connected to variable plasma temperature/neutral hydrogen column density in the region and present results based on background modelling approach. The performed analysis does not allow us to detect the extended X-ray counterpart of HESS J1702-420 of $0.07^\circ - 3^\circ$-radii sizes. The derived upper limits are significantly higher than the expected hadronic model flux of the X-ray counterpart. For the leptonic model the derived limits indicate the magnetic field in the region $B\lesssim 2\mu$G. We argue, that the further advances in the diffuse X-ray counterpart searches could be achieved either with next generation missions or Msec-long observational campaigns with currently operating instruments., Comment: accepted to A&A

Humans express ideas, beliefs, and statements through language. The manner of expression can carry information indicating the author's degree of confidence in their statement. Understanding the certainty level of a claim is crucial in areas such as medicine, finance, engineering, and many others where errors can lead to disastrous results. In this work, we apply a joint model that leverages words and part-of-speech tags to improve hedge detection in text and achieve a new top score on the CoNLL-2010 Wikipedia corpus.

Over the past decade, Finite Element Method (FEM) has served as a foundational numerical framework for approximating the terms of Time Series Expansion (TSE) as solutions to transient Partial Differential Equation (PDE). However, the application of high-order Finite Element (FE) to certain classes of PDEs, such as diffusion equations and the Navier-Stokes (NS) equations, often leads to numerical instabilities. These instabilities limit the number of valid terms in the series, though the efficiency of time series integration even when resummation techniques like the Borel-Pad\'e-Laplace (BPL) integrators are employed. In this study, we introduce a novel variational formulation for computing the terms of a TSE associated with a given PDE using higher-order FEs. Our approach involves the incorporation of artificial diffusion terms on the left-hand side of the equations corresponding to each power in the series, serving as a stabilization technique. We demonstrate that this method can be interpreted as a minimization of an energy functional, wherein the total variations of the unknowns are considered. Furthermore, we establish that the coefficients of the artificial diffusion for each term in the series obey a recurrence relation, which can be determined by minimizing the condition number of the associated linear system. We highlight the link between the proposed technique and the Discrete Maximum Principle (DMP) of the heat equation. We show, via numerical experiments, how the proposed technique allows having additional valid terms of the series that will be substantial in enlarging the stability domain of the BPL integrators.

We model the currently available $\gamma$-ray data from the Fermi Large Area Telescope on Cyg X-3. Thanks to its very strong $\gamma$-ray activity during 2018--2021, the data quality has significantly improved. We study the strong orbital modulation of the $\gamma$-rays observed during at high $\gamma$-ray fluxes. The modulation, as found earlier, is well modeled by anisotropic Compton scattering of the donor blackbody emission by relativistic electrons in a jet strongly misaligned with respect to the orbital axis. We confirm that this model fits well both the average $\gamma$-ray modulation light curve and the spectrum. However, we find that if the jet is aligned with the spin axis of a rotating black hole, it would undergo geodetic precession with the period of $\sim$50 years. However, its presence is ruled out by both the $\gamma$-ray and radio data. Therefore, we consider an alternative model in which the average jet direction jet is aligned, but it is bent to outside the orbit owing to the thrust of the donor stellar wind, and thus precesses at the orbital period. The $\gamma$-ray modulation appears then owing to the variable Doppler boosting of synchrotron self-Compton jet emission. The model also fits well the data. However, the fitted bending angle is much larger than the theoretical one based on the binary and wind parameters as currently known. Thus, both models disagree with important aspects of our current theoretical understanding of the system. We discuss possible ways to find the correct model., Comment: ApJ, in press

Filling cloudy pixels in multispectral satellite imagery is essential for accurate data analysis and downstream applications, especially for tasks which require time series data. To address this issue, we compare the performance of a foundational Vision Transformer (ViT) model with a baseline Conditional Generative Adversarial Network (CGAN) model for missing value imputation in time series of multispectral satellite imagery. We randomly mask time series of satellite images using real-world cloud masks and train each model to reconstruct the missing pixels. The ViT model is fine-tuned from a pretrained model, while the CGAN is trained from scratch. Using quantitative evaluation metrics such as structural similarity index and mean absolute error as well as qualitative visual analysis, we assess imputation accuracy and contextual preservation.

Moving objects are frequently seen in daily life and usually appear blurred in images due to their motion. While general object retrieval is a widely explored area in computer vision, it primarily focuses on sharp and static objects, and retrieval of motion-blurred objects in large image collections remains unexplored. We propose a method for object retrieval in images that are affected by motion blur. The proposed method learns a robust representation capable of matching blurred objects to their deblurred versions and vice versa. To evaluate our approach, we present the first large-scale datasets for blurred object retrieval, featuring images with objects exhibiting varying degrees of blur in various poses and scales. We conducted extensive experiments, showing that our method outperforms state-of-the-art retrieval methods on the new blur-retrieval datasets, which validates the effectiveness of the proposed approach. Code, data, and model are available at https://github.com/Rong-Zou/Retrieval-Robust-to-Object-Motion-Blur.

The field of imbalanced self-supervised learning, especially in the context of tabular data, has not been extensively studied. Existing research has predominantly focused on image datasets. This paper aims to fill this gap by examining the specific challenges posed by data imbalance in self-supervised learning in the domain of tabular data, with a primary focus on autoencoders. Autoencoders are widely employed for learning and constructing a new representation of a dataset, particularly for dimensionality reduction. They are also often used for generative model learning, as seen in variational autoencoders. When dealing with mixed tabular data, qualitative variables are often encoded using a one-hot encoder with a standard loss function (MSE or Cross Entropy). In this paper, we analyze the drawbacks of this approach, especially when categorical variables are imbalanced. We propose a novel metric to balance learning: a Multi-Supervised Balanced MSE. This approach reduces the reconstruction error by balancing the influence of variables. Finally, we empirically demonstrate that this new metric, compared to the standard MSE: i) outperforms when the dataset is imbalanced, especially when the learning process is insufficient, and ii) provides similar results in the opposite case.

Most modern software products incorporate open source components, which requires compliance with each component's licenses. As noncompliance can lead to significant repercussions, organizations often seek advice from legal practitioners to maintain license compliance, address licensing issues, and manage the risks of noncompliance. While legal practitioners play a critical role in the process, little is known in the software engineering community about their experiences within the open source license compliance ecosystem. To fill this knowledge gap, a joint team of software engineering and legal researchers designed and conducted a survey with 30 legal practitioners and related occupations and then held 16 follow-up interviews. We identified different aspects of OSS license compliance from the perspective of legal practitioners, resulting in 14 key findings in three main areas of interest: the general ecosystem of compliance, the specific compliance practices of legal practitioners, and the challenges that legal practitioners face. We discuss the implications of our findings., Comment: 24 pages, 2 figures, FSE 2024

We initiate the study of computing shortest non-separating simple closed curves with some given topological properties on non-orientable surfaces. While, for orientable surfaces, any two non-separating simple closed curves are related by a self-homeomorphism of the surface, and computing shortest such curves has been vastly studied, for non-orientable ones the classification of non-separating simple closed curves up to ambient homeomorphism is subtler, depending on whether the curve is one-sided or two-sided, and whether it is orienting or not (whether it cuts the surface into an orientable one). We prove that computing a shortest orienting (weakly) simple closed curve on a non-orientable combinatorial surface is NP-hard but fixed-parameter tractable in the genus of the surface. In contrast, we can compute a shortest non-separating non-orienting (weakly) simple closed curve with given sidedness in $g^{O(1)}.n\log n$ time, where $g$ is the genus and $n$ the size of the surface. For these algorithms, we develop tools that can be of independent interest, to compute a variation on canonical systems of loops for non-orientable surfaces based on the computation of an orienting curve, and some covering spaces that are essentially quotients of homology covers., Comment: To appear at SoCG 2024

Three main topics were raised in this discussion session, which took place on the 19th of June at the NumHyp-2015 meeting: nonlinear resonance for 1D systems of balance laws, dispersive extensions of standard hyperbolic conservation laws, and the validation of weakly dispersive shallow water wave models. An introductory overview with many bibliographic references is provided for all these topics. Based on kinetic formulation, a numerical strategy that can overcome resonance issues is presented, and a well-balanced (WB) technique for Vlasov-Fokker-Planck equations is outlined. This WB scheme relies on the spectral representation of stationary solutions., Comment: 25 pages, 7 figures, 1 table, 83 references