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The lindgrenite compounds [Cu3(MoO4)2(OH)2] with various architectures and high crystallinity were prepared by a simple surfactant-assisted hydrothermal method. Then, the Cu3Mo2O9 samples were prepared by calcination of the as-synthesized Cu3(MoO4)2(OH)2. The resulting samples have high crystallinity, colloidal properties, high-yield, large-scale production capability with using of nontoxic and inexpensive reagents and water as an environmentally solvent. The scanning electron microscope studies show that the as-prepared lindgrenite nanostructures are well crystallized with rod, sheet and hollow sphere morphologies. Meanwhile, the photoluminescence and magnetic properties of the nanosheet samples have been investigated that the both of Cu3(MoO4)2(OH)2 and Cu3Mo2O9 samples have super paramagnetic behavior at room temperature and in comparison with previous works, Cu3(MoO4)2(OH)2 and Cu3Mo2O9 samples synthesized by the surfactant-assisted hydrothermal method in this work have a very obvious red-shifted PL emission and high intensity.

The HAWC Observatory collected 6 years of extensive data, providing an ideal platform for long-term monitoring of blazars in the Very High Energy (VHE) band, without bias towards specific flux states. HAWC continuously monitors blazar activity at TeV energies, focusing on sources with a redshift of {z \lt 0.3}, based on the Third Fermi-LAT Catalog of High-Energy sources. We specifically focused our analysis on Mrk 421 and Mrk 501, as they are the brightest blazars observed by the HAWC Observatory. With a dataset of 2143 days, this work significantly extends the monitoring previously published, which was based on 511 days of observation. By utilizing HAWC data for the VHE {\gamma}-ray emission in the 300 GeV to 100 TeV energy range, in conjunction with Swift-XRT data for the 0.3 to 10 keV X-ray emission, we aim to explore potential correlations between these two bands. For Mrk 501, we found evidence of a long-term correlation. Additionally, we identified a period in the light curve where the flux was very low for more than two years. On the other hand, our analysis of Mrk 421 measured a strong linear correlation for quasi-simultaneous observations collected by HAWC and Swift-XRT. This result is consistent with a linear dependence and a multiple-zone synchrotron self-Compton model to explain the X-ray and the {\gamma}-ray emission. Finally, as suggested by previous findings, we confirm a harder-when-brighter behavior in the spectral evolution of the flux properties for Mrk 421. These findings contribute to the understanding of blazar emissions and their underlying mechanisms.

Context. Supernova remnants are one potential source class considered a PeVatron (i.e. capable of accelerating cosmic rays above PeV energies). The shock fronts produced after the explosion of the supernova are ideal regions for particle acceleration. IC 443 is a supernova remnant that has been studied extensively at different wavelengths. We study this region using very-high-energy gamma-ray data. Aims. We explore the region of IC 443 using 2966 days of gamma-ray data from the HAWC observatory. We study the emission of this supernova remnant and search for signatures that would show acceleration of (hadronic) cosmic rays at the PeV range. Methods. We use the maximum likelihood estimation and a likelihood ratio test to perform a multi-source fitting search. We find the best-fit morphology and spectrum of the IC 443 region above $\sim$300 GeV that best describes the HAWC data. Results. We observe a point source located at ($\alpha$=94.42$^{\circ}$, $\delta$=22.35$^{\circ}$) that we associate with IC 443. The measured spectrum is a simple power law with an index of -3.14$\pm$0.18, which is consistent with previous TeV observations. We also find a new extended component in the region whose emission is described by a simple power law with an index of -2.49$\pm$0.08 and which we call HAWC J0615+2213. Conclusions. Although we cannot confirm that IC 443 is a hadronic PeVatron, we do not find any sign that the spectrum has a cut off at tens of TeV energies, with the spectrum extending to $\sim$30 TeV. Furthermore, we find a new extended source in the region. While we show evidence that this new source might be a new TeV halo, we defer a detailed analysis of this new source to another publication.

The development of accurate medical image classification models is often constrained by privacy concerns and data scarcity for certain conditions, leading to small and imbalanced datasets. To address these limitations, this study explores the use of generative models, such as Denoising Diffusion Probabilistic Models (DDPM) and Progressive Growing Generative Adversarial Networks (PGGANs), for dataset augmentation. The research introduces a framework to assess the impact of synthetic images generated by DDPM and PGGANs on the performance of four models: a custom CNN, Untrained VGG16, Pretrained VGG16, and Pretrained ResNet50. Experiments were conducted using Random Sampling and Greedy K Sampling to create small, imbalanced datasets. The synthetic images were evaluated using Frechet Inception Distance (FID) and compared to original datasets through classification metrics. The results show that DDPM consistently generated more realistic images with lower FID scores and significantly outperformed PGGANs in improving classification metrics across all models and datasets. Incorporating DDPM-generated images into the original datasets increased accuracy by up to 6%, enhancing model robustness and stability, particularly in imbalanced scenarios. Random Sampling demonstrated superior stability, while Greedy K Sampling offered diversity at the cost of higher FID scores. This study highlights the efficacy of DDPM in augmenting small, imbalanced medical image datasets, improving model performance by balancing the dataset and expanding its size.

Very-high-energy (0.1-100 TeV) gamma-ray emission was observed in HAWC data from the lobes of the microquasar SS 433, making them the first set of astrophysical jets that were resolved at TeV energies. In this work, we update the analysis of SS 433 using 2,565 days of data from the High Altitude Water Cherenkov (HAWC) observatory. Our analysis reports the detection of a point-like source in the east lobe at a significance of $6.6\,\sigma$ and in the west lobe at a significance of $8.2\,\sigma$. For each jet lobe, we localize the gamma-ray emission and identify a best-fit position. The locations are close to the X-ray emission sites "e1" and "w1" for the east and west lobes, respectively. We analyze the spectral energy distributions and find that the energy spectra of the lobes are consistent with a simple power-law $\text{d}N/\text{d}E\propto E^{\alpha}$ with $\alpha = -2.44^{+0.13+0.04}_{-0.12-0.04}$ and $\alpha = -2.35^{+0.12+0.03}_{-0.11-0.03}$ for the east and west lobes, respectively. The maximum energy of photons from the east and west lobes reaches 56 TeV and 123 TeV, respectively. We compare our observations to various models and conclude that the very-high-energy gamma-ray emission can be produced by a population of electrons that were efficiently accelerated.

Microquasars are laboratories for the study of jets of relativistic particles produced by accretion onto a spinning black hole. Microquasars are near enough to allow detailed imaging of spatial features across the multiwavelength spectrum. The recent extension of the spatial morphology of a microquasar, SS 433, to TeV gamma rays \cite{abeysekara2018very} localizes the acceleration of electrons at shocks in the jet far from the black hole \cite{hess2024ss433}. Here we report TeV gamma-ray emission from another microquasar, V4641~Sgr, which reveals particle acceleration at similar distances from the black hole as SS~433. Additionally, the gamma-ray spectrum of V4641 is among the hardest TeV spectra observed from any known gamma-ray source and is detected up to 200 TeV. Gamma rays are produced by particles, either electrons or hadrons, of higher energies. Because electrons lose energy more quickly the higher their energy, such a spectrum either very strongly constrains the electron production mechanism or points to the acceleration of high-energy hadrons. This observation suggests that large-scale jets from microquasars could be more common than previously expected and that microquasars could be a significant source of Galactic cosmic rays. high energy gamma-rays also provide unique constraints on the acceleration mechanisms of extra-Galactic cosmic rays postulated to be produced by the supermassive black holes and relativistic jets of quasars. The distance to quasars limits imaging studies due to insufficient angular resolution of gamma-rays and due to attenuation of the highest energy gamma-rays by the extragalactic background light.

In recent years, interest in vision-language tasks has grown, especially those involving chart interactions. These tasks are inherently multimodal, requiring models to process chart images, accompanying text, underlying data tables, and often user queries. Traditionally, Chart Understanding (CU) relied on heuristics and rule-based systems. However, recent advancements that have integrated transformer architectures significantly improved performance. This paper reviews prominent research in CU, focusing on State-of-The-Art (SoTA) frameworks that employ transformers within End-to-End (E2E) solutions. Relevant benchmarking datasets and evaluation techniques are analyzed. Additionally, this article identifies key challenges and outlines promising future directions for advancing CU solutions. Following the PRISMA guidelines, a comprehensive literature search is conducted across Google Scholar, focusing on publications from Jan'20 to Jun'24. After rigorous screening and quality assessment, 32 studies are selected for in-depth analysis. The CU tasks are categorized into a three-layered paradigm based on the cognitive task required. Recent advancements in the frameworks addressing various CU tasks are also reviewed. Frameworks are categorized into single-task or multi-task based on the number of tasks solvable by the E2E solution. Within multi-task frameworks, pre-trained and prompt-engineering-based techniques are explored. This review overviews leading architectures, datasets, and pre-training tasks. Despite significant progress, challenges remain in OCR dependency, handling low-resolution images, and enhancing visual reasoning. Future directions include addressing these challenges, developing robust benchmarks, and optimizing model efficiency. Additionally, integrating explainable AI techniques and exploring the balance between real and synthetic data are crucial for advancing CU research.

Learning in games provides a powerful framework to design control policies for self-interested agents that may be coupled through their dynamics, costs, or constraints. We consider the case where the dynamics of the coupled system can be modeled as a Markov potential game. In this case, distributed learning by the agents ensures that their control policies converge to a Nash equilibrium of this game. However, typical learning algorithms such as natural policy gradient require knowledge of the entire global state and actions of all the other agents, and may not be scalable as the number of agents grows. We show that by limiting the information flow to a local neighborhood of agents in the natural policy gradient algorithm, we can converge to a neighborhood of optimal policies. If the game can be designed through decomposing a global cost function of interest to a designer into local costs for the agents such that their policies at equilibrium optimize the global cost, this approach can be of interest to team coordination problems as well. We illustrate our approach through a sensor coverage problem.

The dawn of Foundation Models has on the one hand revolutionised a wide range of research problems, and, on the other hand, democratised the access and use of AI-based tools by the general public. We even observe an incursion of these models into disciplines related to human psychology, such as the Affective Computing domain, suggesting their affective, emerging capabilities. In this work, we aim to raise awareness of the power of Foundation Models in the field of Affective Computing by synthetically generating and analysing multimodal affective data, focusing on vision, linguistics, and speech (acoustics). We also discuss some fundamental problems, such as ethical issues and regulatory aspects, related to the use of Foundation Models in this research area.

We study $\ell$-regular partitions by defining a sequence of modular forms of level $\ell$ and quadratic character which encode their $\ell$-adic behavior. We show that this sequence is congruent modulo increasing powers of $\ell$ to level $1$ modular forms of increasing weights. We then prove that certain $\mathbb{Z}/\ell^m\mathbb{Z}$-modules generated by our sequence are isomorphic to certain subspaces of level $1$ cusp forms of weight independent of the power of $\ell$, leading to a uniform bound on the ranks of those modules and consequently to $\ell$-adic relations between $\ell$-regular partition values., Comment: Revised version submitted for publication in Research in Number Theory

Supernova remnants (SNRs) are believed to be capable of accelerating cosmic rays (CRs) to PeV energies. SNR G106.3+2.7 is a prime PeVatron candidate. It is formed by a head region, where the pulsar J2229+6114 and its boomerang-shaped pulsar wind nebula are located, and a tail region containing SN ejecta. The lack of observed gamma ray emission from the two regions of this SNR has made it difficult to assess which region would be responsible for the PeV CRs. We aim to characterize the very-high-energy (VHE, 0.1-100 TeV) gamma ray emission from SNR G106.3+2.7 by determining the morphology and spectral energy distribution of the region. This is accomplished using 2565 days of data and improved reconstruction algorithms from the HAWC Observatory. We also explore possible gamma ray production mechanisms for different energy ranges. Using a multi-source fitting procedure based on a maximum-likelihood estimation method, we evaluate the complex nature of this region. We determine the morphology, spectrum, and energy range for the source found in the region. Molecular cloud information is also used to create a template and evaluate the HAWC gamma ray spectral properties at ultra-high-energies (UHE, >56 TeV). This will help probe the hadronic nature of the highest-energy emission from the region. We resolve one extended source coincident with all other gamma ray observations of the region. The emission reaches above 100~TeV and its preferred log-parabola shape in the spectrum shows a flux peak in the TeV range. The molecular cloud template fit on the higher energy data reveals that the SNR's energy budget is fully capable of producing a purely hadronic source for UHE gamma rays.

HESS J1809-193 is an unidentified TeV source, first detected by the High Energy Stereoscopic System (H.E.S.S.) Collaboration. The emission originates in a source-rich region that includes several Supernova Remnants (SNR) and Pulsars (PSR) including SNR G11.1+0.1, SNR G11.0-0.0, and the young radio pulsar J1809-1917. Originally classified as a pulsar wind nebula (PWN) candidate, recent studies show the peak of the TeV region overlapping with a system of molecular clouds. This resulted in the revision of the original leptonic scenario to look for alternate hadronic scenarios. Marked as a potential PeVatron candidate, this region has been studied extensively by H.E.S.S. due to its emission extending up-to several tens of TeV. In this work, we use 2398 days of data from the High Altitude Water Cherenkov (HAWC) observatory to carry out a systematic source search for the HESS J1809-193 region. We were able to resolve emission detected as an extended component (modelled as a Symmetric Gaussian with a 1 $\sigma$ radius of 0.21 $^\circ$) with no clear cutoff at high energies and emitting photons up-to 210 TeV. We model the multi-wavelength observations for the region HESS J1809-193 using a time-dependent leptonic model and a lepto-hadronic model. Our model indicates that both scenarios could explain the observed data within the region of HESS J1809-193.

We report an observation of ultra-high energy (UHE) gamma rays from the Galactic Center region, using seven years of data collected by the High-Altitude Water Cherenkov (HAWC) Observatory. The HAWC data are best described as a point-like source (HAWC J1746-2856) with a power-law spectrum ($\mathrm{d}N/\mathrm{d}E=\phi(E/26 \,\text{TeV})^{\gamma}$), where $\gamma=-2.88 \pm 0.15_{\text{stat}} - 0.1_{\text{sys}} $ and $\phi=1.5 \times 10^{-15}$ (TeV cm$^{2}$s)$^{-1}$ $\pm\, 0.3_{\text{stat}}\,^{+0.08_{\text{sys}}}_{-0.13_{\text{sys}}}$ extending from 6 to 114 TeV. We find no evidence of a spectral cutoff up to $100$ TeV using HAWC data. Two known point-like gamma-ray sources are spatially coincident with the HAWC gamma-ray excess: Sgr A$^{*}$ (HESS J1745-290) and the Arc (HESS J1746-285). We subtract the known flux contribution of these point sources from the measured flux of HAWC J1746-2856 to exclude their contamination and show that the excess observed by HAWC remains significant ($>$5$\sigma$) with the spectrum extending to $>$100 TeV. Our result supports that these detected UHE gamma rays can originate via hadronic interaction of PeV cosmic-ray protons with the dense ambient gas and confirms the presence of a proton PeVatron at the Galactic Center.

The first TeV gamma-ray source with no lower energy counterparts, TeV J2032+4130, was discovered by HEGRA. It appears in the third HAWC catalog as 3HWC J2031+415 and it is a bright TeV gamma-ray source whose emission has previously been resolved as 2 sources: HAWC J2031+415 and HAWC J2030+409. While HAWC J2030+409 has since been associated with the \emph{Fermi-LAT} Cygnus Cocoon, no such association for HAWC J2031+415 has yet been found. In this work, we investigate the spectrum and energy-dependent morphology of HAWC J2031+415. We associate HAWC J2031+415 with the pulsar PSR J2032+4127 and perform a combined multi-wavelength analysis using radio, X-ray, and $\gamma$-ray emission. We conclude that HAWC J2031+415 and, by extension, TeV J2032+4130 are most probably a pulsar wind nebula (PWN) powered by PSR J2032+4127.

The High-Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory located on the side of the Sierra Negra volcano in Mexico, has been fully operational since 2015. The HAWC collaboration has recently significantly improved their extensive-air-shower reconstruction algorithms, which has notably advanced the observatory performance. The energy resolution for primary gamma rays with energies below 1~TeV was improved by including a noise-suppression algorithm. Corrections have also been made to systematic errors in direction fitting related to the detector and shower plane inclinations, $\mathcal{O}(0.1^{\circ})$ biases in highly inclined showers, as well as enhancements to the core reconstruction. The angular resolution for gamma rays approaching the HAWC array from large zenith angles ($> 37^{\circ}$) has improved by a factor of four at the highest energies ($> 70$~TeV) as compared to previous reconstructions. The inclusion of a lateral distribution function fit to the extensive air shower footprint on the array to separate gamma-ray primaries from cosmic-ray ones, based on the resulting $\chi^{2}$ values, improved the background rejection performance at all inclinations. At large zenith angles, the improvement in significance is a factor of four compared to previous HAWC publications. These enhancements have been verified by observing the Crab Nebula, which is an overhead source for the HAWC Observatory. We show that the sensitivity to Crab-like point sources ($E^{-2.63}$) with locations overhead to 30$^{\circ}$ zenith is comparable or less than 10\% of the Crab Nebula's flux between 2 and 50~TeV. Thanks to these improvements, HAWC can now detect more sources, including the Galactic Center.

Galactic PeVatrons are sources that can accelerate cosmic rays to PeV energies. The high-energy cosmic rays are expected to interact with the surrounding ambient material or radiation, resulting in the production of gamma rays and neutrinos. To optimize for the detection of such associated production of gamma rays and neutrinos for a given source morphology and spectrum, a multi-messenger analysis that combines gamma rays and neutrinos is required. In this study, we use the Multi-Mission Maximum Likelihood framework (3ML) with IceCube Maximum Likelihood Analysis software (i3mla) and HAWC Accelerated Likelihood (HAL) to search for a correlation between 22 known gamma-ray sources from the third HAWC gamma-ray catalog and 14 years of IceCube track-like data. No significant neutrino emission from the direction of the HAWC sources was found. We report the best-fit gamma-ray model and 90% CL neutrino flux limit from the 22 sources. From the neutrino flux limit, we conclude that the gamma-ray emission from five of the sources can not be produced purely from hadronic interactions. We report the limit for the fraction of gamma rays produced by hadronic interactions for these five sources.

Recent studies have demonstrated the emerging capabilities of foundation models like ChatGPT in several fields, including affective computing. However, accessing these emerging capabilities is facilitated through prompt engineering. Despite the existence of some prompting techniques, the field is still rapidly evolving and many prompting ideas still require investigation. In this work, we introduce a method to evaluate and investigate the sensitivity of the performance of foundation models based on different prompts or generation parameters. We perform our evaluation on ChatGPT within the scope of affective computing on three major problems, namely sentiment analysis, toxicity detection, and sarcasm detection. First, we carry out a sensitivity analysis on pivotal parameters in auto-regressive text generation, specifically the temperature parameter $T$ and the top-$p$ parameter in Nucleus sampling, dictating how conservative or creative the model should be during generation. Furthermore, we explore the efficacy of several prompting ideas, where we explore how giving different incentives or structures affect the performance. Our evaluation takes into consideration performance measures on the affective computing tasks, and the effectiveness of the model to follow the stated instructions, hence generating easy-to-parse responses to be smoothly used in downstream applications., Comment: 2 Tables, 1 Figure, preprint submission to ACII 2024

The Galactic gamma-ray diffuse emission (GDE) is emitted by cosmic rays (CRs), ultra-relativistic protons and electrons, interacting with gas and electromagnetic radiation fields in the interstellar medium. Here we present the analysis of TeV diffuse emission from a region of the Galactic Plane over the range in longitude of $l\in[43^\circ,73^\circ]$, using data collected with the High Altitude Water Cherenkov (HAWC) detector. Spectral, longitudinal and latitudinal distributions of the TeV diffuse emission are shown. The radiation spectrum is compatible with the spectrum of the emission arising from a CR population with an "index" similar to that of the observed CRs. When comparing with the \texttt{DRAGON} \textit{base model}, the HAWC GDE flux is higher by about a factor of two. Unresolved sources such as pulsar wind nebulae and TeV halos could explain the excess emission. Finally, deviations of the Galactic CR flux from the locally measured CR flux may additionally explain the difference between the predicted and measured diffuse fluxes.

Recently, the region surrounding eHWC J1842-035 has been studied extensively by gamma-ray observatories due to its extended emission reaching up to a few hundred TeV and potential as a hadronic accelerator. In this work, we use 1,910 days of cumulative data from the High Altitude Water Cherenkov (HAWC) observatory to carry out a dedicated systematic source search of the eHWC J1842-035 region. During the search we have found three sources in the region, namely, HAWC J1844-034, HAWC J1843-032, and HAWC J1846-025. We have identified HAWC J1844-034 as the extended source that emits photons with energies up to 175 TeV. We compute the spectrum for HAWC J1844-034 and by comparing with the observational results from other experiments, we have identified HESS J1843-033, LHAASO J1843-0338, and TASG J1844-038 as very-high-energy gamma-ray sources with a matching origin. Also, we present and use the multi-wavelength data to fit the hadronic and leptonic particle spectra. We have identified four pulsar candidates in the nearby region from which PSR J1844-0346 is found to be the most likely candidate due to its proximity to HAWC J1844-034 and the computed energy budget. We have also found SNR G28.6-0.1 as a potential counterpart source of HAWC J1844-034 for which both leptonic and hadronic scenarios are feasible., Comment: 13 pages, 9 figures, published in ApJ

The decay or annihilation of dark matter particles may produce a steady flux of very-high-energy gamma rays detectable above the diffuse background. Nearby clusters of galaxies provide excellent targets to search for the signatures of particle dark matter interactions. In particular, the Virgo cluster spans several degrees across the sky and can be efficiently probed with a wide field-of-view instrument. The High Altitude Water Cherenkov (HAWC) observatory, due to its wide field of view and sensitivity to gamma rays at an energy scale of 300 GeV--100 TeV is well-suited for this search. Using 2141 days of data, we search for gamma-ray emission from the Virgo cluster, assuming well-motivated dark matter sub-structure models. Our results provide some of the strongest constraints on the decay lifetime of dark matter for masses above 10 TeV., Comment: 7 pages, 3 figures. Accepted for publication in Physical Review D

With the rise of foundation models, a new artificial intelligence paradigm has emerged, by simply using general purpose foundation models with prompting to solve problems instead of training a separate machine learning model for each problem. Such models have been shown to have emergent properties of solving problems that they were not initially trained on. The studies for the effectiveness of such models are still quite limited. In this work, we widely study the capabilities of the ChatGPT models, namely GPT-4 and GPT-3.5, on 13 affective computing problems, namely aspect extraction, aspect polarity classification, opinion extraction, sentiment analysis, sentiment intensity ranking, emotions intensity ranking, suicide tendency detection, toxicity detection, well-being assessment, engagement measurement, personality assessment, sarcasm detection, and subjectivity detection. We introduce a framework to evaluate the ChatGPT models on regression-based problems, such as intensity ranking problems, by modelling them as pairwise ranking classification. We compare ChatGPT against more traditional NLP methods, such as end-to-end recurrent neural networks and transformers. The results demonstrate the emergent abilities of the ChatGPT models on a wide range of affective computing problems, where GPT-3.5 and especially GPT-4 have shown strong performance on many problems, particularly the ones related to sentiment, emotions, or toxicity. The ChatGPT models fell short for problems with implicit signals, such as engagement measurement and subjectivity detection., Comment: 8 pages with references, 2 tables

The employment of foundation models is steadily expanding, especially with the launch of ChatGPT and the release of other foundation models. These models have shown the potential of emerging capabilities to solve problems, without being particularly trained to solve. A previous work demonstrated these emerging capabilities in affective computing tasks; the performance quality was similar to traditional Natural Language Processing (NLP) techniques, but falling short of specialised trained models, like fine-tuning of the RoBERTa language model. In this work, we extend this by exploring if ChatGPT has novel knowledge that would enhance existing specialised models when they are fused together. We achieve this by investigating the utility of verbose responses from ChatGPT about solving a downstream task, in addition to studying the utility of fusing that with existing NLP methods. The study is conducted on three affective computing problems, namely sentiment analysis, suicide tendency detection, and big-five personality assessment. The results conclude that ChatGPT has indeed novel knowledge that can improve existing NLP techniques by way of fusion, be it early or late fusion., Comment: 9 pages, 2 Tables, 1 Figure

Wave phenomena in bianisotropic media have been broadly scrutinized in classical electrodynamics, as these media offer additional degrees of freedom to engineer electromagnetic waves. However, all investigations concerning such systems have so far been limited to stationary (time-invariant) media. Temporally varying the magnetoelectric coupling manifesting bianisotropy engenders a unique prospect to manipulate wave-matter interactions in new ways. In this paper, we theoretically contemplate electromagnetic effects in weakly dispersive bianisotropic media of all classes when the corresponding magnetoelectric coupling parameter suddenly jumps in time, creating a time interface in spatially uniform bianisotropic media. We investigate scattering effects at such time interfaces, revealing novel polarization- and direction-dependent phenomena. We anticipate that our work paves the road for further exploration of time-varying bianisotropic metamaterials (metasurfaces) and bianisotropic photonic time crystals, thus opening up interesting possibilities to control wave polarization and amplitude in reciprocal and nonreciprocal manners.

Recent advancements in wireless local area network (WLAN) technology include IEEE 802.11be and 802.11ay, often known as Wi-Fi 7 and WiGig, respectively. The goal of these developments is to provide Extremely High Throughput (EHT) and low latency to meet the demands of future applications like as 8K videos, augmented and virtual reality, the Internet of Things, telesurgery, and other developing technologies. IEEE 802.11be includes new features such as 320 MHz bandwidth, multi-link operation, Multi-user Multi-Input Multi-Output, orthogonal frequency-division multiple access, and Multiple-Access Point (multi-AP) coordination (MAP-Co) to achieve EHT. With the increase in the number of overlapping APs and inter-AP interference, researchers have focused on studying MAP-Co approaches for coordinated transmission in IEEE 802.11be, making MAP-Co a key feature of future WLANs. Moreover, similar issues may arise in EHF bands WLAN, particularly for standards beyond IEEE 802.11ay. This has prompted researchers to investigate the implementation of MAP-Co over future 802.11ay WLANs. Thus, in this article, we provide a comprehensive review of the state-of-the-art MAP-Co features and their shortcomings concerning emerging WLAN. Finally, we discuss several novel future directions and open challenges for MAP-Co., Comment: The reason for the replacement of the previous version of the paper is due to a change in the author's list. As a result, a new version has been created, which serves as the final draft version before acceptance. This updated version contains all the latest changes and improvements made to the paper

The Galactic Halo is a key target for indirect dark matter detection. The High Altitude Water Cherenkov (HAWC) observatory is a high-energy (~300 GeV to >100 TeV) gamma-ray detector located in central Mexico. HAWC operates via the water Cherenkov technique and has both a wide field of view of 2 sr and a >95% duty cycle, making it ideal for analyses of highly extended sources. We made use of these properties of HAWC and a new background-estimation technique optimized for extended sources to probe a large region of the Galactic Halo for dark matter signals. With this approach, we set improved constraints on dark matter annihilation and decay between masses of 10 and 100 TeV. Due to the large spatial extent of the HAWC field of view, these constraints are robust against uncertainties in the Galactic dark matter spatial profile., Comment: 19 pages, 14 figures

We present a short overview of the TeV-Halos objects as a discovery and a relevant contribution of the High Altitude Water \v{C}erenkov (HAWC) observatory to TeV astrophysics. We discuss history, discovery, knowledge, and the next step through a new and more detailed analysis than the original study in 2017. TeV-Halos will contribute to resolving the problem of the local positron excess observed on the Earth. To clarify the latter, understanding the diffusion process is mandatory., Comment: Work presented in the 21st International Symposium on Very High Energy Cosmic Ray Interactions(ISVHECRI 2022) as part of the Ph. D. Thesis of Ramiro Torres-Escobedo (SJTU, Shanghai, China). Accepted for publication in SciPost Physics Proceedings (ISSN 2666-4003). 11 pages, 3 Figures. Short overview of HAWC and TeV Halos objects until 2022

The High-Altitude Water Cherenkov (HAWC) observatory is a second-generation continuously operated, wide field-of-view, TeV gamma-ray observatory. The HAWC observatory and its analysis techniques build on experience of the Milagro experiment in using ground-based water Cherenkov detectors for gamma-ray astronomy. HAWC is located on the Sierra Negra volcano in M\'exico at an elevation of 4100 meters above sea level. The completed HAWC observatory principal detector (HAWC) consists of 300 closely spaced water Cherenkov detectors, each equipped with four photomultiplier tubes to provide timing and charge information to reconstruct the extensive air shower energy and arrival direction. The HAWC observatory has been optimized to observe transient and steady emission from sources of gamma rays within an energy range from several hundred GeV to several hundred TeV. However, most of the air showers detected are initiated by cosmic rays, allowing studies of cosmic rays also to be performed. This paper describes the characteristics of the HAWC main array and its hardware., Comment: Accepted for publications in Nuclear Inst. and Methods in Physics Research, A (2023) 168253 ( https://www.sciencedirect.com/science/article/abs/pii/S0168900223002437 ); 39 pages, 14 Figures

WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. They can offer extremely high transmission rates but at the cost of low range and penetration. Due to these issues, WiGig systems are unstable and need to rely on frequent handovers to maintain high-quality connections. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use Machine Learning to identify patterns in user behaviors and predict user actions. This prediction is used to do proactive handovers, switching users to access points with better future transmission rates and a more stable environment based on the future state of the user. Results show that not only the proposal is effective at predicting channel data, but the use of such predictions improves system performance and avoids unnecessary handovers., Comment: Submitted to IEEE Network

The deployment of low earth orbit (LEO) satellites with terrestrial networks can potentially increase the efficiency and reduce the cost of relaying content from a data center to a set of edge caches hosted by 6G and beyond enabled macro base stations. In this work, the characteristics of the communication system and the mobility of LEO satellites are thoroughly discussed to describe the channel characteristics of LEO satellites, in terms of their frequency bands, latency, Doppler shifts, fading effects, and satellite access. Three different scenarios are proposed for the relay of data from data centers to edge caches via LEO satellites, which are the "Immediate Forward", "Relay and Forward", and "Store and Forward" scenarios. A comparative problem formulation is utilized to obtain numerical results from simulations to demonstrate the effectiveness and validity as well as the trade-offs of the proposed system model. The simulation results indicate that the integration of LEO satellites in edge caching for 6G and beyond networks decreased the required transmission power for relaying the data from the data center to the edge caches. Future research directions based on the proposed model are discussed.

ChatGPT has shown the potential of emerging general artificial intelligence capabilities, as it has demonstrated competent performance across many natural language processing tasks. In this work, we evaluate the capabilities of ChatGPT to perform text classification on three affective computing problems, namely, big-five personality prediction, sentiment analysis, and suicide tendency detection. We utilise three baselines, a robust language model (RoBERTa-base), a legacy word model with pretrained embeddings (Word2Vec), and a simple bag-of-words baseline (BoW). Results show that the RoBERTa trained for a specific downstream task generally has a superior performance. On the other hand, ChatGPT provides decent results, and is relatively comparable to the Word2Vec and BoW baselines. ChatGPT further shows robustness against noisy data, where Word2Vec models achieve worse results due to noise. Results indicate that ChatGPT is a good generalist model that is capable of achieving good results across various problems without any specialised training, however, it is not as good as a specialised model for a downstream task., Comment: 9 Pages (8 pages + 1 page for references), 1 Figure, 3 Tables

The latest High Altitude Water Cherenkov (HAWC) point-like source catalog up to 56 TeV reported the detection of two sources in the region of the Galactic plane at galactic longitude 52\deg < l < 55\deg, 3HWC J1930+188 and 3HWC J1928+178. The first one is associated with a known TeV source, the supernova remnant SNR G054.1+00.3. It was discovered by one of the currently operating Imaging Atmospheric Cherenkov Telescope (IACT), the Very Energetic Radiation Imaging Telescope Array System (VERITAS), detected by the High Energy Stereoscopic System (H.E.S.S.), and identified as a composite SNR. However, the source 3HWC J1928+178, discovered by HAWC and coincident with the pulsar PSR J1928+1746, was not detected by any IACT despite their long exposure on the region, until a recent new analysis of H.E.S.S. data was able to confirm it. Moreover, no X-ray counterpart has been detected from this pulsar. We present a multicomponent fit of this region using the latest HAWC data. This reveals an additional new source, HAWC J1932+192, which is potentially associated with the pulsar PSR J1932+1916, whose gamma-ray emission could come from the acceleration of particles in its pulsar wind nebula. In the case of 3HWC J1928+178, several possible explanations are explored, in a attempt to unveil the origins of the very-high-energy gamma-ray emission.

We present the results of dark matter (DM) searches in a sample of 31 dwarf irregular (dIrr) galaxies within the field of view of the HAWC Observatory. dIrr galaxies are DM dominated objects, which astrophysical gamma-ray emission is estimated to be negligible with respect to the secondary gamma-ray flux expected by annihilation or decay of Weakly Interacting Massive Particles (WIMPs). While we do not see any statistically significant DM signal in dIrr galaxies, we present the exclusion limits ($95\%~\text{C.L.}$) for annihilation cross-section and decay lifetime for WIMP candidates with masses between $1$ and $100~\text{TeV}$. Exclusion limits from dIrr galaxies are relevant and complementary to benchmark dwarf Spheroidal (dSph) galaxies. In fact, dIrr galaxies are targets kinematically different from benchmark dSph, preserving the footprints of different evolution histories. We compare the limits from dIrr galaxies to those from ultrafaint and classical dSph galaxies previously observed with HAWC. We find that the contraints are comparable to the limits from classical dSph galaxies and $\thicksim2$ orders of magnitude weaker than the ultrafaint dSph limits., Comment: 22 pages, 11 figures, 3 tables

Extended very-high-energy (VHE; 0.1-100 TeV) $\gamma$-ray emission has been observed around several middle-aged pulsars and referred to as ``TeV halos". Their formation mechanism remains under debate. It is also unknown whether they are ubiquitous or related to certain subgroup of pulsars. With 2321 days of observation, the High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory detected VHE $\gamma$-ray emission at the location of the radio-quiet pulsar PSR J0359+5414 with $>6\sigma$ significance. By performing likelihood tests with different spectral and spatial models and comparing the TeV spectrum with multi-wavelength observations of nearby sources, we show that this excess is consistent with a TeV halo associated with PSR J0359+5414, though future observation of HAWC and multi-wavelength follow-ups are needed to confirm this nature. This new halo candidate is located in a non-crowded region in the outer Galaxy. It shares similar properties to the other halos but its pulsar is younger and radio-quiet. Our observation implies that TeV halos could commonly exist around pulsars and their formation does not depend on the configuration of the pulsar magnetosphere.

We report the first detection of a TeV gamma-ray flux from the solar disk (6.3$\sigma$), based on 6.1 years of data from the High Altitude Water Cherenkov (HAWC) observatory. The 0.5--2.6 TeV spectrum is well fit by a power law, dN/dE = $A (E/1 \text{ TeV})^{-\gamma}$, with $A = (1.6 \pm 0.3) \times 10^{-12}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ and $\gamma = -3.62 \pm 0.14$. The flux shows a strong indication of anticorrelation with solar activity. These results extend the bright, hard GeV emission from the disk observed with Fermi-LAT, seemingly due to hadronic Galactic cosmic rays showering on nuclei in the solar atmosphere. However, current theoretical models are unable to explain the details of how solar magnetic fields shape these interactions. HAWC's TeV detection thus deepens the mysteries of the solar-disk emission., Comment: 15 pages, 8 figures including supplementary material. Accepted for publication in Physical Review Letters

Operational bandwidth of resonant circuits is limited by the resonator's size, which is known as the Chu limit. This limit restricts miniaturization of antennas, as the antenna bandwidth is inversely proportional to its size. Here, we propose slow time modulation of resistive elements to engineer bandwidth of small antennas. The temporal modulation of resistance induces virtual impedance that is fully controlled by the modulation parameters. We show how the virtual impedance can be used to optimize the frequency response of a resonant circuit, leading to enhanced matching at multiple frequencies simultaneously. We experimentally verify the proposed technique, demonstrating enhancement of radiation of a broadband modulated signal radiated by a small antenna., Comment: 6 pages, 5 figures, submitted journal letter

Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in practice to annotate all the labels in every training image. Existing works on partial-label learning focus on the case where each training image is labeled with only a subset of its positive/negative labels. To effectively address partial-label classification, this paper proposes an end-to-end Generic Game-theoretic Network (G2NetPL) for partial-label learning, which can be applied to most partial-label settings, including a very challenging, but annotation-efficient case where only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled. In G2NetPL, each unobserved label is associated with a soft pseudo label, which, together with the network, formulates a two-player non-zero-sum non-cooperative game. The objective of the network is to minimize the loss function with given pseudo labels, while the pseudo labels will seek convergence to 1 (positive) or 0 (negative) with a penalty of deviating from the predicted labels determined by the network. In addition, we introduce a confidence-aware scheduler into the loss of the network to adaptively perform easy-to-hard learning for different labels. Extensive experiments demonstrate that our proposed G2NetPL outperforms many state-of-the-art multi-label classification methods under various partial-label settings on three different datasets., Comment: Accepted by BMVC2022