This study introduces a novel method that transforms multimodal physiological signalsphotoplethysmography (PPG), galvanic skin response (GSR), and acceleration (ACC) into 2D image matrices to enhance stress detection using convolutional neural networks (CNNs). Unlike traditional approaches that process these signals separately or rely on fixed encodings, our technique fuses them into structured image representations that enable CNNs to capture temporal and cross signal dependencies more effectively. This image based transformation not only improves interpretability but also serves as a robust form of data augmentation. To further enhance generalization and model robustness, we systematically reorganize the fused signals into multiple formats, combining them in a multi stage training pipeline. This approach significantly boosts classification performance. While demonstrated here in the context of stress detection, the proposed method is broadly applicable to any domain involving multimodal physiological signals, paving the way for more accurate, personalized, and real time health monitoring through wearable technologies.

We report observations of the ultra-high-energy gamma-ray source LHAASO J2108$+$5157, utilizing VERITAS, HAWC, Fermi-LAT, and XMM-Newton. VERITAS has collected $\sim$ 40 hours of data that we used to set ULs to the emission above 200 GeV. The HAWC data, collected over $\sim 2400$ days, reveal emission between 3 and 146 TeV, with a significance of $7.5~\sigma$, favoring an extended source model. The best-fit spectrum measured by HAWC is characterized by a simple power-law with a spectral index of $2.45\pm0.11_{stat}$. Fermi-LAT analysis finds a point source with a very soft spectrum in the LHAASO J2108+5157 region, consistent with the 4FGL-DR3 catalog results. The XMM-Newton analysis yields a null detection of the source in the 2 - 7 keV band. The broadband spectrum can be interpreted as a pulsar and a pulsar wind nebula system, where the GeV gamma-ray emission originates from an unidentified pulsar, and the X-ray and TeV emission is attributed to synchrotron radiation and inverse Compton scattering of electrons accelerated within a pulsar wind nebula. In this leptonic scenario, our X-ray upper limit provides a stringent constraint on the magnetic field, which is $\lesssim 1.5\ \mu$G.

Understanding various humour styles is essential for comprehending the multifaceted nature of humour and its impact on fields such as psychology and artificial intelligence. This understanding has revealed that humour, depending on the style employed, can either have therapeutic or detrimental effects on an individual's health and relationships. Although studies dedicated exclusively to computational-based humour style analysis remain somewhat rare, an expansive body of research thrives within related task, particularly binary humour and sarcasm recognition. In this systematic literature review (SLR), we survey the landscape of computational techniques applied to these related tasks and also uncover their fundamental relevance to humour style analysis. Through this study, we unveil common approaches, illuminate various datasets and evaluation metrics, and effectively navigate the complex terrain of humour research. Our efforts determine potential research gaps and outlined promising directions. Furthermore, the SLR identifies a range of features and computational models that can seamlessly transition from related tasks like binary humour and sarcasm detection to invigorate humour style classification. These features encompass incongruity, sentiment and polarity analysis, ambiguity detection, acoustic nuances, visual cues, contextual insights, and more. The computational models that emerge contain traditional machine learning paradigms, neural network architectures, transformer-based models, and specialised models attuned to the nuances of humour. Finally, the SLR provides access to existing datasets related to humour and sarcasm, facilitating the work of future researchers.

Background: Axion-like particles (ALPs) are hypothetical particles that emerge in numerous theoretical extensions to the Standard Model. Their coupling to electromagnetic field implies that ALPs would mix with photons in the presence of external magnetic fields. As ALP phenomenology is governed by the mass and strength of its coupling, there is a subset of this parameter space in which this mixing would be expected to leave an imprint on the spectra of TeV gamma-ray sources. Data: In 2017, the VERITAS gamma-ray observatory recorded the second day of a dramatic flare of the radio galaxy NGC 1275, embedded at the center of the Perseus galaxy cluster. This serendipitous locale provides a spatially-extended magnetic field of strength O(10$\mu$G) through which escaping photons traverse, making it an excellent target to study ALPs. Methods: We analyze the VERITAS data of NGC 1275's 2017 flare with the gammapy analysis package. Extensive fitting and modeling are performed to ultimately conduct a likelihood analysis used to search for any evidence of a preference for ALPs and to explore the confidence with which constraints can be set. We adopt the CLs method for this study for its conservative approach to setting limits in regimes where the search has limited sensitivity. Results: No evidence for the existence of ALPs is found, and no combination of mass and coupling strength can be excluded at or above 95% confidence level. We provide a map showing the strength of our exclusions in the mass and coupling parameter space. The strongest exclusions are found in the mass range $2 \times 10^{-7}$eV $\lesssim m_a \lesssim 4 \times 10^{-7}$eV and at the coupling strength of $g_{a\gamma} \gtrsim 3 \times 10^{-11}$ GeV$^{-1}$ up to 80% confidence level, which are consistent with previous studies. Conclusions: We find the CLs method to be a trustworthy approach, and advocate for its...

The ALICE detector at the LHC (A Large Ion Collider Experiment) will carry out comprehensive measurements of high energy nucleus-nucleus collisions, in order to study QCD matter under extreme conditions and the phase transtion between con\"ined matter and the Quark-Gluon Plasma (QGP). This report presents our current state of understanding of the Physics Performance of the large acceptance Electromagnetic Calorimeter (EMCal) in the ALICE central detector. The EMCal enhances ALICE's capabilities for jet measurements. The EMCal enables triggering and full reconstruction of high energy jets in ALICE, and augments existing ALICE capabilities to measure high momentum photons and electrons. Combined with ALICE's excellent capabilities to track and identify particles from very low pT to high pT, the EMCal enables a comprehensive study of jet interactions in the medium produced in heavy ion collisions at the LHC.

We present a measurement of the free-streaming length of dark matter (DM) and subhalo abundance around 28 quadruple image strong lenses using observations from JWST MIRI presented in Paper III of this series. We improve on previous inferences on DM properties from lensed quasars by simultaneously reconstructing extended lensed arcs with image positions and relative magnifications (flux ratios). Our forward modeling framework generates full populations of subhalos, line-of-sight halos, and globular clusters, uses an accurate model for subhalo tidal evolution, and accounts for free-streaming effects on halo abundance and concentration. Modeling lensed arcs leads to more-precise model-predicted flux ratios, breaking covariance between subhalo abundance and the free-streaming scale parameterized by the half-mode mass $m_{\rm{hm}}$. Assuming subhalo abundance predicted by the semi-analytic model {\tt{galacticus}} (N-body simulations), we infer (Bayes factor of 10:1) m_{\rm{hm}} < 10^{7.4} \mathrm{M}_{\odot} (m_{\rm{hm}} < 10^{7.2} \mathrm{M}_{\odot}), a 0.4 dex (0.3 dex) improvement relative to omitting lensed arcs. These bounds correspond to lower limits on thermal relic DM particle masses of $7.4$ and $8.4$ keV, respectively. Conversely, assuming DM is cold, we infer a projected mass in subhalos (10^6 < m/M_{\odot}<10^{10.7}) of $1.6_{-1.1}^{+2.4} \times 10^7 \ \mathrm{M}_{\odot} \ \rm{kpc^{-2}}$ at $95 \%$ confidence. This is consistent with {\tt{galacticus}} predictions ($0.6 \times 10^7 \mathrm{M}_{\odot} \ \rm{kpc^{-2}}$), but in tension with recent N-body simulations ($0.3 \times 10^7 \mathrm{M}_{\odot} \ \rm{kpc^{-2}}$). Our results are the strongest limits on WDM, and the most precise measurement of subhalo abundance around strong lenses. Further improvements will follow from the large sample of lenses to be discovered by Euclid, Rubin, and Roman.

The purpose of this paper is to describe asymptotic formulas for determinants of a sum of finite Toeplitz and Hankel matrices with singular generating functions. The formulas are similar to those of the analogous problem for finite Toeplitz matrices for a certain class of symbols. However, the appearance of the Hankel matrices changes the nature of the asymptotics in some instances depending on the location of the singularities. Several concrete examples are also described in the paper.

We establish asymptotic formulas for the determinants of finite Toeplitz + Hankel matrices of size N, as N goes to infinity for singular generating functions defined on the unit circle in the special case where the generating function is even, i.e., where the Toeplitz + Hankel matrices are symmetric.

In this paper we determine the asymptotics of the determinants of truncated Wiener-Hopf plus Hankel operators $\det(W_R(a)\pm H_R(a))$ as $R$ tends to infinity for symbols $a(x)=(x^2/(1+x^2))^\beta$ with the parameter $\beta$ being of small size.

Humour styles can have either a negative or a positive impact on well-being. Given the importance of these styles to mental health, significant research has been conducted on their automatic identification. However, the automated machine learning models used for this purpose are black boxes, making their prediction decisions opaque. Clarity and transparency are vital in the field of mental health. This paper presents an explainable AI (XAI) framework for understanding humour style classification, building upon previous work in computational humour analysis. Using the best-performing single model (ALI+XGBoost) from prior research, we apply comprehensive XAI techniques to analyse how linguistic, emotional, and semantic features contribute to humour style classification decisions. Our analysis reveals distinct patterns in how different humour styles are characterised and misclassified, with particular emphasis on the challenges in distinguishing affiliative humour from other styles. Through detailed examination of feature importance, error patterns, and misclassification cases, we identify key factors influencing model decisions, including emotional ambiguity, context misinterpretation, and target identification. The framework demonstrates significant utility in understanding model behaviour, achieving interpretable insights into the complex interplay of features that define different humour styles. Our findings contribute to both the theoretical understanding of computational humour analysis and practical applications in mental health, content moderation, and digital humanities research.

In this paper we establish several relations between the determinants of the following structured matrices: Hankel matrices, symmetric Toeplitz + Hankel matrices and Toeplitz matrices. Using known results for the asymptotic behavior of Toeplitz determinants, these identities are used in order to obtain Fisher-Hartwig type results on the asymptotics of certain skewsymmetric Toeplitz determinants and certain Hankel determinants.

We develop an analog of the exponential families of Wilf in which the label sets are finite dimensional vector spaces over a finite field rather than finite sets of positive integers. The essential features of exponential families are preserved, including the exponential formula relating the deck enumerator and the hand enumerator.

The effects of local Lorentz violation on dispersion and birefringence of gravitational waves are investigated. The covariant dispersion relation for gravitational waves involving gauge-invariant Lorentz-violating operators of arbitrary mass dimension is constructed. The chirp signal from the gravitational-wave event GW150914 is used to place numerous first constraints on gravitational Lorentz violation.

We report on analysis of englacial radio-frequency (RF) pulser data received over horizontal baselines of 1--5 km, based on broadcasts from two sets of transmitters deployed to depths of up to 1500 meters at the South Pole. First, we analyze data collected usingtwo RF bicone transmitters 1400 meters below the ice surface, and frozen into boreholes drilled for the IceCube experiment in 2011. Additionally, in Dec., 2018, a fat-dipole antenna, fed by one of three high-voltage (~1 kV), fast (~(1-5 ns)) signal generators was lowered into the 1700-m deep icehole drilled for the South Pole Ice Core Experiment (SPICE), approximately 3 km from the geographic South Pole. Signals from transmitters were recorded on the five englacial multi-receiver ARA stations, with receiver depths between 60--200 m. We confirm the long, >1 km RF electric field attenuation length, test our observed signal arrival timing distributions against models, and measure birefringent asymmetries at the 0.15% level.

[Context] The use of defect prediction models, such as classifiers, can support testing resource allocations by using data of the previous releases of the same project for predicting which software components are likely to be defective. A validation technique, hereinafter technique defines a specific way to split available data in training and test sets to measure a classifier accuracy. Time-series techniques have the unique ability to preserve the temporal order of data; i.e., preventing the testing set to have data antecedent to the training set. [Aim] The aim of this paper is twofold: first we check if there is a difference in the classifiers accuracy measured by time-series versus non-time-series techniques. Afterward, we check for a possible reason for this difference, i.e., if defect rates change across releases of a project. [Method] Our method consists of measuring the accuracy, i.e., AUC, of 10 classifiers on 13 open and two closed projects by using three validation techniques, namely cross validation, bootstrap, and walk-forward, where only the latter is a time-series technique. [Results] We find that the AUC of the same classifier used on the same project and measured by 10-fold varies compared to when measured by walk-forward in the range [-0.20, 0.22], and it is statistically different in 45% of the cases. Similarly, the AUC measured by bootstrap varies compared to when measured by walk-forward in the range [-0.17, 0.43], and it is statistically different in 56% of the cases. [Conclusions] We recommend choosing the technique to be used by carefully considering the conclusions to draw, the property of the available datasets, and the level of realism with the classifier usage scenario.

When estimating a phylogeny from a multiple sequence alignment, researchers often assume the absence of recombination. However, if recombination is present, then tree estimation and all downstream analyses will be impacted, because different segments of the sequence alignment support different phylogenies. Similarly, convergent selective pressures at the molecular level can also lead to phylogenetic tree incongruence across the sequence alignment. Current methods for detection of phylogenetic incongruence are not equipped to distinguish between these two different mechanisms and assume that the incongruence is a result of recombination or other horizontal transfer of genetic information. We propose a new recombination detection method that can make this distinction, based on synonymous codon substitution distances. Although some power is lost by discarding the information contained in the nonsynonymous substitutions, our new method has lower false positive probabilities than the comparable recombination detection method when the phylogenetic incongruence signal is due to convergent evolution. We apply our method to three empirical examples, where we analyze: 1) sequences from a transmission network of the human immunodeficiency virus, 2) tlpB gene sequences from a geographically diverse set of 38 Helicobacter pylori strains, and 3) Hepatitis C virus sequences sampled longitudinally from one patient.

In this paper we determine the asymptotics of the determinant of Bessel operators for sufficiently smooth generating functions. These operators are similar to Wiener-Hopf operators with the Fourier transform replaced by the Hankel transform and thus the asymptotics of the determinanst are similar to the well-known Szegö-Akhiezer-Kac formula for truncated Wiener-Hopf determinants. In order to compute the above, we also show that the Bessel operators differ from the Wiener-Hopf by a Hilbert-Schmidt operator.

We present the results of an optical spectroscopic monitoring program targeting NGC 5548 as part of a larger multi-wavelength reverberation mapping campaign. The campaign spanned six months and achieved an almost daily cadence with observations from five ground-based telescopes. The H$\beta$ and He II $\lambda$4686 broad emission-line light curves lag that of the 5100 $Å$ optical continuum by $4.17^{+0.36}_{-0.36}$ days and $0.79^{+0.35}_{-0.34}$ days, respectively. The H$\beta$ lag relative to the 1158 $Å$ ultraviolet continuum light curve measured by the Hubble Space Telescope is roughly $\sim$50% longer than that measured against the optical continuum, and the lag difference is consistent with the observed lag between the optical and ultraviolet continua. This suggests that the characteristic radius of the broad-line region is $\sim$50% larger than the value inferred from optical data alone. We also measured velocity-resolved emission-line lags for H$\beta$ and found a complex velocity-lag structure with shorter lags in the line wings, indicative of a broad-line region dominated by Keplerian motion. The responses of both the H$\beta$ and He II $\lambda$4686 emission lines to the driving continuum changed significantly halfway through the campaign, a phenomenon also observed for C IV, Ly $\alpha$, He II(+O III]), and Si IV(+O IV]) during the same monitoring period. Finally, given the optical luminosity of NGC 5548 during our campaign, the measured H$\beta$ lag is a factor of five shorter than the expected value implied by the $R_\mathrm{BLR} - L_\mathrm{AGN}$ relation based on the past behavior of NGC 5548.

Context: X, formerly known as Twitter, is one of the largest social media platforms and has been widely used for communication during research conferences. While previous studies have examined how users engage with X during these events, limited research has focused on analyzing the content posted by computer science conferences. Objective: This study investigates how conferences from different areas of computer science perform on social media by analyzing their activity, follower engagement, and the content posted on X. Method: We collect posts from 22 computer science conferences and conduct statistical experiments to identify variations in content. Additionally, we perform a manual analysis of the top five posts for each engagement metric. Results: Our findings indicate statistically significant differences in category, sentiment, and post length across computer science conference posts. Among all engagement metrics, likes were the most common way users interacted with conference content. Conclusion: This study provides insights into the social media presence of computer science conferences, highlighting key differences in content, sentiment, and engagement patterns across different venues.