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We continue our investigations of the nature of the linear-response tensors in planar-Hall and planar-thermal Hall configurations, involving three-dimensional nodal-point semimetals, by considering here nodes hosting pseudospin-1 quasiparticles. Such systems exemplify multifold semimetals, as they have three bands crossing at a nodal point. We derive the explicit expressions of the electric, thermoelectric, and thermal coefficients, when the nodes are subjected to the combined influence of an electric field (and/or temperature gradient) and a weak (i.e., nonquantizing) magnetic field. In order to have a complete description, we consider the effects of the Berry curvature and the orbital magnetic moment on an equal footing, both of which originate from the underlying topological features of the bandstructure. Going beyond our previous works, we determine the out-of-plane response comprising the intrinsic anomalous-Hall and the Lorentz-force-contributed currents, and chalk out the effects of internode-scatterings as well. Our theoretical explorations shed light on the mechanisms of transport in multifold semetals, which are being investigated in contemporary experiments., Comment: follow-up paper of arXiv:2405.14844 and arXiv:2408.01422; formulae from arXiv:2411.18434 used

Generative AI has transformed the animation industry. Several models have been developed for image-to-image translation, particularly focusing on converting real-world images into anime through unpaired translation. Scenimefy, a notable approach utilizing contrastive learning, achieves high fidelity anime scene translation by addressing limited paired data through semi-supervised training. However, it faces limitations due to its reliance on paired data from a fine-tuned StyleGAN in the anime domain, often producing low-quality datasets. Additionally, Scenimefy's high parameter architecture presents opportunities for computational optimization. This research introduces NijiGAN, a novel model incorporating Neural Ordinary Differential Equations (NeuralODEs), which offer unique advantages in continuous transformation modeling compared to traditional residual networks. NijiGAN successfully transforms real-world scenes into high fidelity anime visuals using half of Scenimefy's parameters. It employs pseudo-paired data generated through Scenimefy for supervised training, eliminating dependence on low-quality paired data and improving the training process. Our comprehensive evaluation includes ablation studies, qualitative, and quantitative analysis comparing NijiGAN to similar models. The testing results demonstrate that NijiGAN produces higher-quality images compared to AnimeGAN, as evidenced by a Mean Opinion Score (MOS) of 2.192, it surpasses AnimeGAN's MOS of 2.160. Furthermore, our model achieved a Frechet Inception Distance (FID) score of 58.71, outperforming Scenimefy's FID score of 60.32. These results demonstrate that NijiGAN achieves competitive performance against existing state-of-the-arts, especially Scenimefy as the baseline model.

For many purposes, classical plasma dynamics models can work surprisingly well even for strong electromagnetic fields, approaching the Schwinger critical fields, and high frequencies, approaching the Compton frequency. However, the applicability of classical models tends to depend rather sensitively on the details of the problem. In the present paper, we study the specific case of plasma oscillations to draw a line between the classical and quantum relativistic regimes. Due to the field geometry of study, mechanisms like radiation reaction and Breit-Wheeler pair production, which tend to be important for electromagnetic fields, are rather effectively suppressed. Moreover, we find that the polarization current due to the electron spin is generally negligible for frequencies below the Compton frequency, compared to the free current, whose magnitude is well-approximated by the classical Vlasov theory. However, we show that pair creation due to the Schwinger mechanism can sometimes be important for surprisingly modest field strengths, of the order of 10 % of the critical field or even smaller. A rough guideline for when the classical Vlasov theory can be applied is given

Metallic gratings can be used as infrared filters, but their performance is often limited by bandwidth restrictions due to metallic losses. In this work, we propose a metallic groove-slit-groove (GSG) structure that overcomes these limitations by exhibiting a large bandwidth, angularly independent, extraordinary optical transmission. Our design achieves high transmission efficiency in the longwave infrared range, driven by Fano-type resonances created through the interaction between the grooves and the central slit. This mechanism results in a tunable 2 $\mu$m transmission window with high rejection rate. We extend the concept to a two-dimensional GSG array, exhibiting a polarization insensitive 80% transmission window for incident angles up to 50{\deg}, offering significant potential for infrared filtering applications.

The nature of one-loop corrections on long CMB scale modes in models of single field inflation incorporating an intermediate USR phase is under debate. In this work, we investigate the regularization and renormalization of the one-loop corrections of curvature perturbation power spectrum. Employing the UV-IR regularizations and performing the in-in analysis, we calculate the regularized one-loop corrections, including tadpole, in the power spectrum associated with cubic and quartic Hamiltonians. We show that the fully regularized and renormalized fractional loop correction in the power spectrum is controlled by the peak of the power spectrum at the end of USR phase, scaling like $ e^{6 \Delta {\cal N}}$ in which $\Delta {\cal N} $ is the duration of the USR phase. This confirms the original conclusion that the loop corrections can get out of perturbative control if the transition from the intermediate USR phase to the final SR phase is instantaneous and sharp., Comment: 7 pages, 1 figure, 3 appendices

Chikungunya virus (CHIKV) is one of the most relevant arboviruses affecting public health today. It belongs to the Togaviridae family and alphavirus genus, causing an arthritogenic disease known as Chikungunya fever (CHIKF). This multifaceted disease is distinguished from other arbovirus infections by intense arthralgia, which can persist for months or even years in some individuals. The virus has re-emerged as a global health threat in recent decades, originating in Africa and spreading across Asia and America, leading to widespread outbreaks affecting millions. Despite more than 50 years of research on CHIKV pathogenesis, no drugs or vaccines are available. Current management focuses on supportive care to alleviate symptoms and improve patient's quality of life. The ongoing threat posed by CHIKV highlights the need to understand its pathogenesis better. This review provides a comprehensive overview of CHIKV, focusing on host factors, vector-related factors, and complex viral genetic interactions. By exploring these intricate connections, we aim to offer insights that may lead to more effective strategies for preventing and managing this re-emerging global health threat.

The evolution of floating-point computation has been shaped by algorithmic advancements, architectural innovations, and the increasing computational demands of modern technologies, such as artificial intelligence (AI) and high-performance computing (HPC). This paper examines the historical progression of floating-point computation in scientific applications and contextualizes recent trends driven by AI, particularly the adoption of reduced-precision floating-point types. The challenges posed by these trends, including the trade-offs between performance, efficiency, and precision, are discussed, as are innovations in mixed-precision computing and emulation algorithms that offer solutions to these challenges. This paper also explores architectural shifts, including the role of specialized and general-purpose hardware, and how these trends will influence future advancements in scientific computing, energy efficiency, and system design.

We prove that the Kazhdan-Lusztig basis of Specht modules is upper triangular with respect to all generalized Gelfand-Tsetlin bases constructed from any multiplicity-free tower of standard parabolic subgroups., Comment: 10 pages, extended abstract for submission to FPSAC 2025

The modern era always looks into advancements in technology. Design and topology of interconnection networks play a mutual role in development of technology. Analysing the topological properties and characteristics of an interconnection network is not an easy task. Graph theory helps in solving this task analytically and efficiently through the use of numerical parameters known as distance based topological descriptors. These descriptors have considerable applications in various fields of computer science, chemistry, biology, etc. This paper deals with the evaluation of topological descriptors for an n-dimensional multistage interconnection network, the benes network,BB(n). Also, a new variant of interconnection network is derived from the benes network, named as augmented benes network and denoted as BB^* (n). The topological descriptors for the benes derived network are also determined in this work. Further, the benes network and augmented benes network undergoes a comparative analysis based on few network parameters, which helps to understand the efficiency of newly derived benes network. A broadcasting algorithm for the augmented benes network is also provided.

Back-of-the-book indexes are crucial for book readability. Their manual creation is laborious and error prone. In this paper, we consider automating back-of-the-book index extraction for Arabic books to help simplify both the creation and review tasks. Given a back-of-the-book index, we aim to check and identify the accurate occurrences of index terms relative to the associated pages. To achieve this, we first define a pool of candidates for each term by extracting all possible noun phrases from paragraphs appearing on the relevant index pages. These noun phrases, identified through part-of-speech analysis, are stored in a vector database for efficient retrieval. We use several metrics, including exact matches, lexical similarity, and semantic similarity, to determine the most appropriate occurrence. The candidate with the highest score based on these metrics is chosen as the occurrence of the term. We fine-tuned a heuristic method, that considers the above metrics and that achieves an F1-score of .966 (precision=.966, recall=.966). These excellent results open the door for future work related to automation of back-of-the-book index generation and checking.

For a graph $\mathbb{Q}=(\mathbb{V},\mathbb{E})$, the transformation graphs are defined as graphs with vertex set being $\mathbb{V(Q)} \cup \mathbb{E(Q)}$ and edge set is described following certain conditions. In comparison to the structure descriptor of the original graph $\mathbb{Q}$, the topological descriptor of its transformation graphs displays distinct characteristics related to structure. Thus, a compound's transformation graphs descriptors can be used to model a variety of structural features of the underlying chemical. In this work, the concept of transformation graphs are extended giving rise to novel class of graphs, the $(r,s)$- generalised transformation graphs, whose vertex set is union of $r$ copies of $\mathbb{V(Q)}$ and $s$ copies of $\mathbb{E(Q)}$, where, $r, s \in N$ and the edge set are defined under certain conditions. Further, these class of graphs are analysed with the help of first Zagreb index. Mainly, there are eight transformation graphs based on the criteria for edge set, but under the concept of $(r,s)$- generalised transformation graphs, infinite number of graphs can be described and analysed.

Within a perception framework for autonomous mobile and robotic systems, semantic analysis of 3D point clouds typically generated by LiDARs is key to numerous applications, such as object detection and recognition, and scene reconstruction. Scene semantic segmentation can be achieved by directly integrating 3D spatial data with specialized deep neural networks. Although this type of data provides rich geometric information regarding the surrounding environment, it also presents numerous challenges: its unstructured and sparse nature, its unpredictable size, and its demanding computational requirements. These characteristics hinder the real-time semantic analysis, particularly on resource-constrained hardware architectures that constitute the main computational components of numerous robotic applications. Therefore, in this paper, we investigate various 3D semantic segmentation methodologies and analyze their performance and capabilities for resource-constrained inference on embedded NVIDIA Jetson platforms. We evaluate them for a fair comparison through a standardized training protocol and data augmentations, providing benchmark results on the Jetson AGX Orin and AGX Xavier series for two large-scale outdoor datasets: SemanticKITTI and nuScenes., Comment: Accepted to IROS 2024 PPNIV Workshop

Self-supervised contrastive learning is an effective approach for addressing the challenge of limited labelled data. This study builds upon the previously established two-stage patch-level, multi-label classification method for hyperspectral remote sensing imagery. We evaluate the method's performance for both the single-label and multi-label classification tasks, particularly under scenarios of limited training data. The methodology unfolds in two stages. Initially, we focus on training an encoder and a projection network using a contrastive learning approach. This step is crucial for enhancing the ability of the encoder to discern patterns within the unlabelled data. Next, we employ the pre-trained encoder to guide the training of two distinct predictors: one for multi-label and another for single-label classification. Empirical results on four public datasets show that the predictors trained with our method perform better than those trained under fully supervised techniques. Notably, the performance is maintained even when the amount of training data is reduced by $50\%$. This advantage is consistent across both tasks. The method's effectiveness comes from its streamlined architecture. This design allows for retraining the encoder along with the predictor. As a result, the encoder becomes more adaptable to the features identified by the classifier, improving the overall classification performance. Qualitative analysis reveals the contrastive-learning-based encoder's capability to provide representations that allow separation among classes and identify location-based features despite not being explicitly trained for that. This observation indicates the method's potential in uncovering implicit spatial information within the data.

Stunting detection is a significant issue in Indonesian healthcare, causing lower cognitive function, lower productivity, a weakened immunity, delayed neuro-development, and degenerative diseases. In regions with a high prevalence of stunting and limited welfare resources, identifying children in need of treatment is critical. The diagnostic process often raises challenges, such as the lack of experience in medical workers, incompatible anthropometric equipment, and inefficient medical bureaucracy. To counteract the issues, the use of load cell sensor and ultrasonic sensor can provide suitable anthropometric equipment and streamline the medical bureaucracy for stunting detection. This paper also employs machine learning for stunting detection based on sensor readings. The experiment results show that the sensitivity of the load cell sensor and the ultrasonic sensor is 0.9919 and 0.9986, respectively. Also, the machine learning test results have three classification classes, which are normal, stunted, and stunting with an accuracy rate of 98\%., Comment: 14 pages, pre-print

Utilizing JWST MIRI/MRS IFU observations of the kiloparsec scale central regions, we showcase the diversity of ionized gas distributions and kinematics in six nearby Seyfert galaxies included in the GATOS survey. Specifically, we present spatially resolved flux distribution and velocity field maps of six ionized emission lines covering a large range of ionization potentials ($15.8-97.1$ eV). Based on these maps, we showcase the evidence of ionized gas outflows in the six targets, and find some highly disturbed regions in NGC\,5728, NGC\,5506, and ESO137-G034. We propose AGN-driven radio jets plausibly play an important role in triggering these highly disturbed regions. With the outflow rates estimated based on [Ne~{\footnotesize V}] emission, we find the six targets tend to have ionized outflow rates converged to a narrower range than previous finding. These results have important implication for the outflow properties in AGN of comparable luminosity., Comment: 34 pages (11 pages in the appendix), 18 figures in the main text, ApJ in press (accepted on July 26th)

A resolving set for a simple graph $G$ is a subset of vertex set of $G$ such that it distinguishes all vertices of $G$ using the shortest distance from this subset. This subset is a metric basis if it is the smallest set with this property. A resolving set is a fault tolerant resolving set if the removal of any vertex from the subset still leaves it a resolving set. The smallest set satisfying this property is the fault tolerant metric basis, and the cardinality of this set is termed as fault tolerant metric dimension of $G$, denoted by $\beta'(G)$. In this article, we determine the fault tolerant metric dimension of bicyclic graphs of type-I and II and show that it is always $4$ for both types of graphs. We then use these results to form our basis to consider leafless cacti graphs, and calculate their fault tolerant metric dimensions in terms of \textit{inner cycles} and \textit{outer cycles}. We then consider a detailed real world example of supply and distribution center management, and discuss the application of fault tolerant metric dimension in such a scenario. We also briefly discuss some other scenarios where leafless cacti graphs can be used to model real world problems.

Metric dimensions and metric basis are graph invariants studied for their use in locating and indexing nodes in a graph. It was recently established that for bicyclic graph of type-III ($\Theta $-graphs), the metric dimension is $3$ only, when all paths have equal lengths, or when one of the outside path has a length $2$ more than the other two paths. In this article, we refute this claim and show that the case where the middle path is $2$ vertices more than the other two paths, also has metric dimension $3$. We also determine the metric dimension for other values of $p,q,r$ which were omitted in the recent research due to the constraint $p \leq q \leq r$. We also propose a graph-based technique to transform an agricultural supply chain logistics problem into a mathematical model, by using metric basis and metric dimensions. We provide a theoretical groundwork which can be used to model and solve these problems using machine learning algorithms.

Due to their non-iterative nature, fixed Unitary Coupled-Cluster (UCC) ans\"atze are attractive for performing quantum chemistry Variational Quantum Eigensolver (VQE) computations as they avoid pre-circuit measurements on a quantum computer. However, achieving chemical accuracy for strongly correlated systems with UCC requires further inclusion of higher-order fermionic excitations beyond triples increasing circuit depth. We introduce $k$-NI-DUCC, a fixed and Non-iterative Disentangled Unitary Coupled-Cluster compact ansatz, based on specific $"k"$ sets of "qubit" excitations, eliminating the needs for fermionic-type excitations. These elements scale linearly ($\mathcal{O}(n)$) by leveraging Lie algebraic structures, with $n$ being the number of qubits. The key excitations are screened through specific selection criteria, including the enforcement of all symmetries, to ensure the construction of a robust set of generators. NI-DUCC employs $"k"$ products of the exponential of $\mathcal{O}(n)$- anti-Hermitian Pauli operators, where each operator has a length $p$. This results in a fewer two-qubit CNOT gates circuit, $\mathcal{O}(knp)$, suitable for hardware implementations. Tested on LiH, H$_6$ and BeH$_2$, NI-DUCC-VQE achieves both chemical accuracy and rapid convergence even for molecules deviating significantly from equilibrium. It is hardware-efficient, reaching the exact Full Configuration Interaction energy solution at specific layers, while reducing significantly the VQE optimization steps. While NI-DUCC-VQE effectively addresses the gradient measurement bottleneck of ADAPT-VQE-like iterative algorithms, the classical computational cost of constructing the $\mathcal{O}(n)$ set of excitations increases exponentially with the number of qubits. We provide a first implementation for constructing the generators' set able to handle up to 20 qubits and discuss the efficiency perspectives.

We investigate the nature of the linear-response tensors in planar Hall and planar thermal Hall setups, where we subject a Rarita-Schwinger-Weyl (RSW) semimetal to the combined influence of an electric field $\mathbf E $ (and/or temperature gradient $\nabla_{\mathbf r } T$) and a weak (i.e., nonquantizing) magnetic field $\mathbf B $. For computing the in-plane transport components, we have added an elastic deformation which gives rise to a chirality-dependent effective magnetic field $ \mathbf B^{\text{tot}} = \mathbf B + \chi \, \mathbf B_5 $, where $\chi $ is the chirality of an RSW node. We have included the effects of orbital magnetic moment (OMM), in conjunction with the Berry curvature (BC), both of which appear as a consequence of nontrivial topology of the bandstructure. Due to the presence of four bands, RSW semimetals provide a richer structure for obtaining the linear-response coefficients, compared to the Weyl semimetals. In particular, we have found that the OMM-contributed terms may oppose or add up to the BC-only parts, depending on which band we are considering. Last, but not the least, we have determined the out-of-plane response comprising the intrinsic anomalous-Hall and the Lorentz-force-contributed currents, whose nature corroborates the findings of some recent experimental results., Comment: journal version

Instance segmentation is an advanced form of image segmentation which, beyond traditional segmentation, requires identifying individual instances of repeating objects in a scene. Mask R-CNN is the most common architecture for instance segmentation, and improvements to this architecture include steps such as benefiting from bounding box refinements, adding semantics, or backbone enhancements. In all the proposed variations to date, the problem of competing kernels (each class aims to maximize its own accuracy) persists when models try to synchronously learn numerous classes. In this paper, we propose mitigating this problem by replacing mask prediction with a Switch-Split block that processes refined ROIs, classifies them, and assigns them to specialized mask predictors. We name the method MaskUno and test it on various models from the literature, which are then trained on multiple classes using the benchmark COCO dataset. An increase in the mean Average Precision (mAP) of 2.03% was observed for the high-performing DetectoRS when trained on 80 classes. MaskUno proved to enhance the mAP of instance segmentation models regardless of the number and typ

Speech bandwidth expansion is crucial for expanding the frequency range of low-bandwidth speech signals, thereby improving audio quality, clarity and perceptibility in digital applications. Its applications span telephony, compression, text-to-speech synthesis, and speech recognition. This paper presents a novel approach using a high-fidelity generative adversarial network, unlike cascaded systems, our system is trained end-to-end on paired narrowband and wideband speech signals. Our method integrates various bandwidth upsampling ratios into a single unified model specifically designed for speech bandwidth expansion applications. Our approach exhibits robust performance across various bandwidth expansion factors, including those not encountered during training, demonstrating zero-shot capability. To the best of our knowledge, this is the first work to showcase this capability. The experimental results demonstrate that our method outperforms previous end-to-end approaches, as well as interpolation and traditional techniques, showcasing its effectiveness in practical speech enhancement applications.

The purpose of this paper is to obtain a fundamental $L^p-L^{p'}$ estimate for a class of a strongly damped wave equations where the damping operator is given by $-\delta \Delta$ with $\delta \geq 0$ and the constant in the estimate is independent of the damping parameter $\delta.$ The method used here is based on an estimate of the semigroup associated to the linear equation on the Besov spaces and their elementary properties.

We present ALLaM: Arabic Large Language Model, a series of large language models to support the ecosystem of Arabic Language Technologies (ALT). ALLaM is carefully trained considering the values of language alignment and knowledge transfer at scale. Our autoregressive decoder-only architecture models demonstrate how second-language acquisition via vocabulary expansion and pretraining on a mixture of Arabic and English text can steer a model towards a new language (Arabic) without any catastrophic forgetting in the original language (English). Furthermore, we highlight the effectiveness of using parallel/translated data to aid the process of knowledge alignment between languages. Finally, we show that extensive alignment with human preferences can significantly enhance the performance of a language model compared to models of a larger scale with lower quality alignment. ALLaM achieves state-of-the-art performance in various Arabic benchmarks, including MMLU Arabic, ACVA, and Arabic Exams. Our aligned models improve both in Arabic and English from their base aligned models.

We present observations of the type-2 Seyfert NGC7172 obtained with the medium-resolution spectrometer (MRS) of the Mid-Infrared Instrument (MIRI) on board of the JWST. This galaxy hosts one of the lowest ionised gas mass outflow rates (Mout~0.005 M/yr) in a sample of six AGN with similar bolometric luminosities (log Lbol~44erg/s) within the Galactic Activity, Torus and Outflow Survey (GATOS). We aim to understand the properties of the ionised gas outflow, mainly using the emission lines from the neon transitions, that cover a broad range of ionisation potentials (IP) from ~20 eV to ~130 eV. We applied parametric and non-parametric methods to characterise the line emission and kinematics. The low excitation lines (IP<25eV, e.g.[NeII]) trace the rotating disc emission. The high excitation lines (IP>90eV, e.g.[NeV]), which are likely photoionised exclusively by the AGN, are expanding in the direction nearly perpendicular to the disc of the galaxy, with maximum projected velocities of ~350-500 km/s. In particular, [NeV] and [NeVI] lines reveal a biconical ionised gas outflow emerging N-S from the nuclear region, extending at least ~2.5"N and 3.8"S (projected distance of ~450 and 680 pc). Most of the emission arising in the northern part of the cone was not previously detected due to obscuration. Given the almost face-on orientation of the outflow and the almost edge-on orientation of the galaxy, NGC7172 may be a case of weak coupling. Nevertheless, we found evidence for positive feedback in two distinct outflowing clumps at projected distances of 3.1" and 4.3" (i.e. ~560 and 780 pc) SW from the AGN. We estimated a star formation rate in these regions using the [NeII] and [NeIII] luminosities of 0.08 M/yr, that is ~10% of that found in the circumnuclear ring. The star formation activity might have been triggered by the interaction between the ionised gas outflow and the ISM of the galaxy., Comment: 20 pages, 6 figures. Accepted for publication in A&A. Abstract adapted for the arxiv version

Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them in real-world applications to ensure they produce reliable performance. Despite the well-established importance of evaluating LLMs in the community, the complexity of the evaluation process has led to varied evaluation setups, causing inconsistencies in findings and interpretations. To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation. Based on our critical review, we present our perspectives and recommendations to ensure LLM evaluations are reproducible, reliable, and robust., Comment: Accepted at EMNLP 2024 (Main Conference)

The ionisation cones of NGC5728 have a deficit of molecular gas based on millimetre observations of CO(2-1) emission. Although photoionisation from the active nucleus may lead to suppression of this transition, warm molecular gas can still be present. We report the detection of eight mid-infrared rotational H$_2$ lines throughout the central kiloparsec, including the ionisation cones, using integral field spectroscopic observations with JWST/MIRI MRS. The H$_2$ line ratios, characteristic of a power-law temperature distribution, indicate that the gas is warmest where it enters the ionisation cone through disk rotation, suggestive of shock excitation. In the nucleus, where the data can be combined with an additional seven ro-vibrational H$_2$ transitions, we find that moderate velocity (30 km s$^{-1}$) shocks in dense ($10^5$ cm$^{-3}$) gas, irradiated by an external UV field ($G_0 = 10^3$), do provide a good match to the full set. The warm molecular gas in the ionisation cone that is traced by the H$_2$ rotational lines has been heated to temperatures $>200$ K. Outside of the ionisation cone the molecular gas kinematics are undisturbed. However, within the ionisation cone, the kinematics are substantially perturbed, indicative of a radial flow, but one that is quantitatively different from the ionised lines. We argue that this outflow is in the plane of the disk, implying a short 50 pc acceleration zone up to speeds of about 400 km s$^{-1}$ followed by an extended deceleration over $\sim$700 pc where it terminates. The deceleration is due to both the radially increasing galaxy mass, and mass-loading as ambient gas in the disk is swept up., Comment: A&A accepted; 16 pages

The integration of Large Language Models (LLMs) into the healthcare domain has the potential to significantly enhance patient care and support through the development of empathetic, patient-facing chatbots. This study investigates an intriguing question Can ChatGPT respond with a greater degree of empathy than those typically offered by physicians? To answer this question, we collect a de-identified dataset of patient messages and physician responses from Mayo Clinic and generate alternative replies using ChatGPT. Our analyses incorporate novel empathy ranking evaluation (EMRank) involving both automated metrics and human assessments to gauge the empathy level of responses. Our findings indicate that LLM-powered chatbots have the potential to surpass human physicians in delivering empathetic communication, suggesting a promising avenue for enhancing patient care and reducing professional burnout. The study not only highlights the importance of empathy in patient interactions but also proposes a set of effective automatic empathy ranking metrics, paving the way for the broader adoption of LLMs in healthcare.

Arbovirus is a vital, life-threatening disease worldwide and continues to be a significant problem while the world is dealing with the major coronavirus (COVID-19) pandemic. Vectors, mostly mosquitoes and ticks, transmit this disease. Dengue fever, chikungunya, and Zika viruses are the major threats because of their high incidence, public health burden, and clinically significant disease spectrum. These vector-borne disease causes one-fourth of annual deaths, leading to various infectious diseases. The arbovirus represents eight different families and 14 genera; most viruses belong to the family Bunyaviridae, and some also belong to Togaviridae, Reoviridae, and Flaviviridae. The arbovirus disease was isolated first in tropical and subtropical regions of South America and Africa and has high significance because of suitable environmental conditions for virus transmission and vector expansion. Its transmission cycle ranges from simple to highly complex. DENV is the most prevalent, results in febrile illness, and has transmission in 128 different countries. CHIKV causes infection in asymptomatic people, and the problems include nephritis, arthritis, myelitis, and acute encephalopathy. ZIKV-infected 80% of people are asymptomatic and may cause rashes, myalgia, fever, headache, and conjunctivitis. Vaccines for DENV are not clinically available; it is a primary arboviral infection in the world nowadays. The exposure of arbovirus diseases continues to be a global health problem regardless of continuing efforts. This review article will overview major arbovirus diseases and their diagnosis, treatment, and prevention strategies., Comment: 4 Figure, 1 table

Singularities in Newton's gravitation, in general relativity (GR), in Coulomb's law, and elsewhere in classical physics, stem from two ill conceived assumptions: a) there are point-like entities with finite masses, charges, etc., packed in zero volumes, and b) the non-quantum assumption that these point-likes can be assigned precise coordinates and momenta. In the case of GR, we argue that the classical energy-momentum tensor in Einstein's field equation is that of a collection of point particles and is prone to singularity. In compliance with Heisenberg's uncertainty principle, we suggest to replace each constituent of the gravitating matter with a suitable quantum mechanical equivalent, here a Klien-Gordon (KG) or a Yukawa-ameliorated version of it, YKG field. KG and YKG fields are spatially distributed entities. They do not end up in singular spacetime points nor predict singular blackholes. On the other hand, YKG waves reach infinity as $\frac{1}{r}e^{-(\kappa\pm i k)r}$. They create the Newtonian $r^{-2}$ term as well as a non-Newtonian $r^{-1}$ force. The latter is capable of explaining the observed flat rotation curves of spiral galaxies, and is interpretable as an alternative gravity, a dark matter scenario, etc. There are ample observational data on flat rotation curves of spiral galaxies, coded in the Tully-Fisher relation, to support our propositions., Comment: 20 pages, 3 figures, 1 table

Polar dust has been discovered in a number of local Active Galactic Nuclei (AGN), with radiation-driven torus models predicting a wind to be its main driver. However, little is known about its characteristics, spatial extent, or connection to the larger scale outflows. We present the first JWST/MIRI study aimed at imaging polar dust by zooming onto the centre of ESO 428-G14, part of the Galaxy Activity, Torus, and Outflow Survey (GATOS) survey of local AGN. We detect extended mid-infrared (MIR) emission within 200 pc from the nucleus. This polar structure is co-linear with a radio jet and lies perpendicular to a molecular gas lane that feeds and obscures the nucleus. Its morphology bears a striking resemblance to that of gas ionised by the AGN in the narrow-line region. We demonstrate that part of this spatial correspondence is due to contamination within the JWST filter bands from strong emission lines. Correcting for the contamination, we find the morphology of the dust continuum to be more compact, though still clearly extended out to ~ 100 pc. We estimate the emitting dust has a temperature of ~ 120 K. Using simple models, we find that the heating of small dust grains by the radiation from the central AGN and/or radiative jet-induced shocks is responsible for the extended MIR emission. Radiation-driven dusty winds from the torus is unlikely to be important. This has important implications for scales to which AGN winds can carry dust and dense gas out into their host galaxies., Comment: 17 pages, 8 figures, 2 tables. Published in MNRAS

This work performs the convergence analysis of the polytopal nodal discretisation of contact-mechanics (with Tresca friction) recently introduced in [18] in the framework of poro-elastic models in fractured porous media. The scheme is based on a mixed formulation, using face-wise constant approximations of the Lagrange multipliers along the fracture network and a fully discrete first order nodal approximation of the displacement field. The displacement field is enriched with additional bubble degrees of freedom along the fractures to ensure the inf-sup stability with the Lagrange multiplier space. It is presented in a fully discrete formulation, which makes its study more straightforward, but also has a Virtual Element interpretation. The analysis establishes an abstract error estimate accounting for the fully discrete framework and the non-conformity of the discretisation. A first order error estimate is deduced for sufficiently smooth solutions both for the gradient of the displacement field and the Lagrange multiplier. A key difficulty of the numerical analysis is the proof of a discrete inf-sup condition, which is based on a non-standard $H^{-1/2}$-norm (to deal with fracture networks) and involves the jump of the displacements, not their traces. The analysis also requires the proof of a discrete Korn inequality for the discrete displacement field which takes into account fracture networks. Numerical experiments based on analytical solutions confirm our theoretical findings

In this study, we discuss the observation of spin wave interference generated by magnetic oscillators. We employ micromagnetic simulations for two coherent spin Hall nanowire oscillators positioned nearby, horizontally or vertically. The two nanowires produce circular waves with short wavelengths on the order of 100 nm, which interfere with each other. In the horizontal configuration, the spin waves exhibit constructive and destructive fringes, indicating amplification or cancellation of the amplitudes, respectively. The synchronization of spin waves in the current geometry of the two nanowires is facilitated by the combination of dipolar field and propagating spin waves. Additionally, the vertical alignment results in standing spin waves characterized by multiple antinodes and nodes. These observations are interpreted using a wave model that incorporates the superposition principle for each case.

We consider self-interacting scalar fields with a conformal coupling in the dS background and study the quantum corrections from bubble loop diagrams. Incorporating the perturbative in-in formalism, we calculate the quantum corrections in the vacuum zero point energy and pressure of self-interacting fields with the potential $V \propto \Phi^n $ for even values of $n$. We calculate the equation of state corresponding to these quantum corrections and examine the scaling of the divergent terms in the vacuum zero point energy and pressure associated to the dimensional regularization scheme. In particular, we show that for quartic self-interacting scalar field the conformal invariance is respected at two-loop order at the conformal point., Comment: 21 Pages, 4 figures

The term bacteriophage means killer or eater of bacteria. They were initially discovered by F.W. Twort and later on, Felix d'Herelle unveiled them to the world in 1910. Phage therapy has arisen as a favorable option to conventional antibiotics by reducing the multinational problem of increasing antibacterial resistance. These virulent viruses particularly prey on and contaminate bacterial strains and aid in fighting bacterial diseases. Researchers are performing various clinical trials on the bacteriophage to tackle pathogenic bacterial infections, varying from typical illnesses to highly invulnerable biofilms that cannot be treated with antibiotics. The primary experiments demonstrated that phage therapy has fewer consequences than traditional antimicrobial drugs. It is safer to use and show results within a few days. Although phage therapy has a wide range of promising results, but it also encounters diverse obstacles. One is that they are host-specific and can merely be used for personalized therapy. As thousands of bacteria can cause disease, clinicians have to construct a library of phage viruses. For successful treatment, an analysis of versatility, stability, and immune interference related to bacteriophage is necessary. Phage therapy is an excellent substitute for antibiotics as it illustrates a living base for the treatment of infections and it is climate-friendly. It only targets the pathogenic cells and has less influence on the normal microbiota. Regardless of the challenges and problems, phage therapy is approved as a beneficial approach to combating contagious infections.