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The nanoscale optical properties of high-quality MoS$_2$ nanoribbons are investigated using THz nanoscopy based on a scattering-type scanning probe. The nanoribbons comprise a multi-layer core, surrounded by monolayer edges. A featureless complex permittivity spectrum covering the range 0.6-1.6 THz is extracted from experimental time-domain measurements through a minimization procedure, adopting an extended finite-dipole model of the probe-sample interaction. Real-space mapping of the nanoribbon reveals variations in the local permittivity down to the instrument-limited resolution, on the order of 30 nm. Clustering analysis statistically identifies regions of lower apparent permittivity that we attribute to a high curvature at the edges of the nanoribbon causing an increase in local material strain or cross-talk in the measured signal with topography-induced measurement artifacts. The core of the nanoribbon contains two regions that follow tightly distributed, but slightly shifted Gaussian statistics in complex permittivity space, with the real part mean of both distributions lying around 5.4 and compatible with literature values of the static permittivity of thin-film MoS$_2$ reported previously. Our results show that the nanoribbons exhibit a modest degree of dielectric variation at the nanoscale that could be explained by heterogeneous doping or variations in the local defect density. We believe that our approach could be useful for the direct real-space measurement of dielectric disorder in other low-dimensional semiconducting material systems.

This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification accuracy but in the price of retaining too many features. When applied to microarray human cancers, PAM selected 2611 features on average from 10 multi-class datasets. Such a large number of features make it difficult to perform follow up study. One reason behind this problem is the soft thresholding, which is known to produce biased parameter estimate in regression analysis. In this article, we extend the PAM algorithm with two other thresholding methods, hard and order thresholding, and a deep search algorithm to achieve better thresholding parameter estimate. The modified algorithms are extensively tested and compared to the original one based on real data and Monte Carlo studies. In general, the modification not only gave better cancer status prediction accuracy, but also resulted in more parsimonious models with significantly smaller number of features., Comment: 30 pages 9 figures

Informed by the success of the transformer model in various computer vision tasks, we design an end-to-end trainable model for the automatic detection and classification of bleeding and non-bleeding frames extracted from Wireless Capsule Endoscopy (WCE) videos. Based on the DETR model, our model uses the Resnet50 for feature extraction, the transformer encoder-decoder for bleeding and non-bleeding region detection, and a feedforward neural network for classification. Trained in an end-to-end approach on the Auto-WCEBleedGen Version 1 challenge training set, our model performs both detection and classification tasks as a single unit. Our model achieves an accuracy, recall, and F1-score classification percentage score of 98.28, 96.79, and 98.37 respectively, on the Auto-WCEBleedGen version 1 validation set. Further, we record an average precision (AP @ 0.5), mean-average precision (mAP) of 0.7447 and 0.7328 detection results. This earned us a 3rd place position in the challenge. Our code is publicly available via https://github.com/BasitAlawode/WCEBleedGen.

Online shopping has revolutionized the retail industry, providing customers with convenience and accessibility. However, customers often hesitate to purchase wearable products such as watches, jewelry, glasses, shoes, and clothes due to the lack of certainty regarding fit and suitability. This leads to significant return rates, causing problems for both customers and vendors. To address this issue, a platform called the Virtual Trial Room with Computer Vision and Machine Learning is designed which enables customers to easily check whether a product will fit and suit them or not. To achieve this, an AI-generated 3D model of the human head was created from a single 2D image using the DECA model. This 3D model was then superimposed with a custom-made 3D model of glass which is based on real-world measurements and fitted over the human head. To replicate the real-world look and feel, the model was retouched with textures, lightness, and smoothness. Furthermore, a full-stack application was developed utilizing various fornt-end and back-end technologies. This application enables users to view 3D-generated results on the website, providing an immersive and interactive experience.

Altermagnets are distinguished by their unique spin group symmetries, where spin and spatial symmetries are fully decoupled, resulting in nonrelativistic spin splitting (NRSS) of electronic bands. This phenomenon, unlike conventional spin splitting driven by relativistic spin-orbit coupling, has transformative potential in fields such as spintronics, superconductivity and energy-efficient electronics. However, direct observation of NRSS is challenging due to presence of competing phases, low N\'eel temperatures, and the limitations of existing experimental probes to unambiguously capture the associated properties. Here, we integrate theoretical and experimental approaches to uncover NRSS in the intercalated transition metal dichalcogenide CoNb$_4$Se$_8$. Symmetry analysis, density functional theory (DFT), a novel Symmetry-Constrained Adaptive Basis (SCAB), and tight-binding modeling predict the presence of symmetry-enforced spin splitting, which we directly confirm using spin-ARPES for the occupied band structure and a newly developed technique, spin- and angle-resolved electron reflection spectroscopy (spin-ARRES), for the unoccupied states. Together, these complementary tools reveal alternating spin textures consistent with our predicted g-wave altermagnetic order and demonstrate the persistence of NRSS across a broad energy range. Crucially, temperature-dependent measurements show the suppression of NRSS at the N\'eel temperature ($T_N$), providing the first direct evidence of an altermagnetic phase transition. Residual spin splitting above $T_N$ suggests the coexistence of altermagnetic fluctuations and spin-orbit coupling effects, underscoring a complex interplay of mechanisms. By establishing CoNb$_4$Se$_8$ as a prototypical g-wave altermagnet, this work offers a robust framework for understanding NRSS, and lays the foundation for designing energy-efficient spin-based technologies., Comment: 26 pages, 15 figures (5 main, 10 supplementary)

The unique connectivity of kagome lattices gives rise to topological properties, such as flat bands and Dirac cones. When combined with ferromagnetism and a chemical potential near the 2D Dirac points, this structure offers the potential to realize the highly sought-after topological Chern magnetotransport. Recently, there was considerable excitement surrounding this possibility in the ferrimagnetic kagome metal TbMn$_\mathbf{6}$Sn$_\mathbf{6}$. However, density functional theory (DFT) calculations reveal that the 2D Chern gap lies well above the Fermi energy, challenging its relevance in the observed anomalous Hall conductivity. Here, we investigate YMn$_\mathbf{6}$Sn$_\mathbf{5.45}$Ga$_\mathbf{0.55}$, a compound with similar crystallographic, magnetic, and electronic properties to TbMn$_\mathbf{6}$Sn$_\mathbf{6}$. Our findings show that the intrinsic anomalous Hall conductivity in this material, while comparable in magnitude to that in TbMn$_\mathbf{6}$Sn$_\mathbf{6}$, is fully three-dimensional, thus providing experimental evidence that Hall conductivity in this class of materials does not originate from 2D Chern gaps. Additionally, we confirm that the newly proposed empirical scaling relation for extrinsic Hall conductivity is universally governed by spin fluctuations.

Important advances have recently been made in the search for materials with complex multi-phase landscapes that host photoinduced metastable collective states with exotic functionalities. In almost all cases so far, the desired phases are accessed by exploiting light-matter interactions via the imaginary part of the dielectric function through above-bandgap or resonant mode excitation. Nonresonant Raman excitation of coherent modes has been experimentally observed and proposed for dynamic material control, but the resulting atomic excursion has been limited to perturbative levels. Here, we demonstrate that it is possible to overcome this challenge by employing nonresonant ultrashort pulses with low photon energies well below the bandgap. Using mid-infrared pulses, we induce ferroelectric reversal in lithium niobate and phase switching in tin selenide and characterize the large-amplitude mode displacements through femtosecond Raman scattering, second harmonic generation, and x-ray diffraction. This approach, validated by first-principle calculations, defines a novel method for synthesizing hidden phases with unique functional properties and manipulating complex energy landscapes at reduced energy consumption and ultrafast speeds., Comment: 5 figures

The surface ligands in colloidal metal halide perovskites influence not only their intrinsic optoelectronic properties but also their interaction with other materials and molecules. We explore donor-acceptor interactions of CsPbBr3 perovskite nanocrystals with TiO2 nanoparticles and nanotubes by replacing long-chain oleylamine ligands with short-chain butylamines. Through post-synthesis ligand exchange, we functionalize the nanocrystals with butylamine ligands while maintaining their intrinsic properties. In solution, butylamine-capped nanocrystals exhibit reduced photoluminescence intensity with increasing TiO2 concentration but without any change in photoluminescence lifetime. Intriguingly, the Stern-Volmer plot depicts different slopes at low and high TiO2 concentrations, suggesting mixed static and sphere-of-action quenching interactions. Oleylamine-capped nanocrystals in solution, on the other hand, show no interaction with TiO2, as indicated by consistent photoluminescence intensities and lifetimes before and after TiO2 addition. In films, both types exhibit decreased photoluminescence lifetime with TiO2, indicating enhanced donor-acceptor interactions, which are discussed in terms of trap state modification and electron transfer. TiO2 nanotubes enhance nonradiative recombination more in butylamine-capped CsPbBr3 perovskite nanocrystals, emphasizing the role of ligand chain length., Comment: 30 pages, 7 figures

Digital twin technology, traditionally used in industry, is increasingly recognized for its potential to enhance educational experiences. This study investigates the application of industrial digital twins (DTs) in education, focusing on how DT models of varying fidelity can support different stages of Bloom's taxonomy in the cognitive domain. We align Bloom's six cognitive stages with educational levels: undergraduate studies for "Remember" and "Understand," master's level for "Apply" and "Analyze," and doctoral level for "Evaluate" and "Create." Low-fidelity DTs aid essential knowledge acquisition and skill training, providing a low-risk environment for grasping fundamental concepts. Medium-fidelity DTs offer more detailed and dynamic simulations, enhancing application skills and problem-solving. High-fidelity DTs support advanced learners by replicating physical phenomena, allowing for innovative design and complex experiments. Within this framework, large language models (LLMs) serve as mentors, assessing progress, filling knowledge gaps, and assisting with DT interactions, parameter setting, and debugging. We evaluate the educational impact using the Kirkpatrick Model, examining how each DT model's fidelity influences learning outcomes. This framework helps educators make informed decisions on integrating DTs and LLMs to meet specific learning objectives., Comment: 8 pages, 7 figures

We present the first theoretical results on the generation of short-wavelength attosecond vortex beams in semiconductors through their interactions with an intense Laguerre-Gauss beam, in the limit where non-perturbative high-order harmonics are generated. We exploit the details of the novel microscopic mechanism for high-order harmonic generation (HHG) in condensed matter, such as the use of dephasing time included in semiconductor Bloch equations (SBE), the combination of the SBE model with the thin slab model, and the use of experimentally verified scaling laws for various harmonic orders. For our test, we use a zinc oxide crystal as our standard sample, and our vortex beam is characterized by a topological charge of $l=1$. Our time-domain analysis shows that harmonics within the plateau region specifically contribute to the generation of the attosecond vortex beam. Our findings have implications for advancing the understanding of solid-state HHG and leveraging its strengths, such as the use of thin and dense media, for the efficient generation of short-wavelength attosecond vortex beams.

The kagome lattice, known for its strong frustration in two dimensions, hosts a variety of exotic magnetic and electronic states. A variation of this geometry, where the triangular motifs are twisted to further reduce symmetry, has recently revealed even more complex physics. HoAgGe exemplifies such a structure, with magnetic and electronic properties believed to be driven by strong in-plane anisotropy of the Ho spins, effectively acting as a two-dimensional spin ice. In this study, using a combination of magnetization, Hall conductivity measurements, and density functional theory calculations, we demonstrate how various spin-ice states, stabilized by external magnetic fields, influence the Fermi surface topology. More interestingly, we observe sharp transitions in Hall conductivity without concurrent changes in magnetization when an external magnetic field is applied along a particular crystallographic direction, underscoring the role of strong magnetic frustration and providing a new platform for exploring the interplay between magnetic frustration, electronic topology, and crystalline symmetry. These results also highlight the limitations of a simple spin-ice model, suggesting that a more sophisticated framework is necessary to capture the subtle experimental nuances observed., Comment: 17 pages, 11 figures

The development of a desktop Braille printing machine aims to create an affordable, user-friendly device for visually impaired users. This document outlines the entire process, from research and requirement analysis to distribution and support, leveraging the content and guidelines from the GitHub repository,https://github.com/fablabnepal1/Desktop-Braille-Printing-Machine.

Altermagnets (AMs) are a new class of magnetic materials that combine the beneficial spintronics properties of ferromagnets and antiferromagnets, garnering significant attention recently. Here, we have identified altermagnetism in a layered intercalated transition metal diselenide, CoNb$_4$Se$_8$, which crystallizes with an ordered sublattice of intercalated Co atoms between NbSe$_2$ layers. Single crystals are synthesized, and the structural characterizations are performed using single crystal diffraction and scanning tunneling microscopy. Magnetic measurements reveal easy-axis antiferromagnetism below 168 K. Density functional theory (DFT) calculations indicate that A-type antiferromagnetic ordering with easy-axis spin direction is the ground state, which is verified through single crystal neutron diffraction experiments. Electronic band structure calculations in this magnetic state display spin-split bands, confirming altermagnetism in this compound. The layered structure of CoNb$_4$Se$_8$ presents a promising platform for testing various predicted properties associated with altermagnetism.

$TbMn_{6}Sn_{6}$ is a ferrimagnetic material which exhibits a highly unusual phase transition near room temperature where spins remain collinear while the total magnetic moment rotates from out-of-plane to in-plane. The mechanisms underlying this phenomenon have been studied in the quasi-static limit and the reorientation has been attributed to the competing anisotropies of Tb and Mn, whose magnetic moments have very different temperature dependencies. In this work, we present the first measurement of the spin-reorientation transition in $TbMn_{6}Sn_{6}$. By probing very small signals with the transverse magneto-optical Kerr effect (TMOKE) at the Mn M-edge, we show that the re-orientation timescale spans from 12 ps to 24 ps, depending on the laser excitation fluence. We then verify these data with a simple model of spin precession with a temperature-dependent magnetocrystalline anisotropy field to show that the spin reorientation timescale is consistent with the reorientation being driven by very large anisotropies energies on approximately $\approx$ meV scales. Promisingly, the model predicts a possibility of 180o reorientation of the out-of-plane moment over a range of excitation fluences. This could facilitate optically controlled magnetization switching between very stable ground states, which could have useful applications in spintronics or data storage.

The identification of topological Weyl semimetals has recently gained considerable attention. Here, we report the results of density-functional theory calculations regarding the magnetic properties, the electronic structure, and the intrinsic anomalous Hall conductivity of the title compound, which was synthesized already 50 years ago but received little attention, hitherto. We found Cs$_{2}$Co$_{3}$S$_4$ to be a ferrimagnetic half-metal with a total spin magnetic moment of about 3 $\mu_B$ per formula unit. It shows energy band gap of 0.36 eV in the majority-spin channel and a pseudo-gap at the Fermi level in the minority-spin channel. We identified several sets of low-energy Weyl points and traced their dependence on the direction of magnetization. The intrinsic anomalous Hall conductivity is predicted to reach a magnitude up to 500 $\Omega^{-1}$cm$^{-1}$, which is comparable to values obtained in other celebrated Weyl semimetals., Comment: 10 pages, 4 figures

London and Campbell penetration depths were measured in single crystals of the endohedral gallide cluster superconductor, Mo$_{8}$Ga$_{41}$. The full temperature range superfluid density is consistent with the clean isotropic $s-$wave weak-coupling BCS theory without any signs of the second gap or strong coupling. The temperature dependence of the Campbell length is hysteretic between zero-field cooling (ZFC) and field-cooling (FC) protocols, indicating an anharmonic vortex pinning potential. The field dependence of the effective critical current density, $j_{c}\left(H\right)$, reveals an unusual result. While in the ZFC protocol, $j_{c}\left(H\right)$ is monotonically suppressed by the magnetic field, it exhibits a profound ``hidden'' peak effect in the FC protocol, that is, without a vortex density gradient. We suggest a possible novel mechanism for the formation of the peak effect, which involves both static and dynamic aspects.

London, $\lambda_L (T)$, and Campbell, $\lambda_{C} (T)$, penetration depths were measured in single crystals of a topological superconductor candidate $\text{AuSn}_4$. At low temperatures, $\lambda_L (T)$ is exponentially attenuated and, if fitted with the power law, $\lambda(T) \sim T^n$, gives exponents $n>4$, indistinguishable from the isotropic single $s-$wave gap Bardeen-Cooper-Schrieffer (BCS) asymptotic. The superfluid density fits perfectly in the entire temperature range to the BCS theory. The superconducting transition temperature, $T_c = 2.40 \pm 0.05\:\text{K}$, does not change after 2.5 MeV electron irradiation, indicating the validity of the Anderson theorem for isotropic $s-$wave superconductors. Campbell penetration depth before and after electron irradiation shows no hysteresis between the zero-field cooling (ZFC) and field cooling (FC) protocols, consistent with the parabolic pinning potential. Interestingly, the critical current density estimated from the original Campbell theory decreases after irradiation, implying that a more sophisticated theory involving collective effects is needed to describe vortex pinning in this system. In general, our thermodynamic measurements strongly suggest that the bulk response of the $\text{AuSn}_4$ crystals is fully consistent with the isotropic $s-$wave weak-coupling BCS superconductivity.

Due to the unavailability of solar irradiance data for many potential sites of Nepal, the paper proposes predicting solar irradiance based on alternative meteorological parameters. The study focuses on five distinct regions in Nepal and utilizes a dataset spanning almost ten years, obtained from CERES SYN1deg and MERRA-2. Machine learning models such as Random Forest, XGBoost, K-Nearest Neighbors, and deep learning models like LSTM and ANN-MLP are employed and evaluated for their performance. The results indicate high accuracy in predicting solar irradiance, with R-squared(R2) scores close to unity for both train and test datasets. The impact of parameter integration on model performance is analyzed, revealing the significance of various parameters in enhancing predictive accuracy. Each model demonstrates strong performance across all parameters, consistently achieving MAE values below 6, RMSE values under 10, MBE within |2|, and nearly unity R2 values. Upon removal of various solar parameters such as "Solar_Irradiance_Clear_Sky", "UVA", etc. from the datasets, the model's performance is significantly affected. This exclusion leads to considerable increases in MAE, reaching up to 82, RMSE up to 135, and MBE up to |7|. Among the models, KNN displays the weakest performance, with an R2 of 0.7582546. Conversely, ANN exhibits the strongest performance, boasting an R2 value of 0.9245877. Hence, the study concludes that Artificial Neural Network (ANN) performs exceptionally well, showcasing its versatility even under sparse data parameter conditions., Comment: 20 pages, 12 figures, 5 tables

Obfuscation stands as a promising solution for safeguarding hardware intellectual property (IP) against a spectrum of threats including reverse engineering, IP piracy, and tampering. In this paper, we introduce Obfus-chat, a novel framework leveraging Generative Pre-trained Transformer (GPT) models to automate the obfuscation process. The proposed framework accepts hardware design netlists and key sizes as inputs, and autonomously generates obfuscated code tailored to enhance security. To evaluate the effectiveness of our approach, we employ the Trust-Hub Obfuscation Benchmark for comparative analysis. We employed SAT attacks to assess the security of the design, along with functional verification procedures to ensure that the obfuscated design remains consistent with the original. Our results demonstrate the efficacy and efficiency of the proposed framework in fortifying hardware IP against potential threats, thus providing a valuable contribution to the field of hardware security.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. While deep learning has gained substantial popularity for modeling complex relational data, its application to uncovering the spatiotemporal dynamics of the brain is still limited. We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series and employs a specialized graph neural network for the final classification. Our model, DSAM, leverages temporal causal convolutional networks to capture the temporal dynamics in both low- and high-level feature representations, a temporal attention unit to identify important time points, a self-attention unit to construct the goal-specific connectivity matrix, and a novel variant of graph neural network to capture the spatial dynamics for downstream classification. To validate our approach, we conducted experiments on the Human Connectome Project dataset with 1075 samples to build and interpret the model for the classification of sex group, and the Adolescent Brain Cognitive Development Dataset with 8520 samples for independent testing. Compared our proposed framework with other state-of-art models, results suggested this novel approach goes beyond the assumption of a fixed connectivity matrix and provides evidence of goal-specific brain connectivity patterns, which opens up the potential to gain deeper insights into how the human brain adapts its functional connectivity specific to the task at hand., Comment: 18 Pages, 4 figures

Immunotherapy is an effective precision medicine treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma. A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis. Results Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed. Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability., Comment: Published in Neuro-Oncology Advances 2024

Throughout the past few years, various transmission system operators (TSOs) and research institutes have defined several functional specifications for grid-forming (GFM) converters via grid codes, white papers, and technical documents. These institutes and organisations also proposed testing requirements for general inverter-based resources (IBRs) and specific GFM converters. This paper initially reviews functional specifications and testing requirements from several sources to create an understanding of GFM capabilities in general. Furthermore, it proposes an outlook of the defined GFM capabilities, functional specifications, and testing requirements for offshore wind power plant (OF WPP) applications from an original equipment manufacturer (OEM) perspective. Finally, this paper briefly establishes the relevance of new testing methodologies for equipment-level certification and model validation, focusing on GFM functional specifications.

With the STAR experiment at the BNL Relativisic Heavy Ion Collider, we characterize $\sqrt{s_\mathrm{NN}}$ = 200 GeV p+Au collisions by event activity (EA) measured within the pseudorapidity range $eta$ $in$ [-5, -3.4] in the Au-going direction and report correlations between this EA and hard- and soft- scale particle production at midrapidity ($\eta$ $\in$ [-1, 1]). At the soft scale, charged particle production in low-EA p+Au collisions is comparable to that in p+p collisions and increases monotonically with increasing EA. At the hard scale, we report measurements of high transverse momentum (pT) jets in events of different EAs. In contrast with the soft particle production, high-pT particle production and EA are found to be inversely related. To investigate whether this is a signal of jet quenching in high-EA events, we also report ratios of pT imbalance and azimuthal separation of dijets in high- and low-EA events. Within our measurement precision, no significant differences are observed, disfavoring the presence of jet quenching in the highest 30% EA p+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 200 GeV., Comment: 12 page, 9 figures

Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences. Existing approaches often hinge on discriminative models tasked with predicting either the absolute positions of puzzle elements or the permutation actions applied to the original data. Unfortunately, these methods face limitations in effectively solving puzzles with a large number of elements. In this paper, we propose JPDVT, an innovative approach that harnesses diffusion transformers to address this challenge. Specifically, we generate positional information for image patches or video frames, conditioned on their underlying visual content. This information is then employed to accurately assemble the puzzle pieces in their correct positions, even in scenarios involving missing pieces. Our method achieves state-of-the-art performance on several datasets., Comment: 8 pages, 7 figures

The burgeoning development of generative artificial intelligence (GenAI) and the widespread adoption of large language models (LLMs) in educational settings have sparked considerable debate regarding their efficacy and acceptability.Despite the potential benefits, the assimilation of these cutting-edge technologies among educators exhibits a broad spectrum of attitudes, from enthusiastic advocacy to profound skepticism.This study aims to dissect the underlying factors influencing educators' perceptions and acceptance of GenAI and LLMs.We conducted a survey among educators and analyzed the data through the frameworks of the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT). Our investigation reveals a strong positive correlation between the perceived usefulness of GenAI tools and their acceptance, underscoring the importance of demonstrating tangible benefits to educators. Additionally, the perceived ease of use emerged as a significant factor, though to a lesser extent, influencing acceptance. Our findings also show that the knowledge and acceptance of these tools is not uniform, suggesting that targeted strategies are required to address the specific needs and concerns of each adopter category to facilitate broader integration of AI tools.in education.

The effect of annealing on the superconducting properties of niobium single crystals cut from the same master boule was studied by local and global magnetic measurements, as well as scanning tunneling microscopy (STM). The formation of large hydride precipitates was observed in unannealed samples. The variation in structural and magnetic properties was studied after annealing under high vacuum at 800 C, 1400 C, and near the melting point of niobium (2477 C) for a few seconds. The initial samples had a high hydrogen content. Polarized optics and magneto-optical studies show that the formation of large niobium hydride precipitates is suppressed already by 800 C annealing. However, the overall superconducting properties in the annealed samples did not improve after annealing, and in fact, worsened. The superconducting transition temperature decreased, the upper critical field increased, and the pinning strength increased. Parallel studies were conducted using STM, where the sample was annealed initially at 400 C, measured, annealed again at 1700 C, and measured again. These studies revealed a ``dirty'' superconducting gap with a significant spatial variation of tunneling conductance after annealing at 400 C. The clean gap was recovered after annealing at 1700 C. It is likely that these results are due to oxygen redistribution near the surface, which is always covered by oxide layers in as-grown crystals. Overall, the results indicate that vacuum annealing at least up to 1400 C, while expected to remove a large amount of hydrogen, introduces additional nanosized defects, perhaps hydride precipitates, that act as efficient pair-breaking and pinning centers.

Emerging technologies like generative AI tools, including ChatGPT, are increasingly utilized in educational settings, offering innovative approaches to learning while simultaneously posing new challenges. This study employs a survey methodology to examine the policy landscape concerning these technologies, drawing insights from 102 high school principals and higher education provosts. Our results reveal a prominent policy gap: the majority of institutions lack specialized guide-lines for the ethical deployment of AI tools such as ChatGPT. Moreover,we observed that high schools are less inclined to work on policies than higher educational institutions. Where such policies do exist, they often overlook crucial issues, including student privacy and algorithmic transparency. Administrators overwhelmingly recognize the necessity of these policies, primarily to safeguard student safety and mitigate plagiarism risks. Our findings underscore the urgent need for flexible and iterative policy frameworks in educational contexts.

The rapid advancement of artificial intelligence (AI) and the expanding integration of large language models (LLMs) have ignited a debate about their application in education. This study delves into university instructors' experiences and attitudes toward AI language models, filling a gap in the literature by analyzing educators' perspectives on AI's role in the classroom and its potential impacts on teaching and learning. The objective of this research is to investigate the level of awareness, overall sentiment towardsadoption, and the factors influencing these attitudes for LLMs and generative AI-based tools in higher education. Data was collected through a survey using a Likert scale, which was complemented by follow-up interviews to gain a more nuanced understanding of the instructors' viewpoints. The collected data was processed using statistical and thematic analysis techniques. Our findings reveal that educators are increasingly aware of and generally positive towards these tools. We find no correlation between teaching style and attitude toward generative AI. Finally, while CS educators show far more confidence in their technical understanding of generative AI tools and more positivity towards them than educators in other fields, they show no more confidence in their ability to detect AI-generated work.

We study the effects of flux creep on the linear AC response of the vortex lattice in single crystals Ca$_3$Ir$_4$Sn$_{13}$ by measuring the Campbell penetration depth, $\lambda_{\rm \scriptscriptstyle C}(T,H,t)$. Thermal fluctuations release vortices from shallow pinning sites, only for them to become re-trapped by deeper potential wells, causing an initial increase of the effective Labusch parameter, which is proportional to the pinning well curvature. This effect cannot be detected in conventional magnetic relaxation measurements but is revealed by our observation of a nonmonotonic time evolution of $\lambda_{\rm \scriptscriptstyle C}(T,H,t)$, which directly probes the average curvature of the occupied pinning centers. The time evolution of $\lambda_{\rm \scriptscriptstyle C}(T,H,t)$ was measured at different temperatures in samples with different densities of pinning centers produced by electron irradiation. The curves can be collapsed together when plotted on a logarithmic time scale $t \to T\ln{(t/t_0)}$ confirming that the time evolution is driven by flux creep. The $\lambda_{\rm \scriptscriptstyle C}(T,H,t)$ is hysteretic with a noticeable nonmonotonic relaxation in the presence of a vortex density gradient (after zero-field cooling), but is monotonic after field cooling, where the vortex density is uniform. This result quantitatively corroborates the novel picture of vortex creep based on the strong pinning theory.

We study zero-sum differential games with state constraints and one-sided information, where the informed player (Player 1) has a categorical payoff type unknown to the uninformed player (Player 2). The goal of Player 1 is to minimize his payoff without violating the constraints, while that of Player 2 is to violate the state constraints if possible, or to maximize the payoff otherwise. One example of the game is a man-to-man matchup in football. Without state constraints, Cardaliaguet (2007) showed that the value of such a game exists and is convex to the common belief of players. Our theoretical contribution is an extension of this result to games with state constraints and the derivation of the primal and dual subdynamic principles necessary for computing behavioral strategies. Different from existing works that are concerned about the scalability of no-regret learning in games with discrete dynamics, our study reveals the underlying structure of strategies for belief manipulation resulting from information asymmetry and state constraints. This structure will be necessary for scalable learning on games with continuous actions and long time windows. We use a simplified football game to demonstrate the utility of this work, where we reveal player positions and belief states in which the attacker should (or should not) play specific random deceptive moves to take advantage of information asymmetry, and compute how the defender should respond., Comment: Accepted to ICML 2024

We propose a noble, comprehensive and robust agile requirements change management (ARCM) model that addresses the limitations of existing models and is tailored for agile software development in the global software development paradigm. To achieve this goal, we conducted an exhaustive literature review and an empirical study with RCM industry experts. Our study evaluated the effectiveness of the proposed RCM model in a real-world setting and identifies any limitations or areas for improvement. The results of our study provide valuable insights into how the proposed RCM model can be applied in agile global software development environments to improve software development practices and optimize project success rates., Comment: 15 pages, 1 figure

This paper studies control interactions between grid-forming (GFM) converters exhibited by power and frequency oscillations in a weakly connected offshore wind power plant (WPP). Two GFM controls are considered, namely virtual synchronous machine (VSM) and virtual admittance (VAdm) based GFM. The GFM control methods are implemented in wind turbine generators (WTGs) of a verified aggregated model of a WPP and the control interaction between these GFM WTGs is studied for several cases: cases with the same GFM control methods, and cases with different GFM control methods. A sensitivity analysis is performed for the observed oscillations to understand which system parameter affects the oscillations the most. Several solution methods are proposed and the inapplicability of some of the conventional solution methods are elaborated in this paper.

In this paper, we examine the research conducted in the field of Nepali Automatic Speech Recognition (ASR). The primary objective of this survey is to conduct a comprehensive review of the works on Nepali Automatic Speech Recognition Systems completed to date, explore the different datasets used, examine the technology utilized, and take account of the obstacles encountered in implementing the Nepali ASR system. In tandem with the global trends of ever-increasing research on speech recognition based research, the number of Nepalese ASR-related projects are also growing. Nevertheless, the investigation of language and acoustic models of the Nepali language has not received adequate attention compared to languages that possess ample resources. In this context, we provide a framework as well as directions for future investigations., Comment: Accepted in International Conference on Technologies for Computer, Electrical, Electronics & Communication (ICT-CEEL 2023)