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Several initiatives have been undertaken to conceptually model the domain of scholarly data using ontologies and to create respective Knowledge Graphs. Yet, the full potential seems unleashed, as automated means for automatic population of said ontologies are lacking, and respective initiatives from the Semantic Web community are not necessarily connected: we propose to make scholarly data more sustainably accessible by leveraging Wikidata's infrastructure and automating its population in a sustainable manner through LLMs by tapping into unstructured sources like conference Web sites and proceedings texts as well as already existing structured conference datasets. While an initial analysis shows that Semantic Web conferences are only minimally represented in Wikidata, we argue that our methodology can help to populate, evolve and maintain scholarly data as a community within Wikidata. Our main contributions include (a) an analysis of ontologies for representing scholarly data to identify gaps and relevant entities/properties in Wikidata, (b) semi-automated extraction -- requiring (minimal) manual validation -- of conference metadata (e.g., acceptance rates, organizer roles, programme committee members, best paper awards, keynotes, and sponsors) from websites and proceedings texts using LLMs. Finally, we discuss (c) extensions to visualization tools in the Wikidata context for data exploration of the generated scholarly data. Our study focuses on data from 105 Semantic Web-related conferences and extends/adds more than 6000 entities in Wikidata. It is important to note that the method can be more generally applicable beyond Semantic Web-related conferences for enhancing Wikidata's utility as a comprehensive scholarly resource. Source Repository: https://github.com/scholarly-wikidata/ DOI: https://doi.org/10.5281/zenodo.10989709 License: Creative Commons CC0 (Data), MIT (Code), Comment: 17 pages, accepted at EKAW-24

This research dives into exploring the dark mode effects on students of a university. Research is carried out implementing the dark mode in e-Learning sites and its impact on behavior of the users. Students are spending more time in front of the screen for their studies especially after the pandemic. The blue light from the screen during late hours affects circadian rhythm of the body which negatively impacts the health of humans including eye strain and headache. The difficulty that students faced during the time of interacting with various e-Learning sites especially during late hours was analyzed using different techniques of HCI like survey, interview, evaluation methods and principles of design. Dark mode is an option which creates a pseudo inverted adaptable interface by changing brighter elements of UI into a dim-lit friendly environment. It is said that using dark mode will lessen the amount of blue light emitted and benefit students who suffer from eye strain. Students' interactions with dark mode were investigated using a survey, and an e-learning site with a dark mode theme was created. Based on the students' comments, researchers looked into the effects of dark mode on HCI in e-learning sites. The findings indicate that students have a clear preference for dark mode: 79.7% of survey participants preferred dark mode on their phones, and 61.7% said they would be interested in seeing this feature added to e-learning websites., Comment: none

Defects are inevitable during the manufacturing processes of materials. Presence of these defects and their dynamics significantly influence the responses of materials. A thorough understanding of dislocation dynamics of different types of materials under various conditions is essential for analyzing the performance of the materials. Ductility of a material is directly related with the movement and rearrangement of dislocations under applied load. In this work, we look into the dynamics of dislocations in ductile and brittle materials using simplified two dimensional discrete dislocation dynamics (2D-DDD) simulation. We consider Aluminium (Al) and Tungsten (W) as representative examples of ductile and brittle materials respectively. We study the velocity distribution, strain field, dislocation count, and junction formation during interactions of the dislocations within the domain. Furthermore, we study the probability densities of dislocation motion for both materials. In mesoscale, moving dislocations can be considered as particle diffusionm, which are often stochastic and super-diffusive. Classical diffusion models fail to account for these phenomena and the long-range interactions of dislocations. Therefore, we propose the nonlocal transport model for the probability density and obtained the parameters of nonlocal operators using a machine learning framework.

A comparative vacuum ultraviolet spectroscopy study conducted at ISOLDE-CERN of the radiative decay of the $^{229m}$Th nuclear clock isomer embedded in different host materials is reported. The ratio of the number of radiative decay photons and the number of $^{229m}$Th embedded are determined for single crystalline CaF$_2$, MgF$_2$, LiSrAlF$_6$, AlN, and amorphous SiO$_2$. For the latter two materials, no radiative decay signal was observed and an upper limit of the ratio is reported. The radiative decay wavelength was determined in LiSrAlF$_6$ and CaF$_2$, reducing its uncertainty by a factor of 2.5 relative to our previous measurement. This value is in agreement with the recently reported improved values from laser excitation.

This study focuses on EEG-based BMI for detecting voluntary keystrokes, aiming to develop a reliable brain-computer interface (BCI) to simulate and anticipate keystrokes, especially for individuals with motor impairments. The methodology includes extensive segmentation, event alignment, ERP plot analysis, and signal analysis. Different deep learning models are trained to classify EEG data into three categories -- `resting state' (0), `d' key press (1), and `l' key press (2). Real-time keypress simulation based on neural activity is enabled through integration with a tkinter-based graphical user interface. Feature engineering utilized ERP windows, and the SVC model achieved 90.42% accuracy in event classification. Additionally, deep learning models -- MLP (89% accuracy), Catboost (87.39% accuracy), KNN (72.59%), Gaussian Naive Bayes (79.21%), Logistic Regression (90.81% accuracy), and a novel Bi-Directional LSTM-GRU hybrid model (89% accuracy) -- were developed for BCI keyboard simulation. Finally, a GUI was created to predict and simulate keystrokes using the trained MLP model.

Engineering magnetism in layered magnets could result in novel phenomena related to two-dimensional (2D) magnetism, which can be useful for fundamental research and practical applications. Extensive doping efforts such as substitution and intercalation have been adopted to tune antiferromagnetic (AFM) properties in M2P2X6 compounds. The substitutional doping in this material family has mainly focused on bimetallic substitution. Recently, the metal substitution can also be extended to more than two metal elements, leading to medium and high-entropy alloys (MEAs and HEAs), which are fairly underexplored in layered magnetic systems including M2P2X6. In this work, we explored the magnetic properties of the previously unreported Cu- and Cr-substituted Ni2P2S6 i.e., CuxNi2(1-x)CrxP2S6. Our study reveals a relatively systematic evolution of AFM phases with substitution than that observed in traditional bimetallic substitution in M2P2X6. Furthermore, the Cu and Cr substitutions in Ni2P2S6 are found to enhance the ferromagnetic (FM) correlation, which is also accompanied by a possible weak FM phase at low temperatures for the intermediate compositions from 0.32 to 0.80. Our work provides a strategy to establish ferromagnetism in AFM M2P2X6 that can also be used for property tuning in other layered magnets.

Recently, layered materials transition metal thiophosphate MPX3 (M = transition metals, X = S or Se) have gained significant attention because of their rich magnetic, optical, and electronic properties. Specifically, the diverse magnetic structures and the robustness of magnetism in the two-dimensional limit have made them prominent candidates to study two-dimensional magnetism. Numerous efforts such as substitutions and interlayer intercalations have been made to tune the properties of these materials, which has greatly deepened the understanding of the underlying mechanisms that govern the properties. In this work, we focus on modifying the magnetism of Ising-type antiferromagnet FePS3 using electrochemical lithium intercalation. Our work unveils the effectiveness of electrochemical intercalation as a controllable tool to modulating magnetism, including tuning magnetic ordering temperature and inducing low temperature spin-glass state, offering an approach for implementing this material into applications.

Magnetotransport, the response of electrical conduction to external magnetic field, acts as an important tool to reveal fundamental concepts behind exotic phenomena and plays a key role in enabling spintronic applications. Magnetotransport is generally sensitive to magnetic field orientations. In contrast, efficient and isotropic modulation of electronic transport, which is useful in technology applications such as omnidirectional sensing, is rarely seen, especially for pristine crystals. Here we propose a strategy to realize extremely strong modulation of electron conduction by magnetic field which is independent of field direction. GdPS, a layered antiferromagnetic semiconductor with resistivity anisotropies, supports a field-driven insulator-to-metal transition with a paradoxically isotropic gigantic negative magnetoresistance insensitive to magnetic field orientations. This isotropic magnetoresistance originates from the combined effects of a near-zero spin-orbit coupling of Gd3+-based half-filling f-electron system and the strong on-site f-d exchange coupling in Gd atoms. Our results not only provide a novel material system with extraordinary magnetotransport that offers a missing block for antiferromagnet-based ultrafast and efficient spintronic devices, but also demonstrate the key ingredients for designing magnetic materials with desired transport properties for advanced functionalities., Comment: 44 pages, 18 figures

Though the effects of metal dopants on the electrostructural transition of rutile VO$_2$ have been studied for many decades, there is still no consensus explanation for the observed trends. A major challenge has been to separate the impact of a dopant's size from other factors such as its electronic configuration, which stems from the difficulty in directly probing the local bonding environment around a dopant atom. This work addresses the special case of larger dopant ions by combining X-ray total scattering experiments on V$_{0.83}$Mo$_{0.17}$O$_2$ and V$_{0.89}$Nb$_{0.11}$O$_2$ single crystals with multiple Monte Carlo method models to simulate local size effects in the high-temperature tetragonal phase (R). We find that sufficiently long apical metal-oxygen bonds (M$-$O$_{ap}$) induce a strain field in the neighboring chains that locally resembles the metal-metal dimer formation present in the low-temperature distorted structure of VO$_2$ (M1). The dimer mode in the M1 structure is antisymmetric along M$-$O$_{ap}$, however, while the strain-induced pseudodimer motif is symmetric. The implied direct competition between motifs is verified experimentally. This finding provides a new mechanistic parameter toward understanding the phase transition. More generally, the work highlights how local strain fields around dopants can lead to complex distortions that are ordinarily attributed to electronic origins., Comment: Article: 19 pages, 9 figures; Appendix: 4 pages, 2 figures, 3 tables

This paper presents an end-to-end deep learning model for Automatic Speech Recognition (ASR) that transcribes Nepali speech to text. The model was trained and tested on the OpenSLR (audio, text) dataset. The majority of the audio dataset have silent gaps at both ends which are clipped during dataset preprocessing for a more uniform mapping of audio frames and their corresponding texts. Mel Frequency Cepstral Coefficients (MFCCs) are used as audio features to feed into the model. The model having Bidirectional LSTM paired with ResNet and one-dimensional CNN produces the best results for this dataset out of all the models (neural networks with variations of LSTM, GRU, CNN, and ResNet) that have been trained so far. This novel model uses Connectionist Temporal Classification (CTC) function for loss calculation during training and CTC beam search decoding for predicting characters as the most likely sequence of Nepali text. On the test dataset, the character error rate (CER) of 17.06 percent has been achieved. The source code is available at: https://github.com/manishdhakal/ASR-Nepali-using-CNN-BiLSTM-ResNet., Comment: Accepted at 2022 International Conference on Inventive Computation Technologies (ICICT), IEEE

Existing recommendation systems have focused on two paradigms: 1- historical user-item interaction-based recommendations and 2- conversational recommendations. Conversational recommendation systems facilitate natural language dialogues between users and the system, allowing the system to solicit users' explicit needs while enabling users to inquire about recommendations and provide feedback. Due to substantial advancements in natural language processing, conversational recommendation systems have gained prominence. Existing conversational recommendation datasets have greatly facilitated research in their respective domains. Despite the exponential growth in mobile users and apps in recent years, research in conversational mobile app recommender systems has faced substantial constraints. This limitation can primarily be attributed to the lack of high-quality benchmark datasets specifically tailored for mobile apps. To facilitate research for conversational mobile app recommendations, we introduce MobileConvRec. MobileConvRec simulates conversations by leveraging real user interactions with mobile apps on the Google Play store, originally captured in large-scale mobile app recommendation dataset MobileRec. The proposed conversational recommendation dataset synergizes sequential user-item interactions, which reflect implicit user preferences, with comprehensive multi-turn conversations to effectively grasp explicit user needs. MobileConvRec consists of over 12K multi-turn recommendation-related conversations spanning 45 app categories. Moreover, MobileConvRec presents rich metadata for each app such as permissions data, security and privacy-related information, and binary executables of apps, among others. We demonstrate that MobileConvRec can serve as an excellent testbed for conversational mobile app recommendation through a comparative study of several pre-trained large language models.

Tuberculosis remains a significant global health challenge, with millions of new cases reported annually. Recent studies suggest that expanding the accessibility of TB intervention programs can lead to a substantial decrease in both TB incidence and prevalence. This paper initiates by examining a deterministic mathematical model for TB transmission, aiming to analyze the underlying dynamics. Subsequently, an optimal control problem is formulated to enhance TB control measures, encompassing Tuberculosis Preventive Treatment (TPT) and other initiatives targeting malnutrition and diabetes. Through simulation studies, the effectiveness of the control program is assessed. The model dynamics allow us to identify the pseudo-prevalence and incidence. To determine the potential long-term trajectory of TB and to acquire future projections a cost-effectiveness analysis is performed using ACER, AIR, ICER, and four quadrants to compare competing interventions. In conclusion, this work provides valuable insights into TB and strategies for its control and cost effectiveness.

Stochastic differential equations characterized by uncertainty are effective in modelling virus dynamics and provide an alternative to traditional deterministic models. Epidemic models are inevitably subjected to the randomness within the system or the environmental noise. In this paper, we analyze the stochastic within host compartment model for SARS-CoV-2 virus and explore its dynamics. We first examine the existence and positivity of the solution of the model using Ito's formula and the establish the stochastic boundedness and permanence of the model. Exponential stability of the infection free equilibrium state is established. Numerical simulations are conducted to complement the theoretical results. Environmental noise is found to play a crucial role in the dynamics of the disease and can even lead to the extinction of the disease. The model is also extended to a stochastic optimal control problem and the effectiveness of control measures, such as antiviral drugs and immunomodulators is investigated., Comment: 26 pages, 16 figures

We establish an explicit $L^\infty(\Om)$ a priori estimate for weak solutions to subcritical elliptic problems with nonlinearity on the boundary, in terms of the powers of their $H^1(\Om)$ norms. To prove our result, we combine in a novel way Moser type estimates together with elliptic regularity and Gagliardo--Nirenberg interpolation inequality. We illustrate our result with an application to subcritical problems satisfying Ambrosetti-Rabinowitz condition.

Magnetic topological semimetals LnSbTe (Ln = Lanthanide) have attracted intensive attention because of the presence of interplay between magnetism, topological, and electron correlations depending on the choices of magnetic Ln elements. Recently, varying Sb-Te composition has been found to effectively control the electronic and magnetic states in LnSbxTe$_{2-x}$. With this motivation, we report the evolution of magnetic properties with Sb-Te substitution in NdSb$_x$Te$_{2-x+\delta}$. Our work reveals the interesting non-monotonic change in magnetic ordering temperature with varying composition stoichiometry. In addition, reducing the Sb content x drives the reorientation of moments from in-plane (ab-plane) to out-of-plane (c-axis) direction that results in the distinct magnetic structures for two end compounds NdTe$_2$ ($x = 0$) and NdSbTe ($x = 1$). Furthermore, the moment orientation in NdSb$_x$Te$_{2-x+\delta}$ is also found to be strongly tunable upon application of weak magnetic field, leading to rich magnetic phases depending on the composition stoichiometry, temperature, and magnetic field. Such strong tuning of magnetism in this material establishes it as a promising platform for investigating tunable topological states and correlated topological physics., Comment: 35 pages, 5 figures

Tuning magnetic properties in layered magnets is an important route to realize novel phenomenon related to two-dimensional (2D) magnetism. Recently, tuning antiferromagnetic (AFM) properties through substitution and intercalation techniques have been widely studied in MPX3 compounds. Interesting phenomena, such as diverse AFM structures and even the signatures of ferrimagnetism, have been reported. However, long-range ferromagnetic (FM) ordering has remained elusive. In this work, we explored the magnetic properties of the previously unreported Cr-substituted NiPS3. We found that Cr substitution is extremely efficient in controlling spin orientation in NiPS3. Our study reveals a field-induced spin polarization in lightly (9%) Cr-substituted NiPS3, which is likely attributed to the attenuation of AFM interactions and magnetic anisotropy due to Cr doping. Our work provides a possible strategy to achieve FM phase in AFM MPX3, which could be useful for investigating 2D magnetism as well as potential device applications.

Spintronics, an evolving interdisciplinary field at the intersection of magnetism and electronics, explores innovative applications of electron charge and spin properties for advanced electronic devices. The topological Hall effect, a key component in spintronics, has gained significance due to emerging theories surrounding noncoplanar chiral spin textures. This study focuses on Mn2-xZnxSb, a material crystalizing in centrosymmetric space group with rich magnetic phases tunable by Zn contents. Through comprehensive magnetic and transport characterizations, we found that the high-Zn (x>0.6) samples display THE which is enhanced with decreasing temperature, while THE in the low-Zn (x<0.6) samples show an opposite trend. The coexistence of those distinct temperature dependences for THE suggests very different magnetic interactions/structure for different compositions and underscores the strong coupling between magnetism and transport in Mn2-xZnxSb. Our findings contribute to understanding topological magnetism in centrosymmetric tetragonal lattices, establishing Mn2-xZnxSb as a unique platform for exploring tunable transport effects and opening avenues for further exploration in the realm of spintronics., Comment: 23 pages, 6 Figures, Accepted

Recent development in two-dimensional (2D) magnetic materials have motivated the search for new van der Waals magnetic materials, especially Ising-type magnets with strong magnetic anisotropy. Fe-based MPX3 (M = transition metal, X = chalcogen) compounds such as FePS3 and FePSe3 both exhibit an Ising-type magnetic order, but FePSe3 receives much less attention compared to FePS3. This work focuses on establishing the strategy to engineer magnetic anisotropy and exchange interactions in this less-explored compound. Through chalcogen and metal substitutions, the magnetic anisotropy is found to be immune against S substitution for Se whereas tunable only with heavy Mn substitution for Fe. In particular, Mn substitution leads to a continuous rotation of magnetic moments from the out-of-plane direction towards in-plane. Furthermore, the magnetic ordering temperature displays non-monotonic doping dependence for both chalcogen and metal substitutions but due to different mechanisms. These findings provide deeper insight into the Ising-type magnetism in this important van der Waals material, shedding light on the study of other Ising-type magnetic systems as well as discovering novel 2D magnets for potential applications in spintronics.

This paper introduces A2C, a multi-stage collaborative decision framework designed to enable robust decision-making within human-AI teams. Drawing inspiration from concepts such as rejection learning and learning to defer, A2C incorporates AI systems trained to recognise uncertainty in their decisions and defer to human experts when needed. Moreover, A2C caters to scenarios where even human experts encounter limitations, such as in incident detection and response in cyber Security Operations Centres (SOC). In such scenarios, A2C facilitates collaborative explorations, enabling collective resolution of complex challenges. With support for three distinct decision-making modes in human-AI teams: Automated, Augmented, and Collaborative, A2C offers a flexible platform for developing effective strategies for human-AI collaboration. By harnessing the strengths of both humans and AI, it significantly improves the efficiency and effectiveness of complex decision-making in dynamic and evolving environments. To validate A2C's capabilities, we conducted extensive simulative experiments using benchmark datasets. The results clearly demonstrate that all three modes of decision-making can be effectively supported by A2C. Most notably, collaborative exploration by (simulated) human experts and AI achieves superior performance compared to AI in isolation, underscoring the framework's potential to enhance decision-making within human-AI teams.

We report an ASKAP search for associated HI 21-cm absorption against bright radio sources from the Molonglo Reference Catalogue (MRC) 1-Jy sample. The search uses pilot survey data from the ASKAP First Large Absorption Survey in \hi (FLASH) covering the redshift range $0.42 < z < 1.00$. From a sample of 62 MRC 1-Jy radio galaxies and quasars in this redshift range we report three new detections of associated HI 21-cm absorption, yielding an overall detection fraction of $1.8\%^{+4.0\%}_{-1.5\%}$. The detected systems comprise two radio galaxies (MRC 2216$-$281 at $z=0.657$ and MRC 0531$-$237 at $z=0.851$) and one quasar (MRC 2156$-$245 at $z=0.862$). The MRC 0531$-$237 absorption system is the strongest found to date, with a velocity integrated optical depth of $\rm 143.8 \pm 0.4 \ km \ s^{-1}$. All three objects with detected HI 21-cm absorption are peaked-spectrum or compact steep-spectrum (CSS) radio sources, classified based on our SED fits to the spectra. Two of them show strong interplanetary scintillation at 162 MHz, implying that the radio continuum source is smaller than 1 arcsec in size even at low frequencies. Among the class of peaked-spectrum and compact steep-spectrum radio sources, the HI detection fraction is $23\%^{+22\%}_{-13\%}$. This is consistent within $1\sigma$ with a detection fraction of $\approx 42\%^{+21\%}_{-15\%}$ in earlier reported GPS and CSS samples at intermediate redshifts ($0.4 < z < 1.0$). All three detections have a high 1.4 GHz radio luminosity, with MRC 0531$-$237 and MRC 2216$-$281 having the highest values in the sample, $\rm > 27.5 \ W \ Hz^{-1}$. The preponderance of extended radio sources in our sample could partially explain the overall low detection fraction, while the effects of a redshift evolution in gas properties and AGN UV luminosity on the neutral gas absorption still need to be investigated., Comment: 28 pages, 9 figures and 7 Tables. Submitted to MNRAS