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Multi-modal deep metric learning is crucial for effectively capturing diverse representations in tasks such as face verification, fine-grained object recognition, and product search. Traditional approaches to metric learning, whether based on distance or margin metrics, primarily emphasize class separation, often overlooking the intra-class distribution essential for multi-modal feature learning. In this context, we propose a novel loss function called Density-Aware Adaptive Margin Loss(DAAL), which preserves the density distribution of embeddings while encouraging the formation of adaptive sub-clusters within each class. By employing an adaptive line strategy, DAAL not only enhances intra-class variance but also ensures robust inter-class separation, facilitating effective multi-modal representation. Comprehensive experiments on benchmark fine-grained datasets demonstrate the superior performance of DAAL, underscoring its potential in advancing retrieval applications and multi-modal deep metric learning., Comment: 13 pages, 4 fugues, 2 tables

Blazars, a class of active galactic nuclei (AGN) powered by supermassive black holes, are known for their remarkable variability across multiple timescales and wavelengths. With advancements in both ground- and space-based telescopes, our understanding of AGN central engines has significantly improved. However, the mechanisms driving this variability remain elusive, and continue to fascinate both theorists and observers alike. The primary objective of this study is to constrain the X-ray variability properties of the TeV blazar PKS 2155-304. We conduct a comprehensive X-ray spectral and timing analysis, focusing on both long-term and intra-day variability. This analysis uses data from 22 epochs of XMM-Newton EPIC-pn observations, collected over 15 years (2000-2014). To investigate the variability of the source, we applied both timing and spectral analyses. For the timing analysis, we estimated fractional variability, variability amplitude, minimum variability timescales, flux distribution, and power spectral density (PSD). In the spectral analysis, we fitted the X-ray spectra using power-law, log-parabola, and broken power-law (BPL) models to determine the best-fitting parameters. Additionally, we studied the hardness ratio (HR). We observed moderate intra-day variability in most of the light curves. Seven out of the twenty-two observations showed a clear bimodal flux distribution, indicating the presence of two distinct flux states. Our analysis revealed a variable power-law PSD slope. Most HR plots did not show significant variation with flux, except for one observation (OBSID 0124930501), where HR increased with flux (Count/s). The fitted X-ray spectra favored the BPL model for the majority of observations. The findings of this work shed light on the intraday variability of blazars, providing insights into the non-thermal jet processes that drive the observed flux variations.

As more types of transactions move online, there is an increasing need to verify someone's identity remotely. Remote identity verification (RIdV) technologies have emerged to fill this need. RIdV solutions typically use a smart device to validate an identity document like a driver's license by comparing a face selfie to the face photo on the document. Recent research has been focused on ensuring that biometric systems work fairly across demographic groups. This study assesses five commercial RIdV solutions for equity across age, gender, race/ethnicity, and skin tone across 3,991 test subjects. This paper employs statistical methods to discern whether the RIdV result across demographic groups is statistically distinguishable. Two of the RIdV solutions were equitable across all demographics, while two RIdV solutions had at least one demographic that was inequitable. For example, the results for one technology had a false negative rate of 10.5% +/- 4.5% and its performance for each demographic category was within the error bounds, and, hence, were equitable. The other technologies saw either poor overall performance or inequitable performance. For one of these, participants of the race Black/African American (B/AA) as well as those with darker skin tones (Monk scale 7/8/9/10) experienced higher false rejections. Finally, one technology demonstrated more favorable but inequitable performance for the Asian American and Pacific Islander (AAPI) demographic. This study confirms that it is necessary to evaluate products across demographic groups to fully understand the performance of remote identity verification technologies.

The spin structure functions of the proton and the deuteron were measured during the EG4 experiment at Jefferson Lab in 2006. Data were collected for longitudinally polarized electron scattering off longitudinally polarized NH$_3$ and ND$_3$ targets, for $Q^2$ values as small as 0.012 and 0.02 GeV$^2$, respectively, using the CEBAF Large Acceptance Spectrometer (CLAS). This is the archival paper of the EG4 experiment that summaries the previously reported results of the polarized structure functions $g_1$, $A_1F_1$, and their moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$, for both the proton and the deuteron. In addition, we report on new results on the neutron $g_1$ extracted by combining proton and deuteron data and correcting for Fermi smearing, and on the neutron moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$ formed directly from those of the proton and the deuteron. Our data are in good agreement with the Gerasimov-Drell-Hearn sum rule for the proton, deuteron, and neutron. Furthermore, the isovector combination was formed for $g_1$ and the Bjorken integral $\overline \Gamma_1^{p-n}$, and compared to available theoretical predictions. All of our results provide for the first time extensive tests of spin observable predictions from chiral effective field theory ($\chi$EFT) in a $Q^2$ range commensurate with the pion mass. They motivate further improvement in $\chi$EFT calculations from other approaches such as the lattice gauge method., Comment: 33 pages. 26 figures. Data table provided in supplementary material (30 pages)

Stars form in dense cores within molecular clouds and newly formed stars influence their natal environments. How stellar feedback impacts core properties and evolution is subject to extensive investigation. We performed a hierarchical clustering (dendrogram) analysis of a STARFORGE simulation modelling a giant molecular cloud to identify gas overdensities (cores) and study changes in their radius, mass, velocity dispersion, and virial parameter with respect to stellar feedback. We binned these cores on the basis of the fraction of gas affected by protostellar outflows, stellar winds, and supernovae and analysed the property distributions for each feedback bin. We find that cores that experience more feedback influence are smaller. Feedback notably enhances the velocity dispersion and virial parameter of the cores, more so than it reduces their radius. This is also evident in the linewidth-size relation, where cores in higher feedback bins exhibit higher velocities than their similarly sized pristine counterparts. We conclude that stellar feedback mechanisms, which impart momentum to the molecular cloud, simultaneously compress and disperse the dense molecular gas., Comment: Accepted for publication in A&A. 15 pages (1 of Appendix), 8 figures, 13 tables

Volunteer Edge-Cloud (VEC) computing has a significant potential to support scientific workflows in user communities contributing volunteer edge nodes. However, managing heterogeneous and intermittent resources to support machine/deep learning (ML/DL) based workflows poses challenges in resource governance for reliability, and confidentiality for model/data privacy protection. There is a need for approaches to handle the volatility of volunteer edge node availability, and also to scale the confidential data-intensive workflow execution across a large number of VEC nodes. In this paper, we present VECA, a reliable and confidential VEC resource clustering solution featuring three-fold methods tailored for executing ML/DL-based scientific workflows on VEC resources. Firstly, a capacity-based clustering approach enhances system reliability and minimizes VEC node search latency. Secondly, a novel two-phase, globally distributed scheduling scheme optimizes job allocation based on node attributes and using time-series-based Recurrent Neural Networks. Lastly, the integration of confidential computing ensures privacy preservation of the scientific workflows, where model and data information are not shared with VEC resources providers. We evaluate VECA in a Function-as-a-Service (FaaS) cloud testbed that features OpenFaaS and MicroK8S to support two ML/DL-based scientific workflows viz., G2P-Deep (bioinformatics) and PAS-ML (health informatics). Results from tested experiments demonstrate that our proposed VECA approach outperforms state-of-the-art methods; especially VECA exhibits a two-fold reduction in VEC node search latency and over 20% improvement in productivity rates following execution failures compared to the next best method., Comment: Accepted - IC2E 2024 Conference

NGC 6334 is a giant molecular cloud complex with elongated filamentary structure, harbouring OB-stars, HII regions and star forming clumps. To study the emission and velocity structure of the gas in the extended NGC 6334 region, we made observations of the 12CO and 13CO (J=3-2) lines with the APEX telescope. The data provides a spatial resolution of 20 arcsec (~0.16 pc) and sensitivity of ~0.4 K at a spectral resolution of 0.25 km/s. Our observations reveal in the extended NGC 6334 region a connected velocity coherent structure of ~-3.9 km/s over ~80 pc parallel to the galactic plane. The NGC 6334 complex has two connected velocity structures at velocities ~ -9.2 km/s (the bridge features) and ~-20 km/s (the Northern Filament, NGC 6334-NF). We observed local velocity fluctuations at smaller spatial scales along the filament tracing local density enhancement and infall. We investigated the 13CO emission and velocity structure around HII regions and found that most HII regions show signs of molecular gas dispersal from the center and intensity enhancement at their outer radii. Overall NGC 6334 exhibits sequential star formation from west to east. Located in the west, the GM-24 region exhibits bubbles within bubbles and is at a relatively evolved stage of star formation. The NGC 6334 central ridge is undergoing global gas infall and exhibits two gas bridge features possibly connected to the cloud-cloud collision scenario of the NGC 6334-NF and the NGC 6334 main gas component. The relatively quiescent eastern filament (EF1 - G352.1) is a hub-filament in formation which shows the kinematic signature of global gas infall onto the filament. Our observations highlight the important role of H II regions in shaping the molecular gas emission and velocity structure as well as the overall evolution of the molecular filaments in NGC 6334., Comment: 38 pages, 21 figures, Accepted for publication in A&A

This paper explores the analysis and implementation of the Virtual Oscillator Control (VOC) strategy for inverters aiming to enhance stability amidst the ever-increasing generation of renewable energy sources like solar PV. Key objectives include implementation and analysis of a Dead-Zone VOC (DZVOC) three-phase battery-inverter system with an additional voltage control loop, study of its stability and performance in an isolated micro-grid and exploration of their use alongside widely used grid following PV-inverter system. By modeling independent microgrids under various cases with scenarios: VOC inverters of varying capacities and VOC inverters in conjunction with PV inverters, this research addresses critical aspects of power-sharing, compatibility, response times, and fault ride-through potential, as well as improving the voltage droop profile of a general DZVOC control. The simulation is executed in MATLAB SIMULINK and validated with real-time simulation using the Typhoon-HIL 404., Comment: This paper has been accepted for presentation at the 16th IEEE Powercon, 2024

With the increasing use of nonlinear devices in both generation and consumption of power, it is essential that we develop accurate and quick control for active filters to suppress harmonics. Time delays between input and output are catastrophic for such filters which rely on real-time operation. Artificial Neural Networks (ANNs) are capable of modeling complex nonlinear systems through adjustments in their learned parameters. Once properly trained, they can produce highly accurate predictions at an instantaneous time frame. Leveraging these qualities, various complex control systems may be replaced or aided by neural networks to provide quick and precise responses. This paper proposes an ANN-based approach for the prediction of individual harmonic components using minimal inputs. By extracting and analyzing the nature of harmonic component magnitudes obtained from the survey of a particular area through real-time measurements, a sequential pattern in their occurrence is observed. Various neural network architectures are trained using the collected data and their performances are evaluated. The best-performing model, whose losses are minimal, is then used to observe the harmonic cancellation for multiple unseen cases through a simplified simulation in hardware-in-the-loop. These neural network structures, which produce instantaneous and accurate outputs, are effective in harmonic filtering.

Taken together, time-reversal and spatial inversion symmetries impose a two-fold spin degeneracy of the electronic states in crystals. In centrosymmetric materials, this degeneracy can be lifted by introducing magnetism, either via an externally applied field or through internal magnetization. However, a correlated alignment of spins, even in the paramagnetic phase, can lift the spin degeneracy of electronic states. Here, we report an in-depth study of the electronic band structure of the Eu-ternary pnictide EuZn2Sb2 through a combination of high-resolution angle-resolved photoemission spectroscopy measurements and first principles calculations. An analysis of the photoemission lineshapes over a range of incident photon energies and sample temperatures is shown to reveal the presence of band spin degeneracy-lifting in the paramagnetic phase. Our ARPES results are in good agreement with theoretical ferromagnetic-phase calculations, which indicates the importance of ferromagnetic fluctuations in the system. Through our calculations, we predict that spin-polarized bands in EuZn2Sb2 generate a single pair of Weyl nodes. Our observation of band-splitting in EuZn2Sb2 provides a key step toward realizing time-reversal symmetry breaking physics in the absence of long-range magnetic order., Comment: 13 pages, 7 figures

Studies of Galactic HII regions are of crucial importance for studying star formation and the evolution of the interstellar medium. Gaining an insight into their physical characteristics contributes to a more comprehensive understanding of these phenomena. The GLOSTAR project aims to provide a GLObal view on STAR formation in the Milky Way by performing an unbiased and sensitive survey. This is achieved by using the extremely wideband (4{-}8 GHz) C-band receiver of the Karl G. Jansky Very Large Array and the Effelsberg 100 m telescope. Using radio recombination lines observed in the GLOSTAR survey with the VLA in D-configuration with a typical line sensitivity of 1{\sigma} {\sim} 3.0 mJy beam{^-1} at {\sim} 5 km s{^-1} and an angular resolution of 25", we cataloged 244 individual Galactic HII regions and derived their physical properties. We examined the mid-infrared (MIR) morphology of these HII regions and find that a significant portion of them exhibit a bubble-like morphology in the GLIMPSE 8 {\mu}m emission. We also searched for associations with the dust continuum and sources of methanol maser emission, other tracers of young stellar objects, and find that 48\% and 14\% of our HII regions, respectively, are coextensive with those. We measured the electron temperature for a large sample of HII regions within Galactocentric distances spanning from 1.6 to 13.1 kpc and derived the Galactic electron temperature gradient as {\sim} 372 {\pm} 28 K kpc{^-1} with an intercept of 4248 {\pm} 161 K, which is consistent with previous studies., Comment: Accepted for publication in A&A

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

Industry 4.0 has witnessed the rise of complex robots fueled by the integration of Artificial Intelligence/Machine Learning (AI/ML) and Digital Twin (DT) technologies. While these technologies offer numerous benefits, they also introduce potential privacy and security risks. This paper surveys privacy attacks targeting robots enabled by AI and DT models. Exfiltration and data leakage of ML models are discussed in addition to the potential extraction of models derived from first-principles (e.g., physics-based). We also discuss design considerations with DT-integrated robotics touching on the impact of ML model training, responsible AI and DT safeguards, data governance and ethical considerations on the effectiveness of these attacks. We advocate for a trusted autonomy approach, emphasizing the need to combine robotics, AI, and DT technologies with robust ethical frameworks and trustworthiness principles for secure and reliable AI robotic systems., Comment: 10 pages, 3 figures, 1 table

Measuring Deeply Virtual Compton Scattering on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). Neutron targets play a complementary role to transversely polarized proton targets in the determination of the GPD $E$. This poorly known and poorly constrained GPD is essential to obtain the contribution of the quarks' angular momentum to the spin of the nucleon. DVCS on the neutron was measured for the first time selecting the exclusive final state by detecting the neutron, using the Jefferson Lab longitudinally polarized electron beam, with energies up to 10.6 GeV, and the CLAS12 detector. The extracted beam-spin asymmetries, combined with DVCS observables measured on the proton, allow a clean quark-flavor separation of the imaginary parts of the GPDs $H$ and $E$., Comment: 7 pages, 6 figures

The LnSbTe (Ln = Lanthanides) family, like isostructural ZrSiS type compounds, has emerged as a fertile playground for exploring the interaction of electronic correlations and magnetic ordering with the nodal line band topology. Here, we report a detailed electronic band structure investigation of TbSbTe, corroborated by electrical transport, thermodynamic, and magnetic studies. Temperature-dependent magnetic susceptibility and thermodynamic transport studies indicate the onset of antiferromagnetic ordering below TN = 5.1 K. The electronic band structure study, carried out with high-resolution angle-resolved photoemission spectroscopy (ARPES) measurements aided with density functional theory based first-principles calculations reveals presence of nodal lines in the GammaX high symmetry direction, forming a diamond-shaped nodal plane around Gamma high symmetry point. A strongly photon energy dependent nodal feature located at the X point of the surface Brillouin zone, indicating an extended nodal line along X R direction, is also observed. This study elucidates the intricate interplay among symmetry-protected band characteristics, the influence of spin orbit coupling, magnetism, and topological properties., Comment: 21 pages, 4 figures

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

Insurance claims processing involves multi-domain entities and multi-source data, along with a number of human-agent interactions. Use of Blockchain technology-based platform can significantly improve scalability and response time for processing of claims which are otherwise manually-intensive and time-consuming. However, the chaincodes involved within the processes that issue claims, approve or deny them as required, need to be formally verified to ensure secure and reliable processing of transactions in Blockchain. In this paper, we use a formal modeling approach to verify various processes and their underlying chaincodes relating to different stages in insurance claims processing viz., issuance, approval, denial, and flagging for fraud investigation by using linear temporal logic (LTL). We simulate the formalism on the chaincodes and analyze the breach of chaincodes via model checking.

In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the massive surge in industrial big data and advancement in sensing and computational technologies, data-driven Machinery Fault Diagnosis (MFD) solutions based on machine/deep learning approaches have been used ubiquitously in manufacturing. Timely and accurately identifying faulty machine signals is vital in industrial applications for which many relevant solutions have been proposed and are reviewed in many articles. Despite the availability of numerous solutions and reviews on MFD, existing works often lack several aspects. Most of the available literature has limited applicability in a wide range of manufacturing settings due to their concentration on a particular type of equipment or method of analysis. Additionally, discussions regarding the challenges associated with implementing data-driven approaches, such as dealing with noisy data, selecting appropriate features, and adapting models to accommodate new or unforeseen faults, are often superficial or completely overlooked. Thus, this survey provides a comprehensive review of the articles using different types of machine learning approaches for the detection and diagnosis of various types of machinery faults, highlights their strengths and limitations, provides a review of the methods used for condition-based analyses, comprehensively discusses the available machinery fault datasets, introduces future researchers to the possible challenges they have to encounter while using these approaches for MFD and recommends the probable solutions to mitigate those problems. The future research prospects are also pointed out for a better understanding of the field. We believe this article will help researchers and contribute to the further development of the field.

This paper presents BARKPLUG V.2, a Large Language Model (LLM)-based chatbot system built using Retrieval Augmented Generation (RAG) pipelines to enhance the user experience and access to information within academic settings.The objective of BARKPLUG V.2 is to provide information to users about various campus resources, including academic departments, programs, campus facilities, and student resources at a university setting in an interactive fashion. Our system leverages university data as an external data corpus and ingests it into our RAG pipelines for domain-specific question-answering tasks. We evaluate the effectiveness of our system in generating accurate and pertinent responses for Mississippi State University, as a case study, using quantitative measures, employing frameworks such as Retrieval Augmented Generation Assessment(RAGAS). Furthermore, we evaluate the usability of this system via subjective satisfaction surveys using the System Usability Scale (SUS). Our system demonstrates impressive quantitative performance, with a mean RAGAS score of 0.96, and experience, as validated by usability assessments.

Thermal boundary conductance (TBC) across metal diamond interfaces plays a critical role in the thermal management of future diamond based ultrawide bandgap semiconductor devices. Molecular dynamics is a sophisticated method to predict TBC but is limited by the lack of reliable potential describing metal diamond interfaces. In this work, we report the development of machine learning interatomic potentials and the prediction of TBCs of several technologically promising metal diamond interfaces using nonequilibrium molecular dynamics. The predicted TBCs of Al, Zr, Mo, and Au-diamond interfaces are approximately 316, 88, 52, and 55 MW/m2K, respectively, after quantum corrections. The corresponding thermal boundary resistances are equivalent to 0.8 {\mu}m thick of Al, 1.4 {\mu}m Mo, 0.3 {\mu}m Zr, and 5.3 {\mu}m Au, respectively. We also find that the conventional simple models, such as the acoustic mismatch model and diffuse mismatch model, even including the full-band phonon dispersion from first principles, largely misestimate the TBC values because of their inability to include inelastic transmission as well as interfacial structural and bonding details. The quantum-corrected TBC values for the metal diamond interfaces correlate well with the quantum corrected phonon specific heat of metals, instead of diamond. Additionally, our comparative analysis of Debye temperature and elastic modulus in these systems reveals that the former parameter correlates more strongly with the TBC than the latter. These low TBC values need to be considered in future diamond based semiconductor devices., Comment: 20 pages, 6 figures

Cr$_2$Te$_3$ is a ferromagnetic, quasi-two-dimensional layered material with perpendicular magnetic anisotropy, strong spin-orbit coupling, and non-trivial band topology. The non-trivial topology results in an intrinsic anomalous Hall conductivity (AHC) that switches sign under filling and biaxial strain. Thin films can exhibit half metallicity. Using density functional theory combined with maximally localized Wannier functions, we reveal the physical origins of the sensitivity of the sign of the AHC to strain and filling, and we determine the effect of surface termination on the half metallicity. We find that thin films terminated on the Te layers are the most energetically stable, but only the thin films terminated on both sides with the partially occupied Cr layers are half metals. In bulk Cr$_2$Te$_3$, the sensitivity of the sign of the AHC to strain and filling results from the complex Fermi surface comprised of three bands. Filling of local minima and bands near anti-crossings alters the local Berry curvature consistent with the negative to positive switching of the AHC. Similarly, strain depopulates a local minimum, shifts a degenerate point closer to the Fermi energy, and causes two spin-orbit split bands to reverse their order. These findings provide a physical understanding of the evolution of the Berry phase, AHC, and half-metallicity in Cr$_2$Te$_3$., Comment: 14 pages, 11 figures

We use a concerted theory-experiment effort to investigate the formation of chiral real space spin texture when the archetypal Dirac semimetal Cd$_3$As$_2$ is interfaced with In$_{1-x}$Mn$_x$As, a ferromagnetic semiconductor with perpendicular magnetic anisotropy. Our calculations reveal a nonzero off-diagonal spin susceptibility in the Cd$_3$As$_2$ layer due to the Rashba spin-orbit coupling from broken inversion symmetry. This implies the presence of a Dzyaloshinskii-Moriya interaction between local moments in the In$_{1-x}$Mn$_x$As layer, mediated by Dirac electrons in the vicinal Cd$_3$As$_2$ layer, potentially creating the conditions for a real space chiral spin texture. Using electrical magnetoresistance measurements at low temperature, we observe an emergent excess contribution to the transverse magneto-resistance whose behavior is consistent with a topological Hall effect arising from the formation of an interfacial chiral spin texture. This excess Hall voltage varies with gate voltage, indicating a promising electrostatically-tunable platform for understanding the interplay between the helical momentum space states of a Dirac semimetal and chiral real space spin textures in a ferromagnet.

Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses. However, their lack of domain-specific knowledge limits their applicability in healthcare settings, where contextual and comprehensive responses are vital. To address this challenge and enable the generation of patient-centric responses that are contextually relevant and comprehensive, we propose MedInsight:a novel retrieval augmented framework that augments LLM inputs (prompts) with relevant background information from multiple sources. MedInsight extracts pertinent details from the patient's medical record or consultation transcript. It then integrates information from authoritative medical textbooks and curated web resources based on the patient's health history and condition. By constructing an augmented context combining the patient's record with relevant medical knowledge, MedInsight generates enriched, patient-specific responses tailored for healthcare applications such as diagnosis, treatment recommendations, or patient education. Experiments on the MTSamples dataset validate MedInsight's effectiveness in generating contextually appropriate medical responses. Quantitative evaluation using the Ragas metric and TruLens for answer similarity and answer correctness demonstrates the model's efficacy. Furthermore, human evaluation studies involving Subject Matter Expert (SMEs) confirm MedInsight's utility, with moderate inter-rater agreement on the relevance and correctness of the generated responses.

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field.

Commercial wearables from Fitbit, Garmin, and Whoop have recently introduced real-time notifications based on detecting changes in physiological responses indicating potential stress. In this paper, we investigate how these new capabilities can be leveraged to improve stress management. We developed a smartwatch app, a smartphone app, and a cloud service, and conducted a 100-day field study with 122 participants who received prompts triggered by physiological responses several times a day. They were asked whether they were stressed, and if so, to log the most likely stressor. Each week, participants received new visualizations of their data to self-reflect on patterns and trends. Participants reported better awareness of their stressors, and self-initiating fourteen kinds of behavioral changes to reduce stress in their daily lives. Repeated self-reports over 14 weeks showed reductions in both stress intensity (in 26,521 momentary ratings) and stress frequency (in 1,057 weekly surveys)., Comment: In CHI '24 Proceedings of the CHI Conference on Human Factors in Computing Systems Honolulu, HI, USA

In an increasingly interconnected world, where information is the lifeblood of modern society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital systems and information. Additionally, cyber-attacks differ depending on the objective and evolve rapidly to disguise defensive systems. However, a typical cyber-attack demonstrates a series of stages from attack initiation to final resolution, called an attack life cycle. These diverse characteristics and the relentless evolution of cyber attacks have led cyber defense to adopt modern approaches like Machine Learning to bolster defensive measures and break the attack life cycle. Among the adopted ML approaches, Graph Neural Networks have emerged as a promising approach for enhancing the effectiveness of defensive measures due to their ability to process and learn from heterogeneous cyber threat data. In this paper, we look into the application of GNNs in aiding to break each stage of one of the most renowned attack life cycles, the Lockheed Martin Cyber Kill Chain. We address each phase of CKC and discuss how GNNs contribute to preparing and preventing an attack from a defensive standpoint. Furthermore, We also discuss open research areas and further improvement scopes., Comment: 35 pages, 9 figures, 8 tables

Tender notices are usually sought by most of the companies at regular intervals as a means for obtaining the contracts of various projects. These notices consist of all the required information like description of the work, period of construction, estimated amount of project, etc. In the context of Nepal, tender notices are usually published in national as well as local newspapers. The interested bidders should search all the related tender notices in newspapers. However, it is very tedious for these companies to manually search tender notices in every newspaper and figure out which bid is best suited for them. This project is built with the purpose of solving this tedious task of manually searching the tender notices. Initially, the newspapers are downloaded in PDF format using the selenium library of python. After downloading the newspapers, the e-papers are scanned and tender notices are automatically extracted using a neural network. For extraction purposes, different architectures of CNN namely ResNet, GoogleNet and Xception are used and a model with highest performance has been implemented. Finally, these extracted notices are then published on the website and are accessible to the users. This project is helpful for construction companies as well as contractors assuring quality and efficiency. This project has great application in the field of competitive bidding as well as managing them in a systematic manner.

Kagome materials have attracted enormous research interest recently owing to its diverse topological phases and manifestation of electronic correlation due to its inherent geometric frustration. Here, we report the electronic structure of a new distorted kagome metal NdTi3Bi4 using a combination of angle resolved photoemission spectroscopy (ARPES) measurements and density functional theory (DFT) calculations. We discover the presence of two at bands which are found to originate from the kagome structure formed by Ti atoms with major contribution from Ti dxy and Ti dx2-y2 orbitals. We also observed multiple van Hove singularities (VHSs) in its electronic structure, with one VHS lying near the Fermi level EF. In addition, the presence of a surface Dirac cone at the G point and a linear Dirac-like state at the K point with its Dirac node lying very close to the EF indicates its topological nature. Our findings reveal NdTi3Bi4 as a potential material to understand the interplay of topology, magnetism, and electron correlation., Comment: 7 pages, 4 figures

The family of transition metal dipnictides (TMDs) has been of theoretical and experimental interest because this family hosts topological states and extremely large magnetoresistance (MR). Recently, TaAs2, a member of this family, has been predicted to support a topological crystalline insulating state. Here, by using high resolution. Angle resolved photoemission spectroscopy (ARPES), we reveal both closed and open pockets in the metallic Fermi surface and linearly dispersive bands on the (201) surface, along with the presence of extreme MR observed from magneto-transport measurements. A comparison of the ARPES results with first-principles computations show that the linearly dispersive bands on the measured surface of TaAs2 are trivial bulk bands. The absence of symmetry-protected surface state on the (201) surface indicates its topologically dark nature. The presence of open Fermi surface features suggests that the open orbit fermiology could contribute to the extremely large MR of TaAs., Comment: 13 pages, 5 figures

Building extraction $-$ needed for inventory management and planning of urban environment $-$ is affected by the misalignment between labels and off-nadir source imagery in training data. Teacher-Student learning of noise-tolerant convolutional neural networks (CNNs) is the existing solution, but the Student networks typically have lower accuracy and cannot surpass the Teacher's performance. This paper proposes a supervised domain adaptation (SDA) of encoder-decoder networks (EDNs) between noisy and clean datasets to tackle the problem. EDNs are configured with high-performing lightweight encoders such as EfficientNet, ResNeSt, and MobileViT. The proposed method is compared against the existing Teacher-Student learning methods like knowledge distillation (KD) and deep mutual learning (DML) with three newly developed datasets. The methods are evaluated for different urban buildings (low-rise, mid-rise, high-rise, and skyscrapers), where misalignment increases with the increase in building height and spatial resolution. For a robust experimental design, 43 lightweight CNNs, five optimisers, nine loss functions, and seven EDNs are benchmarked to obtain the best-performing EDN for SDA. The SDA of the best-performing EDN from our study significantly outperformed KD and DML with up to 0.943, 0.868, 0.912, and 0.697 F1 scores in the low-rise, mid-rise, high-rise, and skyscrapers respectively. The proposed method and the experimental findings will be beneficial in training robust CNNs for building extraction., Comment: This work has been submitted to Elsevier for possible publication

We studied the electronic structure of a layered antiferromagnetic metal, EuSnP, in the paramagnetic and in the antiferromagnetic phase using angle resolved photoemission spectroscopy (ARPES) alongside density functional theory (DFT) based first principles calculations. The temperature dependence of the magnetic susceptibility measurements exhibits an antiferromagnetic transition at a Neel temperature of 21 K. Employing high resolution ARPES, the valence band structure was measured at several temperatures above and below the Neel temperature, which produced identical spectra independent of temperature. Through analysis of the ARPES results presented here, we attribute the temperature independent spectra to the weak coupling of the Sn, and P conduction electrons with Eu 4f states., Comment: 8 pages, 4 figures