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Face recognition technology has dramatically transformed the landscape of security, surveillance, and authentication systems, offering a user-friendly and non-invasive biometric solution. However, despite its significant advantages, face recognition systems face increasing threats from physical and digital spoofing attacks. Current research typically treats face recognition and attack detection as distinct classification challenges. This approach necessitates the implementation of separate models for each task, leading to considerable computational complexity, particularly on devices with limited resources. Such inefficiencies can stifle scalability and hinder performance. In response to these challenges, this paper introduces an innovative unified model designed for face recognition and detection of physical and digital attacks. By leveraging the advanced Swin Transformer backbone and incorporating HiLo attention in a convolutional neural network framework, we address unified face recognition and spoof attack detection more effectively. Moreover, we introduce augmentation techniques that replicate the traits of physical and digital spoofing cues, significantly enhancing our model robustness. Through comprehensive experimental evaluation across various datasets, we showcase the effectiveness of our model in unified face recognition and spoof detection. Additionally, we confirm its resilience against unseen physical and digital spoofing attacks, underscoring its potential for real-world applications.

Ripples, curvature, and grain boundaries in graphene can significantly alter its thermal conductivity (TC), which is paramount in various applications including thermal management etc. In this study, we conducted extensive equilibrium MD simulations and used the Green-Kubo method to elucidate the impact of ripples on the TC of graphene and the impact of curvature and tilt angles characterizing a grain boundary (GB) on the TC of polycrystalline graphene. Although tensile and compressive strains have been known to control the amount of ripples, the effects of strain and ripple on the TC have not been decoupled. With the help of Green-Kubo simulations on larger graphene samples and simulations based on the spectral energy density method on smaller samples without ripples, we show that both samples show an approximately 30% decrease in TC between tensile strains 3% and 10% when ripples become negligible even in the larger sample. Between 0 and 3% strain, when both ripples and strains are present in the larger sample, we decouple the effect of ripples from strain on the TC and show that ripples alone reduce the TC of the larger unstrained sample by approximately 61%. We also present a unique technique to introduce curvatures in the graphene sheet containing GBs by performing MD simulations under NPT ensembles with a constant pressure of 200 bar along the x-axis. Our analysis suggests that the Green-Kubo TC linearly decreases with curvature; however, the rate of decrease depends on the tilt angles of the grain boundary. An analysis of the TC in graphene samples with GBs of varying tilt angles reveals that the TC strongly depends on the tilt angles. Thus, our research underscores the pivotal role of ripple, curvature, and GB in modulating the thermal conductivity of graphene.

With the increasing use of plastic, the challenges associated with managing plastic waste have become more challenging, emphasizing the need of effective solutions for classification and recycling. This study explores the potential of deep learning, focusing on convolutional neural networks (CNNs) and object detection models like YOLO (You Only Look Once), to tackle this issue using the WaDaBa dataset. The study shows that YOLO- 11m achieved highest accuracy (98.03%) and mAP50 (0.990), with YOLO-11n performing similarly but highest mAP50(0.992). Lightweight models like YOLO-10n trained faster but with lower accuracy, whereas MobileNet V2 showed impressive performance (97.12% accuracy) but fell short in object detection. Our study highlights the potential of deep learning models in transforming how we classify plastic waste, with YOLO models proving to be the most effective. By balancing accuracy and computational efficiency, these models can help to create scalable, impactful solutions in waste management and recycling., Comment: 18 pages, 12 figures

This study examines the impact of monetary factors on the conversion of farmland to renewable energy generation, specifically solar and wind, in the context of expanding U.S. energy production. We propose a new econometric method that accounts for the diverse circumstances of landowners, including their unordered alternative land use options, non-monetary benefits from farming, and the influence of local regulations. We demonstrate that identifying the cross elasticity of landowners' farming income in relation to the conversion of farmland to renewable energy requires an understanding of their preferences. By utilizing county legislation that we assume to be shaped by land-use preferences, we estimate the cross-elasticities of farming income. Our findings indicate that monetary incentives may only influence landowners' decisions in areas with potential for future residential development, underscoring the importance of considering both preferences and regulatory contexts.

Electron-magnon coupling reveals key insights into the interfacial properties between non-magnetic metals and magnetic insulators, influencing charge transport and spin dynamics. Here, we present temperature-dependent Raman spectroscopy and magneto-transport measurements of few-layer graphene (FLG)/antiferromagnetic FePS\(_3\) heterostructures. The magnon mode in FePS\(_3\) softens below 40 K, and effective magnon stiffness decreases with cooling. Magnetotransport measurements show that FLG exhibits negative magnetoresistance (MR) in the heterostructure at low fields (\(\pm 0.2 \, \text{T}\)), persisting up to 100 K; beyond this, MR transitions to positive. Notably, as layer thickness decreases, the coupling strength at the interface reduces, leading to a suppression of negative MR. Additionally, magnetodielectric measurements in the FLG/FePS\(_3\)/FLG heterostructure show an upturn at temperatures significantly below ($T_\text{N}$), suggesting a role for the magnon mode in capacitance, as indicated by hybridization between magnon and phonon bands in pristine FePS\(_3\) \textit{via} magnetoelastic coupling.

Interface-type resistive switching (RS) devices with lower operation current and more reliable switching repeatability exhibits great potential in the applications for data storage devices and ultra-low-energy computing. However, the working mechanism of such interface-type RS devices are much less studied compared to that of the filament-type devices, which hinders the design and application of the novel interface-type devices. In this work, we fabricate a metal/TiOx/TiN/Si (001) thin film memristor by using a one-step pulsed laser deposition. In situ transmission electron microscopy (TEM) imaging and current-voltage (I-V) characteristic demonstrate that the device is switched between high resistive state (HRS) and low resistive state (LRS) in a bipolar fashion with sweeping the applied positive and negative voltages. In situ scanning transmission electron microscopy (STEM) experiments with electron energy loss spectroscopy (EELS) reveal that the charged defects (such as oxygen vacancies) can migrate along the intrinsic grain boundaries of TiOx insulating phase under electric field without forming obvious conductive filaments, resulting in the modulation of Schottky barriers at the metal/semiconductor interfaces. The fundamental insights gained from this study presents a novel perspective on RS processes and opens up new technological opportunities for fabricating ultra-low-energy nitride-based memristive devices.

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pioneers a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient), alongside improvements in mindfulness (7%) and self-reflection (6%). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being., Comment: arXiv admin note: text overlap with arXiv:2404.00487

Using spectral energy density method, we predict the phonon scattering mean lifetimes of polycrystalline graphene (PC-G) having polycrystallinity only along $\rm{x}$-axis with seven different misorientation (tilt) angles at room temperature. Contrary to other studies on PC-G samples, our results indicate strong dependence of the thermal conductivity (TC) on the tilt angles. We also show that the square of the group velocity components along $\rm{x}$ and $\rm{y}$ axes and the phonon lifetimes are uncorrelated and the phonon density of states are almost the same for all samples with different tilt angles. Further, a distribution of the group velocity component along $\rm{x}$ or $\rm{y}$ axis as function of normal frequency is found to be exponentially decaying whereas that of phonon lifetime showed piecewise constant function behavior with respect to frequency. We provide parameters for these distribution functions and suggest another measure of the TC based on these distributions. Finally, we perform a size-dependent analysis for two tilt angles, $21.78^\circ$ and $32.20^\circ$, and find that bulk TC components decrease by around 34% to 62% in comparison to the bulk TC values of the pristine graphene. Our analysis reveals intriguing insights into the interplay between grain orientation, phonon scattering and thermal conductivity in graphene.

A cognitive fully adaptive radar (CoFAR) adapts its behavior on its own within a short period of time in response to changes in the target environment. For the CoFAR to function properly, it is critical to understand its operating environment through estimation of the clutter channel impulse response (CCIR). In general, CCIR is sparse but prior works either ignore it or estimate the CCIR by imposing sparsity as an explicit constraint in their optimization problem. In this paper, contrary to these studies, we develop covariance-free Bayesian learning (CoFBL) techniques for estimating sparse CCIR in a CoFAR system. In particular, we consider a multiple measurement vector scenario and estimate a simultaneously sparse (row sparse) CCIR matrix. Our CoFBL framework reduces the complexity of conventional sparse Bayesian learning through the use of the diagonal element estimation rule and conjugate gradient descent algorithm. We show that the framework is applicable to various forms of CCIR sparsity models: group, joint, and joint-cum-group. We evaluate our method through numerical experiments on a data set generated using RFView, a high-fidelity modeling and simulation tool. We derive Bayesian Cram\'{e}r-Rao bounds for the various considered scenarios to benchmark the performance of our algorithms. Our results demonstrate that the proposed CoFBL-based approaches perform better than the existing popular approaches such as multiple focal underdetermined system solver and simultaneous orthogonal matching pursuit.

We introduce two global-in-time domain decomposition methods, namely the Steklov-Poincare method and the Robin method, for solving a fluid-structure interaction system. These methods allow us to formulate the coupled system as a space-time interface problem and apply iterative algorithms directly to the evolutionary problem. Each time-dependent subdomain problem is solved independently, which enables the use of different time discretization schemes and time step sizes in the subsystems. This leads to an efficient way of simulating time-dependent phenomena. We present numerical tests for both non-physical and physical problems, with various mesh sizes and time step sizes to demonstrate the accuracy and efficiency of the proposed methods.

Electric field-induced giant resistive switching triggered by insulator-to-metal transition (IMT) is one of the promising approaches for developing a new class of electronics often referred to as Mottronics. Achieving this resistive switching by minimal external field at room temperature is of paramount research and technological interest. Mott-IMT is often associated with structural modification, which is very important for optoelectronic and actuator applications. Here, we report a giant resistive switching of about 900 % at room temperature in disordered polycrystalline V2O3-Si thin film stabilized at the IMT phase boundary and associated structural transformation under a small electric field. The increase of electron population in the a1g band under the field is responsible for the Mott gap collapse that drives the structural transition. Furthermore, we also fabricated a room temperature Mott-FET with a channel ON/OFF resistive ratio of about 15. This study provides a fundamental mechanism of the Mott-IMT in V2O3 as well as its device applications., Comment: 16 pages, 5 figures

The introduction of transformers has been an important breakthrough for AI research and application as transformers are the foundation behind Generative AI. A promising application domain for transformers is cybersecurity, in particular the malware domain analysis. The reason is the flexibility of the transformer models in handling long sequential features and understanding contextual relationships. However, as the use of transformers for malware analysis is still in the infancy stage, it is critical to evaluate, systematize, and contextualize existing literature to foster future research. This Systematization of Knowledge (SoK) paper aims to provide a comprehensive analysis of transformer-based approaches designed for malware analysis. Based on our systematic analysis of existing knowledge, we structure and propose taxonomies based on: (a) how different transformers are adapted, organized, and modified across various use cases; and (b) how diverse feature types and their representation capabilities are reflected. We also provide an inventory of datasets used to explore multiple research avenues in the use of transformers for malware analysis and discuss open challenges with future research directions. We believe that this SoK paper will assist the research community in gaining detailed insights from existing work and will serve as a foundational resource for implementing novel research using transformers for malware analysis.

Taking cue from the recent experimental realization of metallic phases in Kagome materials we report the low temperature signatures and thermal scales of Kagome metals and insulators, determined in the framework of the Kagome Hubbard model, using a non perturbative numerical approach. In contrast to the existing consensus we establish a flat band localized insulator in the weak coupling regime which crosses over to a non Fermi liquid (NFL) metal at intermediate coupling, followed by a first order metal-Mott insulator transition in the strong coupling regime. We provide the first accurate estimates of the thermal scales of this model and analyze the NFL phases in terms of resilient quasiparticles and short range magnetic correlations. With our unprecedented access to the low temperature phases and sufficiently large system sizes, we provide the essential benchmarks for the prospective experiments on the Kagome metal and insulators in terms of their thermodynamic, spectroscopic and transport signatures., Comment: 12 pages, 8 figures

As the world continues to face the challenges of climate change, it is crucial to consider the environmental impact of the technologies we use. In this study, we investigate the performance and computational carbon emissions of various transfer learning models for garbage classification. We examine the MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models. Our findings indicate that the EfficientNetV2 family achieves the highest accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model requires more time and produces higher carbon emissions. ResNet50 outperforms ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger carbon footprint. We conclude that EfficientNetV2S is the most sustainable and accurate model with 96.41% accuracy. Our research highlights the significance of considering the ecological impact of machine learning models in garbage classification., Comment: 11 pages, 9 figures

We propose a novel self-supervised framework for retargeting non-parameterized 3D garments onto 3D human avatars of arbitrary shapes and poses, enabling 3D virtual try-on (VTON). Existing self-supervised 3D retargeting methods only support parametric and canonical garments, which can only be draped over parametric body, e.g. SMPL. To facilitate the non-parametric garments and body, we propose a novel method that introduces Isomap Embedding based correspondences matching between the garment and the human body to get a coarse alignment between the two meshes. We perform neural refinement of the coarse alignment in a self-supervised setting. Further, we leverage a Laplacian detail integration method for preserving the inherent details of the input garment. For evaluating our 3D non-parametric garment retargeting framework, we propose a dataset of 255 real-world garments with realistic noise and topological deformations. The dataset contains $44$ unique garments worn by 15 different subjects in 5 distinctive poses, captured using a multi-view RGBD capture setup. We show superior retargeting quality on non-parametric garments and human avatars over existing state-of-the-art methods, acting as the first-ever baseline on the proposed dataset for non-parametric 3D garment retargeting.

Developing novel lead-free ferroelectric materials is crucial for next-generation microelectronic technologies that are energy efficient and environment friendly. However, materials discovery and property optimization are typically time-consuming due to the limited throughput of traditional synthesis methods. In this work, we use a high-throughput combinatorial synthesis approach to fabricate lead-free ferroelectric superlattices and solid solutions of (Ba0.7Ca0.3)TiO3 (BCT) and Ba(Zr0.2Ti0.8)O3 (BZT) phases with continuous variation of composition and layer thickness. High-resolution X-ray diffraction (XRD) and analytical scanning transmission electron microscopy (STEM) demonstrate high film quality and well-controlled compositional gradients. Ferroelectric and dielectric property measurements identify the optimal property point achieved at the morphotropic phase boundary (MPB) with a composition of 48BZT-52BCT. Displacement vector maps reveal that ferroelectric domain sizes are tunable by varying {BCT-BZT}N superlattice geometry. This high-throughput synthesis approach can be applied to many other material systems to expedite new materials discovery and properties optimization, allowing for the exploration of a large area of phase space within a single growth., Comment: submitted

Google's BBR (Bottleneck Bandwidth and Round-trip Propagation Time) approach is used to enhance internet network transmission. It is particularly intended to efficiently handle enormous amounts of data. Traditional TCP (Transmission Control Protocol) algorithms confront the most difficulty in calculating the proper quantity of data to send in order to prevent congestion and bottlenecks. This wastes bandwidth and causes network delays. BBR addresses this issue by adaptively assessing the available bandwidth (also known as bottleneck bandwidth) along the network channel and calculating the round-trip time (RTT) between the sender and receiver. Although when several flows compete for bandwidth, BBR may supply more bandwidth to one flow at the expense of another, resulting in unequal resource distribution. This paper proposes to integrate machine learning with BBR to enhance fairness in resource allocation. This novel strategy can improve bandwidth allocation and provide a more equal distribution of resources among competing flows by using historical BBR data to train an ML model. Further we also implemented a classifier model that is graphic neural network in the congestion control method.

This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a "pen-and-ink" style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design., Comment: Computer Graphics International 2023

As the global population continues to expand, the demand for natural resources increases. Unfortunately, human activities account for 23% of greenhouse gas emissions. On a positive note, remote sensing technologies have emerged as a valuable tool in managing our environment. These technologies allow us to monitor land use, plan urban areas, and drive advancements in areas such as agriculture, climate change mitigation, disaster recovery, and environmental monitoring. Recent advances in AI, computer vision, and earth observation data have enabled unprecedented accuracy in land use mapping. By using transfer learning and fine-tuning with RGB bands, we achieved an impressive 99.19% accuracy in land use analysis. Such findings can be used to inform conservation and urban planning policies., Comment: 10 pages, 7 figures