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Glycated hemoglobin (HbA1c) is the most important factor in diabetes control. Since HbA1c reflects the average blood glucose level over the preceding three months, it is unaffected by the patient's activity level or diet before the test. Noninvasive HbA1c measurement reduces both the pain and complications associated with fingertip piercing to collect blood. Photoplethysmography is helpful for measuring HbA1c without blood samples. Herein, only two wavelengths (615 and 525 nm) were used to estimate HbA1c noninvasively, where two different ratio calibrations were applied and performances were compared to a work that uses three wavelengths. For the fingertip type, the Pearson r values for HbA1c estimates are 0.896 and 0.905 considering ratio calibrations for blood vessel and whole finger models, respectively. Using another value (HbA1c) calibration in addition to ratio calibrations, we can improve this performance, such that the Pearson r values of HbA1c levels are 0.929 and 0.930 for blood vessel and whole finger models, respectively. In the previous study using three wavelengths, the Pearson r values were 0.916 and 0.959 for the blood-vessel and whole-finger models, respectively. Here, the RCF of SpO2 estimation is 0.986 when SpO2 ratio calibration is applied, while in the previous study, the RCF values of SpO2 estimation were 0.983 and 0.986 for the blood-vessel and whole finger models, respectively. Thus, we show that HbA1c estimation using only two wavelengths has comparable performance to previous studies., Comment: 17 pages, 8 figures

Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for predicting material properties. The present study proposes a graph-based representation for modeling medium-entropy alloys (MEAs). Hybrid Monte-Carlo molecular dynamics (MC/MD) simulations are employed to achieve thermally stable structures across various annealing temperatures in an MEA. These simulations generate dump files and potential energy labels, which are used to construct graph representations of the atomic configurations. Edges are created between each atom and its 12 nearest neighbors without incorporating explicit edge features. These graphs then serve as input for a Graph Convolutional Neural Network (GCNN) based ML model to predict the system's potential energy. The GCNN architecture effectively captures the local environment and chemical ordering within the MEA structure. The GCNN-based ML model demonstrates strong performance in predicting potential energy at different steps, showing satisfactory results on both the training data and unseen configurations. Our approach presents a graph-based modeling framework for MEAs and high-entropy alloys (HEAs), which effectively captures the local chemical order (LCO) within the alloy structure. This allows us to predict key material properties influenced by LCO in both MEAs and HEAs, providing deeper insights into how atomic-scale arrangements affect the properties of these alloys., Comment: 28 pages, 9 figures

This study introduces a dual-band plasmonic absorber designed for simultaneous sensing applications in the near-infrared (NIR) and mid-infrared (MIR) regions. The absorber, composed of silver nanostructures on a metal plate with a dielectric spacer, exhibits a combination of localized and gap surface plasmon resonances, resulting in two distinct absorption peaks in theoretical analysis based on the FDTD method. Numerical simulations also validate the sensor's high refractive index sensitivity, enabling the detection of biomolecules, proteins, viruses, and various solutes in aqueous solutions. The absorber demonstrates significant resonance shifts, making it a promising candidate for environmental monitoring, medical diagnostics, and chemical sensing., Comment: 13 pages, 10 figures, submitted to Journal of Optics and Laser Technology for review

The kagome motif is a versatile platform for condensed matter physics, hosting rich interactions between magnetic, electronic, and structural degrees of freedom. In recent years, the discovery of a charge density wave (CDW) in the AV$_3$Sb$_5$ superconductors and structurally-derived bond density waves in FeGe and ScV$_6$Sn$_6$ have stoked the search for new kagome platforms broadly exhibiting density wave (DW) transitions. In this work, we evaluate the known AM$_6$X$_6$ chemistries and construct a stability diagram that summarizes the structural relationships between the $\approx$125 member family. Subsequently we introduce our discovery of the broader LnNb$_6$Sn$_6$ (Ln:Ce-Nd,Sm,Gd-Tm,Lu,Y) family of kagome metals and an analogous DW transition in LuNb$_6$Sn$_6$. Our X-ray scattering measurements clearly indicate a (1/3, 1/3, 1/3) ordering wave vector ($\sqrt{3}\times\sqrt{3}\times3$ superlattice) and diffuse scattering on half-integer $L$-planes. Our analysis of the structural data supports the ``rattling mode'' DW model proposed for ScV$_6$Sn$_6$ and paints a detailed picture of the steric interactions between the rare-earth filler element and the host Nb-Sn kagome scaffolding. We also provide a broad survey of the magnetic properties within the HfFe$_6$Ge$_6$-type LnNb$_6$Sn$_6$ members, revealing a number of complex antiferromagnetic and metamagnetic transitions throughout the family. This work integrates our new LnNb$_6$Sn$_6$ series of compounds into the broader AM$_6$X$_6$ family, providing new material platforms and forging a new route forward at the frontier of kagome metal research.

Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex relationships between human behavior and local contexts in order to identify high-risk groups and strategically allocate limited resources. Traditional approaches to these classes of problems often entail developing manually curated, task-specific features and models to represent human behavior and the natural and built environment, which can be challenging to adapt to new, or even, related tasks. To address this, we introduce a Population Dynamics Foundation Model (PDFM) that aims to capture the relationships between diverse data modalities and is applicable to a broad range of geospatial tasks. We first construct a geo-indexed dataset for postal codes and counties across the United States, capturing rich aggregated information on human behavior from maps, busyness, and aggregated search trends, and environmental factors such as weather and air quality. We then model this data and the complex relationships between locations using a graph neural network, producing embeddings that can be adapted to a wide range of downstream tasks using relatively simple models. We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements. The approach achieves state-of-the-art performance on all 27 geospatial interpolation tasks, and on 25 out of the 27 extrapolation and super-resolution tasks. We combined the PDFM with a state-of-the-art forecasting foundation model, TimesFM, to predict unemployment and poverty, achieving performance that surpasses fully supervised forecasting. The full set of embeddings and sample code are publicly available for researchers., Comment: 28 pages, 16 figures, preprint; v2: updated github url

We present an innovative approach for solving mathematical problems in Bengali, developed for the DL Sprint 3.0 BUET CSE Fest 2024 Competition. Our method uses advanced deep learning models, notably the Qwen 2.5 series, with improvements made through prompt engineering, model quantization, and Tool Integrated Reasoning (TIR) to handle complex calculations. Initially, we explored various model architectures, including fine-tuned Mistral and quantized Qwen models, refining them with translation techniques, Retrieval-Augmented Generation (RAG), and custom dataset curation. Manual hyperparameter tuning optimized parameters like temperature and top-p to enhance model adaptability and accuracy. Removal of RAG and parameter adjustments further improved robustness. Our approach highlights the potential of advanced NLP techniques in solving Bengali mathematical problems.

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

External debt has been identified as the most liable to cause financial crises in developing countries in Asia and Latin America. One recent example of near bankruptcy in Sri Lanka has raised serious concerns among economists about how to anticipate and tackle external debt-related problems. Bangladesh also faced a decline in export income and a sharp rise in import prices amid the aforementioned global shocks. Nevertheless, the international reserves of Bangladesh have never fallen to the level they did in Sri Lanka. This paper examines the relationship between remittance inflows, international reserves, and external debt in Bangladesh and Sri Lanka. Econometric estimations reveal that remittance affects external debt both directly and through international reserves in Bangladesh. The existence of a Dutch Disease effect in the remittance inflows-international reserves relationship has also been confirmed in Bangladesh. We also show that Bangladesh uses international reserves as collateral to obtain more external borrowing, while Sri Lanka, like many other developing countries, accumulates international reserves to deplete in "bad times." Remittances can be seen as one of the significant factors preventing Bangladesh from becoming a sovereign defaulter, whereas Sri Lanka faced that fate.

Collaborative robots are increasingly deployed alongside humans in factories, hospitals, schools, and other domains to enhance teamwork and efficiency. Systems that seamlessly integrate humans and robots into cohesive teams for coordinated and efficient task execution are needed, enabling studies on how robot collaboration policies affect team performance and teammates' perceived fairness, trust, and safety. Such a system can also be utilized to study the impact of a robot's normative behavior on team collaboration. Additionally, it allows for investigation into how the legibility and predictability of robot actions affect human-robot teamwork and perceived safety and trust. Existing systems are limited, typically involving one human and one robot, and thus require more insight into broader team dynamics. Many rely on games or virtual simulations, neglecting the impact of a robot's physical presence. Most tasks are turn-based, hindering simultaneous execution and affecting efficiency. This paper introduces CoHRT (Collaboration System for Human-Robot Teamwork), which facilitates multi-human-robot teamwork through seamless collaboration, coordination, and communication. CoHRT utilizes a server-client-based architecture, a vision-based system to track task environments, and a simple interface for team action coordination. It allows for the design of tasks considering the human teammates' physical and mental workload and varied skill labels across the team members. We used CoHRT to design a collaborative block manipulation and jigsaw puzzle-solving task in a team of one Franka Emika Panda robot and two humans. The system enables recording multi-modal collaboration data to develop adaptive collaboration policies for robots. To further utilize CoHRT, we outline potential research directions in diverse human-robot collaborative tasks., Comment: 8 Pages, Robotics Science and Systems (RSS), Safety and Normative Behaviors in Human-Robot Interaction Workshop 2024 (accepted), https://sites.google.com/view/safe-hri/accepted-papers

Scalar Non-Standard Interactions (SNSI) in neutrinos can arise when a scalar mediator couples to both neutrinos and standard model fermions. This beyond the Standard Model (BSM) scenario is particularly interesting as the SNSI contribution appears as a density-dependent perturbation to the neutrino mass, rather than appearing as a matter-induced potential, and the neutrino oscillation probabilities uniquely depend on the absolute neutrino masses. In this work, we show the complex dependence of the SNSI contributions on the neutrino masses and discuss how the mass of the lightest neutrino would regulate any possible SNSI contribution in both mass ordering scenarios. We derive the analytic expressions for neutrino oscillation probabilities, employing the Cayley-Hamilton theorem, in the presence of diagonal elements of SNSI. The expressions are compact and shows explicit dependence on matter effects and the absolute neutrino masses. The analytic expressions calculated here allow us to obtain the dependence of the SNSI contribution on mass terms of the form $m_1 + m_2$, $m_2 - m_1$, $m_1c_{12}^2 + m_2s_{12}^2,$ $ m_1s_{12}^2 + m_2c_{12}^2$, and $m_3$. We then explore the non-trivial impact of neutrino mass ordering on the SNSI contribution. The dependence of the SNSI contribution on the 3$\nu$ parameters is then thoroughly explored using our analytic expressions., Comment: 40 pages, 11 figures

Text-to-SQLs enables non-expert users to effortlessly retrieve desired information from relational databases using natural language queries. While recent advancements, particularly with Large Language Models (LLMs) like GPT and T5, have shown impressive performance on large-scale benchmarks such as BIRD, current state-of-the-art (SOTA) LLM-based Text-to-SQLs models often require significant efforts to develop auxiliary tools like SQL classifiers to achieve high performance. This paper proposed a novel approach that only needs SQL Quality Measurement to enhance LLMs-based Text-to-SQLs performance. It establishes a SQL quality evaluation mechanism to assess the generated SQL queries against predefined criteria and actual database responses. This feedback loop enables continuous learning and refinement of model outputs based on both syntactic correctness and semantic accuracy. The proposed method undergoes comprehensive validation on the BIRD benchmark, assessing Execution Accuracy (EX) and Valid Efficiency Score (VES) across various Text-to-SQLs difficulty levels. Experimental results reveal competitive performance in both EX and VES compared to SOTA models like GPT4 and T5.

In this study, we address the challenge of speaker recognition using a novel data augmentation technique of adding noise to enrollment files. This technique efficiently aligns the sources of test and enrollment files, enhancing comparability. Various pre-trained models were employed, with the resnet model achieving the highest DCF of 0.84 and an EER of 13.44. The augmentation technique notably improved these results to 0.75 DCF and 12.79 EER for the resnet model. Comparative analysis revealed the superiority of resnet over models such as ECPA, Mel-spectrogram, Payonnet, and Titanet large. Results, along with different augmentation schemes, contribute to the success of RoboVox far-field speaker recognition in this paper, Comment: 5 pages, 2 figures

The discovery of superconductivity in La$_3$Ni$_2$O$_7$ under pressure has motivated the investigation of a parent spin density wave (SDW) state which could provide the underlying pairing interaction. Here, we employ resonant soft x-ray scattering and polarimetry on thin films of bilayer La$_3$Ni$_2$O$_7$ to determine that the magnetic structure of the SDW forms unidirectional diagonal spin stripes with moments lying within the NiO$_2$ plane and perpendicular to $\mathbf{Q}_{SDW}$, but without the strong charge disproportionation typically associated with other nickelates. These stripes form anisotropic domains with shorter correlation lengths perpendicular versus parallel to $\mathbf{Q}_{SDW}$, revealing nanoscale rotational and translational symmetry breaking analogous to the cuprate and Fe-based superconductors, with Bloch-like antiferromagnetic domain walls separating orthogonal domains., Comment: 30 pages including supplementary, 4 figures + 9 supplementary figures

The recent surge in AI-generated songs presents exciting possibilities and challenges. While these inventions democratize music creation, they also necessitate the ability to distinguish between human-composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, these approaches are inadequate for detecting contemporary end-to-end artificial songs where all components (vocals, music, lyrics, and style) could be AI-generated. Additionally, existing datasets lack music-lyrics diversity, long-duration songs, and open-access fake songs. To address these gaps, we introduce SONICS, a novel dataset for end-to-end Synthetic Song Detection (SSD), comprising over 97k songs (4,751 hours) with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long-range temporal dependencies in songs for effective authenticity detection, an aspect entirely overlooked in existing methods. To utilize long-range patterns, we introduce SpecTTTra, a novel architecture that significantly improves time and memory efficiency over conventional CNN and Transformer-based models. In particular, for long audio samples, our top-performing variant outperforms ViT by 8% F1 score while being 38% faster and using 26% less memory. Additionally, in comparison with ConvNeXt, our model achieves 1% gain in F1 score with 20% boost in speed and 67% reduction in memory usage. Other variants of our model family provide even better speed and memory efficiency with competitive performance., Comment: Updated with correction

We present X-ray scattering studies resolving structural twinning and phase separation in the charge density wave (CDW) state of the kagome superconductor CsV$_3$Sb$_5$. The three-dimensional CDW state in CsV$_3$Sb$_5$ is reported to form a complex superposition of Star of David (SoD) or Tri-Hexagonal (TrH) patterns of distortion within its kagome planes, but the out-of-plane stacking is marked by metastability. In order to resolve the impact of this metastability, we present reciprocal space mapping and real-space images of CsV$_3$Sb$_5$ collected across multiple length scales using temperature-dependent high-dynamic range mapping (HDRM) and dark-field X-ray microscopy (DFXM). The experimental data provide evidence for a rich microstructure that forms in the CDW state. Data evidence metastability in the formation of $2\times 2\times 4$ and $2\times 2\times 2$ CDW supercells dependent on thermal history and mechanical deformation. We further directly resolve the real space phase segregation of both supercells as well as a real-space, structural twinning driven by the broken rotational symmetry of the CDW state. Our combined results provide insights into the role of microstructure and twinning in experiments probing the electronic properties of CsV$_3$Sb$_5$ where rotational symmetry is broken by the three-dimensional charge density wave order but locally preserved for any single kagome layer., Comment: 12 pages, 9 figures

Due to Motor Neurone Diseases, a large population remains disabled worldwide, negatively impacting their independence and quality of life. This typically involves a weakness in the hand and forearm muscles, making it difficult to perform fine motor tasks such as writing, buttoning a shirt, or gripping objects. This project presents a vision-enabled rehabilitation hand exoskeleton to assist disabled persons in their hand movements. The design goal was to create an accessible tool to help with a simple interface requiring no training. This prototype is built on a commercially available glove where a camera and embedded processor were integrated to help open and close the hand, using air pressure, thus grabbing an object. An accelerometer is also implemented to detect the characteristic hand gesture to release the object when desired. This passive vision-based control differs from active EMG-based designs as it does not require individualized training. Continuing the research will reduce the cost, weight, and power consumption to facilitate mass implementation.

Developing permanent magnets with fewer critical elements requires understanding hysteresis effects and coercivity through visualizing magnetization reversal. Here, we numerically investigate the effect of the geometry of nanoscale ferromagnetic inclusions in a paramagnetic/non-magnetic matrix to understand the key factors that maximize the magnetic energy product of such nanocomposite systems. Specifically, we have considered a matrix of 3 micron x 3 micron x40 nanometer dimension, which is a sufficiently large volume, two-dimensional representation considering that the ferromagnetic inclusions thickness is less than 3.33% of the lateral dimensions simulated. Using this approach that is representative of bulk behavior while being computationally tractable for simulation, we systematically studied the effect of the thickness of ferromagnetic strips, the separation between the ferromagnetic strips due to the nonmagnetic matrix material, and the length of these ferromagnetic strips on magnetic coercivity and remanence by simulating the hysteresis loop plots for each geometry. Furthermore, we study the underlying micromagnetic mechanism for magnetic reversal to understand the factors that could help attain the maximum magnetic energy densities for ferromagnetic nanocomposite systems in a paramagnetic/non-magnetic material matrix. In this study, we have used material parameters of an exemplary Alnico alloy system, a rare-earth-free, thermally stable nanocomposite, which could potentially replace high-strength NdFeB magnets in applications that don't require large energy products. This can stimulate further experimental work on the fabrication and large-scale manufacturing of RE-free PMs with such nanocomposite systems.

Here we resolve the real-space nature of the high-temperature, short-range charge correlations in the kagome metal ScV$_6$Sn$_6$. Diffuse scattering appears along a frustrated wave vector $\textbf{q}_H=(\frac{1}{3},\frac{1}{3},\frac{1}{2})$ at temperatures far exceeding the charge order transition $T_{CO}=92~\mathrm{K}$, preempting long-range charge order with wave vectors along $\textbf{q}_{\bar{K}}=(\frac{1}{3},\frac{1}{3},\frac{1}{3})$. Using a combination of real space and reciprocal space analysis, we resolve the nature of the interactions between the primary out-of-plane Sc-Sn chain instability and the secondary strain-mediated distortion of the in-plane V kagome network. A minimal model of the diffuse scattering data reveals a high-temperature, short-ranged "zig-zag" phase of in-plane correlations that maps to a frustrated triangular lattice Ising model with antiferromagnetic interactions and provides a real-space understanding of the origin of frustrated charge order in this material., Comment: 7 pages, 4 figures

Since its major release in 2006, the Unified Extensible Firmware Interface (UEFI) has become the industry standard for interfacing a computer's hardware and operating system, replacing BIOS. UEFI has higher privileged security access to system resources than any other software component, including the system kernel. Hence, identifying and characterizing vulnerabilities in UEFI is extremely important for computer security. However, automated detection and characterization of UEFI vulnerabilities is a challenging problem. Static vulnerability analysis techniques are scalable but lack precision (reporting many false positives), whereas symbolic analysis techniques are precise but are hampered by scalability issues due to path explosion and the cost of constraint solving. In this paper, we introduce a technique called STatic Analysis guided Symbolic Execution (STASE), which integrates both analysis approaches to leverage their strengths and minimize their weaknesses. We begin with a rule-based static vulnerability analysis on LLVM bitcode to identify potential vulnerability targets for symbolic execution. We then focus symbolic execution on each target to achieve precise vulnerability detection and signature generation. STASE relies on the manual specification of reusable vulnerability rules and attacker-controlled inputs. However, it automates the generation of harnesses that guide the symbolic execution process, addressing the usability and scalability of symbolic execution, which typically requires manual harness generation to reduce the state space. We implemented and applied STASE to the implementations of UEFI code base. STASE detects and generates vulnerability signatures for 5 out of 9 recently reported PixieFail vulnerabilities and 13 new vulnerabilities in Tianocore's EDKII codebase.

Absence of tamper-proof cattle identification technology was a significant problem preventing insurance companies from providing livestock insurance. This lack of technology had devastating financial consequences for marginal farmers as they did not have the opportunity to claim compensation for any unexpected events such as the accidental death of cattle in Bangladesh. Using machine learning and deep learning algorithms, we have solved the bottleneck of cattle identification by developing and introducing a muzzle-based cattle identification system. The uniqueness of cattle muzzles has been scientifically established, which resembles human fingerprints. This is the fundamental premise that prompted us to develop a cattle identification system that extracts the uniqueness of cattle muzzles. For this purpose, we collected 32,374 images from 826 cattle. Contrast-limited adaptive histogram equalization (CLAHE) with sharpening filters was applied in the preprocessing steps to remove noise from images. We used the YOLO algorithm for cattle muzzle detection in the image and the FaceNet architecture to learn unified embeddings from muzzle images using squared $L_2$ distances. Our system performs with an accuracy of $96.489\%$, $F_1$ score of $97.334\%$, and a true positive rate (tpr) of $87.993\%$ at a remarkably low false positive rate (fpr) of $0.098\%$. This reliable and efficient system for identifying cattle can significantly advance livestock insurance and precision farming., Comment: In claimed novel augmentation techniques, there are some mistakes in equations that convey wrong result, which should not be

Understanding individual ions in solutions is essential for advancing our knowledge of complex chemical systems. However, tracking and detecting ions at the single-ion level in liquid environments remains a challenge. We introduce a strategy for visualization and differentiation of different ions in liquid environment via point defects in hexagonal boron nitride (h-BN) as the ion sensor. Ions interacting with the optically active point defects in h-BN alter emission properties, allowing us to capture these changes and visualize single ions. Using Li+ in organic electrolytes as a model, we observed a spectral shift of over 10 nm upon Li+ addition, and an over 50 nm red shift with applied electric fields due to reactions between Li+ and h-BN point defects. Frequency domain analysis further revealed the rapid dynamics of ion migration and the slow electrochemical reactions. We further spectroscopically differentiated various ions (H+, Li+, Na+, K+, Zn2+, Al3+) in aqueous solution. Each ion, with its distinct electron cloud configuration, interacts distinctively with the electron clouds of h-BN defects, resulting in specific and identifiable spectroscopic signatures. This ion sensing platform enables the direct visualization and differentiation of individual ions in a liquid environment, offering insights into chemical reactions at the single-ion level. This capability presents potential applications in various fields involving ions in liquids, including but not limited to biology, battery technology, and environmental science.

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-- namely, 3D shape classification and semantic segmentation., Comment: Published in Springer Nature (Machine Vision and Applications)

Traditional deep learning approaches for breast cancer classification has predominantly concentrated on single-view analysis. In clinical practice, however, radiologists concurrently examine all views within a mammography exam, leveraging the inherent correlations in these views to effectively detect tumors. Acknowledging the significance of multi-view analysis, some studies have introduced methods that independently process mammogram views, either through distinct convolutional branches or simple fusion strategies, inadvertently leading to a loss of crucial inter-view correlations. In this paper, we propose an innovative multi-view network exclusively based on transformers to address challenges in mammographic image classification. Our approach introduces a novel shifted window-based dynamic attention block, facilitating the effective integration of multi-view information and promoting the coherent transfer of this information between views at the spatial feature map level. Furthermore, we conduct a comprehensive comparative analysis of the performance and effectiveness of transformer-based models under diverse settings, employing the CBIS-DDSM and Vin-Dr Mammo datasets. Our code is publicly available at https://github.com/prithuls/MV-Swin-T, Comment: 4 pages, 2 figures

The Bi${-}$Ni binary system has been of interest due to possible unconventional superconductivity aroused therein, such as time-reversal symmetry breaking in Bi/Ni bilayers or the coexistence of superconductivity and ferromagnetism in Bi$_3$Ni crystals. While Ni acts as a ferromagnetic element in such systems, the role of strong spin-orbit-coupling element Bi in superconductivity has remained unexplored. In this work, we systematically studied the effects of Bi stoichiometry on the superconductivity of Bi$_x$Ni$_{1-x}$ thin films (${x} \approx$ 0.5 to 0.9) fabricated via a composition-spread approach. The superconducting phase map of Bi$_x$Ni$_{1-x}$ thin films exhibited a superconducting composition region attributable to the intermetallic Bi$_3$Ni phase with different amount of excess Bi, revealed by synchrotron X-ray diffraction analysis. Interestingly, the mixed phase region with Bi$_3$Ni and Bi showed unusual increases in the superconducting transition temperature and residual resistance ratio as more Bi impurities were included, with the maximum ${T}_{c}$ ($=$ 4.2 K) observed at $x \approx$ 0.79. A correlation analysis of structural, electrical, and magneto-transport characteristics across the composition variation revealed that the unusual superconducting $"$dome$"$ is due to two competing roles of Bi$\colon$ impurity scattering and carrier doping. We found that the carrier doping effect is dominant in the mild doping regime (0.74 $\leq {x} \leq$ 0.79), while impurity scattering becomes more pronounced at larger Bi stoichiometry.

This paper presents a sensorized vision-enabled prosthetic hand aimed at replicating a natural hand's performance, functionality, appearance, and comfort. The design goal was to create an accessible substitution with a user-friendly interface requiring little to no training. Our mechanical hand uses a camera and embedded processors to perform most of these tasks. The interfaced pressure sensor is used to get pressure feedback and ensure a safe grasp of the object; an accelerometer is used to detect gestures and release the object. Unlike current EMG-based designs, the prototyped hand does not require personalized training. The details of the design, trade-offs, results, and informing the next iteration are presented in this paper.

Scalar non-standard interactions (NSI) presents an exciting pathway for probing potential new physics that extends beyond the Standard Model (BSM). The scalar coupling of neutrinos with matter can appear as a sub-dominant effect that can impact the neutrino oscillation probabilities. The uniqueness of these interactions is that it can directly affect the neutrino mass matrix. This makes oscillations sensitive to the absolute neutrino mass. The effects of scalar NSI scales linearly with matter density which motivates its exploration in long-baseline sector. The presence of scalar NSI can influence the key measurements in the field of neutrino physics, including the precise determination of the leptonic CP phase ($\delta_{CP}$), neutrino mass ordering and the octant of $\theta_{23}$. The precise determination of $\delta_{CP}$ is one of the major goals of DUNE, which is an upcoming long-baseline experiment. A better understanding of the impact of scalar NSI on CP measurement sensitivities is crucial for accurate interpretation of $\delta_{CP}$ phase. In this work, we have explored the impact of the complex off-diagonal scalar NSI elements $\eta_{\alpha\beta}$ and their associated phases $\phi_{\alpha\beta}$ on the CP-measurement sensitivities at DUNE. We have explored the impact of the neutrino mass scale on these sensitivities. We look for constraining these off-diagonal elements for different neutrino mass scales. We also explore their correlation with $\delta_{CP}$, investigating potential degeneracies that can arise due to additional phases. We also perform a correlation study among different scalar NSI elements. We show that the inclusion of the complex scalar NSI elements can significantly modify the CP phase measurements., Comment: 26 pages, 9 figures

The electricity grid has evolved from a physical system to a cyber-physical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things (IoT) devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity co-simulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator., Comment: Accepted to the Proceedings of the National Academy of Sciences (PNAS) 2024

Graphene nanoribbons (GNRs) are unique quasi-one-dimensional (1D) materials that have garnered a lot of research interest in the field of topological insulators. While the topological phases exhibited by GNRs are primarily governed by their chemical structures, the ability to externally control these phases is crucial for their potential utilization in quantum electronics and spintronics. Here we propose a class of GNRs featured by mirror symmetry and four zigzag segments in a unit cell that has unique topological properties induced and controlled by an externally applied electric field. Their band structures manifest two finite gaps which support topological solitons, as described by an effective square-root model. To demonstrate the experimental feasibility, we design and synthesize a representative partially zigzag chevron-type GNR (pzc-GNR) with the desired zigzag segments using a bottom-up approach. First-principles calculations on pzc-GNR reveal band inversions at the two finite gaps by switching the direction of the electric field, which is in accordance with predictions from the square-root Hamiltonian. We show different topological phases can be achieved by controlling the direction of the field and the chemical potential of the system in square-root GNRs. Consequently, upon adding a step-function electric field, solitons states can be generated at the domain wall. We discuss the properties of two types of soliton states, depending on whether the terminating commensurate unit cell is mirror symmetric.

Large Language Models (LLMs) such as ChatGPT and GitHub Copilot have revolutionized automated code generation in software engineering. However, as these models are increasingly utilized for software development, concerns have arisen regarding the security and quality of the generated code. These concerns stem from LLMs being primarily trained on publicly available code repositories and internet-based textual data, which may contain insecure code. This presents a significant risk of perpetuating vulnerabilities in the generated code, creating potential attack vectors for exploitation by malicious actors. Our research aims to tackle these issues by introducing a framework for secure behavioral learning of LLMs through In-Content Learning (ICL) patterns during the code generation process, followed by rigorous security evaluations. To achieve this, we have selected four diverse LLMs for experimentation. We have evaluated these coding LLMs across three programming languages and identified security vulnerabilities and code smells. The code is generated through ICL with curated problem sets and undergoes rigorous security testing to evaluate the overall quality and trustworthiness of the generated code. Our research indicates that ICL-driven one-shot and few-shot learning patterns can enhance code security, reducing vulnerabilities in various programming scenarios. Developers and researchers should know that LLMs have a limited understanding of security principles. This may lead to security breaches when the generated code is deployed in production systems. Our research highlights LLMs are a potential source of new vulnerabilities to the software supply chain. It is important to consider this when using LLMs for code generation. This research article offers insights into improving LLM security and encourages proactive use of LLMs for code generation to ensure software system safety., Comment: 27 pages, Standard Journal Paper submitted to Q1 Elsevier

Land Use Land Cover (LULC) analysis on satellite images using deep learning-based methods is significantly helpful in understanding the geography, socio-economic conditions, poverty levels, and urban sprawl in developing countries. Recent works involve segmentation with LULC classes such as farmland, built-up areas, forests, meadows, water bodies, etc. Training deep learning methods on satellite images requires large sets of images annotated with LULC classes. However, annotated data for developing countries are scarce due to a lack of funding, absence of dedicated residential/industrial/economic zones, a large population, and diverse building materials. BD-SAT provides a high-resolution dataset that includes pixel-by-pixel LULC annotations for Dhaka metropolitan city and surrounding rural/urban areas. Using a strict and standardized procedure, the ground truth is created using Bing satellite imagery with a ground spatial distance of 2.22 meters per pixel. A three-stage, well-defined annotation process has been followed with support from GIS experts to ensure the reliability of the annotations. We performed several experiments to establish benchmark results. The results show that the annotated BD-SAT is sufficient to train large deep learning models with adequate accuracy for five major LULC classes: forest, farmland, built-up areas, water bodies, and meadows., Comment: 26 pages, 15 figures and 12 tables

Large language models have gained tremendous popularity in domains such as e-commerce, finance, healthcare, and education. Fine-tuning is a common approach to customize an LLM on a domain-specific dataset for a desired downstream task. In this paper, we present a valuable resource for fine-tuning LLMs developed for the Spanish language to perform a variety of tasks such as classification, masked language modeling, clustering, and others. Our resource is a collection of handwritten notary records from the seventeenth century obtained from the National Archives of Argentina. This collection contains a combination of original images and transcribed text (and metadata) of 160+ pages that were handwritten by two notaries, namely, Estenban Agreda de Vergara and Nicolas de Valdivia y Brisuela nearly 400 years ago. Through empirical evaluation, we demonstrate that our collection can be used to fine-tune Spanish LLMs for tasks such as classification and masked language modeling, and can outperform pre-trained Spanish models and ChatGPT-3.5/ChatGPT-4o. Our resource will be an invaluable resource for historical text analysis and is publicly available on GitHub.

The use of convolutional neural networks (CNNs) has accelerated the progress of handwritten character classification/recognition. Handwritten character recognition (HCR) has found applications in various domains, such as traffic signal detection, language translation, and document information extraction. However, the widespread use of existing HCR technology is yet to be seen as it does not provide reliable character recognition with outstanding accuracy. One of the reasons for unreliable HCR is that existing HCR methods do not take the handwriting styles of non-native writers into account. Hence, further improvement is needed to ensure the reliability and extensive deployment of character recognition technologies for critical tasks. In this work, the classification of English characters written by non-native users is performed by proposing a custom-tailored CNN model. We train this CNN with a new dataset called the handwritten isolated English character (HIEC) dataset. This dataset consists of 16,496 images collected from 260 persons. This paper also includes an ablation study of our CNN by adjusting hyperparameters to identify the best model for the HIEC dataset. The proposed model with five convolutional layers and one hidden layer outperforms state-of-the-art models in terms of character recognition accuracy and achieves an accuracy of $\mathbf{97.04}$%. Compared with the second-best model, the relative improvement of our model in terms of classification accuracy is $\mathbf{4.38}$%.

Retrieval augmented generation (RAG) provides the capability to constrain generative model outputs, and mitigate the possibility of hallucination, by providing relevant in-context text. The number of tokens a generative large language model (LLM) can incorporate as context is finite, thus limiting the volume of knowledge from which to generate an answer. We propose a two-layer RAG framework for query-focused answer generation and evaluate a proof-of-concept for this framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. The evaluations demonstrate the effectiveness of the two-layer framework in resource constrained settings to enable researchers in obtaining near real-time data from users.

Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, and shared tasks have been organised to facilitate active adverse event surveillance. However, most - if not all - datasets or shared tasks focus on extracting ADEs from a particular type of text. Domain generalisation - the ability of a machine learning model to perform well on new, unseen domains (text types) - is under-explored. Given the rapid advancements in natural language processing, one unanswered question is how far we are from having a single ADE extraction model that is effective on various types of text, such as scientific literature and social media posts. We contribute to answering this question by building a multi-domain benchmark for adverse drug event extraction, which we named MultiADE. The new benchmark comprises several existing datasets sampled from different text types and our newly created dataset - CADECv2, which is an extension of CADEC, covering online posts regarding more diverse drugs than CADEC. Our new dataset is carefully annotated by human annotators following detailed annotation guidelines. Our benchmark results show that the generalisation of the trained models is far from perfect, making it infeasible to be deployed to process different types of text. In addition, although intermediate transfer learning is a promising approach to utilising existing resources, further investigation is needed on methods of domain adaptation, particularly cost-effective methods to select useful training instances. The newly created CADECv2 and the scripts for building the benchmark are publicly available at CSIRO's Data Portal., Comment: Journal of Biomedical Informatics

SMS, or short messaging service, is a widely used and cost-effective communication medium that has sadly turned into a haven for unwanted messages, commonly known as SMS spam. With the rapid adoption of smartphones and Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have taken notice of the significance of SMS for mobile phone users. Consequently, with the emergence of new cybersecurity threats, the number of SMS spam has expanded significantly in recent years. The unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult to successfully fight spam attacks in the cybersecurity domain. In this work, we employ optimized and fine-tuned transformer-based Large Language Models (LLMs) to solve the problem of spam message detection. We use a benchmark SMS spam dataset for this spam detection and utilize several preprocessing techniques to get clean and noise-free data and solve the class imbalance problem using the text augmentation technique. The overall experiment showed that our optimized fine-tuned BERT (Bidirectional Encoder Representations from Transformers) variant model RoBERTa obtained high accuracy with 99.84\%. We also work with Explainable Artificial Intelligence (XAI) techniques to calculate the positive and negative coefficient scores which explore and explain the fine-tuned model transparency in this text-based spam SMS detection task. In addition, traditional Machine Learning (ML) models were also examined to compare their performance with the transformer-based models. This analysis describes how LLMs can make a good impact on complex textual-based spam data in the cybersecurity field.

Substance use disorders (SUDs) are a growing concern globally, necessitating enhanced understanding of the problem and its trends through data-driven research. Social media are unique and important sources of information about SUDs, particularly since the data in such sources are often generated by people with lived experiences. In this paper, we introduce Reddit-Impacts, a challenging Named Entity Recognition (NER) dataset curated from subreddits dedicated to discussions on prescription and illicit opioids, as well as medications for opioid use disorder. The dataset specifically concentrates on the lesser-studied, yet critically important, aspects of substance use--its clinical and social impacts. We collected data from chosen subreddits using the publicly available Application Programming Interface for Reddit. We manually annotated text spans representing clinical and social impacts reported by people who also reported personal nonmedical use of substances including but not limited to opioids, stimulants and benzodiazepines. Our objective is to create a resource that can enable the development of systems that can automatically detect clinical and social impacts of substance use from text-based social media data. The successful development of such systems may enable us to better understand how nonmedical use of substances affects individual health and societal dynamics, aiding the development of effective public health strategies. In addition to creating the annotated data set, we applied several machine learning models to establish baseline performances. Specifically, we experimented with transformer models like BERT, and RoBERTa, one few-shot learning model DANN by leveraging the full training dataset, and GPT-3.5 by using one-shot learning, for automatic NER of clinical and social impacts. The dataset has been made available through the 2024 SMM4H shared tasks., Comment: 7 pages, 1 figure, 4 tables

Objective: To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques. Materials and Methods: We first created a lexicon and regular expression lists from literature-driven stem words for linguistic features of stigmatizing patient labels, doubt markers, and scare quotes within EHRs. The lexicon was further extended using Word2Vec and GPT 3.5, and refined through human evaluation. These lexicons were used to search for matches across 18 million sentences from the de-identified Medical Information Mart for Intensive Care-III (MIMIC-III) dataset. For each linguistic bias feature, 1000 sentence matches were sampled, labeled by expert clinical and public health annotators, and used to supervised learning classifiers. Results: Lexicon development from expanded literature stem-word lists resulted in a doubt marker lexicon containing 58 expressions, and a stigmatizing labels lexicon containing 127 expressions. Classifiers for doubt markers and stigmatizing labels had the highest performance, with macro F1-scores of .84 and .79, positive-label recall and precision values ranging from .71 to .86, and accuracies aligning closely with human annotator agreement (.87). Discussion: This study demonstrated the feasibility of supervised classifiers in automatically identifying stigmatizing labels and doubt markers in medical text, and identified trends in stigmatizing language use in an EHR setting. Additional labeled data may help improve lower scare quote model performance. Conclusions: Classifiers developed in this study showed high model performance and can be applied to identify patterns and target interventions to reduce stigmatizing labels and doubt markers in healthcare systems., Comment: 28 pages, 3 figures, 4 tables. 5 Appendices

Critical National Infrastructure (CNI) encompasses a nation's essential assets that are fundamental to the operation of society and the economy, ensuring the provision of vital utilities such as energy, water, transportation, and communication. Nevertheless, growing cybersecurity threats targeting these infrastructures can potentially interfere with operations and seriously risk national security and public safety. In this paper, we examine the intricate issues raised by cybersecurity risks to vital infrastructure, highlighting these systems' vulnerability to different types of cyberattacks. We analyse the significance of trust, privacy, and resilience for Critical Infrastructure Protection (CIP), examining the diverse standards and regulations to manage these domains. We also scrutinise the co-analysis of safety and security, offering innovative approaches for their integration and emphasising the interdependence between these fields. Furthermore, we introduce a comprehensive method for CIP leveraging Generative AI and Large Language Models (LLMs), giving a tailored lifecycle and discussing specific applications across different critical infrastructure sectors. Lastly, we discuss potential future directions that promise to enhance the security and resilience of critical infrastructures. This paper proposes innovative strategies for CIP from evolving attacks and enhances comprehension of cybersecurity concerns related to critical infrastructure.

In contrast to pedagogies like evidence-based teaching, personalized adaptive learning (PAL) distinguishes itself by closely monitoring the progress of individual students and tailoring the learning path to their unique knowledge and requirements. A crucial technique for effective PAL implementation is knowledge tracing, which models students' evolving knowledge to predict their future performance. Based on these predictions, personalized recommendations for resources and learning paths can be made to meet individual needs. Recent advancements in deep learning have successfully enhanced knowledge tracking through Deep Knowledge Tracing (DKT). This paper introduces generative AI models to further enhance DKT. Generative AI models, rooted in deep learning, are trained to generate synthetic data, addressing data scarcity challenges in various applications across fields such as natural language processing (NLP) and computer vision (CV). This study aims to tackle data shortage issues in student learning records to enhance DKT performance for PAL. Specifically, it employs TabDDPM, a diffusion model, to generate synthetic educational records to augment training data for enhancing DKT. The proposed method's effectiveness is validated through extensive experiments on ASSISTments datasets. The experimental results demonstrate that the AI-generated data by TabDDPM significantly improves DKT performance, particularly in scenarios with small data for training and large data for testing.

A major challenge in Explainable AI is in correctly interpreting activations of hidden neurons: accurate interpretations would help answer the question of what a deep learning system internally detects as relevant in the input, demystifying the otherwise black-box nature of deep learning systems. The state of the art indicates that hidden node activations can, in some cases, be interpretable in a way that makes sense to humans, but systematic automated methods that would be able to hypothesize and verify interpretations of hidden neuron activations are underexplored. This is particularly the case for approaches that can both draw explanations from substantial background knowledge, and that are based on inherently explainable (symbolic) methods. In this paper, we introduce a novel model-agnostic post-hoc Explainable AI method demonstrating that it provides meaningful interpretations. Our approach is based on using a Wikipedia-derived concept hierarchy with approximately 2 million classes as background knowledge, and utilizes OWL-reasoning-based Concept Induction for explanation generation. Additionally, we explore and compare the capabilities of off-the-shelf pre-trained multimodal-based explainable methods. Our results indicate that our approach can automatically attach meaningful class expressions as explanations to individual neurons in the dense layer of a Convolutional Neural Network. Evaluation through statistical analysis and degree of concept activation in the hidden layer show that our method provides a competitive edge in both quantitative and qualitative aspects compared to prior work.

Methane emissions from livestock, particularly cattle, significantly contribute to climate change. Effective methane emission mitigation strategies are crucial as the global population and demand for livestock products increase. We introduce Gasformer, a novel semantic segmentation architecture for detecting low-flow rate methane emissions from livestock, and controlled release experiments using optical gas imaging. We present two unique datasets captured with a FLIR GF77 OGI camera. Gasformer leverages a Mix Vision Transformer encoder and a Light-Ham decoder to generate multi-scale features and refine segmentation maps. Gasformer outperforms other state-of-the-art models on both datasets, demonstrating its effectiveness in detecting and segmenting methane plumes in controlled and real-world scenarios. On the livestock dataset, Gasformer achieves mIoU of 88.56%, surpassing other state-of-the-art models. Materials are available at: github.com/toqitahamid/Gasformer., Comment: 9 pages, 5 figures, this paper has been submitted and accepted for publication at CVPRW 2024

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a DL based automated system for blood cell classification and counting from microscopic blood smear images. We classify total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.25% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier's performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%., Comment: 24 pages, 7 tables and 13 Figures

Rapid advances in the field of Large Language Models (LLMs) have made LLM-based code generation an important area for investigation. An LLM-based code generator takes a prompt as input and produces code that implements the requirements specified in the prompt. Many software requirements include mathematical formulas that specify the expected behavior of the code to be generated. Given a code generation prompt that includes a mathematical formula, a reasonable expectation is that, if the formula is syntactically modified without changing its semantics, the generated code for the modified prompt should be semantically equivalent. We formalize this concept as syntactic robustness and investigate the syntactic robustness of GPT-3.5-Turbo and GPT-4 as code generators. To test syntactic robustness, we generate syntactically different but semantically equivalent versions of prompts using a set of mutators that only modify mathematical formulas in prompts. In this paper, we focus on prompts that ask for code that generates solutions to variables in an equation, when given coefficients of the equation as input. Our experimental evaluation demonstrates that GPT-3.5-Turbo and GPT-4 are not syntactically robust for this type of prompts. To improve syntactic robustness, we define a set of reductions that transform the formulas to a simplified form and use these reductions as a pre-processing step. Our experimental results indicate that the syntactic robustness of LLM-based code generation can be improved using our approach., Comment: 12 pages, 12 figures