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In 2021, approximately five million young people in the United States were not in school or working. This group of young people transitioning to adulthood, roughly ages 16 to 24, are often referred to as "opportunity youth" and sometimes "disconnected youth." Young people from low-income families and from communities of color--such as Native Americans, Blacks, and Latinos--experience higher rates of disconnection, as they face significant obstacles related to poverty and racial inequality. Research shows that disconnection from school and work during these transformative years can have negative, long-term consequences on a range of outcomes--from earnings to incarceration to homeownership to physical and mental well-being--that result in significant costs for these individuals, their communities, and society at large. For most young people, disconnection from school or work is not a steady state. Some people find ways to reconnect as they age, while others remain persistently disconnected over early adulthood. Those who are disconnected for long periods and those who become less connected as they get older are also those who face more barriers to success, including poverty, limited education, and involvement with the criminal legal system. Community-based programs are an important source of support for young people to reconnect to school and work. They may help young people work toward a high school credential, connect to postsecondary education or training, earn an occupational credential, gain employability skills and work experience, and advance in the labor market. But these programs often tend to reach people who are more active in their efforts to reengage in school, training, or employment, whether on their own or with a push from family or friends. Those who are reluctant to seek help--due to past negative experiences in their lives, difficulties in navigating systems, or the hurdles of the program requirements--often fall through the cracks. Dalio Education's Connecticut Opportunity Project (CTOP) partnered with MDRC to conduct an evidence-focused scan of the landscape of programs and practices that are relevant to this specific subset of young people who are furthest from opportunities in their communities: (1) young people who are disconnected from education, training, or employment and are reluctant to actively seek help in reconnecting; and (2) young people reconnecting to education and employment after incarceration and past or current disconnection from education, training, or employment. [This landscape scan was funded by Dalio Education's Connecticut Opportunity Project.]

In the modern context, hand gesture recognition has emerged as a focal point. This is due to its wide range of applications, which include comprehending sign language, factories, hands-free devices, and guiding robots. Many researchers have attempted to develop more effective techniques for recognizing these hand gestures. However, there are challenges like dataset limitations, variations in hand forms, external environments, and inconsistent lighting conditions. To address these challenges, we proposed a novel three-stream hybrid model that combines RGB pixel and skeleton-based features to recognize hand gestures. In the procedure, we preprocessed the dataset, including augmentation, to make rotation, translation, and scaling independent systems. We employed a three-stream hybrid model to extract the multi-feature fusion using the power of the deep learning module. In the first stream, we extracted the initial feature using the pre-trained Imagenet module and then enhanced this feature by using a multi-layer of the GRU and LSTM modules. In the second stream, we extracted the initial feature with the pre-trained ReseNet module and enhanced it with the various combinations of the GRU and LSTM modules. In the third stream, we extracted the hand pose key points using the media pipe and then enhanced them using the stacked LSTM to produce the hierarchical feature. After that, we concatenated the three features to produce the final. Finally, we employed a classification module to produce the probabilistic map to generate predicted output. We mainly produced a powerful feature vector by taking advantage of the pixel-based deep learning feature and pos-estimation-based stacked deep learning feature, including a pre-trained model with a scratched deep learning model for unequalled gesture detection capabilities.

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.

Neural networks are trained to learn an approximate mapping from an input domain to a target domain. Incorporating prior knowledge about true mappings is critical to learning a useful approximation. With current architectures, it is challenging to enforce structure on the derivatives of the input-output mapping. We propose to use a neural network to directly learn the Jacobian of the input-output function, which allows easy control of the derivative. We focus on structuring the derivative to allow invertibility and also demonstrate that other useful priors, such as $k$-Lipschitz, can be enforced. Using this approach, we can learn approximations to simple functions that are guaranteed to be invertible and easily compute the inverse. We also show similar results for 1-Lipschitz functions., Comment: 6 pages, 3 Figures, ICML 2019 INNF Workshop

In video analysis, understanding the temporal context is crucial for recognizing object interactions, event patterns, and contextual changes over time. The proposed model leverages adjacency and semantic similarities between objects from neighboring video frames to construct context-aware temporal object embeddings. Unlike traditional methods that rely solely on visual appearance, our temporal embedding model considers the contextual relationships between objects, creating a meaningful embedding space where temporally connected object's vectors are positioned in proximity. Empirical studies demonstrate that our context-aware temporal embeddings can be used in conjunction with conventional visual embeddings to enhance the effectiveness of downstream applications. Moreover, the embeddings can be used to narrate a video using a Large Language Model (LLM). This paper describes the intricate details of the proposed objective function to generate context-aware temporal object embeddings for video data and showcases the potential applications of the generated embeddings in video analysis and object classification tasks.

The performance of a particle detector derived from nitride read-only memory (NROM) technology is evaluated, with immediate applications in space-based heavy ion radiation monitoring and detection. Irradiation exposures are performed using 40 MeV/u $^{78}$Kr and 10 MeV/u $^4$He particle beams at the Texas A&M University Cyclotron Institute. The results show a strong sensitivity to high-Z heavy ions, and medium sensitivity to low-Z heavy ions.

Social media platforms continue to change our digital relationships nowadays. Therefore, recognizing the complex consequences of infinite scrolling is essential. This paper explores two distinct angles of social media engagement: mindless scrolling and mindful scrolling. This extensive study dives into numerous aspects of social media user behavior and satisfaction via the perspective of multiple surveys. We investigate the psychological exploit of infinite scrolling design to keep users engaged, illuminating its effect on users' emotional well-being. Furthermore, we explore its diverse effects on various groups, such as teenagers, professional people, and pregnant women, to better understand how digital activity differs throughout life phases. Furthermore, our study reveals the psychological consequences of being exposed to unfavorable news material. In the context of nutritional objectives, we examine the problems users confront as well as the significance of scrolling in dietary achievement. By taking into account the demographic effect, we can determine how factors like age, gender, and socioeconomic position affect user behavior. This study presents a comprehensive knowledge of the complicated connection of infinite scrolling with user satisfaction and psychological well-being through a variety of surveys, opening the door for well-informed conversations on online engagement., Comment: under review

This study compares eight pre-trained CNNs for diagnosing keratoconus, a degenerative eye disease. A carefully selected dataset of keratoconus, normal, and suspicious cases was used. The models tested include DenseNet121, EfficientNetB0, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16, and VGG19. To maximize model training, bad sample removal, resizing, rescaling, and augmentation were used. The models were trained with similar parameters, activation function, classification function, and optimizer to compare performance. To determine class separation effectiveness, each model was evaluated on accuracy, precision, recall, and F1-score. MobileNetV2 was the best accurate model in identifying keratoconus and normal cases with few misclassifications. InceptionV3 and DenseNet121 both performed well in keratoconus detection, but they had trouble with questionable cases. In contrast, EfficientNetB0, ResNet50, and VGG19 had more difficulty distinguishing dubious cases from regular ones, indicating the need for model refining and development. A detailed comparison of state-of-the-art CNN architectures for automated keratoconus identification reveals each model's benefits and weaknesses. This study shows that advanced deep learning models can enhance keratoconus diagnosis and treatment planning. Future research should explore hybrid models and integrate clinical parameters to improve diagnostic accuracy and robustness in real-world clinical applications, paving the way for more effective AI-driven ophthalmology tools., Comment: 14 pages, 3 tables, 27 figures

The widespread availability of code-mixed data can provide valuable insights into low-resource languages like Bengali, which have limited datasets. Sentiment analysis has been a fundamental text classification task across several languages for code-mixed data. However, there has yet to be a large-scale and diverse sentiment analysis dataset on code-mixed Bengali. We address this limitation by introducing BnSentMix, a sentiment analysis dataset on code-mixed Bengali consisting of 20,000 samples with $4$ sentiment labels from Facebook, YouTube, and e-commerce sites. We ensure diversity in data sources to replicate realistic code-mixed scenarios. Additionally, we propose $14$ baseline methods including novel transformer encoders further pre-trained on code-mixed Bengali-English, achieving an overall accuracy of $69.8\%$ and an F1 score of $69.1\%$ on sentiment classification tasks. Detailed analyses reveal variations in performance across different sentiment labels and text types, highlighting areas for future improvement.

The interplay of topology, magnetism, and correlations gives rise to intriguing phases of matter. In this study, through state-of-the-art angle-resolved photoemission spectroscopy, density functional theory and dynamical mean-field theory calculations, we visualize a fourfold degenerate Dirac nodal line at the boundary of the bulk Brillouin zone in the antiferromagnet YMn2Ge2. We further demonstrate that this gapless, antiferromagnetic Dirac nodal line is enforced by the combination of magnetism, space-time inversion symmetry and nonsymmorphic lattice symmetry. The corresponding drumhead surface states traverse the whole surface Brillouin zone. YMn2Ge2 thus serves as a platform to exhibit the interplay of multiple degenerate nodal physics and antiferromagnetism. Interestingly, the magnetic nodal line displays a d-orbital dependent renormalization along its trajectory in momentum space, thereby manifesting Hund coupling. Our findings offer insights into the effect of electronic correlations on magnetic Dirac nodal lines, leading to an antiferromagnetic Hund nodal line.

Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into anomaly and background classes. It is vital in agriculture, environment, and military applications such as RSTA (reconnaissance, surveillance, and target acquisition) missions. We previously designed an equal voting ensemble of hyperspectral unmixing and three unsupervised HS-AD algorithms. We later utilized a supervised classifier to determine the weights of a voting ensemble, creating a hybrid of heterogeneous unsupervised HS-AD algorithms with a supervised classifier in a model stacking, which improved detection accuracy. However, supervised classification methods usually fail to detect novel or unknown patterns that substantially deviate from those seen previously. In this work, we evaluate our technique and other supervised and unsupervised methods using general hyperspectral data to provide new insights.

Human emotions are often not expressed directly, but regulated according to internal processes and social display rules. For affective computing systems, an understanding of how users regulate their emotions can be highly useful, for example to provide feedback in job interview training, or in psychotherapeutic scenarios. However, at present no method to automatically classify different emotion regulation strategies in a cross-user scenario exists. At the same time, recent studies showed that instruction-tuned Large Language Models (LLMs) can reach impressive performance across a variety of affect recognition tasks such as categorical emotion recognition or sentiment analysis. While these results are promising, it remains unclear to what extent the representational power of LLMs can be utilized in the more subtle task of classifying users' internal emotion regulation strategy. To close this gap, we make use of the recently introduced \textsc{Deep} corpus for modeling the social display of the emotion shame, where each point in time is annotated with one of seven different emotion regulation classes. We fine-tune Llama2-7B as well as the recently introduced Gemma model using Low-rank Optimization on prompts generated from different sources of information on the \textsc{Deep} corpus. These include verbal and nonverbal behavior, person factors, as well as the results of an in-depth interview after the interaction. Our results show, that a fine-tuned Llama2-7B LLM is able to classify the utilized emotion regulation strategy with high accuracy (0.84) without needing access to data from post-interaction interviews. This represents a significant improvement over previous approaches based on Bayesian Networks and highlights the importance of modeling verbal behavior in emotion regulation., Comment: Accepted to ACII'24

Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce personalized GPT and Context-based Social Media LLM models, utilizing federated learning for privacy and security. Four client entities receive a base GPT-2 model and locally collected social media data, with federated aggregation ensuring up-to-date model maintenance. Subsequent modules focus on categorizing user posts, computing user persona scores, and identifying relevant posts from friends' lists. A quantifying social engagement approach, coupled with matrix factorization techniques, facilitates personalized content suggestions in real-time. An adaptive feedback loop and readability score algorithm also enhance the quality and relevance of content presented to users. Our system offers a comprehensive solution to content filtering and recommendation, fostering a tailored and engaging social media experience while safeguarding user privacy., Comment: This research paper is submitted to ASONAM 2024 conference on Advances in Social Networks Analysis and Mining and going to be published in Springer

A significant part of contemporary research in autonomous vehicles is dedicated to the development of safety critical systems where state-of-the-art artificial intelligence (AI) algorithms, like computer vision (CV), can play a major role. Vision models have great potential for the real-time detection of numerous traffic signs and obstacles, which is essential to avoid accidents and protect human lives. Despite vast potential, computer vision-based systems have critical safety concerns too if the traffic condition drifts over time. This paper represents an analysis of how data drift can affect the performance of vision models in terms of traffic sign detection. The novelty in this research is provided through a YOLO-based fusion model that is trained with drifted data from the CARLA simulator and delivers a robust and enhanced performance in object detection. The enhanced model showed an average precision of 97.5\% compared to the 58.27\% precision of the original model. A detailed performance review of the original and fusion models is depicted in the paper, which promises to have a significant impact on safety-critical automotive systems.

Our paper introduces a novel approach to social network information retrieval and user engagement through a personalized chatbot system empowered by Federated Learning GPT. The system is designed to seamlessly aggregate and curate diverse social media data sources, including user posts, multimedia content, and trending news. Leveraging Federated Learning techniques, the GPT model is trained on decentralized data sources to ensure privacy and security while providing personalized insights and recommendations. Users interact with the chatbot through an intuitive interface, accessing tailored information and real-time updates on social media trends and user-generated content. The system's innovative architecture enables efficient processing of input files, parsing and enriching text data with metadata, and generating relevant questions and answers using advanced language models. By facilitating interactive access to a wealth of social network information, this personalized chatbot system represents a significant advancement in social media communication and knowledge dissemination., Comment: This research paper is submitted to ASONAM 2024 conference on Advances in Social Networks Analysis and Mining and going to be published in Springer

Iris recognition is widely used in several fields such as mobile phones, financial transactions, identification cards, airport security, international border control, voter registration for living persons. However, the possibility of identifying deceased individuals based on their iris patterns has emerged recently as a supplementary or alternative method valuable in forensic analysis. Simultaneously, it poses numerous new technological challenges and one of the most challenging among them is the image segmentation stage as conventional iris recognition approaches have struggled to reliably execute it. This paper presents and compares Deep Learning (DL) models designed for segmenting iris images collected from the deceased subjects, by training SegNet and DeepLabV3+ semantic segmentation methods where using VGG19, ResNet18, ResNet50, MobileNetv2, Xception, or InceptionResNetv2 as backbones. In this study, our experiments demonstrate that our proposed method effectively learns and identifies specific deformations inherent in post-mortem samples and providing a significant improvement in accuracy. By employing our novel method MobileNetv2 as the backbone of DeepLabV3+ and replacing the final layer with a hybrid loss function combining Boundary and Dice loss, we achieve Mean Intersection over Union of 95.54% on the Warsaw-BioBase-PostMortem-Iris-v1 dataset. To the best of our knowledge, this study provides the most extensive evaluation of DL models for post-mortem iris segmentation., Comment: submitted to ijcb 2024 special session

In this paper, we introduce a unified model of early and late dark energy. We call it {\it quintessential early dark energy} model in which early and late dark energy are explained by a single scalar field. In other words two different energy scales are related by a single scalar field potential. To achieve this we introduce the modified steep exponential potential. This potential has a hilltop nature during the early time which consists of a flat region followed by a steep region. This nature of the potential plays a crucial role in achieving early dark energy solution. During recent time, the potential can almost mimic the cosmological constant which can result into late time acceleration. We also constrain and compare the models for steep exponential, modified steep exponential, axionlike and power law potentials by using the available background cosmological data from CMB, BAO (including DESI DR1 2024), supernovae (Pantheon$+$, DESY5 and Union3) and Hubble parameter measurements. The maximum improvement we get in the present value of Hubble parameter compared to the standard $\Lambda$CDM model is for the axionlike potential. For other potentials the constraints are similar to the $\Lambda$CDM model., Comment: 18 pages, 4 tables and 9 figures

In this work, Machine Learning (ML) techniques have been employed to explore the highest performing single-heteronunction thermophotovoltaic cell. Initially, traditional homo junction TPV cells have been explored using ML methodologies for the optimal material combinations. ML methods have notably been devoted to analyze the importance of each parameter in the model, thereby improving the comprehension of the system's behavior and facilitating design optimization. Following this investigation, it has been found that Ge emerged as the most effective emitter layer when paired with the optimal base layer, InGaAsSb compound that possesses a direct bandgap of 0.53 eV. Subsequently, a p-Ge/n-InGaAsSb single-heterojunction TPV cell is introduced executing a device transport model featuring a p-n structure. This cell operated at black body (TBB) and cell temperatures of 1578 K and 300 K, respectively. Through meticulous optimization efforts, the performance of the TPV cell is significantly enhanced resulting in an impressive efficiency of 16.50%. This efficiency is accompanied by a short circuit current, JSC=15.53 A/cm2, an open-circuit voltage, VOC=0.47 V, and a fill factor FF=79.5%. These findings suggest that this structural configuration holds considerable promise for the development of high-performance TPV cells., Comment: 27 pages, 15 figures, 2 tables

Ear recognition as a biometric modality is becoming increasingly popular, with promising broader application areas. While current applications involve adults, one of the challenges in ear recognition for children is the rapid structural changes in the ear as they age. This work introduces a foundational longitudinal dataset collected from children aged 4 to 14 years over a 2.5-year period and evaluates ear recognition performance in this demographic. We present a deep learning based approach for ear recognition, using an ensemble of VGG16 and MobileNet, focusing on both adult and child datasets, with an emphasis on longitudinal evaluation for children., Comment: Submitted to Biosig 2024

Two-dimensional materials with multiple degrees of freedom, including spin, valleys, and orbitals, open up an exciting avenue for engineering multifunctional devices. Beyond spintronics, these degrees of freedom can lead to novel quantum effects such as valley-dependent Hall effects and orbital magnetism, which could revolutionize next-generation electronics. However, achieving independent control over valley polarization and orbital magnetism has been a challenge due to the need for large electric fields. A recent breakthrough involving penta-layer rhombohedral graphene has demonstrated the ability to individually manipulate anomalous Hall signals and orbital magnetic hysteresis, forming what is known as a valley-magnetic quartet. Here, we leverage the electrically tunable Ferro-valleytricity of penta-layer rhombohedral graphene to develop non-volatile memory and in-memory computation applications. We propose an architecture for a dense, scalable, and selector-less non-volatile memory array that harnesses the electrically tunable ferro-valleytricity. In our designed array architecture, non-destructive read and write operations are conducted by sensing the valley state through two different pairs of terminals, allowing for independent optimization of read/write peripheral circuits. The power consumption of our PRG-based array is remarkably low, with only ~ 6 nW required per write operation and ~ 2.3 nW per read operation per cell. This consumption is orders of magnitude lower than that of the majority of state-of-the-art cryogenic memories. Additionally, we engineer in-memory computation by implementing majority logic operations within our proposed non-volatile memory array without modifying the peripheral circuitry. Our framework presents a promising pathway toward achieving ultra-dense cryogenic memory and in-memory computation capabilities.

Manifold optimization (MO) is a powerful mathematical framework that can be applied to optimize functions over complex geometric structures, which is particularly useful in advanced wireless communication systems, such as reconfigurable intelligent surface (RIS)-aided massive MIMO (mMIMO) and extra-large scale massive MIMO (XL-MIMO) systems. MO provides a structured approach to tackling complex optimization problems. By leveraging the geometric properties of the manifold, more efficient and effective solutions can be found compared to conventional optimization methods. This paper provides a tutorial on MO technique and provides some applications of MO in the context of wireless communications systems. In particular, to corroborate the effectiveness of MO methodology, we explore five application examples in RIS-aided mMIMO system, focusing on fairness, energy efficiency (EE) maximization, intracell pilot reuse interference mitigation, and grant-free (GF) random access (RA).

Dark sectors provide beyond Standard Model scenarios which can address unresolved puzzles, such as the observed dark matter abundance or the baryon asymmetry of the Universe. A naturally small portal to the dark sector is obtained if dark-sector interactions stem from a non-Abelian hidden gauge group that couples through kinetic mixing with the hypercharge boson. In this work, we investigate the phenomenology of such a portal of dimension five in the presence of CP violation, focusing on its signatures in fermion electric dipole moments. We show that, currently unbounded regions of the parameter space from dark photon searches can be indirectly probed with upcoming electron dipole moment experiments for dark boson masses in the range $1-100$ GeV. We also discuss two particular scenarios where a $SU(2)_D$ dark gauge group spontaneously breaks into either an Abelian $U(1)_D$ or nothing. In both cases, we show that potentially observable electron dipole moments can be produced in vast regions of the parameter space compatible with current experimental constraints and observed dark matter abundance., Comment: 18 Pages + Appendices. Comments welcome!

Massive multiple-input multiple-output (mMIMO) antenna systems and inter-band carrier aggregation (CA)-enabled multi-band communication are two key technologies to achieve very high data rates in beyond fifth generation (B5G) wireless systems. We propose a joint optimization framework for such systems where the mMIMO antenna spacing selection, precoder optimization, optimum sub-carrier selection and optimum power allocation are carried out simultaneously. We harness the bandwidth gain existing in a tightly coupled base station mMIMO antenna system to avoid sophisticated, non-practical antenna systems for multi-band operation. In particular, we analyze a multi-band communication system using a circuit-theoretic model to consider physical characteristics of a tightly coupled antenna array, and formulate a joint optimization problem to maximize the sum-rate. As part of the optimization, we also propose a novel block iterative water-filling-based sub-carrier selection and power allocation optimization algorithm for the multi-band mMIMO system. A novel sub-carrier windowing-based sub-carrier selection scheme is also proposed which considers the physical constraints (hardware limitation) at the mobile user devices. We carryout the optimizations in two ways: (i) to optimize the antenna spacing selection in an offline manner, and (ii) to select antenna elements from a dense array dynamically. Via computer simulations, we illustrate superior bandwidth gains present in the tightly-coupled colinear and rectangular planar antenna arrays, compared to the loosely-coupled or tightly-coupled parallel arrays. We further show the optimum sum-rate performance of the proposed optimization-based framework under various power allocation schemes and various user capability scenarios.

In this research article, we have primarily focused on the circumstantial investigation of deferred statistical convergence of sequences and investigated some fundamental results compatible with the structure of a probabilistic normed space. Additionally, the idea of deferred statistical Cauchy sequences has been discussed with reference to the structure of a probabilistic normed space., Comment: 12 pages

Federated Learning (FL) has emerged as a transformative approach for enabling distributed machine learning while preserving user privacy, yet it faces challenges like communication inefficiencies and reliance on centralized infrastructures, leading to increased latency and costs. This paper presents a novel FL methodology that overcomes these limitations by eliminating the dependency on edge servers, employing a server-assisted Proximity Evaluation for dynamic cluster formation based on data similarity, performance indices, and geographical proximity. Our integrated approach enhances operational efficiency and scalability through a Hybrid Decentralized Aggregation Protocol, which merges local model training with peer-to-peer weight exchange and a centralized final aggregation managed by a dynamically elected driver node, significantly curtailing global communication overhead. Additionally, the methodology includes Decentralized Driver Selection, Check-pointing to reduce network traffic, and a Health Status Verification Mechanism for system robustness. Validated using the breast cancer dataset, our architecture not only demonstrates a nearly tenfold reduction in communication overhead but also shows remarkable improvements in reducing training latency and energy consumption while maintaining high learning performance, offering a scalable, efficient, and privacy-preserving solution for the future of federated learning ecosystems., Comment: This research article got accepted in COMPSAC conference and going to be published to IEEE

Federated learning has become a significant approach for training machine learning models using decentralized data without necessitating the sharing of this data. Recently, the incorporation of generative artificial intelligence (AI) methods has provided new possibilities for improving privacy, augmenting data, and customizing models. This research explores potential integrations of generative AI in federated learning, revealing various opportunities to enhance privacy, data efficiency, and model performance. It particularly emphasizes the importance of generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs) in creating synthetic data that replicates the distribution of real data. Generating synthetic data helps federated learning address challenges related to limited data availability and supports robust model development. Additionally, we examine various applications of generative AI in federated learning that enable more personalized solutions., Comment: We are going to submit this research article into a conference which is best fit for this topic

Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have been used traditionally, these models often do not consider variations in lifestyles, socioeconomic conditions, or genetic predispositions. Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models. We used data from the UK Biobank study, which included over 500,000 middle-aged participants from different primary healthcare centers in the UK. Data collected at baseline (2006--2010) and during imaging visits after 2014 were used in this study. Baseline characteristics, including sex, age, and the Townsend Deprivation Index, were included. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram and echocardiography data, including left ventricular size and function, cardiac output, and stroke volume, were also used. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are explainable and easily interpretable. We reported the accuracy, precision, recall, and F-1 scores; confusion matrices; and area under the curve (AUC) curves., Comment: 19 pages, 3 figures

Glioblastoma multiforme (GBM), the most aggressive primary brain tumour, exhibits low survival rates due to its rapid growth, infiltrates surrounding brain tissue, and is highly resistant to treatment. One major challenge is oedema infiltration, a fluid build$-$up that provides a path for cancer cells to invade other areas. MRI resolution is insufficient to detect these infiltrating cells, leading to relapses despite chemotherapy and radiotherapy. In this work, we propose a new multiscale mathematical modelling method, to explore the oedema infiltration and predict tumour relapses. To address tumour relapses, we investigated several possible scenarios for the distribution of remaining GBM cells within the oedema after surgery. Furthermore, in this computational modelling investigation on tumour relapse scenarios were investigated assuming the presence of clinically relevant chemo$-$radio therapy numerical results suggest that a higher concentration of GBM cells near the surgical cavity edge led to limited spread and slower progression of tumour relapse. Finally, we explore mathematical and computational avenues for reconstructing relevant shapes for the initial distributions of GBM cells within the oedema from available MRI scans. The results obtained show good overlap between our simulation and the patient serial MRI scans taken 881 days into the treatment. While still under analytical investigation, this work paves the way for robust reconstruction of tumour relapses from available clinical data.

Vision-language models (VLMs) have achieved significant strides in recent times specially in multimodal tasks, yet they remain susceptible to adversarial attacks on their vision components. To address this, we propose Sim-CLIP, an unsupervised adversarial fine-tuning method that enhances the robustness of the widely-used CLIP vision encoder against such attacks while maintaining semantic richness and specificity. By employing a Siamese architecture with cosine similarity loss, Sim-CLIP learns semantically meaningful and attack-resilient visual representations without requiring large batch sizes or momentum encoders. Our results demonstrate that VLMs enhanced with Sim-CLIP's fine-tuned CLIP encoder exhibit significantly enhanced robustness against adversarial attacks, while preserving semantic meaning of the perturbed images. Notably, Sim-CLIP does not require additional training or fine-tuning of the VLM itself; replacing the original vision encoder with our fine-tuned Sim-CLIP suffices to provide robustness. This work underscores the significance of reinforcing foundational models like CLIP to safeguard the reliability of downstream VLM applications, paving the way for more secure and effective multimodal systems.

Learned image compression (LIC) is currently the cutting-edge method. However, the inherent difference between testing and training images of LIC results in performance degradation to some extent. Especially for out-of-sample, out-of-distribution, or out-of-domain testing images, the performance of LIC dramatically degraded. Classical LIC is a serial image compression (SIC) approach that utilizes an open-loop architecture with serial encoding and decoding units. Nevertheless, according to the theory of automatic control, a closed-loop architecture holds the potential to improve the dynamic and static performance of LIC. Therefore, a circular image compression (CIC) approach with closed-loop encoding and decoding elements is proposed to minimize the gap between testing and training images and upgrade the capability of LIC. The proposed CIC establishes a nonlinear loop equation and proves that steady-state error between reconstructed and original images is close to zero by Talor series expansion. The proposed CIC method possesses the property of Post-Training and plug-and-play which can be built on any existing advanced SIC methods. Experimental results on five public image compression datasets demonstrate that the proposed CIC outperforms five open-source state-of-the-art competing SIC algorithms in reconstruction capacity. Experimental results further show that the proposed method is suitable for out-of-sample testing images with dark backgrounds, sharp edges, high contrast, grid shapes, or complex patterns.

Graph Neural Network (GNN) achieves great success for node-level and graph-level tasks via encoding meaningful topological structures of networks in various domains, ranging from social to biological networks. However, repeated aggregation operations lead to excessive mixing of node representations, particularly in dense regions with multiple GNN layers, resulting in nearly indistinguishable embeddings. This phenomenon leads to the oversmoothing problem that hampers downstream graph analytics tasks. To overcome this issue, we propose a novel and flexible truss-based graph sparsification model that prunes edges from dense regions of the graph. Pruning redundant edges in dense regions helps to prevent the aggregation of excessive neighborhood information during hierarchical message passing and pooling in GNN models. We then utilize our sparsification model in the state-of-the-art baseline GNNs and pooling models, such as GIN, SAGPool, GMT, DiffPool, MinCutPool, HGP-SL, DMonPool, and AdamGNN. Extensive experiments on different real-world datasets show that our model significantly improves the performance of the baseline GNN models in the graph classification task.

User activities can influence their subsequent interactions with a post, generating interest in the user. Typically, users interact with posts from friends by commenting and using reaction emojis, reflecting their level of interest on social media such as Facebook, Twitter, and Reddit. Our objective is to analyze user history over time, including their posts and engagement on various topics. Additionally, we take into account the user's profile, seeking connections between their activities and social media platforms. By integrating user history, engagement, and persona, we aim to assess recommendation scores based on relevant item sharing by Hit Rate (HR) and the quality of the ranking system by Normalized Discounted Cumulative Gain (NDCG), where we achieve the highest for NeuMF 0.80 and 0.6 respectively. Our hybrid approach solves the cold-start problem when there is a new user, for new items cold-start problem will never occur, as we consider the post category values. To improve the performance of the model during cold-start we introduce collaborative filtering by looking for similar users and ranking the users based on the highest similarity scores., Comment: This research paper has been accepted in the Social Media Sway: Unraveling the Impact of Social Media on Human Behavior - SMS workshop, to be held in conjunction with the International Conference on Social Networks Analysis and Mining (ASONAM 2024) and will be published in Springer

Social media platforms are extensively used for sharing personal emotions, daily activities, and various life events, keeping people updated with the latest happenings. From the moment a user creates an account, they continually expand their network of friends or followers, freely interacting with others by posting, commenting, and sharing content. Over time, user behavior evolves based on demographic attributes and the networks they establish. In this research, we propose a predictive method to understand how a user evolves on social media throughout their life and to forecast the next stage of their evolution. We fine-tune a GPT-like decoder-only model (we named it E-GPT: Evolution-GPT) to predict the future stages of a user's evolution in online social media. We evaluate the performance of these models and demonstrate how user attributes influence changes within their network by predicting future connections and shifts in user activities on social media, which also addresses other social media challenges such as recommendation systems., Comment: This article has been accepted as a long paper in the MSNDS 2024 workshop, to be held in conjunction with the International Conference on Social Networks Analysis and Mining (ASONAM 2024), September 2-5, 2024. and will be published in Springer

This research is based on the present missile detection technologies in the world and the analysis of these technologies to find a cost effective solution to implement the system in Bangladesh. The paper will give an idea of the missile detection technologies using the electro-optical sensor and the pulse doppler radar. The system is made to detect the target missile. Automatic detection and destruction with the help of ultrasonic sonar, a metal detector sensor, and a smoke detector sensor. The system is mainly based on an ultrasonic sonar sensor. It has a transducer, a transmitter, and a receiver. Transducer is connected with the connected with controller. When it detects an object by following the algorithm, it finds its distance and angle. It can also assure whether the system can destroy the object or not by using another algorithm's simulation., Comment: 67 pages

In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), this paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple atlases of the brain's functional magnetic resonance imaging (fMRI) data through a weighted deep ensemble network. Our approach integrates demographic information into the prediction workflow, which enhances ASD diagnosis performance and offers a more holistic perspective on patient profiling. We experiment with the well-known publicly available ABIDE (Autism Brain Imaging Data Exchange) I dataset, consisting of resting state fMRI data from 17 different laboratories around the globe. Our proposed system achieves 75.20% accuracy on the entire dataset and 96.40% on a specific subset $-$ both surpassing reported ASD diagnosis accuracy in ABIDE I fMRI studies. Specifically, our model improves by 4.4 percentage points over prior works on the same amount of data. The model exhibits a sensitivity of 82.90% and a specificity of 69.70% on the entire dataset, and 91.00% and 99.50%, respectively, on the specific subset. We leverage the F-score to pinpoint the top 10 ROI in ASD diagnosis, such as \emph{precuneus} and anterior \emph{cingulate/ventromedial}. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis. Codes and evaluations are publicly available at TBA., Comment: Md Rakibul Hasan and Xuehan Liu contributed equally to this work

We present a study of complex energy braiding in a 1D non-Hermitian system with $n$th order long range asymmetrical coupling. Our work highlights the emergence of novel topological phenomena in such systems beyond the conventional nearest-neighbor interaction. The modified SSH model displays $n$ distinct knots and links combinations in the complex energy-momentum space under periodic boundary conditions (PBC), which can be controlled by varying the coupling strengths. A topological invariant, namely the braiding index, is introduced to characterize the different complex energy braiding profiles, which depends on the zeros and poles of the characteristic polynomials. Furthermore, we demonstrate that the non-Hermitian skin effect can be localized at one or both ends, signifying conventional or bipolar localization, depending on the sign of the braiding index. Phase transitions between different braiding phases with the same (opposite) sign of the topological invariant occur at Type-1 (Type-2) exceptional points, with Type-1 (Type-2) phase transitions accompanied by single (multiple) exceptional points. We propose an experimental set-up to realize the various braiding schemes based on the RLC circuit framework, which provides an accessible avenue for implementation without recourse to high-dimensional momentum space required in most other platforms., Comment: 12 figures

Can Visual Question Answering (VQA) systems perform just as well when deployed in the real world? Or are they susceptible to realistic corruption effects e.g. image blur, which can be detrimental in sensitive applications, such as medical VQA? While linguistic or textual robustness has been thoroughly explored in the VQA literature, there has yet to be any significant work on the visual robustness of VQA models. We propose the first large-scale benchmark comprising 213,000 augmented images, challenging the visual robustness of multiple VQA models and assessing the strength of realistic visual corruptions. Additionally, we have designed several robustness evaluation metrics that can be aggregated into a unified metric and tailored to fit a variety of use cases. Our experiments reveal several insights into the relationships between model size, performance, and robustness with the visual corruptions. Our benchmark highlights the need for a balanced approach in model development that considers model performance without compromising the robustness.

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape. The advancement of generative artificial intelligence has positioned Multi-modal Large Language Models (MLLMs) as crucial tools for managing healthcare data. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including healthcare data security and freshness issues, affecting the output quality of MLLMs. In this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLMs framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share fresh data, mitigating information asymmetry in data sharing. Finally, we utilize a generative diffusion model-based reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management., Comment: 12 pages, 6 figures

Let $ \mathcal{S}(p) $ be the class of all meromorphic univalent functions defined in the unit disc $ \mathbb{D} $ of the complex plane with a simple pole at $ z=p $ and normalized by the conditions $ f(0)=0 $ and $ f^{\prime}(0)=1 $. In this paper, we find radius of concavity and compute the same for functions in $ \mathcal{S}(p) $ and for some other well-known classes of functions on unit disk. We explore general linear combinations $F(z):=\lambda_1f_1(z)+\cdots+\lambda_{2n} f_{2n}(z),\; \lambda_j\in\mathbb{C} $, $ n\in\mathbb{N} $, of functions belonging to the class $\mathcal{S}(p)$ and some other classes of functions of analytic univalent functions and investigate their radii of univalence, convexity and concavity.

Given the extensive and growing capabilities offered by deep learning (DL), more researchers are turning to DL to address complex challenges in next-generation (xG) communications. However, despite its progress, DL also reveals several limitations that are becoming increasingly evident. One significant issue is its lack of interpretability, which is especially critical for safety-sensitive applications. Another significant consideration is that DL may not comply with the constraints set by physics laws or given security standards, which are essential for reliable DL. Additionally, DL models often struggle outside their training data distributions, which is known as poor generalization. Moreover, there is a scarcity of theoretical guidance on designing DL algorithms. These challenges have prompted the emergence of a burgeoning field known as science-informed DL (ScIDL). ScIDL aims to integrate existing scientific knowledge with DL techniques to develop more powerful algorithms. The core objective of this article is to provide a brief tutorial on ScIDL that illustrates its building blocks and distinguishes it from conventional DL. Furthermore, we discuss both recent applications of ScIDL and potential future research directions in the field of wireless communications.

Disorder is ubiquitous in any quantum many-body system and is usually considered to be an obstacle to the elucidation of the underlying physics of complex systems, but its presence can often introduce exotic phases of matter that cannot generally be realized in a clean system. We report here a detailed experimental and theoretical study of magnetic properties of highly disordered Sr$_3$CuNb$_2$O$_9$ material which exhibits random site mixing between Cu and Nb. The magnetic moments (Cu$^{2+}$) are arranged in a quasi-cubic (three-dimensional) manner, leading to a high degree of frustration with a Curie-Weiss temperature ($\theta_{CW}$) of about -60 K without any long-range magnetic ordering down to 466 mK. These observations suggest that Sr$_3$CuNb$_2$O$_9$ is a candidate for a quantum spin liquid. More interestingly, the susceptibility ($\chi = M/\mu_0H$) and the $C_m/T$ ($C_m$ is the magnetic part of the heat capacity) follow a power-law behavior with decreasing temperature. In addition, $M(T,\mu_0H)$ and $C_m(T,\mu_0H)/T$ show scaling relationships over a wide temperature and field range. This unusual behavior with respect to the conventional behavior of a QSL can be discussed qualitatively as the coexistence of a disorder-induced random spin singlet (RSS) state and a QSL state. A quantitative description has been given by numerical calculations considering a power-law probability distribution $P(J) \propto J^{-\gamma}$ ($J$ is the exchange interaction) of random spin singlets. The parameters extracted from the numerical calculations are in excellent agreement with the experimental data. Furthermore, the analytical results are also consistent with the power-law and scaling behavior of $\chi$ and $C_m(T,\mu_0H)/T$ as a whole. Thus, our comprehensive experimental and theoretical analysis provides evidence for the stabilization of the RSS state in a three-dimensional lattice., Comment: 7 pages, 4 figures. Accepted at Physical Review B (Letter)

A class of non-Hermitian topological sensors (NTOSs) was recently proposed in which the NTOS comprises a non-Hermitian Su-Schrieffer-Heeger chain with a measurant-dependent coupling between the two ends of the chain. The smallest eigenenergy of the system, which serves as the readout signal, has an exponential dependence on the system size at small system sizes but saturates above a critical size. In this study, we further elucidate the dependence of the sensor sensitivity and saturation behavior on the system parameters. We explain how the behavior of the NTOS is characterized by a winding number, which indicates whether the smallest eigenenergy decreases to zero exponentially with the system size or grows exponentially up to a critical size. Interestingly, we further show that by imposing unidirectionality on the coupling between the two ends of a sensor, we can flip the size dependence of the smallest eigenenergy value from an exponentially increasing trend to an exponentially decreasing one. Our findings provide important insights into the saturation phenomenon and the impact of terminal couplings on the sensing characteristics of NTOSs., Comment: 13 pages, 5 figures

Flash X-ray computed tomography (CT) is an important imaging modality for characterization of high-speed dynamic events, such as Kolsky bar impact experiments for the study of mechanical properties of materials subjected to impulsive forces. Due to experimental constraints, the number of X-ray views that can be obtained is typically very sparse in both space and time, requiring strong priors in order to enable a CT reconstruction. In this paper, we propose an effective method for exploiting the cylindrical symmetry inherent in the experiment via a variant of total variation (TV) regularization that operates in cylindrical coordinates, and demonstrate that it outperforms competing approaches.

Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic environment is an active research area, previous studies focused on data collected from a non-stationary microphone placed in the environment or from the perspective of adults. Further, many of these works ignore infants or young children in the environment or have data collected from only a single family where noise from the fixed sound source can be moderate at the infant's position or vice versa. Thus, despite the recent success of large pre-trained models for noise event detection, the performance of these models on infant-centric noise soundscapes in the home is yet to be explored. To bridge this gap, we have collected and labeled noises in home soundscapes from 22 families in an unobtrusive manner, where the data are collected through an infant-worn recording device. In this paper, we explore the performance of a large pre-trained model (Audio Spectrogram Transformer [AST]) on our noise-conditioned infant-centric environmental data as well as publicly available home environmental datasets. Utilizing different training strategies such as resampling, utilizing public datasets, mixing public and infant-centric training sets, and data augmentation using noise and masking, we evaluate the performance of a large pre-trained model on sparse and imbalanced infant-centric data. Our results show that fine-tuning the large pre-trained model by combining our collected dataset with public datasets increases the F1-score from 0.11 (public datasets) and 0.76 (collected datasets) to 0.84 (combined datasets) and Cohen's Kappa from 0.013 (public datasets) and 0.77 (collected datasets) to 0.83 (combined datasets) compared to only training with public or collected datasets, respectively., Comment: Accepted in IEEE/ACM CHASE 2024

Dementia is a mental illness that people live with all across the world. No one is immune. Nothing can predict its onset. The true story of dementia remains unknown globally, partly due to the denial of dementia symptoms and partly due to the social stigma attached to the disease. In recent years, dementia as a mental illness has received a lot of attention from the scientific community and healthcare providers. This paper presents a state of art survey of pervasive technology enabled care and support for people suffering from Alzheimers dementia. We identify three areas of pervasive technology support for dementia patients, focusing on care, wellness and active living. A critical analysis of existing research is presented here, exploring how pervasive computing, artificial intelligence (AI) and the Internet of Things (IoT) are already supporting and providing comfort to dementia patients, particularly those living alone in the community. The work discusses key challenges and limitations of technology-enabled support owing to reasons like lack of accessibility, availability, usability and affordability of technology, limited holistic care approach, and lack of education and information. Future research directions focusing on how pervasive and connected healthcare can better support the well being and mental health impacts of Alzheimers dementia are also highlighted.

Integrating inertial measurement units (IMUs) with large language models (LLMs) advances multimodal AI by enhancing human activity understanding. We introduce SensorCaps, a dataset of 26,288 IMU-derived activity narrations, and OpenSQA, an instruction-following dataset with 257,562 question-answer pairs. Combining LIMU-BERT and Llama, we develop LLaSA, a Large Multimodal Agent capable of interpreting and responding to activity and motion analysis queries. Our evaluation demonstrates LLaSA's effectiveness in activity classification and question answering, highlighting its potential in healthcare, sports science, and human-computer interaction. These contributions advance sensor-aware language models and open new research avenues. Our code repository and datasets can be found on https://github.com/BASHLab/LLaSA., Comment: Under review at ARR (for EMNLP 2024)

Novel states of matter arise in quantum materials due to strong interactions among electrons. A nematic phase breaks the point group symmetry of the crystal lattice and is known to emerge in correlated materials. Here we report the observation of an intra-unit-cell nematic order and signatures of Pomeranchuk instability in the Kagome metal ScV6Sn6. Using scanning tunneling microscopy and spectroscopy, we reveal a stripe-like nematic order breaking the crystal rotational symmetry within the Kagome lattice itself. Moreover, we identify a set of van Hove singularities adhering to the Kagome layer electrons, which appear along one direction of the Brillouin zone while being annihilated along other high-symmetry directions, revealing a rotational symmetry breaking. Via detailed spectroscopic maps, we further observe an elliptical deformation of Fermi surface, which provides direct evidence for an electronically mediated nematic order. Our work not only bridges the gap between electronic nematicity and Kagome physics, but also sheds light on the potential mechanism for realizing symmetry-broken phases in correlated electron systems., Comment: 19 pages, 5 figures

The emerging concept of channel twinning (CT) has great potential to become a key enabler of ubiquitous connectivity in next-generation (xG) wireless systems. By fusing multimodal sensor data, CT advocates a high-fidelity and low-overhead channel acquisition paradigm, which is promising to provide accurate channel prediction in cross-domain and high-mobility scenarios of ubiquitous xG networks. However, the current literature lacks a universal CT architecture to address the challenges of heterogeneous scenarios, data, and resources in xG networks, which hinders the widespread deployment and applications of CT. This article discusses a new modularized CT architecture to bridge the barriers to scene recognition, cooperative sensing, and decentralized training. Based on the modularized design of CT, universal channel modeling, multimodal cooperative sensing, and lightweight twin modeling are described. Moreover, this article provides a concise definition, technical features, and case studies of CT, followed by potential applications of CT-empowered ubiquitous connectivity and some issues requiring future investigations., Comment: submitted to IEEE