Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention to COVID-19: Abstractive Summarization of COVID-19 Research with State-of-the-Art Transformers

The COVID-19 pandemic has led to an over-whelming volume of scientific publications as researchers strive to address the crisis. To alleviate information overload, the COVID-19 Open Research Dataset (CORD-19) was released to help in analyzing large amounts of data and facilitate faster response. Most existing tools based on CORD-19 use extractive summarizers, which suffer from poor coherence and readability. Thus more abstractive summarizers for COVID-19 are needed. Specifically, using state-of-the-art (SOTA) transformers has shown to be successful in summarizing biomedical datasets like arXiv and PubMed. In this study, we finetune two checkpoints of SOTA transformer PEGASUS-X on the CORD-19 dataset: PEGASUS-XBASE-CORD19 and PEGASUS-XBASE-arXiv-CORD19. Our results highlight the importance of finetuning summarizers on domain-specific datasets in the abstractive summarization of COVID-19 research: checkpoints finetuned on CORD-19 out-perform other existing checkpoints and transformers finetuned on more general research datasets (e.g., arXiv and PubMed). After stopword removal in evaluation, we observe that PEGASUS-XBASE-arXiv-CORD19 surpasses PEGASUS-XBASE-CORD19 by a small margin. Our checkpoints still fall behind earlier sequence-to-sequence models; however, this limitation may be due to our constrained GPU resources. Future works, with access to more resources, can further improve our checkpoints for COVID-19 research summarization.
An Efficient Deep Learning Framework for Glaucoma Diagnosis Using Convolution Mixed Transformer Network

In this paper, we propose a deep learning based pipeline to predict the presence of Glaucoma, a neuro-degenerative eye condition, by applying object localization, image segmentation, and image classification techniques. The pipeline takes a full fundus image as input and provides Optic Disc and Optic Cup masks along with the likelihood measure to determining Glaucoma. The pipeline uses a lightweight U-Net model called ResNet based Disc Localization U-Net (RDLU-Net) for Optic Disc localization and Convolution mixed SegFormer Glaucoma Detection Network (CSGDN) for segmenting Optic Disc (OD) and Optic Cup (OC) structures and finally predicting the probability of the presence of Glaucoma by using the classification network in CSGDN. Our approach shows dice scores of 0.9871 and 0.9065 on OD and OC segmentation, respectively by using REFUGE dataset. The proposed frame-work obtains accuracy of 95.47% using MobileNet V2 as the detection network when applied on a custom Glaucoma dataset containing data from REFUGE and DRISHTI-GS datasets.
Black-Box Stealthy Frequency Spectrum Attack on LSTM-based Power Load Forecasting in an Energy Management System with Islanded Microgrid

This research paper introduces a novel approach called Frequency Spectrum Attack (FSA) and assesses its impact on load forecasting and energy management within a microgrid. FSA leverages Fast Fourier Transform to convert load data into the frequency domain, thereby uncovering significant patterns during the learning phase. It strategically adjusts the amplitudes of dominant frequencies within the healthy amplitude range to maintain stealthiness against statistical analysis, contributing to its effectiveness. We evaluate the performance of FSA using LSTM network as a state-of-the-art technology for load forecasting. Our findings reveal that FSA results in a threefold increase in Mean Absolute Error (MAE) compared to normal conditions and a 70% increase compared to noise injection attacks. Additionally, FSA indirectly boosts battery utilization in the Energy Management System (EMS) by 45%. Furthermore, FSA effectively evades detection by frequency monitoring and control units in the microgrid, making frequency deviations inconspicuous.
Observer-Based Sensorless Admittance Control from Demonstrations with Dynamical Systems for Robot-Environment Interaction

Compliant motion adaptation is essential in the task of robot interacting with the environment. This paper presents a motion planning framework that generates and tracks a trajectory to achieve the compliant behavior during interaction. First, motions from multiple demonstrations are modelled by a dynamical system (DS) with Gaussian Mixture Models (GMM), and parameters of DS model are learning online. In case of the contacting force from the environment, admittance control is employed to modify the trajectory to generate a compliant robot motion. The contacting force is estimated by the sensorless approach. Furthermore, in our framework, an adaptive controller is designed to ensure the tracking of the modelled DS motion, and Radial Basis Function neural networks (RBFNNs) are adopted to compensate nonlinear terms of the robot. The proposed framework is not only analyzed in Lyapunov theory, and also verified through simulations study.
Exploiting Pre-trained Architectures for Dual-Stream Classification of LCA-RCA in a Private AngioData

Angiographic images contain a vast amount of information that can be extracted from both the image data itself and the associated metadata. In the field of cardiology, Deep Learning has proven valuable for tasks such as stenosis detection and localization.In this study, we focus on the classification of Left and Right Coronary Artery using Deep Learning architectures applied to frames extracted from angiographic videos. Additionally, we explore the utilization of Machine Learning algorithms applied to the video metadata.Our experiments demonstrate that Deep Learning methods achieve high performance, with fine-tuning of VGG19 and InceptionV3 achieving respectively 93.92% and 94.09% of accuracy. We also investigate the performance of architectures involving two-stream models.Furthermore, we evaluate Machine Learning methods and find that the Random Forest algorithm achieves an accuracy of 89% when utilizing the video metadata.These results highlight the effectiveness of both Deep Learning and Machine Learning approaches for LCA-RCA classification using angiographic data, providing insights into the potential of these methods in cardiology applications.
Synergistic Approach for Computational Analysis of Geomagnetically Induced Currents in Power Grids

Geomagnetically induced currents (GICs) have the potential to be highly disruptive to regular power grid operations. Despite the study of geomagnetic disturbances (GMDs) since the 1940s, comprehensive methods for accurate and precise analysis of their impact, especially in individual grid components, remain limited. This paper describes a method based on the synergistic use of different computational tools for detailed simulation of GMDs in power grids. The proposed methodology allows the effective integration between three main aspects required for accurate determination of GICs and their effects on power systems: (1) location-specific estimation of Earth's magnetic fields at ground level, (2) detailed physics-based ground impedance modeling considering specific geological environment, and (3) power grid modeling considering detailed network and grounding topology. The goal is to generate an end-to-end methodology for GIC calculation in power grids from the magnetic field environment during a geomagnetic event, the soil conductivity profile at the site of interest, and the grid topology data. The resulting methodology was evaluated by means of a sample case that utilizes magnetic field data from an actual event.
Biomechanical evaluation of extra-articular distal humeral fracture treatments using finite element analysis: A preliminary study

Various surgical treatment options have been documented for the management of extra-articular distal humerus fractures. However, the optimal approach remains a subject of controversy, primarily due to the variability in individual fracture characteristics. In this context, the present study aims to investigate the influence of implants on the stability of extra-articular distal humerus fracture fixation and the associated failure mechanism through computational simulations. In this study, three-dimensional models of a locking compression plate (LCP) fixation and an intramedullary nail fixation for extra-articular distal humerus fracture were generated and finite element analysis was performed to compare the effectiveness of these fixations under loading conditions mimicking daily activities. The study focused on evaluating von-Mises stress and displacement on both the implants and bone fragments. The results revealed high-risk regions due to exceeded von-Mises stress, shedding light on the failure mechanism of fixation. To conclude, the results highlighted the superior overall performance of intramedullary nail fixation, particularly under shear force conditions. These findings and the ensuing discussion provide valuable insights and considerations for the further development of a novel fixation system.
Imaging-proteomics co-profiling reveals biologic pathways underlying prognostic MRI features

Patient selection for neoadjuvant chemotherapy in breast cancer remains an unmet clinical need. While quantitative features from medical imaging have been reported to predict treatment responses, the biological meaning of these features is not well understood, which hinders their clinical use. This study aimed to develop a deep learning signature (DLS) using pretreatment MRI to predict responses to neoadjuvant chemotherapy and to identify the underlying pathways through paired MRI and proteomic sequencing data. The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.957. Pathways including mitochondrial energy metabolism, vesicle budding and vascular transportation, protein localization to membranes, mRNA processing, and cytoskeleton-dependent trafficking were differentially regulated in patients showing pCR and were significantly correlated with DLS in the validation dataset. This study offers a biologically interpretable DLS for predicting pCR to neoadjuvant chemotherapy in breast cancer patients, which may guide personalized medication.
Simple Implementation of Terrain Classification Models via Fully Convolutional Neural Networks

This paper introduces a simple implementation of three versions (large, medium, and small) of terrain multi-classification models using Fully Convolutional Neural Networks (FCNNs) for imagery data. The proposed methodology involves labeled and unlabeled data collection from European Space Agency (ESA) WorldCover and Sentinel-2 MultiSpectral Instrument (MSI) on the Google Earth Engine, compressing datasets into Tensorflow records format with 9 diverse terrain types, and handling Google Cloud training computations. There were prepared different dataset portions of 10 megabytes, 200 megabytes, and around a gigabyte files. The experimental results demonstrate the effectiveness of the CNN-based approach, achieving a tolerable 71% accuracy of the Terrain Classification Model (TCM) and robust classification performance. The simplicity and efficiency of the proposed method make it suitable for real-world applications requiring reliable and fast terrain classification.
Selected dietary habits among female adolescents in Hail, Saudi Arabia

Objectives To estimate, among Saudi teenagers, the prevalence of (1) insufficient fruit and vegetable consumption, (2) fast-food consumption, (3) cola-beverage consumption, and to classify respondents' body weight status using BMI. Methods In November 2010, a school-based questionnaire was administered to 180 elementary female students in Hail, KSA (12-16 years). The questionnaire contained data about the intake of fruit, vegetable, fast-food and cola beverage. Anthropometric measurements of weight and height were also obtained. Data were analyzed using frequencies and descriptive statistics Results The daily average of combined servings of fruits & vegetables was 1.65 (SD+1.01) servings. Most students (97.6%) did not eat the recommended amounts of fruits and vegetables. The total sample consumed 2.23 (SD+1.44) fast-food meals per week, and 9.99±8.25 cans of cola per week. The mean BMI was 23.5±5.28 kg/m. The prevalence of overweight and obesity was 15.2% and 11.6% respectively. Conclusions Unhealthy eating habits are common among Saudi female adolescents who tend to consume: less fruits, less vegetables, more fast-food, and more cola beverage. Additionally, the study showed that overweight and obesity were also common. Further studies are recommended to determine the psychosocial correlates of adopting healthy food habits among this segment of Saudi population.
Microaneurysm Detection on Retinal Fundus Images with Multi-Resolution GlobalNet to Support the Diagnosis of Diabetic Retinopathy

Diabetic retinopathy, one of the main causes of blindness in the world, can be diagnosed early by utilizing microaneurysms (MA). The existence of microaneurysms can be detected by performing semantic segmentation on the retinal fundus image. In this paper, deep learning method testing was carried out using the GlobalNet model at various input image resolution values to see the effect of resolution alteration on MA detection in retinal fundus images. The experiment provided AUPR values of $0.391 \pm 0.026,0.387 \pm 0.035$, and $0_{-}394\pm 0.050$, along with Fl scores of $0.361 \pm 0.034,0.361 \pm 0.022$, and $0.360 \pm$ 0.022. Evaluation done at pixel and lesion levels shows that the difference in resolution of the input images does not cause a significant change in the AUPR and Fl scores. However, the resolution alteration does affect the total number of false positive lesions in outputs. In addition, a combination method is developed, providing a better trade-off in sensitivity and precision compared to the GlobalNet segmentation model.
On the Multi-Dimensional Acceleration of Stochastic Blockmodeling for Community Detection

Stochastic block partitioning (SBP) is a community detection algorithm that is highly accurate even on graphs with a complex community structure. However, SBP is much slower than more commonly used algorithms, such as Louvain, making SBP impractical for analyzing large real-world graphs with millions of edges. Thus, we aim to realize fast and accurate community detection on large graphs by accelerating the highly accurate SBP algorithm via sampling, parallel and distributed computing on a cluster as well as algorithmic optimization. We compare our approach to other community detection algorithms, showing that SBP accelerated with our methods on 64 compute nodes is up to 1,163× faster than the official "Graph Challenge" baseline SBP implementation, while still being more accurate than the Louvain and Leiden algorithms on large graphs.
Selective EEG Signal Anonymization using Multi-Objective Autoencoders

The availability of low-cost brain-computer interfaces and related software has enabled application developers to access this technology seamlessly. However, unsupervised access to users' brain signals raises alarms for EEG data privacy and identity protection. This paper explores a new direction to selectively anonymize a person's brain signals resulting from a response to a stimulus. These time-locked potentials containing sensitive user information are masked to allow for the intended task/event classification (brain task activity) while minimizing the accuracy of subject classification (brain identity activity). We study the feasibility of an autoencoder architecture, enveloped with regularizers and a multi-objective loss function, to achieve an optimal utility-privacy trade-off for EEG data application. We observed a drop of 35% in the accuracy of the subject's classification, while suffering only a loss of 14% in the accuracy of the task classification. This algorithm can be applied to multi-channel and multi-subject scenarios, and our results demonstrate a proof-of-concept that we can generalize an anonymizing autoencoder architecture to be applicable to intricate stochastic data such as EEG.
Lightweight and Efficient Air-to-Air Unmanned Aerial Vehicle Detection Neural Networks

This paper introduces a lightweight approach for detecting distant aerial targets using onboard camera mounted on unmanned aerial vehicle (UAV). Building upon YOLOv8, we propose the integration of the C3Ghost algorithm to enhance the backbone network, reducing model parameters. We also employ the effective feature fusion (EFF) module to achieve more comprehensive feature fusion. Additionally, a novel detection box loss function is proposed. The effectiveness of these improvements is validated on a dataset, demonstrating significant performance gains in the task of detecting small targets.
MIMO-NOMA with OSTBC for B5G Cellular Networks with Enhanced Quality of Service

Non-orthogonal multiple access (NOMA) scheme has proved a potential candidate to meet the design challenges of orthogonal multiple access (OMA) schemes. High spectrum efficiency and higher data rates are further achieved by integrating NOMA with multiple input multiple output (MIMO) technology. In this article, we have explored orthogonal space-time block coding (OSTBC) to improve the quality of service (QoS) for the proposed MIMO-NOMA framework. We specifically employed the Alamouti STBC scheme, which provides full diversity for the proposed network. At the transmitter side, OSTBC and power domain multiplexing with superposition coding are utilized, while at the reception side, a low complexity maximum likelihood (ML) detector is used, followed by successive interference cancellation (SIC). Based on bit error rate (BER) evaluations, Monte Carlo simulations are performed to analyze the performance of OSTBC MIMO-NOMA system under various fading channels. The proposed framework is further investigated with a higher modulation scheme and different channel code rates. The investigated Alamouti scheme provides a maximum diversity gain of 2M (M shows the number of receiving antennas) without any need for channel state information at the transmitter end and a less complex ML detector at the receiver end. This research lays the groundwork for designing adaptive modulation and coding methods for future MIMO-NOMA based B5G wireless communication systems.
Embedded Gene Expression Data Based On RidgeClassifier For Alzheimer’s Disease Classification

In bioinformatics, analyzing large-scale gene expression data using machine learning models is a common practice. However, selecting relevant features from high-dimensional data poses a significant challenge. Feature selection techniques aim to address this challenge by reducing data size, selecting important features, and reducing classification time. While filter selection is a fast approach, the selected features may lack biological relevance. Wrapper techniques have shown efficacy in selecting relevant features, but they can be computationally intensive and time-consuming. Embedded techniques bridge this gap by selecting relevant features while maintaining computational efficiency and low cost. This study introduces a framework that utilizes the embedded feature selection based on the RidgeClassifier technique for selecting gene expression data (EGSRC) from the ADNI dataset. Hyperparameter tuning using Optuna is also employed to further improve the performance of the SVM classifier in classification learning. The study evaluates the performance of five different classifiers using various evaluation metrics through cross-validation. SVM achieved the highest scores across all metrics, recording 99.47% accuracy, 99.25% precision, 98.41% specificity, 100% recall, 99.62% F1-score, and 99.20% AUC. The proposed framework’s RC selector outperforms filter, embedded techniques, and state-of-the-art techniques, which demonstrates its robustness.
Clinopyroxenite from the Sulu ultrahigh-pressure terrane, eastern China: Origin and evolution of garnet exsolution in clinopyroxene

Abstract The Rizhao garnet clinopyroxenite occurs as small mantle slices faulted into subducted continental crust in the Sulu ultrahigh-pressure (UHP) terrane, China. Two representative garnet clinopyroxenites with exsolution microstructures were studied: (1) a massive ilmenite-rich specimen, composed of coarse-grained clinopyroxene containing abundant exsolution rods of grossularrich garnet (>20 vol%) + ilmenite (~3-4 vol%) in a fine-grained matrix of clinopyroxene + garnet + ilmenite; and (2) megacrystic garnet-bearing sample, characterized by garnet containing inclusions of clinopyroxene and ilmenite; the clinopyroxene inclusions show exsolution rods and blebs with variable amounts of garnet (1-15 vol%) and minor ilmenite. Both exsolved and matrix garnets have similar grossular-rich compositions. The aggregate composition of clinopyroxene + garnet + ilmenite intergrowth is similar to the whole-rock composition. We propose that the parental phase of exsolved garnet + ilmenite lamellae and clinopyroxene host was either a homogeneous clinopyroxene (hypothesis A) or a majoritic garnet (hypothesis B) that experienced three discrete evolution stages. If the parental phase was clinopyroxene (ABO3), with an Si deficiency in the B site, the Rizhao garnet clinopyroxenite could have an initial assemblage clinopyroxene ± Grt ± Ilm formed at nearsolidus conditions (≤ 1400 °C, at 5 GPa) in the upper mantle. The second stage is defined by garnet exsolution from the primary clinopyroxene involving decreasing temperature and/or increasing pressure, related to continental subduction. Coexisting clinopyroxene host and garnet exsolution (Grtexs) recrystallized at temperatures of ~900 °C at an assumed minimum P of 5 GPa. The third stage is represented by recrystallization of exsolution phases to form the fine-grained matrix of Cpx + Grt + Ilm at 700 °C and ≥ 3 GPa during the early exhumation of the UHP terrane. The speculative hypothesis B implies that the proposed majoritic garnet was from the mantle transition zone (<450 km depth); this suggestion remains to be tested.
Binary Distributed Hybrid Output Consensus Control for Heterogeneous Clustered Networks

This paper investigates the finite-time output consensus of general heterogeneous clustered networks using distributed hybrid communication. To address wide-area scenarios, a more general clustered network is constructed, which includes intercluster intermittent communication and intra-cluster continuous communication. A novel finite-time hybrid control strategy is designed to solve the consensus tracking problem of global clustered networks, ensuring exponential convergence in finite time under the binary distributed and intermittent control methods. Additionally, a distributed hybrid controller is designed to achieve output consensus through feedforward control in a finite time horizon. A novel criterion for the desired finite-time hybrid control is derived using output regulation techniques. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed distributed hybrid control method.
Farmers' Perception and Attitude toward Climate Change in Coastal Ecosystem of West Bengal

Coastal area of India is more prone to the devastating impact of climate change. The present study was conducted in Sunderbans coastal ecosystem of West Bengal which is famous for mangrove forest and aquatic resources but this area is now under the adverse impact of climate change. Total six villages were identified from the two blocks of South 24 Pargana district of West Bengal for the present study. The main body of the research was carried out using a structured survey instrument. It was observed that nearly 38 per cent of the respondents had heard about climate change. Majority of farmers perceived that climate change is real and it is already underway. People of the Sunderbans area had mixed type of attitude toward climate change. They showed positive attitude towards government and scientists’ role in solving climate change related problem and majority of them were worried about indiscriminate destruction of plant and animal species of the Sunderbans area. Attitude of the people also reflected their helplessness orientation toward climate change.