Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Trade-Offs in IVA of Multimodal Neuroimaging using Cross-Platform Multidataset Independent Subspace Analysis

Multidataset independent subspace analysis (MISA) unifies multiple linear blind source separation methods to analyze joint and unique information across multiple datasets. MISA can jointly analyze large multimodal neuroimaging datasets to advance our understanding of the brain from multiple perspectives. However, a systematic evaluation of the trade-offs between problem scale and sample size is still absent in the literature. Aiming to support flexibility and replicability of deep latent variable modeling, and equip practitioners with crucial tools and usage guidelines, we developed a MISA PyTorch module incorporating the linked multi-network architecture and loss function of the original MISA MATLAB. We then investigated critical performance trade-offs between latent space and sample sizes in independent vector analysis (IVA) problems. Both platforms were highly similar in hundreds of simulation settings, demonstrating successful replication of the original framework and flexibility to evaluate multiple configurations. We observed that a larger sample size, fewer datasets and fewer sources can lead to better IVA model performance. We then performed an IVA experiment on a large multimodal neuroimaging dataset and observed high cross-modal correlation linkage among the identified sources in both platforms, supporting MISA’s effectiveness for replicable multimodal linkage detection.
DeepHealth: Geospatial and ML-Based Approach to Identify Health Disparities and Determinants for Improving Pandemic Health Care

The COVID-19 pandemic has exacerbated existing health disparities, and its impact has fallen disproportionately on disadvantaged and vulnerable communities. Racial and ethnic minorities such as Black Americans who are at a particular disadvantage are more likely to be the potential target of COVID-19 infection and are dying at alarmingly high rates. Despite a promising solution of the COVID-19 vaccination offers hope, equitable access to COVID-19 vaccines remains a challenge in the US, which has compounded the existing disparities in cases, hospitalizations, and deaths among racial and ethnic minority groups. The deep and pervasive history of medical racism in the US has led to the vaccine hesitancy in racial and ethnic minorities, and thereby caused the disparities. Although some studies examine determinants of health disparities (e.g., social health determinants), there is a shortage of studies examining the social, structural and constructural health determinants, either alone or in tandem with other determinants. Little research paid attention to leveraging geographic information to trace the social, structural and constructural health determinants, which can provide a lower level of granularity. In this paper, we propose DeepHealth, a geospatial and ML-based (machine learning based) approach to identify diverse determinants (including the social, structural, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities based on multiple COVID-19 datasets and examine the social, structural, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in incidence and mortality rates due to COVID-19 pandemic. Extensive experimental results show the effectiveness of our approach. This research provides new strategies for health disparity identification and determinant tracking with a goal of mitigating health disparities and improving pandemic health care. The research suggests that policymakers should give attention to initiatives that will protect the health of populations (i.e., an upstream approach to reducing health disparities) rather than solely focusing only on providing health and social services.
Fusion of Alphaviruses with Liposomes is a Non-Leaky Process

It has been reported that low-pH-induced fusion of influenza virus with liposomes results in rapid and extensive release of both lowand high-molecular-weight substances from the liposomes (Günther-Ausborn, S., A. Praetor, and T. Stegmann. 1995. J. Biol. Chem. 270:29279-29285; Shangguan, T., D. Alford, and J. Bentz. 1996. Biochemistry 35: 4956-4965). Here, we demonstrate retention of encapsulated watersoluble compounds during fusion of Semliki Forest virus (SFV) or Sindbis virus (SIN) with liposomes at low pH. Under conditions allowing complete fusion of the liposomes, a limited fluorescence dequenching of liposome-encapsulated calcein was observed, particularly for SFV. Also, radioactively labeled inulin or sucrose were largely retained. Freezing and thawing of the virus in the absence of sucrose resulted in an enhanced leakiness of fusion. These results support the notion that the alphavirus fusion event per se is non-leaky and may well involve a discrete hemifusion intermediate. Membrane fusion of alphaviruses is non-leaky 77
Multi-Task Deep Learning and Uncertainty Estimation for Pet Head Motion Correction

Head motion occurring during brain positron emission tomography images acquisition leads to a decrease in image quality and induces quantification errors. We have previously introduced a Deep Learning Head Motion Correction (DL-HMC) method based on supervised learning of gold-standard Polaris Vicra motion tracking device and showed the potential of this method. In this study, we upgrade our network to a multi-task architecture in order to include image appearance prediction in the learning process. This multi-task Deep Learning Head Motion Correction (mtDL-HMC) model was trained on 21 subjects and showed enhanced motion prediction performance compared to our previous DL-HMC method on both quantitative and qualitative results for 5 testing subjects. We also evaluate the trustworthiness of network predictions by performing Monte Carlo Dropout at inference on testing subjects. We discard the data associated with a great motion prediction uncertainty and show that this does not harm the quality of reconstructed images, and can even improve it.
Topological Similarity between Artificial and Biological Neural Networks

Inspired by biological neural networks (BNNs), deep artificial neural networks (ANNs) have largely reshaped artificial intelligence nowadays. Neural encoding and decoding studies have shown how and to what extent the information representation in ANNs functionally resembles the brains. Meanwhile, researchers start to investigate how ANNs’ predictive performance relates to their topological structures by building relational graphs of computational ANNs. These studies bring the opportunity to assess how is the structural organization of ANNs analogous to BNNs. However, further efforts are necessary to answer this question as the graphical metrics and BNNs are limited in previous studies. In this study, we evaluate the topological similarities between several representative ANNs and a battery of BNNs in different species using a rich set of graphical metrics. We sought to answer two questions: 1) What are the appropriate graphical metrics to characterize the topological similarity between ANNs and BNNs? 2) Is the evolution of ANNs analogous to that of BNNs? Our results show that: 1) the ANN-BNN topological similarity patterns are distinguishable in several graphical metrics; 2) The evolution of ANNs to some extent is analogous to that of BNNs. These findings may provide novel clues for designing brain-inspired neural architectures.
Analog Memristor Counter Arrays

Counters are widely used for implementing counting, clocking, and computations. The digital counters are usually implemented with flipflops using clocked mechanisms. Counters can consume a large area on chip, as the number of bits per count increases. Counting large numbers requires higher bits or use design methods making use of memories for scalability. Alternatively, in the paper, we present the use of memristor nodes for counting application. Each counter is implemented with a single memristor node and each count is encoded as a memristive conductive state. The conductance of the nodes in the crossbar is incremented or decremented to represent a count value. Also, the proposed method shows the possibility of implementing a larger number of counters in a single crossbar. The proposed counter design is evaluated using Voltage Threshold Adaptive Memristor (VTEAM) model. The on-chip area and power requirements of the proposed circuit is calculated and compared with counter array design using memristor crossbars.
Multi-national COVID-19 CT Image-label Pairs Synthesis via Few-Shot GANs Adaptation

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has threatened global health for years. Given its transmissibility and high pathogenicity, an accurate and rapid method of diagnosing COVID-19 infection is essential. Numerous deep learning (DL) models have been developed to assist radiologists with chest computed tomography (CT)-based COVID-19 screening. However, the existing COVID-19 CT datasets usually exhibit significant geographic and class imbalances, resulting in the difficulty of generalizing the model across CT datasets from different patient cohorts. One feasible solution to the problem is to generate data for the small target datasets which have only a few available examples by leveraging a large-scale source dataset as pretraining (i.e., few-shot generative model adaptation). To calibrate the target generative models during adaptation, we present LeCam-CLCR to preserve source images’ diversity information via combining contrastive learning (CL) with LeCam regularization, and make full use of the target images via consistency regularization (CR). We demonstrate the effectiveness of our approach by generating realistic and diverse CT image-label pairs of target datasets, and show that it consistently outperforms the state-of-the-art approaches.
PI and Super Twisting Sliding Mode with Smith Predictor Control Structures for SMA Actuators

This paper aims to design three control solutions for nonlinear processes with Shape Memory Alloy (SMA) wire actuators viewed as controlled processes with dead time. The proposed control solutions are the PI controller with Smith predictor configuration, the sliding mode PI controller with Smith predictor configuration and the super twisting sliding mode PI controller with Smith predictor configuration. The parameters of the proposed controllers are optimally tuned using a Grey Wolf optimizer algorithm. An accurate evolved Takagi-Sugeno-Kang fuzzy model of SMA wire actuators is used to validate the control structures by simulations and their control performance is compared and evaluated.
Scale-Specific Auxiliary Multi-Task Contrastive Learning for Deep Liver Vessel Segmentation

Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this respect, an automated liver blood vessel extraction is widely summoned. Despite the significant growth in performance of semantic segmentation methodologies, preserving the complex multi-scale geometry of main vessels and ramifications remains a major challenge. This paper provides a new deep supervised approach for vessel segmentation, with a strong focus on representations arising from the different scales inherent to the vascular tree geometry. In particular, we propose a new clustering technique to decompose the tree into various scale levels, from tiny to large vessels. Then, we extend standard 3D UNet to multi-task learning by incorporating scale-specific auxiliary tasks and contrastive learning to encourage the discrimination between scales in the shared representation. Promising results, depicted in several evaluation metrics, are revealed on the public 3D-IRCADb dataset.
Pathloss Measurement and Modeling for a Campus Block Environment at 2.1GHz and 3.45GHz

Pathloss measurements are carried out at 2.1GHz and 3.45 GHz in a campus block environment with multiple floors. The receiver locations are categorized into indoor, outdoor-to-indoor (O2I), floor 2 and floor 3 locations and correspondingly wall penetration loss and floor attenuation factors are estimated. Two different pathloss models viz., a) the close-in pathloss model with additional explicit wall-loss and floor attenuation factors as well as b) the frequency dependent ABG models are fitted for each of the categories using a minimum mean square error estimator for the model parameters. While the accuracy of the fit, represented by the shadow fading standard deviation, is better for the ABG model, its limitations in the form of breakdown of the physical significance of the model parameters constrained due to limited empirical measurements is observed.
Joint Learning Annotator Calibration and Annotator Preference for Multiple Annotations Optic Disc and Cup Segmentation

The accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images plays an important role in the screening and diagnosis of glaucoma. However, optic disc and cup annotations suffers from annotator variation due to the inherent differences in annotators’ expertise and the inherent blurriness of retinal fundus images. In clinical practice, considering the opinions of multiple annotators can effectively reduce the impact of this annotator-related bias. In this paper, we propose an efficient framework to joint learn annotator calibration and annotator preference for multiple annotations optic disc and cup segmentation, which consists of two main parts. In the first part, we model multi-annotation as a multi-class segmentation problem to learn calibration segmentation. Further, we employ a cascaded architecture that introduces the anatomical knowledge of the optic disc and optic cup, which can effectively improve the segmentation performance of the optic cup. In the second part, each annotator’s preference-involved segmentation is estimated through annotator encoding and conditional convolution learning. Experiments on the RIGA benchmark show that our framework outperforms a range of state-of-the-art (SOTA) multi-annotation segmentation methods.
Attention Based Multi-Label Classification of Diabetic Retinopathy from Optical Coherence Tomography

Diabetic Retinopathy (DR) is a common complication of diabetes that, in severe cases, can result in blindness. Accurate clinical treatment is imperative to prevent these cases and relies considerably on an exact diagnosis of the various symptoms of DR. We aim to advance DR diagnosis by providing a practical tool to automatically classify Optical Coherence Tomography (OCT) scans for DR and to identify and localize DR-related morphological features within the scans. Our system obtains raw OCT input and only sparse clinical annotations at the volume level, which can be obtained automatically from routine electronic medical records.We developed a novel neural network architecture, OCT-Transformer, that obtains state-of-the-art classification results compared to previous models and does so with limited training data. We base our architecture on an attention mechanism and show this to be the driving factor for the boost in performance. We additionally use our model to locate pixels within the input scans that explain its classification.
Autoimmune liver disease

SUMMARY Autoimmunity is regarded as the main mechanism involved in pathogenesis of various liver diseases, such as autoimmune hepatitis (`I˙), primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC), autoimmune cholangitis, overlap syndromes AIH-PBC and AIH-PSC and liver dysfunction due to connective tissue diseases1 . Autoimmune hepatitis is an immune-mediated, autodestructive liver disease with hepatocytes being the target cells of the human immune system. 2,8 Primary biliary cirrrhosis and primary sclerosing cholangitis are also regarded as autoimmune liver diseases with bile duct epithelia being the target cells, resulting in a continuous loss of bile ducts.3 These diseases may occur simultaneously in their full manifestations in about 10-20% of cases, thus constituting an overlap syndrome AIH-PBC or AIH-PSC.4
Miniaturized Dual-Band Dipole Antenna Loaded with U-Shaped Arms

A miniaturized dual-band dipole-based planar antenna for mobile satellite service (MSS) is presented. Four pairs of U-shaped arms are added at the end of the dipole, which can reduce the antenna area to 0.18 λ0 × 0.1 λ0. To improve impedance matching, a pair of trapezoidal structures are loaded at the feeding point. A prototype was designed, fabricated, and measured to verify the idea. Good agreement between the simulations and measurements is obtained. The measured 10-dB impedance bandwidths are 1.83% (1512-1540 MHz) and 1.69% (1647-1675 MHz), respectively, which can fully support the 7-MHz L-band MSS.
Mental Illness Prediction by Refined-Attention Transformer Encoder with Mental RoBERTa

This study presents a mental illness prediction model that combines a retrieval-based chatbot with a Deep Neural Network (DNN). The proposed DNN uses the natural language model of mental RoBERTa to extract sentence feature vectors, and a refined-attention transformer encoder that includes a refined highway and sorting layer to enhance the feature vectors further. The classification task is performed using a fully-connected layer. Our DNN achieves an accuracy of 88.98% and a Macro F1 score of 55.23% on the datasets in references [1] and [2], respectively, demonstrating superior performance compared to conventional models. Furthermore, we applied our DNN to actual doctor-patient consultations, obtaining promising results. Therefore, the DNN proposed herein is highly suitable for early detection of various mental illnesses, and can be effectively integrated with a chatbot for various application services.
Radiocarbon Date List X: Baffin Bay, Baffin Island, Iceland, Labrador Sea, and the Northern North Atlantic

Date List X contains an annotated listing of 213 radiocarbon dates determined on samples from marine and terrestrial environments. The marine samples were collected from the East Greenland, Iceland, Spitzbergen, and Norwegian margins, Baffin Bay, and Labrador Sea. The terrestrial samples were collected from Vestfirdir, Iceland and Baffin Island. The samples were submitted by INSTAAR and researchers affiliated with INSTAAR's Micropaleontology Laboratory under the direction of Dr.’s John T. Andrews and Anne E. Jennings. All of the dates from marine sediment cores were determined from either shells or foraminifera (both benthic and planktic). All dates were obtained by the Accelerator Mass Spectrometry (AMS) method. Regions of concentrated marine research include: Baffin Bay, Baffin Island, Labrador Sea, East Greenland fjords, shelf and slope, Denmark Strait, the southwestern and northwestern Iceland shelves, and Vestfirdir, Iceland. The non-marine radiocarbon dates are from peat, wood, plant microfossils, and mollusc. The radiocarbon dates have been used to address a variety of research objectives such as: 1. determining the timing of northern hemisphere high latitude environmental changes including glacier advance and retreat, and 2. assessing the accuracy of a fluctuating reservoir correction. Thus, most of the dates constrain the timing, rate, and interaction of late Quaternary paleoenvironmental fluctuations in sea level, glacier extent, sediment input, and changes in ocean circulation patterns. Where significant, stratigraphic and sample contexts are presented for each core to document the basis for interpretations.
Semantic Integrity Measurement of Industrial Control Embedded Devices Based on National Secret Algorithm

In the age of 6G and AI, industrial control embedded devices can understand and follow instructions automatically. The key is to properly understand the meaning of tasks rather than just focusing on tiny details. Semantic communication will be widely used in industries. These industrial control embedded devices are crucial for controlling and monitoring industrial processes and any tampering with them could lead to disastrous consequences. Thus, it becomes increasingly important to protect the integrity of these devices semantically. However, existing research in this field either focuses on specific devices or fails to utilize national secret algorithms. To address this issue, we propose an algorithm to measure the semantic integrity of key files that are essential for system startup and control programs based on national secret algorithms. The proposed algorithm uses a unique encryption method to protect key files from unauthorized access and tampering. To validate the effectiveness and applicability of the proposed algorithm, extensive tests were conducted. The test results show that the proposed algorithm is fast and efficient, making it suitable for measuring the integrity of industrial control devices. The development of this algorithm is a significant step towards securing industrial control devices.
Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle.
Research on Software Test Case Generation based on SysML

With the rapid increase of software complexity and reliability requirements, model-based software engineering is becoming more and more valuable. SysML language as the latest modelling language standard has also received more and more attention, and combining it with software testing can improve test efficiency and quality. In this paper, the established software SysML model is taken as the input, the software activity digraph is established, and the black box test profile based on operation is further established. The test sequence set is obtained from the profile according to the traversal algorithm, and the test sequence is reduced to improve the test efficiency. Finally, the elevator control software is selected for example verification, SysML model and test profile are established, and efficient test cases are generated by test sequence reduction.