Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Robust Packet Loss Concealment System With ASR-Guided Representations

Despite the significant advancements and promising performance of deep learning-based packet loss concealment (PLC) systems in transmission systems, their focus on modeling acoustic features for reconstructing lost packets is insufficient to achieve smooth transitions during speech reconstruction. Therefore, to address this limitation, we propose integrating linguistic information derived from a speech recognition system as auxiliary features in the PLC system. By extracting ASR-guided representations and incorporating them using auxiliary loss, we successfully demonstrate a substantial improvement in the perceptual quality and intelligibility of the reconstructed speech. Our evaluation conducted on the wall street journal dataset further validates the effectiveness of our approach through experiments involving different packet loss rates and performance metrics.
A two-stream channel reconstruction and feature attention network for EEG emotion recognition

Research on human emotions based on EEG during multimedia stimuli is an emerging field that has made significant progress in EEG-based emotion classification. However, current studies often neglect the extraction of dynamic information from EEG signals and lack the exploration of local information. Moreover, many existing models are overly complex, demanding an excessive investment in training resources and time. In this paper, we propose a novel, simple two-stream channel reconstruction and feature attention network, named CRFAE-motionNet, for EEG emotion recognition. The main advantage of CRFAEmotionNet is its ability to simultaneously integrate static and dynamic information from EEG signals within a unified network. Additionally, it can extract continuous EEG temporal information through channel reconstruction and utilize feature attention to further explore local information. The proposed network was evaluated using the publicly available DEAP dataset. The experimental results indicate that the proposed CRFAEmotionNet outperforms the state-of-the-art baselines, achieving the accuracy of 98.7% for valence and 98.6% for arousal.
A Dual Stage Resource Efficient ECG Classifier

A dual-stage ECG classifier is proposed for wearable healthcare systems based on a deep convolutional neural network. The dual-stage structure efficiently allocates resources by activating the entire structure only when abnormal beats are detected. Power consumption is reduced by converting multiplication operations into additions using binary weights and coefficient merging techniques. Experimental results on the MITBIH database demonstrate an overall accuracy of 97.94%. FPGA implementation achieves a dynamic power consumption of 41. 05μW at 500kHz frequency. This approach advances wearable healthcare systems, enabling real-time arrhythmia detection and monitoring on edge devices, improving patient care.
Silicon-based CMOS Stress Sensors for Tactile Perception based Smart Skin for Prostheses

There is a growing interest in research related to smart prostheses and exoskeletons for people with muscular disabilities. The goal is to improve their quality of life and allow them to regain some agency over their movements. One of the most critical components in developing any "human-like" electronic skin is its ability to detect force and use it to emulate a touch. This paper presents a fully integrated CMOS-based stress sensor with a high dynamic range (100 kPa to 100 MPa). The designed sensor chip consumes an analog current of ~267 μA in simulations and only takes 10.2 ms for one operation cycle. Moreover, the complete design is CMOS-based, making it easy to manufacture using well-established traditional fabrication techniques. These advantages will help make the sensors easy to commercialize and thus more affordable for the affected patients in the long run.
Automatic Post-Traumatic Stress Disorder Diagnosis via Clinical Transcripts: A Novel Text Augmentation with Large Language Models

Post-traumatic stress disorder (PTSD) is one of the predominant mental disorders in the world. With the development of machine learning (ML), more people have started using natural language processing (NLP) models to make early mental disorder diagnoses, including PTSD. However, these NLP tasks often suffer from data imbalance issues due to the data collection difficulty. Therefore, the current study proposed two novel text augmentation frameworks to cope with data imbalance issues for clinical NLP tasks by leveraging Large Language Models (LLMs). The proposed frameworks utilize two distinct methodologies to augment the original dataset, thereby extending the publicly available Extended Distress Analysis Interview Corpus (E-DAIC) for PTSD. These methodologies involve generating standardized transcripts of PTSD interviews through a zero-shot (ZS) approach and rephrasing the existing training samples in the dataset via a few-shot (FS) approach. The FS and ZS augmented datasets outperform the original EDAIC dataset in automatic PTSD diagnosis. The ZS dataset, with GPT embeddings, achieves the highest performance, demonstrating the potential of LLMs to generate authentic clinical interviews and resolve data imbalance. Despite the FS approach performing slightly inferior to ZS, it still surpasses the original dataset with fewer samples and simplified prompts. The augmented dataset maintains high similarity to the original EDAIC dataset. This research has significant implications, enabling individual ML researchers to leverage powerful LLMs for innovative applications, reducing labour and time costs. LLMs can generate synthetic data at a fraction of the expense of recruiting human volunteers, facilitating future clinical NLP tasks. The approach offers flexibility in generating realistic and professional content through prompt design.
Focusing Intracranial Aneurysm Lesion Segmentation by Graph Mask2Former with Local Refinement in DSA Images

Intracranial Aneurysm (IA) lesion segmentation is significant for IA treatment, which is one of the high death rate and deformity cerebrovascular diseases. Segmenting the IA lesions accurately is still challenging in digital subtraction angiography (DSA) images due to blurred boundaries, imaging noise, and intracranial vascular morphologies. In this paper, we are the first time to propose a novel instance segmentation network architecture, Graph Mask2Former, to segment IA lesions automatically based on DSA images. Specifically, we apply a graph convolution module to reassign label information, aiming to adjust the confidence weight of error instances adaptively. Furthermore, we design a local refinement module to refine the coarse mask output. The extensive experiments on the clinical IA- DSA and LiTS datasets show that our method outperforms recent state-of-the-art methods. This paper also provides the visual analysis to explain the inherent behavior of our method.
Internet of Things (IoT) Enabled Image Segmentation Model For Lung Disease Classification: An Approach Based On Particle Swarm Optimization

In the last decades, the domain of IoT has been explored by research community due to its vast real time applications. A combination of deep learning and IoT is well accepted worldwide as using deep learning, IoT devices can be easily converted into intelligence devices. Moreover, these devices are capable enough to take the decision based on real-time data. However, deployment of deep learning model is not so easy in IoT devices as these devices are constraint with limited computational power and storage space. Generally, deep learning architectures are large in terms of storage space, and due to the complication of model, it required resources to generate the output. To overcome the storage space and the large resource barrier, we proposed the method based on the particle swarm optimization technique for compression of the UNet architecture for its easy deployment on IoT devices for semantic segmentation usages. In this paper, all the intermediate steps involved for this compression of UNet using PSO is well explained with suitable examples. Experimentally, it has been proven that the proposed algorithm compresses the UNet architecture in the chest radiograph data set by 77% after 0. 68% decrease in accuracy with an improvement in the inference time by 2.23X.
Modeling of compressible homogeneous sheared non-isentropic turbulence: comparison between isothermal and isobaric initial conditions results

In this study, we use Rapid Distortion Theory (RDT) to simulate compressible homogeneous sheared turbulence in the non-isentropic hypothesis of fluctuating flow. This linear model permits us to analyze compressibility effects on structure and thermal properties of the turbulence. The analysis concerns the root mean square (rms) of thermodynamic variables fluctuations and correlations between them. RDT results are obtained for isothermal and isobaric initial conditions. A comparison between these results is carried out for different values of initial gradient Mach number, which is a compressibility indicator parameter.
Formal Verification of Attitude Control Systems Using Geometric Barrier Functions

Compared to safe obstacle avoidance in the position space, ensuring the safety of the attitude system in today's aerial vehicle operations is more challenging due to the non-Euclidean nature of the attitude space and the underactuated nature of the system. To address this issue, we first propose the geometric exponential barrier condition (GEBC) to produce barrier certificates on the manifold, by which attitude safety requirements can be encoded globally into the verification problems of attitude control systems. Then, we use exponential coordinates to characterize GEBCs, which makes them describable in terms of quantifier-free real arithmetic logic (QF-NRA) and efficiently solvable by current satisfiability modulo theories (SMT) solvers. A performance criterion is further discussed where we propose an effective algorithm to construct safe operational regions with different controllers, which can help with nominal controller selection and tuning. Finally, we demonstrate our approach in a quadrotor system and analyze the safe performance of two PD controllers on the proposed safe operation criterion.
Coordinated Control of Load Tap Changer Transformers for Voltage Regulation and Voltage Hunting Prevention: A Switched Systems Approach

We propose a coordinated control strategy for load tap changer (LTC) transformers in high voltage radial transmission systems that are connected to higher voltage grids and active distribution networks. We utilize switched systems modeling tools to capture the non-smooth characteristics of the LTCs. Our approach employs a state- and time-dependent switching logic to regulate the voltage at a specific node while preventing stability issues produced by the voltage hunting phenomenon. Then, we derive sufficient tuning conditions for the LTC control parameters, namely, deadband widths and time delays, as a function of the tap magnitude of the LTCs. These conditions ensure the existence of an exponentially stable equilibrium point of the closed-loop switched system and the exponential convergence of its output, i.e., the regulated voltage, to a desired set. Finally, a numerical example shows the proposed strategy's superior performance over an uncoordinated scenario.
Improving Medical Image Denoising via a Lightweight Plug-and-play Module

Medical image denoising, as a part of medical image processing, is significant for the assessment and diagnosis of diseases. To improve the medical image denoising performance of existing deep learning methods, we propose a lightweight plug-and-play module (LP2M) with low complexity, which can be plugged before current image denoising methods. Specifically, the proposed LP2M contains three stacked Convolutional Neural Network (CNN) based blocks: a image receptor block, an adaptive receptive field selection block and a high-low frequency processing block. The image receptor block can perceive color or grayscale images and perform preliminary processing. The adaptive receptive field selection block includes two parallel paths with different receptive fields (i.e., convolution kernel sizes) and the adaptive weighting operation, which can process multi-scale information in the image. The high-low frequency processing block consists of a low frequency pathway using convolutional layers with large kernel sizes, and a high frequency pathway using covolutional layers with small kernel sizes, which can process the low and high frequency components in images. Extensive validation experiments are performed on five state-of-the-art denoising methods on multiple medical image datasets for three different medical image denoising tasks (X-ray image denoising, magnetic resonance image denoising and dermoscopic image denoising). Experimental results show that our proposed LP2M can effectively improve the results of these five state-of-the-art methods for three denoising tasks with only increasing 0.996K parameters and 63.112M FLOPs, and it can provide potential direction for improving image denoising.
Fusing Local-Global Facial Features by NFFT for Automatic Depression Estimation

With the increasing number of depressed patients and the development of computer vision technology, the study of individual automatic depression estimation (ADE) methods based on facial images has attracted much attention in recent years. Most existing works focus on obtaining informative features from the whole images with advancing deep learning models, while the local tiny changes and the fusion of different features have been paid less attention. In this paper, a two-branch predicting model with a elaborate transfomer block NFFT is designed to combine global and local features extracted from whole images and image patches for predicting the depression score precisely. Besides, a classification head is added to guide the regression results for improving accuracy. Experiment results on the AVEC2014 dataset (MAE=5.81, RMSE=7.49) demonstrate that the proposed model outperforms other methods, and the extended experiments on one new dataset (CCPL) are conducted to validate the generalizability and robustness of our model.
Investigating Consistency Constraints in Heterogeneous Multi-task Learning for Medical Image Processing

Multi-task learning (MTL), a learning paradigm that leverages the relationships among different tasks to concurrently improve their prediction performance, has been widely applied in the medical image processing field. To tackle the challenge of inconsistent task-specific predictions in early MTL methods, recent works primarily focus on incorporating certain consistency constraints into model training for specific MTL settings, which is difficult to generalize to heterogeneous medical image processing tasks. Meanwhile, the synergy of different consistency objectives has not been well explored. In this paper, we address these gaps by proposing a novel multi-task consistency regularization scheme. It consists of two complementary consistency constraints: Task-level Output Consistency (TOC) which constrains different tasks to give consistent predictions and Feature-level Representation Consistency (FRC) which enforces feature representation consistency between target and auxiliary tasks. Furthermore, we present an optimal strategy to select appropriate constraints for any given set of tasks. Extensive experiments on three popular benchmark datasets were conducted in both the few-shot and semi-supervised settings. The results demonstrate our proposed multi-task consistency regularization scheme improves MTL performance by 13.7% in Dice score on average. Additionally, we show that FRC is effective under all MTL settings, achieving an average 6.3% gain in accuracy.
HiSwin UT: Hybrid Swin Transformers Network for Segmenting 3D in vivo Two-photon Images of Rodent Cerebrovasculature

Two-photon microscopy is currently the preferred technique for in vivo vasculature imaging. The ability to generate high-resolution 3D images of blood vessels could improve our understanding of normal vascular physiology and disease pathogenesis. Automatic segmenting and mapping of these 3D blood vessel networks remain challenging due to vascular geometries' complexity and signal-to-noise ratio limitations. In this paper, we propose a novel segmentation network named HYbrid Swin Unet Transformers (HiSwin UT) to address this problem. The network explores the use of the Swin transformer to segment 3D volumetric vessels in vivo images acquired by two-photon microscopy. We propose to use a hybrid of hierarchical Swin transformers with 3D convolutional neural networks (3D CNNs) as both the encoder and the decoder. The Swin transformer guards guide the 3D CNN laborers at every stage in both the encoding and decoding paths to enlarge the receptive field of the network. Specifically, the 3D vasculature images are projected into a sequence of embedding and fed into the network. The encoders extract features at five different resolutions by using a combination of sifted windows self-attention layers and residual convolutional layers. Then these latent multi-scale features are connected to each decoder via skip connection to cater to the diverse range of vessels in terms of their sizes and shapes. We evaluate this HiSwin UT model on the public two-photon vasculature dataset, extensive experimental results demonstrate the superiority of our network compared with the state-of-the-art models. The source code of the HiSwin UT will be publicly available at https://github.com/UTINK/HiSwinUT.
Enhancing Cancer Gene Prediction through Aligned Fusion of Multiple PPI Networks Using Graph Transformer Models

Despite the considerable progress that has been made in cancer research, identifying cancer genes remains a significant challenge due to the intricate nature of the disease. Given the importance of incorporating gene interaction relationships in identifying potential cancer genes, Graph Neural Networks (GNNs) have garnered increasing attention for their ability to model gene associations effectively. Particularly, recent studies have demonstrated the superiority of GNNs in deciphering the knowledge embedded in Protein-Protein Interaction (PPI) networks for cancer gene prediction. However, these studies primarily focused on a single PPI network, overlooking the valuable insights encapsulated within other PPI networks. Additionally, previous endeavors often centered around pan-cancer datasets, neglecting the importance of predicting specific cancer genes. To address these limitations, we present a novel method called MPIT, which employs Graph Transformer Networks (GTNs) to identify specific cancer driver genes. MPIT effectively integrates data from diverse PPI and multi-omics data via the alignment and fusion of gene representations learned from different PPI networks. We collect three distinct cancer cell line datasets to assess the model performance. Our experimental findings demonstrate the superiority of MPIT over the existing methods, achieving the state-of-the-art performance across all three datasets.
SEDGCN: Sentiment Enhanced Dual Graph Convolutional Networks for Detecting Adverse Drug Reactions

In the realm of medicine and healthcare, adverse drug reactions (ADRs) are a significant contributor to mortality and morbidity. Consequently, it is of paramount importance to closely observe the adverse effects of marketed drugs to minimize associated risks. While current methods for Adverse Drug Reaction (ADR) detection have demonstrated notable efficacy, a significant number of researchers have failed to acknowledge the integral role that sentiment information plays in this process. In this paper, we propose Sentiment Enhanced Dual Graph Convolutional Networks (SEDGCN), a novel method for ADRs detection by incorporating sentiment information. In particular, we first introduce the concept of prompt learning and reformulate the ADR detection task as an aspect-level sentiment analysis task. Subsequently, we construct a sentimentenhanced dependency matrix for each sentence to capture the sentiment knowledge and syntactic information of the sentence. The matrix is then input into the graph convolutional networks to obtain a graph representation of the sentence. Finally, to capture global information, we construct a heterogeneous graph based on all words and sentences and fuse this heterogeneous graph with the sentence-level graph representation for ADR detection. Extensive experimentation on two publicly available datasets, namely TwiMed and Twitter, yielded F1 scores of 78.24% and 75.43%, respectively. These results underscore the efficacy of our proposed model.
A Time-Guided Method for Constructing Combined Medical Event Chains

Medical events, such as diagnostic events, treatment events and examination events, and the relationships between these medical events are of importance in medical research. Moreover, the temporal relationship is one of the most basic medical event relationships. However, people often cannot clearly obtain the relationship between medical events directly from electronic medical records. This defect can lead to inconvenient query construction, inefficient query execution, and poor query performance when queries need to be made for certain diseases or symptoms. In this paper, we propose a method to construct a time-guided medical event chain, which combines medical events of patients with the same disease or symptoms according to the temporal relationship. We use BERT and Bi-LSTM joint encoding to learn contextual information to detect medical event trigger words, and test the effectiveness of our model on MLEE dataset. Finally, we extract medical event chains on the CCKS2020 evaluation dataset. Through the event chain, patients and doctors can more easily understand the trend of the disease and analyze the disease and the treatment effect, and it is helpful for some complex clinical research. Moreover, medical event chain can be easily integrated into existing medical knowledge graph (such as CMeKG) for auxiliary diagnosis.
A Wireless Power and Bidirectional Data Telemetry Prototype Using a Single Inductive Link for Biomedical Implants

This paper presents a prototype of a wireless power and half-duplex data telemetry subsystem using a single inductive link for implantable biomedical applications. In order to maintain continuity of power delivery for bidirectional data telemetry, half-duplex frequency-shift keying (FSK) and resonance-shift keying (RSK) are employed for the downlink and uplink, respectively. Frequency splitting characteristics of short-distance inductive link is utilized to determine the modulation frequencies of FSK, while the RSK is realized by switching a resonant capacitor at the secondary coil. The characteristics of the inductive link combining FSK and RSK are analyzed in detail as the theory basis for device parameter design. A prototype designed with discrete components delivers 125-mW continuous power with 127- and 700-Kbps data rates for the FSK downlink and RSK uplink, respectively, which acts as a reference of chip implementation for implantable wireless power and bidirectional data links.