Miracle-dz/UniFAR-FTUs huggingface dataset:
Formin protein FMNL1 is a biomarker for tumor-infiltrating immune cells and associated with well immunotherapeutic response

Background: Increased studies on the basis of bulk RNA-sequencing (RNA-seq) data of cancer identify numbers of immune-related genes which may play potential regulatory roles in the tumor microenvironment (TME) without in-depth validation. Methods: In the current study, the immunological correlation and cell subpopulation expression pattern of FMNL1 were analyzed using public data. In addition, the cell subpopulation expression pattern of FMNL1 was also deeply validated using single-cell RNA-sequencing (scRNA-seq) and multiplexed quantitative immunofluorescence (mQIF). Results: Bulk FMNL1 mRNA was related to better prognosis in hepatocellular carcinoma (HCC) and was able to identify immuno-hot tumor in not only HCC but also multiple cancer types. Bulk FMNL1 mRNA also predicted the response to immunotherapy in multiple cancers. Further validation using scRNA-seq and mQIF revealed that FMNL1 was a biomarker for immune cells. Conclusions: FMNL1 is a biomarker for immune cells in not only hepatocellular carcinoma, but also multiple cancer types. Moreover, immune infiltration analysis using the bulk RNA-seq data would be further validated using scRNA-seq and/or mQIF to describe the cell subpopulation expression pattern in tumor tissues for more in-depth and appropriate understanding.
Research on Visual-Inertial Fusion SLAM Based on Mean Filtering Algorithm

Visual SLAM has rich application scenarios and can work both indoors and outdoors. However, it is greatly affected by ambient light and cannot work in dark places. There are cumulative errors in map construction, which poses challenging requirements for precise positioning and low computing costs. To solve this problem, this paper introduces a SLAM system based on fusion of vision and inertial measurement unit (IMU). Aiming at the angle error existing in the raw data of the IMU sensor, a mean filtering algorithm is designed in this paper. The IMU data is filtered and the visual information is used as input to the SLAM system to improve the positioning accuracy and robustness of the system. Experimental results prove that the mean filtering algorithm for IMU data processing can effectively decrease the angular error of the system. The implementation of the proposed method shows its potential in practical applications, especially in environments with high precision requirements.
AdaptST: Adaptive Fusion-based Spatio-Temporal Networks for Urban Flow Prediction

Urban flow prediction is of great importance to intelligent traffic system. Recently, it has attracted the attention of many researchers due to the vital impact of deep learning and big data. There are complex nonlinear dependencies such as spatio-temporal dependencies and external factors existing in the urban flow, which makes the accurate prediction challenging. Previous work has applied various deep neural networks in the task, in order to capture complicated dependencies. However, many of them rarely take into consideration all kinds of spatio-temporal dependencies, ignoring or weakening the influence of certain dependencies. In this paper, we propose a method entitled AdaptST (Adaptive fusion-based spatio-temporal networks) based on two kind of adaptive fusions in spatio-temporal networks. One of adaptive fusions is designed in the variant of ConvLSTM (Convolutional LSTM) cell, which fuses multiscale local and global features selectively instead of adding them directly. Another is adopted at the end of the long-term component to further boost the performance by attention-based weighted summation of feature maps at different time steps. Experimental results on real-world traffic dataset verify the effectiveness of our methods.
Zero-Voltage Transition Based Dual-Switch Enhanced Gain Boost Converter

In this paper, a zero-voltage transition technique is introduced to the switching capacitor-based boost topology. The unique aspect of this topology is that it is capable of achieving high voltage transformation ratios even at smaller duty ratios. To address the common issue of switch current and voltage overlap prevailing in hard-switched converters, soft turn-ON of switches is implemented for the switching capacitor-based boost converter. Soft turn-ON of switches effectively realized through the well established zero-voltage transition (ZVT) network. An added advantage of this ZVT network is that the diodes undergo with zero-current switching (ZCS), thereby minimizing the losses in the diodes. The paper establishes theoretical analysis followed by simulation studies on the switching capacitor-based boost topology for concept demonstration.
Fine-Grained Detection of Apnea-Hypopnea Events Based on Transformer Network in Audio Recordings

Sleep Apnea Hypopnea Syndrome (SAHS) is a common sleep disorder that affects approximately 7% of adults worldwide. The clinical gold standard for diagnosis, polysomnography (PSG), is expensive and unsuitable for long-term monitoring. In recent years, home sleep monitoring methods based on sensors and signal processing technologies have become popular. This paper aims to develop a method for detecting apnea and hypopnea events using only audio signals without causing discomfort to the user. To overcome the limitations of traditional methods in dealing with time-frequency domain dependencies and the problems of low accuracy and ineffectiveness of audio event segmentation at a single scale, a segmentation model based on Transformer and multi-scale feature fusion is proposed to effectively utilize global information and multi-scale features to achieve localization of event start and end times. Experiments on the PSG-AUDIO public dataset achieve a Dice similarity coefficient of 0.98 and the segment-based f1-score of 0.95, demonstrating the great potential of the method in detecting SAHS and providing a new way for non-invasive home sleep monitoring.
Receding Bid Optimization Method with Real-Time Feedback and Prediction for Sponsored Search Advertising on Taobao

Since the popularity of the Internet, sponsored search advertising has become one of the most vital marketing methods in e-commerce. As one of its cores, the bid optimization problem seeks a bidding strategy that helps advertisers maximize their objectives subject to commercial constraints. We model it as a Markov Decision Process (MDP); however, directly solving such MDPs is inefficient and impractical due to the complexity, computation, and magnitude. Thus, we propose the receding bid optimization method. Then the problem is transformed into a constructed bidding formulation with finite parameters. Meanwhile, we show that parameters can be solved by data-driven linear programming (LP) based on real-time feedback on constraints. Also, the optimality of the bidding formulation is proven. Furthermore, this method must be computation-friendly, considering Taobao's magnitude and reality consumption. Therefore, a down-sampling process is proposed, which achieves over 90% accuracy with only 3% of data. Besides, an auction scale prediction module is added to improve the adaptiveness under dynamic markets. Finally, two-month-long online experiments validate its outstanding performance compared to several state-of-the-art methods. Nowadays, the proposed method has been deployed on Taobao and is used for nearly billions of auctions every day.
Transformer based Twitter Trending Topics Sentiment Drift Analysis in Real Time

Analyzing sentiment drifts in real time Twitter trending topics poses significant challenges due to the multitude of trends associated with specific locations. This paper presents an enhanced framework, called trending TSDA-BERT, which builds upon previous works [2, 4] to perform sentiment drift analysis for trending topics in real time. The framework incorporates topic modeling to classify trending hashtags into distinct domains and utilizes a Random Forest Regression (RFR) model to predict the sentiment drift forecasting measure for trending topics and performs sentiment drift analysis on topics surpassing a predefined threshold. Experimental evaluations conducted on a sample of real time trending hashtags demonstrate the framework’s effectiveness, achieving an accuracy of SS.3% in real time. While the results provided serve as proof of concept for a limited set of topics, the proposed framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams across various topics. This research contributes to the advancement of real time sentiment drift analysis in the dynamic landscape of Twitter trending topics even in the presence of misleading profiles and tweets.
Few-shot Underwater Acoustic Target Recognition Based on Siamese Network

Underwater acoustic target recognition is a widely investigated issue in the field of underwater acoustics. Many good results have been reported for underwater acoustic target recognition. However, in practical applications, the strong demand for labeled data for underwater acoustic target recognition is a big obstacle. In order to solve this problem, researchers have explored few-shot learning and unsupervised methods in various papers. A Siamese network is proposed which is composed of one-dimensional convolution and Long Short-Term Memory (LSTM) neural networks, called 1DCLSN. A structure for 1DCLSN is designed which combines contrastive information with label information and obtains satisfactory recognition results. In addition, the contrastive loss function with a different clustering term is modified to improve the performance. With only few labeled training samples, the performance of the proposed approach is better than those of other deep learning methods. The experiment shows the great potential of our method.
Asymmetrical Eleven-Level Multilevel Inverter with Low Switching Count and Voltage Stress

This research presents an eleven-level multilevel inverter with decreased switching count and voltage stress. There are only 7 switches in the architecture, including 1 bidirectional switch and 2 capacitors. The inverter circuit is given a gate pulse using a straightforward nearest level control (NLC), which is a fundamental switching frequency-based approach. To demonstrate the advantages of the presented topology, a comparison with recent topologies in terms of switches, diodes, sources, and capacitors employed in numbers is made. According to the IEEE 519–2014 standard, the total harmonic distortion (THD) is 8.6%, which is almost within acceptable bounds. Eventually, using the software PLECS and MATLAB (SIMULINK), the recommended architecture has been operationally and functionally simulated. Its experimental validation is also being done on the setup for various loading circumstances.
A Novel Tandem Ducted Fan UAV Attitude Control Based on Cascade PID Controller

In this paper, we introduce a novel tandem ducted fan unmanned aerial vehicle (UAV) and focus on the attitude control problem of it. Firstly, the structural layout of this new vehicle is introduced, then the force analysis and mathematical formula are performed based on kinematic and dynamical formulas. Secondly, this section is not only focuses on the degsign of controller but also the analysis of its advantages. For example, how they can improve system performance through frequency domain analysis, implementation of specific algorithms. Thirdly, an outdoor flight experiment is completed. The experimental process is as follows: after having the appropriate parameters, the vehicle is first allowed to fly to a height of about 10 meters and hover down, and then three attitude angle changes are performed. Finally, we conclude that the cascade PID controller for this type of UAV attitude control is simple, practical and can cope with many flight conoditions and indicate the shortcomings of this dissertation and future work.
An Effective Automated Framework for Oral Cancer Detection by Enhanced Convolutional Neural Networks

Oral cancer, an aggressive form of cancer affecting the mouth and throat, has been on an alarming worldwide increase due to tobacco use and smoking. Oral cancer presents various challenges such as high incidence rates, delayed diagnosis, inadequate treatment plans and late prognoses and outcomes; early detection is crucial in improving prognosis outcomes and survival prospects. Machine learning techniques have gained prominence for improving cancer diagnosis while decreasing mortality and morbidity; in recent years they were used to detect oral cancer, providing highly accurate classification tasks yet limited capabilities in terms of feature extraction capabilities. To address these limitations, we are currently employing a deep-learning approach. To this end, we have devised an automated framework encompassing feature extraction, preprocessing and classification. Contrast-limited adaptive Histogram Equalization (CLACHE), used for effective pre-processing purposes to increase image contrast and resolution, is one form of pre-processing while Gray Level Cooccurrence Matrix extracts statistical texture-based features; finally, a Modified Convolutional Neural Net (MCNN) was employed for accurate classification of oral cancer cases. Utilizing Kaggle Repository dataset, we used it to assess the performance of our framework, while comparing it against other high-performance methods. Our proposed framework had an accuracy rating of 99.13% which surpasses current state-of-the art approaches.
Multi-satellite localization based on QR decomposition

Multi-satellite localization is a common localization method, which mainly uses time difference of arrival (TDOA) for localization. Many algorithms have been proposed for multi-satellite localization. However, due to the non-convex characteristics of the optimization problem, these algorithms tend to fall into local minima and degrade the localization accuracy. In this paper, we consider the position constraint that the target is located on the Earth surface, and convert the non-convex quadratic equation constraints to linear constraints in the iterative process. This paper proposed a localization algorithm based on QR decomposition, which can guarantee convergence to the global optimal solution. It is demonstrated through simulations that the algorithm can achieve the corresponding Cramer-Rao lower bound (CRLB), and the localization accuracy can be effectively improved by considering the position constraint of the target.
A Deep Learning Approach for Lunar Impact Crater Detection Based on YOLO v7 and CBAM Attention Mechanism

Impact craters are the most common geomorphic unit on the lunar surface, and there are a large number of impact craters of different sizes and morphologies on the lunar surface. Lunar impact craters are a key basis for lunar geological studies, and their study allows for the exploration of the Moon and other planets. Therefore, this paper builds a convolutional neural network YOLO V7_CBAM based on YOLO V7 and the attention mechanism for identifying lunar impact craters. Based on the full-moon CCD images and DEM images provided by NASA, a comparison test was conducted and the precision of YOLO V7_CBAM based on both images was higher than that of YOLO V7, from 72.31% to 74.65% based on CCD images; and from 70.60% to 71.70% based on DEM images.
Few-Shot Segmentation via Channel Attention and Supervision Augmentation

The effect of the semantic segmentation methods has been significantly improved by introducing deep convolutional neural networks (CNNs) in recent years. But, these approaches need quantities of data with pixel-level labels and have poor generalizability. Few-shot segmentation (FSS) is subsequently proposed, which can complete the segmentation of unseen classes with only a small amount of annotated data. However, existing approaches have the following two problems. First, they ignored the connection between feature channels of support features and query features. Second, they did not fully mine the information in the support set. These two problems harm the segmentation performance. In this study, we design a new FSS approach to alleviate the above two problems. Our model consists of two parts: the channel attention module is designed to fully mine the connection of the feature channels of support and query set, and the supervision augmentation module is designed to fully utilize the information in the support set and strengthen the supervision of the model. Extensive experiments verify that our model achieves 0.9% and 1.3% mIoU (mean intersection over union) improvements over the baseline on the PASCAL-5i dataset for 1-shot and 5-shot tasks, respectively, and our approach is more lightweight than other state-of-the-art (SOTA) approaches.
Sparse Representation Analysis of Space Object Visible Imagery based on Compressive Sensing

Compressive sensing (CS) technology combines sampling and compression in the traditional information acquisition process. According to CS, the amount of measurement data is far less than that of traditional sampling, which can effectively alleviate the pressure of spaceborne data storage and processing in space observation field. Sparse representation is the prior factor of applying CS in space object visible imagery. This paper quantitatively studies the sparse representation of space object scene in various orthogonal complete bases and different representation methods. The sparse representation performance of space object image in two datasets is analyzed under different sparsity settings and evaluated by overall image error and structural similarity indexes. The simulation results show that the sparsely represented space object image can keep the image quality and structural information in most cases. The conclusion verifies the admirable sparsity of space object image for CS, and supports the further research on the space observation of compression imaging method.
Weighted Ensemble Algorithm for Aerial Imaging Based Mosquito Breeding Sites Classification

This work proposes an automated detection system that identifies potential mosquito breeding sites using drone images and an ensemble learning system. Four learning architectures, namely shallow convolutional neural network (CNN) with RGB images, shallow CNN with LAB color images, Support Vector Machine (SVM), and few-shot learning (FSL), were compared for detecting potential mosquito breeding sites, i.e water pools, in aerial images. The research adopted a supervised learning approach and utilizes a labeled custom dataset of aerial images to train these models. The CNN trained on LAB color space achieved the highest classification accuracy at testing which is 90.01%, followed by the CNN trained on RGB color space with an accuracy of 84.29%. The SVM and FSL models achieved a testing accuracy level of 79% and 80% respectively. Later these four learning models were strategically combined using a weighted ensemble approach to combine the strengths of each model in order to increase the accuracy of the overall system. The proposed ensemble architecture has the potential to support public health inspectors in identifying potential mosquito breeding sites, i.e water pools, especially in places that are inaccessible to humans, with high accuracy. Hence, these findings carry significant implications for improving vector-borne disease control strategies and reducing the burden of vector-borne diseases.
Distributed Path Planning for UAVs Based on $\mathbf{A}^{\ast }$ Algorithm of Dubins ath

This paper proposes a path planning method for unmanned aerial vehicles(UAV). Aiming at the problem that the traditional grid-based $\mathrm{A}^{\ast}$ search algorithm does not consider flight performance constraints, which cause the path discontinuity. This paper adopts the Dubins curve to find effective nodes, and combines the idea of $\mathrm{A}^{\ast}$ heuristic search to generate obstacle avoidance paths composed of Dubins curves for UAVs. This paper then combines the proposed algorithm with leader-follower control algorithm and applies the algorithm to multiple UAVs formation. The simulation and experimental results show that the method is feasible. Results shows it takes advantage over the commonly used $\mathrm{A}^{\ast}$ algorithm applied on a grid map in aspects of the optimality and efficiency, and it can achieve obstacle avoidance in formation flying.
An Improved DVB-S2 Phase Coarse Synchronization Algorithm and Its FPGA Implementation

This paper proposes an improved phase coarse synchronization algorithm, which can be applied to the DVB-S2 system. The improved algorithm can handle the uncorrected residual frequency offset from the frequency synchronization module. Compared with the original algorithm, the residual frequency offset is nearly doubled threshold, and reduces the complexity of FPGA implementation and saves hardware resource overhead. The scheme is implemented on Xilinx Virtex-7 VC709 field programmable gate array, and simulation verifies that the scheme can achieve bit error performance below 10-7 within the supported residual frequency offset range.
A Dynamic Feature Regression Network for Industrial Soft Sensor Modeling

Soft sensor has been extensively applied for online estimation of the key quality variables in modern industrial processes, which is extremely important for the process to achieve efficient monitoring and smooth control. To build an accurate data-driven model, the dynamic correlation and strong nonlinearity of process sequential data must be considered in soft sensor modeling. Therefore, a dynamic feature regression network (DFR) is proposed in this paper for industrial soft sensor modeling, consisting of a dynamic feature extraction network and a feature regression network to explore different industrial data features. First, the dynamic feature extraction network maps time series samples to a set of hidden dynamic features, while the feature regression network performs deeper feature extraction and output regression on key quality variables. Furthermore, since both networks consist of unsupervised feature extraction and supervised feature regression, a large scale of unlabeled samples can be utilized in the semisupervised learning of model parameters. Finally, the feasibility and efficacy of the proposed model are verified through the coal-to-ethylene glycol process data to predict the carbon monoxide content.
DCIM-3DRec: A 3D Reconstruction Accelerator with Digital Computing-in-Memory and Octree-Based Scheduler

Learning-based 3D reconstruction has evolved rapidly with promising quality, while it requires high-performance hardware for interactive applications. In this work, a reconstruction accelerator called DCIM-3DRec is presented which leverages digital computing-in-memory (DCIM) design to facilitate learning-based reconstruction deployment on realtime and low-power edge platforms. The DCIM-3DRec is designed with the following features: a reconfigurable DCIM macro array for high data reuse and macro utilization, and an Octree-based subdivision scheduler for efficient management of 3D space prediction. The DCIM-3DRec accelerator is implemented and evaluated in TSMC 55 nm technology, with a DCIM macro efficiency of 19.4 TOPS/W at INT8. Overall, the DCIM-3DRec accelerator achieves 23× performance gain and four orders of magnitude energy efficiency improvement compared to a Nvidia RTX3090 GPU.
Distributed Observer-Based Simultaneous Fault Detection and Formation Control of Multi-UAVs

This paper studies the formation control and simultaneous fault detection of multi-UAVs. Firstly, a distributed observer is constructed for the second-order particle model of each UAV. Secondly, a novel formation controller is designed based on the distributed observer. Then, the faulty UAV is detected by the residual signal. Finally, based on Lyapunov theory and Projection Lemma, sufficient conditions for UAVs formation are obtained. The effectiveness of the proposed method is verified by simulation.