Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Finite Time Prescribed Performance Fault Tolerant Control for Spacecraft Attitude Maneuver

In this paper, an active fault tolerant control method for spacecraft against actuator faults, uncertainties and disturbances is investigated. First, an adaptive iterative learning observer with improved adaptive law is proposed, which greatly improves the accuracy and speed of fault estimation. Then, a novel adaptive finite time prescribed performance fault tolerant controller is proposed, which has flexible performance constraints according to faults and control references, with better robustness and lower conservatism, breaking the limitation of fixed performance constraint. Next, an online optimal control allocation strategy is designed to achieve high-performance actuator allocation under saturation and fault constraints. Finally, through numerical simulation, the effectiveness and robustness of the proposed scheme are illustrated by comparing with existing methods.
Life-course reconstruction for mobile individuals in an Early Bronze Age society in Central Europe: Concept of the project and first results for the cemetery of Singen (Germany)

The German Ministry of Education and Research sponsored project “Life course reconstruction of mobile individuals in sedentary societies”, run by the Chair of Protoand Prehistory, University of Leipzig, and by the Dept. of Human Evolution, Max Planck Institute of Evolutionary Anthropology (MPIEVA), investigates mobility as part of the human life course in cultural groups during the beginnings of the Bronze and Iron Ages in Central Europe. Starting with the basic question ‘What role do individual mobility and the integration of foreign persons play in the development of prehistoric sedentary societies? ’ the archaeological subproject investigates the links between the mobility of individuals and gender roles and between the former and the transfer of technologies in prehistoric sedentary societies. The results of isotope analyses now offer the chance to compare data on geographical provenance with cultural data. So the research of the MPI-EVA’s component of the project focuses on the analysis of five stable isotopes – strontium, oxygen, sulphur, carbon, and nitrogen – to reconstruct dietary habits and provenance in different life stages. The project focuses on the Early Bronze Age cemetery of Singen (district of Constance) and the Early Iron Age burial mound the Magdalenenberg near Villingen-Schwenningen (Schwarzwald-Baar district), both in Baden-Württemberg, Germany.
Sparse Coding-based Multichannel Spike Sorting with the Locally Competitive Algorithm

Spike sorting is a crucial step in the analysis of multichannel neural signals that enables the identification of individual neurons’ activity. However, the limited availability of low-power neuromorphic spike sorting methods is due to the difficulty of processing high-density multichannel extracellular neural signals. In this study, we propose to use the locally competitive algorithm (LCA) that has been previously implemented on neuromorphic hardware as a novel feature extraction method for spike sorting. Based on a bio-inspired neural network, LCA can learn a signal-dependent dictionary of spatiotemporal features and give highly sparse representations. The proposed approach results in better sorting accuracy at low signal-to-noise ratios compared to k-SVD, a well-known sparse coding model, and the principal component analysis (PCA), a classical approach in spike sorting. This network-based solution paves the way for neuromorphic processing of multichannel neural signals in future brain implants.
DFNodule: a Novel Deformable Faster R-CNN for Lung Nodule Detection

In computer-aided diagnosis systems, lung nodule detection plays a crucial role in the overall framework. In this work, we propose a new three-dimensional deformable convolutional neural network (dcnn) method for lung nodule detection based on the Faster R-CNN framework. We incorporate deformable convolutions to design a hybrid convolutional module, which enhances feature extraction in the lung nodule detection model. By leveraging the deformable convolutions’ characteristics, the network is capable of capturing the diverse morphological variations of lung nodules, addressing challenges such as large morphological variations and the inability to capture unified image features. This improves the accuracy of the lung nodule detection algorithm. Additionally, we employ a second-stage network to further discriminate suspected nodules, which enhances the recognition of non-nodule tissues and reduces false positive nodules. To comprehensively evaluate the performance of various lung nodule detection models, we conducted experiments using the publicly available LUNA16 dataset. Our method surpasses other detection algorithms in terms of CPM, achieving a 1.5% improvement. Particularly, the nodule recognition rate is significantly improved at lower false positive rates. In addition, in other metrics such as F1-score, AP, we also achieved 0.6%, 2% improvement.
Modeling and Optimal Control for a Class of One Dimensional Counter-Swarm Problems with Distributed Point Actuation

This paper presents a class of counter-swarm problems wherein the mean-field behavior of the swarm is modeled as an infinite dimensional system. This work considers two classes of problems, one in which the spatial distribution of the agents is fixed and one in which it is dynamic and driven by the standard continuity equation in mechanics. The counter-swarm objectives are formulated as optimal control problems and solved numerically using deep Q-networks.
Enhanced Word Embedding with CNN using Word Order Information For Sentiment Analysis

On several e-commerce platforms, internet users share opinions. Understanding people's sentiments and opinions is necessary. GloVe, which uses word contexts and matrix vectorization, is an effective word vector learning method. Vector-learning systems have improved with this method. However, the GloVe model ignores word order in context. This paper presents Positional Embedding, which integrates word order into GloVe word embeddings. We found that our Word Order Vector (PEWOVe) word embeddings method outperforms GloVe in sentiment classification. Amazon data was used to test the proposed technique. The proposed model is 2.7%, 2.5%, and 2.1 % more accurate than baseline models on GE, GEC, and GE+PE.
ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field

Autism Spectrum Disorder (ASD) is a heterogeneous disorder in children, and the current clinical diagnosis is accomplished using behavioral, cognitive, developmental, and language metrics. These clinical metrics can be imperfect measures as they are subject to high test-retest variability, and are influenced by assessment factors such as environment, social structure, or comorbid disorders. Advances in neuroimaging coupled with machine-learning provides an opportunity to develop methods that are more quantifiable, and reliable than existing clinical techniques. In this paper, we design and develop a deep-learning model that operates on functional magnetic resonance imaging (fMRI) data, and can classify between ASD and neurotypical brains. We introduce a novel strategy to transform time-series data extracted from fMRI signals into Gramian Angular Field (GAF) while locking in the temporal and spatial patterns in the data. Our motivation is to design and develop a novel framework that could encode the time-series, acquired from fMRI data, into images that can be used by deep-learning architectures that have been successful in computer vision. In our proposed framework called ASD-GResTM, we used a Convolutional Neural Network (CNN) to extract useful features from GAF images. We then used a Long Short-Term Memory (LSTM) layer to learn the activities between the regions. Finally, the output representations of the last LSTM layer are applied to a single-layer perceptron (SPL) to get the final classification. Our extensive experimentation demonstrates high accuracy across 4 centers, and outperforms state-of-the-art models on two centers with an increase in the accuracy of 17.58% and 6.7%, respectively as compared to the state of the art. Our model achieved the maximum accuracy of 81.78% with high degree of sensitivity and specificity. All training, validation, and testing was accomplished using openly available ABIDE-I benchmarking dataset.
A High-Voltage, Implantable ASIC for Active Interfaces of the Vagus Nerve

Versatility of neural interfaces is a key factor in the efficacy of bioelectronic solutions for treating neurological and cardiac disorders. High-density and highly selective interfaces demand a small form factor, resulting in very high electrode impedances that require a large compliance voltage. This paper presents the design of a low-noise, electrode-driving ASIC in 180-nm high-voltage CMOS technology to achieve concurrent stimulation and recording for vagus nerve interfaces. Inter-channel crosstalk is minimized using pole-shifting. The ASIC has a variable recording gain of 60 to 74 dB and programmable, stimulating currents of 4 to 124 μA, which account for variations in the electrode characteristics. The ASIC occupies an area of 2.31 mm2. Measured results show clear biphasic stimulus pulses ranging in widths from 10 to 500 μs. To address the impact of artifacts on biopotential recording, the recording amplifier has a very low input-referred noise of 2.92 μVrms over a variable bandwidth that ranges from 300 Hz to 1.4 kHz or 6.6 kHz with or without pole shifting, respectively.
PLFormer: Prompt Learning for Early Warning of Unplanned Extubation in ICU

Patients’ Unplanned Extubation (UEX) behaviors in ICU have adverse effects on their postoperative recovery. Therefore, it is necessary to design a early warning systems to detect UEX tendency. However, the fineness and rapidity of UEX behaviors, coupled with the complexity of the ICU environment, renders the utilization of RGB monitory videos for early warning extremely challenging. To address the aforementioned challenges, we propose a PLFormer to make early warning of UEX behaviors in ICU by using the prompt learning approach. Specifically, we provide click prompts to the Track Anything model (TAM) with the ability to segment and track patient regions, producing a mask sequence. Then we introduce the Prompt-Guided Adaptive Fusion (PAF) module, which utilizes mask prompts to guide the model’s attention towards the dynamically active region at both global and local levels. Subsequently, we incorporate the ST-Transformer to delve deeply into the spatial fine-grained representation and long-term temporal dependency properties of UEX behaviors. Experimental results demonstrate that our PLFormer achieves state-of-the-art performance on an ICU monitory dataset.
Utilizing Transfer Learning in the Udacity Simulator to Train a Self-Driving Car for Steering Angle Prediction

Recently, the study of self-driving autonomous vehicles has gained great popularity. The development of automated driving will change humans’ lives which can provide a fully safe transportation system for them. Such a system requires huge computing power with a high cost, So the simulator is a good choice to train and test self-driving cars as an initial step before implementing them in real. This paper provides a study on how a self-driving car is trained for steering angle prediction and following the road lane in the Udacity simulator via the input data from the car's front-center camera. It also explores the utilization of pre-trained Keras transfer learning models in self-driving cars. The methodology involves collecting the necessary driving data and preprocessing it. Then the transfer learning models are used as base models. The models are trained on a part of the lake track road of the Udacity simulator and tested on the rest part of the road. Also, the trained models have been evaluated by mean square error, the coefficient of determination R square, and Root mean square error. The car on the simulator acts autonomously very well and follows the road’s lane using the center camera which shows promising performance. The findings contribute to the advancement of autonomous driving technology and provide insights for further research in this field.
Plug and Play Prior Regularized Algorithm for Acoustic Resolution Photoacoustic Microscopy Bioimaging System Enhancement

As a novel biomedical imaging setup, acoustic resolution photoacoustic microscopy (AR-PAM) employs photoacoustic effect for visualizing vasculature and bio-structure. Nevertheless, this modality suffers from low imaging resolution which greatly limited the application for clinical usage. Model based and data driven algorithms have been developed to enhance the imaging resolution and quality, however, there exists limitations in both kinds of algorithms respectively. In this paper, we proposed a Plug and Play prior regularized AR-PAM imaging enhancement algorithm, which combined the merits of both model based and data driven algorithms, where the prior term is learned by a denoising network and served as sub-module to the optimization framework. The physical model is also derived based on acoustic beamforming theory that characterizes the AR-PAM degradation mechanism and directly incorporated into the algorithm. Finally, the proposed algorithm is applied to enhance the simulated and in vivo AR-PAM imaging results to prove its performance. Perceptually, the proposed algorithm achieves the best enhancement quality when compared with total variation based algorithm. Quantitatively, the evaluation metrics also achieve the best values for simulated and in vivo enhancement results.
Brain Tumor Segmentation from MR Images using Customized U-net for a Smaller Dataset

In medical image analysis, deep learning has emerged as a powerful tool for solving complex tasks such as segmentation. This research presents an original approach using a customized U-net model for automatic brain tumor segmentation in Magnetic resonance imaging scans. The model's performance is thoroughly assessed using metrics like Mean Dice Similarity Coefficient, Sensitivity, Specificity, and Accuracy. The study evaluates the model's proficiency on 50 test images from in-house and established BRaTS 2020 datasets. The proposed U-net model showcases its superiority by achieving high Dice Similarity Coefficient scores, indicating a solid alignment with reference segmentations. It also demonstrates impressive sensitivity and specificity values, signifying its capacity to capture tumor regions and true negatives accurately. The results demonstrate that the proposed model effectively segments tumors on a relatively smaller dataset.
SAR Tomography Reconstruction using ISTA and GLRT Techniques

SAR Tomography (TomoSAR) leverages the utilization of multiple baselines to gather data about an area, enabling the acquisition of information not only in the range-azimuth direction but also in the elevation direction. This additional information in the elevation direction primarily consists of point scatterers, with one or more scatterers typically present within a single resolution cell. This research paper examines two distinct methodologies for detecting multiple scatterers in TomoSAR: the conventional Generalized Likelihood Ratio Test (GLRT) technique and a compressive sensing (CS) approach known as the Iterative Soft-Thresholding Algorithm (ISTA). The results are examined for a simulated case of two scatterers and a real case using the TerraSAR-X dataset.
Integrating Heterogeneous Biological Networks and Ontologies for Improved Protein Function Prediction with Graph Neural Networks

Elucidating protein functions is critical to advance our understanding of biological systems. However, the majority of proteins lack functional annotations due to the pace of manual knowledge-driven curation of the Gene Ontology (GO) terms. Automatic function prediction (AFP) aims to computationally infer these missing annotations by integrating sequence, interaction, and ontology data. Current AFP methods have yet to fully utilize heterogeneous relationships across multi-omics networks between non-coding RNAs, proteins, and the GO hierarchies. To address this, we introduce LATTE2GO, a novel graph neural network that integrates protein, RNA, and GO function entities and their interactions into a unified representation. By extracting higher-order associations with attention mechanism, LATTE2GO achieved significant gains over previous graph-based AFP techniques on CAFA4 benchmarks. Our analyses revealed modeling specific protein-protein interactions (PPI) and GO relationships increases accuracy in predicting molecular functions and biological processes. Overall, LATTE2GO demonstrates that heterogeneous graph neural networks can integrate diverse omics knowledge to advance systems-level understanding of protein roles within the complex milieu of functional and physical interaction networks.
Control of Polytopic LPV Systems with Uncertain Initial Conditions

This paper focuses on the control design and analysis for nonstationary linear parameter-varying systems with affine parameter dependence and uncertain initial conditions. The uncertain initial state and the disturbance input are allowed to reside in two separate norm balls. Convex analysis and synthesis conditions are derived, and a reachability analysis result for systems with pointwise-bounded inputs is developed, enabling the construction of ellipsoids in which the state or some output of interest lies at specified time instants. The usefulness of the proposed approach is demonstrated through an illustrative example involving a two-mass rotational system.
Framework for Protein Structures Conformation Analysis

Protein three-dimensional structure drives binding interactions, thus enabling proteins to perform their functions. However, a structure can undergo structural changes due to the changing environment. Analysis of datasets covering these structural states is challenging in terms of complexity and time consumption. Therefore, we propose a database framework designed to store, retrieve, and visualize information about changing structure conformations. The framework enables navigation between the series of transformations and investigation of each transformation individually via a web application. The framework sets the stage for a more accessible analysis of the dynamic molecular processes.
Signal Comparison Average Consensus Algorithm Under Binary-Valued Communications

The average consensus problem under binary-valued communications is investigated in the paper. Measurement noises and fixed quantizers are considered. A signal comparison algorithm is proposed for the problem. Neither the noise distribution information nor assumptions on the states' approximate locations are required for the algorithm design. The signal comparison algorithm is proved to achieve average consensus both in the almost sure and mean square sense. The algorithm's mean square convergence rate is also calculated. The efficiency of the algorithm is demonstrated by a numerical example.
Diffusion-Enhanced Graph Attention Network for Cancer Type Classification

The prediction of cancer types through protein expression levels is of paramount significance for effective patient treatment. In recent years, numerous methods for cancer classification based on protein expression levels have emerged. Most of these methods treat proteins as independent agents, disregarding the intricate interactions among them. A minority of methods considers the interactions between proteins but oversimplifies protein interactions by assuming equal strength and neglecting intermediate impacts. To address the essence of varying intensity of protein interactions and distant intermediate correlations among proteins in cancer classification, this paper introduces a novel diffusion-enhanced graph neural network, named DEGAT. DEGAT establishes a weighted protein-protein interaction network to account for the diverse strengths of protein interactions. It then employs a diffusion process to quantify the multi-hop influence between proteins and feeds this information to a graph attention network for accurate cancer type prediction. Experiments conducted on both breast cancer and pan-cancer datasets demonstrate the superior performance of DEGAT compared to other state-of-the-art methods. Further analysis substantiates the robustness of DEGAT and underscores its potential for real-world applications.
FLAREFUZZ: Fuzzing Javascript Engines via Deep Learning

During the past few years, Fuzz testing has been a popular technique for finding bugs in software. It continuously inputs a large number of test cases into the target program and monitors the execution status to discover potential vulnerabilities. The efficiency of fuzzing tools heavily relies on the quality of test cases. However, Fuzz testing faces a challenge due to the specific input requirements of many software applications. The JavaScript engine is used to interpret and execute JavaScript files, and it requires that the input files should meet the syntax and semantic rules of JavaScript. Traditional fuzzing tools usually ignore the file format requirements and struggle to generate valid test cases, which makes it difficult to expose deep vulnerabilities in JavaScript engines. To address this challenge and enhance the quality of test cases, this paper proposes a novel fuzz testing approach leveraging deep learning techniques, named FlareFuzz. FlareFuzz utilizes a GRU network to learn syntax and semantic knowledge from AST and CFG of JavaScript programs. The structured representation of the code contains rich semantic information, which helps the deep learning model capture the latent semantic rules from the corpus. In addition, the performance of the GRU network makes it more suitable for fuzz testing since it is faster than LSTM and effectively captures long-distance dependencies in sequences. Thus, the introduction of GRU helps FlareFuzz achieve high efficiency. Evaluations indicate that FlareFuzz performs better than previous approaches in generating test cases. Furthermore, experiments conducted on mainstream JavaScript engines demonstrate that FlareFuzz also performs well in real scenarios.
Performance Optimization of Angle-Based Network Localization

Recent advances in sensor network localization have enabled sensor nodes to localize themselves by using the measurements of inter-node angles. According to our earlier work, the proposed angle-based localization algorithms' performance, particularly, the convergence rate, is relatively poor, which, however, has not been adequately addressed in the existing literature. Motivated by this, this paper aims to improve the performance of angle-based localization algorithms, specifically, the stability margin, convergence rate and robustness against measurement noises. Firstly, we show that the stability margin, convergence rate and robustness of angle-based localization algorithms are commonly determined by one parameter, namely, the minimum eigenvalue of the network's localization matrix. Secondly, we formulate the performance optimization problem as an eigenvalue optimization problem, and show the non-differentiability of the eigenvalue optimization problem. By carefully choosing the decision variable, we utilize interior-point methods to obtain an optimal solution to the eigenvalue optimization problem. Finally, simulation examples validate the improvement of the algorithms' performance.