Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Media Misinformation Propagation and Detection

Misinformation, also known as "fake news," is a growing problem in social media. It refers to the dissemination of false information, intentionally or unintentionally, in digital media platforms such as Facebook, Twitter, and Instagram. The issue has gained global attention due to its potential to cause harm and pose a threat to democracy. Misinformation in social media has significant consequences such as loss of trust in institutions and the stoking of fear and anger in people. The causes and mechanisms of spreading fake news are complex, involving psychological, social, and technological factors. To tackle the problem, it is necessary to develop interventions that address these factors and to enhance media literacy among the public, so people can tell fact from fiction. The objective of this research paper is to perform a comprehensive review of the techniques and tools that are used to cause, spread, and detect misinformation in social media, as well as perform in-depth experiments with the open-source tools and datasets to evaluate and compare those techniques. We believe the research work’s outcome will benefit researchers, educators, and students through dissemination of the knowledge and resources, including the open-source tools and data acquired by the research work.
Federated Learning Based Satellite-Marine Integrated Training for Marine Edge Intelligence

Marine communication refers to the communication conducted between land and ocean or within the ocean. It constitutes a crucial component in achieving global network coverage. Within the existing marine-edge environments, a vast amount of edge data is generated. Leveraging the stability of ocean buoys, maritime edge training methods are poised to play a pivotal role in marine communication. However, due to the formidable challenges associated with ocean infrastructure development and the inherent characteristics of dispersed and privacy-sensitive ocean data, conventional edge training methodologies find substantial obstacles in implementation. To address these challenges, we propose a federated learning based Satellite-Marine integrated training approach, aiming to enhance marine-edge intelligence while safeguarding privacy. Firstly, we perform unsupervised clustering of marine-edge data, dynamically partitioning the edge network and designating marine-edge servers as central nodes. Subsequently, within each edge server, edge data is cached, and deep residual networks along with deep learning models are established to facilitate feature extraction from the marine-edge data. Finally, satellites are employed as global network hubs, enabling vertical federated learning to achieve aggregation of edge features. Experimental results demonstrate that, in the realm of regression prediction, compared to support vector machine and random forest models, the proposed network model in this study exhibits reduced error and higher efficiency.
PatrolGo: Efficient Security Patrol Route Planning to Catch Intruders

Solving security patrol route planning in large-scale realistic scenarios is a very challenging problem. Here we propose a route planning algorithm PatrolGo based on Monte Carlo Tree Search (MCTS). In this algorithm, the strategy network selects the actions of security resources, and the situation network evaluates the current situation. These deep neural networks are training from the confrontation between PatrolGo and the attacker model. We assume that the attacker and the defender act alternately, and the attacker model is fixed. Specifically, we first apply MCTS to the grid security game scenario. Furthermore, we added the deep neural network to the MCTS algorithm to enhance its efficiency in selecting and evaluating stages. Finally, the PatrolGo algorithm tree search process can provide prescriptive security resources action. We provide a new scheme for security patrol route planning. The simulation results show that the scheme has state-of-the-art performance and effective strategic prescript.
DeepDiffer: Find Deep Learning Compiler Bugs via Priority-guided Differential Fuzzing

Recently, Deep learning (DL) compilers have been widely developed to optimize the deployment of DL models. These DL compilers transform DL models into high-level intermediate representation (IR) and then into low-level IR, ultimately generating optimized codes for different hardware targets. However, DL compilers are not immune to generating incorrect code, leading to potentially severe consequences. Testing techniques for low-level IR are limited, and efficient approaches for detecting some categories of non-crashing bugs are lacking. In this paper, we address the limitations of existing low-level IR DL compiler testing techniques and introduce DeepDiffer, a priority-guided differential testing framework designed to detect bugs resulting from low-level optimizations in the DL compiler, specifically TVM. We propose a novel DL compiler coverage metric and establish an optimization goal to maximize the detection of valuable differences between DL compilers. Our experiments demonstrate that DeepDiffer outperforms existing low-level IR fuzzers, detecting a wider range of bug types. In fact, DeepDiffer has successfully identified 13 bugs in TVM, which can be categorized into 9 distinct root causes, and 9 bugs are first found. We have submitted these bugs to the TVM community, where they have been confirmed.
A Spiking Cerebellar Model Enhanced Gradient Neural Solution to Time-Varying Linear Equations With Application to Robot Motion Planning

Time-varying linear equations (TVLEs) are prevalent in science and engineering, especially in artificial intelligence and robotics. When solving TVLEs, the existing recurrent neural networks (RNNs) either suffer from non-vanishing errors, iterative computations at each time step, or the involvement of matrix inversion and time derivatives of coefficients. Inspired by the human cerebellum's role in fine-tuning and coordinating motor commands, this paper presents a spiking cerebellar model enhanced gradient neural network (SCM-GNN), where the GNN and the SCM generate an approximate solution and a corrective signal, respectively. The SCM-GNN model embraces spiking neural networks' potential merits including biological plausibility, and it is a non-iterative solution that performs only one iteration of computations at each time step with no matrix inversion and time derivatives of coefficients involved. For performance validations, the SCM-GNN is successfully applied to a numerical example and a cyclical motion planning scheme of a robot tracking a user-specified path, validating its effectiveness and practicality.
A 2MHz, 97.4% Peak Efficiency, Heterogenous GaN/BCD 48-to-12 V Cascaded Switched Capacitor Converter With ZVS Auxiliary Technique

It has been demonstrated that switched capacitor (SC) converters have the potential to achieve higher efficiency and higher power density than magnetic-based converter. With two conventiona12-to-1 SC converters cascaded, this article presents a 2MHz, 12-54V input intermediate bus converter (IBC) mainly to realize the typical use of 48-to-12 V (fixed 4:1 step-down ratio) for hybrid electrical vehicle and other modern advanced industry. Based on tridimensional heterogeneous integration (3D-HI) of gallium nitride (GaN) and bipolar-CMOS-double-diffused meta-loxide-semiconductor (DMOS) (BCD) technology, the novel driving strategy and zero voltage switching (ZVS) auxiliary technique suggested in this work, the proposed highly-integrated cascade SC converter sized $3.74\times 3.09$ mm2 achieves one of the best overall metrics among the state of the arts. Verified through post-simulation, it features a peak efficiency of 97.4% and power density of 1.87 W/mm2.
Real-Time Low-Earth Orbit Detector Implementation for Chirp-Based Preamble Communication Systems

Internet of Things (IoT) has been undergoing a revolution with the deployment of gateways in low earth orbit (LEO). This revolution offers what IoT has been promising since its inception, namely the connection of an object regard-less of its position on Earth. Several physical layers have shown to have the sensitivity for such communications. In this paper, we propose a solution allowing an IoT network operator using LEO satellite to detect different uplink IoT communication technologies based on a same preamble. The proposed approach has been implemented on two ARM cores (Cortex-A72 and Cortex-A9) compliant with the spatial constraints. The experiments carried out demonstrate the realtime capability of the proposed approach even on low-end processor targets with approximately 10% of the CPU time.
Use of human iPSCs and kidney organoids to develop a cysteamine/mTOR inhibition combination therapy to treat cystinosis

Cystinosis is a lysosomal storage disease caused by mutations in CTNS, encoding a cystine transporter, and in its severest form is characterized by cystine accumulation, renal proximal tubule dysfunction and kidney failure. Cystinosis is treated with the cystine-depleting drug cysteamine, however this only slows progression of the disease and there is an urgent need for better treatments. Here, we have generated and characterized the first human induced pluripotent stem cell (iPSC) and kidney organoid models of cystinosis. These models exhibit elevated cystine and cysteine levels, enlarged lysosomes and a block in basal autophagy flux. Cysteamine treatment ameliorates this phenotype except for the basal autophagy flux defect. We found that treatment with Everolimus, an inhibitor of the mTOR pathway, reduces the number of large lysosomes and activates autophagy but does not rescue the cystine/cysteine loading defect. However, dual treatment of cystinotic iPSCs or kidney organoids with cysteamine and Everolimus corrects all of the observed phenotypes indicating that a combination therapy has therapeutic potential to improve the treatment of cystinosis.
An Ultra-Fast Transient Digital-LDO with Adaptive Rising-Edge Logic Control

An ultra-fast transient response Digital Low Dropout Regulator (D-LDO) is proposed. The D-LDO incorporates adaptive rising-edge logic control and adaptive current allocation technique to enhance the transient response speed and overshoot. The proposed D-LDO is simulated in a 180nm CMOS process and achieves a quiescent current of $18.4 \mu \mathrm{A}$ when operated at input and output voltages of 1.2V and 1.1V. It achieves a maximum current efficiency of 99.0% and demonstrates a response time of $0.72 \mu \mathrm{s}$, with the undershoot and overshoot voltages at 103 mV and 63 mV, when subjected to load current switching between 1mA and 5mA.
An AC-Feasible Linear Model in Distribution Networks With Energy Storage

With the increasing deployment of distributed energy resources (DERs), dispatching DERs subject to operational constraints in distribution networks draws much attention. One challenge is the non-convexities in 1) system-wide AC power flow constraints and 2) the individual complementarity constraint of energy storage. To resolve this challenge, this paper studies an AC-feasible linear model in distribution networks with energy storage, including its formulation, analysis, and applications. First, an AC-feasible linear model is formulated as a set of linear constraints on controllable DERs and uncontrollable power demand by 1) converting the non-convex system-wide constraints into linear constraints based on the Brouwer's fixed-point theorem and the second-order Taylor expansion, and 2) replacing the non-convex individual complementarity constraint of energy storage with one properly designed linear constraint. Furthermore, to analyze the power demand level at which the proposed linear model can provide a solution, this paper proposes an examination-based projection method under the Monte Carlo framework to handle projections of thousands of dimensions from linear constraints over time periods. Finally, the potential applications of our AC-feasible linear model are discussed. Numerical experiments are conducted in the IEEE 33-bus and 136-bus test systems to demonstrate the effectiveness of the proposed methods.
Predictive Fault-Tolerant Control with Robust Dynamic Output Feedback for Discrete Uncertain Systems with Time Delays

In response to discrete industrial processes characterized by unmeasurable states, actuator faults, and time delays, a novel robust predictive fault-tolerant control approach based on dynamic output feedback is proposed. First, we establish an innovative iterative error model that expands the notion of output tracking error to guarantee superior tracking performance for the system. Subsequently, based on the dynamic output feedback method, a robust predictive fault-tolerant controller is developed utilizing this model. By employing Lyapunov stability theory, linear matrix inequality (LMI) techniques, relaxation strategies, and variable transformation methods, stability conditions are given in the form of LMI. Real-time optimal control gains are obtained by online solving of these stability conditions, leading to a significant enhancement in control performance. The feasibility and effectiveness of the proposed approach are finally demonstrated through a simulation case study involving the TTS20 water tank.
Layered Reinforcement Learning Design for Safe Flight Control of UAV in Urban Environments

Unmanned aerial vehicles(UAVs) have gained prominence due to their high maneuverability, robust controllability, and reliability. Nowadays, reinforcement learning has attracted increasing attention among researchers. In this paper, a data-based PID control method is proposed to craft a trajectory tracking controller for UAV to address the problem that adjusting controller parameters can be a complex task. Furthermore, aiming at the issue of sparse rewards in intricate urban environments, the concept of hierarchical reinforcement learning is proposed to decompose the overall task into smaller. The proximal policy optimization algorithm is then proposed to train UAV, enabling it to make informed decisions within urban environments. The experimental results demonstrate the effectiveness of our autonomous flight strategies proposed for the UAV, showing both their efficacy and high performance.
A 27.1 dB Gain and 6.85 dB NF Low-Noise Amplifier Based on the Novel Gain-Noise Plane

This paper presents a 160 GHz cascode low-noise amplifier (LNA) in $0.13 \mu \mathrm{m}$ SiGe BiCMOS with the gain-noise plane approach. The collaborative power gain and noise optimization is performed by analyzing the optimal region of the LNA and the character of the gain-state point on the gain-noise plane. Besides, the optimized stagger-tuned technology is derived theoretically to decrease the in-band noise performance degradation. The proposed cascode LNA achieves a power gain $(G_{p})$ of 27.1 dB, bandwidth of 31 GHz (148-179-GHz), noise figure (NF) of 6.85 dB, input-referred 1dB power compression point (IP1dB) of -23.9 dBm and power consumption of 31.6 mW.
A 4% Impedance Variation JEDEC Compatible ZQ Calibrator for DDR3, DDR4 and DDR5 SDRAM

This paper design and implement a common ZQ Calibration circuit, it can finish all calibration procedures within 60ns by 6 steps, and comparator is the important component in ZQ Calibrator, this paper utilizes a 10 kHz chopper to cancel the offset, and the offset is lower than 2mV after DCOC. this paper use multi voltage reference points for DDR3/DDR4/DDR5 calibration, DDR3 use 0.5*VDDQ as calibration point while DDR4/5 use 0.8*VDDQ. What’s more, DDR3 and DDR4 ZQ calibrator utilize external clock while DDR5 needs to use self-oscillator clock, so both external clock mode and internal clock mode are implemented in this paper, and the internal OSC period variation is lower than 10% by utilizing voltage and temperature compensation. Lastly, a scan test circuit is added in this design to facilitate ZQ calibrator test.
Joint Channel Estimation and Signal Detection in MIMO-NOMA Wireless Systems Using Deep Learning

This paper presents a the performance of deep learning (DL) in joint channel estimation and multi-user detection in non-orthogonal multiple access (NOMA) based multiple-input multiple-output (MIMO) wireless systems. Generally, in NOMA systems, the sequential decoding of multiple users is performed by the conventional successive interference cancellation (SIC) method at the receiver. Due to the effects of error propagation, with SIC, the accuracy of detection is dependent on the accuracy of detection of preceding users. DL-based MIMO-NOMA receiver is intended to detect messages for several users in a single step, without explicit estimation of the channel state information. The DL-based receiver for the MIMO-NOMA system can be implemented by a deep neural network (DNN) that conducts the estimation of channel parameters and the detection of signals jointly. The DNN is trained offline and is then used to directly detect the transmitted data during the online testing phase. Results indicate that the DL-based systems outperform the systems with traditional channel estimation methods and are more tolerant to variation in the number of pilot symbols and length of cyclic prefixes (CP). It is validated that the DL-based scheme is able to mitigate the SIC-based detector's error propagation effects. It is also observed that the DL method can perform better than the least square (LS) and minimum mean square error (MMSE) detector even in severe inter-symbol interference (ISI).
Modified U-Net Architecture for Diabetic Retinopathy Fundus Image Segmentation

Diabetic retinopathy is an ocular condition that has long-term effects on diabetic patients, leading to severe visual impairment. However, due to the high interclass similarity in terms of color, shape, and contrast among various lesions associated with diabetic retinopathy, the task of segmenting them proves to be extremely challenging. To address these challenges, we proposed a model based on the nmODE(neural memory Ordinary Differential Equation) block and the U-net backbone. In this model, the nmODE block is embedded within the decoder of the U-net architecture, where the output of each decoder layer serves as input to the nmODE block, and the output of the nmODE block subsequently serves as input to the next layer of the decoder. Extensive experiments were conducted on the IDRiD dataset, demonstrating that the nmODE block effectively enhances the computational capabilities of the network and achieves remarkable performance.
A Smart Cane with Fall Detection System for People with Visual Impairments

According to the World Health Organization, at least 2.2 billion people suffer from near or distance vision impairment, and this number is expected to increase as the population ages. The prevalence of vision impairment in low- and middle-income regions is four times greater than in high-income regions, which poses a significant challenge for visually impaired individuals in terms of accessibility, mobility, and independence. It is imperative that assistive technology be affordable. Among visually impaired individuals, aging is associated with a decrease in independence and an increased risk of falling. A study conducted by HealthInAging.org estimated that approximately 30% of adults over 65 fall each year, and this risk is more than double for those who are visually impaired. These individuals are also limited in their activities by a fear of falling. We propose a smart white cane that includes an emergency fall alert system to alert appropriate personnel and the family of a visually impaired individual who has fallen. Users receive haptic feedback when approaching obstacles using ultra-sensors. A smart cane detects a fall and sends a text message to the appropriate persons in the event of a fall with more than 95% accuracy. Our contribution includes a detection algorithm, a falling alert system, and a wireless communication approach.
Cardiac Effect of Acupuncture on the Neiguan Point

Background: The acupuncture on the Neiguan point (PC6) is used in the traditional Chinese medicine to calm the mind and relieve pain. In this study, we assess whether the stimulation of this point through acupuncture influences the heart rate. Methods: A resting 3-lead ECG was evaluated for 71 subjects in sinus rhythm during a 5-phase protocol (rest-sham acupuncture - rest - acupuncture on Neiguan point - rest). After a filtering procedure, the R peaks were detected and the RR intervals computed. The changes in the RR series after stimulation were assessed in both acupuncture phases (sham and on the Neiguan point) as the slope of the RR interval series post stimulation. Results: Comparing the RR changes in the Neiguan point acupuncture with the ones of the sham acupuncture for all subjects, we found a higher slope for the Neiguan point acupuncture (Neiguan: $2.90\pm 11.70vs$ sham: -1.02 $\pm 10.95, p=0.015)$. This effect disappeared over time: indeed, computing the slopes of RR series further away in time from the acupuncture, a significant difference was not found between the two phases. Conclusion: These results suggest that acupuncture on the Neiguan point has a transient vagal effect on the heart, that should be considered in clinical practice.
CONTAMINANTS IN LAKE ERIE FISH COMMUNITIES: A BAYESIAN EVALUATION

Increasing awareness about the presence and ecological ramifications of toxic, persistent and bioaccumulative contaminants within the Great Lakes system spurred the implementation of numerous bans and emissions restrictions over the past few decades. Due to their high trophic status in food webs and the critical link they serve with human consumers, fish species have historically been monitored to assess the relative success of such remedial efforts within the region and to simultaneously ascertain the current risks posed to local humans. Using Bayesian dynamic linear modelling, this project first aimed to evaluate temporal trends of various organochlorine contaminants within Lake Erie fish communities, the results of which generally indicated decreasing trends through time. The second half of this study used a similar Bayesian approach to propose a framework for updating fish consumption advisories, with specific attention paid to the acknowledgment of uncertainty and natural variability when producing such consumption guidelines.
Lightweight Deep Learning Algorithm for Visual Odometry

Visual odometry is a widely-used approach for the relative localization problem in autonomous vehicles. However, achieving high-accuracy pose estimation in a dynamic driving environment while meeting safety requirements is challenging. Existing geometry-based visual odometry algorithms require individual design and fine-tuning. Therefore, we propose a new monocular visual odometry system based on a neural network for feature extraction and matching. The efficiency of measurement is a primary consideration due to safety requirements in complex environments, and reducing computational cost is crucial for enabling real-time processing on embedded platforms with limited hardware resources. Our system estimates poses directly from video sequences, and our main contribution is an efficient neural network model that reduces computational costs while maintaining accuracy. Our proposed model is suitable for applications such as robotics and autonomous driving, which require a lightweight, low-latency network model. Experimental results show that our system is relatively accurate to ground truth, and our lightweight neural network model is a significant contribution to visual odometry by reducing computational costs.