Miracle-dz/UniFAR-FTUs huggingface dataset:
Universal Adversarial Attacks for Visual Odometry Systems

Visual Odometry (VO) has gained significant attention as a critical technology with broad applications in autonomous navigation, augmented reality, and related domains. However, recent research indicates that VO systems are susceptible to adversarial attacks, leading to compromised accuracy and potential system failure. Traditional adversarial attack algorithms, primarily relying on random perturbations or objective function minimization, are not suitable for VO algorithms. In this paper, we present a novel and general adversarial attack algorithm specifically designed for targeting the yaw and translation components of visual odometry, while increasing the Euclidean distance between adjacent frames. Through a comprehensive analysis of VO algorithm characteristics, we propose an effective approach to disrupt VO system operation. Extensive experimental results demonstrate that the proposed attack algorithm significantly reduces the localization accuracy of VO algorithms while exhibiting robustness and generality. The findings of this research contribute to enhancing the security and stability of deep learning-based visual odometry algorithms, providing valuable insights and guidance for practical applications.
Automatic Prediction of the Origin in Outflow Tract Ventricular Arrhythmias with Machine Learning Combining Clinical Data and Electrocardiogram Analysis

Identifying the site of origin in outflow tract ventricular arrhythmias (OTVAs) is crucial for the success of radiofrequency ablation procedures. Despite recent progress, this task remains challenging and too dependent on clinician's expertise, since origin estimation merely relied on visual inspection of the electrocardiogram (ECG). This study presents an automatic system to identify the ventricular origin in OTVA with machine learning algorithms. The system comprises two cascading classifier models that utilize raw electrocardiogram (ECG) signals, relevant ECG signal features, and clinical data. It was trained using data from four different databases. The final model achieved an accuracy of 95.45%. Furthermore, we identified specific regions in the ECG signal, such as the transition between the R and the S waves in V3 and V4 and the beginning of the QRS complex in V2, which are key when estimating the OTVA origin.
MicroKGCL: A Knowledge Graph for Root Cause Localization of Feedback Issues in Microservices

The popularity of the microservices architecture has led to an evident trend of integration in system design, enabling systems to integrate numerous services to meet the diverse requirements of users. However, with extensive integration, it is difficult to quickly identify the root cause of the issues. In this paper, we propose MicroKGCL, a knowledge graph for root cause localization of feedback issues in microservices. Through the knowledge graph constructed with historical cases, MicroKGCL explores the potential relationship between user feedback and the root cause of system issues, it analyzes possible issues and ranks candidate root causes. In detail, in order to provide a more accurate representation of the feedback profile, we design a multi-modal embedding block, which utilizes a contrastive learning model and BERT model to extract feedback features from visual and content modalities. Experimental results demonstrate the superiority of the proposed MicroKGCL in terms of both MRR and Hits@n by comparing with the baselines. Moreover, to further verify the MicroKGCL, it has been deployed in the real production environment of Ant Group, achieving a top-3 hit ratio of 75% and a coverage ratio of 81.8%.
Research on Malicious Traffic Detection based on Word Embedding Algorithms and Neural Networks

With the rapid development of society, network technology continues to advance. At the same time, people are also facing threats from malicious traffic. To address this issue, this paper proposes a malicious traffic detection method based on word vectorization algorithm and neural networks. This method combines tasks such as packet parsing, header data grayscale image generation, and message feature extraction to extract effective feature information. By combining convolutional neural networks (CNN), it detects malicious traffic hidden within normal traffic. Experimental results demonstrate that compared to traditional machine learning methods, the proposed approach exhibits higher efficiency and accuracy in identifying malicious traffic attacks, thereby better-protecting network users from malicious attacks.
Spatial Temporal Graph Transformers for Probabilistic Load Forecasting

Ensuring a balance between electricity supply and demand is crucial for effective energy generation and scheduling. To this end, accurate probabilistic energy prediction is essential. In this paper, we introduce a novel transformer-based framework, Spatial Temporal Graph Transformers (STGT), designed to estimate probability distributions for future time series data. By combining Graph Neural Networks (GNNs) with Transformers, STGT is able to extract both temporal and spatial features, ultimately resulting in more reliable and accurate probability predictions. The architecture of STGT consists of a transformer decoder to capture historical load data and a graph neural network to model the spatial relationships present across different user loads. Additionally, quantile regression is utilized to model load probability distributions. Through extensive experiments conducted on real-world load time series datasets, we demonstrate that STGT outperforms alternative approaches in both point prediction and probabilistic prediction accuracy.
Topology Optimized Integrated SiN Mode Converters

We present compact, low-loss silicon nitride mode converters designed using topology optimization and fabricated on a commercial silicon foundry. We experimentally demonstrate efficient mode conversion (-0.32 dB for TE<inf>00</inf> to TE<inf>01</inf> and -0.52 dB for TE<inf>00</inf> to TE<inf>02</inf>), both within 0.4 dB of simulation.
A Comparison of Methodologies for Pulmonary Veins Segmentation in High Definition Voltage Maps of Patients with Atrial Fibrillation

This paper compares three methodologies used to segment images extracted from the atria of patients with atrial fibrillation (AF). We collected voltage maps of 120 patients' left atrium before being treated for AF with tissue ablation. The high-definition voltage maps (HDVM) were extracted with the Rythmia software system from Boston Scientific and subsequently analyzed offline in MATLAB. During the analysis, the atrium is segmented into three main structures: body, pulmonary veins (PVs), and mitral valve (MV). Method I is based on a manual elimination of the PV by the operator. Method II uses a semi-automatic method based on geometric considerations coupled with a threshold for voltage value. Method III, in addition, uses geometric features, i.e., the geometric curvature, to eliminate the maps' PVs. We quantitatively compare the two first methods' efficiency and ease of use. In particular, the values of two electrical biomarkers associated with the maps are computed before and after the PV's elimination process. This allows us to classify them according to their sensitivity with respect to the cleaning of the PVs. Finally, we discuss which methodology is more adequate to perform the PVs and MV segmentation in light of improving the precision of the resulting maps.
Machine Learning at the Edge Using Neural Network Processor

Increasingly, power, space, and cooling constrained embedded computing assets need to run machine learning applications to make autonomous, low latency decisions within operational environments. Where GPUs, CPUs, and FPGAs aren't suitable for these constraints, custom ASICs with their non-standard development processes and frameworks are the best available option for bringing the power of machine learning to the edge. This paper discusses a neural network processing architecture and why it surpasses all size, power, throughput, and weight requirements while maintaining performance and allowing interoperability with commercial vendors license comprehensive development environment and machine learning libraries. We compare this neural network processor architecture with existing AI and machine learning platforms that leverage common architectures such as GPUs, CPUs, and FPGAs. The plug and play nature of the neural network processor architecture is optimized for applications such as EO-IR (Electro-optical/Infrared) Sensor and Radar Systems without respinning the hardware, reducing costs. This paper explores best practices for deploying common AI and machine learning models for object detection, super resolution, and natural language processing on a neural network processor at the edge. The neural network processor offer an alternative for deployment of autonomous machine learning at the edge that brings the power of a robust development ecosystem together with an architecture favorable for power and space constrained use cases.
A Machine Learning Approach for Outcome Prediction in Postanoxic Coma Patients Using Frequency Domain Features

In this work, we describe the creation of our machine-learning-based solution for coma prognosis after cardiac arrest using longitudinal EEG and ECG recordings for the “Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023”. Our team, “ComaToast”, had its best submission ranked 28 out of 36 teams selected worldwide, with a challenge score of 0.381 on the official leaderboard for the hidden test set. We use a combination of age and signal features from EEG and ECG recordings. Frequency domain features, specifically mean power spectral density from 4 different bands of frequencies (Delta, Theta, Alpha and Beta) and mean Burst Suppression Ratio, were extracted from pre-processed EEG recordings from the first and last available recording for a given patient. Features like mean and standard deviations were extracted along channels for ECG recordings. After imputing missing values, these features are fed to an XGBoost classifier for the final binary classification of the outcome prediction task. The features are fed to a random forest regressor to predict the CPC outcome for every patient. A solution like ours, which uses a simple model and training technique, may be more viable than deep-learning solutions in general use cases. In our final model, our approach achieved a 5-fold cross-validation score of 0.34 on the public train set.
Architecture-Aware Currying

In near-data computing (NDC), computation is brought into data, as opposed to bringing data to computation. While there is prior work focusing on different NDC opportunities, there is no study, to our knowledge, that investigates the importance of “neighborhood” in NDC. This paper explores the neighborhood concept in multithreaded programs that run on on-chip network-based manycore systems. We define the concept of “neighborhood”, in terms of on-chip network links, and use it to formulate the NDC problem. We propose a “generic” compiler algorithm, called “architecture-aware currying”, that uses the neighborhood concept to implement NDC. So, a core can perform some portions of computation with the nearby data and postpone the remainder of the computation until the remaining data become nearby. It can also perform computations - with nearby data - on behalf of other cores. Our experimental evaluation shows that the proposed compiler algorithm outperforms state-of-the-art data locality optimization strategies.
Artificial Neural Network and Peer-to-Peer Communications at the Grid-Edge to Mitigate Cyber Attacks on Distributed Photovoltaic Inverters

Resilient control of photovoltaic (PV) inverters using a local Artificial Neural Network (ANN) and peer-to-peer communications can maintain grid services during a cyberattack. High penetrations of PV systems presents grid performance challenges, and alterations to connected systems can introduce additional problems. To tackle these issues, this paper introduces a methodology for controlling PV inverters that are under attack using the Laterally Primed Adaptive Resonance Theory (LAPART) ANN to predict the best reactive power control input when communications between the central command center are down or cannot be trusted. This work tested the approach using a 6-bus feeder model with a high penetration of PV. The experiment found that the algorithm can predict the appropriate reactive power setting with high accuracy, and when embedded inside the grid model, the algorithm can predict a reactive power that improved system voltages.
The Effects of Global Damp Heat Ageing on Debonding of Polyolefin Glass Laminates

The long-term reliability of photovoltaic (PV) modules depends significantly on the encapsulation material. Commonly used ethylene vinyl acetate copolymers (EVA) are prone to degradation such as environment-induced deacetylation. Furthermore, the polarity of EVA enables cation migration associated with potential induced degradation (PID) of PV modules. Less polar encapsulants allow for better water and ion barrier properties. Hence, polyolefin elastomers (POE) have been established as a promising alternative to EVA. However, so far only few studies have been dealing with the long-term reliability of POE encapsulants especially close to relevant interfaces of PV module laminates. Therefore, in this study global ageing experiments were performed on EVA and POE double glass laminates, which were exposed to damp heat (DH; 85°C, 85 %rh) for up to 10000h. The aged double glass specimens were characterized with microscopic methods, compressive shear testing and X-ray photoelectron spectroscopy (XPS). Both laminate types revealed pronounced haziness after 10000h of DH exposure. While the change in haziness was attributable to water uptake of the EVA/glass laminate, ageing at the POE/glass interface led to the formation of calcium and sodium carbonate crystals, which act as scattering domains. Interestingly, no significant change in compressive shear strength was ascertained for unaged (Oh) and DH aged (10000h) EVA laminates. In contrast, POE specimens revealed a significantly higher compressive shear strength (+80%) after 10000h of DH exposure. XPS measurements confirmed excellent ion barrier properties of POE. No Na and Ca ions were detectable within the bulk, whereas a significant increase of these elements was found on the fractured surfaces of POE laminates failing in a cohesive manner within the glass substrates.
Learning Based Adaptation of Proportional Derivative Controller for a Novel Rotary Slosh Dynamics Model

The disturbance in the free liquid surface caused by the movement of a partially filled liquid container is referred to as sloshing. A wide variety of application fields, such as liquid cargo carriers, liquidized natural gas and petroleum storage tanks, the beverage industry, rocket engine fuel tanks, molten metal transfer in steel plants, spacecraft trajectory or attitude manoeuvres, etc frequently experience the sloshing phenomenon. Rotary slosh develops in a partially filled liquid container as a result of 2-dimensional movement of the container. In order to solve the rotary slosh minimization problem, this paper proposes an adaptation of proportional derivative (PD) controller parameters using deep reinforcement learning (DRL) framework. In this paper, for the first time a novel rotary slosh dynamics model is proposed. A spherical pendulum on cart is used to represent the rotary slosh when the cart with partially filled liquid container is moving on a specified path in a 2-D plane. A DRL-based adaptation of PD controller parameters is proposed in order to produce near-optimal performance even with unknown perturbations in the system model. The decision making of the proposed DRL-based PD controller for the rotary slosh minimization problem is verified in a Python simulation environment, and its performance with unknown perturbations is compared with conventional PD controller approach to validate the performance and robustness of the controller due to the learning based framework.
The Aggressive Oversubscribing Scheduling for Interactive Jobs on a Supercomputing System

As interactive usages of supercomputing systems become popular, especially in the AI and machine learning (ML) fields, the systems are expected to provide resources in real time. As interactive jobs have different features from traditional batch jobs, the systems should be designed to accept both types of jobs efficiently. This paper shows that the aggressive oversubscribing scheduling, in which multiple jobs share computational resources regardless of job types, can effectively process hybrid jobs. This paper investigates behaviors of the real interactive jobs with fluctuating CPU utilization. And a simulation method is described, which combines existing workload trace data and data on CPU utilization. Through the evaluation, we demonstrate oversubscribing scheduling achieves a short response time for interactive jobs. Also our solution eliminates the necessity of configuring dedicated queues for job types and achieves robustness towards the change of demand of interactive jobs.
A Fuzzy Adaptive Zeroing Neural Network with Noise Tolerance for Time-varying Stein Matrix Equation Solving

Zeroing Neural Network (ZNN) are an effective tool for solving time-varying problems in engineering and computing fields. However, the ZNN model fails to achieve stable convergence in the presence of external noise disturbances. The time-varying Stein matrix equation, as a surrogate of the Lyapunov matrix equation, has essential status in practical engineering applications. Therefore, in order to solve the time-varying Stein matrix equation more effectively, a new fuzzy adaptive tolerant-noise zeroing neural network (FATNZNN) is proposed. Different from the traditional zeroing neural network (ZNN) and tolerant-noise zeroing neural network (TNZNN), FATNZNN has obvious superior characteristics of strengthened convergence and robustness. The superiority of FATNZNN comes from the following two aspect. The FATNZNN has the convergence parameter generated by the Takagi-Sugeno fuzzy logic controller (TSFLC) which can be adaptively adjusted according to the steady-state residual. FATNZNN has an integral term that can resist the effects of interference with different noise. In addition, a new nonlinear activation function, exp-sign activation function (ESAF), is constructed and applied to the FATNZNN model to speed up its convergence. A numerical example with multiple simulations verifies the convergence and robust of FATNZNN from different conditions.
A Graph-Based Scene Encoder for Vehicle Trajectory Prediction Using the Diffusion Model

To boost autonomous driving safety even further, autonomous vehicles must be able to predict future trajectory states, allowing them to grasp their surroundings in real time, just like a human driver. In this paper, we conduct research on the defects of poor multimodal richness and unstable training of existing vehicle trajectory prediction models, and propose an improved model MID++. In order to better grasp the interaction between agents and road elements, we mainly introduce an GNN-based ALEncoder to replace the traditional CNN block. Meanwhile, an importance sampling strategy is exploited in the training process. According to the experimental results on Argoverse 2 dataset, our model enhances the accuracy and variety of trajectory predictions and significantly shortens the training time.
Performance Characterization of Popular DNN Models on Out-of-Order CPUs

DNN popularity, which is driving advances in a growing number of fields, has increased the amount of computing resources running this kind of applications at an unprecedent rate. Specialized hardware, such as GPUs or ASIC-based accelerators, has been the preferred platform to run these applications. However, the ubiquity of DNN models is rapidly extending the presence of this software to general-purpose CPUs. For this reason, there is a pressing need to gain understanding of the main features of state-of-the-art DNN models to adapt CPU microarchitecture accordingly. In this paper we investigated a representative set of DNN models and, based on data collected from real hardware, we evaluated how efficiently they utilize the underlying system. We analyzed overall system performance, as well as the amount of vectorization provided by CPU-optimized frameworks. We quantified the performance loss caused by processor backend, and the contribution of memory hierarchy and functional units to it. We compared the backend utilization of DNN applications to popular benchmarks such as SPEC CPU2017 and found a lower balance in the use of the elements that make up the processor microarchitecture. Although many workloads seem to be constrained by functional unit availability, in a significant group of applications we found a non-negligible impact of memory hierarchy on performance.
A CPU-FPGA Holistic Source-To-Source Compilation Approach for Partitioning and Optimizing C/C++ Applications

A common approach for improving performance uses FPGAs to accelerate critical code regions, which often involves two processes: hardware/software partitioning, which identifies regions to offload to the FPGA; and optimizing those regions (e.g., through HLS directives). As both processes are separate and usually applied in sequence, the interplay between them is unnatural, and it is unclear how the choices made in one step can benefit the choices made in the other step. This paper presents our work-in-progress for combining partitioning and optimization into a single holistic process. First, our source-to-source compiler builds a task-based representation from the input application. Then, a greedy algorithm builds clusters of tasks and assigns each cluster to either hardware (FPGA) or software (CPU). The algorithm iteratively refines the clusters and offloading decisions by: a) minimizing the communication costs between clusters by assigning tasks that work with shared data to the same cluster; b) reducing the global execution time by applying code optimizations to the tasks in each cluster. We show the impact of our holistic approach to a motivating edge detection example and compare the results when applying partitioning and code optimizations as independent steps. The results show that a holistic partitioning can lead to a speedup of up to $28.7\times$ when compared to a simple offloading of the application to an FPGA.
Analysis of Reward Formulation Based on Mean Arterial Pressure in Reinforcement Learning for Critically Ill Septic Patient

Aims: Optimal reward formulation in Reinforcement Learning (RL) is still uncertain. The aim of this study is to show that formulating a reward in RL for sepsis treatment using Mean Arterial Pressure (MAP) is a viable solution and can improve patient outcomes. Methods: The data were extracted from the MIMIC-III database. Patient data from 20,496 intensive care unit (ICU) stays were modeled with two different Markov Decision Processes that differed in reward formulation. The Mortality Model had a reward function linked only to 90-day mortality, and the Target MAP Model had an additional reward component that penalized the RL agent if the patient's MAP fell below 65 mmHg. Results: The Target MAP Model achieved the best results with a 95% lower bound (LB) of estimated policy value equal to 88.64 compared to 86.01 obtained from the Mortality Model despite having a more penalizing reward. The Target MAP Model in hypotensive patients uses less intravenous fluids and resorts more often to aggressive dosages of vasopressors. Conclusions: The results show that tying the reward to MAP is a viable approach, and the less sparse reward that comes with tying the reward to high temporal resolution cardiovascular features allows to evaluate single actions rather than the whole sequences of actions leading to the final outcome, allowing the RL agent to learn a better policy.