Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilient Navigation Based on Multimodal Measurements and Degradation Identification for High-Speed Autonomous Race Cars

This paper presents a localization system robust against unreliable measurements and a resilient navigation system recovering from localization failures for Indy autonomous challenge (IAC). The IAC is a competition with full-scale autonomous race cars that drive at speeds up to 300 kph. Owing to high-speed and heavy vibration in the car, a GPS/INS system is prone to degrade causing critical localization errors, which leads to catastrophic accidents.In order to address this issue, we propose a robust localization system that probabilistically evaluates the credibility of multi-modal measurements. At a correction step of the Kalman filter, a degradation identification method with a novel hyper-parameter derived from Bayesian decision theory is introduced to choose the most credible measurement values in real-time. Since the racing condition is so harsh that even our robust localization method can fail for a short period of time, we present a resilient navigation system that enables the race car to continue to follow the race track in the event of a localization failure. Our system uses direct perception information in planning and execution until the completion of localization recovery.The proposed localization system is first validated in a simulation with real measurement data contaminated by large artificial noises. The experimental validation during an actual race is also presented. The last part of our paper shows the results from the real-world tests where our system recovers from failures and prevents accidents in real-time, which proves the resilience of the proposed navigation system.
GPLDet: A Strong Anchor-free Object Detector for Gastric Precancerous Lesions

Object detection techniques play a significant role in image diagnosis of gastric precancerous lesions. However, most of the established approaches in this area are anchor-based for prediction of gastroscopic pictures. FCOS is a state-of-the-art anchor-free object detector. However, we observed that there are two pitfalls of FCOS for gastroscope image analysis: 1) When the target center is not at the center of the bounding box, center-ness does not necessarily work. 2) The component that focuses on non-adjacent feature fusion, represented by balanced feature pyramid (BFP), is crucial in multiscale feature enhancement, but it uses the non-local block in the refinement, which introduces a high computational cost. Therefore, we propose GPLDet to handle the above challenges. It integrates dual center-ness fusion and crisscross balanced feature pyramid (CCBFP). Dual center-ness fusion contains both semantic and location information of the target, which can enrich the feature content of the center-ness. CCBFP performs better than BFP in multi-scale feature enhancement with 18.5% fewer parameters. To evaluate the performance of GPLDet, we perform extensive experiments on a collected Gastric Precancerous Lesions dataset (GPL dataset) and two public datasets namely HyperKvasir and Kvasir-Instrument. In particular, experimental results show that GPLDet can achieve the best results on these three datasets.
An Extensive Study on Model Architecture and Program Representation in the Domain of Learning-based Automated Program Repair

Bug fixing is one of the most time-consuming and resource-intensive tasks in the software development life cycle. Automated Program Repair (APR) might be able to help in this process, but it still has to overcome many obstacles. Deep learning models have shown promise for automated program repair in recent years, but their effectiveness can depend on the representation of the source code used as input. In this paper, we conduct an experimental study to compare the performance of deep learning models on two popular programming languages, Java and JavaScript, using three different code representations: raw text, command sequences, and abstract syntax trees (ASTs). We also experiment with varying models, including T5, CodeT5, (for solving sequence-to-sequence tasks) RoBERTa, and GPTNeo (to encode/decode AST graph information). We evaluate the models on a set of real-world defects from open-source projects and compare the performance, and the repair patches generated by the models. Our results show that training on command sequence representation outperforms most other configurations. We achieve a best of 19.88% accuracy on the java-small dataset, and 11.87% on java-medium, using text representation. Using command sequence representation, we achieve 30.64% on java-small and 18.53% on the medium dataset. However, when representing the source with ast+text information, our models significantly underperform compared to other representations, achieving results below one percent. Our findings contribute to a better understanding of the strengths and limitations of deep learning models for automated program repair and provide practical guidance for their use in practice.
MPCNet: GNSS Multipath Error Compensation Network via Multi-task learning

In a multipath channel environment, classifying non-line-of-sight (NLOS) Global Navigation Satellite (GNSS) satellites and compensating multipath ranging error (MRE) is the most important task for improving GNSS positioning accuracy in urban areas. Recently, Signal-to-noise ratio (SNR), pseudorange, and other measurements have been used to classify NLOS satellites, but these measurements have limited representation of NLOS channel characteristics. In this paper, we propose a Multipath error Compensation Network (MPCNet) that uses an Autocorrelation function (ACF) output and 3D Geographic Information System (GIS) as inputs to classify NLOS satellites and compensate for MRE. MPCNet is composed of two heads for each task and a shared network that learns relevant information about the multipath channel environment from the ACF output. The performance evaluation of MPCNet was performed in a real urban environment, and the NLOS classification accuracy was compared with that of conventional deep learning-based NLOS classifiers, and the positioning performance of conventional positioning methods was also compared. MPCNet showed an NLOS classification performance of about 97% and an improvement in positioning accuracy of about 57% compared to conventional positioning methods, demonstrating that it is a robust and accurate MRE compensation technique in multipath environment.
NetDiceSyn: Multi-Property Probabilistic Verification of Network Configurations

In recent years, probabilistic network analysis has received significant attention as a crucial aspect of network management. With the increasing use of distributed routing protocols in networks, computing the probabilities of multiple properties becomes a challenging task, as different property violations derive from different failure scenarios. While some previous work has attempted to prune the space of failure scenarios by identifying a set of links whose failure does not change whether a property holds, this approach often requires multiple calls to the system, leading to redundant computation when verifying multiple properties. To solve this problem, we propose NetDiceSyn, a scalable multi-property probabilistic network configuration analyzer. As verifying each property requires exploring a state tree, NetDiceSyn proposes to explore all state trees synchronously and compute a shared data plane for multiple states in different state trees, thus reducing the redundant computation between different state trees and verifying multiple properties simultaneously. Besides, we design a state sharing method to reduce the redundant storage of states during synchronous exploration. Additionally, we propose abstract topology to merge equivalent states within a state tree to speed up probabilistic verification in series link networks. Extensive experiments are conducted with real-world network topologies, and the results show that NetDiceSyn outperforms state-of-the-art methods, providing up to an order of magnitude speedup when verifying hundreds of properties for networks with hundreds of links.
Failure-Tolerant Task Offloading for Vehicular Fog Computing

Vehicular fog computing (VFC) as one of the newly emergent computing paradigms has attracted widespread attention recently, which brings the computing and storage resources of cloud computing to the mobile terminal devices – smart vehicles. Despite the advantage of VFC, tasks are more susceptible to failures, owing to the limited computing capabilities of vehicles as well as the unstable wireless communication links during service provisioning. However, current works seldom pay attention to it. In this paper we strive to design failure-tolerant task offloading strategy in VFC, by considering different kinds of failures during task offloading. We also pose strict latency requirement on the response delay for each task in the optimization period. The goal of this paper is to minimize the average response latency for all the offloaded tasks in the optimization period by allocating the offloading requests to the fog vehicles. The optimization problem is formulated as one variant of multiple knapsack problem that is NP-hard. To solve this problem in polynomial time, we put forward a heuristic greedy algorithm to solve it. The extensive simulation is conducted to validate its efficiency and effectiveness and the results show its advantages compared to the baseline approaches.
Predicting Driver Behavior on the Highway with Multi-Agent Adversarial Inverse Reinforcement Learning

For the implementation of autonomous or highly automated driving functions, predicting the driver behavior of the surrounding road users is highly relevant. This work investigates the prediction of driver behavior on the highway. For this purpose, different approaches, such as rule-based functions or behavior cloning models, have been proposed in the literature. In this work, a realization with Adversarial Inverse Reinforcement Learning (AIRL) is implemented. AIRL combines behavior cloning and reinforcement learning approaches. Based on Generative Adversarial Networks (GAN), it uses competition between a discriminator and a generator. This paper uses Proximal Policy Optimization (PPO) as the generator in AIRL. Further, the prediction is realized in a multi-agent fashion to achieve the the most accurate interaction-aware prediction. Our evaluation shows that this work achieves better accuracy than rule-based models and comparable accuracy to the state-of-the-art methods, but additionally has an extremely low inference time.
Miniature, 3-D Printed RF Quadrupole Mass Filters for Cubesats

We report the design, fabrication, and characterization of the first monolithically 3-D printed, hyperbolic, compact RF quadrupole mass filters. The devices are made via multimaterial extrusion. We also developed compact electronics to drive the quadrupoles that are compatible with the size, weight, and power constraints of deployable platforms, such as CubeSats (<3 W, up to 400 Vpp sinusoidal amplitude, 1–3 MHz, >2000 voltage steps for 100:1 resolution). Characterization of prototypes in vacuum shows the devices can scan the 1–250 amu mass range and correctly identify Ar.
Time, $E_8$, and the Standard Model

Based upon the unique and simple starting point of the continuous flow of time a physical theory is derived through an analysis of the elementary arithmetic composition and symmetries of this one-dimensional progression. We describe how the explicit development of the theory leads to a prediction of the unique and largest exceptional Lie group E8 as the full ‘symmetry of time’, and hence as the unification group for the physical theory. This proposal results from the identification of a series of esoteric properties of the Standard Model of particle physics from a series of intermediate augmentations in the ‘multi-dimensional form of time’. These physical properties derive from the breaking of the full symmetry of time through the necessary interposition of an external 4-dimensional spacetime arena, itself constructed from a 4-dimensional form of time, as the background to all observations. The basic conceptual picture is presented together with reviews of a number of references regarding E8 structures which may provide a significant guide in pursuing the goal of converging upon a complete unified theory.
Damper Winding Resistance Impact on Synchronous Machine Transients – Case Study

This paper presents preliminary results from the mathematical model of the synchronous turbo-machine that is under development. The model is based on the winding function theory and is derived in the natural frame of reference. After a brief introduction to the synchronous machine swing equation, the influence of the cage winding resistance on the damping effect is investigated in a specific, ideal case leading to rotor oscillations- an instantaneous increase in driving torque.
Isotope effects on L-H threshold and confinement in tokamak plasmas

The dependence of plasma transport and confinement on the main hydrogenic ion isotope mass is of fundamental importance for understanding turbulent transport and, therefore, for accurate extrapolations of confinement from present tokamak experiments, which typically use a single hydrogen isotope, to burning plasmas such as ITER, which will operate in deuterium–tritium mixtures. Knowledge of the dependence of plasma properties and edge transport barrier formation on main ion species is critical in view of the initial, low-activation phase of ITER operations in hydrogen or helium and of its implications on the subsequent operation in deuterium–tritium. The favourable scaling of global energy confinement time with isotope mass, which has been observed in many tokamak experiments, remains largely unexplained theoretically. Moreover, the mass scaling observed in experiments varies depending on the plasma edge conditions. In preparation for upcoming deuterium–tritium experiments in the JET tokamak with the ITER-like Be/W Wall (JET-ILW), a thorough experimental investigation of isotope effects in hydrogen, deuterium and tritium plasmas is being carried out, in order to provide stringent tests of plasma energy, particle and momentum transport models. Recent hydrogen and deuterium isotope experiments in JET-ILW on L-H power threshold, L-mode and H-mode confinement are reviewed and discussed in the context of past and more recent isotope experiments in tokamak plasmas, highlighting common elements as well as contrasting observations that have been reported. The experimental findings are discussed in the context of fundamental aspects of plasma transport models.
Erosion and deuterium retention of CLF-1 steel exposed to deuterium plasma

In recent years reduced activation ferritic martensitic steel has been proposed as the plasma-facing material in remote regions of the first wall. This study reports the erosion and deuterium retention behaviours in CLF-1 steel exposed to deuterium (D) plasma in a linear experimental plasma system as function of incident ion energy and fluence. The incident D ion energy ranges from 30 to 180 eV at a flux of 4 × 1021 D m−2 s−1 up to a fluence of 1025 D m−2. SEM images revealed a clear change of the surface morphology as functions of incident fluence and impinging energy. The mass loss results showed a decrease of the total sputtering yield of CLF-1 steel with increasing incident fluence by up to one order of magnitude. The total sputtering yield of CLF-1 steel after 7.2 × 1024 D m−2 deuterium plasma exposure reduced by a factor of 4 compared with that of pure iron, which can be attributed to the enrichment of W at the surface due to preferential sputtering of iron and chromium. After D plasma exposure, the total deuterium retention in CLF-1 steel samples measured by TDS decreased with increasing incident fluence and energy, and a clear saturation tendency as function of incident fluence or energy was also observed.
Joint Global and Local Feature Learning Based on Facial StO2 for Stress Recognition

Stress is an emotional state that is inevitable in social life, it is of great importance to accurately identify different types of stress. In this paper, we use human facial tissue oxygen saturation (StO2) to identify four states of an individual’s baseline, emotional stress, high-intensity physical stress and low-intensity physical stress. For the stress classification, we proposed a network called GLNet that combines global and local StO2 features. Specifically, GLNet learns local features from facial regions that provide the effective stress information and global features from the whole face. Then, the two features are fused at decision-level and classified. In addition, a new data augmentation method was proposed to address the data imbalance, which generates new data by constructing a mixed network called MixNet that fuses the depth features of multiple data. Experimental results show that MixNet can effectively alleviate the problem of data imbalance and GLNet combined with the data expanded by MixNet achieved the best performance compared to previous methods of StO2 stress recognition.
Quasi-static major and minor strain-stress loops in textured polycrystalline Fe81.6Ga18.4 Galfenol

The ΔE effect (Young's modulus variation of magnetostrictive materials) is useful for tunable vibration absorption and stiffness control. The ΔE effect of iron-gallium (Galfenol) has not been fully characterized. In this study, major and minor strain-stress loops were measured under different bias magnetic fields in solid, research grade, ⟨100⟩-oriented, highly-textured polycrystalline Fe81.6Ga18. 4 Galfenol. A 1 Hz, constant amplitude compressive stress was applied from −0.5 MPa to −63.3 MPa for major loop responses. Minor loops were generated by simultaneously applying a 4 Hz, 2.88 MPa amplitude sinusoidal stress and different bias stresses ranging from −5.7 MPa to −41.6 MPa in increments of about 7.2 MPa. Bias magnetic fields were applied in two ways, a constant field in the sample obtained using a proportional-integral (PI) controller and a constant current in the excitation coils. The ΔE effect was quantified from major and minor loop measurements. The maximum ΔE effect is 54.84% and 39.01% for consta.
An Efficient Anonymous Authentication Technique for TMIS under the Smart Home Environment

The Internet of Things (IoT) is rapidly evolving, and one new paradigm is the “smart home,” which allows people to control their intelligent home gadgets from anywhere in the world via the Internet. Sensitive information gathered by smart devices is at risk of being intercepted and exploited unlawfully by a malicious third party due to insecure communication routes between the client and the smart devices. Through Telecare Medical Information Systems (TMIS), patients can get several types of healthcare delivery services in the comfort of their homes. Access to Electronic Medical Records (EMR) by patients or clinicians requires authentication, patient privacy protection, security, and data confidentiality. Thus, developing an effective anonymous authentication mechanism is crucial to ensure secure communications in the smart home setting. This paper describes a novel and effective security scheme using extended Chebyshev chaotic maps in the context of smart home environments. Analytical results show that the proposed authentication technique is secure from replay attacks, impersonation attacks, offline password guessing attacks, stolen verification table attacks, patient privacy protection, perfect forward secrecy, and known key security. Finally, the projected authentication technique gives superior results to the preliminary techniques.
Automatic Disengagement Scenario Reconstruction Based on Urban Test Drives of Automated Vehicles

In recent years, scenario-based testing has gained increased attention as a potentially efficient strategy for validating the overall safety of automated vehicles. However, which scenarios are of interest for testing and how to systematically generate the test instances remain as unanswered questions. In this work, we interpret the importance of incorporating automated vehicle disengagement scenarios into scenario-based testing. Accordingly, we design and implement a fully automatic pipeline to reconstruct the essential and error-reduced disengagement scenarios based on imperfect perception measurement data from real test drives in an urban environment. Our concept is developed based on 137 disengagement data snippets and two additional datasets for handling false positives and false negatives in the original measurements. We use additional disengagement snippets for validating the performance of the pipeline. We exhibit representative reconstructed scenarios to show a successful restoration of the reality and quantitatively demonstrate the correct functioning of the methods in the pipeline regarding filtering irrelevant objects and handling the perception inaccuracies.
Optimal control of mean-field backward doubly stochastic systems driven by Itô-Lévy processes

ABSTRACT In this paper, we introduce a new class of backward doubly stochastic differential equations (in short BDSDE) called mean-field backward doubly stochastic differential equations (in short MFBDSDE) driven by Itô-Lévy processes and study the partial information optimal control problems for backward doubly stochastic systems driven by Itô-Lévy processes of mean-field type, in which the coefficients depend on not only the solution processes but also their expected values. First, using the method of contraction mapping, we prove the existence and uniqueness of the solutions to this kind of MFBDSDE. Then, by the method of convex variation and duality technique, we establish a sufficient and necessary stochastic maximum principle for the stochastic system. Finally, we illustrate our theoretical results by an application to a stochastic linear quadratic optimal control problem of a mean-field backward doubly stochastic system driven by Itô-Lévy processes.
Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets

This work shows the benefit of Neural Architecture Search (NAS) for robust and efficient radar neural network design. Radar-based neural networks have received increasing attention in the development of Advanced Driver-Assistance Systems. However, many state-of-the-art networks are designed for specific tasks and datasets, often disregarding network efficiency, leading to a lack of robust methods for efficient radar network design. This paper leverages a differentiable NAS method called DASH to automate the design of radar neural networks. We evaluate our method across multiple tasks and datasets to assess its robustness. Additionally, we introduce Efficient-DASH, which leverages compound scaling to discover more efficient networks. Our results show that DASH outperforms a state-of-the-art radar network across tasks, while maintaining strong performance when generalizing to other datasets. Efficient-DASH finds architectures that are more efficient than the baseline network, while still reporting performance gains over 4.5%. This demonstrates the potential of NAS for robust and efficient radar neural network design.