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Thermal aging and ultraviolet (UV) irradiation are used as methods to prepare samples of kraft paper spanning its full lifecycle within a high-voltage transformer, from new to end of life. The mechanical break strain is correlated with the degree of polymerization (DP), representing a progressive deterioration of mechanical properties during aging. Subsequent exposure to oil flow mimicking both normal plant operation and reclamation activities shows that aged paper exposed to high rates of flow experiences noticeable surface roughening. Despite this, no measurable erosion occurs even after long-term temperature/flow cycling in used mineral oil containing a notable particulate content. This indicates that kraft paper is unlikely to be eroded by normal reclamation activities, even in a severely aged asset. This confirms the validity of using this approach to extend plant life. [ABSTRACT FROM AUTHOR]

Primality generation is the cornerstone of several essential cryptographic systems. The problem has been a subject of deep investigations, but there is still a substantial room for improvements. Typically, the algorithms used have two parts – trial divisions aimed at eliminating numbers with small prime factors and primality tests based on an easy-to-compute statement that is valid for primes and invalid for composites. In this paper, we will showcase a technique that will eliminate the first phase of the primality testing algorithms. The computational simulations show a reduction of the primality generation time by about 30 percent in the case of 1024-bit RSA key pairs. This can be particularly beneficial in the case of decentralized environments for shared RSA keys as the initial trial division part of the key generation algorithms can be avoided at no cost. This also significantly reduces the communication complexity. Another essential contribution of the paper is the introduction of a new one-way function that is computationally simpler than the existing ones used in public-key cryptography. This function can be used to create new random number generators, and it also could be potentially used for designing entirely new public-key encryption systems. [ABSTRACT FROM AUTHOR]

It is known that the determination of the good-dice-in-bad-neighborhoods (GDBNs) has been regarded as an effective technique to reduce the value of the defect parts per million (DPPM) by identifying and rejecting the suspicious dice even though they are good in testing. Instead of examining eight immediate neighbors in a small-sized $3\times 3$ window or exploiting simple linear regression, a large-sized window can be used to recognize the broad-sighted neighborhoods and accurately infer the suspiciousness level for any given die. In this paper, the artificial neural networks (ANN)-based method can be proposed to solve the GDBN identification. Furthermore, two enhanced techniques can be further presented to improve the inference accuracy of the original ANN-based method by considering the variation of the time-dependent wafer patterns and the wafer-to-wafer relationship between two adjacent wafers. After applying the two enhanced techniques, the business profits can be improved in the new ANN-based method. Various experiments on two datasets clearly reveal the superiority of the proposed ANN-based method over the other existing methods. In addition to the reduction of the DPPM value, the new ANN-based method can achieve the 1.5X–2X better reduction in the cost of the return merchandise authorization (RMA). On the other hand, the experimental results show that the similar result can also be obtained in the other lower-yield products. By using the new ANN-based method, the relationships on bad dice cross wafers can be captured and the highly-accurate inference results can be simultaneously maintained. [ABSTRACT FROM AUTHOR]

Since the introduction of adaptive steganography, most of the recent research works seek at designing cost functions that are evaluated against steganalysis methods. While those approaches have been successful, they rely on intuitive principles and ad-hoc costs associated with each pixel or Discrete Cosine Transform (DCT) coefficient. Beyond the empirical assessments, the insights one can get from such approaches are very limited. On the opposite, this paper presents an original method for steganography in JPEG images that exploits a statistical model of the DCT coefficients. Within the framework of hypothesis testing theory, we use a statistical model of covers to derive the analytical expression of the most powerful detector. The objective of the steganographer is to minimize the statistical performance of this “omniscient detector” which represents a “worst-case” scenario for security. This paper shows how this method allows designing effective steganography, in terms of both security and computational complexity, in the two main use cases: when having only one single JPEG image and when the uncompressed image is available, case also known as Side-Informed (SI). A wide range of numerical comparisons shows that the proposed method outperforms the current state-of-the-art especially against the latest and most accurate steganalysis approaches based on Deep Learning. [ABSTRACT FROM AUTHOR]

Background: More than 30 years ago, Harmon distinguished developmental research articles (RAs), which propose a solution to a problem, from experimental RAs, but the developmental format has received little attention. Literature review: Genre analysis of RAs has been largely restricted to articles following the standard experimental/Introduction, Methods, Results, Discussion (IMRD) format, thereby excluding many developmental engineering articles. Recently, a textbook proposed Introduction, Process, Testing, Conclusion (IPTC) as a prototypical format for electrical engineering RAs, but this format has not yet been demonstrated from a corpus. Research questions: 1. What is the macrostructure of electrical engineering RAs? 2. What are the characteristic features of each division of electrical engineering RAs? Methodology: Section headings, wordcount, and notable features were analyzed for 75 RAs from 15 electrical engineering journals and compared with both IPTC and Harmon's developmental structure. Results: Only one article, a case study, followed IMRD. Sixty-seven developmental RAs followed the IPTC format. These are distinguished by the second division (P), where the new solution is described, written in extended style, comprising several sections with headings specific to the research. A paragraph at the end of the Introduction describing the organization of the paper, the location of the theoretical framework and testing methods, and a ubiquitous Conclusion also differ from IMRD. Seven developmental RAs exhibited a hybrid format with the well-known IMRD section headings superimposed on an IPTC structure. Conclusions: Most electrical engineering articles are developmental and follow IPTC format. This can inform future genre analysis research and has pedagogical implications for teaching engineering writing. [ABSTRACT FROM AUTHOR]

You are reading the Editorial of the Special Section of IEEE Transactions on Device and Materials Reliability with a collection of the best papers of the 2021 (27th) edition of IEEE IOLTS, an established IEEE symposium which focuses on all critical challenges and important solutions for computing and electronic circuits and systems robust design. Robustness from the IOLTS technical point of view spans across the technology, circuit, microarchitecture, architecture, system, and software layers. [ABSTRACT FROM AUTHOR]

Trends in the automotive industry confirm that the demand for testing of embedded systems, especially advanced driver assistance systems (ADAS), will grow dramatically in the near future. This paper proposes a new solution that automates the detection of software defects in embedded systems. The solution consists of a data-driven sampling algorithm to intelligently sample the testing space by sequentially generating test cases. Moreover, it segregates different defects from each other and identifies the signals that trigger each. The results are compared against other automated methods for defect identification and analysis, and it is found that this novel solution is able to identify defects more rapidly. In addition, it correctly separates defects and reliably reproduces each distinct defect. [ABSTRACT FROM AUTHOR]

The pathogen of the ongoing coronavirus disease 2019 (COVID-19) pandemic is a newly discovered virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Testing individuals for SARS-CoV-2 plays a critical role in containing COVID-19. For saving medical personnel and consumables, many countries are implementing group testing against SARS-CoV-2. However, existing group testing methods have the following limitations: (1) The group size is determined without theoretical analysis, and hence is usually not optimal. This adversely impacts the screening efficiency. (2) These methods neglect the fact that mixing samples together usually leads to substantial dilution of the SARS-CoV-2 virus, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. We propose an eXpectation Maximization based Adaptive Group Testing (XMAGT) method. The basic idea is to adaptively adjust its testing strategy between a group testing strategy and an individual testing strategy such that the expected number of samples identified by a single test is larger. During the screening process, the XMAGT method can estimate the ratio of positive samples. With this ratio, the XMAGT method can determine a group size under which the group testing strategy can achieve a maximal expected number of negative samples and the sensitivity of tests is higher than a user-specified threshold. Experimental results show that the XMAGT method outperforms existing methods in terms of both efficiency and sensitivity. [ABSTRACT FROM AUTHOR]

This paper widens the application of a testing protocol for overhead line insulators introduced by TERNA. Testing results relevant to three groups of Room Temperature Vulcanized pre-coated cap-and-pin insulators removed from High Voltage Alternate Current and Extra-High Voltage Alternate Current Italian lines after different years of service are reported and statistically processed; the third group is treated here for the first time and new results for the other two groups are included. Among the various tests prescribed in the protocol, focus is on the Quick Flashover test, with the aim to examine and compare the flashover voltages of all three groups as a function of service time. The dependence of flashover voltage on electric field is also analyzed and a model correlating flashover voltage with field and time is proposed for the first time. It is confirmed that the flashover voltages provided by the Quick Flashover test can be considered as a diagnostic property of Room Temperature Vulcanized insulators, as they drop with the ageing time and the electric field. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DERs), such as responsive loads and energy storage systems, can help grid operators to more effectively balance supply and demand and manage network constraints. However, consumer acceptance of load coordination schemes depends on ensuring customer quality of service (QoS), thus requiring the careful management of device-level constraints. Simultaneously managing both device-level and system-level objectives is a difficult problem. Most potential solutions to the DER coordination problem are in some way distributed, with decision logic running both at the device (such as a thermostat) and the coordinator, with potentially complex communications between devices and one or more coordinators. It is difficult to effectively test distributed DER control schemes, particularly when there are thousands of devices with both device-level and grid-level constraints and asynchronous communications. This paper addresses this challenge by presenting a novel real-time grid-and-DER cyber-physical co-simulation platform for testing advanced DER coordination schemes and characterizing the behaviors of a DER fleet. The co-simulation platform is particularly suitable for: $i$) testing the real-time performance of a large fleet of DERs delivering advanced grid services; $ii$) characterizing the distribution of states for a large fleet of managed DERs; and $iii$) incorporating practical limitations of DERs and communications and analyzing the effects on fleet-wide performance. To illustrate these benefits, we demonstrate how the platform can be used to test a fleet with thousands of DERs coordinated using Packetized Energy Management (PEM) and interacting with a realistic transmission system model. [ABSTRACT FROM AUTHOR]

The Industry 4.0 (I4.0) is a paradigm settled down by the introduction of the Internet of things (IoT) into the production and manufacturing environment. I4.0 promotes the connection of physical items such as sensors, devices, and enterprise assets, to each other and to the Internet. The information that flows through these items is vital because it serves to make relevant decisions. One of the main features of I4.0 is its adaptability to the human needs, this means that the items included in the I4.0 network are heterogeneous and they are large in number. The majority of I4.0 papers, which are focused on testing, describe a specific system or part of the I4.0 network. We have not found any paper that undertakes the testing of multiple connected IoT devices that will receive, process, and make decisions according to the complex and real data that travel through the network. In this article, we present IoT-TEG (Test Event Generator) 4.0, which is based on the test event generator system IoT-TEG. IoT-TEG 4.0 provides two new main contributions: the generation of test cases, which can include all the different types of data that the connected I4.0 devices under study can manage, and real-time testing. Additionally, its validation using real IoT programs is included and the results show that IoT-TEG 4.0 allows us to conduct tests that mimic real IoT system behaviors. [ABSTRACT FROM AUTHOR]

Objective: This paper presents preliminary methods of incorporating the pathological conditions of cardiac arrhythmias and valvular stenosis in hybrid mock circulation loop (hMCL) operation for the enhanced verification and validation of mechanical circulatory support devices such as VADs. Methods: The MGH/MF Waveform datasets from PhysioNet database (including both nominal and clinically diagnosed arrhythmic ECG measurements) as well as cardiovascular system model updates are used to recreate arrhythmic events and valvular stenosis in vitro. Results: Preliminary results show the hMCL can recreate each tested cardiac event within 2% and 4% mean error for reference pressure tracking in the aortic and left ventricular pressure chambers, respectively. Further, frequency spectrum analysis comparisons using the magnitude-squared coherence analysis shows close alignment between measured arrhythmic and hMCL realized pressure frequency content. Conclusion: The generation of cardiac arrhythmias and valvular stenosis around a VAD via both model and acute measurement based methods was achieved. Significance: Pathological conditions such as cardiac arrhythmias and valvular stenosis are limited in documentation despite the large percentage of patients who experience these events. This paper provides a means to begin incorporating these events into hardware-in-the-loop mock circulatory systems for next generation VAD validation and verification. [ABSTRACT FROM AUTHOR]

This paper presents a three-dimensional (3D) localization and attitude estimation system to track a Unmanned Aerial Vehicle (UAV) using a single camera without prior knowledge of the environment. The hardware system consists of a Dynamic Vision Sensing (DVS) camera, a circle-shaped blinking marker made by Light-Emitting Diodes (LEDs), and a base station computer. The algorithm for spatial localization and attitude estimation includes a temporal video filter, triangulation-based location and attitude estimation, and 3D real-time plotting with a graphical user interface (GUI). The temporal video filter processes the image stream from the DVS camera to identify the frequency of the marker and removes the background image. The circle-shaped marker creates an ellipse in the image, whose diameter length and angles are utilized for calculating the location and attitude of the UAV, which offers a low-computing overhead. The proposed system has been evaluated in hardware flight testing. The results are compared with the benchmark data from both the infrared motion capturing system for localization and the on-board inertial measurement units of the UAV for attitude estimation. The accuracy and detection range surpasses similar state-of-the-art systems. The proposed method provides a simple yet accurate solution for tracking the location and attitude of a UAV. [ABSTRACT FROM AUTHOR]

Safety validation is a challenge for releasing automated driving, even though substantial effort has been made in this field. This paper proposes an online safety validation method within the frame of virtual assessment of automation in field operation (VAAFO). The basic idea of VAAFO is that a virtual automated vehicle runs in the background, while the physical vehicle is driven in an automated or manual mode. The virtual automated vehicle receives input from real sensors, but has no access to the actuators. Thus, the risk-free nature of simulation-based testing and the validity of field operational testing are combined. The testing of automated vehicles is accelerated by applying the approach in customer vehicles. In this paper, we elaborate on and implement this approach. Essential parameters are studied and specified, while the necessary coordinate transformation is performed. Triggers are defined and concretized by a newly developed criticality index. Two derived simulated cases and a real-world case are utilized to illustrate the VAAFO approach. The study cases show that the safety of automated vehicles can be assessed online and critical scenarios can be discovered by the defined triggers under the framework of VAAFO. The results demonstrate that the proposed approach is able to safely and efficiently test automated vehicles online under real driving conditions and discover unknown unsafe scenarios. [ABSTRACT FROM AUTHOR]

Autonomous vehicles must be comprehensively evaluated before deployed in cities and highways. However, most existing evaluation approaches for autonomous vehicles are static and lack adaptability, so they are usually inefficient in generating challenging scenarios for tested vehicles. In this paper, we propose an adaptive evaluation framework to efficiently evaluate autonomous vehicles in adversarial environments generated by deep reinforcement learning. Considering the multimodal nature of dangerous scenarios, we use ensemble models to represent different local optimums for diversity. We then utilize a nonparametric Bayesian method to cluster the adversarial policies. The proposed method is validated in a typical lane-change scenario that involves frequent interactions between the ego vehicle and the surrounding vehicles. Results show that the adversarial scenarios generated by our method significantly degrade the performance of the tested vehicles. We also illustrate different patterns of generated adversarial environments, which can be used to infer the weaknesses of the tested vehicles. [ABSTRACT FROM AUTHOR]

To solve the low spatial and/or temporal resolution problem which the conventional hyperspectral cameras often suffer from, coded hyperspectral imaging systems have attracted more attention recently. Recovering a hyperspectral image (HSI) from its corresponding coded image is an ill-posed inverse problem, and learning accurate prior of HSI is essential to solve this inverse problem. In this paper, we present an effective convolutional neural network (CNN) based method for coded HSI reconstruction, which learns the deep prior from the external dataset as well as the internal information of input coded image with spatial-spectral constraint. Specifically, we first develop a CNN-based channel attention reconstruction network to effectively exploit the spatial-spectral correlation of the HSI. Then, the reconstruction network is learned by leveraging an arbitrary external hyperspectral dataset to exploit the general spatial-spectral correlation under adversarial loss. Finally, we customize the network by internal learning with spatial-spectral constraint and total variation regularization for each coded image, which can make use of the internal imaging model to learn specific prior for current desirable image and effectively avoids overfitting. Experimental results using both synthetic data and real images show that our method outperforms the state-of-the-art methods on several popular coded hyperspectral imaging systems under both comprehensive quantitative metrics and perceptive quality. [ABSTRACT FROM AUTHOR]

Deep learning (DL) brings autonomous vehicles (AVs) close to reality. However, the witness of many safety issues has raised a big concern about the reliability of AVs. To solve this problem, much research has been done to test deep learning-driven AVs. Generally, once a test input is produced, a developer needs to manually check its expected output. However, there often exists massive unlabeled test data (e.g., raw context traces in the real world). It is impractical to manually label all test inputs. Despite some works on automatic generation of test oracles, they are either task-specific or constrained to synthetic inputs. In this paper, we present a general and extensible framework, DeepSuite, to mitigate the manual effort of generating test oracles. The intuition behind is that not all test inputs are equally worth labelling. With limited testing budget, it is desirable to label a test suite with high diversity and a reasonable size. Due to the large search space, to optimize such test suites is of great challenge. To address it, DeepSuite employs a three-phase optimization method (i.e., selection, crossover, and mutation) to iteratively select representative but non-redundant test suites. Such conflicting profit/cost objectives are attained through a genetic algorithm with a well-defined multi-objective fitness function. In the experiments, we first show that the diversity of tests can be revealed by test criteria. Then, experiments on three widely-used datasets demonstrated the effectiveness of DeepSuite in generating test suites with competitive testing coverage and 68.42% smaller size, which greatly improves the data collection efficiency of testing DL-driven autonomous vehicles. [ABSTRACT FROM AUTHOR]

The scenario-based test method is the research hotspot of automated vehicle (AV) validation and verification (V&V), and testing with hazardous scenarios is of important means. An Optimization Searching (OS) method for enhanced generation in hazardous scenarios is proposed in this paper to efficiently explore functional boundary scenarios in a huge logical state space. The method is computationally tractable, and its generated experimental parameters are optimized using past test results. The method includes five essential modules. The Exploration and Exploitation module uses the Multi-arm bandit method to obtain the greatest sum of the $TTC^{\mathbf {-1}}$ (Time To Collision). The Parameter Moving Probability Determination module uses an analytic hierarchy process to ensure that influential parameters are more likely to move. The Step Size Determination module is built with Levy-step to find a greater number of hazardous scenarios. The Memory Function module is used to avoid repeat experiments that can reduce computing efficiency. The Result Analysis module creates a hazard parameter space for subsequent tests. We tested an ACC (Adaptive Cruise Control) algorithm with a specified logical scenario in the virtual environment built by PreScan. The results showed that the OS method can effectively discover the dangerous range with the tested ACC algorithm, and its test speed can reach more than five times that of an exhaustive algorithm without prior knowledge. [ABSTRACT FROM AUTHOR]

Identifying the same persons across different views plays an important role in many vision applications. In this paper, we study this important problem, denoted as Multi-view Multi-Human Association (MvMHA), on multi-view images that are taken by different cameras at the same time. Different from previous works on human association across two views, this paper is focused on more general and challenging scenarios of more than two views, and none of these views are fixed or priorly known. In addition, each involved person may be present in all the views or only a subset of views, which are also not priorly known. We develop a new end-to-end deep-network based framework to address this problem. First, we use an appearance-based deep network to extract the feature of each detected subject on each image. We then compute pairwise-similarity scores between all the detected subjects and construct a comprehensive affinity matrix. Finally, we propose a Deep Assignment Network (DAN) to transform the affinity matrix into an assignment matrix, which provides a binary assignment result for MvMHA. We build both a synthetic dataset and a real image dataset to verify the effectiveness of the proposed method. We also test the trained network on other three public datasets, resulting in very good cross-domain performance. [ABSTRACT FROM AUTHOR]

Viruses are the most abundant biological entities on earth, and play vital roles in many aspects of microbial communities. As major human pathogens, viruses have caused huge mortality and morbidity to human society in history. Metagenomic sequencing methods could capture all microorganisms from microbiota, with sequences of viruses mixed with these of other species. Therefore, it is necessary to identify viral sequences from metagenomes. However, existing methods perform poorly on identifying short viral sequences. To solve this problem, a deep learning based method, RNN-VirSeeker, is proposed in this paper. RNN-VirSeeker was trained by sequences of 500bp sampled from known Virus and Host RefSeq genomes. Experimental results on the testing set have shown that RNN-VirSeeker exhibited AUROC of 0.9175, recall of 0.8640 and precision of 0.9211 for sequences of 500bp, and outperformed three widely used methods, VirSorter, VirFinder, and DeepVirFinder, on identifying short viral sequences. RNN-VirSeeker was also used to identify viral sequences from a CAMI dataset and a human gut metagenome. Compared with DeepVirFinder, RNN-VirSeeker identified more viral sequences from these metagenomes and achieved greater values of AUPRC and AUROC. RNN-VirSeeker is freely available at https://github.com/crazyinter/RNN-VirSeeker. [ABSTRACT FROM AUTHOR]

The ongoing coronavirus disease 2019 (COVID-19) is a pandemic causing millions of deaths, devastating social and economic disruptions. Testing individuals for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of COVID-19, is critical for mitigating and containing COVID-19. Many countries are implementing group testing strategies against COVID-19 to improve testing capacity and efficiency while saving required workloads and consumables. A group of individuals’ nasopharyngeal/oropharyngeal (NP/OP) swab samples is mixed to conduct one test. However, existing group testing methods neglect the fact that mixing samples usually leads to substantial dilution of viral ribonucleic acid (RNA) of SARS-CoV-2, which seriously impacts the sensitivity of tests. In this paper, we aim to screen individuals infected with COVID-19 with as few tests as possible, under the premise that the sensitivity of tests is high enough. To achieve this goal, we propose an Adaptive Group Testing (AdaGT) method. By collecting information on the number of positive and negative samples that have been identified during the screening process, the AdaGT method can estimate the ratio of positive samples in real-time. Based on this ratio, the AdaGT algorithm adjusts its testing strategy adaptively between an individual testing strategy and a group testing strategy. The group size of the group testing strategy is carefully selected to guarantee that the sensitivity of each test is higher than a predetermined threshold and that this group contains at most one positive sample on average. Theoretical performance analysis on the AdaGT algorithm is provided and then validated in experiments. Experimental results also show that the AdaGT algorithm outperforms existing methods in terms of efficiency and sensitivity. Note to Practitioners—Real-time reverse transcription-polymerase chain reaction (rRT-PCR) tests provide scope for automation and are one of the most widely used laboratory methods for detecting the SARS-CoV-2 virus. This paper is motivated by the following challenges: (1) Many countries are experiencing an acute shortage of professionals and consumables for conducting rRT-PCR tests; (2) Group sizes of existing group testing methods against COVID-19 may not be optimal, which adversely impacts the efficiency of the screening of the SARS-CoV-2 virus; (3) Existing group testing methods do not consider the fact that the sensitivity of rRT-PCR tests usually decreases with the group size. The objective of this paper is to improve the efficiency and sensitivity of large-scale screening against COVID-19. For achieving this goal, we propose an Adaptive Group Testing (AdaGT) algorithm, which has the following advantages: (1) It can improve the efficiency for screening the SARS-CoV-2 virus, mainly by adaptively adjusting its testing strategy between an individual testing strategy and a group testing strategy based upon an estimated ratio of positive samples during the screening process; (2) It can guarantee a high sensitivity of the rRT-PCR tests by determining the group sizes of the group testing strategy based upon some constraints; (3) We derive an appropriate threshold for the estimated ratio of positive samples such that the AdaGT algorithm can achieve a minimum average number of rRT-PCR tests and can be directly employed in practical applications. [ABSTRACT FROM AUTHOR]

Coded computing has proved its efficiency in handling a straggler issue in distributed computing framework. It uses error correcting codes to mitigate the effect of the stragglers. However, in a coded distributed computing framework, there may exist Byzantine workers who send the wrong computation results to a master in order to contaminate the overall computation output. Therefore, it is essential to identify Byzantine workers from their computation results in coded computing. In this paper, we consider Byzantine attack identification problem in coded computing for distributed matrix multiplication tasks. We propose a new coding scheme which facilitates the efficient Byzantine attack identification, namely locally testable codes. We also suggest a hierarchical group testing method for Byzantine attack identification. We claim the required number of tests for group testing in our scheme, and show that it requires smaller number of tests than the conventional group testing method for the existing coded computing schemes. [ABSTRACT FROM AUTHOR]

Type III sleep studies record cardio-respiratory channels only. Compared with polysomnography, which also records electrophysiological channels, they present many advantages: they are less expensive, less time-consuming, and more likely to be performed at home. However, their accuracy is limited by missing sleep information. That is why many studies present specific cardio-respiratory parameters to assess the causal effects of sleep stages upon cardiac or respiratory activities. For this paper, we gathered many parameters proposed in literature, leading to 1,111 features. The pulse oximeter, the PneaVoX sensor (recording tracheal sounds), respiratory inductance plethysmography belts, the nasal cannula and the actimeter provided the 112 worthiest ones for automatic sleep scoring. Then, a 3-step model was implemented: classification with a multi-layer perceptron, sleep transition rules corrections (from the AASM guidelines), and sequence corrections using a Viterbi hidden Markov model. The whole process was trained and tested using 300 and 100 independent recordings provided from patients suspected of having sleep breathing disorders. Results indicated that the system achieves substantial agreement with manual scoring for classifications into 2 stages (wake vs. sleep: mean Cohen’s Kappa $\kappa$ of 0.63 and accuracy rate $Acc$ of 87.8%) and 3 stages (wake vs. R stage vs. NREM stage: mean $\kappa$ of 0.60 and $Acc$ of 78.5%). It indicates that the method could provide information to help specialists while diagnosing sleep. The presented model had promising results and may enhance clinical diagnosis. [ABSTRACT FROM AUTHOR]

Deep embedding learning plays a key role in learning discriminative feature representations, where the visually similar samples are pulled closer and dissimilar samples are pushed away in the low-dimensional embedding space. This paper studies the unsupervised embedding learning problem by learning such a representation without using any category labels. This task faces two primary challenges: mining reliable positive supervision from highly similar fine-grained classes, and generalizing to unseen testing categories. To approximate the positive concentration and negative separation properties in category-wise supervised learning, we introduce a data augmentation invariant and instance spreading feature using the instance-wise supervision. We also design two novel domain-agnostic augmentation strategies to further extend the supervision in feature space, which simulates the large batch training using a small batch size and the augmented features. To learn such a representation, we propose a novel instance-wise softmax embedding, which directly perform the optimization over the augmented instance features with the binary discrmination softmax encoding. It significantly accelerates the learning speed with much higher accuracy than existing methods, under both seen and unseen testing categories. The unsupervised embedding performs well even without pre-trained network over samples from fine-grained categories. We also develop a variant using category-wise supervision, namely category-wise softmax embedding, which achieves competitive performance over the state-of-of-the-arts, without using any auxiliary information or restrict sample mining. [ABSTRACT FROM AUTHOR]

Cyber-physical systems are becoming more intelligent with the adoption of heterogeneous sensor networks and machine learning capabilities that deal with an increasing amount of input data. While this complexity aims to solve problems in various domains, it adds new challenges for the system assurance. One issue is the rise in the number of abnormal behaviors that affect system performance due to possible sensor faults and attacks. The combination of safety risks, which are usually caused by random sensor faults and security risks that can happen during any random system state, makes the full coverage testing of the cyber-physical system challenging. Existing techniques are inadequate to deal with complex safety and security co-risks against cyber-physical systems. In this paper, we propose AST-SafeSec, an analysis methodology for both safety and security aspects that utilizes reinforcement learning to identify the most likely adversarial paths at various normal or failure states of a cyber-physical system that can influence system behavior through its sensor data. The methodology is evaluated using an autonomous vehicle scenario by incorporating a security attack into the stochastic sensor elements of a vehicle. Evaluation results show that the methodology analyzes the interaction of malicious attacks with random faults and identifies the incident caused by the interactions and the most likely path that leads to the incident. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables multiple clients to train a unified model without disclosing their private data. However, susceptibility to membership inference attacks (MIAs) arises due to the natural inclination of FL models to overfit on the training data during the training process, thereby enabling MIAs to exploit the subtle differences in the FL model’s parameters, activations, or predictions between the training and testing data to infer membership information. It is worth noting that most if not all existing MIAs against FL require access to the model’s internal information or modification of the training process, yielding them unlikely to be performed in practice. In this paper, we present with TEAR the first evidence that it is possible for an honest-but-curious federated client to perform MIA against an FL system, by exploring the Temporal Evolution of the Adversarial Robustness between the training and non-training data. We design a novel adversarial example generation method to quantify the target sample’s adversarial robustness, which can be utilized to obtain the membership features to train the inference model in a supervised manner. Extensive experiment results on five realistic datasets demonstrate that TEAR can achieve a strong inference performance compared with two existing MIAs, and is able to escape from the protection of two representative defenses. [ABSTRACT FROM AUTHOR]

This paper considers the problem of adaptive group testing for isolating up to $k$ defective items from a population of size $n$. There exist restrictions or preferences which determine how the items can be pooled for testing. A graphical model formalizes the pooling restrictions and preferences. Such graph-constrained group testing is investigated in three settings: populations with defectives, populations facing the potential presence of inhibitors, and populations with community structures. Adaptive group testing frameworks are provided for each setting. In populations without inhibitors, existing non adaptive frameworks can isolate the defective items perfectly with $\Theta(k \log n/k)$ number of tests, where is the $\beta$ -mixing time of a random walk over the underlying graph. This paper provides a two-stage framework that can perfectly isolate up to $k$ defective items for a regular graph using $\Theta(k2 \log n k + k)$ number of tests, thus achieving an approximate gain of a factor of $k$ over the non-adaptive frameworks. This twostage framework's principles are extended to community-structured graphs and graphs with up to $r$ inhibitor items. In particular, when inhibitors are present in the graph, a four-stage group testing framework is proposed. The results show that in the regime $r= O(k)$ for a fully connected graph, $\Theta ((k+r)\log n/(k+r) + r\log n)$ tests are sufficient for isolating the defective items. This matches the corresponding necessary condition on tests which scales $(k+r)\log n$. The adaptive graphconstrained group testing framework is also empirically evaluated. [ABSTRACT FROM AUTHOR]

Smart sensors in smart grids provide real-time data and status of bidirectional flows of energy for monitoring, protection, and control of grid operations to improve reliability and resilience. Smart sensor data interoperability is a major challenge for smart grids. This paper proposes a methodology for modeling interoperability of smart sensors in terms of interactions using labeled transition systems and finite state processes in order to quantitatively and automatically measure and assess the interoperability, identify and resolve interoperability issues, and improve interoperability. A generic interoperability model of synchronous message passing from a sender to a receiver is built based on the proposed methodology. A case study is provided to apply this methodology for modeling interoperability between the Institute of Electrical and Electronics Engineers C37.118 phasor measurement unit-based smart sensors and phasor data concentrators. The interoperability model can be used for the quantitative and automated measurement and assessment of the interoperability of phasor measurement unit-based smart sensors and phasor data concentrators to address interoperability issues. This methodology can also be applied to modeling interoperability of smart sensors based on other standard communication protocols in order to achieve and assure sensor data interoperability in smart grids. [ABSTRACT FROM AUTHOR]

Person re-identification (ReID) aims at matching identities across disjoint cameras. Its fundamental difficulty lies in associating images across individual cameras, where a key clue, i.e., identity appearance, is prone to the environmental factors of cameras and, consequently, subject to distinct image distributions due to the environmental differences between cameras. To associate images from training cameras, ReID methods strongly demand expensive inter-camera annotations for learning the relations between the distribution of these cameras, yet trained models are still not guaranteed to transfer well to unseen cameras. This problem significantly limits the application of ReID. This paper rethinks the working mechanism of conventional ReID approaches and puts forward a new solution. With an effective operator named Camera-based Batch Normalization (CBN), we guarantee an invariant input distribution independent of all cameras. Thus, the training and testing procedures are always conducted under the same input distribution. This alignment brings three benefits. First, ReID models enjoy better abilities to generalize across testing scenarios with unseen cameras and transfer across multiple training sets. Second, it makes better use of intra-camera annotations, which have been undervalued before due to the lack of cross-camera information. Ideally, the cost of inter-camera annotations can be largely reduced. Third, cross-modality tasks can be better defined through aligning visible/infrared cameras’ distributions. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Stochastic gradient descent (SGD) has become the method of choice for training highly complex and nonconvex models since it can not only recover good solutions to minimize training errors but also generalize well. Computational and statistical properties are separately studied to understand the behavior of SGD in the literature. However, there is a lacking study to jointly consider the computational and statistical properties in a nonconvex learning setting. In this paper, we develop novel learning rates of SGD for nonconvex learning by presenting high-probability bounds for both computational and statistical errors. We show that the complexity of SGD iterates grows in a controllable manner with respect to the iteration number, which sheds insights on how an implicit regularization can be achieved by tuning the number of passes to balance the computational and statistical errors. As a byproduct, we also slightly refine the existing studies on the uniform convergence of gradients by showing its connection to Rademacher chaos complexities. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of testing for equality between the location parameters of two unknown symmetric distributions that may belong to different families. Under this framework, we develop a new robust extension of the two-sample Hotelling test (HT). The proposed extension, called measure-transformed HT (MT-HT), operates by applying a transform to the probability measures of some reshaped versions of the two compared data sets. The considered measure transform is structured by a non-negative function, called MT-function, that weights the data points. In the paper we show that proper selection of the involved MT-functions can result in significant enhancement of the decision performance in the presence of non-Gaussian distributions with heavy tails. The advantages of the proposed MT-HT are illustrated in simulation studies that involve synthetic measurements. Additionally, the MT-HT is illustrated for anomaly detection in a blurred and noisy video stream. [ABSTRACT FROM AUTHOR]

This paper deals with adaptive radar detection problems where several alternative hypotheses may be plausible. This kind of problems naturally extends the conventional binary tests that often occur in radar (as well as in other application fields) by including a further uncertainty degree related to the number of unknown signal parameters (model order). Such a modification consequently leads to multiple composite alternative hypotheses. In the companion paper (Addabbo et al., 2021), we have defined a new design framework which allows us to come up with decision schemes for these hypothesis testing problems by exploiting the Kullback-Leibler Information Criterion and without resorting to heuristic design criteria. The architectures devised within the proposed framework consist of the sum between the compressed log-likelihood ratio and a penalty term inherited from model order selection rules. Such a penalty term accounts for the number of unknown parameters to overcome the limitation of the generalized likelihood ratio test in the presence of nested hypotheses. In the present paper, we apply the new design framework to different detection problems related to both real aperture and (polarimetric) synthetic aperture radar. The analysis is carried out in comparison with suitable competitors (possibly based upon heuristic design criteria) and shows that the architectures devised within the proposed theoretically-founded design framework represent an effective means to deal with detection problems where the uncertainty on some parameters leads to multiple alternative hypotheses. [ABSTRACT FROM AUTHOR]

Since a large amount of data can be obtained in the oil production process nowadays and the operation environment is increasingly complicated, it is necessary to research unsupervised and robust fault detection methods for improving safety. In this paper, an online Bayesian-based technique with a novel decision fusion algorithm is proposed for unsupervised fault detection in the pumping unit. First, a new strategy to detect the working condition of the pumping unit by dynamometer card as well as five process measured variables is proposed. To deal with high-dimension data and outliers in dynamometer card, a robust Douglas-Peucker algorithm is developed for obtaining compressed data. A chord ratio index evaluating deviation degree of observations is defined, which can be used for removing outliers during approximation. Two norms are introduced for choosing the threshold in the proposed Douglas-Peucker algorithm. Moreover, a Bayesian-based online change point detection model is attempted for detecting univariate faults in the pumping unit. A decision fusion method derived from Bayesian probability formula is proposed for fusing univariate fault detection results. At last, the power of the proposed method is evaluated by numerical simulations and a real oil production process. [ABSTRACT FROM AUTHOR]

Automatic speech emotion recognition provides computers with critical context to enable user understanding. While methods trained and tested within the same dataset have been shown successful, they often fail when applied to unseen datasets. To address this, recent work has focused on adversarial methods to find more generalized representations of emotional speech. However, many of these methods have issues converging, and only involve datasets collected in laboratory conditions. In this paper, we introduce Adversarial Discriminative Domain Generalization (ADDoG), which follows an easier to train “meet in the middle” approach. The model iteratively moves representations learned for each dataset closer to one another, improving cross-dataset generalization. We also introduce Multiclass ADDoG, or MADDoG, which is able to extend the proposed method to more than two datasets, simultaneously. Our results show consistent convergence for the introduced methods, with significantly improved results when not using labels from the target dataset. We also show how, in most cases, ADDoG and MADDoG can be used to improve upon baseline state-of-the-art methods when target dataset labels are added and in-the-wild data are considered. Even though our experiments focus on cross-corpus speech emotion, these methods could be used to remove unwanted factors of variation in other settings. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) has been applied in seizure detection to deal with differences between different subjects or tasks. In this paper, we consider cross-subject seizure detection that does not rely on patient history records, that is, acquiring knowledge from other subjects through TL to improve seizure detection performance. We propose a novel domain adaptation method, named the Cluster Embedding Joint-Probability-Discrepancy Transfer (CEJT), for data distribution structure learning. Specifically, 1) The joint probability distribution discrepancy is minimized to reduce the distribution shift in the source and target domains, and strengthen the discriminative knowledge of classes. 2) A clustering is performed on the target domain, and the class centroids of sources is used as the clustering prototype of the target domain to enhance data structure. It is worth noting that the manifold regularization is used to improve the quality of clustering prototypes. In addition, a correlation-alignment-based source selection metric (SSC) is designed for most favorable subject selection, reducing the computational cost as well as avoiding some negative transfer. Experiments on 15 patients with focal epilepsy from the Children’s Hospital, Zhejiang University School of Medicine (CHZU) database shown that CEJT outperforms several state-of-the-art approaches, and can promote the application of seizure detection. [ABSTRACT FROM AUTHOR]

For practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning method called temporal episode relation learning (TERL). TERL models MI with only limited trials from the target subject by the ability to compare MI trials through episode-based training. It can be directly applied to a new user without being re-trained, which is vital to improve user experience and realize real-world MIBCI applications. We develop a new and effective approach where, unlike the original episode learning, the temporal pattern between trials in each episode is encoded during the learning to boost the classification performance. We also perform an online evaluation simulation, in addition to the offline analysis that the previous studies only conduct, to better understand the performance of different approaches in real-world scenario. Extensive experiments are completed on four publicly available MIBCI datasets to evaluate the proposed TERL. Results show that TERL outperforms baseline and recent state-of-the-art methods, demonstrating competitive performance for subject-specific MIBCI where few trials are available from a target subject and a considerable number of trials from other source subjects. [ABSTRACT FROM AUTHOR]

Confronted with the COVID-19 health crisis, the year 2020 represented a turning point for the entire world. It paved the way for health-care systems to reaffirm their foundations by using different technologies such as sensors, wearables, mobile applications, drones, robots, Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT). A lot of domains have been renovated such as diagnosis, treatment, and monitoring, as well as previously unprecedented domains such as contact tracing. Contact tracing, in conjunction with the emergence, spread, and public compliance for vaccines, was a critical step for controlling and limiting the spread of the pandemic. Traditional contact tracing is usually dependent on individuals ability to recall their interactions, which is challenging and yet not effective. Consequently, further development and usage of automated, privacy-preserving, digital contact-tracing was required. As the pandemic is coming to an end, it is vital to collect and learn the effective used technologies that aided in fighting the virus in order to be prepared for any future pandemics and to be aware of any literature gaps that must be filled. This paper surveys state-of-the-art architectures, platforms, and applications combating COVID-19 at each phase of the five basic contact tracing phases, including case identification, contacts identification and rapid exposure notification, surveillance, regular follow up and prevention. In addition, there is a phase of preparation and post-pandemic services for current and needed future technology that will aid in the fight against any incoming infectious diseases. [ABSTRACT FROM AUTHOR]

As a lightweight method, runtime verification aims to check whether one program execution satisfies a desired property. For online runtime verification, the approach efficiency and property expressiveness are two key points restricting its wide application. In this paper, we propose a distributed network-based parallel runtime verification approach to verifying full regular temporal properties for a suitable subset of C (named by Xd-C) programs in an online manner. With this approach, an Xd-C program is translated into an equivalent Modeling, Simulation and Verification Language (MSVL) program, and a desired property is specified as a Propositional Projection Temporal Logic (PPTL) formula; during the program execution, segments of the generated state sequence are verified in parallel by distributed multi-core machines. Experimental results show that, our approach has a speedup of 2.5X-5.0X over the state-of-art runtime verification approaches and supports full regular temporal properties, meaning that our approach can not only take full advantage of computing and storage resources in a distributed network, but also support more expressive properties. [ABSTRACT FROM AUTHOR]

In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios. Accordingly, in this paper, we turn to neural architecture search (NAS) and make the first attempt to bring NAS techniques into the ZSL realm. Specifically, we propose a differentiable GAN architecture search method over a specifically designed search space for zero-shot learning, referred to as ZeroNAS. Considering the relevance and balance of the generator and discriminator, ZeroNAS jointly searches their architectures in a min-max player game via adversarial training. Extensive experiments conducted on four widely used benchmark datasets demonstrate that ZeroNAS is capable of discovering desirable architectures that perform favorably against state-of-the-art ZSL and generalized zero-shot learning (GZSL) approaches. Source code is at https://github.com/caixiay/ZeroNAS. [ABSTRACT FROM AUTHOR]

A highly automated vehicle (HAV) is a safety-critical system. Therefore, a verification and validation (V&V) process that rigorously evaluates the safety of HAVs is necessary before their release to the market. In this paper, we propose an interaction-aware safety evaluation framework for the HAV and apply it to the roundabout entering scenario. Instead of assuming that the primary other vehicles (POVs) take predetermined maneuvers, we model the POVs as game-theoretic agents. To capture a wide variety of interactions between the POVs and the vehicle under test (VUT), we use level- $k$ game theory and social value orientation (SVO) to characterize the interactive behaviors and train a diverse library of POVs using reinforcement learning. The game-theoretic library, together with initial conditions, form a rich testing space for the two-POV roundabout scenario. On the other hand, we propose an adaptive test case generation scheme based on adaptive sampling, stochastic optimization and upper confidence bound (UCB) algorithm to efficiently generate customized challenging cases for the VUT from the testing space. In simulations, the proposed testing space design captured a wide range of interactive patterns at the roundabout scenario. The proposed test case generation scheme was found to cover the failure modes of the VUT more effectively compared to other test case generation approaches. [ABSTRACT FROM AUTHOR]

Game engines are increasingly used as simulation platforms by the autonomous vehicle community to develop vehicle control systems and test environments. A key requirement for simulation-based development and verification is determinism, since a deterministic process will always produce the same output given the same initial conditions and event history. Thus, in a deterministic simulation environment, tests are rendered repeatable and yield simulation results that are trustworthy and straightforward to debug. However, game engines are seldom deterministic. This paper reviews and identifies the potential causes and effects of non-deterministic behaviours in game engines. A case study using CARLA, an open-source autonomous driving simulation environment powered by Unreal Engine, is presented to highlight its inherent shortcomings in providing sufficient precision in experimental results. Different configurations and utilisations of the software and hardware are explored to determine an operational domain where the simulation precision is sufficiently high i.e. variance between repeated executions becomes negligible for development and testing work. Finally, a method of a general nature is proposed, that can be used to find the domains of permissible variance in game engine simulations for any given system configuration. [ABSTRACT FROM AUTHOR]

Digital twins found their genesis in the halls of NASA and the methods of product lifecycle management. Rapidly evolving trends around the proliferation of sensors, the Internet of Things, Industry 4.0, and cyber-physical systems have spurred the growth of digital twins. This paper reviews digital twins and their use in connected and automated vehicles (CAVs). Strictly speaking, digital twins must have communication between a physical system and its model, as opposed to similar methodologies that achieve indirect communication through iteration, or that substitute different parts of a system simulation with bits of hardware or software for testing. In practice, new methodologies for testing CAVs are sufficiently complex and difficult to apply simple labels. This is seen in our review of vehicular digital twins. Several gaps and challenges are apparent for the continued advancement of digital twin applications. We note some developing areas as traffic management centers, digital maps, onboard diagnostics, and logistics. Digital twins foster model reuse and encourage the use of multiple models at different scales of resolution. The role of digital twins will continue to grow as models become more tightly integrated to the physical systems they represent. This will drive such models towards uniqueness (matching a particular vehicle or road), adaptability (evolving with changing conditions and subject to wear and tear), and interpretability (conveying useful information to a human user). A maturing connected infrastructure and the development of smart cities will cause the number of new digital twin services to explode in a myriad of unforeseen ways. [ABSTRACT FROM AUTHOR]

Detection of the number of signals corrupted by high-dimensional noise is a fundamental problem in signal processing and statistics. This paper focuses on a general setting where the high-dimensional noise has an unknown complicated heterogeneous variance structure. We propose a sequential test which utilizes the edge singular values (i.e., the largest few singular values) of the data matrix. It also naturally leads to a consistent sequential testing estimate of the number of signals. We describe the asymptotic distribution of the test statistic in terms of the Tracy-Widom distribution. The test is shown to be accurate and have full power against the alternative, both theoretically and numerically. The theoretical analysis relies on establishing the Tracy-Widom law for a large class of Gram type random matrices with non-zero means and completely arbitrary variance profiles, which can be of independent interest. [ABSTRACT FROM AUTHOR]

The recent success of generative adversarial networks and variational learning suggests that training a classification network may work well in addressing the classical two-sample problem, which asks to differentiate two densities given finite samples from each one. Network-based methods have the computational advantage that the algorithm scales to large datasets. This paper considers using the classification logit function, which is provided by a trained classification neural network and evaluated on the testing set split of the two datasets, to compute a two-sample statistic. To analyze the approximation and estimation error of the logit function to differentiate near-manifold densities, we introduce a new result of near-manifold integral approximation by neural networks. We then show that the logit function provably differentiates two sub-exponential densities given that the network is sufficiently parametrized, and for on or near manifold densities, the needed network complexity is reduced to only scale with the intrinsic dimensionality. In experiments, the network logit test demonstrates better performance than previous network-based tests using classification accuracy, and also compares favorably to certain kernel maximum mean discrepancy tests on synthetic datasets and hand-written digit datasets. [ABSTRACT FROM AUTHOR]

The work presented in this two-part paper focuses on a prediction method of the radiated susceptibility of integrated circuit and printed circuit board from near-field scan injection, in order to anticipate risks of noncompliance due to design weakness. In Part I, a worst-case estimator of the far-field induced voltage on a PCB trace was proposed. Based on it, an estimator of the radiated susceptibility of a printed circuit board based on near-field scan results is derived and validated through two validation case studies. [ABSTRACT FROM AUTHOR]

The performance of person re-identification (re-ID) is easily affected by illumination variations caused by different shooting times, places and cameras. Existing illumination-adaptive methods usually require annotating cross-camera pedestrians on each illumination scale, which is unaffordable for a long-term person retrieval system. The cross-illumination person retrieval problem presents a great challenge for accurate person matching. In this paper, we propose a novel method to tackle this task, which only needs to annotate pedestrians on one illumination scale. Specifically, (i) we propose a novel Illumination Estimation and Restoring framework (IER) to estimate the illumination scale of testing images taken at different illumination conditions and restore them to the illumination scale of training images, such that the disparities between training images with uniform illumination and testing images with varying illuminations are reduced. IER achieves promising results on illumination-adaptive dataset and proving itself a proper baseline for cross-illumination person re-ID. (ii) we propose a Mixed Training strategy using both Original and Reconstructed images (MTOR) to further improve model performance. We generate reconstructed images that are consistent with the original training images in content but more similar to the restored images in style. The reconstructed images are combined with the original training images for supervised training to further reduce the domain gap between original training images and restored testing images. To verify the effectiveness of our method, some simulated illumination-adaptive datasets are constructed with various illumination conditions. Extensive experimental results on the simulated datasets validate the effectiveness of the proposed method. The source code is available at https://github.com/FadeOrigin/IUReId. [ABSTRACT FROM AUTHOR]

Extended Affine (EA) equivalence is the equivalence relation between two vectorial Boolean functions $F$ and $G$ such that there exist two affine permutations $A$ , $B$ , and an affine function $C$ satisfying $G = A \circ F \circ B + C$. While the problem has a simple formulation, it is very difficult in practice to test whether two functions are EA-equivalent. This problem has two variants: EA-partitioning deals with partitioning a set of functions into disjoint EA-equivalence classes, and EA-recovery is about recovering the tuple $(A,B,C)$ if it exists. In this paper, we present a new algorithm that efficiently solves the EA-recovery problem for quadratic functions. Although its worst-case complexity occurs when dealing with APN functions, it supersedes, in terms of performance, all previously known algorithms for solving this problem for all quadratic functions and in any dimension, even in the case of APN functions. This approach is based on the Jacobian matrix of the functions, a tool whose study in this context can be of independent interest. The best approach for EA-partitioning in practice mainly relies on class invariants. We provide an overview of the known invariants along with a new one based on the ortho-derivative. This new invariant is applicable to quadratic APN functions, a specific type of functions that is of great interest, and of which tens of thousands need to be sorted into distinct EA-classes. Our ortho-derivative-based invariant is very fast to compute, and it practically always distinguishes between EA-inequivalent quadratic APN functions. [ABSTRACT FROM AUTHOR]

The core value of simulation-based autonomy tests is to create densely extreme traffic scenarios to test the performance and robustness of the algorithms and systems. Test scenarios are usually designed or extracted manually from the real-world data, which is inefficient with a remarkable domain gap compared with testing in real scenarios. Therefore, it is crucial to automatically generate realistic and diverse dynamic traffic scenarios making autonomy tests efficient. Moreover, scenario generation is expected to be interpretable, controllable, and diversified, which can be hard to achieve simultaneously by methods based on rules or deep networks. In this paper, we propose a dynamic traffic scenario generation method called SceGene, inspired by genetic inheritance and mutation processes in biological intelligence. SceGene applies biological processes, such as crossover and mutation, to exchange and mutate the content of scenarios, and involves the natural selection process to control generation direction. SceGene has three main parts: 1) a new representation method for describing the traffic scenarios’ feature; 2) a new scenario generation algorithm based on crossover, mutation, and selection; and 3) an abnormal scenario information repair method based on the microscopic driving model. Evaluation on the public traffic scenario dataset shows that SceGene can ensure highly realistic and diversified scenario generation in an interpretable and controllable way, significantly improving the efficiency of the simulation-based autonomy tests. [ABSTRACT FROM AUTHOR]

This paper considers the problem of inhibiting smearing effects for multiple sensor fault isolation. Although the reconstruction-based approach has received considerable attention and achieved excellent results, the investigation into nonlinear systems has been relatively limited. A new reconstruction-based input training neural network aided by the bound and branch method (BAB-RBITNN) is proposed for the nonlinear systems so as to achieve the desired performance of multiple sensor fault isolation. In BAB-RBITNN, a hypothetical faulty direction is naturally integrated into the objective testing function as residual weights for reducing the negative impacts of inaccurate sensor data, taking advantage of the non-static ITNN testing process. The weighted objective function is then considered as the fitness function to obtain the minimum predicted residual by the covariance matrix adaptation evolutionary strategy algorithm. The corresponding optimized fitness is regarded as the reconstruction-based index, which is the critical indicator of fault isolation in the proposed method. Besides, the BAB method is implemented to handle the combinatorial optimization problem instead of a brute search for intelligently pinpointing the real faulty sensors. The efficiency of the proposed method is shown through a validation example and two industrial examples. Comparisons with other methods, including reconstruction-based principal component analysis and reconstruction-based auto-associative neural network techniques, are also presented. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) is considered as a critical technique in 5 G New Radio (NR), due to its capability to significantly improve data rate and link reliability for the radio link. To make it a reality, it is essential to evaluate massive MIMO performance in realistic spatial fading channel conditions. Specifically, dynamic spatial channel emulation is crucial to evaluate the massive MIMO beam operations. Due to the high cost associated with both the traditional conduced testing method and the standardized multi-probe anechoic chamber (MPAC) based Over-the-Air (OTA) testing setup for massive MIMO performance testing, fading channel emulation for massive MIMO testing using a conductive phase matrix setup has been proposed in the literature. However, it is still a challenging problem to emulate dynamic spatial channels in a cost-effective and efficient manner for the conductive phase matrix setup, since traditional algorithms suffer from high computational complexity and system cost. This paper proposes a fast and accurate framework to emulate dynamic spatial channels in the phase matrix setup, where the basic idea is to mimic a virtual MPAC setup. By doing so, we can mimic MPAC setup with flexible probe configurations. Furthermore, a novel successive cancellation strategy and a closed-form algorithm are proposed to determine the probe locations and probe weights, respectively, which can significantly reduce the computational complexity associated with dynamic channel emulation. Simulation results show that the proposed algorithm is highly accurate and computationally efficient, compared to state-of-art solutions. Further, we apply our developed algorithm to emulate measured non-stationary channels and excellent emulation accuracy can be achieved. The proposed method can be employed for emulating dynamic spatial channels, which is of great importance for massive MIMO performance testing. [ABSTRACT FROM AUTHOR]