Autonomous driving systems (ADSs) must be sufficiently tested to ensure their safety. Though various ADS testing methods have shown promising results, they are limited to a fixed vehicle characteristics setting (VCS). The impact of variations in vehicle characteristics (e.g., mass, tire friction) on the safety of ADSs has not been sufficiently and systematically studied. Such variations are often due to wear and tear, production errors and so on, which may lead to unexpected driving behaviours of ADSs. To this end, in this article, we propose a method, named SafeVar , to systematically find minimum variations to the original vehicle characteristics setting, which affect the safety of the ADS deployed on the vehicle. To evaluate the effectiveness of SafeVar , we employed two ADSs and conducted experiments with two driving scenarios. Results show that SafeVar , equipped with NSGA-II, generates more critical settings that put the vehicle into unsafe situations, as compared with the baseline algorithm. We also identified critical vehicle characteristics and reported to which extent varying their settings put the ADS vehicle into unsafe situations.

Depression detection using Electroencephalogram (EEG) signals obtained from wearable medical-assisted diagnostic systems has become a well-established approach in the field of affective disorders. However, despite recent advancements, on-board Artificial Intelligence (AI) models still demand substantial computational resources, presenting significant challenges for deployment on resource-constrained wearable medical devices. Embedded Multi-core Processors (MPs) offer a promising solution for accelerating these models. However, the limited computational capabilities of embedded MPs, combined with the structural diversity of AI models, complicate resource allocation and increase associated costs. To address these challenges, we propose a Memory-Aware Multi-Objective Iterative Local Search (MAMILS) algorithm to optimize task scheduling, thereby improving the efficiency of AI model deployment on wearable EEG devices. Experimental results across seven AI models demonstrate that, the MAMILS approach yields substantial improvements in key performance indicators: Total Energy Consumption ($\bm {TEC}$) with an average reduction of 47.57%,$\bm {Makespan}$with an average reduction of 48.75%, and$\bm {Throughput}$with an average increase of 198.37%, all while maintaining satisfactory classification performance for both Machine Learning (ML) and Deep Learning (DL) models. Especially, on-board deployment of EEGNeX achieves an accuracy of 93.4%, sensitivity of 91.6%, and specificity of 95.8%. Further analysis indicates that, when integrated with wearable EEG sensors and executable on-board AI models, the proposed MAMILS optimization strategy shows significant promise in facilitating the widespread adoption of low-power, real-time diagnostic systems for depression detection.

While deep learning techniques have greatly advanced automated electrocardiography (ECG) interpretation, models trained on homogeneous, single-site datasets often fail to generalize due to confounding batch effects arising from differences in equipment, patient demographics, and acquisition protocols. To address this, we propose a Bayesian-inspired conditional adversarial multi-task framework that simultaneously optimizes ECG classification and removes confounding variables, with the adversarial site-ID serving as a proxy variable to ensure marginalization over the learned conditional distribution of site-specific variability. Compared with the baseline, our foundation model achieved better generalization across all key metrics on both the internal and public datasets. t-SNE visualizations of the learned latent space further confirmed successful de-confounding, revealing well-mixed, site-invariant clusters. This straightforward approach thus offers a robust, interpretable solution for scalable ECG classification across heterogeneous clinical environments.

The advancement of wearable electronic technology has facilitated the integration of smart wearable devices into artificial intelligence (AI)-driven medical assisted diagnosis. Embedded multi-core processors (MPs) have gradually emerged as pivotal hardware components for smart wearable medical diagnostic devices due to their high performance and flexibility. However, embedded MPs face the challenge of balancing performance, power consumption, and load-balancing. In response, we introduce a Pareto-based iterated local search (PILS) algorithm for task scheduling, which systematically optimizes multiple objectives, alongside a task list model to reduce the dimension of the decision space and enhance scheduling performance. In addition, we present a two-stage discretization scheme to ensure that the proposed algorithm offers meaningful guidance throughout the scheduling process. Simulation and on-board testing results show that the proposed algorithm effectively optimizes energy consumption, task execution time, and load balancing in embedded MPs task scheduling, indicating the potential of the proposed algorithm in enhancing the performance of smart wearable medical diagnostic devices powered by embedded MPs.

Graph Convolutional Network (GCN) is extensively utilized in the domain of Knowledge Graph Completion (KGC) which is aiming to predict the absent entities or relationships within Knowledge Graph (KG). The majority of traditional GCN-based models employ the method of passing messages layer by layer to capture the characteristics of distant neighbors. However, this approach fails to effectively integrate the semantic feature information of multi-hop neighbors due to the significant complexity of relations when attempting to learn continuous vectors for entities. To overcome this limitation, this study introduces Multi-Hop Message Passing Graph Convolutional Network (MMPGCN), a novel framework for GCNs that effectively integrates feature information from indirect neighbors in a novel manner. Furthermore, the graph attention mechanism is utilized in the proposed model to differentiate the weights of various indirect neighbors. A robust and expressive balancing gate mechanism is designed to integrate the information from both direct and indirect neighbors to produce the ultimate representation. The approach is assessed using FB15k237 and WN18RR datasets, and it demonstrates superior performance in comparison to state-of-the-art methods for KGC task.

MOTIVATION: DNA methylation within gene body and promoters in cancer cells is well documented. An increasing number of studies showed that cytosine-phosphate-guanine (CpG) sites falling within other regulatory elements could also regulate target gene activation, mainly by affecting transcription factors (TFs) binding in human cancers. This led to the urgent need for comprehensively and effectively collecting distinct cis-regulatory elements and TF-binding sites (TFBS) to annotate DNA methylation regulation. RESULTS: We developed a database (CanMethdb, http://meth.liclab.net/CanMethdb/) that focused on the upstream and downstream annotations for CpG-genes in cancers. This included upstream cis-regulatory elements, especially those involving distal regions to genes, and TFBS annotations for the CpGs and downstream functional annotations for the target genes, computed through integrating abundant DNA methylation and gene expression profiles in diverse cancers. Users could inquire CpG-target gene pairs for a cancer type through inputting a genomic region, a CpG, a gene name, or select hypo/hypermethylated CpG sets. The current version of CanMethdb documented a total of 38 986 060 CpG-target gene pairs (with 6 769 130 unique pairs), involving 385 217 CpGs and 18 044 target genes, abundant cis-regulatory elements and TFs for 33 TCGA cancer types. CanMethdb might help biologists perform in-depth studies of target gene regulations based on DNA methylations in cancer. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/chunquanlipathway/CanMethdb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this article, a fractional-order sliding mode control (FOSMC) scheme is proposed for mitigating harmonic distortions in the power system, whereby a self-constructing recurrent fuzzy neural network (SCRFNN) is used to weaken the effect of compound nonlinearity caused by unknown uncertainties and environmental fluctuations. The fractional-order sliding mode controller (SMC) is constructed to maintain the control system to be asymptotically stable and a fractional-order calculus is introduced into an SMC to soften the sliding manifold design and realize chattering reduction. Considering parameter variations existing in the power system model, SCRFNN is adopted to approximate the unknown dynamics, which is able to dynamically update network structure by optimizing the fuzzy division, and a feedback connection is incorporated into the feedforward neural network, which is regarded as a storage unit to enhance the capability of coping with temporal problem. The control scheme combining the FOSMC with the SCRFNN can make the tracking error and its time derivative converge to zero. Experimental studies demonstrate the validity of the designed scheme, and comprehensive comparisons illustrate its superiority in harmonic suppression and high robustness.

With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, P = 0.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP.

Narrowband Internet of Things (NB-IoT) is a new communication technology designed for machine type communications (MTC), which needs to support much more devices compared to Long Term Evolution (LTE). But the random access (RA) process of it is also based on the slotted-ALOHA mechanism like LTE, which is not that suitable for machine to machine (M2M) communications. When a device's access attempt failed, the time of backoff and access class barring (ACB) is random. This would lead to a lot of meaningless failed attempts when the access load is heavy. And the access successful probability and energy efficiency will be very low. In this paper, we propose a novel access protocol based on resource grouping and distributed queuing (RGDQ) mechanism to effectively solve the massive access issue in NB-IoT. Firstly, we apply DQ mechanism into the access process of NB-IoT. Afterwards, we newly propose an arrival-based access resource grouping mechanism (RG) to reduce the access delay caused by the queuing process of DQ. In addition, we develop an analytical model to accurately estimate the access performance of the proposed protocol. Finally, computer simulations are also performed to validate the accuracy of the analytical model and verify the proposed protocol in comparison with the NB-IoT standard and conventional DQ access schemes.

Rapid 3D reconstruction of environments has become an active research topic due to the importance of 3D models in a huge number of applications, be it in Augmented Reality (AR), architecture or other commercial areas. In this paper we present a novel system that allows for the generation of a coarse 3D model of the environment within several seconds on mobile smartphones. By using a very fast and flexible algorithm a set of panoramic images is captured to form the basis of wide field-of-view images required for reliable and robust reconstruction. A cheap on-line space carving approach based on Delaunay triangulation is employed to obtain dense, polygonal, textured representations. The use of an intuitive method to capture these images, as well as the efficiency of the reconstruction approach allows for an application on recent mobile phone hardware, giving visually pleasing results almost instantly.

Off-line model reconstruction relies on an image collection phase and a slow reconstruction phase, requiring a long time to verify a model obtained from an image sequence is acceptable. We propose a new model acquisition system, called ProFORMA, which generates a 3D model on-line as the input sequence is being collected. As the user rotates the object in front of a stationary camera, a partial model is reconstructed and displayed to the user to assist view planning. The model is also used by the system to robustly track the pose of the object. Models are rapidly produced through a Delaunay tetrahe-dralisation of points obtained from on-line structure from motion estimation, followed by a probabilistic tetrahedron carving step to obtain a textured surface mesh of the object. © 2009. The copyright of this document resides with its authors.

ProFORMA, an on-line reconstruction system for textured objects rotated by a user's hand, can be coupled with augmented reality (AR) to allow users to rapidly generate textured 3D models. We demonstrate how the use of an overlaid mesh model and 3D arrow can be used to assist the user in view planning, guiding the user to collect new keyframes from desirable views. The method described is particularly suited for use with AR headsets, providing guidance with minimal user input and allowing in situ modelling using the head-mounted camera (ProFORMA does not require a completely stationary camera).

Synchronization is an important feature in the design of high-speed Viterbi decoders for punctured convolutional codes. Since some punctured codes might show invariance (total or partial) to phase rotations or other transformations, it is difficult to determine their synchronization status using a simple method. Necessary and sufficient conditions for a code to be totally invariant to an affine class of symbol transformations have been derived by A. Mogre et al. (see ibid., vol.48, p.1066-9, 2000) in conjunction with invariance compensation techniques at the receiver. Detection of these invariances is usually achieved based on a synchronization pattern. We propose a method to replace this pattern by a cyclic redundancy check code, since such codes are already present in many communications systems. We also investigate the effects of partial invariances, which can occur in several ways. After deriving some sufficient conditions for a code to exhibit partial invariance, we show that for rate k/n convolutional codes with 2k>n, the types of partial invariances considered have negligible effect on the error performance and, therefore, can be ignored at the receiver.

A new simplified approach for QAM signal error analysis in hybrid AM/QAM lightwave systems is proposed. The method allows fast and accurate BER calculation.

Analogue video cavalcade services in cable access television (CATV) networks usually require a very high carrier-to-noise ratio (CNR) in order to provide a good quality picture. In consequence, practical lightwave hybrid amplitude modulation (AM) M-ary quadrature amplitude modulation (M-QAM) systems are found to suffer only infrequent clipping impulse noise, which can be provably modeled as a Poisson arriving pulse train. Based on this knowledge, a new expression for the probability density function (PDF) of the clipping noise at the output of a QAM demodulation matched filter is given which can be numerically evaluated with high accuracy. The bit-error rate (BER) performance prediction is then carried out for M-QAM signals within the hybrid system in the presence of an additive mixture of Gaussian and clipping impulse noise. The agreement between the analytical results and the experimental data is quite good.