BACKGROUND: Despite the identification of some pan-cancer driver genes through international collaborative initiatives, the discovery of key cancer driver genes remains a formidable challenge. This limitation continues to hinder progress in critical areas, such as early diagnosis, prognostic evaluation, and precision medicine. Consequently, the accurate identification of key driver genes for specific cancer types has become a central focus of bioinformatics research. RESULTS: We present MODCAN, a novel semi-supervised algorithm based on multi-omics features and differential co-association networks. MODCAN facilitates the meaningful stratification of tumor samples into distinct subtypes, enabling a comprehensive exploration of multi-omics features that reflect the inherent heterogeneity of tumors. By constructing differential co-association networks, MODCAN reveals the unique genetic interactions characteristic of each subtype, thereby facilitating the complementary integration of information. CONCLUSIONS: When applied to ten cancer datasets from TCGA, MODCAN significantly outperforms both existing supervised and unsupervised learning algorithms, exhibiting superior performance in terms of precision, recall, and AUPR. Furthermore, MODCAN demonstrates substantial advantages in predicting potential tumor-specific driver genes. Notably, these genes not only exhibit strong specificity for their respective cancers, but also reveal tumor heterogeneity across distinct subtypes.

This article presents a novel intrinsic image transfer (IIT) algorithm for image illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework composed of illumination, reflectance and content photo-realistic losses, respectively. Each loss is first defined on the corresponding sub-layers factorized by an intrinsic image decomposition and then reduced under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. We illustrate that all losses, with the aid of an "exemplar" image, can be directly defined on images without the necessity of taking an intrinsic image decomposition, thereby giving a closed-form solution to image illumination manipulation. We also demonstrate its versatility and benefits to several illumination-related tasks: illumination compensation, image enhancement and tone mapping, and high dynamic range (HDR) image compression, and show their high-quality results on natural image datasets.

In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimization scheme. The model is derived from the observations that semi-sparsity prior knowledge is universally applicable in situations where sparsity is not fully admitted such as in the polynomial-smoothing surfaces. We illustrate that such priors can be identified into a generalized $L_{0}$ -norm minimization problem in higher-order gradient domains, giving rise to a new "feature-aware" filter with a powerful simultaneous-fitting ability in both sparse singularities (corners and salient edges) and polynomial-smoothing surfaces. Notice that a direct solver to the proposed model is not available due to the non-convexity and combinatorial nature of $L_{0}$ -norm minimization. Instead, we propose to solve it approximately based on an efficient half-quadratic splitting technique. We demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications.

Energy demand and supply vary from area to area, where an unbalanced load may occur and endanger the system security constraints and cause significant differences in the locational marginal price (LMP) in the power system. With the increasing proportion of local renewable energy (RE) sources in microgrids that are connected to the power grid and the growing number of electric vehicle (EV) charging loads, the imbalance will be further magnified. In this article, we first model the EV charging network as a cyber–physical system (CPS) that is coupled with both the transportation networks and the smart grids. Then, we propose an EV charging station recommendation algorithm. With a proper charging scheduling algorithm deployed, the synergy between the transportation network and the smart grid can be created. The EV charging activity will no longer be a burden for power grids, but a load-balancing tool that can transfer energy between the unbalanced distribution grids. The proposed system model is validated via simulations. The results show that the proposed algorithms can optimize the EV charging behaviors, reduce charging costs, and effectively balance the regional load profiles of the grids.

Multiple Object Tracking (MOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. Although many researchers have proposed effective method, they failed in crowd scene. The reason is that body detection and tracking is used in existing MOT methods. In crowd scene, many detections are missed detect and many overlap body bounding boxes decrease the quality of data association. To handle this issue, this paper propsed an online novel multiple pedestrians tracking, which is based on head detection. We first fuse the head and body detection to improve the detection result. Then, we use the head detection bounding box to replace the body detection bounding box for tracking. Finally, the experimental results demonstrate the effectiveness of our proposed method and achieve best performance with the state-of-the-art MOT trackers.

This article investigates the consensus tracking problem of the heterogeneous multivehicle systems (MVSs) under a repeatable control environment. First, a unified iterative learning control (ILC) algorithm is presented for all autonomous vehicles, each of which is governed by both discrete- and continuous-time nonlinear dynamics. Then, several consensus criteria for MVSs with switching topology and external disturbances are established based on our proposed distributed ILC protocols. For discrete-time systems, all vehicles can perfectly track to the common reference trajectory over a specified finite time interval, and the corresponding digraphs may not have spanning trees. Existing approaches dealing with the continuous-time systems generally require that all vehicles have strictly identical initial conditions, being too ideal in practice. We relax this unpractical assumption and propose an extra distributed initial state learning protocol such that vehicles can take different initial states, leading to the fact that the finite time tracking is achieved ultimately regardless of the initial errors. Finally, a numerical example demonstrates the effectiveness of our theoretical results.

This paper proposes a novel two-input, two-output architecture of photo core transform (PCT) in JPEG XR. First, the lifting operations of PCT are optimized such that some operations can be reused. Then the time multiplexing technique is used by combining lifting steps to design a hardware-efficient architecture. Experimental results based on field programmable gate array (FPGA) demonstrate that our architecture has good performance in reducing hardware resources and power consumption, which could be an efficient alternative in low-cost applications.

Mass spectrometry (MS) has become one of the major detection technologies for high-throughput proteomics. The preprocessing of mass spectra is crucial for its subsequent analysis like biomarker discovery or protein identification. Wavelet transform is gradually becoming an important methodology in the MS data preprocessing. This paper reviews the application of wavelet transforms in quality control, smoothing and peak detection of MS data preprocessing. It also proposes an improved Discrete Wavelet Transform (DWT) smoothing algorithm, which utilizes the cross-level DWT coefficients information during smoothing. Most of the algorithms described in this paper are included or will be included in the BioconductorMassSpecWaveletpackage.