In cancer research, identifying cancer subtypes and evaluating prognosis are crucial for personalized diagnosis and treatment of cancer. With the advancement of high-throughput sequencing technologies, multi-omics data has become essential for cancer classification and prognostic analysis. By integrating deep learning techniques, it is possible to more accurately identify cancer subtypes, providing a robust basis for personalized treatment of cancer patients. In this study, we propose a convolutional autoencoder prognostic model incorporating a channel attention mechanism (CA-CAE). The model utilizes multi-omics data to predict survival-associated cancer subtypes and identify prognostic genes. We applied CA-CAE to multiple cancer types, successfully identifying subtypes in 15 distinct cancer types and revealing significant survival differences among these subtypes. Moreover, compared to traditional statistical methods and other deep learning approaches, CA-CAE demonstrated superior performance in predicting survival outcomes.

The chemical industry is faced with the urgent challenge of effectively harnessing the vast amounts of time-series data generated by thousands of sensors, which is essential for forecasting chemical states, achieving accurate real-time control of production processes. Traditional forecasting methods suffer from high computational latency and struggle with the complexity of spatiotemporal dependencies. As a result, modeling this data becomes challenging. This paper introduces a novel approach, referred to as ASTNet, designed to address these challenges. ASTNet integrates an asynchronous spatiotemporal modeling framework that combines temporal and spatial encoders, enabling concurrent learning of temporal and spatial dependencies while reducing computational latency. Additionally, it introduces a gated graph fusion mechanism that adaptively combines static (meta) and evolving (dynamic) sensor graphs, enhancing the handling of heterogeneous sensor data and spatial correlations. Extensive experiments on three real-world chemical sensor datasets demonstrate that ASTNet outperforms SOTA methods in terms of both prediction accuracy and computational efficiency, making ASTNet successfully deployed in chemical engineering industrial scenarios.

Sparse linear solvers play a crucial role in transistor-level circuit simulation, especially for large-scale post-layout circuit simulation when considering complex parasitic effects. As semiconductor technology advances rapidly, the increasing sizes of circuits result in sparse linear solvers that require extended execution times and additional memory resources. Consequently, high-performance sparse linear solvers emerge as pivotal tools to facilitate rapid circuit simulation and verification. However, circuit matrices frequently exhibit high sparsity and non-uniform distributions of nonzero elements, compounding the challenge of achieving efficient acceleration. Recently, the flourishing developments in machine learning technology and the continuous enhancement of hardware capabilities have presented new opportunities for accelerating sparse linear solvers. This paper provides a perspective review of these technological advancements, while also highlighting the challenges and future opportunities in this evolving landscape.

The relentless pursuit of energy-efficient electronic devices necessitates advanced methodologies for low-power design verification, with a particular focus on mitigating power supply noise. The challenges posed by shrinking voltage margins in low-power designs lead to a significant demand for rapid and accurate power supply noise simulation and verification techniques. Too large supply noise inevitably results in the raise of supply level, thereby hurting the lower power design target. Spectral methods have demonstrated as a great alternative to produce a sparse sub-matrix with spectral-similarity property as the preconditioner to efficiently reduce the iteration number and solve the linear system for supply noise verification. However, existing methods either suffer from high computational complexity or rely on approximations to reduce computational time. Therefore, a novel approach is needed to efficiently generate high-quality preconditioners. In this paper, we propose a two-stage spectral-aware algorithm to address these challenges. Our approach has three main highlights. Firstly, by introducing spectral-aware weights, we can better assess the priority of edges and construct high-quality spanning trees with the minimum relative condition number. Secondly, by leveraging eigenvalue transformation strategies, we can quickly and accurately recover off-tree edges that are spectrally critical, avoiding time-consuming iterative computations. Thirdly, we proposed a fast computation method to further decrease the computational complexity of the effective resistance. Compared with two SOTA methods, GRASS and feGRASS, our approach demonstrates higher accuracy and efficiency in preconditioner generation (37.3x and 2.13x speedup, respectively) as well as significant improvements in accelerating the linear solver for power supply noise analysis in power grid simulation and other Laplacian graphs (5.16x and 1.70x speedup, respectively).

Operating system (OS) extensions are more popular than ever. For example, Linux BPF is marketed as a "superpower" that allows user programs to be downloaded into the kernel, verified to be safe and executed at kernel hook points. So, BPF extensions have high performance and are often placed at performance-critical paths for tracing and filtering.

The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers’ preference information region. Therefore, we propose a multi-objective optimization algorithm, referred to as the double-grid interactive preference based MOEA (DIP-MOEA), which explicitly takes the preferences of decision makers (DMs) into account. First, according to the optimization objective of the practical multi-objective optimization problems and the preferences of DMs, the membership functions are mapped to generate a decision preference grid and a preference error grid. Then, we put forward two dominant modes of population, preference degree dominance and preference error dominance, and use this advantageous scheme to update the population in these two grids. Finally, the populations in these two grids are combined with the DMs’ preference interaction information, and the preference multi-objective optimization interaction is performed. To verify the performance of DIP-MOEA, we test it on two kinds of problems, i.e., the basic DTLZ series functions and the multi-objective knapsack problems, and compare it with several different popular preference-based MOEAs. Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs, quickly provides the test results, and has better performance in the distribution of the Pareto front solution set.

The choice of a storage technology for video-on-demand servers depends on a variety of factors. The primary technical factor is the number of video streams that the technologies can provide, while the initial cost of the memory technologies will play a major role in the final system price. This article compares various storage technologies and storage architectures for video-on-demand servers and analyzes the relative costs and performance of each.>