Prediction of cancer drug response based on deep learning methods has become the basis for personalized medicine. A large amount of multi-omics data on human cell lines and anti-cancer drugs are available for this task. Feature extraction from multi-omics data is essential for this task. Driver mutations affect key signaling pathways, which can significantly alter the sensitivity of tumor cells to drugs and affect the prediction of drug response. However, the contribution of cancer driver mutations to model performance has not been fully investigated. We propose AFMCDR, a novel approach that addresses key challenges in predicting cancer drug response. AFMCDR innovatively utilizes multi-omics data on driver mutations in cell lines and multidimensional drug data, filling a gap in understanding how these data affect drug response mechanisms, a key research area; we propose a hierarchical feature enhancement network that captures both the histological feature level and deep associations at the biological entity level; and we propose a biomedical crossnetwork attention mechanism to capture associations between biomedical entities. In addition, we design a representation learning method that combines drug responses with biomedical information. Finally, we propose a biological network-based adaptive fusion mechanism for dynamically optimizing the fusion of multi-omics features. Experiments on two benchmark datasets show that AFMCDR outperforms existing methods in predicting cancer drug responses. Visualization analysis shows that driver mutation features significantly improves the performance of drug response prediction. These findings confirm that AFMCDR is a powerful tool for predicting cancer drug response. The source code and datasets of AFMCDR are available at https://anonymous.4open.science/r/AFMCDR-DA65/.

Segmenting organs at risk from head and neck CT images is a prerequisite for the treatment of head and neck cancer using intensity modulated radiotherapy. However, accurate and automatic segmentation of organs at risk is a challenging task due to the low contrast of soft tissue and image artifact in CT images. Shape priors have been proved effective in addressing this challenging task. However, conventional methods incorporating shape priors often suffer from sensitivity to shape initialization and also shape variations across individuals. In this paper, we propose a novel approach to incorporate shape priors into a hierarchical learning-based model. The contributions of our proposed approach are as follows: 1) a novel mechanism for critical vertices identification is proposed to identify vertices with distinctive appearances and strong consistency across different subjects; 2) a new strategy of hierarchical vertex regression is also used to gradually locate more vertices with the guidance of previously located vertices; and 3) an innovative framework of joint shape and appearance learning is further developed to capture salient shape and appearance features simultaneously. Using these innovative strategies, our proposed approach can essentially overcome drawbacks of the conventional shape-based segmentation methods. Experimental results show that our approach can achieve much better results than state-of-the-art methods.

Cache replacement policy plays an important role in guaranteeing the availability of cache blocks, reducing miss rates, and improving applications' overall performance. However, recent research efforts on improving replacement policies require either significant additional hardware or major modifications to the organization of the existing cache. In this study, we propose the PAC-PLRU cache replacement policy. PAC-PLRU not only utilizes but also judiciously salvages the prediction information discarded from a widely-adopted stride prefetcher. The main idea behind PAC-PLRU is utilizing the prediction results generated by the existing stride prefetcher and preventing these predicted cache blocks from being replaced in the near future. Experimental results show that leveraging the PAC-PLRU with a stride prefetcher reduces the average L2 cache miss rate by 91% over a baseline system with only PLRU policy, and by 22% over a system using PLRU with an unconnected stride prefetcher. Most importantly, PAC-PLRU only requires minor modifications to existing cache architecture to get these benefits. The proposed PAC-PLRU policy is promising in fostering the connection between prefetching and replacement policies, and have a lasting impact on improving the overall cache performance.

A compression quality prediction model is proposed for grey images coding with JPEG2000. With this model, the compression quality (PSNR) could be estimated according to the given compression ratio (CR) and the image activity measures (IAM) without coding images. The image activity measure is the weighted sum of the IAM values based on the 1-pixel-distance and 2-pixel-distance gradients along horizontal and vertical directions. We have shown that IAM is a function of the image variance and autocorrelation coefficients. Based on Shannon's rate-distortion theorem, a theoretical justification is provided for the correlation of IAM with PSNR. Experimental results show that the prediction error is lower than 1 dB for more than 70% sample images when CR is higher than 15. The prediction error is less than 2 dB for over 90% images. This prediction performance is acceptable for general applications.

A quality prediction model is proposed for color images coded with JPEG2000. This model estimates the quality (PSNR) of a color image at a given compression ratio without coding. The image activity measure as the image feature and compression ratio are taken as the input of the model. Experimental results show that the prediction error is less than 1dB for more than 75% images, and less than 2dB for over 95% images. The computation of the prediction process is much lower than that of the compression and quality calculation.

With the digital technology developments, Digital Cinema outperforms traditional films and attracts the movie industry. The first edition Digital Cinema System Specification was released by Digital Cinema Initiatives, LLC (DCI) in 2005, which advances the Digital Cinema development. We have developed a Digital Cinema Playback system compliant with the DCI Specification. It can extract and play the 2 K-resolution component from the 2 K/4 K DCP (Digital Cinema Package) files at 24 fps, and support an up to 350 Mbps video streaming service. Connected to Barco projector and Dolby digital device, this system can display the standard fully or partially encrypted test materials fluently and correctly. The synchronization of audio and video meets the DCI Specification.

Rough set theory is a relatively new mathematical tool to deal with imprecise, incomplete and inconsistent data. A method of multispectral image classification using rough set theory is proposed. First, to decrease computational time and complexity, band reduction of multispectral image using attribute reduct concept in rough set theory and information entropy is performed. Then, mixture model initial parameters of remote sensing image are mapped from crude classes, which are generated using equivalent relation. Finally image cluster is obtained unsupervised with Gaussian mixture model whose parameters are refined by Expectation Maximization algorithm. The proposed method is performed on a multispectral image, and the experimental results show the feasibility and effectiveness of the algorithm by means of comparison and analysis.

Rate and quality-constrained rate control is very effective for video applications, since it can maintain constant quality while eliminating local rate spikes. An adaptive rate control method based on image activity measures (ARCIAM) is proposed for Motion JPEG2000. The algorithm can efficiently adjust the compressed bit rates to meet the rate and quality constraints for different types of colour sequences. It predicts the rate-distortion (RD) slope thresholds for frames based on image activity measure (IAM) before entropy coding. Thus, the encoding of unnecessary coding passes can be avoided. Experimental results show that the proposed method can meet the quality and rate constraints, and can achieve quality performance comparable to post-compression rate distortion optimisation at the same bit rate. Under 3 bpp and 40 dB constraints, ARCIAM can reduce the total encoding time and tier-1 encoding time for sequences by more than 20 and 30%, respectively. Moreover, the memory requirement for buffering bit-streams and RD-slope information is reduced by more than 30% on average. Besides, the method can be incorporated into the line-based JPEG2000 encoding system easily, and has been implemented on Kakadu.

In satellite images processing, image restoration is an important pre-processing step. As some traditional deblurring methods like Wiener filtering often obtain an either smooth or noisy solution, whereas some edge-preserving methods are iterative and time-consuming; a modified Fourier-wavelet deconvolution method (ForWaRD) is proposed in this paper. This method could be implemented forward directly with two steps. From the view of practical engineering, this approach could obtain pretty good results in a fast speed, so it is a suitable method in satellite image restoration area.