Multiple-input-multiple-output (MIMO) optical wireless communication (OWC) is a promising technology capable of providing high data rates. However, interuser interference can negatively impact system performance. Existing studies employ CVX-based beamforming designs to mitigate this issue, but these approaches suffer from high computational complexity. To tackle this challenge, this article studies the low-complexity beamforming design to maximize the sum rate. Specifically, we first utilize the fractional programming (FP) method to decompose the original beamforming problem into a series of convex subproblems and propose a block coordinate descent (BCD)-based beamforming framework. Then, we provide closed-form or semi-closed-form solutions for these subproblems. For the general approximate sum rate maximization, we drive the optimal semi-closed-form beamforming structure based on the Karush-Kuhn–Tucker (KKT) conditions. Furthermore, we propose a closed-form solution for the upper bound maximization by taking advantage of the separability of optical power constraints. Finally, numerical results indicate that the proposed beamforming designs reduce the computational complexity significantly while keeping the sum rate performance.

In this work, by re-examining the "matching" nature of Anomaly Detection (AD), we propose a novel AD framework that simultaneously enjoys new records of AD accuracy and dramatically high running speed. In this framework, the anomaly detection problem is solved via a cascade patch retrieval procedure that retrieves the nearest neighbors for each test image patch in a coarse-to-fine fashion. Given a test sample, the top-K most similar training images are first selected based on a robust histogram matching process. Secondly, the nearest neighbor of each test patch is retrieved over the similar geometrical locations on those "most similar images", by using a carefully trained local metric. Finally, the anomaly score of each test image patch is calculated based on the distance to its "nearest neighbor" and the "non-background" probability. The proposed method is termed "Cascade Patch Retrieval" (CPR) in this work. Different from the previous patch-matching-based AD algorithms, CPR selects proper "targets" (reference images and patches) before "shooting" (patch-matching). On the well-acknowledged MVTec AD, BTAD and MVTec-3D AD datasets, the proposed algorithm consistently outperforms all the comparing SOTA methods by remarkable margins, measured by various AD metrics. Furthermore, CPR is extremely efficient. It runs at the speed of 113 FPS with the standard setting while its simplified version only requires less than 1 ms to process an image at the cost of a trivial accuracy drop. The code of CPR is available at https://github.com/flyinghu123/CPR.

The capacity region for single input single out broadcast channel (SISO BC) is achieved by non-orthogonal multiple access (NOMA), which utilizes successive interference cancellation (SIC) to mitigate the inter-user interference. However, the complexity of SIC is high. To balance between the sum rate performance and the complexity of receivers, in this paper, we explore rate splitting multiple access (RSMA) in visible light communications (VLC). We formulate the joint rate allocation and power control problem to maximize the sum-rate under both quality of service (QoS) and SIC constraints. To solve this non-convex problem, a successive convex approximation (SCA) based algorithm is proposed to obtain a local optimal solution. Numerical results show that 1-layer RSMA is able to achieve very close performance to NOMA with much reduced complexity. Jointly considering the performance and complexity of the system, 1-layer RSMA is an attractive alternative to NOMA in SISO VLC networks.

BACKGROUND: Scaffold proteins play a critical role in an increasing number of biological signaling processes, including simple tethering mechanism, regulating selectivity in pathways, shaping cellular behaviors. While many databases document the signaling pathways, few databases are devoted to the scaffold proteins that medicate signal transduction. RESULTS: Here, we have developed a user-friendly database, ScaPD, to describe computationally predicted, experimentally validated scaffold proteins and associated signaling pathways. It currently contains 273 scaffold proteins and 1118 associated signaling pathways. The database allows users to search, navigate and download the scaffold protein-mediated signaling networks. CONCLUSIONS: Manually curated and predicted scaffold protein data will be a foundation for further investigation of the scaffold protein in the signal transduction. With maintained up-to-date data, ScaPD ( http://bioinfo.wilmer.jhu.edu/ScaPD ) will be a valuable resource for understanding how individual signaling pathways are regulated.

The dominance of the major transcript isoform relative to other isoforms from the same gene generated by alternative splicing (AS) is essential to the maintenance of normal cellular physiology. However, the underlying principles that determine such dominance remain unknown. Here, we analyzed the physical AS process and found that it can be modeled by a stochastic minimization process, which causes the scaled expression levels of all transcript isoforms to follow the same Weibull extreme value distribution. Surprisingly, we also found a simple equation to describe the median frequency of transcript isoforms of different dominance. This two-parameter Weibull model provides the statistical distribution of all isoforms of all transcribed genes, and reveals that previously unexplained observations concerning relative isoform expression derive from these principles.

The past decade has witnessed the increasing demands on data-driven business intelligence that led to the proliferation of data-intensive applications. A managed object-oriented programming language such as Java is often the developer's choice for implementing such applications, due to its quick development cycle and rich community resource. While the use of such languages makes programming easier, their automated memory management comes at a cost. When the managed runtime meets Big Data, this cost is significantly magnified and becomes a scalability-prohibiting bottleneck. This paper presents a novel compiler framework, called Facade, that can generate highly-efficient data manipulation code by automatically transforming the data path of an existing Big Data application. The key treatment is that in the generated code, the number of runtime heap objects created for data types in each thread is (almost) statically bounded, leading to significantly reduced memory management cost and improved scalability. We have implemented Facade and used it to transform 7 common applications on 3 real-world, already well-optimized Big Data frameworks: GraphChi, Hyracks, and GPS. Our experimental results are very positive: the generated programs have (1) achieved a 3%--48% execution time reduction and an up to 88X GC reduction; (2) consumed up to 50% less memory, and (3) scaled to much larger datasets.

Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process.

Communication traces are increasingly important, both for parallel applications' performance analysis/optimization, and for designing next-generation HPC systems. Meanwhile, the problem size and the execution scale on supercomputers keep growing, producing prohibitive volume of communication traces. To reduce the size of communication traces, existing dynamic compression methods introduce large compression overhead with the job scale. We propose a hybrid static-dynamic method that leverages information acquired from static analysis to facilitate more effective and efficient dynamic trace compression. Our proposed scheme, Cypress, extracts a program communication structure tree at compile time using inter-procedural analysis. This tree naturally contains crucial iterative computing features such as the loop structure, allowing subsequent runtime compression to "fill in", in a "top-down" manner, event details into the known communication template. Results show that Cypress reduces intra-process and inter-process compression overhead up to 5× and 9× respectively over state-of-the-art dynamic methods, while only introducing very low compiling overhead.

SUMMARY: Phosphorylation plays an important role in cellular signal transduction. Current phosphorylation-related databases often focus on the phosphorylation sites, which are mainly determined by mass spectrometry. Here, we present PhosphoNetworks, a phosphorylation database built on a high-resolution map of phosphorylation networks. This high-resolution map of phosphorylation networks provides not only the kinase-substrate relationships (KSRs), but also the specific phosphorylation sites on which the kinases act on the substrates. The database contains the most comprehensive dataset for KSRs, including the relationships from a recent high-throughput project for identification of KSRs using protein microarrays, as well as known KSRs curated from the literature. In addition, the database also includes several analytical tools for dissecting phosphorylation networks. PhosphoNetworks is expected to play a prominent role in proteomics and phosphorylation-related disease research. AVAILABILITY AND IMPLEMENTATION: http://www.phosphonetworks.org

MOTIVATION: The vertebrate retina is a complex neuronal tissue, and its development, normal functioning and response to injury and disease is subject to a variety of genetic factors. To understand better the regulatory and functional relationships between the genes expressed within the retina, we constructed an interactive gene network of the mouse retina by applying a Bayesian statistics approach to information derived from a variety of gene expression, protein-protein interaction and gene ontology annotation databases. RESULTS: The network contains 673 retina-related genes. Most of them are obtained through manual literature-based curation, while the others are the genes preferentially expressed in the retina. These retina-related genes are linked by 3403 potential functional associations in the network. The prediction on the gene functional association using the Bayesian approach outperforms predictions using only one source of information. The network includes five major gene clusters, each enriched in different biological activities. There are several applications to this network. First, we identified approximately 50 hub genes that are predicted to play particularly important roles in the function of the retina. Some of them are not yet well studied. Second, we can predict novel gene functions using 'guilt by association' method. Third, we also predicted novel retinal disease-associated genes based on the network analysis. AVAILABILITY: To provide easy access to the retinal network, we constructed an interactive web tool, named MoReNet, which is available at http://bioinfo.wilmer.jhu.edu/morenet/.