Regulation-incorporated Gene Expression Network-based Heterogeneity Analysis

Gene expression-based heterogeneity analysis has been extensively conducted. In recent studies, it has been shown that network-based analysis, which takes a system perspective and accommodates the interconnections among genes, can be more informative than that based on simpler statistics. Gene expressions are highly regulated. Incorporating regulations in analysis can better delineate the "sources" of gene expression effects. Although conditional network analysis can somewhat serve this purpose, it does render enough attention to the regulation relationships. In this article, significantly advancing from the existing heterogeneity analyses based only on gene expression networks, conditional gene expression network analyses, and regression-based heterogeneity analyses, we propose heterogeneity analysis based on gene expression networks (after accounting for or "removing" regulation effects) as well as regulations of gene expressions. A high-dimensional penalized fusion approach is proposed, which can determine the number of sample groups and parameter values in a single step. An effective computational algorithm is proposed. It is rigorously proved that the proposed approach enjoys the estimation, selection, and grouping consistency properties. Extensive simulations demonstrate its practical superiority over closely related alternatives. In the analysis of two breast cancer datasets, the proposed approach identifies heterogeneity and gene network structures different from the alternatives and with sound biological implications

Two kinds of average approximation accuracy

Rough set theory places great importance on approximation accuracy, which is used to gauge how well a rough set model describes a target concept. However, traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set model. To overcome this, two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is proposed. The first is the relative average approximation accuracy, which is based on all sets in the universe and has several basic properties. The second is the absolute average approximation accuracy, which is based on undefinable sets and has yielded significant conclusions. We also explore the relationship between these two types of average approximation accuracy. Finally, the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis

In diverse fields ranging from finance to omics, it is increasingly common that data is distributed and with multiple individual sources (referred to as ``clients'' in some studies). Integrating raw data, although powerful, is often not feasible, for example, when there are considerations on privacy protection. Distributed learning techniques have been developed to integrate summary statistics as opposed to raw data. In many of the existing distributed learning studies, it is stringently assumed that all the clients have the same model. To accommodate data heterogeneity, some federated learning methods allow for client-specific models. In this article, we consider the scenario that clients form clusters, those in the same cluster have the same model, and different clusters have different models. Further considering the clustering structure can lead to a better understanding of the ``interconnections'' among clients and reduce the number of parameters. To this end, we develop a novel penalization approach. Specifically, group penalization is imposed for regularized estimation and selection of important variables, and fusion penalization is imposed to automatically cluster clients. An effective ADMM algorithm is developed, and the estimation, selection, and clustering consistency properties are established under mild conditions. Simulation and data analysis further demonstrate the practical utility and superiority of the proposed approach

Distinction in corrosion resistance of selective laser melted Ti-6Al-4V alloy on different planes

Electrochemical measurements and microstructural analysis were performed to study the corrosion resistance of different planes of Ti-6Al-4V alloy manufactured by selective laser melting (SLM). The electrochemical results suggest that its XY-plane possesses a better corrosion resistance compared to XZ-plane in 1 M HCl solution, in spite of slight difference in 3.5 wt.% NaCl solution, suggesting that the different planes exhibit more pronounced distinction in corrosion resistance in harsher solution system. The inferior corrosion resistance of XZ-plane is attributed to the presence of more α′ martensite and less β-Ti phase in microstructure for XZ-plane than for XY-plane of the SLM-produced Ti-6Al-4V allo

Improvements of Blockchain’s Block Broadcasting:An Incentive Approach

In order to achieve a truthful distributed ledger, homogeneous nodes in Blockchain systems will propagate messages on a P2P network so that they can synchronize the status of the ledger. Currently, blockchain systems target on achieving better scalability and higher throughput to support divergent applications which will lead to heavier message propagation, especially the broadcasting of blocks. The heavier traffic on the P2P network will cause longer latency of block synchronization, which may damage system consistency and expose the system to potential attacks. Even worse, when heavy communication consumes a lot of network capacity, nodes in the P2P network may not relay blocks to save their bandwidth. This may damage the efficiency of network synchronization. In order to alleviate the problems, we propose an improved block broadcasting protocol which elaborates block data sharding and financial incentive mechanisms. In the proposed scheme, a block is sliced into pieces in order to keep the network traffic smooth and speed up content delivery. Any node which relays a piece of the block will get benefits with financial rewards. By applying data sharding, our proposed scheme speed up the block broadcasting and therefore shorten the synchronization time by 90\%, which is shown in our simulation experiments. In addition, we carry out game theoretical analysis to prove that nodes are efficiently incentivized to relay blocks honestly and actively

Effect of direct current electric field intensity and electrolyte layer thickness on oxygen reduction in simulated atmospheric environment

The effect of direct current (DC) electric field and electrolyte layer thickness on oxygen reduction in simulated atmospheric environment were investigated using electrochemical measurements. The results show that the limiting diffusion current density (ilim) decreases with increasing the thin electrolyte layers (TELs) thickness but it increases with increasing the DC electric field intensity. The potential shifts negatively with the DC electric field. It is found that the DC electric field enables OH− ions to quickly migrate from the solution/electrode interface to the electrolyte layer. All these features promote the cathodic reduction process thereby enhancing the metal corrosion rate

CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception

Perception is crucial in the realm of autonomous driving systems, where bird's eye view (BEV)-based architectures have recently reached state-of-the-art performance. The desirability of self-supervised representation learning stems from the expensive and laborious process of annotating 2D and 3D data. Although previous research has investigated pretraining methods for both LiDAR and camera-based 3D object detection, a unified pretraining framework for multimodal BEV perception is missing. In this study, we introduce CALICO, a novel framework that applies contrastive objectives to both LiDAR and camera backbones. Specifically, CALICO incorporates two stages: point-region contrast (PRC) and region-aware distillation (RAD). PRC better balances the region- and scene-level representation learning on the LiDAR modality and offers significant performance improvement compared to existing methods. RAD effectively achieves contrastive distillation on our self-trained teacher model. CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements. Notably, CALICO outperforms the baseline method by 10.5% and 8.6% on NDS and mAP. Moreover, CALICO boosts the robustness of multimodal 3D object detection against adversarial attacks and corruption. Additionally, our framework can be tailored to different backbones and heads, positioning it as a promising approach for multimodal BEV perception