Conflict-free replicated data types (CRDTs) are highly available and performant data replication solutions for distributed applications. However, their eventual consistency guarantees are often insufficient for ensuring application correctness, especially in the presence of Byzantine failures. Naively applying traditional consensus and Byzantine fault tolerance (BFT) protocols to CRDT updates for stronger guarantees, while intuitive, negates the performance benefits of CRDTs. We introduce a novel programming model called reliable CRDTs that expands CRDTs with additional guarantees: users can query strongly or eventually consistent values, enforce a total order among selected operations, and define data-type level invariants while remaining operational in the presence of Byzantine failures. Reliable CRDTs enable the use of CRDTs in scenarios where strong consistency is needed while maintaining their performance advantages. We present an implementation of reliable CRDTs named Janus. It enhances CRDTs with the aforementioned features by functioning as a middleware that facilitates CRDT communication and asynchronously runs a BFT consensus protocol. Our evaluation demonstrates that Janus achieves 21× higher throughput than naively applying state-of-the-art BFT protocols such as HotStuff achieves, and it remains responsive even under heavy loads.

In this work, we propose a neural network based rate control algorithm for Versatile Video Coding (VVC). The proposed method relies on the modeling of the Rate-Quantization (R-Q) and Distortion-Quantization (D-Q) relationships in a data driven manner based upon the characteristics of prediction residuals. In particular, a pre-analysis framework is adopted, in an effort to obtain the prediction residuals which govern the Rate-Distortion (R-D) behaviors. By inferring from the prediction residuals with deep neural networks, the Coding Tree Unit (CTU) level R-Q and D-Q model parameters are derived, which could efficiently guide the optimal bit allocation. Subsequently, the coding parameters, including Quantization Parameter (QP) and$\lambda $, at both frame and CTU levels, are obtained according to allocated bit-rates. We implement the proposed rate control algorithm on VVC Test Model (VTM-13.0). Experimental results exhibit that the proposed rate control algorithm achieves 0.77% BD-Rate savings under Low Delay B (LDB) configurations when compared to the default rate control algorithm used in VTM-13.0. For Random Access (RA) configurations, 1.77% BD-Rate savings can be observed. Furthermore, with better bit-rate estimation, more stable buffer status can be observed, further demonstrating the advantages of the proposed rate control method.

Conflict-free replicated data types (CRDTs) are popular for optimistic replication and ensuring strong eventual consistency (SEC) in distributed systems. However, reversibility is an underdeveloped functionality for CRDTs, despite its usefulness in system restoration from an erroneous state or undoing unwanted operations. In this paper, we define the concept and design of reversible CRDTs (rCRDTs). Reverse operations compensate for the effect of reversed updates, and they extend existing CRDT interfaces. Three abstractions for reversibility are proposed: reversing a single update, multiple causally related updates, and multiple logically related updates that capture the user intention behind the updates. Moreover, a replicated and distributed key-value store, rKVCRDT, is implemented as a proof of concept that integrates the support of reversible CRDTs. The rCRDTs' evaluation show that although adding reversibility affects the system's performance, the end result depends on multiple factors and varies based on the underlying CRDTs. System designers must consider the trade-off between the benefit of reversibility and the performance impact.

In this work, we propose a novel rate control algorithm for Versatile Video Coding (VVC) standard based on its distinct rate-distortion characteristics. By modelling the transform coefficients with the composite Cauchy distribution, higher accuracy compared with traditional distributions has been achieved. Based on the transform coefficient modelling, the theoretically derived R-Q and D-Q models which have been shown to deliver higher accuracy in characterizing RD characteristics for sequences with different content are incorporated into the rate control process. Furthermore, to establish an adaptive bit allocation scheme, the dependency between different levels of frames is modelled by a dependency factor to describe relationship between the reference and to-be-coded frames. Given the derived R-Q and D-Q relationships, as well as the dependency factor, an adaptive bit allocation scheme is developed for optimal bits allocation. We implement the proposed algorithm on VVC Test Model (VTM) 3.0. Experiments show that due to proper bit allocation, for low delay configuration the proposed algorithm can achieve 1.03% BD-Rate saving compared with the default rate control algorithm and 2.96% BD-Rate saving compared with fixed-QP scheme. Moreover, 1.29% BD-Rate saving and higher control accuracy have also been observed under the random access configuration.

Endometrial cancer (UCEC) is a complex malignant tumor which remains the third cause of cancer-related death of women. Although UCEC has been repeatedly researched in recent years, the molecular heterogeneity of UCEC made it hard to predict clinical outcomes of this malignant disease. This study was aimed to access the prognostic value of long non-coding RNAs (lncRNAs) in UCEC patients and to identify potential lncRNA biomarker for improving the prognosis and diagnosis of patient. We performed a comprehensive genome-wide analysis of gene expression profiles and clinical data downloaded from The Cancer Genome Atlas (TCGA). After filtering out the differentially expressed genes (DEGs) from expression profiles, the co-expression modules were constructed by weighted gene co-expression network analysis. In the analysis of key modules, four hub genes (TPX2, KIF2C, AURKA and CDCA5) was filtered out, the three hub long non-coding RNAs (ENSG00000258884, ENSG00000274825 and ENSG00000271936) were discovered and verified to be potential prognostic biomarkers.