Publications

Recently, a new image scrambling (i.e., encryption) scheme without bandwidth expansion was proposed based on two-dimensional discrete prolate spheroidal sequences. A comprehensive cryptanalysis is given here on this image scrambling scheme, showing that it is not sufficiently secure against various cryptographical attacks including ciphertext-only attack, known/chosen-plaintext attack, and chosen-ciphertext attack. Detailed cryptanalytic results suggest that the image scrambling scheme can only be used to realize perceptual encryption but not to provide content protection for digital images.

Measuring water holdup and characterizing the flow behavior of an oil–water two-phase flow is a contemporary and challenging problem of significant importance in industry. To address this problem, we develop a new method to design a new four-sector distributed conductance sensor. Specifically, we first use the finite-element method (FEM) to investigate the sensitivity distribution of the electric field and then calculate its response on the measurement electrodes. Based on the FEM analysis results, we extract two optimizing indexes to describe and find the optimum geometry for the four-sector distributed conductance sensor. We carry out oil–water two-phase flow experiments in a vertical upward pipe to validate the designed sensor implemented in the measurement of water holdup. In addition, we use the multivariate pseudo Wigner distribution (MPWD) method to analyze the multivariate signals from the four-sector distributed sensor. Our analytical and experimental results indicate that the four-sector distributed conductance sensor enables measuring water holdup and the MPWD allows uncovering local flow behavior revealing different oil–water flow patterns.

The last decade has seen an explosion of research in network controllability. The present article reviews some basic concepts, significant progress, important results and recent advances in the studies of the controllability of networked linear dynamical systems, regarding the relationship of the network topology, node-system dynamics, external control inputs and inner dynamical interactions with the controllability of such complex networked dynamical systems. Different approaches to analyzing the network controllability are evaluated. Some advanced topics on the selection of driver nodes, optimization of network controllability and control energy are discussed. Potential applications to real-world networked systems are also described. Finally, a near-future research outlook is highlighted.

Graphs are naturally used to describe the structures of various real-world systems in biology, society, computer science etc., where subgraphs or motifs as basic blocks play an important role in function expression and information processing. However, existing research focuses on the basic statistics of certain motifs, largely ignoring the connection patterns among them. Recently, a subgraph network (SGN) model is proposed to study the potential structure among motifs, and it was found that the integration of SGN can enhance a series of graph classification methods. However, SGN model lacks diversity and is of quite high time complexity, making it difficult to widely apply in practice. In this paper, we introduce sampling strategies into SGN, and design a novel sampling subgraph network model, which is scale-controllable and of higher diversity. We also present a hierarchical feature fusion framework to integrate the structural features of diverse sampling SGNs, so as to improve the performance of graph classification. Extensive experiments demonstrate that, by comparing with the SGN model, our new model indeed has much lower time complexity (reduced by two orders of magnitude) and can better enhance a series of graph classification methods (doubling the performance enhancement).

The limited bandwidth of PCIe has emerged as the critical bottleneck for large language model (LLM) performance, such as prefix cache fetching and model switching. Although intra-server multipath data transfer between GPU and host memory is theoretically possible, heterogeneous protocols such as PCIe and NVLink currently limit the bandwidth between host memory and GPUs to that of a single PICe link. This limitation resuals in underutilized intra-server bandwidth. To address this issue, we propose Multipath Memory Access (MMA), a scheme that, to the best of our knowledge, is the first to enalbe efficient multipath data transfer between GPU and host memory. MMA supports seamless deployment via dynamic library injection, enabling LLM applications to benefit from MMA without requiring any code modification. In our testbed, MMA significantly improves the data transfer bandwidth between the GPU and memory, achieving a peak bandwidth of 245 GB/s-representing a 4.62x speedup compared to the natice single-path bandwidth. End-to-end evaluations demonstrate that MMA reduces the time-to-first-token (TTFT) for LLM serving by 1.14x to 2.38x and decreases model-switching latency in vLLM's sleep mode by 1.12x to 2.48x.