Distribution matching is a key technique in machine learning, with applications in generative models, domain adaptation, and algorithmic fairness. A related but less explored challenge is generating a distribution that aligns with multiple underlying distributions, often with conflicting objectives, known as a Pareto optimal distribution. In this paper, we develop a general theory based on information geometry to construct the Pareto set and front for the entire exponential family under KL and inverse KL divergences. This formulation allows explicit derivation of the Pareto set and front for multivariate normal distributions, enabling applications like multiobjective variational autoencoders (MOVAEs) to generate interpolated image distributions. Experimental results on real-world images demonstrate that both algorithms can generate high-quality interpolated images across multiple distributions.

With the end of Moore’s law and Dennard scaling, waferscale systems or processors that integrate multiple pre-tested known good dies (KGDs) on a waferscale-interposer are new approaches to further improve the chiplet-based system’s performance. This article explores the network on wafer (NoW) architecture of waferscale switching system under several physical constraints. A software-based approach is proposed to redefine the topological property. A five-level butterfly fat-tree (BFT)-like logical topology with 8.96-Tb/s (896 ports$\times 10$Gb/s/port) switching bandwidth is achieved based on 2-D-mesh-like physical topology. We show that the proposed BFT-like topology with breadth-first-search (BFS) based traffic balanced routing algorithm reduces 55.6% hops, 41.4% transmission delay, and improves 24.2% throughput compared to 2-D-mesh-like topology under different traffic distributions. This BFT-like waferscale switching system is suitable for high-performance computing and data centers. In addition, the numerical analysis shows that the waferscale package can provide significant power efficiency and latency advantages compared to the typical single-chip package, which mainly benefits from the short-reach IO requirements. Note that the proposed waferscale switching system is compatible with high-switch-capacity dies with advanced process technology, which can further improve system performance. Finally, we present the physical implementations for the waferscale system with heterogeneous dies.

The popularity of 4K videos is on the rise. However, streaming such high-quality videos over mm Wave to several users presents significant challenges due to directional communication, fluctuating channels, and high bandwidth demands. To address these challenges, this paper introduces an innovative 4K layered video multicast streaming system. We (i) develop a video quality model tailored for layered video coding, (ii) optimize resource allocation, scheduling, and beamforming based on the channel conditions of different users, and (iii) design a streaming strategy that integrates fountain code to eliminate redundancy in multicast groups, coupled with a Leaky-Bucket approach for congestion control. We implement our system on Commodity-Off- The-Shelf (COTS) WiGig devices and demonstrate its effectiveness through comprehensive testbed and emulation experiments.

System on Wafer (SoW) based on chiplets may be implanted with hardware Trojans (HTs) by untrustworthy third‐party chiplet vendors. However, traditional HTs protection techniques cannot guarantee complete protection against HTs, which poses a great challenge to the hardware security of SoW. In this paper, we propose a computing architecture based on endogenous security theory—dynamic heterogeneous redundant computing architecture (DHRCA) that can tolerate and detect HTs at runtime. The security of our approach is analyzed by building a generalized stochastic coloring petri net (GSCPN) model of DHRCA. The simulation results based on the GSCPN model show that our method can improve the system security probability to 0.8690 and the system availability probability to 0.9750 in the steady state compared with typical triple‐mode redundancy and runtime monitoring methods. Furthermore, the impact of different attack and defense strategies on system security of different methods is simulated and analyzed in this paper.

This paper presents the framework of a power-dispatching automation system (PDAS) based on a virtual private cloud, which extracts advantages of technologies, such as a virtual private cloud, a virtual private network, an https protocol, etc. Unlike existing on-premise or public cloud hosted PDASs that are vulnerable and may be subject to blackouts due to extreme conditions, such as natural disasters or cyberattacks, the proposed framework operates more safely and securely under those conditions, while at the same time meeting the reliability requirements for power systems as well. This paper presents the theory, architectural design, characteristics, and implementation process of the system, as well as its reliability and cyber security measurements. The annual failure time of the proposed system is reduced from 61.2 to about 0.43 min, about 142 times less than those of existing PDASs. This framework has been implemented in the Lipu power system in China since May 2016. Results show that the proposed system is reliable, safe, and cost-effective, with a bright prospect for future applications.

Power system outages/blackouts, especially weather related, are becoming more and more frequent, incurring significant economic and social costs. The ability to restore power services quickly after a blackout is crucial for power system resilience. Power system restoration is an extremely complicated process, involving multiple steps, highly combinatorial operational decisions, and highly nonlinear technical constraints, which make restoration planning an exceptionally challenging task. This paper will first introduce the restoration process and operations, examine important issues in restoration, and survey the state of the art in the research and practice of power system restoration planning. Then, we will focus on the commonly used buildup restoration planning strategy, in which the system is sectionalized into smaller subsystems with initial power sources, and then the subsystems are restored in parallel. Due to the complexity, existing approaches treat the sectionalization and restoration separately, leading to a suboptimal restoration plan. We will introduce an integrated restoration planning approach to improve the quality of restoration plans globally (such as shorter overall restoration time) by using mathematical programming and simulation in an interactive and iterative way. Case studies will be provided to illustrate the effectiveness of the proposed approach.