Understanding user profiling on social media poses significant challenges due to the intertwined complexities of network structures, user interactions, and the multi-dimensional nature of the data. To reduce visual clutter and offer complementary perspectives for engagingly exploring user profiling within these networks, we propose Social Media Island, an interactive 3D metaphoric visualization system. Our system uses 3D mountain metaphors to visualize user profiling, capturing user influence and activity, while tree metaphors visualize the information forwarding process. To support a flexible scope for users to explore in a more intriguing way, we design various interactions such as cutting the mountain to split out a subset with similarity to some extent for further exploration. By using these 3D visualizations with user interactions, Social Media Island facilitates immersion in the data, the fluid exploration of user influence, topic evolution, and the spread of information. The effectiveness of metaphors is evaluated by user studies, and that of the system is evaluated through two case studies.

Dual-functional radar and communication (DFRC) system is capable of sensing potential eavesdroppers close to the DFRC base station (BS) and further ensuring secure transmission using physical layer security technologies based on the obtained location information of eavesdroppers. However, such security can be threatened by a malicious intelligent reflecting surface (IRS) that simultaneously changes both radar and communication channels. To reveal this threat, an IRS-based active attack scheme is proposed in this paper under the assumption of a prevalent DFRC framework. In this scheme, the attacker, equipped with a malicious IRS, carefully controls and optimizes the IRS phase shifts in each stage of the DFRC framework to reduce its reflected radar echo power to the BS and/or strength the wiretap link gain for pilot spoofing attack according to the statistical channel state information. Numerical results show that our proposed attack scheme can significantly reduce the secrecy rate of the legitimate user, while avoid being detected by the DFRC BS.

The metaverse is a visual world that blends the physical world and digital world. At present, the development of the metaverse is still in the early stage, and there lacks a framework for the visual construction and exploration of the metaverse. In this paper, we propose a framework that summarizes how graphics, interaction, and visualization techniques support the visual construction of the metaverse and user-centric exploration. We introduce three kinds of visual elements that compose the metaverse and the two graphical construction methods in a pipeline. We propose a taxonomy of interaction technologies based on interaction tasks, user actions, feedback and various sensory channels, and a taxonomy of visualization techniques that assist user awareness. Current potential applications and future opportunities are discussed in the context of visual construction and exploration of the metaverse. We hope this paper can provide a stepping stone for further research in the area of graphics, interaction and visualization in the metaverse.

Symmetrical multilevel diversity coding (SMDC) is a classical model for coding over distributed storage. In this setting, a simple separate encoding strategy known as superposition coding was shown to be optimal in terms of achieving the minimum sum rate and the entire admissible rate region of the problem. The proofs utilized carefully constructed induction arguments, for which the classical subset entropy inequality played a key role. This paper consists of two parts. In the first part, the existing optimality proofs for classical SMDC are revisited, with a focus on their connections to subset entropy inequalities. Initially, a new sliding-window subset entropy inequality is introduced and then used to establish the optimality of superposition coding for achieving the minimum sum rate under a weaker source-reconstruction requirement. Finally, a subset entropy inequality recently proved by Madiman and Tetali is used to develop a new structural understanding of the work of Yeung and Zhang on the optimality of superposition coding for achieving the entire admissible rate region. Building on the connections between classical SMDC and the subset entropy inequalities developed in the first part, in the second part the optimality of superposition coding is extended to the cases where there is either an additional all-access encoder or an additional secrecy constraint.

Symmetrical Multilevel Diversity Coding (SMDC) is a network compression problem introduced by Roche (1992) and Yeung (1995). This paper considers a generalization of SMDC for which, in addition to the randomly accessible encoders, there is also an all-access encoder. It is shown that a simple separate coding strategy known as superposition coding is optimal in terms of achieving the entire admissible rate region of the problem. Key to our proof is to identify the supporting hyperplanes that define the boundary of the admissible rate region and then builds on the result of Yeung and Zhang on a generalization of Han's subset inequality.

In this paper, an explicit rate control framework for Ethernet applications, especially data centers, is described. The framework guarantees zero packet drops at the congested switch and fast convergence to fair and stable state. In order to manage the congestion, design choices on 2-point and 3- point structures, the reactive and proactive signaling, explicit and implicit rate controls are compared. Then the core component of the framework, queue control, is carefully studied. Furthermore, we show that this framework can seamlessly cooperate with IEEE 802.3x PAUSE mechanism to recover from severe congestion scenarios. Numerical results are provided to support the claims.

IEEE 802.1 standards committee is working on a new specification for congestion notification in Ethernet networks. The goal of this work is to enable application of Ethernet in backend datacenter applications. Such applications typically use fiber channel and infiniband due to their loss-free characteristics. A backward congestion notification (BCN) scheme is one of the candidate schemes proposed to avoid long delays and minimize loss in Ethernet networks. This paper presents an analysis of this scheme. We develop an analytical model to analyze the stability and the rate of convergence of the scheme. It is shown that BCN achieves proportional fairness and not max-min fairness under certain circumstances. Simulation results are presented that validate the analytical results.

The performance analysis of the zero-forcing successive interference cancellation (ZF-SIC) detector in vertical Bell Labs layered space-time (V-BLAST) is generally considered difficult, mainly because of the nonlinear error propagation effect. In this paper, we first present the nearly exact bit error rate (BER) analysis of ZF-SIC V-BLAST with binary phase-shift keying (BPSK) modulation in the richly scattered Rayleigh-fading multiple-antenna channel. We derive the closed-form expressions of the error probability of each substream under a given number of transmit antennas. We further give a general recursive procedure to calculate the BER of each substream with arbitrary number of transmit and receive antennas. The procedure we give is easy to follow and is feasible in evaluating the BER performance of the V-BLAST with ZF-SIC receiver. It can be further utilized to develop an optimal transmission strategy for the open-loop V-BLAST. Our analytical results show excellent matches with the Monte Carlo simulation results.