Due to the growing need for bandwidth starving Multi-View Videos (MVV) in virtual reality, TV, and education, effectively allocating the resources of next-generation wireless technologies for MVV streams becomes increasingly crucial. To achieve high utility for MVV users, this article proposes a cross-layer resource allocation mechanism to leverage video synthesizing schemes (such as Depth-Image-Based Rendering (DIBR) for efficient MVV streaming with massive MIMO). First, we formulate a new problem,antenna allocation with video synthesis(AAVS), and prove its NP-hardness. Then, we design an approximation algorithm namedUtility-based Multi-View Synthesis(UMVS) with the analytical performance provided, and dynamic scenarios are addressed by augmenting UMVS with deep reinforcement learning. Data-driven simulation results show that UMVS outperforms existing antenna allocation schemes by at least 10%, and the DRL extension provides an additional 6% improvement in system utility under congested scenarios.

Due to the growing need for Multi-View Videos (MVV) in advertisement, TV, and education, effectively allocating the resources of next-generation wireless technologies to provide MVV streams is challenging. To achieve the high utility for MVV users, this paper proposes a cross-layer resource allocation mechanism to leverage video synthesizing schemes (such as Depth-Image-Based Rendering (DIBR)) for efficient MVV streaming with massive MIMO. We formulate a new problem Antenna Allocation with Video Synthesizing (AAVS) and prove its NP-hardness. Then, we design an algorithm, named Marginal Utility-Based Iteration (MUBI). The performance is evaluated by the data-driven simulations, and the results manifest that MUBI outperforms the baselines regarding the total utility of users.

Techniques of domain adaptation have been applied to address cross-domain recognition problems. In particular, such techniques favor the scenarios in which labeled data can be obtained at the source domain, but only few labeled target domain data are available during the training stage. In this paper, we propose a domain adaptation approach which is able to transfer source domain labeled data to the target domain, so that one can collect a sufficient amount of training data at that domain for recognition purposes. By advancing low-rank matrix decomposition for obtaining representative cross-domain data, our proposed model aims at transferring source domain labeled data to the target domain while preserving class label information. This introduces additional discriminating ability into our model, and thus improved recognition can be expected. Empirical results on cross-domain image datasets confirm the use of our proposed model for solving cross-domain recognition problems.

Upon receiving the output sequence streaming from a sequential encoder, a decoder reconstructs the corresponding input sequence that streamed to the encoder. Such an encoding and decoding scheme is commonly encountered in communication, cryptography, signal processing, and other applications. Given an encoder specification, decoder design can be error-prone and time consuming. Its automation may help designers improve productivity and justify encoder correctness. Though recent advances showed promising progress, there is still no complete method that decides whether a decoder exists for a finite state transition system. The quest for completely automatic decoder synthesis remains. This paper presents a complete and practical approach to automating decoder synthesis via incremental Boolean satisfiability solving and Craig interpolation. Experiments show that, for decoder-existent cases, our method synthesizes decoders effectively; for decoder-nonexistent cases, our method concludes the nonexistence instantly while prior methods may fail. Case studies are also conducted in synthesizing decoders for linear error-correcting codes.

Upon receiving the output sequence streaming from a sequential encoder, a decoder reconstructs the corresponding input sequence that streamed to the encoder. Such an encoding and decoding scheme is commonly encountered in communication, cryptography, signal processing, and other applications. Given an encoder specification, decoder design can be error-prone and time consuming. Its automation may help designers improve productivity and justify encoder correctness. Though recent advances showed promising progress, there is still no complete method that decides whether a decoder exists for a finite state transition system. The quest for completely automatic decoder synthesis remains. This paper presents a complete and practical approach to automating decoder synthesis via incremental SAT solving and Craig interpolation. Experiments show that, for decoder-existent cases, our method synthesizes decoders effectively; for decoder-nonexistent cases, our method concludes the non-existence instantly while prior methods may fail.