In coding theory, codes are usually designed with a certain level of randomness to facilitate analysis and accommodate different channel conditions. However, the resulting random code constructed can be suboptimal in practical implementations. Represented by a bipartite graph, the Batched Sparse Code (BATS Code) is a randomly constructed erasure code that utilizes network coding to achieve near-optimal performance in wireless multi-hop networks. In the performance analysis in the previous research, it is implicitly assumed that the coded batches in the BATS code are independent. This assumption holds only asymptotically when the number of input symbols is infinite, but it does not generally hold in a practical setting where the number of input symbols is finite, especially when the code is constructed randomly. We show that dependence among the batches significantly degrades the code’s performance. In order to control the batch dependence through graphical design, we propose constructing the BATS code in a structured manner. A hardware-friendly structured BATS code called the Cyclic-Shift BATS (CS-BATS) code is proposed, which constructs the code from a small base graph using light-weight cyclic-shift operations. We demonstrate that when the base graph is properly designed, a higher decoding rate and a smaller complexity can be achieved compared with the random BATS code.

The network coding enhances performance in network communications and distributed storage by increasing throughput and robustness while reducing latency. Batched sparse (BATS) codes are a class of capacity-achieving network codes, but their practical applications are hindered by their structure, computational intensity, and power demands of finite field (FF) operations. Most literature focuses on algorithmic-level techniques to improve the coding efficiency. Optimization with an algorithm/hardware co-designing approach has long been neglected. Leveraging the unique structure of BATS codes, we first present cyclic-shift BATS (CS-BATS), a hardware-friendly variant. Next, we propose a simple but effective bounded-value (BV) generator, to reduce the size of a finite field multiplier by up to 70%. Finally, we report on a scalable and resource-efficient field-programmable gate array (FPGA)-based network coding accelerator that achieves a throughput of 27 Gb/s, a speedup of more than 300 over software.

Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-Computer Interface. However, reconstructing high-quality images with correct semantics from brain recordings is a challenging problem due to the complex underlying representations of brain signals and the scarcity of data annotations. In this work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned Latent Diffusion Model for Human Vision Decoding. Firstly, we learn an effective self-supervised representation of fMRI data using mask modeling in a large latent space inspired by the sparse coding of information in the primary visual cortex. Then by augmenting a latent diffusion model with double-conditioning, we show that MinD-Vis can reconstruct highly plausible images with semantically matching details from brain recordings using very few paired annotations. We benchmarked our model qualitatively and quantitatively; the experimental results indicate that our method outperformed state-of-the-art in both semantic mapping (100-way semantic classification) and generation quality (FID) by 66% and 41% respectively. An exhaustive ablation study was also conducted to analyze our framework.

Reconstructing human vision from brain activities has been an appealing task that helps to understand our cognitive process. Even though recent research has seen great success in reconstructing static images from non-invasive brain recordings, work on recovering continuous visual experiences in the form of videos is limited. In this work, we propose Mind-Video that learns spatiotemporal information from continuous fMRI data of the cerebral cortex progressively through masked brain modeling, multimodal contrastive learning with spatiotemporal attention, and co-training with an augmented Stable Diffusion model that incorporates network temporal inflation. We show that high-quality videos of arbitrary frame rates can be reconstructed with Mind-Video using adversarial guidance. The recovered videos were evaluated with various semantic and pixel-level metrics. We achieved an average accuracy of 85% in semantic classification tasks and 0.19 in structural similarity index (SSIM), outperforming the previous state-of-the-art by 45%. We also show that our model is biologically plausible and interpretable, reflecting established physiological processes.

BATched Sparse codes (BATS codes) are a class of random linear network code designed for wireless multi-hop networks with packet loss. The encoder of a BATS code generates batches where each batch contains a number of coded packets. As the outer code is a matrix generalization of the fountain code, the ordinary batch construction scheme relies on a degree distribution with a random packet sampling scheme. In practical applications, we want a batch construction scheme which achieves a high decoding rate at the destination. A natural question to ask is: Is there any batch construction scheme which achieves a higher decoding rate than the ordinary one? We give an affirmative answer to this question by formulating the batch construction scheme as a multi-armed bandit problem and solving it with a deep reinforcement learning method with the degree distribution as a guiding prior. The BATS code generated by our proposed method achieves a higher decoding rate with improved efficiency compared with the ordinary batch construction scheme.

Batched network coding (BNC) is a low-complexity variant of random linear network coding (RLNC) that encodes the file to be sent into batches, each consisting of a few coded packets. Although many works are focusing on the overhead reduction of RLNC, the overhead reduction for BNC is rarely studied. Different types of overheads in BNC interact and can potentially increase the overall overhead significantly. In this paper, we formulate a minimization problem for BNC on the number of symbols we need to send at the source node by tuning the number of packets divided from the file, which has the same meaning as tuning the packet size. It is hard to optimize the discrete zigzag-like objective efficiently. However, in practical scenarios, the encoder has to optimize the problem in real-time for different file size and redundancy requirements. We propose an efficient heuristic for this purpose and show its accuracy by numerical evaluations.