The Vision Transformer (ViT) has demonstrated remarkable performance in Self-Supervised Learning (SSL) for 3D medical image analysis. Masked AutoEncoder (MAE) for feature pre-training can further unleash the potential of ViT on various medical vision tasks. However, due to large spatial sizes with much higher dimensions of 3D medical images, the lack of hierarchical design for MAE may hinder the performance of downstream tasks. In this paper, we propose a novel Mask in Mask (MiM) pre-training framework for 3D medical images, which aims to advance MAE by learning discriminative representation from hierarchical visual tokens across varying scales. We introduce multiple levels of granularity for masked inputs from the volume, which are then reconstructed simultaneously ranging at both fine and coarse levels. Additionally, a cross-level alignment mechanism is applied to adjacent level volumes to enforce anatomical similarity hierarchically. Furthermore, we adopt a hybrid backbone to enhance the hierarchical representation learning efficiently during the pre-training. MiM was pre-trained on a large scale of available 3D volumetric images, i.e., Computed Tomography (CT) images containing various body parts. Extensive experiments on twelve public datasets demonstrate the superiority of MiM over other SSL methods in organ/tumor segmentation and disease classification. We further scale up the MiM to large pre-training datasets with more than 10k volumes, showing that large-scale pre-training can further enhance the performance of downstream tasks. Code is available at https://github.com/JiaxinZhuang/MiM.

The problem of clock synchronization is studied in an arbitrary network ${\mathcal{G}} = ({\mathcal{V}},{\mathcal{E}})$ with $|{\mathcal{V}}|$ server nodes and $|{\mathcal{E}}|$ edges. Every pair of adjacent servers has a time discrepancy (edge information) that is only known approximately to one or both of the two adjacent servers. A master node aims to coordinate the otherwise independent clocks of the servers by eliminating the loop-wise offset surplus in the network. The goal is to minimize the communication cost between server nodes and the master node. Optimal schemes are found for the two cases where each time discrepancy is known by 1) both adjacent servers, and 2) only one of the adjacent servers. Notably, the scheme for the first case is robust to a straggler (slow or failed server). An algorithm that outperforms the natural (uncoded) baseline is proposed for the general setting that is a mix of the two cases. Classes of such mixed setting are identified where the algorithm represents the optimal solution.

Biomechanical forces intimately contribute to cardiac morphogenesis. However, volumetric imaging to investigate the cardiac mechanics with high temporal and spatial resolution remains an imaging challenge. We hereby integrated light-field microscopy (LFM) with light-sheet fluorescence microscopy (LSFM), coupled with a retrospective gating method, to simultaneously access myocardial contraction and intracardiac blood flow at 200 volumes per second. While LSFM allows for the reconstruction of the myocardial function, LFM enables instantaneous acquisition of the intracardiac blood cells traversing across the valves. We further adopted deformable image registration to quantify the ventricular wall displacement and particle tracking velocimetry to monitor intracardiac blood flow. The integration of LFM and LSFM enabled the time-dependent tracking of the individual blood cells and the differential rates of segmental wall displacement during a cardiac cycle. Taken together, we demonstrated a hybrid system, coupled with our image analysis pipeline, to simultaneously capture the myocardial wall motion with intracardiac blood flow during cardiac development.

Continuous monitoring of crop is an essential task of agricultural practices for detection of diseases or pests, precision irrigation and fertilization. The state of the art monitoring and imaging systems use aerial imaging to obtain visual feedback and multi-spectral imagery to determine crop growth factors. The main idea is that the features can be automatically calculated and assessed after pre-processing the images. After pre-processing, then the parameters can be computed using image processing techniques. For example, key leaf function traits leaf life span, leaf mass per area can be calculated. Our findings indicate that plant health assessment could be moved from lab and expensive monitoring tools to ubiquitous silicon technology based cost effect solutions without much loss of accuracy.

DNA becomes an attractive storage medium in recent years for its ultra-high density, millennial-long endurance, and efficient replication. In this work we consider DNA storage that uses the affordable and portable nanopore sequencing as the reading mechanics. Unlike traditional data storage systems, errors occur asymmetrically among the four types of nucleotide bases of DNA. Quaternary codes can be employed for error correction, but suffer from high complexity. In this paper, we design binary LDPC codes with a turbo-like decoder for the DNA storage channel. Simulation results show that our binary LDPC codes have a similar bit-error rate but with a speed up by a factor of 4 compared to quaternary codes.

We present a new algorithm for blind estimation of the symbol timing and frequency offset in a time-varying frequency-selective Rayleigh fading multipath channel for OFDM systems. It exploits the intrinsic structure of OFDM signals and only relies on a second-order moment without knowledge of the probability distribution function of the received signals. Under the minimum mean-square-error (MMSE) sense, the proposed estimators are totally optimum and easily implemented. Furthermore, we expand the estimation range of the frequency offset estimator and improve the timing estimator to be independent of the frequency offset. A more generalized channel model is considered. It is characterized by its power delay profile and time-varying scattering function. The channel has high reliability for a real mobile environment.