Siyang Li

We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each pixel that enables identifying all pixels belonging to the same object. Though trained on static images, the instance embeddings are stable over consecutive video frames, which allows us to link objects together over time. Thus, we adapt the instance networks trained on static images to video object segmentation and incorporate the embeddings with objectness and optical flow features, without model retraining or online fine-tuning. The proposed method outperforms state-of-the-art unsupervised segmentation methods in the DAVIS dataset and the FBMS dataset.

Rechargeable aqueous zinc-metal batteries, considered as the possible post-lithium-ion battery technology for large-scale energy storage, face severe challenges such as dendrite growth and hydrogen evolution side reaction (HER) on Zn negative electrode. Herein, a three-dimensional Cu-In alloy interface is developed through a facile potential co-replacement route to realize uniform Zn nucleation and HER anticatalytic effect simultaneously. Both theoretical calculations and experimental results demonstrate that this bifunctional Cu-In alloy interface inherits the merits of low Zn-nucleation overpotential and high HER overpotential from individual copper and indium constituents, respectively. Moreover, the dynamical self-reconstruction during cycling leads to an HER-anticatalytic and zincophilic gradient hierarchical structure, enabling highly reversible Zn chemistry with dendrite-free Zn (002) deposition and inhibited HER. Moreover, the improved interface stability featured by negligible pH fluctuations in the diffusion layer and suppressed by-product formation is evidenced by in-situ scanning probe technology, Raman spectroscopy, and electrochemical gas chromatography. Consequently, the lifespan of the CuIn@Zn symmetric cell is extended to more than one year with a voltage hysteresis of 6 mV. Importantly, the CuIn@Zn negative electrode is also successfully coupled with high-loading iodine positive electrode to fabricate Ah-level (1.1 Ah) laminated pouch cell, which exhibits a capacity retention of 67.9% after 1700 cycles.

PURPOSE: Monte Carlo (MC) dose calculation is generally superior to analytical dose calculation (ADC) used in commercial TPS to model the dose distribution especially for heterogeneous sites, such as lung and head/neck patients. The purpose of this study was to provide a validated, fast, and open-source MC code, MCsquare, to assess the impact of approximations in ADC on clinical pencil beam scanning (PBS) plans covering various sites. METHODS: First, MCsquare was validated using tissue-mimicking IROC lung phantom measurements as well as benchmarked with the general purpose Monte Carlo TOPAS for patient dose calculation. Then a comparative analysis between MCsquare and ADC was performed for a total of 50 patients with 10 patients per site (including liver, pelvis, brain, head-and-neck, and lung). Differences among TOPAS, MCsquare, and ADC were evaluated using four dosimetric indices based on the dose-volume histogram (target Dmean, D95, homogeneity index, V95), a 3D gamma index analysis (using 3%/3 mm criteria), and estimations of tumor control probability (TCP). RESULTS: Comparison between MCsquare and TOPAS showed less than 1.8% difference for all of the dosimetric indices/TCP values and resulted in a 3D gamma index passing rate for voxels within the target in excess of 99%. When comparing ADC and MCsquare, the variances of all the indices were found to increase as the degree of tissue heterogeneity increased. In the case of lung, the D95s for ADC were found to differ by as much as 6.5% from the corresponding MCsquare statistic. The median gamma index passing rate for voxels within the target volume decreased from 99.3% for liver to 75.8% for lung. Resulting TCP differences can be large for lung (≤10.5%) and head-and-neck (≤6.2%), while smaller for brain, pelvis and liver (≤1.5%). CONCLUSIONS: Given the differences found in the analysis, accurate dose calculation algorithms such as Monte Carlo simulations are needed for proton therapy, especially for disease sites with high heterogeneity, such as head-and-neck and lung. The establishment of MCsquare can facilitate patient plan reviews at any institution and can potentially provide unbiased comparison in clinical trials given its accuracy, speed and open-source availability.