Self-training with dual uncertainty for semi-supervised medical image segmentation

In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimatio

High-temperature electrical and thermal transport properties of fully filled skutterudites RFe_(4)Sb_(12) (R = Ca, Sr, Ba, La, Ce, Pr, Nd, Eu, and Yb)

Fully filled skutterudites RFe_(4)Sb_(12) (R = Ca, Sr, Ba, La, Ce, Pr, Nd, Eu, and Yb) have been prepared and the high-temperature electrical and thermal transport properties are investigated systematically. Lattice constants of RFe_(4)Sb_(12) increase almost linearly with increasing the ionic radii of the fillers, while the lattice expansion in filled structure is weakly influenced by the filler valence charge states. Using simple charge counting, the hole concentration in RFe_(4)Sb_(12) with divalent fillers (R = Ca, Sr, Ba, Eu, and Yb) is much higher than that in RFe4Sb12 with trivalent fillers (R = La, Ce, Pr, and Nd), resulting in relatively high electrical conductivity and low Seebeck coefficient. It is also found that RFe_(4)Sb_(12) filled skutterudites having similar filler valence charge states exhibit comparable electrical conductivity and Seebeck coefficient, and the behavior of the temperature dependence, thereby leading to comparable power factor values in the temperature range from 300 to 800 K. All RFe_(4)Sb_(12) samples possess low lattice thermal conductivity. The correlation between the lattice thermal resistivity WL and ionic radii of the fillers is discussed and a good relationship of W_L ~ (r_(cage)−r_(ion))^3 is observed in lanthanide metal filled skutterudites. CeFe_(4)Sb_(12), PrFe_(4)Sb_(12), and NdFe_(4)Sb_(12) show the highest thermoelectric figure of merit around 0.87 at 750 K among all the filled skutterudites studied in this work

A Generic Multi-Player Transformation Algorithm for Solving Large-Scale Zero-Sum Extensive-Form Adversarial Team Games

Many recent practical and theoretical breakthroughs focus on adversarial team multi-player games (ATMGs) in ex ante correlation scenarios. In this setting, team members are allowed to coordinate their strategies only before the game starts. Although there existing algorithms for solving extensive-form ATMGs, the size of the game tree generated by the previous algorithms grows exponentially with the number of players. Therefore, how to deal with large-scale zero-sum extensive-form ATMGs problems close to the real world is still a significant challenge. In this paper, we propose a generic multi-player transformation algorithm, which can transform any multi-player game tree satisfying the definition of AMTGs into a 2-player game tree, such that finding a team-maxmin equilibrium with correlation (TMECor) in large-scale ATMGs can be transformed into solving NE in 2-player games. To achieve this goal, we first introduce a new structure named private information pre-branch, which consists of a temporary chance node and coordinator nodes and aims to make decisions for all potential private information on behalf of the team members. We also show theoretically that NE in the transformed 2-player game is equivalent TMECor in the original multi-player game. This work significantly reduces the growth of action space and nodes from exponential to constant level. This enables our work to outperform all the previous state-of-the-art algorithms in finding a TMECor, with 182.89, 168.47, 694.44, and 233.98 significant improvements in the different Kuhn Poker and Leduc Poker cases (21K3, 21K4, 21K6 and 21L33). In addition, this work first practically solves the ATMGs in a 5-player case which cannot be conducted by existing algorithms.Comment: 9 pages, 5 figures, NIPS 202

Real-time study of rapid spread of antibiotic resistance plasmid in biofilm using microfluidics

Gene transfer in biofilms is known to play an important role in antibiotic resistance dissemination. However, the process remains poorly understood. In this study, microfluidics with time-lapse imaging was used for real-time monitoring of plasmid-mediated horizontal gene transfer (HGT) in biofilms. Pseudomonas putida KT2440 harboring an antibiotic resistance plasmid RP4 was chosen as the donor while Escherichia coli and activated sludge bacteria were used as the recipient cells. Dynamic features of the transfer process, including the transfer rate, cell growth rate and kinetic changes of the transfer frequency, were determined. It was found that the routes for gene transfer strongly depend on the structure and composition of a biofilm. While intraspecies HGT is essential to initiate a transfer event, the secondary retransfer from transconjugants to the same species is more efficient and can cause cascading gene spread in single-strain biofilms. For the activated sludge biofilm, only small and scattered colonies formed and vertical gene transfer appears to be the dominant route after initial intraspecies transfer. Furthermore, more than 46% of genera in the activated sludge were permissive to plasmid RP4, many of which are associated with human pathogens. These phenomena imply early prevention and interruptions to biofilm structure could provide an effect way to inhibit rapid antibiotic resistance gene spread and reduce the likelihood of catastrophic events associated with antibiotic resistance

1,5-Bis(4-chloro­phen­yl)-3-(2-thien­yl)pentane-1,5-dione

In the title mol­ecule, C21H16Cl2O2S, the five-membered ring is rotationally disordered between two orientations in a 1:1 ratio. In the crystal structure, weak inter­molecular C—H⋯O hydrogen bonds link mol­ecules related by translation along the a axis into chains, which are further combined into layers parallel to the bc plane via C—H⋯π inter­actions. The crystal studied was a racemic twin with a 0.37 (19):0.63 (19) domain ratio