Hierarchical Uncertainty Estimation for Medical Image Segmentation Networks

Learning a medical image segmentation model is an inherently ambiguous task, as uncertainties exist in both images (noise) and manual annotations (human errors and bias) used for model training. To build a trustworthy image segmentation model, it is important to not just evaluate its performance but also estimate the uncertainty of the model prediction. Most state-of-the-art image segmentation networks adopt a hierarchical encoder architecture, extracting image features at multiple resolution levels from fine to coarse. In this work, we leverage this hierarchical image representation and propose a simple yet effective method for estimating uncertainties at multiple levels. The multi-level uncertainties are modelled via the skip-connection module and then sampled to generate an uncertainty map for the predicted image segmentation. We demonstrate that a deep learning segmentation network such as U-net, when implemented with such hierarchical uncertainty estimation module, can achieve a high segmentation performance, while at the same time provide meaningful uncertainty maps that can be used for out-of-distribution detection.Comment: 8 pages, 3 figure

Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction

Effective long-term predictions have been increasingly demanded in urban-wise data mining systems. Many practical applications, such as accident prevention and resource pre-allocation, require an extended period for preparation. However, challenges come as long-term prediction is highly error-sensitive, which becomes more critical when predicting urban-wise phenomena with complicated and dynamic spatial-temporal correlation. Specifically, since the amount of valuable correlation is limited, enormous irrelevant features introduce noises that trigger increased prediction errors. Besides, after each time step, the errors can traverse through the correlations and reach the spatial-temporal positions in every future prediction, leading to significant error propagation. To address these issues, we propose a Dynamic Switch-Attention Network (DSAN) with a novel Multi-Space Attention (MSA) mechanism that measures the correlations between inputs and outputs explicitly. To filter out irrelevant noises and alleviate the error propagation, DSAN dynamically extracts valuable information by applying self-attention over the noisy input and bridges each output directly to the purified inputs via implementing a switch-attention mechanism. Through extensive experiments on two spatial-temporal prediction tasks, we demonstrate the superior advantage of DSAN in both short-term and long-term predictions.Comment: 11 pages, an ACM SIGKDD 2020 pape

Advances in All-Inorganic Perovskite Nanocrystal-Based White Light Emitting Devices

Metal halide perovskites (MHPs) are exceptional semiconductors best known for their intriguing properties, such as high absorption coefficients, tunable bandgaps, excellent charge transport, and high luminescence yields. Among various MHPs, all-inorganic perovskites exhibit benefits over hybrid compositions. Notably, critical properties, including chemical and structural stability, could be improved by employing organic-cation-free MHPs in optoelectronic devices such as solar cells and light-emitting devices (LEDs). Due to their enticing features, including spectral tunability over the entire visible spectrum with high color purity, all-inorganic perovskites have become a focus of intense research for LEDs. This Review explores and discusses the application of all-inorganic CsPbX3 nanocrystals (NCs) in developing blue and white LEDs. We discuss the challenges perovskite-based LEDs (PLEDs) face and the potential strategies adopted to establish state-of-the-art synthetic routes to obtain rational control over dimensions and shape symmetry without compromising the optoelectronic properties. Finally, we emphasize the significance of matching the driving currents of different LED chips and balancing the aging and temperature of individual chips to realize efficient, uniform, and stable white electroluminescence

Lonicera japonica polysaccharides attenuate ovalbumin-induced allergic rhinitis by regulation of Th17 cells in BALB/c mice

Lonicera japonica Thunb. has been widely used as food ingredients and healthy drinks in the Asian countries, which was reported to possess some good activities. However, it remains unknown in the immunomodulation of Lonicera japonica polysaccharides (LJP) on allergic rhinitis (AR). This study aimed to investigate the impact of LJP on ovalbumin-induced AR in BALB/c mice model. LJP significantly inhibited AR symptoms and eosinophil number in nasal mucosa. Besides, the increased serum levels of IgE, TNF-α, IL-1β and IL-17 were markedly decreased when AR mice were treated with LJP. The mRNA expression levels of IL-4, IL-5, IL-6, IL-17, IL-23, ROR-γt and STAT3 in OVA group were increased, and SOCS3 was reduced, while LJP inhibited the changes. The present study indicated that LJP suppressed the inflammatory response in AR sensitized by ovalbumin, showing that LJP has the potential to treat AR through the regulation of Th17 cells

Ergodic stationary distribution of stochastic virus mutation model with time delay

The virus mutation can increase the complexity of the infectious disease. In this paper, the dynamical characteristics of the virus mutation model are discussed. First, we built a stochastic virus mutation model with time delay. Second, the existence and uniqueness of global positive solutions for the proposed model is proved. Third, based on the analysis of the ergodic stationary distribution for the model, we discuss the influence mechanism between the different factors. Finally, the numerical simulation verifies the theoretical results