Typing Composable Coroutines

Coroutine, as a powerful programming construct, is widely used in asynchronous applications to replace thread-based programming or the callback hell. Using coroutines makes code more readable and maintainable, for its ability to transfer control while keeping the literal scope. However, reasoning about coroutine behavior can be challenging without proper typing. We propose a type notation and calculus for composing asymmetric, first-class, stackless coroutines. Given the types of a list of coroutines, we can compute a composed type matching the collective behavior of the coroutines, so that the input and output can be type-checked by a type system. Our coroutine types can model the data received by or yielded from a coroutine, which be of coroutine types as well. On top of our type calculus, we discuss its soundness and evaluation issues, then provide four application scenarios of our coroutine types. Not only can our types be used in modern programming languages, such as Python, but also model program behaviors in OCaml and even Prolog

Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data. Many adversarial-based UDA methods involve high-instability training and have to carefully tune the optimization procedure. Some non-adversarial UDA methods employ a consistency regularization on the target predictions of a student model and a teacher model under different perturbations, where the teacher shares the same architecture with the student and is updated by the exponential moving average of the student. However, these methods suffer from noticeable negative transfer resulting from either the error-prone discriminator network or the unreasonable teacher model. In this paper, we propose an uncertainty-aware consistency regularization method for cross-domain semantic segmentation. By exploiting the latent uncertainty information of the target samples, more meaningful and reliable knowledge from the teacher model can be transferred to the student model. In addition, we further reveal the reason why the current consistency regularization is often unstable in minimizing the distribution discrepancy. We also show that our method can effectively ease this issue by mining the most reliable and meaningful samples with a dynamic weighting scheme of consistency loss. Experiments demonstrate that the proposed method outperforms the state-of-the-art methods on two domain adaptation benchmarks, $i.e.,$ GTAV $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes

Context-Aware Mixup for Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. Existing UDA-based semantic segmentation approaches always reduce the domain shifts in pixel level, feature level, and output level. However, almost all of them largely neglect the contextual dependency, which is generally shared across different domains, leading to less-desired performance. In this paper, we propose a novel Context-Aware Mixup (CAMix) framework for domain adaptive semantic segmentation, which exploits this important clue of context-dependency as explicit prior knowledge in a fully end-to-end trainable manner for enhancing the adaptability toward the target domain. Firstly, we present a contextual mask generation strategy by leveraging the accumulated spatial distributions and prior contextual relationships. The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels. Besides, provided the context knowledge, we introduce a significance-reweighted consistency loss to penalize the inconsistency between the mixed student prediction and the mixed teacher prediction, which alleviates the negative transfer of the adaptation, e.g., early performance degradation. Extensive experiments and analysis demonstrate the effectiveness of our method against the state-of-the-art approaches on widely-used UDA benchmarks.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

DMT: Dynamic Mutual Training for Semi-Supervised Learning

Recent semi-supervised learning methods use pseudo supervision as core idea, especially self-training methods that generate pseudo labels. However, pseudo labels are unreliable. Self-training methods usually rely on single model prediction confidence to filter low-confidence pseudo labels, thus remaining high-confidence errors and wasting many low-confidence correct labels. In this paper, we point out it is difficult for a model to counter its own errors. Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors. With this new viewpoint, we propose mutual training between two different models by a dynamically re-weighted loss function, called Dynamic Mutual Training (DMT). We quantify inter-model disagreement by comparing predictions from two different models to dynamically re-weight loss in training, where a larger disagreement indicates a possible error and corresponds to a lower loss value. Extensive experiments show that DMT achieves state-of-the-art performance in both image classification and semantic segmentation. Our codes are released at https://github.com/voldemortX/DST-CBC .Comment: Reformatte

Loss of PDZK1 expression activates PI3K/AKT signaling via PTEN phosphorylation in gastric cancer

Phosphorylation of PTEN plays an important role in carcinogenesis and progression of gastric cancer. However, the underlying mechanism of PTEN phosphorylation regulation remains largely elusive. In the present study, PDZK1 was identified as a novel binding protein of PTEN by association of PTEN through its carboxyl terminus and PDZ domains of PDZK1. By direct interaction with PTEN, PDZK1 inhibited the phosphorylation of PTEN at S380/T382/T383 cluster and further enhanced the capacity of PTEN to suppress PI3K/AKT activation. PDZK1 suppressed gastric cancer cell proliferation by diminishing PI3K/AKT activation via inhibition of PTEN phosphorylation in vitro and in vivo. The expression of PDZK1 was frequently downregulated in gastric cancer specimens and correlated with progression and poor prognosis of gastric cancer patients. Downregulation of PDZK1 was associated with PTEN inactivation, AKT signaling and cell proliferation activation in clinical specimens. Thus, low levels of PDZK1 in gastric cancer specimens lead to increase proliferation of gastric cancer cells via phosphorylation of PTEN at the S380/T382/T383 cluster and constitutively activation of PI3K/AKT signaling, which results in poor prognosis of gastric cancer patients

Antibacterial activity and mechanism of sanguinarine against Staphylococcus aureus by interfering with the permeability of the cell wall and membrane and inducing bacterial ROS production

Staphylococcus aureus (SA) is representative of gram-positive bacteria. Sanguinarine chloride hydrate (SGCH) is the hydrochloride form of sanguinarine (SG), one of the main extracts of Macleaya cordata (M. cordata). There are few reports on its antibacterial mechanism against SA. Therefore, in this study, we investigated the in vitro antibacterial activity and mechanism of SGCH against SA. The inhibitory zone, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) were measured, and the bactericidal activity curve was plotted. In addition, the micromorphology, alkaline phosphatase (AKP) activity, Na+K+, Ca2+Mg2+-adenosine triphosphate (ATP) activity, intracellular reactive oxygen species (ROS), and fluorescein diacetate (FDA) were observed and detected. The results showed that the inhibitory zone of SGCH against SA was judged as medium-sensitive; the MIC and MBC were 128 and 256 μg/mL, respectively; in the bactericidal activity curve, SGCH with 8 × MIC could completely kill SA within 24 h. SGCH was able to interfere with the integrity and permeability of the SA cell wall and membrane, as confirmed by the scanning electron microscopy (SEM) images, the increase in extracellular AKP and Na+ K+, Ca2+ Mg2+-ATP activities as well as the fluorescein diacetate (FDA) staining experiment results. Moreover, a high concentration of SGCH could induce SA to produce large amounts of ROS. In summary, these findings revealed that SGCH has a preferable antibacterial effect on SA, providing an experimental and theoretical basis for using SG as an antibiotic substitute in animal husbandry and for the clinical control and treatment of diseases caused by SA

Typing Requirement Model as Coroutines

Model-Driven Engineering (MDE) is a technique that aims to boost productivity in software development and ensure the safety of critical systems. Central to MDE is the refinement of high-level requirement models into executable code. Given that requirement models form the foundation of the entire development process, ensuring their correctness is crucial. RM2PT is a widely used MDE platform that employs the REModel language for requirement modeling. REModel contains contract sections and other sections including a UML sequence diagram. This paper contributes a coroutine-based type system that represents pre- and post-conditions in the contract sections in a requirement model as the receiving and yielding parts of coroutines, respectively. The type system is capable of composing coroutine types, so that users can view functions as a whole system and check their collective behavior. By doing so, our type system ensures that the contracts defined in it are executed as outlined in the accompanied sequence diagram. We assessed our approach using four case studies provided by RM2PT, validating the accuracy of the models

An electrically conductive SiBCN film prepared via polymer-derived ceramic and chemical vapor deposition methods

High-temperature surface acoustic wave (SAW) sensors for multi-parameter simultaneous measurement are widely needed in the fields of aerospace and energy. For the SAW sensors that can be operated up to 1200 degrees C, high temperature conductive coatings are required to match with the YCa4O(BO3)(3) (YCOB) single crystal, which is the only available piezoelectric ceramics now for use at 1200 degrees C. Promising high temperature conductive films comprised of SiBCN ceramics were coated via chemical vapor deposition (CVD), polymer-derived ceramic (PDC) route and PDC plus CVD method, respectively. The microstructure, morphology and electrical properties of the films were characterized in detail. It was found that a film with few defects and good conductive performance can be obtained at 1000 degrees C by PDC plus CVD route. However, a BN film has to be deposited on YCOB wafer first to suppress its decomposition in inert atmospheres around 1000 degrees C in the case of using CVD method. The overall conductivity change is primarily due to the increase in graphitization degree of free carbon in PDC-SiBCN