Fully Composable and Adequate Verified Compilation with Direct Refinements between Open Modules (Technical Report)

Verified compilation of open modules (i.e., modules whose functionality depends on other modules) provides a foundation for end-to-end verification of modular programs ubiquitous in contemporary software. However, despite intensive investigation in this topic for decades, the proposed approaches are still difficult to use in practice as they rely on assumptions about the internal working of compilers which make it difficult for external users to apply the verification results. We propose an approach to verified compositional compilation without such assumptions in the setting of verifying compilation of heterogeneous modules written in first-order languages supporting global memory and pointers. Our approach is based on the memory model of CompCert and a new discovery that a Kripke relation with a notion of memory protection can serve as a uniform and composable semantic interface for the compiler passes. By absorbing the rely-guarantee conditions on memory evolution for all compiler passes into this Kripke Memory Relation and by piggybacking requirements on compiler optimizations onto it, we get compositional correctness theorems for realistic optimizing compilers as refinements that directly relate native semantics of open modules and that are ignorant of intermediate compilation processes. Such direct refinements support all the compositionality and adequacy properties essential for verified compilation of open modules. We have applied this approach to the full compilation chain of CompCert with its Clight source language and demonstrated that our compiler correctness theorem is open to composition and intuitive to use with reduced verification complexity through end-to-end verification of non-trivial heterogeneous modules that may freely invoke each other (e.g., mutually recursively)

Amodal Segmentation Based on Visible Region Segmentation and Shape Prior

Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to the whole image. This is against the human's amodal perception, where human uses the visible part and the shape prior knowledge of the target to infer the occluded region. To mimic the behavior of human and solve the ambiguity in the learning, we propose a framework, it firstly estimates a coarse visible mask and a coarse amodal mask. Then based on the coarse prediction, our model infers the amodal mask by concentrating on the visible region and utilizing the shape prior in the memory. In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation. Consequently, the amodal mask would not be affected by what the occlusion is given the same visible regions. The leverage of shape prior makes the amodal mask estimation more robust and reasonable. Our proposed model is evaluated on three datasets. Experiments show that our proposed model outperforms existing state-of-the-art methods. The visualization of shape prior indicates that the category-specific feature in the codebook has certain interpretability.Comment: Accepted by AAAI 202

Shaping nanoparticles with hydrophilic compositions and hydrophobic properties as nanocarriers for antibiotic delivery

Inspired by the lotus effect in nature, surface roughness engineering has led to novel materials and applications in many fields. Despite the rapid progress in superhydrophobic and superoleophobic materials, this concept of Mother Nature’s choice is yet to be applied in the design of advanced nanocarriers for drug delivery. Pioneering work has emerged in the development of nanoparticles with rough surfaces for gene delivery; however, the preparation of nanoparticles with hydrophilic compositions but with enhanced hydrophobic property at the nanoscale level employing surface topology engineering remains a challenge. Herein we report for the first time the unique properties of mesoporous hollow silica (MHS) nanospheres with controlled surface roughness. Compared to MHS with a smooth surface, rough mesoporous hollow silica (RMHS) nanoparticles with the same hydrophilic composition show unusual hydrophobicity, leading to higher adsorption of a range of hydrophobic molecules and controlled release of hydrophilic molecules. RMHS loaded with vancomycin exhibits an enhanced antibacterial effect. Our strategy provides a new pathway in the design of novel nanocarriers for diverse bioapplications

Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer

ObjectiveThe aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.Materials and methodsThis retrospective study included 95 women (mean age, 48.1 years; range, 29–77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test.ResultsThe overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort.ConclusionThe combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR

Using Glycated Albumin and Stimulated C-Peptide to Define Partial Remission in Type 1 Diabetes

ObjectiveTo propose a new definition of partial remission (PR) for patients with type 1 diabetes (T1D) of all-ages using insulin dose and glycated albumin (GA), and find the optimal cut-off values for stimulated C-peptide to diagnose PR in different age-groups.Research Design and MethodsPatients with newly diagnosed T1D (n=301) were included. GA/insulin dose was used to diagnose PR, and insulin dose-adjusted glycated albumin (IDAGA) was proposed to facilitate clinical application. The optimal diagnostic levels of IDAGA and stimulated C-peptide were determined in different age-groups (≤ 12y, 12-18y and ≥ 18y). Furthermore, the diagnostic consistency between different PR definitions was studied.ResultsGA≤ 23%/insulin dose ≤ 0.5u/kg/day was used to define PR, and IDAGA (GA (%) + 40 * insulin dose(u/kg/day)) ≤ 40 was feasible in all age-groups. Whereas, the optimal diagnostic level showed difference for stimulated C-peptide (265.5, 449.3 and 241.1 pmol/L for the ≤ 12y, 12-18y and ≥ 18y age-group, respectively). About 40% of patients met the PR definition by stimulated C-peptide but not GA/insulin dose or IDAGA, who showed dyslipidemia and higher insulin resistance.ConclusionsA new definition of the PR phase is proposed using GA/insulin dose, and the calculated IDAGA≤ 40 applies to all age-groups. The stimulated C-peptide to diagnose PR is the highest in the 12-18y age-group, which reflects the effect of puberty on metabolism. For patients with insulin resistance, it is not recommended to use stimulated C-peptide alone to diagnose PR