Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning

In contrastive self-supervised learning, the common way to learn discriminative representation is to pull different augmented "views" of the same image closer while pushing all other images further apart, which has been proven to be effective. However, it is unavoidable to construct undesirable views containing different semantic concepts during the augmentation procedure. It would damage the semantic consistency of representation to pull these augmentations closer in the feature space indiscriminately. In this study, we introduce feature-level augmentation and propose a novel semantics-consistent feature search (SCFS) method to mitigate this negative effect. The main idea of SCFS is to adaptively search semantics-consistent features to enhance the contrast between semantics-consistent regions in different augmentations. Thus, the trained model can learn to focus on meaningful object regions, improving the semantic representation ability. Extensive experiments conducted on different datasets and tasks demonstrate that SCFS effectively improves the performance of self-supervised learning and achieves state-of-the-art performance on different downstream tasks

Grand canonical Monte Carlo simulation on adsorption of aniline on the ice surface

Aniline has been found to have frequent environmental occurrence and high toxicity. However, little study has been performed on its environmental fate. Here, we employed Grand Canonical Monte Carlo simulations (GCMC) to investigate the adsorption behavior of aniline on hexagonal ice surface at 200 K using our modified force field of aniline and TIP5P force field of water. The results indicate that the adsorption isotherm of aniline exhibits a “monolayer saturation plateau”, starting with a rapid increase, then a plateau, and finally a condensed phase. Under very low surface coverage, the adsorption isotherm apparently follows Langmuir type adsorption isotherm although anilines can be adsorbed to various sites. Within the range of the apparent Langmuir-type adsorption isotherm, adsorbed anilines are independent from each other and most anilines are almost parallel to the ice surface and form two N−H•••O hydrogen bonds. With the increase of coverage, the adsorbed anilines can interact with each other, resulting in the deviation from the apparent Langmuir-type adsorption isotherm. In addition, the adsorption energy from GCMC simulation (−65.91 kJ mol−1) is well consistent that from our validating quantum chemistry calculation (−69.34 kJ mol−1), further confirming the reliability of our GCMC simulation results.Peer reviewe

Estimating ammonia emissions from cropland in China based on the establishment of agro-region-specific models

ACKNOWLEDGMENTS This work was financially supported by Natural Science Foundation of China under a grant numbers 41877546 and U1612441, and a BBSRC-Newton Funded project (BB/N013484/1). This work also contributes to the activities of Top-notch Academic Programs Project of Jiangsu Higher Education Institution of China (PPZY2015A061), and Program for Student Innovation through Research and Training (1913A22).Peer reviewedPostprin

Gas foaming of electrospun poly(L-lactide-co-caprolactone)/silk fibroin nanofiber scaffolds to promote cellular infiltration and tissue regeneration

Electrospun nanofibers emulate extracellular matrix (ECM) morphology and architecture; however, small pore size and tightly-packed fibers impede their translation in tissue engineering. Here we exploited in situ gas foaming to afford three-dimensional (3D) poly(L-lactide-co-ε-caprolactone)/silk fibroin (PLCL/SF) scaffolds, which exhibited nanotopographic cues and a multilayered structure. The addition of SF improved the hydro philicity and biocompatibility of 3D PLCL scaffolds. Three-dimensional scaffolds exhibited larger pore size (38.75 ± 9.78 μm2 ) and high porosity (87.1% ± 1.5%) than that of their 2D counterparts. 3D scaffolds also improved the deposition of ECM components and neo-vessel regeneration as well as exhibited more numbers of CD163+/ CCR7+ cells after 2 weeks implantation in a subcutaneous model. Collectively, 3D PLCL/SF scaffolds have broad implications for regenerative medicine and tissue engineering applications.info:eu-repo/semantics/publishedVersio

SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation

Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a notable challenge in acquiring a substantial amount of pre-training data, particularly within the medical field. To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis. Our strategy harnesses the potential of multi-view information by incorporating two principal components. In the pre-training phase, we deploy a masked multi-view encoder devised to concurrently train masked multi-view observations through a range of diverse proxy tasks. These tasks span image reconstruction, rotation, contrastive learning, and a novel task that employs a mutual learning paradigm. This new task capitalizes on the consistency between predictions from various perspectives, enabling the extraction of hidden multi-view information from 3D medical data. In the fine-tuning stage, a cross-view decoder is developed to aggregate the multi-view information through a cross-attention block. Compared with the previous state-of-the-art self-supervised learning method Swin UNETR, SwinMM demonstrates a notable advantage on several medical image segmentation tasks. It allows for a smooth integration of multi-view information, significantly boosting both the accuracy and data-efficiency of the model. Code and models are available at https://github.com/UCSC-VLAA/SwinMM/.Comment: MICCAI 2023; project page: https://github.com/UCSC-VLAA/SwinMM