Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning

Kriging aims at estimating the attributes of unsampled geo-locations from observations in the spatial vicinity or physical connections, which helps mitigate skewed monitoring caused by under-deployed sensors. Existing works assume that neighbors' information offers the basis for estimating the attributes of the unobserved target while ignoring non-neighbors. However, non-neighbors could also offer constructive information, and neighbors could also be misleading. To this end, we propose ``Contrastive-Prototypical'' self-supervised learning for Kriging (KCP) to refine valuable information from neighbors and recycle the one from non-neighbors. As a pre-trained paradigm, we conduct the Kriging task from a new perspective of representation: we aim to first learn robust and general representations and then recover attributes from representations. A neighboring contrastive module is designed that coarsely learns the representations by narrowing the representation distance between the target and its neighbors while pushing away the non-neighbors. In parallel, a prototypical module is introduced to identify similar representations via exchanged prediction, thus refining the misleading neighbors and recycling the useful non-neighbors from the neighboring contrast component. As a result, not all the neighbors and some of the non-neighbors will be used to infer the target. To encourage the two modules above to learn general and robust representations, we design an adaptive augmentation module that incorporates data-driven attribute augmentation and centrality-based topology augmentation over the spatiotemporal Kriging graph data. Extensive experiments on real-world datasets demonstrate the superior performance of KCP compared to its peers with 6% improvements and exceptional transferability and robustness. The code is available at https://github.com/bonaldli/KCPComment: Accepted in AISTATS 202

MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery

We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In teacher-student model, data augmentation is usually adopted on unlabeled data to regularize the consistent training between teacher and student. We start from a key perspective that fixed relative locations and variable sizes of different organs can provide distribution information where a multi-organ CT scan is drawn. Thus, we treat the prior anatomy as a strong tool to guide the data augmentation and reduce the mismatch between labeled and unlabeled images for semi-supervised learning. More specifically, we propose a data augmentation strategy based on partition-and-recovery N$^3$ cubes cross- and within- labeled and unlabeled images. Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch). For within-branch, we further propose to refine the quality of pseudo labels by blending the learned representations from small cubes to incorporate local attributes. Our method is termed as MagicNet, since it treats the CT volume as a magic-cube and N$^3$-cube partition-and-recovery process matches with the rule of playing a magic-cube. Extensive experiments on two public CT multi-organ datasets demonstrate the effectiveness of MagicNet, and noticeably outperforms state-of-the-art semi-supervised medical image segmentation approaches, with +7% DSC improvement on MACT dataset with 10% labeled images. Code is available at https://github.com/DeepMed-Lab-ECNU/MagicNet.Comment: Accepted by CVPR 202

Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration

Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-guided radiotherapy. Existing multi-modality image registration algorithms rely on statistical-based similarity measures or local structural image representations. However, the former is sensitive to locally varying noise, while the latter is not discriminative enough to cope with complex anatomical structures in multimodal scans, causing ambiguity in determining the anatomical correspondence across scans with different modalities. In this paper, we propose a modality-agnostic structural representation learning method, which leverages Deep Neighbourhood Self-similarity (DNS) and anatomy-aware contrastive learning to learn discriminative and contrast-invariance deep structural image representations (DSIR) without the need for anatomical delineations or pre-aligned training images. We evaluate our method on multiphase CT, abdomen MR-CT, and brain MR T1w-T2w registration. Comprehensive results demonstrate that our method is superior to the conventional local structural representation and statistical-based similarity measures in terms of discriminability and accuracy.Comment: Accepted by CVPR202

Effect of water stratification and mixing on phytoplankton functional groups: a case study of Xikeng Reservoir, China

A shift in reservoir stratification and mixing significantly affects the water column ecosystem, which in turn leads to changes in phytoplankton abundance and community structure. To explore the effects of stratification and mixing on the phytoplankton community structure of a diversion reservoir, a 1-year survey was divided into a stratification period in 2020, a mixing period in 2020, and a stratification period in 2021, and redundancy analysis (RDA), variance partitioning analysis (VPA) and Pearson correlation analysis were used to analyse the key drivers affecting the phytoplankton functional groups, using Xikeng Reservoir as a case study. During the study period, 8 phyla, 69 genera and 9 major functional groups were observed in this reservoir. The dominant functional groups varied significantly, being X1 in the stratified period in 2020; P and D in the mixing period in 2020; and D, X1, and M in the stratified period in 2021. The phytoplankton diversity index was greater in the mixing period than in the stratification period, in agreement with the results of the aquatic ecological status evaluation (Q index, higher in the mixing period than in the stratification period). However, phytoplankton diversity of Xikeng Reservoir was of limited value in assessing the degree of water pollution, so should be considered in combination with the Q index. Water temperature (WT), mixing depth (Zmix), nitrogen–phosphorus ratio (N/P), and total nitrogen (TN) were important drivers of phytoplankton functional group dynamics in different periods. The study provides a valuable reference for assessing the relationship between environmental factors and phytoplankton communities, as well as for the evaluation and conservation of aquatic ecosystems in southern China's water diversion reservoirs

Effects of nonaromatic through‐bond conjugation and through‐space conjugation on the photoluminescence of nontraditional luminogens

Abstract Photoluminescence (PL) mechanisms of nontraditional luminogens (NTLs) have attracted great interest, and they are generally explained with intra/intermolecular through‐space conjugation (TSC) of nonconventional chromophores. Here a new concept of nonaromatic through‐bond conjugation (TBC) is proposed and it is proved that it plays an important role in the PL of NTLs. The PL behaviors of the three respective isomers of cyclohexanedione and gemdimethyl‐1,3‐cyclohexanedione were studied and correlated with their chemical and aggregate structures. These compounds show different fluorescence emissions as well as different concentration, excitation and solvent‐dependent emissions. The compounds which undergo keto‐enol tautomerism and hence with a conjugated ketone‐enol structure (i.e., nonaromatic TBC) show more red‐shifted emissions. TBC effect reduces the energy gaps and facilitates the formation of stronger TSC in the aggregate state. The compounds in the ketone‐enol form are also prone to occur excited state intra/intermolecular proton transfer (ESIPT). The cooperative effect of nonaromatic TBC and TSC determines the PL behaviors of NTLs. This work provides a novel understanding of the PL mechanisms of NTLs and is of great importance for directing the design and synthesis of novel NTLs

Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy

An improved density-based spatial clustering of applications with noise (IDBSCAN) analysis approach based on kurtosis and sample entropy (SE) is presented for the identification of operational state in order to provide accurate monitoring of spindle operation condition. This is because of the low strength of the shock signal created by bearing of precision spindle of misalignment or imbalanced load, and the difficulties in extracting shock features. Wavelet noise reduction begins by dividing the recorded vibration data into equal lengths. Features like kurtosis and entropy in the frequency domain are used to generate feature vectors that indicate the bearing operation state. IDBSCAN cluster analysis is then utilized to establish the ideal neighborhood radius (Eps) and the minimum number of objects contained within the neighborhood radius (MinPts) of the vector set, which are combined to identify the bearing operating condition features. Finally, utilizing data from the University of Cincinnati, the approach was validated and assessed, attaining a condition detection accuracy of 99.2%. As a follow-up, the spindle’s vibration characteristics were studied utilizing an unbalanced bearing’s load bench. Bearing state recognition accuracy was 98.4%, 98.4%, and 96.7%, respectively, under mild, medium, and overload circumstances, according to the results of the experimental investigation. Moreover, it shows that conditions of bearings under various unbalanced loads can be precisely monitored using the proposed method without picking up on specific sorts of failures

Effects of Through-Bond and Through-Space Conjugations on the Photoluminescence of Small Aromatic and Aliphatic Aldimines

Through-bond conjugation (TBC) and/or through-space conjugation (TSC) determine the photophysical properties of organic luminescent compounds. No systematic studies have been carried out to understand the transition from aromatic TBC to non-aromatic TSC on the photoluminescence of organic luminescent compounds. In this work, a series of small aromatic and aliphatic aldimines were synthesized. For the aromatic imines, surprisingly, N,1-diphenylmethanimine with the highest TBC is non-emissive, while N-benzyl-1-phenylmethanimine and N-cyclohexyl-1-phenylmethanimine emit bright fluorescence in aggregate states. The aliphatic imines are all emissive, and their maximum emission wavelength decreases while the quantum yield increases with a decrease in steric hindrance. The imines show concentration-dependent and excitation-dependent emissions. Theoretical calculations show that the TBC extents in the aromatic imines are not strong enough to induce photoluminescence in a single molecule state, while the intermolecular TSC becomes dominant for the fluorescence emissions of both aromatic and aliphatic imines in aggregate states, and the configurations and spatial conformations of the molecules in aggregate states play a key role in the formation of effective TSC. This study provides an understanding of how chemical and spatial structures affect the formation of TBC and TSC and their functions on the photoluminescence of organic luminescent materials

Polypseudorotaxane Constructed from Cationic Polymer with Cucurbit[7]uril for Controlled Antibacterial Activity

This letter is aimed to develop a general strategy to fabricate polypseudorotaxanes with controlled antibacterial activity based on cationic polymers. As a proof of concept, the commercially available antibacterial cationic polymer, ε-poly-l-lysine hydrochloride, was chosen for the demonstration. Using host–guest chemistry, cucurbit[7]­uril (CB[7]), a water-soluble macrocyclic host, was employed to bind with the positive charge and hydrophobic component on ε-poly-l-lysine hydrochlorides for antibacterial regulation. In this way, by tuning the ratio of CB[7] to the cationic polymer, the antibacterial polypseudorotaxane can be obtained, and the antibacterial efficiency can be well tuned from 5% to 100%. This line of research will enrich the field of cationic polymers and polypseudorotaxanes with important functions on precise control over antibacterial activity