25 research outputs found
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 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-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
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), nitrogenâphosphorus 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 throughâbond conjugation and throughâspace 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 throughâspace conjugation (TSC) of nonconventional chromophores. Here a new concept of nonaromatic throughâbond 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 gemdimethylâ1,3âcyclohexanedione were studied and correlated with their chemical and aggregate structures. These compounds show different fluorescence emissions as well as different concentration, excitation and solventâdependent emissions. The compounds which undergo ketoâenol tautomerism and hence with a conjugated ketoneâenol structure (i.e., nonaromatic TBC) show more redâshifted emissions. TBC effect reduces the energy gaps and facilitates the formation of stronger TSC in the aggregate state. The compounds in the ketoneâenol 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 spindleâs vibration characteristics were studied utilizing an unbalanced bearingâs 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, Δ-poly-l-lysine hydrochloride,
was chosen for the demonstration. Using hostâguest chemistry,
cucurbit[7]Âuril (CB[7]), a water-soluble macrocyclic host, was employed
to bind with the positive charge and hydrophobic component on Δ-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
Axial Compression Behavior of Symmetrical Full-Scale Concrete Filled Double Skin Steel Tube Stub Columns
To investigate the bearing behavior of symmetrical full-scale different strength concrete filled double skin steel tube (CFDST) stub columns, 19 full-scale specimens were designed, considering the slenderness ratio (λ); the compression strength of core concrete and sandwich concrete (fcki, fcko), the thickness of the inner and outer steel tubes (ti, to); the diameter of inner and outer steel tubes (Di, Do); and the tensile strength of the inner and outer steel tubes (fyki, fyko) as the main parameters. Nonlinear constitutive models for concrete considering constraint effect were adopted, and a finite element (FE) model was established using ABAQUS software. By comparing the results between simulations and experiments, the rationality of the modeling method was verified. Based on the FE model, the parameter analysis for CFDST columns were conducted, and the force mechanism, stress distribution, and deformation process were analyzed. The results showed that the axial compression bearing capacity (Nsu) increased significantly with an increase in fcki, to, Do, and fcko, while Nsu decreased gradually with an increase in λ. Finally, according to the calculated results of the specimens, the calculation formula for Nsu of full-scale composite columns was statistically regressed using 1stOpt software and showed a good agreement with the FE