262 research outputs found
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Polymer Gels: Kinetics, Dynamics Studies and Their Applications as Biomaterials
The polymer gels especially hydrogels have a very special structure and useful features such as unusual volume phase transition, compatibility with biological systems, and sensitivity to environmental stimuli (temperature, pH value, electric field, light and more), which lead to many potential applications in physical and biochemical fields. This research includes: (1) the theoretical and experimental studies of polymer gels on swelling kinetics, spinodal decomposition, and solution convection in gel matrix; (2) applications of polymer gels in wound dressing, tissue-simulating optical phantom and gel display. The kinetics of gel swelling has been theoretically analyzed by considering coupled motions of both solvent and polymer network. Analytical solutions of the solvent and the network movement are derived from collective diffusion equations for a long cylindrical and a large disk gel. Kinetics of spinodal decomposition of N-isopropylacrylamide (NIPA) polymer gel is investigated using turbidity and ultrasonic techniques. By probing movement of domains, a possible time-dependent gel structure in the spinodal decomposition region is presented. Theoretical studies of solution convection in gel matrix have been done and more analysis on dimensionless parameters is provided. To enhance the drug uptake and release capacity of silicone rubber (SR), NIPA hydrogel particles have been incorporated into a SR membrane. This SR/NIPA composite gel has promising attributes for wound dressing and other uses. Tissue-simulating optical phantom has been synthesized and studied using NIPA solution trapped inside a hydrogel. Polymer gels with engineered surface patterns were implemented. NIPA gel deposited on the surface of an acrylamide gel can be used as responsive gel display. A dynamically measurement technique of local shear modulus and swelling ratio of gel is presented based on an engineered periodic surface pattern as square array
DSMNet: Deep High-precision 3D Surface Modeling from Sparse Point Cloud Frames
Existing point cloud modeling datasets primarily express the modeling
precision by pose or trajectory precision rather than the point cloud modeling
effect itself. Under this demand, we first independently construct a set of
LiDAR system with an optical stage, and then we build a HPMB dataset based on
the constructed LiDAR system, a High-Precision, Multi-Beam, real-world dataset.
Second, we propose an modeling evaluation method based on HPMB for object-level
modeling to overcome this limitation. In addition, the existing point cloud
modeling methods tend to generate continuous skeletons of the global
environment, hence lacking attention to the shape of complex objects. To tackle
this challenge, we propose a novel learning-based joint framework, DSMNet, for
high-precision 3D surface modeling from sparse point cloud frames. DSMNet
comprises density-aware Point Cloud Registration (PCR) and geometry-aware Point
Cloud Sampling (PCS) to effectively learn the implicit structure feature of
sparse point clouds. Extensive experiments demonstrate that DSMNet outperforms
the state-of-the-art methods in PCS and PCR on Multi-View Partial Point Cloud
(MVP) database. Furthermore, the experiments on the open source KITTI and our
proposed HPMB datasets show that DSMNet can be generalized as a post-processing
of Simultaneous Localization And Mapping (SLAM), thereby improving modeling
precision in environments with sparse point clouds.Comment: To be published in IEEE Geoscience and Remote Sensing Letters (GRSL
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
Transfer Learning (TL) has shown great potential to accelerate Reinforcement
Learning (RL) by leveraging prior knowledge from past learned policies of
relevant tasks. Existing transfer approaches either explicitly computes the
similarity between tasks or select appropriate source policies to provide
guided explorations for the target task. However, how to directly optimize the
target policy by alternatively utilizing knowledge from appropriate source
policies without explicitly measuring the similarity is currently missing. In
this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL
by taking advantage of this idea. Our framework learns when and which source
policy is the best to reuse for the target policy and when to terminate it by
modeling multi-policy transfer as the option learning problem. PTF can be
easily combined with existing deep RL approaches. Experimental results show it
significantly accelerates the learning process and surpasses state-of-the-art
policy transfer methods in terms of learning efficiency and final performance
in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202
TIPE2 regulates periodontal inflammation by inhibiting NF-κB p65 phosphorylation
The roles and molecular mechanisms of tumor necrosis factor-α-induced protein 8-like 2 (TIPE2) in periodontitis remain largely unknown.
Objective: This study aimed to determine the expression of TIPE2 and NF-κB p65 in rat Porphyromonas gingivalis-induced periodontics in vivo. Methodology: Periodontal inflammation and alveolar bone resorption were analyzed using western blotting, micro-computed tomography, TRAP staining, immunohistochemistry, and immunofluorescence. THP-1 monocytes were stimulated using 1 μg/ml Pg. lipopolysaccharide (Pg.LPS) to determine the expression of TIPE2 in vitro. TIPE2 mRNA was suppressed by siRNA transfection, and the transfection efficiency was proven using western blotting and real-time PCR. The NF-κB pathway was activated by treating the cells with 1 μg/ml Pg.LPS to explore related mechanisms. Results: The expression of both TIPE2 and NF-κB p65 was increased in the gingival tissues of rat periodontitis compared with normal tissues. Positive expression of TIPE2 was distributed in inflammatory infiltrating cells and osteoclasts in the marginal lacunae of the alveolar bone. However, strong positive expression of TIPE2 in THP-1 was downregulated after Pg.LPS stimulation. TIPE2 levels negatively correlated with TNF-α and IL-1β. Decreased TIPE2 in THP-1 further promoted NF-κB p65 phosphorylation. Mechanistically, TIPE2 knockdown upregulated NF-κB signaling pathway activity. Conclusions: Taken together, these findings demonstrate that TIPE2 knockdown aggravates periodontal inflammatory infiltration via NF-κB pathway. Interventions aimed at increasing TIPE2 may help in the therapeutic applications for periodontitis
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
We investigate the problem of learning with noisy labels in real-world
annotation scenarios, where noise can be categorized into two types: factual
noise and ambiguity noise. To better distinguish these noise types and utilize
their semantics, we propose a novel sample selection-based approach for noisy
label learning, called Proto-semi. Proto-semi initially divides all samples
into the confident and unconfident datasets via warm-up. By leveraging the
confident dataset, prototype vectors are constructed to capture class
characteristics. Subsequently, the distances between the unconfident samples
and the prototype vectors are calculated to facilitate noise classification.
Based on these distances, the labels are either corrected or retained,
resulting in the refinement of the confident and unconfident datasets. Finally,
we introduce a semi-supervised learning method to enhance training. Empirical
evaluations on a real-world annotated dataset substantiate the robustness of
Proto-semi in handling the problem of learning from noisy labels. Meanwhile,
the prototype-based repartitioning strategy is shown to be effective in
mitigating the adverse impact of label noise. Our code and data are available
at https://github.com/fuxiAIlab/ProtoSemi
Towards Long-term Annotators: A Supervised Label Aggregation Baseline
Relying on crowdsourced workers, data crowdsourcing platforms are able to
efficiently provide vast amounts of labeled data. Due to the variability in the
annotation quality of crowd workers, modern techniques resort to redundant
annotations and subsequent label aggregation to infer true labels. However,
these methods require model updating during the inference, posing challenges in
real-world implementation. Meanwhile, in recent years, many data labeling tasks
have begun to require skilled and experienced annotators, leading to an
increasing demand for long-term annotators. These annotators could leave
substantial historical annotation records on the crowdsourcing platforms, which
can benefit label aggregation, but are ignored by previous works. Hereby, in
this paper, we propose a novel label aggregation technique, which does not need
any model updating during inference and can extensively explore the historical
annotation records. We call it SuperLA, a Supervised Label Aggregation method.
Inside this model, we design three types of input features and a
straightforward neural network structure to merge all the information together
and subsequently produce aggregated labels. Based on comparison experiments
conducted on 22 public datasets and 11 baseline methods, we find that SuperLA
not only outperforms all those baselines in inference performance but also
offers significant advantages in terms of efficiency
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