Chords of longest circuits of graphs

This thesis is on a long standing open conjecture proposed by one of the most prominent mathematicians, Dr. C. Thomassen: Every longest circuit of 3-connected graph has a chord. In 1987, C. Q. Zhang proved that every longest circuit of a 3-connected planar graph G has a chord if G is cubic or if the minimum degree is at least 4. In 1997, Carsten Thomassen proved that every longest circuit of 3-connected cubic graph has a chord.;In this dissertation, we prove the following three independent partial results: (1) Every longest circuit of a 3-connected graph embedded in a projective plane with minimum degree at least has a chord (Theorem 2.3.1). (2) Every longest circuit of a 3-connected cubic graph has at least two chords. Furthermore if the graph is also a planar, then every longest circuit has at least three chords (Theorem 3.2.6, 3.2.7). (3) Every longest circuit of a 4-connected graph embedded in a torus or Klein bottle has a chord.;We get these three independent results with three totally different approaches: Connectivity (Tutte circuit), second Hamilton circuit, and charge and discharge methods

The Directional Transport of Self-Propelled Ellipsoidal Particles Confined in 2D Channel

Transport phenomenon of self-propelled ellipsoidal particles confined in a smooth corrugated channel with a two-dimensional asymmetric potential and Gaussian colored noise is investigated. Effects of the channel, potential and coloured noise are discussed. The moving direction changes from along x axis to opposite x axis with increasing load f. Proper size of pore is good at the directional transport, but too large or too small pore size will inhibit the transport speed. Large x axis noise intensity will inhibit the directional transport phenomena. Proper y axis noise intensity will help to the directional transport. Transport reverse phenomenon appears with increasing self-propelled speed v0. Perfect sphere particle is more easier for directional transport than needlelike ellipsoid particle

PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery

Satellite imagery analysis plays a vital role in remote sensing, but the information loss caused by cloud cover seriously hinders its application. This study presents a high-performance cloud removal architecture called Progressive Multi-scale Attention Autoencoder (PMAA), which simultaneously leverages global and local information. It mainly consists of a cloud detection backbone and a cloud removal module. The cloud detection backbone uses cloud masks to reinforce cloudy areas to prompt the cloud removal module. The cloud removal module mainly comprises a novel Multi-scale Attention Module (MAM) and a Local Interaction Module (LIM). PMAA establishes the long-range dependency of multi-scale features using MAM and modulates the reconstruction of the fine-grained details using LIM, allowing for the simultaneous representation of fine- and coarse-grained features at the same level. With the help of diverse and multi-scale feature representation, PMAA outperforms the previous state-of-the-art model CTGAN consistently on the Sen2_MTC_Old and Sen2_MTC_New datasets. Furthermore, PMAA has a considerable efficiency advantage, with only 0.5% and 14.6% of the parameters and computational complexity of CTGAN, respectively. These extensive results highlight the potential of PMAA as a lightweight cloud removal network suitable for deployment on edge devices. We will release the code and trained models to facilitate the study in this direction.Comment: 8 pages, 5 figure

ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction

Spatiotemporal prediction aims to generate future sequences by paradigms learned from historical contexts. It holds significant importance in numerous domains, including traffic flow prediction and weather forecasting. However, existing methods face challenges in handling spatiotemporal correlations, as they commonly adopt encoder and decoder architectures with identical receptive fields, which adversely affects prediction accuracy. This paper proposes an Asymmetric Receptive Field Autoencoder (ARFA) model to address this issue. Specifically, we design corresponding sizes of receptive field modules tailored to the distinct functionalities of the encoder and decoder. In the encoder, we introduce a large kernel module for global spatiotemporal feature extraction. In the decoder, we develop a small kernel module for local spatiotemporal information reconstruction. To address the scarcity of meteorological prediction data, we constructed the RainBench, a large-scale radar echo dataset specific to the unique precipitation characteristics of inland regions in China for precipitation prediction. Experimental results demonstrate that ARFA achieves consistent state-of-the-art performance on two mainstream spatiotemporal prediction datasets and our RainBench dataset, affirming the effectiveness of our approach. This work not only explores a novel method from the perspective of receptive fields but also provides data support for precipitation prediction, thereby advancing future research in spatiotemporal prediction.Comment: 0 pages, 5 figure

PlantDet: A benchmark for Plant Detection in the Three-Rivers-Source Region

The Three-River-Source region is a highly significant natural reserve in China that harbors a plethora of untamed botanical resources. To meet the practical requirements of botanical research and intelligent plant management, we construct a large-scale dataset for Plant detection in the Three-River-Source region (PTRS). This dataset comprises 6965 high-resolution images of 2160*3840 pixels, captured by diverse sensors and platforms, and featuring objects of varying shapes and sizes. Subsequently, a team of botanical image interpretation experts annotated these images with 21 commonly occurring object categories. The fully annotated PTRS images contain 122, 300 instances of plant leaves, each labeled by a horizontal rectangle. The PTRS presents us with challenges such as dense occlusion, varying leaf resolutions, and high feature similarity among plants, prompting us to develop a novel object detection network named PlantDet. This network employs a window-based efficient self-attention module (ST block) to generate robust feature representation at multiple scales, improving the detection efficiency for small and densely-occluded objects. Our experimental results validate the efficacy of our proposed plant detection benchmark, with a precision of 88.1%, a mean average precision (mAP) of 77.6%, and a higher recall compared to the baseline. Additionally, our method effectively overcomes the issue of missing small objects. We intend to share our data and code with interested parties to advance further research in this field.Comment: 10 pages, 5 figure

Cynaropicrin inhibits lung cancer proliferation by targeting EGFR/AKT signaling pathway

Purpose: To investigate the anti-proliferative effect of cynaropicrin on lung cancer cell lines, and the underlying molecular mechanism. Methods: The effect of cynaropicrin treatment on the viabilities of H1975 and H460 cells was measured using Cell Counting Kit-8. Apoptosis was analysed by annexin-V/FITC staining, while protein expressions were assayed by western blotting. Results: Treatment of H1975 and H460 cells with cynaropicrin at doses of 0.25 – 2.0 μM led to a marked reduction in their viability (p < 0.05). In cynaropicrin-treated H1975 and H460 cells, there was significant increase in apoptosis, when compared to control cells. Caspase-3 and caspase-9 levels were also significantly increased in H1975 and H460 cells on treatment with cynaropicrin at doses of 0.25 and 2.0 μM while treatment with cynaropicrin at doses of 0.25 - 2.0 μM significantly down-regulated the mRNA expression of CCND1 in the two cell lines (p < 0.05). Cynaropicrin markedly inhibited mRNA and protein expressions of EGFR, and also downregulated AKT in H1975 and H460 cells (p < 0.05). However, cynaropicrin significantly increased the expressions of miR-202 and miR-370. Conclusion: Cynaropicrin exerts anti-proliferative and proapoptotic effects on H1975 and H460 lung cancer cells via deactivation of EGFR/AKT signaling pathway. Moreover, it upregulated the expressions of miR-202 and miR-370 in these cells. Thus, cynaropicrin has potentials for the treatment of lung cancer

High-Fidelity Lake Extraction via Two-Stage Prompt Enhancement: Establishing a Novel Baseline and Benchmark

The extraction of lakes from remote sensing images is a complex challenge due to the varied lake shapes and data noise. Current methods rely on multispectral image datasets, making it challenging to learn lake features accurately from pixel arrangements. This, in turn, affects model learning and the creation of accurate segmentation masks. This paper introduces a unified prompt-based dataset construction approach that provides approximate lake locations using point, box, and mask prompts. We also propose a two-stage prompt enhancement framework, LEPrompter, which involves prompt-based and prompt-free stages during training. The prompt-based stage employs a prompt encoder to extract prior information, integrating prompt tokens and image embeddings through self- and cross-attention in the prompt decoder. Prompts are deactivated once the model is trained to ensure independence during inference, enabling automated lake extraction. Evaluations on Surface Water and Qinghai-Tibet Plateau Lake datasets show consistent performance improvements compared to the previous state-of-the-art method. LEPrompter achieves mIoU scores of 91.48% and 97.43% on the respective datasets without introducing additional parameters or GFLOPs. Supplementary materials provide the source code, pre-trained models, and detailed user studies.Comment: 8 pages, 7 figure