Edge-aware Feature Aggregation Network for Polyp Segmentation

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes. In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation, which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation. Specifically, we first present an Edge-aware Guidance Module (EGM) to combine the low-level features with the high-level features to learn an edge-enhanced feature, which is incorporated into each decoder unit using a layer-by-layer strategy. Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation. Further, a Cross-level Fusion Module (CFM) is proposed to effectively integrate the cross-level features, which can exploit the local and global contextual information. Finally, the outputs of CFMs are adaptively weighted by using the learned edge-aware feature, which are then used to produce multiple side-out segmentation maps. Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness.Comment: 20 pages 8 figure

Pattern formation and bifurcation analysis of delay induced fractional-order epidemic spreading on networks

The spontaneous emergence of ordered structures, known as Turing patterns, in complex networks is a phenomenon that holds potential applications across diverse scientific fields, including biology, chemistry, and physics. Here, we present a novel delayed fractional-order susceptible-infected-recovered-susceptible (SIRS) reaction-diffusion model functioning on a network, which is typically used to simulate disease transmission but can also model rumor propagation in social contexts. Our theoretical analysis establishes the Turing instability resulting from delay, and we support our conclusions through numerical experiments. We identify the unique impacts of delay, average network degree, and diffusion rate on pattern formation. The primary outcomes of our study are: (i) Delays cause system instability, mainly evidenced by periodic temporal fluctuations; (ii) The average network degree produces periodic oscillatory states in uneven spatial distributions; (iii) The combined influence of diffusion rate and delay results in irregular oscillations in both time and space. However, we also find that fractional-order can suppress the formation of spatiotemporal patterns. These findings are crucial for comprehending the impact of network structure on the dynamics of fractional-order systems.Comment: 23 pages, 9 figure

Recent Advances in Ambipolar Transistors for Functional Applications

Ambipolar transistors represent a class of transistors where positive (holes) and negative (electrons) charge carriers both can transport concurrently within the semiconducting channel. The basic switching states of ambipolar transistors are comprised of common offâ state and separated onâ state mainly impelled by holes or electrons. During the past years, diverse materials are synthesized and utilized for implementing ambipolar charge transport and their further emerging applications comprising ambipolar memory, synaptic, logic, and lightâ emitting transistors on account of their special bidirectional carrierâ transporting characteristic. Within this review, recent developments of ambipolar transistor field involving fundamental principles, interface modifications, selected semiconducting material systems, device structures, ambipolar characteristics, and promising applications are highlighted. The existed challenges and prospective for researching ambipolar transistors in electronics and optoelectronics are also discussed. It is expected that the review and outlook are well timed and instrumental for the rapid progress of academic sector of ambipolar transistors in lighting, display, memory, as well as neuromorphic computing for artificial intelligence.Ambipolar transistors represent transistors that allow synchronous transport of electrons and holes and their accumulation within semiconductors. This review provides a comprehensive summary of recent advances in various semiconducting materials realized in ambipolar transistors and their functional memory, synapse, logic, as well as lightâ emitting applications.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151885/1/adfm201902105_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151885/2/adfm201902105.pd

Molecular Phylogeny of the Ant Subfamily Formicinae (Hymenoptera, Formicidae) from China Based on Mitochondrial Genes

To resolve long-standing discrepancies in the relationships among genera within the ant subfamily Formicinae, a phylogenetic study of Chinese Formicine ants based on three mitochondria genes (Cyt b, COI, COII) was conducted. Phylogenetic trees obtained in the current study are consistent with several previously reported trees based on morphology, and specifically confirm and reinforce the classifications made by Bolton (1994). The tribes Lasiini, Formicini, Plagiolepidini and Camponotini are strongly supported, while Oecophyllini has moderate support despite being consistent across all analyses. We have also established that the genus Camponotus and Polyrhachis are indeed not monophyletic. Additionally, we found strong evidence for Polyrhachis paracamponota, as described by Wu and Wang in 1991, to be corrected as Camponotus based on molecular, morphological and behavioral data

GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting

Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93\%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/zyj-111/GCformer

Rethinking the competition between detection and ReID in Multi-Object Tracking

Due to balanced accuracy and speed, joint learning detection and ReID-based one-shot models have drawn great attention in multi-object tracking(MOT). However, the differences between the above two tasks in the one-shot tracking paradigm are unconsciously overlooked, leading to inferior performance than the two-stage methods. In this paper, we dissect the reasoning process of the aforementioned two tasks. Our analysis reveals that the competition of them inevitably hurts the learning of task-dependent representations, which further impedes the tracking performance. To remedy this issue, we propose a novel cross-correlation network that can effectively impel the separate branches to learn task-dependent representations. Furthermore, we introduce a scale-aware attention network that learns discriminative embeddings to improve the ReID capability. We integrate the delicately designed networks into a one-shot online MOT system, dubbed CSTrack. Without bells and whistles, our model achieves new state-of-the-art performances on MOT16 and MOT17. Our code is released at https://github.com/JudasDie/SOTS