Spatial Transform Decoupling for Oriented Object Detection

Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks. However, their potential in rotation-sensitive scenarios has not been fully explored, and this limitation may be inherently attributed to the lack of spatial invariance in the data-forwarding process. In this study, we present a novel approach, termed Spatial Transform Decoupling (STD), providing a simple-yet-effective solution for oriented object detection with ViTs. Built upon stacked ViT blocks, STD utilizes separate network branches to predict the position, size, and angle of bounding boxes, effectively harnessing the spatial transform potential of ViTs in a divide-and-conquer fashion. Moreover, by aggregating cascaded activation masks (CAMs) computed upon the regressed parameters, STD gradually enhances features within regions of interest (RoIs), which complements the self-attention mechanism. Without bells and whistles, STD achieves state-of-the-art performance on the benchmark datasets including DOTA-v1.0 (82.24% mAP) and HRSC2016 (98.55% mAP), which demonstrates the effectiveness of the proposed method. Source code is available at https://github.com/yuhongtian17/Spatial-Transform-Decoupling

PSYCHOSOCIAL FACTORS LEAD TO DELINQUENCY ITENTION ON ONLINE PEER-TO-PEER LENDING PLATFORM: A SURVEY EVIDENCE

In recent years, online P2P lending grows remarkably. Past studies mainly used direct second-hand data from P2P platforms to conclude many factors are related to people\u27s delinquency and default behaviours, lacking further exploration on how people\u27s social and psychological status could impact their behaviour during the borrowing and repayment process. On foundation of general strain theory (GST) and the model of frame selection (MFS), we used survey method and collected data from more than 700 Chinese subjects. A two-stage structural equation model was proposed. In the first stage, we investigated how people\u27s psychosocial factors (e.g. economic capacity, sense of fairness and sociability etc.) could shape their individual feelings and attitudes in social context (e.g. life satisfaction and self-esteem) as well as morality. In the second stage, we tested the relationship between life satisfaction, self-esteem, moral norm and people\u27s delinquency intention on P2P lending platform. The empirical results suggest that higher psychosocial status will be conductive to better individual feelings of life satisfaction and self-esteem. Moreover, better psychosocial factors will mostly lead to a higher moral norm of people. Therefore, these favourable feelings and morality further contribute to less delinquency intention on P2P lending platform. Our research has both academic and practical implications

Fast-iTPN: Integrally Pre-Trained Transformer Pyramid Network with Token Migration

We propose integrally pre-trained transformer pyramid network (iTPN), towards jointly optimizing the network backbone and the neck, so that transfer gap between representation models and downstream tasks is minimal. iTPN is born with two elaborated designs: 1) The first pre-trained feature pyramid upon vision transformer (ViT). 2) Multi-stage supervision to the feature pyramid using masked feature modeling (MFM). iTPN is updated to Fast-iTPN, reducing computational memory overhead and accelerating inference through two flexible designs. 1) Token migration: dropping redundant tokens of the backbone while replenishing them in the feature pyramid without attention operations. 2) Token gathering: reducing computation cost caused by global attention by introducing few gathering tokens. The base/large-level Fast-iTPN achieve 88.75%/89.5% top-1 accuracy on ImageNet-1K. With 1x training schedule using DINO, the base/large-level Fast-iTPN achieves 58.4%/58.8% box AP on COCO object detection, and a 57.5%/58.7% mIoU on ADE20K semantic segmentation using MaskDINO. Fast-iTPN can accelerate the inference procedure by up to 70%, with negligible performance loss, demonstrating the potential to be a powerful backbone for downstream vision tasks. The code is available at: github.com/sunsmarterjie/iTPN.Comment: The tiny/small/base-level models report new records on ImageNet-1K. Code: github.com/sunsmarterjie/iTP

A Compound Fuzzy Disturbance Observer Based on Sliding Modes and Its Application on Flight Simulator

A compound fuzzy disturbance observer based on sliding modes is developed, and its application on flight simulator is presented. Fuzzy disturbance observer (FDO) is an effective method in nonlinear control. However, traditional FDO is confined to monitor dynamic disturbance, and the frequency bandwidth of the system is restricted. Sliding mode control (SMC) compensates the high-frequency component of disturbance while it is limited by the chattering phenomenon. The proposed method uses the sliding mode technique to deal with the uncompensated dynamic equivalent disturbance. The switching gain of sliding mode control designed according to the error of disturbance estimation is a small value. Therefore, the proposal also helps to decrease the chattering. The validity of the proposal method is confirmed by experiments on flight simulator

ChatterBox: Multi-round Multimodal Referring and Grounding

In this study, we establish a baseline for a new task named multimodal multi-round referring and grounding (MRG), opening up a promising direction for instance-level multimodal dialogues. We present a new benchmark and an efficient vision-language model for this purpose. The new benchmark, named CB-300K, spans challenges including multi-round dialogue, complex spatial relationships among multiple instances, and consistent reasoning, which are beyond those shown in existing benchmarks. The proposed model, named ChatterBox, utilizes a two-branch architecture to collaboratively handle vision and language tasks. By tokenizing instance regions, the language branch acquires the ability to perceive referential information. Meanwhile, ChatterBox feeds a query embedding in the vision branch to a token receiver for visual grounding. A two-stage optimization strategy is devised, making use of both CB-300K and auxiliary external data to improve the model's stability and capacity for instance-level understanding. Experiments show that ChatterBox outperforms existing models in MRG both quantitatively and qualitatively, paving a new path towards multimodal dialogue scenarios with complicated and precise interactions. Code, data, and model are available at: https://github.com/sunsmarterjie/ChatterBox.Comment: 17 pages, 6 tables, 9 figurs. Code, data, and model are available at: https://github.com/sunsmarterjie/ChatterBo

Arc fault detection using artificial intelligence: Challenges and benefits

This systematic review aims to investigate recent developments in the area of arc fault detection. The rising demand for electricity and concomitant expansion of energy systems has resulted in a heightened risk of arc faults and the likelihood of related fires, presenting a matter of considerable concern. To address this challenge, this review focuses on the role of artificial intelligence (AI) in arc fault detection, with the objective of illuminating its advantages and identifying current limitations. Through a meticulous literature selection process, a total of 63 articles were included in the final analysis. The findings of this review suggest that AI plays a significant role in enhancing the accuracy and speed of detection and allowing for customization to specific types of faults in arc fault detection. Simultaneously, three major challenges were also identified, including missed and false detections, the restricted application of neural networks and the paucity of relevant data. In conclusion, AI has exhibited tremendous potential for transforming the field of arc fault detection and holds substantial promise for enhancing electrical safety

The Natural Compound Myricetin Effectively Represses the Malignant Progression of Prostate Cancer by Inhibiting PIM1 and Disrupting the PIM1/CXCR4 Interaction

Background/Aims: Natural compounds are a promising resource for anti-tumor drugs. Myricetin, an abundant flavonoid found in the bark and leaves of bayberry, shows multiple promising anti-tumor functions in various cancers. Methods: The cytotoxic, pro-apoptotic, and anti-metastatic effects of myricetin on prostate cancer cells were investigated in both in vitro and in vivo studies. Short-hairpin RNA knockdown of the proviral integration site for Moloney murine leukemia virus-1 (PIM1), pull-down and co-immunoprecipitation assays, and an intracellular Ca2+ flux assay were used to investigate the potential underlying mechanism of myricetin. ONCOMINE database data mining and immunohistochemical analysis of prostate cancer tissues were used to evaluate the expression of PIM1 and CXCR4, as well as the correlation between PIM1 and CXCR4 expression and the clinicopathologic characteristics and prognoses of prostate cancer patients. Results: Myricetin exerted selective cytotoxic, pro-apoptotic, and anti-metastatic effects on prostate cancer cells by inhibiting PIM1 and disrupting the PIM1/CXCR4 interaction. Moreover, PIM1 and CXCR4 were coexpressed and associated with aggressive clinicopathologic traits and poor prognosis in prostate cancer patients. Conclusion: These results offer preclinical evidence for myricetin as a potential chemopreventive and therapeutic agent for precision medicine tailored to prostate cancer patients characterized by concomitant elevated expression of PIM1 and CXCR4

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly