Adapting Vision Transformer for Efficient Change Detection

Most change detection models based on vision transformers currently follow a "pretraining then fine-tuning" strategy. This involves initializing the model weights using large scale classification datasets, which can be either natural images or remote sensing images. However, fully tuning such a model requires significant time and resources. In this paper, we propose an efficient tuning approach that involves freezing the parameters of the pretrained image encoder and introducing additional training parameters. Through this approach, we have achieved competitive or even better results while maintaining extremely low resource consumption across six change detection benchmarks. For example, training time on LEVIR-CD, a change detection benchmark, is only half an hour with 9 GB memory usage, which could be very convenient for most researchers. Additionally, the decoupled tuning framework can be extended to any pretrained model for semantic change detection and multi temporal change detection as well. We hope that our proposed approach will serve as a part of foundational model to inspire more unified training approaches on change detection in the future

Learning-by-Doing in Non-Homogeneous Tasks: An Empirical Study of Content Creator Performance on A Music Streaming Platform

With the development of high-speed internet and better mobile connections, online streaming platforms with user-generated videos are becoming popular. The success of these platforms relies on content creators who can effectively enhance user engagement (e.g., subscribing to a content creator’s channel). As opposed to homogeneous production scenarios (e.g., assembling automobiles in a factory), creating user-generated videos is a more complex task in which learning might happen. In this study, we empirically test the effect of prior experience on content creators’ performance. Furthermore, we examine the role of specialization in learning. We use a dataset from NetEase Cloud Music, one of the most popular music streaming platforms in China, with 21,549 content creators and 252,762 user-generated videos. The findings indicate that: (1) prior experience has a positive effect on creators’ performance; (2) specialized experience across distinct video categories has a nonlinear effect on creators’ performance. These results have implications for improving user engagement for online user-generated video streaming platforms

R&D modes and firm performance in high-tech companies: A research based on cross-boundary ambidexterity and network structures

This paper draws on the cross-boundary ambidexterity theory to propose that four different R&D modes impact firm performance differently and that cooperative network structure moderates the above relationships. The theoretical model is tested by using financial and patent data of 587 high-tech firms for 10 consecutive years in China. We find that different R&D modes have different impacts on a firm’s financial and innovative performance, and network structure plays different moderating roles. Practically, this work guides high-tech enterprises to optimize their resource allocation, select the most appropriate R&D mode, and establish efficient cooperative networks

General practitioners' knowledge of ageing and attitudes towards older people in China

Author version made available in accordance with Publisher copyright. 12 month embargo from date of publication [Oct 9 2013]. This is the accepted version of the following article: [Yang, Y., Xiao, L. D., Ullah, S. and Deng, L. (2013), General practitioners' knowledge of ageing and attitudes towards older people in China. Australasian Journal on Ageing. ], which has been published in final form at [doi: 10.1111/ajag.12105]. In addition, authors may also transmit, print and share copies with colleagues, provided that there is no systematic distribution of the submitted version, e.g. posting on a listserve, network or automated delivery.Aim To explore general practitioners (GPs)' knowledge of ageing, attitudes towards older people and factors affecting their knowledge and attitudes in a Chinese context. Methods Four hundred GPs were surveyed using the Chinese version of the Aging Semantic Differential (CASD) and the Chinese version of the Facts on Aging Quiz (CFAQ1) scale. Results The CASD scores indicated that GPs had a neutral attitude towards older people. The CFAQ1 scores indicated a low level of knowledge about ageing. GPs' awareness of the mental and social facts of ageing was poorer compared to that of physical facts. Male GPs had a significantly higher negative bias score than female GPs. No other variables had a statistically significant influence on knowledge and attitudes. Conclusions The findings suggest the need for education interventions for GPs regarding knowledge of ageing and also provide evidence to guide future development of continuing medical programs for this group of medical doctors

Distinct composition and amplification dynamics of transposable elements in sacred lotus (Nelumbo nucifera Gaertn.)

Sacred lotus (Nelumbo nucifera Gaertn.) is a basal eudicot plant with a unique lifestyle, physiological features, and evolutionary characteristics. Here we report the unique profile of transposable elements (TEs) in the genome, using a manually curated repeat library. TEs account for 59% of the genome, and hAT (Ac/Ds) elements alone represent 8%, more than in any other known plant genome. About 18% of the lotus genome is comprised of Copia LTR retrotransposons, and over 25% of them are associated with non-canonical termini (non-TGCA). Such high abundance of non-canonical LTR retrotransposons has not been reported for any other organism. TEs are very abundant in genic regions, with retrotransposons enriched in introns and DNA transposons primarily in flanking regions of genes. The recent insertion of TEs in introns has led to significant intron size expansion, with a total of 200 Mb in the 28 455 genes. This is accompanied by declining TE activity in intergenic regions, suggesting distinct control efficacy of TE amplification in different genomic compartments. Despite the prevalence of TEs in genic regions, some genes are associated with fewer TEs, such as those involved in fruit ripening and stress responses. Other genes are enriched with TEs, and genes in epigenetic pathways are the most associated with TEs in introns, indicating a dynamic interaction between TEs and the host surveillance machinery. The dramatic differential abundance of TEs with genes involved in different biological processes as well as the variation of target preference of different TEs suggests the composition and activity of TEs influence the path of evolution

Masked Transformer for Electrocardiogram Classification

Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. With the advent of advanced algorithms, various deep learning models have been adopted for ECG tasks. However, the potential of Transformers for ECG data is not yet realized, despite their widespread success in computer vision and natural language processing. In this work, we present a useful masked Transformer method for ECG classification referred to as MTECG, which expands the application of masked autoencoders to ECG time series. We construct a dataset comprising 220,251 ECG recordings with a broad range of diagnoses annoated by medical experts to explore the properties of MTECG. Under the proposed training strategies, a lightweight model with 5.7M parameters performs stably well on a broad range of masking ratios (5%-75%). The ablation studies highlight the importance of fluctuated reconstruction targets, training schedule length, layer-wise LR decay and DropPath rate. The experiments on both private and public ECG datasets demonstrate that MTECG-T significantly outperforms the recent state-of-the-art algorithms in ECG classification

Nurse-led cognitive screening model for older adults in primary care

Author version made available in accordance with publisher copyright. Under 12 month embargo from date of publication [26 September 2014]. This is the accepted version of the following article: [Yang, Y., Xiao, L. D., Deng, L., Wang, Y., Li, M. and Ullah, S. (2014), Nurse-led cognitive screening model for older adults in primary care. Geriatrics & Gerontology International.], which has been published in final form at [doi: 10.1111/ggi.12339]. In addition, authors may also transmit, print and share copies with colleagues, provided that there is no systematic distribution of the submitted version, e.g. posting on a listserve, network or automated delivery.Aim The present study aimed to establish a nurse-led cognitive screening model for community-dwelling older adults with subjective memory complaints from seven communities in Chongqing, China, and report the findings of this model. Methods Screenings took place from July 2012 to June 2013. Cognitive screening was incorporated into the annual health assessment for older adults with subjective memory complaints in a primary care setting. Two community nurses were trained to implement the screening using the Mini-Mental State Examination and Montreal Cognitive Assessment. Results Of 733 older adults, 495 (67.5%) reported having subjective memory complaints. Of the 249 individuals who participated in the cognitive screening, 102 (41%) had mild cognitive impairment, whereas 32 (12.9%) had cognitive impairment. A total of 80 participants (78.4%) with mild cognitive impairment agreed to participate in a memory support program. Participants with cognitive impairment were referred to specialists for further examination and diagnosis; only one reported that he had seen a specialist and had been diagnosed with dementia. Conclusions Incorporating cognitive screening into the annual health assessment for older adults with subjective memory complaints was feasible, though referral rates from primary care providers remained unchanged. The present study highlights the urgent need for simple screenings as well as community-based support services in primary care for older adults with cognitive or mild cognitive impairments

S-T CRF: Spatial-Temporal Conditional Random Field for Human Trajectory Prediction

Trajectory prediction is of significant importance in computer vision. Accurate pedestrian trajectory prediction benefits autonomous vehicles and robots in planning their motion. Pedestrians' trajectories are greatly influenced by their intentions. Prior studies having introduced various deep learning methods only pay attention to the spatial and temporal information of trajectory, overlooking the explicit intention information. In this study, we introduce a novel model, termed the \textbf{S-T CRF}: \textbf{S}patial-\textbf{T}emporal \textbf{C}onditional \textbf{R}andom \textbf{F}ield, which judiciously incorporates intention information besides spatial and temporal information of trajectory. This model uses a Conditional Random Field (CRF) to generate a representation of future intentions, greatly improving the prediction of subsequent trajectories when combined with spatial-temporal representation. Furthermore, the study innovatively devises a space CRF loss and a time CRF loss, meticulously designed to enhance interaction constraints and temporal dynamics, respectively. Extensive experimental evaluations on dataset ETH/UCY and SDD demonstrate that the proposed method surpasses existing baseline approaches