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Accurate and computationally efficient means for classifying human activities have been the subject of extensive research efforts. Most current research focuses on extracting complex features to achieve high classification accuracy. We propose a template selection approach based on Dynamic Time Warping, such that complex feature extraction and domain knowledge is avoided. We demonstrate the predictive capability of the algorithm on both simulated and real smartphone data.

Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on mul-tiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design.

Background and Objective:Silent cerebrovascular disease (SCD), comprised of silent brain infarction (SBI) and white matter disease (WMD), is commonly found incidentally on neuroimaging scans obtained in routine clinical care. However, their prognostic significance is not known. We aimed to estimate the incidence of, and risk increase in, future stroke in patients with incidentally-discovered SCD.Methods:Patients in Kaiser Permanente Southern California (KPSC) health system aged ≥ 50, without prior ischemic stroke, transient ischemic attack, or dementia/Alzheimer’s disease receiving a head CT or MRI between 2009-2019 were included. SBI and WMD were identified by natural language processing (NLP) from the neuroimage report.Results:Among 262,875 individuals receiving neuroimaging, NLP identified 13,154 (5.0%) with SBI and 78,330 (29.8%) with WMD. The incidence of future stroke was 32.5 (95% CI 31.1, 33·9) per 1,000 patient-years for patients with SBI; 1.·3 (95% CI 18.9, 19.8) for patients with WMD and 6.8 (95% CI 6.7, 7.0) for patients without SCD. The crude HR associated with SBI was 3.40 (95% CI 3.25 to 3.56); and for WMD was 2.63 (95% CI 2.54 to 2·71). With MRI-discovered SBI, the adjusted HR was 2.95 (95% CI 2.53 to 3.44) for those < age 65 and 2.15 (95% CI 1.91 to 2.41) for those ≥ age 65. With CT scan, the adjusted HR was 2.48 (95% CI 2.19 to 2.81) for those < age 65 and 1.81 (95% CI 1.71 to 1.91) for those >= age 65. The adjusted HR associated with a finding of WMD was 1.76 (95% CI 1.69 to 1.82) and was not modified by age or imaging modality.Discussion:Incidentally-discovered SBI and WMD are common and associated with increased risk of subsequent symptomatic stroke representing an important opportunity for stroke prevention.