Rethinking Data Augmentation in Knowledge Distillation for Object Detection

Knowledge distillation (KD) has shown its effectiveness for object detection, where it trains a compact object detector under the supervision of both AI knowledge (teacher detector) and human knowledge (human expert). However, existing studies treat the AI knowledge and human knowledge consistently and adopt a uniform data augmentation strategy during learning, which would lead to the biased learning of multi-scale objects and insufficient learning for the teacher detector causing unsatisfactory distillation performance. To tackle these problems, we propose the sample-specific data augmentation and adversarial feature augmentation. Firstly, to mitigate the impact incurred by multi-scale objects, we propose an adaptive data augmentation based on our observations from the Fourier perspective. Secondly, we propose a feature augmentation method based on adversarial examples for better mimicking AI knowledge to make up for the insufficient information mining of the teacher detector. Furthermore, our proposed method is unified and easily extended to other KD methods. Extensive experiments demonstrate the effectiveness of our framework and improve the performance of state-of-the-art methods in one-stage and two-stage detectors, bringing at most 0.5 mAP gains.Comment: 8 pages, 5 figure

Laser-Activatable CuS Nanodots to Treat Multidrug-Resistant Bacteria and Release Copper Ion to Accelerate Healing of Infected Chronic Nonhealing Wounds

Chronic nonhealing wounds have imposed serious challenges in the clinical practice, especially for the patients infected with multidrug-resistant microbes. Herein, we developed an ultrasmall copper sulfide (covellite) nanodots (CuS NDs) based dual functional nanosystem to cure multidrug-resistant bacteria-infected chronic nonhealing wound. The nanosystem could eradicate multidrug-resistant bacteria and expedite wound healing simultaneously owing to the photothermal effect and remote control of copper-ion release. The antibacterial results indicated that the combination treatment of photothermal CuS NDs with photothermal effect initiated a strong antibacterial effect for drug-resistant pathogens including methicillin-resistant Staphylococcus aureus (MRSA) and extended-spectrum beta-lactamase Escherichia coli both in vitro and in vivo. Meanwhile, the released Cu2+ could promote fibroblast cell migration and endothelial cell angiogenesis, thus accelerating wound-healing effects. In MRSA-infected diabetic mice model, the nanosystem exhibited synergistic wound healing effect of infectious wounds in vivo and demonstrated negligible toxicity and nonspecific damage to major organs. The combination of ultrasmall CuS NDs with photothermal therapy displayed enhanced therapeutic efficacy for chronic nonhealing wound in multidrug-resistant bacterial infections, which may represent a promising class of antibacterial strategy for clinical translation.Peer reviewe

TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models

Automatic Speech Recognition (ASR) models need to be optimized for specific hardware before they can be deployed on devices. This can be done by tuning the model's hyperparameters or exploring variations in its architecture. Re-training and re-validating models after making these changes can be a resource-intensive task. This paper presents TODM (Train Once Deploy Many), a new approach to efficiently train many sizes of hardware-friendly on-device ASR models with comparable GPU-hours to that of a single training job. TODM leverages insights from prior work on Supernet, where Recurrent Neural Network Transducer (RNN-T) models share weights within a Supernet. It reduces layer sizes and widths of the Supernet to obtain subnetworks, making them smaller models suitable for all hardware types. We introduce a novel combination of three techniques to improve the outcomes of the TODM Supernet: adaptive dropouts, an in-place Alpha-divergence knowledge distillation, and the use of ScaledAdam optimizer. We validate our approach by comparing Supernet-trained versus individually tuned Multi-Head State Space Model (MH-SSM) RNN-T using LibriSpeech. Results demonstrate that our TODM Supernet either matches or surpasses the performance of manually tuned models by up to a relative of 3% better in word error rate (WER), while efficiently keeping the cost of training many models at a small constant.Comment: Meta AI; Submitted to ICASSP 202

Individualized prevention of proton pump inhibitor related adverse events by risk stratification

Proton pump inhibitors (PPIs) are commonly used for gastric acid-related disorders, but their safety profile and risk stratification for high-burden diseases need further investigation. Analyzing over 2 million participants from five prospective cohorts from the US, the UK, and China, we found that PPI use correlated with increased risk of 15 leading global diseases, such as ischemic heart disease, diabetes, respiratory infections, and chronic kidney disease. These associations showed dose-response relationships and consistency across different PPI types. PPI-related absolute risks increased with baseline risks, with approximately 82% of cases occurring in those at the upper 40% of the baseline predicted risk, and only 11.5% of cases occurring in individuals at the lower 50% of the baseline risk. While statistical association does not necessarily imply causation, its potential safety concerns suggest that personalized use of PPIs through risk stratification might guide appropriate decision-making for patients, clinicians, and the public

Neutrophil-to-lymphocyte ratio and incident end-stage renal disease in Chinese patients with chronic kidney disease: results from the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE)

Abstract Background Chronic kidney disease (CKD) leads to end-stage renal failure and cardiovascular events. An attribute to these progressions is abnormalities in inflammation, which can be evaluated using the neutrophil-to-lymphocyte ratio (NLR). We aimed to investigate the association of NLR with the progression of end stage of renal disease (ESRD), cardiovascular disease (CVD) and all-cause mortality in Chinese patients with stages 1–4 CKD. Methods Patients with stages 1–4 CKD (18–74 years of age) were recruited at 39 centers in 28 cities across 22 provinces in China since 2011. A total of 938 patients with complete NLR and other relevant clinical variables were included in the current analysis. Cox regression analysis was used to estimate the association between NLR and the outcomes including ESRD, CVD events or all-cause mortality. Results Baseline NLR was related to age, hypertension, serum triglycerides, total serum cholesterol, CVD history, urine albumin to creatinine ratio (ACR), chronic kidney disease-mineral and bone disorder (CKD-MBD), hyperlipidemia rate, diabetes, and estimated glomerular filtration rate (eGFR). The study duration was 4.55 years (IQR 3.52–5.28). Cox regression analysis revealed an association of NLR and the risk of ESRD only in patients with stage 4 CKD. We did not observe any significant associations between abnormal NLR and the risk of either CVD or all-cause mortality in CKD patients in general and CKD patients grouped according to the disease stages in particular. Conclusion Our results suggest that NLR is associated with the risk of ESRD in Chinese patients with stage 4 CKD. NLR can be used in risk assessment for ESRD among patients with advanced CKD; this application is appealing considering NLR being a routine test. Trial registration ClinicalTrials.gov Identifier NCT03041987. Registered January 1, 2012. (retrospectively registered) ( https://www.clinicaltrials.gov/ct2/show/NCT03041987?term=Chinese+Cohort+Study+of+Chronic+Kidney+Disease+%28C-STRIDE%29&rank=1 )https://deepblue.lib.umich.edu/bitstream/2027.42/148285/1/12967_2019_Article_1808.pd

A protocol of Chinese expert consensuses for the management of health risk in the general public

IntroductionNon-communicable diseases (NCDs) represent the leading cause of mortality and disability worldwide. Robust evidence has demonstrated that modifiable lifestyle factors such as unhealthy diet, smoking, alcohol consumption and physical inactivity are the primary causes of NCDs. Although a series of guidelines for the management of NCDs have been published in China, these guidelines mainly focus on clinical practice targeting clinicians rather than the general population, and the evidence for NCD prevention based on modifiable lifestyle factors has been disorganized. Therefore, comprehensive and evidence-based guidance for the risk management of major NCDs for the general Chinese population is urgently needed. To achieve this overarching aim, we plan to develop a series of expert consensuses covering 15 major NCDs on health risk management for the general Chinese population. The objectives of these consensuses are (1) to identify and recommend suitable risk assessment methods for the Chinese population; and (2) to make recommendations for the prevention of major NCDs by integrating the current best evidence and experts’ opinions.Methods and analysisFor each expert consensus, we will establish a consensus working group comprising 40–50 members. Consensus questions will be formulated by integrating literature reviews, expert opinions, and an online survey. Systematic reviews will be considered as the primary evidence sources. We will conduct new systematic reviews if there are no eligible systematic reviews, the methodological quality is low, or the existing systematic reviews have been published for more than 3 years. We will evaluate the quality of evidence and make recommendations according to the GRADE approach. The consensuses will be reported according to the Reporting Items for Practice Guidelines in Healthcare (RIGHT)

Effect of cooking processes on tilapia aroma and potential umami perception

1 Tilapia is a freshwater fish group with a sustainable prospect but suffers off-notes appearing during 2 cooking processes. To promote pleasant odorants by thermal cooking processes, tilapia fillets were 3 cooked in different ways (roasting, microwave-heating, boiling and steaming). Their aroma profiles were 4 analysed with special focus on off-notes and umami-enhancing odorants by Principal Component 5 Analysis, and correlated with the heating time, colour, moisture and water activity by Partial Least 6 Squares Regression analysis. Results showed that the "green" and "earthy" off-notes were highly 7 correlated with the boiling process (excess of water, short heating time), while most of the umami-8 enhancing odorants had a high association with the roasting process (low water content, long heating 9 time, better Maillard reaction). This study indicated that roasting is the most adapted cooking process 10 promoting Maillard-derived aromas, umami-enhancing aromas and meanwhile, reducing off-notes. This 11 research helps in understanding the off-note generation in tilapia and promoting desirable umami-12 enhancing odorants