Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices

Internet of Things (IoT) devices bring us rich sensor data, such as images capturing the environment. One prominent approach to understanding and utilizing such data is image classification which can be effectively solved by deep learning (DL). Combined with cross-entropy loss, softmax has been widely used for classification problems, despite its limitations. Many efforts have been made to enhance the performance of softmax decision-making models. However, they require complex computations and/or re-training the model, which is computationally prohibited on low-power IoT devices. In this paper, we propose a light-weight framework to enhance the performance of softmax decision-making models for DL. The proposed framework operates with a pre-trained DL model using softmax, without requiring any modification to the model. First, it computes the level of uncertainty as to the model’s prediction, with which misclassified samples are detected. Then, it makes a probabilistic control decision to enhance the decision performance of the given model. We validated the proposed framework by conducting an experiment for IoT car control. The proposed model successfully reduced the control decision errors by up to 96.77% compared to the given DL model, and that suggests the feasibility of building DL-based IoT applications with high accuracy and low complexity

Endocrine and Metabolic Illnesses in Young Adults with Prader–Willi Syndrome

Prader–Willi syndrome (PWS) is a rare genetic disorder characterized by an insatiable appetite that leads to morbid obesity. Previous studies reported health problems in adults with PWS. However, studies on younger adults are lacking, and there are no specific studies of endocrine and metabolic illness in this age group. We performed a retrospective cohort study of 68 individuals with PWS aged 19 to 34 years at Samsung Medical Center. The prevalence of endocrine and metabolic illnesses were compared with those in an age-, sex-, and BMI-matched healthy control group. Young adults with PWS had a higher prevalence of metabolic syndrome (35.3% vs. 4.4%), type 2 diabetes mellitus (50.0% vs. 5.4%), hypertension (30.8% vs. 16.1%), dyslipidemia (38.2% vs. 14.7%), decreased bone density (26.4% vs. 0.9%), and sleep apnea (32.3% vs. 4.4%) than controls (all p < 0.05). The PWS group that maintained recombinant human growth (rhGH) treatment in adulthood had a lower probability of having a BMI ≥ 30 at the last follow-up (odds ratio = 0.106 (0.012–0.948), p = 0.045). Endocrine and metabolic illnesses in individuals with PWS may have already started in the early teens; therefore, appropriate screening and early intervention are important. Better understanding of the natural history of PWS and age-related complications will lead to better-quality medical care for individuals with PWS