Treatment Learning Causal Transformer for Noisy Image Classification

Current top-notch deep learning (DL) based vision models are primarily based on exploring and exploiting the inherent correlations between training data samples and their associated labels. However, a known practical challenge is their degraded performance against "noisy" data, induced by different circumstances such as spurious correlations, irrelevant contexts, domain shift, and adversarial attacks. In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy by jointly estimating their treatment effects. Motivated from causal variational inference, we propose a transformer-based architecture, Treatment Learning Causal Transformer (TLT), that uses a latent generative model to estimate robust feature representations from current observational input for noise image classification. Depending on the estimated noise level (modeled as a binary treatment factor), TLT assigns the corresponding inference network trained by the designed causal loss for prediction. We also create new noisy image datasets incorporating a wide range of noise factors (e.g., object masking, style transfer, and adversarial perturbation) for performance benchmarking. The superior performance of TLT in noisy image classification is further validated by several refutation evaluation metrics. As a by-product, TLT also improves visual salience methods for perceiving noisy images.Comment: Accepted to IEEE WACV 2023. The first version was finished in May 201

Sensor Fabrication Method for in Situ Temperature and Humidity Monitoring of Light Emitting Diodes

In this work micro temperature and humidity sensors are fabricated to measure the junction temperature and humidity of light emitting diodes (LED). The junction temperature is frequently measured using thermal resistance measurement technology. The weakness of this method is that the timing of data capture is not regulated by any standard. This investigation develops a device that can stably and continually measure temperature and humidity. The device is light-weight and can monitor junction temperature and humidity in real time. Using micro-electro-mechanical systems (MEMS), this study minimizes the size of the micro temperature and humidity sensors, which are constructed on a stainless steel foil substrate (40 μm-thick SS-304). The micro temperature and humidity sensors can be fixed between the LED chip and frame. The sensitivities of the micro temperature and humidity sensors are 0.06 ± 0.005 (Ω/°C) and 0.033 pF/%RH, respectively

YamSat: the First Picosatellite being Developed in Taiwan

This paper describes the current planning and design of the YamSat, the first picosatellite being developed in Taiwan. The design, analysis, manufacture, integration, test and operation of the YamSat will be performed by the National Space Program Office (NSPO), Taiwan, R.O.C, in cooperation with other domestic organizations and companies. It is a member of the CubeSat [1], 10cm x 10cm x 10cm size and within 1kg mass. The major objective of the YamSat is to qualify in space the components and technology developed in Taiwan, including a micro-spectrometer payload using Micro Electro Mechanical Systems (MEMS) technology. The YamSat will be ready for flight in the middle of 2002

Neuromagnetic amygdala response to pain-related fear as a brain signature of fibromyalgia

Fibromyalgia (FM) is a chronic pain condition characterized by impaired emotional regulation. This study explored the brain response to pain-related fear as a potential brain signature of FM

Potassium {4-[(3S,6S,9S)-3,6-dibenzyl-9-isopropyl-4,7,10-trioxo-11–oxa-2,5,8-triazadodecyl]phenyl}trifluoroborate

[[abstract]]The reported compound 4 was synthesized and fully characterized by 1H NMR, 13C NMR, 11B NMR, 19F NMR, and high resolution mass spectrometry.[[booktype]]電子版[[countrycodes]]CH