The Chevalley-Gras formula over global fields

In this article we give an adelic proof of the Chevalley-Gras formula for global fields, which itself is a generalization of the ambiguous class number formula. The idea is to reduce the formula to the Hasse norm theorem, the local and global reciprocity laws. We also give an adelic proof of the Chevalley-Gras formula for $0$-th divisor class group in the function field case, which extends a result of Rosen.Comment: minor revisio

An Intelligent Auxiliary Vacuum Brake System

The purpose of this paper focuses on designing an intelligent, compact, reliable, and robust auxiliary vacuum brake system (VBS) with Kalman filter and self-diagnosis scheme. All of the circuit elements in the designed system are integrated into one programmable system-on-chip (PSoC) with entire computational algorithms implemented by software. In this system, three main goals are achieved: (a) Kalman filter and hysteresis controller algorithms are employed within PSoC chip by software to surpass the noises and disturbances from hostile surrounding in a vehicle. (b) Self-diagnosis scheme is employed to identify any breakdown element of the auxiliary vacuum brake system. (c) Power MOSFET is utilized to implement PWM pump control and compared with relay control. More accurate vacuum pressure control has been accomplished as well as power energy saving. In the end, a prototype has been built and tested to confirm all of the performances claimed above

Revisiting the problem of audio-based hit song prediction using convolutional neural networks

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP

Pentacene-Based Thin-Film Transistors With a Solution-Process Hafnium Oxide Insulator

Abstract—Pentacene-based organic thin-film transistors with solution-process hafnium oxide (HfOx) as gate insulating layer have been demonstrated. The solution-process HfOx could not only exhibit a high-permittivity (κ = 11) dielectric constant but also has good dielectric strength. Moreover, the root-mean-square surface roughness and surface energy (γs) on the surface of the HfOx layer were 1.304 nm and 34.24 mJ/cm2, respectively. The smooth, as well as hydrophobic, surface of HfOx could facilitate the direct deposition of the pentacene film without an additional polymer treatment layer, leading to a high field-effect mobility of 3.8 cm2/(V · s). Index Terms—Hafnium oxide, high permittivity, organic thinfilm transistor (OTFT), solution process, surface energy