Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.Comment: To appear in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, 2018. (IJCAI-ECAI 2018

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

Accurately Estimate Tourism Impacts: Tourism Satellite Account and Input-Output Analysis

The main purpose of TSA is to provide a system of statistics consistent with the national account. If TSA can be extended by incorporating the economic evaluation and estimation models, it can serve not only as a statistic framework but also an analytical tool. In this paper, we discuss the framework of Taiwan TSA, and an evaluation system of Taiwan TSA, including a tourism expenditure model, a tourism input-output model, and a tourism CGE model. We also use the tourism input-output model to calculate the impacts of a tourism policy (opening up to Chinese tourism) on the economy

Infinitely Many Eigenfunctions for Polynomial Problems: Exact Results

Let Fx, y=asxys+as-1xys-1+⋯+a0x be a real-valued polynomial function in which the degree s of y in Fx, y is greater than or equal to 1. For any polynomial yx, we assume that T:Rx→Rx is a nonlinear operator with Tyx=Fx, yx. In this paper, we will find an eigenfunction yx∈Rx to satisfy the following equation: Fx, yx=ayx for some eigenvalue a∈R and we call the problem Fx, yx=ayx a fixed point like problem. If the number of all eigenfunctions in Fx, yx=ayx is infinitely many, we prove that (i) any coefficients of Fx, y, asx, as-1x,…, a0x, are all constants in R and (ii) yx is an eigenfunction in Fx, yx=ayx if and only if yx∈R

Postural and cognitive precursors of post-bout motion sickness and concussion-related symptoms in boxers

University of Minnesota Ph.D. August 2014. Major: Kinesiology. Advisor: Thomas A. Stoffregen. 1 computer file (PDF); vii, 106 pages, appendix A.Background: Motion sickness is characterized by subjective symptoms that include dizziness and nausea. Studies have shown that subjective symptoms of motion sickness are preceded by differences in standing body sway between those who experience the symptoms and those who are not. Boxers often report dizziness and nausea immediately after bouts. We predicted that pre-bout standing body sway would differ between boxers who experienced post-bout motion sickness and those who did not. Methodology/Principal Findings: We collected data on standing body sway before bouts. During measurement of body sway participants performed two visual tasks. In addition, we varied stance width (the distance between the heels). Postural testing was conducted separately before and after participants' regular warm-up routines. After bouts, we collected self-reports of motion sickness incidence and symptoms. Results revealed that standing body sway was greater after warm-up than before warm-up, and that wider stance width was associated with reduced sway. Eight of 15 amateur boxers reported motion sickness after a bout. Two statistically significant interactions revealed that standing body sway before bouts differed between participants who reported post-bout motion sickness and those who did not. Conclusions/Significance: The results suggest that susceptibility to motion sickness in boxers may be manifested in characteristic patterns of body sway. It may be possible to use pre-bout data on postural sway to predict susceptibility to post-bout motion sickness

Decay Constants of Pseudoscalar DD-mesons in Lattice QCD with Domain-Wall Fermion

We present the first study of the masses and decay constants of the pseudoscalar $D$ mesons in two flavors lattice QCD with domain-wall fermion. The gauge ensembles are generated on the $24^3 \times 48$ lattice with the extent $N_s = 16$ in the fifth dimension, and the plaquette gauge action at $\beta = 6.10$, for three sea-quark masses with corresponding pion masses in the range $260-475$ MeV. We compute the point-to-point quark propagators, and measure the time-correlation functions of the pseudoscalar and vector mesons. The inverse lattice spacing is determined by the Wilson flow, while the strange and the charm quark masses by the masses of the vector mesons $\phi(1020)$ and $J/\psi(3097)$ respectively. Using heavy meson chiral perturbation theory (HMChPT) to extrapolate to the physical pion mass, we obtain $f_D = 202.3(2.2)(2.6)$ MeV and $f_{D_s} = 258.7(1.1)(2.9)$ MeV.Comment: 15 pages, 3 figures. v2: the statistics of ensemble (A) with m_sea = 0.005 has been increased, more details on the systematic error, to appear in Phys. Lett.

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan

We study Covid-19 spreading dynamics underlying 84 curves of daily Covid-19 infection rates pertaining to 84 districts belonging to the largest seven cities in Taiwan during her pristine surge period. Our computational developments begin with selecting and extracting 18 features from each smoothed district-specific curve. This step of computing effort allows unstructured data to be converted into structured data, with which we then demonstrate asymmetric growth and decline dynamics among all involved curves. Specifically, based on Theoretical Information measurements of conditional entropy and mutual information, we compute major factors of order-1 and order-2 that reveal significant effects on affecting the curves' peak value and curvature at peak, which are two essential features characterizing all the curves. Further, we investigate and demonstrate major factors determining the geographic and social-economic induced behavioral effects by encoding each of these 84 districts with two binary characteristics: North-vs-South and Unban-vs-suburban. Furthermore, based on this data-driven knowledge on the district scale, we go on to study fine-scale behavioral effects on infectious disease spreading through similarity among 96 age-group-specific curves of daily infection rate within 12 urban districts of Taipei and 12 suburban districts of New Taipei City, which counts for almost one-quarter of the island nation's total population. We conclude that human living, traveling, and working behaviors do implicitly affect the spreading dynamics of Covid-19 across Taiwan profoundly

How to Backdoor Diffusion Models?

Diffusion models are state-of-the-art deep learning empowered generative models that are trained based on the principle of learning forward and reverse diffusion processes via progressive noise-addition and denoising. To gain a better understanding of the limitations and potential risks, this paper presents the first study on the robustness of diffusion models against backdoor attacks. Specifically, we propose BadDiffusion, a novel attack framework that engineers compromised diffusion processes during model training for backdoor implantation. At the inference stage, the backdoored diffusion model will behave just like an untampered generator for regular data inputs, while falsely generating some targeted outcome designed by the bad actor upon receiving the implanted trigger signal. Such a critical risk can be dreadful for downstream tasks and applications built upon the problematic model. Our extensive experiments on various backdoor attack settings show that BadDiffusion can consistently lead to compromised diffusion models with high utility and target specificity. Even worse, BadDiffusion can be made cost-effective by simply finetuning a clean pre-trained diffusion model to implant backdoors. We also explore some possible countermeasures for risk mitigation. Our results call attention to potential risks and possible misuse of diffusion models