Learning from Multi-View Multi-Way Data via Structural Factorization Machines

Real-world relations among entities can often be observed and determined by different perspectives/views. For example, the decision made by a user on whether to adopt an item relies on multiple aspects such as the contextual information of the decision, the item's attributes, the user's profile and the reviews given by other users. Different views may exhibit multi-way interactions among entities and provide complementary information. In this paper, we introduce a multi-tensor-based approach that can preserve the underlying structure of multi-view data in a generic predictive model. Specifically, we propose structural factorization machines (SFMs) that learn the common latent spaces shared by multi-view tensors and automatically adjust the importance of each view in the predictive model. Furthermore, the complexity of SFMs is linear in the number of parameters, which make SFMs suitable to large-scale problems. Extensive experiments on real-world datasets demonstrate that the proposed SFMs outperform several state-of-the-art methods in terms of prediction accuracy and computational cost.Comment: 10 page

Optical music recognition of the singer using formant frequency estimation of vocal fold vibration and lip motion with interpolated GMM classifiers

The main work of this paper is to identify the musical genres of the singer by performing the optical detection of lip motion. Recently, optical music recognition has attracted much attention. Optical music recognition in this study is a type of automatic techniques in information engineering, which can be used to determine the musical style of the singer. This paper proposes a method for optical music recognition where acoustic formant analysis of both vocal fold vibration and lip motion are employed with interpolated Gaussian mixture model (GMM) estimation to perform musical genre classification of the singer. The developed approach for such classification application is called GMM-Formant. Since humming and voiced speech sounds cause periodic vibrations of the vocal folds and then the corresponding motion of the lip, the proposed GMM-Formant firstly operates to acquire the required formant information. Formant information is important acoustic feature data for recognition classification. The proposed GMM-Formant method then uses linear interpolation for combining GMM likelihood estimates and formant evaluation results appropriately. GMM-Formant will effectively adjust the estimated formant feature evaluation outcomes by referring to certain degree of the likelihood score derived from GMM calculations. The superiority and effectiveness of presented GMM-Formant are demonstrated by a series of experiments on musical genre classification of the singer

Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG

This study explores the responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited fifty-five outpatients with TRD who were randomised into three approximately equal-sized groups (A: 0.5 mg/kg ketamine; B: 0.2 mg/kg ketamine; and C: normal saline) under double-blind conditions. The ketamine responses were measured by EEG signals and Hamilton Depression Rating Scale (HDRS) scores. At baseline, responders showed a significantly weaker EEG theta power than did non- responders (p < 0.05). Responders exhibited a higher EEG alpha power but lower EEG alpha asymmetry and theta cordance at post-treatment than at baseline (p < 0.05). Furthermore, our baseline EEG predictor classified responders and non-responders with 81.3 +- 9.5% accuracy, 82.1 +- 8.6% sensitivity and 91.9 +- 7.4% specificity. In conclusion, the rapid antidepressant effects of mixed doses of ketamine are associated with prefrontal EEG power, asymmetry and cordance at baseline and early post-treatment changes. The prefrontal EEG patterns at baseline may account for recognising ketamine effects in advance. Our randomised, double- blind, placebo-controlled study provides information regarding clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.Comment: This revised article is submitting to IEEE TBM

Developments of Machine Learning Schemes for Dynamic Time-Wrapping-Based Speech Recognition

This paper presents a machine learning scheme for dynamic time-wrapping-based (DTW) speech recognition. Two categories of learning strategies, supervised and unsupervised, were developed for DTW. Two supervised learning methods, incremental learning and priority-rejection learning, were proposed in this study. The incremental learning method is conceptually simple but still suffers from a large database of keywords for matching the testing template. The priority-rejection learning method can effectively reduce the matching time with a slight decrease in recognition accuracy. Regarding the unsupervised learning category, an automatic learning approach, called &quot;most-matching learning, &quot; which is based on priority-rejection learning, was developed in this study. Most-matching learning can be used to intelligently choose the appropriate utterances for system learning. The effectiveness and efficiency of all three proposed machine-learning approaches for DTW were demonstrated using keyword speech recognition experiments

ψ(3770)\psi(3770) and BB meson exclusive decay B→ψ(3770)KB \to \psi(3770) K in QCD factorization

Belle has observed surprisingly copious production of $\psi(3770)$ in $B$ meson decay $B\to \psi(3770)K$, of which the rate is comparable to that of $B\to \psi(3686)K$. We study this puzzling process in the QCD factorization approach with the effect of S-D mixing considered. We find that the soft scattering effects in the spectator interactions play an essential role. With a proper parametrization for the higher twist soft end-point singularities associated with kaon, and with the S-D mixing angle $\theta=-12^{\circ}$, the calculated decay rates can be close to the data. Implications of these soft spectator effects to other charmonium production in $B$ exclusive decays are also emphasized.Comment: journal versio

A Method to Integrate GMM, SVM and DTW for Speaker Recognition

This paper develops an effective and efficient scheme to integrate Gaussian mixture model (GMM), support vector machine (SVM), and dynamic time wrapping (DTW) for automatic speaker recognition. GMM and SVM are two popular classifiers for speaker recognition applications. DTW is a fast and simple template matching method, and it is frequently seen in applications of speech recognition. In this work, DTW does not play a role to perform speech recognition, and it will be employed to be a verifier for verification of valid speakers. The proposed combination scheme of GMM, SVM and DTW, called SVMGMM-DTW, for speaker recognition in this study is a two-phase verification process task including GMM-SVM verification of the first phase and DTW verification of the second phase. By providing a double check to verify the identity of a speaker, it will be difficult for imposters to try to pass the security protection; therefore, the safety degree of speaker recognition systems will be largely increased. A series of experiments designed on door access control applications demonstrated that the superiority of the developed SVMGMM-DTW on speaker recognition accuracy

Possible retardation effects of quark confinement on the meson spectrum

The reduced Bethe-Salpeter equation with scalar confinement and vector gluon exchange is applied to quark-antiquark bound states. The so called intrinsic flaw of Salpeter equation with static scalar confinement is investigated. The notorious problem of narrow level spacings is found to be remedied by taking into consideration the retardation effect of scalar confinement. Good fit for the mass spectrum of both heavy and light quarkomium states is then obtained.Comment: 14 pages in LaTex for

A Crucial Test for Color-Octet Production Mechanism in Z^0 Decays

The direct production rates of $D$-wave charmonia in the decays of $Z^0$ is evaluated. The color-octet production processes $Z^0\rightarrow ^3D_J(c\bar c) q\bar q$ are shown to have distinctively large branching ratios, the same order of magnitude as that of $J/\psi$ prodution, as compared with other $D$-wave charmonium production mechanisms. This may suggest a crucial channel to test the color-octet mechanism as well as to observe the $D$-wave charmonium states in $Z^0$ decays. In addition, a signal for the $^3D_J$ charmonium as strong as $J/\psi$ or $\psi^\prime$ with large transverse momentum at the Tevatron should also be observed.Comment: 14 pages in LaTex (3 figures in PS-file