Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation

Speech emotion recognition plays an important role in building more intelligent and human-like agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help address the target corpus. However, domain shift between the corpora poses a serious challenge, making domain shift adaptation difficult to function even on the recognition of positive/negative emotions. In this work, we propose class-wise adversarial domain adaptation to address this challenge by reducing the shift for all classes between different corpora. Experiments on the well-known corpora EMODB and Aibo demonstrate that our method is effective even when only a very limited number of target labeled examples are provided.Comment: 5 pages, 3 figures, accepted to ICASSP 201

Constraints on large-extra-dimensions model through 125 GeV Higgs pair production at the LHC

Based on the analysis of 5 fb^-1 of data at the LHC, the ATLAS and CMS collaborations have presented evidence for a Higgs boson with a mass in the 125 GeV range. We consider the 125 GeV neutral Higgs pair production process in the context of large-extra-dimensions (LED) model including the Kaluza-Klein (KK)excited gravitons at the LHC. We consider the standard model(SM) Higgs pair production in gluon-gluon fusion channel and pure LED effects through graviton exchange as well as their interferences. It is shown that such interferences should be included; the LED model raises the transverse momentum (Pt)and invariant mass (M_HH) distributions at high scales of Pt and M_HH of the Higgs pair production. By using the Higgs pair production we could set the discovery limit on the cutoff scale M_S up to 6 TeV for delta = 2 and 4.5 TeV for delta = 6.Comment: 6 figure

On Tree-Based Neural Sentence Modeling

Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at https://github.com/ExplorerFreda/TreeEnc.Comment: To Appear at EMNLP 201

Cross-Modal Translation and Alignment for Survival Analysis

With the rapid advances in high-throughput sequencing technologies, the focus of survival analysis has shifted from examining clinical indicators to incorporating genomic profiles with pathological images. However, existing methods either directly adopt a straightforward fusion of pathological features and genomic profiles for survival prediction, or take genomic profiles as guidance to integrate the features of pathological images. The former would overlook intrinsic cross-modal correlations. The latter would discard pathological information irrelevant to gene expression. To address these issues, we present a Cross-Modal Translation and Alignment (CMTA) framework to explore the intrinsic cross-modal correlations and transfer potential complementary information. Specifically, we construct two parallel encoder-decoder structures for multi-modal data to integrate intra-modal information and generate cross-modal representation. Taking the generated cross-modal representation to enhance and recalibrate intra-modal representation can significantly improve its discrimination for comprehensive survival analysis. To explore the intrinsic crossmodal correlations, we further design a cross-modal attention module as the information bridge between different modalities to perform cross-modal interactions and transfer complementary information. Our extensive experiments on five public TCGA datasets demonstrate that our proposed framework outperforms the state-of-the-art methods.Comment: Accepted by ICCV202

A new approach to understanding the frequency response of mineral oil

Dielectric spectroscopy is non-invasive diagnostic method and can give information about dipole relaxation, electrical conduction and structure of molecules. Since the creation of charge carriers in mineral oil is not only from dissociation but also injection from electrodes, the injection current cannot be simply ignored. The polarization caused by the charge injection has been studied in this paper. Based on our research, if the mobility of the injected charge carriers is fast enough so that they can reach the opposite electrode, the current caused by the injection will contribute only to the imaginary part of the complex permittivity and this part of the complex permittivity will decrease with the frequency with a slope of -1 which is in a good agreement with the experimental result. The classic ionic drift and diffusion model and this injection model will be combined to make an improved model. In this paper, the frequency responses of three different kinds of mineral oils have been measured, and this modified model has been used to simulate the experiment result. Since there is only one unknown parameter in this improved model, a better understanding of the frequency response in mineral oil can be achieve