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Jian Huang

Publications and source records attributed to Jian Huang.

At least 181 records · Page 10Linked to original sources
arXiv2020

High Speed Mid-Wave Infrared Uni-traveling Carrier Photodetector

Mid-wave infrared (MWIR) frequency comb is expected to dramatically improve the precision and sensitivity of molecular spectroscopy. For high resolution application, high speed MWIR photodetector is one of the key components, however, the commercially available high speed MWIR photodetector only has sub-GHz bandwidth currently. In this paper, we demonstrate, for the first time to our knowledge, a high speed mid-wave infrared (MWIR) uni-traveling carrier photodetector based on InAs/GaSb type-II superlattice (T2SL) at room temperature. The device exhibits a cutoff wavelength of 5.6μm, and 3dB bandwidth of 6.58 GHz for a 20μm diameter device at 300K. These promising results show the device has potential to be utilized in high speed applications such as frequency comb spectroscopy, free space communication and others. The limitations on the high frequency performance of the photodetectors are also discussed.

physics.app-ph↗
arXiv2020

On the relation between hard X-ray photon index versus accretion rate for super-Eddington accreting quasars

We investigate whether the hard X-ray photon index ($Γ$) versus accretion rate correlation for super-Eddington accreting quasars is different from that for sub-Eddington accreting quasars. We construct a sample of 113 bright quasars from the Sloan Digital Sky Survey Data Release 14 quasar catalog, including 38 quasars as the super-Eddington subsample and 75 quasars as the sub-Eddington subsample. We derive black-hole masses using a simple-epoch virial mass formula based on the ${\rm Hβ}$ lines, and we use the standard thin disk model to derive the dimensionless accretion rates ($\dot{\mathscr{M}}$) for our sample. The X-ray data for these quasars are collected from the Chandra and XMM-Newton archives. We fit the hard X-ray spectra using a single power-law model to obtain $Γ$ values. We find a statistically significant ($R_{\rm S}=0.43$, $p=7.75\times{10}^{-3}$) correlation between $Γ$ and $\dot{\mathscr{M}}$ for the super-Eddington subsample. The $Γ$-$\dot{\mathscr{M}}$ correlation for the sub-Eddington subsample is also significant, but weaker ($R_{\rm S}=0.30$, $p=9.98\times{10}^{-3}$). Linear regression analysis shows that ${\rm Γ}=(0.34\pm0.11){\rm log}{\dot{\mathscr{M}}}+(1.71\pm0.17)$ and ${\rm Γ}=(0.09\pm0.04){\rm log}{\dot{\mathscr{M}}}+(1.93\pm0.04)$ for the super- and sub-Eddington subsamples, respectively. The $Γ$-$\dot{\mathscr{M}}$ correlations of the two subsamples are different, suggesting different disk-corona connections in these two types of systems. We propose one qualitative explanation of the steeper $Γ$-$\dot{\mathscr{M}}$ correlation in the super-Eddington regime that involves larger seed photon fluxes received by the compact coronae from the thick disks in super-Eddington accreting quasars.

astro-ph.HE↗
arXiv2020

QoS-Based Source and Relay Secure Optimization Design with Presence of Channel Uncertainty

In this letter, we study relay-aided networks with presence of single eavesdropper. We provide joint beamforming design of the source and relay that can minimize the overall power consumption while satisfying our predefined quality-of-service (QoS) requirements. Additionally, we investigate the case that the channel between relay and eavesdropper suffers from channel uncertainty. Finally, simulation results are provided to verify the effectiveness of our algorithm.

eess.SP↗
arXiv2020

Learning Implicit Generative Models with Theoretical Guarantees

We propose a \textbf{uni}fied \textbf{f}ramework for \textbf{i}mplicit \textbf{ge}nerative \textbf{m}odeling (UnifiGem) with theoretical guarantees by integrating approaches from optimal transport, numerical ODE, density-ratio (density-difference) estimation and deep neural networks. First, the problem of implicit generative learning is formulated as that of finding the optimal transport map between the reference distribution and the target distribution, which is characterized by a totally nonlinear Monge-Ampère equation. Interpreting the infinitesimal linearization of the Monge-Ampère equation from the perspective of gradient flows in measure spaces leads to the continuity equation or the McKean-Vlasov equation. We then solve the McKean-Vlasov equation numerically using the forward Euler iteration, where the forward Euler map depends on the density ratio (density difference) between the distribution at current iteration and the underlying target distribution. We further estimate the density ratio (density difference) via deep density-ratio (density-difference) fitting and derive explicit upper bounds on the estimation error. Experimental results on both synthetic datasets and real benchmark datasets support our theoretical findings and demonstrate the effectiveness of UnifiGem.

cs.LG↗
arXiv2020

$\ell_0$-Regularized High-dimensional Accelerated Failure Time Model

We develop a constructive approach for $\ell_0$-penalized estimation in the sparse accelerated failure time (AFT) model with high-dimensional covariates. Our proposed method is based on Stute's weighted least squares criterion combined with $\ell_0$-penalization. This method is a computational algorithm that generates a sequence of solutions iteratively, based on active sets derived from primal and dual information and root finding according to the KKT conditions. We refer to the proposed method as AFT-SDAR (for support detection and root finding). An important aspect of our theoretical results is that we directly concern the sequence of solutions generated based on the AFT-SDAR algorithm. We prove that the estimation errors of the solution sequence decay exponentially to the optimal error bound with high probability, as long as the covariate matrix satisfies a mild regularity condition which is necessary and sufficient for model identification even in the setting of high-dimensional linear regression. We also proposed an adaptive version of AFT-SDAR, or AFT-ASDAR, which determines the support size of the estimated coefficient in a data-driven fashion. We conduct simulation studies to demonstrate the superior performance of the proposed method over the lasso and MCP in terms of accuracy and speed. We also apply the proposed method to a real data set to illustrate its application.

stat.ME↗
arXiv2020

A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models

Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size. In this paper, we develop a support detection and root finding procedure to learn the high dimensional sparse generalized linear models and denote this method by GSDAR. Based on the KKT condition for $\ell_0$-penalized maximum likelihood estimations, GSDAR generates a sequence of estimators iteratively. Under some restricted invertibility conditions on the maximum likelihood function and sparsity assumption on the target coefficients, the errors of the proposed estimate decays exponentially to the optimal order. Moreover, the oracle estimator can be recovered if the target signal is stronger than the detectable level. We conduct simulations and real data analysis to illustrate the advantages of our proposed method over several existing methods, including Lasso and MCP.

stat.ML↗
arXiv2020

Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction

Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approaches that preserve only the global data structure, such as principal component analysis (PCA), are usually sensitive to outliers. Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph. We propose a novel linear dimensionality reduction approach, supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN), to integrate neighborhood-free supervised discriminative sparse PCA and projected clustering with adaptive neighbors. As a result, both global and local data structures, as well as the label information, are used for better dimensionality reduction. Classification experiments on nine high-dimensional datasets validated the effectiveness and robustness of our proposed SDSPCAAN.

cs.LG↗
arXiv2020

Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization

Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models; however, they may not be easily optimized when the data size is large, and/or the data dimensionality is high. This paper proposes a mini-batch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy classifiers. It integrates two novel techniques: 1) uniform regularization (UR), which forces the rules to have similar average contributions to the output, and hence to increase the generalization performance of the TSK classifier; and, 2) batch normalization (BN), which extends BN from deep neural networks to TSK fuzzy classifiers to expedite the convergence and improve the generalization performance. Experiments on 12 UCI datasets from various application domains, with varying size and dimensionality, demonstrated that UR and BN are effective individually, and integrating them can further improve the classification performance.

cs.LG↗
arXiv2019

Generation of macroscopic entangled cat states in a molecular cavity-QED system

Macroscopic entangled cat states not only are significant in the demonstration of the fundamentals of quantum physics, but also have wide applications in modern quantum technologies such as continuous-variable quantum information processing and quantum metrology. Here we propose a scheme for generation of macroscopic entangled cat states in a molecular cavity-QED system, which is composed of an organic molecule (including electronic and vibrational states) coupled to a single-mode cavity field. By simultaneously modulating the resonance frequencies of the molecular vibration and the cavity field, the molecular vibrational displacement can be enhanced significantly and hence macroscopic entangled cat states between the molecular vibrational mode and the cavity mode can be created. We also study quantum coherence effects in the generated states by calculating the joint Wigner function and the degree of entanglement. The dissipation effects are included by considering the state generation in the open-system case. Our results will pave the way to the study of quantum physics and quantum chemistry in molecular cavity-QED systems.

quant-ph↗
arXiv2019

Domain adversarial learning for emotion recognition

In practical applications for emotion recognition, users do not always exist in the training corpus. The mismatch between training speakers and testing speakers affects the performance of the trained model. To deal with this problem, we need our model to focus on emotion-related information, while ignoring the difference between speaker identities. In this paper, we look into the use of the domain adversarial neural network (DANN) to extract a common representation between different speakers. The primary task is to predict emotion labels. The secondary task is to learn a common representation where speaker identities can not be distinguished. By using the gradient reversal layer, the gradients coming from the secondary task are used to bring the representations for different speakers closer. To verify the effectiveness of the proposed method, we conduct experiments on the IEMOCAP database. Experimental results demonstrate that the proposed framework shows an absolute improvement of 3.48% over state-of-the-art strategies.

eess.AS↗
arXiv2019

Conversational Emotion Analysis via Attention Mechanisms

Different from the emotion recognition in individual utterances, we propose a multimodal learning framework using relation and dependencies among the utterances for conversational emotion analysis. The attention mechanism is applied to the fusion of the acoustic and lexical features. Then these fusion representations are fed into the self-attention based bi-directional gated recurrent unit (GRU) layer to capture long-term contextual information. To imitate real interaction patterns of different speakers, speaker embeddings are also utilized as additional inputs to distinguish the speaker identities during conversational dialogs. To verify the effectiveness of the proposed method, we conduct experiments on the IEMOCAP database. Experimental results demonstrate that our method shows absolute 2.42% performance improvement over the state-of-the-art strategies.

cs.CL↗
arXiv2019

Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition

Prior works on speech emotion recognition utilize various unsupervised learning approaches to deal with low-resource samples. However, these methods pay less attention to modeling the long-term dynamic dependency, which is important for speech emotion recognition. To deal with this problem, this paper combines the unsupervised representation learning strategy -- Future Observation Prediction (FOP), with transfer learning approaches (such as Fine-tuning and Hypercolumns). To verify the effectiveness of the proposed method, we conduct experiments on the IEMOCAP database. Experimental results demonstrate that our method is superior to currently advanced unsupervised learning strategies.

eess.AS↗
arXiv2019

Expression Analysis Based on Face Regions in Read-world Conditions

Facial emotion recognition is an essential and important aspect of the field of human-machine interaction. Past research on facial emotion recognition focuses on the laboratory environment. However, it faces many challenges in real-world conditions, i.e., illumination changes, large pose variations and partial or full occlusions. Those challenges lead to different face areas with different degrees of sharpness and completeness. Inspired by this fact, we focus on the authenticity of predictions generated by different pairs. For example, if only the mouth areas are available and the emotion classifier predicts happiness, then there is a question of how to judge the authenticity of predictions. This problem can be converted into the contribution of different face areas to different emotions. In this paper, we divide the whole face into six areas: nose areas, mouth areas, eyes areas, nose to mouth areas, nose to eyes areas and mouth to eyes areas. To obtain more convincing results, our experiments are conducted on three different databases: facial expression recognition + ( FER+), real-world affective faces database (RAF-DB) and expression in-the-wild (ExpW) dataset. Through analysis of the classification accuracy, the confusion matrix and the class activation map (CAM), we can establish convincing results. To sum up, the contributions of this paper lie in two areas: 1) We visualize concerned areas of human faces in emotion recognition; 2) We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis. Our findings can be combined with findings in psychology to promote the understanding of emotional expressions.

cs.CV↗
arXiv2019

Speech Emotion Recognition via Contrastive Loss under Siamese Networks

Speech emotion recognition is an important aspect of human-computer interaction. Prior work proposes various end-to-end models to improve the classification performance. However, most of them rely on the cross-entropy loss together with softmax as the supervision component, which does not explicitly encourage discriminative learning of features. In this paper, we introduce the contrastive loss function to encourage intra-class compactness and inter-class separability between learnable features. Furthermore, multiple feature selection methods and pairwise sample selection methods are evaluated. To verify the performance of the proposed system, we conduct experiments on The Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, a common evaluation corpus. Experimental results reveal the advantages of the proposed method, which reaches 62.19% in the weighted accuracy and 63.21% in the unweighted accuracy. It outperforms the baseline system that is optimized without the contrastive loss function with 1.14% and 2.55% in the weighted accuracy and the unweighted accuracy, respectively.

cs.CV↗
arXiv2019

Exploration of heterogeneous treatment effects via concave fusion

Understanding treatment heterogeneity is essential to the development of precision medicine, which seeks to tailor medical treatments to subgroups of patients with similar characteristics. One of the challenges to achieve this goal is that we usually do not have a priori knowledge of the grouping information of patients with respect to treatment. To address this problem, we consider a heterogeneous regression model by assuming that the coefficient for treatment variables are subject-dependent and belong to different subgroups with unknown grouping information. We develop a concave fusion penalized method for automatically estimating the grouping structure and the subgroup-specific treatment effects, and derive an alternating direction method of multipliers algorithm for its implementation. We also study the theoretical properties of the proposed method and show that under suitable conditions there exists a local minimizer that equals the oracle least squares estimator with a priori knowledge of the true grouping information with high probability. This provides theoretical support for making statistical inference about the subgroup-specific treatment effects based on the proposed method. We evaluate the performance of the proposed method by simulation studies and illustrate its application by analyzing the data from the AIDS Clinical Trials Group Study.

stat.ME↗
arXiv2019

On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression

Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently optimize its parameters, how to balance the trade-off between cooperations and competitions among the rules, how to overcome the curse of dimensionality, how to increase its generalization ability, etc. Literature has shown that by making appropriate connections between fuzzy systems and other machine learning approaches, good practices from other domains may be used to improve the fuzzy systems, and vice versa. This paper gives an overview on the functional equivalence between Takagi-Sugeno-Kang fuzzy systems and four classic machine learning approaches -- neural networks, mixture of experts, classification and regression trees, and stacking ensemble regression -- for regression problems. We also point out some promising new research directions, inspired by the functional equivalence, that could lead to solutions to the aforementioned problems. To our knowledge, this is so far the most comprehensive overview on the connections between fuzzy systems and other popular machine learning approaches, and hopefully will stimulate more hybridization between different machine learning algorithms.

cs.LG↗