MODMA dataset: a Multi-model Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-model open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis.

KoPA: Automated Kronecker Product Approximation

We consider the matrix approximation induced by the Kronecker product decomposition. We propose to approximate a given matrix by the sum of a few Kronecker products, which we refer to as the Kronecker product approximation (KoPA). Because the Kronecker product is an extensions of the outer product from vectors to matrices, KoPA extends the low rank approximation, and include the latter as a special case. KoPA also offers a greater flexibility over the low rank approximation, since it allows the user to choose the configuration, which are the dimensions of the two smaller matrices forming the Kronecker product. On the other hand, the configuration to be used is usually unknown, and has to be determined from the data in order to achieve the optimal balance between accuracy and parsimony. We propose to use extended information criteria to select the configuration. Under the paradigm of high dimensional analysis, we show that the proposed procedure is able to select the true configuration with probability tending to one, under suitable conditions on the signal-to-noise ratio. We demonstrate the superiority of KoPA over the low rank approximations through numerical studies, and a benchmark image example.

Stable Matrix Completion using Properly Configured Kronecker Product Decomposition

Matrix completion problems are the problems of recovering missing entries in a partially observed high dimensional matrix with or without noise. Such a problem is encountered in a wide range of applications such as collaborative filtering, global positioning and remote sensing. Most of the existing matrix completion algorithms assume a low rank structure of the underlying complete matrix and perform reconstruction through the recovery of the low-rank structure using singular value decomposition. In this paper, we propose an alternative and more flexible structure for the underlying true complete matrix for the purpose of matrix completion and denoising. Specifically, instead of assuming a low matrix rank, we assume the underlying complete matrix has a low Kronecker product rank structure. Such a structure is often seen in the matrix observations in signal processing and image processing applications. The Kronecker product structure also includes low rank singular value decomposition structure commonly used as one of its special cases. The extra flexibility assumed for the underlying structure allows for using much less number of parameters but also raises the challenge of determining the proper Kronecker product configuration to be used. In this article, we propose to use a class of information criteria for the determination of the proper configuration and study its empirical performance in matrix completion problems. Simulation studies show promising results that the true underlying configuration can be accurately selected by the information criteria and the accompanying matrix completion algorithm can produce more accurate matrix recovery with less number of parameters than the standard matrix completion algorithms.

Hybrid Kronecker Product Decomposition and Approximation

Discovering the underlying low dimensional structure of high dimensional data has attracted a significant amount of researches recently and has shown to have a wide range of applications. As an effective dimension reduction tool, singular value decomposition is often used to analyze high dimensional matrices, which are traditionally assumed to have a low rank matrix approximation. In this paper, we propose a new approach. We assume a high dimensional matrix can be approximated by a sum of a small number of Kronecker products of matrices with potentially different configurations, named as a hybird Kronecker outer Product Approximation (hKoPA). It provides an extremely flexible way of dimension reduction compared to the low-rank matrix approximation. Challenges arise in estimating a hKoPA when the configurations of component Kronecker products are different or unknown. We propose an estimation procedure when the set of configurations are given and a joint configuration determination and component estimation procedure when the configurations are unknown. Specifically, a least squares backfitting algorithm is used when the configuration is given. When the configuration is unknown, an iterative greedy algorithm is used. Both simulation and real image examples show that the proposed algorithms have promising performances. The hybrid Kronecker product approximation may have potentially wider applications in low dimensional representation of high dimensional data

Asynchronous "Events" are Better For Motion Estimation

Event-based camera is a bio-inspired vision sensor that records intensity changes (called event) asynchronously in each pixel. As an instance of event-based camera, Dynamic and Active-pixel Vision Sensor (DAVIS) combines a standard camera and an event-based camera. However, traditional models could not deal with the event stream asynchronously. To analyze the event stream asynchronously, most existing approaches accumulate events within a certain time interval and treat the accumulated events as a synchronous frame, which wastes the intensity change information and weakens the advantages of DAVIS. Therefore, in this paper, we present the first neural asynchronous approach to process event stream for event-based camera. Our method asynchronously extracts dynamic information from events by leveraging previous motion and critical features of gray-scale frames. To our best knowledge, this is the first neural asynchronous method to analyze event stream through a novel deep neural network. Extensive experiments demonstrate that our proposed model achieves remarkable improvements against the state-of-the-art baselines.

Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification

Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a shallow tree-based algorithm, called Bonsai, which promotes diversity of the label space and easily scales to millions of labels. Bonsai relaxes the two main constraints of the recently proposed tree-based algorithm, Parabel, which partitions labels at each tree node into exactly two child nodes, and imposes label balanced-ness between these nodes. Instead, Bonsai encourages diversity in the partitioning process by (i) allowing a much larger fan-out at each node, and (ii) maintaining the diversity of the label set further by enabling potentially imbalanced partitioning. By allowing such flexibility, it achieves the best of both worlds - fast training of tree-based methods, and prediction accuracy better than Parabel, and at par with one-vs-rest methods. As a result, Bonsai outperforms state-of-the-art one-vs-rest methods such as DiSMEC in terms of prediction accuracy, while being orders of magnitude faster to train. The code for \bonsai is available at https://github.com/xmc-aalto/bonsai.

Dual Ask-Answer Network for Machine Reading Comprehension

There are three modalities in the reading comprehension setting: question, answer and context. The task of question answering or question generation aims to infer an answer or a question when given the counterpart based on context. We present a novel two-way neural sequence transduction model that connects three modalities, allowing it to learn two tasks simultaneously and mutually benefit one another. During training, the model receives question-context-answer triplets as input and captures the cross-modal interaction via a hierarchical attention process. Unlike previous joint learning paradigms that leverage the duality of question generation and question answering at data level, we solve such dual tasks at the architecture level by mirroring the network structure and partially sharing components at different layers. This enables the knowledge to be transferred from one task to another, helping the model to find a general representation for each modality. The evaluation on four public datasets shows that our dual-learning model outperforms the mono-learning counterpart as well as the state-of-the-art joint models on both question answering and question generation tasks.

Multi-Perspective Neural Architecture for Recommendation System

Currently, there starts a research trend to leverage neural architecture for recommendation systems. Though several deep recommender models are proposed, most methods are too simple to characterize users' complex preference. In this paper, for a fine-grain analysis, users' ratings are explained from multiple perspectives, based on which, we propose our neural architecture. Specifically, our model employs several sequential stages to encode the user and item into hidden representations. In one stage, the user and item are represented from multiple perspectives and in each perspective, the representations of user and item put attentions to each other. Last, we metric the output representations of final stage to approach the users' rating. Extensive experiments demonstrate that our method achieves substantial improvements against baselines.