Person re-identification (re-id) is a fundamental technique to associate various person images, captured by differentsurveillance cameras, to the same person. Compared to the single image based person re-id methods, video-based personre-id has attracted widespread attentions because extra space-time information and more appearance cues that can beused to greatly improve the matching performance. However, most existing video-based person re-id methods equally treatall video frames, ignoring their quality discrepancy caused by object occlusion and motions, which is a common phenomenonin real surveillance scenario. Based on this finding, we propose a novel video-based person re-id method via self paced weighting (SPW). Firstly, we propose a self paced outlier detection method to evaluate the noise degree of video sub sequences. Thereafter, a weighted multi-pair distance metric learning approach is adopted to measure the distance of two person image sequences. Experimental results on two public datasets demonstrate the superiority of the proposed method over current state-of-the-art work.

In the past decade, various sparse learning based unsupervised feature selection methods have been developed. However, most existing studies adopt a two-step strategy, i.e., selecting the top-m features according to a calculated descending order and then performing K-means clustering, resulting in a group of sub-optimal features. To address this problem, we propose a Dependence Guided Unsupervised Feature Selection (DGUFS) method to select features and partition data in a joint manner. Our proposed method enhances the inter-dependence among original data, cluster labels, and selected features. In particular, a projection-free feature selection model is proposed based on l20-norm equality constraints. We utilize the learned cluster labels to fill in the information gap between original data and selected features. Two dependence guided terms are consequently proposed for our model. More specifically, one term increases the dependence of desired cluster labels on original data, while the other term maximizes the dependence of selected features on cluster labels to guide the process of feature selection. Last but not least, an iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the constrained minimization problem efficiently. Extensive experiments on different datasets consistently demonstrate that our proposed method significantly outperforms state-of-the-art baselines.

Molecular dynamics (MD) is a powerful computational method for simulating molecular behavior. Deep neural networks provide a novel method of generating MD data efficiently, but there is no architecture that mitigates the well-known exposure bias accumulated by multi-step generations. In this paper, we propose a multi-step time series generator using a deep neural network based on Wasserstein generative adversarial nets. Instead of sparse real data, our model evolves a latent variable z that is densely distributed in a low-dimensional space. This novel framework successfully mitigates the exposure bias. Moreover, our model can evolve part of the system (Feature extraction) with any time step (Step skip), which accelerates the efficient generation of MD data. The applicability of this model is evaluated through three different systems: harmonic oscillator, bulk water, and polymer melts. The experimental results demonstrate that our model can generate time series of the MD data with sufficient accuracy to calculate the physical and important dynamical statistics.

Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the underlying information explicitly. We propose CF-UIcA, a neural co-autoregressive model for CF tasks, which exploits the structural correlation in the domains of both users and items. The co-autoregression allows extra desired properties to be incorporated for different tasks. Furthermore, we develop an efficient stochastic learning algorithm to handle large scale datasets. We evaluate CF-UIcA on two popular benchmarks: MovieLens 1M and Netflix, and achieve state-of-the-art performance in both rating prediction and top-N recommendation tasks, which demonstrates the effectiveness of CF-UIcA.

Urban air pollution has attracted much attention these years for its adverse impacts on human health. While monitoring stations have been established to collect pollutant statistics, the number of stations is very limited due to the high cost. Thus, inferring fine-grained urban air quality information is becoming an essential issue for both government and people. In this paper, we propose a generic neural approach, named ADAIN, for urban air quality inference. We leverage both the information from monitoring stations and urban data that are closely related to air quality, including POIs, road networks and meteorology. ADAIN combines feedforward and recurrent neural networks for modeling static and sequential features as well as capturing deep feature interactions effectively. A novel attempt of ADAIN is an attention-based pooling layer that automatically learns the weights of features from different monitoring stations, to boost the performance. We conduct experiments on a real-world air quality dataset and our approach achieves the highest performance compared with various state-of-the-art solutions.

With the growing volume of publications in the Computer Science (CS) discipline, tracking the research evolution and predicting the future research trending topics are of great importance for researchers to keep up with the rapid progress of research. Within a research area, there are many top conferences that publish the latest research results. These conferences mutually influence each other and jointly promote the development of the research area. To predict the trending topics of mutually influenced conferences, we propose a correlated neural influence model, which has the ability to capture the sequential properties of research evolution in each individual conference and discover the dependencies among different conferences simultaneously. The experiments conducted on a scientific dataset including conferences in artificial intelligence and data mining show that our model consistently outperforms the other state-of-the-art methods. We also demonstrate the interpretability and predictability of the proposed model by providing its answers to two questions of concern, i.e., what the next rising trending topics are and for each conference who the most influential peer is.

This paper studies the problem of multilingual causal reasoning in resource-poor languages. Existing approaches, translating into the most probable resource-rich language such as English, suffer in the presence of translation and language gaps between different cultural area, which leads to the loss of causality. To overcome these challenges, our goal is thus to identify key techniques to construct a new causality network of cause-effect terms, targeted for the machine-translated English, but without any language-specific knowledge of resource-poor languages. In our evaluations with three languages, Korean, Chinese, and French, our proposed method consistently outperforms all baselines, achieving up-to 69.0% reasoning accuracy, which is close to the state-of-the-art accuracy 70.2% achieved on English.

Knowledge representation learning aims at modeling knowledge graph by encoding entities and relations into a low dimensional space. Most of the traditional works for knowledge embedding need negative sampling to minimize a margin-based ranking loss. However, those works construct negative samples through a random mode, by which the samples are often too trivial to fit the model efficiently. In this paper, we propose a novel knowledge representation learning framework based on Generative Adversarial Networks (GAN). In this GAN-based framework, we take advantage of a generator to obtain high-quality negative samples. Meanwhile, the discriminator in GAN learns the embeddings of the entities and relations in knowledge graph. Thus, we can incorporate the proposed GAN-based framework into various traditional models to improve the ability of knowledge representation learning. Experimental results show that our proposed GAN-based framework outperforms baselines on triplets classification and link prediction tasks.

The technique called splitting sets has been proven useful in simplifying the investigation of Answer Set Programming (ASP). In this paper, we investigate the splitting set theorem for LP MLN that is a new extension of ASP created by combining the ideas of ASP and Markov Logic Networks (MLN). Firstly, we extend the notion of splitting sets to LP MLN programs and present the splitting set theorem for LP MLN . Then, the use of the theorem for simplifying several LP MLN inference tasks is illustrated. After that, we give two parallel approaches for solving LP MLN programs via using the theorem. The preliminary experimental results show that these approaches are alternative ways to promote an LP MLN solver.

In real world, the regions occupied by these entities may the location of an object, involve dealing with spatial properties have holes (e.g., Store A may have a small garden in the and relations of objects. For higher precision of solutions, middle) or may be disconnected (e.g., Store A may consist if data is available, quantitative approaches can be of two parts across a small street). Moreover, the given set of employed to find metric solutions for these tasks. On the constraints may be incomplete (i.e., qualitative spatial relations other hand, for some applications (e.g., exploration of an between some spatial objects are not known) or some unknown environment), quantitative data may not always be constraints may involve disjunctions (e.g., missing child is available due to incomplete knowledge about the environment; to the south of Store A or to the north of Store B). In such and, for some applications (e.g., that involve humanrobot cases, with uncertainty or incomplete knowledge, checking interactions) sociable and understandable interactions the consistency of a given set of constraints is NPcomplete and acceptable explanations are often more desirable than (Table 1).