Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

Joint Topic-Semantic-aware Social Recommendation for Online Voting

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017

Lower Bounds for Achieving Synchronous Early Stopping Consensus with Orderly Crash Failures

In this paper, we discuss the consensus problem for synchronous distributed systems with orderly crash failures. For a synchronous distributed system of n processes with up to t crash failures and f failures actually occur, first, we present a bivalency argument proof to solve the open problem of proving the lower bound, min (t + 1, f + 2) rounds, for early-stopping synchronous consensus with orderly crash failures, where t < n - 1. Then, we extend the system model with orderly crash failures to a new model in which a process is allowed to send multiple messages to the same destination process in a round and the failing processes still respect the order specified by the protocol in sending messages. For this new model, we present a uniform consensus protocol, in which all non-faulty processes always decide and stop immediately by the end of f + 1 rounds. We prove that the lower bound of early stopping protocols for both consensus and uniform consensus are f + 1 rounds under the new model, and our proposed protocol is optimal.Singapore-MIT Alliance (SMA

Life History of Lepidostoma hirtum in an Iberian Stream and its Role in Organic Matter Processing

Abstract The goal of this research was to determine the role of Lepidostoma hirtum Fabricius 1775 in the fragmentation of allochtonous organic material, in a segment of a mountain river in central Portugal. For this purpose, we measured leaf fragmentation and growth rates at four temperatures (9, 12, 15 and 18 C) and four leaf types (alder, Alnus glutinosa L.; oak, Quercus andegavensis Hy; poplar, Populus canadensis Moench; and chestnut, Castanea sativa Mill.). Growth rates ranged from 0.012 to 0.049 mg AFDW day-1 with no significant effect of temperature and leaf type. Fragmentation/consumption rates were significantly higher for alder (1.62 mg animal-1 day-1) than for other leaf types, and significantly lower at 9 C (0.70 mg animal-1 day-1) than at any other temperature (1.12 mg animal-1 day-1). In the studied stream, L. hirtum larvae had a univoltine life history, with an asynchronous development. Secondary production of L. hirtum ranged from 53.95 mg m-2 year-1 (pools) to 63.12 mg m-2 year-1 (riffles). Annual P/B ratios differ between habitats: they were 4.01 year-1 for pools and 4.49 year-1 for riffles. Considering the average density of this species in the study river and their consumption rates, this species has the potential to fragment 8.6 times the mean annual standing stock of organic matter, in the study location