Wasserstein Introspective Neural Networks

We present Wasserstein introspective neural networks (WINN) that are both a generator and a discriminator within a single model. WINN provides a significant improvement over the recent introspective neural networks (INN) method by enhancing INN's generative modeling capability. WINN has three interesting properties: (1) A mathematical connection between the formulation of the INN algorithm and that of Wasserstein generative adversarial networks (WGAN) is made. (2) The explicit adoption of the Wasserstein distance into INN results in a large enhancement to INN, achieving compelling results even with a single classifier --- e.g., providing nearly a 20 times reduction in model size over INN for unsupervised generative modeling. (3) When applied to supervised classification, WINN also gives rise to improved robustness against adversarial examples in terms of the error reduction. In the experiments, we report encouraging results on unsupervised learning problems including texture, face, and object modeling, as well as a supervised classification task against adversarial attacks.Comment: Accepted to CVPR 2018 (Oral

Dimensionality reduction of networked systems with separable coupling-dynamics: theory and applications

Complex dynamical systems are prevalent in various domains, but their analysis and prediction are hindered by their high dimensionality and nonlinearity. Dimensionality reduction techniques can simplify the system dynamics by reducing the number of variables, but most existing methods do not account for networked systems with separable coupling-dynamics, where the interaction between nodes can be decomposed into a function of the node state and a function of the neighbor state. Here, we present a novel dimensionality reduction framework that can effectively capture the global dynamics of these networks by projecting them onto a low-dimensional system. We derive the reduced system's equation and stability conditions, and propose an error metric to quantify the reduction accuracy. We demonstrate our framework on two examples of networked systems with separable coupling-dynamics: a modified susceptible-infected-susceptible model with direct infection and a modified Michaelis-Menten model with activation and inhibition. We conduct numerical experiments on synthetic and empirical networks to validate and evaluate our framework, and find a good agreement between the original and reduced systems. We also investigate the effects of different network structures and parameters on the system dynamics and the reduction error. Our framework offers a general and powerful tool for studying complex dynamical networks with separable coupling-dynamics.Comment: 15 pages, 5 figure

Systematic analysis of Rfx2 target genes in vertebrate multiciliated cells

Multiciliated cells (MCCs) drive directional fluid flow in diverse tubular organs and are essential for development and homeostasis of the vertebrate central nervous system, airway, and reproductive tracts. These cells are characterized by dozens or hundreds of long, motile cilia that beat in a coordinated and polarized manner. In recent years, genomic studies have not only elucidated the transcriptional hierarchy for MCC specification, but also identified myriad new proteins that govern MCC ciliogenesis, cilia beating, or cilia polarization. Interestingly, this burst of genomic data has also highlighted the obvious importance of the “ignorome,” that large fraction of vertebrate genes that remain only poorly characterized. Understanding the function of novel proteins with little prior history of study presents a special challenge, especially when faced with large numbers of such proteins. Here, we explored the MCC ignorome by defining the subcellular localization of 260 poorly defined proteins in vertebrate MCCs in vivo. Based on this localization data, we selected some targets of MCC ignorome for further functional studies because they could possibly play key roles in the regulation of ciliogenesis. We characterized Myo5c as the motor for basal body apical migration, vi Arhgef18 as the RhoA signaling activator at the basal bodies, and Dennd2b as a regulator of actin network formation and ciliogenesis. All of these findings have deepened our understanding about molecular mechanisms of related cellular process. This study exemplifies the power of high content protein localization screening as the bridging step between large-scale omics data and functional study of specific proteins.Cellular and Molecular Biolog

Shear behaviour of inorganic polymer concrete beams reinforced with basalt FRP bars and stirrups

Inorganic polymer concrete (IPC) reinforced with basalt fibre reinforced polymer (BFRP) was proposed as a promising substitute of conventional reinforced concrete for structures to enhance their sustainability and durability. This paper, for the first time, presents a systematic study, experimental, theoretical and numerical, of shear behaviour of IPC beams reinforced with BFRP bars and stirrups considering the effects of stirrup spacing (S = 80, 100 and 150 mm) and shear span-to-depth ratio (λ = 1.5, 2.0 and 2.5). Result indicates that all BFRP-IPC beams fail in shear as a result of BFRP stirrup rupture and shear-compression failure. Compared to S, λ has a more pronounced influence on shear performance of BFRP reinforced IPC beams, with a maximum reduction of ultimate shear load by 29.4%. The simulation results show good agreement with experimental data, while the theoretical predictions according to existing design provisions for FRP reinforced concrete have a discrepancy of more than 30% with experiments due to lack of consideration of λ. Modified equations taking into account the effect of λ were then derived and used to predict the shear capacity of BFRP reinforced IPC beams, which agrees well with experimental data with an average discrepancy of only around 5%

Options and Evaluations on Propulsion Systems of LNG Carriers

The LNG carriers are undergoing a period of rapid and profound change, with much larger size ships and novel propulsion systems emerging for fulfilling the market trends of LNG shipping industry. There are various proposed propulsion solutions for LNG carriers, ranging from the conventional steam turbine and dual fuel diesel electric propulsion, until more innovative ideas such as slow speed dual fuel diesel engine, combined gas turbine electric & steam system, and hybrid propulsion based on steam turbine and gas engine. Since propulsion system significantly influenced the ship’s capital, emission regulation compliance and navigation safety, the selection of a proper propulsion option with technical feasibility and economic viability for LNG carriers is currently a major concern from the shipping industry and thus must be comprehensively assessed. In this context, this chapter investigated the main characteristics of these propulsion options in terms of BOG treatment, fuel consumption, emission standards compliance, and plant reliability. Furthermore, comparisons among different propulsion system were also carried out and related evaluation was presented