Selective Categories and Linear Canonical Relations

A construction of Wehrheim and Woodward circumvents the problem that compositions of smooth canonical relations are not always smooth, building a category suitable for functorial quantization. To apply their construction to more examples, we introduce a notion of highly selective category, in which only certain morphisms and certain pairs of these morphisms are "good". We then apply this notion to the category $\mathbf{SLREL}$ of linear canonical relations and the result ${\rm WW}(\mathbf{SLREL})$ of our version of the WW construction, identifying the morphisms in the latter with pairs $(L,k)$ consisting of a linear canonical relation and a nonnegative integer. We put a topology on this category of indexed linear canonical relations for which composition is continuous, unlike the composition in $\mathbf{SLREL}$ itself. Subsequent papers will consider this category from the viewpoint of derived geometry and will concern quantum counterparts

Compact Quiescent Galaxies at Intermediate Redshifts

From several searches of the area common to the Sloan Digital Sky Survey and the United Kingdom Infrared Telescope Infrared Deep Sky Survey, we have selected 22 luminous galaxies between $z \sim$ 0.4 and $z \sim$ 0.9 that have colors and sizes similar to those of the compact quiescent galaxies at $z>2$. By exploring structural parameters and stellar populations, we found that most of these galaxies actually formed most of their stars at $z<2$ and are generally less compact than those found at $z > 2$. Several of these young objects are disk-like or possibly prolate. This lines up with several previous studies which found that massive quiescent galaxies at high redshifts often have disk-like morphologies. If these galaxies were to be confirmed to be disk-like, their formation mechanism must be able to account for both compactness and disks. On the other hand, if these galaxies were to be confirmed to be prolate, the fact that prolate galaxies do not exist in the local universe would indicate that galaxy formation mechanisms have evolved over cosmic time. We also found five galaxies forming over 80% of their stellar masses at $z>2$. Three of these galaxies appear to have been modified to have spheroid-like morphologies, in agreement with the scenario of "inside-out" buildup of massive galaxies. The remaining galaxies, SDSS\,J014355.21+133451.4 and SDSS\,J115836.93+021535.1, have truly old stellar populations and disk-like morphologies. These two objects would be good candidates for nearly unmodified compact quiescent galaxies from high redshifts that are worth future study.Comment: Accepted for publication in Ap

A Product Line Systems Engineering Process for Variability Identification and Reduction

Software Product Line Engineering has attracted attention in the last two decades due to its promising capabilities to reduce costs and time to market through reuse of requirements and components. In practice, developing system level product lines in a large-scale company is not an easy task as there may be thousands of variants and multiple disciplines involved. The manual reuse of legacy system models at domain engineering to build reusable system libraries and configurations of variants to derive target products can be infeasible. To tackle this challenge, a Product Line Systems Engineering process is proposed. Specifically, the process extends research in the System Orthogonal Variability Model to support hierarchical variability modeling with formal definitions; utilizes Systems Engineering concepts and legacy system models to build the hierarchy for the variability model and to identify essential relations between variants; and finally, analyzes the identified relations to reduce the number of variation points. The process, which is automated by computational algorithms, is demonstrated through an illustrative example on generalized Rolls-Royce aircraft engine control systems. To evaluate the effectiveness of the process in the reduction of variation points, it is further applied to case studies in different engineering domains at different levels of complexity. Subject to system model availability, reduction of 14% to 40% in the number of variation points are demonstrated in the case studies.Comment: 12 pages, 6 figures, 2 tables; submitted to the IEEE Systems Journal on 3rd June 201

Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future

Deep Reinforcement Learning for Dialogue Generation

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues