Irregular Convolutional Neural Networks

Convolutional kernels are basic and vital components of deep Convolutional Neural Networks (CNN). In this paper, we equip convolutional kernels with shape attributes to generate the deep Irregular Convolutional Neural Networks (ICNN). Compared to traditional CNN applying regular convolutional kernels like ${3\times3}$, our approach trains irregular kernel shapes to better fit the geometric variations of input features. In other words, shapes are learnable parameters in addition to weights. The kernel shapes and weights are learned simultaneously during end-to-end training with the standard back-propagation algorithm. Experiments for semantic segmentation are implemented to validate the effectiveness of our proposed ICNN.Comment: 7 pages, 5 figures, 3 table

Synthesis of graphene based materials and other applications as energy storage materials and Ni (II) ions adsorbant

PhD ThesisToday, with the increasing global concern regarding energy savings, CO2 emission and environmental protection, the development of low cost and environmentally friendly materials for electrodes in energy storage devices and adsorbent in wastewater treatment becomes important. Graphene, as a new materials, has attracted lots of attention due to its high current carrying capacity and high surface area. These properties give graphene the huge potential to be used as electrode materials for energy storage devices and adsorbant materials for heavy metal ions. However, the complicate synthesis methods and long reaction time limit its industrial scale up application. In this thesis, the research is focused on development of graphene based composite materials produced by fast, green and energy saving synthesis methods and study their usage as electrodes and for Ni (II) ions removal by analysing the electrochemical properties and Ni (II) ions absorb capacity. Beside graphene, bismuth has also been considered as safe and non-toxic material. In addition, a large amount of bismuth is produced as a by-product of the copper and tin refining industry. The long Fermi wavelength and high Hall coefficient give bismuth the possibility to reach high electronic conductivity with controlled structure. Therefore, bismuth compounds were selected to decorate graphene for the electrode materials. In this study, reduced graphene oxide bismuth composite (rGO/Bi, Bi2O3-GO, rGO/Bi2O2CO3) were synthesis at 60 C or room temperature with short reaction time of 3 hrs. These composite materials exhibit nano-structure and good electrochemical properties, such as high specific capacity and long cycling life. In the rGO/Bi composite materials, bismuth particles with size around 20 to 50 nm were wrapped and protected by graphene layers from oxidation. This composite materials achieves a specific capacity value of 773 C g-1, which is in the range of its theoretical value. In the Bi2O3-GO composite material, Bi2O3 shows a flower-like shape and linked by graphene oxide layer. This material reaches a specific capacity value as high as 559 C g-1. In the rGO/Bi2O2CO3 composite materials, nanosized bismuth subcarbonate were attached on the graphene layers. This composite material shows stable cycling performance even afi ter 4500 cycles. With the low cost of initial materials, simple synthesis methods, low reaction temperature, short reaction time, high specific capacity value and stable long cycling life, graphene bismuth compounds could be the promising candidates for the future electrodes used in electrochemical energy storage devices. The ability of Ni (II) ions removal by graphene oxide (GO) with sodium dodecyl sulphate (SDS) was also studied. Previous studies have proved that Ni is an excellent catalyst for carbon dioxide reforming. A robust Ni (II) ions removal absorbant is needed in order for this technology to become widely acceptable. SDS has been widely used as the industrial surfactant in toothpaste and shampoo. By adding SDS to decorate GO, it helps prevent graphene oxide sheets from stacking back together and then further enlarge the GO’s capacity of Ni (II) ions removal. In this work, SDS was added to modify graphene oxide surface by a one-step easy-to-handle method at room temperature. The effect of time on adsorption, initial concentration of Ni (II) ions and pH value of the Ni (II) ion solutions with GO and GO-SDS were analyzed. The driving force of the adsorption of Ni (II) ions on GO-SDS is proved to be by electrostatic attraction, Ni (II) ions are adsorbed on the GO surface chemically and by ion exchange. By using SDS modified GO, the Ni (II) ions adsorption capacity was increased dramatically from 20.19 mg g-1 to 55.16 mg g-1 in respect to pure GO.School of Chemical Engineering and Advanced Materials, Newcastle University, National Institute for Materials Science, Tsukuba, Japa

Partner Choice and Morality: Preference Evolution under Stable Matching

We present a model that investigates preference evolution with endogenous matching. In the short run, individuals' subjective preferences simultaneously determine who they choose to match with and how they behave in the social interactions with their matched partners, which result in material payoffs for them. Material payoffs in turn affect how preferences evolve in the long run. To properly model the "match-to-interact" process, we combine stable matching and equilibrium concepts. Our analysis unveils that endogenous matching gives rise to the "we is greater than me" moral perspective. This perspective is underpinned by a preference that exhibits both homophily and efficiency, which enables individuals to reach a consensus of a collective ``we" that transcends the boundaries of the individual "I" and "you." Such a preference stands out in the evolutionary process because it is able to force positive assortative matching and efficient play among individuals carrying the same preference type. Under incomplete information, a strong form of homophily, which we call parochialism, is necessary for a preference to prevail in evolution, because stronger incentives are required to engage in self-sorting with information friction

End-to-End Quantum-like Language Models with Application to Question Answering

Language Modeling (LM) is a fundamental research topic ina range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Specifically, based on word embeddings, we design a new density matrix, which represents a sentence (e.g., a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models

Flow Dynamics of a Dodecane Jet in Oxygen Crossflow at Supercritical Pressures

In advanced aero-propulsion engines, kerosene is often injected into the combustor at supercritical pressures, where flow dynamics is distinct from the subcritical counterpart. Large-eddy simulation combined with real-fluid thermodynamics and transport theories of a N-dodecane jet in oxygen crossflow at supercritical pressures is presented. Liquid dodecane at 600 K is injected into a supercritical oxygen environment at 700 K at different supercritical pressures and jet-to-crossflow momentum flux ratios (J). Various vortical structures are discussed in detail. The results shown that, with the same jet-to-crossflow velocity ratio of 0.75, the upstream shear layer (USL) is absolutely unstable at 6.0 MPa (J = 7.1) and convectively unstable at 3.0 MPa (J = 13.2). This trend is consistent with the empirical criterion for the stability characteristics of a jet in crossflow at subcritical pressures (Jcr = 10). While decreasing J to 7.1 at 3.0 MPa, however, the dominant Strouhal number of the USL varies along the upstream jet trajectory, and the USL becomes convectively unstable. Such abnormal change in stability behavior can be attributed to the real-fluid effect induced by strong density stratification at pressure of 3.0 MPa, under which a point of inflection in the upstream mixing layer renders large density gradient and tends to stabilize the USL. The stability behavior with varying pressure and J is further corroborated by linear stability analysis. The analysis of spatial mixing deficiencies reveals that the mixing efficiency is enhanced at a higher jet-to-crossflow momentum flux ratio

Learning Efficient Convolutional Networks through Irregular Convolutional Kernels

As deep neural networks are increasingly used in applications suited for low-power devices, a fundamental dilemma becomes apparent: the trend is to grow models to absorb increasing data that gives rise to memory intensive; however low-power devices are designed with very limited memory that can not store large models. Parameters pruning is critical for deep model deployment on low-power devices. Existing efforts mainly focus on designing highly efficient structures or pruning redundant connections for networks. They are usually sensitive to the tasks or relay on dedicated and expensive hashing storage strategies. In this work, we introduce a novel approach for achieving a lightweight model from the views of reconstructing the structure of convolutional kernels and efficient storage. Our approach transforms a traditional square convolution kernel to line segments, and automatically learn a proper strategy for equipping these line segments to model diverse features. The experimental results indicate that our approach can massively reduce the number of parameters (pruned 69% on DenseNet-40) and calculations (pruned 59% on DenseNet-40) while maintaining acceptable performance (only lose less than 2% accuracy)

Design and control of a linear electromagnetic actuation system for active vehicle suspensions

Traditionally, automotive suspension designs have been a compromise between the three conflicting criteria of road holding, load carrying and passenger comfort. The Linear Electromagnetic Actuation System (LEA) design presented here offers an active solution with the potential to meet the requirements of all three conditions. Using a tubular permanent magnet brushless AC machine with rare earth magnets, thrust densities of over 6 x 105 N/m3 can be achieved with a power requirement of around 50W RMS, much less than equivalent hydraulic systems. The paper examines the performance of the system for both the quarter car and full vehicle simulation, considering high level control of vehicle ride and chassis roll, with the vehicle model being parameterized for a target Jaguar XJ test vehicle. Results demonstrate the ability for 100% roll cancellation with significant improvements in ride quality over the passive Jaguar system