Pixelated Semantic Colorization

While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed convolutional neural network includes two branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more realistic and finer results compared to the colorization state-of-the-art

Pixelated semantic colorization

While many image colorization algorithms have recently shown the capability of producing plausible color versions from grayscale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed network includes two branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on Pascal VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more realistic and finer results compared to the colorization state-of-the-art

Experimental and analytic study of a hybrid solar/biomass rural heating system

© 2019 Elsevier Ltd This paper presents a dedicated analytic and experimental study of a hybrid solar/biomass space heating system incorporating a micro-channel solar thermal panels-array, a biomass boiler and a dedicated control algorithm. This system enables the smart and joint use of solar and biomass energies to provide a comfortable indoor environment. The in-situ testing of the system was undertaken and the data obtained from the testing were analysed using Grubbs method to formulate the experimental thermal efficiency equation for the solar panels-array and the heat conversion factor equation for the combined heat storage/exchanging water tank. The annual energy performance of the hybrid system was investigated using a professional building energy simulation program (EnergyPlus), which can predict the heat load profile of house, the ratio of energy usage from solar/biomass sources and the primary energy/exergy efficiencies. The thermal efficiency of the solar thermal panels-array is in the range of 60%–70%. The heat storage water tank has a heat conversion factor in the range of 0.94–0.98. The heat load index per unit area is 46.86 W/m2 and cumulative heating energy consumption with 100 m2 house is 24.3 GJ during a heating season. The total annual energy demand of the solar/biomass heating system is around 35.91 GJ, of which the sun provides 63.31% and biomass provides 36.69%. The primary energy and exergy efficiencies of the solar/biomass rural heating system are 67.66% and 16.17% respectively. However, when the total input electrical exergy is traced back to its primary energy source, i.e. a coal-fired power plant, the exergy efficiency falls from 23.14% to 7.27%. Compared to the traditional primary energy supply system, the energy conversion effect and effective utilization degree of the solar/biomass heating system are relatively higher

An optimized quantum minimum searching algorithm with sure-success probability and its experiment simulation with Cirq

Finding a minimum is an essential part of mathematical models, and it plays an important role in some optimization problems. Durr and Hoyer proposed a quantum searching algorithm (DHA), with a certain probability of success, to achieve quadratic speed than classical ones. In this paper, we propose an optimized quantum minimum searching algorithm with sure-success probability, which utilizes Grover-Long searching to implement the optimal exact searching, and the dynamic strategy to reduce the iterations of our algorithm. Besides, we optimize the oracle circuit to reduce the number of gates by the simplified rules. The performance evaluation including the theoretical success rate and computational complexity shows that our algorithm has higher accuracy and efficiency than DHA algorithm. Finally, a simulation experiment based on Cirq is performed to verify its feasibility.Comment: 15 pages, 8 figures. arXiv admin note: text overlap with arXiv:1908.07943 by other author

Unconstrained Face Recognition Using A Set-to-Set Distance Measure on Deep Learned Features

Recently considerable efforts have been dedicated to unconstrained face recognition, which requires to identify faces "in the wild" for a set of images and/or video frames captured without human intervention. Unlike traditional face recognition that compares one-to-one medium (either a single image or a video frame) only, we consider a problem of matching sets with heterogeneous contents of both images and videos. In this paper, we propose a novel Set-to-Set (S2S) distance measure to calculate the similarity between two sets with the aim to improve the accuracy of face recognition in real-world situations such as extreme poses or severe illumination conditions. Our S2S distance adopts the kNN-average pooling for the similarity scores computed on all the media in two sets, making the identification far less susceptible to the poor representations (outliers) than traditional feature-average pooling and score-average pooling. Furthermore, we show that various metrics can be embedded into our S2S distance framework, including both predefined and learned ones. This allows to choose the appropriate metric depending on the recognition task in order to achieve the best results. To evaluate the proposed S2S distance, we conduct extensive experiments on the challenging set-based IJB-A face dataset, which demonstrate that our algorithm achieves the stateof- the-art results and is clearly superior to the baselines including several deep learning based face recognition algorithms

Phononic real Chern insulator with protected corner modes in graphynes

Higher-order topological insulators have attracted great research interest recently. Different from conventional topological insulators, higher-order topological insulators do not necessarily require spin-orbit coupling, which makes it possible to realize them in spinless systems. Here, we study phonons in 2D graphyne family materials. By using first-principle calculations and topology/symmetry analysis, we find that phonons in both graphdiyne and $\gamma$-graphyne exhibit a second-order topology, which belongs to the specific case known as real Chern insulator. We identify the nontrivial phononic band gaps, which are characterized by nontrivial real Chern numbers enabled by the spacetime inversion symmetry. The protected phonon corner modes are verified by the calculation on a finite-size nanodisk. Our study extends the scope of higher-order topology to phonons in real materials. The spatially localized phonon modes could be useful for novel phononic applications.Comment: 6 pages, 5figure

Metamemory judgments have dissociable reactivity effects on item and interitem relational memory

Making metamemory judgments reactively changes item memory itself. Here we report the first investigation of reactive influences of making judgments of learning (JOLs) on interitem relational memory-specifically, temporal (serial) order memory. Experiment 1 found that making JOLs impaired order reconstruction. Experiment 2 observed minimal reactivity on free recall and negative reactivity on temporal clustering. Experiment 3 demonstrated a positive reactivity effect on recognition memory, and Experiment 4 detected dissociable effects of making JOLs on order reconstruction (negative) and forced-choice recognition (positive) by using the same participants and stimuli. Finally, a meta-analysis was conducted to explore reactivity effects on word list learning and to investigate whether test format moderates these effects. The results show a negative reactivity effect on interitem relational memory (order reconstruction), a modest positive effect on free recall, and a medium-to-large positive effect on recognition. Overall, these findings imply that even though making metacognitive judgments facilitates item-specific processing, it disrupts relational processing, supporting the item-order account of the reactivity effect on word list learning. (PsycInfo Database Record (c) 2023 APA, all rights reserved)