Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. However, recent work has challenged this belief, showing that complex encoder-decoder architectures perform similarly to nearest-neighbor baselines or simple linear decoder models that exploit large amounts of per category data in standard benchmarks. On the other hand settings where 3D shape must be inferred for new categories with few examples are more natural and require models that generalize about shapes. In this work we demonstrate experimentally that naive baselines do not apply when the goal is to learn to reconstruct novel objects using very few examples, and that in a \emph{few-shot} learning setting, the network must learn concepts that can be applied to new categories, avoiding rote memorization. To address deficiencies in existing approaches to this problem, we propose three approaches that efficiently integrate a class prior into a 3D reconstruction model, allowing to account for intra-class variability and imposing an implicit compositional structure that the model should learn. Experiments on the popular ShapeNet database demonstrate that our method significantly outperform existing baselines on this task in the few-shot setting

The use of traditional Chinese medicine among the Chinese immigrants in the United Kingdom: An intersectionality perspective

\ua9 2024. This study queries why Traditional Chinese Medicine (TCM) has remained an important health care choice for the Chinese population living in the UK after decades of settlement. Data was gathered through participant observation and unstructured interviews in a TCM clinic in London. 105 h of observation and 3 in-depth unstructured interviews were conducted. Data analysis was done using a modified grounded theory (M-GT). This study focused on the activities and views of the clinical staff and the use of the clinic by different user groups. The analysis used an intersectionality approach to understand health behaviours. The study found that the utilisation of TCM was deeply related to multiple factors simultaneously, including, immigrant identity, language problems, limited access to mainstream health services, social isolation, and the health demands of aging Chinese immigrants. These factors worked together to make TCM an essential healthcare resource primarily to serve the aged Chinese people, young students and vulnerable immigrants, especially those who are undocumented. The study suggests that future research and policy making should consider the multiple, simultaneous dilemmas faced by social groups. Policymakers should take into consideration these dynamics and their likely impact on healthcare delivery policies. Further research also needs to be done to ensure the safety and efficacy of TCM

Structural Studies of the von Willebrand Factor D’ domain and its Binding Mechanism to Factor VIII

Hemophilia A is an X-linked disorder that results in uncontrolled bleeding, which is caused by a lack of activity for blood coagulation factor VIII, an essential protein cofactor in the clotting cascade. Factor VIII consists of multiple domains, and binding disruptions between factor VIII and its circulatory partner, von Willebrand Factor, may cause von Willebrand disease. Von Willebrand Disease type 2N is an autosomal recessive disease, and it is caused by binding disruptions between the D’ domain (also known as TIL’E’) of von Willebrand Factor and a3 domain of factor VIII. A 2.9Å Cryoelectron microscopy structure of the FVIII:vWF complex was recently published, and this crystal structure further described the interactions between FVIII and vWF. To further understand the most severe types of von Willebrand Disease type 2N, site-directed mutagenesis of vWF was employed to enhance the understanding of binding disruptions between vWF and FVIII. The designed mutants were transformed, expressed, and purified for further experimental studies. Binding assays were conducted between the mutants against our bioengineered chimeric structure factor VIII, ET3i, via pull-down assays, sedimentation assays, as well as quantitative studies via biolayer interferometry. The results of this present study included the investigation of binding studies between the D’ domain of von Willebrand factor and the a3 domain of factor VIII. The results obtained in this study were interpreted, compared, and concluded to be consistent with the newest cryo-EM structure

Unusual Thermodynamics on the Fuzzy 2-Sphere

Higher spin Dirac operators on both the continuum sphere($S^2$) and its fuzzy analog($S^2_F$) come paired with anticommuting chirality operators. A consequence of this is seen in the fermion-like spectrum of these operators which is especially true even for the case of integer-spin Dirac operators. Motivated by this feature of the spectrum of a spin 1 Dirac operator on $S_F^2$, we assume the spin 1 particles obey Fermi-Dirac statistics. This choice is inspite of the lack of a well defined spin-statistics relation on a compact surface such as $S^2$. The specific heats are computed in the cases of the spin $\frac{1}{2}$ and spin 1 Dirac operators. Remarkably the specific heat for a system of spin $\frac{1}{2}$ particles is more than that of the spin 1 case, though the number of degrees of freedom is more in the case of spin 1 particles. The reason for this is inferred through a study of the spectrums of the Dirac operators in both the cases. The zero modes of the spin 1 Dirac operator is studied as a function of the cut-off angular momentum $L$ and is found to follow a simple power law. This number is such that the number of states with positive energy for the spin 1 and spin $\frac{1}{2}$ system become comparable. Remarks are made about the spectrums of higher spin Dirac operators as well through a study of their zero-modes and the variation of their spectrum with degeneracy. The mean energy as a function of temperature is studied in both the spin $\frac{1}{2}$ and spin 1 cases. They are found to deviate from the standard ideal gas law in 2+1 dimensions.Comment: 19 pages, 7 figures. The paper has been significantly modified. Main results are unchange

A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for realtime implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems

Evaluation of energy and indoor environmental performance of a UK passive house dwelling

The preliminary findings of the energy and indoor environmental performance of a Passive House dwelling in North East of England is presented in this paper. This dwelling is designed to comply with the Passive House Standard (certified by the International Passive House Association) which aims to reduce energy consumption and carbon emissions. The property benefits from advanced building fabric design and materials, PV array, mechanical ventilation with heat recovery system (MVHR) and high efficiency domestic hot water storage vessel to minimise operational carbon emissions. Power generated by the PV panel, imported grid electricity and mains gas consumption of this house are monitored by a proprietary monitoring package; and data of indoor temperature, relative humidity and resident occupancy at several different locations in the dwelling are also recorded. A computational model of this property was developed using DesignBuilder software. The model was validated using the data monitored on site; and is used to predict and evaluate the performance of the house. The initial findings of this study shows the advantages of Passive House in achieving high thermal comfort and good indoor air quality with much lower energy consumption compares to the national averag

The electric field alignment of short carbon fibres to enhance the toughness of epoxy composites

An investigation is presented on increasing the fracture toughness of epoxy/short carbon fibre (SCF) composites by alignment of SCFs using an externally applied alternating current (AC) electric field. Firstly, the effects of SCF length, SCF content and AC electric field strength on the rotation of the SCFs suspended in liquid (i.e. uncured) epoxy resin are investigated. Secondly, it is shown the mode I fracture toughness of the cured epoxy composites increases with the weight fraction of SCFs up to a limiting value (5 wt%). Thirdly, the toughening effect is greater when the SCFs are aligned in the composite normal to the direction of crack growth. The SCFs increases the fracture toughness by inducing multiple intrinsic and extrinsic toughening mechanisms, which are identified. Based on the identified toughening mechanisms, an analytical model is proposed to predict the enhancement to the fracture toughness due to AC electric field alignment of the SCFs