Conditional variances in UK regional house prices

The returns of house price indices for the 13 UK regions are modelled using time series processes, including conditional variances. The first conclusion is that the UK follows the USA, with some regions displaying time-varying variances and others with constant variances. Secondly, there is limited evidence of an asymmetric component in six of the seven regions displaying autoregressive conditional heteroskedasticity. Thirdly, the results suggest that there are three distinct housing markets in the UK, based on common structures within their mean and variance processes, and that South West England is the region driving the other time-varying variances. Variances conditionnelles dans les prix regionaux de l'immobilier au Royaume-Uni Resume Les resultats de l'indice des prix de l'immobilier pour les 13 regions du Royaume-Uni sont modelises ici au moyen de procedes de series chronologiques, y compris des variances conditionnelles. La premiere conclusion est que le Royaume-Uni suit les Etats-Unis, certaines regions presentant des variances temporelles, d'autres des variances constantes. Deuxiemement, on releve peu de traces d'un composant asymetrique dans six des sept regions presentant une heteroscedasticite conditionnelle autoregressive. Troisiemement, les resultats indiquent qu'il y aurait trois marches de l'immobilier distincts au Royaume-Uni, sur la base de structures communes dans le cadre de leurs procedes moyens et de variance, et que le sud-ouest de l'Angleterre est la region qui dynamise les autres variances temporelles. Varianzas condicionales en los precios regionales de la vivienda en el Reino Unido Extracto Las cifras de los indices de precios de la vivienda en 13 regiones del Reino Unido se modelan utilizando procesos de series temporales, incluyendo varianzas condicionales. La primera conclusion es que el Reino Unido sigue a los EE UU, con varias regiones que muestran varianzas fluctuantes con el tiempo y otras con varianzas constantes. En segundo lugar, existe evidencia limitada de un componente asimetrico en seis de las siete regiones que muestran una heteroesquedacidad condicional autorregresiva. En tercer lugar, los resultados sugieren que existen tres mercados distintivos de la vivienda en el Reino Unido, basados en estructuras comunes dentro de sus procesos de media y varianza, y que el sudoeste de Inglaterra es la region que dirige las otras varianzas fluctuantes con el tiemp

Extracting 3D parametric curves from 2D images of Helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively

∞\infty-Diff: Infinite Resolution Diffusion with Subsampled Mollified States

We introduce $\infty$-Diff, a generative diffusion model which directly operates on infinite resolution data. By randomly sampling subsets of coordinates during training and learning to denoise the content at those coordinates, a continuous function is learned that allows sampling at arbitrary resolutions. In contrast to other recent infinite resolution generative models, our approach operates directly on the raw data, not requiring latent vector compression for context, using hypernetworks, nor relying on discrete components. As such, our approach achieves significantly higher sample quality, as evidenced by lower FID scores, as well as being able to effectively scale to higher resolutions than the training data while retaining detail

The importance of the glycosylation of antimicrobial peptides: natural and synthetic approaches.

Glycosylation is one of the most prevalent post-translational modifications of a protein, with a defining impact on its structure and function. Many of the proteins involved in the innate or adaptive immune response, including cytokines, chemokines, and antimicrobial peptides (AMPs), are glycosylated, contributing to their myriad activities. The current availability of synthetic coupling and glycoengineering technology makes it possible to customise the most beneficial glycan modifications for improved AMP stability, microbicidal potency, pathogen specificity, tissue or cell targeting, and immunomodulation

Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting

Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery. By allowing the study of growth, structure, and behaviour of the ROI in the planning phase, critical information can be obtained, increasing the likelihood of a successful operation. Usually, segmentations are performed manually or via machine learning methods trained on manual annotations. In contrast, this paper proposes a fully automatic, unsupervised inpainting-based brain tumour segmentation system for T1-weighted MRI. First, a deep convolutional neural network (DCNN) is trained to reconstruct missing healthy brain regions. Then, upon application, anomalous regions are determined by identifying areas of highest reconstruction loss. Finally, superpixel segmentation is performed to segment those regions. We show the proposed system is able to segment various sized and abstract tumours and achieves a mean and standard deviation Dice score of 0.771 and 0.176, respectively

Dynamic Unary Convolution in Transformers

It is uncertain whether the power of transformer architectures can complement existing convolutional neural networks. A few recent attempts have combined convolution with transformer design through a range of structures in series, where the main contribution of this paper is to explore a parallel design approach. While previous transformed-based approaches need to segment the image into patch-wise tokens, we observe that the multi-head self-attention conducted on convolutional features is mainly sensitive to global correlations and that the performance degrades when these correlations are not exhibited. We propose two parallel modules along with multi-head self-attention to enhance the transformer. For local information, a dynamic local enhancement module leverages convolution to dynamically and explicitly enhance positive local patches and suppress the response to less informative ones. For mid-level structure, a novel unary co-occurrence excitation module utilizes convolution to actively search the local co-occurrence between patches. The parallel-designed Dynamic Unary Convolution in Transformer (DUCT) blocks are aggregated into a deep architecture, which is comprehensively evaluated across essential computer vision tasks in image-based classification, segmentation, retrieval and density estimation. Both qualitative and quantitative results show our parallel convolutional-transformer approach with dynamic and unary convolution outperforms existing series-designed structures

Robust 3D U-Net Segmentation of Macular Holes

Macular holes are a common eye condition which result in visual impairment. We look at the application of deep convolutional neural networks to the problem of macular hole segmentation. We use the 3D U-Net architecture as a basis and experiment with a number of design variants. Manually annotating and measuring macular holes is time consuming and error prone. Previous automated approaches to macular hole segmentation take minutes to segment a single 3D scan. Our proposed model generates significantly more accurate segmentations in less than a second. We found that an approach of architectural simplification, by greatly simplifying the network capacity and depth, exceeds both expert performance and state-of-the-art models such as residual 3D U-Nets