We present volunteer feedback on the digitally created sockets and provide expert commentary on the use of digital tools in upper-limb socket manufacturing. We show that it is possible to utilise 3D scanning and printing, but only if the process is informed by expert knowledge. We bring examples to demonstrate how and why the process may go wrong. Finally, we provide discussion on why progress in modernising the manufacturing of upper-limb sockets has been slow yet it is still too early to rule out digital methods.The dominant markup language for Web visualizations---Scalable Vector Graphics (SVG)---is comparatively easy to learn, and is open, accessible, customizable via CSS, and searchable via the DOM, with easy interaction handling and debugging. Because these attributes allow visualization creators to focus on design on implementation details, tools built on top of SVG, such as D3.js, are essential to the visualization community. However, slow SVG rendering can limit designs by effectively capping the number of on-screen data points, and this can force visualization creators to switch to Canvas or WebGL. These are less flexible (e.g., no search or styling via CSS), and harder to learn. We introduce Scalable Scalable Vector Graphics (SSVG) to reduce these limitations and allow complex and smooth visualizations to be created with SVG. SSVG automatically translates interactive SVG visualizations into a dynamic virtual DOM (VDOM) to bypass the browser's slow 'to specification' rendering by intercepting JavaScript function calls. De-coupling the SVG visualization specification from SVG rendering, and obtaining a dynamic VDOM, creates flexibility and opportunity for visualization system research. SSVG uses this flexibility to free up the main thread for more interactivity and renders the visualization with Canvas or WebGL on a web worker. Together, these concepts create a drop-in JavaScript library which can improve rendering performance by 3-9X with only one line of code added. To demonstrate applicability, we describe the use of SSVG on many example visualizations including published visualization research. A free copy of this paper, collected data, and source code are available at osf.io/ge8wp.Link prediction aims at inferring missing links or predicting future ones based on the currently observed network. This topic is important for many applications such as social media, bioinformatics and recommendation systems. Most existing methods focus on homogeneous settings and consider only low-order pairwise relations while ignoring either the heterogeneity or high-order complex relations among different types of nodes, which tends to lead to a sub-optimal embedding result. This paper presents a method named Heterogeneous Hypergraph Variational Autoencoder (HeteHG-VAE) for link prediction in heterogeneous information networks (HINs). It first maps a conventional HIN to a heterogeneous hypergraph with a certain kind of semantics to capture both the high-order semantics and complex relations among nodes, while preserving the low-order pairwise topology information of the original HIN. Then, deep latent representations of nodes and hyperedges are learned by a Bayesian deep generative framework from the heterogeneous hypergraph in an unsupervised manner. Moreover, a hyperedge attention module is designed to learn the importance of different types of nodes in each hyperedge. The major merit of HeteHG-VAE lies in its ability of modeling multi-level relations in heterogeneous settings. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed method.Proximal operators are of particular interest in optimization problems dealing with non-smooth objectives because in many practical cases they lead to optimization algorithms whose updates can be computed in closed form or very efficiently. A well-known example is the proximal operator of the vector L1 norm, which is given by the soft-thresholding operator. In this paper we study the proximal operator of the mixed L1,oo matrix norm and show that it can be computed in closed form by applying the well-known soft-thresholding operator to each column of the matrix. However, unlike the vector L1 norm case where the threshold is constant, in the mixed L1,oo norm case each column of the matrix might require a different threshold and all thresholds depend on the given matrix. We propose a general iterative algorithm for computing these thresholds, as well as two efficient implementations that further exploit easy to compute lower bounds for the mixed norm of the optimal solution. Experiments on large-scale synthetic and real data indicate that the proposed methods can be orders of magnitude faster than state-of-the-art methods.Poisson observations for videos are important models in computer vision. In this paper, we study the third-order tensor completion problem with Poisson observations. The main aim is to recover a tensor based on a small number of its Poisson observation entries.  https://www.selleckchem.com/products/sulfopin.html  A existing matrix-based method may be applied to this problem via the matricized version of the tensor. However, this method does not leverage on the global low-rankness of a tensor and may be substantially suboptimal. Our approach is to consider the maximum likelihood estimate of the Poisson distribution, and utilize the Kullback-Leibler divergence for the data-fitting term to measure the observations and the underlying tensor. Moreover, we propose to employ a transformed tensor nuclear norm (TTNN) ball constraint and a bounded constraint of each entry, where the TTNN is used to get a lower transformed multi-rank tensor with suitable unitary transformation. We show that the upper bound of the estimator of the proposed model is less than that of the existing matrix-based method. Also a lower error bound is established. An alternating direction method of multipliers is developed to solve the resulting model. Extensive numerical experiments are presented to demonstrate the effectiveness of our proposed model.This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is crucial. To that end, the two modalities need to be first encoded into real-valued vectors and then projected into a common space. In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Our novelty is two-fold. First, different from prior art that resorts to a specific single-level encoder, the proposed network performs multi-level encoding that represents the rich content of both modalities in a coarse-to-fine fashion. Second, different from a conventional common space learning algorithm which is either concept based or latent space based, we introduce hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space.