Wand: $$360^{circ }$$ video projection mapping using a $$360^{circ }$$ camera

Virtual Reality - In a surrounding projection-based display environment (e.g., a dome theater), the viewers can enjoy a $$360^{circ }$$ video with a strong sense of immersion. Building a thriving...     

Integrated damping parameter and control design in structural systems for \bf{\mathcal{H}^2} and {\mathcal{H}^{\infty}} specifications

The paper presents a linear matrix inequality (LMI)-based approach for the simultaneous optimal design of output feedback control gains and damping parameters in structural systems with collocated actuators and sensors. The proposed integrated design is based on simplified $\mathcal{H}^2$ and $\mathcal{H}^{\infty}$ norm upper bound calculations for collocated structural systems. Using these upper bound results, the combined design of the damping parameters of the structural system and the output feedback controller to satisfy closed-loop $\mathcal{H}^2$ or $\mathcal{H}^{\infty}$ performance specifications is formulated as an LMI optimization problem with respect to the unknown damping coefficients and feedback gains. Numerical examples motivated from structural and aerospace engineering applications demonstrate the advantages and computational efficiency of the proposed technique for integrated structural and control design. The effectiveness of the proposed integrated design becomes apparent, especially in very large scale structural systems where the use of classical methods for solving Lyapunov and Riccati equations associated with $\mathcal{H}^2$ and $\mathcal{H}^{\infty}$ designs are time-consuming or intractable.     

$$hbox {CTL}^{star }$$ family-based model checking using variability abstractions and modal transition systems

International Journal on Software Tools for Technology Transfer - Variational systems can produce a (potentially huge) number of related systems, known as products or variants, by using features...     

Pseudorandom functions in \textit{TC}^{0} and cryptographic limitations to proving lower bounds

This paper investigates which complexity classes inside NC can contain pseudorandom function generators (PRFGs). Under the Decisional Diffie-Hellman assumption (a common cryptographic assumption) TC⁰ \textit{TC}^{0} 4 contains PRFGs. No lower complexity classes with this property are currently known. On the other hand, we use effective lower bound arguments to show that some complexity classes cannot contain PRFGs. This provides evidence for the following conjecture: Any effective lower bound argument for a complexity class can be turned into an efficient distinguishing algorithm which proves that this class cannot     

Unextendible maximally entangled bases in $${\mathbb {C}}^{pd}\otimes {\mathbb {C}}^{qd}$$

The construction of unextendible maximally entangled bases is tightly related to quantum information processing like local state discrimination. We put forward two constructions of UMEBs in \({\mathbb {C}}^{pd}\otimes {\mathbb {C}}^{qd}\)(\(p\le q\)) based on the constructions of UMEBs in \({\mathbb {C}}^{d}\otimes {\mathbb {C}}^{d}\) and in \({\mathbb {C}}^{p}\otimes {\mathbb {C}}^{q}\), which generalizes the results in Guo (Phys Rev A 94:052302, 2016) by two approaches. Two different 48-member UMEBs in \({\mathbb {C}}^{6}\otimes {\mathbb {C}}^{9}\) have been constructed in detail.     

PRESC ^{2}: efficient self-reconfiguration of cache strategies for elastic caching platforms

Elastic caching platforms (ECPs) play an important role in accelerating the performance of Web applications. Several cache strategies have been proposed for ECPs to manage data access and distributions while maintaining the service availability. In our earlier research, we have demonstrated that there is no “one-fits-all” strategy for heterogeneous scenarios and the selection of the optimal strategy is related with workload patterns, cluster size and the number of concurrent users. In this paper, we present a new reconfiguration framework named PRESC $^{2}$ . It applies machine learning approaches to determine an optimal cache strategy and supports online optimization of performance model through trace-driven simulation or semi-supervised classification. Besides, the authors also propose a robust cache entries synchronization algorithm and a new optimization mechanism to further lower the adaptation costs. In our experiments, we find that PRESC $^{2}$ improves the elasticity of ECPs and brings big performance gains when compared with static configurations.     

Anisotropic Nonconforming { EQ}_1^{rot} Quadrilateral Finite Element Approximation to Second Order Elliptic Problems

The main aim of this paper is to study the nonconforming $EQ_1^{rot}$ quadrilateral finite element approximation to second order elliptic problems on anisotropic meshes. The optimal order error estimates in broken energy norm and $L^2$ -norm are obtained, and three numerical experiments are carried out to confirm the theoretical results.     

\upmu \mathrm{DC}^2: unified data collection for data centers

Modern data centers are playing an important role in a world full of information and communication technologies (ICTs). Many efforts have been paid to build a more efficient, cleaner data center for economic, social, and environmental benefits. This objective is being enabled by emerging technologies such as cloud computing and software-defined networking (SDN). However, a data center is inherently heterogeneous, consisting of servers, networking devices, cooling devices, power supply devices, etc., resulting in daunting challenges in its management and control. Previous approaches typically focus on only a single domain, for example, traditional cloud computing for server resource (e.g., computing resource and storage resource) management and SDN for network management. In a similar context of networking device heterogeneity, network function virtualization has been proposed to offer a standard abstract interface to manage all networking devices. In this research, we take the challenge of building a suit of unified middleware to monitor and control the three intrinsic subsystems in a data centre, including ICT, power, and cooling. Specifically, we present \(\upmu \mathrm{DC}^2\) , a unified scalable IP-based data collection system for data center management with elevated extensibility, as an initial step to offer a unified platform for data center operations. Our system consists of three main parts, i.e., data-source adapters for information collection over various subsystems in a data center, a unified message bus for data transferring, and a high-performance database for persistent data storage. We have conducted performance benchmark for the key building components, namely messaging server and database, confirming that our system is scalable for a data center with high device density and real-time management requirements. Key features, such as configuration files, dynamical module loading, and data compression, enhance our implementation with high extensibility and performance. The effectiveness of our proposed data collection system is verified by sample applications, such as, traffic flow migration for load balancing, VM migration for resource reservation, and server power management for hardware safety. This research lays out a foundation for a unified data centre management in future.     

Subspace Learning by $$\ell ^{0}$$-Induced Sparsity

Subspace clustering methods partition the data that lie in or close to a union of subspaces in accordance with the subspace structure. Such methods with sparsity prior, such as sparse subspace clustering (SSC) (Elhamifar and Vidal in IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781, 2013) with the sparsity induced by the \(\ell ^{1}\)-norm, are demonstrated to be effective in subspace clustering. Most of those methods require certain assumptions, e.g. independence or disjointness, on the subspaces. However, these assumptions are not guaranteed to hold in practice and they limit the application of existing sparse subspace clustering methods. In this paper, we propose \(\ell ^{0}\)-induced sparse subspace clustering (\(\ell ^{0}\)-SSC). In contrast to the required assumptions, such as independence or disjointness, on subspaces for most existing sparse subspace clustering methods, we prove that \(\ell ^{0}\)-SSC guarantees the subspace-sparse representation, a key element in subspace clustering, for arbitrary distinct underlying subspaces almost surely under the mild i.i.d. assumption on the data generation. We also present the “no free lunch” theorem which shows that obtaining the subspace representation under our general assumptions can not be much computationally cheaper than solving the corresponding \(\ell ^{0}\) sparse representation problem of \(\ell ^{0}\)-SSC. A novel approximate algorithm named Approximate \(\ell ^{0}\)-SSC (A\(\ell ^{0}\)-SSC) is developed which employs proximal gradient descent to obtain a sub-optimal solution to the optimization problem of \(\ell ^{0}\)-SSC with theoretical guarantee. The sub-optimal solution is used to build a sparse similarity matrix upon which spectral clustering is performed for the final clustering results. Extensive experimental results on various data sets demonstrate the superiority of A\(\ell ^{0}\)-SSC compared to other competing clustering methods. Furthermore, we extend \(\ell ^{0}\)-SSC to semi-supervised learning by performing label propagation on the sparse similarity matrix learnt by A\(\ell ^{0}\)-SSC and demonstrate the effectiveness of the resultant semi-supervised learning method termed \(\ell ^{0}\)-sparse subspace label propagation (\(\ell ^{0}\)-SSLP).     

Mutually unbiased special entangled bases with Schmidt number 2 in $${\mathbb {C}}^3 \otimes {\mathbb {C}}^{4k}$$

A way of constructing special entangled basis with fixed Schmidt number 2 (SEB2) in \({\mathbb {C}}^3 \otimes {\mathbb {C}}^{4k}(k\in z^+,3\not \mid k)\) is proposed, and the conditions mutually unbiased SEB2s (MUSEB2s) satisfy are discussed. In addition, a very easy way of constructing MUSEB2s in \({\mathbb {C}}^3 \otimes {\mathbb {C}}^{4k}(k=2^l)\) is presented. We first establish the concrete construction of SEB2 and MUSEB2s in \({\mathbb {C}}^3 \otimes {\mathbb {C}}^{4}\) and \({\mathbb {C}}^3 \otimes {\mathbb {C}}^{8}\), respectively, and then generalize them into \({\mathbb {C}}^3 \otimes {\mathbb {C}}^{4k}(k\in z^+,3\not \mid k)\) and display the condition that MUSEB2s satisfy; we also give general form of two MUSEB2s as examples in \({\mathbb {C}}^3 \otimes {\mathbb {C}}^{4k}(k=2^l)\).     

A unified approximate reasoning theory suitable for both propositional calculus system \mathcal{L}^* and predicate calculus system \mathcal{K}^*

The concepts of metric R ₀-algebra and Hilbert cube of type R ₀ are introduced. A unified approximate reasoning theory in propositional caculus system $\mathcal{L}^* $ and predicate calculus system $\mathcal{K}^* $ is established semantically as well as syntactically, and a unified complete theorem is obtained.     

Phase field computations for surface diffusion and void electromigration in {\mathbb{R}^3}

We consider a finite element approximation of a phase field model for the evolution of voids by surface diffusion in an electrically conducting solid. The phase field equations are given by a degenerate Cahn–Hilliard equation with an external forcing induced by the electric field. We describe the iterative scheme used to solve the resulting nonlinear discrete equations and present some numerical experiments in three space dimensions. The first author was supported by the EPSRC grant EP/C548973/1.     

$$hbox {TG}^2$$ TG 2 : text-guided transformer GAN for restoring document readability and perceived quality

International Journal on Document Analysis and Recognition (IJDAR) - Most image enhancement methods focused on restoration of digitized textual documents are limited to cases where the text...     

Distributed {{\mathcal H}^2} -matrices for non-local operators

${{\mathcal H}^2}$ -matrices can be used to approximate dense n × n matrices resulting from the discretization of certain non-local operators (e.g., Fredholm-type integral operators) in ${{\mathcal O}(n k)}$ units of storage, where k is a parameter controlling the accuracy of the approximation. Since typically k ? n holds, this representation is much more efficient than the conventional representation by a two-dimensional array. For very large problem dimensions, the amount of available storage becomes a limiting factor for practical algorithms. A popular way to provide sufficiently large amounts of storage at relatively low cost is to use a cluster of inexpensive computers that are connected by a network. This paper presents a method for managing an ${{\mathcal H}^2}$ -matrix on a distributed-memory cluster that can be proven to be of almost optimal parallel efficiency.