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).     

The categories L-\mathbf{Top}_{0} and L-\mathbf{Sob} as epireflective hulls

We show that the category \(L\) - \(\mathbf{Top}_{0}\) of \(T_{0}\) - \(L\) -topological spaces is the epireflective hull of Sierpinski \(L\) -topological space in the category \(L\) - \(\mathbf{Top}\) of \(L\) -topological spaces and the category \(L\) - \(\mathbf{Sob}\) of sober \(L\) -topological spaces is the epireflective hull of Sierpinski \(L\) -topological space in the category \(L\) - \(\mathbf{Top}_{0}\) .     

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.     

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.     

On Projection Matrices $$\mathcal{P}^k \to \mathcal{P}^2 ,k = 3,...,6, $$ and their Applications in Computer Vision

Projection matrices from projective spaces have long been used in multiple-view geometry to model the perspective projection created by the pin-hole camera. In this work we introduce higher-dimensional mappings for the representation of various applications in which the world we view is no longer rigid. We also describe the multi-view constraints from these new projection matrices (where k > 3) and methods for extracting the (non-rigid) structure and motion for each application.     

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 \({\mathbb {C}}^3 \otimes {\mathbb {C}}^{4k}(k=2^l)\).     

Branching Time Logics \mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha } with Operations Until and Since Based on Bundles of Integer Numbers, Logical Consecutions, Deciding Algorithms

This paper is intended as an attempt to describe logical consequence in branching time logics. We study temporal branching time logics $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ which use the standard operations Until and Next and dual operations Since and Previous (LTL, as standard, uses only Until and Next). Temporal logics $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ are generated by semantics based on Kripke/Hinttikka structures with linear frames of integer numbers $\mathcal {Z}$ with a single node (glued zeros). For $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ , the permissible branching of the node is limited by α (where 1≤α≤ω). We prove that any logic $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ is decidable w.r.t. admissible consecutions (inference rules), i.e. we find an algorithm recognizing consecutions admissible in $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ . As a consequence, it implies that $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ itself is decidable and solves the satisfiability problem.     

A hybrid \text{ M}5^\prime -genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process

Recent years have seen various rapid prototyping (RP) processes such as fused deposition modelling (FDM) and three-dimensional printing being used for fabricating prototypes, leading to shorter product development times and less human intervention. The literature reveals that the properties of RP built parts such as surface roughness, strength, dimensional accuracy, build cost, etc are related to and can be improved by the appropriate settings of the input process parameters. Researchers have formulated physics-based models and applied empirical modelling techniques such as regression analysis and artificial neural network for the modelling of RP processes. Physics-based models require in-depth understanding of the processes which is a formidable task due to their complexity. The issue of improving trustworthiness of the prediction ability of empirical models on test (unseen) samples is paid little attention. In the present work, a hybrid M5 \(^{\prime }\) -genetic programming (M5 \(^{\prime }\) -GP) approach is proposed for empirical modelling of the FDM process with an attempt to resolve this issue of ensuring trustworthiness. This methodology is based on the error compensation achieved using a GP model in parallel with a M5 \(^{\prime }\) model. The performance of the proposed hybrid model is compared to those of support vector regression (SVR) and adaptive neuro fuzzy inference system (ANFIS) model and it is found that the M5 \(^{\prime }\) -GP model has the goodness of fit better than those of the SVR and ANFIS models.     

Practical $\phi_{0}$-stability of switched stochastic nonlinear systems and corresponding stochastic perturbation theory

The aim of this paper is to study the practical $\phi_{0}$-stability in probability (P$\phi_{0}$SiP) and practical $\phi_{0}$-stability in pth mean (P$\phi_{0}$SpM) of switched stochastic nonlinear systems. Sufficient conditions on such practical properties are obtained by using the comparison principle and the cone-valued Lyapunov function methods. Also, based on an extended comparison principle, a perturbation theory of switched stochastic systems is given.     

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.     

Ring Theory-Based Evolutionary Algorithm and its application to D{0-1} KP

In this paper, we propose a new view for designing an evolutionary algorithm by using algebraic theory to solve the combinatorial optimization problem. Using the addition, multiplication and inverse operation of the direct product of rings, we first propose two evolution operators: the global exploration operator (R-GEO) and the local development operator (R-LDO). Then, by utilizing the R-GEO and R-LDO to generate individuals and applying the greedy selection strategy to generate a new population, we propose a new algorithm – the Ring Theory-Based Evolutionary Algorithm (RTEA) – for the combinatorial optimization problem. Moreover, we give a new method for solving the discounted {0-1} knapsack problem (D{0–} KP) by using the RTEA. To verify the performance of the RTEA, we use it and existing algorithms to solve four kinds of large-scale instances of the D{0-1} KP. The computational results show that the RTEA performs better than the others, and it is more suitable for solving the D{0-1} KP problem. Moreover, it indicates that using algebraic theory to design evolutionary algorithms is feasible and effective.     

The method of interlineation of vector functions \vec{w} (x, y, z, t) on a system of vertical straight lines and its application in crosshole seismic tomography

The authors propose a method to construct interlineation operators for vector functions $ \vec{w} $ (x, y, z, t) on a system of arbitrarily located vertical straight lines. The method allows calculating the vector $ \vec{w} $ at each point (x, y, z) between straight lines Γ _k for any instant of time t ≥ 0. They are proposed to be used to construct a crosshole accelerometer to model Earth’s crust on the basis of seismic sounding data $ {{\vec{w}}_k}\left( {z,t} \right),\,k=\overline{1,M} $ , about the vector of acceleration $ \vec{w} $ (x, y, z, t) received by accelerometers at each chink Γ _k .     

An Enhanced tabu search algorithm to minimize a bi-criteria objective in batching and scheduling problems on unrelated-parallel machines with desired lower bounds on batch sizes

This paper addresses a sequence- and machine-dependent batch scheduling problem on a set of unrelated-parallel machines where the objective is to minimize a linear combination of total weighted completion time and total weighted tardiness. In particular, batch scheduling disregards the group technology assumptions by allowing for the possibility of splitting pre-determined groups of jobs into batches with respect to desired lower bounds on batch sizes. With regard to bounds on batch sizes, the MILP model is developed as two integrated batching and scheduling phases to present the problem. A benchmark of small-size instances on group scheduling shows the superior performance of batch scheduling up to 37% reduction in the objective function value. An efficient meta-heuristic algorithm based on tabu search with multi-level diversification and multi-tabu structure is developed at three levels, which moves back and forth between batching and scheduling phases. To eliminate searching in ineffective neighborhoods and thus enhance computational efficiency of search algorithms, several lemmas are proposed and proven. The results of applying lemmas reflect up to 40% reduction in computational times. Comparing the optimal solutions found by CPLEX and tabu search shows that the tabu search algorithm could find solutions, at least as good as CPLEX but in incredibly shorter computational time. In order to trigger the search algorithm, two different initial solution finding mechanisms have been developed and implemented. Also, due to lack of a qualified benchmark for unrelated-parallel machines, a comprehensive data generation mechanism has been developed in a way that it fairly reflects the real world situations encountered in practice. The machine availability times and job release times are considered to be dynamic and the run time of each job differs on different machines based upon the capability of the machines.