Complete discriminant evaluation and feature extraction in kernel space for face recognition

This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to limited number of training samples. A weighting function is proposed to circumvent undue scaling of eigenvectors corresponding to the unreliable small and zero eigenvalues. Eigenfeatures are then extracted by the discriminant evaluation in the whole kernel space. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results on FERET, ORL and GT databases show that our approach consistently outperforms other kernel based face recognition methods.

Actions Arising from Intersection and Union

An action is a pair of sets, C and S, and a function \(f:C\times S \rightarrow C\). Rothschild and Yalcin gave a simple axiomatic characterization of those actions arising from set intersection, i.e. for which the elements of C and S can be identified with sets in such a way that elements of S act on elements of C by intersection. We introduce and axiomatically characterize two natural classes of actions which arise from set intersection and union. In the first class, the \(\uparrow \!\!\downarrow \)-actions, each element of S is identified with a pair of sets \((s^\downarrow ,s^\uparrow )\), which act on a set c by intersection with \(s^\downarrow \) and union with \(s^\uparrow \). In the second class, the \(\uparrow \!\!\downarrow \)-biactions, each element of S is labeled as an intersection or a union, and acts accordingly on C. We give intuitive examples of these actions, one involving conversations and another a university’s changing student body. The examples give some motivation for considering these actions, and also help give intuitive readings of the axioms. The class of \(\uparrow \!\!\downarrow \)-actions is closely related to a class of single-sorted algebras, which was previously treated by Margolis et al., albeit in another guise (hyperplane arrangements), and we note this connection. Along the way, we make some useful, though very general, observations about axiomatization and representation problems for classes of algebras.     

Nonatomic dual bakery algorithm with bounded tokens

A simple mutual exclusion algorithm is presented that only uses nonatomic shared variables of bounded size, and that satisfies bounded overtaking. When the shared variables behave atomically, it has the first-come-first-served property (FCFS). Nonatomic access makes information vulnerable. The effects of this can be mitigated by minimizing the information and by spreading it over more variables. The design approach adopted here begins with such mitigating efforts. These resulted in an algorithm with a proof of correctness, first for atomic variables. This proof is then used as a blueprint for the simultaneous development of the algorithm for nonatomic variables and its proof. Mutual exclusion is proved by means of invariants. Bounded overtaking and liveness under weak fairness are proved with invariants and variant functions. Liveness under weak fairness is formalized and proved in a set-theoretic version of temporal logic. All these assertions are verified with the proof assistant PVS. We heavily rely on the possibility offered by a proof assistant like PVS to reuse proofs developed for one context in a different context.     

DOLAR: virtualizing heterogeneous information spaces to support their expansion

Users expect applications to successfully cope with the expansion of information as necessitated by the continuous inclusion of novel types of content. Given that such content may originate from ‘not‐seen thus far’ data collections and/or data sources, the challenging issue is to achieve the return of investment on existing services, adapting to new information without changing existing business‐logic implementation. To address this need, we introduce DOLAR (Data Object Language And Runtime), a service‐neutral framework which virtualizes the information space to avoid invasive, time‐consuming, and expensive source‐code extensions that frequently break applications. Specifically, DOLAR automates the introduction of new business‐logic objects in terms of the proposed virtual ‘content objects’. Such user‐specified virtual objects align to storage artifacts and help realize uniform ‘store‐to‐user’ data flows atop heterogeneous sources, while offering the reverse ‘user‐to‐store’ flows with identical effectiveness and ease of use. In addition, the suggested virtual object composition schemes help decouple business logic from any content origin, storage and/or structural details, allowing applications to support novel types of items without modifying their service provisions. We expect that content‐rich applications will benefit from our approach and demonstrate how DOLAR has assisted in the cost‐effective development and gradual expansion of a production‐quality digital library. Experimentation shows that our approach imposes minimal overheads and DOLAR‐based applications scale as well as any underlying datastore(s). Copyright © 2011 John Wiley & Sons, Ltd.     

Fully-adaptive algorithms for long-lived renaming

Long-lived renaming allows processes to repeatedly get distinct names from a small name space and release these names. This paper presents two long-lived renaming algorithms in which the name a process gets is bounded above by the number of processes currently occupying a name or performing one of these operations. The first algorithm is asynchronous, uses LL/SC objects, and has step complexity that is linear in the number of processes, c, currently getting or releasing a name. The second is synchronous, uses registers and counters, and has step complexity that is polylogarithmic in c. Both tolerate any number of process crashes.     

Automated discovery of local search heuristics for satisfiability testing

The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.     

Optimal neuronal tuning for finite stimulus spaces

The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. For three dimensions and higher, there is an optimal tuning width.     

On Kalman filtering over fading wireless channels with controlled transmission powers

We study stochastic stability of centralized Kalman filtering for linear time-varying systems equipped with wireless sensors. Transmission is over fading channels where variable channel gains are counteracted by power control to alleviate the effects of packet drops. We establish sufficient conditions for the expected value of the Kalman filter covariance matrix to be exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing power control policies which minimize the total sensor power budget. In deriving the optimal power control laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases.     

Recycle of valuable products from oily cold rolling mill sludge

Oily cold rolling mill （CRM） sludge contains lots of iron and alloying elements along with plenty of hazardous organic components, which makes it as an attractive secondary source and an environmental contaminant at the same time. The compound methods of ＂vacuum distillation ＋ oxidizing roasting＂ and ＂vacuum distillation ＋ hydrogen reduction＂ were employed for the recycle of oily cold rolling mill sludge. First, the sludge was dynamically vacuum distilled in a rotating furnace at 50 r/rain and 600℃ for 3 h, which removed almost hazardous organic components, obtaining 89.2wt% ferrous resultant. Then, high purity ferric oxide powders （99.2wt%） and reduced iron powders （98.9wt%） were obtained when the distillation residues were oxidized and reduced, respectively. The distillation oil can be used for fuel or chemical feedstock, and the distillation gases can be collected and reused as a fuel.