Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

Formation of single-walled carbon nanotube via the interaction of graphene nanoribbons : ab initio density functional calculations

The interaction of bare graphene nanoribbons (GNRs) was investigated by ab initio density functional theory calculations with both the local density approximation (LDA) and the generalized gradient approximation (GGA). Remarkably, two bare 8-GNRs with zigzag-shaped edges are predicted to form an (8, 8) armchair single-wall carbon nanotube (SWCNT) without any obvious activation barrier. The formation of a (10, 0) zigzag SWCNT from two bare 10-GNRs with armchair-shaped edges has activation barriers of 0.23 and 0.61 eV for using the LDA and the revised PBE exchange correlation functional, respectively, Our results suggest a possible route to control the growth of specific types SWCNT via the interaction of GNRs.

Role of lithium vacancies in accelerating the dehydrogenation kinetics on a LiBH4(010) surface : an Ab initio study

In this work, ab initio density functional calculations were performed to explore the effect of surface lithium vacancies on the initial dehydrogenation kinetics of lithium borohydride. We found that some B−H bonds in neighboring BH4-1 complexes around the vacancy became elongated (weakened). The activation barriers for the recombination of H atoms to form H2 were decreased from 3.64 eV for the stoichiometrically complete LiBH4(010) surface to 1.53 and 0.23 eV in the presence of mono- and di-vacancies, respectively. Our results indicate that the creation of Li vacancies may play a critical role in accelerating the dehydrogenation kinetics of LiBH4.

Mechanisms of glycerol dehydration

Dehydration of neutral and protonated glycerol was investigated using quantum mechanical calculations (CBS-QB3). Calculations on neutral glycerol show that there is a high barrier for simple 1,2-dehydration, E-a = 70.9 kcal mol(-1), which is lowered to 65.2 kcal mol(-1) for pericyclic 1,3-dehydration. In contrast, the barriers for dehydration of protonated glycerol are much lower. Dehydration mechanisms involving hydride transfer, pinacol rearrangement, or substitution reactions have barriers between 20 and 25 kcal mol(-1). Loss of water from glycerol via substitution results in either oxirane or oxetane intermediates, which can interconvert over a low barrier. Subsequent decomposition of these intermediates proceeds via either a second dehydration step or loss of formaldehyde. The computed mechanisms for decomposition of protonated glycerol are supported by the gas-phase fragmentation of protonated glycerol observed using a triple-quadrupole mass spectrometer.

A mass spectrometry study of XCO+, X = Si, Ge : Is SiCO+ a main group carbonyl? Comments on the bonding in ground state SiCO and the Si,C,O (+) potential energy surface

The cation\[Si,C,O](+) has been generated by 1) the electron ionisation (EI) of tetramethoxysilane and 2) chemical ionisation (CI) of a mixture of silane and carbon monoxide. Collisional activation (CA) experiments performed for mass-selected \[Si,C,O](+), generated by using both methods, indicate that the structure is not inserted OSiC+; however, a definitive structural assignment as Si+-CO, Si+-OC or some cyclic variant is impossible based on these results alone. Neutralisation-reionisation (+NR+) experiments for EI-generated \[Si,C,O](+) reveal a small peak corresponding to SiC+, but no detectable SiO+ signal, and thus establishes the existence of the Si+-CO isomer. CCSD(T)//B3LYP calculations employing a triple-zeta basis set have been used to explore the doublet and quartet potential-energy surfaces of the cation, as well as some important neutral states The results suggest that both Si+-CO and Si+ - OC isomers are feasible; however, the global minimum is (2)Pi SiCO+. Isomeric (2)Pi SiOC+ is 12.1 kcal mol(-1) less stable than (2)Pi SiCO+, and all quartet isomers are much higher in energy. The corresponding neutrals Si-CO and Si-OC are also feasible, but the lowest energy Si - OC isomer ((3)A") is bound by only 1.5 kcal mol(-1). We attribute most, if nor all, of the recovery signal in the +NR' experiment to SiCO+ survivor ions. The nature of the bonding in the lowest energy isomers of Si+ -(CO,OC) is interpreted with the aid of natural bond order analyses, and the ground stale bonding of SiCO+ is discussed in relation to classical analogues such as metal carbonyls and ketenes.

Best basis-based intelligent machine fault diagnosis

The wavelet packet transform decomposes a signal into a set of bases for time–frequency analysis. This decomposition creates an opportunity for implementing distributed data mining where features are extracted from different wavelet packet bases and served as feature vectors for applications. This paper presents a novel approach for integrated machine fault diagnosis based on localised wavelet packet bases of vibration signals. The best basis is firstly determined according to its classification capability. Data mining is then applied to extract features and local decisions are drawn using Bayesian inference. A final conclusion is reached using a weighted average method in data fusion. A case study on rolling element bearing diagnosis shows that this approach can greatly improve the accuracy ofdiagno sis.

Remaining useful life prediction using elliptical basis function network and Markov Chain

This paper presents a novel method for remaining useful life prediction using the Elliptical Basis Function (EBF) network and a Markov chain. The EBF structure is trained by a modified Expectation-Maximization (EM) algorithm in order to take into account the missing covariate set. No explicit extrapolation is needed for internal covariates while a Markov chain is constructed to represent the evolution of external covariates in the study. The estimated external and the unknown internal covariates constitute an incomplete covariate set which are then used and analyzed by the EBF network to provide survival information of the asset. It is shown in the case study that the method slightly underestimates the remaining useful life of an asset which is a desirable result for early maintenance decision and resource planning.

A new approach for power system black-start decision-making with vague set theory

Power system restoration after a large area outage involves many factors, and the procedure is usually very complicated. A decision-making support system could then be developed so as to find the optimal black-start strategy. In order to evaluate candidate black-start strategies, some indices, usually both qualitative and quantitative, are employed. However, it may not be possible to directly synthesize these indices, and different extents of interactions may exist among these indices. In the existing black-start decision-making methods, qualitative and quantitative indices cannot be well synthesized, and the interactions among different indices are not taken into account. The vague set, an extended version of the well-developed fuzzy set, could be employed to deal with decision-making problems with interacting attributes. Given this background, the vague set is first employed in this work to represent the indices for facilitating the comparisons among them. Then, a concept of the vague-valued fuzzy measure is presented, and on that basis a mathematical model for black-start decision-making developed. Compared with the existing methods, the proposed method could deal with the interactions among indices and more reasonably represent the fuzzy information. Finally, an actual power system is served for demonstrating the basic features of the developed model and method.

Understanding the genetic basis of invasiveness

Invasive species provide excellent study systems to evaluate the ecological and evolutionary processes that contribute to the colonization of novel environments. While the ecological processes that contribute to the successful establishment of invasive plants have been studied in detail, investigation of the evolutionary processes involved in successful invasions has only recently received attention. In particular, studies investigating the genomic and gene expression differences between native and introduced populations of invasive species are just beginning and are required if we are to understand how plants become invasive. In the current issue of Molecular Ecology, Hodgins et al. () tackle this unresolved question, by examining gene expression differences between native and introduced populations of annual ragweed, Ambrosia artemisiifolia. The study identifies a number of potential candidate genes based on gene expression differences that may be responsible for the success of annual ragweed in its introduced range. Furthermore, genes involved in stress response are over-represented in the differentially expressed gene set. Future experiments could use functional studies to test whether changes in gene expression at these candidate genes do in fact underlie changes in growth characteristics and reproductive output observed in this and other invasive species.

Inelastic finite element analysis of composite beams on the basis of the plastic hinge approach

This paper emphasizes material nonlinear effects on composite beams with recourse to the plastic hinge method. Numerous combinations of steel and concrete sections form arbitrary composite sections. Secondly, the material properties of composite beams vary remarkably across its section from ductile steel to brittle concrete. Thirdly, concrete is weak in tension, so composite section changes are dependent on load distribution. To this end, the plastic zone approach is convenient for inelastic analysis of composite sections that can evaluate member resistance, including material nonlinearities, by routine numerical integration with respect to every fiber across the composite section. As a result, many researchers usually adopt the plastic zone approach for numerical inelastic analyses of composite structures. On the other hand, the plastic hinge method describes nonlinear material behaviour of an overall composite section integrally. Consequently, proper section properties for use in plastic hinge spring stiffness are required to represent the material behaviour across the arbitrary whole composite section. In view of numerical efficiency and convergence, the plastic hinge method is superior to the plastic zone method. Therefore, based on the plastic hinge approach, how to incorporate the material nonlinearities of the arbitrary composite section into the plastic hinge stiffness formulation becomes a prime objective of the present paper. The partial shear connection in this paper is by virtue of the effective flexural rigidity as AISC 1993 [American Institute of Steel Construction (AISC). Load and resistance factor design specifications. 2nd ed., Chicago; 1993]. Nonlinear behaviour of different kinds of composite beam is investigated in this paper, including two simply supported composite beams, a cantilever and a two span continuous composite beam.

A query-basis approach to parametrizing novelty-biased cumulative gain

Novelty-biased cumulative gain (α-NDCG) has become the de facto measure within the information retrieval (IR) community for evaluating retrieval systems in the context of sub-topic retrieval. Setting the incorrect value of parameter α in α-NDCG prevents the measure from behaving as desired in particular circumstances. In fact, when α is set according to common practice (i.e. α = 0.5), the measure favours systems that promote redundant relevant sub-topics rather than provide novel relevant ones. Recognising this characteristic of the measure is important because it affects the comparison and the ranking of retrieval systems. We propose an approach to overcome this problem by defining a safe threshold for the value of α on a query basis. Moreover, we study its impact on system rankings through a comprehensive simulation.

Social networking and digital gaming media convergence : Classification and its consequences for appropriation

Abstract Within the field of Information Systems, a good proportion of research is concerned with the work organisation and this has, to some extent, restricted the kind of application areas given consideration. Yet, it is clear that information and communication technology deployments beyond the work organisation are acquiring increased importance in our lives. With this in mind, we offer a field study of the appropriation of an online play space known as Habbo Hotel. Habbo Hotel, as a site of media convergence, incorporates social networking and digital gaming functionality. Our research highlights the ethical problems such a dual classification of technology may bring. We focus upon a particular set of activities undertaken within and facilitated by the space – scamming. Scammers dupe members with respect to their ‘Furni’, virtual objects that have online and offline economic value. Through our analysis we show that sometimes, online activities are bracketed off from those defined as offline and that this can be related to how the technology is classified by members – as a social networking site and/or a digital game. In turn, this may affect members’ beliefs about rights and wrongs. We conclude that given increasing media convergence, the way forward is to continue the project of educating people regarding the difficulties of determining rights and wrongs, and how rights and wrongs may be acted out with respect to new technologies of play online and offline.

Convergence-guaranteed time-varying RRT path planning for profiling floats in 4-Dimensional flow

This paper presents an extension to the Rapidly-exploring Random Tree (RRT) algorithm applied to autonomous, drifting underwater vehicles. The proposed algorithm is able to plan paths that guarantee convergence in the presence of time-varying ocean dynamics. The method utilizes 4-Dimensional, ocean model prediction data as an evolving basis for expanding the tree from the start location to the goal. The performance of the proposed method is validated through Monte-Carlo simulations. Results illustrate the importance of the temporal variance in path execution, and demonstrate the convergence guarantee of the proposed methods.

Stability and convergence of a new finite volume method for a two-sided space-fractional diffusion equation

In this paper, we consider a two-sided space-fractional diffusion equation with variable coefficients on a finite domain. Firstly, based on the nodal basis functions, we present a new fractional finite volume method for the two-sided space-fractional diffusion equation and derive the implicit scheme and solve it in matrix form. Secondly, we prove the stability and convergence of the implicit fractional finite volume method and conclude that the method is unconditionally stable and convergent. Finally, some numerical examples are given to show the effectiveness of the new numerical method, and the results are in excellent agreement with theoretical analysis.