Window annealing for pixel-labeling problems

The pixel labeling problems in computer vision are often formulated as energy minimization tasks. Algorithms such as graph cuts and belief propagation are prominent; however, they are only applicable for specific energy forms. For general optimization, Markov Chain Monte Carlo (MCMC) based simulated annealing can estimate the minima states very slowly.This paper presents a sampling paradigm for faster optimization. First, in contrast to previous MCMCs, the role of detailed balance constraint is eliminated. The reversible Markov chain jumps are essential for sampling an arbitrary posterior distribution, but they are not essential for optimization tasks. This allows a computationally simple window cluster sample. Second, the proposal states are generated from combined sets of local minima which achieve a substantial increase in speed compared to uniformly labeled cluster proposals. Third, under the coarse-to-fine strategy, the maximum window size variable is incorporated along with the temperature variable during simulated annealing. The proposed window annealing is experimentally shown to be many times faster and capable of finding lower energy compared to the previous Gibbs and Swendsen-Wang cut (SW-cut) sampler. In addition, the proposed method is compared with other deterministic algorithms like graph cut, belief propagation, and spectral method in their own specific energy forms. Window annealing displays competitive performance in all domains.     

An efficient joint channel assignment and QoS routing protocol for IEEE 802.11 multi-radio multi-channel wireless mesh networks

With the emerging of video, voice over IP (VoIP) and other real-time multimedia services, more and more people pay attention to quality of service (QoS) issues in terms of the bandwidth, delay and jitter, etc. As one effective way of broadband wireless access, it has become imperative for wireless mesh networks (WMNs) to provide QoS guarantee. Existing works mostly modify QoS architecture dedicated for ad hoc or sensor networks, and focus on single radio and single channel case. Meanwhile, they study the QoS routing or MAC protocol from view of isolated layer. In this paper, we propose a novel cross-layer QoS-aware routing protocol on OLSR (CLQ-OLSR) to support real-time multimedia communication by efficiently exploiting multi-radio and multi-channel method. By constructing multi-layer virtual logical mapping over physical topology, we implement two sets of routing mechanisms, physical modified OLSR protocol (M-OLSR) and logical routing, to accommodate network traffic. The proposed CLQ-OLSR is based on a distributed bandwidth estimation scheme, implemented at each node for estimating the available bandwidth on each associated channel. By piggybacking the bandwidth information in HELLO and topology control (TC) messages, each node disseminates information of topology and available bandwidth to other nodes in the whole network in an efficient way. From topology and bandwidth information, the optimized path can be identified. Finally, we conduct extensive simulation to verify the performance of CLQ-OLSR in different scenarios on QualNet platform. The results demonstrate that our proposed CLQ-OLSR outperforms single radio OLSR, multi-radio OLSR and OLSR with differentiated services (DiffServ) in terms of network aggregate throughput, end-to-end packet delivery ratio, delay and delay jitter with reasonable message overheads and hardware costs. In particular, the network aggregate throughput for CLQ-OLSR can almost be improved by 300% compared with the single radio case.     

A novel sequential watermark detection model for efficient traceback of secret network attack flows

Network watermarking schemes have been proposed to trace secret network attack flows transferred through stepping stones as well as anonymous channels. However, most existing network flow watermark detection techniques focus on a fixed sample size of network data to achieve the required accuracy. Irrespective of the uncertainty or information content of successive observations, such detection techniques will result in low efficiency of watermark detection. We herein propose a novel sequential watermark detection model (SWDM) supporting three sequential detectors for efficient traceback of network attack flows. By exploiting the sequential probability ratio test approach, we first propose the intuitive paired-intervals-based optimum watermark detector (POWD) and the single-interval-based optimum watermark detector (SOWD) under the assumption of known parameters of the observed attack flow. We then propose the sequential sign watermark detector (SSWD) that operates on two-level quantized observations for nonparametric watermark detection. Based on our SWDM model, a statistical analysis of sequential detectors, with no assumptions or limitations concerning the distribution of the timing of packets, proves their effectiveness despite traffic timing perturbations. The experiments using a large number of synthetically-generated SSH traffic flows demonstrate that there is a significant advantage in using our sequential watermark detectors based on the proposed SWDM model over the existing fixed sample size watermark detector (FSWD). Compared to the FSWD detector, the POWD detector achieves almost 28% savings in the average number of packets. Especially, given the required probability of detection errors, the SOWD detector and the SSWD detector can achieve almost 47% and 29% savings, respectively, in the average number of required packets, thus resulting in not only guaranteed rates of detection errors but also high efficiency of flow traceback.     

Exploring Virtual Librarianship: Second Life Library 2.0

ABSTRACT

In April 2006, Alliance Library System and Online Programming for All Libraries partnered to start the Second Life Library 2.0 in the online world Second Life and in August 2006 purchased an island on the Teen grid of Teen Second Life. Second Life is a virtual world, a descendant of multiplayer online games. Educational institutions have recently been drawn into Second Life's robust economy and vibrant cultural life. The Second Life Library 2.0 works with librarians from around the world and with other educational partners such as TechSoup, WorldBridges, and the New Media Consortium. Second Life Library 2.0 provides “traditional” library services such as ready reference, book discussions, and search assistance, but, at heart, it explores what it means to be a virtual library in a virtual world.     

Structure based virtual screening-driven identification of monastrol as a potent urease inhibitor

Virtual screening uses computer based methods to discover new ligands on the basis of biological structures. Among all virtual screening methods structure based docking has received considerable attention. In an attempt to identify new ligands as urease inhibitors, structure-based virtual screening (SBVS) of an in-house database of 10,000 organic compounds was carried out. The X-ray crystallographic structure of Bacillus pasteurii (BP) in complex with acetohydroxamic acid (PDB Code 4UBP) was used as a protein structure. As a starting point, ~10,000 compounds of our in-house database were analyzed to check redundancy and the compounds found repeated were removed from the database. Finally 6993 compounds were docked into the active site of BP urease using GOLD and MOE-Dock software. A remarkable feature of this study was the identification of monastrol, a well-known KSP inhibitor already in clinical trials, as a novel urease inhibitor. The hits identified were further evaluated by molecular docking and on examination of the affinity predictions, twenty-seven analogs of monastrol were synthesized by a multicomponent Biginelli reaction followed by their in vitro screening as urease inhibitors. Finally twelve compounds were identified as new urease inhibitors. The excellent in vitro activity suggested that these compounds may serve as viable lead compounds for the treatment of urease related problems.     

A hierarchical dense deformable model for 3D face reconstruction from skull

3D face reconstruction from skull has been investigated deeply by computer scientists in the past two decades because it is important for identification. The dominant methods construct 3D face from the soft tissue thickness measured at a set of landmarks on skull. The quantity and position of the landmarks are very vital for 3D face reconstruction, but there is no uniform standard for the selection of the landmarks. Additionally, the acquirement of the landmarks on skull is difficult without manual assistance. In this paper, an automatic 3D face reconstruction method based on a hierarchical dense deformable model is proposed. To construct the model, the skull and face samples are acquired by CT scanner and represented as dense triangle mesh. Then a non-rigid dense mesh registration algorithm is presented to align all the samples in point-to-point correspondence. Based on the aligned samples, a global deformable model is constructed, and three local models are constructed from the segmented patches of the eye, nose and mouth. For a given skull, the globe and local deformable models are iteratively matched with it, and the reconstructed facial surface is obtained by fusing the globe and local reconstruction results. To validate the presented method, a measurement in the coefficient domain of a face deformable model is defined. The experimental results indicate that the proposed method has good performance for 3D face reconstruction from skull.     

Feature Extraction Based on Maximum Nearest Subspace Margin Criterion

Based on the classification rule of sparse representation-based classification (SRC) and linear regression classification (LRC), we propose the maximum nearest subspace margin criterion for feature extraction. The proposed method can be seen as a preprocessing step of SRC and LRC. By maximizing the inter-class reconstruction error and minimizing the intra-class reconstruction error simultaneously, the proposed method significantly improves the performances of SRC and LRC. Compared with linear discriminant analysis, the proposed method avoids the small sample size problem and can extract more features. Moreover, we extend LRC to overcome the potential singular problem. The experimental results on the extended Yale B (YALE-B), AR, PolyU finger knuckle print and the CENPARMI handwritten numeral databases demonstrate the effectiveness of the proposed method.     

Delay Dependent Exponential Stability for Fuzzy Recurrent Neural Networks with Interval Time-Varying Delay

In this paper, the problem of delay-dependent exponential stability for fuzzy recurrent neural networks with interval time-varying delay is investigated. The delay interval has been decomposed into multiple non equidistant subintervals, on these interval Lyapunov-Krasovskii functionals (LKFs) are constructed to study stability analysis. Employing these LKFs, an exponential stability criterion is proposed in terms of Linear Matrix Inequalities (LMIs) which can be easily solved by MATLAB LMI toolbox. Numerical example is given to illustrate the effectiveness of the proposed method.     

Adoption and use of Java generics

Support for generic programming was added to the Java language in 2004, representing perhaps the most significant change to one of the most widely used programming languages today. Researchers and language designers anticipated this addition would relieve many long-standing problems plaguing developers, but surprisingly, no one has yet measured how generics have been adopted and used in practice. In this paper, we report on the first empirical investigation into how Java generics have been integrated into open source software by automatically mining the history of 40 popular open source Java programs, traversing more than 650 million lines of code in the process. We evaluate five hypotheses and research questions about how Java developers use generics. For example, our results suggest that generics sometimes reduce the number of type casts and that generics are usually adopted by a single champion in a project, rather than all committers. We also offer insights into why some features may be adopted sooner and others features may be held back.     

Behaviour and space in agent-based modelling: Poverty patterns in East Kalimantan,Indonesia

Applied models of policy interventions are increasingly expected to consider households’ responses to these interventions, which makes agent-based modelling popular in applied policy situations. Implementing an adequate level of agent heterogeneity and mapping it into a spatial environment are critical factors of such applied modelling. However, policy applications demand the characterisation and parameterisation of behavioural response functions of heterogeneous agents and the spatial distribution of heterogeneous agents, which are neither highly transparent nor greatly tested steps in implementing agent-based models. This paper describes an agent-based model of fuel price changes for a case study in East Kalimantan, Indonesia, and specifically: (a) the characterisation and parameterisation approach, (b) resulting agent types for approximating behavioural heterogeneity, and (c) emerging spatial poverty and deforestation patterns. The model highlights the spatial dynamics of poverty dynamics, indicating that the direct impact of deforestation on poverty among forest-dwelling communities is to trigger their migration into peri-urban areas. Overall, the model suggests that poverty increases in response to fuel price reductions.