Making a shallow network deep: Conversion of a boosting classifier into a decision tree by boolean optimisation

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimization problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit-a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems. © 2011 Springer Science+Business Media, LLC.

Multiple classifier boosting and tree-structured classifiers

Visual recognition problems often involve classification of myriads of pixels, across scales, to locate objects of interest in an image or to segment images according to object classes. The requirement for high speed and accuracy makes the problems very challenging and has motivated studies on efficient classification algorithms. A novel multi-classifier boosting algorithm is proposed to tackle the multimodal problems by simultaneously clustering samples and boosting classifiers in Section 2. The method is extended into an online version for object tracking in Section 3. Section 4 presents a tree-structured classifier, called Super tree, to further speed up the classification time of a standard boosting classifier. The proposed methods are demonstrated for object detection, tracking and segmentation tasks. © 2013 Springer-Verlag Berlin Heidelberg.

A new technique for boosting efficiency of silicon solar cells

A new type of photovoltaic system with higher generation power density has been studied in detail. The feature of the system is a V-shaped module (VSM) with two tilted monocrystalline solar cells. Compared to solar cells in a flat orientation, the VSM enhances external quantum efficiency and leads to an increase of 31% in power conversion efficiency. Due to the VSM technique, short-circuit current density was raised from 24.94 to 33.7mA/cm(2), but both fill factor and open-circuit voltage were approximately unchanged. For the VSM similar results (about 30% increase) were obtained for solar cells fabricated by using mono-crystal line silicon wafers with only conventional background impurities. (c) 2004 Elsevier B.V. All rights reserved.

Visual-Context Boosting for Eye Detection

Eye detection plays an important role in many practical applications. This paper presents a novel two-step scheme for eye detection. The first step models an eye by a newly defined visual-context pattern (VCP), and the second step applies semisupervised boosting for precise detection. VCP describes both the space and appearance relations between an eye region (region of eye) and a reference region (region of reference). The context feature of a VCP is extracted by using the integral image. Aiming to reduce the human labeling efforts, we apply semisupervised boosting, which integrates the context feature and the Haar-like features for precise eye detection. Experimental results on several standard face data sets demonstrate that the proposed approach is effective, robust, and efficient. We finally show that this approach is ready for practical applications.

Boosting Nearest Neighbor Classifiers for Multiclass Recognition

This paper introduces an algorithm that uses boosting to learn a distance measure for multiclass k-nearest neighbor classification. Given a family of distance measures as input, AdaBoost is used to learn a weighted distance measure, that is a linear combination of the input measures. The proposed method can be seen both as a novel way to learn a distance measure from data, and as a novel way to apply boosting to multiclass recognition problems, that does not require output codes. In our approach, multiclass recognition of objects is reduced into a single binary recognition task, defined on triples of objects. Preliminary experiments with eight UCI datasets yield no clear winner among our method, boosting using output codes, and k-nn classification using an unoptimized distance measure. Our algorithm did achieve lower error rates in some of the datasets, which indicates that, in some domains, it may lead to better results than existing methods.

Boosting high-intensity focused ultrasound-induced anti-tumor immunity using a sparse-scan strategy that can more effectively promote dendritic cell maturation.

BACKGROUND: The conventional treatment protocol in high-intensity focused ultrasound (HIFU) therapy utilizes a dense-scan strategy to produce closely packed thermal lesions aiming at eradicating as much tumor mass as possible. However, this strategy is not most effective in terms of inducing a systemic anti-tumor immunity so that it cannot provide efficient micro-metastatic control and long-term tumor resistance. We have previously provided evidence that HIFU may enhance systemic anti-tumor immunity by in situ activation of dendritic cells (DCs) inside HIFU-treated tumor tissue. The present study was conducted to test the feasibility of a sparse-scan strategy to boost HIFU-induced anti-tumor immune response by more effectively promoting DC maturation. METHODS: An experimental HIFU system was set up to perform tumor ablation experiments in subcutaneous implanted MC-38 and B16 tumor with dense- or sparse-scan strategy to produce closely-packed or separated thermal lesions. DCs infiltration into HIFU-treated tumor tissues was detected by immunohistochemistry and flow cytometry. DCs maturation was evaluated by IL-12/IL-10 production and CD80/CD86 expression after co-culture with tumor cells treated with different HIFU. HIFU-induced anti-tumor immune response was evaluated by detecting growth-retarding effects on distant re-challenged tumor and tumor-specific IFN-gamma-secreting cells in HIFU-treated mice. RESULTS: HIFU exposure raised temperature up to 80 degrees centigrade at beam focus within 4 s in experimental tumors and led to formation of a well-defined thermal lesion. The infiltrated DCs were recruited to the periphery of lesion, where the peak temperature was only 55 degrees centigrade during HIFU exposure. Tumor cells heated to 55 degrees centigrade in 4-s HIFU exposure were more effective to stimulate co-cultured DCs to mature. Sparse-scan HIFU, which can reserve 55 degrees-heated tumor cells surrounding the separated lesions, elicited an enhanced anti-tumor immune response than dense-scan HIFU, while their suppressive effects on the treated primary tumor were maintained at the same level. Flow cytometry analysis showed that sparse-scan HIFU was more effective than dense-scan HIFU in enhancing DC infiltration into tumor tissues and promoting their maturation in situ. CONCLUSION: Optimizing scan strategy is a feasible way to boost HIFU-induced anti-tumor immunity by more effectively promoting DC maturation.

Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.

MOTIVATION: Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries. RESULTS: We have proposed a high-dimensional variable selection method by incorporating stability selection to control false discovery. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries. The proposed method is applied to study the associations of 2339 common single-nucleotide polymorphisms (SNPs) with overall survival among cutaneous melanoma (CM) patients. The results have confirmed that BRCA2 pathway SNPs are likely to be associated with overall survival, as reported by previous literature. Moreover, we have identified several new Fanconi anemia (FA) pathway SNPs that are likely to modulate survival of CM patients. AVAILABILITY AND IMPLEMENTATION: The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues. CONTACT: yili@umich.edu.

Potent functional antibody responses elicited by HIV-I DNA priming and boosting with heterologous HIV-1 recombinant MVA in healthy Tanzanian adults.

UNLABELLED: Vaccine-induced HIV antibodies were evaluated in serum samples collected from healthy Tanzanian volunteers participating in a phase I/II placebo-controlled double blind trial using multi-clade, multigene HIV-DNA priming and recombinant modified vaccinia Ankara (HIV-MVA) virus boosting (HIVIS03). The HIV-DNA vaccine contained plasmids expressing HIV-1 gp160 subtypes A, B, C, Rev B, Gag A, B and RTmut B, and the recombinant HIV-MVA boost expressed CRF01_AE HIV-1 Env subtype E and Gag-Pol subtype A. While no neutralizing antibodies were detected using pseudoviruses in the TZM-bl cell assay, this prime-boost vaccination induced neutralizing antibodies in 83% of HIVIS03 vaccinees when a peripheral blood mononuclear cell (PBMC) assay using luciferase reporter-infectious molecular clones (LucR-IMC) was employed. The serum neutralizing activity was significantly (but not completely) reduced upon depletion of natural killer (NK) cells from PBMC (p=0.006), indicating a role for antibody-mediated Fcγ-receptor function. High levels of antibody-dependent cellular cytotoxicity (ADCC)-mediating antibodies against CRF01_AE and/or subtype B were subsequently demonstrated in 97% of the sera of vaccinees. The magnitude of ADCC-mediating antibodies against CM235 CRF01_AE IMC-infected cells correlated with neutralizing antibodies against CM235 in the IMC/PBMC assay. In conclusion, HIV-DNA priming, followed by two HIV-MVA boosts elicited potent ADCC responses in a high proportion of Tanzanian vaccinees. Our findings highlight the potential of HIV-DNA prime HIV-MVA boost vaccines for induction of functional antibody responses and suggest this vaccine regimen and ADCC studies as potentially important new avenues in HIV vaccine development. TRIAL REGISTRATION: Controlled-Trials ISRCTN90053831 The Pan African Clinical Trials Registry ATMR2009040001075080 (currently PACTR2009040001075080).

Multilevel refinement for combinatorial optimisation: boosting metaheuristic performance

The multilevel paradigm as applied to combinatorial optimisation problems is a simple one, which at its most basic involves recursive coarsening to create a hierarchy of approximations to the original problem. An initial solution is found, usually at the coarsest level, and then iteratively refined at each level, coarsest to finest, typically by using some kind of heuristic optimisation algorithm (either a problem-specific local search scheme or a metaheuristic). Solution extension (or projection) operators can transfer the solution from one level to another. As a general solution strategy, the multilevel paradigm has been in use for many years and has been applied to many problem areas (for example multigrid techniques can be viewed as a prime example of the paradigm). Overview papers such as [] attest to its efficacy. However, with the exception of the graph partitioning problem, multilevel techniques have not been widely applied to combinatorial problems and in this chapter we discuss recent developments. In this chapter we survey the use of multilevel combinatorial techniques and consider their ability to boost the performance of (meta)heuristic optimisation algorithms.

Boosting Blue Growth in a Mild Sea: Analysis of the Synergies Produced by a Multi-Purpose Offshore Installation in the Northern Adriatic, Italy

In the near future, the oceans will be subjected to a massive development of marine infrastructures, including offshore wind, tidal and wave energy farms and constructions for marine aquaculture. The development of these facilities will unavoidably exert environmental pressures on marine ecosystems. It is therefore crucial that the economic costs, the use of marine space and the environmental impacts of these activities remain within acceptable limits. Moreover, the installation of arrays of wave energy devices is still far from being economically feasible due to many combined aspects, such as immature technologies for energy conversion, local energy storage and moorings. Therefore, multi-purpose solutions combining renewable energy from the sea (wind, wave, tide), aquaculture and transportation facilities can be considered as a challenging, yet advantageous, way to boost blue growth. This would be due to the sharing of the costs of installation and using the produced energy locally to feed the different functionalities and optimizing marine spatial planning. This paper focuses on the synergies that may be produced by a multi-purpose offshore installation in a relatively calm sea, i.e., the Northern Adriatic Sea, Italy, and specifically offshore Venice. It analyzes the combination of aquaculture, energy production from wind and waves, and energy storage or transfer. Alternative solutions are evaluated based on specific criteria, including the maturity of the technology, the environmental impact, the induced risks and the costs. Based on expert judgment, the alternatives are ranked and a preliminary layout of the selected multi-purpose installation for the case study is proposed, to further allow the exploitation of the synergies among different functionalities.

Software transactional memory as a building block for parallel embedded realtime systems

The recent trends of chip architectures with higher number of heterogeneous cores, and non-uniform memory/non-coherent caches, brings renewed attention to the use of Software Transactional Memory (STM) as a fundamental building block for developing parallel applications. Nevertheless, although STM promises to ease concurrent and parallel software development, it relies on the possibility of aborting conflicting transactions to maintain data consistency, which impacts on the responsiveness and timing guarantees required by embedded real-time systems. In these systems, contention delays must be (efficiently) limited so that the response times of tasks executing transactions are upper-bounded and task sets can be feasibly scheduled. In this paper we assess the use of STM in the development of embedded real-time software, defending that the amount of contention can be reduced if read-only transactions access recent consistent data snapshots, progressing in a wait-free manner. We show how the required number of versions of a shared object can be calculated for a set of tasks. We also outline an algorithm to manage conflicts between update transactions that prevents starvation.

Marketing plan for online aqctivity of Rota Vicentina boosting direct website sales

The nature tourism experienced a great expansion of its market with the appearance of different lifestyles. In this Work Project a study regarding the website direct sales of Rota Vicentina was developed. Its website shows the idea of being solely an information structure and not a purchase one, leading to a current absence of online sales. Hence, it is suggested the modification of its business model, using different instruments and channels. Some digital marketing recommendations were developed in order to boost website sales, such as a platform for online reviews, remarketing campaigns and social media activity.