A user parameter-free approach for mining robust sequential classification rules

Recent years have witnessed extensive studies of graph classification due to the rapid increase in applications involving structural data and complex relationships. To support graph classification, all existing methods require that training graphs should be relevant (or belong) to the target class, but cannot integrate graphs irrelevant to the class of interest into the learning process. In this paper, we study a new universum graph classification framework which leverages additional “non-example” graphs to help improve the graph classification accuracy. We argue that although universum graphs do not belong to the target class, they may contain meaningful structure patterns to help enrich the feature space for graph representation and classification. To support universum graph classification, we propose a mathematical programming algorithm, ugBoost, which integrates discriminative subgraph selection and margin maximization into a unified framework to fully exploit the universum. Because informative subgraph exploration in a universum setting requires the search of a large space, we derive an upper bound discriminative score for each subgraph and employ a branch-and-bound scheme to prune the search space. By using the explored subgraphs, our graph classification model intends to maximize the margin between positive and negative graphs and minimize the loss on the universum graph examples simultaneously. The subgraph exploration and the learning are integrated and performed iteratively so that each can be beneficial to the other. Experimental results and comparisons on real-world dataset demonstrate the performance of our algorithm.     

A Survey on Data‐driven Dictionary‐based Methods for 3D Modeling

Dictionaries are very useful objects for data analysis, as they enable a compact representation of large sets of objects through the combination of atoms. Dictionary‐based techniques have also particularly benefited from the recent advances in machine learning, which has allowed for data‐driven algorithms to take advantage of the redundancy in the input dataset and discover relations between objects without human supervision or hard‐coded rules. Despite the success of dictionary‐based techniques on a wide range of tasks in geometric modeling and geometry processing, the literature is missing a principled state‐of‐the‐art of the current knowledge in this field. To fill this gap, we provide in this survey an overview of data‐driven dictionary‐based methods in geometric modeling. We structure our discussion by application domain: surface reconstruction, compression, and synthesis. Contrary to previous surveys, we place special emphasis on dictionary‐based methods suitable for 3D data synthesis, with applications in geometric modeling and design. Our ultimate goal is to enlight the fact that these techniques can be used to combine the data‐driven paradigm with design intent to synthesize new plausible objects with minimal human intervention. This is the main motivation to restrict the scope of the present survey to techniques handling point clouds and meshes, making use of dictionaries whose definition depends on the input data, and enabling shape reconstruction or synthesis through the combination of atoms.     

C1 continuous h-adaptive least-squares spectral element method for phase-field models

To describe the interfacial dynamics between two phases using the phase-field method, the interfacial region needs to be close enough to a sharp interface so as to reproduce the correct physics. Due to the high gradients of the solution within the interfacial region and consequent high computational cost, the use of the phase-field method has been limited to the small-scale problems whose characteristic length is similar to the interfacial thickness. By using finer mesh at the interface and coarser mesh in the rest of computational domain, the phase-field methods can handle larger scale of problems with realistic interface thicknesses. In this work, a $C^1$ continuous $h$-adaptive mesh refinement technique with the least-squares spectral element method is presented. It is applied to the Navier–Stokes-Cahn–Hilliard (NSCH) system and the isothermal Navier–Stokes–Korteweg (NSK) system. Hermite polynomials are used to give global differentiability in the approximated solution, and a space–time coupled formulation and the element-by-element technique are implemented. Two refinement strategies based on the solution gradient and the local error estimators are suggested, and they are compared in two numerical examples.     

Random Pattern and Frequency Generation Using a Photonic Reservoir Computer with Output Feedback

Reservoir computing is a bio-inspired computing paradigm for processing time dependent signals. The performance of its analogue implementations matches other digital algorithms on a series of benchmark tasks. Their potential can be further increased by feeding the output signal back into the reservoir, which would allow to apply the algorithm to time series generation. This requires, in principle, implementing a sufficiently fast readout layer for real-time output computation. Here we achieve this with a digital output layer driven by a FPGA chip. We demonstrate the first opto-electronic reservoir computer with output feedback and test it on two examples of time series generation tasks: frequency and random pattern generation. We obtain very good results on the first task, similar to idealised numerical simulations. The performance on the second one, however, suffers from the experimental noise. We illustrate this point with a detailed investigation of the consequences of noise on the performance of a physical reservoir computer with output feedback. Our work thus opens new possible applications for analogue reservoir computing and brings new insights on the impact of noise on the output feedback.     

Characterization and distribution of mechanically competent mineralized tissue in micropores of β-tricalcium phosphate bone substitutes

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Phase transformations during processing and in vitro degradation of porous calcium polyphosphates

A 2-Step sinter/anneal treatment has been reported previously for forming porous CPP as biodegradable bone substitutes [9]. During the 2-Step annealing treatment, the heat treatment used strongly affected the rate of CPP degradation in vitro. In the present study, x-ray diffraction and ³¹P solid state nuclear magnetic resonance were used to determine the phases that formed using different heat treating processes. The effect of in vitro degradation (in PBS at 37 °C, pH 7.1 or 4.5) was also studied. During CPP preparation, β-CPP and γ-CPP were identified in powders formed from a calcium monobasic monohydrate precursor after an initial calcining treatment (10 h at 500 °C). Melting of this CPP powder (at 1100 °C), quenching and grinding formed amorphous CPP powders. Annealing powders at 585 °C (Step-1) resulted in rapid sintering to form amorphous porous CPP. Continued annealing to 650 °C resulted in crystallization to form a multi-phase structure of β-CPP primarily plus lesser amounts of α-CPP, calcium ultra-phosphates and retained amorphous CPP. Annealing above 720 °C and up to 950 °C transformed this to β-CPP phase. In vitro degradation of the 585 °C (Step-1 only) and 650 °C Step-2 annealed multi-phase samples occurred significantly faster than the β-CPP samples formed by Step-2 annealing at or above 720 °C. This faster degradation was attributable to preferential degradation of thermodynamically less stable phases that formed in samples annealed at 650 °C (i.e. α-phase, ultra-phosphate and amorphous CPP). Degradation in lower pH solutions significantly increased degradation rates of the 585 and 650 °C annealed samples but had no significant effect on the β-CPP samples.     

Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine

This study evaluates the potential of object-based image analysis in combination with supervised machine learning to identify urban structure type patterns from Landsat Thematic Mapper (TM) images. The main aim is to assess the influence of several critical choices commonly made during the training stage of a learning machine on the classification performance and to give recommendations for classifier-dependent intelligent training. Particular emphasis is given to assess the influence of size and class distribution of the training data, the approach of training data sampling (user-guided or random) and the type of training samples (squares or segments) on the classification performance of a Support Vector Machine (SVM). Different feature selection algorithms are compared and segmentation and classifier parameters are dynamically tuned for the specific image scene, classification task, and training data. The performance of the classifier is measured against a set of reference data sets from manual image interpretation and furthermore compared on the basis of landscape metrics to a very high resolution reference classification derived from light detection and ranging (lidar) measurements. The study highlights the importance of a careful design of the training stage and dynamically tuned classifier parameters, especially when dealing with noisy data and small training data sets. For the given experimental set-up, the study concludes that given optimized feature space and classifier parameters, training an SVM with segment-shaped samples that were sampled in a guided manner and are balanced between the classes provided the best classification results. If square-shaped samples are used, a random sampling provided better results than a guided selection. Equally balanced sample distributions outperformed unbalanced training sets.     

Visual Analysis of Governing Topological Structures in Excitable Network Dynamics

To understand how topology shapes the dynamics in excitable networks is one of the fundamental problems in network science when applied to computational systems biology and neuroscience. Recent advances in the field discovered the influential role of two macroscopic topological structures, namely hubs and modules. We propose a visual analytics approach that allows for a systematic exploration of the role of those macroscopic topological structures on the dynamics in excitable networks. Dynamical patterns are discovered using the dynamical features of excitation ratio and co‐activation. Our approach is based on the interactive analysis of the correlation of topological and dynamical features using coordinated views. We designed suitable visual encodings for both the topological and the dynamical features. A degree map and an adjacency matrix visualization allow for the interaction with hubs and modules, respectively. A barycentric‐coordinates layout and a multi‐dimensional scaling approach allow for the analysis of excitation ratio and co‐activation, respectively. We demonstrate how the interplay of the visual encodings allows us to quickly reconstruct recent findings in the field within an interactive analysis and even discovered new patterns. We apply our approach to network models of commonly investigated topologies as well as to the structural networks representing the connectomes of different species. We evaluate our approach with domain experts in terms of its intuitiveness, expressiveness, and usefulness.     

Bootstrapping Inference in the IDP Knowledge Base System

Declarative systems aim at solving tasks by running inference engines on a specification, to free their users from having to specify how a task should be tackled. In order to provide such functionality, declarative systems themselves apply complex reasoning techniques, and, as a consequence, the development of such systems can be laborious work. In this paper, we demonstrate that the declarative approach can be applied to develop such systems, by tackling the tasks solved inside a declarative system declaratively. In order to do this, a meta-level representation of those specifications is often required. Furthermore, by using the language of the system for the meta-level representation, it opens the door to bootstrapping: an inference engine can be improved using the inference it performs itself.One such declarative system is the IDP knowledge base system, based on the language \(\rm FO(\cdot)^{\rm IDP}\), a rich extension of first-order logic. In this paper, we discuss how \(\rm FO(\cdot)^{\rm IDP}\) can support meta-level representations in general and which language constructs make those representations even more natural. Afterwards, we show how meta-\(\rm FO(\cdot)^{\rm IDP}\) can be applied to bootstrap its model expansion inference engine. We discuss the advantages of this approach: the resulting program is easier to understand, easier to maintain, and more flexible.