Isolation kernel: the X factor in efficient and effective large scale online kernel learning

Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. Current state-of-the-art large scale online kernel learning focuses on improving efficiency. Two key approaches to gain efficiency through approximation are (1) limiting the number of support vectors, and (2) using an approximate feature map. They often employ a kernel with a feature map with intractable dimensionality. While these approaches can deal with large scale datasets efficiently, this outcome is achieved by compromising predictive accuracy because of the approximation. We offer an alternative approach that puts the kernel used at the heart of the approach. It focuses on creating a sparse and finite-dimensional feature map of a kernel called Isolation Kernel. Using this new approach, to achieve the above aim of large scale online kernel learning becomes extremely simple—simply use Isolation Kernel instead of a kernel having a feature map with intractable dimensionality. We show that, using Isolation Kernel, large scale online kernel learning can be achieved efficiently without sacrificing accuracy.

     

Morphology study of oriented SmBCO film deposited by MOCVD

c-axis-oriented SmBa_2Cu_3O_7(SmBCO) films have been deposited on(100)- LaA1O_3(LAO)substrate by metal organic chemical vapor deposition(MOCVD) technique.The effects of deposition temperature(T_(dep)) and total pressure(P_(tot)) on the orientation and microstructure of SmBCO films were investigated.The orientation of SmBCO films transformed from α-axis to c-axis with increasing of T_(dep) from 900 to 1 100℃.At T_(dep)=1 050℃,SmBCO films had c-axis orientation and tetragon surface.At P_(tot)~(dep)=400-800 Pa and T_(dep)=1 050 ℃,totally c-axis-oriented SmBCO films were obtained.The R_(dep) of SmBCO films increased firstly and then decreased with increasing P_(tot).The surface of SmBCO films exhibited tetragon morphology at 1 050 ℃ and400 Pa.Maximum thickness of SmBCO film deposited was 1.2μm at P_(tot)= 600 Pa,and the corresponding R_(dep)was 7.2 μm·h~(-1).     

Lab-scale feasibility study on new elastomer comprising chloroprene and acrylonitrile for oil and gas applications

The limited options of suitable elastomers with adequate cost-performance balance drive the necessity to introduce new materials in the oil & gas (O&G) application space. The relevance of a recently developed copolymer of chloroprene and acrylonitrile (referred to as acrylonitrile–chloroprene rubber or NCR) to O&G applications is described in this technical contribution. The new elastomer demonstrates adequate physical properties and reasonably good high and low-temperature capabilities. It offers good resistance to several aqueous and non-aqueous fluids with low volume swelling and retains its physical properties to reasonable extents while exposed to hot test oils. Acrylonitrile–chloroprene rubber has been found to sustain “sour gas” exposure. Good abrasion resistance, high tear strength, and remarkably high flex-fatigue resistance coupled with low heat build-up reflect its durability under dynamic conditions. In addition, acrylonitrile–chloroprene rubber can very well withstand the rapid gas decompression test at 25°C. This unique combination of attributes may allow acrylonitrile–chloroprene rubber to be considered as a candidate material for high-performance O&G applications.     

Predicting viewer-perceived activity/dominance in soccer games with stick-breaking HMM using data from a fixed set of cameras

We attempted to predict activity/dominance for soccer games, where activity is defined as the degree of activity of the game as perceived by the viewer, whereas dominance is the degree at which the viewer perceives a particular team to dominate over the other team. Such activity/dominance information would help a layman viewer understand the game. It would also enable construction of an automatic digest creation system that extracts scenes having high activity/dominance. There are two facets of this study: 1. The main part of the underlying prediction model consists of a Stick-Breaking Hidden Markov Model, where the data automatically estimates the number of states of the Markov process behind the data. 2. The data used in this paper is vector time-series data consisting of player, referee, and ball positions, together with team information, acquired by a set of fixed cameras. The problem was approached with a Bayesian framework where learning and prediction were implemented by three different methods: Markov Chain Monte Carlo, Expectation Maximization, and Variational Bayes. The proposed method was tested using a dataset consisting of 10 professional soccer games and was compared against standard regression methods.     

Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space.     

Gender Differences in Mouse and Cursor Movements

Computer cursor and mouse activities such as moving, pointing, selecting, and dragging are essential parts of everyday interactions. Yet it is unknown how men and women differ in the way they move computer cursors. This study examines gender differences in movements of computer cursors. In one experiment, the authors measured trajectories of computer cursors every 20 ms in a simple choice-reaching task and tested the extent to which movement features related to controlling and targeting diverge between male and female participants. Results showed significant gender differences in cursor motions. Female participants deviated from the straight path toward the target location to a larger degree than did male participants, and female participants showed more backward motions (deviating backward from the target location) than did male participants. Implications for sources of these gender differences, user interface and input device design, and musculoskeletal disorders in women are also discussed.     

An Example-based Procedural System for Element Arrangement

We present a method for synthesizing two dimensional (2D) element arrangements from an example. The main idea is to combine texture synthesis techniques based‐on a local neighborhood comparison and procedural modeling systems based‐on local growth. Given a user‐specified reference pattern, our system analyzes neigh‐borhood information of each element by constructing connectivity. Our synthesis process starts with a single seed and progressively places elements one by one by searching a reference element which has local features that are the most similar to the target place of the synthesized pattern. To support creative design activities, we introduce three types of interaction for controlling global features of the resulting pattern, namely a spray tool, a flow field tool, and a boundary tool. We also introduce a global optimization process that helps to avoid local error concentrations. We illustrate the feasibility of our method by creating several types of 2D patterns.     

Teaching materials to enhance the visual expression of Web pages for students not in art or design majors

The explosive growth of the Internet has made the knowledge and skills for creating Web pages into general subjects that all students should learn. It is now common to teach the technical side of the production of Web pages and many teaching materials have been developed. However teaching the aesthetic side of Web page design has been neglected, and students not in art or design majors are normally confronted with difficulties when they do expressive design of Web pages.     

Adaptive control design for a class of nonsmooth nonlinear systems with matched and linearly parameterized uncertainty

This paper presents an adaptive control design for a class of nonsmooth nonlinear systems with matched uncertainty, which is linearly parameterized with a known discontinuous function. The design framework is based on the concept of the Filippov solution as the classical Lyapunov theory for smooth systems cannot be applied to establish the stability of the adaptive control system due to the presence of the discontinuity. It is proved that as an adaptive control system, the global Lyapunov stability with the convergence of the state of the controlled system to the origin can be achieved by evaluating the monotonicity of the Lyapunov function in the state space, particularly on the discontinuous surface, while the uniqueness of the solution of the closed‐loop system is not necessarily guaranteed. Some interesting numerical examples are demonstrated with simulation results. Copyright © 2008 John Wiley & Sons, Ltd.