Agent Mining: The Synergy of Agents and Data Mining

Autonomous agents and multiagent systems (or agents) and data mining and knowledge discovery (or data mining) are two of the most active areas in information technology. Ongoing research has revealed a number of intrinsic challenges and problems facing each area, which can't be addressed solely within the confines of the respective discipline. A profound insight of bringing these two communities together has unveiled a tremendous potential for new opportunities and wider applications through the synergy of agents and data mining. With increasing interest in this synergy, agent mining is emerging as a new research field studying the interaction and integration of agents and data mining. In this paper, we give an overall perspective of the driving forces, theoretical underpinnings, main research issues, and application domains of this field, while addressing the state-of-the-art of agent mining research and development. Our review is divided into three key research topics: agent-driven data mining, data mining-driven agents, and joint issues in the synergy of agents and data mining. This new and promising field exhibits a great potential for groundbreaking work from foundational, technological and practical perspectives.     

Guest Editors' Introduction: Agents and Data Mining

On top of two active research streams, agents and data mining, a most recent and exciting trend is their interaction and integration. Agent mining has emerged as a very promising field due to its unique contributions to complementary and innovative methodologies, techniques, and applications for complex problem-solving. This editorial summarizes the structure of this special issue.     

Internal behaviour provided by nonlinear l1-optimal controllers

An l₁-optimal linear time-invariant (LTI) compensator may have an order significantly higher than that of the plant, even when the state is measurable. Recently there has been work exploring the use of nonlinear static feedback controllers which provide near optimal performance. Here we consider a class of nonlinear state feedback controllers and derive superposition-like bounds on both the plant state and the controlled output in the event that the plant initial condition, the disturbance, and the noise are all non-zero.     

Function Estimation by Feedforward Sigmoidal Networks with Bounded Weights

We address the problem of estimating a function f: [0,1] ^d [-L,L] by using feedforward sigmoidal networks with a single hidden layer and bounded weights. The only information about the function is provided by an identically independently distributed sample generated according to an unknown distribution. The quality of the estimate is quantified by the expected cost functional and depends on the sample size. We use Lipschitz properties of the cost functional and of the neural networks to derive the relationship between performance bounds and sample sizes within the framework of Valiant's probably approximately correct learning.     

Dynamic programming and convex clustering

In this paper the notion of convexity of clusterings for the given ordering of units is introduced. In the case when at least one (optimal) solution of the clustering problem is convex, dynamic programming leads to a polynomial algorithm with complexityO(kn ³). We prove that, for several criterion functions, convex optimal clusterings exist when dissimilarity is pyramidal for a given ordering of units.This research was supported in part by the Research Council of Slovenia.     

Disruptive infrared image sensors enabled by quantum dots

Sensors based on quantum dot photodiodes promise quality and accessibility improvement of infrared imaging. We demonstrate miniaturization by sub-2-μm pixel pitch arrays. Functionality is confirmed with external quantum efficiencies above 40% at 1450 nm. Monolithic integration enables high throughput and wide deployment of short-wave infrared (SWIR) imagers in applications that previously could not afford them.     

Sediment Flocs: Settling Velocity, Flocculation Factor, and Optical Backscatter

Improved relationships for the flocculation factor and floc settling velocity are proposed. They differ from the existing formulas by accounting for the effect of floc porosity on the hydrodynamic drag and for the fractal properties of the floc projection on the plane. These relationships predict that the fractal dimension d influences the floc settling velocity Ws only if d ≥ 2. A flocculation effect on optical backscatter is also considered, and a fractal-based relationship for quantifying this effect is proposed. This relationship serves as a basis for a simple optical backscatter sensor (OBS) based technique for estimating some fractal-related parameters of sediment flocs involved in the proposed relationships for the settling velocity and flocculation factor. The technique is tested using data from three field sites. The potential effect of particle size on backscatter efficiency is highlighted, and an approach for accounting for this effect is suggested.     

Effects of Heat Treatment on the Structure and Mechanical Characteristics at 20-1100°C for Chromium Film Condensate

Studies have been made on the structure, phase composition, and mechanical properties of 0.25 mm film condensates made from Cr – 0.35 La – 0.2 Ta alloy by vacuum arc sputtering on to a copper substrate at a condensation temperature of 650°C. The specimens have been tested in tension in the initial state and after annealing at 900-1300°C. The poor mechanical properties of the condensates at room temperature are due to the high internal stresses on account of the high density of vacancy defects within the crystallites, which can lead to failure at grain boundaries at comparatively low stresses. Annealing at 1100°C annihilates the defects and starts recrystallization. The increase in the yield point at test temperatures of 700-800°C is probably due to ageing which may be due to the high density of vacation defects and the high mobility of them, which is due to temperature-dependent changes. Intercrystallite failure and exfoliation at a neck in the specimen are examined during tensin tests.     

“Pseudogap” State of Novel Superconductors: Energy Scales and Structural and Diamagnetic Transitions

High T_c cuprates are characterized by three characteristic energy scales: T_c, T*_c, and T*. The lowest scale, T_c, corresponds to the usual transition to the dissipationless state (R=0) with a macroscopic phase coherence. The higher energy scale, T*_c, describes the diamagnetic transition. The region T*_c>T>T_c is characterized by the presence of superconducting regions embedded in a normal metallic matrix. The highest energy scale (T*) corresponds to the formation of the structure, phase separation, and to the opening of the CDW gap.