The importance of neighbourhood size in self organising systems

In recent times, the analysis of SOM (self-organising map) performance has concentrated on optimising the gain decay, rather than the size, form and decay of the neighbourhood function. We propose that the size, form and decay of region size plays a much more significant role in the learning, and especially in the development, of topographic feature maps. In this paper, a biologically-derived SOM model is presented. This model is able to select a single winning neuron and to form Gaussian outputs about this winner, without the need for a meta-level decision-making structure to artificially select a winner and fit a Gaussian output to that winner. Using this model, some fundamental characteristics of the relationship between neighbourhood size and SOM output states are demonstrated.<br /

An empirical study of neighbourhood decay in Kohonen\u27s self organizing map

In this paper, empirical results are presented which suggest that size and rate of decay of region size plays a much more significant role in the learning, and especially the development, of topographic feature maps. Using these results as a basis, a scheme for decaying region size during SOM training is proposed. The proposed technique provides near optimal training time. This scheme avoids the need for sophisticated learning gain decay schemes, and precludes the need for a priori knowledge of likely training times. This scheme also has some potential uses for continuous learning

Path finding on a spherical SOM using the distance transform and floodplain analysis

Data visualization has become an important tool for analyzing very complex data. In particular, spatial visualization enables users to view data in a intuitive manner. It has typically been used to externalize clusters and their relationships which exist in highly complex multidimensional data. We envisage that not only cluster formation and relationships but also other types of information, such as temporal changes of datum, can be extracted through the spatialization. In this paper, we investigate an application of trajectory/path analysis carried out using a Self-Organizing Map as a spatialization method. We propose an application of distance transformations to the Geodesic Self-Organizing Map. This new approach allows a user to visually inspect the trajectory of multidimensional knowledge pieces on a two-dimensional space. The trajectories discovered through this approach are essentially the shortest paths between two points on the Self-Organizing Map. However, those paths might go outside of the input dataspace due to the connectivity of neurons imposed by the grid structure. We also present a method to find the shortest path, which falls within the input dataspace using simple floodplain analysis

Interaction Paradigms for Bare-Hand Interaction with Large Displays at a Distance

Non

Low-cost interactive active monocular range finder

This paper describes a low-cost interactive active monocular range finder and illustrates the effect of introducing interactivity to the range acquisition process. The range finder consists of only one camera and a laser pointer, to which three LEDs are attached. When a user scans the laser along surfaces of objects, the camera captures the image of spots (one from the laser, and the others from LEDs), and triangulation is carried out using the camera\u27s viewing direction and the optical axis of the laser. The user interaction allows the range finder to acquire range data in which the sampling rate varies across the object depending on the underlying surface structures. Moreover, the processes of separating objects from the background and/or finding parts in the object can be achieved using the operator\u27s knowledge of the objects

Path finding on a spherical SOM using the distance transform and floodplain analysis

Data visualization has become an important tool for analyzing very complex data. In particular, spatial visualization enables users to view data in a intuitive manner. It has typically been used to externalize clusters and their relationships which exist in highly complex multidimensional data. We envisage that not only cluster formation and relationships but also other types of information, such as temporal changes of datum, can be extracted through the spatialization. In this paper, we investigate an application of trajectory/path analysis carried out using a Self-Organizing Map as a spatialization method. We propose an application of distance transformations to the Geodesic Self-Organizing Map. This new approach allows a user to visually inspect the trajectory of multidimensional knowledge pieces on a two-dimensional space. The trajectories discovered through this approach are essentially the shortest paths between two points on the Self-Organizing Map. However, those paths might go outside of the input dataspace due to the connectivity of neurons imposed by the grid structure. We also present a method to find the shortest path, which falls within the input dataspace using simple floodplain analysis

Symbolic representation and distributed matching strategies for schematics

This paper describes object-centered symbolic representation and distributed matching strategies of 3D objects in a schematic form which occur in engineering drawings and maps. The object-centered representation has a hierarchical structure and is constructed from symbolic representations of schematics. With this representation, two independent schematics representing the same object can be matched. We also consider matching strategies using distributed algorithms. The object recognition is carried out with two matching methods: (1) matching between an object model and observed data at the lowest level of the hierarchy, and (2) constraints propagation. The first is carried out with symbolic Hopfield-type neural networks and the second is achieved via hierarchical winner-takes-all algorithms<br /

Differences in Ocular Complications Between Candida albicans and Non-albicans Candida Infection Analyzed by Epidemiology and a Mouse Ocular Candidiasis Model

Objectives:Candida species are a major cause of hospital infections, including ocular candidiasis, but few studies have examined the propensities of specific species to invade the eye or the unique immunological responses induced. This study examined the frequency and characteristics of species-specific Candida eye infections by epidemiology and experiments using a mouse ocular candidiasis model.Methods: We reviewed medical records of candidemia patients from January 2012 to March 2017. We also evaluated ocular fungal burden, inflammatory cytokine and chemokine profiles, and inflammatory cell profiles in mice infected with Candida albicans, Candida glabrata, or Candida parapsilosis.Results: During the study period, 20 ocular candidiasis cases were diagnosed among 99 candidemia patients examined by ophthalmologists. Although C. parapsilosis was the most frequent candidemia pathogen, only C. albicans infection was significantly associated with ocular candidiasis by multivariate analysis. In mice, ocular fungal burden and inflammatory mediators were significantly higher during C. albicans infection, and histopathological analysis revealed invading C. albicans surrounded by inflammatory cells. Ocular neutrophil and inflammatory monocyte numbers were significantly greater during C. albicans infection.Conclusion:Candida albicans is strongly associated with ocular candidiasis due to greater capacity for invasion, induction of inflammatory mediators, and recruitment of neutrophils and inflammatory monocytes

経皮的腎瘻を造設した水腎症合併進行癌症例の検討

経皮的腎瘻(PNS)を造設した水腎症合併進行癌症例33例を対象に, 原疾患の種類, PNS造設前のperformance status(PS), PNS造設後の在宅期間, カテーテル交換回数, カテーテルの種類, 合併症について調べた.その結果, 生存期間中央値は3.0ヵ月であった.全症例の69.7%は退院が不可能であった.30例は原疾患により死亡していた.原疾患別の生存期間中央値は上部消化管癌, 大腸癌, 泌尿器科癌, 婦人科癌でそれぞれ1.5, 5.5, 3.0, 3.0ヵ月であった(p=0.0116).18例(54.5%)で腎盂腎炎, カテーテル閉塞, カテーテル抜去などの合併症を経験していた.カテーテルの種類と合併症頻度に関連は見られなかった.以上, PNSは水腎症合併進行癌症例に対し適した尿路変向術であり, 一側の造設により致死的な腎後性腎不全を回避できる.しかし必ずしも患者のQOL改善は期待できず, いかなるカテーテルを用いても合併症のリスクは不可避であると考えられたWe investigated the clinical courses of 33 patients with advanced malignancies who has undergone percutaneous nephrostomy (PNS) to clarify the efficacy and validity of PNS and evaluated the results according to the type of catheter. The pigtail, Malecot and balloon catheters were used. All the catheters were exchanged every 4 weeks, if necessary for larger ones. The site of the primary disease, preoperative performance status, duration of time spent at home after PNS construction, number of times the catheter was changed, type and size of the catheter and complications associated with PNS were compiled. The median overall survival was 3.0 months. Twenty-three patients (69.7%) were never discharged from hospital. Thirty patients died of their primary diseases. The median overall survival of patients with cancers of the upper gastrointestinal system, large bowel, urological organs and gynecologic organs were 1.5, 5.5, 3.0 and 3.0 months, respectively (p=0.0116). Eighteen patients (54.5%) experienced complications such as pyelonephritis, obstruction, dislodgement and so on. PNS is an appropriate urinary diversion for patients with hydronephrosis with advanced cancer and unilateral construction is effective for treating end-stage obstructive renal failure. However, PNS rarely improves the patient's quality of life and the risk of complications is unavoidable regardless of the type of catheter used