Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

We present a Gaussian kernel loss function and training algorithm for convolutional neural networks that can be directly applied to both distance metric learning and image classification problems. Our method treats all training features from a deep neural network as Gaussian kernel centres and computes loss by summing the influence of a feature's nearby centres in the feature embedding space. Our approach is made scalable by treating it as an approximate nearest neighbour search problem. We show how to make end-to-end learning feasible, resulting in a well formed embedding space, in which semantically related instances are likely to be located near one another, regardless of whether or not the network was trained on those classes. Our approach outperforms state-of-the-art deep metric learning approaches on embedding learning challenges, as well as conventional softmax classification on several datasets.Comment: Accepted in the International Conference on Image Processing (ICIP) 2018. Formerly titled Nearest Neighbour Radial Basis Function Solvers for Deep Neural Network

A Limnological Study of Ricks Pond and the Gulpha Creek Drainage in Garland County, Arkansas

A limnological investigation of Ricks Pond and the Gulpha Creek drainage of Garland County, Arkansas was conducted between 1 June 1978, and 21 August 1978. Water samples taken from ten stations on three different dates indicated that the stream and pond systems were typical in water quality characteristics of other small, high gradient streams and impoundments in the Ouachita Mountains of Arkansas. In Ricks Pond, thermal stratification occurred along with the development of an oxygen deficient zone below a depth of one meter. Other water quality parameters indicated that Ricks Pond is a moderately productive ecosystem, with the productivity limited by the nitrogen species. The fecal coliform bacterial counts were very low, indicating no direct input of excessive amounts of fecal matter into the system during the present study. However, a Hot Springs city sewer line runs through the pond, and two manholes emerge from the pond\u27s surface. The possibility exists that this sewer line could discharge raw sewage into Ricks Pond during periods of high water. A biological investigation was also conducted in the study area, and lists of the phytoplankton, periphyton, higher aquatic vegetation, zooplankton, benthic macroinvertebrates, and fishes are presented. Twenty-seven species of fishes were collected from the Gulpha Creek drainage, and no rare or endangered forms were found. Ricks Pond is best-suited for the establishment of a put- and-take fishery for channel catfish, Ictalurus punctatus. The following recommendations were made for the establishment of such a fishery: (1) Renovation of the pond by draining and deepening it; (2) Removal of the sewer line from the pond; (3) Stocking of catchable size channel catfish at the rate of approximately 300-400 pounds per acre; (4) Periodic monitoring of the water quality

The 3D widgets for exploratory scientific visualization

Computational fluid dynamics (CFD) techniques are used to simulate flows of fluids like air or water around such objects as airplanes and automobiles. These techniques usually generate very large amounts of numerical data which are difficult to understand without using graphical scientific visualization techniques. There are a number of commercial scientific visualization applications available today which allow scientists to control visualization tools via textual and/or 2D user interfaces. However, these user interfaces are often difficult to use. We believe that 3D direct-manipulation techniques for interactively controlling visualization tools will provide opportunities for powerful and useful interfaces with which scientists can more effectively explore their datasets. A few systems have been developed which use these techniques. In this paper, we will present a variety of 3D interaction techniques for manipulating parameters of visualization tools used to explore CFD datasets, and discuss in detail various techniques for positioning tools in a 3D scene

Deficits in Conditional Discrimination Learning in Children with ADHD are Independent of Delay Aversion and Working Memory

Adaptive behavior requires the adjustment of one\u27s behavioral repertoire to situational demands. The learning of situationally appropriate choice behavior can be operationalized as a task of Conditional Discrimination Learning (CDL). CDL requires the acquisition of hierarchical reinforcement relations, which may pose a particular challenge for children with Attention Deficit Hyperactivity Disorder (ADHD), particularly in light of documented deficits in short-term/working memory and delay aversion in ADHD. Using an arbitrary Delayed Matching-To-Sample task, we investigated whether children with ADHD (N = 46), relative to Typically Developing children (TD, N = 55), show a deficit in CDL under different choice delays (0, 8, and 16 seconds) and whether these differences are mediated by short-term/working memory capacity and/or delay aversion. Children with ADHD demonstrated poorer CDL than TD children under 8 and 16-second delays. Non-delayed CDL performance did not differ between groups. CDL differences were not mediated by short-term/working memory performance or delay aversion. Moreover, CDL performance under an 8-second delay was a better predictor of clinical status than short-term/working memory performance or delay aversion. CDL, under conditions of delay, is impaired in children with ADHD. This may lead to difficulties discriminating between different situational demands and adapting behavior according to the prevailing reward contingencies or expectations

Scheduling time-critical graphics on multiple processors

This paper describes an algorithm for the scheduling of time-critical rendering and computation tasks on single- and multiple-processor architectures, with minimal pipelining. It was developed to manage scientific visualization scenes consisting of hundreds of objects, each of which can be computed and displayed at thousands of possible resolution levels. The algorithm generates the time-critical schedule using progressive-refinement techniques; it always returns a feasible schedule and, when allowed to run to completion, produces a near-optimal schedule which takes advantage of almost the entire multiple-processor system