An Optimal Scale for Edge Detection

Many problems in early vision are ill posed. Edge detection is a typical example. This paper applies regularization techniques to the problem of edge detection. We derive an optimal filter for edge detection with a size controlled by the regularization parameter $\\ lambda $ and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter $\\lambda $ is derived from regularization analysis for the case of small values of $\\lambda$. We also discuss the method of Generalized Cross Validation for obtaining the optimal filter scale. Finally, we use our framework to explain two perceptual phenomena: coarsely quantized images becoming recognizable by either blurring or adding noise.

Neural Network Exploration Using Optimal Experiment Design

We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of a neural network learner. We demonstrate that these techniques allow the learner to minimize its generalization error by exploring its domain efficiently and completely. We conclude that, while not a panacea, OED-based query/action has much to offer, especially in domains where its high computational costs can be tolerated.

Near-Optimal Distributed Failure Circumscription

Small failures should only disrupt a small part of a network. One way to do this is by marking the surrounding area as untrustworthy --- circumscribing the failure. This can be done with a distributed algorithm using hierarchical clustering and neighbor relations, and the resulting circumscription is near-optimal for convex failures.

Optimal Approximations of the Frequency Moments

We give a one-pass, O~(m^{1-2/k})-space algorithm for estimating the k-th frequency moment of a data stream for any real k>2. Together with known lower bounds, this resolves the main problem left open by Alon, Matias, Szegedy, STOC'96. Our algorithm enables deletions as well as insertions of stream elements.

Cloning and structure analysis of hydrogenase gene from Chlamydomonas reinhardtii SE

The cDNA of Chlamydomonas reinhardtii SE encoding hydrogenase (HydA2) was obtained from the total RNA of C reinhardtii SE by RT-PCR. The DNA of hydrogenase was amplified by PCR from the genomic DNA of C reinhardtii SE. The cDNA and DNA of hydrogenase were sequenced, respectively. The structure of hydrogenase gene was analyzed by biology software. The open reading frame predicts that the hydrogenase is composed of 3584 bp encoding 505 amino acids in length with a predicted M.W. of 53.69 kDa. Ten exons (including 1518 bp) and nine introns (including 2066 bp) have been found in the hydrogenase, and there were two potential N-glycosylate sites, eight protein kinase C phosphorylation site, eight casein kinase H phosphorylation site and one sulphorylation in the sequence. The theory pI was 6.15. Total number of negatively charged residues (Asp + Glu) and positively charged residues (Arg + Lys) were 55 and 61, respectively. (c) 2005 Elsevier Ltd. All rights reserved.

Optimal light regime for the cultivation of transgenic Laminaria japonica gametophytes in a bubble-column bioreactor

Fluctuating light intensity had a more significant impact on growth of gametophytes of transgenic Laminaria japonica in a 2500 ml bubble-column bioreactor than constant light intensity. A fluctuating light intensity between 10 and 110 mu E m(-2) s(-1), with a photoperiod of 14 h:10 h light:dark, was the best regime for growth giving 1430 mg biomass l(-1).

Determination of ionic resistance and optimal composition in the anodic catalyst layers of DMFC using AC impedance

In the present study, a method based on transmission-line mode for a porous electrode was used to measure the ionic resistance of the anode catalyst layer under in situ fuel cell operation condition. The influence of Nafion content and catalyst loading in the anode catalyst layer on the methanol electro-oxidation and direct methanol fuel cell (DMFC) performance based on unsupported Pt-Ru black was investigated by using the AC impedance method. The optimal Nafion content was found to be 15 wt% at 75 degrees C. The optimal Pt-Ru loading is related to the operating temperature, for example, about 2.0 mg/cm(2) for 75-90 degrees C, 3.0 mg/cm2 for 50 degrees C. Over these values, the cell performance decreased due to the increases in ohmic and mass transfer resistances. It was found that the peak power density obtained was 217 mW/cm(2) with optimal catalyst and Nafion loading at 75 degrees C using oxygen. (c) 2005 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.

Hamilton?Jacobi?Bellman equations for quantum optimal feedback control

Gough, John; Belavkin, V.P.; Smolianov, O.G., (2005) 'Hamilton?Jacobi?Bellman equations for quantum optimal feedback control', Journal of Optics B: Quantum and Semiclassical Optics 7 pp.S237-S244 RAE2008

Cloning and characterization of a putative fructosyltransferase and two putative invertase genes from the temperate grass Lolium temulentum L.

J. A. Gallagher, A. J. Cairns and C. J. Pollock (2004). Cloning and characterization of a putative fructosyltransferase and two putative invertase genes from the temperate grass Lolium temulentum L. Journal of Experimental Botany, 55 (397) pp.557-569 Sponsorship: BBSRC RAE2008

Construction of a Festuca pratensis BAC library for map-based cloning in IFestulolium substitution lines

Iain S. Donnison, Donal M. O Sullivan, Ann Thomas, Peter Canter, Beverley Moore, Ian Armstead, Howard Thomas, Keith J. Edwards and Ian P. King (2005). Construction of a Festuca pratensis BAC library for map-based cloning in Festulolium substitution lines. Theoretical and Applied Genetics, 110 (5) pp.846-851 Sponsorship: BBSRC;BBSRC RAE2008

Optimal Scheduling of Secondary Content for Aggregation in Video-on-Demand Systems

Dynamic service aggregation techniques can exploit skewed access popularity patterns to reduce the costs of building interactive VoD systems. These schemes seek to cluster and merge users into single streams by bridging the temporal skew between them, thus improving server and network utilization. Rate adaptation and secondary content insertion are two such schemes. In this paper, we present and evaluate an optimal scheduling algorithm for inserting secondary content in this scenario. The algorithm runs in polynomial time, and is optimal with respect to the total bandwidth usage over the merging interval. We present constraints on content insertion which make the overall QoS of the delivered stream acceptable, and show how our algorithm can satisfy these constraints. We report simulation results which quantify the excellent gains due to content insertion. We discuss dynamic scenarios with user arrivals and interactions, and show that content insertion reduces the channel bandwidth requirement to almost half. We also discuss differentiated service techniques, such as N-VoD and premium no-advertisement service, and show how our algorithm can support these as well.

Object Detection at the Optimal Scale with Hidden State Shape Models

Hidden State Shape Models (HSSMs) [2], a variant of Hidden Markov Models (HMMs) [9], were proposed to detect shape classes of variable structure in cluttered images. In this paper, we formulate a probabilistic framework for HSSMs which provides two major improvements in comparison to the previous method [2]. First, while the method in [2] required the scale of the object to be passed as an input, the method proposed here estimates the scale of the object automatically. This is achieved by introducing a new term for the observation probability that is based on a object-clutter feature model. Second, a segmental HMM [6, 8] is applied to model the "duration probability" of each HMM state, which is learned from the shape statistics in a training set and helps obtain meaningful registration results. Using a segmental HMM provides a principled way to model dependencies between the scales of different parts of the object. In object localization experiments on a dataset of real hand images, the proposed method significantly outperforms the method of [2], reducing the incorrect localization rate from 40% to 15%. The improvement in accuracy becomes more significant if we consider that the method proposed here is scale-independent, whereas the method of [2] takes as input the scale of the object we want to localize.

Spatial Pattern Learning, Catastophic Forgetting and Optimal Rules of Synaptic Transmission

It is a neural network truth universally acknowledged, that the signal transmitted to a target node must be equal to the product of the path signal times a weight. Analysis of catastrophic forgetting by distributed codes leads to the unexpected conclusion that this universal synaptic transmission rule may not be optimal in certain neural networks. The distributed outstar, a network designed to support stable codes with fast or slow learning, generalizes the outstar network for spatial pattern learning. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns across a target field of nodes. The distributed outstar replaces the outstar source node with a source field, of arbitrarily many nodes, where the activity pattern may be arbitrarily distributed or compressed. Learning proceeds according to a principle of atrophy due to disuse whereby a path weight decreases in joint proportion to the transmittcd path signal and the degree of disuse of the target node. During learning, the total signal to a target node converges toward that node's activity level. Weight changes at a node are apportioned according to the distributed pattern of converging signals three types of synaptic transmission, a product rule, a capacity rule, and a threshold rule, are examined for this system. The three rules are computationally equivalent when source field activity is maximally compressed, or winner-take-all when source field activity is distributed, catastrophic forgetting may occur. Only the threshold rule solves this problem. Analysis of spatial pattern learning by distributed codes thereby leads to the conjecture that the optimal unit of long-term memory in such a system is a subtractive threshold, rather than a multiplicative weight.

Dynamic Programming for Optimal Control of Set-Up Scheduling with Neural Network Modifications

This paper demonstrates an optimal control solution to change of machine set-up scheduling based on dynamic programming average cost per stage value iteration as set forth by Cararnanis et. al. [2] for the 2D case. The difficulty with the optimal approach lies in the explosive computational growth of the resulting solution. A method of reducing the computational complexity is developed using ideas from biology and neural networks. A real time controller is described that uses a linear-log representation of state space with neural networks employed to fit cost surfaces.

Genetically Generated Neural Networks II: Searching for an Optimal Representation

Genetic Algorithms (GAs) make use of an internal representation of a given system in order to perform optimization functions. The actual structural layout of this representation, called a genome, has a crucial impact on the outcome of the optimization process. The purpose of this paper is to study the effects of different internal representations in a GA, which generates neural networks. A second GA was used to optimize the genome structure. This structure produces an optimized system within a shorter time interval.