Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on multi-scale (pyramidal) feature ensemble architecture image using optical coherence tomography (OCT) images. First, scale-adaptive neural developed to produce inputs extraction and learning. The larger input sizes yield...

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In this paper, we examine the reliability of multi-path multi-stage interconnection networks (MINs) of different class. The bounded expressions for the time-dependent reliability for 16 x 16 Zeta Networks (ZTNs) and 16 x 16 Augmented Shuffle-Exchange Networks (ASENs) are derived. Furthermore, reliability upper bound and lower bound that is useful for the analysis of large networks is derived. T...

In this paper we try to solve the Temporal Resource Reallocation Problem (TRR-P) in multi-agent systems by having the agents negotiate periods of time during which they can have use of resources. Our work is based on and extends a previous work in which a multistage negotiation framework that defines the way agents negotiate over resources and time is defined. The approach in this framework sug...

Many techniques have been proposed for segmenting feature point trajectories tracked through a video sequence into independent motions, but objects in the scene are usually assumed to undergo general 3-D motions. As a result, the segmentation accuracy considerably deteriorates in realistic video sequences in which object motions are nearly degenerate. In this paper, we propose a multi-stage uns...

In early stages of drug development there is often uncertainty about the most promising among a set of different treatments. In order to ensure the best use of resources it is important to decide which, if any, of the treatments should be taken forward for further testing. Multi-arm multi-stage (MAMS) trials provide gains in efficiency over separate randomised trials of each treatment. They all...

Abstract This paper presents a new and general approach, named “Stage- t Scenario Dominance,” to solve the risk-averse multi-stage stochastic mixed-integer programs (M-SMIPs). Given monotonic objective function, our method derives partial ordering of scenarios by pairwise comparing realization uncertain parameters at each time stage under scenario. Specifically, we derive bounds implications fr...

A multi-stage Multi-User Detection (MUD) scheme designed for asynchronous fast Frequency-Hopping/Multilevel Frequency-Shift-Keying (fast-FH/MFSK) systems is proposed, in which each signal detection interval is divided into sub-intervals and the MUD is applied to each sub-interval. In our scheme the MUD exploits the explicit knowledge of the hopping addresses assigned to users. The received sign...

Adaptive designs that are based on group-sequential approaches have the benefit of being efficient as stopping boundaries can be found that lead to good operating characteristics with test decisions based solely on sufficient statistics. The drawback of these so called “pre-planned adaptive” designs is that unexpected design changes are not possible without impacting the error rates. “Flexible ...

Multi-task learning (MTL) seeks to improve the generalization performance by sharing information among multiple tasks. Many existing MTL approaches aim to learn the lowrank structure on the weight matrix, which stores the model parameters of all tasks, to achieve task sharing, and as a consequence the trace norm regularization is widely used in the MTL literature. A major limitation of these ap...