The original problem of group testing consists in the identification of defective items in a collection, by applying tests on groups of items that detect the presence of at least one defective element in the group. Th...
详细信息
The original problem of group testing consists in the identification of defective items in a collection, by applying tests on groups of items that detect the presence of at least one defective element in the group. The aim is then to identify all defective items of the collection with as few tests as possible. This problem is relevant in several fields, among which biology and computer sciences. In the present article we consider that the tests applied to groups of items returns a load, measuring how defective the most defective item of the group is. In this setting, we propose a simple non-adaptative algorithm allowing the detection of all defective items of the collection. Items are put on an n x n grid and pools are organised as lines, columns and diagonals of this grid. This method improves on classical group testing algorithms using only the binary response of the test. Group testing recently gained attraction as a potential tool to solve a shortage of COVID-19 test kits, in particular for RT-qPCR. These tests return the viral load of the sample and the viral load varies greatly among individuals. Therefore our model presents some of the key features of this problem. We aim at using the extra piece of information that represents the viral load to construct a one-stage pool testing algorithm on this idealized version. We show that under the right conditions, the total number of tests needed to detect contaminated samples can be drastically diminished.
The mixture of factor analyzers (MFA) model has emerged as a useful tool to perform dimensionality reduction and model-based clustering for heterogeneous data. In seeking the most appropriate number of factors (q) of ...
详细信息
The mixture of factor analyzers (MFA) model has emerged as a useful tool to perform dimensionality reduction and model-based clustering for heterogeneous data. In seeking the most appropriate number of factors (q) of a MFA model with the number of components (g) fixed a priori, a two-stage procedure is commonly implemented by firstly carrying out parameter estimation over a set of prespecified numbers of factors, and then selecting the best q according to certain penalized likelihood criteria. When the dimensionality of data grows higher, such a procedure can be computationally prohibitive. To overcome this obstacle, we develop an automated learning scheme, called the automated MFA (AMFA) algorithm, to effectively merge parameter estimation and selection of q into a one-stage algorithm. The proposed AMFA procedure that allows for much lower computational cost is also extended to accommodate missing values. Moreover, we explicitly derive the score vector and the empirical information matrix for calculating standard errors associated with the estimated parameters. The potential and applicability of the proposed method are demonstrated through a number of real datasets with genuine and synthetic missing values.
Previous research has shown that there are two architectures for speech-to-speech translation (S2ST) system implementation. one is client-server based systems that are built on the server computer, which means they ar...
详细信息
Previous research has shown that there are two architectures for speech-to-speech translation (S2ST) system implementation. one is client-server based systems that are built on the server computer, which means they are not available anytime or anywhere. The other is portable stand-alonedevices, which lack real-time performance. Therefore, this work presents a hardware-software co-design of a speech translation embedded system for portable S2ST applications. This system is characterized by small size, low cost, real-time operation, and high portability. In order to realize the proposed S2ST system, we have designed the ARM-based system-on-a-programmable-chip (SoPC) architecture, the speech translation intellectual property, and the software procedures of the proposed SoPC. The entire design was implemented on ALTERA EPXA10. The English-to-Mandarin translation process can be completed within 0.5 second at a 40 MHz clock frequency with 1,200 translation patterns. The maximum frequency is 46.22 MHz, and the usage of logic elements is 19,318 (50% of the total number of logic elements of the EPXA10 device).
暂无评论