Algorithm for generating optimal tests for exclusive-or networks

1991 ◽  
Vol 138 (2) ◽  
pp. 93 ◽  
Author(s):  
W.H. Debany ◽  
C.R.P. Hartmann ◽  
T.J. Snethen
Keyword(s):  
Optimal Tests ◽  
Exclusive Or

Blockchain technology for port logistics capability: Exclusive or sharing

2021 ◽  
Vol 149 ◽  
pp. 347-392
Author(s):  
Junjin Wang ◽  
Jiaguo Liu ◽  
Fan Wang ◽  
Xiaohang Yue
Keyword(s):  
Blockchain Technology ◽  
Port Logistics ◽  
Exclusive Or ◽  
Logistics Capability

scholarly journals A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Karim El-Laithy ◽  
Martin Bogdan
Keyword(s):  
Reinforcement Learning ◽  
Spike Timing ◽  
Neural Representation ◽  
Model Parameters ◽  
Learning Framework ◽  
Reference Target ◽  
Wide Range ◽  
Spiking Network ◽  
Dynamic Synapses ◽  
Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

ASYMPTOTICALLY UMP PANEL UNIT ROOT TESTS—THE EFFECT OF HETEROGENEITY IN THE ALTERNATIVES

2014 ◽  
Vol 31 (3) ◽  
pp. 539-559 ◽  
Author(s):  
I. Gaia Becheri ◽  
Feike C. Drost ◽  
Ramon van den Akker
Keyword(s):  
Unit Root ◽  
Generalized Least Squares ◽  
Applied Economics ◽  
Unit Root Tests ◽  
Finite Sample ◽  
Asymptotic Power ◽  
Optimal Tests ◽  
Independent Panel ◽  
Local Asymptotic Power ◽  
Uniformly Most Powerful

In a Gaussian, heterogeneous, cross-sectionally independent panel with incidental intercepts, Moon, Perron, and Phillips (2007, Journal of Econometrics 141, 416–459) present an asymptotic power envelope yielding an upper bound to the local asymptotic power of unit root tests. In case of homogeneous alternatives this envelope is known to be sharp, but this paper shows that it is not attainable for heterogeneous alternatives. Using limit experiment theory we derive a sharp power envelope. We also demonstrate that, among others, one of the likelihood ratio based tests in Moon et al. (2007, Journal of Econometrics 141, 416–459), a pooled generalized least squares (GLS) based test using the Breitung and Meyer (1994, Applied Economics 25, 353–361) device, and a new test based on the asymptotic structure of the model are all asymptotically UMP (Uniformly Most Powerful). Thus, perhaps somewhat surprisingly, pooled regression-based tests may yield optimal tests in case of heterogeneous alternatives. Although finite-sample powers are comparable, the new test is easy to implement and has superior size properties.

Adding Lateral Inhibition to a Simple Feedforward Network Enables It to Perform Exclusive-Or

1998 ◽  
Vol 10 (2) ◽  
pp. 277-280
Author(s):  
Leslie S. Smith
Keyword(s):  
Lateral Inhibition ◽  
Recurrent Network ◽  
External Input ◽  
Output Function ◽  
Output Unit ◽  
Feedforward Network ◽  
Point Attractor ◽  
Exclusive Or

A simple laterally inhibited recurrent network that implementse xclusive-or is demonstrated. The network consists of two mutually inhibitory units with logistic output function, each receiving one external input and each connected to a simple threshold output unit. The mutually inhibitory units settle into a point attractor. We investigate the range of steepness of the logistic and the range of inhibitory weights for which the network can perform exclusive-or.

scholarly journals Expression of sugarcane genes induced by inoculation with Gluconacetobacter diazotrophicus and Herbaspirillum rubrisubalbicans

2001 ◽  
Vol 24 (1-4) ◽  
pp. 199-206 ◽  
Author(s):  
Eduardo de Matos Nogueira ◽  
Fabiano Vinagre ◽  
Hana Paula Masuda ◽  
Claudia Vargas ◽  
Vânia Lúcia Muniz de Pádua ◽  
...  
Keyword(s):  
Expressed Sequence Tag ◽  
Expression Profiles ◽  
Inorganic Nitrogen ◽  
Gene Expression Profiles ◽  
Nitrogen Fertilizers ◽  
Gluconacetobacter Diazotrophicus ◽  
Nitrogen Fixing ◽  
Preferential Expression ◽  
Sugarcane Varieties ◽  
Exclusive Or

Several Brazilian sugarcane varieties have the ability to grow with little addition of inorganic nitrogen fertilizers, showing high contributions of Biological Nitrogen Fixation (BNF). A particular type of nitrogen-fixing association has been described in this crop, where endophytic diazotrophs such as Gluconacetobacter diazotrophicus and Herbaspirillum spp. colonize plant tissues without causing disease symptoms. In order to gain insight into the role played by the sugarcane in the interaction between this plant and endophytic diazotrophs, we investigated gene expression profiles of sugarcane plants colonized by G. diazotrophicus and H. rubrisubalbicans by searching the sugarcane expressed sequence tag SUCEST Database (<A HREF="http://sucest.lad.ic.unicamp.br/en/">http://sucest.lad.ic.unicamp.br/en/</A>). We produced an inventory of sugarcane genes, candidates for exclusive or preferential expression during the nitrogen-fixing association. This data suggests that the host plant might be actively involved in the establishment of the interaction with G. diazotrophicus and H. rubrisubalbicans.

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