160,443 research outputs found
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity
We present three enhancements to existing encoder-decoder models for
open-domain conversational agents, aimed at effectively modeling coherence and
promoting output diversity: (1) We introduce a measure of coherence as the
GloVe embedding similarity between the dialogue context and the generated
response, (2) we filter our training corpora based on the measure of coherence
to obtain topically coherent and lexically diverse context-response pairs, (3)
we then train a response generator using a conditional variational autoencoder
model that incorporates the measure of coherence as a latent variable and uses
a context gate to guarantee topical consistency with the context and promote
lexical diversity. Experiments on the OpenSubtitles corpus show a substantial
improvement over competitive neural models in terms of BLEU score as well as
metrics of coherence and diversity
Integrating local knowledge with tree diversity analyses to optimize on-farm tree species composition for ecosystem service delivery in coffee agroforestry systems of Uganda
Coffee agroforestry systems deliver ecosystem services (ES) critical for rural livelihoods like food but also disservices that constrain livelihoods like fostering coffee-pests. Since such ES are tree-based, maximizing ES and limiting constraints requires knowledge on optimizing on-farm tree composition especially trees adapted to local conditions. The study was in three sites along a rainfall gradient in Central Uganda where we: assessed tree diversity in coffee agroforestry; ranked tree suitability for providing ES according to farmers' knowledge; and then proposed an approach for optimizing on-farm tree composition for delivery of ES. We collected data on tree diversity and, farmers' knowledge of tree species and the ES they provide. Farmers ranked ES in order of importance to their livelihoods ('Needs rank') and ranked trees according to suitability for providing ES. Using Bradley Terry modeling, we grouped trees into 'ES groups' according to suitability for providing different ES and ranked 'ES groups' according to tree diversity ('Diversity rank'). Tree-suitability for providing ES and importance of ES to farmers varied with rainfall regime but tree diversity did not match farmers' needs for ES. We propose the FaD–FaN (matching farm tree diversity to farmers' needs) approach for optimizing tree species composition with respect to tree-suitability for farmers' priority ES. Farmers locally prioritize ES needed and identify trees that best serve such ES. The approach then focuses on modifying on-farm tree diversity to match/suit farmers' priority ES. The FaD–FaN approach caters for varying socio-ecological conditions; it's adaptable for other coffee and cocoa-growing areas worldwide
Code diversity in multiple antenna wireless communication
The standard approach to the design of individual space-time codes is based
on optimizing diversity and coding gains. This geometric approach leads to
remarkable examples, such as perfect space-time block codes, for which the
complexity of Maximum Likelihood (ML) decoding is considerable. Code diversity
is an alternative and complementary approach where a small number of feedback
bits are used to select from a family of space-time codes. Different codes lead
to different induced channels at the receiver, where Channel State Information
(CSI) is used to instruct the transmitter how to choose the code. This method
of feedback provides gains associated with beamforming while minimizing the
number of feedback bits. It complements the standard approach to code design by
taking advantage of different (possibly equivalent) realizations of a
particular code design. Feedback can be combined with sub-optimal low
complexity decoding of the component codes to match ML decoding performance of
any individual code in the family. It can also be combined with ML decoding of
the component codes to improve performance beyond ML decoding performance of
any individual code. One method of implementing code diversity is the use of
feedback to adapt the phase of a transmitted signal as shown for 4 by 4
Quasi-Orthogonal Space-Time Block Code (QOSTBC) and multi-user detection using
the Alamouti code. Code diversity implemented by selecting from equivalent
variants is used to improve ML decoding performance of the Golden code. This
paper introduces a family of full rate circulant codes which can be linearly
decoded by fourier decomposition of circulant matrices within the code
diversity framework. A 3 by 3 circulant code is shown to outperform the
Alamouti code at the same transmission rate.Comment: 9 page
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