66 research outputs found
AN ANALYSIS OF OKLAHOMA DIRECT MARKETING OUTLETS: CASE STUDY OF PRODUCE FARMERS' MARKETS
The objectives of this study are to examine consumer preferences among various marketing channels including direct marketing in Oklahoma, and to analyze the impact of various demographic variables on purchasing choice. Specifically, this research focuses on the links between demographic factors and shopping preferences. Data from consumers' survey in 21 farmers' markets in Oklahoma will be used to analyze consumer preferences using an ordered logistic regression analysis method. Farmers' market producers and market managers were also surveyed. The results of this study identify consumer characteristics that influence produce demand and consequently growers' return at Oklahoma farmers' market.Consumer/Household Economics,
End-to-End Localization and Ranking for Relative Attributes
We propose an end-to-end deep convolutional network to simultaneously
localize and rank relative visual attributes, given only weakly-supervised
pairwise image comparisons. Unlike previous methods, our network jointly learns
the attribute's features, localization, and ranker. The localization module of
our network discovers the most informative image region for the attribute,
which is then used by the ranking module to learn a ranking model of the
attribute. Our end-to-end framework also significantly speeds up processing and
is much faster than previous methods. We show state-of-the-art ranking results
on various relative attribute datasets, and our qualitative localization
results clearly demonstrate our network's ability to learn meaningful image
patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201
BATS: Binary ArchitecTure Search
This paper proposes Binary ArchitecTure Search (BATS), a framework that
drastically reduces the accuracy gap between binary neural networks and their
real-valued counterparts by means of Neural Architecture Search (NAS). We show
that directly applying NAS to the binary domain provides very poor results. To
alleviate this, we describe, to our knowledge, for the first time, the 3 key
ingredients for successfully applying NAS to the binary domain. Specifically,
we (1) introduce and design a novel binary-oriented search space, (2) propose a
new mechanism for controlling and stabilising the resulting searched
topologies, (3) propose and validate a series of new search strategies for
binary networks that lead to faster convergence and lower search times.
Experimental results demonstrate the effectiveness of the proposed approach and
the necessity of searching in the binary space directly. Moreover, (4) we set a
new state-of-the-art for binary neural networks on CIFAR10, CIFAR100 and
ImageNet datasets. Code will be made available
https://github.com/1adrianb/binary-nasComment: accepted to ECCV 202
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks
Deep neural networks (DNNs) have demonstrated success for many supervised
learning tasks, ranging from voice recognition, object detection, to image
classification. However, their increasing complexity might yield poor
generalization error that make them hard to be deployed on edge devices.
Quantization is an effective approach to compress DNNs in order to meet these
constraints. Using a quasiconvex base function in order to construct a binary
quantizer helps training binary neural networks (BNNs) and adding noise to the
input data or using a concrete regularization function helps to improve
generalization error. Here we introduce foothill function, an infinitely
differentiable quasiconvex function. This regularizer is flexible enough to
deform towards and penalties. Foothill can be used as a binary
quantizer, as a regularizer, or as a loss. In particular, we show this
regularizer reduces the accuracy gap between BNNs and their full-precision
counterpart for image classification on ImageNet.Comment: Accepted in 16th International Conference of Image Analysis and
Recognition (ICIAR 2019
(Dynamic) Prompting might be all you need to repair Compressed LLMs
Large language models (LLMs), while transformative for NLP, come with
significant computational demands, underlining the need for efficient,
training-free compression. Notably, despite the marked improvement in
training-free compression for the largest of LLMs, our tests using LLaMA-7B and
OPT-6.7b highlight a significant performance drop in several realistic
downstream tasks. Investigation into the trade-off between resource-intensive
post-compression re-training highlights the prospect of prompt-driven recovery
as a lightweight adaption tool. However, existing studies, confined mainly to
perplexity evaluations and simple tasks, fail to offer unequivocal confidence
in the scalability and generalizability of prompting. We tackle this
uncertainty in two key ways. First, we uncover the vulnerability of naive
prompts in LLM compression as an over-reliance on a singular prompt per input.
In response, we propose inference-time dynamic prompting (IDP), a mechanism
that autonomously chooses from a set of curated prompts based on the context of
each individual input. Second, we delve into a scientific understanding of why
"prompting might be all you need post-LLM compression." Our findings suggest
that compression does not irretrievably erase LLM model knowledge but displace
it, necessitating a new inference path. IDP effectively redirects this path,
enabling the model to tap into its inherent yet displaced knowledge and thereby
recover performance. Empirical tests affirm the value of IDP, demonstrating an
average performance improvement of 1.24% across nine varied tasks spanning
multiple knowledge domains
Resistance of wheat cultivars to bird cherry-oat aphid, Rhopalosiphum padi (Hem.: Aphididae)
The bird cherry-oat aphid, Rhopalosiphum padi (L.), is polyphagous with a nearly worldwide distribution and known as an important pest of wheat and the main vector of barley yellow dwarf virus. In this study, the possibility of antixenosis, antibiosis and tolerance of six common wheat cultivars of Chamran, Darab 2, Shiraz, Ghods, Marvdasht and Niknezhad was investigated at 2-3 leaf growth stage in the Fars province, Iran. The experiments were conducted at 24 ± 5°C, 65 ± 5% R.H. and natural light in a greenhouse, using a randomized complete design. The analysis of variance in regard to the number of adult aphids attracted to each cultivar, was indicative of significant differences among the cultivars (P < 0.01). The highest (21 ± 0.71) and the lowest (11.6 ± 0.51) mean number of adult aphids attracted per plant was observed on Shiraz and Darab 2, respectively. The antibiosis test, based on nymphs per female was significantly different among the cultivars (P < 0.01) whose average values were 62.05, 55.84, 49.89, 47.63, 42.76 and 40.65 nymphs per female on Niknezhad, Shiraz, Ghods, Marvdash, Chamran, and Darab 2, respectively. The tolerance studies based on the damage index, showed that Chamran and Darab 2, with the lowest damage index (1.33), were the most tolerant cultivars while Shiraz and Niknezhad, with the highest damage indexes of 3.00 and 3.67 respectively, were the most susceptible cultivars. The cutivars Niknezhad and Shiraz are found to be susceptible, Ghods and Marvdasht partially resistant, and Chamran and Darab 2 resistant to the bird cherry - oat aphid
Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing
Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals.
Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the
cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature
selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially
increase classification accuracy and reduce computational complexity by identifying important features from the
original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature
selection method that combines the output of four filter methods to achieve an optimum selection. We then
perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark
dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce
the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to
other classification techniques
Effect of Austenitizing Temperature on Mechanical Properties of the Mixed Bainite - Martensite Microstructure in CrMoV Steel
Seletividade de produtos fitossanitários sobre o ácaro predador Agistemus brasiliensis Matioli, Ueckermann & Oliveira (Acari: Stigmaeidae)
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