Search results for "NETWORKS"
showing 10 items of 3260 documents
Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning
2022
The estimation of biophysical variables is at the core of remote sensing science, allowing a close monitoring of crops and forests. Deriving temporally resolved and spatially explicit maps of parameters of interest has been the subject of intense research. However, deriving products from optical sensors is typically hampered by cloud contamination and the trade-off between spatial and temporal resolutions. In this work we rely on the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to generate long gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectances. An artificial neural network is trained on PROSAIL inversion to p…
Exposing the structure of an Arctic food web
2015
15 pages; International audience; How food webs are structured has major implications for their stability and dynamics. While poorly studied to date, arctic food webs are commonly assumed to be simple in structure, with few links per species. If this is the case, then different parts of the web may be weakly connected to each other, with populations and species united by only a low number of links. We provide the first highly resolved description of trophic link structure for a large part of a high-arctic food web. For this purpose, we apply a combination of recent techniques to describing the links between three predator guilds (insectivorous birds, spiders, and lepidopteran parasitoids) a…
On the Suitability of Neural Networks as Building Blocks for the Design of Efficient Learned Indexes
2022
With the aim of obtaining time/space improvements in classic Data Structures, an emerging trend is to combine Machine Learning techniques with the ones proper of Data Structures. This new area goes under the name of Learned Data Structures. The motivation for its study is a perceived change of paradigm in Computer Architectures that would favour the use of Graphics Processing Units and Tensor Processing Units over conventional Central Processing Units. In turn, that would favour the use of Neural Networks as building blocks of Classic Data Structures. Indeed, Learned Bloom Filters, which are one of the main pillars of Learned Data Structures, make extensive use of Neural Networks to improve…
Mini-COVIDNet
2021
Mini-COVIDNet is a efficient lightweight deep neural network for ultrasound-based point-of-care detection of COVID-19.
Semantic Word Error Rate for Sentence Similarity
2016
Sentence similarity measures have applications in several tasks, including: Machine Translation, Paraphrase Iden- tification, Speech Recognition, Question-answering and Text Summarization. However, measures designed for these tasks are aimed at assessing equivalence rather than resemblance, partly departing from human cognition of similarity. While this is reasonable for these activities, it hinders the applicability of sentence similarity measures to other tasks. We therefore propose a new sentence similarity measure specifically designed for resemblance evaluation, in order to cover these fields better. Experimental results are discussed.
Monolingual and cross-lingual intent detection without training data in target languages
2021
Due to recent DNN advancements, many NLP problems can be effectively solved using transformer-based models and supervised data. Unfortunately, such data is not available in some languages. This research is based on assumptions that (1) training data can be obtained by the machine translating it from another language
A magnetic skyrmion as a non-linear resistive element - a potential building block for reservoir computing
2017
Inspired by the human brain, there is a strong effort to find alternative models of information processing capable of imitating the high energy efficiency of neuromorphic information processing. One possible realization of cognitive computing are reservoir computing networks. These networks are built out of non-linear resistive elements which are recursively connected. We propose that a skyrmion network embedded in frustrated magnetic films may provide a suitable physical implementation for reservoir computing applications. The significant key ingredient of such a network is a two-terminal device with non-linear voltage characteristics originating from single-layer magnetoresistive effects,…
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
2019
Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural ne…
Drug-induced chromatin accessibility changes associate with sensitivity to liver tumor promotion
2019
This work explores quantitative chromatin accessibility, transcriptional and cis-acting gene regulatory variations underlying mouse strain–specific differences in drug-induced liver tumor promotion sensitivity.
Cognitive resource allocation determines the organization of personal networks
2018
Significance The way we organize our social relationships is key to understanding the structure of our society. We propose a quantitative theory to tackle this issue, assuming that our capacity to maintain relationships is limited and that different types of relationships require different investments. The theory accounts for well-documented empirical evidence on personal networks, such that connections are typically arranged in layers of increasing size and decreasing emotional content. More interestingly, it predicts that when the number of available relationships is small, this structure is inverted, having more close relationships than acquaintances. We provide evidence of the existence…