167 research outputs found
Deep learning for computer vision constrained by limited supervision
This thesis presents the research work conducted on developing algo- rithms capable of training neural networks for image classification and re- gression in low supervision settings. The research was conducted on publicly available benchmark image datasets as well as real world data with appli- cations to herbage quality estimation in an agri-tech scope at the VistaMilk SFI centre. Topics include label noise and web-crawled datasets where some images have an incorrect classification label, semi-supervised learning where only a small part of the available images have been annotated by humans and unsupervised learning where the images are not annotated. The principal contributions are summarized as follows. Label noise: a study highlighting the dual in- and out-of-distribution nature of web-noise; a noise detection metric than can independently retrieve each noise type; an observation of the linear separability of in- and out-of-distribution images in unsupervised contrastive feature spaces; two noise-robust algorithms DSOS and SNCF that iteratively improve the state-of-the-art accuracy on the mini-Webvision dataset. Semi-supervised learning: we use unsupervised features to propagate labels from a few labeled examples to the entire dataset; ReLaB an algorithm that allows to decrease the classification error up to 8% with one labeled representative image on CIFAR-10. Biomass composition estimation from images: two semi-supervised approaches that utilize unlabeled images either through an approximate annotator or by adapting semi-supervised algorithms from the image classification litterature. To scale the biomass to drone images, we use super-resolution paired with semi-supervised learning. Early results on grass biomass estimation show the feasibility of automating the process with accuracies on par or better than human experts. The conclusion of the thesis will summarize the research contributions and discuss thoughts on future research that I believe should be tackled in the field of low supervision computer vision
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Modeling Random Networks with Heterogeneous Reciprocity
Reciprocity, or the tendency of individuals to mirror behavior, is a key
measure that describes information exchange in a social network. Users in
social networks tend to engage in different levels of reciprocal behavior.
Differences in such behavior may indicate the existence of communities that
reciprocate links at varying rates. In this paper, we develop methodology to
model the diverse reciprocal behavior in growing social networks. In
particular, we present a preferential attachment model with heterogeneous
reciprocity that imitates the attraction users have for popular users, plus the
heterogeneous nature by which they reciprocate links. We compare Bayesian and
frequentist model fitting techniques for large networks, as well as
computationally efficient variational alternatives. Cases where the number of
communities are known and unknown are both considered. We apply the presented
methods to the analysis of a Facebook wallpost network where users have
non-uniform reciprocal behavior patterns. The fitted model captures the
heavy-tailed nature of the empirical degree distributions in the Facebook data
and identifies multiple groups of users that differ in their tendency to reply
to and receive responses to wallposts
Sequential Inference with the Mallows Model
The Mallows model is a widely used probabilistic model for analysing rank data. It assumes that a collection of n items can be ranked by each assessor and then summarised as a permutation of size n. The associated probability distribution is defined on the permutation space of these items. A hierarchical Bayesian framework for the Mallows model, named the Bayesian Mallows model, has been developed recently to perform inference and to provide uncertainty estimates of the model parameters. This framework typically uses Markov chain Monte Carlo (MCMC) methods to simulate from the target posterior distribution. However, MCMC can be considerably slow when additional computational effort is presented in the form of new ranking data. It can therefore be difficult to update the Bayesian Mallows model in real time. This thesis extends the Bayesian Mallows model to allow for sequential updates of its posterior estimates each time a collection of new preference data is observed. The posterior is updated over a sequence of discrete time steps with fixed computational complexity. This can be achieved using Sequential Monte Carlo (SMC) methods. SMC offers a standard alternative to MCMC by constructing a sequence of posterior distributions using a set of weighted samples. The samples are propagated via a combination of importance sampling, resampling and moving steps. We propose an SMC framework that can perform sequential updates for the posterior distribution for both a single Mallows model and a Mallows mixture each time we observe new full rankings in an online setting. We also construct a framework to conduct SMC with partial rankings for a single Mallows model. We propose an alternative proposal distribution for data augmentation in partial rankings that incorporates the current posterior estimates of the Mallows model parameters in each SMC iteration. We also extend the framework to consider how the posterior is updated when known assessors provide additional information in their partial ranking. We show how these corrections in the latent information are performed to account for the changes in the posterior
Operational Research: methods and applications
This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
Operational research:methods and applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
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Regime-Specific Quant Generative Adversarial Network: A Conditional Generative Adversarial Network for Regime-Specific Deepfakes of Financial Time Series
Data Availability Statement: The data that support the findings of this study are available from Bloomberg LLP but restrictions apply to the availability of this data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Bloomberg LLP.Simulating financial time series (FTS) data consistent with non-stationary, empirical market behaviour is difficult, but it has valuable applications for financial risk management. A better risk estimation can improve returns on capital and capital efficiency in investment decision making. Challenges to modelling financial risk in market crisis environments are anomalous asset price behaviour and a lack of historical data to learn from. This paper proposes a novel semi-supervised approach for generating regime-specific ‘deep fakes’ of FTS data using generative adversarial networks (GANs). The proposed architecture, a regime-specific Quant GAN (RSQGAN), is a conditional GAN (cGAN) that generates class-conditional synthetic asset return data. Conditional class labels correspond to distinct market regimes that have been detected using a structural breakpoint algorithm to segment FTS into regime classes for simulation. Our RSQGAN approach accurately simulated univariate time series behaviour consistent with specific empirical regimes, outperforming equivalently configured unconditional GANs trained only on crisis regime data. To evaluate the RSQGAN performance for simulating asset return behaviour during crisis environments, we also propose four test metrics that are sensitive to path-dependent behaviour and are also actionable during a crisis environment. Our RSQGAN model design borrows from innovation in the image GAN domain by enabling a user-controlled hyperparameter for adjusting the fit of synthetic data fidelity to real-world data; however, this is at the cost of synthetic data variety. These model features suggest that RSQGAN could be a useful new tool for understanding risk and making investment decisions during a time of market crisis.This research received no external funding
Circuit analysis of the <i>Drosophila</i> brain using connectivity-based neuronal classification reveals organization of key communication pathways
AbstractWe present a functionally relevant, quantitative characterization of the neural circuitry of Drosophila melanogaster at the mesoscopic level of neuron types as classified exclusively based on potential network connectivity. Starting from a large neuron-to-neuron brain-wide connectome of the fruit fly, we use stochastic block modeling and spectral graph clustering to group neurons together into a common “cell class” if they connect to neurons of other classes according to the same probability distributions. We then characterize the connectivity-based cell classes with standard neuronal biomarkers, including neurotransmitters, developmental birthtimes, morphological features, spatial embedding, and functional anatomy. Mutual information indicates that connectivity-based classification reveals aspects of neurons that are not adequately captured by traditional classification schemes. Next, using graph theoretic and random walk analyses to identify neuron classes as hubs, sources, or destinations, we detect pathways and patterns of directional connectivity that potentially underpin specific functional interactions in the Drosophila brain. We uncover a core of highly interconnected dopaminergic cell classes functioning as the backbone communication pathway for multisensory integration. Additional predicted pathways pertain to the facilitation of circadian rhythmic activity, spatial orientation, fight-or-flight response, and olfactory learning. Our analysis provides experimentally testable hypotheses critically deconstructing complex brain function from organized connectomic architecture
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