5,782 research outputs found
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap
Global point cloud registration is essential in many robotics tasks like loop
closing and relocalization. Unfortunately, the registration often suffers from
the low overlap between point clouds, a frequent occurrence in practical
applications due to occlusion and viewpoint change. In this paper, we propose a
graph-theoretic framework to address the problem of global point cloud
registration with low overlap. To this end, we construct a consistency graph to
facilitate robust data association and employ graduated non-convexity (GNC) for
reliable pose estimation, following the state-of-the-art (SoTA) methods.
Unlike previous approaches, we use semantic cues to scale down the dense
point clouds, thus reducing the problem size. Moreover, we address the
ambiguity arising from the consistency threshold by constructing a pyramid
graph with multi-level consistency thresholds. Then we propose a cascaded
gradient ascend method to solve the resulting densest clique problem and obtain
multiple pose candidates for every consistency threshold. Finally, fast
geometric verification is employed to select the optimal estimation from
multiple pose candidates. Our experiments, conducted on a self-collected indoor
dataset and the public KITTI dataset, demonstrate that our method achieves the
highest success rate despite the low overlap of point clouds and low semantic
quality. We have open-sourced our code
https://github.com/HKUST-Aerial-Robotics/Pagor for this project.Comment: Accepted by IROS202
Reinforcement learning in large state action spaces
Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios.
This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory).
In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Studies on genetic and epigenetic regulation of gene expression dynamics
The information required to build an organism is contained in its genome and the first
biochemical process that activates the genetic information stored in DNA is transcription.
Cell type specific gene expression shapes cellular functional diversity and dysregulation
of transcription is a central tenet of human disease. Therefore, understanding
transcriptional regulation is central to understanding biology in health and disease.
Transcription is a dynamic process, occurring in discrete bursts of activity that can be
characterized by two kinetic parameters; burst frequency describing how often genes
burst and burst size describing how many transcripts are generated in each burst. Genes
are under strict regulatory control by distinct sequences in the genome as well as
epigenetic modifications. To properly study how genetic and epigenetic factors affect
transcription, it needs to be treated as the dynamic cellular process it is. In this thesis, I
present the development of methods that allow identification of newly induced gene
expression over short timescales, as well as inference of kinetic parameters describing
how frequently genes burst and how many transcripts each burst give rise to. The work is
presented through four papers:
In paper I, I describe the development of a novel method for profiling newly transcribed
RNA molecules. We use this method to show that therapeutic compounds affecting
different epigenetic enzymes elicit distinct, compound specific responses mediated by
different sets of transcription factors already after one hour of treatment that can only
be detected when measuring newly transcribed RNA.
The goal of paper II is to determine how genetic variation shapes transcriptional bursting.
To this end, we infer transcriptome-wide burst kinetics parameters from genetically
distinct donors and find variation that selectively affects burst sizes and frequencies.
Paper III describes a method for inferring transcriptional kinetics transcriptome-wide
using single-cell RNA-sequencing. We use this method to describe how the regulation of
transcriptional bursting is encoded in the genome. Our findings show that gene specific
burst sizes are dependent on core promoter architecture and that enhancers affect burst
frequencies. Furthermore, cell type specific differential gene expression is regulated by
cell type specific burst frequencies.
Lastly, Paper IV shows how transcription shapes cell types. We collect data on cellular
morphologies, electrophysiological characteristics, and measure gene expression in the
same neurons collected from the mouse motor cortex. Our findings show that cells
belonging to the same, distinct transcriptomic families have distinct and non-overlapping
morpho-electric characteristics. Within families, there is continuous and correlated
variation in all modalities, challenging the notion of cell types as discrete entities
Hybrid time-dependent Ginzburg-Landau simulations of block copolymer nanocomposites: nanoparticle anisotropy
Block copolymer melts are perfect candidates to template the position of colloidal nanoparticles in the nanoscale, on top of their well-known suitability for lithography applications. This is due to their ability to self-assemble into periodic ordered structures, in which nanoparticles can segregate depending on the polymer-particle interactions, size and shape. The resulting coassembled structure can be highly ordered as a combination of both the polymeric and colloidal properties. The time-dependent Ginzburg-Landau model for the block copolymer was combined with Brownian dynamics for nanoparticles, resulting in an efficient mesoscopic model to study the complex behaviour of block copolymer nanocomposites. This review covers recent developments of the time-dependent Ginzburg-Landau/Brownian dynamics scheme. This includes efforts to parallelise the numerical scheme and applications of the model. The validity of the model is studied by comparing simulation and experimental results for isotropic nanoparticles. Extensions to simulate nonspherical and inhomogeneous nanoparticles are discussed and simulation results are discussed. The time-dependent Ginzburg-Landau/Brownian dynamics scheme is shown to be a flexible method which can account for the relatively large system sizes required to study block copolymer nanocomposite systems, while being easily extensible to simulate nonspherical nanoparticles
Four Lectures on the Random Field Ising Model, Parisi-Sourlas Supersymmetry, and Dimensional Reduction
Numerical evidence suggests that the Random Field Ising Model loses
Parisi-Sourlas SUSY and the dimensional reduction property somewhere between 4
and 5 dimensions, while a related model of branched polymers retains these
features in any . These notes give a leisurely introduction to a recent
theory, developed jointly with A. Kaviraj and E. Trevisani, which aims to
explain these facts. Based on the lectures given in Cortona and at the IHES in
2022.Comment: 55 pages, 11 figures; v2 - minor changes, mentioned forthcoming work
by Fytas et a
Evaluation of image quality and reconstruction parameters in recent PET-CT and PET-MR systems
In this PhD dissertation, we propose to evaluate the impact of using different PET isotopes for
the National Electrical Manufacturers Association (NEMA) tests performance evaluation of the
GE Signa integrated PET/MR. The methods were divided into three closely related categories:
NEMA performance measurements, system modelling and evaluation of the image quality of
the state-of-the-art of clinical PET scanners. NEMA performance measurements for
characterizing spatial resolution, sensitivity, image quality, the accuracy of attenuation and
scatter corrections, and noise equivalent count rate (NECR) were performed using clinically
relevant and commercially available radioisotopes. Then we modelled the GE Signa integrated
PET/MR system using a realistic GATE Monte Carlo simulation and validated it with the result of
the NEMA measurements (sensitivity and NECR). Next, the effect of the 3T MR field on the
positron range was evaluated for F-18, C-11, O-15, N-13, Ga-68 and Rb-82. Finally, to evaluate the image
quality of the state-of-the-art clinical PET scanners, a noise reduction study was performed
using a Bayesian Penalized-Likelihood reconstruction algorithm on a time-of-flight PET/CT
scanner to investigate whether and to what extent noise can be reduced. The outcome of this
thesis will allow clinicians to reduce the PET dose which is especially relevant for young
patients. Besides, the Monte Carlo simulation platform for PET/MR developed for this thesis will
allow physicists and engineers to better understand and design integrated PET/MR systems
Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond
[ES] Esta tesis se enmarca en la intersección entre las técnicas modernas de Machine Learning, como las Redes Neuronales Profundas, y el modelado probabilÃstico confiable. En muchas aplicaciones, no solo nos importa la predicción hecha por un modelo (por ejemplo esta imagen de pulmón presenta cáncer) sino también la confianza que tiene el modelo para hacer esta predicción (por ejemplo esta imagen de pulmón presenta cáncer con 67% probabilidad). En tales aplicaciones, el modelo ayuda al tomador de decisiones (en este caso un médico) a tomar la decisión final. Como consecuencia, es necesario que las probabilidades proporcionadas por un modelo reflejen las proporciones reales presentes en el conjunto al que se ha asignado dichas probabilidades; de lo contrario, el modelo es inútil en la práctica. Cuando esto sucede, decimos que un modelo está perfectamente calibrado.
En esta tesis se exploran tres vias para proveer modelos más calibrados. Primero se muestra como calibrar modelos de manera implicita, que son descalibrados por técnicas de aumentación de datos. Se introduce una función de coste que resuelve esta descalibración tomando como partida las ideas derivadas de la toma de decisiones con la regla de Bayes. Segundo, se muestra como calibrar modelos utilizando una etapa de post calibración implementada con una red neuronal Bayesiana. Finalmente, y en base a las limitaciones estudiadas en la red neuronal Bayesiana, que hipotetizamos que se basan en un prior mispecificado, se introduce un nuevo proceso estocástico que sirve como distribución a priori en un problema de inferencia Bayesiana.[CA] Aquesta tesi s'emmarca en la intersecció entre les tècniques modernes de Machine Learning, com ara les Xarxes Neuronals Profundes, i el modelatge probabilÃstic fiable. En moltes aplicacions, no només ens importa la predicció feta per un model (per ejemplem aquesta imatge de pulmó presenta cà ncer) sinó també la confiança que té el model per fer aquesta predicció (per exemple aquesta imatge de pulmó presenta cà ncer amb 67% probabilitat). En aquestes aplicacions, el model ajuda el prenedor de decisions (en aquest cas un metge) a prendre la decisió final. Com a conseqüència, cal que les probabilitats proporcionades per un model reflecteixin les proporcions reals presents en el conjunt a què s'han assignat aquestes probabilitats; altrament, el model és inútil a la prà ctica. Quan això passa, diem que un model està perfectament calibrat.
En aquesta tesi s'exploren tres vies per proveir models més calibrats. Primer es mostra com calibrar models de manera implÃcita, que són descalibrats per tècniques d'augmentació de dades. S'introdueix una funció de cost que resol aquesta descalibració prenent com a partida les idees derivades de la presa de decisions amb la regla de Bayes. Segon, es mostra com calibrar models utilitzant una etapa de post calibratge implementada amb una xarxa neuronal Bayesiana. Finalment, i segons les limitacions estudiades a la xarxa neuronal Bayesiana, que es basen en un prior mispecificat, s'introdueix un nou procés estocà stic que serveix com a distribució a priori en un problema d'inferència Bayesiana.[EN] This thesis is framed at the intersection between modern Machine Learning techniques, such as Deep Neural Networks, and reliable probabilistic modeling. In many machine learning applications, we do not only care about the prediction made by a model (e.g. this lung image presents cancer) but also in how confident is the model in making this prediction (e.g. this lung image presents cancer with 67% probability). In such applications, the model assists the decision-maker (in this case a doctor) towards making the final decision. As a consequence, one needs that the probabilities provided by a model reflects the true underlying set of outcomes, otherwise the model is useless in practice. When this happens, we say that a model is perfectly calibrated.
In this thesis three ways are explored to provide more calibrated models. First, it is shown how to calibrate models implicitly, which are decalibrated by data augmentation techniques. A cost function is introduced that solves this decalibration taking as a starting point the ideas derived from decision making with Bayes' rule. Second, it shows how to calibrate models using a post-calibration stage implemented with a Bayesian neural network. Finally, and based on the limitations studied in the Bayesian neural network, which we hypothesize that came from a mispecified prior, a new stochastic process is introduced that serves as a priori distribution in a Bayesian inference problem.Maroñas Molano, J. (2022). Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181582TESI
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