1,480 research outputs found
Risk Minimization, Regret Minimization and Progressive Hedging Algorithms
This paper begins with a study on the dual representations of risk and regret
measures and their impact on modeling multistage decision making under
uncertainty. A relationship between risk envelopes and regret envelopes is
established by using the Lagrangian duality theory. Such a relationship opens a
door to a decomposition scheme, called progressive hedging, for solving
multistage risk minimization and regret minimization problems. In particular,
the classical progressive hedging algorithm is modified in order to handle a
new class of linkage constraints that arises from reformulations and other
applications of risk and regret minimization problems. Numerical results are
provided to show the efficiency of the progressive hedging algorithms.Comment: 21 pages, 2 figure
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
Risk-Adaptive Learning of Seismic Response using Multi-Fidelity Analysis
Performance-based earthquake engineering often requires a large number of sophisticated nonlinear time-history analyses and is therefore demanding both with regard to computing resources and technical expertise. We develop a risk-adaptive statistical learning method based on multi-fidelity analysis that enables engineers to conservatively predict structural response using only low-fidelity analyses such as Pushover analyses. Using a structural model of a 35-story building in California and a training data set consisting of nonlinear time-history and pushover analyses for 160 ground motions, we accurately and conservatively predict maximum story drift ratio, top-story drift ratio, and normalized base shear under the effect of 40 ground motions not seen during the training
Graph Neural Networks for Pressure Estimation in Water Distribution Systems
Pressure and flow estimation in Water Distribution Networks (WDN) allows
water management companies to optimize their control operations. For many
years, mathematical simulation tools have been the most common approach to
reconstructing an estimate of the WDN hydraulics. However, pure physics-based
simulations involve several challenges, e.g. partially observable data, high
uncertainty, and extensive manual configuration. Thus, data-driven approaches
have gained traction to overcome such limitations. In this work, we combine
physics-based modeling and Graph Neural Networks (GNN), a data-driven approach,
to address the pressure estimation problem. First, we propose a new data
generation method using a mathematical simulation but not considering temporal
patterns and including some control parameters that remain untouched in
previous works; this contributes to a more diverse training data. Second, our
training strategy relies on random sensor placement making our GNN-based
estimation model robust to unexpected sensor location changes. Third, a
realistic evaluation protocol considers real temporal patterns and additionally
injects the uncertainties intrinsic to real-world scenarios. Finally, a
multi-graph pre-training strategy allows the model to be reused for pressure
estimation in unseen target WDNs. Our GNN-based model estimates the pressure of
a large-scale WDN in The Netherlands with a MAE of 1.94mHO and a MAPE of
7%, surpassing the performance of previous studies. Likewise, it outperformed
previous approaches on other WDN benchmarks, showing a reduction of absolute
error up to approximately 52% in the best cases.Comment: submitted to Water Resources Research. Huy Truong and Andr\'es Tello
contributed equally to this wor
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better
generalization performance. Currently, deep learning models with multilayer
processing architecture is showing better performance as compared to the
shallow or traditional classification models. Deep ensemble learning models
combine the advantages of both the deep learning models as well as the ensemble
learning such that the final model has better generalization performance. This
paper reviews the state-of-art deep ensemble models and hence serves as an
extensive summary for the researchers. The ensemble models are broadly
categorised into ensemble models like bagging, boosting and stacking, negative
correlation based deep ensemble models, explicit/implicit ensembles,
homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised,
semi-supervised, reinforcement learning and online/incremental, multilabel
based deep ensemble models. Application of deep ensemble models in different
domains is also briefly discussed. Finally, we conclude this paper with some
future recommendations and research directions
Distributionally Robust Optimization: A Review
The concepts of risk-aversion, chance-constrained optimization, and robust
optimization have developed significantly over the last decade. Statistical
learning community has also witnessed a rapid theoretical and applied growth by
relying on these concepts. A modeling framework, called distributionally robust
optimization (DRO), has recently received significant attention in both the
operations research and statistical learning communities. This paper surveys
main concepts and contributions to DRO, and its relationships with robust
optimization, risk-aversion, chance-constrained optimization, and function
regularization
Predictive cognition in dementia: the case of music
The clinical complexity and pathological diversity of neurodegenerative diseases impose immense challenges for diagnosis and the design of rational interventions. To address these challenges, there is a need to identify new paradigms and biomarkers that capture shared pathophysiological processes and can be applied across a range of diseases. One core paradigm of brain function is predictive coding: the processes by which the brain establishes predictions and uses them to minimise prediction errors represented as the difference between predictions and actual sensory inputs. The processes involved in processing unexpected events and responding appropriately are vulnerable in common dementias but difficult to characterise. In my PhD work, I have exploited key properties of music – its universality, ecological relevance and structural regularity – to model and assess predictive cognition in patients representing major syndromes of frontotemporal dementia – non-fluent variant PPA (nfvPPA), semantic-variant PPA (svPPA) and behavioural-variant FTD (bvFTD) - and Alzheimer’s disease relative to healthy older individuals. In my first experiment, I presented patients with well-known melodies containing no deviants or one of three types of deviant - acoustic (white-noise burst), syntactic (key-violating pitch change) or semantic (key-preserving pitch change). I assessed accuracy detecting melodic deviants and simultaneously-recorded pupillary responses to these deviants. I used voxel-based morphometry to define neuroanatomical substrates for the behavioural and autonomic processing of these different types of deviants, and identified a posterior temporo-parietal network for detection of basic acoustic deviants and a more anterior fronto-temporo-striatal network for detection of syntactic pitch deviants. In my second chapter, I investigated the ability of patients to track the statistical structure of the same musical stimuli, using a computational model of the information dynamics of music to calculate the information-content of deviants (unexpectedness) and entropy of melodies (uncertainty). I related these information-theoretic metrics to performance for detection of deviants and to ‘evoked’ and ‘integrative’ pupil reactivity to deviants and melodies respectively and found neuroanatomical correlates in bilateral dorsal and ventral striatum, hippocampus, superior temporal gyri, right temporal pole and left inferior frontal gyrus. Together, chapters 3 and 4 revealed new hypotheses about the way FTD and AD pathologies disrupt the integration of predictive errors with predictions: a retained ability of AD patients to detect deviants at all levels of the hierarchy with a preserved autonomic sensitivity to information-theoretic properties of musical stimuli; a generalized impairment of surprise detection and statistical tracking of musical information at both a cognitive and autonomic levels for svPPA patients underlying a diminished precision of predictions; the exact mirror profile of svPPA patients in nfvPPA patients with an abnormally high rate of false-alarms with up-regulated pupillary reactivity to deviants, interpreted as over-precise or inflexible predictions accompanied with normal cognitive and autonomic probabilistic tracking of information; an impaired behavioural and autonomic reactivity to unexpected events with a retained reactivity to environmental uncertainty in bvFTD patients. Chapters 5 and 6 assessed the status of reward prediction error processing and updating via actions in bvFTD. I created pleasant and aversive musical stimuli by manipulating chord progressions and used a classic reinforcement-learning paradigm which asked participants to choose the visual cue with the highest probability of obtaining a musical ‘reward’. bvFTD patients showed reduced sensitivity to the consequence of an action and lower learning rate in response to aversive stimuli compared to reward. These results correlated with neuroanatomical substrates in ventral and dorsal attention networks, dorsal striatum, parahippocampal gyrus and temporo-parietal junction. Deficits were governed by the level of environmental uncertainty with normal learning dynamics in a structured and binarized environment but exacerbated deficits in noisier environments. Impaired choice accuracy in noisy environments correlated with measures of ritualistic and compulsive behavioural changes and abnormally reduced learning dynamics correlated with behavioural changes related to empathy and theory-of-mind. Together, these experiments represent the most comprehensive attempt to date to define the way neurodegenerative pathologies disrupts the perceptual, behavioural and physiological encoding of unexpected events in predictive coding terms
Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes
Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanztomographie-Bildern für eine Hirnparzellierung zu nutzen
Predicting the Outcome of Cognitive Training in Parkinson’s Disease using Magnetic Resonance Imaging
Motivation: Cognitive impairment is an important symptom of Parkinson’s Disease (PD),
usually having a substantial negative impact on the quality of life of patients, families,
and caregivers. Cognitive Training (CT) have been proven effective in halting the process
of cognitive decline in PD. However, the efficacy of CT is unpredictable from subject to
subject.
Objective: Investigate the possibility of predicting the outcome of CT in PD patients
with Mild Cognitive Impairment using structural and functional Magnetic Resonance
Imaging (MRI) data.
Methods: Before CT, a sample of 42 PD patients underwent structural and functional
MRI. Graph measures were then extracted from their structural and functional con nectomes and used as features for random forest (RFo) and decision tree (DT) machine
learning (ML) regression algorithms with and without prior latent component analysis
(LCA). CT response was evaluated by assessing the outcomes of the Tower of London
task pre- and post-treatment. Finally, the 4 ML models were used to predict CT response
and their performances were assessed. Post hoc analyses were conducted to investigate
whether these algorithms could predict age using connectomic measures on a sample of
80 PD patients.
Results: The performances of the aforementioned algorithms did not differ signifi cantly from the baseline performance predicting the subject-specific CT outcome. The
performance of the RFo without LCA differed significantly from the baseline performance
in the age prediction task for the sample of 80 patients.
Conclusion: Notwithstanding the lack of statistical significance in predicting our
xicognitive outcomes, the relative success of the age prediction task points towards the
potential of this approach. We hypothesise that bigger sample sizes are needed in order
to predict the outcome of CT using ML
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