962 research outputs found
Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Model reconstruction from temporal data for coupled oscillator networks
In a complex system, the interactions between individual agents often lead to
emergent collective behavior like spontaneous synchronization, swarming, and
pattern formation. The topology of the network of interactions can have a
dramatic influence over those dynamics. In many studies, researchers start with
a specific model for both the intrinsic dynamics of each agent and the
interaction network, and attempt to learn about the dynamics that can be
observed in the model. Here we consider the inverse problem: given the dynamics
of a system, can one learn about the underlying network? We investigate
arbitrary networks of coupled phase-oscillators whose dynamics are
characterized by synchronization. We demonstrate that, given sufficient
observational data on the transient evolution of each oscillator, one can use
machine learning methods to reconstruct the interaction network and
simultaneously identify the parameters of a model for the intrinsic dynamics of
the oscillators and their coupling.Comment: 27 pages, 7 figures, 16 table
A Preliminary Investigation of Machine Learning Approaches for Mobility Monitoring from Smartphone Data
In this work we investigate the use of machine learning models for the management and monitoring of sustainable mobility, with particular reference to the transport mode recognition. The specific aim is to automatize the detection of the user’s means of transport among those considered in the data collected with an App installed on the users smartphones, i.e. bicycle, bus, train, car, motorbike, pedestrian locomotion. Preliminary results show the potentiality of the analysis for the introduction of reliable advanced, machine learning based, monitoring systems for sustainable mobility. © 2020, Springer Nature Switzerland AG
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
In this work, we utilize T1-weighted MR images and StackNet to predict fluid
intelligence in adolescents. Our framework includes feature extraction, feature
normalization, feature denoising, feature selection, training a StackNet, and
predicting fluid intelligence. The extracted feature is the distribution of
different brain tissues in different brain parcellation regions. The proposed
StackNet consists of three layers and 11 models. Each layer uses the
predictions from all previous layers including the input layer. The proposed
StackNet is tested on a public benchmark Adolescent Brain Cognitive Development
Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of
82.42 on the combined training and validation set with 10-fold
cross-validation. In addition, the proposed StackNet also achieves a mean
squared error of 94.25 on the testing data. The source code is available on
GitHub.Comment: 8 pages, 2 figures, 3 tables, Accepted by MICCAI ABCD-NP Challenge
2019; Added ND
Reconstructing dynamical networks via feature ranking
Empirical data on real complex systems are becoming increasingly available.
Parallel to this is the need for new methods of reconstructing (inferring) the
topology of networks from time-resolved observations of their node-dynamics.
The methods based on physical insights often rely on strong assumptions about
the properties and dynamics of the scrutinized network. Here, we use the
insights from machine learning to design a new method of network reconstruction
that essentially makes no such assumptions. Specifically, we interpret the
available trajectories (data) as features, and use two independent feature
ranking approaches -- Random forest and RReliefF -- to rank the importance of
each node for predicting the value of each other node, which yields the
reconstructed adjacency matrix. We show that our method is fairly robust to
coupling strength, system size, trajectory length and noise. We also find that
the reconstruction quality strongly depends on the dynamical regime
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Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach
The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research
Memory-Efficient Incremental Learning Through Feature Adaptation
We introduce an approach for incremental learning that preserves feature
descriptors of training images from previously learned classes, instead of the
images themselves, unlike most existing work. Keeping the much
lower-dimensional feature embeddings of images reduces the memory footprint
significantly. We assume that the model is updated incrementally for new
classes as new data becomes available sequentially.This requires adapting the
previously stored feature vectors to the updated feature space without having
access to the corresponding original training images. Feature adaptation is
learned with a multi-layer perceptron, which is trained on feature pairs
corresponding to the outputs of the original and updated network on a training
image. We validate experimentally that such a transformation generalizes well
to the features of the previous set of classes, and maps features to a
discriminative subspace in the feature space. As a result, the classifier is
optimized jointly over new and old classes without requiring old class images.
Experimental results show that our method achieves state-of-the-art
classification accuracy in incremental learning benchmarks, while having at
least an order of magnitude lower memory footprint compared to image-preserving
strategies
Salts of 5-amino-2-sulfonamide-1,3,4-thiadiazole, a structural and analog of acetazolamide, show interesting carbonic anhydrase inhibitory properties, diuretic, and anticonvulsant action
Three salts of 5-amino-2-sulfonamide-1,3,4-thiadiazole (Hats) were prepared and characterized by physico-chemical methods. The p-toluensulfonate, the methylsulfonate, and the chlorhydrate monohydrate salts of Hats were evaluated as carbonic anhydrase (CA, EC 4.2.1.1) inhibitors (CAIs) and as anticonvulsants and diuretics, since many CAIs are clinically used as pharmacological agents. The three Hats salts exhibited diuretic and anticonvulsant activities with little neurotoxicity. The human (h) isoforms hCA I, II, IV, VII, IX, and XII were inhibited in their micromolar range by these salts, whereas pathogenic beta and gamma CAs showed similar, weak inhibitory profiles.Fil: Diaz, Jorge R. A.. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Área Química General e Inorgánica; ArgentinaFil: Camí, Gerardo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Investigaciones en Tecnología Química. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Investigaciones en Tecnología Química; ArgentinaFil: Liu González, Malva. Universidad de Valencia; EspañaFil: Vega, Daniel Roberto. Comisión Nacional de Energía Atómica. Gerencia del Área de Investigaciones y Aplicaciones no Nucleares. Gerencia de Física (Centro Atómico Constituyentes); Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vullo, Daniela. Università degli Studi di Firenze; ItaliaFil: Juárez, Américo. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; ArgentinaFil: Pedregosa, Jose Carmelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Investigaciones en Tecnología Química. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Investigaciones en Tecnología Química; ArgentinaFil: Supuran, Claudiu T.. Università degli Studi di Firenze; Itali
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
The rising volume of datasets has made training machine learning (ML) models
a major computational cost in the enterprise. Given the iterative nature of
model and parameter tuning, many analysts use a small sample of their entire
data during their initial stage of analysis to make quick decisions (e.g., what
features or hyperparameters to use) and use the entire dataset only in later
stages (i.e., when they have converged to a specific model). This sampling,
however, is performed in an ad-hoc fashion. Most practitioners cannot precisely
capture the effect of sampling on the quality of their model, and eventually on
their decision-making process during the tuning phase. Moreover, without
systematic support for sampling operators, many optimizations and reuse
opportunities are lost.
In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML
training. BlinkML allows users to make error-computation tradeoffs: instead of
training a model on their full data (i.e., full model), BlinkML can quickly
train an approximate model with quality guarantees using a sample. The quality
guarantees ensure that, with high probability, the approximate model makes the
same predictions as the full model. BlinkML currently supports any ML model
that relies on maximum likelihood estimation (MLE), which includes Generalized
Linear Models (e.g., linear regression, logistic regression, max entropy
classifier, Poisson regression) as well as PPCA (Probabilistic Principal
Component Analysis). Our experiments show that BlinkML can speed up the
training of large-scale ML tasks by 6.26x-629x while guaranteeing the same
predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201
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