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Algorithms, Volume 14, Issue 3

March 2021 - 33 articles

Cover Story: In this work, we propose a deep learning architecture (BrainGNN) that learns the connectivity structure while learning to classify subjects. It simultaneously trains a graphical neural network on this graph and learns to select a sparse subset of brain regions important to the prediction task. We demonstrate the model’s state-of-the-art classification performance on a schizophrenia fMRI dataset and show how introspection leads to disorder-relevant findings. The graphs learned by the model exhibit strong class discrimination, and the identified sparse subset of relevant regions is consistent with the schizophrenia literature. View this paper
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Articles (33)

  • Article
  • Open Access
16 Citations
6,025 Views
31 Pages

16 March 2021

We study the problem of quickly computing point-to-point shortest paths in massive road networks with traffic predictions. Incorporating traffic predictions into routing allows, for example, to avoid commuter traffic congestions. Existing techniques...

  • Article
  • Open Access
3 Citations
3,189 Views
14 Pages

15 March 2021

To reconstruct point geometry from multiple images, computation of the fundamental matrix is always necessary. With a new optimization criterion, i.e., the re-projective 3D metric geometric distance rather than projective space under RANSAC (Random S...

  • Article
  • Open Access
14 Citations
8,245 Views
36 Pages

Local Data Debiasing for Fairness Based on Generative Adversarial Training

  • Ulrich Aïvodji,
  • François Bidet,
  • Sébastien Gambs,
  • Rosin Claude Ngueveu and
  • Alain Tapp

14 March 2021

The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial t...

  • Article
  • Open Access
14 Citations
4,491 Views
30 Pages

14 March 2021

Thick ellipsoids were recently introduced by the authors to represent uncertainty in state variables of dynamic systems, not only in terms of guaranteed outer bounds but also in terms of an inner enclosure that belongs to the true solution set with c...

  • Article
  • Open Access
8 Citations
3,169 Views
18 Pages

8 March 2021

Continuous-time linear systems with uncertain parameters are widely used for modeling real-life processes. The uncertain parameters, contained in the system and input matrices, can be constant or time-varying. In the latter case, they may represent s...

  • Article
  • Open Access
2 Citations
2,422 Views
10 Pages

6 March 2021

A choice to use a seat belt is largely dependent on the psychology of the vehicles’ occupants, and thus those decisions are expected to be characterized by preference heterogeneity. Despite the importance of seat belt use on the safety of the roadway...

  • Article
  • Open Access
44 Citations
6,761 Views
16 Pages

Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method

  • Shijin Yuan,
  • Cheng Wang,
  • Bin Mu,
  • Feifan Zhou and
  • Wansuo Duan

4 March 2021

A typhoon is an extreme weather event with strong destructive force, which can bring huge losses of life and economic damage to people. Thus, it is meaningful to reduce the prediction errors of typhoon intensity forecasting. Artificial and deep neura...

  • Article
  • Open Access
3,917 Views
28 Pages

DynASP2.5: Dynamic Programming on Tree Decompositions in Action

  • Johannes K. Fichte,
  • Markus Hecher,
  • Michael Morak and
  • Stefan Woltran

2 March 2021

Efficient exact parameterized algorithms are an active research area. Such algorithms exhibit a broad interest in the theoretical community. In the last few years, implementations for computing various parameters (parameter detection) have been estab...

  • Article
  • Open Access
12 Citations
3,624 Views
12 Pages

2 March 2021

Identifying and ranking the node influence in complex networks is an important issue. It helps to understand the dynamics of spreading process for designing efficient strategies to hinder or accelerate information spreading. The idea of decomposing n...

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Algorithms - ISSN 1999-4893