Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.Comment: 27 pages, 17 figure

Deep learning in medical image registration: introduction and survey

Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers

Relative Geologic Time By Dynamic Time Warping

This thesis considers an approach to tackle a core problem within seismic interpretation, which is bringing an autonomously generated interpretation of the seismic data, which is now known as a Relative Geologic Time. The proposed method readily utilizes the method of Dynamic Time Warping, which is an established method within signal processing. Using Dynamic Time Warping is thought to replicate similar interpretations an interpreter would conduct when fulfilling an interpretation of the subsurface. Utilizing Dynamic Time Warping to seismic data results in a fully autonomous interpretation of the subsurface, conducted in minutes and seconds. The method is simple and extendable, which can easily be further expanded. The workflow established during the thesis work results in a method that successfully produces an RGT volume. However, problems related to the method must be improved to enhance the outcome further and diminish errors present in the result. Furthermore, even with problems associated with the method, potential solutions are described in detail in the discussion and appendix. Discussion affiliated with previous attempts in solving Relative Geologic Time volumes is emphasized. The research conducted in Dynamic Time Warping is promising and emits potential for further research. LaTeX setup by Gunn and Patel (2017)

A biologically inspired meta-control navigation system for the Psikharpax rat robot

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics

Using neural networks based on epigenomic maps for predicting the transcriptional regulation measured by CRISPR/Cas9

[EN] Because of the great impact that the genomic editing with CRISPR/CAS9 has had in the recent years, and the great advances that it brings to biotechnology a great need of information has arisen. However researches struggle to find a definate pattern with these experiments making a very long process of trial and error to find an optimal solution for a particular experiment. With this project we intend to optimize the genomic edition with the newest advance CRISPR/Cas9, to find the optimal insertion site we design a mathematical model based on neural networks. During this process we had to deal with huge amount of information from the genome so we had to develop a way to filter and handle it efficiently. For this project we are going to focus in Arabidopsis Thaliana which is a very common plant in genomic edition and has many resources available online.Barberá Mourelle, A. (2016). Using neural networks based on epigenomic maps for predicting the transcriptional regulation measured by CRISPR/Cas9. http://hdl.handle.net/10251/69318.TFG