MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities

In this paper, we introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. A second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalising on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

ReHabgame: A non-immersive virtual reality rehabilitation system with applications in neuroscience

This paper proposes the use of a non-immersive virtual reality rehabilitation system ”ReHabgame” developed using Microsoft KinectT M and the ThalmicT M Labs Myo gesture control armband. The ReHabgame was developed based on two third-person video games that provide a feasible possibility of assessing postural control and functional reach tests. It accurately quantifies specific postural control mechanisms including timed standing balance, functional reach tests using real-time anatomical landmark orientation, joint velocity, and acceleration while end trajectories were calculated using an inverse kinematics algorithm. The game was designed to help patients with neurological impairment to be subjected to physiotherapy activity and practice postures of daily activities. The subjective experience of the ReHabgame was studied through the development of an Engagement Questionnaire (EQ) for qualitative, quantitative and Rasch model. The Monte-Carlo Tree Search (MCTS) and Random object (ROG) generator algorithms were used to adapt the physical and gameplay intensity in the ReHabgame based on the Motor Assessment Scale (MAS) and Hierarchical Scoring System (HSS). Rasch analysis was conducted to assess the psychometric characteristics of the ReHabgame and to identify if these are any misfitting items in the game. Rasch rating scale model (RSM) was used to assess the engagement of players in the ReHabgame and evaluate the effectiveness and attractiveness of the game. The results showed that the scales assessing the rehabilitation process met Rasch expectations of reliability, and unidimensionality. Infit and outfit mean squares values are in the range of (0.68 − 1.52) for all considered 16 items. The Root Mean Square Residual (RMSR) and the person separation reliability were acceptable. The item/person map showed that the persons and items were clustered symmetrically

Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference

On the relationship between neuronal codes and mental models

Das übergeordnete Ziel meiner Arbeit an dieser Dissertation war ein besseres Verständnis des Zusammenhangs von mentalen Modellen und den zugrundeliegenden Prinzipien, die zur Selbstorganisation neuronaler Verschaltung führen. Die Dissertation besteht aus vier individuellen Publikationen, die dieses Ziel aus unterschiedlichen Perspektiven angehen. Während die Selbstorganisation von Sparse-Coding-Repräsentationen in neuronalem Substrat bereits ausgiebig untersucht worden ist, sind viele Forschungsfragen dazu, wie Sparse-Coding für höhere, kognitive Prozesse genutzt werden könnte noch offen. Die ersten zwei Studien, die in Kapitel 2 und Kapitel 3 enthalten sind, behandeln die Frage, inwieweit Repräsentationen, die mit Sparse-Coding entstehen, mentalen Modellen entsprechen. Wir haben folgende Selektivitäten in Sparse-Coding-Repräsentationen identifiziert: mit Stereo-Bildern als Eingangsdaten war die Repräsentation selektiv für die Disparitäten von Bildstrukturen, welche für das Abschätzen der Entfernung der Strukturen zum Beobachter genutzt werden können. Außerdem war die Repräsentation selektiv für die die vorherrschende Orientierung in Texturen, was für das Abschätzen der Neigung von Oberflächen genutzt werden kann. Mit optischem Fluss von Eigenbewegung als Eingangsdaten war die Repräsentation selektiv für die Richtung der Eigenbewegung in den sechs Freiheitsgraden. Wegen des direkten Zusammenhangs der Selektivitäten mit physikalischen Eigenschaften können Repräsentationen, die mit Sparse-Coding entstehen, als frühe sensorische Modelle der Umgebung dienen. Die kognitiven Prozesse hinter räumlichem Wissen ruhen auf mentalen Modellen, welche die Umgebung representieren. Wir haben in der dritten Studie, welche in Kapitel 4 enthalten ist, ein topologisches Modell zur Navigation präsentiert, Es beschreibt einen dualen Populations-Code, bei dem der erste Populations-Code Orte anhand von Orts-Feldern (Place-Fields) kodiert und der zweite Populations-Code Bewegungs-Instruktionen, basierend auf der Verknüpfung von Orts-Feldern, kodiert. Der Fokus lag nicht auf der Implementation in biologischem Substrat oder auf einer exakten Modellierung physiologischer Ergebnisse. Das Modell ist eine biologisch plausible, einfache Methode zur Navigation, welche sich an einen Zwischenschritt emergenter Navigations-Fähigkeiten in einer evolutiven Navigations-Hierarchie annähert. Unser automatisierter Test der Sehleistungen von Mäusen, welcher in Kapitel 5 beschrieben wird, ist ein Beispiel von Verhaltens-Tests im Wahrnehmungs-Handlungs-Zyklus (Perception-Action-Cycle). Das Ziel dieser Studie war die Quantifizierung des optokinetischen Reflexes. Wegen des reichhaltigen Verhaltensrepertoires von Mäusen sind für die Quantifizierung viele umfangreiche Analyseschritte erforderlich. Tiere und Menschen sind verkörperte (embodied) lebende Systeme und daher aus stark miteinander verwobenen Modulen oder Entitäten zusammengesetzt, welche außerdem auch mit der Umgebung verwoben sind. Um lebende Systeme als Ganzes zu studieren ist es notwendig Hypothesen, zum Beispiel zur Natur mentaler Modelle, im Wahrnehmungs-Handlungs-Zyklus zu testen. Zusammengefasst erweitern die Studien dieser Dissertation unser Verständnis des Charakters früher sensorischer Repräsentationen als mentale Modelle, sowie unser Verständnis höherer, mentalen Modellen für die räumliche Navigation. Darüber hinaus enthält es ein Beispiel für das Evaluieren von Hypothesn im Wahr\-neh\-mungs-Handlungs-Zyklus.The superordinate aim of my work towards this thesis was a better understanding of the relationship between mental models and the underlying principles that lead to the self-organization of neuronal circuitry. The thesis consists of four individual publications, which approach this goal from differing perspectives. While the formation of sparse coding representations in neuronal substrate has been investigated extensively, many research questions on how sparse coding may be exploited for higher cognitive processing are still open. The first two studies, included as chapter 2 and chapter 3, asked to what extend representations obtained with sparse coding match mental models. We identified the following selectivities in sparse coding representations: with stereo images as input, the representation was selective for the disparity of image structures, which can be used to infer the distance of structures to the observer. Furthermore, it was selective to the predominant orientation in textures, which can be used to infer the orientation of surfaces. With optic flow from egomotion as input, the representation was selective to the direction of egomotion in 6 degrees of freedom. Due to the direct relation between selectivity and physical properties, these representations, obtained with sparse coding, can serve as early sensory models of the environment. The cognitive processes behind spatial knowledge rest on mental models that represent the environment. We presented a topological model for wayfinding in the third study, included as chapter 4. It describes a dual population code, where the first population code encodes places by means of place fields, and the second population code encodes motion instructions based on links between place fields. We did not focus on an implementation in biological substrate or on an exact fit to physiological findings. The model is a biologically plausible, parsimonious method for wayfinding, which may be close to an intermediate step of emergent skills in an evolutionary navigational hierarchy. Our automated testing for visual performance in mice, included in chapter 5, is an example of behavioral testing in the perception-action cycle. The goal of this study was to quantify the optokinetic reflex. Due to the rich behavioral repertoire of mice, quantification required many elaborate steps of computational analyses. Animals and humans are embodied living systems, and therefore composed of strongly enmeshed modules or entities, which are also enmeshed with the environment. In order to study living systems as a whole, it is necessary to test hypothesis, for example on the nature of mental models, in the perception-action cycle. In summary, the studies included in this thesis extend our view on the character of early sensory representations as mental models, as well as on high-level mental models for spatial navigation. Additionally it contains an example for the evaluation of hypotheses in the perception-action cycle

Autonomous Drone Landings on an Unmanned Marine Vehicle using Deep Reinforcement Learning

This thesis describes with the integration of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV, also commonly known as drone) in a single Multi-Agent System (MAS). In marine robotics, the advantage offered by a MAS consists of exploiting the key features of a single robot to compensate for the shortcomings in the other. In this way, a USV can serve as the landing platform to alleviate the need for a UAV to be airborne for long periods time, whilst the latter can increase the overall environmental awareness thanks to the possibility to cover large portions of the prevailing environment with a camera (or more than one) mounted on it. There are numerous potential applications in which this system can be used, such as deployment in search and rescue missions, water and coastal monitoring, and reconnaissance and force protection, to name but a few. The theory developed is of a general nature. The landing manoeuvre has been accomplished mainly identifying, through artificial vision techniques, a fiducial marker placed on a flat surface serving as a landing platform. The raison d'etre for the thesis was to propose a new solution for autonomous landing that relies solely on onboard sensors and with minimum or no communications between the vehicles. To this end, initial work solved the problem while using only data from the cameras mounted on the in-flight drone. In the situation in which the tracking of the marker is interrupted, the current position of the USV is estimated and integrated into the control commands. The limitations of classic control theory used in this approached suggested the need for a new solution that empowered the flexibility of intelligent methods, such as fuzzy logic or artificial neural networks. The recent achievements obtained by deep reinforcement learning (DRL) techniques in end-to-end control in playing the Atari video-games suite represented a fascinating while challenging new way to see and address the landing problem. Therefore, novel architectures were designed for approximating the action-value function of a Q-learning algorithm and used to map raw input observation to high-level navigation actions. In this way, the UAV learnt how to land from high latitude without any human supervision, using only low-resolution grey-scale images and with a level of accuracy and robustness. Both the approaches have been implemented on a simulated test-bed based on Gazebo simulator and the model of the Parrot AR-Drone. The solution based on DRL was further verified experimentally using the Parrot Bebop 2 in a series of trials. The outcomes demonstrate that both these innovative methods are both feasible and practicable, not only in an outdoor marine scenario but also in indoor ones as well