Cholesky-factorized sparse Kernel in support vector machines

Support Vector Machine (SVM) is one of the most powerful machine learning algorithms due to its convex optimization formulation and handling non-linear classification. However, one of its main drawbacks is the long time it takes to train large data sets. This limitation is often aroused when applying non-linear kernels (e.g. RBF Kernel) which are usually required to obtain better separation for linearly inseparable data sets. In this thesis, we study an approach that aims to speed-up the training time by combining both the better performance of RBF kernels and fast training by a linear solver, LIBLINEAR. The approach uses an RBF kernel with a sparse matrix which is factorized using Cholesky decomposition. The method is tested on large artificial and real data sets and compared to the standard RBF and linear kernels where both the accuracy and training time are reported. For most data sets, the result shows a huge training time reduction, over 90\%, whilst maintaining the accuracy

Long-duration robot autonomy: From control algorithms to robot design

The transition that robots are experiencing from controlled and often static working environments to unstructured and dynamic settings is unveiling the potential fragility of the design and control techniques employed to build and program them, respectively. A paramount of example of a discipline that, by construction, deals with robots operating under unknown and ever-changing conditions is long-duration robot autonomy. In fact, during long-term deployments, robots will find themselves in environmental scenarios which were not planned and accounted for during the design phase. These operating conditions offer a variety of challenges which are not encountered in any other discipline of robotics. This thesis presents control-theoretic techniques and mechanical design principles to be employed while conceiving, building, and programming robotic systems meant to remain operational over sustained amounts of time. Long-duration autonomy is studied and analyzed from two different, yet complementary, perspectives: control algorithms and robot design. In the context of the former, the persistification of robotic tasks is presented. This consists of an optimization-based control framework which allows robots to remain operational over time horizons that are much longer than the ones which would be allowed by the limited resources of energy with which they can ever be equipped. As regards the mechanical design aspect of long-duration robot autonomy, in the second part of this thesis, the SlothBot, a slow-paced solar-powered wire-traversing robot, is presented. This robot embodies the design principles required by an autonomous robotic system 1in order to remain functional for truly long periods of time, including energy efficiency, design simplicity, and fail-safeness. To conclude, the development of a robotic platform which stands at the intersection of design and control for long-duration autonomy is described. A class of vibration-driven robots, the brushbots, are analyzed both from a mechanical design perspective, and in terms of interaction control capabilities with the environment in which they are deployed.Ph.D

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy

Apprentissage supervisés sous contraintes

As supervised learning occupies a larger and larger place in our everyday life, it is met with more and more constrained settings. Dealing with those constraints is a key to fostering new progress in the field, expanding ever further the limit of machine learning---a likely necessary step to reach artificial general intelligence. Supervised learning is an inductive paradigm in which time and data are refined into knowledge, in the form of predictive models. Models which can sometimes be, it must be conceded, opaque, memory demanding and energy consuming. Given this setting, a constraint can mean any number of things. Essentially, a constraint is anything that stand in the way of supervised learning, be it the lack of time, of memory, of data, or of understanding. Additionally, the scope of applicability of supervised learning is so vast it can appear daunting. Usefulness can be found in areas including medical analysis and autonomous driving---areas for which strong guarantees are required. All those constraints (time, memory, data, interpretability, reliability) might somewhat conflict with the traditional goal of supervised learning. In such a case, finding a balance between the constraints and the standard objective is problem-dependent, thus requiring generic solutions. Alternatively, concerns might arise after learning, in which case solutions must be developed under sub-optimal conditions, resulting in constraints adding up. An example of such situations is trying to enforce reliability once the data is no longer available. After detailing the background (what is supervised learning and why is it difficult, what algorithms will be used, where does it land in the broader scope of knowledge) in which this thesis integrates itself, we will discuss four different scenarios. The first one is about trying to learn a good decision forest model of a limited size, without learning first a large model and then compressing it. For that, we have developed the Globally Induced Forest (GIF) algorithm, which mixes local and global optimizations to produce accurate predictions under memory constraints in reasonable time. More specifically, the global part allows to sidestep the redundancy inherent in traditional decision forests. It is shown that the proposed method is more than competitive with standard tree-based ensembles under corresponding constraints, and can sometimes even surpass much larger models. The second scenario corresponds to the example given above: trying to enforce reliability without data. More specifically, the focus in on out-of-distribution (OOD) detection: recognizing samples which do not come from the original distribution the model was learned from. Tackling this problem with utter lack of data is challenging. Our investigation focuses on image classification with convolutional neural networks. Indicators which can be computed alongside the prediction with little additional cost are proposed. These indicators prove useful, stable and complementary for OOD detection. We also introduce a surprisingly simple, yet effective summary indicator, shown to perform well across several networks and datasets. It can easily be tuned further as soon as samples become available. Overall, interesting results can be reached in all but the most severe settings, for which it was a priori doubtful to come up with a data-free solution. The third scenario relates to transferring the knowledge of a large model in a smaller one in the absence of data. To do so, we propose to leverage a collection of unlabeled data which are easy to come up with in domains such as image classification. Two schemes are proposed (and then analyzed) to provide optimal transfer. Firstly, we proposed a biasing mechanism in the choice of unlabeled data to use so that the focus is on the more relevant samples. Secondly, we designed a teaching mechanism, applicable for almost all pairs of large and small networks, which allows for a much better knowledge transfer between the networks. Overall, good results are obtainable in decent time provided the collection of data actually contains relevant samples. The fourth scenario tackles the problem of interpretability: what knowledge can be gleaned more or less indirectly from data. We discuss two subproblems. The first one is to showcase that GIFs (cf. supra) can be used to derive intrinsically interpretable models. The second consists in a comparative study between methods and types of models (namely decision forests and neural networks) for the specific purpose of quantifying how much each variable is important in a given problem. After a preliminary study on benchmark datasets, the analysis turns to a concrete biological problem: inferring gene regulatory network from data. An ambivalent conclusion is reached: neural networks can be made to perform better than decision forests at predicting in almost all instances but struggle to identify the relevant variables in some situations. It would seem that better (motivated) methods need to be proposed for neural networks, especially in the face of highly non-linear problems

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios