The market manipulation regime in the European Union

Market manipulation is an illegal practice that enables a person can profit from practices that artificially raise or lower the prices of an instrument in the financial markets. Its prohibition is based on the 2003 Market Abuse Directive in the EU. The current market manipulation regime was broadly considered as a big success except for enforcement and supervisory inconsistencies in the Member States at the initial. A review of the market manipulation regime began at the end of 2007, which became quickly incorporated into the wider EU crisis-era reform program. A number of weaknesses of current regime have been identified, which include regulatory gaps caused by the development of trading venues and financial products, regulatory gaps concerning cross-border and cross-markets manipulation (particular commodity markets), legal uncertainty as a result of various implementation, and inefficient supervision and enforcement. On 12 June 2014, a new regulatory package of market abuse, Market Abuse Regulation and Directive on criminal sanctions for market abuse, has been adopted. And several changes will be made concerning the EU market manipulation regime. A wider scope of the regime and a new prohibition of attempted market manipulation will ensure the prevention of market manipulation at large. The AMPs will be subject to strict scrutiny of ESMA to reduce divergences in implementation. In order to enhance efficiency of supervision and enforcement, powers of national competent authorities will be strengthened, ESMA is imposed more power to settle disagreement between national regulators, and the administrative and criminal sanctioning regimes are both further harmonized. In addition, the protection of fundamental rights is stressed by the new market manipulation regime, and some measures are provided to guarantee its realization. Further, the success EU market manipulation regime could be of significant reference to China, helping China to refine its immature regime.

Design and Control of Robotic Hands

The application of dexterous robotic hands out of research laboratories has been limited by the intrinsic complexity that these devices present. This is directly reflected as an economically unreasonable cost and a low overall reliability. Within the research reported in this thesis it is shown how the problem of complexity in the design of robotic hands can be tackled, taking advantage of modern technologies (i.e. rapid prototyping), leading to innovative concepts for the design of the mechanical structure, the actuation and sensory systems. The solutions adopted drastically reduce the prototyping and production costs and increase the reliability, reducing the number of parts required and averaging their single reliability factors. In order to get guidelines for the design process, the problem of robotic grasp and manipulation by a dual arm/hand system has been reviewed. In this way, the requirements that should be fulfilled at hardware level to guarantee successful execution of the task has been highlighted. The contribution of this research from the manipulation planning side focuses on the redundancy resolution that arise in the execution of the task in a dexterous arm/hand system. In literature the problem of coordination of arm and hand during manipulation of an object has been widely analyzed in theory but often experimentally demonstrated in simplified robotic setup. Our aim is to cover the lack in the study of this topic and experimentally evaluate it in a complex system as a anthropomorphic arm hand system.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Imagery of a moving object: the role of occipital cortex and human MT/V5+

Visual imagery – similar to visual perception – activates feature-specific and category-specific visual areas. This is frequently observed in experiments where the instruction is to imagine stimuli that have been shown immediately before the imagery task. Hence, feature-specific activation could be related to the short-term memory retrieval of previously presented sensory information. Here, we investigated mental imagery of stimuli that subjects had not seen before, eliminating the effects of short-term memory. We recorded brain activation using fMRI while subjects performed a behaviourally controlled guided imagery task in predefined retinotopic coordinates to optimize sensitivity in early visual areas. Whole brain analyses revealed activation in a parieto-frontal network and lateral–occipital cortex. Region of interest (ROI) based analyses showed activation in left hMT/V5+. Granger causality mapping taking left hMT/V5+ as source revealed an imagery-specific directed influence from the left inferior parietal lobule (IPL). Interestingly, we observed a negative BOLD response in V1–3 during imagery, modulated by the retinotopic location of the imagined motion trace. Our results indicate that rule-based motion imagery can activate higher-order visual areas involved in motion perception, with a role for top-down directed influences originating in IPL. Lower-order visual areas (V1, V2 and V3) were down-regulated during this type of imagery, possibly reflecting inhibition to avoid visual input from interfering with the imagery construction. This suggests that the activation in early visual areas observed in previous studies might be related to short- or long-term memory retrieval of specific sensory experiences.

The Failure of Silence and the Manipulation of Memory: How Spain's pact of oblivion has failed to rid the country of its troubled past

This thesis is an analysis of Spain’s development from dictatorship to democracy in light of the trauma that it endured during the Spanish Civil War of 1936 – 1939 and the dictatorship of Francisco Franco, which lasted until 1975. Drawing from the work of Maurice Halbwachs and Pierre Nora, this thesis seeks to use the concepts of collective memory and lieux de mémoire to analyze what role memory has played in Spanish society from 1939 to the present day. Theanalysis begins with an overview of the Spanish Civil War and Franco’s ensuing dictatorship in order to establish an understanding of the trauma endured by Spain and its people. Of importance will be the manner in which the presentation of history became manipulated anddistorted under Franco as the dictator sought to control the country’s collective memory. With this background in mind, the thesis then turns to analyze how the memory of Spain’s past has affected the country’s development in two eras: during its transition to democracy in the 1970s and in the present day. Of central importance is the pact of silence that was established during the transition to democracy, which was a tacit agreement among the Spanish people to notdiscuss the past. This pact of silence still clouds Spain’s memory today and affects modern discourse concerning the past. Yet it is clear that Spain has not been reconciled to its past, as the provocation of history inevitably results in tension and controversy. The central contention of this thesis is that the pact of silence that surrounds Spain’s past has not eliminated the trauma of the Civil War and dictatorship, as demonstrated by the controversy stirred up by people, groups and places in the present day. This contention has repercussions for the study of history as a whole, as it indicates that the past cannot be muted in order to achievereconciliation; rather, it suggests that we must engage the past in order to be reconciled to it.

Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis.