Les ribozymes sont des ARN naturels ou artificiels possédant une activité catalytique. Les ribozymes artificiels ont été identifiés in vitro par la méthode SELEX, et plusieurs d'entre eux ont été caractérisés par des études cinétiques. Ces molécules sont impliquées dans des réactions de clivage, de ligation, de modification d'extrémités d'ARN, de polymérisation, de phosphorylation et d'activation de groupements acyl. Parce qu'elle est nécessaire à la traduction, l'aminoacylation des ARN joue un rôle évolutif important dans la transition du monde de l'ARN vers le monde moderne de l'ADN et des protéines, et elle est centrale à l'établissement du code génétique. Plusieurs ribozymes catalysant le transfert d'acides aminés à partir de cofacteurs activants ont pu être isolés et caractérisés depuis une vingtaine d'années, ce qui a documenté la possibilité d'aminoacylation d'ARNt en l'absence des aminoacyl ARNt synthétases. En développant un nouveau protocole SELEX basé sur l'oxydation au périodate, le but de notre travail est de découvrir de nouveau ribozymes d'une taille de l'ordre d'une vingtaine de nucléotides pouvant combiner la catalyse de l'activation des acides aminé et la transestérification. Bien que des molécules catalysant l'une ou l'autre des deux réactions ont été identifiées, aucun ribozyme n'existe à ce jour qui puisse utiliser des acides aminés libres et un cofacteur activant pour réaliser l'aminoacylation en 3' dans un même milieu réactionnel. La sélection de molécules actives dans une approche SELEX exige la présence de régions constantes sur les deux extrémités des séquences pools aléatoires initiaux. Ces régions sont nécessaires pour l'amplification par PCR, mais elles imposent des contraintes importantes pour l'identification de ribozymes car elles peuvent complètement inhiber leur activité par interférence structurelle. Nous présentons un protocole optimisé qui minimise la taille de ces régions constantes. D'autre part, notre nouveau design est très spécifique pour la sélection d'ARN aminoacylés sur l'extrémité 3'. Ce protocole a été utilisé pour réaliser 6 à 7 cycles de sélection avec différents pools, et un enrichissement en séquences spécifiques a pu être mis en évidence. Bien que certains tests avec les pools sélectionnés a révélé une activité possible, des essais avec des séquences spécifiques de ces pools n'ont pour l'instant pas pu confirmer l'activité catalytique recherchée. Un protocole basé sur le même principe de sélection a été utilisé dans une étude parallèle pour identifier les ARN aminoacylés présents dans l'ARN total d'Escherichia coli. Dans ce deuxième travail, note but est d'identifier tous les d'ARN aminoacylés par séquençage massif, avec à la clé la découverte possible de molécules autres que les ARNt et ARNtm. En utilisant les ARNt comme modèle, nous nous sommes aperçus qu'un protocole RNAseq standard n'était pas adapté à cause des bases modifiées présentes sur ces molécules. Nous avons développé et mis au point un nouveau protocole pour l'identification de n'importe quelle séquence aminoacylée en 3'. La nouvelle approche présentée devrait permette l'étude exhaustive de l'aminoacylation de toutes les séquences présentes dans l'ARN total. / Ribozymes are natural or in vitro selected RNA molecules possessing a catalytic activity. Artificial ribozymes have been extensively investigated by in vitro SELEX experiments, and characterized by kinetic assays. Ribozymes are involved in RNA cleavage, ligation, capping, polymerization, phosphorylation and acyl activation. Because it is required for translation, RNA aminoacylation plays an important role in the evolution from the late RNA world to the modern DNA and protein world, and is central to the genetic code. Several ribozymes catalyzing amino acid transfer from various activating groups have already been selected and characterized in the past two decades, documenting the possibility of tRNA aminoacylation in the absence of aminoacyl tRNA synthetase. With a newly designed SELEX protocol based on periodate oxydation, the aim of our investigation is to uncover small ribozymes of the order of 20 nucleotides that could catalyze both amino acid activation and transesterification. Although molecules catalyzing either reaction have been identified, no existing ribozyme could use free amino acids and activating cofactor(s) as substrates for 3' esterification in a single reactional context. The selection of active molecules in a SELEX procedure requires the presence of constant tracks on both ends of the sequences constituting the initial random pools. These tracks are required for PCR amplification, but they impose significant burden to the identification of ribozymes because they can prevent any activity through structural inhibition. We present an optimized protocol that significantly minimizes the size of these constant tracks. At the same time, our newly design protocol is very specific for the selection of 3'-end aminoacylated RNA. Working with this protocol, we performed 6 to 7 cycles of selection with different pools, and observed an enrichement with specific sequences. Although some experiments performed with entire pools did reveal a possible activity, no activity could be so far confirmed with specific sequences. A similar protocol was also applied in a parallel study to identify aminoacylated RNA from total RNA in Escherichia coli. In this other approach, our goal is to possibly identify new classes of aminoacylated RNA while using the deep sequencing technology. Using tRNA to validate our protocol, we realized that a standard RNAseq procedure could not work due to the presence of modified bases. We established a new method for bank preparation to identify any sequence aminoacylated at the 3' end. Ultimately, this new approach will allow us to study the level of aminoacylation of any sequence present in total RNA.

Autonomous Vehicles need precise information as to the Drive-able space in order to be able to safely navigate. In recent years deep learning and Semantic Segmentation have attracted intense research. It is a highly advancing and rapidly evolving field that continues to provide excellent results. Research has shown that deep learning is emerging as a powerful tool in many applications. The aim of this study is to develop a deep learning system to estimate the Free Drive-able space. Building on the state of the art deep learning techniques, semantic segmentation will be used to replace the need for highly accurate maps, that are expensive to license. Free Drive-able space is defined as the drive-able space on the correct side of the road, that can be reached without a collision with another road user or pedestrian. A state of the art deep network will be trained with a custom data-set in order to learn complex driving decisions. Motivated by good results, further deep learning techniques will be applied to measure distance from monocular images. The findings demonstrate the power of deep learning techniques in complex driving decisions. The results also indicate the economic and technical feasibility of semantic segmentation over expensive high definition maps.

<p>In this dissertation, we document a few of our recent attempts in bridging the gap between the fast evolving deep learning research and the vast industry needs for dealing with computer vision challenges. More specifically, we developed novel deep-learning-based techniques for the following application-driven computer vision challenges: image super-resolution with quality restoration, motion estimation by optical flow, object detection for shape reconstruction, and object segmentation for motion tracking. Those four topics cover the computer vision hierarchy from the low level where digital images are processed to restore missing information for better human perception, to middle level where certain objects of interest are recognized and their motions are analyzed, finally to high level where the scene captured in the video footage will be interpreted for further analysis. In the process of building the whole-package of  ready-to-deploy solutions, we center our efforts on designing and training the most suitable convolutional neural networks for the particular computer vision problem at hand. Complementary procedures for data collection, data annotation,  post-processing of network outputs tailored for specific application needs, and deployment details will also be discussed where necessary. We hope our work demonstrates the applicability and versatility of convolutional neural networks for real-world computer vision tasks on a broad spectrum, from seeing better to understanding better.</p>

Tor is a Privacy-Enhancing Technology based on onion routing which lets its users browse the web anonymously. Even though the traffic is encrypted in multiple layers, traffic analysis can still be used to gather information from meta-data such as time, size, and direction of the traffic. A Website Fingerprinting (WF) attack is characterized by monitoring traffic locally to the user in order to predict the destination website based on the observed patterns. TrafficSliver is a proposed defence against WF attacks which splits the traffic on multiple paths in the Tor network. This way, a local attacker is assumed to only be able to observe a subset of all the user's total traffic. The initial evaluation of TrafficSliver against Deep Fingerprinting (DF), the state-of-the-art WF attack, showed promising results for the defence, reducing the accuracy of DF from over 98% down to less than 7% without adding artificial delays or dummy traffic. In this thesis, we further evaluate TrafficSliver against DF beyond what was done in the original work by De la Cadena et al. by using a richer data representation and finding out whether it is possible to utilize simulated training data to improve the accuracy of the attack. By introducing directional time as a richer data representation and increasing the size of the training dataset using a simulator, the accuracy of DF was improved against TrafficSliver on three different datasets. Against the original dataset provided by the authors of TrafficSliver, the accuracy was initially 7.1% and then improved to 49.9%. The results were confirmed by using two additional datasets with TrafficSliver, where the accuracy was improved from 5.4% to 44.9% and from 9.8% to 37.7%. / Tor är ett personlig-integritetsverktyg baserat på onion routing som låter sina användare anonymnt besöka hemsidor på internet. Även om trafiken är enkrypterad i flera lager, kan trafikanalys användas för att utvinna information från metadata som exempelvis: tid, storlek och riktning av trafik. En Website Fingerprinting (WF)-attack karaktäriseras av att övervaka trafik nära användaren för att sedan avgöra vilken hemsida som besökts utifrån mönster. TrafficSliver är ett föreslaget försvar mot WF-attacker genom att dela upp trafiken på flera vägar genom nätverket. Detta gör att en attackerare antas endast kunna se en delmängd av användarens totala trafik. Den första utvärderingen av TrafficSliver mot Deep Fingerprinting (DF), spjutspetsen inom WF-attacker, visade lovande resultat för försvaret genom att reducera träffsäkerheten av DF från över 98% till mindre än 7% utan att lägga till artificiella fördröjningar eller falsk trafik. I denna uppsats strävar vi att fortsätta utvärderingen av TrafficSliver mot DF utöver vad som redan har gjorts av De la Cadena et al. med en rikare datarepresentation och en undersökning huruvida det går att använda simulerad data för att träna attacker mot försvaret. Genom att introducera riktad tid och öka mängden data för att träna attacken, ökades träffsäkerheten av DF mot TrafficSliver på tre distinkta dataset. Mot det dataset som samlades in av TrafficSliver var träffsäkerheten inledelsevis 7.1% och sedan förbättrad med hjälp av riktad tid och större mängder av simulerad träningsdata till 49.9%. Dessa resultat bekräftades även för två ytterligare dataset med TrafficSliver, där träffsäkerheten blev förbättrad från 5.4% till 44.9% och från 9.8% till 37.7%.

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京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24725号 / 情博第813号 / 新制||情||137(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 吉川 正俊 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This dissertation presents novel methods for addressing important challenges related to the robustness of Deep Neural Networks (DNNs) for 3D understanding and in 3D setups. Our research focuses on two main areas, adversarial robustness on 3D data and setups and the robustness of DNNs to realistic 3D scenarios. One paradigm for 3D understanding is to represent 3D as a set of 3D points and learn functions on this set directly. Our first work, AdvPC, addresses the issue of limited transferability and ease of defense against current 3D point cloud adversarial attacks. By using a point cloud Auto-Encoder to generate more transferable attacks, AdvPC surpasses state-of-the-art attacks by a large margin on 3D point cloud attack transferability. Additionally, AdvPC increases the ability to break defenses by up to 38\% as compared to other baseline attacks on the ModelNet40 dataset. Another paradigm of 3D understanding is to perform 2D processing of multiple images of the 3D data. The second work, MVTN, addresses the problem of selecting viewpoints for 3D shape recognition using a Multi-View Transformation Network (MVTN) to learn optimal viewpoints. It combines MVTN with multi-view approaches leading to state-of-the-art results on standard benchmarks ModelNet40, ShapeNet Core55, and ScanObjectNN. MVTN also improves robustness to realistic scenarios like rotation and occlusion. Our third work analyzes the Semantic Robustness of 2D Deep Neural Networks, addressing the problem of high sensitivity toward semantic primitives in DNNs by visualizing the DNN global behavior as semantic maps and observing the interesting behavior of some DNNs. Additionally, we develop a bottom-up approach to detect robust regions of DNNs for scalable semantic robustness analysis and benchmarking of different DNNs. The fourth work, SADA, showcases the problem of lack of robustness in DNNs specifically for the safety-critical applications of autonomous navigation, beyond the simple classification setup. We present a general framework (BBGAN) for black-box adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task. BBGAN is trained to generate failure cases that consistently fool a trained agent on tasks such as object detection, self-driving, and autonomous UAV racing.

<p>Deep learning has been the driving force behind many successful data science achievements. However, the deep neural network (DNN) that forms the basis of deep learning is</p> <p>often over-parameterized, leading to training, prediction, and interpretation challenges. To</p> <p>address this issue, it is common practice to apply an appropriate penalty to each connection</p> <p>weight, limiting its magnitude. This approach is equivalent to imposing a prior distribution</p> <p>on each connection weight from a Bayesian perspective. This project offers a systematic investigation into the selection of the penalty function or prior distribution. Specifically, under</p> <p>the general theoretical framework of posterior consistency, we prove that consistent sparse</p> <p>deep learning can be achieved with a variety of penalty functions or prior distributions.</p> <p>Examples include amenable regularization penalties (such as MCP and SCAD), spike-and?slab priors (such as mixture Gaussian distribution and mixture Laplace distribution), and</p> <p>polynomial decayed priors (such as the student-t distribution). Our theory is supported by</p> <p>numerical results.</p> <p><br></p>