10,166 research outputs found

    Online Network Source Optimization with Graph-Kernel MAB

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    We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks, such that the reward obtained from a priori unknown network processes is maximized. The uncertainty calls for online learning, which suffers however from the curse of dimensionality. To achieve sample efficiency, we describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. This enables a data-efficient learning framework, whose learning rate scales with the dimension of the spectral representation model instead of the one of the network. We then propose Grab-UCB, an online sequential decision strategy that learns the parameters of the spectral representation while optimizing the action strategy. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy We introduce a computationally simplified solving method, Grab-arm-Light, an algorithm that walks along the edges of the polytope representing the objective function. Simulations results show that the proposed online learning algorithm outperforms baseline offline methods that typically separate the learning phase from the testing one. The results confirm the theoretical findings, and further highlight the gain of the proposed online learning strategy in terms of cumulative regret, sample efficiency and computational complexity

    Evaluation Methodologies in Software Protection Research

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    Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 572 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks

    Reinforcement learning in large state action spaces

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    Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios. This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory). In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    Optimising water quality outcomes for complex water resource systems and water grids

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    As the world progresses, water resources are likely to be subjected to much greater pressures than in the past. Even though the principal water problem revolves around inadequate and uncertain water supplies, water quality management plays an equally important role. Availability of good quality water is paramount to sustainability of human population as well as the environment. Achieving water quality and quantity objectives can be conflicting and becomes more complicated with challenges like, climate change, growing populations and changed land uses. Managing adequate water quality in a reservoir gets complicated by multiple inflows with different water quality levels often resulting in poor water quality. Hence, it is fundamental to approach this issue in a more systematic, comprehensive, and coordinated fashion. Most previous studies related to water resources management focused on water quantity and considered water quality separately. However, this research study focused on considering water quantity and quality objectives simultaneously in a single model to explore and understand the relationship between them in a reservoir system. A case study area was identified in Western Victoria, Australia with water quantity and quality challenges. Taylors Lake of Grampians System in Victoria, Australia receives water from multiple sources of differing quality and quantity and has the abovesaid problems. A combined simulation and optimisation approach was adopted to carry out the analysis. A multi-objective optimisation approach was applied to achieve optimal water availability and quality in the storage. The multi-objective optimisation model included three objective functions which were: water volume and two water quality parameters: salinity and turbidity. Results showed competing nature of water quantity and quality objectives and established the trade-offs. It further showed that it was possible to generate a range of optimal solutions to effectively manage those trade-offs. The trade-off analysis explored and informed that selective harvesting of inflows is effective to improve water quality in storage. However, with strict water quality restriction there is a considerable loss in water volume. The robustness of the optimisation approach used in this study was confirmed through sensitivity and uncertainty analysis. The research work also incorporated various spatio-temporal scenario analyses to systematically articulate long-term and short-term operational planning strategies. Operational decisions around possible harvesting regimes while achieving optimal water quantity and quality and meeting all water demands were established. The climate change analysis revealed that optimal management of water quantity and quality in storage became extremely challenging under future climate projections. The high reduction in storage volume in the future will lead to several challenges such as water supply shortfall and inability to undertake selective harvesting due to reduced water quality levels. In this context, selective harvesting of inflows based on water quality will no longer be an option to manage water quantity and quality optimally in storage. Some significant conclusions of this research work included the establishment of trade-offs between water quality and quantity objectives particular to this configuration of water supply system. The work demonstrated that selective harvesting of inflows will improve the stored water quality, and this finding along with the approach used is a significant contribution to decision makers working within the water sector. The simulation-optimisation approach is very effective in providing a range of optimal solutions, which can be used to make more informed decisions around achieving optimal water quality and quantity in storage. It was further demonstrated that there are range of planning periods, both long-term (>10 years) and short-term (<1 year), all of which offer distinct advantages and provides useful insights, making this an additional key contribution of the work. Importantly, climate change was also considered where it was found that diminishing water resources, particularly to this geographic location, makes it increasingly difficult to optimise both quality and quantity in storage providing further useful insights from this work.Doctor of Philosoph

    Machine learning approach towards predicting turbulent fluid flow using convolutional neural networks

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    Using convolutional neural networks, we present a novel method for predicting turbulent fluid flow through an array of obstacles in this thesis. In recent years, machine learning has exploded in popularity due to its ability to create accurate data driven models and the abundance of available data. In an attempt to understand the characteristics of turbulent fluid flow, we utilise a novel convolutional autoencoder neural network to predict the first ten POD modes of turbulent fluid flow. We find that the model is able to predict the first two POD modes well although and with less accuracy for the remaining eight POD modes. In addition, we find that the ML-predicted POD modes are accurate enough to be used to reconstruct turbulent flow that adequately captures the large-scale details of the original simulation

    Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach

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    We study the Sparse Plus Low-Rank decomposition problem (SLR), which is the problem of decomposing a corrupted data matrix into a sparse matrix of perturbations plus a low-rank matrix containing the ground truth. SLR is a fundamental problem in Operations Research and Machine Learning which arises in various applications, including data compression, latent semantic indexing, collaborative filtering, and medical imaging. We introduce a novel formulation for SLR that directly models its underlying discreteness. For this formulation, we develop an alternating minimization heuristic that computes high-quality solutions and a novel semidefinite relaxation that provides meaningful bounds for the solutions returned by our heuristic. We also develop a custom branch-and-bound algorithm that leverages our heuristic and convex relaxations to solve small instances of SLR to certifiable (near) optimality. Given an input nn-by-nn matrix, our heuristic scales to solve instances where n=10000n=10000 in minutes, our relaxation scales to instances where n=200n=200 in hours, and our branch-and-bound algorithm scales to instances where n=25n=25 in minutes. Our numerical results demonstrate that our approach outperforms existing state-of-the-art approaches in terms of rank, sparsity, and mean-square error while maintaining a comparable runtime

    Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles

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    This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets

    Knowledge-based Modelling of Additive Manufacturing for Sustainability Performance Analysis and Decision Making

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    Additiivista valmistusta on pidetty käyttökelpoisena monimutkaisissa geometrioissa, topologisesti optimoiduissa kappaleissa ja kappaleissa joita on muuten vaikea valmistaa perinteisillä valmistusprosesseilla. Eduista huolimatta, yksi additiivisen valmistuksen vallitsevista haasteista on ollut heikko kyky tuottaa toimivia osia kilpailukykyisillä tuotantomäärillä perinteisen valmistuksen kanssa. Mallintaminen ja simulointi ovat tehokkaita työkaluja, jotka voivat auttaa lyhentämään suunnittelun, rakentamisen ja testauksen sykliä mahdollistamalla erilaisten tuotesuunnitelmien ja prosessiskenaarioiden nopean analyysin. Perinteisten ja edistyneiden valmistusteknologioiden mahdollisuudet ja rajoitukset määrittelevät kuitenkin rajat uusille tuotekehityksille. Siksi on tärkeää, että suunnittelijoilla on käytettävissään menetelmät ja työkalut, joiden avulla he voivat mallintaa ja simuloida tuotteen suorituskykyä ja siihen liittyvän valmistusprosessin suorituskykyä, toimivien korkea arvoisten tuotteiden toteuttamiseksi. Motivaation tämän väitöstutkimuksen tekemiselle on, meneillään oleva kehitystyö uudenlaisen korkean lämpötilan suprajohtavan (high temperature superconducting (HTS)) magneettikokoonpanon kehittämisessä, joka toimii kryogeenisissä lämpötiloissa. Sen monimutkaisuus edellyttää monitieteisen asiantuntemuksen lähentymistä suunnittelun ja prototyyppien valmistuksen aikana. Tutkimus hyödyntää tietopohjaista mallinnusta valmistusprosessin analysoinnin ja päätöksenteon apuna HTS-magneettien mekaanisten komponenttien suunnittelussa. Tämän lisäksi, tutkimus etsii mahdollisuuksia additiivisen valmistuksen toteutettavuuteen HTS-magneettikokoonpanon tuotannossa. Kehitetty lähestymistapa käyttää fysikaalisiin kokeisiin perustuvaa tuote-prosessi-integroitua mallinnusta tuottamaan kvantitatiivista ja laadullista tietoa, joka määrittelee prosessi-rakenne-ominaisuus-suorituskyky-vuorovaikutuksia tietyille materiaali-prosessi-yhdistelmille. Tuloksina saadut vuorovaikutukset integroidaan kaaviopohjaiseen malliin, joka voi auttaa suunnittelutilan tutkimisessa ja täten auttaa varhaisessa suunnittelu- ja valmistuspäätöksenteossa. Tätä varten testikomponentit valmistetaan käyttämällä kahta metallin additiivista valmistus prosessia: lankakaarihitsaus additiivista valmistusta (wire arc additive manufacturing) ja selektiivistä lasersulatusta (selective laser melting). Rakenteellisissa sovelluksissa yleisesti käytetyistä metalliseoksista (ruostumaton teräs, pehmeä teräs, luja niukkaseosteinen teräs, alumiini ja kupariseokset) testataan niiden mekaaniset, lämpö- ja sähköiset ominaisuudet. Lisäksi tehdään metalliseosten mikrorakenteen karakterisointi, jotta voidaan ymmärtää paremmin valmistusprosessin parametrien vaikutusta materiaalin ominaisuuksiin. Integroitu mallinnustapa yhdistää kerätyn kokeellisen tiedon, olemassa olevat analyyttiset ja empiiriset vuorovaikutus suhteet, sekä muut tietopohjaiset mallit (esim. elementtimallit, koneoppimismallit) päätöksenteon tukijärjestelmän muodossa, joka mahdollistaa optimaalisen materiaalin, valmistustekniikan, prosessiparametrien ja muitten ohjausmuuttujien valinnan, lopullisen 3d-tulosteun komponentin halutun rakenteen, ominaisuuksien ja suorituskyvyn saavuttamiseksi. Valmistuspäätöksenteko tapahtuu todennäköisyysmallin, eli Bayesin verkkomallin toteuttamisen kautta, joka on vankka, modulaarinen ja sovellettavissa muihin valmistusjärjestelmiin ja tuotesuunnitelmiin. Väitöstyössä esitetyn mallin kyky parantaa additiivisien valmistusprosessien suorituskykyä ja laatua, täten edistää kestävän tuotannon tavoitteita.Additive manufacturing (AM) has been considered viable for complex geometries, topology optimized parts, and parts that are otherwise difficult to produce using conventional manufacturing processes. Despite the advantages, one of the prevalent challenges in AM has been the poor capability of producing functional parts at production volumes that are competitive with traditional manufacturing. Modelling and simulation are powerful tools that can help shorten the design-build-test cycle by enabling rapid analysis of various product designs and process scenarios. Nevertheless, the capabilities and limitations of traditional and advanced manufacturing technologies do define the bounds for new product development. Thus, it is important that the designers have access to methods and tools that enable them to model and simulate product performance and associated manufacturing process performance to realize functional high value products. The motivation for this dissertation research stems from ongoing development of a novel high temperature superconducting (HTS) magnet assembly, which operates in cryogenic environment. Its complexity requires the convergence of multidisciplinary expertise during design and prototyping. The research applies knowledge-based modelling to aid manufacturing process analysis and decision making in the design of mechanical components of the HTS magnet. Further, it explores the feasibility of using AM in the production of the HTS magnet assembly. The developed approach uses product-process integrated modelling based on physical experiments to generate quantitative and qualitative information that define process-structure-property-performance interactions for given material-process combinations. The resulting interactions are then integrated into a graph-based model that can aid in design space exploration to assist early design and manufacturing decision-making. To do so, test components are fabricated using two metal AM processes: wire and arc additive manufacturing and selective laser melting. Metal alloys (stainless steel, mild steel, high-strength low-alloyed steel, aluminium, and copper alloys) commonly used in structural applications are tested for their mechanical-, thermal-, and electrical properties. In addition, microstructural characterization of the alloys is performed to further understand the impact of manufacturing process parameters on material properties. The integrated modelling approach combines the collected experimental data, existing analytical and empirical relationships, and other data-driven models (e.g., finite element models, machine learning models) in the form of a decision support system that enables optimal selection of material, manufacturing technology, process parameters, and other control variables for attaining desired structure, property, and performance characteristics of the final printed component. The manufacturing decision making is performed through implementation of a probabilistic model i.e., a Bayesian network model, which is robust, modular, and can be adapted for other manufacturing systems and product designs. The ability of the model to improve throughput and quality of additive manufacturing processes will boost sustainable manufacturing goals
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