2,491 research outputs found

    A review of differentiable digital signal processing for music and speech synthesis

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    The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Evaluating Symbolic AI as a Tool to Understand Cell Signalling

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    The diverse and highly complex nature of modern phosphoproteomics research produces a high volume of data. Chemical phosphoproteomics especially, is amenable to a variety of analytical approaches. In this thesis we evaluate novel Symbolic AI based algorithms as potential tools in the analysis of cell signalling. Initially we developed a first order deductive, logic-based model. This allowed us to identify previously unreported inhibitor-kinase relationships which could offer novel therapeutic targets for further investigation. Following this we made use of the probabilistic reasoning of ProbLog to augment the aforementioned Prolog based model with an intuitively calculated degree of belief. This allowed us to rank previous associations while also further increasing our confidence in already established predictions. Finally we applied our methodology to a Saccharomyces cerevisiae gene perturbation, phosphoproteomics dataset. In this context we were able to confirm the majority of ground truths, i.e. gene deletions as having taken place as intended. For the remaining deletions, again using a purely symbolic based approach we were able to provide predictions on the rewiring of kinase based signalling networks following kinase encoding gene deletions. The explainable, human readable and white-box nature of this approach were highlighted, however its brittleness due to missing, inconsistent or conflicting background knowledge was also examined

    Modelling 3D humans : pose, shape, clothing and interactions

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    Digital humans are increasingly becoming a part of our lives with applications like animation, gaming, virtual try-on, Metaverse and much more. In recent years there has been a great push to make our models of digital humans as real as possible. In this thesis we present methodologies to model two key characteristics of real humans, their appearance and actions. This thesis covers four innovations: (i) MGN, the first approach to reconstruct 3D garments and body shape underneath, as separate meshes, from a few RGB images of a person. This allows, for the first time, real world applications like texture transfer, garment transfer and virtual try-on in 3D, using just images. (ii) IPNet, a neural network, that leverages implicit functions for detailed reconstruction and registers the reconstructed mesh with the parametric SMPL model to make it controllable for real world tasks like animation and editing. (iii) LoopReg, a novel formulation that makes 3D registration task end-to-end differentiable for the first time. Semi-supervised LoopReg outperforms contemporary supervised methods using ∼100x less supervised data. (iv) BEHAVE the first dataset and method to track full body real interactions between humans and movable objects. All our code, MGN digital wardrobe and BEHAVE dataset are publicly available for further research.Digital humans are increasingly becoming a part of our lives with applications like animation, gaming, virtual try-on, Metaverse and much more. In recent years there has been a great push to make our models of digital humans as real as possible. In this thesis we present methodologies to model two key characteristics of real humans, their appearance and actions. This thesis covers four innovations: (i) MGN, the first approach to reconstruct 3D garments and body shape underneath, as separate meshes, from a few RGB images of a person. This allows, for the first time, real world applications like texture transfer, garment transfer and virtual try-on in 3D, using just images. (ii) IPNet, a neural network, that leverages implicit functions for detailed reconstruction and registers the reconstructed mesh with the parametric SMPL model to make it controllable for real world tasks like animation and editing. (iii) LoopReg, a novel formulation that makes 3D registration task end-to-end differentiable for the first time. Semi-supervised LoopReg outperforms contemporary supervised methods using ∼100x less supervised data. (iv) BEHAVE the first dataset and method to track full body real interactions between humans and movable objects. All our code, MGN digital wardrobe and BEHAVE dataset are publicly available for further research.Der digitale Mensch wird immer mehr zu einem Teil unseres Lebens mit Anwendungen wie Animation, Spielen, virtuellem Ausprobieren, Metaverse und vielem mehr. In den letzten Jahren wurden große Anstrengungen unternommen, um unsere Modelle digitaler Menschen so real wie möglich zu gestalten. In dieser Arbeit stellen wir Methoden zur Modellierung von zwei Schlüsseleigenschaften echter Menschen vor: ihr Aussehen und ihre Handlungen. Wir schlagen MGN vor, den ersten Ansatz zur Rekonstruktion von 3D-Kleidungsstücken und der darunter liegenden Körperform als separate Netze aus einigen wenigen RGB-Bildern einer Person. Wir erweitern das weit verbreitete SMPL-Körpermodell, das nur unbekleidete Formen darstellt, um auch Kleidungsstücke zu erfassen (SMPL+G). SMPL+G kann mit Kleidungsstücken bekleidet werden, die entsprechend dem SMPL-Modell posiert und geformt werden können. Dies ermöglicht zum ersten Mal reale Anwendungen wie Texturübertragung, Kleidungsübertragung und virtuelle Anprobe in 3D, wobei nur Bilder verwendet werden. Wir unterstreichen auch die entscheidende Einschränkung der netzbasierten Darstellung für digitale Menschen, nämlich die Fähigkeit, hochfrequente Details darzustellen. Daher untersuchen wir die neue implizite funktionsbasierte Darstellung als Alternative zur netzbasierten Darstellung (einschließlich parametrischer Modelle wie SMPL) für digitale Menschen. Typischerweise mangelt es den Methoden, die auf letzteren basieren, an Details, während ersteren die Kontrolle fehlt. Wir schlagen IPNet vor, ein neuronales Netzwerk, das implizite Funktionen für eine detaillierte Rekonstruktion nutzt und das rekonstruierte Netz mit dem parametrischen SMPL-Modell registriert, um es kontrollierbar zu machen. Auf diese Weise wird das Beste aus beiden Welten genutzt. Wir untersuchen den Prozess der Registrierung eines parametrischen Modells, wie z. B. SMPL, auf ein 3D-Netz. Dieses jahrzehntealte Problem im Bereich der Computer Vision und der Graphik erfordert in der Regel einen zweistufigen Prozess: i) Herstellung von Korrespondenzen zwischen dem Modell und dem Netz, und ii) Optimierung des Modells, um den Abstand zwischen den entsprechenden Punkten zu minimieren. Dieser zweistufige Prozess ist nicht durchgängig differenzierbar. Wir schlagen LoopReg vor, das eine neue, auf impliziten Funktionen basierende Darstellung des Modells verwendet und die Registrierung differenzierbar macht. Semi-überwachtes LoopReg übertrifft aktuelle überwachte Methoden mit ∼100x weniger überwachten Daten. Die Modellierung des menschlichen Aussehens ist notwendig, aber nicht ausreichend, um realistische digitale Menschen zu schaffen. Wir müssen nicht nur modellieren, wie Menschen aussehen, sondern auch, wie sie mit ihren umgebenden Objekten interagieren. Zu diesem Zweck präsentieren wir mit BEHAVE den ersten Datensatz von realen Ganzkörper-Interaktionen zwischen Menschen und beweglichen Objekten. Wir stellen segmentierte Multiview-RGBDFrames zusammen mit registrierten SMPL- und Objekt-Fits sowie Kontaktannotationen in 3D zur Verfügung. Der BEHAVE-Datensatz enthält ∼15k Frames und seine Erweiterung enthält ∼400k Frames mit Pseudo-Ground-Truth-Annotationen. Unsere BEHAVE-Methode verwendet diesen Datensatz, um ein neuronales Netz zu trainieren, das die Person, das Objekt und die Kontakte zwischen ihnen gemeinsam verfolgt. In dieser Arbeit untersuchen wir die oben genannten Ideen und bieten eine eingehende Analyse unserer Schlüsselideen und Designentscheidungen. Wir erörtern auch die Grenzen unserer Ideen und schlagen künftige Arbeiten vor, um nicht nur diese Grenzen anzugehen, sondern auch die Forschung weiter auszubauen. Unser gesamter Code, die digitale Garderobe und der Datensatz sind für weitere Forschungen öffentlich zugänglich

    Mathematical optimization and machine learning to support PCB topology identification

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    In this paper, we study an identification problem for schematics with different concurring topologies. A framework is proposed, that is both supported by mathematical optimization and machine learning algorithms. Through the use of Python libraries, such as scikit-rf, which allows for the emulation of network analyzer measurements, and a physical microstrip line simulation on PCBs, data for training and testing the framework are provided. In addition to an individual treatment of the concurring topologies and subsequent comparison, a method is introduced to tackle the identification of the optimum topology directly via a standard optimization or machine learning setup: An encoder-decoder sequence is trained with schematics of different topologies, to generate a flattened representation of the rated graph representation of the considered schematics. Still containing the relevant topology information in encoded (i.e., flattened) form, the so obtained latent space representations of schematics can be used for standard optimization of machine learning processes. Using now the encoder to map schematics on latent variables or the decoder to reconstruct schematics from their latent space representation, various machine learning and optimization setups can be applied to treat the given identification task. The proposed framework is presented and validated for a small model problem comprising different circuit topologies.</p

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    Tools for efficient Deep Learning

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    In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption. We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work. This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C. Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets. All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces

    Implicit Shape Model Trees: Recognition of 3-D Indoor Scenes and Prediction of Object Poses for Mobile Robots

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    For a mobile robot, we present an approach to recognize scenes in arrangements of objects distributed over cluttered environments. Recognition is made possible by letting the robot alternately search for objects and assign found objects to scenes. Our scene model "Implicit Shape Model (ISM) trees" allows us to solve these two tasks together. For the ISM trees, this article presents novel algorithms for recognizing scenes and predicting the poses of searched objects. We define scenes as sets of objects, where some objects are connected by 3-D spatial relations. In previous work, we recognized scenes using single ISMs. However, these ISMs were prone to false positives. To address this problem, we introduced ISM trees, a hierarchical model that includes multiple ISMs. Through the recognition algorithm it contributes, this article ultimately enables the use of ISM trees in scene recognition. We intend to enable users to generate ISM trees from object arrangements demonstrated by humans. The lack of a suitable algorithm is overcome by the introduction of an ISM tree generation algorithm. In scene recognition, it is usually assumed that image data is already available. However, this is not always the case for robots. For this reason, we combined scene recognition and object search in previous work. However, we did not provide an efficient algorithm to link the two tasks. This article introduces such an algorithm that predicts the poses of searched objects with relations. Experiments show that our overall approach enables robots to find and recognize object arrangements that cannot be perceived from a single viewpoint.Comment: 22 pages, 24 figures; For associated video clips, see https://www.youtube.com/playlist?list=PL3RZ_UQY_uOIfuIJNqdS8wDMjTjOAeOm
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