5,511 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

    The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems

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    The Travel Salesman Problem (TSP) consists in finding the minimal-length closed tour that connects the entire group of nodes of a given graph. We propose to solve such a combinatorial optimization problem with the AddACO algorithm: it is a version of the Ant Colony Optimization method that is characterized by a modified probabilistic law at the basis of the exploratory movement of the artificial insects. In particular, the ant decisional rule is here set to amount in a linear convex combination of competing behavioral stimuli and has therefore an additive form (hence the name of our algorithm), rather than the canonical multiplicative one. The AddACO intends to address two conceptual shortcomings that characterize classical ACO methods: (i) the population of artificial insects is in principle allowed to simultaneously minimize/maximize all migratory guidance cues (which is in implausible from a biological/ecological point of view) and (ii) a given edge of the graph has a null probability to be explored if at least one of the movement trait is therein equal to zero, i.e., regardless the intensity of the others (this in principle reduces the exploratory potential of the ant colony). Three possible variants of our method are then specified: the AddACO-V1, which includes pheromone trail and visibility as insect decisional variables, and the AddACO-V2 and the AddACO-V3, which in turn add random effects and inertia, respectively, to the two classical migratory stimuli. The three versions of our algorithm are tested on benchmark middle-scale TPS instances, in order to assess their performance and to find their optimal parameter setting. The best performing variant is finally applied to large-scale TSPs, compared to the naive Ant-Cycle Ant System, proposed by Dorigo and colleagues, and evaluated in terms of quality of the solutions, computational time, and convergence speed. The aim is in fact to show that the proposed transition probability, as long as its conceptual advantages, is competitive from a performance perspective, i.e., if it does not reduce the exploratory capacity of the ant population w.r.t. the canonical one (at least in the case of selected TSPs). A theoretical study of the asymptotic behavior of the AddACO is given in the appendix of the work, whose conclusive section contains some hints for further improvements of our algorithm, also in the perspective of its application to other optimization problems

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks

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    Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, Computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology

    Improving Oblivious Reconfigurable Networks with High Probability

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    Oblivious Reconfigurable Networks (ORNs) use rapidly reconfiguring switches to create a dynamic time-varying topology. Prior theoretical work on ORNs has focused on the tradeoff between maximum latency and guaranteed throughput. This work shows that by relaxing the notion of guaranteed throughput to an achievable rate with high probability, one can achieve a significant improvement in the latency/throughput tradeoff. For a fixed maximum latency, we show that almost twice the maximum possible guaranteed throughput rate can be achieved with high probability. Alternatively for a fixed throughput value, relaxing to achievement with high probability decreases the maximum latency to almost the square root of the latency required to guarantee the throughput rate. We first give a lower bound on the best maximum latency possible given an achieved throughput rate with high probability. This is done using an LP duality style argument. We then give a family of ORN designs which achieves these tradeoffs. The connection schedule is based on the Vandermonde Basis Scheme of Amir, Wilson, Shrivastav, Weatherspoon, Kleinberg, and Agarwal, although the period and routing scheme differ significantly. We prove achievable throughput with high probability by interpreting the amount of flow on each edge as a sum of negatively associated variables, and applying a Chernoff bound. This gives us a design with maximum latency that is tight with our lower bound (up to a log factor) for almost all constant throughput values.Comment: 19 pages, 1 figur

    When Deep Learning Meets Polyhedral Theory: A Survey

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    In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing. Meanwhile, the structure of neural networks converged back to simpler representations based on piecewise constant and piecewise linear functions such as the Rectified Linear Unit (ReLU), which became the most commonly used type of activation function in neural networks. That made certain types of network structure \unicode{x2014}such as the typical fully-connected feedforward neural network\unicode{x2014} amenable to analysis through polyhedral theory and to the application of methodologies such as Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) for a variety of purposes. In this paper, we survey the main topics emerging from this fast-paced area of work, which bring a fresh perspective to understanding neural networks in more detail as well as to applying linear optimization techniques to train, verify, and reduce the size of such networks

    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

    Regional groundwater levels in crystalline aquifers: structural domains, groundwater level monitoring, and factors controlling the response time and variability

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    This thesis aims to determine the degree to which fracture networks control the response time and fluctuation of groundwater levels in regional crystalline aquifers in comparison to topography, sediment deposits, precipitation and snowmelt. In this respect, the compartmentalization of the crystalline aquifer into structural domains is necessary, in order to take into account the heterogeneity of the crystalline aquifer in relation to the different fracture networks existing in the rock mass. Field investigations were conducted in the Lanaudiere region, Quebec, Canada, where the underlying crystalline rock outcrops in several locations, allowing access to outcrops for fracture sampling. In addition, four unequipped boreholes drilled into the crystalline rock were available for fracture sampling. Typically, fracture sampling involves the collection of multiple fracture samples, which involve numerous fracture clusters. Grouping fracture samples into structural domains is generally useful for geologists, hydrogeologists, and geomechanicians as a region of fractured rocks is subdivided into sub-regions with similar behavior in terms of their hydromechanical properties. One of the commonly used methods to group fracture samples into structural domains is Mahtab and Yegulalp's method, considering the orientation of fracture clusters and ignoring several fracture parameters, such as fracture spacing, aperture, and persistence, that are important for fluid circulation in the rock mass. In this thesis, we proposed a new cluster-based similarity method that considers cluster orientation as well as the aperture, persistence and spacing. In addition, a method for compartmentalizing a given study area into structural domains using Voronoi diagrams has also been proposed. The proposed method is more suitable than the previous method for applications in hydrogeology and rock mechanics, especially for regional studies of fluid flow in the rock mass. The study of response time and variability of groundwater levels requires a groundwater level monitoring network. The inclusion of private boreholes in these monitoring networks can provide a cost-effective means of obtaining a larger data set; however, the use of these boreholes is limited by the fact that frequent pumping, in these boreholes, generates outliers in the recorded time series. In this thesis, a slope criterion is applied to identify and remove outliers from groundwater level time series from exploited private boreholes. Nevertheless, the removal of outliers creates a missing value problem, which biases the subsequent time series analysis. Thus, 14 imputation methods were used to replace missing values. The proposed approach is applied to groundwater level time series from a monitoring network of 20 boreholes in the Lanaudiere region, Quebec, Canada. The slope criterion is shown to be very effective in identifying outliers in exploited private boreholes. Among the characteristics of the missing value pattern, the gap size and gap position in the time series are the most important parameters that affect the performance of the imputation methods. Among the imputation methods tested, linear and Stineman interpolations, and Kalman filtering were the most effective. This thesis demonstrates that privately operated boreholes can be used for groundwater monitoring by removing outliers and imputing missing values. At local and regional scales, groundwater level is controlled by several factors. The most commonly studied factors are climatic, geologic and geomorphologic controls on groundwater level variability and response time, and in many cases only one controlling factor is considered in the analysis. However, many other factors can affect groundwater level variability and response time, such as the sediment deposit properties and fracture network characteristics in crystalline aquifers. In this study, a more inclusive approach is used to consider climatic, geomorphological, and fracture network parameters as potential controlling factors. A total of 18 parameters were analyzed for interrelationships as each controlling factor is described by several parameters. The study analyzed a two-year record of groundwater levels in 20 boreholes, drilled into the crystalline rock of the Canadian Shield in the Lanaudière region, Québec, Canada. Factors associated to geomorpgology and fracture network are related to groundwater level variability and its response time. Of the various parameters analyzed in each control factor, sediment thickness and local slope of the geomorphological factor, as well as average persistence and equivalent hydraulic conductivity of the fracture network factor, are most closely related to groundwater level variability and response time. However, further studies are needed to elucidate the physical processes behind certain interrelationships between fracture network parameters and groundwater level variability parameters. Cette thèse a pour but de déterminer le degré auquel les réseaux de fractures contrôlent le temps de réponse et la fluctuation du niveau des eaux souterraines dans les aquifères cristallins régionaux par rapport à la topographie, aux dépôts de sédiments, aux précipitations et à la fonte des neiges. À cet égard, la compartimentation de l'aquifère cristallin en domaines structuraux est nécessaire, afin de prendre en compte l'hétérogénéité de l'aquifère cristallin par rapport aux différents réseaux de fractures existants dans le massif rocheux. Des investigations de terrain ont été menées dans la région de Lanaudière, Québec, Canada, où la roche cristalline sous-jacente affleure à plusieurs endroits, permettant un accès aux affleurements pour l'échantillonnage des fractures. De plus, quatre forages non équipés, forés dans la roche cristalline, étaient disponibles pour l'échantillonnage des fractures. Habituellement, l'échantillonnage de fractures comprend la collecte de plusieurs échantillons de fractures, qui impliquent de nombreux groupes de fractures. Le regroupement des échantillons de fractures en domaines structuraux est généralement utile pour les géologues, les hydrogéologues et les géomécaniciens dans la mesure où une région de roches fracturées est subdivisée en sous-régions ayant un comportement similaire en termes de propriétés hydromécaniques. L'une des méthodes couramment utilisées pour regrouper les échantillons de fractures en domaines structuraux est celle de Mahtab and Yegulalp, considérant l'orientation des clusters de fractures et ignorant plusieurs paramètres de fractures, tels que l'espacement, l'ouverture et la persistance des fractures, qui sont importants pour la circulation des fluides dans le massif rocheux. Dans cette thèse, nous avons proposé une nouvelle méthode de similarité basée sur les clusters qui considère l'orientation des clusters ainsi que l'ouverture, la persistance et l'espacement des clusters. En outre, une méthode pour la compartimentation d'une zone d'étude donnée en domaines structuraux au moyen de diagrammes de Voronoï a également été proposée. La méthode proposée est plus adaptée que la méthode précédente pour des applications en hydrogéologie et en mécanique des roches, notamment pour les études régionales de la circulation des fluides dans la masse rocheuse. L'étude du temps de réponse et de la variabilité du niveau des eaux souterraines nécessite un réseau de surveillance du niveau des eaux souterraines. L'inclusion de forages privés dans ces réseaux de surveillance peut fournir un moyen peu coûteux d'obtenir un ensemble plus large de données ; cependant, l'utilisation de ces forages est limitée par le fait que le pompage fréquent de ces forages génère des valeurs aberrantes dans les séries temporelles enregistrées. Dans cette thèse, un critère de pente est appliqué pour identifier et éliminer les valeurs aberrantes des séries temporelles du niveau des eaux souterraines provenant de forages privés exploités. Néanmoins, l'élimination des valeurs aberrantes crée un problème de valeurs manquantes, ce qui biaise l'analyse ultérieure des séries temporelles. Ainsi, 14 méthodes d'imputation ont été utilisées pour remplacer les valeurs manquantes. L'approche proposée est appliquée aux séries temporelles du niveau des eaux souterraines provenant d'un réseau de surveillance de 20 forages dans la région de Lanaudière, Québec, Canada. Le critère de pente s'avère très efficace pour identifier les valeurs aberrantes dans les forages privés exploités. Parmi les caractéristiques du modèle de valeurs manquantes, la taille et la position des lacunes dans la série temporelle sont les paramètres les plus importants qui affectent les performances des méthodes d'imputation. Parmi les méthodes d'imputation testées, les interpolations linéaires et de Stineman, ainsi que le filtrage de Kalman ont été les plus efficaces. La présente thèse démontre que les forages privés exploités peuvent être utilisés pour la surveillance des eaux souterraines en éliminant les valeurs aberrantes et en imputant les valeurs manquantes. À l'échelle locale et régionale, le niveau des eaux souterraines est contrôlé par plusieurs facteurs. Les facteurs les plus couramment étudiés sont les contrôles climatiques, géologiques et géomorphologiques sur la variabilité du niveau des eaux souterraines et le temps de réponse, et dans de nombreux cas, un seul facteur de contrôle est pris en compte dans l'analyse. Cependant, de nombreux autres facteurs peuvent affecter la variabilité du niveau des eaux souterraines et le temps de réponse, tels que les propriétés des dépôts de sédiments et les caractéristiques du réseau de fractures dans les aquifères cristallins. Dans cette étude, une approche plus globale est utilisée pour considérer les paramètres climatiques, géomorphologiques et du réseau de fractures comme des facteurs de contrôle potentiels. Au total, 18 paramètres ont été analysés pour déterminer les interrelations, sachant que chaque facteur de contrôle est décrit par plusieurs paramètres. L'étude a analysé un jeu de données de deux ans sur les niveaux d'eau souterraine dans 20 forages réalisés dans la roche cristalline du Bouclier canadien dans la région de Lanaudière, au Québec, Canada Les facteurs liés à la géomorphologie et au réseau de fractures sont liés à la variabilité du niveau des eaux souterraines et à son temps de réponse. Parmi les divers paramètres analysés dans chaque facteur de contrôle, l'épaisseur des sédiments et la pente locale du facteur géomorphologique, ainsi que la persistance moyenne et la conductivité hydraulique équivalente du facteur réseau de fractures, sont les plus étroitement liés à la variabilité du niveau des eaux souterraines et à son temps de réponse. Toutefois, des études complémentaires sont nécessaires pour élucider les processus physiques à l'origine de certaines interrelations entre les paramètres du réseau de fractures et les paramètres de variabilité du niveau des eaux souterraines
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