2,774 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Guided rewriting and constraint satisfaction for parallel GPU code generation
Graphics Processing Units (GPUs) are notoriously hard to optimise for manually due to their scheduling and memory hierarchies. What is needed are good automatic code generators and optimisers for such parallel hardware. Functional approaches such as Accelerate, Futhark and LIFT leverage a high-level algorithmic Intermediate Representation (IR) to expose parallelism and abstract the implementation details away from the user. However, producing efficient code for a given accelerator remains challenging. Existing code generators depend on the user input to choose a subset of hard-coded optimizations or automated exploration of implementation search space. The former suffers from the lack of extensibility, while the latter is too costly due to the size of the search space. A hybrid approach is needed, where a space of valid implementations is built automatically and explored with the aid of human expertise.
This thesis presents a solution combining user-guided rewriting and automatically generated constraints to produce high-performance code. The first contribution is an automatic tuning technique to find a balance between performance and memory consumption. Leveraging its functional patterns, the LIFT compiler is empowered to infer tuning constraints and limit the search to valid tuning combinations only.
Next, the thesis reframes parallelisation as a constraint satisfaction problem. Parallelisation constraints are extracted automatically from the input expression, and a solver is used to identify valid rewriting. The constraints truncate the search space to valid parallel mappings only by capturing the scheduling restrictions of the GPU in the context of a given program. A synchronisation barrier insertion technique is proposed to prevent data races and improve the efficiency of the generated parallel mappings.
The final contribution of this thesis is the guided rewriting method, where the user encodes a design space of structural transformations using high-level IR nodes called rewrite points. These strongly typed pragmas express macro rewrites and expose design choices as explorable parameters. The thesis proposes a small set of reusable rewrite points to achieve tiling, cache locality, data reuse and memory optimisation.
A comparison with the vendor-provided handwritten kernel ARM Compute Library and the TVM code generator demonstrates the effectiveness of this thesis' contributions. With convolution as a use case, LIFT-generated direct and GEMM-based convolution implementations are shown to perform on par with the state-of-the-art solutions on a mobile GPU. Overall, this thesis demonstrates that a functional IR yields well to user-guided and automatic rewriting for high-performance code generation
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
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
Automatic Control of General Anesthesia: New Developments and Clinical Experiments
L’anestesia generale è uno stato di coma farmacologicamente indotto, temporaneo e reversibile. Il suo obiettivo consiste nel provocare la perdita totale della coscienza e nel sopprimere la percezione del dolore. Essa costituisce un aspetto fondamentale per la medicina moderna in quanto consente di praticare interventi chirurgici invasivi senza causare ansia e dolore al paziente. Nella pratica clinica dell’anestesia totalmente endovenosa questi effetti vengono generalmente ottenuti mediante la somministrazione simultanea del farmaco ipnotico propofol e del farmaco analgesico remifentanil. Il dosaggio di questi farmaci viene gestito dal medico anestesista basandosi su linee guida farmacologiche e monitorando la risposta clinica del paziente. Recenti sviluppi nelle tecniche di elaborazione dei segnali fisiologici hanno consentito di ottenere degli indicatori quantitativi dello stato anestetico del paziente. Tali indicatori possono essere utilizzati come segnali di retroazione per sistemi di controllo automatico dell'anestesia. Lo sviluppo di questi sistemi ha come obiettivo quello di fornire uno strumento di supporto per l'anestesista.
Il lavoro presentato in questa tesi è stato svolto nell'ambito del progetto di ricerca riguardante il controllo automatico dell'anestesia attivo presso l'Università degli Studi di Brescia. Esso è denominato ACTIVA (Automatic Control of Total IntraVenous Anesthesia) ed è il risultato della collaborazione tra il Gruppo di Ricerca sui Sistemi di Controllo dell’Università degli Studi di Brescia e l’Unità Operativa Anestesia e Rianimazione 2 degli Spedali Civili di Brescia. L’obiettivo del progetto ACTIVA consiste nello sviluppo teorico, nell’implementazione e nella validazione clinica di strategie di controllo innovative per il controllo automatico dell’anestesia totalmente endovenosa. Nel dettaglio, in questa tesi vengono inizialmente presentati i risultati sperimentali ottenuti con strutture di controllo basate sull'algoritmo PID e PID ad eventi per la somministrazione di propofol e remifentanil. Viene poi presentato lo sviluppo teorico e la validazione clinica di strutture di controllo predittivo basate su modello. Successivamente vengono presentati i risultati di uno studio in simulazione riguardante una soluzione di controllo innovativa che consente all'anestesista di regolare esplicitamente il bilanciamento tra propofol e remifentanil. Infine, vengono presentati gli sviluppi teorici ed i relativi studi in simulazione riguardanti soluzioni di controllo personalizzate per le fasi di induzione e mantenimento dell'anestesia.General anesthesia is a state of pharmacologically induced, temporary and reversible coma. Its goal is to cause total loss of consciousness and suppress the perception of pain. It constitutes a fundamental aspect of modern medicine as it allows invasive surgical procedures to be performed without causing anxiety and pain to the patient. In the clinical practice of total intravenous anesthesia, these effects are generally obtained by the simultaneous administration of the hypnotic drug propofol and of the analgesic drug remifentanil. The dosing of these drugs is managed by the anesthesiologist on the basis of pharmacological guidelines and by monitoring the patient's clinical response. Recent developments in physiological signal processing techniques have introduced the possibility to obtain quantitative indicators of the patient's anesthetic state. These indicators can be used as feedback signals for automatic anesthesia control systems. The development of these systems aims to provide a support tool for the anesthesiologist.
The work presented in this thesis has been carried out in the framework of the research project concerning the automatic control anesthesia at the University of Brescia. The project is called ACTIVA (Automatic Control of Total IntraVenous Anesthesia) and is the result of the collaboration between the Research Group on Control Systems of the University of Brescia and the Anesthesia and Intensive Care Unit 2 of the Spedali Civili di Brescia. The objective of the ACTIVA project consists in the theoretical development, implementation, and clinical validation of innovative control strategies for the automatic control of total intravenous anesthesia. In detail, in this thesis the experimental results obtained with control structures based on the PID and on event-based PID controllers for the administration of propofol and remifentanil are initially presented. The theoretical development and clinical validation of model predictive control strategies is then proposed. Next, the results of a simulation study regarding an innovative control solution that allows the anesthesiologist to explicitly adjust the balance between propofol and remifentanil are given. Finally, the theoretical developments and the relative simulation studies concerning personalized control solutions for induction and maintenance phases of anesthesia are explained
A robotic platform for precision agriculture and applications
Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited.
As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles.
The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances.
Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles.
The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed.
Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork.
The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.Le tecniche agricole sono state migliorate nel corso dei secoli per soddisfare la crescente domanda di aumento della popolazione mondiale. I recenti progressi tecnologici in termini di piattaforme robotiche possono essere sfruttati in questo contesto.
Poiché la gestione del frutteto è una delle applicazioni più impegnative, a causa della sua struttura arborea e della necessaria interazione con l'ambiente, è stata oggetto di ricerca per fornire una soluzione personalizzata che sviluppi un nuovo concetto di veicolo agricolo.
Il risultato si è concretizzato in un veicolo cingolato leggero, capace di effettuare una navigazione autonoma sia nello scenario di pieno campo che all'interno dei frutteti (navigazione interfilare). La progettazione meccanica, insieme all'implementazione del software, sono stati dettagliati per evidenziarne i punti di forza, accanto ad alcuni ulteriori miglioramenti previsti per incrementarne le prestazioni complessive.
I test di stabilità statica hanno dimostrato che il veicolo può resistere a ripidi pendii. Sono stati inoltre studiati miglioramenti per affinare la stima dello slittamento che si verifica durante le manovre di svolta, tipico dei veicoli cingolati.
L'architettura software è stata implementata utilizzando il framework Robot Operating System (ROS), in modo da sfruttare i pacchetti disponibili relativi a componenti base, come le interfacce dei sensori, e consentendo al contempo un'implementazione personalizzata degli algoritmi di navigazione sviluppati.
I test in condizioni reali all'interno dei frutteti sperimentali dell'università hanno dimostrato la robustezza e la stabilità della soluzione con oltre 800 ore di lavoro sul campo.
Il veicolo ha permesso di attivare e svolgere un'ampia gamma di attività agricole in maniera autonoma, come l'irrorazione, la falciatura e la raccolta di dati sul campo. Questi ultimi possono essere sfruttati per stimare automaticamente le proprietà più rilevanti del frutteto, come il conteggio e la calibratura dei frutti, la stima delle proprietà della chioma e la raccolta autonoma dei frutti con stime post-raccolta
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
DESIGN AND VERIFICATION OF AUTONOMOUS SYSTEMS IN THE PRESENCE OF UNCERTAINTIES
Autonomous Systems offer hope towards moving away from mechanized, unsafe, manual, often inefficient practices. The last decade has seen several small, but important, steps towards making this dream into reality. These advancements have helped us to achieve limited autonomy in several places, such as, driving, factory floors, surgeries, wearables, and home assistants, etc. Nevertheless, autonomous systems are required to operate in a wide range of environments with uncertainties (viz., sensor errors, timing errors, dynamic nature of the environment, etc.). Such environmental uncertainties, even when present in small amounts, can have drastic impact on the safety of the system—thus hampering the goal of achieving higher degree of autonomy, especially in safety critical domains. To this end, the dissertation shall discuss formaltechniques that are able to verify and design autonomous systems for safety, even under the presence of such uncertainties, allowing for their trustworthy deployment in the real world. Specifically, the dissertation shall discuss monitoring techniques for autonomous systems from available (noisy) logs, and safety-verification techniques of autonomous system controllers under timing uncertainties. Secondly, using heterogeneous learning-based cloud computing models that can balance uncertainty in output and computation cost, the dissertation will present techniques for designing safe and performance-optimal autonomous systems.Doctor of Philosoph
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