743 research outputs found

    Heterogeneous Acceleration for 5G New Radio Channel Modelling Using FPGAs and GPUs

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Systematic AI Approach for AGI: Addressing Alignment, Energy, and AGI Grand Challenges

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    AI faces a trifecta of grand challenges the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume unsustainable amounts of energy during model training and daily operations.Making things worse, the amount of computation required to train each new AI model has been doubling every 2 months since 2020, directly translating to increases in energy consumption.The leap from AI to AGI requires multiple functional subsystems operating in a balanced manner, which requires a system architecture. However, the current approach to artificial intelligence lacks system design; even though system characteristics play a key role in the human brain from the way it processes information to how it makes decisions. Similarly, current alignment and AI ethics approaches largely ignore system design, yet studies show that the brains system architecture plays a critical role in healthy moral decisions.In this paper, we argue that system design is critically important in overcoming all three grand challenges. We posit that system design is the missing piece in overcoming the grand challenges.We present a Systematic AI Approach for AGI that utilizes system design principles for AGI, while providing ways to overcome the energy wall and the alignment challenges.Comment: International Journal on Semantic Computing (2024) Categories: Artificial Intelligence; AI; Artificial General Intelligence; AGI; System Design; System Architectur

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Constraint-based simulation of virtual crowds

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    Central to simulating pedestrian crowds is their motion and behaviour. It is required to understand how pedestrians move to simulate and predict scenarios with crowds of people. Pedestrian behaviours enhance the range of motions people can demonstrate, resulting in greater variety, believability, and accuracy. Models with complex computations and motion have difficulty in being extended with additional behaviours. This is because the structure of these models are not designed in a way that is generally compatible with collision avoidance behaviours. To address this issue, this thesis will research a possible pedestrian model that can simulate collision response with a wide range of additional behaviours. The model will do so by using constraints, a limit on the velocity of a person's movement. The proposed model will use constraints as the core computation. By describing behaviours in terms of constraints, these behaviours can be combined with the proposed model. Pedestrian simulations strike a balance between model complexity and runtime speed. Some models focus entirely on the complexity and accuracy of people, while other models focus on creating believable yet lightweight and performant simulations. Believable crowds look realistic to human observation, but do not match up to numerical analysis under scrutiny. The larger the population, and the more complex the motion of people, the slower the simulation will run. One route for improving performance of software is by using Graphical Processing Units (GPUs). GPUs are devices with theoretical performance that far outperforms equivalent multi-core CPUs. Research literature tends to focus on either the accuracy, or the performance optimisations of pedestrian crowd simulations. This suggests that there is opportunity to create more accurate models that run relatively quickly. Real time is a useful measure of model runtime. A simulation that runs in real time can be interactive and respond live to user input. By increasing the performance of the model, larger and more complex models can be simulated. This in turn increases the range of applications the model can represent. This thesis will develop a performant pedestrian simulation that runs in real time. It will explore how suitable the model is for GPU acceleration, and what performance gains can be obtained by implementing the model on the GPU

    Validation and Verification of Safety-Critical Systems in Avionics

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    This research addresses the issues of safety-critical systems verification and validation. Safety-critical systems such as avionics systems are complex embedded systems. They are composed of several hardware and software components whose integration requires verification and testing in compliance with the Radio Technical Commission for Aeronautics standards and their supplements (RTCA DO-178C). Avionics software requires certification before its deployment into an aircraft system, and testing is mandatory for certification. Until now, the avionics industry has relied on expensive manual testing. The industry is searching for better (quicker and less costly) solutions. This research investigates formal verification and automatic test case generation approaches to enhance the quality of avionics software systems, ensure their conformity to the standard, and to provide artifacts that support their certification. The contributions of this thesis are in model-based automatic test case generations approaches that satisfy MC/DC criterion, and bidirectional requirement traceability between low-level requirements (LLRs) and test cases. In the first contribution, we integrate model-based verification of properties and automatic test case generation in a single framework. The system is modeled as an extended finite state machine model (EFSM) that supports both the verification of properties and automatic test case generation. The EFSM models the control and dataflow aspects of the system. For verification, we model the system and some properties and ensure that properties are correctly propagated to the implementation via mandatory testing. For testing, we extended an existing test case generation approach with MC/DC criterion to satisfy RTCA DO-178C requirements. Both local test cases for each component and global test cases for their integration are generated. The second contribution is a model checking-based approach for automatic test case generation. In the third contribution, we developed an EFSM-based approach that uses constraints solving to handle test case feasibility and addresses bidirectional requirements traceability between LLRs and test cases. Traceability elements are determined at a low-level of granularity, and then identified, linked to their source artifact, created, stored, and retrieved for several purposes. Requirements’ traceability has been extensively studied but not at the proposed low-level of granularity

    Diffusion MRI tractography for oncological neurosurgery planning:Clinical research prototype

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    Jornadas Nacionales de InvestigaciĂłn en Ciberseguridad: actas de las VIII Jornadas Nacionales de InvestigaciĂłn en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de InvestigaciĂłn en Ciberseguridad (8ÂŞ. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernizaciĂłn tecnolĂłxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
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