1,090 research outputs found

    The Sparse Abstract Machine

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    We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM defines a streaming dataflow abstraction with sparse primitives that encompass a large space of scheduled tensor algebra expressions. SAM dataflow graphs naturally separate tensor formats from algorithms and are expressive enough to incorporate arbitrary iteration orderings and many hardware-specific optimizations. We also present Custard, a compiler from a high-level language to SAM that demonstrates SAM's usefulness as an intermediate representation. We automatically bind from SAM to a streaming dataflow simulator. We evaluate the generality and extensibility of SAM, explore the performance space of sparse tensor algebra optimizations using SAM, and show SAM's ability to represent dataflow hardware.Comment: 18 pages, 17 figures, 3 table

    An optimal approach to the design of experiments for the automatic characterisation of biosystems

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    The Design-Build-Test-Learn cycle is the main approach of synthetic biology to re-design and create new biological parts and systems, targeting the solution for complex and challenging paramount problems. The applications of the novel designs range from biosensing and bioremediation of water pollutants (e.g. heavy metals) to drug discovery and delivery (e.g. cancer treatment) or biofuel production (e.g. butanol and ethanol), amongst others. Standardisation, predictability and automation are crucial elements for synthetic biology to efficiently attain these objectives. Mathematical modelling is a powerful tool that allows us to understand, predict, and control these systems, as shown in many other disciplines such as particle physics, chemical engineering, epidemiology and economics. Yet, the inherent difficulties of using mathematical models substantially slowed their adoption by the synthetic biology community. Researchers might develop different competing model alternatives in absence of in-depth knowledge of a system, consequently being left with the burden of with having to find the best one. Models also come with unknown and difficult to measure parameters that need to be inferred from experimental data. Moreover, the varying informative content of different experiments hampers the solution of these model selection and parameter identification problems, adding to the scarcity and noisiness of laborious-to-obtain data. The difficulty to solve these non-linear optimisation problems limited the widespread use of advantageous mathematical models in synthetic biology, broadening the gap between computational and experimental scientists. In this work, I present the solutions to the problems of parameter identification, model selection and experimental design, validating them with in vivo data. First, I use Bayesian inference to estimate model parameters, relaxing the traditional noise assumptions associated with this problem. I also apply information-theoretic approaches to evaluate the amount of information extracted from experiments (entropy gain). Next, I define methodologies to quantify the informative content of tentative experiments planned for model selection (distance between predictions of competing models) and parameter inference (model prediction uncertainty). Then, I use the two methods to define efficient platforms for optimal experimental design and use a synthetic gene circuit (the genetic toggle switch) to substantiate the results, computationally and experimentally. I also expand strategies to optimally design experiments for parameter identification to update parameter information and input designs during the execution of these (on-line optimal experimental design) using microfluidics. Finally, I developed an open-source and easy-to-use Julia package, BOMBs.jl, automating all the above functionalities to facilitate their dissemination and use amongst the synthetic biology community

    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

    Northeastern Illinois University, Academic Catalog 2023-2024

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    https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp

    Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms

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    We demonstrate a high-performance vendor-agnostic method for massively parallel solving of ensembles of ordinary differential equations (ODEs) and stochastic differential equations (SDEs) on GPUs. The method is integrated with a widely used differential equation solver library in a high-level language (Julia's DifferentialEquations.jl) and enables GPU acceleration without requiring code changes by the user. Our approach achieves state-of-the-art performance compared to hand-optimized CUDA-C++ kernels, while performing 20100×20-100\times faster than the vectorized-map (\texttt{vmap}) approach implemented in JAX and PyTorch. Performance evaluation on NVIDIA, AMD, Intel, and Apple GPUs demonstrates performance portability and vendor-agnosticism. We show composability with MPI to enable distributed multi-GPU workflows. The implemented solvers are fully featured, supporting event handling, automatic differentiation, and incorporating of datasets via the GPU's texture memory, allowing scientists to take advantage of GPU acceleration on all major current architectures without changing their model code and without loss of performance.Comment: 11 figure

    SoC-based FPGA architecture for image analysis and other highly demanding applications

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    Al giorno d'oggi, lo sviluppo di algoritmi si concentra su calcoli efficienti in termini di prestazioni ed efficienza energetica. Tecnologie come il field programmable gate array (FPGA) e il system on chip (SoC) basato su FPGA (FPGA/SoC) hanno dimostrato la loro capacità di accelerare applicazioni di calcolo intensive risparmiando al contempo il consumo energetico, grazie alla loro capacità di elevato parallelismo e riconfigurazione dell'architettura. Attualmente, i cicli di progettazione esistenti per FPGA/SoC sono lunghi, a causa della complessità dell'architettura. Pertanto, per colmare il divario tra le applicazioni e le architetture FPGA/SoC e ottenere un design hardware efficiente per l'analisi delle immagini e altri applicazioni altamente demandanti utilizzando lo strumento di sintesi di alto livello, vengono prese in considerazione due strategie complementari: tecniche ad hoc e stima delle prestazioni. Per quanto riguarda le tecniche ad-hoc, tre applicazioni molto impegnative sono state accelerate attraverso gli strumenti HLS: discriminatore di forme di impulso per i raggi cosmici, classificazione automatica degli insetti e re-ranking per il recupero delle informazioni, sottolineando i vantaggi quando questo tipo di applicazioni viene attraversato da tecniche di compressione durante il targeting dispositivi FPGA/SoC. Inoltre, in questa tesi viene proposto uno stimatore delle prestazioni per l'accelerazione hardware per prevedere efficacemente l'utilizzo delle risorse e la latenza per FPGA/SoC, costruendo un ponte tra l'applicazione e i domini architetturali. Lo strumento integra modelli analitici per la previsione delle prestazioni e un motore design space explorer (DSE) per fornire approfondimenti di alto livello agli sviluppatori di hardware, composto da due motori indipendenti: DSE basato sull'ottimizzazione a singolo obiettivo e DSE basato sull'ottimizzazione evolutiva multiobiettivo.Nowadays, the development of algorithms focuses on performance-efficient and energy-efficient computations. Technologies such as field programmable gate array (FPGA) and system on chip (SoC) based on FPGA (FPGA/SoC) have shown their ability to accelerate intensive computing applications while saving power consumption, owing to their capability of high parallelism and reconfiguration of the architecture. Currently, the existing design cycles for FPGA/SoC are time-consuming, owing to the complexity of the architecture. Therefore, to address the gap between applications and FPGA/SoC architectures and to obtain an efficient hardware design for image analysis and highly demanding applications using the high-level synthesis tool, two complementary strategies are considered: ad-hoc techniques and performance estimator. Regarding ad-hoc techniques, three highly demanding applications were accelerated through HLS tools: pulse shape discriminator for cosmic rays, automatic pest classification, and re-ranking for information retrieval, emphasizing the benefits when this type of applications are traversed by compression techniques when targeting FPGA/SoC devices. Furthermore, a comprehensive performance estimator for hardware acceleration is proposed in this thesis to effectively predict the resource utilization and latency for FPGA/SoC, building a bridge between the application and architectural domains. The tool integrates analytical models for performance prediction, and a design space explorer (DSE) engine for providing high-level insights to hardware developers, composed of two independent sub-engines: DSE based on single-objective optimization and DSE based on evolutionary multi-objective optimization

    Electronic Devices for the Combination of Electrically Controlled Drug Release, Electrostimulation, and Optogenetic Stimulation for Nerve Tissue Regeneration

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    [ES] La capacidad de las células madre para proliferar formando distintas células especializadas les otorga la potencialidad de servir de base para terapias efectivas para patologías cuyo tratamiento era inimaginable hasta hace apenas dos décadas. Sin embargo, esta capacidad se encuentra mediada por estímulos fisiológicos, químicos, y eléctricos, específicos y complejos, que dificultan su traslación a la rutina clínica. Por ello, las células madre representan un campo de estudio en el que se invierten amplios esfuerzos por parte de la comunidad científica. En el ámbito de la regeneración nerviosa, para modular su desarrollo y diferenciación el tratamiento farmacológico, la electroestimulación, y la estimulación optogenética son técnicas que están consiguiendo prometedores resultados. Es por ello por lo que en la presente tesis se ha desarrollado un conjunto de sistemas electrónicos para permitir la aplicación combinada de estas técnicas in vitro, con perspectiva a su aplicación in vivo. Hemos diseñado una novedosa tecnología para la liberación eléctricamente controlada de fármacos. Esta tecnología está basada en nanopartículas de sílice mesoporosa y puertas moleculares de bipiridina-heparina. Las puertas moleculares son electroquímicamente reactivas, y encierran los fármacos en el interior de las nanopartículas, liberándolos ante un estímulo eléctrico. Hemos caracterizado esta tecnología, y la hemos validado mediante la liberación controlada de rodamina en cultivos celulares de HeLa. Para la combinación de liberación controlada de fármacos y electroestimulación hemos desarrollado dispositivos que permiten aplicar los estímulos eléctricos de forma configurable desde una interfaz gráfica de usuario. Además, hemos diseñado un módulo de expansión que permite multiplexar las señales eléctricas a diferentes cultivos celulares. Además, hemos diseñado un dispositivo de estimulación optogenética. Este tipo de estimulación consiste en la modificación genética de las células para que sean sensibles a la radiación lumínica de determinada longitud de onda. En el ámbito de la regeneración de tejido mediante células precursoras neurales, es de interés poder inducir ondas de calcio, favoreciendo su diferenciación en neuronas y la formación de circuitos sinápticos. El dispositivo diseñado permite obtener imágenes en tiempo real mediante microscopía confocal de las respuestas transitorias de las células al ser irradiadas. El dispositivo se ha validado irradiando neuronas modificadas con luz pulsada de 100 ms. También hemos diseñado un dispositivo electrónico complementario de medida de irradiancia con el doble fin de permitir la calibración del equipo de irradiancia y medir la irradiancia en tiempo real durante los experimentos in vitro. Los resultados del uso de los bioactuadores en procesos complejos y dinámicos, como la regeneración de tejido nervioso, son limitados en lazo abierto. Uno de los principales aspectos analizados es el desarrollo de biosensores que permitiesen la cuantización de ciertas biomoléculas para ajustar la estimulación suministrada en tiempo real. Por ejemplo, la segregación de serotonina es una respuesta identificada en la elongación de células precursoras neurales, pero hay otras biomoléculas de interés para la implementación de un control en lazo cerrado. Entre las tecnologías en el estado del arte, los biosensores basados en transistores de efecto de campo (FET) funcionalizados con aptámeros son realmente prometedores para esta aplicación. Sin embargo, esta tecnología no permitía la medición simultánea de más de una biomolécula objetivo en un volumen reducido debido a las interferencias entre los distintos FETs, cuyos terminales se encuentran inmersos en la solución. Por ello, hemos desarrollado instrumentación electrónica capaz de medir simultáneamente varios de estos biosensores, y la hemos validado mediante la medición simultánea de pH y la detección preliminar de serotonina y glutamato.[CA] La capacitat de les cèl·lules mare per a proliferar formant diferents cèl·lules especialitzades els atorga la potencialitat de servir de base per a teràpies efectives per a patologies el tractament de les quals era inimaginable fins fa a penes dues dècades. No obstant això, aquesta capacitat es troba mediada per estímuls fisiològics, químics, i elèctrics, específics i complexos, que dificulten la seua translació a la rutina clínica. Per això, les cèl·lules mare representen un camp d'estudi en el qual s'inverteixen amplis esforços per part de la comunitat científica. En l'àmbit de la regeneració nerviosa, per a modular el seu desenvolupament i diferenciació el tractament farmacològic, l'electroestimulació, i l'estimulació optogenética són tècniques que estan aconseguint prometedors resultats. És per això que en la present tesi s'ha desenvolupat un conjunt de sistemes electrònics per a permetre l'aplicació combinada d'aquestes tècniques in vitro, amb perspectiva a la seua aplicació in vivo. Hem dissenyat una nova tecnologia per a l'alliberament elèctricament controlat de fàrmacs. Aquesta tecnologia està basada en nanopartícules de sílice mesoporosa i portes moleculars de bipiridina-heparina. Les portes moleculars són electroquímicament reactives, i tanquen els fàrmacs a l'interior de les nanopartícules, alliberant-los davant un estímul elèctric. Hem caracteritzat aquesta tecnologia, i l'hem validada mitjançant l'alliberament controlat de rodamina en cultius cel·lulars de HeLa. Per a la combinació d'alliberament controlat de fàrmacs i electroestimulació hem desenvolupat dispositius que permeten aplicar els estímuls elèctrics de manera configurable des d'una interfície gràfica d'usuari. A més, hem dissenyat un mòdul d'expansió que permet multiplexar els senyals elèctrics a diferents cultius cel·lulars. A més, hem dissenyat un dispositiu d'estimulació optogenètica. Aquest tipus d'estimulació consisteix en la modificació genètica de les cèl·lules perquè siguen sensibles a la radiació lumínica de determinada longitud d'ona. En l'àmbit de la regeneració de teixit mitjançant cèl·lules precursores neurals, és d'interés poder induir ones de calci, afavorint la seua diferenciació en neurones i la formació de circuits sinàptics. El dispositiu dissenyat permet obtindré imatges en temps real mitjançant microscòpia confocal de les respostes transitòries de les cèl·lules en ser irradiades. El dispositiu s'ha validat irradiant neurones modificades amb llum polsada de 100 ms. També hem dissenyat un dispositiu electrònic complementari de mesura d'irradiància amb el doble fi de permetre el calibratge de l'equip d'irradiància i mesurar la irradiància en temps real durant els experiments in vitro. Els resultats de l'ús dels bioactuadors en processos complexos i dinàmics, com la regeneració de teixit nerviós, són limitats en llaç obert. Un dels principals aspectes analitzats és el desenvolupament de biosensors que permeteren la quantització de certes biomolècules per a ajustar l'estimulació subministrada en temps real. Per exemple, la segregació de serotonina és una resposta identificada amb l'elongació de les cèl·lules precursores neurals, però hi ha altres biomolècules d'interés per a la implementació d'un control en llaç tancat. Entre les tecnologies en l'estat de l'art, els biosensors basats en transistors d'efecte de camp (FET) funcionalitzats amb aptàmers són realment prometedors per a aquesta aplicació. No obstant això, aquesta tecnologia no permetia el mesurament simultani de més d'una biomolècula objectiu en un volum reduït a causa de les interferències entre els diferents FETs, els terminals dels quals es troben immersos en la solució. Per això, hem desenvolupat instrumentació electrònica capaç de mesurar simultàniament diversos d'aquests biosensors i els hem validat amb mesurament simultani del pH i la detecció preliminar de serotonina i glutamat.[EN] The stem cells' ability to proliferate to form different specialized cells gives them the potential to serve as the basis for effective therapies for pathologies whose treatment was unimaginable until just two decades ago. However, this capacity is mediated by specific and complex physiological, chemical, and electrical stimuli that complicate their translation to clinical routine. For this reason, stem cells represent a field of study in which the scientific community is investing a great deal of effort. In the field of nerve regeneration, to modulate their development and differentiation, pharmacological treatment, electrostimulation, and optogenetic stimulation are techniques that are achieving promising results. For this reason, we have developed a set of electronic systems to allow the combined application of these techniques in vitro, with a view to their application in vivo. We have designed a novel technology for the electrically controlled release of drugs. This technology is based on mesoporous silica nanoparticles and bipyridine-heparin molecular gates. The molecular gates are electrochemically reactive and entrap the drugs inside the nanoparticles, releasing them upon electrical stimulus. We have characterized this technology and validated it by controlled release of rhodamine in HeLa cell cultures. For combining electrostimulation and controlled drug release we have developed devices that allow applying the different electrical stimuli in a configurable way from a graphical user interface. In addition, we have designed an expansion module that allows multiplexing electrical signals to different cell cultures. In addition, we have designed an optogenetic stimulation device. This type of stimulation consists of genetically modifying cells to make them sensitive to light radiation of a specific wavelength. In tissue regeneration using neural precursor cells, it is interesting to be able to induce calcium waves, favoring the cell differentiation into neurons and the formation of synaptic circuits. The designed device enable the obtention of real-time images through confocal microscopy of the transient responses of cells upon irradiation. The device has been validated by irradiating modified neurons with 100 ms pulsed light stimulation. We have also designed a complementary electronic irradiance measurement device to allow calibration of the irradiator equipment and measuring irradiance in real time during in vitro experiments. The results of using bioactuators in complex and dynamic processes, such as nerve tissue regeneration, are limited in an open loop. One of the main aspects analyzed is the development of biosensors that would allow quantifying of specific biomolecules to adjust the stimulation provided in real time. For instance, serotonin secretion is an identified response of neural precursor cells elongation, among other biomolecules of interest for the implementation of a closed-loop control. Among the state-of-the-art technologies, biosensors based on field effect transistors (FETs) functionalized with aptamers are promising for this application. However, this technology did not allow the simultaneous measurement of more than one target biomolecule in a small volume due to interferences between the different FETs, whose terminals are immersed in the solution. This is why we have developed electronic instrumentation capable of simultaneously measuring several of these biosensors, and we have validated it with the simultaneous pH measurement and the preliminary detection of serotonin and glutamate.Monreal Trigo, J. (2023). Electronic Devices for the Combination of Electrically Controlled Drug Release, Electrostimulation, and Optogenetic Stimulation for Nerve Tissue Regeneration [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19384

    Decryption Failure Attacks on Post-Quantum Cryptography

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    This dissertation discusses mainly new cryptanalytical results related to issues of securely implementing the next generation of asymmetric cryptography, or Public-Key Cryptography (PKC).PKC, as it has been deployed until today, depends heavily on the integer factorization and the discrete logarithm problems.Unfortunately, it has been well-known since the mid-90s, that these mathematical problems can be solved due to Peter Shor's algorithm for quantum computers, which achieves the answers in polynomial time.The recently accelerated pace of R&D towards quantum computers, eventually of sufficient size and power to threaten cryptography, has led the crypto research community towards a major shift of focus.A project towards standardization of Post-quantum Cryptography (PQC) was launched by the US-based standardization organization, NIST. PQC is the name given to algorithms designed for running on classical hardware/software whilst being resistant to attacks from quantum computers.PQC is well suited for replacing the current asymmetric schemes.A primary motivation for the project is to guide publicly available research toward the singular goal of finding weaknesses in the proposed next generation of PKC.For public key encryption (PKE) or digital signature (DS) schemes to be considered secure they must be shown to rely heavily on well-known mathematical problems with theoretical proofs of security under established models, such as indistinguishability under chosen ciphertext attack (IND-CCA).Also, they must withstand serious attack attempts by well-renowned cryptographers both concerning theoretical security and the actual software/hardware instantiations.It is well-known that security models, such as IND-CCA, are not designed to capture the intricacies of inner-state leakages.Such leakages are named side-channels, which is currently a major topic of interest in the NIST PQC project.This dissertation focuses on two things, in general:1) how does the low but non-zero probability of decryption failures affect the cryptanalysis of these new PQC candidates?And 2) how might side-channel vulnerabilities inadvertently be introduced when going from theory to the practice of software/hardware implementations?Of main concern are PQC algorithms based on lattice theory and coding theory.The primary contributions are the discovery of novel decryption failure side-channel attacks, improvements on existing attacks, an alternative implementation to a part of a PQC scheme, and some more theoretical cryptanalytical results
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