2,427 research outputs found

    Preference and perceptions:Studies in behavioural health economics

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    Preference and perceptions:Studies in behavioural health economics

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    ENHANCING CLOUD SYSTEM RUNTIME TO ADDRESS COMPLEX FAILURES

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    As the reliance on cloud systems intensifies in our progressively digital world, understanding and reinforcing their reliability becomes more crucial than ever. Despite impressive advancements in augmenting the resilience of cloud systems, the growing incidence of complex failures now poses a substantial challenge to the availability of these systems. With cloud systems continuing to scale and increase in complexity, failures not only become more elusive to detect but can also lead to more catastrophic consequences. Such failures question the foundational premises of conventional fault-tolerance designs, necessitating the creation of novel system designs to counteract them. This dissertation aims to enhance distributed systems’ capabilities to detect, localize, and react to complex failures at runtime. To this end, this dissertation makes contributions to address three emerging categories of failures in cloud systems. The first part delves into the investigation of partial failures, introducing OmegaGen, a tool adept at generating tailored checkers for detecting and localizing such failures. The second part grapples with silent semantic failures prevalent in cloud systems, showcasing our study findings, and introducing Oathkeeper, a tool that leverages past failures to infer rules and expose these silent issues. The third part explores solutions to slow failures via RESIN, a framework specifically designed to detect, diagnose, and mitigate memory leaks in cloud-scale infrastructures, developed in collaboration with Microsoft Azure. The dissertation concludes by offering insights into future directions for the construction of reliable cloud systems

    Backpropagation Beyond the Gradient

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    Automatic differentiation is a key enabler of deep learning: previously, practitioners were limited to models for which they could manually compute derivatives. Now, they can create sophisticated models with almost no restrictions and train them using first-order, i. e. gradient, information. Popular libraries like PyTorch and TensorFlow compute this gradient efficiently, automatically, and conveniently with a single line of code. Under the hood, reverse-mode automatic differentiation, or gradient backpropagation, powers the gradient computation in these libraries. Their entire design centers around gradient backpropagation. These frameworks are specialized around one specific task—computing the average gradient in a mini-batch. This specialization often complicates the extraction of other information like higher-order statistical moments of the gradient, or higher-order derivatives like the Hessian. It limits practitioners and researchers to methods that rely on the gradient. Arguably, this hampers the field from exploring the potential of higher-order information and there is evidence that focusing solely on the gradient has not lead to significant recent advances in deep learning optimization. To advance algorithmic research and inspire novel ideas, information beyond the batch-averaged gradient must be made available at the same level of computational efficiency, automation, and convenience. This thesis presents approaches to simplify experimentation with rich information beyond the gradient by making it more readily accessible. We present an implementation of these ideas as an extension to the backpropagation procedure in PyTorch. Using this newly accessible information, we demonstrate possible use cases by (i) showing how it can inform our understanding of neural network training by building a diagnostic tool, and (ii) enabling novel methods to efficiently compute and approximate curvature information. First, we extend gradient backpropagation for sequential feedforward models to Hessian backpropagation which enables computing approximate per-layer curvature. This perspective unifies recently proposed block- diagonal curvature approximations. Like gradient backpropagation, the computation of these second-order derivatives is modular, and therefore simple to automate and extend to new operations. Based on the insight that rich information beyond the gradient can be computed efficiently and at the same time, we extend the backpropagation in PyTorch with the BackPACK library. It provides efficient and convenient access to statistical moments of the gradient and approximate curvature information, often at a small overhead compared to computing just the gradient. Next, we showcase the utility of such information to better understand neural network training. We build the Cockpit library that visualizes what is happening inside the model during training through various instruments that rely on BackPACK’s statistics. We show how Cockpit provides a meaningful statistical summary report to the deep learning engineer to identify bugs in their machine learning pipeline, guide hyperparameter tuning, and study deep learning phenomena. Finally, we use BackPACK’s extended automatic differentiation functionality to develop ViViT, an approach to efficiently compute curvature information, in particular curvature noise. It uses the low-rank structure of the generalized Gauss-Newton approximation to the Hessian and addresses shortcomings in existing curvature approximations. Through monitoring curvature noise, we demonstrate how ViViT’s information helps in understanding challenges to make second-order optimization methods work in practice. This work develops new tools to experiment more easily with higher-order information in complex deep learning models. These tools have impacted works on Bayesian applications with Laplace approximations, out-of-distribution generalization, differential privacy, and the design of automatic differentia- tion systems. They constitute one important step towards developing and establishing more efficient deep learning algorithms

    Synthesis of multifunctional glyco-pseudodendrimers and glyco-dendrimers and their investigation as anti-Alzheimer agents

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    As the world population is aging, the cases of Alzheimer’s Disease (AD) are increasing. AD is a disorder of the brain which is characterized by the aggregation of amyloid beta (Aβ) plaques. This leads to the death of numerous brain cells thus affecting the cognitive and motor functions of the individual. Till date, no cure for the disease is available. Aβ are peptides with 40/42 amino acid residues but, their exact mechanism(s) of action in AD is under debate. Having different amino acid residues makes them susceptible to form hydrogen bonds. Dendrimers with sugar units are often referred to as glycopolymers and have been shown to have potential anti-amyloidogenic activity. However, they also have drawbacks, the synthesis involves multiple tedious steps, and dendrimers themselves offer only a limited number of functional units. Pseudodendrimers are another class of branched polymers based on hyperbranched polymers. Unlike the dendrimers, they are easy to synthesize with a dense shell of functional units on the surface. One of the main goals in this dissertation is the synthesis and characterization of pseudodendrimers and dendrimers based on 2,2-bis(hydroxymethyl)-propionic acid (bis-MPA), an aliphatic polyester scaffold, as it offers biocompatibility and easy degradability. Furthermore, they are decorated with mannose units on the surface using a ‘click’ reaction forming glyco-pseudodendrimers and glyco-dendrimers. A detailed characterization of their structures and physical properties was undertaken using techniques such as size exclusion chromatography, asymmetric flow field flow fractionation (AF4), and dynamic light scattering. The second main focus of this work has been to investigate the interaction of synthesized glyco-pseudodendrimers and glyco-dendrimers with Aβ 40 peptides. For this task, five different concentrations of the synthesized glycopolymers were tested with Aβ 40 using the Thioflavin T assay. The results of the synthesized polymers which produced the best results of showing maximum anti-aggregation behavior against Aβ 40 were confirmed with circular dichroism spectroscopy. AF4 was also used to investigate Aβ 40-glycopolymer aggregates, which has never been done before and constitutes the highlight of this dissertation. Atomic force microscopy was used to image Aβ 40-glycopseudodenrimer aggregates. A basic but important step in the development of drug delivery platforms is to evaluate the toxicity of the drugs synthesized. In this work, preliminary studies of the cytotoxicity of glyco-pseudodendrimers were performed in two different cell lines. Thus, this study comprises a preliminary investigation of the anti-amyloidogenic activity of glyco-pseudodendrimers synthesized on an aliphatic polyester backbone.:Abstract List of Tables List of Figures Abbreviations 1 Introduction 1.1 Objectives of the work 1.2 Thesis overview 2 Fundamentals and Literature 2.1 Alzheimer’s Disease and its impact 2.1.1 Neurological diagnosis of AD 2.1.2 Histopathology of AD 2.1.3 Amyloid precursor protein (APP) and its role in AD 2.2. Amyloid Beta (Aβ) peptide 2.2.1 Aβ peptide 2.2.2. Location and function 2.2.3 Amyloid hypothesis 2.2.4 The mechanism of Aβ aggregation 2.2.5 Amyloid fibrils 2.2.6 Toxicity of Aβ 2.3 Research methods to study Aβ aggregates 2.3.1 Models to study the mode of action of aggregates 2.3.2 Endogenous Aβ aggregates and synthetic aggregates 2.3.3 Strategies to alter aggregation of amyloids 2.4 Treatment and therapeutics 2.4.1 Current therapeutics 2.4.2 Current therapeutic research 2.4.2.1 Reduction of Aβ production 2.4.2.2 Reduction of Aβ plaque accumulation 2.4.2.2.1 Anti-amyloid aggregation agents 2.4.2.2.2 Metals 2.4.2.2.3 Immunotherapy 2.4.2.2.4 Dendrimers as potential anti-amyloidogenic agent 2.6 Dendrimers 2.6.1 Definition 2.6.2 Structure Table of Contents 2.6.3 Synthesis 2.6.4 Properties 2.7 Pseudodendrimers - a sub-class of hyperbranched polymer 2.7.1 Definition 2.7.2 Structure 2.7.3 Synthesis 3 Analytical Techniques 3.1 Size Exclusion Chromatography Coupled to Light Scattering (SEC-MALS) 3.2 Asymmetric Flow Field Flow Fractionation (AF4) 3.3 Dynamic Light Scattering 3.4 Molecular Dynamics Simulation 3.5 Nuclear Magnetic Resonance Spectroscopy 3.6 Thioflavin T fluorescence 3.6.1 Kinetic analysis 3.7 Circular Dichroism Spectroscopy 3.8 Atomic Force Microscopy 3.9 Cytotoxic assay 3.9.1 MTT assay 3.9.2 Determining the level of reactive oxygen species 3.9.3 Changes in mitochondrial transmembrane potential 3.9.4 Flow cytometric detection of phosphatidyl serine exposure 4 Experimental Details and Methodology 4.1 Details of chemicals/components used 4.1.1 Other materials 4.1.2 Peptide preparation 4.1.3 Buffer preparation 4.1.4 Fibril growth conditions 4.2 Synthesis and characterization of polymers 4.2.1 Synthesis and characterization of pseudodendrimers and dendrimers 4.2.1.1 Synthesis of hyperbranched polymer (1) 4.2.1.2 Synthesis of protected monomer 4.2.1.2.1 bis-MPA acetonide (2) 4.2.1.2.2 bis-MPA-acetonide anhydride (3) 4.2.1.3 Synthesis of protected pseudodendrimers (4, 6 and 8) and protected dendrimers (10, 12, and 14) 4.2.1.4 Deprotection of pseudodendrimers (5,7, and 9) and dendrimers (11,13 and 15) 4.2.2 Synthesis of glyco-pseudodendrimers and glyco-dendrimers 4.2.2.1 Pentynoic anhydride (16) 4.2.2.2 Synthesis of pentinate modified pseudodendrimers (17, 18 and 19) and dendrimers (20, 21 and 22) 4.2.2.3 3-Azido-1-propanol (23) 4.2.2.4 Mannose propyl azide tetraacetate (24) Table of Contents 4.2.2.5 Mannosepropylazide (25) 4.2.2.6 Glyco-pseudodendrimers (Gl-P) (26, 27 and 28) and glyco- dendrimers (Gl-D) (29, 30 and 31) 4.3 Analytical techniques and their general details 4.3.1 SEC-MALS - Instrumentation, software and analysis 4.3.2 AF4 - Instrumentation, software and analysis 4.3.2.1 Sample preparation 4.3.2.2 Method development for analysis of Gl-P and Gl-D 4.3.2.3 Method development for analysis of Aβ 40 and its interaction with Gl-P and Gl-D 4.3.3 Batch DLS - Instrumentation, software and analysis 4.3.3.1 Sample preparation 4.3.4 Theoretical calculations and molecular dynamics simulations 4.3.4.1 Ab-initio calculations 4.3.4.2 Modelling of the polymer structures 4.3.4.2.1 Pseudodendrimers 4.3.4.2.2 Dendrimers 4.3.4.2.3 Modification of the polymers with special end groups 4.3.4.2.4 Preparing of the THF solvent box 4.3.4.2.5 Solvation of the polymer structures 4.3.4.3 Molecular dynamics simulations 4.3.4.3.1 Evaluation of the simulation trajectories 4.4 Investigation of interaction of Gl-P and Gl-D with amyloid beta (Aβ 40) 4.4.1 ThT Assay - Instrumentation and software 4.4.1.1 Sample preparation 4.4.1.2 Kinetics based on ThT assay- software and data analysis 4.4.2 CD spectroscopy - Instrumentation and software 4.4.2.1 Sample preparation 4.4.3 AFM - Instrumentation and software 4.4.3.1 Substrate and sample preparation 4.4.3.2 Height determination and counting procedures 4.4.3.3 Topography and diameter 4.5 Cytotoxicity 4.5.1 Zeta potential 4.5.2 Cell culturing 4.5.3 Sample preparation 4.5.4 MTT assay 4.5.5 Changes in mitochondrial transmembrane potential (JC-1 method) 4.5.6 Flow cytometric detection of phosphatidyl serine exposure (Annexin V and PI method) 5 Results and Discussion 5.1 Synthesis and characterization of glyco-pseudodendrimers and glyco- dendrimers 5.1.1 Synthesis and characterization of hyperbranched polyester Table of Contents 5.1.2 Synthesis and characterization of pseudodendrimers P-G1-OH, P-G2-OH and P-G3-OH 5.1.3 Synthesis and characterization of dendrimers D-G4-OH, D-G5-OH and D-G6-OH 5.1.4 Synthesis and characterization of Gl-P and Gl-D 5.1.4.1 Molecular size determination of Gl-P and Gl-D using SEC 5.1.4.2 Particle size determination using batch DLS 5.1.4.3 Apparent densities 5.1.4.4 Molecular size determination of Gl-P and Gl-D using AF4 ..... 5.1.5 Molecular dynamics simulation 5.2 Investigation of interaction of Gl-P and Gl-D with amyloid beta (Aβ 40) ...... 5.2.1 ThT Assay 5.2.1.1 Kinetics based on ThT assay 5.2.2 CD spectroscopy 5.2.3 Time dependent AF4 5.3.2.1 Separation of Aβ 40 by AF4 5.3.2.2 Aβ 40 amyloid aggregation in the presence of Gl-P and Gl-D 5.2.4 AFM 5.2.4.1 Height 5.2.4.2 Topography and diameter 5.2.4.3 Length 5.2.4.4 Morphology 5.2.5 Cytotoxicity 5.2.5.1 MTT assay 5.2.5.2 Changes in mitochondrial transmembrane potential 5.2.5.3 Flow cytometric detection of phosphatidyl serine exposure 6 Conclusions and Outlook 7 Bibliography Appendix Acknowledgement

    Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

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    Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community\u27s attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques\u27 design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research

    ACiS: smart switches with application-level acceleration

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    Network performance has contributed fundamentally to the growth of supercomputing over the past decades. In parallel, High Performance Computing (HPC) peak performance has depended, first, on ever faster/denser CPUs, and then, just on increasing density alone. As operating frequency, and now feature size, have levelled off, two new approaches are becoming central to achieving higher net performance: configurability and integration. Configurability enables hardware to map to the application, as well as vice versa. Integration enables system components that have generally been single function-e.g., a network to transport data—to have additional functionality, e.g., also to operate on that data. More generally, integration enables compute-everywhere: not just in CPU and accelerator, but also in network and, more specifically, the communication switches. In this thesis, we propose four novel methods of enhancing HPC performance through Advanced Computing in the Switch (ACiS). More specifically, we propose various flexible and application-aware accelerators that can be embedded into or attached to existing communication switches to improve the performance and scalability of HPC and Machine Learning (ML) applications. We follow a modular design discipline through introducing composable plugins to successively add ACiS capabilities. In the first work, we propose an inline accelerator to communication switches for user-definable collective operations. MPI collective operations can often be performance killers in HPC applications; we seek to solve this bottleneck by offloading them to reconfigurable hardware within the switch itself. We also introduce a novel mechanism that enables the hardware to support MPI communicators of arbitrary shape and that is scalable to very large systems. In the second work, we propose a look-aside accelerator for communication switches that is capable of processing packets at line-rate. Functions requiring loops and states are addressed in this method. The proposed in-switch accelerator is based on a RISC-V compatible Coarse Grained Reconfigurable Arrays (CGRAs). To facilitate usability, we have developed a framework to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. In the third work, we extend ACiS to support fused collectives and the combining of collectives with map operations. We observe that there is an opportunity of fusing communication (collectives) with computation. Since the computation can vary for different applications, ACiS support should be programmable in this method. In the fourth work, we propose that switches with ACiS support can control and manage the execution of applications, i.e., that the switch be an active device with decision-making capabilities. Switches have a central view of the network; they can collect telemetry information and monitor application behavior and then use this information for control, decision-making, and coordination of nodes. We evaluate the feasibility of ACiS through extensive RTL-based simulation as well as deployment in an open-access cloud infrastructure. Using this simulation framework, when considering a Graph Convolutional Network (GCN) application as a case study, a speedup of on average 3.4x across five real-world datasets is achieved on 24 nodes compared to a CPU cluster without ACiS capabilities

    Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices

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    Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements. This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open-source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state-of-the-art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, preprocessing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community

    Energy-efficient and real-time wearable for wellbeing-monitoring IoT system based on SoC-FPGA

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    Wearable devices used for personal monitoring applications have been improved over the last decades. However, these devices are limited in terms of size, processing capability and power consumption. This paper proposes an efficient hardware/software embedded system for monitoring bio-signals in real time, including a heart rate calculator using PPG and an emotion classifier from EEG. The system is suitable for outpatient clinic applications requiring data transfers to external medical staff. The proposed solution contributes with an effective alternative to the traditional approach of processing bio-signals offline by proposing a SoC-FPGA based system that is able to fully process the signals locally at the node. Two sub-systems were developed targeting a Zynq 7010 device and integrating custom hardware IP cores that accelerate the processing of the most complex tasks. The PPG sub-system implements an autocorrelation peak detection algorithm to calculate heart rate values. The EEG sub-system consists of a KNN emotion classifier of preprocessed EEG features. This work overcomes the processing limitations of microcontrollers and general-purpose units, presenting a scalable and autonomous wearable solution with high processing capability and real-time response.info:eu-repo/semantics/publishedVersio
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