Increasing salinity is a worldwide problem, but the knowledge on how salt enters the roots of plants remains largely unknown. Non-selective cation channels (NSCCs) have been suggested to be the major pathway for the entry of sodium ions (Na+) in several species. The hypothesis tested in this research is that PQ loop (PQL) proteins could form NSCCs, mediate some of the Na+ influx into the root and contribute to ion accumulation and the inhibition of growth in saline conditions. This is based on previous preliminary evidence indicating similarities in the properties of NSCC currents and currents mediated by PQL proteins, such as the inhibition of an inward cation current mediated by PQL proteins by high external calcium and pH acidification. PQL family members belonging to clade one in Arabidopsis and barley were characterized using a reverse genetics approach, electrophysiology and high-throughput phenotyping. Expression of AtPQL1a and HvPQL1 in HEK293 cells increased Na+ and K+ inward currents in whole cell membranes. However, when GFP-tagged PQL proteins were transiently overexpressed in tobacco leaf cells, the proteins appeared to localize to intracellular membrane structures. Based on q-RT-PCR, the levels of mRNA of AtPQL1a, AtPQL1b and AtPQL1c is higher in salt stressed plants compared to control plants in the shoot tissue, while the mRNA levels in the root tissue did not change in response to stress. Salt stress responses of lines with altered expression of AtPQL1a, AtPQL1b and AtPQL1c were examined using RGB and chlorophyll fluorescence imaging of plants growing in soil in a controlled environment chamber. Decreases in the levels of expression of AtPQL1a, AtPQL1b and AtPQL1c resulted in larger rosettes, when measured seven days after salt stress imposition. Interestingly, overexpression of AtPQL1a also resulted in plants having larger rosettes in salt stress conditions. Differences between the mutants and the wild-type plants were not observed at earlier stages, suggesting that PQLs might be involved in long-term responses to salt stress. These results contribute towards a better understanding of the role of PQLs in salinity tolerance and provide new targets for crop improvement.

Deep Neural Networks are state-of the art algorithms for various image and natural language processing tasks. These networks are composed of billions of operations working on an input to produce the desired result. Along with this computational complexity, these workloads are also massively parallel in nature. These inherent properties make deep neural networks an excellent target for custom acceleration. The main challenge faced by such accelerators is achieving a compromise between power consumption, software programmability, and resource utilization for the varied compute and data access patterns presented by DNN workloads. In this work, I present Inference Engine, a scalable and efficient DNN accelerator designed to be agnostic to the type of DNN workload. Inference Engine was designed to provide near peak hardware resource utilization, minimize data transfer, and offer a programmer friendly instruction set. Inference engine scales at the level of individually programmable clusters, each of which contains several hundred compute resources. It provides an instruction set designed to exploit parallelism within the workload while also allowing freedom for compiler based exploration of data access patterns.

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The support of mobile workers with mobile IT solutions can create tremendous improvements in mobile business processes of a company. The main characteristic of such a mobile system is the ability to connect via a (mobile) network to a central server, e.g. in order to access customer data. This paper presents a detailed description of the four main software architectures for mobile client/server-based systems and their main characteristics. Beyond, typical business requirements in mobile environments like the location of use, data topicality, interaction requirements, synchronisation mechanisms and many more are mapped onto each of these architectures. The presented results can be used for discussing concurrent business needs as well as for deriving a mobile system architecture based on these needs.

The support of mobile workers with mobile IT solutions can create dremendous improvements in mobile business processes of a company. The main charateristic of such a mobile system is the ability to connect via a (mobile) network to a central server, e.g. in order to access customer data. The frequency and the location of the use, data topicality, interaction requirements and many more are central aspects when developing a suitable system architecture. This paper provides a detailed decription of the four main software architectures for mobile systems and their main charateristics. Beyond, typical business requirements are developed, the implications for the system architecture for each of them is shown.

Visualizing large datasets poses challenges in terms of how to create visualization applications with good performance. Due to the amount of data, transfer speed and processing speed may lead to waiting times that cause users to abandon the application. It is therefore important to select methods and techniques that can handle the data in as efficient a way as possible. The aim of this study was to investigate if a server-client architecture had better performance in a visualization web application than a purely client-side architecture in terms of selected performance metrics and network load, and whether the selection of implementation language and tools affected the performance of the server-client architecture implementation. To answer these questions, a visualization application was implemented in three different ways: a purely client-side implementation, a server-client implementation using Node.js for the server, and a server-client implementation using Flask for the server. The results showed that the purely client-side architecture suffered from a very long page loading time and high network load but was able to process data quickly in response to user actions in the application. The server-client architecture implementations could load the page faster, but responding to requests took longer, whereas the amount of data transferred was much lower. Furthermore, the server-client architecture implemented with a Node.js server performed better on all metrics than the application implemented with a Flask server. Overall, when taking all measurements into consideration, the Node.js server architecture may be the best choice among the three when working with a large dataset, although the longer response time compared to the purely client-side architecture may cause the application to seem less responsive.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23326号 / 情博第762号 / 新制||情||130(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 大木 英司, 教授 守倉 正博, 教授 岡部 寿男 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

<p>Deep Neural Networks (DNNs) are a class of machine learning algorithms that are widelysuccessful in various computer vision tasks. DNNs filter input images and videos with manyconvolution operations in each layer to extract high-quality features and achieve high ac-curacy. Although highly accurate, the state-of-the-art DNNs usually require server-gradeGPUs, and are too energy, computation and memory-intensive to be deployed on most de-vices. This is a significant problem because billions of mobile and embedded devices that donot contain GPUs are now equipped with high definition cameras. Running DNNs locallyon these devices enables applications such as emergency response and safety monitoring,because data cannot always be offloaded to the Cloud due to latency, privacy, or networkbandwidth constraints.</p> <p>Prior research has shown that a considerable number of a DNN’s memory accesses andcomputation are redundant when performing computer vision tasks. Eliminating these re-dundancies will enable faster and more efficient DNN inference on low-power embedded de-vices. To reduce these redundancies and thereby reduce the energy consumption of DNNs,this thesis proposes a novel Tree-based Unidirectional Neural Network (TRUNK) architec-ture. Instead of a single large DNN, multiple small DNNs in the form of a tree work togetherto perform computer vision tasks. The TRUNK architecture first finds thesimilaritybe-tween different object categories. Similar object categories are grouped intoclusters. Similarclusters are then grouped into a hierarchy, creating a tree. The small DNNs at every nodeof TRUNK classify between different clusters. During inference, for an input image, oncea DNN selects a cluster, another DNN further classifies among the children of the cluster(sub-clusters). The DNNs associated with other clusters are not used during the inferenceof that image. By doing so, only a small subset of the DNNs are used during inference,thus reducing redundant operations, memory accesses, and energy consumption. Since eachintermediate classification reduces the search space of possible object categories in the image,the small efficient DNNs still achieve high accuracy.</p> <p>In this thesis, we identify the computer vision applications and scenarios that are wellsuited for the TRUNK architecture. We develop methods to use TRUNK to improve the efficiency of the image classification, object counting, and object re-identification problems.We also present methods to adapt the TRUNK structure for different embedded/edge ap-plication contexts with different system architectures, accuracy requirements, and hardware constraints.</p> <p>Experiments with TRUNK using several image datasets reveal the effectiveness of theproposed solution to reduce memory requirement by∼50%, inference time by∼65%, energyconsumption by∼65%, and the number of operations by∼45% when compared with existingDNN architectures. These experiments are conducted on consumer-grade embedded systems:NVIDIA Jetson Nano, Raspberry Pi 3, and Raspberry Pi Zero. The TRUNK architecturehas only marginal losses in accuracy when compared with the state-of-the-art DNNs.</p>

The need to secure critical infrastructure (CI) systems against attacks is a topic that has been discussed recently in literature. Many examples of attacks against CI exist, such as the physical attack on the Pacific Gas and Electric Metcalf substation in 2013 that caused millions of dollars in damage or the Stuxnet cyber-attack which was identified in 2010 that caused damage to Iran’s nuclear program and alerted the world to the existence of cyber weapons. As a result of these types of events in which vulnerabilities in CI are exploited, it is important to have a comprehensive systems approach for assessing the vulnerabilities in CI systems. This dissertation seeks to provide a method for engineers to use system and human architectures to perform vulnerability assessment (VA) and decision analysis to enable decision makers to make tradeoffs on how to use their resources to protect CI against attacks.There are several gaps in literature in how to use system and human architectures to perform VA to protect CI from damage. First, no method exists that uses a model based approach and human and system architectures to perform a comprehensive analysis of CI to develop decision analysis models to aid decision makers in determining the most effective use of security resources to secure their CI systems. It is important that such models be comprehensive by including industry standards, system and human architectures, attack scenarios, subject matter expert opinion and models for analysis to help decision makers determine the best security investments. Second, there is not an established method to develop detailed mathematical models from an operational activity diagram that represents an attack scenario. This is important because the translation from architecture to high fidelity models will enable CI asset owners to make tradeoffs on security resource use. Finally, there is no method to evaluate the role of humans in a CI VA based on human views of the system. This dissertation provides an approach to use human and system architectures to perform VA and decision analysis to fill these gaps.

Monitoring, tracking, and analyzing the dynamic growth of a living organism is essential to understanding its response to changes in its surrounding environment. Imaging tools to study these dynamics at spatial and temporal scales with optimal resolution rely on high-performance instrumentations. These systems are generally costly, stationary, and not flexible. In addition, performing non-destructive high-throughput phenotyping to extract roots' structural and morphological features remains challenging. We developed the MultipleXLab: a modular, mobile, and cost-effective robotic root imager to tackle these limitations. Among its advantages associated with a large field-of-view, integrated programmable plant-growth lighting, and high magnification with a high resolving power, the system is useful for a wide range of biological applications. We have also created the MultipleXLab Advanced; this configuration turns the system into a mobile environmental chamber by also featuring temperature control and automated irrigation. Another system we developed was the MultipleXLab Advanced Fluorescence to allow fluorescence imaging with a resolution that competes with a fluorescence binocular or even a fluorescence microscope. Furthermore, we have implemented various technologies and techniques to facilitate 3D imaging and quantification, ranging from X-ray micro-Computed Tomography to 3D segmentation of tissues, cells, and cellular compartments within the cell imaged using Confocal Laser Scanning Microscopy. For future research, we have conceptualized an upscaled system named MultipleXLabXL. This larger system will allow tracking, monitoring, and quantifying root growth of a much higher number of seedlings for more extended periods.