マルチレベル並列化とアプリケーション指向データレイアウトを用いるハードウェアアクセラレータの設計と実装

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 稲葉 雅幸, 東京大学教授 須田 礼仁, 東京大学教授 五十嵐 健夫, 東京大学教授 山西 健司, 東京大学准教授 稲葉 真理, 東京大学講師 中山 英樹University of Tokyo(東京大学

Using High-Order Prior Belief Predictions in Hierarchical Temporal Memory for Streaming Anomaly Detection

Autonomous streaming anomaly detection can have a significant impact in any domain where continuous, real-time data is common. Often in these domains, datasets are too large or complex to hand label. Algorithms that require expensive global training procedures and large training datasets impose strict demands on data and are accordingly not fit to scale to real-time applications that are noisy and dynamic. Unsupervised algorithms that learn continuously like humans therefore boast increased applicability to these real-world scenarios. Hierarchical Temporal Memory (HTM) is a biologically constrained theory of machine intelligence inspired by the structure, activity, organization and interaction of pyramidal neurons in the neocortex of the primate brain. At the core of HTM are spatio-temporal learning algorithms that store, learn, recall and predict temporal sequences in an unsupervised and continuous fashion to meet the demands of real-time tasks. Unlike traditional machine learning and deep learning encompassed by the act of complex functional approximation, HTM with the surrounding proposed framework does not require any offline training procedures, any massive stores of training data, any data labels, it does not catastrophically forget previously learned information and it need only make one pass through the temporal data. Proposed in this thesis is an algorithmic framework built upon HTM for intelligent streaming anomaly detection. Unseen in earlier streaming anomaly detection work, the proposed framework uses high-order prior belief predictions in time in the effort to increase the fault tolerance and complex temporal anomaly detection capabilities of the underlying time-series model. Experimental results suggest that the framework when built upon HTM redefines state-of-the-art performance in a popular streaming anomaly benchmark. Comparative results with and without the framework on several third-party datasets collected from real-world scenarios also show a clear performance benefit. In principle, the proposed framework can be applied to any time-series modeling algorithm capable of producing high-order predictions

Type 3 adenylyl cyclase, neuronal primary cilia, and hippocampus-dependent memory formation

Primary cilia are microtubule-based cellular antennae present in most vertebrate cells including neurons. Neuronal primary cilia have abundant expression of G-protein coupled receptors (GPCRs) and downstream cAMP signaling components such as type 3 adenylyl cyclase (AC3). The deflects of neuronal cilia is associated with many memory-related disorders, such as intellectual disability. Thus far, little is known about how neuronal primary cilia regulate neuronal activity and affect hippocampal memory formation. Episodic memory is thought to be encoded by sparsely distributed memory-eligible neurons in the hippocampus and neocortex. However, it is not clear how memory-eligible neurons interact with one another to form and retrieve a memory. The objectives of my dissertation are to determine the roles of AC3 in regulating cortical protein phosphorylation, to examine the cellular mechanism of episodic memory formation, and to examine how neuronal primary cilia regulate trace fear memory formation. Project 1: Compare protein phosphorylation levels in the prefrontal cortex between AC3 knockout (KO) and wildtype (WT) mice. AC3 represents a key enzyme mediating ciliary cAMP signaling in neurons and is genetically associated with major depressive disorder (MDD) and autism spectrum disorders (ASD). The major downstream effector protein of cAMP in cells is protein kinase A (PKA), whose activation leads to the phosphorylation of numerous proteins to propagate the signaling downstream. In my mass spectrometry-based phosphoproteomic study using conditional AC3 KO mice, I identified thousands of peptides from prefrontal cortical tissues, some of which are differentially phosphorylated in AC3 WT and KO samples. In addition, this effort led to identification of over two hundred proteins, whose phosphorylation were sex-biased. Surprisingly, a high percentage of these targets (31%) are autism-associated proteins/genes. Hence, this study provides the first phosphoproteomic evidence suggesting that sex-biased protein phosphorylation may contribute to the sexual dimorphism of autism. Project 2: Investigate how hippocampal neurons are recruited to interact with each other to encode a trace fear memory. Using in vivo calcium imaging in freely behaving mice, I found that a small portion of highly active hippocampal neurons (termed primed neurons) are actively engaged in memory formation and retrieval. I found that induction of activity synchronization among primed neurons from random dynamics is critical for trace memory formation and retrieval. My work has provided direct in vivo evidence to challenge the long-held paradigm that activation and re-activation of memory cells encodes and retrieves memory, respectively. These findings support a new mechanistic model for associative memory formation, in that primed neurons connect with each other to forge a new circuit, bridging a conditional stimulus with an unconditional stimulus. Project 3: Develop an analytical method to identify primed neurons and determine the roles of neuronal primary cilia on hippocampal neuronal priming and trace memory formation. Neuronal primary cilia are “cellular antennae” which sense and transduce extracellular signals into neuronal soma. However, to date little is known about how neuronal primary cilia influence neuronal functions and hippocampal memory. I utilized conditional Ift88 knockout mice (to ablate cilia) as loss-of-function models. I found that inducible conditional Ift88 KOs display more severe learning deficits compared to their littermate controls. Cilia-ablated mice showed reduced overall neuronal activity, decreased number of primed neurons, and failed to form burst synchronization. These data support the conclusion that alteration of neuronal primary cilia impairs trace fear memory by decreasing hippocampal neuronal priming and the formation of burst synchronization. This study also provides evidence to support the importance of burst synchronization among primed neurons on memory formation and retrieval

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Role of herpes simplex virus 1 protein ICP47 in antigen presentation and pathogenesis

The herpes simplex virus (HSV) immunomodulatory protein, ICP47, conceals infected cells from CD8+ T cells by inhibiting the presentation of peptides on MHC class I. The mechanism by which ICP47 exerts this function is by binding to the transporter associated with antigen processing (TAP) protein, blocking peptide transport and loading onto MHC I molecules in the ER. The earliest studies of ICP47 supported by biochemical and in vitro observations noted marked species specificity with human but not mouse TAP being inhibited by this protein. However, later work demonstrated that ICP47 can contribute to HSV neurovirulence in mice. The discordance between biochemical and in vivo data leaves our understanding of ICP47 and its role in evading CD8+ T cells incomplete. Data from our laboratory suggested that ICP47 is likely to be expressed during the establishment and maintenance of HSV-1 latency, however, its exact function during these stages of infection is unknown. Therefore, in this study, we sought to re-visit the discrepancies discussed above and investigate the role of ICP47 during HSV-1 infection. We utilised different strains of HSV and mice, as well as an alternate infection model and unique methods to quantify the effect of ICP47 on levels of antigen presentation. In our mouse model, where HSV is confined to the peripheral nervous system, deletion of ICP47 from HSV-1 KOS did not alter lesion development, virus load, spread or reactivation. Likewise, latency was unaffected by ICP47 deficiency as determined using a sensitive Cre-marking mouse model. Further observations from the Cre-marking mouse model revealed that unlike the ICP47 promoter inserted in an ectopic locus, native promoters did not induce additional neuronal marking by Cre beyond lytic infection. We evaluated the reasons behind the difference in marking using newly generated recombinant viruses. Subsequent flank infection of ROSA26R mice with these viruses showed that the local genomic context is also important for regulation of gene expression. By contrast to our in vivo pathogenesis data, we were able to show that ICP47 does inhibit antigen presentation significantly on HSV-infected mouse cells using in vitro antigen presentation assays. However, in mouse cells, antigen presentation was ablated by 44%, compared to an 85% reduction in human cells. As CD8+ T cells have been shown to recognize very few peptide-MHC I complexes on the surface of target cells, it is important to consider the efficiency at which ICP47 inhibits human and mouse TAP. Therefore, we used mass spectrometry to identify and quantify MHC I bound peptides derived from HSV-1 during viral infection. We found that more peptide sequences were presented on mouse cells infected with ICP47 null virus compared to those infected with wild-type virus. We quantified the presentation of 14 of these peptides and the contribution of ICP47 to this process in human and mouse cells. We found that ICP47 almost entirely blocks human TAP-mediated peptide presentation, though the degree of inhibition was somewhat peptide-specific. Conversely, the effect of ICP47 on mouse TAP was far less profound, resulting in only up to five-fold reduction in MHC-peptide abundance. In conclusion, this study shows that despite significant inhibition of antigen presentation in mouse cells, ICP47 may not be an effective immune modulator in mice and suggests a need for re-evaluation of suitable mouse models

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

ISMCR 1994: Topical Workshop on Virtual Reality. Proceedings of the Fourth International Symposium on Measurement and Control in Robotics

This symposium on measurement and control in robotics included sessions on: (1) rendering, including tactile perception and applied virtual reality; (2) applications in simulated medical procedures and telerobotics; (3) tracking sensors in a virtual environment; (4) displays for virtual reality applications; (5) sensory feedback including a virtual environment application with partial gravity simulation; and (6) applications in education, entertainment, technical writing, and animation

Intra-host HIV-1 evolution and the co-receptor switch

The course of an infection with the human immunodeficiency virus type 1 (HIV-1) is characterised by three phases: primary infection, chronic infection and acquired immunodeficiency syndrome (AIDS). These stages are defined based on levels of the number of CD4-positive T-helper cells (CD4+). This characteristic three-staged classification is also reflected in the course of the viral divergence and in the emergence of viral diversity. It is known that the V3 loop, a region encoded in the HIV envelope gene, is important for T cell infection. The CD4 receptor of the cells is used as primary receptor for viral cell entry, and the CCR5 or CXCR4 are the most important co-receptors that are necessary for cell entry. In about half of all patients, HIV switches from CCR5 towards CXCR4 usage during the late stage of infection, which hints at the onset of AIDS. Since the co-receptor tropism is determined by the V3 loop sequence, an understanding of the mechanisms of its evolution and of the circumstances leading to the co-receptor switch is of high interest. In the first part of the present work, we analysed longitudinal patient data, comprising information on CD4+ cell count, viral load, medication, coinfections and V3 loop sequences. We examined the correlations among the clinical and evolutionary data as well as the co-receptor usage over time, guided by different questions: Is the course of disease one-directional? Can successful drug therapy influence co-receptor usage? What are the genetic differences between CCR5- and CXCR4-tropic viruses? Due to the weak statistical support of our data, we only found few indications that successful HAART therapy influences the course of disease and the direction of the coreceptor switch. We hypothesise that successful therapy can pause or roll back the course of infection, enabling the CD4+ cells to recover to high levels of immune pressure. A suppression of the viral load further can displace X4-tropic viral variants in the viral population in favour of R5-tropic variants. In the second part of this work, we derived a fitness function to approximate the replication capacity of R5 and X4-tropic viruses. Based on a set of V3 loop sequences gathered from the Los Alamos HIV data base, the fitness function is composed of two components: the main fitness term describes the amino acid preferences found in the R5 and the X4 consensus sequence, and the additional epistatic term describes the effects of double mutations. While the impact of the main and epistatic fitness contribution can be influenced by a weighting parameter, an additional parameter controls the importance of available CCR5 and CXCR4 positive target cells. The fitness function enabled us to observe the differences of the underlying R5 and X4 fitness landscapes. A comparison of the sequence data set showed that the R5-tropic viral sequences were highly conserved, in contrast to the X4 sequences. Network analyses confirmed the higher sequence variability of the X4 sequences, which we found to be distributed over a larger sequence space. Interestingly, our analyses revealed that the most weakly conserved sequence positions of the X4 data set were very sensible to mutations. Upon an alteration of the most weakly conserved nt positions, the X4 sequences showed an increased probability to acquire stop condons and to loose their replicative capacity. The last part of the work describes an in silico approach of the V3 loop evolution based on the R5 and X4 fitness function. Simulations enable us to mimic the sequence evolution in silico, and to monitor the course of the viral diversity and divergence as well as the mean fitness of the simulated viral population over time. First results indicated that our simulation is able to imitate the evolutionary course of the viral diversity and divergence of an HIV infection. In our simulations, the sequence evolution followed a chemically sensible course. Amino acids that differed from the favoured chemical properties were first replaced by amino acids belonging to the favourable chemical class and finally converged into the dominant amino acid in the specific sequence position. The present project was designed to prepare the ground for deeper insights into the evolutionary dynamics of the HIV V3 loop. Our work enabled us to gain broader knowledge of the properties of R5- and X4-tropic viral sequences

Efficient Knowledge Extraction from Structured Data

Knowledge extraction from structured data aims for identifying valid, novel, potentially useful, and ultimately understandable patterns in the data. The core step of this process is the application of a data mining algorithm in order to produce an enumeration of particular patterns and relationships in large databases. Clustering is one of the major data mining tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized, and the similarity of objects from different clusters is minimized. In this thesis, we advance the state-of-the-art data mining algorithms for analyzing structured data types. We describe the development of innovative solutions for hierarchical data mining. The EM-based hierarchical clustering method ITCH (Information-Theoretic Cluster Hierarchies) is designed to propose solid solutions for four different challenges. (1) to guide the hierarchical clustering algorithm to identify only meaningful and valid clusters. (2) to represent each cluster content in the hierarchy by an intuitive description with e.g. a probability density function. (3) to consistently handle outliers. (4) to avoid difficult parameter settings. ITCH is built on a hierarchical variant of the information-theoretic principle of Minimum Description Length (MDL). Interpreting the hierarchical cluster structure as a statistical model of the dataset, it can be used for effective data compression by Huffman coding. Thus, the achievable compression rate induces a natural objective function for clustering, which automatically satisfies all four above mentioned goals. The genetic-based hierarchical clustering algorithm GACH (Genetic Algorithm for finding Cluster Hierarchies) overcomes the problem of getting stuck in a local optimum by a beneficial combination of genetic algorithms, information theory and model-based clustering. Besides hierarchical data mining, we also made contributions to more complex data structures, namely objects that consist of mixed type attributes and skyline objects. The algorithm INTEGRATE performs integrative mining of heterogeneous data, which is one of the major challenges in the next decade, by a unified view on numerical and categorical information in clustering. Once more, supported by the MDL principle, INTEGRATE guarantees the usability on real world data. For skyline objects we developed SkyDist, a similarity measure for comparing different skyline objects, which is therefore a first step towards performing data mining on this kind of data structure. Applied in a recommender system, for example SkyDist can be used for pointing the user to alternative car types, exhibiting a similar price/mileage behavior like in his original query. For mining graph-structured data, we developed different approaches that have the ability to detect patterns in static as well as in dynamic networks. We confirmed the practical feasibility of our novel approaches on large real-world case studies ranging from medical brain data to biological yeast networks. In the second part of this thesis, we focused on boosting the knowledge extraction process. We achieved this objective by an intelligent adoption of Graphics Processing Units (GPUs). The GPUs have evolved from simple devices for the display signal preparation into powerful coprocessors that do not only support typical computer graphics tasks but can also be used for general numeric and symbolic computations. As major advantage, GPUs provide extreme parallelism combined with a high bandwidth in memory transfer at low cost. In this thesis, we propose algorithms for computationally expensive data mining tasks like similarity search and different clustering paradigms which are designed for the highly parallel environment of a GPU, called CUDA-DClust and CUDA-k-means. We define a multi-dimensional index structure which is particularly suited to support similarity queries under the restricted programming model of a GPU. We demonstrate the superiority of our algorithms running on GPU over their conventional counterparts on CPU in terms of efficiency