82 research outputs found

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system β€” GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Three Risky Decades: A Time for Econophysics?

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    Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trappedβ€”the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be builtβ€”only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

    Telecommunications Networks

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    This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

    Urban Informatics

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    This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become β€˜smart’ and β€˜sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of β€˜big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

    Urban Informatics

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    This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become β€˜smart’ and β€˜sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of β€˜big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

    Listening with great expectations: A study of predictive natural speech processing

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    μž₯μ†Œμ™€ κ·Έ κ°€μΉ˜λ₯Ό μ €μž₯ν•˜λŠ” λ°°μΈ‘κ³Ό 쀑간 ν•΄λ§ˆμ˜ 차별적 μ—­ν• 

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : μžμ—°κ³Όν•™λŒ€ν•™ λ‡ŒμΈμ§€κ³Όν•™κ³Ό, 2021.8. κ°•λ―Όμˆ˜.μ˜€λž˜μ „λΆ€ν„° ν•΄λ§ˆλŠ” μžμ‹ μ˜ κ²½ν—˜, 즉 일화 μ‚¬κ±΄μ˜ 기얡에 ν•„μˆ˜μ μΈ μ˜μ—­μœΌλ‘œ μ•Œλ €μ Έμ™”μŠ΅λ‹ˆλ‹€. μ΄λŸ¬ν•œ 일화 μ‚¬κ±΄μ—λŠ” νŠΉμ • μž₯μ†Œμ—μ„œ κ²ͺ은 감정적 κ²½ν—˜λ“€μ΄ κΈ°μ–΅μœΌλ‘œ μ €μž₯λ©λ‹ˆλ‹€. μ΄λŸ¬ν•œ 일화 κΈ°μ–΅μ˜ νŠΉμ„±μ„ κ³ λ €ν•˜λ©΄, ν•΄λ§ˆλŠ” 감정 정보λ₯Ό μ²˜λ¦¬ν•  κ°€λŠ₯성이 맀우 λ†’κ³ , μ‹€μ œλ‘œ 쀑간 ν•΄λ§ˆμ™€ 볡츑 ν•΄λ§ˆλŠ” νŽΈλ„μ²΄λ‘œλΆ€ν„° ν•΄λΆ€ν•™μ μœΌλ‘œ 직접 μ—°κ²°λ˜μ–΄ μžˆμŠ΅λ‹ˆλ‹€. λ˜ν•œ 쀑간 ν•΄λ§ˆλŠ” λ°°μΈ‘ ν•΄λ§ˆλ‘œλΆ€ν„° λ§Žμ€ 곡간 정보λ₯Ό λ°›μ•„λ“œλ¦°λ‹€κ³  μ•Œλ €μ ΈμžˆμŠ΅λ‹ˆλ‹€. λ”°λΌμ„œ 쀑간 ν•΄λ§ˆλŠ” μž₯μ†Œμ˜ μœ„μΉ˜μ™€ κ·Έ μž₯μ†Œμ—μ„œ κ²½ν—˜ν•œ 감정정보λ₯Ό μ—°ν•©ν•  κ°€λŠ₯성이 λ†’μŠ΅λ‹ˆλ‹€. ν•˜μ§€λ§Œ, 쀑간 ν•΄λ§ˆμ˜ μ΄λŸ¬ν•œ μž₯μ†Œ-감정 μ—°ν•© κΈ°μ–΅μ˜ 역할은 거의 μ•Œλ €μ§€μ§€ μ•Šμ•˜λ‹€. κ·Έλž˜μ„œ, μ €λŠ” 쀑간 해맀가 νŠΉμ • κ³΅κ°„μ—μ„œ λ°œμƒν•˜λŠ” μ‚¬κ±΄μ˜ κ°€μΉ˜λ₯Ό μ €μž₯ν•˜λŠ”λ° μ€‘μš”ν•˜κ³ , λ°°μΈ‘ ν•΄λ§ˆλŠ” μ •ν™•ν•œ μœ„μΉ˜ 정보λ₯Ό ν‘œμƒν•˜λŠ”λ° μ€‘μš”ν•˜λ‹€λŠ” 가섀을 μ„Έμ› μŠ΅λ‹ˆλ‹€. 이λ₯Ό κ²€μ¦ν•˜κΈ° μœ„ν•΄ μ₯μ˜ λ°°μΈ‘κ³Ό 쀑간 ν•΄λ§ˆμ˜ κ°œλ³„ λ‰΄λŸ°μ„ λ™μ‹œμ— λ¦¬μ½”λ”©ν•˜μ˜€μœΌλ©°, μ„ ν˜Έλ„κ°€ λ‹€λ₯Έ 먹이λ₯Ό μ΄μš©ν•΄ μž₯μ†Œμ˜ κ°€μΉ˜ 정보λ₯Ό λ³€ν™”μ‹œν‚€λŠ” μ‹€ν—˜μ„ μ§„ν–‰ν–ˆμŠ΅λ‹ˆλ‹€. 이 ν•™μœ„ λ…Όλ¬Έμ˜ 첫 νŒŒνŠΈμ—μ„œλŠ” μ₯κ°€ 2차원 κ³΅κ°„μ—μ„œ 자유둭게 λŒμ•„λ‹€λ‹ λ•Œμ˜ λ°°μΈ‘λΆ€ν„° λ³΅μΈ‘ν•΄λ§ˆμ˜ μž₯μ†Œ 세포가 μ–΄λ–»κ²Œ λ‹¬λΌμ§€λŠ”μ§€ μ‚΄νŽ΄λ³΄μ•˜μŠ΅λ‹ˆλ‹€. ꡬ체적으둜, μ€‘κ°„ν•΄λ§ˆλ³΄λ‹€ λ°°μΈ‘ ν•΄λ§ˆμ—μ„œ μž₯μ†Œ 선택적 ν™œλ™μ΄ 더 κ°•ν•˜κ²Œ λ‚˜νƒ€λ‚¬μœΌλ©°, 볡츑 ν•΄λ§ˆμ—μ„œλŠ” μž₯μ†Œ μ„Έν¬μ˜ ν™œλ™μ΄ 거의 κ΄€μ°°λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€. λ‘λ²ˆμ§Έ νŒŒνŠΈμ—μ„œ, ν•΄λ§ˆκ°€ ν•„μš”μ—†λŠ” κ°„λ‹¨ν•œ κ³Όμ œμ—μ„œ 먹이의 κ°€μΉ˜κ°€ 바뀐 이후에, λ°°μΈ‘κ³Ό 볡츑 ν•΄λ§ˆμ˜ μž₯μ†Œ μ„Έν¬μ˜ 곡간 ν‘œμƒ λ³€ν™”λ₯Ό μ‚΄νŽ΄λ³΄μ•˜μŠ΅λ‹ˆλ‹€. 주어진 κ³΅κ°„μ—μ„œ 제곡되던 λ§›μžˆλŠ” 먹이가 λ§›μ—†λŠ” 먹이둜 λ°”λ€Œκ³  λ‚˜λ©΄, 쀑간 ν•΄λ§ˆμ˜ μž₯μ†Œμ„Έν¬λŠ” 재빠λ₯΄κ²Œ 곡간 ν‘œμƒμ„ μž¬λ°°μ—΄ν•˜κ²Œ λ©λ‹ˆλ‹€. ν•˜μ§€λ§Œ, λ™μΌν•œ μ‘°μž‘μ—μ„œ λ°°μΈ‘ ν•΄λ§ˆμ˜ μž₯μ†Œμ„Έν¬λŠ” 곡간 ν‘œμƒμ„ μΌμ •ν•˜κ²Œ μœ μ§€ν•˜μ˜€μŠ΅λ‹ˆλ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ, μ„Έλ²ˆμ§Έ νŒŒνŠΈμ—μ„œλŠ” ν•΄λ§ˆκ°€ ν•„μš”ν•œ κΈ°μ–΅ κ³Όμ œμ—μ„œ κ°€μΉ˜-의쑴적 곡간 μž¬λ°°μ—΄μ„ μΆ”κ°€μ μœΌλ‘œ μ•Œμ•„λ³΄μ•˜μŠ΅λ‹ˆλ‹€. T λͺ¨μ–‘μ˜ λ―Έλ‘œμ—μ„œ μž₯μ†Œ μ„ ν˜Έ 과제λ₯Ό μ§„ν–‰ν•˜λŠ” λ™μ•ˆ, 쀑간 ν•΄λ§ˆμ˜ μž₯μ†Œ μ„Έν¬λŠ” λ§›μžˆλŠ” 먹이가 λ‚˜μ˜€λŠ” 곡간을 μ§‘μ€‘μ μœΌλ‘œ ν‘œμƒν•˜λ©°, μ΄λŸ¬ν•œ μ§‘μ€‘λœ ν‘œμƒμ€ λ§›μžˆλŠ” 먹이의 μœ„μΉ˜κ°€ λ°”λ€Œμ–΄λ„ λ™μΌν•˜κ²Œ κ΄€μ°°λ©λ‹ˆλ‹€. 반면, λ°°μΈ‘ ν•΄λ§ˆ μž₯μ†Œ μ„Έν¬μ˜ 곡간 ν‘œμƒμ€ μ΄λŸ¬ν•œ μ‘°μž‘μ— 거의 영ν–₯을 받지 μ•ŠμŠ΅λ‹ˆλ‹€. 그리고 이 μž₯μ†Œ μ„ ν˜Έ ν•™μŠ΅μ„ ν•˜λŠ” λ™μ•ˆ, λ°°μΈ‘ ν•΄λ§ˆλ³΄λ‹€ 쀑간 ν•΄λ§ˆμ˜ 신경망 μƒνƒœκ°€ λΉ λ₯΄κ²Œ λ³€ν•˜λŠ” λͺ¨μŠ΅μ„ λ³΄μ˜€μŠ΅λ‹ˆλ‹€. μ’…ν•©ν•˜μžλ©΄, μœ„ 결과듀은 λ°°μΈ‘ ν•΄λ§ˆμ™€ 볡츑 ν•΄λ§ˆλŠ” μ„œλ‘œ λ‹€λ₯Έ κΈ°λŠ₯을 맑고 μžˆλ‹€λŠ” 점을 λ³΄μ—¬μ€λ‹ˆλ‹€. 즉, λ°°μΈ‘ ν•΄λ§ˆλŠ” λ™λ¬Όμ˜ μ •ν™•ν•œ μž₯μ†Œλ₯Ό ν‘œμƒν•˜λŠ”λ° νŠΉν™”λ˜μ–΄ 있으며, 쀑간 ν•΄λ§ˆλŠ” μž₯μ†Œμ™€ κ·Έ κ°€μΉ˜ 정보λ₯Ό μ—°ν•©ν•˜λŠ” 역할을 맑고 μžˆμŠ΅λ‹ˆλ‹€. μ΄λŸ¬ν•œ λ°œκ²¬μ€ 쀑간 ν•΄λ§ˆκ°€ 행동 선택과 λ°€μ ‘ν•œ 정보λ₯Ό μ²˜λ¦¬ν•˜λ©°, μ΄λŸ¬ν•œ 정보λ₯Ό λ‚΄μΈ‘ 전두엽을 톡해 λ‹€λ₯Έ λ‡Œ μ˜μ—­κ³Ό μ†Œν†΅ν•˜λŠ” κΈ°λŠ₯적으둜 μ€‘μš”ν•œ μ˜μ—­μ΄λΌλŠ” 것을 μ‹œμ‚¬ν•©λ‹ˆλ‹€.It has long been postulated that the hippocampus is vital for memorizing autobiographical episodic events. Because an episodic event often entails memories for certain places associated with their emotional and motivational significance, it is promising that the hippocampus processes spatial information in conjunction with its associated valence. Among the hippocampal subregions (i.e., dorsal, intermediate, and ventral), the amygdala, which plays key roles in processing valence information, sends direct axonal projection to the intermediate and ventral hippocampus. Also, there are extensive recurrent collaterals and associational projections (presumably spatial information) from the dorsal hippocampus to the intermediate hippocampus. Thus, the intermediate hippocampus may integrate emotional/motivational information in association with locational information. However, it is largely unknown that how the intermediate hippocampus process value-associated spatial information processing. Therefore, I hypothesized that encoding the value of an event at a specific location takes priority in the intermediate hippocampus, compared to the dorsal hippocampus, whose priority resides in representing the precise location of an animal, presumably in the cognitive map. To test this hypothesis, I simultaneously recorded single units from the dorsal and intermediate hippocampus while rats performed a battery of tasks in which the level of motivational significance of a place was controlled by foods with different palatability. In this dissertation of Chapter 1, I examined the changes in spatial firing patterns along the dorsoventral axis while rats foraged in an open field maze. Specifically, spatially selective firing was more eminent in the dorsal than in the intermediate hippocampus, and spatial signals were hardly observed in the ventral hippocampus. In Chapter 2, after changes in reward value during non-mnemonic tasks, differential global remappings of place cells were found between the dorsal and intermediate hippocampus. When more-palatable reward (i.e., sunflower seeds) were replaced with less-palatable one (Cheerios) in a given location, place cells in the intermediate hippocampus remapped immediately. In contrast, place fields recorded from the dorsal hippocampus maintained their spatial representations stably in the same manipulation. In Chapter 3, value-dependent remappings were further investigated in hippocampal-dependent tasks. During the place-preference task in the T-maze, place fields obtained from the intermediate hippocampus accumulated near the arm associated with more-preferred rewards, and overrepresented patterns shifted toward opposite arm after the locations of more-preferred and less-preferred rewards were reversed. However, spatial representations of place cells in the dorsal hippocampus were rarely affected by such manipulation. And, during the acquisition of the place-preference task, the ensemble network state in the iHP changed faster than that in the dHP. Taken together, our results suggest that there are functional segregations between the dorsal and intermediate subregions of the hippocampus. That is, the dorsal hippocampus is specialized in representing the animal's precise locations in the environment, whereas the intermediate hippocampus takes part in the integration of spatial information and its motivational values. These findings imply that the intermediate hippocampus is a functionally significant hippocampal subregion through which critical action-related information (i.e., spatial information from the dorsal hippocampus and emotional/motivational information from the amygdala) is integrated and communicated to the rest of the brain via the medial prefrontal cortex.BACKGROUND AND HYPOTHESIS. 1 1.1 BACKGROUND 1 1.1.1 Episodic memory and hippocampus. 2 1.1.2 Introduction of the rodent hippocampal researches. 2 1.1.3 Single-cell recording from the rodent hippocampus 4 1.1.3.1 Basic firing properties of place cells 4 1.1.3.2 Spatial representation of place cells. 5 1.1.3.3 Non-spatial representation of place cells 6 1.1.3.4 Value representation in the hippocampus. 6 1.1.4 Difference in anatomical connectivities along the dorsoventral axis. 7 1.1.5 Difference in functions along the dorsoventral axis. 10 1.2 HYPOTHESIS 12 CHAPTER 1. 13 2.1 Introduction. 14 2.2 Methods. 15 2.2.1 Subjects. 15 2.2.2 Maze familiarization and pre-training 15 2.2.3 Surgical implantation of the hyperdrive. 15 2.2.4 Electrophysiological recording procedures 16 2.2.5 Histological verification of tetrode tracks 16 2.2.6 Unit isolation 16 2.2.7 Basic firing properties 17 2.2.8 Definition of place fields 17 2.2.9 Theta-modulation and burst index 18 2.3 Results. 19 2.3.1 Anatomical boundary between dorsal, intermediate and ventral hippocampus. 19 2.3.2 Comparison of basic firing properties between hippocampal subregions 20 2.3.3 Degree of spatially selective firing patterns sharply decreased at the border between dHP and iHP. 23 2.4 Discussion. 28 CHAPTER 2. 30 3.1 Introduction. 31 3.2 Methods. 32 3.2.1 Behavior paradigm. 32 3.2.1.1 Food preference test. 32 3.2.1.2 Spatial alternation task. 33 3.2.2 Post-surgical training and main recording 33 3.2.3 Constructing the population rate map. 34 3.2.4 Categorization of place field responses 34 3.2.5 Reward-type coding analysis. 34 3.2.6 Speed-correlated cells. 35 3.3 Results. 35 3.3.1 Rat's food preference for sunflower seeds and Froot Loops over Cheerios. 35 3.3.2 Place cells in iHP, but not dHP, encode changes in motivational values of place via global remapping. 36 3.3.3 Identity of reward type is coded in the iHP by rate remapping, but not in the dHP. 49 3.3.4 Neural activity of single cells of vHP in response to motivational value changes. 51 3.4.5 Immediate coding of the changes in motivational values in iHP, but not in dHP. 53 3.4 Discussion 60 CHAPTER 3. 64 4.1 Introduction 65 4.2 Methods 65 4.2.1 Behavior paradigm. 65 4.2.2 Principal component analysis for neural ensemble state 66 4.2.3 Synchronization of spiking activity. 67 4.3 Results. 68 4.3.1 Overrepresentation of the motivationally significant place by the place cells in iHP, but not in dHP 68 4.2.2 Rapid changes of the ensemble network changes in iHP, compared to those in dHP. 77 4.2.3 Place cells in the dHP and iHP co-fire more strongly during a mnemonic task than non-mnemonic tasks. 79 4.4 Discussion 82 GENERAL DISCUSSION. 87 5.1 Conclusion 88 5.2 Limitation 88 5.3 Implication and perspective. 89 5.4 Future research direction. 93 BIBLIOGRAPHY 94 ACKNOWLEDGMENT 111 ꡭ문초둝 112λ°•
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