7,340 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 — 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

    Bridging RL Theory and Practice with the Effective Horizon

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    Deep reinforcement learning (RL) works impressively in some environments and fails catastrophically in others. Ideally, RL theory should be able to provide an understanding of why this is, i.e. bounds predictive of practical performance. Unfortunately, current theory does not quite have this ability. We compare standard deep RL algorithms to prior sample complexity prior bounds by introducing a new dataset, BRIDGE. It consists of 155 MDPs from common deep RL benchmarks, along with their corresponding tabular representations, which enables us to exactly compute instance-dependent bounds. We find that prior bounds do not correlate well with when deep RL succeeds vs. fails, but discover a surprising property that does. When actions with the highest Q-values under the random policy also have the highest Q-values under the optimal policy, deep RL tends to succeed; when they don't, deep RL tends to fail. We generalize this property into a new complexity measure of an MDP that we call the effective horizon, which roughly corresponds to how many steps of lookahead search are needed in order to identify the next optimal action when leaf nodes are evaluated with random rollouts. Using BRIDGE, we show that the effective horizon-based bounds are more closely reflective of the empirical performance of PPO and DQN than prior sample complexity bounds across four metrics. We also show that, unlike existing bounds, the effective horizon can predict the effects of using reward shaping or a pre-trained exploration policy

    Development of a SQUID magnetometry system for cryogenic neutron electric dipole moment experiment

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    A measurement of the neutron electric dipole moment (nEDM) could hold the key to understanding why the visible universe is the way it is: why matter should predominate over antimatter. As a charge-parity violating (CPV) quantity, an nEDM could provide an insight into new mechanisms that address this baryon asymmetry. The motivation for an improved sensitivity to an nEDM is to find it to be non-zero at a level consistent with certain beyond the Standard Model theories that predict new sources of CPV, or to establish a new limit that constrains them. CryoEDM is an experiment that sought to better the current limit of ∣dn∣<2.9×10−26 e |d_n| < 2.9 \times 10^{-26}\,e\,cm by an order of magnitude. It is designed to measure the nEDM via the Ramsey Method of Separated Oscillatory Fields, in which it is critical that the magnetic field remains stable throughout. A way of accurately tracking the magnetic fields, moreover at a temperature ∼0.5 \sim 0.5\,K, is crucial for CryoEDM, and for future cryogenic projects. This thesis presents work focussing on the development of a 12-SQUID magnetometry system for CryoEDM, that enables the magnetic field to be monitored to a precision of 0.1 0.1\,pT. A major component of its infrastructure is the superconducting capillary shields, which screen the input lines of the SQUIDs from the pick up of spurious magnetic fields that will perturb a SQUID's measurement. These are shown to have a transverse shielding factor of >1×107> 1 \times 10^{7}, which is a few orders of magnitude greater than the calculated requirement. Efforts to characterise the shielding of the SQUID chips themselves are also discussed. The use of Cryoperm for shields reveals a tension between improved SQUID noise and worse neutron statistics. Investigations show that without it, SQUIDs have an elevated noise when cooled in a substantial magnetic field; with it, magnetostatic simulations suggest that it is detrimental to the polarisation of neutrons in transport. The findings suggest that with proper consideration, it is possible to reach a compromise between the two behaviours. Computational work to develop a simulation of SQUID data is detailed, which is based on the Laplace equation for the magnetic scalar potential. These data are ultimately used in the development of a linear regression technique to determine the volume-averaged magnetic field in the neutron cells. This proves highly effective in determining the fields within the 0.1 0.1\,pT requirement under certain conditions

    Exact Community Recovery in the Geometric SBM

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    We study the problem of exact community recovery in the Geometric Stochastic Block Model (GSBM), where each vertex has an unknown community label as well as a known position, generated according to a Poisson point process in Rd\mathbb{R}^d. Edges are formed independently conditioned on the community labels and positions, where vertices may only be connected by an edge if they are within a prescribed distance of each other. The GSBM thus favors the formation of dense local subgraphs, which commonly occur in real-world networks, a property that makes the GSBM qualitatively very different from the standard Stochastic Block Model (SBM). We propose a linear-time algorithm for exact community recovery, which succeeds down to the information-theoretic threshold, confirming a conjecture of Abbe, Baccelli, and Sankararaman. The algorithm involves two phases. The first phase exploits the density of local subgraphs to propagate estimated community labels among sufficiently occupied subregions, and produces an almost-exact vertex labeling. The second phase then refines the initial labels using a Poisson testing procedure. Thus, the GSBM enjoys local to global amplification just as the SBM, with the advantage of admitting an information-theoretically optimal, linear-time algorithm

    Hospital-Wide Inpatient Flow Optimization

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    An ideal that supports quality and delivery of care is to have hospital operations that are coordinated and optimized across all services in real-time. As a step toward this goal, we propose a multistage adaptive robust optimization approach combined with machine learning techniques. Informed by data and predictions, our framework unifies the bed assignment process across the entire hospital and accounts for present and future inpatient flows, discharges as well as bed requests – from the emergency department, scheduled surgeries and admissions, and outside transfers. We evaluate our approach through simulations calibrated on historical data from a large academic medical center. For the 600-bed institution, our optimization model was solved in seconds, reduced off-service placement by 24% on average, and boarding delays in the emergency department and post-anesthesia units by 35% and 18% respectively. We also illustrate the benefit from using adaptive linear decision rules instead of static assignment decisions

    Ypsilanti Histories: A Look Back at the Last Fifty Years

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    In commemoration of their city\u27s bicentennial, the people of Ypsilanti look back on the dramatic changes that the last fifty years brought to this small town in southeastern Michigan. Drawing on archival research, published sources, and personal recollections, Ypsilanti Histories explores the government, educational institutions, businesses, community organizations, neighborhoods, and individuals that have defined Ypsilanti since 1973. As befits the rich diversity of the community, Ypsilanti Histories captures a range of experiences. It explores the controversies that have rocked the city from the university mascot to school consolidation, while also celebrating the city\u27s oldest African American civic organization and the pioneering Ypsilanti Heritage Foundation. Beloved businesses like the Ypsilanti Food Coop and the Ypsilanti Thrift Shop are profiled here as are some of the city\u27s greatest heroes including a Medal of Honor recipient. The effects of deindustrialization are documented as are the challenges that this brought to Michigan Avenue, Depot Town, and various neighborhoods. Education has long been central to Ypsilanti\u27s history, and Ypsilanti Histories examines changes at the city\u27s high school and Eastern Michigan University. The authors of Ypsilanti Histories are amateur and professional historians who call Ypsilanti home. Many personally witnessed the events they describe, and some played a key role in the histories they tell. Ypsilanti Histories: A Look Back at the Last Fifty Years is edited by John McCurdy, Bill Nickels, Evan Milan, and Sarah Zawacki.https://commons.emich.edu/books/1011/thumbnail.jp

    Quality of experience and access network traffic management of HTTP adaptive video streaming

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    The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

    Microwave-shielded ultracold polar molecules

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    Since the realization of Bose--Einstein condensates and degenerate Fermi gases, ultracold atoms with tunable interactions have become an essential platform for studying quantum many-body phenomena. Notable examples include the realization of BCS--BEC crossover and the simulation of the Bose/Fermi Hubbard model. Ultracold polar molecules could enrich the quantum gas toolbox with their long-range dipole-dipole interaction, which offers not only new opportunities in many-body physics, such as realizing the topological superfluid and the extended Hubbard model, but also applications in quantum chemistry, quantum computation, and precision measurements. However, the large number of internal degrees of freedom of molecules present a significant challenge in both cooling them to quantum degeneracy and controlling their interactions. Unlike atomic gases, a dense molecular sample suffers from fast collisional losses, preventing the implementation of evaporative cooling and the observation of scattering resonances. In this thesis, we describe how we solved the long-standing issue of collisional losses by microwave shielding, created a degenerate Fermi gas of NaK molecules, and discovered a new type of scattering resonances via which we created the first ultracold tetratomic molecules in the 100-nK regime. By synchronizing the rotation of polar molecules with a circularly polarized microwave electric field, we equip the molecular sample with a highly tunable intermolecular potential. This not only stabilizes the gas against inelastic collisions but also enables field-linked scattering resonances for precise control over scattering lengths. At long range, the molecules interact via their induced rotating dipole moments. As they approach each other, their orientations realign to produce a repulsive force, thereby mitigating inelastic collisions at close distances. With an elastic-to-inelastic collision ratio of 500, we have achieved evaporative cooling of the molecular gas down to 21 nK and 0.36 times the Fermi temperature, setting a new record for the coldest polar molecular gas to date. Thanks to the collisional stability of microwave-shielded molecules, we can directly load them into predominantly a single layer of a magic 3D optical lattice, achieving a peak filling fraction of 24%. These ultracold molecules, owing to their long lifetimes in their ground state and their long-range dipolar coupling, provide a unique platform to study quantum magnetism. With the achieved high filling fraction, we are prepared to study non-equilibrium spin dynamics such as rotational synchronization and spin squeezing. We demonstrated that the interaction between microwave-shielded polar molecules is highly tunable via the microwave power, detuning, and polarization. When the interaction potential is deep enough to host field-linked bound states at the collisional threshold, a shape resonance is induced, allowing us to tune the scattering rate by three orders of magnitude. The field-linked resonances enables controls over the scattering length in a similar fashion as Feshbach resonance for ultracold atoms, promising the realization of strongly correlated phases, such as dipolar pp-wave superfluid. It also paves the way to investigate the interplay between short-range and long-range interactions in novel quantum matters, such as exotic supersolid. Moreover, through a field-linked resonance, we associated for the first time weakly bound tetratomic molecules in the 100-nK regime, with a phase space density of 0.04. The transition from a Fermi gas of diatomic molecules to a Bose gas of tetratomic molecules paves the way for dipolar BCS--BEC crossover. With microwave-shielded polar molecules, we have realized a quantum gas featuring highly tunable long-range interactions. The technique is universal to polar molecules with a sufficiently large dipole moment, and thus offers a general strategy for cooling and manipulating polar molecules, and for associating weakly bound ultracold polyatomic molecules. Utilizing the toolbox developed in ultracold atoms, this platform possesses the potential to unlock an entirely new realm of quantum simulation of many-body physics
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