9,332 research outputs found
Stagnation-point Brinkman flow of nanofluid on a stretchable plate with thermal radiation.
The study is an analytical exploration of hybrid nanofluid flow at a stagnation-point with Brinkman effect on a stretchable plate with thermal radiation. All of the aforementioned factors were taken into account when developing the mathematical model based on the Navier–Stokes equations for nanofluids, leading to a system of partial differential equations. Using suitable scaling, these equations are reduced to system of ordinary differential equations. The outcome of the system of ordinary differential equations are solved analytically and closed-form solutions are obtained in terms of incomplete error function. The results are analysed for the many significant flow characteristics with the profiles of velocity and temperature explored graphically. The amount of the heat transfer is increased due to the interaction between nanoparticles and the wall, and the wall surface is cooled when wall suction is present
Deep generative models for network data synthesis and monitoring
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
The medical applications of hyperpolarized Xe and nonproton magnetic resonance imaging
Hyperpolarized 129Xe (HP 129Xe) magnetic resonance imaging (MRI) is a relatively young
field which is experiencing significant advancements each year. Conventional proton MRI is
widely used in clinical practice as an anatomical medical imaging due to its superb soft tissue
contrast. HP 129Xe MRI, on the other hand, may provide valuable information about internal organs
functions and structure. HP 129Xe MRI has been recently clinically approved for lung imaging in
the United Kingdom and the United States. It allows quantitative assessment of the lung function
in addition to structural imaging. HP 129Xe has unique properties of anaesthetic, and may transfer
to the blood stream and be further carried to the highly perfused organs. This gives the opportunity
to assess brain perfusion with HP 129Xe and perform molecular imaging. However, the further
progression of the HP 129Xe utilization for brain perfusion quantification and molecular imaging
implementation is limited by the absence of certain crucial milestones.
This thesis focused on providing important stepping stones for the further development of
HP 129Xe molecular imaging and brain imaging. The effect of glycation on the spectroscopic
characteristics of HP 129Xe was studied in whole sheep blood with magnetic resonance
spectroscopy. An additional peak of HP 129Xe bound to glycated hemoglobin was observed. This
finding should be implemented in the spectroscopic HP 129Xe studies in patients with diabetes. [...
Spatial Blind Source Separation in the Presence of a Drift
Multivariate measurements taken at different spatial locations occur frequently in practice. Proper analysis of such data needs to consider not only dependencies on-sight but also dependencies in and in-between variables as a function of spatial separation. Spatial Blind Source Separation (SBSS) is a recently developed unsupervised statistical tool that deals with such data by assuming that the observable data is formed by a linear latent variable model. In SBSS the latent variable is assumed to be constituted by weakly stationary random fields which are uncorrelated. Such a model is appealing as further analysis can be carried out on the marginal distributions of the latent variables, interpretations are straightforward as the model is assumed to be linear, and not all components of the latent field might be of interest which acts as a form of dimension reduction. The weakly stationarity assumption of SBSS implies that the mean of the data is constant for all sample locations, which might be too restricting in practical applications. Therefore, an adaptation of SBSS that uses scatter matrices based on differences was recently suggested in the literature. In our contribution we formalize these ideas, suggest a novel adapted SBSS method and show its usefulness on synthetic data and illustrate its use in a real data application
On Lasso estimator for the drift function in diffusion models
In this paper we study the properties of the Lasso estimator of the drift
component in the diffusion setting. More specifically, we consider a
multivariate parametric diffusion model observed continuously over the
interval and investigate drift estimation under sparsity constraints.
We allow the dimensions of the model and the parameter space to be large. We
obtain an oracle inequality for the Lasso estimator and derive an error bound
for the -distance using concentration inequalities for linear functionals
of diffusion processes. The probabilistic part is based upon elements of
empirical processes theory and, in particular, on the chaining method
Multimodal MRI analysis using deep learning methods
Magnetic resonance imaging (MRI) has been widely used in scientific and clinical research. It is a non-invasive medical imaging technique that reveals anatomical structures and provides useful information for investigators to explore aging and pathological processes. Different MR modalities offer different useful properties. Automatic MRI analysis algorithms have been developed to address problems in many applications such as classification, segmentation, and disease diagnosis. Segmentation and labeling algorithms applied to brain MRIs enable evaluations of the volumetric changes of specific structures in neurodegenerative diseases. Reconstruction of fiber orientations using diffusion MRI is beneficial to obtain better understanding of the underlying structures.
In this thesis, we focused on development of deep learning methods for MRI analysis using different image modalities. Specifically, we applied deep learning techniques on different applications, including segmentation of brain structures and reconstruction of tongue muscle fiber orientations. For segmentation of brain structures, we developed an end-to-end deep learning algorithm for ventricle parcellation of brains with ventriculomegaly using T1-w MR images. The deep network provides robust and accurate segmentation results in subjects with high variability in ventricle shapes and sizes. We developed another deep learning method to automatically parcellate the thalamus into a set of thalamic nuclei using T1-w MRI and features from diffusion MRI. The algorithm incorporates a harmonization step to make the network adapt to input images with different contrasts.
We also studied the strains associated with tongue muscles during speech production using multiple MRI modalities. To enable this study, we first developed a deep network to reconstruct crossing tongue muscle fiber orientations using diffusion MRI. The network was specifically designed for the human tongue and accounted for the orthogonality property of the tongue muscles. Next, we proposed a comprehensive pipeline to analyze the strains associated with tongue muscle fiber orientations during speech using diffusion MRI, and tagged and cine MRI. The proposed pipeline provides a solution to analyze the cooperation between muscle groups during speech production
Weierstrass Bridges
We introduce a new class of stochastic processes called fractional
Wiener-Weierstrass bridges. They arise by applying the convolution from the
construction of the classical, fractal Weierstrass functions to an underlying
fractional Brownian bridge. By analyzing the -th variation of the fractional
Wiener-Weierstrass bridge along the sequence of -adic partitions, we
identify two regimes in which the processes exhibit distinct sample path
properties. We also analyze the critical case between those two regimes for
Wiener-Weierstrass bridges that are based on standard Brownian bridge. We
furthermore prove that fractional Wiener-Weierstrass bridges are never
semimartingales, and we show that their covariance functions are typically
fractal functions. Some of our results are extended to Weierstrass bridges
based on bridges derived from a general continuous Gaussian martingale.Comment: 40 pages, 2 figure
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