Demixing Sines and Spikes Using Multiple Measurement Vectors

In this paper, we address the line spectral estimation problem with multiple measurement corrupted vectors. Such scenarios appear in many practical applications such as radar, optics, and seismic imaging in which the signal of interest can be modeled as the sum of a spectrally sparse and a blocksparse signal known as outlier. Our aim is to demix the two components and for that, we design a convex problem whose objective function promotes both of the structures. Using positive trigonometric polynomials (PTP) theory, we reformulate the dual problem as a semi-definite program (SDP). Our theoretical results states that for a fixed number of measurements N and constant number of outliers, up to O(N) spectral lines can be recovered using our SDP problem as long as a minimum frequency separation condition is satisfied. Our simulation results also show that increasing the number of samples per measurement vectors, reduces the minimum required frequency separation for successful recovery.Comment: 9 pages, 3 figure

Spatial distortion in MRI with application to stereotactic neurosurgery

The aim of this work was to implement a thorough method for quantifying the errors introduced to frame-based neurosurgical stereotactic procedures by the use of MRI. Chang & Fitzpatrick's reversed gradient distortion correction method was used, in combination with a phantom, to measure these errors. Spatial distortion in MR images of between 1 mm and 2 mm was measured. Further analysis showed that this typically introduced an additional error in the coordinate of the actual treatment point of 0.7 mm. The implications of this are discussed. The main source of distortion in the MR images used for stereotaxis was found to be the head ring. A comparison between imaging sequences and MR scanners revealed that the spatial distortion depends mainly on the bandwidth per pixel of the sequence rather than other differences in the imaging sequences. By comparison with a phase map distortion correction technique, the imaging parameters required to allow successful distortion correction with the reversed gradient method were identified. The most important was the use of full Fourier spin echo acquisitions. The reversed gradient correction method was applied to two contemporary EPI techniques. Considerable improvement was seen in the production of ADC maps after the images had been corrected for distortion. The method also was shown to be valid in application to BOLD fMRI data

DETECTING BRAIN-WIDE INTRINSIC CONNECTIVITY NETWORKS USING fMRI IN MICE

Functional neuroimaging methods in mice are essential for unraveling complex neuronal networks that underlie maladaptive behavior in neurological disorder models. By using fMRI to detect intrinsic connectivity networks in mice, we can examine large scale alteration in brain activity and functional connectivity to establish causal associations in brain network changes. The work presented in this dissertation is organized into five chapters. Chapter 1 provides the necessary background required to understand how functional neuroimaging tools such as fMRI detect signal changes attributed to spontaneous neuronal activity of intrinsic connectivity networks in mice. Chapter 2 describes the development of our isotropic fMRI acquisition sequence in mice and semi-automated pipeline for mouse fMRI data. Naïve mouse fMRI scans were used to validated the pipeline by reliably and reproducibly extracting intrinsic connectivity networks. Chapter 3 establishes the development and validation of a novel superparamagenetic iron-oxide nanoparticle to enhance fMRI signal sensitivity. Chapter 4 studies the effects norepinephrine released by locus coeruleus neurons on the default mode network in mice. Norepinephrine release selectively enhanced neuronal activity and connectivity in the Frontal module of the default mode network by suppressing information flow from the Retrosplenial-Hippocampal to the Association modules. Chapter 5 addresses the implications of our findings and addresses the limitations and future studies that can be conducted to expand on this research.Doctor of Philosoph

Spatial distortion in MRI with application to stereotactic neurosurgery

The aim of this work was to implement a thorough method for quantifying the errors introduced to frame-based neurosurgical stereotactic procedures by the use of MRI. Chang & Fitzpatrick's reversed gradient distortion correction method was used, in combination with a phantom, to measure these errors. Spatial distortion in MR images of between 1 mm and 2 mm was measured. Further analysis showed that this typically introduced an additional error in the coordinate of the actual treatment point of 0.7 mm. The implications of this are discussed. The main source of distortion in the MR images used for stereotaxis was found to be the head ring. A comparison between imaging sequences and MR scanners revealed that the spatial distortion depends mainly on the bandwidth per pixel of the sequence rather than other differences in the imaging sequences. By comparison with a phase map distortion correction technique, the imaging parameters required to allow successful distortion correction with the reversed gradient method were identified. The most important was the use of full Fourier spin echo acquisitions. The reversed gradient correction method was applied to two contemporary EPI techniques. Considerable improvement was seen in the production of ADC maps after the images had been corrected for distortion. The method also was shown to be valid in application to BOLD fMRI data

Quantification en imagerie optique diffuse cérébrale : analyse du signal et étude du problème direct

Physiologie -- Physiologie cérébrale humaine -- Couplage neurovasculaire -- Les vaisseaux sanguins cérébraux : les principaux sinus -- Imagerie optique diffuse et analyse du signal -- Définition du problème -- Problème direct -- Problème inverse -- Méthode d'analyse en imagerie optique diffuse -- Neuronavigation IRM-IOD -- IRM fonctionnelle -- Physique de l'IRM -- Neuronavigation -- Équipement de neuronavigation et de visualisation -- Analyse de la sensibilité -- Inverted responses in diffuse optical imaging and their correlation with negative BOLD signal -- Materials and methods -- Results -- Quantification -- Problème direct en imagerie optique diffuse -- Segmentation des tissus à partir de données IRMa -- Simulation Monte Carlo -- Formulation du problème -- Discrétisation et mise en oeuvre numérique -- Hybrid boundary element method applied to volumetric diffuse optical tomography -- Definition of the problem -- Born approximation -- Numerical discretization -- Results

Wide-field optical imaging of neurological disorders and sleep in mice

Neuroimaging has revolutionized the way in which we understand the hierarchical organization of the amazingly complex, interconnected human brain. Neuroimaging techniques, like functional magnetic resonance imaging (fMRI), have provided high quality structural and functional data, providing multiple in-depth analyses and biomarkers of disease processes. In animal models, mechanistic studies can uncover root pathologies that aren’t explorable in humans. In mice, brain functional connectivity (FC) can be measured via Optical Intrinsic Signal (OIS) imaging – a modality that measures vascular reactivity as a surrogate for neural activity via quantification of fluctuations in oxygenated-hemoglobin (similar to the blood oxygen level dependent (BOLD) signal used in fMRI). Another advantage of optical neuroimaging in mice is the expression of genetically encoded calcium indicators (GECIs), which provide cell-specific and network-level functional imaging of brain activity at speeds up to at least 4Hz. Imaging in higher frequency bands (compared to \u3c0.2Hz in fMRI or other hemoglobin-based imaging modalities) allows for resolution of neural specific phenomena on the order of milliseconds, such as the global ∼1Hz slow oscillation that is characteristic of anesthesia and non-rapid eye movement (NREM) sleep. We imaged mice expressing the GECI GCaMP6 in excitatory neurons while awake, in NREM (verified by EEG), or under ketamine/xylazine (K/X) or Dexmedetomidine (Dex) anesthesia and reconcile discrepancies between activity dynamics observed with hemoglobin vs. calcium (GCaMP6) imaging. Alterations in correlation structure were most obvious in delta band calcium NREM and anesthesia data, resulting in maps with large regions of polarized positive and negative correlations covering the field-of-view (FOV). We use principal component analysis (PCA) to provide evidence that the slow oscillation superimposes on FC rather than replaces FC patterns typical of the alert state. While consciousness state can oscillate on the order of seconds, many studies of disease processes are most informative across a longer period of time. Surgical preparations coupled with optical imaging allow for longitudinal experiments on varying timescales. For example, sequalae of subarachnoid hemorrhage (SAH) include vasospasm, microvessel thrombi, and other delayed cerebral ischemic (DCI) events around 3 days post SAH. These DCI events have been shown to coincide with up-regulation of the neuroprotective peptide Sirtuin1 (SIRT1), using an endovascular perforation mouse model. Here, we display global FC disruption caused by SAH and DCI events in parallel with behavioral deterioration. Normal brain connectivity and behavior was maintained during SAH and DCI via two different treatments targeting SIRT1 activation. SIRT1-specific (resveratrol) and non-specific (hypoxic conditioning) treatments both protected against the FC deficits induced by SAH and DCI, with the latter providing the largest protective effect. This indicates that conditioning-based strategies targeting SIRT1-directed mechanisms provide multifaceted neurovascular protection in experimental SAH – data that further supports the overarching hypothesis that conditioning- based therapy is a powerful approach with great potential for improving patient outcome after aneurysmal SAH. Studies involving focal injury (e.g., stroke, SAH) usually exhibit functional deficits surrounding the injured tissue, however, it is less clear how diffuse processes, such as novel models of acute septic encephalopathy (i.e., Delirium), and encephalitis caused by Zika virus infection, alter brain dynamics. Septic encephalopathy leads to major and costly burdens for a large percentage of admitted hospital patients. Elderly patients are at an increased risk, especially those with dementia. Current treatments are aimed at sedation to combat mental status changes and are not aimed at the underlying cause of encephalopathy. Indeed, the underlying pathology linking together peripheral infection and altered neural function has not been established, largely because good, acutely accessible readouts of encephalopathy in animal models do not exist. In-depth behavioral testing in animals lasts multiple days, outlasting the time frame of acute encephalopathy. Here, we propose optical fluorescent imaging of neural FC as a readout of encephalopathy in a mouse model of acute sepsis. Imaging and basic behavioral assessment was performed at baseline, Hr8, Hr24, and Hr72 following injection of either lipopolysaccharide (LPS) or phosphate buffered saline (PBS). Neural FC strength decreased at Hr8 and returned to baseline by Hr72 in somatosensory and parietal cortical regions. Additionally, neural fluctuations transiently declined at Hr8 and returned to baseline by Hr72. Both FC strength and neural fluctuation tone correlated with behavioral neuroscore indicating this imaging methodology is a sensitive and acute readout of encephalopathy. Zika virus (ZIKV) emerged as a prominent global health concern due to the severe neurologic injury in infants born to adults who had ZIKV infection during pregnancy. However, neurologic manifestations in healthy adults were subsequently reported during Zika pandemics in South America and Southeast Asia. In this population, infection can result in severe cases of encephalitis and have lasting impacts on cognition, and learning and memory, even after recovery from acute infection. Recent studies have uncovered extensive ZIKV- related neural apoptosis within the trisynaptic circuit involving the entorhinal cortex, the cornu ammonis, and the dentate gyrus of the hippocampus in adult mice. However, there are many contributing regions and circuits involved in cognition and learning and memory outside of this trisynaptic circuit. Communication within the cortex and between the cortex and hippocampus is necessary for a variety of neurological processes, such as performing cognitive tasks or for memory consolidation during sleep. Here, we investigate cortical networks and connectivity utilizing wide-field optical fluorescence imaging. We demonstrate that functional deficits congregate in regions of cortex that are highly communicative with hippocampus, such as somatosensory and retrosplenial cortices. Further, we prove that these functional imaging deficits are correlated with other metrics of disease severity, such as encephalitis score and increased delta power, providing a potentially useful clinical biomarker of disease. Finally, these imaging deficits resolve after recovery from acute infection. While optical methods have obvious advantages when used to study animal models, the technique is relatively novel (compared to fMRI) therefore, there are many avenues for data processing algorithms to improve. Similar to fMRI, historically, optical methods use a remarkably simple bivariate Pearson-based approach to mapping FC, leading to quick and easy-to-interpret models of brain networks but also susceptibility to global sources of variance (e.g., motion, Mayer waves). Previously, we demonstrated the binarizing effect of the slow oscillation on FC during NREM and K/X anesthesia. While PCA effectively removed the slow oscillation, it is reasonable to assume that a biological process cannot be completely explained in algebraically orthogonal components. Therefore, we pioneer a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data in mice in an effort to map connectivity with less susceptibility to confounding variance. Calcium dynamics in all brain pixels are holistically weighted via support vector regression to predict activity in a region of interest (ROI). This approach yielded remarkably high prediction accuracy, suggesting the optimized pixel weights represent multivariate functional connectivity (MFC) strength with the ROI. Additionally, MFC maps were largely impervious to the slow oscillation. Moreover, MFC maps more closely aligned with anatomical connectivity as modeled through axonal projection images, than FC maps. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to standard FC. These results show that MFC has several performance and conceptual advantages over standard FC and should be considered more broadly within the FC analysis community. Further, with study of diffuse processes (e.g., LPS and ZIKV infection), statistical developments are crucial to solve the multiple comparisons problem when examining all cortical regions within the FOV. Therefore, part of this thesis focuses on the development of a streamlined, open source, user friendly data processing toolbox that contains multiple statistical approaches to make the aforementioned studies possible. Together, the following presents the multiple ways wide-field optical imaging can be used to learn more about the brain’s functional architecture in health and disease

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Strategic Latency Unleashed: The Role of Technology in a Revisionist Global Order and the Implications for Special Operations Forces

The article of record may be found at https://cgsr.llnl.govThis work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-59693This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-5969