CMB-S4 Science Book, First Edition

This book lays out the scientific goals to be addressed by the next-generation ground-based cosmic microwave background experiment, CMB-S4, envisioned to consist of dedicated telescopes at the South Pole, the high Chilean Atacama plateau and possibly a northern hemisphere site, all equipped with new superconducting cameras. CMB-S4 will dramatically advance cosmological studies by crossing critical thresholds in the search for the B-mode polarization signature of primordial gravitational waves, in the determination of the number and masses of the neutrinos, in the search for evidence of new light relics, in constraining the nature of dark energy, and in testing general relativity on large scales

Origin and characterization of disks substructures, and their relation to stellar hosts

Planets are formed from the gas and dust content available in planet-forming disks around young stars, creating substructures in their density, thermal, and chemical distribution. Characterizing those substructures can provide constraints on the planet-formation potential of each disk. To improve our understanding of how planets are formed around the stars that are the most common in our galaxy, very low mass stars and binary stars, I studied high spatial resolution observations of dust and gas emission from these objects. To maximize information recovery, I analyzed these datasets with visibility-based methods. The results demonstrate that substructured emission in the dust continuum is present in all spatially resolved disks around very low mass stars, which could be explained by ongoing planet formation. In circumbinary disks, the combination of hydro-models and observations suggest that measuring the eccentricity gradient as a function of radii can be used as a tracer for the presence of Saturn-like planets embedded in the disks. On the other hand, for multiple disk systems, I showed the feasibility of recovering the orbital motion of young objects through the relative movement of their disks, which is crucial to interpreting the emission substructures

Modeling trends and periodic components in geodetic time series: a unified approach

Geodetic time series are usually modeled with a deterministic approach that includes trend, annual, and semiannual periodic components having constant amplitude and phase-lag. Although simple, this approach neglects the time-variability or stochasticity of trend and seasonal components, and can potentially lead to inadequate interpretations, such as an overestimation of global navigation satellite system (GNSS) station velocity uncertainties, up to masking important geophysical phenomena. In this contribution, we generalize previous methods for determining trends and seasonal components and address the challenge of their time-variability by proposing a novel linear additive model, according to which (i) the trend is allowed to evolve over time, (ii) the seasonality is represented by a fractional sinusoidal waveform process (fSWp), accounting for possible non-stationary cyclical long-memory, and (iii) an additional serially correlated noise captures the short term variability. The model has a state space representation, opening the way for the evaluation of the likelihood and signal extraction with the support of the Kalman filter (KF) and the associated smoothing algorithm. Suitable enhancements of the basic methodology enable handling data gaps, outliers, and offsets. We demonstrate the advantage of our method with respect to the benchmark deterministic approach using both observed and simulated time series and provide a fair comparison with the Hector software. To that end, various geodetic time series are considered which illustrate the ability to capture the time-varying stochastic seasonal signals with the fSWp

Indoor Mapping and Reconstruction with Mobile Augmented Reality Sensor Systems

Augmented Reality (AR) ermöglicht es, virtuelle, dreidimensionale Inhalte direkt innerhalb der realen Umgebung darzustellen. Anstatt jedoch beliebige virtuelle Objekte an einem willkürlichen Ort anzuzeigen, kann AR Technologie auch genutzt werden, um Geodaten in situ an jenem Ort darzustellen, auf den sich die Daten beziehen. Damit eröffnet AR die Möglichkeit, die reale Welt durch virtuelle, ortbezogene Informationen anzureichern. Im Rahmen der vorliegenen Arbeit wird diese Spielart von AR als "Fused Reality" definiert und eingehend diskutiert. Der praktische Mehrwert, den dieses Konzept der Fused Reality bietet, lässt sich gut am Beispiel seiner Anwendung im Zusammenhang mit digitalen Gebäudemodellen demonstrieren, wo sich gebäudespezifische Informationen - beispielsweise der Verlauf von Leitungen und Kabeln innerhalb der Wände - lagegerecht am realen Objekt darstellen lassen. Um das skizzierte Konzept einer Indoor Fused Reality Anwendung realisieren zu können, müssen einige grundlegende Bedingungen erfüllt sein. So kann ein bestimmtes Gebäude nur dann mit ortsbezogenen Informationen augmentiert werden, wenn von diesem Gebäude ein digitales Modell verfügbar ist. Zwar werden größere Bauprojekt heutzutage oft unter Zuhilfename von Building Information Modelling (BIM) geplant und durchgeführt, sodass ein digitales Modell direkt zusammen mit dem realen Gebäude ensteht, jedoch sind im Falle älterer Bestandsgebäude digitale Modelle meist nicht verfügbar. Ein digitales Modell eines bestehenden Gebäudes manuell zu erstellen, ist zwar möglich, jedoch mit großem Aufwand verbunden. Ist ein passendes Gebäudemodell vorhanden, muss ein AR Gerät außerdem in der Lage sein, die eigene Position und Orientierung im Gebäude relativ zu diesem Modell bestimmen zu können, um Augmentierungen lagegerecht anzeigen zu können. Im Rahmen dieser Arbeit werden diverse Aspekte der angesprochenen Problematik untersucht und diskutiert. Dabei werden zunächst verschiedene Möglichkeiten diskutiert, Indoor-Gebäudegeometrie mittels Sensorsystemen zu erfassen. Anschließend wird eine Untersuchung präsentiert, inwiefern moderne AR Geräte, die in der Regel ebenfalls über eine Vielzahl an Sensoren verfügen, ebenfalls geeignet sind, als Indoor-Mapping-Systeme eingesetzt zu werden. Die resultierenden Indoor Mapping Datensätze können daraufhin genutzt werden, um automatisiert Gebäudemodelle zu rekonstruieren. Zu diesem Zweck wird ein automatisiertes, voxel-basiertes Indoor-Rekonstruktionsverfahren vorgestellt. Dieses wird außerdem auf der Grundlage vierer zu diesem Zweck erfasster Datensätze mit zugehörigen Referenzdaten quantitativ evaluiert. Desweiteren werden verschiedene Möglichkeiten diskutiert, mobile AR Geräte innerhalb eines Gebäudes und des zugehörigen Gebäudemodells zu lokalisieren. In diesem Kontext wird außerdem auch die Evaluierung einer Marker-basierten Indoor-Lokalisierungsmethode präsentiert. Abschließend wird zudem ein neuer Ansatz, Indoor-Mapping Datensätze an den Achsen des Koordinatensystems auszurichten, vorgestellt

ADVANCEMENTS IN QUANTITATIVE PERFUSION MAGNETIC RESONANCE IMAGING (MRI) OF DEMENTIA

Alzheimer's disease (AD) affects a considerable, and increasing, part of the population. Early diagnosis of AD is very important to permit effective therapy, and minimize AD's social and economic burden. The goal of our research is to evaluate the changes of cerebral perfusion (i.e., blood flow) in the early stages of AD and the effects from hypertension.We studied volunteers with Mild Cognitive Impairment (MCI) and early AD from the Pittsburgh cohort of the Cardiovascular Health Study (CHS) Cognitive Study during a four-year follow-up. Previously, studies used referral patients who typically have more advanced AD. No perfusion data concerning the early and transitional disease stages are currently available from population studies (i.e., subjects who have been monitored longitudinally in time). There are no common techniques for perfusion quantification and image analysis so that inconsistencies are observed between/within studies, modalities, and researchers. Several advancements were achieved in preparation for the cohort study. First, we improved the accuracy and speed of brain perfusion quantification. Second, we improved the accuracy of image registration to a reference brain using quantitative validation of a registration method and performance comparison with a popular registration method. Third, we improved the method of statistical analysis for evaluating the changes of perfusion between groups. Fourth, we evaluated the changes of cerebral perfusion between cognitive groups (controls, MCIs, ADs), and hypertension and normo-tensive subgroups.Individual perfusion maps were improved by measuring and incorporating individual arrival time, saturation effects, and individual inversion efficiency. A fully deformable registration technique was shown to be more accurate than standard techniques like statistical parametric mapping to detect local perfusion changes. All of the published literature for perfusion up-to-date reported decreased perfusion in AD, but we found hyperperfusion in some regions. The regional findings imply that a hemodynamic process, at the capillary level, accompanied the neurodegenerative process. Hypertensive normal cognitive controls demonstrated hypoperfusion in regions usually involved in AD pathology. However, the effect of hypertension was attenuated after the onset of the pathological cognitive process

Interdisciplinary application of nonlinear time series methods

This paper reports on the application to field measurements of time series methods developed on the basis of the theory of deterministic chaos. The major difficulties are pointed out that arise when the data cannot be assumed to be purely deterministic and the potential that remains in this situation is discussed. For signals with weakly nonlinear structure, the presence of nonlinearity in a general sense has to be inferred statistically. The paper reviews the relevant methods and discusses the implications for deterministic modeling. Most field measurements yield nonstationary time series, which poses a severe problem for their analysis. Recent progress in the detection and understanding of nonstationarity is reported. If a clear signature of approximate determinism is found, the notions of phase space, attractors, invariant manifolds etc. provide a convenient framework for time series analysis. Although the results have to be interpreted with great care, superior performance can be achieved for typical signal processing tasks. In particular, prediction and filtering of signals are discussed, as well as the classification of system states by means of time series recordings.Comment: 86 pages, 26 figure

Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination

Sparse coding may be a general strategy of neural systems for augmenting memory capacity. In Drosophila melanogaster, sparse odor coding by the Kenyon cells of the mushroom body is thought to generate a large number of precisely addressable locations for the storage of odor-specific memories. However, it remains untested how sparse coding relates to behavioral performance. Here we demonstrate that sparseness is controlled by a negative feedback circuit between Kenyon cells and the GABAergic anterior paired lateral (APL) neuron. Systematic activation and blockade of each leg of this feedback circuit showed that Kenyon cells activated APL and APL inhibited Kenyon cells. Disrupting the Kenyon cell–APL feedback loop decreased the sparseness of Kenyon cell odor responses, increased inter-odor correlations and prevented flies from learning to discriminate similar, but not dissimilar, odors. These results suggest that feedback inhibition suppresses Kenyon cell activity to maintain sparse, decorrelated odor coding and thus the odor specificity of memories

An investigation into the dynamical and statistical properties of dominant ocean surface waves using close-range remote sensing

Denne avhandlingen er basert på forskningsresultat som behandler statistiske og dynamiske egenskaper av dominante vinddrevne overflatebølger i åpent hav. Med uttrykket dominante bølger refererer vi her til de største bølgene, med størst energi, i en gitt sjøtilstand. Bølgedrevne prosesser er viktige både i klimasammenheng via atmosfære--hav interaksjon som drives i stor grad av bølgebrytning, samt for kommersiell og rekreasjonell offshorevirksomhet p.g.a. risikoen for å bli utsatt for f.eks. ekstreme enkeltbølger. Både bølgebrytning og ekstrembølgestatistikk er i skrivende stund ufullstendig representert i teoretiske og numeriske modeller. Arbeidet som presenteres i denne avhandlingen undersøker de ovennevnte temaene ved bruk av bølgeobservasjoner som er primært samlet inn på Ekofiskfeltet i den sentrale delen av Nordsjøen. Observasjonsdatasettene består av en langtidstidsserie av laser-altimetermålinger og stereoskopiske videodata fra Ekofisk, samt videomålinger av brytende bølger fra et forskningstokt i nordre Stillehavet. Forskningsresultatene er presentert i artikkelform med to publiserte verk og ett innlevert manuskript. Det blir påvist en tydelig forbindelse mellom økt bølgebrytning og dominante bølgegrupper, et resultat som tidligere har blitt påvist i laboratorie- og modelleksperiment, men sjeldent ved bruk av feltobservasjoner. Tredimensjonale stereo-rekonstruksjoner viser også at ekstreme bølgekammer, både brytende og ikke-brytende, følger nylig utviklet teori om ikke-lineær bølgegruppedynamikk. Dette funnet har konsekvenser f.eks. for estimering av geometriske og kinematiske bølgeegenskaper såsom steilhet og kamhastighet fra endimensjonale tidsseriemålinger. Som følge av en langtidsanalyse av endimensjonal bølgestatistikk blir det vist at enrettet, langkammet og bratt sjø mest sannsynlig leder til ekstreme enkeltbølger med statistiske egenskaper som avviker systematisk fra ordinære statistiske modeller. Tredimensjonal, kortsiktig tid-rom-statistikk av ekstreme bølgekammer blir også undersøkt v.h.a. stereomålingene fra Ekofisk. Her blir det vist at statistiske modeller utvidet fra endimensjonale til tredimensjonale bølgefelt i snitt er velegnet til å beskrive forekomsten av de høyeste bølgekammene, spesielt for relativt store tid-rom segment.The research presented in this thesis characterizes statistical and dynamical aspects of dominant wind-generated surface gravity waves inferred from field observations in intermediate-to-deep water. Dominant waves are the most energetic waves in a sea state, and as such, understanding their behavior is important in both engineering and geophysical contexts. Large waves impart considerable impact forces on marine structures such as oil and gas platforms and offshore wind turbines, and these forces may multiply manyfold when waves break. Wave breaking in deep water, often referred to as whitecapping, is also a key, though incompletely understood, process regulating the transfer of momentum, gas and heat across the air-sea interface, and must thus be accurately parameterized in large-scale weather and climate models. Current theory holds that the wave breaking process is closely linked kinematically and dynamically to the group structure inherent in ocean surface wave fields. Wave group dynamics is also believed to govern the characteristic shape and motion of so-called extreme or rogue waves, whose correct statistical description is central to many offshore activities. The work presented herein shows, using state-of-the-art stereoscopic imaging techniques employed at the Ekofisk platform complex in the central North Sea, that large-scale wave breaking activity in the open ocean is strongly enhanced in dominant wave groups. The topic of wave group-modulated wave breaking has received considerable attention in the past two decades from theoretical, numerical and laboratory perspectives; however, quantitative field studies of the phenomenon remain comparatively rare. The current results also support the general notion that the dominant waves in a given sea state regulate the breaking of shorter waves. The statistics of extreme wave crest elevations is investigated using a novel long-term laser altimeter data set, also located at the Ekofisk field. The validity of the extreme values is verified using a newly developed despiking methodology, and the quality controlled data set, which covers storm events over an 18-year period, is used to investigate the effects of wave steepness and directionality on crest height statistics. Narrow directional spread combined with high wave steepness is found to lead to crest height statistics that deviate the most from standard linear and second-order formulations. Finally, geometric wave shape and crest speed dynamics are analyzed for the highest wave crests encountered in three-dimensional, spatially and temporally resolved segments of the stereo-reconstructed sea surface fields. The directly measured crest steepness is found to conform to the classical breaking limit of Stokes, whereas crest steepness estimated from one-dimensional time series measurements using the linear gravity-wave dispersion relation are systematically higher. This may be at least in part explained by the observation that the directly measured crest speed just before, during and after the moment of maximum crest elevation slows down compared to the linear gravity-wave phase speed estimate. For the first time, the crest speed slowdown is shown with field measurements to apply to both breaking and non-breaking dominant wave crests.Doktorgradsavhandlin