Computer simulations of two-dimensional colloidal crystals in confinement

Monte Carlo simulations are used to study the effect of confinement on a crystal of point particles interacting with an inverse power law potential in d=2 dimensions. This system can describe colloidal particles at the air-water interface, a model system for experimental study of two-dimensional melting. It is shown that the state of the system (a strip of width D) depends very sensitively on the precise boundary conditions at the two ``walls'' providing the confinement. If one uses a corrugated boundary commensurate with the order of the bulk triangular crystalline structure, both orientational order and positional order is enhanced, and such surface-induced order persists near the boundaries also at temperatures where the system in the bulk is in its fluid state. However, using smooth repulsive boundaries as walls providing the confinement, only the orientational order is enhanced, but positional (quasi-) long range order is destroyed: The mean-square displacement of two particles n lattice parameters apart in the y-direction along the walls then crosses over from the logarithmic increase (characteristic for $d=2$) to a linear increase (characteristic for d=1). The strip then exhibits a vanishing shear modulus. These results are interpreted in terms of a phenomenological harmonic theory. Also the effect of incommensurability of the strip width D with the triangular lattice structure is discussed, and a comparison with surface effects on phase transitions in simple Ising- and XY-models is made

Computer simulations of microstructures related to the olivine spinel transition

Tiefherd-Beben, die im oberen Erdmantel in einer Tiefe von ca. 400 km auftreten, werden gewöhnlich mit dem in gleicher Tiefe auftretenden druckabhängigen, polymorphen Phasenübergang von Olivine (α-Phase) zu Spinel (β-Phase) in Verbindung gebracht. Es ist jedoch nach wie vor unklar, wie der Phasenübergang mit dem mechanischen Versagen des Mantelmaterials zusammenhängt. Zur Zeit werden im Wesentlichen zwei Modelle diskutiert, die entweder Mikrostrukturen, die durch den Phasenübergang entstehen, oder aber die rheologischen Veränderungen des Mantelgesteins durch den Phasenübergang dafür verantwortlich machen. Dabei sind Untersuchungen der Olivin→Spinel Umwandlung durch die Unzugänglichkeit des natürlichen Materials vollständig auf theoretische Überlegungen sowie Hochdruck-Experimente und Numerische Simulationen beschränkt. Das zentrale Thema dieser Dissertation war es, ein funktionierendes Computermodell zur Simulation der Mikrostrukturen zu entwickeln, die durch den Phasenübergang entstehen. Des Weiteren wurde das Computer Modell angewandt um die mikrostrukturelle Entwicklung von Spinelkörnern und die Kontrollparameter zu untersuchen. Die Arbeit ist daher in zwei Teile unterteilt: Der erste Teil (Kap. 2 und 3) behandelt die physikalischen Gesetzmäßigkeiten und die prinzipielle Funktionsweise des Computer Modells, das auf der Kombination von Gleichungen zur Errechnung der kinetischen Reaktionsgeschwindigkeit mit Gesetzen der Nichtgleichgewichtsthermodynamik unter nicht-hydostatischen Bedingungen beruht. Das Computermodell erweitert ein Federnetzwerk der Software latte aus dem Programmpaket elle. Der wichtigste Parameter ist dabei die Normalspannung auf der Kornoberfläche von Spinel. Darüber hinaus berücksichtigt das Programm die Latenzwärme der Reaktion, die Oberflächenenergie und die geringe Viskosität von Mantelmaterial als weitere wesentliche Parameter in der Berechnung der Reaktionskinetic. Das Wachstumsverhalten und die fraktale Dimension von errechneten Spinelkörnern ist dabei in guter Übereinstimmung mit Spinelstrukturen aus Hochdruckexperimenten. Im zweiten Teil der Arbeit wird das Computermodell angewandt, um die Entwicklung der Oberflächenstruktur von Spinelkörnern unter verschiedenen Bedigungen zu eruieren. Die sogenannte ’anticrack theory of faulting’, die den katastrophalen Verlauf der Olivine→Spinel Umwandlung in olivinhaltigem Material unter differentieller Spannung durch Spannungskonzentrationen erklärt, wurde anhand des Computermodells untersucht. Der entsprechende Mechanismus konnte dabei nicht bestätigt werden. Stattdessen können Oberflächenstrukturen, die Ähnlichkeiten zu Anticracks aufweisen, durch Unreinheiten des Materials erklärt werden (Kap. 4). Eine Reihe von Simulationen wurde der Herleitung der wichtigsten Kontrollparameter der Reaktion in monomineralischem Olivin gewidmet (Kap. 5 and Kap. 6). Als wichtigste Einflüsse auf die Kornform von Spinel stellten sich dabei die Hauptnormalspannungen auf dem System sowie Heterogenitäten im Wirtsminerals und die Viskosität heraus. Im weiteren Verlauf wurden die Nukleierung und das Wachstum von Spinel in polymineralischen Mineralparagenesen untersucht (Kap. 7). Die Reaktionsgeschwindigkeit der Olivine→Spinel Umwandlung und die Entwicklung von Spinelnetzwerken und Clustern wird durch die Gegenwart nicht-reaktiver Minerale wie Granat oder Pyroxen erheblich beschleunigt. Die Bildung von Spinelnetzwerken hat das Potential, die mechanischen Eigenschaften von Mantelgestein erheblich zu beeinflussen, sei es durch die Bildung potentieller Scherzonen oder durch Gerüstbildung. Dieser Lokalisierungprozess des Spinelwachstums in Mantelgesteinen kann daher ein neues Erklärungsmuster für Tiefbeben darstellen.

Simulations of amphiphilic peptides at the air-water interface

Amphiphile Peptide, Pro-Glu-(Phe-Glu)n-Pro, Pro-Asp-(Phe-Asp)n-Pro, und Phe-Glu-(Phe-Glu)n-Phe, können so aus n alternierenden Sequenzen von hydrophoben und hydrophilen Aminosäuren konstruiert werden, dass sie sich in Monolagen an der Luft-Wasser Grenzfläche anordnen. In biologischen Systemen können Strukturen an der organisch-wässrigen Grenzfläche als Matrix für die Kristallisation von Hydroxyapatit dienen, ein Vorgang der für die Behandlung von Osteoporose verwendet werden kann. In der vorliegenden Arbeit wurden Computersimulationenrneingesetzt, um die Strukturen und die zugrunde liegenden Wechselwirkungen welche die Aggregation der Peptide auf mikroskopischer Ebene steuern, zu untersuchen. Atomistische Molekulardynamik-Simulationen von einzelnen Peptidsträngen zeigen, dass sie sich leicht an der Luft-Wasser Grenzfläche anordnen und die Fähigkeit haben, sich in β-Schleifen zu falten, selbst für relativ kurze Peptidlängen (n = 2). Seltene Ereignisse wie diese (i.e. Konformationsänderungen) erfordern den Einsatz fortgeschrittener Sampling-Techniken. Hier wurde “Replica Exchange” Molekulardynamik verwendet um den Einfluss der Peptidsequenzen zu untersuchen. Die Simulationsergebnisse zeigten, dass Peptide mit kürzeren azidischen Seitenketten (Asp vs. Glu) gestrecktere Konformationen aufwiesen als die mit längeren Seitenketten, die in der Lage waren die Prolin-Termini zu erreichen. Darüber hinaus zeigte sich, dass die Prolin-Termini (Pro vs. Phe) notwendig sind, um eine 2D-Ordnung innerhalb derrnAggregate zu erhalten. Das Peptid Pro-Asp-(Phe-Asp)n-Pro, das beide dieser Eigenschaften enthält, zeigt das geordnetste Verhalten, eine geringe Verdrehung der Hauptkette, und ist in der Lage die gebildeten Aggregate durch Wasserstoffbrücken zwischen den sauren Seitenketten zu stabilisieren. Somit ist dieses Peptid am besten zur Aggregation geeignet. Dies wurde auch durch die Beurteilung der Stabilität von experimentnah-aufgesetzten Peptidaggregaten, sowie der Neigung einzelner Peptide zur Selbstorganisation von anfänglich ungeordneten Konfigurationen unterstützt. Da atomistische Simulationen nur auf kleine Systemgrößen und relativ kurze Zeitskalen begrenzt sind, wird ein vergröbertes Modell entwickelt damit die Selbstorganisation auf einem größeren Maßstab studiert werden kann. Da die Selbstorganisation an der Grenzfläche vonrnInteresse ist, wurden existierenden Vergröberungsmethoden erweitert, um nicht-gebundene Potentiale für inhomogene Systeme zu bestimmen. Die entwickelte Methode ist analog zur iterativen Boltzmann Inversion, bildet aber das Update für das Interaktionspotential basierend auf der radialen Verteilungsfunktion in einer Slab-Geometrie und den Breiten des Slabs und der Grenzfläche. Somit kann ein Kompromiss zwischen der lokalen Flüssigketsstruktur und den thermodynamischen Eigenschaften der Grenzfläche erreicht werden. Die neue Methode wurde für einen Wasser- und einen Methanol-Slab im Vakuum demonstriert, sowie für ein einzelnes Benzolmolekül an der Vakuum-Wasser Grenzfläche, eine Anwendung die von besonderer Bedeutung in der Biologie ist, in der oft das thermodynamische/Grenzflächenpolymerisations-Verhalten zusätzlich der strukturellen Eigenschaften des Systems erhalten werden müssen. Daraufrnbasierend wurde ein vergröbertes Modell über einen Fragment-Ansatz parametrisiert und die Affinität des Peptids zur Vakuum-Wasser Grenzfläche getestet. Obwohl die einzelnen Fragmente sowohl die Struktur als auch die Wahrscheinlichkeitsverteilungen an der Grenzfläche reproduzierten, diffundierte das Peptid als Ganzes von der Grenzfläche weg. Jedoch führte eine Reparametrisierung der nicht-gebundenen Wechselwirkungen für eines der Fragmente der Hauptkette in einem Trimer dazu, dass das Peptid an der Grenzfläche blieb. Dies deutet darauf hin, dass die Kettenkonnektivität eine wichtige Rolle im Verhalten des Petpids an der Grenzfläche spielt.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Evoluzione delle user interface: dalla metafora del desktop al Wearable Computing

Negli ultimi decenni abbiamo assistito ad una graduale evoluzione delle interfacce utente e della tecnologia. Sono stati introdotti nuovi dispositivi mobile e wearable che negli ultimi anni hanno subito un incremento tecnologico esponenziale arrivando a fondersi con la vita di tutti i giorni. Le classiche interfacce grafiche WIMP, la metafora del desktop e le linee guida di progettazione fino ad ora sviluppate non risultano ideali per la nuova tecnologia di wearable computing. Il proposito che la tesi vuole andare ad affrontare è proprio quello di indagare lo sviluppo di nuove user inteface basate sulla tecnologia wearable ed in particolare per smart glasses.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Correlation properties of spontaneous motor activity in healthy infants: a new computer-assisted method to evaluate neurological maturation

Qualitative assessment of spontaneous motor activity in early infancy is widely used in clinical practice. It enables the description of maturational changes of motor behavior in both healthy infants and infants who are at risk for later neurological impairment. These assessments are, however, time-consuming and are dependent upon professional experience. Therefore, a simple physiological method that describes the complex behavior of spontaneous movements (SMs) in infants would be helpful. In this methodological study, we aimed to determine whether time series of motor acceleration measurements at 40-44 weeks and 50-55 weeks gestational age in healthy infants exhibit fractal-like properties and if this self-affinity of the acceleration signal is sensitive to maturation. Healthy motor state was ensured by General Movement assessment. We assessed statistical persistence in the acceleration time series by calculating the scaling exponent α via detrended fluctuation analysis of the time series. In hand trajectories of SMs in infants we found a mean α value of 1.198 (95 % CI 1.167-1.230) at 40-44 weeks. Alpha changed significantly (p = 0.001) at 50-55 weeks to a mean of 1.102 (1.055-1.149). Complementary multilevel regression analysis confirmed a decreasing trend of α with increasing age. Statistical persistence of fluctuation in hand trajectories of SMs is sensitive to neurological maturation and can be characterized by a simple parameter α in an automated and observer-independent fashion. Future studies including children at risk for neurological impairment should evaluate whether this method could be used as an early clinical screening tool for later neurological compromise.

High-resolution computer simulation of the dynamics of isoelectric focusing using carrier ampholytes: focusing with concurrent electrophoretic mobilization is an isotachophoretic process

Focusing of four hemoglobins with concurrent electrophoretic mobilization was studied by computer simulation. A dynamic electrophoresis simulator was first used to provide a detailed description of focusing in a 100-carrier component, pH 6-8 gradient using phosphoric acid as anolyte and NaOH as catholyte. These results are compared to an identical simulation except that the catholyte contained both NaOH and NaCl. A stationary, steady-state distribution of carrier components and hemoglobins is produced in the first configuration. In the second, the chloride ion migrates into and through the separation space. It is shown that even under these conditions of chloride ion flux a pH gradient forms. All amphoteric species acquire a slight positive charge upon focusing and the whole pattern is mobilized towards the cathode. The cathodic gradient end is stable whereas the anodic end is gradually degrading due to the continuous accumulation of chloride. The data illustrate that the mobilization is a cationic isotachophoretic process with the sodium ion being the leading cation. The peak height of the hemoglobin zones decreases somewhat upon mobilization, but the zones retain a relatively sharp profile, thus facilitating detection. The electropherograms that would be produced by whole column imaging and by a single detector placed at different locations along the focusing column are presented and show that focusing can be commenced with NaCl present in the catholyte at the beginning of the experiment. However, this may require detector placement on the cathodic side of the catholyte/sample mixture interface.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Computer algebra for x-ray spectral reconstruction between 6 and 25 MV

A previously presented algorithm for the reconstruction of bremsstrahlung spectra from transmission data has been implemented into MATHEMATICA. Spectra vectorial algebra has been used to solve the matrix system A * F = T. The new implementation has been tested by reconstructing photon spectra from transmission data acquired in narrow beam conditions, for nominal energies of 6, 15, and 25 MV. The results were in excellent agreement with the original calculations. Our implementation has the advantage to be based on a well-tested mathematical kernel. Furthermore it offers a comfortable user interface.

Development of a computer-assisted high-pressure Injection device for vertebroplasty

A novel computer-assisted injection device for the delivery of highly viscous bone cements in vertebroplasty is presented. It addresses the shortcomings of manual injection systems ranging from low-pressure and poor level of control to device failure. The presented instrument is capable of generating a maximum pressure of 5000 kPa in traditional 6-ml syringes and provides an advanced control interface for precise cement delivery from outside radiation fields emitted by intraoperative imaging systems. The integrated real-time monitoring of injection parameters, such as flow-rate, volume, pressure, and viscosity, simplifies consistent documentation of interventions and establishes a basis for the identification of safe injection protocols on the longer term. Control algorithms prevent device failure due to overloading and provide means to immediately stop cement flow to avoid leakage into adjacent tissues.

Design and implementation of a 3D computer game controller using inertial MEMS sensors

Though 3D computer graphics has seen tremendous advancement in the past two decades, most available mechanisms for computer interaction in 3D are high cost and targeted for industry and virtual reality applications. Recent advances in Micro-Electro-Mechanical-System (MEMS) devices have brought forth a variety of new low-cost, low-power, miniature sensors with high accuracy, which are well suited for hand-held devices. In this work a novel design for a 3D computer game controller using inertial sensors is proposed, and a prototype device based on this design is implemented. The design incorporates MEMS accelerometers and gyroscopes from Analog Devices to measure the three components of the acceleration and angular velocity. From these sensor readings, the position and orientation of the hand-held compartment can be calculated using numerical methods. The implemented prototype is utilizes a USB 2.0 compliant interface for power and communication with the host system. A Microchip dsPIC microcontroller is used in the design. This microcontroller integrates the analog to digital converters, the program memory flash, as well as the core processor, on a single integrated circuit. A PC running Microsoft Windows operating system is used as the host machine. Prototype firmware for the microcontroller is developed and tested to establish the communication between the design and the host, and perform the data acquisition and initial filtering of the sensor data. A PC front-end application with a graphical interface is developed to communicate with the device, and allow real-time visualization of the acquired data.

A Stereoscopic Volume Rendered Brain Atlas

The anatomy of the human brain is organized as a complex arrangement of interrelated structures in three dimensional space. To facilitate the understanding of both structure and function, we have created a volume rendered brain atlas (VRBA) with an intuitive interface that allows real-time stereoscopic rendering of brain anatomy. The VRBA incorporates 2-dimensional and 3-dimensional texture mapping to display segmented brain anatomy co-registered with a T1 MRI. The interface allows the user to remove and add any of the 62 brain structures, as well as control the display of the MRI dataset. The atlas also contains brief verbal and written descriptions of the different anatomical regions to correlate structure with function. A variety of stereoscopic projection methods are supported by the VRBA and provide an abstract, yet simple, way of visualizing brain anatomy and 3-dimensional relationships between different nuclei.

Microstates in language-related brain potential maps show noun-verb differences

Brain processing of grammatical word class was studied analyzing event-related potential (ERP) brain fields. Normal subjects observed a randomized sequence of single German nouns and verbs on a computer screen, while 20-channel ERP field map series were recorded separately for both word classes. Spatial microstate analysis was applied, based on the observation that series of ERP maps consist of epochs of quasi-stable map landscapes and based on the rationale that different map landscapes must have been generated by different neural generators and thus suggest different brain functions. Space-oriented segmentation of the mean map series identified nine successive, different functional microstates, i.e., steps of brain information processing characterized by quasi-stable map landscapes. In the microstate from 116 to 172 msec, noun-related maps differed significantly from verb-related maps along the left–right axis. The results indicate that different neural populations represent different grammatical word classes in language processing, in agreement with clinical observations. This word class differentiation as revealed by the spatial–temporal organization of neural activity occurred at a time after word input compatible with speed of reading.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.