iCaRL: Incremental Classifier and Representation Learning

A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail.Comment: Accepted paper at CVPR 201

XUV-IR pump-probe experiments: Exploring nuclear and electronic correlated quantum dynamics in the hydrogen molecule

Wave packet dynamics and autoionization of doubly excited states in molecules can be studied by combining an intense, short-pulse infrared (IR) laser and a extreme ultraviolet (XUV) source with a Reaction Microscope, which allows for coincident measurements of ions and electrons. Furthermore, this detection system is capable of measuring the three dimensional momentum of each charged particle involved in the ionization process. This technique was used to investigate the autoionization of doubly excited H2 molecules, a process that occurs on a timescale of a few femtoseconds. Since this reaction time is of the order of the molecular motion, the nuclei can no longer be regarded as stationary. The coupling of the dissociation dynamics of H2+ to the corresponding electron, which is emitted through the autoionization of doubly excited states, leads to a symmetry breaking in the dissociation. In the conducted measurements, this translates into a localization of coincident electron-ion pairs. In order to study the temporal dynamics of these processes, the molecules were further probed with delayed IR pulses, revealing dynamics within the autoionization

Homogenized yarn-level cloth

We present a method for animating yarn-level cloth effects using a thin-shell solver. We accomplish this through numerical homogenization: we first use a large number of yarn-level simulations to build a model of the potential energy density of the cloth, and then use this energy density function to compute forces in a thin shell simulator. We model several yarn-based materials, including both woven and knitted fabrics. Our model faithfully reproduces expected effects like the stiffness of woven fabrics, and the highly deformable nature and anisotropy of knitted fabrics. Our approach does not require any real-world experiments nor measurements; because the method is based entirely on simulations, it can generate entirely new material models quickly, without the need for testing apparatuses or human intervention. We provide data-driven models of several woven and knitted fabrics, which can be used for efficient simulation with an off-the-shelf cloth solver

Relationship between molecular pathogen detection and clinical disease in febrile children across Europe: a multicentre, prospective observational study

BackgroundThe PERFORM study aimed to understand causes of febrile childhood illness by comparing molecular pathogen detection with current clinical practice.MethodsFebrile children and controls were recruited on presentation to hospital in 9 European countries 2016-2020. Each child was assigned a standardized diagnostic category based on retrospective review of local clinical and microbiological data. Subsequently, centralised molecular tests (CMTs) for 19 respiratory and 27 blood pathogens were performed.FindingsOf 4611 febrile children, 643 (14%) were classified as definite bacterial infection (DB), 491 (11%) as definite viral infection (DV), and 3477 (75%) had uncertain aetiology. 1061 controls without infection were recruited. CMTs detected blood bacteria more frequently in DB than DV cases for N. meningitidis (OR: 3.37, 95% CI: 1.92-5.99), S. pneumoniae (OR: 3.89, 95% CI: 2.07-7.59), Group A streptococcus (OR 2.73, 95% CI 1.13-6.09) and E. coli (OR 2.7, 95% CI 1.02-6.71). Respiratory viruses were more common in febrile children than controls, but only influenza A (OR 0.24, 95% CI 0.11-0.46), influenza B (OR 0.12, 95% CI 0.02-0.37) and RSV (OR 0.16, 95% CI: 0.06-0.36) were less common in DB than DV cases. Of 16 blood viruses, enterovirus (OR 0.43, 95% CI 0.23-0.72) and EBV (OR 0.71, 95% CI 0.56-0.90) were detected less often in DB than DV cases. Combined local diagnostics and CMTs respectively detected blood viruses and respiratory viruses in 360 (56%) and 161 (25%) of DB cases, and virus detection ruled-out bacterial infection poorly, with predictive values of 0.64 and 0.68 respectively.InterpretationMost febrile children cannot be conclusively defined as having bacterial or viral infection when molecular tests supplement conventional approaches. Viruses are detected in most patients with bacterial infections, and the clinical value of individual pathogen detection in determining treatment is low. New approaches are needed to help determine which febrile children require antibiotics.FundingEU Horizon 2020 grant 668303

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Personen Klassifikation mit konvolutionellen Neuronalen Netzwerken

Zusammenfassung in deutscher SpracheAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersGroßangelegte Objekterkennungswettbewerbe wie zum Beispiel das ImageNet Large Scale Visual Object Recognition Challenge oder Microsoft Common Objects in Context haben gezeigt, dass faltende neuronale Netzwerke den Stand der Technik der Leistung in Computer Vision Aufgaben wie Objektdetektion und Bildklassifikation erreichen. Faltende neuronale Netzwerke profitieren von Datensätzen aus hunderttausenden Bildern, die mehr Intra-Klassen-Variabilitäten abdecken und helfen, robuste und invariante Merkmale zu lernen. Allerdings sind dies Datensätze für allgemeine Objekterkennung und es existiert kein Datensatz für Personenerkennung, der ähnliche Ausmaße hat. Daher werden Daten von über 30 Datensätzen für Personendetektion, Personenklassifikation, Personensegmentation und Personenverfolgung gesammelt, um einen Topf von Datenquellen für Personenerkennung zu formen. Es wird eine Methode für das Extrahieren von anwendungsspezifischen Daten aus diesem Topf und das Trainieren von einem faltenden neuronalen Netzwerk für binäre Personenklassifikation vorgeschlagen. Weiters werden die Leistungsverbesserungen durch Subklassenannotation der Nicht-Personen- Klasse analysiert und eine Fehlerquote von 2.82% wird erreicht. Resultate zeigen, dass die Verwendung von unserem Personenerkennungsdatensatz als Vor-Trainings-Datensatz für Personenklassifikationsaufgaben, welche Trainings-Datensätze von nur wenigen tausenden Bildern haben, zu einer Genauigkeitssteigerung von über 8% führt, was in Folge zu einer Gesamtpräzision von über 99% führt. Die Qualität unseres Datensatzes wird weiters durch zusätzliche Evaluierung nachgewiesen. Darüber hinaus betonen die Ergebnisse die Komplexität der Auswahl einer geeigneten Architektur eines faltenden neuronalen Netzwerks und bezeugen die erhöhte Robustheit beim Training durch die Verwendung von Subklassenannotationen bezüglich Initialisierung und Lösungsalgorithmen.Large-scale object recognition challenges such as the ImageNet Large Scale Visual Object Recognition Challenge or the Microsoft Common Objects in Context challenge have shown that convolutional neural networks achieve state-of-the-art performance on computer vision problems like object detection and image classification. Convolutional neural networks benefit from datasets of hundreds of thousands of images, which cover more intraclass variabilities and aid in learning robust and invariant features. However, these datasets are designed for general object recognition and no dataset of similar dimensions exist for person recognition. Therefore, data is collected from over 30 datasets for person detection, classification, segmentation and tracking to form a pool of data sources for person recognition. A method of extracting application-specific data from this pool and training a convolutional neural network for binary person classification is proposed. Additionally, performance improvements of subclass labeling are analyzed for the nonperson class and an error rate of 2.82% is achieved. Results demonstrate that using our person recognition dataset as a pre-training set for person classification tasks with training sets of only up to a few thousand images leads to an increase in accuracy of over 8% to a total accuracy of over 99%. The quality of our dataset is demonstrated by additional evaluation. Furthermore, results emphasize the complexity of convolutional neural network architecture choice and indicate increased robustness in training with subclass labeling with regards to initialization and solver algorithms.7

ISTA Thesis

The complex yarn structure of knitted and woven fabrics gives rise to both a mechanical and visual complexity. The small-scale interactions of yarns colliding with and pulling on each other result in drastically different large-scale stretching and bending behavior, introducing anisotropy, curling, and more. While simulating cloth as individual yarns can reproduce this complexity and match the quality of real fabric, it may be too computationally expensive for large fabrics. On the other hand, continuum-based approaches do not need to discretize the cloth at a stitch-level, but it is non-trivial to find a material model that would replicate the large-scale behavior of yarn fabrics, and they discard the intricate visual detail. In this thesis, we discuss three methods to try and bridge the gap between small-scale and large-scale yarn mechanics using numerical homogenization: fitting a continuum model to periodic yarn simulations, adding mechanics-aware yarn detail onto thin-shell simulations, and quantitatively fitting yarn parameters to physical measurements of real fabric. To start, we present a method for animating yarn-level cloth effects using a thin-shell solver. We first use a large number of periodic yarn-level simulations to build a model of the potential energy density of the cloth, and then use it to compute forces in a thin-shell simulator. The resulting simulations faithfully reproduce expected effects like the stiffening of woven fabrics and the highly deformable nature and anisotropy of knitted fabrics at a fraction of the cost of full yarn-level simulation. While our thin-shell simulations are able to capture large-scale yarn mechanics, they lack the rich visual detail of yarn-level simulations. Therefore, we propose a method to animate yarn-level cloth geometry on top of an underlying deforming mesh in a mechanics-aware fashion in real time. Using triangle strains to interpolate precomputed yarn geometry, we are able to reproduce effects such as knit loops tightening under stretching at negligible cost. Finally, we introduce a methodology for inverse-modeling of yarn-level mechanics of cloth, based on the mechanical response of fabrics in the real world. We compile a database from physical tests of several knitted fabrics used in the textile industry spanning diverse physical properties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation, using homogenized shell models to speed up computation and adding some small-but-necessary extensions to yarn-level models used in computer graphics

Erzeugung und Charakterisierung ultrakurzer Lichtimpulse für die Generation Hoher Harmonischer Strahlung

In atomic and molecular physics experiments extremely short laser pulses, mostly in the sub ten-femtosecond range, are of strong interest. The shorter the pulses are and correspondingly, with growing bandwidth, the more important dispersion control and management becomes. In this diploma thesis a new setup for spectral broadening involving self phase modulation via lamentation and subsequent recompression is presented. Moreover pulse characterization through a dedicated interferometric autocorrelation setup with nearly zero dispersion has been achieved. The initial pulses reveal a duration of 32 fs at a repetition rate of 8000 Hz and a single pulse energy of about 1 mJ. An extensive investigation of important quantities, in uencing the lamentation process, like pressure, focussing parameters and the interaction length was performed. Their optimization yielded a broadening of the fundamental spectra of about a factor of 5 supporting lightpulses down to a fourierlimited duration of 4 fs with 1.5 optical cycles. These values, as well as the appearance of smaller pre- and postpulse structures were con rmed by autocorrelation measurements of the pulses. Even though the full reconstruction of the time dependent electric eld of the pulses is impossible via autocorrelation, one can retrieve quantitative information about the pulse chirp by simulating the complete autocorrelation signal, including the second-order dispersion parameter

Osteomyelitis of the Patella in a 10-Year-Old Girl: A Case Report and Review of the Literature

The incidence of osteomyelitis constantly declines. While the disease most commonly affects the long bones, involvement of the patella is rarely seen. Due to this rarity and the variable clinical presentation, diagnosis is often delayed. The present case report describes a 10-year-old female patient with a delayed diagnosis of patella osteomyelitis. The diagnostic procedures and the treatment regimen are described. Additionally, a detailed literature review of the available publications reporting osteomyelitis of the patella in children is presented

Mechanics-aware deformation of yarn pattern geometry

Triangle mesh-based simulations are able to produce satisfying animations of knitted and woven cloth; however, they lack the rich geometric detail of yarn-level simulations. Naive texturing approaches do not consider yarn-level physics, while full yarn-level simulations may become prohibitively expensive for large garments. We propose a method to animate yarn-level cloth geometry on top of an underlying deforming mesh in a mechanics-aware fashion. Using triangle strains to interpolate precomputed yarn geometry, we are able to reproduce effects such as knit loops tightening under stretching. In combination with precomputed mesh animation or real-time mesh simulation, our method is able to animate yarn-level cloth in real-time at large scales