Semantic Segmentation and Completion of 2D and 3D Scenes

Semantic segmentation is one of the fundamental problems in computer vision. This thesis addresses various tasks, all related to the fine-grained, i.e. pixel-wise or voxel-wise, semantic understanding of a scene. In the recent years semantic segmentation by 2D convolutional neural networks has become as much as a default pre-processing step for many other computer vision tasks, since it outputs very rich spatially resolved feature maps and semantic labels that are useful for many higher level recognition tasks. In this thesis, we make several contributions to the field of semantic scene understanding using an image or a depth measurement, recorded by different types of laser sensors, as input. Firstly, we propose a new approach to 2D semantic segmentation of images. It consists of an adaptation of an existing approach for real time capability under constrained hardware demands that are required by a real life drone. The approach is based on a highly optimized implementation of random forests combined with a label propagation strategy. Next, we shift our focus to what we believe is one of the important next forefronts in computer vision: To give machines the ability to anticipate and extrapolate beyond what is captured in a single frame by a camera or depth sensor. This anticipation capability is what allows humans to efficiently interact with their environment. The need for this ability is most prominently displayed in the behaviour of today's autonomous cars. One of their shortcomings is that they only interpret the current sensor state, which prevents them from anticipating events which would require an adaptation of their driving policy. The result is a lot of sudden breaks and non-human-like driving behaviour, which can provoke accidents or negatively impact the traffic flow. Therefore we first propose a task to spatially anticipate semantic labels outside the field of view of an image. The task is based on the Cityscapes dataset, where each image has been center cropped. The goal is to train an algorithm that predicts the semantic segmentation map in the area outside the cropped input region. Along with the task itself, we propose an efficient iterative approach based on 2D convolutional neural networks by designing a task adapted loss function. Afterwards, we switch to the 3D domain. In three dimensions the goal shifts from assigning pixel-wise labels towards the reconstruction of the full 3D scene using a grid of labeled voxels. Thereby one has to anticipate the semantics and geometry in the space that is occluded by the objects themselves from the viewpoint of an image or laser sensor. The task is known as 3D semantic scene completion and has recently caught a lot of attention. Here we propose two new approaches that advance the performance of existing 3D semantic scene completion baselines. The first one is a two stream approach where we leverage a multi-modal input consisting of images and Kinect depth measurements in an early fusion scheme. Moreover we propose a more memory efficient input embedding. The second approach to semantic scene completion leverages the power of the recently introduced generative adversarial networks (GANs). Here we construct a network architecture that follows the GAN principles and uses a discriminator network as an additional regularizer in the 3D-CNN training. With our proposed approaches in semantic scene completion we achieve a new state-of-the-art performance on two benchmark datasets. Finally we observe that one of the shortcomings in semantic scene completion is the lack of a realistic, large scale dataset. We therefore introduce the first real world dataset for semantic scene completion based on the KITTI odometry benchmark. By semantically annotating alls scans of a 10 Hz Velodyne laser scanner, driving through urban and countryside areas, we obtain data that is valuable for many tasks including semantic scene completion. Along with the data we explore the performance of current semantic scene completion models as well as models for semantic point cloud segmentation and motion segmentation. The results show that there is still a lot of space for improvement for either tasks so our dataset is a valuable contribution for future research into these directions

Surgical Therapy of Atrial Fibrillation

Atrial fibrillation (AF) can be found in an increasing number of cardiac surgical patients due to a higher patient's age and comorbidities. Atrial fibrillation is known, however, to be a risk factor for a greater mortality, and one aim of intraoperative AF treatment is to approximate early and long-term survival of AF patients to survival of patients with preoperative sinus rhythm. Today, surgeons are more and more able to perform less complex, that is, minimally invasive cardiac surgical procedures. The evolution of alternative ablation technologies using different energy sources has revolutionized the surgical therapy of atrial fibrillation and allows adding the ablation therapy without adding significant risk. Thus, the surgical treatment of atrial fibrillation in combination with the cardiac surgery procedure allows to improve the postoperative long-term survival and to reduce permanent anticoagulation in these patients. This paper focuses on the variety of incisions, lesion sets, and surgical techniques, as well as energy modalities and results of AF ablation and also summarizes future trends and current devices in use

A user material interface for the Peridynamic Peridigm framework.

User materials (UMAT) in finite element codes allow the researchers or engineers to apply their own material routines. Simple software interfaces are specified to represent the material behavior in software. In order to use these already existing and often validated models to Peridynamics a UMAT interface is presented. It allows the simplified use of already existing material routines in the peridynamic framework Peridigm. The interface is based on the Abaqus UMAT definition and allows the integration of Fortran routines directly into Peridigm. The integration of already existing UMAT routines based in Peridigm eliminates the need for redevelopment and reprogramming material models from classical continuum mechanics theory. In addition, the same material model implementations are applicable in finite element as well as peridynamic simulations. This opens up new possibilities for analysis, verification and comparison. With this interface many material routines can be reused and applied to progressive failure analysis. The source code is stored in a GitHub repository

A comparative-advantage approach to government debt maturity’,

Abstract We study optimal government debt maturity in a model where investors derive monetary services from holding riskless short-term securities. In a setting where the government is the only issuer of such riskless paper, it trades off the monetary premium associated with short-term debt against the refinancing risk implied by the need to roll over its debt more often. We then extend the model to allow private financial intermediaries to compete with the government in the provision of short-term, money-like claims. We argue that if there are negative externalities associated with private money creation, the government should tilt its issuance more towards short maturities. The idea is that the government may have a comparative advantage relative to the private sector in bearing refinancing risk, and hence should aim to partially crowd out the private sector's use of short-term debt

A proposal for early dosing regimens in heart transplant patients receiving thymoglobulin and calcineurin inhibition

There is currently no consensus regarding the dose or duration of rabbit antithymocyte globulin (rATG) induction in different types of heart transplant patients, or the timing and intensity of initial calcineurin inhibitor (CNI) therapy in rATG-treated individuals. Based on limited data and personal experience, the authors propose an approach to rATG dosing and initial CNI administration. Usually rATG is initiated immediately after exclusion of primary graft failure, although intraoperative initiation may be appropriate in specific cases. A total rATG dose of 4.5 to 7.5 mg/kg is advisable, tailored within that range according to immunologic risk and adjusted according to immune monitoring. Lower doses (eg, 3.0 mg/kg) of rATG can be used in patients at low immunological risk, or 1.5 to 2.5 mg/kg for patients with infection on mechanical circulatory support. The timing of CNI introduction is dictated by renal recovery, varying between day 3 and day 0 after heart transplantation, and the initial target exposure is influenced by immunological risk and presence of infection. Rabbit antithymocyte globulin and CNI dosing should not overlap except in high-risk cases. There is a clear need for more studies to define the optimal dosing regimens for rATG and early CNI exposure according to risk profile in heart transplantation