Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization

Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning

Query processing of pre-partitioned data using Sandwich Operators

textabstractIn this paper we present the Sandwich Operators, an elegant approach to exploit pre-sorting or pre-grouping from clustered storage schemes in operators such as Aggregation/Grouping, HashJoin, and Sort of a database management system. Thereby, each of these operator types is "sandwiched" by two new operators, namely PartitionSplit and PartitionRestart. PartitionSplit splits the input relation into its smaller independent groups on which the sandwiched operator is executed. After a group is processed PartitionRestart is used to trigger the execution on the following group. Executing one of these operator types with the help of the Sandwich Operators introduces minimal overhead and does not penalty performance of the sandwiched operator as its implementation remains unchanged. On the contrary, we show that sandwiched execution of an operator results in lower memory consumption and faster execution time. PartitionSplit and PartitionRestart replace special implementations of partitioned versions of these operator. Sandwich Operators also turn blocking operators in streaming operators, resulting in faster response times for the first query results

Modeling Micro-Porous Surfaces for Secondary Electron Emission Control to Suppress Multipactor

This work seeks to understand how the topography of a surface can be engineered to control secondary electron emission (SEE) for multipactor suppression. Two unique, semi-empirical models for the secondary electron yield (SEY) of a micro-porous surface are derived and compared. The first model is based on a two-dimensional (2D) pore geometry. The second model is based on a three-dimensional (3D) pore geometry. The SEY of both models is shown to depend on two categories of surface parameters: chemistry and topography. An important parameter in these models is the probability of electron emissions to escape the surface pores. This probability is shown by both models to depend exclusively on the aspect ratio of the pore (the ratio of the pore height to the pore diameter). The increased accuracy of the 3D model (compared to the 2D model) results in lower electron escape probabilities with the greatest reductions occurring for aspect ratios less than two. In order to validate these models, a variety of micro-porous gold surfaces were designed and fabricated using photolithography and electroplating processes. The use of an additive metal-deposition process (instead of the more commonly used subtractive metal-etch process) provided geometrically ideal pores which were necessary to accurately assess the 2D and 3D models. Comparison of the experimentally measured SEY data with model predictions from both the 2D and 3D models illustrates the improved accuracy of the 3D model. For a micro-porous gold surface consisting of pores with aspect ratios of two and a 50% pore density, the 3D model predicts that the maximum total SEY will be one. This provides optimal engineered surface design objectives to pursue for multipactor suppression using gold surfaces

Automatic Schema Design for Co-Clustered Tables

Schema design of analytical workloads provides opportunities to index, cluster, partition and/or materialize. With these opportunities also the complexity of finding the right setup rises. In this paper we present an automatic schema design approach for a table co-clustering scheme called Bitwise Dimensional Co-Clustering, aimed at schemas with a moderate amount dimensions, but not limited to typical star and snowflake schemas. The goal is to design one primary schema and keep the knobs to turn to a minimum while providing a robust schema for a wide range of queries. In our approach a clustered schema is derived by trying to apply dimensions throughout the whole schema and co-cluster as many tables as possible according to at least one common dimension. Our approach is based on the assumption that initially foreign key relationships and a set of dimensions are defined based on classic DDL

A new BIST scheme for low-power and high-resolution DAC testing

A BIST scheme for testing on chip DAC is presented in this paper. We discuss the generation of on chip testing stimuli and the measurement of digital signals with a narrow-band digital filter. We validate the scheme with software simulation and point out the possibility of ADC BIST with verified DACicus-journals

Comparison of Potential Sites in China for Erecting a Hybrid Solar Tower Power Plant with Air Receiver

AbstractIn this work transient simulation results of a hybrid solar tower power plant with openvolumetric receiver technology are presented for several locations in China. The openvolumetric receiver uses ambient air as heat transfer fluid and the hybridization can be realized with additional firing. The solar receiver and/or the additional firing heat up the air which is then passed through a boiler of a conventional Rankine cycle. The simulated plantis based on the configuration of the solar thermal test and demonstration power plant located in Jülich (STJ). The investigatedplant operates in hybrid - parallelmode which allows a constant power generation. The meteorological data for the different sites in China was taken from the software Meteonorm in a time resolution of one hour. The solar tower power simulation tool was developed in the simulation environment MATLAB/Simulink

Editorial: Fibrosis and inflammation in tissue pathophysiology

In adult mammals, tissue damage activates a wound healing response with acute inflammation followed by either complete repair (for low-grade damage or in highly regenerative tissues, such as the liver) or replacement fibrosis (for extensive damage or in poorly regenerative tissues, such as the myocardium). Persistent damage and repeated insults sustain continuous activation of repair pathways leading to chronic inflammation, progressive tissue fibrosis and sclerosis. Despite the evolutionary advantage conferred by scarring as a rapid repair mechanism, chronic fibrosis leads to tissue adverse remodeling and impaired function. Persistent low-level inflammation and fibrosis are observed in many pathological conditions (e.g. hypertension, obesity, diabetes, genetic diseases), and lead to further complications including atherosclerosis and ischemic events, organ failure, autoimmune diseases, cancer, aging, and reduced resilience to infectious diseases. Pathological fibrosis plays a major role in a wide range of diseases, accounting for an increasingly large fraction of mortality cases worldwide. While recent advances have unveiled many environmental and genetic causes of fibrotic disorders, a better understanding of both ubiquitous and tissue-specific regulatory pathways and cellular dynamics could help to design new targeted therapies, and to identify the etiology of idiopathic diseases. Within this Research Topic, we invite submission of articles (reviews, original research, or methodology articles) on the pathophysiological role of fibrosis and inflammation in different tissues. Areas to be covered include, but are not limited to: - genetic and environmental causes of persistent low-level inflammation and fibrosis (e.g. autoimmunity, hypertension, obesity, diabetes, genetic diseases, latent infections); - comorbidities including systemic sclerosis, neurological disorders, organ failure (heart, skeletal muscle, kidney, liver, lungs), cancer, and reduced resilience to infectious diseases; - in vivo (animal models) and in vitro (organoids, tissue culture) modelling of fibrotic diseases for the discovery of novel therapeutic targets and potential tissue-specific treatments; - vascular responses to inflammation and inflammation of vascular tissues; - system biology approaches to identify molecular and cellular networks leading to chronic inflammation and fibrosis

PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Strategy for the Implementation of an Industrial Land Bank

The City of Cleveland plans to create a long-term vision for industrial and commercial land reuse in order to better serve the business and neighborhood interests of its citizens. The implementation of an industrial land bank is one critical way in which to fulfill this goal. This study aimed to develop a strategy to aid the city in the operation and management of rehabilitating commercial and industrial properties for reuse. The objectives of the project were to: * Incorporate a strategy understood by senior managers at the city that identifies a broad economic redevelopment vision, especially for brownfields. * Include in the plan strategies for financing the acquisition and/or transfer of properties into the land bank. * Establish elements in the plan to include both short- and long-term implementation