Induced Delocalization by Correlation and Interaction in the one-dimensional Anderson Model

We consider long-range correlated disorder and mutual interacting particles according to a dipole-dipole coupling as modifications to the one-dimensional Anderson model. Technically we rely on the (numerical) exact diagonalization of the system's Hamilitonian. From the perspective of different localization measures we confirm and extend the picture of the emergence of delocalized states with increasing correlations. Beside these studies a definition for multi-particle localization is proposed. In the case of two interacting bosons we observe a sensitivity of localization with respect to the range of the particle-particle interaction and insensitivity to the coupling's sign, which should stimulate new theoretical approaches and experimental investigations with e.g. dipolar cold quantum gases. This revised manuscript is much more explicit compared to the initial version of the paper. Major extensions have been applied to Sects. II and III where we updated and added figures and we more extensively compared our results to the literature. Furthermore, Sect. III additionally contains a phenomenological line of reasoning that bridges from delocalization by correlation to delocalization by interaction on the basis of the multi-particle Hamilton matrix

Map Generation from Large Scale Incomplete and Inaccurate Data Labels

Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc.. In this paper we present progress in developing an algorithmic pipeline and distributed compute system that automates the process of map creation using high resolution aerial images. Unlike previous studies, most of which use datasets that are available only in a few cities across the world, we utilizes publicly available imagery and map data, both of which cover the contiguous United States (CONUS). We approach the technical challenge of inaccurate and incomplete training data adopting state-of-the-art convolutional neural network architectures such as the U-Net and the CycleGAN to incrementally generate maps with increasingly more accurate and more complete labels of man-made infrastructure such as roads and houses. Since scaling the mapping task to CONUS calls for parallelization, we then adopted an asynchronous distributed stochastic parallel gradient descent training scheme to distribute the computational workload onto a cluster of GPUs with nearly linear speed-up.Comment: This paper is accepted by KDD 202

On a Numerical Framework for Functional Renormalization of Quantum Statistical Physics

The subject of this thesis intends to investigate and put forward the method of functional renormalization within the field of quantum statistical physics. Our focus is on a (generic) truncation scheme that is suited to flexibly resolve two important mathematical objects of physical relevance: The (inverse) propagator and the effective potential, respectively. In the former case our effort aims at a proper resolution of the momentum dependence which is related to the particles dispersion relation. The effective potential contains valuable thermodynamic information on e.g. the equation of state and the system’s phase diagram. A main achievement related to our study is the implementation of a numerical library, libfrg, which sets up a generic framework for high performance parallel computing in conjunction with the method of functional renormalization. By licensing it under the GNU GPL it is tailored to foster shared development by the community of scientists with research focus on this branch of physics

Quantification of Carbon Sequestration in Urban Forests

Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere. However, the lack of efficient quantification methods of carbon stored in trees renders it difficult to track the process. We present an approach to estimate the carbon storage in trees based on fusing multi-spectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species -- key attributes to carbon storage quantification. We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from aerial imagery in order to determine the tree's biomass. Specifically, we estimate a total of $52,000$ tons of carbon sequestered in trees for New York City's borough Manhattan

Geospatial Discovery Network (GeoDN): A Large-Scale Data Mining Perspective

Lightning presentation of activities in the Earth Observation Data Science department @ German Aerospace Center related to "Large-Scale Data Mining" in the context of the DLR terrabyte initiative, application of self-supervised learning to cross-data center analytics, and climate action geodata analytics to network with academia, corporate, and governmental organizations such as MIT, Oxford University, Stony Brook University, Columbia University, the New York Academy of Sciences, NASA, NOAA, ECCC, IBM Research, and Argonne National Laboratory

Large-Scale Geo-Data Mining for Good

The ever-increasing amount of earth observation data provides us an ample basis to sense, understand, and visualize the health of our planet. Machine learning enables us to value our home through mining massive amounts of geo-information provided by satellites and airborne measurements once curated for scalable access by a Big Geospatial Data "digital twin" platform. My presentation intends to bridge the "AI Ethics" to the "Big Data & Global Human Behavior" session through a technical overview of remote sensor technologies demonstrating their value for applications in archaeology, urban mapping, and biomass estimation relevant to various ethical aspects. I invite you to enter a vital, interdisciplinary discussion on a. How to leverage machine learning and remote sensing to improve the local climate in (mega)cities for the well-being of its urban population; and how to address ethical concerns related? b. How artificial intelligence and earth observation have the capacity to help protect the Amazon rainforest led by fair principles incorporating the "perspectives of all stakeholders" such as endangered species, local farmers, archaeologists, and governments? What are the current limitations of these technologies vis-a-vis protection of human rights and ethics; and how do we overcome limitations? c. How do we transparently implement AI-based environmental management inspired by the United Nation's Sustainable Development Goals

Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing

Self-supervised learning guided by masked image modelling, such as Masked AutoEncoder (MAE), has attracted wide attention for pretraining vision transformers in remote sensing. However, MAE tends to excessively focus on pixel details, thereby limiting the model's capacity for semantic understanding, in particular for noisy SAR images. In this paper, we explore spectral and spatial remote sensing image features as improved MAE-reconstruction targets. We first conduct a study on reconstructing various image features, all performing comparably well or better than raw pixels. Based on such observations, we propose Feature Guided Masked Autoencoder (FG-MAE): reconstructing a combination of Histograms of Oriented Graidents (HOG) and Normalized Difference Indices (NDI) for multispectral images, and reconstructing HOG for SAR images. Experimental results on three downstream tasks illustrate the effectiveness of FG-MAE with a particular boost for SAR imagery. Furthermore, we demonstrate the well-inherited scalability of FG-MAE and release a first series of pretrained vision transformers for medium resolution SAR and multispectral images.Comment: 13 pages, 8 figure

Urban Forests for Carbon Sequestration and Heat Island Mitigation

Urban forests serve both as a carbon sequestration pool and heat island mitigation tool. Climate change will increase the frequency and severity of urban heat islands. Thus, new urban planning strategies demand our attention. Based on multimodal, remotely sensed data, we map the tree density, its carbon sequestered, and its impact on urban heat islands for Long Island, NY and Dallas, TX. Using local climate zones we investigate concepts of urban planning through optimized tree planting and adjusting building designs to mitigate urban heat islands

EFFICIENT CONTENT NAVIGATION WITH A SINGLE DIMENSION OF INPUT

In general, the present disclosure describes navigating user interfaces that accept one-dimensional input, such as a vehicle dashboard console, a smart watch display, and/or the like. For example, a user interface may include a scrollable menu where the user is limited to just scrolling up or down the menu without dedicated next, previous, or select buttons. Rather than requiring a user to scroll all the way to a fixed location in the list, e.g. the top of the list, to navigate a menu hierarchy in order to select a back, select, next, up, close, or other affordance button, the techniques described herein may modify the user interface such that the affordance button is more easily accessible and may require fewer user inputs to navigate to and select. For example, the affordance button may be hidden until a user indicates an intent to activate the affordance button, the affordance button may remain at a top of the visible area of a scrollable menu even as the user scrolls the list beyond the first page of the list, or the affordance button may move around the user interface such that it remains adjacent to the cursor as the user navigates through the list