1. How Can We Use Data to Predict the Length of a Shadow? - NSTA
Missing: Sunless mining
Elementary | Daily Do
2. GENERATE_SHADOWS - Blue Marble Geographics
Missing: Sunless World mining
Calculates sun shadows on a surface based on terrain, 3D vector obstructions, and sun angle. The calculations can happen over a range of times and heights above ground. The outputs can include a grid of the count or percent of times that a particular cell is shaded, a point cloud of shadow count/percent at different heights, and a series of shadow mask layers showing the shadow at each given time/height.
3. Demonstration of Robust and Efficient Quantum Property Learning ... - arXiv
Feb 27, 2024 · We propose the robust shallow shadows protocol. Our protocol uses Bayesian inference to learn the experimentally relevant noise model and mitigate it in ...
Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few measurements. While random single qubit measurements are experimentally friendly and suitable for learning low-weight Pauli observables, they perform poorly for nonlocal observables. Prepending a shallow random quantum circuit before measurements maintains this experimental friendliness, but also has favorable sample complexities for observables beyond low-weight Paulis, including high-weight Paulis and global low-rank properties such as fidelity. However, in realistic scenarios, quantum noise accumulated with each additional layer of the shallow circuit biases the results. To address these challenges, we propose the robust shallow shadows protocol. Our protocol uses Bayesian inference to learn the experimentally relevant noise model and mitigate it in postprocessing. This mitigation introduces a bias-variance trade-off: correcting for noise-induced bias comes at the cost of a larger estimator variance. Despite this increased variance, as we demonstrate on a superconducting quantum processor, our protocol correctly recovers state properties such as expectation values, fidelity, and entanglement entropy, while maintaining a lower sample complexity compared to the random single qubit measurement scheme. We also theoretically analyze the effects...
4. Cloud and Cloud-Shadow Detection for Applications in Mapping Small ...
This paper uses an efficient two-step machine-learning approach using freely available tools to detect clouds and shadows in the context of mapping small-scale ...
Small-scale placer mining in Colombia takes place in rural areas and involves excavations resulting in large footprints of bare soil and water ponds. Such excavated areas comprise a mosaic of challenging terrains for cloud and cloud-shadow detection of Sentinel-2 (S2A and S2B) data used to identify, map, and monitor these highly dynamic activities. This paper uses an efficient two-step machine-learning approach using freely available tools to detect clouds and shadows in the context of mapping small-scale mining areas, one which places an emphasis on the reduction of misclassification of mining sites as clouds or shadows. The first step is comprised of a supervised support-vector-machine classification identifying clouds, cloud shadows, and clear pixels. The second step is a geometry-based improvement of cloud-shadow detection where solar-cloud-shadow-sensor geometry is used to exclude commission errors in cloud shadows. The geometry-based approach makes use of sun angles and sensor view angles available in Sentinel-2 metadata to identify potential directions of cloud shadow for each cloud projection. The approach does not require supplementary data on cloud-top or bottom heights nor cloud-top ruggedness. It assumes that the location of dense clouds is mainly impacted by meteorological conditions and that cloud-top and cloud-base heights vary in a predefined manner. The methodology has been tested over an intensively excavated and well-studied pilot site and shows 50% more de...
5. Sun Shadows project - Educapoles.org
Jan 26, 2012 · These measurements can be used in geography lessons about the movements of the earth in our solar system, in trigonometry classes and even in ...
26.01.2012
6. A Sea of Data: Apophenia and Pattern (Mis-)Recognition - Journal #72
And if so, is there a way to ever “unscramble” the “shadows” Amani has been left with? Notes. 1. See →. 2. “The SIGINT World Is Flat, ...
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7. Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance ...
... shadow due to canopy shape and Sun zenith angle (SZA). The size of ... global land cover characteristics database and IGBP DISCover from 1 km AVHRR data.
Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square...
8. Visualizing Data Shadow Donation - OpenEdition Journals
During that time, various fields, from psychology to biology, physics, and chemistry, similarly asserted that the social and natural world could be understood ...
En el contexto de la dataficación, el capitalismo de la vigilancia participativa y la incipiente retórica sobre el conocimiento científico a partir del Big Data, existe la práctica de la donación de datos en la sombra (data shadow donation). Esta actividad relacional hace referencia a la donación digital de colecciones de datos personales a plataformas de investigación o de caridad. Al basarse en la donación de datos por parte de los usuarios, la donación en la sombra de datos opera fuera del ámbito comercial y se presenta, en primer lugar, como un instrumento de compromiso cívico y, en segundo lugar, como una expresión de valor público y control compartido. A partir de un proyecto de investigación etnográfica sobre dicho fenómeno se creó una instalación. Este artículo presenta esta obra de arte y se centra en el contenido, en el estilo y en cómo se experimentaron y emplearon las nuevas tecnologías para resolver los retos de visualización de forma creativa. Además, este artículo contribuye a los debates sobre los datos como visualidad, las nuevas tecnologías de los medios de comunicación y la inclusión de sujetos y espectadores como creadores de imágenes.
9. Data mining you, by Tom Engelhardt (Le Monde diplomatique ...
... sunless forest of the U.S. intelligence world. In cost, for example, it barely tops the $1.7 billion headquarters complex in Virginia that the National ...
by Tom Engelhardt (Le Monde diplomatique - English edition, April 2012)
10. Data Mining: What it is and why it matters | SAS
Uncover new insights from data through the use of predictive analytics. Data Mining in Today's World. Data mining is a cornerstone of analytics, helping you ...
Data mining uses machine learning, statistics and artificial intelligence to find patterns, anomalies and correlations across a large universe of data – and to predict outcomes. Discover how it works.
11. Review: Shadow Algorithms Data Miner by Andrew Woo - cybereality
Missing: Sunless World mining
Shadow Algorithms Data Miner by Andrew Woo was an interesting find for me. The oddly titled book was released over 2 years ago, yet there wasn’t a single review on Amazon (where I purchased the e-book). However, I read the table of contents and it seemed pretty extensive
12. Data mining in a complex world - Northeastern Global News
Jan 29, 2013 · Assistant professor of computer and information science Yizhou Sun develops data-mining algorithms that take advantage of that complexity.
The real world is an enormously complex network in which everything is interconnected. Assistant professor of computer and information science Yizhou Sun develops data-mining algorithms that take advantage of that complexity.
