High-field superconducting nested coil magnet

Superconducting magnet, employed in conjunction with five types of superconducting cables in a nested solenoid configuration, produces total, central magnetic field strengths approaching 70 kG. The multiple coils permit maximum information on cable characteristics to be gathered from one test

Rectangular configuration improves superconducting cable

Superconducting cable for a cryogenic electromagnet with improved mechanical and thermal properties consists of a rectangular cross-sectioned combination of superconductor and normal conductor. The conductor cable has superconductors embedded in a metallic coating with high electrical and mechanical conductivity at liquid helium temperatures

Stranded superconducting cable of improved design

High-current cable developed in liquid helium cooled magnets uses aluminum wire interspersed with the superconductor strands. The aluminum maintains higher electrical conductivity, is light in weight, and has low thermal capacity

Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.Comment: In Proc. 30th AAAI Conference on Artificial Intelligenc

Warming and Crop Production in the US and Beyond

This presentation will discuss what we currently know about how crops respond to warming, where the biggest impacts over the next few decades might be, and what we can do to adapt.Title VI National Resource Center Grant (P015A060066)unpublishednot peer reviewe

Tile2Vec: Unsupervised representation learning for spatially distributed data

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language -- words appearing in similar contexts tend to have similar meanings -- to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations on three datasets. Our learned representations significantly improve performance in downstream classification tasks and, similar to word vectors, visual analogies can be obtained via simple arithmetic in the latent space.Comment: 8 pages, 4 figures in main text; 9 pages, 11 figures in appendi

The Exclusionary Rule Redux--Again

The exclusionary rule itself is not very complicated: if the police obtain evidence by means that violate a person’s rights under the Fourth Amendment, the evidence is not admissible against that person in a criminal trial. The basic provision, however, has been freighted with innumerable epicycles, and epicycles on epicycles ever since it was made part of Fourth Amendment jurisprudence. The exclusionary rule survives in a kind of doctrinal purgatory, neither accepted fully into the constitutional canon nor cast into the outer darkness. It survives, but its reach is uncertain, its rational questioned, and its value doubted. Hudson v. Michigan and Herring v. United States again pose the question what the rule’s future is, or rather, whether it has a future