State-of-the-art and gaps for deep learning on limited training data in remote sensing

Deep learning usually requires big data, with respect to both volume and variety. However, most remote sensing applications only have limited training data, of which a small subset is labeled. Herein, we review three state-of-the-art approaches in deep learning to combat this challenge. The first topic is transfer learning, in which some aspects of one domain, e.g., features, are transferred to another domain. The next is unsupervised learning, e.g., autoencoders, which operate on unlabeled data. The last is generative adversarial networks, which can generate realistic looking data that can fool the likes of both a deep learning network and human. The aim of this article is to raise awareness of this dilemma, to direct the reader to existing work and to highlight current gaps that need solving.Comment: arXiv admin note: text overlap with arXiv:1709.0030

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Studies on Bredt's Rule

The object of the first part of this thesis was to determine whether a bridgehead double bond is more stable when located in the larger or smaller bridge of a bicycle. For this purpose a unique Bredt compound (A) was prepared. This is the only verified example of an aromatic ring located on two bridges of a bicycle. The compound was examined (1H n. m. r., 13C n. m. r., u.v., and Raman) for evidence of bond-fixation (Mills-Nixon effect) within the aromatic ring. An x-ray analysis was not possible, but other workers have made available molecular mechanics calculations on (A), Although there was spectroscopic evidence that the benzene ring is non-planar and molecular mechanics calculations show that some bonds are unusually short, there was no indication that it is other than aromatic. On the basis of the calculations, it would appear that, contrary to published evidence, the bridgehead double bond is more stable when located in the smaller bridge. The preferred conformation of the molecule, as deduced spectroscopically, was in agreement with that calculated for (A). The second part of the thesis describes an attempt to use ring annulation-scission methods to synthesise macrocycles from available five and six membered rings. The necessary tricyclic (B) and benzo-tricyclic (C) precursors were obtained, but various attempts to cleave the bridges of these compounds proved more difficult than expected. Thus, the amide derived from (B) by Beckmann rearrangement of its oxime, could be hydrolysed, but rapidly recyclised on standing

Six Districts Begin the Principal Pipeline Initiative

This first report of an ongoing evaluation of The Wallace Foundation's Principal Pipeline Initiative describes the six participating school districts' plans and activities during the first year of their grants. The evaluation, conducted by Policy Studies Associates and the RAND Corporation, isintended to inform policy makers and practitioners about the process of carrying out new policies and practices for school leadership and about the results of investments in the Principal Pipeline Initiative. This report is based on collection and analysis of qualitative data, including the districts' proposals, work plans, and progress reports and semi-structured interviews in spring 2012 with 91 administrators employed by districts and their partner institutions. Leaders in all districts report wanting to enlarge their pools of strong applicants for principal positions and to identify and cultivate leadership talent as early as possible in educators' careers.Districts are actively working on allrequired pipeline components: (1) with stakeholder participation, they have developed standards and identified competencies for principals, which they plan to use to guide principal training, hiring, evaluation, and support; (2) they are initiating or strengthening partnerships with university training programs; (3) for hiring, they have standard performance tasks and are developing systems to capture data on candidates' experience; (4) they have diagnostic evaluation tools and are working to build the capacity of principals' supervisors and mentors to support principals' skill development. In addition, all are also bolstering district-run training programs for graduates of university training programs who aspire to become principals

Reducing Incarceration in Philadelphia

Reducing incarceration is an important public health priority. There is now widespread recognition that criminal justice systems are a significant source of public health harm. They sometimes penalize individuals without improving community health, or create improvements that are offset by the considerable individual and communal harms associated with incarceration and with the collateral consequences of criminal convictions. Philadelphia has become a leader in implementing criminal justice reforms. In 2010, the District Attorney’s Office initiated changes to reduce overcharging. In the last seven years, the First Judicial District has created nine specialized diversion programs, with seven specifically aimed at addressing the underlying causes of criminal activity. These programs vary in design but share key features. All attempt to prevent future criminal activity by diverting offenders away from incarceration and into community supervision. Programs also provide access to appropriate social and health services, and utilize a more collaborative approach between prosecutors, defense attorneys, judges and social services staff. This more efficient use of resources allows greater attention to more serious and violent crimes in Philadelphia.https://repository.upenn.edu/publichealth_databriefs/1000/thumbnail.jp

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System