Hot Spots Regulation and Environmental Justice

This paper analyzes whether regulating “hot spots” of toxic air pollution by increasing the spatial resolution of regulation could address environmental justice (EJ) concerns. To examine this question, this paper develops a decision model of a regulator choosing emission controls within a net cost minimizing framework. An empirical application of the model using air toxic emission data for Escambia and Santa Rosa Counties in Florida estimates the emission standards and spatial distribution of risks at a coarse and a finer spatial resolutions. Implications for EJ are analyzed by combining the simulated spatial risk distributions at the two resolutions with the demographic data. Results indicate that different measures of EJ point to different conclusions regarding the question of whether finer resolution regulation alleviates EJ concerns. The paper concludes with a discussion of the implications for EJ policy

Expanding the Family of Grassmannian Kernels: An Embedding Perspective

Modeling videos and image-sets as linear subspaces has proven beneficial for many visual recognition tasks. However, it also incurs challenges arising from the fact that linear subspaces do not obey Euclidean geometry, but lie on a special type of Riemannian manifolds known as Grassmannian. To leverage the techniques developed for Euclidean spaces (e.g, support vector machines) with subspaces, several recent studies have proposed to embed the Grassmannian into a Hilbert space by making use of a positive definite kernel. Unfortunately, only two Grassmannian kernels are known, none of which -as we will show- is universal, which limits their ability to approximate a target function arbitrarily well. Here, we introduce several positive definite Grassmannian kernels, including universal ones, and demonstrate their superiority over previously-known kernels in various tasks, such as classification, clustering, sparse coding and hashing

Spatial Regulation of Air Toxics Hot Spots

This paper analyzes the potential implications, in terms of net social costs and distribution of risks and abatement costs, of a policy to address the problem of air toxics “hot spots.” The policy we analyze involves regulation of air toxics sources at increasingly finer spatial resolutions. We develop a model of a decisionmaker choosing emission standards within a net cost minimization framework. Empirical application of the model to two counties in Florida demonstrates that regulation at finer resolutions could involve trade-offs between net social costs and equitable distribution of risks and, in some settings, between individual and population risks

Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions

We present a comparative evaluation of various techniques for action recognition while keeping as many variables as possible controlled. We employ two categories of Riemannian manifolds: symmetric positive definite matrices and linear subspaces. For both categories we use their corresponding nearest neighbour classifiers, kernels, and recent kernelised sparse representations. We compare against traditional action recognition techniques based on Gaussian mixture models and Fisher vectors (FVs). We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation). Despite recent advancements for handling manifolds, manifold based techniques obtain the lowest performance and their kernel representations are more unstable in the presence of challenging conditions. The FV approach obtains the highest accuracy under ideal conditions. Moreover, FV best deals with moderate scale and translation changes

Teaching deep neural networks to localize sources in super-resolution microscopy by combining simulation-based learning and unsupervised learning

Single-molecule localization microscopy constructs super-resolution images by the sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and a new training algorithm which enables this deep network to solve the Bayesian inverse problem of detecting and localizing single molecules. Our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. Our training algorithm combines simulation-based supervised learning with autoencoder-based unsupervised learning to make it more robust against mismatch in the generative model. We demonstrate the performance of our method on datasets imaged using a variety of point spread functions and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy in data with low fluorophore density, they are confounded by high densities. Our method significantly outperforms the state of the art at high densities and thus, enables faster imaging than previous approaches. Our work also more generally shows how to train deep networks to solve challenging Bayesian inverse problems in biology and physics

Superfund, Hedonics, and the Scales of Environmental Justice

Environmental justice (EJ) is prominent in environmental policy, yet EJ research is plagued by debates over methodological procedures. A well-established economic approach, the hedonic price method, can offer guidance on one contentious aspect of EJ research: the choice of the spatial unit of analysis. Environmental managers charged with preventing or remedying inequities grapple with these framing problems. This article reviews the theoretical and empirical literature on unit choice in EJ, as well as research employing hedonic pricing to assess the spatial extent of hazardous waste site impacts. The insights from hedonics are demonstrated in a series of EJ analyses for a national inventory of Superfund sites. First, as evidence of injustice exhibits substantial sensitivity to the choice of spatial unit, hedonics suggests some units conform better to Superfund impacts than others. Second, hedonic estimates for a particular site can inform the design of appropriate tests of environmental inequity for that site. Implications for policymakers and practitioners of EJ analyses are discussed

The Immunomodulatory Role of Adjuvants in Vaccines Formulated with the Recombinant Antigens Ov-103 and Ov-RAL-2 against Onchocerca volvulus in Mice.

BACKGROUND: In some regions in Africa, elimination of onchocerciasis may be possible with mass drug administration, although there is concern based on several factors that onchocerciasis cannot be eliminated solely through this approach. A vaccine against Onchocerca volvulus would provide a critical tool for the ultimate elimination of this infection. Previous studies have demonstrated that immunization of mice with Ov-103 and Ov-RAL-2, when formulated with alum, induced protective immunity. It was hypothesized that the levels of protective immunity induced with the two recombinant antigens formulated with alum would be improved by formulation with other adjuvants known to enhance different types of antigen-specific immune responses. METHODOLOGY/ PRINCIPAL FINDINGS: Immunizing mice with Ov-103 and Ov-RAL-2 in conjunction with alum, Advax 2 and MF59 induced significant levels of larval killing and host protection. The immune response was biased towards Th2 with all three of the adjuvants, with IgG1 the dominant antibody. Improved larval killing and host protection was observed in mice immunized with co-administered Ov-103 and Ov-RAL-2 in conjunction with each of the three adjuvants as compared to single immunizations. Antigen-specific antibody titers were significantly increased in mice immunized concurrently with the two antigens. Based on chemokine levels, it appears that neutrophils and eosinophils participate in the protective immune response induced by Ov-103, and macrophages and neutrophils participate in immunity induced by Ov-RAL-2. CONCLUSIONS/SIGNIFICANCE: The mechanism of protective immunity induced by Ov-103 and Ov-RAL-2, with the adjuvants alum, Advax 2 and MF59, appears to be multifactorial with roles for cytokines, chemokines, antibody and specific effector cells. The vaccines developed in this study have the potential of reducing the morbidity associated with onchocerciasis in humans

Wafer scale manufacturing of high precision micro-optical components through X-ray lithography yielding 1800 Gray Levels in a fingertip sized chip

We present a novel x-ray lithography based micromanufacturing methodology that offers scalable manufacturing of high precision optical components. It is accomplished through simultaneous usage of multiple stencil masks made moveable with respect to one another through custom made micromotion stages. The range of spectral flux reaching the sample surface at the LiMiNT micro/nanomanufacturing facility of Singapore Synchrotron Light Source (SSLS) is about 2 keV to 10 keV, offering substantial photon energy to carry out deep x-ray lithography. In this energy range, x-rays penetrate through resist materials with only little scattering. The highly collimated rectangular beam architecture of the x-ray source enables a full 4″ wafer scale fabrication. Precise control of dose deposited offers determined chain scission in the polymer to required depth enabling 1800 discrete gray levels in a chip of area 20 mm$^{2}$ and with more than 2000 within our reach. Due to its parallel processing capability, our methodology serves as a promising candidate to fabricate micro/nano components of optical quality on a large scale to cater for industrial requirements. Usage of these fine components in analytical devices such as spectrometers and multispectral imagers transforms their architecture and shrinks their size to pocket dimension. It also reduces their complexity and increases affordability while also expanding their application areas. Consequently, equipment based on these devices is made available and affordable for consumers and businesses expanding the horizon of analytical applications. Mass manufacturing is especially vital when these devices are to be sold in large quantities especially as components for original equipment manufacturers (OEM), which has also been demonstrated through our work. Furthermore, we also substantially improve the quality of the micro-components fabricated, 3D architecture generated, throughput, capability and availability for industrial application. Manufacturing 1800 Gray levels or more through other competing techniques is either limited due to multiple process steps involved or due to unacceptably long time required owing to their pencil beam architecture. Our manufacturing technique presented here overcomes both these shortcomings in terms of the maximum number of gray levels that can be generated, and the time required to generate the same

Publisher Correction: Deep learning enables fast and dense single-molecule localization with high accuracy

In the version of this Article initially published, Jacob H. Macke and Jonas Ries were not listed as corresponding authors. Their contact information and designation as corresponding authors are now included. The error has been corrected in the online version of the Article