Closing the Loophole: A Case Study of Organizing for More Equitable and Affordable Access to Health Care in San Francisco

This paper presents in-depth case study of a successful hybrid political and community organizing campaign to ensure equitable access to health care through the perspective of a grassroots San Francisco community-based organization, the Chinese Progressive Association (CPA), which has been organizing low-income Chinese immigrants for over four decades. First, it outlines the Health Care Security Ordinance (HCSO), which, since its passage in 2006, has established a near-universal health care access program, helping to make health care accessible and affordable to individuals living and working in San Francisco. Then it presents the campaign to save the HCSO, focusing on CPA’s participation in the HCSO coalition. Finally, it discusses health care as it relates to the San Francisco’s affordability crisis and the political economic context in which it is taking place. Despite the limitations inherent in small case studies like this one, it nevertheless provides a valuable opportunity to better understand how one politically progressive city attempted to address the problem of grossly inequitable health care access through the lens of community organizing, advocacy, and coalition building. San Francisco, like many major American cities today, is being confronted with rapid gentrification and growing economic inequality—the backdrop to the HCSO. Through innovative experiments in social responsibility like the HCSO, however, the city has made leaps in health care access. It concludes with lessons learned from local organizing and advocacy to save the HCSO as these may inform other local efforts to promote health care for all

Decision-Directed Hybrid RIS Channel Estimation with Minimal Pilot Overhead

To reap the benefits of reconfigurable intelligent surfaces (RIS), channel state information (CSI) is generally required. However, CSI acquisition in RIS systems is challenging and often results in very large pilot overhead, especially in unstructured channel environments. Consequently, the RIS channel estimation problem has attracted a lot of interest and also been a subject of intense study in recent years. In this paper, we propose a decision-directed RIS channel estimation framework for general unstructured channel models. The employed RIS contains some hybrid elements that can simultaneously reflect and sense the incoming signal. We show that with the help of the hybrid RIS elements, it is possible to accurately recover the CSI with a pilot overhead proportional to the number of users. Therefore, the proposed framework substantially improves the system spectral efficiency compared to systems with passive RIS arrays since the pilot overhead in passive RIS systems is proportional to the number of RIS elements times the number of users. We also perform a detailed spectral efficiency analysis for both the pilot-directed and decision-directed frameworks. Our analysis takes into account both the channel estimation and data detection errors at both the RIS and the BS. Finally, we present numerous simulation results to verify the accuracy of the analysis as well as to show the benefits of the proposed decision-directed framework.Comment: submitted for journal publication, 13 pages, 7 figure

Biodegradation of phenol by Pseudomonas pictorum on immobilized with chitin

Biodegradation of phenol using Pseudomonas pictorum (ATCC 23328) a potential biodegradant of phenol was investigated under different operating conditions. Chitin was chosen as a support material and then partially characterized physically and chemically. The pH of the solution was varied over a range of 7 – 9. The maximum adsorption and degradation capacity of bacteria immobilized with chitin at 30oC when the phenol concentration was 0.200 mg/L is at pH 7.0. The results showed that the equilibrium data for all phenol-degradation sorbent systems fitted the Langmuir, Freundlich and Redlich-Peterson model best. Kinetic modeling of phenol degradation was done using the pseudo-first order and pseudo-second order rate expression. The biodegradation data generally fit the intraparticle diffusion rate equation from which biodegradation rate constant, diffusion rate constant were determined

The H-alpha Luminosity Function and Star Formation Rate Volume Density at z=0.8 from the NEWFIRM H-alpha Survey

[Abridged] We present new measurements of the H-alpha luminosity function (LF) and SFR volume density for galaxies at z~0.8. Our analysis is based on 1.18$\mu$m narrowband data from the NEWFIRM H-alpha Survey, a comprehensive program designed to capture deep samples of intermediate redshift emission-line galaxies using narrowband imaging in the near-infrared. The combination of depth ($\approx1.9\times10^{-17}$ erg s$^{-1}$ cm$^{-2}$ in H-alpha at 3$\sigma$) and areal coverage (0.82 deg$^2$) complements other recent H-alpha studies at similar redshifts, and enables us to minimize the impact of cosmic variance and place robust constraints on the shape of the LF. The present sample contains 818 NB118 excess objects, 394 of which are selected as H-alpha emitters. Optical spectroscopy has been obtained for 62% of the NB118 excess objects. Empirical optical broadband color classification is used to sort the remainder of the sample. A comparison of the LFs constructed for the four individual fields reveals significant cosmic variance, emphasizing that multiple, widely separated observations are required. The dust-corrected LF is well-described by a Schechter function with L*=10^{43.00\pm0.52} ergs s^{-1}, \phi*=10^{-3.20\pm0.54} Mpc^{-3}, and \alpha=-1.6\pm0.19. We compare our H-alpha LF and SFR density to those at z<1, and find a rise in the SFR density \propto(1+z)^{3.4}, which we attribute to significant L* evolution. Our H-alpha SFR density of 10^{-1.00\pm0.18} M_sun yr^{-1} Mpc^{-3} is consistent with UV and [O II] measurements at z~1. We discuss how these results compare to other H-alpha surveys at z~0.8, and find that the different methods used to determine survey completeness can lead to inconsistent results. This suggests that future surveys probing fainter luminosities are needed, and more rigorous methods of estimating the completeness should be adopted as standard procedure.Comment: 19 pages (emulate-ApJ format), 16 figures, 5 tables, published in ApJ. Modified to match ApJ versio

DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs

Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.Comment: 6 pages, 8 figures, submitted for publication. arXiv admin note: text overlap with arXiv:2008.0375

Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.Comment: 12 pages, 10 figure

Hong Kong, The United Nations International Crime Victim Survey: Final Report of the 2006 Hong Kong UNICVS

Final Report of the 2006 Hong Kong UNICVSpublished_or_final_versio

CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction

In this paper, we present CrudeOilNews, a corpus of English Crude Oil news for event extraction. It is the first of its kind for Commodity News and serve to contribute towards resource building for economic and financial text mining. This paper describes the data collection process, the annotation methodology and the event typology used in producing the corpus. Firstly, a seed set of 175 news articles were manually annotated, of which a subset of 25 news were used as the adjudicated reference test set for inter-annotator and system evaluation. Agreement was generally substantial and annotator performance was adequate, indicating that the annotation scheme produces consistent event annotations of high quality. Subsequently the dataset is expanded through (1) data augmentation and (2) Human-in-the-loop active learning. The resulting corpus has 425 news articles with approximately 11k events annotated. As part of active learning process, the corpus was used to train basic event extraction models for machine labeling, the resulting models also serve as a validation or as a pilot study demonstrating the use of the corpus in machine learning purposes. The annotated corpus is made available for academic research purpose at https://github.com/meisin/CrudeOilNews-Corpus.Comment: Accepted at LREC 2022. arXiv admin note: text overlap with arXiv:2105.0821