Working 9 to Non-Stop: The Fair Housing Act's Sexual Harassment Protections for Domestic, Agricultural, and Other Live-In Workers

Live-in workers, for whom their bosses are typically also their landlords, are often trapped in sexually harassing situations that feel as though they have no practical or legal redress, especially when the worker’s harasser can both fire and evict them in one fell swoop. This Note explores the novel possibility of using fair housing law, including the Fair Housing Act (“FHA”) and state/local fair housing statutes, as a tool to provide legal protections to workers with employer-provided housing (“live-in workers”) who experience sexual harassment or violence in the workplace. There is currently very little case law in which live-in workers have brought fair housing and employment discrimination claims simultaneously, and functionally no case law in which attorneys have brought both claims for live-in worker sexual harassment cases. This Note argues that, under existing fair housing law, many live-in workers should be eligible to bring claims under the FHA and equivalent state laws that prohibit discrimination in housing. As a result, the FHA and equivalent state claims can provide sexual harassment and assault protections for workers, including domestic workers and farmworkers, who may not receive protections under federal or state employment discrimination law. Furthermore, this Note argues that the FHA can provide supplemental or stronger protections from sexual harassment for live-in workers than traditional Title VII or employment discrimination claims. It accordingly suggests that plaintiffs facing harassment or sexual assault in live-in industries should pursue fair housing claims in addition to or in place of Title VII and employment discrimination claims, in order to achieve maximum protection and relief.

VINYL: Variant prIoritizatioN by survivaL analysis

Motivation Clinical applications of genome re-sequencing technologies typically generate large amounts of data that need to be carefully annotated and interpreted to identify genetic variants potentially associated with pathological conditions. In this context, accurate and reproducible methods for the functional annotation and prioritization of genetic variants are of fundamental importance. Results In this paper, we present VINYL, a flexible and fully automated system for the functional annotation and prioritization of genetic variants. Extensive analyses of both real and simulated datasets suggest that VINYL can identify clinically relevant genetic variants in a more accurate manner compared to equivalent state of the art methods, allowing a more rapid and effective prioritization of genetic variants in different experimental settings. As such we believe that VINYL can establish itself as a valuable tool to assist healthcare operators and researchers in clinical genomics investigations. Availability VINYL is available at http://beaconlab.it/VINYL and https://github.com/matteo14c/VINYL. Supplementary information Supplementary data are available at Bioinformatics online.

Parametric Convolutional Neural Network for Radar-based Human Activity Classification Using Raw ADC Data

Radar sensors offer a promising and effective sensing modality for<br>human activity classification. Human activity classification enables several smart<br>homes applications for energy saving, human-machine interface for gesture<br>controlled appliances and elderly fall-motion recognition. Present radar-based<br>activity recognition system exploit micro-Doppler signature by generating Doppler<br>spectrograms or video of range-Doppler images (RDIs), followed by deep neural<br>network or machine learning for classification. Although, deep convolutional neural<br>networks (DCNN) have been shown to implicitly learn features from raw sensor<br>data in other fields, such as camera and speech, yet for the case of radar DCNN<br>preprocessing followed by feature image generation, such as video of RDI or<br>Doppler spectrogram, is required to develop a scalable and robust classification<br>or regression application. In this paper, we propose a parametric convolutional<br>neural network that mimics the radar preprocessing across fast-time and slow-time<br>radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for<br>classification of various human activities. It is demonstrated that our proposed<br>solution shows improved results compared to equivalent state-of-art DCNN solutions<br>that rely on Doppler spectrogram or video of RDIs as feature images.

Parametric Convolutional Neural Network for Radar-based Human Activity Classification Using Raw ADC Data

Radar sensors offer a promising and effective sensing modality for<br>human activity classification. Human activity classification enables several smart<br>homes applications for energy saving, human-machine interface for gesture<br>controlled appliances and elderly fall-motion recognition. Present radar-based<br>activity recognition system exploit micro-Doppler signature by generating Doppler<br>spectrograms or video of range-Doppler images (RDIs), followed by deep neural<br>network or machine learning for classification. Although, deep convolutional neural<br>networks (DCNN) have been shown to implicitly learn features from raw sensor<br>data in other fields, such as camera and speech, yet for the case of radar DCNN<br>preprocessing followed by feature image generation, such as video of RDI or<br>Doppler spectrogram, is required to develop a scalable and robust classification<br>or regression application. In this paper, we propose a parametric convolutional<br>neural network that mimics the radar preprocessing across fast-time and slow-time<br>radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for<br>classification of various human activities. It is demonstrated that our proposed<br>solution shows improved results compared to equivalent state-of-art DCNN solutions<br>that rely on Doppler spectrogram or video of RDIs as feature images.

VINYL: Variant prIoritizatioN bY survivaL analysis

MotivationClinical applications of genome re-sequencing technologies typically generate large amounts of data that need to be carefully annotated and interpreted to identify genetic variants associated with pathological conditions. In this context, accurate and reproducible methods for the functional annotation and prioritization of genetic variants are of fundamental importance, especially when large volumes of data - like those produced by modern sequencing technologies - are involved.ResultsIn this paper, we present VINYL, a highly accurate and fully automated system for the functional annotation and prioritization of genetic variants in large scale clinical studies. Extensive analyses of both real and simulated datasets suggest that VINYL show higher accuracy and sensitivity when compared to equivalent state of the art methods, allowing the rapid and systematic identification of potentially pathogenic variants in different experimental settings.

‘The tiger’s leap’: The role of history in legitimating the authority of modern Chinese planners

Much has been written about the ways in which recent economic reforms have changed Chinese cities. Chinese planning is often discussed as a profession in an equivalent state of flux, as urban planners struggle to develop a new concept of their role that can find a coherent middle course between conflicting priorities: the pursuit of market-driven growth; the demands of a centralised and relatively authoritarian state; and the need to maintain a participative openness to local communities. This paper questions this emphasis on coherence within professionalism, arguing that planners in modern China define their role by sliding between very different sets of values and priorities. The term ‘public interest’ acts as a pivot in this negotiation, allowing the tensions between competing rationales to be downplayed. Furthermore, the challenges of the contemporary context do not entail leaving history behind but rather using it as a creative resource for ideas of legitimacy, authority and professionalism. Inspired by Walter Benjamin’s reflections on history, we argue that earlier models of professional authority from the Confucian and socialist traditions are mined and reinvented to cope with the uncertainties of professional decision-making in a highly conflicted present.