3 research outputs found
Beyond and Behind Platforms and Algorithms: Exploring the Lived Experiences of Gig Workers
No full textWhile the literature on gig work is expanding rapidly, many are the issues that need to be answered in order to fully understand the lived experiences of gig workers and illuminate the dynamics of gig work. Despite it is widely recognized that gig workers constitute an heterogenous workforce, for instance, seminal works have focused on finding similarities among gig workers across platforms, while the mechanisms behind different gig workers’ behaviors and perceptions are still widely obscure. Moreover, most of the literature focuses on what gig workers do individually on platforms, but not – or only cursorily – on how these workers manage the interplay between their online and offline activities. Specifically, comprehending how the online dimensions of work blur or integrate with offline aspects of gig workers’ lives – such as family condition or family needs, the presence of alternative, offline jobs, the cultural context of the community and country of origin – is of significant importance. This symposium addresses these issues by examining what happens behind and beyond platforms, and by presenting four papers looking at different gig workers’ experiences and different forms of interplay between online and offline aspects of gig work.
A Multi-National Ethnography of Ride-Hailing in the Global South
Author: Lindsey Cameron; The Wharton School, U. of Pennsylvania
Author: Bobbi Thomason; Pepperdine Graziadio Business School
Understanding African Digital Platform Workers’ Behaviours through the Lens of Omoluwabi Ethos
Author: Ayomikun Idowu; U. of Sussex Business School
Gig workers and Wellbeing: How is Algorithmic Work related to Work-Life Balance?
Author: Francesca Bellesia; Dep. of Sciences and Methods for Engineering, U. of Modena and Reggio Emilia
Author: Fabiola Bertolotti; U. of Modena and Reggio Emilia
Author: Elisa Mattarelli; San Jose State U.
Gig work in organizations: Trends and perspectives from Human Resource Management professionals
Author: Ksenia Keplinger; Max Planck Institute for Intelligent Systems
Author: Aizhan Tursunbayeva; Parthenope U. of Naples
Author: Vindhya Singh; Max Planck Institute for Intelligent Systems
Author: Stefano Di Lauro; U. Mercatoru
A role for alpha4(non-alpha6)* nicotinic acetylcholine receptors in motor behavior
No full textFirst author Lindsey G. Soll r is a doctoral student in the Neuroscience program in the Graduate School of Biomedical Sciences (GSBS) at UMass Medical School.Nicotinic acetylcholine receptors (nAChRs) containing either the alpha4 and/or alpha6 subunit are robustly expressed in dopaminergic nerve terminals in dorsal striatum where they are hypothesized to modulate dopamine (DA) release via acetylcholine (ACh) stimulation from cholinergic interneurons. However, pharmacological blockade of nAChRs or genetic deletion of individual nAChR subunits, including alpha4 and alpha6, in mice, yields little effect on motor behavior. Based on the putative role of nAChRs containing the alpha4 subunit in modulation of DA in dorsal striatum, we hypothesized that mice expressing a single point mutation in the alpha4 nAChR subunit, Leu9'Ala, that renders nAChRs hypersensitive to agonist, would exhibit exaggerated differences in motor behavior compared to WT mice. To gain insight into these differences, we challenged WT and Leu9'Ala mice with the alpha4beta2 nAChR antagonist dihydro-beta-erythroidine (DHbetaE). Interestingly, in Leu9'Ala mice, DHbetaE elicited a robust, reversible motor impairment characterized by hypolocomotion, akinesia, catalepsy, clasping, and tremor; whereas the antagonist had little effect in WT mice at all doses tested. Pre-injection of nicotine (0.1 mg/kg) blocked DHbetaE-induced motor impairment in Leu9'Ala mice confirming that the phenotype was mediated by antagonism of nAChRs. In addition, SKF82958 (1 mg/kg) and amphetamine (5 mg/kg) prevented the motor phenotype. DHbetaE significantly activated more neurons within striatum and substantia nigra pars reticulata in Leu9'Ala mice compared to WT animals, suggesting activation of the indirect motor pathway as the circuit underlying motor dysfunction. ACh evoked DA release from Leu9'Ala striatal synaptosomes revealed agonist hypersensitivity only at alpha4(non-alpha6)* nAChRs. Similarly, alpha6 nAChR subunit deletion in an alpha4 hypersensitive nAChR (Leu9'Ala/alpha6 KO) background had little effect on the DHbetaE-induced phenotype, suggesting an alpha4(non-alpha6)* nAChR-dependent mechanism. Together, these data indicate that alpha4(non-alpha6)* nAChR have an impact on motor output and may be potential molecular targets for treatment of disorders associated with motor impairment.Neuroscienc
simpeg-research/Astic-2020-JointInversion: Joint inversion of synthetic potential fields data based on the DO-27 kimberlite pipe
No full textSummary
We present a framework for petrophysically and geologically guided inversion to perform multi-physics joint inversions. Petrophysical and geological information is included in a multi-dimensional Gaussian mixture model that regularizes the inverse problem. The inverse problem we construct consists of a suite of three cyclic optimizations over the geophysical, petrophysical and geological information. The two additional problems over the petrophysical and geological data are used as a coupling term. They correspond to updating the geophysical reference model and regularization weights. This guides the inverse problem towards reproducing the desired petrophysical and geological characteristics. The objective function that we define for the inverse problem is comprised of multiple data misfit terms: one for each geophysical survey and one for the petrophysical properties and geological information. Each of these misfit terms has its own target misfit value which we seek to fit in the inversion. Our framework is modular and extensible and this allows us to combine multiple geophysical methods in a joint inversion and to distribute open-source code and reproducible examples. To illustrate the gains made by multi-physics inversions, we apply our framework to jointly invert, in 3D, synthetic potential fields data based on the DO- kimberlite pipe case study (Northwest Territories, Canada). The pipe contains two distinct kimberlite facies embedded in a host rock. We show that inverting the datasets individually, even with petrophysical information, leads to a binary geologic model consisting of background or kimberlite. A joint inversion, with petrophysical information, can differentiate the two main kimberlite facies of the pipe.
Contents
Geology surfaces folder: Geology surfaces built from drillholes
Forward folder: Scripts to forward model the magnetic and gravity data from the geological surfaces
L2 inversion folder: Jupyter notebooks to run Tikhonov (L2) inversions and Sparse (Lp-Lq) inversion of the gravity and magnetic data.
Petrophysics folder: Jupyter notebook to build the GMM objects for all the PGI inversions.
PGI individual inversion: Jupyter notebooks to perform PGI inversions of the gravity and magnetic data individually, each with the PK and HK signature respectively.
PGI joint inversion: Jupyter notebook to perform the joint PGI inversion with full petrophysical knowledge
PGI Joint no petrophysical means: Jupyter notebook to perform the joint PGI inversion without petrophysical knowledge
Usage
Dependencies are specified in requirements.txt
pip install -r requirements.txt
To run the notebooks locally, you will need to have python installed, preferably through anaconda .
You can then clone this repository. From a command line, run
git clone https://github.com/simpeg-research/Astic-2020-JointInversion.git
Then cd into the Astic-2020-JointInversion directory:
cd Astic-2020-JointInversion
To setup your software environment, we recommend you use the provided conda environment
conda env create -f environment.yml
conda activate pgijoint-environment
alternatively, you can install dependencies through pypi
pip install -r requirements.txt
You can then launch Jupyter
jupyter notebook
Jupyter will then launch in your web browser.
Running the notebooks
Each cell of code can be run with shift + enter or you can run the entire notebook by selecting cell, Run All in the toolbar.
For more information on running Jupyter notebooks, see the Jupyter Documentation
Issues
Please make an issue if you encounter any problems while trying to run the notebooks.
Citations
If you build upon or use these examples in your work, please cite:
Astic, T., L. J. Heagy, and D. W. Oldenburg, 2020, Joint geophysical, petrophysical and geologic inversion using a dynamic Gaussian mixture model, submitted to Geophysical Journal International.
Astic, T., and D. W. Oldenburg, 2019, A framework for petrophysically and geologically guided geophysical inversion using a dynamic Gaussian mixture model prior: Geophysical Journal International, 219, 1989-2012. https://doi.org/10.1093/gji/ggz389
Astic, T. and D. W. Oldenburg, 2018, Petrophysically guided geophysical inversion using a dynamic Gaussian mixture model prior. In SEG Technical Program Expanded Abstracts 2018 (pp. 2312-2316). https://doi.org/10.1190/segam2018-2995155.1
@article{AsticJoint,
author = {Thibaut Astic and Lindey J. Heagy and Douglas W. Oldenburg},
title = {Joint geophysical, petrophysical and geologic inversion using a dynamic Gaussian mixture model},
journal = {Submitted to Geophysical Journal International},
year = {2020}
}
@article{ggz389,
author = {Astic, Thibaut and Oldenburg, Douglas W},
title = "{A framework for petrophysically and geologically guided geophysical inversion using a dynamic Gaussian mixture model prior}",
journal = {Geophysical Journal International},
volume = {219},
number = {3},
pages = {1989-2012},
year = {2019},
month = {08},
issn = {0956-540X},
doi = {10.1093/gji/ggz389},
url = {https://doi.org/10.1093/gji/ggz389},
eprint = {http://oup.prod.sis.lan/gji/article-pdf/219/3/1989/30144784/ggz389.pdf},
}
@inbook{Astic2018,
author = {Thibaut Astic and Douglas W. Oldenburg},
title = {Petrophysically guided geophysical inversion using a dynamic Gaussian mixture model prior},
booktitle = {SEG Technical Program Expanded Abstracts 2018},
chapter = {},
pages = {2312-2316},
year = {2018},
doi = {10.1190/segam2018-2995155.1},
URL = {https://library.seg.org/doi/abs/10.1190/segam2018-2995155.1},
eprint = {https://library.seg.org/doi/pdf/10.1190/segam2018-2995155.1}
}
If you are using `SimPEG`, please cite:
Cockett, Rowan, Seogi Kang, Lindsey J. Heagy, Adam Pidlisecky, and Douglas W. Oldenburg. "SimPEG: An Open Source Framework for Simulation and Gradient Based Parameter Estimation in Geophysical Applications" Computers & Geosciences, September 2015. https://doi.org/10.1016/j.cageo.2015.09.015.
@article{Cockett2015,
author = {Cockett, Rowan and Kang, Seogi and Heagy, Lindsey J. and Pidlisecky, Adam and Oldenburg, Douglas W.},
doi = {10.1016/j.cageo.2015.09.015},
issn = {00983004},
journal = {Computers and Geosciences},
keywords = {Electromagnetics,Geophysics,Inversion,Numerical modeling,Object-oriented programming,Sensitivities},
pages = {142--154},
publisher = {Elsevier},
title = {{SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications}},
url = {http://dx.doi.org/10.1016/j.cageo.2015.09.015},
volume = {85},
year = {2015}
}
If you are using the Electromagnetics Simulations & Inversions capabilities of `SimPEG`, please cite:
Lindsey J. Heagy, Rowan Cockett, Seogi Kang, Gudni K. Rosenkjaer, Douglas W. Oldenburg. "A framework for simulation and inversion in electromagnetics" Computers & Geosciences, September 2017. https://doi.org/10.1016/j.cageo.2017.06.018
@article{heagy2017framework,
title = {A framework for simulation and inversion in electromagnetics},
journal = {Computers \& Geosciences},
volume = {107},
pages = {1-19},
year = {2017},
issn = {0098-3004},
doi = {https://doi.org/10.1016/j.cageo.2017.06.018},
url = {http://www.sciencedirect.com/science/article/pii/S0098300416303946},
author = {Lindsey J. Heagy and Rowan Cockett and Seogi Kang and Gudni K. Rosenkjaer and Douglas W. Oldenburg},
keywords = {Geophysics, Numerical modelling, Finite volume, Sensitivities, Object oriented}
}
License
These scripts and notebooks are licensed under the MIT License which allows academic and commercial re-use and adaptation of this work
