Negotiating Ubiquitous Surveillance

Screen Bodies ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 23-38
Author(s):  
Ira J. Allen
Keyword(s):  
Popular Culture ◽  
Discrete Data ◽  
Life Chances ◽  
Screen Shot ◽  
Data Points

Surveillance now is ubiquitous—each of us is decomposed along multiple axes into discrete data points, and then recomposed on screens and in combinatory algorithms that organize our life chances. Such surveillance is directly screened in popular culture, however, quite rarely. It is hard to see ubiquitous surveillance, and the harder something powerful is to see, the more powerful it tends to be. The essays of this Screen Shot offer perspective on various concrete instances of contemporary surveillance, both ubiquitous and granular, and in so doing offer tools for negotiating its suffusive presence in and organization of our lives.

Implementation of “Epic Healthy Planet” registry for survivorship care coordination.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 337-337
Author(s):  
Karen Kinahan ◽  
Bijal Desai ◽  
Michele Volpentesta ◽  
Margo Klein ◽  
Melissa Duffy ◽  
...  
Keyword(s):  
Medical Oncology ◽  
Discrete Data ◽  
Mandatory Reporting ◽  
Survivorship Care ◽  
Management Tools ◽  
Noticeable Increase ◽  
Episode Of Care ◽  
Data Points ◽  
User Friendly

337 Background: The evolving Commission on Cancer (CoC) reporting mandate and institution’s growing survivorship program led to identifying the need for systematic tracking of survivorship patients, surveillance tests, return appointments and referrals placed. Our aim was to develop an electronic medical record (EMR) integrated registry utilizing discrete data fields to assist our team in tracking key elements of high-quality survivorship care. Methods: Stakeholders from our survivorship team (APP/RN), medical oncology, psychology, research, operations and IT analytics reached consensus on essential discrete EMR fields to be included in the registry. For implementation we utilized the EPIC module, “Healthy Planet”, where patients enter the registry by initiating an “Episode of Care” at their initial survivorship visit. SmartForm fields create unique discrete patient data points identified by the stakeholders. Results: The following domains were identified as important elements of care that require tracking in a dedicated survivorship program. The registry domains populate from two sources: 1) currently existing EMR data fields, 2) domains with no currently discrete data (e.g. lymphedema, peripheral neuropathy) were captured in the developed SmartForm (see Table). From January 1, 2019 to June 1, 2021, 778 patients were entered into the registry. Since September 4, 2020, 112 patient follow-up appointment reminders were sent via EMR which has led to a noticeable increase in return appointments. SmartForm data fields are being amended as additional malignancy types are added to our survivorship program. Conclusions: The utilization of Healthy Planet is an effective and user-friendly way to track survivorship return appointments, remind providers of diagnostic tests that are due, and track referrals for CoC reporting. As the numbers of cancer survivors continues to increase, systematic population management tools are essential to ensure adherence to survivorship guideline recommendations, follow-up care and mandatory reporting.[Table: see text]

scholarly journals SimulaD: A Novel Feature Selection Heuristics For Discrete Data

2021 ◽  
Author(s):  
Mohammad Reza Besharati ◽  
Mohammad Izadi
Keyword(s):  
Optimization Problem ◽  
Window Size ◽  
Discrete Data ◽  
Limited Range ◽  
Simulated Distillation ◽  
Machine Learning Methods ◽  
Parameters Tuning ◽  
Data Points ◽  
Conventional Machine ◽  
Range Of Values

Abstract For discrete big data which have a limited range of values, Conventional machine learning methods cannot be applied because we see clutter and overlapping of classes in such data: many data points from different classes overlap. In this paper we introduce a solution for this problem through a novel heuristics method. By applying a running average (with a window-size= d) we could transform Discrete data to broad-range, Continuous values. When we have more than 2 columns and one of them is containing data about the tags of classification (Class Column), we could compare and sort the features (Non-class Columns) based on the R2 coefficient of the regression for running averages. The parameters tuning could help us to select the best features (the non-class columns which have the best correlation with the Class Column). “Window size” and “Ordering” could be tuned to achieve the goal. This optimization problem is hard and we need an Algorithm (or Heuristics) for simplifying this tuning. We demonstrate a novel heuristics, Called Simulated Distillation (SimulaD), which could help us to gain a somehow good results with this optimization problem.

scholarly journals On the Calculation of Sample Entropy Using Continuous and Discrete Human Gait Data

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 764 ◽  
Author(s):  
John McCamley ◽  
William Denton ◽  
Andrew Arnold ◽  
Peter Raffalt ◽  
Jennifer Yentes
Keyword(s):  
Range Of Motion ◽  
Sampling Rate ◽  
Discrete Data ◽  
Sample Entropy ◽  
Biological Data ◽  
Human Gait ◽  
Joint Angles ◽  
Data Types ◽  
Data Points ◽  
Differential Relationship

Sample entropy (SE) has relative consistency using biologically-derived, discrete data >500 data points. For certain populations, collecting this quantity is not feasible and continuous data has been used. The effect of using continuous versus discrete data on SE is unknown, nor are the relative effects of sampling rate and input parameters m (comparison vector length) and r (tolerance). Eleven subjects walked for 10-minutes and continuous joint angles (480 Hz) were calculated for each lower-extremity joint. Data were downsampled (240, 120, 60 Hz) and discrete range-of-motion was calculated. SE was quantified for angles and range-of-motion at all sampling rates and multiple combinations of parameters. A differential relationship between joints was observed between range-of-motion and joint angles. Range-of-motion SE showed no difference; whereas, joint angle SE significantly decreased from ankle to knee to hip. To confirm findings from biological data, continuous signals with manipulations to frequency, amplitude, and both were generated and underwent similar analysis to the biological data. In general, changes to m, r, and sampling rate had a greater effect on continuous compared to discrete data. Discrete data was robust to sampling rate and m. It is recommended that different data types not be compared and discrete data be used for SE.

scholarly journals A Growing Educational Divide in the COVID-19 Economy Is Especially Pronounced among Parents

2021 ◽  
Vol 7 ◽  
pp. 237802312097980
Author(s):  
Christel Kesler ◽  
Sarah Bash
Keyword(s):  
Labor Force ◽  
Current Population Survey ◽  
Population Survey ◽  
Cumulative Risk ◽  
Demographic Characteristics ◽  
Parental Status ◽  
Life Chances ◽  
Current Population ◽  
Data Points

Economic disruption related to coronavirus disease 2019 (COVID-19) continued through the summer of 2020, affecting the lives of millions of Americans. In this visualization, the authors use recent data from the Current Population Survey to examine Americans’ cumulative risk for labor force detachment during the pandemic. The individuals in the analysis were interviewed eight times: in April, May, June, and July of 2019 and 2020. The authors document respondents’ employment experiences during the 2020 pandemic, using the 2019 data points as a baseline for comparison. Increasing detachment from the labor force varies by basic demographic characteristics (gender and parental status), but a more important divide in the COVID-19 economy is education, an already fundamental determinant of Americans’ life chances. The educational divide is especially pronounced among parents, with important repercussions for inequalities among children.

scholarly journals Covariance-Invariant Mapping of Data Points to Nonlinear Models

2021 ◽  
Author(s):  
Wolfgang Grimm
Keyword(s):  
Real Time ◽  
Nonlinear Models ◽  
Discrete Data ◽  
Mapping Algorithm ◽  
Data Points ◽  
Special Case

A centroid- and covariance-invariant deterministic mapping of sets of discrete data points to nonlinear models is introduced. Conditions for bijectivity of this mapping are developed. Since the mapping can be accomplished by look-up tables for the special case of equally-spaced data, the resulting mapping algorithm is considered computationally fast. This could be attractive for real-time operations.

Rapoc: the Rosseland and Planck opacity converter. A user-friendly and fast opacity program for Python

2021 ◽  
Author(s):  
Lorenzo V. Mugnai ◽  
Darius Modirrousta-Galian
Keyword(s):  
Temperature Dependence ◽  
Temperature Profile ◽  
Discrete Data ◽  
Complex Treatment ◽  
Atmospheric Models ◽  
Gaseous Species ◽  
Atmospheric Modelling ◽  
Data Points ◽  
The Mean ◽  
User Friendly

<p>We present a novel code that converts the widely-used wavelength-dependent opacities of gaseous species into Rosseland and Planck mean opacities (RPMs). RAPOC (Rosseland and Planck Opacity Converter) is a straightforward and efficient Python code that makes use of ExoMol and DACE data as well as any other user-defined data, provided that it is within the correct format. Furthermore, RAPOC has the useful ability of rapidly interpolating between discrete data points, therefore allowing for a complete incorporation in atmospheric models. </p> <p>Whereas RPMs should not be used as a replacement for more rigorous opacity analyses, they have certain benefits. For example, RPMs  allow  one  to  use  Grey  or  semi-Grey  models  when  analysing  gaseous environments;  which  are  simpler,  have  exact  solutions,  and  can  be  used  as benchmarks  for  more  rigorous  approaches. By incorporating the pressure and temperature dependence of RPMs, RAPOC provides a more complex treatment of the mean opacities than what is sometimes used within the literature, notably assuming constant values or adopting simple analytic formulations.  We report  examples  of RAPOC opacities  that  are  incorporated  into  a  semi-Grey  model  to produce the temperature profile of HD 209458 b that is then compared to the realisations of the more rigorous POSEIDON code.</p> <p>The RAPOC code will provide the exoplanetary community a new tool for atmospheric modelling. For a quick installation in one's machinery, the “pip install rapoc” command can be used.</p>

3-Axis NC Rough Tool-Path Generation from Discrete Data Points of Reliefs

2010 ◽  
Vol 43 ◽  
pp. 484-487
Author(s):  
Wei Liu ◽  
Lai Shui Zhou ◽  
Lu Ling An
Keyword(s):  
Tool Path ◽  
Point Clouds ◽  
Discrete Data ◽  
Tool Path Generation ◽  
Path Generation ◽  
Direction Parallel ◽  
Tool Paths ◽  
Tool Offset ◽  
Data Points ◽  
Ito Method

This paper presents an algorithm through which 3-axis NC rough tool-paths can be directly generated from discrete data points. Based on Inverse Tool Offset (ITO) method, the algorithm generates direction-parallel (DP) tool paths for relief point clouds. The algorithm includes four steps: dividing data points into 3D cell grids; constructing inverse tool model and calculating the grids intersecting the surface of inverse tool; obtaining the grids containing cutter location points; calculating tool paths. The experiment results indicate that the algorithm of the rough tool paths is efficient.

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