Jets report card: Adam Gase deserves credit for Raiders destruction
by Brian CostelloGrading the Jets after their 34-3 win over the Oakland Raiders on Sunday.
Offense
Adam Gase was in a groove calling plays and the Jets offense looks rejuvenated. They gained 401 yards, the second straight week they hit 400 yards. QB Sam Darnold (20 of 29, 315 yards, 2 passing TDs, 1 rushing TD, 127.8 rating) did not turn the ball over and made some beautiful throws. WR Robby Anderson (4 catches, 86 yards, 1 TD) had his best game in weeks. The offensive line opened some holes and allowed just one sack. It was a complete performance by the offense.
Grade: A
Defense
Jamal Adams said the Jets were upset they gave up the three points. That is about the only negative you can take away from this one. They shut down Josh Jacobs (10 rushes, 34 yards) and forced QB Derek Carr (15 of 27, 127 yards, 1 INT, 52.5 rating) to the bench in the third quarter. Gregg Williams’ group was suffocating, allowing 208 total yards. CB Brian Poole capped it off with a pick-six that put the game away.
Grade: A
Special teams
The only group dragging down the GPA this week, and it is only because of two miscues. Kicker Sam Ficken missed a 48-yard field goal, but did hit two others. Punt returner Braxton Berrios inexplicably let a punt drop inside the Jets’ 10. He later had a return of 26 yards, so he made up for it. The coverage team was good, bottling up Raiders returner Trevor Davis.
Grade: B
Coaching
If you were ripping Adam Gase a month ago, you have to give him credit now. Gase kept the Jets together and has them on a three-game winning streak. His play-calling was terrific on Sunday and he had the offense rolling.
Grade: A


State agency says suicide deaths rose 45% since 2007 in Ohio
COLUMBUS, Ohio (AP) - The state Department of Health says the number of suicides in Ohio increased 45% from 2007 to 2018 and by more than half for young people.
Republican Gov. Mike DeWine responded to Wednesday’s report by saying his RecoveryOhio initiative seeks to address mental illness and issues leading to suicide.
Findings in the report include:
- The number of suicides for those age 10 to 24 increased 56% while the rate increased 64% from 2007 to 2018.
- The rate for people 65 and older increased nearly 48% between 2007 and 2018.
- White, non-Hispanic males had the highest suicide rate.
- The suicide rate among black males increased 54% from 2014 to 2018.
- Five people die by suicide every day in Ohio with over 1,800 suicide deaths last year.
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DotNetNuke® Feedback Module v. 06.04.00 -- RELEASE NOTES
Requirements: DotNetNuke version 06.02.01 or later, ASP.Net 3.5 SP1 or later.
Full details on the changes can be found in great detail at http://dnnfeedback.codeplex.com/workitem/list/basic.
This release brings a number of enhancements, the major one being the inclusion of Akismet support.
Enhancements
- Added ability to disable sending of mail to receipients until feedback is published.
- Added setting to allow configuration of page size in Moderation/Management view.
Changes
- Changed margins to be percentages from fixed with to improve usability on small screen sizes.
- Code tidied up and improved for Azure installation.
Bug Fixes
- Included workaround for saving Sent To Roles to handle bug introduced in DNN 6.2.0.


The stplanr package provides functions to access and analyse data for transportation research, including origin-destination analysis, route allocation and modelling travel patterns.
overline
- Aggregate overlaying route lines and data intelligently
calc_catchment
- Create a 'catchment area' to show the areas serving a destination
route_cyclestreet
- Finds the fastest routes for cyclists between two places.


Another Tech Blog That Will Change the Way You Experience Reality:
Group_by and Goodbye
December 14th, 2014
Hey folks, it's that time of the week again. For those of you that have been holding your breath for the entirety of the week I now release you from your self-imposed bondage. Guys, please don't not breath. No matter how excited you are to see what I've got to teach you guys this week, not breathing is a game where not only does nobody win, but it's also a game that commits the worst crime a game can commit: being lame. So before we even dive into the technical stuff let's all agree; not breathing is lame. Good, now let's actually talk about programming. Today we are going to talk about my favorite method that this specific blog post is about – group_by.
You may have noticed that there is something that makes the names of Ruby methods pretty handy and that is that if you are paying attention they tell you what function they perform. Taking that premise then let's look at the name of group_by. Because I believe that inside of your skull is a vibrant/beautiful mind I trust that just by looking at group_by you probably guessed that it's task is to take one thing (a collection) and group it by certain parameters that you have set, by using a block. The resulting group is done in the form of a hash. That's all well and good, but what does that look like? Well, my knowledge thirsty amigo, it looks like this:
(1..10).group_by {|i| i%2} #=> {0=>[2, 4, 6, 8, 10] 1=> [1, 2, 5, 7, 9]}
Looking at the code written and going from left to right we have a range of 1 through 10, that are being grouped by the results of taking their remainders after being divided by 2. After that what we are given is a hash, where the key is the result of the block (that is the possible remainders) and the value is an array composed of the integers in that array that correspond to that remainder. What this means is that even integers like 2, 4, and 6 that have a remainder of 0 after being divided by 2 are place d in the array with 0 as it's key. Makes a massive load of sense right? And for the sake of me looking as thorough and competent as I can let me also tell you that you can have strings in your array as well.  As you can see the group_by method is one that is straight forward and can certainly come in handy, especially if you are dealing with a large pool of information (people in a phone book) that would be easier to deal with in smaller groups of your choosing.


The Newton Boys (1998)
Director: Richard Linklater Cast: Matthew McConaughey, Skeet Ulrich, Ethan Hawke, Vincent D'Onofrio, Julianna Margulies, Dwight Yokam, Gail Gronauer Screenplay: Richard Linklater, Claude Stanush, Clark Lee Walker Producers: Anne Walker-McBay Runtime: 122 min. US Distribution: Twentieth Century Fox Rated PG-13: violence, language
By Nathaniel R. Atcheson<EMAIL_ADDRESS>It must have been nice to live around 1920, when one could stumble into a bank and belligerently order the clerks to hand over all the money, and then stumble back out on to the street and mosey back to the horse, and then get away, having "not been seen." The Newton brothers were smarter than this, but their tactics are frequently as conspicuous. Apparently it didn't matter, because they are the most successful bank robbers in American history.
And their story makes a darn good movie. The Newton Boys is a pleasant surprise: I expected something far less than this entertaining, well-acted, and thoughtful film.
Matthew McConaughey has the largest role as Willis Newton, the brother who engineers most of the bank robberies. At first, his accomplices are his two younger brothers, Joe (Skeet Ulrich) and Jess (Ethan Hawke), and Brentwood Glasscock (Dwight Yokam), the bomb expert. Later, their brother Dock (Vincent D'Onofrio) gets out of jail and catches up with them.
Willis masterminds each robbery, and is responsible for upping the stakes as time goes on. They start off small, blowing up safes in the middle of the night and taking the money. Soon, they move up to breaking into two banks simultaneously. Not long after this, they are going to the bigger cities and taking a lot more money. Willis justifies his actions by saying, "It's just one thief stealing from another," and the good thing is we believe him (at least a little bit).
Bank robbery is a serious thing, but I liked the light tone that director Richard Linklater has taken with this film. The bank robberies are simultaneously suspenseful and funny; they are also well-filmed, and enhanced by an appropriate musical score. The sets are authentic, and certainly not inexpensive: huge blocks of city streets from this time period have been constructed for the film, and the result is a picture that is visually flawless.
But what makes this such an enjoyable film are the characters, and the actors who portray them. McConaughey adds one more bit of proof that he can hold his own as a leading man; he manages to make Willis the crazed, eccentric man that he is, while still demanding our like and sympathy. And later, when he meets his love, Louise Brown (Julianna Margulies), we truly believe that he cares for her. Surprisingly, this is one of the best-written romances I've seen in a while, for the female character is fleshed-out, and Margulies plays her part well.
The rest of the cast is made up of a group of talented young actors who are all making names for themselves. Hawke, who plays the most jovial (and most volatile) of the brothers, is perhaps the most entertaining to watch. Ulrich is quiet and reserved as Joe, and he does a good job adding dimension to his character. D'Onofrio, who has been playing small parts for a while, is good here, though not given a lot of screen time.
Don't leave when the credits begin: interviews with the real Willis and Joe Newton role through the credits. After seeing the film, it's vastly entertaining to watch these old men talk about what they did so many years ago (and Willis is still justifying the robberies). And it also serves to show how well McConaughey and Ulrich play their roles.
The Newton Boys is a charming, entertaining film. Its success lies in its simplicity, and I don't understand why all film makers can't go by this easy set of guidelines: make the characters round and interesting, use talented actors, treat the material with respect, and everything else will usually fall into place.
*** out of **** (7/10, B)
Visit FILM PSYCHOSIS at http://www.pyramid.net/natesmovies
Nathaniel R. Atcheson
The review above was posted to the
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|
SuanShu, a Java numerical and statistical library | |||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.Garch
public class Garch
This class does fitting for the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model.
This implementation does the fitting by maximizing the likelihood function, using the gradient information.
| Constructor Summary | |
|---|---|
Garch(TimeSeries xt,
int p,
int q)
Fit the GARCH(p, q) model to the time series. |
|
Garch(TimeSeries xt,
int p,
int q,
int maxIterations)
Fit the GARCH(p, q) model to the time series. |
| Method Summary | |
|---|---|
RealVectorFunction |
dLogLikelihood(double[] e_t2,
int p,
int q)
the gradient of the log-likelihood function for a set of observations The gradient log-likelihood takes θ as the inputs. |
RealScalarFunction |
logLikelihood(double[] e_t2,
int p,
int q)
the log-likelihood function for a set of observations The log-likelihood takes θ as the inputs. |
GarchModel |
model()
Get the fitted GARCH model. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public Garch(TimeSeries xt, int p, int q, int maxIterations)
xt
- a time series of the observationsq
- the ARCH orderp
- the GARCH part ordermaxIterations
- the maximum number of iterations in the numerical optimization algorithmpublic Garch(TimeSeries xt, int p, int q)
xt
- a time series of the observationsq
- the ARCH orderp
- the GARCH part order| Method Detail |
|---|
public GarchModel model()
public RealScalarFunction logLikelihood(double[] e_t2, int p, int q)
The log-likelihood takes θ as the inputs.
e_t2
- squared observationsq
- the ARCH orderp
- the GARCH part order
public final RealVectorFunction dLogLikelihood(double[] e_t2, int p, int q)
The gradient log-likelihood takes θ as the inputs.
e_t2
- the squared observationsp
- the number of AR termsq
- the number of MA terms
|
SuanShu, a Java numerical and statistical library | |||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data structures and R using concepts from 'Rcpp' which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses 'Rcpp' to connect to another third-party library. The 'autoconf' script, 'inline' plugin and example package can all be used as a stanza to write a similar package against another library.
| Version: | 0.3.6 |
| Imports: | Rcpp (≥ 0.11.0), stats |
| LinkingTo: | Rcpp |
| Suggests: | RUnit, inline, knitr, rmarkdown, pinp |
| Published: | 2018-06-10 |
| Author: | Dirk Eddelbuettel and Romain Francois |
| Maintainer: | Dirk Eddelbuettel <edd at debian.org> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU GSL |
| Materials: | README NEWS ChangeLog |
| CRAN checks: | RcppGSL results |
| Reference manual: | RcppGSL.pdf |
| Vignettes: |
RcppGSL RcppGSL-unitTests |
| Package source: | RcppGSL_0.3.6.tar.gz |
| Windows binaries: | r-devel: RcppGSL_0.3.6.zip, r-release: RcppGSL_0.3.6.zip, r-oldrel: RcppGSL_0.3.6.zip |
| OS X binaries: | r-release: RcppGSL_0.3.6.tgz, r-oldrel: RcppGSL_0.3.6.tgz |
| Old sources: | RcppGSL archive |
| Reverse linking to: | catSurv, coga, fingerPro, libstableR, mixlink, mvabund, RcppZiggurat, smam |
Please use the canonical form https://CRAN.R-project.org/package=RcppGSL to link to this page.


A core outcome set for clinical trials on non-specific low back pain: study protocol for the development of a core domain set
Department of Health Sciences, Faculty of Earth & Life Sciences, EMGO+ Institute for Health and Care Research, VU University, de Boelelaan 1085, 1081HV Amsterdam, The Netherlands
Department of Epidemiology & Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, van der Boechorstraat 7, 1081 BT Amsterdam, The Netherlands
Departments of Family Medicine, Internal Medicine, Public Health and Preventive Medicine, Oregon Institute of Occupational Health Sciences, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
The George Institute for Global Health, Sydney Medical School, The University of Sydney, PO Box M201, Missenden Rd, NSW 2050, Australia
Monash Department of Clinical Epidemiology, Cabrini Institute and Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia - Cabrini Medical Centre, 183 Wattletree Rd, Melbourne, VIC, Australia
Consumer Representative, Cochrane Collaboration Back Review Group, Maple Grove, MN 55311, USA
Masters and Doctoral Programs in Physical Therapy, Universidade Cidade de Sao Paulo, Rua Cesário Galeno 448, Sao Paulo, Brazil
Arthritis Research UK Primary Care Centre, Institute of Primary Care and Health Sciences, Keele University, Keele, Staffordshire ST5 5BG, UK
Oslo and Akershus University College of Applied Sciences, Faculty of Health Sciences and FORMI, Oslo University Hospital, Ullevaal N-0407, Oslo, Norway
Department of General Practice, Erasmus MC, University Medical Center, PO Box 20403000CA Rotterdam, The Netherlands
Spanish Back Pain Research Network (REIDE), Fundacion Kovacs, Paseo de Mallorca 36, 07012 Palma de Mallorca, Spain
Department of Orthopaedic Surgery, Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon NH03756, USA
Department of Neurosurgery, Leiden University Medical Center, Postzone J11-R, Postbus 9600, 2300RC Leiden, The Netherlands
Consumer Representative, Cochrane Collaboration Back Review Group, Newbury, MA 01951, USA
Department of Anesthesiology & Pain Medicine and Center for Pain Research on Impact, Measurement & Effectiveness, University of Washington, 1949 NE Pacific Street, Seattle, WA 98195-6540, USA
Low back pain (LBP) is one of the most disabling and costly disorders affecting modern society, and approximately 90% of patients are labelled as having non-specific LBP (NSLBP). Several interventions for patients with NSLBP have been assessed in clinical trials, but heterogeneous reporting of outcomes in these trials has hindered comparison of results and performance of meta-analyses. Moreover, there is a risk of selective outcome reporting bias. To address these issues, the development of a core outcome set (COS) that should be measured in all clinical trials for a specific health condition has been recommended. A standardized set of outcomes for LBP was proposed in 1998, however, with evolution in COS development methodology, new instruments, interventions, and understanding of measurement properties, it is appropriate to update that proposal. This protocol describes the methods used in the initial step in developing a COS for NSLBP, namely, establishing a core domain set that should be measured in all clinical trials.
An International Steering Committee including researchers, clinicians, and patient representatives from four continents was formed to guide the development of this COS. The approach of initiatives like Core Outcome Measures in Effectiveness Trials (COMET) and Outcome Measures in Rheumatology (OMERACT) was followed. Participants were invited to participate in a Delphi study aimed at generating a consensus-based core domain set for NSLBP. A list of potential core domains was drafted and presented to the Delphi participants who were asked to judge which domains were core. Participant suggestions about overlap, aggregation, or addition of potential core domains were addressed during the study. The patients’ responses were isolated to assess whether there was substantial disagreement with the rest of the Delphi panel. A priori thresholds for consensus were established before each Delphi round. All participants’ responses were analysed from a quantitative and qualitative perspective to ascertain that no substantial discrepancies between the two approaches emerged.
We present the initial step in developing a COS for NSLBP. The next step will be to determine which measurement instruments adequately cover the domains.
Low back pain (LBP) is one of the most disabling and costly disorders affecting modern society, and approximately 90% of patients are labelled as having non-specific LBP (NSLBP). Several interventions for patients with NSLBP have been assessed in clinical trials, but heterogeneous reporting of outcomes in these trials has hindered comparison of results and performance of meta-analyses. Moreover, there is a risk of selective outcome reporting bias. To address these issues, the development of a core outcome set (COS) that should be measured in all clinical trials for a specific health condition has been recommended. A standardized set of outcomes for LBP was proposed in 1998, however, with evolution in COS development methodology, new instruments, interventions, and understanding of measurement properties, it is appropriate to update that proposal. This protocol describes the methods used in the initial step in developing a COS for NSLBP, namely, establishing a core domain set that should be measured in all clinical trials.
An International Steering Committee including researchers, clinicians, and patient representatives from four continents was formed to guide the development of this COS. The approach of initiatives like Core Outcome Measures in Effectiveness Trials (COMET) and Outcome Measures in Rheumatology (OMERACT) was followed. Participants were invited to participate in a Delphi study aimed at generating a consensus-based core domain set for NSLBP. A list of potential core domains was drafted and presented to the Delphi participants who were asked to judge which domains were core. Participant suggestions about overlap, aggregation, or addition of potential core domains were addressed during the study. The patients’ responses were isolated to assess whether there was substantial disagreement with the rest of the Delphi panel. A priori thresholds for consensus were established before each Delphi round. All participants’ responses were analysed from a quantitative and qualitative perspective to ascertain that no substantial discrepancies between the two approaches emerged.
We present the initial step in developing a COS for NSLBP. The next step will be to determine which measurement instruments adequately cover the domains.
Low back pain (LBP) is the leading global contributor to years lived with disability (YLDs) and the sixth global contributor to disability-adjusted life years (DALYs) 1 2 . The costs associated with LBP are high and they represent a substantial burden to society 3 . Approximately 90% of patients with LBP are labelled as having non-specific LBP (NSLBP), a diagnosis based on exclusion of a specific cause or pathology 4 . A wide range of health interventions for patients with NSLBP has been evaluated in clinical trials 5 . The results of clinical trials addressing similar interventions are often summarized in systematic reviews 6 , but authors of these reviews have reported that outcomes are inconsistently measured and reported across trials 7 8 . This limits ability to compare findings between studies or pool data for formal meta-analyses. Furthermore, inconsistency can be the result of selective outcome reporting bias (selective reporting of favourable outcomes) which can strongly affect the conclusions of systematic reviews 9 .
To reduce heterogeneity in outcomes measured across clinical trials, development of core outcome sets (COSs) in specific health conditions has been advocated 10 . A COS is an agreed minimum set of outcomes that should be measured and reported in all clinical trials for a particular health condition 11 . A COS might increase reporting of important outcomes, reduce the risk of selective outcome reporting, and increase the feasibility of conducting meta-analyses 11 . The existence of a COS also ensures that authors of clinical trials report on outcomes that are relevant for all stakeholders 11 . Editors of Cochrane Review Groups have agreed that the availability of COSs would enhance reliability of the reviews 12 .
In 1998, following an expert panel discussion held at the second international LBP Forum (The Hague, Netherlands), a proposal for a standardized set of outcomes in LBP clinical research was published 13 . Five domains were proposed for measurement (pain symptoms, back-related function, generic wellbeing, disability social role, and satisfaction with care) and specific measurement instruments were recommended for each domain 13 . This proposal has been highly cited and widely adopted over the years. However, a workshop discussion among LBP researchers during the 2012 twelfth LBP Forum (Odense, Denmark) underlined the wish to update the existing recommendations considering recent advancements in the fields of COS development and clinimetrics 11 14 . There were two primary reasons: to explore if relevant domains were missing, and to critically appraise the recommended measurement instruments in light of a large body of newer data on measurement properties of instruments for LBP 15 .
The aim of this study is to update the recommended domains for LBP clinical research 13 through the development of a COS. The Core Outcome Measures in Effectiveness Trials (COMET) 11 and the Outcome Measures in Rheumatology (OMERACT) 16 initiatives provide methodological guidance, involving a stepwise approach, to development of a COS. The first step is to determine which domains should be measured in all clinical trials (‘what’ to measure, the ‘core domain set’), and the second is to determine the instruments that should be used to assess the domains (‘how’ to measure, the ‘core outcome measurement set’) 11 17 . This protocol presents the methods that will be adopted to reach a consensus on the core domain set.
Our goals are to present a detailed context for reporting in the results manuscript, and to provide a resource for COS developers of any health condition. Despite encouragement of the COMET initiative to make COS protocols publicly available, few have been published 18 19 , providing the motivation for this paper.
An International Steering Committee was formed to initiate and support the development of this COS. Members of the Steering Committee were chosen to represent various disciplines, geographical areas, and types of expertise. As a result, the Steering Committee includes 18 members among whom: 14 are researchers from different disciplines with ample experience in LBP clinical research (RD, CL, RB, LC, NF, MG, BK, FK, CM, AP, WP, DT, MvT, RO), three are experts in consensus-based research procedures (CT, MB, DT), six are authors of several publications in the field of clinimetrics (CT, RB, LC, FK, MG, RO), six are healthcare providers involved in the management of patients with LBP (MB, RB, LC, FK, AP, WP), and two are patient representatives (TC, MS). The members represent Europe, Australia, and North and South America.
The project team consisted of one investigator (AC), appointed to coordinate the day-to-day management of the project, and three members of the Steering Committee working at the same institution (CT, MB, RO). During face-to-face meetings, the project team established the methodology and addressed key aspects. The other members of the Steering Committee were contacted by email regarding critical decisions; when more than half of the members agreed on a decision, this was followed unless substantial and convincing arguments were raised by one or more members in disagreement. Definitions of key concepts and terms used in this study protocol follow those recently outlined by the OMERACT initiative 17 and are presented in Table 1.
|
Concept |
Definition |
|
Health Condition |
A situation of impaired health. |
|
Health Intervention |
An activity performed by, for, with, or on behalf of a client(s) whose purpose is to improve individual or population health, to alter or diagnose the course of a health condition, or to improve functioning. |
|
Core Area |
An aspect of health or a health condition that needs to be measured to appropriately assess the effects of a health intervention (core areas are broad concepts consisting of a number of more specific concepts called domains). |
|
Domain or Subdomain |
Component of core area: a concept to be measured, a further specification of an aspect of health, categorized within a core area. |
|
Outcome |
Any identified result in a (sub)domain arising from exposure to a casual factor or a health intervention. |
|
Measurement Instrument |
A tool to measure a quality or quantity of a variable, in this context a (sub)domain or a contextual factor. |
|
Outcome Measurement Instrument |
A measurement instrument chosen to assess outcome(s). |
|
Core Domain Set |
For study of health interventions, the minimum set of domains and subdomains necessary to adequately cover all core areas (fully measure all relevant concepts of a specific health condition within a specified scope); it describes what to measure. |
|
Core Outcome Measurement Set |
The minimum set of outcome measurement instruments that must be administered in each intervention study of a certain health condition within a specified setting to adequately cover a corresponding core domain set; it describes how to measure. |
|
Scope |
The set of factors that describes the studies and circumstances to which the core outcome set will apply. This is determined by the study questions and includes the health condition(s), target population, interventions, and so forth. |
|
Contextual Factor |
Variable that is not an outcome of the study, but needs to be recognized (and measured) to understand the study results. This includes potential confounders and effect modifiers. |
The Steering Committee recommended that this COS should apply to measuring efficacy or effectiveness of health interventions in clinical trials for patients with NSLBP, defined as ‘low back pain not attributable to a recognizable, known specific pathology (eg infection, tumour, fracture, axial spondyloarthritis)’ 20 . All interventions for NSLBP are targeted by this COS, regardless of type, setting, or mode of administration. In line with COMET definition 11 , this does not imply that primary outcomes of a clinical trial should always be those of the COS or that outcome measures should be restricted to the domains of the COS. However, domains of this COS should be considered for inclusion in all clinical trials on NSLBP, besides the measurement of trial-specific domains. There is no intent that this COS should be considered as a requirement for regulatory approval of drugs or devices, or that an intervention should demonstrate benefits on all COS measures to be judged efficacious or effective.
Other goals of this COS are to provide a recommendation on reporting of adverse events (AEs) in clinical trials on NSLBP and to identify contextual factors (such as confounders and effect modifiers) that should be measured alongside core domains (Table 1). However, it is out of our scope to reach consensus on contextual factors that should be measured and reported in all clinical trials. For measurement and reporting of contextual factors, we refer to the prominent work of the National Institutes of Health (NIH) Task Force that recently published a report about minimum baseline measures for clinical studies on chronic LBP 21 .
The Steering Committee identified initiatives that could potentially overlap with this study at an early stage of this project. None of these initiatives had the same scope as this COS. In this subsection, we report where partial conceptual overlap between this COS and the other initiatives could lie:
1. The Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) has developed a consensus-based set of outcome domains and measurement instruments for clinical trials on chronic pain 22 23 . IMMPACT leaves ample space for the definition of more specialized COSs for some specific subgroups of patients with pain, such as this COS which focuses only on patients with NSLBP.
2. A core set for LBP based on the International Classification of Functioning (ICF) was developed by linking the health issues associated with LBP to the categories of the ICF framework 24 . These core sets are available for use in clinical studies or to guide assessment of patients with LBP, but they do not have a specific focus on the measurement of outcomes in clinical trials.
3. The recent NIH Task Force recommended baseline research standards for clinical studies on chronic LBP (CLBP) 21 . These recommendations are proposed to describe, stratify, and compare reports on patients with chronic LBP, but they are not prescriptive for the reporting of core outcomes in clinical trials.
4. The International Consortium for Health Outcomes Measurement (ICHOM) has recently developed a standardized set of outcomes for LBP 25 . The ICHOM initiative aims at targeting ‘all providers around the world’ and all LBP disorders. This suggests a rather broad scope focused on the provision of healthcare, rather than a specific focus on core outcomes for all health interventions assessed in clinical trials for NSLBP.
Different stakeholders can be involved in the development of a COS: researchers, healthcare providers, patients, managers, government agencies, and industry representatives 11 . The involvement of multiple stakeholders in the consensus process for a COS is strongly recommended by COS methodologists 11 16 26 . For this COS, the Steering Committee decided to focus on four groups of stakeholders:
1. Healthcare researchers: these are professionals working in all fields of clinical research relevant for NSLBP (such as orthopaedics, physiotherapy, psychology, chiropractic, anaesthesiology, rheumatology, physical medicine, and rehabilitation), methodologists, or statisticians who currently work only as researchers. All researchers involved in this project should be authors of published scientific articles on clinical research for LBP.
2. Healthcare providers: these are professionals from different disciplines who have clinical experience in the management of patients with NSLBP.
3. Professionals who work both as healthcare researchers and providers: a separate category is made for these professionals because they might bring a perspective on core outcomes that may differ from that of people who perform only one of the two jobs.
4. Patients: this group is composed of people who have or have had NSLBP, and who sought healthcare for their NSLBP. Previous research has shown that it is limiting not to include patients in the development of a COS 16 . Patients have the perspective of living with the health condition and this may substantially differ from those of researchers and providers.
The decision to focus on these groups of stakeholders was based on practical considerations related to the resources of time and money available for the project. Members of the Steering Committee represented all these stakeholder groups, and representatives of each were invited to participate in the Delphi study used to reach consensus on this core domain set.
A comprehensive framework of health can be beneficial in developing a COS, favouring the content validity of the end product. A recent review identified five conceptual frameworks that could be relevant for COSs development 27 . However, only the five Ds (discomfort, disability, drug toxicity, dollar cost, and death) and the ICF framework have been used to develop COSs in different health areas, and they cover somewhat different areas of outcome 27 . The OMERACT initiative has developed a new framework that aims at including all key aspects of a health condition to ensure comprehensiveness of COSs 17 .
The OMERACT Filter 2.0 framework was created to broaden the ICF framework and combine it with Wilson and Cleary’s model of health-related quality of life 28 . The framework is subdivided into core areas (Table 1) that encompass the complete content of what is measurable in a clinical trial, including both patient-centred and intervention-specific information. It includes three core areas that describe the ‘impact of health conditions’ (‘death’, ‘life impact’, and ‘resource use and economic impact’) and one core area that describes ‘pathophysiological manifestations’. An explanation of the four core areas of the OMERACT framework is presented in Table 2. OMERACT recommends the inclusion of at least one domain from each core area in every COS 17 . However, our Steering Committee decided that, initially, no requirements related to the core areas should be applied to the domains that may become part of this core domain set. Regarding ‘death’, it was noted that this is a mandatory reporting requirement for all clinical trials, but a rare event in NSLBP. Regarding ‘pathophysiological manifestations’, it was noted that they might be out of the scope of some clinical trials, given that NSLBP is defined by its lack of known pathophysiology. The Delphi study will be used to evaluate whether substantial and convincing arguments generate further discussion on this within the Steering Committee. The OMERACT Filter 2.0 framework was used by the Steering Committee to help the development of a list of potential core domains and for discussion during the Delphi study.
|
Core Area |
Specification |
|
Death a |
This core area includes possible specifications of death, such as generic or disease-specific (all-cause versus disease-specific mortality), and intervention-specific (for example, death due to surgery). |
|
Life Impact a |
This core area can include domains of the ICF 35 (such as activity and participation) and domains within the concept of health-related quality of life 28 (such as functional status, general health perceptions, and overall quality of life). |
|
Resource Use/Economical Impact a |
This core area describes the economic impact of health conditions both on society and on the individual. In fact, the presence of a health condition and its treatment incur resource use. |
|
Pathophysiological Manifestations b |
This core area is to assess whether or not the effect of the intervention specifically targets the pathophysiology of the health condition. Pathophysiology can include psychosocial manifestations. Example domains are: ICF body function, reversible manifestations (including modifiable risk factors and actual manifestations of ill health), and irreversible manifestations (including unmodifiable risk factors and damage). This area can also encompass all biomarkers and surrogate outcomes. |
A Delphi technique was used in this study, as in some other COS efforts 26 . This method is usually used to gain consensus among a group of experts or informed respondents that constitute the Delphi panel 29 . The respondents take part anonymously in sequential questionnaires that constitute different rounds. After each round, the group responses are fed back to the panellists who can reconsider their views based on this report of the group views 29 . The Delphi method avoids situations in which the group is dominated by the views of a few prominent personalities.
Before running the Delphi procedure, one member of the project team (AC) selected the people who were invited to be members of the panel. The project team and the Steering Committee took responsibility for drawing a list of potential core domains that was used in the Delphi study. The pre-Delphi steps described in this subsection have been completed, while the Delphi procedure itself is nearing completion.
We decided that at least 80 experts were required to participate in each round of the Delphi. Based on previous experience with Delphi studies 14 , we calculated a minimum response rate of 40% that led to the conclusion that at least 200 people had to be invited in the first round. A list of researchers who had extensively published on LBP was compiled using an approach aimed at minimizing selection bias. First, a search in Web of Science was performed to identify authors of at least 25 publications on LBP over the last 10 years (between 2003 and 2013). Those who were authors of at least two clinical trials or one systematic review of clinical trials on NSLBP were considered eligible. One reviewer (AC) screened titles and abstracts of publications of each author against the inclusion criteria. Only researchers with a retrievable email address were selected for invitation to the Delphi procedure. To ensure that notable researchers were not excluded from this list, convenience sampling was added to the systematic search. Members of the Steering Committee were asked to indicate the names of five researchers from different disciplines that should absolutely be included in this consensus exercise. Names recommended by the Steering Committee were added to the existing list.
Healthcare providers for the Delphi panel were recruited through convenience sampling. Each member of the Steering Committee was asked to identify a minimum of 10 clinicians from different disciplines who had clinical experience in managing patients with NSLBP.
Patients were also recruited through convenience sampling. Inclusion criteria for patients were: present or past history of NSLBP, attendance of healthcare for their back complaint, and fluent understanding of written English. Three members of the Steering Committee from different geographical locations (CL, MS, and RO) were asked to identify providers who could have direct contact to patients. Patients identified by these providers were contacted by email, provided further information about the study, and invited to participate. Those who agreed were sent an informative document giving explanations of technical terms that could be encountered during the questionnaires. Those who confirmed their interest were invited to take part in the Delphi study.
All members of the Steering Committee were also invited to take part in the Delphi study. This implies that they expressed their opinion twice regarding aspects discussed previously within the Steering Committee and then in the Delphi study (for example, regarding AEs). It was decided not to exclude their opinion from the Delphi results, where responses remained anonymous and could be seen by the whole panel. We considered that this fact could outweigh the disadvantage of having their opinion counting twice for some issues. The final list of selected panellists was known only to the project team member who compiled it (AC), and was not shared with the Steering Committee or any other panellist.
To make a list of (sub)domains that are routinely used as outcomes in efficacy or effectiveness trials for NSLBP, a search was performed of five recent Cochrane systematic reviews) 8 30 31 32 (Oosterhuis T et al. unpublished data). This first list of domains was subsequently enriched by adding (sub)domains included in the comprehensive ICF core set for LBP 24 and in a conceptual model developed to characterize the burden of LBP 33 . The comprehensive ICF core set and the conceptual model on the burden of LBP were adopted in this developmental phase because they both considered the patients’ perspective 33 34 . Subdomains that seemed to cover the same domain were grouped together to make a first draft list of domains (version one) where domains were classified into one of the core areas included in the OMERACT framework (Table 2). To find appropriate terms and definitions for each domain, we consulted the ICF Framework 35 , the Health Framework of the Patient-Reported Outcomes Measurement Information System (PROMIS) 36 , the Wilson and Cleary Model 28 , and IMMPACT 22 23 . When a definition was not found in one of these frameworks, it was searched in individual published papers.
Different features of the version one list of domains were discussed among the project team. A second literature search based on points of discussion was conducted and led to the development of the version two list. Feedback for version two was invited from other members of the Steering Committee, who were asked to provide critical comments on each domain. The members were also asked to indicate if there were important missing potential core domains and to indicate if they thought that certain domains were too broad or should be aggregated. The project team adjusted version two according to this input and formulated the final list of potential core domains (version three) that was used during the Delphi study.
A minimum of two Delphi rounds (including both closed and open-ended questions) was planned a priori. Considering that it is not feasible to weight responses from different stakeholder groups, all panel members were invited to participate, irrespective of the number from each stakeholder group. Panel members were invited to participate in each round of the Delphi study, unless they explicitly indicated during the study that they did not wish to receive further invitations.
The project team designed the Delphi questionnaires, sent invitations and reminders to panel members, analysed the responses, and formulated the feedback reports. Questionnaires used during the whole Delphi procedure were pilot-tested by three members of the project team (CT, MB, RO) and by one or two selected panellists. For this Delphi study, the online software SurveyMonkey (SurveyMonkey, Palo Alto, United States) was used and invitations for participation were sent by email. Each round was online for three or four weeks and reminder emails were sent approximately every seven days after the initial invitation. Recommendations of a group of COS methodologists for the use of the Delphi method were followed in this study 26 .
In the first round, participants were initially given information about the study and about the formulation of the questionnaire. They completed questions about their educational and professional background, their experience with clinical research relevant for NSLBP, and whether they were invited to participate as patients. The version three list of potential core domains, subdivided into core areas, was used to rate their importance. In the survey, the order of core areas and the order of domains within core areas were randomized. Panel members were asked to indicate if each domain was important enough to be included in this core domain set; response options were ‘Yes’, ‘No’ and ‘Unsure/I do not know’. Participants were strongly encouraged to provide arguments for their choices and to suggest modifications of definitions or wording of the domains. Participants were then asked to indicate if they considered that there was a large conceptual overlap between some domains, to suggest whether some domains had be aggregated, and to suggest potential core domains not included in the list. They were also asked to indicate what they would consider to be an ideal number of domains for this COS.
Finally, participants were asked to assess whether they agreed on a specific approach for the reporting of AEs. This approach highlighted that AEs cannot be considered as one of the potential core domains because they could affect multiple core areas and domains. Therefore, only AEs occurring outside of core domains could be listed specifically as AEs, because those occurring within a defined core domain would be summarized within the results for that domain. Panellists were asked to provide reasons for their responses.
The responses of round one were analysed and collated in the feedback report. Frequencies for the response options on the importance of domains were calculated for the whole panel. Responses to open questions were checked to evaluate if substantial arguments emerged against the overall trend of frequencies. Responses of the patients’ group were highlighted and analysed separately to assess if they differed from the other panel responses.
The project team established a priori that domains for which more than 60% of the responders chose the response option ‘No’ and less than 20% chose the response option ‘Yes’ would be dropped from the list of potential core domains. The project team considered the suggestions for the aggregation of certain domains and the strength of the arguments. Suggested missing core domains were added to the list for the next round. Descriptive statistics were used to summarize responses on the ideal number of domains and on the reporting of AEs.
The feedback report was provided to the panel members along with the second round of the survey. A proposal was presented for deleting domains that did not have at least 67% of first round respondents in favour or unsure about inclusion in the core domain set. Other proposals were driven by comments of the first round and concerned exclusion, aggregation, or retention of other potential core domains. For example, if in round one several comments were made about the breadth of one domain covering other more specific domains, a proposal was made to erase this broad domain. Panellists were asked to indicate whether or not they agreed with all the proposals and a priori consensus was set at 67% respondent agreement. Panellists were also asked to judge if missing core domains suggested in the first round were important enough to be included in the list. Response options were the same as the first round and these domains were added to the list of potential core domains if 67% of respondents were in favour or unsure of their inclusion.
Upon completion of the second round, responses were analysed and combined in the feedback report. As in the first round, patients’ responses were analysed separately to assess if they had different opinions from the rest of the panel. Results were discussed by the project team, which assessed whether substantial arguments emerged against the overall consensus obtained with quantitative answers.
The feedback report of the second round was presented to all participants invited to the third round. Based on previous results, the potential core domains left in the list were presented in the third round to ask if each domain was indeed core. Response options were the same as the first round and participants were given the opportunity to provide arguments for their choices.
Frequencies for the response options were calculated for the whole panel and for each of the four stakeholder groups separately to evaluate if discrepancies existed between the groups. Reasons for all choices were checked to ensure that no convincing arguments were raised against the dominating group response. A priori consensus was set at 67% of the panel agreeing that a domain was core, with domains reaching this threshold to be included in this COS. If there were clear discrepancies between stakeholder groups or controversial arguments emerged, the results were presented to the Steering Committee that made final decisions.
As this project does not involve experiments with patients or study subjects, according to the Dutch Medical Research in Human Subjects Act (WMO), it is exempt from ethical approval in The Netherlands. No ethical approval is required in other countries from which other patients were invited to take part in the Delphi study (United States and Italy). All patients involved were asked for their consent before participation in the Delphi study, and all procedures were conducted according to the Declaration of Helsinki.
Development of a COS is an iterative approach that includes, firstly, determination of the domains that should be measured and reported and, secondly, determination of which outcome measurement instruments should be used to measure the domains 11 17 . This study protocol presents the methodology that has been adopted for development of a consensus-based core domain set for clinical trials of health interventions for NSLBP. It forms the first step in updating the proposal of standardized measurement in LBP made in 1998 by Deyo et al. 13 . Conceptualisation and design of the second step for this COS will now need to be established, and recently published methodological guidance 37 38 in this field could act as a useful reference for formulating the core outcome measurement set for NSLBP.
The third round of the Delphi study has recently been completed and the results will be analysed and reviewed by the Steering Committee. A publication reporting the results of the Delphi study will be submitted for publication in late 2014.
AE: Adverse event; COMET: Core Outcome Measures Effectiveness Trials; COS: Core outcome set; DALYs: Disability-adjusted life years; ICF: International Classification of Functioning; ICHOM: International Consortium for Health Outcomes Measurement; IMMPACT: Initiative on the Methods, Measurement, and Pain Assessment in Clinical Trials; LBP: Low back pain; NIH: National Institutes of Health; NSLBP: Non-specific low back pain; OMERACT: Outcome Measures Rheumatoid Arthritis Clinical Trials; PROMIS: Patient Reported Outcomes Measurement Information System; YLDs: Years lived with disability.
The authors declare that they have no competing interests.
AC, CT, MB and RO conceived and designed the study. AC conducted the study and wrote the first draft of this manuscript. CT, MB and RO conducted the study and provided important intellectual revision of the manuscript. RD and CL participated in conception and design of the study and provided important intellectual revision of the manuscript. RB, TC, LC, NF, MG, BK, FK, CM, AP, WP, MS, DT, and MvT participated in conception and design of the study, and revised the manuscript. All authors read and approved the final version of this manuscript.
This research project was funded by the Wetenschappelijk College Fysiotherapie (WCF) of the Royal Dutch Society for Physical Therapy (KNGF). The funding body did not have any role in trial design, collection, analysis, and interpretation of data, nor in writing the manuscript and deciding to submit this manuscript for publication. The views expressed here are those of the authors and do not necessarily reflect those of their funding bodies.
Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
A systematic review of low back pain cost of illness studies in the United States and internationally
Diagnosis and treatment of low back pain
Low back pain symptoms show a similar pattern of improvement following a wide range of primary care treatments: a systematic review of randomized clinical trials
2009 updated method guidelines for systematic reviews in the Cochrane Back Review Group
Exercise therapy for treatment of non-specific low back pain
Spinal manipulative therapy for acute low back pain
The impact of outcome reporting bias in randomized controlled trials on a cohort of systematic reviews
Standardising outcomes for clinical trials and systematic reviews
Developing core outcome sets for clinical trials: issues to consider
Can a core outcome set improve the quality of systematic reviews?—a survey of the Co-ordinating Editors of Cochrane Review Groups
Outcome measures for low back pain research. A proposal for standardized use
The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes
Core Outcomes for Clinical Research in Low Back Pain: Revisited
OMERACT: an international initiative to improve outcome measurement in rheumatology
Developing core outcome measurement sets for clinical trials: OMERACT Filter 2.0
MOMENT – management of otitis media with effusion in cleft palate: protocol for a systematic review of the literature and identification of a core outcome set using a Delphi survey
The CONSENSUS study: protocol for a mixed methods study to establish which outcomes should be included in a core outcome set for oropharyngeal cancer
Non-specific low back pain
Report of the NIH task force on research standards for chronic low back pain
Core outcome domains for chronic pain clinical trials: IMMPACT recommendations
Core outcome measures for chronic pain clinical trials: IMMPACT recommendations
ICF core sets for low back pain
The ICHOM Standard Set for Low Back Pain
Using the Delphi technique to determine which outcomes to measure in clinical trials: recommendations for the future based on a systematic review of existing studies
Can we decide which outcomes should be measured in every clinical trial? A scoping review of the existing conceptual frameworks and processes to develop core outcome sets
Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes
Diagnostic classification of patients with low back pain: report on a survey of physical therapy experts
Behavioural treatment for chronic low back pain
Rehabilitation after lumbar disc surgery
Multidisciplinary biopsychosocial rehabilitation for chronic low back pain
A validity-driven approach to the understanding of the personal and societal burden of low back pain: development of a conceptual and measurement model
ICF core sets for low back pain: do they include what matters to patients?
ICF Browser
PROMIS Domain Framework
Developing core outcome sets: methods for identifying and including patient-reported outcomes (PROs)
Core outcome measures in effectiveness trials (COMET) initiative: protocol for an international Delphi study to achieve consensus on how to select outcome measurement instruments for outcomes included in a ‘core outcome set’


MasterMind
MasterMind is a board game with two players; in this
case the user chooses the secret and the program does the guessing.
Here is a sample interaction:
examples> ./MasterMind
Welcome to Mastermind!
Choose your secret. Press return when ready.
My guess: Red Blue Blue Red
Answer (two integers): 1 1
My guess: Blue Black Yellow Red
Answer (two integers): 2 0
My guess: Green Black Red Red
Answer (two integers): 0 0
My guess: Blue Blue Yellow White
Answer (two integers): 3 0
My guess: Blue Blue Yellow Blue
Answer (two integers): 3 0
My guess: Blue Blue Yellow Yellow
Answer (two integers): 4 0
Yippee!
Do you want to play again? (y/n) n
examples>
The program is rather long and is appended at the end of this page.
We only note a few things:
- The program makes use of two library modules
Data.Functional.List and RandomGenerator. These
are parts of the (presently very experimental) Timber library.
- The program complains if the user gives contradictory answers,
asks for the secret and explains the user's mistake.
module MasterMind where
import Data.Functional.List
import RandomGenerator
import POSIX
data Colour = Red | Blue | Green | Yellow | Black | White
default eqColour :: Eq Colour
showColour :: Show Colour
parseColour :: Parse Colour
eqAnswer :: Eq Answer
type Guess = [Colour]
data Answer = Answer Int Int
instance showAnswer :: Show Answer where
show (Answer e n) = show e ++ " " ++ show n
instance showGuess :: Show Guess where
show ss = unwords (map show ss)
type Board = [(Guess,Answer)]
allColours = [Red, Blue, Green, Yellow, Black, White]
allCodes :: Int -> [Guess]
allCodes 0 = [[]]
allCodes n = concat [ [c:cs | c <- allColours]
| cs <- allCodes (n-1)]
mkAnswer :: String -> Answer
mkAnswer cs = Answer e n
where [e,n] = map parse (words cs)
answer :: Guess -> Guess -> Answer
answer guess code = Answer e n
where
e = equals guess code
n = sum [ min (count c guess) (count c code)
| c <- allColours] - e
count c xs = length [x | x <- xs, x==c]
equals [] [] = 0
equals (x:xs) (y:ys)
| x==y = 1 + equals xs ys
| otherwise = equals xs ys
contradictions :: Board -> Guess -> Board
contradictions board c =
[ (g,r) | (g,r) <- board, answer g c /= r ]
consistent :: Board -> [Guess] -> [Guess]
consistent board cs =
[ c | c <- cs, null (contradictions board c) ]
data State = Idle | JustGuessed | GameOver | GetSecret
root env = class
gen = new baseGen (microsecOf env.startTime)
board := []
cs := []
state := Idle
shift = do
r <- gen.next
n = r `mod` (length cs)
ys = take n cs
zs = drop n cs
cs := zs ++ ys
startGame = do
board := []
cs := []
env.stdout.write
"Choose your secret. Press return when ready.\n"
state := Idle
mkGuess = do
if null cs then
env.stdout.write "Contradictory answers!\n"
env.stdout.write "Tell me your secret: "
state := GetSecret
else
shift
env.stdout.write ("My guess: "++show (head cs)++"\n")
env.stdout.write "Answer (two integers): "
state := JustGuessed
checkQuit = do
env.stdout.write "Do you want to play again? (y/n) "
state := GameOver
inpHandler inp = action
case state of
Idle ->
cs := allCodes 4
mkGuess
JustGuessed ->
case map parse (words inp) of
[Right e, Right n] ->
if e == 4 then
env.stdout.write "Yippee!\n"
checkQuit
else
c:cs' = cs
board := (c,ans) : board
cs := consistent board cs'
mkGuess
_ -> env.stdout.write "Answer must be two integers separated by spaces; try again\n"
GameOver ->
if head inp == 'y' then
startGame
else
env.exit 0
GetSecret ->
case map parse (words inp) of
es | length es==4
&& all isRight es ->
ss = map fromRight es
(g',r'):_ = contradictions board ss
env.stdout.write ("When I guessed "++show g'++
", you answered "++show r'++".\n")
env.stdout.write ("Correct answer should have been "++
show (answer g' ss)++".\n")
checkQuit
_ -> env.stdout.write "Secret must be four colours separated by spaces; try again\n"
result
action
env.stdin.installR inpHandler
env.stdout.write "Welcome to Mastermind!\n"
startGame


I'm a software engineer and full-stack web developer who likes to build things that work and things that matter.
Find Out MoreI'm a software engineer from the San Francisco Bay Area who has also been a biochemist, musician, and badminton coach. My recent projects include a music streaming web application built on a Rails backend and a React/Redux frontend, a daily biometric tracking application built on the MEAN stack, and a turn-based tank artillery browser game built with JavaScript. Check them out below!
Projects


%+header%>
<%:Shutdown your device%>
<%- local c = require("luci.model.uci").cursor():changes() if c and next(c) then -%><%:Warning: There are unsaved changes that will be lost after shutdown!%>
<%- end if not shutdown then -%><%:Perform a system shutdown%>
<%- else -%><%:Please wait: device is shutting down...%>
<%- end -%> <%+footer%>


Before Change
read_weights = var(read_weights)
// condition read weights by their usages
relevant_usages = usage.gather(1, read_positions.squeeze())
read_weights = (read_weights.squeeze(1) * relevant_usages).unsqueeze(1)
(b, m, w) = memory.size()
read_vectors = memory.gather(1, read_positions.squeeze().unsqueeze(2).expand(b, self.K+1, w))
After Change
(b, m, w) = memory.size()
visible_memory = memory.gather(1, read_positions.unsqueeze(2).expand(b, r*k+1, w))
read_weights = F.softmax(θ(visible_memory, keys), dim=2)
read_vectors = T.bmm(read_weights, visible_memory)
return read_vectors, read_positions, read_weights, visible_memory


{% load static %}
Home - BeyondBias
BeyondBias
Home
Experience news differently.
BeyondBias exposes you to new perspectives and interesting ideas.
Analyze!
Other Perspectives
{% for link,title,summary,fact,bias in links.link %} {% if forloop.counter <= 3%}
{{title}}
{{summary}}
Read More
{% endif %} {% endfor %}


Every startup needs a cheesy pitch line where you compare your company to an existing service and then tell what sets your startup apart. Enter yours below:


{% extends "generator/base.html" %} {% load static %} {% block content %}
This website generates random passwords as per your choice (◠‿◠).


Mission
Build a web platform for global environmental health research that fosters collaboration across disciplines and national boundaries--accelerating discovery, advancing outbreak detection, and improving prevention. Make the platform available to multiple disciplines and regions as skapes.org
Development status
This version (v0.4) was released in June 2011 to showcase web approaches to global health research. This website and alpha application are under active development. Through open and public development practices (e.g. Launchpad) we hope to to encourage the global environmental health and open source communities to join us in the development of a new analytical cyberinfrasture NOTE: This alpha release (v.04) is not yet stable ... it is a proof of concept and contents may change on short notice. Please join our Google Group Mailing List (sidebar) for updates or visit the documentation on our Trac Wiki. Please check back often.
Opening a web of knowledge discovery
Global public health in the 21st century will require careful attention to processes of global environmental change. These challenges necessitate open, interdisciplinary access to cross-cutting tools and data. The flow of information is currently limited by a lack of tools to integrate datasets, maps, and local knowledge. Advances in computing hold the promise of a new cyberinfrastructure for studying global environmental change and human health. Healthscapes leverages these advances to address: difficulties finding relevant information, limited access to analytical tools, and poor knowledge transfer across a fractured social network.
Acknowledgements
This website is being developed with numerous open source technologies, including: web2py, openstreetmap, python, geoserver, openlayers, geoext, extjs, jquery, and others. Our technology stack is inspired and powered by the work of the Sahana Foundation (open source disaster management system). As such, it is designed extend the geospatial analytical capacity of the Sahana Eden platform.


Eric Wong. Union Bank Of China, 99 Queens Road Central Hong Kong, Dear Friend, I write you in good faith, hoping that you will treat this letter as a desperate search for assistance in a matter that shall benefit both ofus.A random search based on your locality from the Internet produced youraddress, Please i apologise for this medium of contact as it appears the only definate way i can have assistance with this matter. I am Mr Eric Wong, Head of Private Banking, Union Bank Of China,also account Manager to a deceased Immigrant property magnate who was based in the U.K. and had vast business interest in Hong Kong ,also referred to as my client.On Monday, 31 July, 2000, 13:22 GMT 14:22 UK , my client, his wife and their two Children died in the Air France concord plane crash bound for New York in their plan for a world cruise I have been managing his Bank accounts here in the Hong Kong, and some Properties which he had put out for sale. The properties which have long been put out on sale have been paid for, and the money paid has long been deposited into his local bank account here. I have contacted you to assist in repatriating the money and estate left behind by my client before they get confiscated or declared unserviceable by my Bank. Particularly, the bank has issued me with a notice to provide any of his next of kin or have the account confiscated within a short period of time.My late client has an account is valued at 18 Million United States Dollars Only I therefore made further investigation,with the personel department and discovered that did not declare any kin or relations in all his official documents, including his Bank Deposit paperwork in my Bank. This sum of US$18million is still sitting in my Bank and the interest is being rolled over with the principal sum at the end of each year. No one will ever come forward to claim it. According to Laws of Hon Kong, at the expiration of 5 (five) years, the money will revert to the ownership of the Hong Kong Government if nobody applies to claim the fund. I now seek your consent to present you as the next of kin to my deceased client, so that the proceeds of his account valued at 18 Million United States Dollars Only can be paid to you, I will like you to provide immediately your full names and address so that the attorney will prepare the necessary documents and affidavits that will put you in place as the next of kin. The money will be paid to you for us to share in the ratio of 60% for me and 35% for you and 5%, after completion,for Expenses Incurred in the course of the transaction .There is no risk at all as all the paperwork for this transaction will be done by the attorney and with my position as the Head Private Banking guarantees the successful execution of this transaction. If you are interested, please reply immediately to my email<EMAIL_ADDRESS>All I require is your honest co-operation to enable us see this deal through. I guarantee that this will be executed under a legitimate arrangement that will protect you from any breach of the law. Trust me as his Manager of his Estates, as i have all the needed information, that this is a risk-free relationship.You should observe utmost confidentiality,and rest assured that this transaction would be most profitable for both of us because I shall require your assistance to invest my share in your country. Awaiting your urgent reply. Thanks and regards. Eric Wong.


SE-DASH
Home
DNS
Redfish
Others
Server Name:
dal-server1.walmart.com
dfw-server1.walmart.com
dfw-server2.walmart.com
dal-server2.walmart.com
dal-server3.walmart.com
oneops-server1.walmart.com
oneops-server2.walmart.com
HardWare Name:
FirmWare Upgrade
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Os Related
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| Stream stream | The Stream object to read the string from. |
| uint pad | The amount of characters that the string is padded with. |
| Stream stream | The Stream object to write the string to. |
| string data | The data to write to the stream. |
| uint pad | How many bytes to pad the string to using null characters. |
| T[] array | The array to search. |
| T find | The element to find. |


Class Definition: FullAtomModel
FullAtomModel is basically an object that stores the relevent information in a PDB file. PDB files are parsed into structures, not objects. This class is to give flexibility to the full atom model.
Contents
classdef FullAtomModel < handle
%FULLATOMMODEL Contains a parsed pdb file % Contains just relevant information from the pdb structure
Properties section
This section defines the properties of the FullAtomModel class
properties (SetAccess = private) Name % The name should be the pdb ID, or custom name of choice Model % This contains the model, structured just like in a pdb structure end
Methods section
Here the various functions that can operate on a FullAtomModel are defined
methods
Instantiation function
function FAM = FullAtomModel(name) % This creates a new instance of FullAtomModel if nargin < 1 % Assign a name if one is specified, otherwise call it un-named name='un-named'; end FAM.Name = name; % The Model property will automatically be set to an empty matrix [] end
ans = FullAtomModel handle Properties: Name: 'un-named' Model: []
Puts actual information into the model from a pdb structure
function PopulateFAM(FAM,pdb) if isfield(pdb.Model,'HeterogenAtom') % If HeterogenAtoms are found in the pdb structure, include them FAM.Model=[pdb.Model.Atom pdb.Model.HeterogenAtom]; else FAM.Model = pdb.Model.Atom; % Otherwise, just include what is in Model. X-ray is assumed, not NMR end end
Takes a subset of a FullAtomModel
function CutFAM(FAM,subset,fam_orig) if nargin < 3 % If a second FAM is not supplied, cut the first one FAM.Model=FAM.Model(subset); else FAM.Model=fam_orig.Model(subset); % If a second FAM is supplied, copy a subset of it into the first one end end
end
end


I have the standard emontx enclosure here. I can't figure out how to fit my AA batteries inside. There isn't much room for them. I can get them inside with the two metal clip but I have to push quite hard to insert them. I don't know how it's been done on the boxed emontx picture on the website (although the plastic does look a bit bent). Not only that but even with the two clips and the batteries hardly fitting I still need to add another clip the other end to connect + with - between the two batteries.
Does anyone have a clean solution to get board and batteries together nicely in this box? I'm interested.
I though about cutting the plactic separation maybe. Or another idea is to use AAA batteries (which fit very nicely but less Ah obviously).
Re: Emontx enclosure - How do you fit the AA batteries?
I think that the short answer is that they don't fit!
It looks as though the enclosures were intended for a PP3 - 9V battery, ie with both connections on the same side, and it's extremely tight to fit 2 x AA batteries in there. As you have already noticed, you can see the case bulging in the photo top left.
I cut a small length of stainless steel plate, bent the two edges up slightly, and hot glued it to the plastic case, to provide a connection between the + & - of the AA batteries.
Works, but again it distorts the case.
Paul
Re: Emontx enclosure - How do you fit the AA batteries?
Hi. I managed to fit two AA batteries in my TX box. I admit I had to push a bit hard.
I connected the + and - with a folded piece of tin foil.


--- title: "Tag: resources" subtitle: "Posts and resources filtered by tag: resources." permalink: /tags/Resources tag: Resources tag_style: rgba( 247, 118, 205, 100%) --- Cities Posts


|
Herkulex_English
v1
C++ API for Herkulex English version
|
#include <KeyboardFunctions.hpp>
Public Member Functions | |
| char | getNonBlockingTriggers () |
| Get non-blocking keyboard triggers (skip until triggered) More... | |
| char | getBlockingTriggers () |
| Get blocking keyboard triggers (wait until triggered) More... | |
| void | demo () |
| A test/demo of the linux kbhit function. More... | |
Private Member Functions | |
| int | _kbhit () |
| A Linux implementation of Windows _kbhit non-blocking function to wait for user's key pressed. More... | |
| char | _getch () |
| A Linux implementation of Windows _getch to obtain the keypressed from the buffer. More... | |
Keyboard function class
A implementation of the _kbhit() function for Linux.
In windows _kbhit() is suppose to implement non-blocking user input checking function. If the function returns a nonzero value, a keystroke is waiting in the buffer. The program can then call _getch or _getche to get the keystroke.
Non-blocking means that inputs can interrupt the current ongoing process, but not let the program wont wait for user input indefinitely like scanf
In Windows , conio.h implements the _kbhit function for us.
In Linux, we have to implement our own, because it lacks a _kbhit function.
Edited from the source written by the following
|
private |
A Linux implementation of Windows _getch to obtain the keypressed from the buffer.
|
private |
A Linux implementation of Windows _kbhit non-blocking function to wait for user's key pressed.
| void KeyboardFunctions::demo | ( | ) |
A test/demo of the linux kbhit function.
Calling this function will run a non-blocking keyboard detection loop.
Once the 'E' key is pressed, the program will stop.
| char KeyboardFunctions::getBlockingTriggers | ( | ) |
Get blocking keyboard triggers (wait until triggered)
Calling this function will block the program using a while(1) loop.
Will only return when a keyboard is pressed/triggered
Trigger Code:
-look at the define pre-compiled macros defined in header file
| char KeyboardFunctions::getNonBlockingTriggers | ( | ) |
Get non-blocking keyboard triggers (skip until triggered)
Calling this function will NOT block the program
return the triggered code
Trigger Code:
-look at the define pre-compiled macros defined in header file


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