A sample blog entry that is only being created for testing purposes. The aim is to read a JSON object file that links to markdown, which is then parsed and displayed in the browser.
A sample blog entry that is only being created for testing purposes. The aim is to read a JSON object file that links to markdown, which is then parsed and displayed in the browser.
A sample blog entry that is only being created for testing purposes. The aim is to read a JSON object file that links to markdown, which is then parsed and displayed in the browser.


Namespace: BioFSharp.Mz Parent Module: Db
addOrUpDateInPlace param dbsequence
createDBSequence (...)
createDBSequenceParamTable cn
createDBSequenceTable cn
createDBSequenceWith (...)
insertDBSequenceToDb (...)
prepareInsertDBSequence cn tr
prepareInsertDBSequenceParam cn tr
prepareSelectDBSequencebyAccession cn tr
prepareSelectDBSequencebyID cn tr
prepareSelectDBSequencebyName cn tr
prepareSelectDBSequencebyRowVersion (...)
prepareSelectDBSequencebySearchDBID (...)


Hi, I'm Alex
I studied Computer Science at UC Berkeley.
I build at the intersection of collaborative intelligence, cyber-physical automation, and high performance distributed computing to accelerate scientific research.
I value learning in public, open source, and reproducible research.
Learn more
ExperienceBioinformatics Software Engineer at Tempus, May 2021 - PresentWriting modular pipeline components used in oncogenomic assays, and automating variant characterization systems on GCP.Software Engineering Intern at Google, May - August 2018Worked on the Device Inventory team on a project related to data engineering and information retrieval for a schemaless database of corporate device assets.Software Engineering Intern at Intuit, May - August 2017Fullstack work that powered performance analytics for core infrastructure.EducationB.A. in Computer Science at UC Berkeley, 2016 - 2020Organizations - Upsilon Pi Epsilon (CS Honor Society), iGEM, CHAOS, Ukrainian Club, West Coast Swing, Running Club
PortfolioAcademixCreated a functional demo using Free Busy and Google Maps APIs to automate planning meetings.Mirror Mirror on the WallDesigned a “Smart Mirror” that responds to voice commands to play music, give weather forecasts, etc. powered by a Raspberry Pi and a dynamic web interface, winning Honorable Mention and Crowd Favorite awards.JumboJobsA website that allows the user to search for specific industries in different locations to see job growth, average income, and more.Ray TracerRendered a 3D scene complete with patterns and shadows, earned second place in a programmatic art contest.Works In ProgressProjects at various stages of development
ContactThe best way to reach me is to shoot me an email at {{site.user.email}}.If you are interested in working, networking, or collaborating with me, do not hesitate to reach out.


If you clicked this, your dog is probably a dalmatian!
The spots on the dalmatian are like the stars on the sky. In ancient times, their spots were used for story telling. Then they were eaten alive and dalmations still seek revenge on humanity.Be careful...:(


A Snaker who can't play basketball is not a good programmer.
"When nothing seems to help,I go and look at a stonecutter hammering away at his rock perhaps a hundred times without as much as a crack showing in it.
“当一切都看起来无济于事的时候，我去看一个石匠敲石头。他一连敲了100次，石头仍然纹丝不动。但当他敲第101次的时候，石头裂为两半。可我知道，让石头裂开的不是那最后一击，而是前面的一百次敲击的结果。”——雅各布·里斯
Yet at the hundred and first blowit will split in two,and I know it was not that blow that did it——but all that had gone before."——Jacob Riis
喜欢走走拍拍，穷且技术一般
邓呆子是我一生的信仰
同时努力赶超陈医生的发际线
篮球队队长，然而打不过队中高手
自学四种泳姿，幻想救上白富美
滑雪爱好新 get√，没有然而
装有古锭刀的许褚发动裸衣后
酒火杀装有藤甲空手牌的诸葛亮
到底会掉几点血


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This track contains chromatin interaction data generated using the 5C (Chromatin Conformation Capture Carbon Copy) method by the ENCODE group (Dekker Lab) located at the University of Massachusetts, Worcester, MA. This track shows the significant looping interactions between transcriptional start sites (TSS) and distal regulatory elements in the context of the 44 ENCODE pilot regions spanning 1% of the human genome.
Although the DNA is a linear sequence, the chromatin, which is packed and organized inside the nucleus, does not function linearly. This is most clearly illustrated by the fact that genes are often regulated by elements that are located hundreds of kilobases away in the linear genome. Imaging techniques have shown that regulatory elements can act over large genomic distances by engaging in direct physical interactions with target genes, resulting in the formation of chromatin loops. Based on these observations, we have envisaged that the spatial organization of the genome resembles a three-dimensional network that is driven by physical associations between genes and regulatory elements, both in cis (within the same chromosome) and in trans (between different chromosomes) (Dekker, 2006).
Apart from imaging technology which is labor intensive and low-throughput, long-range chromatin looping interactions can be detected using the Chromosome Conformation Capture (3C) technology (Dekker et al., 2002). The 3C method employs formaldehyde cross-linking to covalently link interacting chromatin segments in intact cells. Cells are subsequently lysed and chromatin is digested with a restriction enzyme of choice. The digested fragments are then ligated under dilute conditions to facilitate intramolecular ligation. The result is a genome-wide interaction library of ligation products corresponding to all possible chromatin interactions. Specific ligation products can then be detected by PCR using specific primer pairs.
The 5C method was developed to dramatically increase 3C throughput (Dostie et al., 2006; Dostie and Dekker, 2007). The 5C method greatly increases the scale of chromatin interaction detection by replacing the PCR detection step of 3C with ligation-mediated amplification (LMA). LMA is advantageous due to a much higher level of multiplexing by using thousands of primers in a single reaction to detect millions of chromatin interactions (ligation junctions) in parallel. The LMA step effectively "copies" 3C ligation products into much smaller 5C ligation products that precisely correspond to ligation junctions formed during the 3C procedure. The products of the multiplexed LMA reaction constitute the 5C library. The composition of the 5C library is determined using high-throughput DNA sequencing.
In the graphical display, the significant looping interactions in cis (i.e., from the same ENCODE pilot regions) are represented by blocks and connected by a horizontal line. Users can opt to filter the significant looping interactions according to their respective z-score (scaled to 0-1000) by using the built-in genome browser display score threshold.
Metadata for a particular subtrack can be found by clicking the down arrow in the list of subtracks.
The following types of data are available for download:
The aim of the pilot study was to generate a "connectivity map" between transcription start sites (TSS) and distal regulatory elements within the 44 ENCODE PILOT regions.
In the current scheme, 5C primers were designed for all HindIII restriction fragments. Reverse primers were designed on fragments containing the TSS of annotated genes. Forward primers were designed on all other fragments. This design allowed for the interrogation of all TSS with all other restriction fragments, thus generating an interaction map between TSS and regulatory elements. For gene desert ENCODE pilot regions (for example ENr313), an altered scheme of forward and reverse primers was designed.
Primers were selected for relative uniqueness using a custom 15-mer frequency table and BLAST. A custom hexamer barcode was added to each primer to ensure the sequence was unique relative to the primer pool being used. Primers were also selected for the appropriate melting temperature and GC-content and a universal tail sequence for amplification.
The 44 ENCODE regions were analyzed in two groups using two separate 5C primer pools. The first group (ENm) contained the manually-picked ENCODE regions, ENm001-014 and ENr313. The second group (ENr) contained the 30 randomly-picked ENCODE regions. The two 5C primer pools were made by pooling 5C primers for interrogating long-range interactions in the two groups of ENCODE regions. The primer pool for the ENm group contained a total of 3,150 primers (476 reverse 5C primers and 2674 forward 5C primers). This primer pool allowed interrogation of a total of 1,272,824 interactions. Of these, 83,427 interactions were between fragments that were both located in the same ENCODE region. This primer pool for the ENr group contained a total of 3,152 primers (505 reverse 5C primers and 2647 forward 5C primers). This primer pool allowed interrogation of a total of 1,336,735 interactions. Of these, 34,859 interactions were between fragments that were both located in the same ENCODE region. In total, 981 reverse primers and 5,321 forward primers were designed (corresponding to ~77.1% (6,302/8,174) of all HindIII fragments in the 44 ENCODE regions).
Currently, data for two biological replicates have been generated for ENCODE Tier I (GM12878 and K562), Tier II (HeLa-S3), and H1 human embryonic stem cells (H1-hESC), spanning 14 ENCODE manual regions along with one random region (ENr313) as well as 30 random regions separately using high-throughput paired-end sequencing in the Illumina GA2 platform. The looping interactions, which are detected in both the biological replicates, are considered significant.
This is Release 2 (July 2012). There is no new data for this release all new data has the version number appended to the name (e.g., V2). Peak files have been reanalyzed and more complete Raw Data files have been submitted.
All provided data were produced by the Dekker Lab at UMass Medical School, Worcester, MA. The following personnel contributed to the project (contacts):
Additional information and/or vizualization tools can be found on the Dekker Lab website.
Baù D, Sanyal A, Lajoie BR, Capriotti E, Byron M, Lawrence JB, Dekker J, Marti-Renom MA. The three-dimensional folding of the α-globin gene domain reveals formation of chromatin globules. Nat Struct Mol Biol. 2011 Jan;18(1):107-14.
Dekker J. The three 'C' s of chromosome conformation capture: controls, controls, controls. Nat. Methods. 2006;3(1):17-21.
Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science. 2002 Feb 15;295(5558):1306-11.
Dostie J, Dekker J. Mapping networks of physical interactions between genomic elements using 5C technology. Nature Protocols. 2007;2(4):988-1002.
Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL, Honan TA, Rubio ED, Krumm A, Lamb J, Nusbaum C et al. Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 2006 Oct;16(10):1299-309.
Lajoie BR, van Berkum NL, Sanyal A, Dekker J. My5C: web tools for chromosome conformation capture studies. Nat. Methods. 2009;6(1):690-91.
Data users may freely use ENCODE data, but may not, without prior consent, submit publications that use an unpublished ENCODE dataset until nine months following the release of the dataset. This date is listed in the Restricted Until column, above. The full data release policy for ENCODE is available here.


Before Change
"Eigenvectors", "eigenvecnotimplemented", m)
// The eigenvectors are given in the same order as the eigenvalues.
try:
eigenvects = sorted(eigenvects,
key=lambda v_c: (abs(v_c[0]), -v_c[0]),
reverse=True)
// Try to sort the results as complex numbers
except TypeError:
try:
eigenvects = sorted(eigenvects,
key=lambda v_c: -abs(v_c[0]))
// Don"t sort the results at all
except TypeError:
pass
result = []
for val, count, basis in eigenvects:
// Select the i"th basis vector, convert matrix to vector,
// and convert from sympy
After Change
return evaluation.message(
"Eigenvectors", "eigenvecnotimplemented", m)
if all(v.is_complex for (v, _, _) in eigenvects):
eigenvects.sort(key=lambda v: (abs(v[0]), - re(v[0]), - im(v[0])),
reverse=True)
else:
eigenvects.sort(key=lambda v: from_sympy(v[0]).get_sort_key())
result = []
for val, count, basis in eigenvects:
// Select the i"th basis vector, convert matrix to vector,
// and convert from sympy


Region: Elbonia Japan Italy US and Canada Rest of the Americas Rest of the World
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Staurastrum Zygnema land gene plant green cpDNAs Chaetosphaeridium chloroplast cpDNA intron genome sequences genes Marchantia algae introns sequence [GenBank: group {border-style regions zygnematalean plants px;} #ffffff;} DNA charophycean evolution Mesostigma order repeat content cpDNAs, cpDNAs. genomes large splicing units analyses ancestor counterparts cpDNA, region relative small (Table GTAT common number using (Fig. Gene absence analysis complete cpDNA. features genes, identified present px; repeats represent rpl rps sites size tandem {font-family (%) Plant Sci Staurastrum, Turmel Zygnema, Zygnematales clusters font-size highly insertion inverted lineage rRNA revealed shown single-copy ATAC AY]; Biol Genes Lemieux MatK Mol Otis Palmer Table algal amino cellular denote double; intergenic lack larger loss matK presence shared streptophyte tRNA trnK(uuu) (in Acad CTTA Chloroplast Figure Natl Note Proc USA absent algae. ancestral blocks border-color border-width breakpoints changes conserved containing dispersed displayed filled genomic groups introns) lost map organization probably rearrangements sequencing solid; streptophytes {font-style {font-weight ./j.-...x AY] Chaetosphaeridium, Evol McCourt NC_] NC_]; ORF ORFs Phycol RNA Res TTCTA Zygnema. Zygnematales. acids alga based bold cell closely compared composition considered courier; desmids differ direct distinct divergence early em; event evidence evolutionary exhibit form genome. given however, indicated inferred insertions kit level lineages maturase mitochondrial monophyletic open order, origin outside protein pt;} rbcL relationships represented respectively. reverse share short site spacers species structure subunit support trans-spliced viride


Research Articles and Letters
- Keyi Han, Xuanwen Hua, Vishwa Vasani, Ge-Ah R. Kim, Wenhao Liu, Shuichi Takayama, and Shu Jia. "3D super-resolution live-cell imaging with radial symmetry and Fourier light-field microscopy." Biomedical Optics Express 13, no. 11 (2022): 5574-5584.
- Xuanwen Hua, Wenhao Liu, and Shu Jia. "High-resolution Fourier light-field microscopy for volumetric multi-color live-cell imaging." Optica 8, no. 5 (2021): 614-620.
- Wang, Jian, Xuanwen Hua, Changliang Guo, Wenhao Liu, and Shu Jia. "Airy-beam tomographic microscopy." Optica 7, no. 7 (2020): 790-793.
- Mandracchia, Biagio, Xuanwen Hua, Changliang Guo, Jeonghwan Son, Tara Urner, and Shu Jia. "Fast and accurate sCMOS noise correction for fluorescence microscopy." Nature communications 11, no. 1 (2020): 1-12. [Nature Methods highlight: Say goodbye to sCMOS noise. (Read more)]
- Guo, Changliang, Wenhao Liu, Xuanwen Hua, Haoyu Li, and Shu Jia. "Fourier light-field microscopy." Optics Express 27, no. 18 (2019): 25573-25594.
- Hua, Xuanwen, Changliang Guo, Jian Wang, Deborah Kim-Holzapfel, Bryce Schroeder, Wenhao Liu, Junhua Yuan, Jarrod French, and Shu Jia. "Depth-extended, high-resolution fluorescence microscopy: whole-cell imaging with double-ring phase (DRiP) modulation." Biomedical optics express 10, no. 1 (2019): 204-214.
- Munagavalasa, Sravan, Bryce Schroeder, Xuanwen Hua, and Shu Jia. "Spatial and spectral imaging of point-spread functions using a spatial light modulator." Optics communications 404 (2017): 51-54.
Conference Publications
- Hua, Xuanwen, Biagio Mandracchia, Wenhao Liu and Shu Jia. "Volumetric and optofluidic live-cell imaging using high-resolution Fourier light-field microscopy (HR-FLFM)." In Proc. SPIE 11964, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XX, 2022.
- Hua, Xuanwen and Shu Jia. "3D Live Cell Imaging Using High-Resolution Fourier Light-Field Microscopy." In Frontiers in Optics, pp. FW7E-1. Optical Society of America, 2020.
- Mandracchia, Biagio, Hua, Xuanwen, Changliang Guo, Jeonghwan Son, Tara Urner, and Shu Jia. "Correction of CMOS-related noise in fluorescence microscopy." In Frontiers in Optics, pp. JM6B-19. Optical Society of America, 2020.
- Hua, Xuanwen, Changliang Guo, Wenhao Liu, and Shu Jia. "Depth-Extended High-Resolution Microscopy with Double-Ring Phase Modulation." In Novel Techniques in Microscopy, pp. NS1B-7. Optical Society of America, 2019.
- Liu, Wenhao, Changliang Guo, Xuanwen Hua, and Shu Jia. "Fourier Light-Field Microscopy: An Integral Model and Experimental Verification." In Bio-Optics: Design and Application, pp. DT1B-4. Optical Society of America, 2019.
- Mandracchia, Biagio, Xuanwen Hua, Changliang Guo, and Shu Jia. "Automatic Correction of Pixel-dependent Noise: Towards the Ideal sCMOS Camera." In Novel Techniques in Microscopy, pp. JW4C-8. Optical Society of America, 2019.


{% extends 'pages_base.html' %} {% block container %}
Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution 3.0 Italian license.


I'm sure there are tons of tiny elves working on this.


EURO - US DOLLAR RATE
Share options
Indexes
EUR/USD pivot points
EUR/USD FORECAST POLL
The latest data for the Currencies Forecast Poll, released by FXStreet on Friday the 3rd shows a distribution of 60.33% short-term Bearish among the 15 participants.
EUR/USD Trading positions
The Average Sell Price among our contributors is 1.3380, with no visible cluster ahead.Traders positioning shows Average Buy Price at 1.3350 , and a lot of liquidity clustered around 1.3320.)
Bullish percentage index
The EUR-Bullish Percentage Index soars above the 70% level, considered overbougth territory
Cot net positioning
Net implied commercial USD long positions increased modestly over the previous week, while speculators pared their long positions


{% extends "layouts/base.html" %} {% block title %}Delete Survey{% endblock %} {%block content %}
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My favorite site is GitHub!
Astute leader with a tenacious appetite for data-driven continuous improvement, operational excellence, and refined results. Fearless and proven people, process, and project manager experienced in directing high-functioning teams of 2 to 500. Resourceful entrepreneurial mindset focused on customer-centric creative solutions, market-led strategic decision-making, and anticipating future needs while conceptualizing innovative future states. Resilient action-oriented change agent very well-versed in building trust through promoting open and honest communication, fostering collaboration, and creating a positive and inviting culture based on integrity, service, and obsessive attention to detail.
Experience
MISSION DIRECTOR, USAF; FT. MEADE, MD — 2016-2018
- Directed simultaneous real-time med/high altitude intelligence, surveillance, and reconnaissance missions managing crews of 50-75 operators, supervisors, and contractors.
- ASSISTANT CHIEF MILITARY LANGUAGE INSTRUCTOR, USAF; MONTEREY, CA — 2012 - 2016
- Managed a team of 15-25 military and contract language instructors overseeing a college of 200+ native-speaking professors and 350+ officer/enlisted language students.
- BORDER COORDINATION CENTER INTELLIGENCE ANALYST, USAF; AFGHANISTAN — 2010 - 2010
- Deployed with the Army to various Forward Operating Bases conducting intelligence, airfield, biometrics, security, combat, construction, planning, and training operations.
- DISTRIBUTED MISSION SUPERVISOR, USAF; FT. GORDON, GA — 2009 - 2011
- Led team of 25 operators prosecuting high-altitude ISR missions while coordinating with multiple world-wide distributed mission sites, agencies, organizations, and aircrews.
- CRYPTOLOGIC LANGUAGE ANALYST, USAF; FT. GORDON, GA — 2006 - 2009
- Provided direct threat warning to U.S. and allied aircrews as a subject matter expert and instructor rated operator on target language operations working ISR missions via DCGS.
Education
- American Military University — BASc - Technical Management (PM w/Cert in EOD) - 2020
- Community College of the Air Force — AAS - Intelligence Studies and Technology - 2017
- Defense Language Institute Foreign Language Center — AA - Hindi - 2012
- Defense Language Institute Foreign Language Center — AA - Hebrew - 2004
- Enlisted Aircrew Undergraduate Course — Certificate - 2003
Skills and Interests
Adaptability, AI, Biotech, Blockchain, Critical Analysis, Customer Service, EQ, Growth, Helping Others, Je ne sais quoi, Scalability, Servant Leadership, Strategic Planning


Source code for hdim
from .hdim import *
[docs]def ISTA( X, Y, Beta_0, convergence_criteria, Lambda, L_0 = 0.1, use_screening_rules = True ):
"""
Use the Iterative Shrinkage Thresholding Algorithm ( ISTA ) to iteratively solve the LASSO.
Args:
X: An n x p 2D array representing the design matrix.
Y: A 1 x p 1D array representing the predictors
Beta_0: A 1 x p 1D array describing initial condition for Beta.
convergence_criteria: Can be either an positive integer descrbing the number
if iterations that the solve should be run for, or a positive float describing
the smallest allowable Duality Gap.
Lambda: Regularization hyper-parameter for the LASSO ( e.g. Lambda x || Beta ||_1 )
L_0: Learning rate used by the backtracking line search.
use_screening_rules: Enable or disable GAPSAFE screening rules. Enabling
these rules MAY speed up the algorithm.
Returns:
numpy array containing the coefficients of Beta after the specified convergence
criteria has been meet.
Raises:
ValueError: Raised if convergence_criteria is neither a positive integer
or a positive float.
"""
if( convergence_criteria <= 0 ):
raise ValueError( "Convergence criteria must be positive int or float.")
if use_screening_rules:
solver = hdim.ista( L_0 )
else:
solver = hdim.screened_ista( L_0 )
return solver( X, Y, Beta_0, Lambda, convergence_criteria )
[docs]def FISTA( X, Y, Beta_0, convergence_criteria, Lambda, L_0 = 0.1, use_screening_rules = True ):
"""
Use the Fast Iterative Shrinkage Thresholding Algorithm ( FISTA ) to iteratively solve the LASSO.
Args:
X: An n x p 2D array representing the design matrix.
Y: A 1 x p 1D array representing the predictors
Beta_0: A 1 x p 1D array describing initial condition for Beta.
convergence_criteria: Can be either an positive integer descrbing the number
if iterations that the solve should be run for, or a positive float describing
the smallest allowable Duality Gap.
Lambda: Regularization hyper-parameter for the LASSO ( e.g. Lambda x || Beta ||_1 )
L_0: Learning rate used by the backtracking line search.
use_screening_rules: Enable or disable GAPSAFE screening rules. Enabling
these rules MAY speed up the algorithm.
Returns:
numpy array containing the coefficients of Beta after the specified convergence
criteria has been meet.
Raises:
ValueError: Raised if convergence_criteria is neither a positive integer
or a positive float.
"""
if( convergence_criteria <= 0 ):
raise ValueError( "Convergence criteria must be positive int or float.")
if use_screening_rules:
solver = hdim.fista( Beta_0, L_0 )
else:
solver = hdim.screened_fista( Beta_0, L_0 )
return solver( X, Y, Beta_0, Lambda, convergence_criteria )
[docs]def CoordinateDescent( X, Y, Beta_0, Lambda, convergence_criteria, use_screening_rules = True ):
"""
Use Coordinate Descent to iteratively solve the LASSO.
Generally performs better than sub-gradient descent methods such as ISTA or FISTA.
Args:
X: An n x p 2D array representing the design matrix.
Y: A 1 x p 1D array representing the predictors
Beta_0: A 1 x p 1D array describing initial condition for Beta.
convergence_criteria: Can be either an positive integer descrbing the number
if iterations that the solve should be run for, or a positive float describing
the smallest allowable Duality Gap.
Lambda: Regularization hyper-parameter for the LASSO ( e.g. Lambda x || Beta ||_1 )
use_screening_rules: Enable or disable GAPSAFE screening rules. Enabling
these rules MAY speed up the algorithm.
Returns:
numpy array containing the coefficients of Beta after the specified convergence
criteria has been meet.
Raises:
ValueError: Raised if convergence_criteria is neither a positive integer
or a positive float.
"""
if use_screening_rules:
solver = hdim.CD( X, Y, Beta_0 )
else:
solver = hdim.CD_SR( X, Y, Beta_0 )
return solver( X, Y, Beta_0, Lambda, convergence_criteria )
[docs]def FOS( X, Y, Beta_0, solver = hdim.SolverType_cd, screening_rules = True ):
"""
Use the Fast and Optimal Support ( FOS ) method to estimate the support for
a given LASSO problem.
Args:
X: An n x p 2D array representing the design matrix.
Y: A 1 x p 1D array representing the predictors
Beta_0: A 1 x p 1D array describing initial condition for Beta.
solver: The type of iterative solver used internally. Can used
sub-gradient descent methods or coordinate descent, both of which
can use GAPSAFE screening rules or not.
Returns:
numpy array containing the estimated support and intercept term.
"""
fos_object = hdim.fos()
fos_object( X, Y, solver )
return fos_object.ReturnCoefficients()


About 6 months ago, for 1GAM, Johannes and I spent a month tinkering with LibPD (the end result was Synapse). LibPD, for those of you who don’t know, is a library for working with Pure-Data, a visual programming tool for procedural audio. Out of the box, it doesn’t work nicely with Unity, but there’s a repository called libpd4unity that simplifies the process.
Libpd4unity isn’t suited to really in depth PD development in Unity (at the moment it seems to only support loading one patch at a time), but you can still do some interesting things with it. So today, I’m going to go over the process of setting up libpd4unity with Unity.
If you’re on mac, you may be a bit disappointed to see that there isn’t a mac compatible pd library in the libpd4unity class, so the first step for us is to compile a .bundle for mac. If you’re on windows, skip down to the actual programming.
Thankfully this is pretty straightfoward, if a bit weird:
Note: You will need to download Libpd4Unity
Ok now that that’s out of the way, it’s time for some fun stuff. First off, copy the LibPD folder from libpd4unity/Assets, and paste it into the assets folder of your project.
Next, make an Assets/Resources folder. This is a special folder that allows you to specify resources that you want to have available to Unity at runtime. Put your patches in this folder (or a subfolder of it). If you don’t have a patch to work with, or want to follow along exactly with this demo, you can grab the simple sine patch from the repo for this post’s example project (patch courtesy of johannesg.com ).
Now that all the housekeeping is taken care of, it’s time to actually interact with a patch program from Unity. LibPd4Unity comes with an example script called LibPdFilterRead.cs that will serve as the basic outline for our class, but we’re going to tailor ours to suit our needs a bit better.
using UnityEngine;
using System.Collections;
using LibPDBinding;
using System;
using System.Runtime.InteropServices;
public class OSCControl : MonoBehaviour
{
public string patch;
public bool playOnAwake = false;
public bool patchIsStereo = false;
private int patchName;
private bool islibpdready;
private string path;
private GCHandle dataHandle;
private IntPtr dataPtr;
private float freq = 500;
The script I’m going to build here interacts with the sample patch linked above.
Lets go through these variables:
Now let’s get to some functionality
void Awake ()
{
path = Application.dataPath + "/Resources/" + patch;
if ( playOnAwake)loadPatch ();
}
public void loadPatch ()
{
if(!islibpdready)
{
if (!patchIsStereo) LibPD.OpenAudio (1,1, 48000);
else LibPD.OpenAudio(2,2,48000);
}
patchName = LibPD.OpenPatch (path);
LibPD.ComputeAudio (true);
islibpdready = true;
}
Awake isn’t all that interesting, except to show off how to get the actual file path to the patch. Also note that loadPath() needs to be called before we can start working with pd.
loadPatch is the standard initialization sequence for working with libPd.
I’m going to hold off on the good stuff until the end, so we’re going to skip from the initialization process down to the cleanup process. This is a little more involved than the usual in C# because we are explicitly telling the garbage collector to not interact with the data stream, so we need to do a bit of manual memory management. This is taken directly from the example project in LibPd4Unity.
public void closePatch ()
{
LibPD.ClosePatch (patchName);
LibPD.Release ();
}
void OnApplicationQuit ()
{
closePatch ();
}
public void OnDestroy()
{
dataHandle.Free();
dataPtr = IntPtr.Zero;
}
I don’t have a good explanation for why we don’t need to free the dataHandle on close patch, so if anyone has an idea, shoot me a message on twitter and I can update the post. Otherwise, this is boilerplate code that will need to be added to every class that you write that will handle loading a Pd program.
And now finally, the good stuff!
public void OnAudioFilterRead (float[] data, int channels)
{
if(dataPtr == IntPtr.Zero)
{
dataHandle = GCHandle.Alloc(data,GCHandleType.Pinned);
dataPtr = dataHandle.AddrOfPinnedObject();
}
if (LibPD.Process(32, dataPtr, dataPtr)==0) {
LibPD.SendFloat(patchName + "freq1", freq);
LibPD.SendFloat(patchName + "freq2", freq);
}
}
void OnGUI()
{
Rect r = new Rect(Screen.width/2 - 50 ,
Screen.height/2 - 150,
100,
300);
freq = GUI.VerticalSlider(r,freq,1000, 400);
Rect r2 = new Rect(Screen.width/2-30,
Screen.height/2 - 30,
80,
30);
GUI.Box(r2, ""+freq+" hz");
}
OnAudioFilterRead is the callback method used by LibPd4Unity’s library. It will be called whenever the internal audio buffer has been filled. I’m really not sure why we’re checking that libPD.Process returns 0, although I assume that’s LibPD’s “all good” return value. Inside that block you can see how to pass messages to the currently running patch. What tripped me up for awhile was both the need to prepend the target value’s name with the int name of the loaded patch, and the need to leave off the “$0” part of the variable name, which is displayed when you open the patch in pd.
Everything should now work fine in the editor, but if you’re on mac, your journey is not over yet!

Apparently Unity really really hates people who use libpd. Thankfully, there is a solution!
All of this is necessary because Unity’s build process doesn’t like the libpdcsharp bundle, and attempts to copy it multiple times (creating that ugly error), and completely ignores the patch file in Resources because it doesn’t recognize the file extension. Thankfully, all that’s needed to resolve this a mildly annoying process.
If you’ve made it this far, you should now have a unity project that can interact with Pure Data plugins, and can actually create builds! Congratulations! If you’ve hit any difficulties or need further clarification on something I’ve said here, you can download a sample project from my dropbox, or send me a message on Twitter. Hope this tutorial helped!


You can use this page to generate badges for your favorite Bioconda packages, suitable for screen and poster presentations.
Update the values below to change the badges. When you're happy, just click one of the buttons below a badge to download it in .svg
or .png
format.


Death of Fife hotel guest found in fire escape stairwell ‘not suspicious’
Suspicious circumstances have been ruled out in the death of a guest at a Fife hotel.
by Cheryl PeeblesA police probe was launched when a man was found dead in Drummonds Hotel, Markinch, last Wednesday.
Part of the hotel was cordoned off as detectives investigated.
It is understood the man was found in a fire escape stairwell by a member of staff at the Balbirnie Street hotel, which is part of the OYO franchise group.
A police spokeswoman said on Monday: “There are no suspicious circumstances surrounding the death.
“The Procurator Fiscal has been informed.”
The hotel’s owners and OYO decided to close the premises for a week and guests were relocated.
However, the restaurant and lounge reopened on Friday.
The hotel said: “We are deeply shocked and saddened by what has happened and offer our deepest condolences to the family of this guest.
“We are assisting the authorities however we can with their investigation and are also doing everything possible to support our staff.”
It added: “We thank everyone for their patience and understanding through these sad times.”


The Internet Protocol Datagram (Version 4) is made up of the following: A version number (this is version 4) which is composed of 4 bits, a header length which is to determine where the IP datagram header begins (4 bits long), a TOS (Type of Service) to specify whether or not the datagram requires reliability, thoroughput, etc., a datagram length which can be composed of 32 bits to tell how large the datagram is, a TTL field which tells how long the packet should "stay alive" before being dropped, a protocol field to indicate what's going through the transport layer (UDP, TCP, etc.), a checksum to check for corrupted bits or data loss, a source and destination IP address, an options field (allows the IP header to be extended), and a data payload field.
IP Datagram Fragmentation
A router has a MTU (Maximum Transmission Unit), which can only allow for a specific number of bytes to be transferred at one time. If a router receives a packet with more bytes than the MTU, then IP fragmentation occurs to even out the load.
When IP fragmentation occurs, a packet is split into tinier packets, each being consistent with the router's MTU, and then those packets are sent out to hosts with flags ranging from 1 to 0 (0 indicates the ending packets), with the same identifiers (that way the routers know that all the packets are meant to be together). The tinier packets also have bits that determine where the packet is meant to be assembled amongst the many bytes (for instance, one packet may start at 1000 bytes from the very first packet).
IP fragmentation attacks ("Teardrop" Attacks)
This type of attack is when an attacker sends fragmented IP packets that have no end (that is, there's no flag that ends the packet). This quickly consumes the end hosts' processing power, and causes a DoS (Denial of Service). It should be noted that these types of attacks don't usually work anymore, unless they're Teardrop attacks.
Teardrop attacks are similar since they also use fragmentation. However, these types of attacks take advantage of how an OS reassembles packets in general. Teardrop attacks are specific to certain vulnerabilities and generally don't work as universally as general fragmentation attacks.
Glorious IPv6
IPv6 has a similar structure to an IPv4 packet, save a few differences: Fragmentation has been completely dropped, and is no longer allowed (to stop the DoS attacks mentioned above).
Most headers are used in the "Next Header" portion of the packet, so as to minimize the size of the IP datagram. A hop limit is introduced as similiar to a TTL, where the packet can only go through a certain number of routers before being dropped. A flow label to indicate some ToS.


Day 3 of my trip, and day 1 of the conference kicked off calmly. I made a cup of coffee with my Aeropress & props to Aneel for planting the idea a bunch of months ago by showing me how he uses it.
Good morning #HashiConf. Cheers! pic.twitter.com/TxfqtBVmCS
— Λdrøn (@Adron) September 7, 2016
While picking up some breakfast, I realized, how one could create a conference of just hallway activity. I always find myself hacking in a hallway at every conference I’ve ever been to.
For my respective tshirt of the day, I had to give some love and rep for Devops Day PDX. It was also an excellent conference that a number of organizers did a great job with.
After breakfast all of the speakers, attendees, and everybody went into the main room for the keynote. We gathered, two big screens displayed beside the podium. Once everyone got seated Seth Vargo came out to kick off they keynote and introduce the speakers.
After the keynote I chatted about some thoughts on Vault and related security technology.
I started to walk onward toward the speakers room again but ran into Bryan Cantrill (CTO @ Joyent, lover of Oracle) and James Nugent. They discussed a number of things, one being certain failings of Go. This was interesting to me as I’m just starting to hack around with the language and now have a list of things I will ensure I look into within the toolchain itself. I also wish I could have recorded this conversation for you dear reader, because let me tell ya, Bryan has more overheards (you know, OH on twitter) that need to be published on Twitter than I could even hope to record. We’re talking about 10-20 notable overheard quotes per second, he’s boss on phrasing.
I’ll have more on Vault, some of the topics that came up in conversation, and more in subsequent blog entries. But for now I was happy with the simple things in life, like tweeting via paper. :-o #shocks #muchsmartass
Paper pic.twitter.com/vFBkijD8Io
— Λdrøn (@Adron) September 7, 2016
As scheduled (thanks for wrangling us speakers Kristen, you did an excellent job!) Evan and I were setup and ready precisely on time. We kicked off our presentation at exactly 3:35pm. More details (slide deck, code, links, etc) on the talk can be found on the page I’ve created for the talk here and whenever the video of the talk is available I’ll post a link there and update with a link here.
The talk went exceptionally. Both Evan and I were happy with how things turned out. Namely, here are a few high points:
- Code was committed live on Github for features related to create GCP Projects with Terraform. The respective resources will be available soon as they’re ready for a subsequent build.
- Evan did not suffer any consequences from the heat of standing behind the curtain, as wizards sometimes do.
- Our key points of conversation topics came across well, funny parts delivered with laughs, but more importantly there were more than a few people after the talk that also wanted to contribute and discuss further these ideas. The notion of bridging together one’s autopilot pattern based, 12-factor app style, intelligently monitored systems style architectures into application projects was something that is on more than a few people’s minds. I’ll have more on this as conversations continue, and hope you dear reader may want to jump into that conversation too.
Literally hiding behind the curtains at #hashiconf as @adron does his part of the talk. #spooky pic.twitter.com/9w65aQ916T
— Evan Brown (@evandbrown) September 7, 2016
After the presentation conversations went on for another 2 hours or so. But after that a number of Googlers and myself headed off for Oxford Market, Pliny the Elder, and adventure in Napa.
If you’d like to dive into the conversations around application organization, configuration, maintenance, and management of said projects, do sign up to Thrashing Code and I’ll be pushing forward that conversation and herding all of us cats into a medium in which we can discuss these things further.


Structures
The following structures are available globally.
-
Type representing the name of a property
See moreDeclaration
Swift
public struct PropertyName
extension PropertyName: PropertyNameProtocol
extension PropertyName: ExpressibleByStringLiteral
-
The
BindingRef
type acts as a lightweight Swift reference to an underlyingGBinding
instance. It exposes methods that can operate on this data type throughBindingProtocol
conformance. UseBindingRef
only as anunowned
reference to an existingGBinding
instance.GBinding
is the representation of a binding between a property on aGObject
instance (or source) and another property on anotherGObject
instance (or target). Whenever the source property changes, the same value is applied to the target property; for instance, the following binding:(C Language Example):
g_object_bind_property (object1, "property-a", object2, "property-b", G_BINDING_DEFAULT);
will cause the property named “property-b” of
object2
to be updated every timeg_object_set()
or the specific accessor changes the value of the property “property-a” ofobject1
.It is possible to create a bidirectional binding between two properties of two
GObject
instances, so that if either property changes, the other is updated as well, for instance:(C Language Example):
g_object_bind_property (object1, "property-a", object2, "property-b", G_BINDING_BIDIRECTIONAL);
will keep the two properties in sync.
It is also possible to set a custom transformation function (in both directions, in case of a bidirectional binding) to apply a custom transformation from the source value to the target value before applying it; for instance, the following binding:
(C Language Example):
g_object_bind_property_full (adjustment1, "value", adjustment2, "value", G_BINDING_BIDIRECTIONAL, celsius_to_fahrenheit, fahrenheit_to_celsius, NULL, NULL);
will keep the “value” property of the two adjustments in sync; the
celsius_to_fahrenheit
function will be called whenever the “value” property ofadjustment1
changes and will transform the current value of the property before applying it to the “value” property ofadjustment2
.Vice versa, the
fahrenheit_to_celsius
function will be called whenever the “value” property ofadjustment2
changes, and will transform the current value of the property before applying it to the “value” property ofadjustment1
.Note that
GBinding
does not resolve cycles by itself; a cycle likeobject1:propertyA -> object2:propertyB object2:propertyB -> object3:propertyC object3:propertyC -> object1:propertyA
might lead to an infinite loop. The loop, in this particular case, can be avoided if the objects emit the
GObject::notify
signal only if the value has effectively been changed. A binding is implemented using theGObject::notify
signal, so it is susceptible to all the various ways of blocking a signal emission, likeg_signal_stop_emission()
org_signal_handler_block()
.A binding will be severed, and the resources it allocates freed, whenever either one of the
GObject
instances it refers to are finalized, or when theGBinding
instance loses its last reference.Bindings for languages with garbage collection can use
g_binding_unbind()
to explicitly release a binding between the source and target properties, instead of relying on the last reference on the binding, source, and target instances to drop.
See moreGBinding
is available since GObject 2.26Declaration
Swift
public struct BindingRef : BindingProtocol
-
The
CClosureRef
type acts as a lightweight Swift reference to an underlyingGCClosure
instance. It exposes methods that can operate on this data type throughCClosureProtocol
conformance. UseCClosureRef
only as anunowned
reference to an existingGCClosure
instance.A
See moreGCClosure
is a specialization ofGClosure
for C function callbacks.Declaration
Swift
public struct CClosureRef : CClosureProtocol
-
The
ClosureRef
type acts as a lightweight Swift reference to an underlyingGClosure
instance. It exposes methods that can operate on this data type throughClosureProtocol
conformance. UseClosureRef
only as anunowned
reference to an existingGClosure
instance.A
GClosure
represents a callback supplied by the programmer. It will generally comprise a function of some kind and a marshaller used to call it. It is the responsibility of the marshaller to convert the arguments for the invocation fromGValues
into a suitable form, perform the callback on the converted arguments, and transform the return value back into aGValue
.In the case of C programs, a closure usually just holds a pointer to a function and maybe a data argument, and the marshaller converts between
GValue
and native C types. The GObject library provides theGCClosure
type for this purpose. Bindings for other languages need marshallers which convert betweenGValues
and suitable representations in the runtime of the language in order to use functions written in that language as callbacks. Useg_closure_set_marshal()
to set the marshaller on such a custom closure implementation.Within GObject, closures play an important role in the implementation of signals. When a signal is registered, the
c_marshaller
argument tog_signal_new()
specifies the default C marshaller for any closure which is connected to this signal. GObject provides a number of C marshallers for this purpose, see the g_cclosure_marshal_() functions. Additional C marshallers can be generated with the glib-genmarshal utility. Closures can be explicitly connected to signals withg_signal_connect_closure()
, but it usually more convenient to let GObject create a closure automatically by using one of the g_signal_connect_() functions which take a callback function/user data pair.Using closures has a number of important advantages over a simple callback function/data pointer combination:
Closures allow the callee to get the types of the callback parameters, which means that language bindings don’t have to write individual glue for each callback type.
The reference counting of
GClosure
makes it easy to handle reentrancy right; if a callback is removed while it is being invoked, the closure and its parameters won’t be freed until the invocation finishes.g_closure_invalidate()
and invalidation notifiers allow callbacks to be automatically removed when the objects they point to go away.
Declaration
Swift
public struct ClosureRef : ClosureProtocol
-
The
See moreClosureNotifyDataRef
type acts as a lightweight Swift reference to an underlyingGClosureNotifyData
instance. It exposes methods that can operate on this data type throughClosureNotifyDataProtocol
conformance. UseClosureNotifyDataRef
only as anunowned
reference to an existingGClosureNotifyData
instance.Declaration
Swift
public struct ClosureNotifyDataRef : ClosureNotifyDataProtocol
-
The
EnumClassRef
type acts as a lightweight Swift reference to an underlyingGEnumClass
instance. It exposes methods that can operate on this data type throughEnumClassProtocol
conformance. UseEnumClassRef
only as anunowned
reference to an existingGEnumClass
instance.The class of an enumeration type holds information about its possible values.
See moreDeclaration
Swift
public struct EnumClassRef : EnumClassProtocol
-
The
EnumValueRef
type acts as a lightweight Swift reference to an underlyingGEnumValue
instance. It exposes methods that can operate on this data type throughEnumValueProtocol
conformance. UseEnumValueRef
only as anunowned
reference to an existingGEnumValue
instance.A structure which contains a single enum value, its name, and its nickname.
See moreDeclaration
Swift
public struct EnumValueRef : EnumValueProtocol
-
The
FlagsClassRef
type acts as a lightweight Swift reference to an underlyingGFlagsClass
instance. It exposes methods that can operate on this data type throughFlagsClassProtocol
conformance. UseFlagsClassRef
only as anunowned
reference to an existingGFlagsClass
instance.The class of a flags type holds information about its possible values.
See moreDeclaration
Swift
public struct FlagsClassRef : FlagsClassProtocol
-
The
FlagsValueRef
type acts as a lightweight Swift reference to an underlyingGFlagsValue
instance. It exposes methods that can operate on this data type throughFlagsValueProtocol
conformance. UseFlagsValueRef
only as anunowned
reference to an existingGFlagsValue
instance.A structure which contains a single flags value, its name, and its nickname.
See moreDeclaration
Swift
public struct FlagsValueRef : FlagsValueProtocol
-
The
InitiallyUnownedRef
type acts as a lightweight Swift reference to an underlyingGInitiallyUnowned
instance. It exposes methods that can operate on this data type throughInitiallyUnownedProtocol
conformance. UseInitiallyUnownedRef
only as anunowned
reference to an existingGInitiallyUnowned
instance.All the fields in the GInitiallyUnowned structure are private to the
See moreGInitiallyUnowned
implementation and should never be accessed directly.Declaration
Swift
public struct InitiallyUnownedRef : InitiallyUnownedProtocol
-
The
InitiallyUnownedClassRef
type acts as a lightweight Swift reference to an underlyingGInitiallyUnownedClass
instance. It exposes methods that can operate on this data type throughInitiallyUnownedClassProtocol
conformance. UseInitiallyUnownedClassRef
only as anunowned
reference to an existingGInitiallyUnownedClass
instance.The class structure for the GInitiallyUnowned type.
See moreDeclaration
Swift
public struct InitiallyUnownedClassRef : InitiallyUnownedClassProtocol
-
The
InterfaceInfoRef
type acts as a lightweight Swift reference to an underlyingGInterfaceInfo
instance. It exposes methods that can operate on this data type throughInterfaceInfoProtocol
conformance. UseInterfaceInfoRef
only as anunowned
reference to an existingGInterfaceInfo
instance.A structure that provides information to the type system which is used specifically for managing interface types.
See moreDeclaration
Swift
public struct InterfaceInfoRef : InterfaceInfoProtocol
-
The
ObjectRef
type acts as a lightweight Swift reference to an underlyingGObject
instance. It exposes methods that can operate on this data type throughObjectProtocol
conformance. UseObjectRef
only as anunowned
reference to an existingGObject
instance.All the fields in the GObject structure are private to the
See moreGObject
implementation and should never be accessed directly.Declaration
Swift
public struct ObjectRef : ObjectProtocol
-
The
ObjectClassRef
type acts as a lightweight Swift reference to an underlyingGObjectClass
instance. It exposes methods that can operate on this data type throughObjectClassProtocol
conformance. UseObjectClassRef
only as anunowned
reference to an existingGObjectClass
instance.The class structure for the GObject type.
(C Language Example):
See more// Example of implementing a singleton using a constructor. static MySingleton *the_singleton = NULL; static GObject* my_singleton_constructor (GType type, guint n_construct_params, GObjectConstructParam *construct_params) { GObject *object; if (!the_singleton) { object = G_OBJECT_CLASS (parent_class)->constructor (type, n_construct_params, construct_params); the_singleton = MY_SINGLETON (object); } else object = g_object_ref (G_OBJECT (the_singleton)); return object; }
Declaration
Swift
public struct ObjectClassRef : ObjectClassProtocol
-
The
ObjectConstructParamRef
type acts as a lightweight Swift reference to an underlyingGObjectConstructParam
instance. It exposes methods that can operate on this data type throughObjectConstructParamProtocol
conformance. UseObjectConstructParamRef
only as anunowned
reference to an existingGObjectConstructParam
instance.The GObjectConstructParam struct is an auxiliary structure used to hand
See moreGParamSpec
/GValue
pairs to theconstructor
of aGObjectClass
.Declaration
Swift
public struct ObjectConstructParamRef : ObjectConstructParamProtocol
-
The
ParamSpecRef
type acts as a lightweight Swift reference to an underlyingGParamSpec
instance. It exposes methods that can operate on this data type throughParamSpecProtocol
conformance. UseParamSpecRef
only as anunowned
reference to an existingGParamSpec
instance.GParamSpec
is an object structure that encapsulates the metadata required to specify parameters, such as e.g.GObject
properties.Parameter names #
A property name consists of segments consisting of ASCII letters and digits, separated by either the
-
or_
character. The first character of a property name must be a letter. These are the same rules as for signal naming (seeg_signal_new()
).When creating and looking up a
See moreGParamSpec
, either separator can be used, but they cannot be mixed. Using-
is considerably more efficient, and is the ‘canonical form’. Using_
is discouraged.Declaration
Swift
public struct ParamSpecRef : ParamSpecProtocol
-
The
ParamSpecBooleanRef
type acts as a lightweight Swift reference to an underlyingGParamSpecBoolean
instance. It exposes methods that can operate on this data type throughParamSpecBooleanProtocol
conformance. UseParamSpecBooleanRef
only as anunowned
reference to an existingGParamSpecBoolean
instance.A
See moreGParamSpec
derived structure that contains the meta data for boolean properties.Declaration
Swift
public struct ParamSpecBooleanRef : ParamSpecBooleanProtocol
-
The
ParamSpecBoxedRef
type acts as a lightweight Swift reference to an underlyingGParamSpecBoxed
instance. It exposes methods that can operate on this data type throughParamSpecBoxedProtocol
conformance. UseParamSpecBoxedRef
only as anunowned
reference to an existingGParamSpecBoxed
instance.A
See moreGParamSpec
derived structure that contains the meta data for boxed properties.Declaration
Swift
public struct ParamSpecBoxedRef : ParamSpecBoxedProtocol
-
The
ParamSpecCharRef
type acts as a lightweight Swift reference to an underlyingGParamSpecChar
instance. It exposes methods that can operate on this data type throughParamSpecCharProtocol
conformance. UseParamSpecCharRef
only as anunowned
reference to an existingGParamSpecChar
instance.A
See moreGParamSpec
derived structure that contains the meta data for character properties.Declaration
Swift
public struct ParamSpecCharRef : ParamSpecCharProtocol
-
The
ParamSpecClassRef
type acts as a lightweight Swift reference to an underlyingGParamSpecClass
instance. It exposes methods that can operate on this data type throughParamSpecClassProtocol
conformance. UseParamSpecClassRef
only as anunowned
reference to an existingGParamSpecClass
instance.The class structure for the GParamSpec type. Normally, GParamSpec classes are filled by
See moreg_param_type_register_static()
.Declaration
Swift
public struct ParamSpecClassRef : ParamSpecClassProtocol
-
The
ParamSpecDoubleRef
type acts as a lightweight Swift reference to an underlyingGParamSpecDouble
instance. It exposes methods that can operate on this data type throughParamSpecDoubleProtocol
conformance. UseParamSpecDoubleRef
only as anunowned
reference to an existingGParamSpecDouble
instance.A
See moreGParamSpec
derived structure that contains the meta data for double properties.Declaration
Swift
public struct ParamSpecDoubleRef : ParamSpecDoubleProtocol
-
The
ParamSpecEnumRef
type acts as a lightweight Swift reference to an underlyingGParamSpecEnum
instance. It exposes methods that can operate on this data type throughParamSpecEnumProtocol
conformance. UseParamSpecEnumRef
only as anunowned
reference to an existingGParamSpecEnum
instance.A
See moreGParamSpec
derived structure that contains the meta data for enum properties.Declaration
Swift
public struct ParamSpecEnumRef : ParamSpecEnumProtocol
-
The
ParamSpecFlagsRef
type acts as a lightweight Swift reference to an underlyingGParamSpecFlags
instance. It exposes methods that can operate on this data type throughParamSpecFlagsProtocol
conformance. UseParamSpecFlagsRef
only as anunowned
reference to an existingGParamSpecFlags
instance.A
See moreGParamSpec
derived structure that contains the meta data for flags properties.Declaration
Swift
public struct ParamSpecFlagsRef : ParamSpecFlagsProtocol
-
The
ParamSpecFloatRef
type acts as a lightweight Swift reference to an underlyingGParamSpecFloat
instance. It exposes methods that can operate on this data type throughParamSpecFloatProtocol
conformance. UseParamSpecFloatRef
only as anunowned
reference to an existingGParamSpecFloat
instance.A
See moreGParamSpec
derived structure that contains the meta data for float properties.Declaration
Swift
public struct ParamSpecFloatRef : ParamSpecFloatProtocol
-
The
ParamSpecGTypeRef
type acts as a lightweight Swift reference to an underlyingGParamSpecGType
instance. It exposes methods that can operate on this data type throughParamSpecGTypeProtocol
conformance. UseParamSpecGTypeRef
only as anunowned
reference to an existingGParamSpecGType
instance.A
See moreGParamSpec
derived structure that contains the meta data forGType
properties.Declaration
Swift
public struct ParamSpecGTypeRef : ParamSpecGTypeProtocol
-
The
ParamSpecIntRef
type acts as a lightweight Swift reference to an underlyingGParamSpecInt
instance. It exposes methods that can operate on this data type throughParamSpecIntProtocol
conformance. UseParamSpecIntRef
only as anunowned
reference to an existingGParamSpecInt
instance.A
See moreGParamSpec
derived structure that contains the meta data for integer properties.Declaration
Swift
public struct ParamSpecIntRef : ParamSpecIntProtocol
-
The
ParamSpecInt64Ref
type acts as a lightweight Swift reference to an underlyingGParamSpecInt64
instance. It exposes methods that can operate on this data type throughParamSpecInt64Protocol
conformance. UseParamSpecInt64Ref
only as anunowned
reference to an existingGParamSpecInt64
instance.A
See moreGParamSpec
derived structure that contains the meta data for 64bit integer properties.Declaration
Swift
public struct ParamSpecInt64Ref : ParamSpecInt64Protocol
-
The
ParamSpecLongRef
type acts as a lightweight Swift reference to an underlyingGParamSpecLong
instance. It exposes methods that can operate on this data type throughParamSpecLongProtocol
conformance. UseParamSpecLongRef
only as anunowned
reference to an existingGParamSpecLong
instance.A
See moreGParamSpec
derived structure that contains the meta data for long integer properties.Declaration
Swift
public struct ParamSpecLongRef : ParamSpecLongProtocol
-
The
ParamSpecObjectRef
type acts as a lightweight Swift reference to an underlyingGParamSpecObject
instance. It exposes methods that can operate on this data type throughParamSpecObjectProtocol
conformance. UseParamSpecObjectRef
only as anunowned
reference to an existingGParamSpecObject
instance.A
See moreGParamSpec
derived structure that contains the meta data for object properties.Declaration
Swift
public struct ParamSpecObjectRef : ParamSpecObjectProtocol
-
The
ParamSpecOverrideRef
type acts as a lightweight Swift reference to an underlyingGParamSpecOverride
instance. It exposes methods that can operate on this data type throughParamSpecOverrideProtocol
conformance. UseParamSpecOverrideRef
only as anunowned
reference to an existingGParamSpecOverride
instance.This is a type of
See moreGParamSpec
type that simply redirects operations to another paramspec. All operations other than getting or setting the value are redirected, including accessing the nick and blurb, validating a value, and so forth. Seeg_param_spec_get_redirect_target()
for retrieving the overidden property.GParamSpecOverride
is used in implementingg_object_class_override_property()
, and will not be directly useful unless you are implementing a new base type similar to GObject.Declaration
Swift
public struct ParamSpecOverrideRef : ParamSpecOverrideProtocol
-
The
ParamSpecParamRef
type acts as a lightweight Swift reference to an underlyingGParamSpecParam
instance. It exposes methods that can operate on this data type throughParamSpecParamProtocol
conformance. UseParamSpecParamRef
only as anunowned
reference to an existingGParamSpecParam
instance.A
See moreGParamSpec
derived structure that contains the meta data forG_TYPE_PARAM
properties.Declaration
Swift
public struct ParamSpecParamRef : ParamSpecParamProtocol
-
The
ParamSpecPointerRef
type acts as a lightweight Swift reference to an underlyingGParamSpecPointer
instance. It exposes methods that can operate on this data type throughParamSpecPointerProtocol
conformance. UseParamSpecPointerRef
only as anunowned
reference to an existingGParamSpecPointer
instance.A
See moreGParamSpec
derived structure that contains the meta data for pointer properties.Declaration
Swift
public struct ParamSpecPointerRef : ParamSpecPointerProtocol
-
The
ParamSpecPoolRef
type acts as a lightweight Swift reference to an underlyingGParamSpecPool
instance. It exposes methods that can operate on this data type throughParamSpecPoolProtocol
conformance. UseParamSpecPoolRef
only as anunowned
reference to an existingGParamSpecPool
instance.A
See moreGParamSpecPool
maintains a collection ofGParamSpecs
which can be quickly accessed by owner and name. The implementation of theGObject
property system uses such a pool to store theGParamSpecs
of the properties all object types.Declaration
Swift
public struct ParamSpecPoolRef : ParamSpecPoolProtocol
-
The
ParamSpecStringRef
type acts as a lightweight Swift reference to an underlyingGParamSpecString
instance. It exposes methods that can operate on this data type throughParamSpecStringProtocol
conformance. UseParamSpecStringRef
only as anunowned
reference to an existingGParamSpecString
instance.A
See moreGParamSpec
derived structure that contains the meta data for string properties.Declaration
Swift
public struct ParamSpecStringRef : ParamSpecStringProtocol
-
The
ParamSpecTypeInfoRef
type acts as a lightweight Swift reference to an underlyingGParamSpecTypeInfo
instance. It exposes methods that can operate on this data type throughParamSpecTypeInfoProtocol
conformance. UseParamSpecTypeInfoRef
only as anunowned
reference to an existingGParamSpecTypeInfo
instance.This structure is used to provide the type system with the information required to initialize and destruct (finalize) a parameter’s class and instances thereof. The initialized structure is passed to the
See moreg_param_type_register_static()
The type system will perform a deep copy of this structure, so its memory does not need to be persistent across invocation ofg_param_type_register_static()
.Declaration
Swift
public struct ParamSpecTypeInfoRef : ParamSpecTypeInfoProtocol
-
The
ParamSpecUCharRef
type acts as a lightweight Swift reference to an underlyingGParamSpecUChar
instance. It exposes methods that can operate on this data type throughParamSpecUCharProtocol
conformance. UseParamSpecUCharRef
only as anunowned
reference to an existingGParamSpecUChar
instance.A
See moreGParamSpec
derived structure that contains the meta data for unsigned character properties.Declaration
Swift
public struct ParamSpecUCharRef : ParamSpecUCharProtocol
-
The
ParamSpecUIntRef
type acts as a lightweight Swift reference to an underlyingGParamSpecUInt
instance. It exposes methods that can operate on this data type throughParamSpecUIntProtocol
conformance. UseParamSpecUIntRef
only as anunowned
reference to an existingGParamSpecUInt
instance.A
See moreGParamSpec
derived structure that contains the meta data for unsigned integer properties.Declaration
Swift
public struct ParamSpecUIntRef : ParamSpecUIntProtocol
-
The
ParamSpecUInt64Ref
type acts as a lightweight Swift reference to an underlyingGParamSpecUInt64
instance. It exposes methods that can operate on this data type throughParamSpecUInt64Protocol
conformance. UseParamSpecUInt64Ref
only as anunowned
reference to an existingGParamSpecUInt64
instance.A
See moreGParamSpec
derived structure that contains the meta data for unsigned 64bit integer properties.Declaration
Swift
public struct ParamSpecUInt64Ref : ParamSpecUInt64Protocol
-
The
ParamSpecULongRef
type acts as a lightweight Swift reference to an underlyingGParamSpecULong
instance. It exposes methods that can operate on this data type throughParamSpecULongProtocol
conformance. UseParamSpecULongRef
only as anunowned
reference to an existingGParamSpecULong
instance.A
See moreGParamSpec
derived structure that contains the meta data for unsigned long integer properties.Declaration
Swift
public struct ParamSpecULongRef : ParamSpecULongProtocol
-
The
ParamSpecUnicharRef
type acts as a lightweight Swift reference to an underlyingGParamSpecUnichar
instance. It exposes methods that can operate on this data type throughParamSpecUnicharProtocol
conformance. UseParamSpecUnicharRef
only as anunowned
reference to an existingGParamSpecUnichar
instance.A
See moreGParamSpec
derived structure that contains the meta data for unichar (unsigned integer) properties.Declaration
Swift
public struct ParamSpecUnicharRef : ParamSpecUnicharProtocol
-
The
ParamSpecValueArrayRef
type acts as a lightweight Swift reference to an underlyingGParamSpecValueArray
instance. It exposes methods that can operate on this data type throughParamSpecValueArrayProtocol
conformance. UseParamSpecValueArrayRef
only as anunowned
reference to an existingGParamSpecValueArray
instance.A
See moreGParamSpec
derived structure that contains the meta data forGValueArray
properties.Declaration
Swift
public struct ParamSpecValueArrayRef : ParamSpecValueArrayProtocol
-
The
ParamSpecVariantRef
type acts as a lightweight Swift reference to an underlyingGParamSpecVariant
instance. It exposes methods that can operate on this data type throughParamSpecVariantProtocol
conformance. UseParamSpecVariantRef
only as anunowned
reference to an existingGParamSpecVariant
instance.A
GParamSpec
derived structure that contains the meta data forGVariant
properties.When comparing values with
See moreg_param_values_cmp()
, scalar values with the same type will be compared withg_variant_compare()
. Other non-nil
variants will be checked for equality withg_variant_equal()
, and their sort order is otherwise undefined.nil
is ordered before non-nil
variants. Twonil
values compare equal.Declaration
Swift
public struct ParamSpecVariantRef : ParamSpecVariantProtocol
-
The
ParameterRef
type acts as a lightweight Swift reference to an underlyingGParameter
instance. It exposes methods that can operate on this data type throughParameterProtocol
conformance. UseParameterRef
only as anunowned
reference to an existingGParameter
instance.The GParameter struct is an auxiliary structure used to hand parameter name/value pairs to
See moreg_object_newv()
.Declaration
Swift
public struct ParameterRef : ParameterProtocol
-
The
SignalInvocationHintRef
type acts as a lightweight Swift reference to an underlyingGSignalInvocationHint
instance. It exposes methods that can operate on this data type throughSignalInvocationHintProtocol
conformance. UseSignalInvocationHintRef
only as anunowned
reference to an existingGSignalInvocationHint
instance.The
See moreGSignalInvocationHint
structure is used to pass on additional information to callbacks during a signal emission.Declaration
Swift
public struct SignalInvocationHintRef : SignalInvocationHintProtocol
-
The
SignalQueryRef
type acts as a lightweight Swift reference to an underlyingGSignalQuery
instance. It exposes methods that can operate on this data type throughSignalQueryProtocol
conformance. UseSignalQueryRef
only as anunowned
reference to an existingGSignalQuery
instance.A structure holding in-depth information for a specific signal. It is filled in by the
See moreg_signal_query()
function.Declaration
Swift
public struct SignalQueryRef : SignalQueryProtocol
-
The
TypeClassRef
type acts as a lightweight Swift reference to an underlyingGTypeClass
instance. It exposes methods that can operate on this data type throughTypeClassProtocol
conformance. UseTypeClassRef
only as anunowned
reference to an existingGTypeClass
instance.An opaque structure used as the base of all classes.
See moreDeclaration
Swift
public struct TypeClassRef : TypeClassProtocol
-
The
TypeFundamentalInfoRef
type acts as a lightweight Swift reference to an underlyingGTypeFundamentalInfo
instance. It exposes methods that can operate on this data type throughTypeFundamentalInfoProtocol
conformance. UseTypeFundamentalInfoRef
only as anunowned
reference to an existingGTypeFundamentalInfo
instance.A structure that provides information to the type system which is used specifically for managing fundamental types.
See moreDeclaration
Swift
public struct TypeFundamentalInfoRef : TypeFundamentalInfoProtocol
-
The
TypeInfoRef
type acts as a lightweight Swift reference to an underlyingGTypeInfo
instance. It exposes methods that can operate on this data type throughTypeInfoProtocol
conformance. UseTypeInfoRef
only as anunowned
reference to an existingGTypeInfo
instance.This structure is used to provide the type system with the information required to initialize and destruct (finalize) a type’s class and its instances.
The initialized structure is passed to the
See moreg_type_register_static()
function (or is copied into the providedGTypeInfo
structure in theg_type_plugin_complete_type_info()
). The type system will perform a deep copy of this structure, so its memory does not need to be persistent across invocation ofg_type_register_static()
.Declaration
Swift
public struct TypeInfoRef : TypeInfoProtocol
-
The
TypeInstanceRef
type acts as a lightweight Swift reference to an underlyingGTypeInstance
instance. It exposes methods that can operate on this data type throughTypeInstanceProtocol
conformance. UseTypeInstanceRef
only as anunowned
reference to an existingGTypeInstance
instance.An opaque structure used as the base of all type instances.
See moreDeclaration
Swift
public struct TypeInstanceRef : TypeInstanceProtocol
-
The
TypeInterfaceRef
type acts as a lightweight Swift reference to an underlyingGTypeInterface
instance. It exposes methods that can operate on this data type throughTypeInterfaceProtocol
conformance. UseTypeInterfaceRef
only as anunowned
reference to an existingGTypeInterface
instance.An opaque structure used as the base of all interface types.
See moreDeclaration
Swift
public struct TypeInterfaceRef : TypeInterfaceProtocol
-
The
TypeModuleRef
type acts as a lightweight Swift reference to an underlyingGTypeModule
instance. It exposes methods that can operate on this data type throughTypeModuleProtocol
conformance. UseTypeModuleRef
only as anunowned
reference to an existingGTypeModule
instance.GTypeModule
provides a simple implementation of theGTypePlugin
interface. The model ofGTypeModule
is a dynamically loaded module which implements some number of types and interface implementations. When the module is loaded, it registers its types and interfaces usingg_type_module_register_type()
andg_type_module_add_interface()
. As long as any instances of these types and interface implementations are in use, the module is kept loaded. When the types and interfaces are gone, the module may be unloaded. If the types and interfaces become used again, the module will be reloaded. Note that the last unref cannot happen in module code, since that would lead to the caller’s code being unloaded beforeg_object_unref()
returns to it.Keeping track of whether the module should be loaded or not is done by using a use count - it starts at zero, and whenever it is greater than zero, the module is loaded. The use count is maintained internally by the type system, but also can be explicitly controlled by
g_type_module_use()
andg_type_module_unuse()
. Typically, when loading a module for the first type,g_type_module_use()
will be used to load it so that it can initialize its types. At some later point, when the module no longer needs to be loaded except for the type implementations it contains,g_type_module_unuse()
is called.
See moreGTypeModule
does not actually provide any implementation of module loading and unloading. To create a particular module type you must derive fromGTypeModule
and implement the load and unload functions inGTypeModuleClass
.Declaration
Swift
public struct TypeModuleRef : TypeModuleProtocol
-
The
TypeModuleClassRef
type acts as a lightweight Swift reference to an underlyingGTypeModuleClass
instance. It exposes methods that can operate on this data type throughTypeModuleClassProtocol
conformance. UseTypeModuleClassRef
only as anunowned
reference to an existingGTypeModuleClass
instance.In order to implement dynamic loading of types based on
See moreGTypeModule
, theload
andunload
functions inGTypeModuleClass
must be implemented.Declaration
Swift
public struct TypeModuleClassRef : TypeModuleClassProtocol
-
The
TypePluginRef
type acts as a lightweight Swift reference to an underlyingGTypePlugin
instance. It exposes methods that can operate on this data type throughTypePluginProtocol
conformance. UseTypePluginRef
only as anunowned
reference to an existingGTypePlugin
instance.The GObject type system supports dynamic loading of types. The
GTypePlugin
interface is used to handle the lifecycle of dynamically loaded types. It goes as follows:- The type is initially introduced (usually upon loading the module the first time, or by your main application that knows what modules introduces what types), like this:
(C Language Example):
new_type_id = g_type_register_dynamic (parent_type_id, "TypeName", new_type_plugin, type_flags);
where
new_type_plugin
is an implementation of theGTypePlugin
interface.The type’s implementation is referenced, e.g. through
g_type_class_ref()
or throughg_type_create_instance()
(this is being called byg_object_new()
) or through one of the above done on a type derived fromnew_type_id
.This causes the type system to load the type’s implementation by calling
g_type_plugin_use()
andg_type_plugin_complete_type_info()
onnew_type_plugin
.At some point the type’s implementation isn’t required anymore, e.g. after
g_type_class_unref()
org_type_free_instance()
(called when the reference count of an instance drops to zero).This causes the type system to throw away the information retrieved from
g_type_plugin_complete_type_info()
and then it callsg_type_plugin_unuse()
onnew_type_plugin
.Things may repeat from the second step.
So basically, you need to implement a
GTypePlugin
type that carries a use_count, once use_count goes from zero to one, you need to load the implementation to successfully handle the upcomingg_type_plugin_complete_type_info()
call. Later, maybe after succeeding use/unuse calls, once use_count drops to zero, you can unload the implementation again. The type system makes sure to callg_type_plugin_use()
andg_type_plugin_complete_type_info()
again when the type is needed again.
See moreGTypeModule
is an implementation ofGTypePlugin
that already implements most of this except for the actual module loading and unloading. It even handles multiple registered types per module.Declaration
Swift
public struct TypePluginRef : TypePluginProtocol
-
The
TypePluginClassRef
type acts as a lightweight Swift reference to an underlyingGTypePluginClass
instance. It exposes methods that can operate on this data type throughTypePluginClassProtocol
conformance. UseTypePluginClassRef
only as anunowned
reference to an existingGTypePluginClass
instance.The
See moreGTypePlugin
interface is used by the type system in order to handle the lifecycle of dynamically loaded types.Declaration
Swift
public struct TypePluginClassRef : TypePluginClassProtocol
-
The
TypeQueryRef
type acts as a lightweight Swift reference to an underlyingGTypeQuery
instance. It exposes methods that can operate on this data type throughTypeQueryProtocol
conformance. UseTypeQueryRef
only as anunowned
reference to an existingGTypeQuery
instance.A structure holding information for a specific type. It is filled in by the
See moreg_type_query()
function.Declaration
Swift
public struct TypeQueryRef : TypeQueryProtocol
-
The
TypeValueTableRef
type acts as a lightweight Swift reference to an underlyingGTypeValueTable
instance. It exposes methods that can operate on this data type throughTypeValueTableProtocol
conformance. UseTypeValueTableRef
only as anunowned
reference to an existingGTypeValueTable
instance.The
See moreGTypeValueTable
provides the functions required by theGValue
implementation, to serve as a container for values of a type.Declaration
Swift
public struct TypeValueTableRef : TypeValueTableProtocol
-
The
ValueRef
type acts as a lightweight Swift reference to an underlyingGValue
instance. It exposes methods that can operate on this data type throughValueProtocol
conformance. UseValueRef
only as anunowned
reference to an existingGValue
instance.An opaque structure used to hold different types of values. The data within the structure has protected scope: it is accessible only to functions within a
See moreGTypeValueTable
structure, or implementations of the g_value_*() API. That is, code portions which implement new fundamental types.GValue
users cannot make any assumptions about how data is stored within the 2 elementdata
union, and theg_type
member should only be accessed through theG_VALUE_TYPE()
macro.Declaration
Swift
public struct ValueRef : ValueProtocol
-
The
ValueArrayRef
type acts as a lightweight Swift reference to an underlyingGValueArray
instance. It exposes methods that can operate on this data type throughValueArrayProtocol
conformance. UseValueArrayRef
only as anunowned
reference to an existingGValueArray
instance.A
See moreGValueArray
contains an array ofGValue
elements.Declaration
Swift
public struct ValueArrayRef : ValueArrayProtocol
-
The
WeakRefRef
type acts as a lightweight Swift reference to an underlyingGWeakRef
instance. It exposes methods that can operate on this data type throughWeakRefProtocol
conformance. UseWeakRefRef
only as anunowned
reference to an existingGWeakRef
instance.A structure containing a weak reference to a
GObject
. It can either be empty (i.e. point tonil
), or point to an object for as long as at least one “strong” reference to that object exists. Before the object’sGObjectClass.dispose
method is called, everyGWeakRef
associated with becomes empty (i.e. points tonil
).Like
GValue
,GWeakRef
can be statically allocated, stack- or heap-allocated, or embedded in larger structures.Unlike
g_object_weak_ref()
andg_object_add_weak_pointer()
, this weak reference is thread-safe: converting a weak pointer to a reference is atomic with respect to invalidation of weak pointers to destroyed objects.If the object’s
See moreGObjectClass.dispose
method results in additional references to the object being held, anyGWeakRefs
taken before it was disposed will continue to point tonil
. IfGWeakRefs
are taken after the object is disposed and re-referenced, they will continue to point to it until its refcount goes back to zero, at which point they too will be invalidated.Declaration
Swift
public struct WeakRefRef : WeakRefProtocol
-
Undocumented
Declaration
Swift
public struct GBinding
-
Undocumented
Declaration
Swift
public struct GParamSpecPool
-
Undocumented
Declaration
Swift
public struct GValueArray
-
Undocumented
Declaration
Swift
public struct GTypePlugin


public final class Forms
extends java.lang.Object
Form
s and you can change pages at your wish.
So in the end, you can have prepared any number of Form
s you like,
even from different web pages, and manipulate them as you please.| Modifier and Type | Method and Description |
|---|---|
void |
closeDocument()
Close currently open
Document in singleton instance of module Forms . |
org.jsoup.nodes.Document |
getDocument()
Get current
Document |
static Forms |
getInstance()
Method for getting singleton instance of module
Forms , which you need to work with this module. |
void |
openDocument(org.jsoup.nodes.Document document)
Open
Document in singleton instance of module Forms . |
void |
openDocument(java.net.URL url)
Should create
Document instance from given url parameter and open it. |
Form |
selectForm(java.lang.String identifier)
Should select form element by identifier from currently opened page/document and return it.
|
public static Forms getInstance()
Forms
, which you need to work with this module.Forms
public void openDocument(java.net.URL url) throws java.io.IOException
Document
instance from given url parameter and open it.url
- of page which you want to openjava.io.IOException
- in case of bad format urlpublic void openDocument(org.jsoup.nodes.Document document)
Document
in singleton instance of module Forms
.
If Document
parameter is null
, its like no document was opened.document
- Document
to be opened in singleton instance of module Forms
public void closeDocument()
Document
in singleton instance of module Forms
.public org.jsoup.nodes.Document getDocument()
Document
Document
public Form selectForm(java.lang.String identifier) throws java.net.MalformedURLException
null
.
When found element is not form, application will try found another matching element, but if at page isn't any matching element application return null
.
When on the page is more then one elements with same identifier, application will select first matching form element.


Publications
-
- Authors: Danilo S. Carvalho
- In: Coluna Conexão Ciência e Saúde - Revista Ciência Hoje
- Year: 2023
- Subject: Explainable and safe AI, Popular science
-
- Authors: Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann, Andre Freitas
- In: (preprint)
- Year: 2023
- Subject: Explainable and safe AI, Abstract Meaning Representation, Natural Language Inference
- DOI: 10.48550/arXiv.2308.03581
-
- Authors: Danilo S. Carvalho, Lucas L. Felipe, Priscila C. Albuquerque, Fabio Zicker, Bruna P. Fonseca
- In: Scientometrics
- Year: 2023
- Subject: COVID-19, Research Leadership, Bibliometrics
- DOI: 10.1007/s11192-023-04754-x
-
- Authors: Danilo S. Carvalho, Giangiacomo Mercatali, Yingji Zhang, Andre Freitas
- In: Findings of the European chapter of Association for Computational Linguistics (Findings of EACL)
- Year: 2023
- Subject: Explainable and safe AI, Deep Neural models, Natural Language Processing
-
- Authors: Marco Valentino, Danilo S. Carvalho, André Freitas
- In: (preprint)
- Year: 2023
- Subject: Explainable AI, Deep Neural models, Natural Language Processing
- DOI: 10.48550/arXiv.2305.07303
-
- Authors: Mauricio Jacobo-Romero, Danilo S. Carvalho, Andre Freitas
- In: (preprint)
- Year: 2023
- Subject: Computational Engineering, AI Automation, Productivity, Signal Processing, Process Mining
- DOI: 10.48550/arXiv.2302.04785
-
- Authors: Danilo S. Carvalho, Edoardo Manino, Julia Rozanova, Lucas Cordeiro, André Freitas
- In: (preprint)
- Year: 2022
- Subject: Explainable and safe AI, Metamorphic Testing, Deep Neural models, Natural Language Processing
- DOI: 10.48550/arXiv.2212.04310
-
- Authors: Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann, André Freitas
- In: (preprint)
- Year: 2022
- Subject: Explainable and safe AI, Deep Neural models, Natural Language Processing
- DOI: 10.48550/arXiv.2210.06230
-
- Authors: Mauricio Jacobo-Romero, Danilo S. Carvalho, Andre Freitas
- In: (preprint)
- Year: 2022
- Subject: AI Automation, Productivity, Signal Processing, Process Mining
- DOI: 10.48550/arXiv.2210.01252
-
- Authors: Edoardo Manino, Julia Rozanova, Danilo Carvalho, Andre Freitas, Lucas Cordeiro
- In: Findings of the Association for Computational Linguistics: ACL 2022
- Year: 2022
- Subject: Explainable and safe AI, Metamorphic Testing, Deep Neural models, Natural Language Processing
- DOI: 10.18653/v1/2022.findings-acl.185
-
- Authors: Edoardo Manino, Danilo Carvalho, Yi Dong, Julia Rozanova, Xidan Song, Mustafa A. Mustafa, Andre Freitas, Gavin Brown, Mikel Luján, Xiaowei Huang, Lucas Cordeiro
- In: AAAI's Workshops on Artificial Intelligence Safety (SafeAI)
- Year: 2022
- Subject: Explainable and safe AI
-
- Authors: Eric Ferreira dos Santos, Danilo Silva de Carvalho, Jonice Oliveira
- In: Proc. of the Brazilian Symposium on Multimedia and the Web
- Year: 2021
- Subject: Bot Detection, Social Network Analysis, Natural Language Processing
- DOI: 10.1145/3470482.3479452
-
- Authors: Danilo S. Carvalho, Lydia Bares, Kelyane Silva
- In: Journal of Data and Information Science
- Year: 2020
- Subject: Patent statistics
- DOI: 10.2478/jdis-2020-0025
-
- Authors: Eric F. dos Santos, Danilo S. Carvalho, Lívia Ruback, Jonice Oliveira
- In: Companion Proceedings of The 2019 World Wide Web Conference
- Year: 2019
- Subject: Bot Detection, Social Network Analysis, Natural Language Processing
- DOI: 10.1145/3308560.3317599
-
- Authors: Danilo S. Carvalho, Minh-Le Nguyen
- In: Proc. of the 9th International Conference on Knowledge and Systems Engineering (KSE) 2017
- Year: 2017
- Subject: Term Definition Vector, Vectorial Representation, Natural Language Processing
- DOI: 10.1109/KSE.2017.8119465
-
- Authors: Danilo S. Carvalho, Minh-Le Nguyen, Hiroyuki Iida
- In: Proc. of Advances in Computer Games (ACG) 2017
- Year: 2017
- Subject: Statistical Analysis, Checkers, Computer Games
- DOI: https://doi.org/10.1007/978-3-319-71649-7_18
-
- Authors: Danilo S. Carvalho, Vu Tran, Khanh Van Tran, Minh-Le Nguyen
- In: Proc. of the 4th Competition on Legal Information Extraction and Entailment (COLIEE) 2017
- Year: 2017
- Subject: Legal Text Analysis, Language Modeling, Term Order Probabilities, Natural Language Processing
- DOI: 10.29007/2xzw
-
- Authors: Danilo S. Carvalho, Minh-Le Nguyen
- In: Proc. of the 25th European Symposium on Artificial Neural Networks (ESANN) 2017
- Year: 2017
- Subject: Document Segmentation, Term Order Probabilities, Artificial Neural Networks, Machine Learning, Natural Language Processing
-
- Authors: Danilo S. Carvalho, Minh-Le Nguyen
- In: Proc. of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 2017
- Year: 2017
- Subject: Definitional Semantics, Term Definition Vector, Vectorial Representation, Natural Language Processing
- DOI: https://doi.org/10.18653/v1/e17-1085
-
- Authors: Danilo S. Carvalho, Vu Duc Tran, Khanh Van Tran, Minh-Le Nguyen
- In: Proc. of the 10th International Workshop on Juris-informatics (JURISIN) 2016
- Year: 2016
- Subject: Legal Text Analysis, Language Modeling, Natural Language Processing
-
- Authors: Douglas O. Cardoso, Danilo S. Carvalho, Daniel S.F. Alves, Diego F.P. Souza, Hugo C.C. Carneiro, Carlos E. Pedreira, Priscila M.V. Lima, Felipe M.G. França
- In: Neurocomputing
- Year: 2016
- Subject: Credit Analysis, Temporal Clustering, Weightless Neural Networks, Machine Learning
- DOI: 10.1016/j.neucom.2015.06.105
-
- Authors: Danilo S. Carvalho, Minh-Tien Nguyen, Tran Xuan Chien, Minh-Le Nguyen
- In: New Frontiers in Artificial Intelligence (Lecture Notes in Artificial Intelligence)
- Year: 2016
- Subject: Legal Text Analysis, Language Modeling, Natural Language Processing
- DOI: 10.1007/978-3-319-50953-2
-
- Authors: Danilo S. Carvalho, Çaǧatay Çalli, André Freitas, Edward Curry
- In: Proc. of the 13th International Semantic Web Conference (ISWC), Posters & Demonstrations Track, Volume 1272
- Year: 2014
- Subject: Explicit Semantic Analysis, Vectorial Representation, Natural Language Processing
-
- Authors: Danilo S. Carvalho, Felipe M. G. França, Priscila M. V. Lima
- In: Proc. of the 2014 Brazilian Conference on Intelligent Systems, BRACIS 2014
- Year: 2014
- Subject: Semantic Relation Extraction, Patents, Weightless Neural Networks, Natural Language Processing
- DOI: 10.1109/BRACIS.2014.17
-
- Authors: Douglas de O. Cardoso, Danilo S. Carvalho, Daniel S. F. Alves, Diego F. P. de Souza, Hugo C. C. Carneiro, Carlos E. Pedreira, Priscila M. V. Lima, Felipe M. G. França
- In: Proc. of the 22th European Symposium on Artificial Neural Networks, ESANN 2014
- Year: 2014
- Subject: Credit Analysis, Temporal Clustering, Weightless Neural Networks, Machine Learning
-
- Authors: Paulo Coutinho, Hugo C. C. Carneiro, Danilo S. Carvalho, Felipe M. G. França
- In: Proc. of the 22th European Symposium on Artificial Neural Networks, ESANN 2014
- Year: 2014
- Subject: WiSARD, Weightless Neural Networks, Machine Learning
-
- Authors: Rafael L. Carvalho, Danilo S. Carvalho, Priscila M. V. Lima, Félix Mora-Camino, Felipe M. G. França
- In: Proc. of the 22th European Symposium on Artificial Neural Networks, ESANN 2014
- Year: 2014
- Subject: Video Object Tracking, WiSARD, Weightless Neural Networks, Machine Learning
-
- Authors: André Freitas, Rafael Vieira, Edward Curry and Danilo S. Carvalho, João Carlos Pereira da Silva
- In: Proc. of the 19th International Conference on Natural Language Processing and Information Systems (NLDB) 2014
- Year: 2014
- Subject: Linked Data, Natural Language Processing
- DOI: 10.1007/978-3-319-07983-7_6
-
- Authors: Danilo S. Carvalho, Hugo C. C. Carneiro, Felipe M. G. França, Priscila M. V. Lima
- In: Proc. of the 21st European Symposium on Artificial Neural Networks, ESANN 2013
- Year: 2013
- Subject: WiSARD, Weightless Neural Networks, Overtraining Avoidance, Machine Learning
-
- Authors: Danilo S. Carvalho, André Freitas, João C.P. da Silva
- In: Proc. of Extended Semantic Web Conference
- Year: 2013
- Subject: Semantic Relation Extraction, Linked Data, Natural Language Processing
- DOI: 10.1007/978-3-642-41242-4_31
-
- Authors: André Freitas, Seán O’Riain, Edward Curry, João C.P. da Silva, Danilo S. Carvalho
- In: Proc. of Database and Expert Systems Applications (DEXA), 2013 24th International Workshop
- Year: 2013
- Subject: Semantic Relation Extraction, Linked Data, Natural Language Processing
- DOI: 10.1109/DEXA.2013.21
-
- Authors: André Freitas, Danilo S. Carvalho, João C.P. da Silva, Seán O’Riain, Edward Curry
- In: Proc. of the 1st Workshop on the Web of Linked Entities,(ISWC)
- Year: 2012
- Subject: Semantic Relation Extraction, Linked Data, Natural Language Processing


TABLE OF INTERACTIONS WITH
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE
From the French ANSM drug interactions document of
September 2016, p. 113
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
DEXTROMETHORPHAN RxNorm: 3289 ATC: R05DA09 |
Risk of appearance of serotonin syndrome: diarrhea, tachycardia, sweats, trembling, confusion, even coma |
Not Recommended |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
MAOI-B CLASS CODE: N04BD0-001 |
Risk of rapid elevation of blood pressure, due to loss of selectivity on the monoamine oxydase, especially in case of eating food rich in tyramine (cheese, beer, ...) |
CONTRAINDICATION |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
SELECTIVE SEROTONIN REUPTAKE INHIBITORS (SSRIs) CLASS CODE: N06AB |
Risk of appearance of a serotonin syndrome: diarrhea, tachycardia, sweats, trembling, confusion, even coma |
Not recommended If administering these substances together cannot be avoided, very strict clinical monitoring. Begin the administration at minimum recommended dosages. |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
MIXED ADRENERGIC-SEROTONINERGIC MEDICATIONS CLASS CODE: N06A |
Risk of appearance of a serotonin syndrome: diarrhea, tachycardia, sweats, confusion, even coma |
Not recommended If administering these substances together cannot be avoided, very strict clinical monitoring. Begin the administration of the substances together with minimum recommended dosages. |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
ST JOHN'S WORT as St John's Wort Extract RxNorm: 258326 ATC: not found |
Risk of appearance of a serotonin syndrome |
Precaution for use Regular clinical and biological monitoring, especially at the beginning of the administration of the substances together. |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
PETHIDINE RxNorm: 6754 ATC: N02AB02 N02AG03 |
Risk of appearance of a serotonin syndrome: diarrhea, tachycardia, sweats, trembling, confusion, even coma. |
CONTRAINDICATION |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
ALPHA AND BETA SYMPATHOMIMETICS (IM AND IV ROUTES) CLASS CODE: C01CA |
By extrapolation from the non selective MAOIs: risk of increase of the pressor action. |
Precaution for use To be used only under strict medical monitoring. |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
INDIRECT SYMPATHOMIMETICS CLASS CODE: C01CA-R01BA |
Risk of constriction of blood vessels and/or rapid elevation of blood pressure |
Contraindication: with the bupropion Not recommended: with the other indirect sympathomimetics |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
TRAMADOL RxNorm: 10689 ATC: N02AX02 |
Risk of appearance of a serotonin syndrome: diarrhea, sweats, trembling, confusion, even coma |
Not Recommended |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
TRIPTANS METABOLIZED BY THE MAO CLASS CODE: N02CC-002 |
Risk of arterial hypertension, of constriction of coronary arteries |
CONTRAINDICATION |
REVERSIBLE MAOI-A, INCLUDING LINEZOLIDE AND METHYLENE BLUE CLASS CODE: NO6AG-001 |
TRIPTANS NOT METABOLIZED BY THE MAO CLASS CODE: NO2CC-003 |
Risk of arterial hypertension, of constriction of coronary arteries |
Not Recommended |


First open http://www.planetfour.org in a separate tab and sign in (clicking on the link should open the P4 webpage in a separate window). Once you log in you can close the window/tab and come back here.
The easiest way to get the gold standard data is to use a url query to make the Planet Four website show a specific image. The links are below for the images assigned to you for this week.
For each link below - click and it should take you to a new tab. Make your markings and then hit 'Finish' and then 'Next'. After that close the tab/window and come back here and classify the next link.
If you think a particular image was way especially hard then instead of 'Next' click discuss and add the talk hashtag #hard2see or you can send me the link
Note once you've gone to a link it should change color and your browswer should remember going there so you can track of what you've done and is still left to do. Also give the interace a second to load and retrieve the image (you might see the P4 home page flash first).
Reminder: First open http://www.planetfour.org in a separate tab and sign in (clicking on the link should open the P4 webpage in a separate window). Once you log in you can close the window/tab and come back here.
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APF0000mp6
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APF0000r0s
APF0000ky8
APF0000ifj
APF0000k9b
APF0000stc


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