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--- layout: home description: "" sub-title: "Detector Gadget" header-img: "img/home2-bg.jpg" ---
Since the discovery of Green Fluorescent Protein, it has revolutionized
fluorescent imaging of biomolecules inside cells and animal tissue. A similar
fluorescent signal can be obtained with the RNA aptamer Spinach, which also
allows direct optical monitoring of RNA in vivo. The qualitative detection of a
small molecule is enabled by fusing Spinach with a small molecule aptamer,
however, quantitative detection has never been achieved.
Here we show the construction of a modified biosensor system for concentration
dependent detection of the metabolite S-adenosylmethionine, SAM. By adjusting
the stability of the linker region of the Spinach-SAM construct, the aptamer
folding is made dependent on the concentration of the metabolite. An orthogonal
aptamer-fluorophore pair termed Mango-TO3 serves as an internal control of RNA transcription levels.
Combining several concentration dependent constructs allows us to construct a biosensor
with a broadened dynamic range.


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Gush
Summons a gush of water to damage an enemy unit, reducing their movement speed and armor. Lasts 4 seconds.
| Mana Cost: | 120/120/120/120 |
| Cooldown: | 12 |
| ABILITY: | Unit Target |
| AFFECTS: | Enemy Units |
| DAMAGE TYPE: | Magical |
| PIERCES SPELL IMMUNITY: | No |
| DAMAGE: | 110 / 160 / 210 / 260 |
| SLOW: | 40% / 40% / 40% / 40% |
| ARMOR REDUCTION: | 2 / 3 / 4 / 5 |
During his championship of the Sunken Isles, Leviathan gained mastery over the open sea.
Kraken Shell
Thickens Tidehunter's hide to passively block a portion of any incoming physical attack damage. The hide also removes negative status effects if the damage taken crosses a threshold. Does not stack with items that provide Damage Block.
| ABILITY: | Passive |
| DAMAGE BLOCK: | 12 / 24 / 36 / 48 |
| DAMAGE THRESHOLD: | 600 / 550 / 500 / 450 |
| THRESHOLD TIMER: | 6 / 6 / 6 / 6 |
Claddish navymen tell tales of a mighty sea-faring beast that suffered spear and sword but continued to lay waste to the fleet.
Anchor Smash
Tidehunter swings his mighty anchor to damage nearby enemies and reduce their attack damage.
| Mana Cost: | 30/40/50/60 |
| Cooldown: | 7/6/5/4 |
| ABILITY: | No Target |
| DAMAGE TYPE: | Physical |
| PIERCES SPELL IMMUNITY: | Yes |
| DAMAGE: | 75 / 125 / 175 / 225 |
| DAMAGE REDUCTION: | 45% / 50% / 55% / 60% |
| DURATION: | 6 / 6 / 6 / 6 |
| RADIUS: | 375 |
Stolen from one of Admiral Kunkka's flagships, Leviathan's heavy anchor proves useful as a melee weapon.
Ravage
Slams the ground, causing tentacles to erupt in all directions, damaging and stunning all nearby enemy units.
| Mana Cost: | 150/225/325 |
| Cooldown: | 150 |
| ABILITY: | No Target |
| DAMAGE TYPE: | Magical |
| PIERCES SPELL IMMUNITY: | No |
| DAMAGE: | 200 / 290 / 380 |
| RADIUS: | 1025 |
| DURATION: | 2.02 / 2.32 / 2.77 |
Calling to the abyssal god Maelrawn has resulted in entire armadas being lost at sea.


buzzword is a platform for investigating language use in large collections of documents.
Some of the texts have been extracted from PDF data through the process of optical character recognition. Because OCR is not always perfectly accurate, for these resources, you are able to
If you have PDFs of your own, you can submit them for automatic OCR. Buzzword will provide a correction interface for your data, and allow you to accept corrections made by others.
{% for corpus in page_obj.object_list %}
{{corpus.name}}
{{corpus.name}}
{{corpus.desc}}
{% if corpus.pdfs %}
{% endif %}
{% endfor %}
{% include "start_corpora_nav.html" %}
Or, upload your own...
Browse
View digitised collections
Visualise
Generate high-quality, interactive charts and heatmaps showing frequencies or keyness of words, lemmata, or grammatical features
Concordance (keyword-in-context)
View search results in their immediate context, to get a better understanding of what they mean
Improve
Submit improvements to the automatic transcriptions


This is just a demo. Uploaded files are not stored. This does not handle your file uploads on the server. You have to implement the code to receive and store the file yourself.
alias:date
(999) 999-9999
(999) 999-9999 x99999
+33 999 999 999
99-9999999
999-99-9999
a*-999-a999
PO: aaa-999-***
99%
right alignment
left alignment


M_drawplus(3f) - [M_drawplus] Additional routines using the M_DRAW graphics library (LICENSE:MIT)
Synopsis
Description
Library Function Descriptions
Author
License
use M_drawplus, only : spirograph ,call_draw ,draw_interpret use M_drawplus, only : arrowhead ,plain_rect ,rdpnt ,rdbox ,barcode use M_drawplus, only : uconic ,ellipse ,uarc ,polyline2, seefont use M_drawplus, only : smoot, ismoo,ismoo1,ismoo2,ismoo3,perin use M_drawplus, only : ismoo,ismoo1,ismoo2,ismoo3, perin
M_drawplus(3f) is a collection of higher level graphic routines that call the base M_draw(3f) graphics routines.
subroutine rdbox(returnx1,returny1,returnx2,returny2,ikey) reads two points and outline defined box and return points subroutine rdpnt(oldx,oldy,sx,sy,ibut) reads coordinates of point locator clicked at
subroutine polyline2(arrx(:),arry(:))
subroutine arrowhead(x1,y1,x2,y2,size,idraw)
subroutine uarc
subroutine uconic(x,y,p,e,theta1,theta2,orientation)
subroutine ellipse(xpage,ypage,rmaj,rmin,angle,th0,thf,ipen)
subroutine spirograph(xcenter,ycenter,sunr0,planet0,offset0,radius,ilines,ang,angs,ifill)
subroutine smoot, ismoo,ismoo1,ismoo2,ismoo3,perin
subroutine plain_rect(x1,y1,x2,y2)
subroutine seefont(fontin)
subroutine barcode
subroutine call_draw
subroutine draw_interpret
John S. Urban
MIT License
| M_drawplus (3) | January 01, 2020 |


Chicken
Chrome can help keep you safe from data breaches,bad extensions, and more chicken Chrome can help keep you safe from data breaches,bad extensions, and more chicken
Beef
Chrome can help keep you safe from data breaches,bad extensions, and more beef Chrome can help keep you safe from data breaches,bad extensions, and more chicken
Sushi
Chrome can help keep you safe from data breaches,bad extensions, and more suchi Chrome can help keep you safe from data breaches,bad extensions, and more chicken


class: inverse, center, middle

Zoned-In and Zoned-Out:
Changes in School Attendance Zones over Time

 .small[Magdalena Bennett
*McCombs School of Business, The University of Texas at Austin* ]
 .small[AEFP Conference
March 24th, 2023] --- # Attendance zones, new schools, and segregation - Schools can either **.darkorange[attract]** or **.darkorange[push away]** residents depending on socioeconomic characteristics .small[(Hasan & Kumar, 2019; Gibbons, Machin, & Silva 2013; Figlio & Lucas, 2004)] --

 - Neighborhoods have important effects on **.darkorange[long-term outcomes]** .small[(Chetty et al. 2020)] --

 - **.darkorange[Racial and socioeconomic disparities]** in the school system also have long-term effects on students .small[(Reardon, 2016; Billings et al., 2014)] -- .box-7Trans[New public schools opening → Changes in attendance zones] --- # This paper .pull-left[ - How do **.darkorange[changes in attendance zones (AZ)]** affect: - Zoned-in areas (i.e. neighborhoods)? - Zoned-out areas (i.e. neighborhoods and schools)? ] .pull-right[ .center[ ![:scale 90%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map_TX.png) ] ] --- # This paper .pull-left[ - How do **.darkorange[changes in attendance zones (AZ)]** affect: - Zoned-in areas (i.e. neighborhoods)? - Zoned-out areas (i.e. neighborhoods and schools)? - **.darkorange[New high schools]** in Texas that changed AZ `\(\rightarrow\)` mostly gentrified areas ] .pull-right[ .center[ ![:scale 90%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map_TX.png) ] ] --- # This paper .pull-left[ - How do **.darkorange[changes in attendance zones (AZ)]** affect: - Zoned-in areas (i.e. neighborhoods)? - Zoned-out areas (i.e. neighborhoods and schools)? - **.darkorange[New high schools]** in Texas that changed AZ `\(\rightarrow\)` mostly gentrified areas - **.darkorange[Outcomes of interest]**: - Differences in scores and score gaps between race/ethnicity - Differences in school composition - Differences in neighborhood composition ] .pull-right[ .center[ ![:scale 90%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map_TX.png) ] ] --- # What do we mean by zoned-in and zoned-out areas? -- .pull-left[ **Zoned-in area**: Attendance zone for a new school *S'* .center[ ![:scale 100%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map1.png)] ] -- .pull-right[ **Zoned-out area**: New attendance zone for school *S* after the opening of schools *S'*. .center[ ![:scale 100%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map2.png)] ] --- # What do we mean by zoned-in and zoned-out areas? .pull-left[ **Zoned-in area**: Attendance zone for a new school *S'* .center[ ![:scale 100%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map1.png)] ] .pull-right[ **Spillover school**: School which had a part of their catchment area zoned-in to S'. .center[ ![:scale 100%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map2.png)] ] --- # Data - **.darkorange[Common Core Data (CCD) [2005-2019]]**: Administrative data from NCES, including demographic and socioeconomic characteristics of schools. --

 - **.darkorange[Texas Educacion Agency (TEA) data [2005-2019]]**: Performance data for schools over time. --

 - **.darkorange[Attendance zones maps [2009-2017]]**: Geographic data for school boundaries over time from SABS, SABINS, and Maponics. --

 - **.darkorange[Census and American Community Survey (ACS) data [2010-2019]]**: Demographic information at the census tract level --

 - **.darkorange[Housing Prices data [2005-2019]]**: Information about housing prices over time from CoreLogic and Zillow *(coming soon)*. --- # Identification Strategy: An Augmented Synthetic Control Method - Use a **.darkorange[weighted average]** of similar districts/schools/AZ/neighborhoods to create a comparison group for affected areas. -- - Under **.darkorange[Augmented Synthetic Control Method]** (ASCM) (Ben-Michael et al., 2020) there is a correction for poor fit: `$$\hat{Y}^{aug}_{1T}(0) = \sum_{W_i=0}\gamma_iY_{iT}+\color{#db5f12}{(\hat{m}_{iT}(\mathbf{X_i})-\sum_{W_i=0}\gamma_i\hat{m}_{iT}(\mathbf{X_i}))}$$` .small[ - `\(m_{iT}\)`: Estimator for `\(Y_{iT}(0)\)` - Extrapolation for "bias correction". - If ridge regression is used `\(\rightarrow\)` penalization for extrapolation.] -- - Proposal of **.darkorange[sensitivity analysis to hidden bias]** .small[(Rosenbaum, 2002; Keele et al., 2019)]: - *How much should an unobserved confounder affect the probability of treatment (i.e. new school opening there vs in a control area) to explain away the results we find?* --- # Broader picture: What happens to districts? .pull-left[ - Compare districts with a new school between 2012 and 2016 vs districts with no new schools. - ASCM for different characteristics, adjusting for other baseline covariates (e.g. number of schools, enrollment, %FRPL, % race/ethnicity) ] .pull-right[ .center[ ![](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map0_districts.png)] ] --- # Districts with new schools increase gap in Math
 .center[ ![:scale 85%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/districts_gap_math.png)] --- # ... no significant change in Reading
 .center[ ![:scale 85%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/districts_gap_read.png)] --- # What happens within districts? .pull-left[ - Identify **.darkorange[6 new high schools]** between 2012-2016 that change AZ. - Compare attendance zones *within districts* to create a counterfactual. ] .pull-right[ .center[ ![](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map_zoned_in_out.png)] ] --- # What happens within districts? .pull-left[ - Identify **.darkorange[6 new high schools]** between 2012-2016 that change AZ. - Compare attendance zones *within districts* to create a counterfactual.
 - **.darkorange[Important caveat]**: - Limited sample size `\(\rightarrow\)` under-powered. - Trends are suggestive. ] .pull-right[ .center[ ![](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/map_zoned_in_out.png)] ] --- # Zoned-in Areas: How do neighborhoods change? - No major changes in % white population (left) or % African American population (right) .pull-left[ .center[ ![](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/zones_in_white.png)] ] .pull-right[ .center[ ![](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/zones_in_black.png)] ] --- # Zoned-in Areas: How do neighborhoods change? - Suggestive increase in % of college educated population .center[ ![:scale 60%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/zones_in_college.png)] --- # Zoned-out Areas: How are schools affected? - **.darkorange[Demographics]**: Decreasing trend in white students enrollment (left) vs increasing trend in African American enrollment (right). .pull-left[ .center[ ![](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/zones_out_white_students.png)] ] .pull-right[ .center[ ![](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/zones_out_black_students.png)] ] --- # Zoned-out Areas: How are schools affected? - **.darkorange[Performance]**: Decreasing trend % of proficiency (sharper for African American students). .center[ ![:scale 60%](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/zones_out_scores.png)] --- # Zoned-out Areas: What about neighborhoods? - Increasing trend in % of African-American population (left) and decreasing trend in % college-educated population (right). .pull-left[ .center[ ![](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/zones_out_black.png)] ] .pull-right[ .center[ ![](https://raw.githubusercontent.com/maibennett/presentations/main/content/presentations/AttendanceZones/AEFP_20230324/images/zones_out_college.png)] ] --- # Wrapping up - Attendance zones have a huge role in **.darkorange[shaping neighborhoods]** and nearby areas. --

 - Importance of understanding the effects of **.darkorange[new schools]** and their location and boundaries. --

 - Effects of housing prices? Long-run outcomes? --

 - **.darkorange[Next steps]**: - Include other states (e.g. CA) - Analyze housing prices over time. - Heterogeneity in effect for gentrified neighborhoods? --- class: inverse, center, middle

Zoned-In and Zoned-Out:
Changes in School Attendance Zones over Time

 .small[Magdalena Bennett
*McCombs School of Business, The University of Texas at Austin* ]
 .small[AEFP Conference
March 24th, 2023]


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Sticking to your treatment schedule takes discipline. But the more you do something, the easier it gets, and HIV treatment is no different. Here are some ideas that may help you stick to your HIV treatment plan:
Take your medication at the same time each day and keep it in the same place.
Watch your supply of medication.


In the early 2015, several cases of patients presenting symptoms of mild fever, rash, conjunctivitis and arthralgia were reported in the northeastern Brazil. Although all patients lived in a dengue endemic area, molecular and serological diagnosis for dengue resulted negative. Chikungunya virus infection was also discarded. Subsequently, Zika virus (ZIKV) was detected by reverse transcription-polymerase chain reaction from the sera of eight patients and the result was confirmed by DNA sequencing. Phylogenetic analysis suggests that the ZIKV identified belongs to the Asian clade. This is the first report of ZIKV infection in Brazil.
Zika virus (ZIKV) is an emerging arthropod borne virus (arbovirus) transmitted by Aedes ( Stegomyia) mosquitoes. The virus belongs to the genus Flavivirus, family Flaviviridae, and is closely related to other flaviviruses of public health relevance including dengue (DENV), yellow fever and West Nile viruses ( Pierson & Diamond 2013 , Faye et al. 2014) .
ZIKV was first isolated from a rhesus monkey in the Zika Forest of Uganda, in 1947 ( Dick et al. 1952 ). For half a century, the virus was described as causing sporadic human infections in Africa and Asia, until 2007 when a Zika fever epidemics took place in Yap Island, Micronesia ( Duffy et al. 2009 ). In 2013, a large epidemic was reported in French Polynesia, concomitant with a dengue epidemic caused by serotypes 1 and 3. ZIKV has been considered as emergent since 2007 ( Cao-Lormeau et al. 2014 , Ioos et al. 2014 ).
In the early 2015, records of patients presenting a "dengue-like syndrome" appeared in the public health service in the city of Natal (05°47’42"S 35°12’32"O), state of Rio Grande do Norte, Brazil. A physician specialist in infectious disease evaluated the patients and the clinical signs and symptoms and laboratory findings indicated a non-DENV and non-Chikungunya virus (CHIKV) infection. Symptoms included arthralgia, oedema of extremities, mild fever, maculopapular rashes frequently pruritic, headaches, retroorbital pain, no purulent conjunctivitis, vertigo, myalgia and digestive disorder.
By March 2015, the Molecular Virology Laboratory of Carlos Chagas Institute, Oswaldo Cruz Institute, state of Paraná, Brazil, received 21 acute-phase serum specimens from those patients exhibiting the "dengue-like syndrome". Sera were transported and kept at -80ºC until use. The protocol was approved by the Brazilian Ethical Committee (42481115.7.0000.5248).
After RNA extraction, reverse transcription-polymerase chain reaction (RT-PCR) was performed as a rapid molecular tool to detect viral infection in acute-phase samples. Viral RNA was extracted from 140 µL of sera by using QIAamp viral RNA mini kit (Qiagen, USA) according to the manufacturer’s instructions.
The sera were first tested for DENV by RT-PCR ( Lanciotti et al. 1992 ), ELISA for non-structural protein 1 antigen detection (Dengue Early ELISA, PanBio) and anti-DENV IgM (Dengue IgM Capture ELISA, PanBio), all yielded negative results. Moreover, all samples rendered negative results in RT-PCR assays for CHIKV infection ( Lanciotti et al. 2007 ).
Since clinical symptoms were compatible with ZIKV infection, RT-PCR assay to specifically detect ZIKV RNA was performed, as described by Faye et al. (2008) , with the modification that, instead of a one step, a two step protocol was used (cDNA synthesis followed by the PCR).
Amplicons of 364 bp encompassing the envelope protein coding region were obtained for eight out of 21 samples ( Fig. 1). Two of the resultant amplicons were purified with High Pure PCR Purification kit (Roche) and sequenced in an ABI 3500 Genetic Analyzer (Applied Biosystems). Sequences were assembled using the Assembler tool (hpa-bioinformatics.org.uk/cgi-bin/assembly_tool/seq_assemble.cgi?no=2) (GenBank accessions KR815989 and KR815990) and their identity were confirmed by Basic Local Alignment Search Tool (BLAST) and compared to other ZIKV sequences. Results demonstrated a high identity with sequences from the ZIKV Asian lineage. Despite the similarity of the clinical findings, the remaining samples resulted negative in RT-PCR, probably due to the short (and low) viraemia period (between the 3rd and 5th day after the onset of clinical symptoms).
Phylogenetic analysis of the sequences ( Fig. 2) placed the Brazilian strains in a clade with sequences from the Asian lineage. The highest identity scores in BLAST were observed with the H/PF/2013 (99%, GenBank accession KJ776791), CK-ISL 2014 (99%, GenBank accession KJ634273) and FSS13025 (99%, GenBank accession JN860885) isolates. The two generated sequences were identical between themselves in the region that was analysed ( Fig. 2) and was identified as 15095_BR_2015. These results corroborate previously published data about the spread of the ZIKV Asian lineage that was first reported in the outbreaks of the Pacific Islands ( Musso et al. 2014 ). To track the introduction of ZIKV in Brazil a retrospective and more extensive analysis of additional samples from other Brazilian regions is needed, as well as a complete viral genetic characterisation. However, one plausible hypothesis is the arrival of the new emergent virus during the soccer World Cup in 2014.
Seven of the eight patients with confirmed ZIKV disease were female, the median age was 39 years (range, 18-65), all of them lived in Natal and had relatives with the same symptoms. The most commonly reported symptoms were maculopapular rash (100%) ( Fig. 3) and pain. The pain scale was applied for six of them and the pain levels were high in most patients, with levels of zero (1/7), seven (2/7), nine (1/7) or 10 (2/7). The pain duration ranged from two-15 days, except for one patient who had pain for more than 21 days. Other symptoms included headache (6/8), retroorbital pain (4/8) and myalgia (6/8). Myalgia was not intense. Joint pain was reported for seven patients, including pain in the hands (5), ankle (4), elbow (4), knee and wrist (3) and foot (2); one patient reported cervical pain.
During the clinical examination, all the patients were haemodynamically normal and periarticular swelling ( Fig. 3) was present in six of them, with predominance in the hands (50%). Fever was reported in six patients, but high fever (around 39ºC) in only two. The duration of fever ranged from one-eight days. Submandibular or cervical lymphadenopathy ( Fig. 3) occurred in three patients. No purulent conjunctivitis was observed. ZIKV can produce a clinical relevant disease, which lasts for approximately two weeks. The patients observed in Natal had intense pain resembling CHIKV infection, but the clinical course was shorter. To date, no death or complications were associated with ZIKV illness in Natal.
The blood cell count revealed that only two patients had leukopaenia, the others had normal levels of leukocytes and neutrophils and platelets were normal in all of them. The tourniquet tests were negative in seven of the patients.
We report here the first identification of ZIKV as the causative agent of an outbreak in the northeastern Brazil. It represents the first autochthonous transmission of ZIKV in the country. Although most of the patients had mild illness, clinicians and public health officials should be aware of the risk of expansion of this new emerging virus, especially given the naïve immunological status of the Brazilian population. Spreading of the disease in the country might occur by virtue of the large population mobility and the widespread occurrence of the transmitting vectors. Furthermore the complex epidemiological context with the co-circulation of DENV, CHIKV and ZIKV cannot be neglected. DENV and ZIKV ( Dupont-Rouzeyrol et al. 2015 ) or DENV and CHIKV ( Caron et al. 2012 ) co-infections have already been reported.
The effect of the concurrent outbreaks caused by these three different arboviruses is unknown. ZIKV disease was associated with neurological and autoimmune complications in a context of concurrent circulation with DENV in French Polynesia ( Roth et al. 2014 ). DENV also circulates in Natal. Further studies are needed to unveil whether co-infection and subsequent infection by different arboviruses can affect the course of the disease, the occurrence of severe cases and the ways of transmission (vertical, perinatal, sexual) ( Foy et al. 2011 , Besnard et al. 2014 , Musso et al. 2015 ). Of interest, recent paper by Gourinat et al. (2015) shows evidence of virus secretion in urine for more than 10 days after onset of disease.
The correct clinical and laboratorial differential diagnosis between acute ZIKV, CHIKV and DENV infection will contribute to the patients’ prognosis and to drive surveillance actions.
To the Sequencing Platform of the Department of Biochemistry and Molecular Biology of the Federal University of Paraná, for viral genomic sequencing, to Dr Helisson Faoro, for discussion, and to Dra Andreia Suzukawa, for technical assistance.
Financial support: FIOCRUZ, CNPq, CAPES, Fundação Araucária


Lab 6
By:AndyJiang
Date:10/19/2018
Move Left
Move Right
Move Down
Move Up
Disappear
Come Back
Small
Normal
Big
Circle
square
Crazy
Stop
Stop All
Stop and go to the end


CHASERS A film review by Tim Voon Copyright 1997 Tim Voon
Directed by Dennis Hopper, Written by Jon Batteer, John Rice and Dan Gilroy Cast Tom Berenger William McNamara, Erika Eleniak, Crispin Glover, Dean Stockwell, Gary Busey, Seymour Cassell, Federic Forrest, Marilu Henner, Dennis Hopper
This movie is a poor excuse for a comedy. There is no plot and plenty of running about with nothing better to do (I guess that's why they called it CHASERS because there is no other relation to the plot that I can see.)
The story revolves around two navy officers (Berenger and McNamara) who are assigned to transport a beautiful, young female navy officer (Eleniak) to her court martial. She tries to escape numerous times; they fall in a pit where they all have to strip in order to escape; the younger officer (McNamara) sleeps with his prisoner (totally unethical), then falls in love with her and, the Captain (Berenger) takes pity on them and helps them to escape to Mexico.
All this in the name of comedy? What utter rubbish! At least my video recorder had a good work out with my constant thumb-pressing on the fast forward button of my remote control.
Poor direction, writing and acting.
Comment: Don't bother.
Timothy Voon e-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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Please direct comments/criticisms of the review to relevant newsgroups.
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Related links: index of all rec.arts.movies.reviews reviews


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My favorite places
These are my favorite cites I have lived in
Tokyo, Japan
Portland, OR
New York CIty, NY
I hope I get the chance to experince many more!


leaf leaves curvature Chinese rp- rapa miR rc- curvature. curving downward cabbage curved plants genotypes rp-, types Arabidopsis heading longitudinal miRNA transverse Plant different miRNAs upward rc-, head {border-style wavy gene leafy px;} #ffffff;} accessions blades genes heads margins degree direction ssp. angle levels miRa rosette transgenic Figure accumulation cabbage, formation non-heading expression leaves, petiole inward mutants rr- (Figure Brassica significant using (ssp. family lines margins, outermost petioles regions var. RNA Table analysis angles blade blades, compared development distance genotype genotypes. mutant px; similar {font-family (Figures CIs Heading However, axis carrying crops direction, font-size genes. higher inner innermost miRab target upregulation (Liu Allen Biol. Brp-MIRg Cell Flat Liu Science Transverse Wavy angle, associated changes deficient directions double; flat global increased leaves. local pekinensis performed position rapa. related showed transversely turnip type various wild-type < (Student's Arabidopsis, Arabidopsis. Bartel China Curr. Leaf MIRg Overexpression PCR Serrated Wang auxin border-color border-width cabbage. cell chinensis control crop data differences division downwardly flattening folding growth incline indices morphological outer plant plants, progressively referred regulation relationship respectively. serrated shape small solid; t-test) used {font-style {font-weight (Palatnik ./tpc.. Bai Blades BrpREV-, CUC Curvature Development Downward Expression Genetic HYL Leafy Leaves MIRa Mao No. Sci. Xie activated affect affected apical article binary bold caused characterization classified courier; curved, developmental difference. distinct edible em; formula function high important incurved inwardly measurement meristem pAA::BrpMIRg. pekinensis), plants; polarity pt;} rapa, region respectively, results seedlings showing shown stages targets thaliana upwardly vegetative


I've created a very simple Android app for Emoncms. The app collects the Json data from a users account and lists them. The development is in an early stage, the code is on Github and the api-key is hardcoded (you'll have to change it to show your own data).
Code is here: https://github.com/bphermansson/Emondata_Light
Screenshot:
Re: Created a simple Android app
Some progress has been made. The Api-key is no longer hard-coded, now it's editable in the app. I also created an Apk if anybody likes to test the app.
After the Api-key has been entered the app has to be restarted, this will be fixed in the next version.
Re: Created a simple Android app
Wow, nice work. Just tried to install. I get "The file type 2015-10-13-10-27-15-175null is not supported now!" error. I tried on my phone running Android 5.1 and on my tablet running android 6.0 and I get same error.
Re: Created a simple Android app
Ok... I've tested on two different phones and it works. It seems your filename is wrong, did you download the attachement "Emondata_Light-release-1.0.apk_.zip"? You have to unzip the file and allow the phone to install from unknown sources,
I might put the app on Google Play later on for easier install.
Re: Created a simple Android app
Hello,
Tried installing your app, (unzipped it first) but get this error: "There was a problem parsing the package"
Sideloading is enabled. Device is a tablet running Android 4.2.2
Re: Created a simple Android app
I've used Android 4.4.2 as lowest possible Android version, a bit stupid maybe :) I shall recompile so that it works on earlier versions too.
Re: Created a simple Android app
Hi Patrik,
Got this installed tonight on my Moto x running Android 5.1. I had to use adb sideload.
App loads ok and lets me past in my API then save. Then I exit the app and close it by swiping it away from the running apps. When I try and load it again I get an error "Unfortunately Emoncms data has stopped". The only way to recover from this is clear the app data then I'm back to the API enter screen. Is there any logfiles I can use to help you debug?
App looks promising, I look forward to using it to quickly check my feeds. useful to point to other emoncms servers other *than* emoncms.org and be able to select feeds that are shown.
Keep up the good work.
Re: Created a simple Android app
Thanks!
I tried to replicate your error with no success, but I don't remove the app from running apps after I saved the API.
I'll start modyfing the code so you don't have to reload the app after entering the API, let's see how that works. In the meantime I've compiled it for lower Android versions, the attached file works on my Sony Tipo with Android 4.0.4.
Edit: I can't attach the file, but you'll find it at Github, https://github.com/bphermansson/Emondata_Light/tree/master/app


2 Kings 25:13
And the pillars of brass that were in the house of the LORD, and the bases, and the brazen sea that was in the house of the LORD, did the Chaldees break in pieces, and carried the brass of them to Babylon.


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Aaron Bolduc
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I am interested in wireless network from two perspective, i.e., economics and geometry. More specifically, I study network economics and intelligent probabilistic network:
– Network Economics – Capacity – Coverage – Connectivity – Computation – Crowdsourcing – Cognitive Radio – Compressive Sensing – Communication Complexity – Intelligent Networks
I am also interested in developing systems including:
– Intelligent Terminals: smart phone, and mobile internet – Intelligent Sensing: inteligent transportation system – Intelligent Services: crowdsourcing, smart car, smart four-rotor aircraft – Intelligent Networks
Here are some more details of the system work.
Our spark system web is available ( spark.sjtu.edu.cn )
Our smart big data network is available ( smartbdn.sjtu.edu.cn )


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Welcome back,
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Modules


Google Earth Enterprise Documentation Home | Google Earth Enterprise client
EC bandwidth requirements
User behavior dramatically influences bandwidth requirements. However, even with a good set of log files, bandwidth usage for Google Earth is difficult to measure. When assessing how much bandwidth you need, keep in mind the following considerations:
- Google Earth servers use HTTP, which is inherently stateless. This means that it's very difficult to measure concurrent users because it's not clear when users log out of the client or when users move to another application and leave the client running.
- The Google Earth client has a local cache, so after a user has visited one area on the globe, those tiles are not re-requested from the server. Consequently, bandwidth usage is affected by the size of the local cache and how often users revisit areas.
- User traffic comes in bursts, not a steady stream. When a client flies to a new location, the new set of tiles are sent to the client, causing a spike in traffic. After the client stops in one place, traffic goes to zero. You should set bandwidth requirements high enough to accommodate spikes in usage.


Choose a Partner
| Partner | Contact | Number/Email | Kids | City | Unmet Need | Distance to Restaurant |
|---|---|---|---|---|---|---|
| Arrowood Elementary School | Megan Formie |<PHONE_NUMBER> | 25 | Calgary, AB | M T W Th F S Su | XXX Kilometers |
| Eagle Crest Academy | Ursola McGavin |<EMAIL_ADDRESS>| 40 | Toronto, ON | M T W Th F S Su | XXX Kilometers |
| Deer Valley Middle School | Vicky Inwood |<PHONE_NUMBER> | 15 | Waterloo, ON | M T W Th F S Su | XXX Kilometers |
Choose a Restaurant
| Restaurant | Contact | Number/Email | Meals | City | Distance to Program |
|---|---|---|---|---|---|
| Popeyes | Elise Atack |<EMAIL_ADDRESS>| 100 | Calgary, AB | XXX Kilometers |
| Quiznos | Benjamin Yacob |<PHONE_NUMBER> | 50 | Toronto, ON | XXX Kilometers |
| Tim Horton's | Maddie Claricoates |<PHONE_NUMBER> | 40 | Toronto, ON | XXX Kilometers |
Copyright © 2019 Feeding Canadian Kids. All rights reserved.


Template design by Andreas Viklund
I am a PhD student at Arizona State University
Contact information: mghimire@asu.edu


French man abducted in Mexico released: Foreign ministry
PARIS (Reuters) - A French man who was abducted in Mexico on Sunday has been released, the French Foreign ministry said on Monday.
According to Mexican media reports, the Frenchman was abducted with a Mexican while on a trip to the Nevado national park in Toluca.
The French ministry had provided no further details about the man, but said it was working with local authorities to find him.
The ministry said on Monday the Mexican national traveling with him was also freed.
A dozen years of gang-fuelled violence have claimed well over 200,000 lives in Mexico while murders hit record levels last year.


Websites often display content in multiple columns (like a magazine or a newspaper).
HTML Layout Elements
HTML has several semantic elements that define the different parts of a web page:
- <header> - Defines a header for a document or a section
- <nav>- Defines a set of navigation links
- <section> - Defines a section in a document
- <article> - Defines an independent, self-contained content
- <aside>- Defines content aside from the content (like a sidebar)
- <footer> -Defines a footer for a document or a section
- <details>- Defines additional details that the user can open and close on demand
- <summary> -Defines a heading for the <details>element
HTML Layout Techniques
There are four different techniques to create multicolumn layouts. Each technique has its pros and cons:
- CSS framework
- CSS float property
- CSS flexbox
- CSS grid
CSS Frameworks
If you want to create your layout fast, you can use a CSS framework, like W3.CSS or Bootstrap.
CSS Float Layout
It is common to do entire web layouts using the CSS float property. Float is easy to learn - you just need to remember how the float and clear properties work. Disadvantages: Floating elements are tied to the document flow, which may harm the flexibility. Learn more about float in our CSS Float and Clear chapter.
CSS Flexbox Layout
Use of flexbox ensures that elements behave predictably when the page layout must accommodate different screen sizes and different display devices.
CSS Grid Layout
The CSS Grid Layout Module offers a grid-based layout system, with rows and columns, making it easier to design web pages without having to use floats and positioning.


#include <QtCore/QString>
#include <QtCore/QStringList>
Go to the source code of this file.
Classes | |
| class | QtArgHelpPrinterIface |
| Interface for the help printer. More... | |
Functions | |
| bool | isUnique (const QString &name, const QStringList &list) |
| void | markUnrequiredPartOfTheName (QString &name, const QStringList &rawNames) |
| Mark unrequired part of the name with '[' and ']' characters. |
Copyright (c) 2010-2012 Igor Mironchik
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
| bool isUnique | ( | const QString & | name, | |
| const QStringList & | list | |||
| ) | [inline] |
| void markUnrequiredPartOfTheName | ( | QString & | name, | |
| const QStringList & | rawNames | |||
| ) | [inline] |
Mark unrequired part of the name with '[' and ']' characters.
| name | Name to mark unrequired part. |
| rawNames | All available names. |


Presentation
Skills
Tools
Experiences
TV8 - local TV station
Forbach (From early June to mid-August 2020)
A period of 10 weeks during which I was able to express myself on the creation of mock-ups of websites and on the creation of digital media for social networks.
Pictopia - Communication agency
Sarreguemines (2019)
Period of 4 weeks on which I was able to improve my skills on the creation of models, the modification of several websites and creation of one website. (Artylab). I was able to provide training for clients on a plugin but also on the use of WordPress (DIVI).
Training
Web and graphic design training, 3 years after high school
EiSINe - Charleville-Mézières (2020/2021)
Logical continuation of the last years, continuity of the learning of the web, ant the graphic design with adobe suit, , management of projects with agile methods tools. Realizations of dynamic models and responsive web supports, with a modern UI/UX appearance.
Web and graphic design training
Haguenau (2018/2020)
Learning of web, graphic design video and motion design with adobe suite, communication, marketing, management projects, ... Achievements responsive models and websites, concepts of UI/UX and accessibility.
Baccalaureate STI2D
Shoeneck (2016/2018)
Acknowledgement, approach of tools and knowledge computer science. Learning information chains and simple algorithms.


{% extends "base.html" %} {% block title %}It's time to estimate!{% endblock %} {% block content %}
Poker planning is a powerful tool, to make faster and more accurate estimations.
Sign in


{% extends "layout.html" %} {% block content %} Subject Courses one two Computer Science Course classes one CS 48 three Section section 4:00pm-4:50pm two three Search {% endblock %}


Argentina
Australia
Brazil
Canada
Chile
China
Egypt
England
France
Germany
India
Indonesia
Kenya
Mexico
New Zealand
South Africa
USA


{% extends "layout.html" %} {% block page_title %}Admin screen{% endblock %} {% block propositionHeader %} {{>includes/propositional_navigation_sprint_6}} {% endblock %} {% block content %} ALPHA This is a new service – your feedback will help us to improve it. Log-in and password change successful Please choose from the following options: Service Centre Link Job Centre Link Benefit Centre Link Management Information Link Admin Link {% endblock %}
ALPHA This is a new service – your feedback will help us to improve it.
Link


001/* 002 * Licensed to the Apache Software Foundation (ASF) under one 003 * or more contributor license agreements. See the NOTICE file 004 * distributed with this work for additional information 005 * regarding copyright ownership. The ASF licenses this file 006 * to you under the Apache License, Version 2.0 (the 007 * "License"); you may not use this file except in compliance 008 * with the License. You may obtain a copy of the License at 009 * 010 * http://www.apache.org/licenses/LICENSE-2.0 011 * 012 * Unless required by applicable law or agreed to in writing, 013 * software distributed under the License is distributed on an 014 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY 015 * KIND, either express or implied. See the License for the 016 * specific language governing permissions and limitations 017 * under the License. 018 */ 019package org.apache.shiro.mgt; 020 021import org.apache.shiro.authc.AuthenticationException; 022import org.apache.shiro.authc.AuthenticationToken; 023import org.apache.shiro.authc.Authenticator; 024import org.apache.shiro.authz.Authorizer; 025import org.apache.shiro.session.mgt.SessionManager; 026import org.apache.shiro.subject.Subject; 027import org.apache.shiro.subject.SubjectContext; 028 029 030/** 031 * A {@code SecurityManager} executes all security operations for all Subjects (aka users) across a 032 * single application. 033 *
 034 * The interface itself primarily exists as a convenience - it extends the {@link org.apache.shiro.authc.Authenticator}, 035 * {@link Authorizer}, and {@link SessionManager} interfaces, thereby consolidating 036 * these behaviors into a single point of reference. For most Shiro usages, this simplifies configuration and 037 * tends to be a more convenient approach than referencing {@code Authenticator}, {@code Authorizer}, and 038 * {@code SessionManager} instances separately; instead one only needs to interact with a single 039 * {@code SecurityManager} instance. 040 *
 041 * In addition to the above three interfaces, this interface provides a number of methods supporting 042 * {@link Subject} behavior. A {@link org.apache.shiro.subject.Subject Subject} executes 043 * authentication, authorization, and session operations for a single user, and as such can only be 044 * managed by {@code A SecurityManager} which is aware of all three functions. The three parent interfaces on the 045 * other hand do not 'know' about {@code Subject}s to ensure a clean separation of concerns. 046 *
 047 * Usage Note: In actuality the large majority of application programmers won't interact with a SecurityManager 048 * very often, if at all. Most application programmers only care about security operations for the currently 049 * executing user, usually attained by calling 050 * {@link org.apache.shiro.SecurityUtils#getSubject() SecurityUtils.getSubject()}. 051 *
 052 * Framework developers on the other hand might find working with an actual SecurityManager useful. 053 * 054 * @see org.apache.shiro.mgt.DefaultSecurityManager 055 * @since 0.2 056 */ 057public interface SecurityManager extends Authenticator, Authorizer, SessionManager { 058 059 /** 060 * Logs in the specified Subject using the given {@code authenticationToken}, returning an updated Subject 061 * instance reflecting the authenticated state if successful or throwing {@code AuthenticationException} if it is 062 * not. 063 *
 064 * Note that most application developers should probably not call this method directly unless they have a good 065 * reason for doing so. The preferred way to log in a Subject is to call 066 * subject.{@link org.apache.shiro.subject.Subject#login login(authenticationToken)} (usually after 067 * acquiring the Subject by calling {@link org.apache.shiro.SecurityUtils#getSubject() SecurityUtils.getSubject()}). 068 *
 069 * Framework developers on the other hand might find calling this method directly useful in certain cases. 070 * 071 * @param subject the subject against which the authentication attempt will occur 072 * @param authenticationToken the token representing the Subject's principal(s) and credential(s) 073 * @return the subject instance reflecting the authenticated state after a successful attempt 074 * @throws AuthenticationException if the login attempt failed. 075 * @since 1.0 076 */ 077 Subject login(Subject subject, AuthenticationToken authenticationToken) throws AuthenticationException; 078 079 /** 080 * Logs out the specified Subject from the system. 081 *
 082 * Note that most application developers should not call this method unless they have a good reason for doing 083 * so. The preferred way to logout a Subject is to call 084 * {@link org.apache.shiro.subject.Subject#logout Subject.logout()}, not the 085 * {@code SecurityManager} directly. 086 *
 087 * Framework developers on the other hand might find calling this method directly useful in certain cases. 088 * 089 * @param subject the subject to log out. 090 * @since 1.0 091 */ 092 void logout(Subject subject); 093 094 /** 095 * Creates a {@code Subject} instance reflecting the specified contextual data. 096 *
 097 * The context can be anything needed by this {@code SecurityManager} to construct a {@code Subject} instance. 098 * Most Shiro end-users will never call this method - it exists primarily for 099 * framework development and to support any underlying custom {@link SubjectFactory SubjectFactory} implementations 100 * that may be used by the {@code SecurityManager}. 101 *
Usage
 102 * After calling this method, the returned instance is not bound to the application for further use. 103 * Callers are expected to know that {@code Subject} instances have local scope only and any 104 * other further use beyond the calling method must be managed explicitly. 105 * 106 * @param context any data needed to direct how the Subject should be constructed. 107 * @return the {@code Subject} instance reflecting the specified initialization data. 108 * @see SubjectFactory#createSubject(SubjectContext) 109 * @see Subject.Builder 110 * @since 1.0 111 */ 112 Subject createSubject(SubjectContext context); 113 114}


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OneDrive is the one place for your files that you can access from virtually anywhere. Read our blog to hear the latest news and updates!
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Visual studio, VS, Express, editor, vs editor, visual studio editor, vsx, extensibility, vs extensibility, project, vs project, visual studio ...
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office.microsoft.com
This article describes some important limitations that you might need to know for different SharePoint Online plans in Office 365. For example, it ...
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The Official Microsoft ASP.NET Site
www.asp.net
ASP.NET is a free web framework for building great Web sites and Web applications using HTML, CSS and JavaScript.


Across: 1. Transfers carbon trade out to Bob (6) 4. Blimey! Lovely decorative feature! (7) 9. Trim pagan bird (9) 10. Dyke-builder left behind lights (5) 11. Peter comes back for Flynn (5) 12. Sea flows back around Rupert through holes (9) 13. View about the French trickery (7) 15.  Second time change of date has been declared (6) 19. Illegal smoking jackets? (7) 22. Gary raved about cemetery! (9) 24. Tom’s dad beat cockney woman (5) 26. Subject suggested by best one hundred (5) 27. Stubborn saint to be converted (9) 28. Now, put some clothes on to make speech! (7) 29. Youngster is caught by Tyne flowing round Gateshead (6) Down: 1. Bring down in order to keep record (7) 2. One sinks into cleaner seat (5) 3. The French politician’s superficial glow (9) 4. Happy with what lies inside (7) 5. Settle in Australian thoroughfare? (5) 6. Fashionable dealer accepts Queen’s conclusion (9) 7. Silent characters join up (6) 8. Dumbfounded hag sat about (6) 14. Spoke harshly after forester is wrongfully apprehended (9) 16. Represent Filey ex-MP (9) 18. Churchmen dance so badly! (7) 19. Vegetable cooked by constable? (6) 20. Slippery customer enters, holding Penny (7) 21. She throws hat into cooker! (6) 23. Host caught me out in eastern capitals (5) 25. Man holds river shellfish (5)


2010 NIST Speaker Recognition Evaluation Test Set, Linguistic Data Consortium (LDC) catalog number LDC2017S06 and ISBN 1-58563-795-5, was developed by LDC and NIST (National Institute of Standards and Technology). It contains 2,255 hours of American English telephone speech and speech recorded over a microphone channel involving an interview scenario used as test data in the NIST-sponsored 2010 Speaker Recognition Evaluation (SRE).
The ongoing series of SRE yearly evaluations conducted by NIST are intended to be of interest to researchers working on the general problem of text independent speaker recognition. To this end the evaluations are designed to be simple, to focus on core technology issues, to be fully supported and to be accessible to those wishing to participate.
The 2010 evaluation was similar to the 2008 evaluation by including in the training and test conditions for the core test not only conversational telephone speech (CTS) recorded over ordinary telephone channels, but also CTS and conversational interview speech recorded over a room microphone channel. Unlike prior evaluations, some of the conversational telephone style speech was collected in a manner to produce particularly high, or particularly low, vocal effort on the part of the speaker of interest.
The speech recordings in this release were collected in 2009 and 2010 by LDC at its Human Subjects Collection facility in Philadelphia. This collection was part of the Mixer 6 project, which was designed to support the development of robust speaker recognition technology by providing carefully collected and audited speech from a large pool of speakers recorded simultaneously across numerous microphones.
The telephone speech segments include two-channel excerpts of approximately 10 seconds and 5 minutes. There are also summed-channel excerpts in the range of 5 minutes. The microphone excerpts are 3-15 minutes in duration. As in prior evaluations, intervals of silence were not removed. The data included in this release is 8 bit ulaw with a sample rate of 8000.
In addition to evaluation data, this package also consists of answer keys, trial and train files, development data and evaluation documentation.
Please see file.tbl for a complete file list as well as checksums for this publication.
Additional information, updates, bug fixes may be available in the LDC catalog entry for this corpus at LDC2017S06.
Portions © 2009, 2010, 2017 Trustees of the University of Pennsylvania


and his Magic Robotic Nonsense Generator
What is your name?
Enter a number
Compute
Try Another Number


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 99
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 99
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
# print( str(image_width) + "X" + str(image_height) )
# print( image_channels )
# print( z_dim )
# print( "\n" )
realInputs = tf.placeholder( tf.float32, [ None, image_width, image_height, image_channels ] , name="real_inputs" )
generatedInputs = tf.placeholder( tf.float32, [ None, z_dim ], name="z_inputs")
learningRate = tf.placeholder( dtype=tf.float32, name="learning_rate" )
# print( "\n" )
# print( realInputs )
# print( generatedInputs )
# print( learningRate )
# print( "\n" )
return realInputs, generatedInputs, learningRate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
# print( images )
# print( reuse )
# print( "\n" )
with tf.variable_scope( 'discriminator', reuse=reuse ):
alpha = 0.03
# Input layer
# 28x28x3
conv1 = tf.layers.conv2d( images, 56, 5, strides=2, padding='same' )
max1 = tf.maximum( alpha * conv1, conv1 )
# 16x16x56
conv2 = tf.layers.conv2d( max1, 112, 5, strides=2, padding='same' )
conv2Batch = tf.layers.batch_normalization( conv2, training=True )
max2 = tf.maximum( alpha * conv2Batch, conv2Batch )
# 8x8x112
conv3 = tf.layers.conv2d( max2, 224, 5, strides=2, padding='same' )
conv3Batch = tf.layers.batch_normalization( conv3, training=True )
max3 = tf.maximum( alpha * conv3Batch, conv3Batch )
# 4x4x224
Flatten = tf.reshape( max3, ( -1, 4*4*224 ) )
discriminatorLogits = tf.layers.dense( Flatten, 1 )
discriminatorOutput = tf.sigmoid( discriminatorLogits )
# print( discriminatorLogits )
# print( discriminatorOutput )
# print( "\n" )
return discriminatorOutput, discriminatorLogits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
# print( z )
# print( out_channel_dim )
# print( "\n" )
with tf.variable_scope( 'generator', reuse=not is_train ):
alpha = 0.03
# Fully connected Layer
# Convolution Layer
# 7x7x448
conv1 = tf.layers.dense( z, 7*7*448 )
# Reshape
conv1Reshape = tf.reshape( conv1, ( -1, 7, 7, 448 ))
conv1Batch = tf.layers.batch_normalization( conv1Reshape, training=is_train )
max1 = tf.maximum( alpha * conv1Batch, conv1Batch )
# 14x14x224
conv2Reshape = tf.layers.conv2d_transpose( max1, 224, 5, strides=2, padding='same')
conv2Batch = tf.layers.batch_normalization( conv2Reshape, training=is_train)
max2 = tf.maximum( alpha * conv2Batch, conv2Batch)
# # 28x28x112
# conv3Reshape = tf.layers.conv2d_transpose( max2, 112, out_channel_dim, strides=2, padding='same')
# conv3Batch = tf.layers.batch_normalization( conv3Reshape, training=is_train)
# max3 = tf.maximum( alpha * conv3Batch, conv3Batch)
# Logits
generatorLogits = tf.layers.conv2d_transpose(max2, out_channel_dim, 5, strides=2, padding='same')
# 28x28x3
generatorOutput = tf.tanh( generatorLogits )
# print( generatorLogits )
# print( generatorOutput )
# print( "\n" )
return generatorOutput
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
# print( input_real )
# print( input_z )
# print( out_channel_dim )
# print( "\n" )
# G and D Models
generatorModel = generator( input_z, out_channel_dim, is_train=True )
discriminatorRealModel, discriminatorRealLogits = discriminator( input_real )
discriminatorFakeModel, discriminatorFakeLogits = discriminator( generatorModel, reuse=True)
# G and D Losses
discriminatorRealLoss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits( logits=discriminatorRealLogits,
labels=tf.ones_like( discriminatorRealModel ) ) )
discriminatorFeakLoss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits( logits=discriminatorFakeLogits,
labels=tf.zeros_like( discriminatorFakeModel ) ) )
generatorLoss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits( logits=discriminatorFakeLogits,
labels=tf.ones_like( discriminatorFakeModel ) ) )
discriminatorLoss = discriminatorRealLoss + discriminatorFeakLoss
# print( generatorLoss )
# print( discriminatorLoss )
# print( "\n" )
return discriminatorLoss, generatorLoss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
# print( d_loss )
# print( g_loss )
# print( learning_rate )
# print( beta1 )
# print( "\n" )
# Get all variables
trainableVariables = tf.trainable_variables()
discriminatorVariables = [variable for variable in trainableVariables if variable.name.startswith('discriminator') ]
generatorVariables = [variable for variable in trainableVariables if variable.name.startswith('generator') ]
# Optimizers
with tf.control_dependencies( tf.get_collection( tf.GraphKeys.UPDATE_OPS ) ):
discriminatorTrainOperation = tf.train.AdamOptimizer( learning_rate, beta1=beta1 ).minimize( d_loss, var_list=discriminatorVariables )
generatorTrainOperation = tf.train.AdamOptimizer( learning_rate, beta1=beta1 ).minimize( g_loss, var_list=generatorVariables )
return discriminatorTrainOperation, generatorTrainOperation
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
import pickle as pkl
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
# print( epoch_count )
# print( batch_size )
# print( z_dim )
# print( learning_rate )
# print( beta1 )
# print( get_batches )
# print( data_shape )
# print( data_image_mode )
# print( "\n" )
# Model Inputs
realInputs, generatedInputs, learningRate = model_inputs( data_shape[1], data_shape[2], data_shape[3], z_dim )
# Model Loss
discriminatorLoss, generatorLoss = model_loss( realInputs, generatedInputs, data_shape[3] )
# Model Optimizers
discriminatorOptimazer, generatorOptimazer = model_opt( discriminatorLoss, generatorLoss, learningRate, beta1)
# print( discriminatorOptimazer )
# print( generatorOptimazer )
# print( "\n" )
steps = 0
with tf.Session() as sess:
sess.run( tf.global_variables_initializer() )
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
image_input = 1 * batch_images
steps+=1
# Sample Random Noise
sampleBatch = np.random.uniform( -1, 1, size=( batch_size, z_dim ) )
# Optimizers
_ = sess.run(discriminatorOptimazer, feed_dict={ realInputs: image_input, generatedInputs: sampleBatch, learningRate: learning_rate})
#_ = sess.run(generatorOptimazer, feed_dict={ realInputs: image_input, generatedInputs: sampleBatch, learningRate: learning_rate})
_ = sess.run(generatorOptimazer, feed_dict={ realInputs: image_input, generatedInputs: sampleBatch, learningRate: learning_rate})
if steps % 2 == 0:
# get the losses and print them out
discriminatorTrainLoss = sess.run( discriminatorLoss, { generatedInputs: sampleBatch, realInputs: batch_images, learningRate: learning_rate })
generatorTrainLoss = generatorLoss.eval( { generatedInputs: sampleBatch } )
print("Epoch {}/{}...".format( epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(discriminatorTrainLoss),
"Generator Loss: {:.4f}".format(generatorTrainLoss))
if steps == 1 or steps % 25 == 0:
n_images = 9
show_generator_output(sess, n_images, generatedInputs, data_shape[3], data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 128
z_dim = 128
learning_rate = 0.0003
beta1 = 0.3
# batch_size = 128
# z_dim = 256
# learning_rate = 0.0003
# beta1 = 0.7
# batch_size = 128
# z_dim = 128
# learning_rate = 0.0003
# beta1 = 0.7
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 128
z_dim = 128
learning_rate = 0.0003
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Todays blog post is less technical than the previous ones. I would like to talk about some events that happened in the last time.
I am a registered Apple developer since November 2013. Since then I collected a lot of experiences, built a lot of apps and gained a ton of knowledge. But every product I built was for someone else, no matter if it was a company or a person. Even the first app I built (the iOS app for my university HTW Dresden) was not published via my personal account but an enterprise account of the university.
Do not get me wrong, there is nothing wrong with building apps for others and I am very proud of every app I built or contributed to, especially the ones that are used by thousands or even millions of people. But over the years I payed nearly 400€ just for being registered at Apples developer program without using the service like it should be used: for submitting apps.
Last week I had a crazy idea, I wondered what Apple thinks about Get Schwifty. They already accepted versions of it for beta testing via Testflight before (with one exception because I used the Swift logo as app icon) but that does not mean they would also approve it for the App Store. So I added a must-have feature: the ability to stop running scripts. I changed the info text from ‘thanks for testing Get Schwifty’ to ‘thanks for using Get Schwifty’. I filled out the necessary information in iTunesConnect (since I have no dedicated website for the app I named this blog instead) and clicked on Submit to Review.
Just a day later Apple really approved my app, so I am thrilled to announce that ‘Get Schwifty’ is now available on the Apple App Store. I am also really glad to work on a new feature which was initiated by a pull request on Github.
I am also very excited to announce another project which I had planned together with Dominik. We had the idea back in October last year. We wanted to create a German podcast about iOS, macOS, Objective-C and Swift-development. We have a lot of ideas and topics for future episodes so we just started recording the first one which is already available in our RSS-Feed.
The first episode is about WWDC about which I also talked in a previous blog post before. I am really excited where this journey will bring us. The next episode is already planned and we will have a great guest in the show. The episode will hit your podcast player (I recommend Overcast) on 24th of April if you subscribed to our feed. 😬
Thanks for reading this far! The next blog post will have a technical topic again, Typscript! Stay tuned and whish me luck for this week’s friday which is the day Apple announces the scholarship winners for this years WWDC.


Frequently Asked Questions
We thought you might be having some doubts
Q.1How do I install FlatCharge?
Installing flatcharge is as easy as blowing a whistle. You just need to have inverter lines in your home or workplace and then all you need to do is call a tehnician to drill holes in the wall to hang the FlatCharge and then plug it and it's ready.
Q.2How do I start using Flatcharge?
After you have successfully completed the installation of Flatcharge all you need to do is power it on and setup your smartphone application. You see how easy it is.
Q.3How do I setup the Flatcharge with my smartphone?
You have to download the smartphone application from Google Playstore or Apple Appstore and then just follow the instructions after installing the app.
Q.4What is Flatcharge?
Flatcharge is a smart power backup system for your house and office which is very advanced and cost effective.
Q.5Do we get any warranty for Flatcharge?
Yes, you get 8 years of warranty with the Flatcharge.
Q.6How the Flatcharge is better than the other power backup systems?
Flatcharge is very different than the traditional batteries and inverters. The traditional battery have less energy density and short lifespan whereas the Flatcharge works for atleast 8 years with very advance features.
Q.7What is EPD in FlatCharge?
EPD stands for Emergency POwer Delivery. When you run out of juice in Flatcharge and you are in utter need of power delivery then you can enable the Emergency Power Delivery option on your smartphone to get the limited power in the hour of need. Notice that you can only use this feature for 5 times in a month.
Q.8Do I need to connect any inverter with the Flatcharge?
No, Flatcharge comes with a inbuilt advance inverter which is power efficient, silent and small in size which is perfectly compatible with the Flatcharge.
Q.9What features does the smartphone app has?
The smartphone app had many features like
Estimated Power Usage
Estimated Time to Discharge
Estimated Time to Charge
Battery Health, etc
Q.10Do I need wifi to connect my smartphone to Flatcharge?
No, the Flatcharge connect to your phone using Bluetooth.
Q.11What platforms are compatible with Flatcharge Smartphone app?
The smartphone app is available for both the iOS and the Android OS.
Q.12What happens when Flatcharge is not supplying power?
When Flatcharge is not supplying power then either it must be discharged or the wiring is not done properly.
Q.13Is Flatcharge compatible with Solar Panels, if yes how?
Yes, the Flatcharge is totally compatible with the Solar Panels. You need to buy the Solar Panels on your own and the smart solar module which enables the Flatcharge to properly sync with the Solar Panels.
Q.14Can the Flatcharge provides power supply and charge at the same time?
Yes, Flatcharge can charge from Grid or Solar Energy and at the same time provide power supply to you.
Q.15How does the Flatcharge let me have control of use of Energy?
You have all the control through the smartphone app. You can anytime turn the charging, power delivery on or off with just a touch on your phone.
Q.16Can I purchase only the battery of Flatcharge without inverter and connect it to my previously owned inverter?
No, the Flatcharge is different than the other traditional inverter and batteries and thus it needs different inverter which is specially designed to be used with it.
Q.17What kind of batteries for energy storage does the Flatcharge uses? Are they safe?
The Flatcharge uses the Lithium -ion batteries inside it which are properly attached, insulated and have a cooling system inside. There is very short chance of the short circuit inside it which is about one in a million.


Education
Pathways in Technology Early College High School, Brooklyn, NY
A high school program where students learned the traditional core subjects,
but also received an education in computer science and completed two years of college
work with the college of New York City College of Technology.
New York City College of Technology, Brooklyn, NY
Major: Computer Information Systems
Graduated June 2017
Degree: Associates in Applied Science
Queens College, Queens, NY
Major: Computer Science
September 2017- Present
Degree: Bachelors Degree in Science
Work Experience
IBM, New York, NY
Summer 2014
Corporate Citizenship Intern
In this internship, I collaborated with a team to develop a pitch for a gaming company called Rockstar Games.
- Continued to develop my HTML and CSS skills
- Learned more about user interface for web and mobile app development
- Enhanced my presentation skills
- Created PowerPoints using both Microsoft and Prezi
- Practiced scrum and agile working styles
PS.12, New York
January-June 2016
Tutor
- I worked with students from ages 7-11 on STEM projects that included science fair projects and special experiments.
Skills and Expertise
- Intermediate Experience in C++
- Fluent in HTML and CSS, Bootstrap
- Intermediate with JavaScript, Python, SQL
- Experience with Microsoft Office Suite: PowerPoint, Word, Excel
- Experience with Adobe Photoshop
Special Projects
PTECH Hackathon 2014
Worked for 24 hours with a team of three to code a
Role Playing Game (RPG) using coding language RUBY on a game
design platform called RPG Maker VX Pro.
Hackathon 2015 @ Microsoft
Worked for ten with a team of ten to make a game using the JavaScript library Phazer.
MLH Hackathon 2017 @ Queens College
Worked in a group of 4 to make a food app.
Also did multiple workshops.
Hacking with Amazon Alexa
Created an app in a testing environment for Amazon Alexa.


"Translating and analyzing big data using charts, graphics, and images is becoming more and more necessary for decision-making."
Association of Public Data Users
A numerical simulation is conducted to study the numerical behavior of these Interest Rates. This computer simulation is used to explore and gain new insights into the impact of the temporal decision that determined them and to estimate and highlight specific interconnection levels between them.
A numerical simulation is conducted to study the numerical behavior of these Interest Rates. This computer simulation is used to explore and gain new insights into the impact of the temporal decision that determined them and to estimate and highlight specific interconnection levels between them.
Data Science and Analytics: Modelling Volatility in Major World Stock Markets
Mining World Exchanges Market Data-sets using Mathematical Modelling & Numerical Analysis with Machine Learning Methods to coupled systems of partial integro-differential equations and jump-diffusion processes:
The insight analysis uses R programming language to process the results ( R is a software environment for statistical computing
and graphics supported by the R Foundation for Statistical Computing ). The interactive data visualizations in web browsers uses the NVD3 data visualization library.
Author scientific profile:
Statistics and Applied Mathematics for Data Analytics, Identify opportunities to apply Mathematical Statistics, Numerical Methods, Machine Learning and Pattern Recognition to investigate and implement solutions to the field of Data Content Analytics. Data prediction via computational methods to predict from massive amounts of data (Big Data Content). These methods included clustering, regression, survival analysis, neural network, classification , ranking, deep discrepancy learning .


func BroadcastReceive(broadcast Broadcast, addr string, responses chan common.Status, errs chan error)
broadReceive waits until it receives the response from broadcast stream
func BroadcastSend(broadcast Broadcast, addr string, envelope *common.Envelope) error
broadcastSend sends transaction envelope to orderer service
func BroadcastWaitForResponse(responses chan common.Status, errs chan error) (common.Status, error)
broadcastWaitForResponse reads from response and errs chans until responses chan is closed
func CreateDeliverEnvelope(channelId string, creator []byte, signer SignerIdentity, cert *tls.Certificate) (*common.Envelope, error)
create a signed envelope with SeekPosition_Newest for block
func CreateEnvelope(data []byte, header *common.Header, signer SignerIdentity) (*common.Envelope, error)
CreateEnvelope creates a common.Envelope with given tx bytes, header, and Signer
func CreateHeader(txType common.HeaderType, channelId string, creator []byte, tlsCertHash []byte) (string, *common.Header, error)
CreateHeader creates common.Header for a token transaction tlsCertHash is for client TLS cert, only applicable when ClientAuthRequired is true
func DeliverReceive(df DeliverFiltered, address string, txid string, eventCh chan TxEvent) error
func DeliverSend(df DeliverFiltered, address string, envelope *common.Envelope) error
func DeliverWaitForResponse(ctx context.Context, eventCh chan TxEvent, txid string) (bool, error)
DeliverWaitForResponse waits for either eventChan has value (i.e., response has been received) or ctx is timed out This function assumes that the eventCh is only for the specified txid If an eventCh is shared by multiple transactions, a loop should be used to listen to events from multiple transactions
func ValidateClientConfig(config *ClientConfig) error
Broadcast defines the interface that abstracts grpc calls to broadcast transactions to orderer
type Broadcast interface { Send(m *common.Envelope) error Recv() (*ab.BroadcastResponse, error) CloseSend() error }
Client represents the client struct that calls Prover and TxSubmitter
type Client struct { SigningIdentity tk.SigningIdentity Prover Prover TxSubmitter FabricTxSubmitter }
func (c *Client) Issue(tokensToIssue []*token.TokenToIssue) ([]byte, error)
func (c *Client) Transfer(tokenIDs [][]byte, shares []*token.RecipientTransferShare) ([]byte, error)
Transfer is the function that the client calls to transfer his tokens. Transfer takes as parameter an array of token.RecipientTransferShare that identifies who receives the tokens and describes how the tokens are distributed.
ClientConfig will be updated after the CR for token client config is merged, where the config data will be populated based on a config file.
type ClientConfig struct { ChannelId string MspDir string MspId string TlsEnabled bool OrdererCfg ConnectionConfig CommitPeerCfg ConnectionConfig ProverPeerCfg ConnectionConfig }
ConnectionConfig contains data required to establish grpc connection to a peer or orderer
type ConnectionConfig struct { Address string TlsRootCertFile string ServerNameOverride string }
DeliverClient defines the interface to create a DeliverFiltered client
type DeliverClient interface { // NewDeliverFilterd returns a DeliverFiltered NewDeliverFiltered(ctx context.Context, opts ...grpc.CallOption) (DeliverFiltered, error) // Certificate returns tls certificate for the deliver client to commit peer Certificate() *tls.Certificate }
func NewDeliverClient(config *ClientConfig) (DeliverClient, error)
DeliverFiltered defines the interface that abstracts deliver filtered grpc calls to commit peer
type DeliverFiltered interface { Send(*common.Envelope) error Recv() (*pb.DeliverResponse, error) CloseSend() error }
type FabricTxSubmitter interface { // Submit allows the client to build and submit a fabric transaction for fabtoken that has as // payload a serialized tx; it takes as input an array of bytes // and returns an error indicating the success or the failure of the tx submission and an error // explaining why. Submit(tx []byte) error }
OrdererClient defines the interface to create a Broadcast
type OrdererClient interface { // NewBroadcast returns a Broadcast NewBroadcast(ctx context.Context, opts ...grpc.CallOption) (Broadcast, error) // Certificate returns tls certificate for the orderer client Certificate() *tls.Certificate }
func NewOrdererClient(config *ClientConfig) (OrdererClient, error)
type Prover interface { // RequestImport allows the client to submit an issue request to a prover peer service; // the function takes as parameters tokensToIssue and the signing identity of the client; // it returns a response in bytes and an error message in the case the request fails. // The response corresponds to a serialized TokenTransaction protobuf message. RequestImport(tokensToIssue []*token.TokenToIssue, signingIdentity tk.SigningIdentity) ([]byte, error) // RequestTransfer allows the client to submit a transfer request to a prover peer service; // the function takes as parameters a fabtoken application credential, the identifiers of the tokens // to be transfererd and the shares describing how they are going to be distributed // among recipients; it returns a response in bytes and an error message in the case the // request fails RequestTransfer(tokenIDs [][]byte, shares []*token.RecipientTransferShare, signingIdentity tk.SigningIdentity) ([]byte, error) }
type ProverPeer struct { ChannelID string ProverClient token.ProverClient RandomnessReader io.Reader Time TimeFunc }
func (prover *ProverPeer) CreateSignedCommand(payload interface{}, signingIdentity tk.SigningIdentity) (*token.SignedCommand, error)
func (prover *ProverPeer) RequestImport(tokensToIssue []*token.TokenToIssue, signingIdentity tk.SigningIdentity) ([]byte, error)
func (prover *ProverPeer) RequestTransfer( tokenIDs [][]byte, shares []*token.RecipientTransferShare, signingIdentity tk.SigningIdentity) ([]byte, error)
type Signer interface { // Sign signs the given payload and returns a signature Sign([]byte) ([]byte, error) }
SignerIdentity signs messages and serializes its public identity to bytes
type SignerIdentity interface { Signer // Serialize returns a byte representation of this identity which is used to verify // messages signed by this SignerIdentity Serialize() ([]byte, error) }
type TimeFunc func() time.Time
TxEvent contains information for token transaction commit If application wants to be notified when a token transaction is committed, do the following: - create a event chan with size 1 or bigger, e.g. txChan := make(chan TxEvent, 1) - call client.SubmitTransactionWithChan(txBytes, txChan) - implement a function to read TxEvent from txChan so that it will be notified when transaction is committed or failed
type TxEvent struct { Txid string Committed bool CommitPeer string Err error }
type TxSubmitter struct { Config *ClientConfig Signer SignerIdentity Creator []byte OrdererClient OrdererClient DeliverClient DeliverClient }
func NewTxSubmitter(config *ClientConfig) (*TxSubmitter, error)
NewTransactionSubmitter creates a new TxSubmitter from token client config
func (s *TxSubmitter) CreateTxEnvelope(txBytes []byte) (string, *common.Envelope, error)
func (s *TxSubmitter) SubmitTransaction(txEnvelope *common.Envelope, waitTimeInSeconds int) (committed bool, txId string, err error)
SubmitTransaction submits a token transaction to fabric. It takes TokenTransaction bytes and waitTimeInSeconds as input parameters. The 'waitTimeInSeconds' indicates how long to wait for transaction commit event. If it is 0, the function will not wait for transaction to be committed. If it is greater than 0, the function will wait until timeout or transaction is committed, whichever is earlier
func (s *TxSubmitter) SubmitTransactionWithChan(txEnvelope *common.Envelope, eventCh chan TxEvent) (committed bool, txId string, err error)
SubmitTransactionWithChan submits a token transaction to fabric with an event channel. This function does not wait for transaction commit and returns as soon as the orderer client receives the response. The application will be notified on transaction completion by reading events from the eventCh. When the transaction is committed or failed, an event will be added to eventCh so that the application will be notified. If eventCh has buffer size 0 or its buffer is full, an error will be returned.


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This test ensures layout properly respects shape-inside when it is set/unset, or when shape-outside is set/unset for a value of "outside-shape". The test requires the Ahem font, and in each case the green squares should be contained by a blue outline.