Light Up the Night coming to Stettler Town and Country Museum
First annual gingerbread house contest added to event
By Jessica Jones
For the Independent
Be merry and bright at Stettler Town and Country Museum’s fifth annual Light Up the Night event kicking off on Friday, Dec. 6th.
Light Up the Night is sure to brighten up the holiday season as hundreds of lights, as well as numerous individual displays, decorate 26 buildings for a gleaming light show on site.
Karen Wahlund, Stettler Town and Country Museum manager, says local businesses and family and community service groups have set up the displays that light up about a half kilometre around the loop of the site.
Visitors are able to walk the park or ride in a wagon to check out the displays.
“This is a fantastic Christmas light display,” Wahlund said.
“It started in 2014 and has grown every year and more and more people keep coming as the word is getting out.”
This year’s event will also feature a gingerbread contest. Contestants are encouraged to drop off their gingerbread houses at the Museum before opening night on Dec. 6th, and during the night hours, visitors are able to vote on the houses. A bake sale will also kick off opening night, Wahlund mentioned.
Light Up the Night runs every Friday and Saturday in December from 6-9 p.m., with the addition of the days before Christmas, Dec. 22nd, 23rd, and 24th.
Light Up the Night is a fundraiser for the Stettler Town and Country Museum so donations are appreciated.
For more information, contact Karen at the Museum weekdays from 10 a.m.- 3 p.m. at<PHONE_NUMBER>. The Museum is located at 6502 44 Ave.


Multimodal Knowledge Alignment with Reinforcement Learning
Youngjae Yu*, Jiwan Chung*, Heeseung Yun, Jack Hessel, Jae Sung Park, Ximing Lu, Prithviraj Ammanabrolu, Rowan Zellers, Ronan Le Bras, Gunhee Kim, Yejin Choi (* equal contribution)
Arxiv, Link
PROSOCIALDIALOG:A Prosocial Backbone for Conversational Agents
Hyunwoo Kim*, Youngjae Yu*, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten Sap (* equal contribution)
Arxiv, Link
MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound
Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, Yejin Choi
In CVPR 2022, Link
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
Yanpeng Zhao, Jack Hessel, Youngjae Yu, Ximing Lu, Rowan Zellers, Yejin Choi
In NAACL 2022, Link
NeuroLogic A* esque Decoding: Constrained Text Generation with Lookahead Heuristics
Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A Smith, Yejin Choi
In NAACL 2022, Link
MERLOT: Multimodal Neural Script Knowledge Models
Rowan Zellers*, Ximing Lu*, Jack Hessel*, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, Yejin Choi (* equal contribution)
In NeurIPS 2021, Link
Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning
Sangho Lee*, Jiwan Chung*, Youngjae Yu, Gunhee Kim, Thomas Breuel, Gal Chechik, Yale Song (* equal contribution)
In ICCV 2021, Link
Pano-AVQA: Grounded Audio-Visual Question Answering on 360° Videos
Heeseung Yun, Youngjae Yu, Wonsuk Yang and Gunhee Kim
In ICCV 2021, Link
Transitional Adaptation of Pretrained Models for Visual Storytelling
Youngjae Yu*, Jiwan Chung*, Jongseok Kim, Heeseung Yun and Gunhee Kim (* equal contribution)
In CVPR 2021, Link
Self-Supervised Learning of Compressed Video Representations
Youngjae Yu, Sangho Lee, Gunhee Kim, Yale Song
In ICLR 2021, Link
Parameter Efficient Multimodal Transformers for Video Representation Learning
Sangho Lee, Youngjae Yu, Thomas Breuel, Jan Kautz, Gunhee Kim, Yale Song
In ICLR 2021, Link
Dual Compositional Learning in Interactive Image Retrieval
Jongseok Kim, Youngjae Yu, Hoeseong Kim, Gunhee Kim
In AAAI 2021, Link
Character Grounding and Re-Identification in Story of Videos and Text Descriptions
Youngjae Yu, Jongseok Kim, Heeseung Yun, Jiwan Chung, Gunhee Kim
In ECCV 2020 (Spotlight), Link
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
Youngjae Yu, Jongseok Kim, Gunhee Kim
In ECCV 2018, Link
A Memory Network Approach for Story-based Temporal Summarization of 360° Videos
Sangho Lee, Jinyoung Sung Youngjae Yu, Gunhee Kim
In CVPR 2018, Link
A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360° Video
Youngjae Yu,Sangho Lee, Joonil Na, Jaeyun Kang, Gunhee Kim
In AAAI 2018, Link
Supervising Neural Attention Models for Video Captioning by Human Gaze Data
Youngjae Yu,Jongwook Choi, Yeonhwa Kim, Kyoung Yu, Sang-Hun Lee, Gunhee Kim
In CVPR 2017, Link
End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering
Youngjae Yu,Hyungjin Ko, Jongwook Choi, Gunhee Kim
In CVPR 2017 (Spotlight), Link
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, Gunhee Kim
In CVPR 2017 (Spotlight), Link
TimesVector: A Vectorized Clustering Approach to the Analysis of Time Series Transcriptome Data from Multiple Phenotypes
Inuk Jung, Kyuri Jo, Hyejin Kang, Hongryul Ahn, Youngjae Yu, Sun Kim
In GIW 2016
Video Question Answering with Spatio-Temporal Reasoning
Yunseok Jang, Yale Song, Chris Dongju Kim, Youngjae Yu, Youngjin Kim, Gunhee Kim
In IJCV 2019, Link
TimesVector: A Vectorized Clustering Approach to the Analysis of Time Series Transcriptome Data from Multiple Phenotypes
Inuk Jung, Kyuri Jo, Hyejin Kang, Hongryul Ahn, Youngjae Yu, Sun Kim
In Bioinformatics 2017, Link
Cycled Compositional Learning between Images and Text
Jongseok Kim *, Youngjae Yu * , Seunghwan Lee, Gunhee Kim
In CVPR 2020, Third Workshop on Computer Vision for Fashion, Art and Design , Link
Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context
Hankyol Lee, Youngjae Yu, Gunhee Kim
In ACL 2020, Second Workshop on Figurative Language Processing, Link
CurlingNet: Compositional Learning between Images and Text for Fashion IQ Data
Youngjae Yu, Seunghwan Lee, Yuncheol Choi, Gunhee Kim
In ICCV 2019, Second Workshop on Computer Vision for Fashion, Art and Design , Link
Encoding Video and Label Priors for Multi-label Video Classification on YouTube-8M dataset
Seil Na, Youngjae Yu, Sangho Lee, Jisung Kim, Gunhee Kim
In CVPR 2017, YouTube-8M Large-Scale Video Understanding, Link
Video Captioning and Retrieval Models with Semantic Attention
Youngjae Yu,Hyungjin Ko, Jongwook Choi, Gunhee Kim
In ECCV 2016, LSMDC & VisStory, Link
Correlation Based Feature Selection and Pattern Clustering Method for Time
Series Gene Expression Data of Drought Stressed Rice
Youngjae Yu, Sun Kim
Young Investigator, (Advisor : Yejin Choi)
Research Scientist
Graduate Student, (Advisor : Gunhee Kim)
2 year Compulsory Military Service. Served as a sergeant in R.O.K Marine Corp
Bachelor studies in Dept of Computer Science & Engineering, Seoul Nation University(SNU) @ Korea


(Social Sciences)
| Ranking | University | Number of undergraduate alumni who received a Ph.D. degree (2003-2012) | Total of students that graduated with bachelor degree (1998-2007) | Percentage of undergraduate alumni that received a Ph.D. degree |
|---|---|---|---|---|
| 1 | Swarthmore College | 134 | 3577 | 3.75% |
| 2 | Reed College | 87 | 2829 | 3.08% |
| 3 | Harvard University | 482 | 17956 | 2.68% |
| 4 | University of Chicago | 258 | 10002 | 2.58% |
| 5 | Grinnell College | 85 | 3407 | 2.49% |
| 6 | Bryn Mawr College | 74 | 3035 | 2.44% |
| 7 | Amherst College | 90 | 4207 | 2.14% |
| 8 | Macalester College | 89 | 4189 | 2.12% |
| 9 | Oberlin College | 139 | 6895 | 2.02% |
| 10 | Pomona College | 76 | 3783 | 2.01% |
| 11 | Carleton College | 92 | 4621 | 1.99% |
| 12 | Wesleyan University (CT) | 144 | 7278 | 1.98% |
| 13 | Williams College | 102 | 5188 | 1.97% |
| 14 | Princeton University | 212 | 11267 | 1.88% |
| 15 | Yale University | 245 | 13329 | 1.84% |
| 16 | St John's College (MD) | 17 | 1004 | 1.69% |
| 17 | Haverford College | 47 | 2907 | 1.62% |
| 18 | Vassar College | 97 | 6032 | 1.61% |
| 19 | Brown University | 242 | 15123 | 1.60% |
| 20 | Stanford University | 270 | 17242 | 1.57% |
| 21 | Wellesley College | 93 | 5980 | 1.56% |
| 22 | Hampshire College | 39 | 2623 | 1.49% |
| 23 | Dartmouth College | 157 | 10626 | 1.48% |
| 24 | Beloit College | 39 | 2729 | 1.43% |
| 25 | Bard College | 38 | 2862 | 1.33% |


--- layout: post title: flight date: type: post published: false status: draft categories: [] tags: [] meta: _edit_last: '2' _publicize_twitter_user: "@MommyKnowsJen" author: login: jen email<EMAIL_ADDRESS>display_name: Jennifer Elam first_name: Jen last_name: Elam ---
OH MY! Flight trampoline park is da bomb! We snagged their groupon and hit Open jump over New Years and it was such a hi! Bounce until you can't move the kids totally blew off steam
My mom use to tell me that there were 2 kinds of people in this world - the movers and the shakers - Ok maybe it was the movers and the shakers and the non-movers but I feel like once you hit 35 - there really should only be one type of person and that's a mover! So let's go with that.
You want that body!  And it doesn't matter where or how just get up! You like the gym so you can surround yourself with hard body's! GO! You like to be outside and run for the moment! Do IT!
Today, we tried out Flight Trampoline. It's a wall to wall warehouse where you can jump until you're weak in the knees! I jumped my heart out!
Hope you found your move today!


BUS CHECK-IN IS DONE! |
YOU HAVE SUCCESFULLY REGISTERED IN THE {{ bus|upper }} BUS. PLEASE ARRIVE THERE 30 MINUTES BEFORE TIME OF DEPARTURE |
| YOUR NAME: | {{ confirmation.payment.name|upper }} {{ confirmation.payment.last_name|upper }} |
| YOUR BUS COLOR: | {{ bus|upper }} |
| CONFIRMATION NUMBER: | {{ confirmation.code }} |
| DEPARTURE DATE: | {{ confirmation.payment.trip.date|upper }} at 22:00. |
| TRIP: | {{ confirmation.payment.trip.name|upper }} |


Open Source Projects
Master-Portfolio
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LottoReum is decenterized ใบ้เลขเด็ด Platform. By using the power of Arduino, Rasbery Pi, Etheruem and Web3JS.


Wayward_Pines
Imagine the perfect American town... beautiful homes, manicured lawns, children playing safely in the streets. Now imagine never being able to leave. You have no communication with the outside world. You think you're going insane. You must be in Wayward Pines.


--- title: Prokaryotic Genome GC Content author: John Christian Gaby date: '2020-09-15' slug: prokaryotic-genome-gc-content categories: ["genome calculations"] tags: ["genomics","GC%","GC content"] subtitle: 'Visualizing the distribution of prokaryotic genome GC content within phyla.' summary: 'In this blogpost, I explore the range of genome GC contents using a data table of 11,710 refseq prokaryotic genomes.' authors: ["John Christian Gaby"] lastmod: '2020-09-15T22:53:36+02:00' featured: no image: caption: '' focal_point: '' preview_only: no projects: [] draft: false ---
Prokaryotic genomes vary in GC content. Indeed, the percent GC content of a prokaryotic genome is considered a characteristic of the organism, and closely-related organisms often have similar GC contents. Even at the phylum level of the taxnomic hierarchy for prokaryotes, organisms may share a similar range of GC contents. For example, Actinobacteria are sometimes referred to as the high GC gram positives while Firmicutes are referred to as the low GC gram positives. In this blogpost, I will use the same data table of 11,710 RefSeq representative prokaryotic genomes that was used in a prior blogpost where I examined genome size, except I will analyze the column indicating the percent GC content of the genome.
Let’s load all the required libraries upfront so that one doesn’t have to search each code snippet for the required packages.
library(ggplot2)
library(tidyverse)
## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ✔ purrr 0.3.3
## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
I will begin by loading the aforementioned table of prokaryotic genome metadata, and then I will geneate summary data to examine the range of GC contents for the prokaryotes represented in that table.
setwd("/home/chrisgaby/github/My_Website/")
# Read the table into a tibble.
prokaryote.genomes.table <- read_csv(file = "./static/files/prokaryotes.csv")
# Display the minimum, maximum, mean, median, and 1st and 3rd quartiles
# for the genome sizes.
summary(prokaryote.genomes.table$`GC%`)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22.40 41.00 54.80 53.18 65.50 77.00
It looks like GC% ranges from 22.4 to 77%, a difference of 54.6%!
Let’s examine the distribution of GC% for all the prokaryotic genomes.
qplot(data = prokaryote.genomes.table,
x = `GC%`,
binwidth = 1)
The distribution is bimodal.
Note that the descriptors used in the table to refer to taxonomy include multi-phylum groupings like “FCB group”, “PVC group”, and “Terrabacteria”. Hence, we’ll separate the “Organism Groups” column with this information into 3 new columns for the taxonomic levels (TL) designated TL1, TL2, and TL3, and then we’ll examine the GC% distribution for the 6 most abundant groups at TL2.
# Split the Organism Groups column into new columns named according to their
# respective taxonomy level, abbreviated TL1, TL2, and TL3.
prokaryote.genomes.table.split <- separate(data=prokaryote.genomes.table,
col = `Organism Groups`,
sep = ";",
into = c("TL1", "TL2", "TL3"))
TL2.subset.names <- names(which(summary(factor(prokaryote.genomes.table.split$TL2)) > 90))
prokaryote.genomes.table.split.reduced <- prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL2 == TL2.subset.names[1] |
prokaryote.genomes.table.split$TL2 == TL2.subset.names[2] |
prokaryote.genomes.table.split$TL2 == TL2.subset.names[3] |
prokaryote.genomes.table.split$TL2 == TL2.subset.names[4] |
prokaryote.genomes.table.split$TL2 == TL2.subset.names[5] |
prokaryote.genomes.table.split$TL2 == TL2.subset.names[6],]
qplot(data=prokaryote.genomes.table.split.reduced,
x = `GC%`,
geom = "density",
color = TL2,
fill = TL2,
alpha = I(0.2))
It looks like some phyla have a bimodal GC% distribution too.
Let’s separate these TL2 groups into their own facets.
qplot(data=prokaryote.genomes.table.split.reduced,
x = `GC%`,
geom = "density",
color = TL2,
fill = TL2,
facets = TL2 ~ .)
The Proteobacteria and Spirochaetes have bimodal genome GC% distributions, and thus it seems there are taxa within these phyla with distinct GC% contents.
Some of these designations like PVC group and Terrabacteria group are supercategories that contain several traditional phyla.
Let’s divide the Terrabacteria group up into the phyla that comprise it.
# Display the taxa within Terrabacteria.
unique(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL2 ==
"Terrabacteria group","TL3"])
## # A tibble: 9 x 1
## TL3
## <chr>
## 1 Firmicutes
## 2 Actinobacteria
## 3 Cyanobacteria/Melainabacteria group
## 4 Tenericutes
## 5 Deinococcus-Thermus
## 6 Chloroflexi
## 7 unclassified Terrabacteria group
## 8 Armatimonadetes
## 9 Abditibacteriota
The Terrabacteria group contains both the high and low GC gram positives Actinobacteria and Firmicutes. Let’s see if we have enough data points in each phylum to create another distribution.
# The number of rows for each phylum indicates the number of datapoints
# for each phylum
nrow(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 ==
"Actinobacteria",])
## [1] 2319
nrow(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 ==
"Firmicutes",])
## [1] 2217
nrow(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 ==
"Cyanobacteria/Melainabacteria group",])
## [1] 134
nrow(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 ==
"Tenericutes",])
## [1] 159
nrow(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 ==
"Deinococcus-Thermus",])
## [1] 73
nrow(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 ==
"Chloroflexi",])
## [1] 43
nrow(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 ==
"unclassified Terrabacteria group",])
## [1] 1
nrow(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 ==
"Armatimonadetes",])
## [1] 3
nrow(prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 ==
"Abditibacteriota",])
## [1] 1
It looks like only Actinobacteria, Firmicutes, Cyanobacteria/Melainabacteria group, and Tenericutes have more than 100 data points. We’ll proceed with these 4 phyla for the following visualization.
# Create a Terrabacteria dataframe with only the 4 phyla haveing over 100 datapoints.
Terra.4TL2.df <- prokaryote.genomes.table.split[prokaryote.genomes.table.split$TL3 == "Actinobacteria" |
prokaryote.genomes.table.split$TL3 == "Firmicutes" |
prokaryote.genomes.table.split$TL3 == "Cyanobacteria/Melainabacteria group" |
prokaryote.genomes.table.split$TL3 == "Tenericutes",]
# Create the plot faceted by the information in the TL3 column.
qplot(data=Terra.4TL2.df,
x = `GC%`,
geom = "density",
color = TL3,
fill = TL3,
facets = TL3 ~ .)
The Firmicutes have a median GC% of 40.3% vs. the Actinobacteria whose median GC% is 69.9%.
The Firmicutes exhibit a wide distribution of genome GC%, thereby leading to a standard deviation of 7.3900331, whereas the Tenericutes have a relatively narrow distribution, with a corresponding standard deviation of 2.7652308.
Genome GC content can vary by more than 50% depending on the organism. Closely-related organisms tend to have similar GC%. For example, Actinobacteria are high GC%, gram positive organisms whose median GC% was determined to be 70% herein, while the Firmicutes are low GC%, gram postitive organisms whose median GC% was 40% herein. Based upon the current genome representation in this dataset, some phyla like the the Tenericutes have a narrow distribution of GC% whereas others like the Firmicutes have a wide range. Still other phyla like the Proteobacteria and Spirochaetes appear bimodal, suggesting that there are lower order taxa in these phyla with distinct genome GC% contents.


Legend:
- Box - represents aggregation time period with min and max values as its lower and higher bars.
- Solid Line - average values for the period.
- Dashed Line - historical min/max values for the same period.
* - click on a chart to draw horizontal line for easy comparison of values.
* - hover over box to show period details.


{% extends "list_base.html" %} {% block pageheader %}
| Customer | Invoice # | Invoice Date | Sales Type | Total Amount | Details |
|---|---|---|---|---|---|
| {{ invoice.customer }} | {{ invoice.invoice_no }} | {{ invoice.invoice_date }} | {{ invoice.get_sales_type_display }} | {{ invoice.amount }} | details | {% endfor %}


1. The contest is open to anyone with a knack for programming.
2. It is a team contest with each team having a maximum of two members from the same institution.
3. Teams will be ranked as per the number of problems solved.
4.Ties will be broken by the total time for each user in ascending order of time.
5.Total time is the sum of the time consumed for each problem solved. The time consumed for a solved problem is the time elapsed from the beginning of the contest to the submission of the first accepted run plus 20 penalty minutes for every previously rejected run for that problem (Wrong answer, Time limit exceeded, Runtime Error, Compilation Error). There is no time consumed for a problem that is not solved.
6.The contest will consist of multiple rounds, with teams progressing in every round.
7.The problem statements will be available in four different languages: English, Mandarin, Russian and Vietnamese.


Welcome Neighbor Unit!
Hello neighbor user, it's great to interface with you today. Why don't you insert a number and let this unit report a story?
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British author Lee Child applying for Irish passport due to Brexit
The novelist says he is ‘not a huge fan of Britain’.
British writer Lee Child, creator of the Jack Reacher thriller series, is reportedly applying for an Irish passport due to Brexit.
Child, whose real name is James Grant, has sold more than 100 million books.
He lives between the UK and the US but is entitled to an Irish passport as his father was born in Belfast.
The author said he wants an Irish passport to avoid the possibility of post-Brexit travel restrictions in Europe.
“I haven’t got the passport yet but I will do very soon, it feels like I’m cheating to be honest as I will get in the back door,” Child told The Irish Times.
He was speaking in Berlin as part of a European “Friendship Tour”, where Child and fellow British authors Kate Mosse, Jojo Moyes and Ken Follett spoke out against Brexit.
“I am not a huge fan of Britain, even though I was born there.
“It’s a silly and frustrating country, which is why I moved to America,” Child said.
He also criticised Prime Minister Boris Johnson, portraying him as an elitist.
“If he had gone to my school he would not now be Prime Minister,” said Child.
Child’s new book, Blue Moon, has just been released.


Developed by Valve Corporation, SteamVR™ Tracking is a complete hardware and software system that lets objects determine, in real time, where they are located within a tracked 3D space. Valve has made SteamVR™ Tracking available to companies without licensing fees.
The SteamVR™ Tracking system consists of three major blocks: Base stations that scan the room with infrared light, tracked objects, and a host PC to perform position calculations.
A tracked room has one or two base stations. Each base station outputs flashes of omni-directional infrared light along with swept planes of infrared laser light. The flash of IR light is called the Optical Sync and synchronizes all of the light to digital sensors in the room to time zero. Each sensor on a tracked object then waits to be scanned by the rotating plane of laser light. Since the sheet of laser light is rotating at a constant velocity, the angle from the base station to each light sensor can be computed by measuring the time from the sync light pulse (time zero) to the time when the plane of light hits the sensor. This sync and sweep process is repeated in the horizontal and vertical directions to provide a horizontal and vertical angle for each sensor on a tracked object.
Tracked objects can have up to 32 TS4231 Light to Digital Converters. When base station IR light is incident on a site’s photodiode, that site’s TS4231 will output a digital pulse. The TS4231 can detect very low IR light levels in high noise environments. The output each light to digital detector is sent to a Watchman function that aggregates the sync and swept laser timings from all sensor locations. These light time stamps are then sent to the PC via a wired USB connection or a wireless Bluetooth link. Tracked objects can also contain an IMU to provide a fusion of tracking information back to the PC.
The Host PC uses SteamVR™ software and the SteamVR™ API to compute the tracked object’s location. The PC software takes as input the time stamp reports for the up to 32 light-to-digital sensor inputs. These inputs are processed by a fitting engine that computes the object’s X,Y,Z location along with the rotation about the X,Y, and Z axis to deliver a real time pose of each object.
Triad Semiconductor’s TS4231 Light-to-Digital Converter (2nd Gen) is an integral part of a SteamVR™ Tracking system. You can find more details below but in summary, a tracked object, in a SteamVR™ Tracking system, has a number of Triad’s TS4231 devices located on the object to detect infrared light from position basestations located in the room.
Triad’s TS4231 Light-to-Digital Converter Integrated Circuit converts the SteamVR™ Tracking IR light into a digital signal indicating the angle of the sensor site from each tracking base station. The TS4231 is a size-optimized 9-bump WLCSP optimized for volume production and for small size and power.
Find out moreThe TS3633-CM1 is an excellent way to prototype SteamVR Tracking designs. The CM1 contains the TS3633 IC, a photodiode and the resistors and capacitors needed for a complete Light-to-Digital prototyping solution. The components are part of a PCB assembly with castellated pads on 100-mil centers to facilitate rapid prototyping of SteamVR Tracking solutions.
Find out moreFrom room scale applications to console controllers and more, Triad's SteamVR hardware provides you with the tools and capability to build tomorrow's VR hardware today.


SOTLAS (SOTA Atlas) is an online map of Summits On the Air (SOTA) summits with detailed information about summits, activators, spots and alerts.


Teen boy dies in hospital after he was found in West Dunbartonshire park
The youth - believed to be 16-years-old - died in hospital after he was discovered in Christie Park, Alexandria, West Dunbartonshire
by Stephen Stewart, https://www.dailyrecord.co.uk/authors/stephen-stewart/A teenage boy has tragically died after he was found lying in a park.
The youth - who is believed to have been 16-years-old - died in hospital after he was discovered by a dog walker in Christie Park, Alexandria, West Dunbartonshire.
Police have now launched a probe into the death after emergency services were scrambled to the incident less than 200 yards from Christie Park primary school, at around 12.35pm.
The teen was found alive and was taken by ambulance to hospital where he later died.
Locals saw several police cars in the surrounding streets, as well as forensic officers scouring a cordoned off area just after 3pm.
A Police Scotland spokeswoman said: " Emergency services attended at Christie Park in Alexandria at 12.34pm on Saturday after a member of the public requested assistance.
“A male youth was taken to hospital, where he has subsequently died. The death is being treated as unexplained and inquiries are ongoing."


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acl acl2013 acl2013-lda knowledge-graph by maker-knowledge-mining
topicId: wordWeight*wordName (topN-words)
topic #0: 0.009*similarity + 0.006*inference + 0.005*wordnet + 0.005*monolingual + 0.004*prediction + 0.004*text + 0.004*probability + 0.004*parsers + 0.004*measures + 0.004*lexical
topic #1: 0.001*boundary + 0.001*hrg + 0.001*autoencoder + 0.001*rae + 0.001*treewidth + 0.001*combinatory + 0.001*subgraphs + 0.001*bn + 0.001*hyperedge + 0.001*autoencoders
topic #2: 0.004*adjunction + 0.004*tsg + 0.003*cvg + 0.002*plsa + 0.002*fst + 0.002*adjunctions + 0.001*auxiliary + 0.001*eigenmaps + 0.001*kin + 0.001*laplacian
topic #3: 0.002*msd + 0.002*sl + 0.002*sign + 0.002*msds + 0.001*articulator + 0.001*neurons + 0.001*pdlsl + 0.001*romanian + 0.001*layer + 0.001*propositional
topic #4: 0.006*unlabeled + 0.003*ssl + 0.003*embeddings + 0.002*mtr + 0.002*disagreement + 0.002*mukherjee + 0.002*paraphrasing + 0.002*embedding + 0.002*ndcg + 0.002*ranking
topic #5: 0.003*cws + 0.003*similes + 0.002*simplification + 0.002*simile + 0.002*sanskrit + 0.001*cw + 0.001*primitives + 0.001*metaphor + 0.001*figurative + 0.001*schank
topic #6: 0.006*annotation + 0.006*alignment + 0.006*summarization + 0.005*alignments + 0.005*state + 0.005*quality + 0.005*parse + 0.004*summaries + 0.004*annotators + 0.004*summary
topic #7: 0.003*flaw + 0.002*metaphor + 0.002*liwc + 0.002*flaws + 0.001*valence + 0.001*hunalign + 0.001*date + 0.001*farsi + 0.001*cleanup + 0.001*dct
topic #8: 0.001*distant + 0.001*subjectivity + 0.001*decipherment + 0.001*sr + 0.001*passage + 0.001*subjective + 0.001*distantly + 0.001*xf + 0.001*quadratic + 0.001*cut
topic #9: 0.002*itg + 0.001*meetings + 0.001*abstracts + 0.001*slotvp + 0.001*swap + 0.001*mono + 0.001*slotnp + 0.001*ami + 0.001*icsi + 0.001*neubig
topic #10: 0.002*polish + 0.001*kennedy + 0.001*oswald + 0.001*slavonic + 0.001*przepi + 0.001*fuzziness + 0.001*lucene + 0.001*znany + 0.001*malarz + 0.001*zabi
topic #11: 0.008*chinese + 0.006*parser + 0.005*relations + 0.005*relation + 0.005*verb + 0.005*pairs + 0.004*pair + 0.004*algorithm + 0.004*parsing + 0.004*term
topic #12: 0.002*malay + 0.002*katakana + 0.001*iban + 0.001*lookup + 0.001*ws + 0.001*monosemic + 0.001*morin + 0.001*polysemous + 0.001*chiao + 0.001*prochasson
topic #13: 0.002*lvcs + 0.002*causal + 0.002*pj + 0.001*lix + 0.001*hungarian + 0.001*vincze + 0.001*biomedical + 0.001*ppl + 0.001*dsim + 0.001*lvc
topic #14: 0.004*cfg + 0.004*constituent + 0.003*median + 0.003*penn + 0.003*coarse + 0.003*ecs + 0.003*palmer + 0.003*formalism + 0.002*derivations + 0.002*vp
topic #15: 0.005*ranking + 0.004*bias + 0.003*aspects + 0.003*overlap + 0.003*network + 0.002*field + 0.002*subjectivity + 0.002*der + 0.002*influence + 0.002*observations
topic #16: 0.004*segmentation + 0.003*gslda + 0.002*punctuations + 0.002*compressive + 0.002*extractive + 0.002*chinese + 0.001*yue + 0.001*sighan + 0.001*woodsend + 0.001*ctb
topic #17: 0.002*scf + 0.002*scfs + 0.002*dpp + 0.001*caption + 0.001*captions + 0.001*sps + 0.001*berg + 0.001*ordonez + 0.001*visually + 0.001*korhonen
topic #18: 0.003*fv + 0.002*tolerance + 0.001*intolerant + 0.001*edus + 0.001*tipping + 0.001*korhonen + 0.001*tolerant + 0.001*joanis + 0.001*parisien + 0.001*rhetorical
topic #19: 0.003*toponym + 0.002*ostag + 0.002*qa + 0.002*toponyms + 0.002*moschitti + 0.002*keyword + 0.001*eda + 0.001*ptk + 0.001*yeah + 0.001*xx
topic #20: 0.003*affix + 0.002*lattice + 0.002*rating + 0.001*review + 0.001*coreg + 0.001*agglutinative + 0.001*regressor + 0.001*confidently + 0.001*qian + 0.001*uyghur
topic #21: 0.003*zt + 0.002*factoids + 0.002*freebase + 0.002*emotion + 0.002*addressee + 0.002*hoffmann + 0.002*crossword + 0.001*factoid + 0.001*detections + 0.001*kb
topic #22: 0.003*normal + 0.002*grained + 0.002*consonants + 0.001*simplification + 0.001*vowel + 0.001*consonant + 0.001*coarse + 0.001*vowels + 0.001*transfering + 0.001*nasal
topic #23: 0.003*emotion + 0.002*dispersion + 0.002*stacking + 0.002*quest + 0.002*tabular + 0.001*ensemble + 0.001*writer + 0.001*geology + 0.001*devset + 0.001*poon
topic #24: 0.007*bilingual + 0.007*document + 0.006*user + 0.006*segmentation + 0.006*discourse + 0.006*documents + 0.004*lda + 0.004*distribution + 0.004*representation + 0.004*corpora
topic #25: 0.003*causal + 0.002*mtus + 0.002*friendfeed + 0.002*osm + 0.002*tsm + 0.001*reddit + 0.001*urls + 0.001*tsunamis + 0.001*microblogging + 0.001*grammaticality
topic #26: 0.011*sentiment + 0.007*tree + 0.006*tweets + 0.006*feature + 0.006*training + 0.005*languages + 0.005*sentences + 0.005*news + 0.005*social + 0.005*twitter
topic #27: 0.003*edgar + 0.002*icsi + 0.001*bigrams + 0.001*ilp + 0.001*nlu + 0.001*monserrate + 0.001*tac + 0.001*bigram + 0.001*virtual + 0.001*meinedo
topic #28: 0.003*unlabeled + 0.003*orientation + 0.002*community + 0.002*tracking + 0.002*dialog + 0.002*mos + 0.002*deceptive + 0.002*diverse + 0.002*union + 0.002*preference
topic #29: 0.003*td + 0.002*recurrent + 0.002*deceptive + 0.002*factored + 0.002*truthful + 0.002*propminer + 0.002*born + 0.001*rnnlm + 0.001*mikolov + 0.001*flm
topic #30: 0.003*verbnet + 0.002*wsd + 0.002*mace + 0.002*underspecified + 0.002*metonymic + 0.002*semlink + 0.001*inventories + 0.001*vp + 0.001*literal + 0.001*eng
topic #31: 0.003*ucca + 0.003*pivot + 0.003*wordnets + 0.002*lingpy + 0.002*historical + 0.002*cognate + 0.002*wiktionary + 0.001*ccgs + 0.001*scene + 0.001*cc
topic #32: 0.002*supertags + 0.002*ptb + 0.001*sb + 0.001*cd + 0.001*malt + 0.001*mst + 0.000*deepbank + 0.000*ivanova + 0.000*bohnet + 0.000*vrelid
topic #33: 0.002*uncertainty + 0.002*monsters + 0.002*pro + 0.001*svmn + 0.001*gram + 0.001*uncertain + 0.001*szarvas + 0.001*orgy + 0.001*acceptance + 0.001*gy
topic #34: 0.002*duc + 0.002*docsys + 0.002*docbase + 0.002*docorig + 0.001*biography + 0.001*fudannlp + 0.001*generations + 0.001*mds + 0.001*weather + 0.001*cuid
topic #35: 0.006*entity + 0.005*semantic + 0.005*topic + 0.005*query + 0.005*distributional + 0.005*topics + 0.004*proceedings + 0.004*entities + 0.004*grammar + 0.004*models
topic #36: 0.002*icarus + 0.002*quantisation + 0.001*hyperplanes + 0.001*lsh + 0.001*political + 0.001*bits + 0.001*schrodt + 0.001*bit + 0.001*neighbours + 0.001*nearest
topic #37: 0.001*tad + 0.001*ontologies + 0.000*alexe + 0.000*rajasekar + 0.000*krishnamurthy + 0.000*films + 0.000*brave + 0.000*stanoi + 0.000*ioana + 0.000*modules
topic #38: 0.002*connor + 0.001*fes + 0.001*crisis + 0.001*zielinski + 0.001*bollen + 0.001*ritter + 0.001*brendan + 0.001*castillo + 0.001*sakaki + 0.001*chamberlain
topic #39: 0.002*harmony + 0.002*vowels + 0.002*vowel + 0.001*inflection + 0.001*phonmatrix + 0.001*scripts + 0.001*consonants + 0.001*gen + 0.001*vcv + 0.001*diffs
topic #40: 0.003*specification + 0.002*lms + 0.002*relaxation + 0.002*specifications + 0.002*conjuncts + 0.002*reorderings + 0.002*rlt + 0.002*embedding + 0.002*css + 0.002*adnn
topic #41: 0.002*att + 0.002*flat + 0.002*nested + 0.001*businesses + 0.001*iaa + 0.001*dialog + 0.001*recommendation + 0.001*pagerank + 0.001*dialogs + 0.001*mcrae
topic #42: 0.008*dependency + 0.007*phrase + 0.006*sentence + 0.005*reordering + 0.005*clustering + 0.005*decoding + 0.005*translation + 0.005*association + 0.005*source + 0.004*clusters
topic #43: 0.002*padding + 0.001*idle + 0.001*clu + 0.001*bldl + 0.001*dh + 0.001*sagae + 0.001*bldr + 0.000*xh + 0.000*blml + 0.000*tldr
topic #44: 0.002*qsd + 0.002*pet + 0.002*scoping + 0.001*bow + 0.001*burger + 0.001*bootstrapped + 0.001*manshadi + 0.001*durme + 0.001*outscoping + 0.001*scopal
topic #45: 0.004*jokes + 0.003*derivational + 0.003*zv + 0.003*dissect + 0.002*zu + 0.002*compo + 0.002*funny + 0.002*joke + 0.001*lk + 0.001*zk
topic #46: 0.003*gusp + 0.002*lis + 0.002*italian + 0.002*sign + 0.001*oovs + 0.001*flight + 0.001*connectives + 0.001*aewlis + 0.001*cloud + 0.001*atis
topic #47: 0.002*vsem + 0.002*punctuation + 0.002*crawl + 0.002*commoncrawl + 0.001*upp + 0.001*cpp + 0.001*transcribed + 0.001*bovw + 0.001*punctuations + 0.001*ract
topic #48: 0.005*vectors + 0.005*pos + 0.005*arguments + 0.004*predicate + 0.004*words + 0.004*learning + 0.004*vector + 0.004*classes + 0.004*class + 0.004*meaning
topic #49: 0.002*qa + 0.002*mbrar + 0.001*mbr + 0.001*jeopardy + 0.001*answer + 0.000*ghm + 0.000*aar + 0.000*aqx + 0.000*qai + 0.000*passages
topic #50: 0.002*mcdc + 0.001*misclassified + 0.001*pebl + 0.001*signature + 0.001*reuters + 0.001*msrpar + 0.001*elkan + 0.001*svs + 0.001*yk + 0.001*sts
topic #51: 0.003*tm + 0.003*vsm + 0.002*lms + 0.002*neural + 0.001*ntt + 0.001*price + 0.001*semtree + 0.001*stock + 0.001*subcorpora + 0.001*duh
topic #52: 0.003*fcg + 0.002*derivational + 0.002*turkish + 0.002*temporal + 0.002*steels + 0.001*signal + 0.001*fluid + 0.001*typing + 0.001*luc + 0.001*derivationally
topic #53: 0.002*pargram + 0.002*glyph + 0.001*pargrambank + 0.001*bailey + 0.001*lfg + 0.001*ink + 0.001*pixel + 0.001*typesetting + 0.001*tracy + 0.001*pixels
topic #54: 0.003*cohesion + 0.001*seg + 0.001*angeli + 0.001*mira + 0.001*devices + 0.001*week + 0.001*kleine + 0.001*reiteration + 0.001*nil + 0.001*friday
topic #55: 0.003*chronic + 0.002*typesetting + 0.002*critics + 0.002*breaks + 0.002*break + 0.001*critic + 0.001*typeset + 0.001*anticipation + 0.001*anct + 0.001*raggedness
topic #56: 0.002*headline + 0.002*ciphers + 0.002*khapra + 0.002*listener + 0.002*brainsup + 0.002*headlines + 0.002*literal + 0.001*creative + 0.001*heady + 0.001*agents
topic #57: 0.003*dialog + 0.002*paralex + 0.002*factoids + 0.001*qazvinian + 0.001*surveys + 0.001*chelba + 0.001*hik + 0.001*wikianswers + 0.001*radev + 0.001*ngrams
topic #58: 0.003*taboo + 0.002*humor + 0.002*dshmm + 0.002*hk + 0.001*atc + 0.001*humorous + 0.001*dk + 0.001*contextualized + 0.001*clinical + 0.001*cdt
topic #59: 0.003*webanno + 0.002*apposition + 0.002*appositions + 0.001*polls + 0.001*bilinear + 0.001*intention + 0.001*lbp + 0.001*ww + 0.001*brat + 0.001*uu
topic #60: 0.004*rouge + 0.002*deception + 0.002*parma + 0.002*conroy + 0.001*rf + 0.001*tac + 0.001*aesop + 0.001*responsiveness + 0.001*mtc + 0.001*rankel
topic #61: 0.002*pyramid + 0.002*scus + 0.002*scu + 0.002*ordering + 0.001*haji + 0.001*lvc + 0.001*zeman + 0.001*swedish + 0.001*nilsson + 0.001*genetic
topic #62: 0.003*argo + 0.003*metaphor + 0.002*lmf + 0.002*sumo + 0.001*metaphors + 0.001*literal + 0.001*metaphorical + 0.001*rdf + 0.001*indonet + 0.001*ili
topic #63: 0.003*stance + 0.003*debate + 0.002*debates + 0.002*polarities + 0.001*polarity + 0.001*reconstruction + 0.001*coders + 0.001*cj + 0.001*weed + 0.001*wd
topic #64: 0.004*crowdsourcing + 0.004*hypernym + 0.003*hypergraph + 0.003*definitional + 0.003*chang + 0.002*bipartite + 0.002*supervision + 0.002*mooney + 0.002*paths + 0.002*workers
topic #65: 0.002*hamlet + 0.002*reducible + 0.001*dir + 0.001*shakespeare + 0.001*gertrude + 0.001*psdtmopv + 0.001*ch + 0.001*othello + 0.001*psetsotp + 0.001*reducibility
topic #66: 0.004*catenae + 0.002*pivot + 0.002*pivoting + 0.001*persian + 0.001*subjectivity + 0.001*lb + 0.001*subjective + 0.001*libter + 0.001*connectivity + 0.001*bootstrapped
topic #67: 0.005*neural + 0.003*weimar + 0.003*network + 0.002*layer + 0.002*embeddings + 0.002*mnb + 0.002*collobert + 0.002*valency + 0.002*potthast + 0.002*atemporal
topic #68: 0.004*pas + 0.003*annomarket + 0.002*gate + 0.001*cloud + 0.001*cunningham + 0.001*services + 0.001*equivalences + 0.001*paas + 0.001*upload + 0.001*ambiguities
topic #69: 0.003*pal + 0.002*variational + 0.001*chatterbot + 0.001*approximation + 0.001*fellowship + 0.001*chatterbots + 0.001*field + 0.001*ftoiorn + 0.001*counseling + 0.001*bp
topic #70: 0.006*question + 0.005*questions + 0.005*trees + 0.005*graph + 0.004*parsing + 0.004*visual + 0.004*character + 0.004*temporal + 0.004*zhang + 0.004*event
topic #71: 0.003*anaphora + 0.003*ctb + 0.002*charniak + 0.002*bengali + 0.002*guitar + 0.002*pronoun + 0.002*modifications + 0.002*curran + 0.002*hyena + 0.002*essays
topic #72: 0.004*politeness + 0.003*binarization + 0.003*submodular + 0.002*britannica + 0.002*subtrees + 0.001*subtree + 0.001*requests + 0.001*gi + 0.001*irtg + 0.001*compressed
topic #73: 0.003*matcher + 0.001*freebase + 0.001*kite + 0.001*ubl + 0.001*satellite + 0.001*ecological + 0.001*behaviours + 0.001*lextender + 0.001*schema + 0.001*conservation
topic #74: 0.003*morph + 0.002*morphs + 0.001*weibo + 0.001*xilai + 0.001*hsiung + 0.001*censorship + 0.001*uncensored + 0.000*alias + 0.000*buhou + 0.000*chongqing
topic #75: 0.003*linggle + 0.003*docent + 0.002*quotes + 0.002*quote + 0.001*hardmeier + 0.001*beach + 0.001*keefe + 0.001*carbon + 0.001*ovix + 0.001*tax
topic #76: 0.002*elementary + 0.002*mrl + 0.002*sister + 0.002*mrs + 0.002*stock + 0.001*geoquery + 0.001*dpm + 0.001*lag + 0.001*nl + 0.001*kwiatkowski
topic #77: 0.002*cws + 0.001*proceeding + 0.001*namt + 0.001*informal + 0.001*fcrf + 0.001*pinyin + 0.001*levenberg + 0.001*lclr + 0.001*ck + 0.001*itg
topic #78: 0.002*bericht + 0.001*subcategorization + 0.001*mitarbeiter + 0.001*multir + 0.001*npnom + 0.001*inflected + 0.001*npdat + 0.001*kollege + 0.001*npacc + 0.001*kollegen
topic #79: 0.002*empowerment + 0.001*minority + 0.001*competition + 0.001*preference + 0.001*irt + 0.001*ros + 0.001*carolyn + 0.001*penstein + 0.001*trk + 0.001*mayfield
topic #80: 0.003*quantum + 0.002*connotation + 0.002*lemmatisation + 0.002*cutting + 0.002*prc + 0.002*operator + 0.001*qc + 0.001*gramtype + 0.001*assessors + 0.001*nom
topic #81: 0.002*ehdp + 0.002*timeline + 0.001*epoch + 0.001*evolutionary + 0.001*hdp + 0.001*dish + 0.001*epochs + 0.001*yan + 0.001*mtk + 0.001*rouge
topic #82: 0.003*geolocation + 0.002*acoustic + 0.001*facial + 0.001*plausibility + 0.001*modalities + 0.001*video + 0.001*geotagged + 0.001*livetest + 0.001*stacking + 0.001*afp
topic #83: 0.003*numerical + 0.002*realisation + 0.002*thesaurus + 0.001*restaurant + 0.001*sparky + 0.001*moby + 0.001*dethlefs + 0.001*manager + 0.001*vik + 0.001*ferret
topic #84: 0.003*rst + 0.002*sensespotting + 0.002*entailment + 0.002*facet + 0.001*thadani + 0.001*auc + 0.001*rel + 0.001*manli + 0.001*faceted + 0.001*muscles
topic #85: 0.003*smatch + 0.002*travatar + 0.002*tss + 0.002*wcl + 0.001*amr + 0.001*gf + 0.001*wcls + 0.001*sieve + 0.001*reconcile + 0.001*lattices
topic #86: 0.003*caseframes + 0.002*agreements + 0.002*peer + 0.001*subgroups + 0.001*signed + 0.001*caseframe + 0.001*discussant + 0.001*perceptrons + 0.001*discussants + 0.001*summarizers
topic #87: 0.002*weibo + 0.001*collective + 0.001*aggregators + 0.001*spans + 0.001*endriss + 0.001*stymne + 0.001*microblog + 0.001*aggregator + 0.000*parenthetical + 0.000*integrity
topic #88: 0.008*sense + 0.007*classification + 0.007*projection + 0.006*coreference + 0.005*users + 0.005*polarity + 0.005*classifier + 0.004*disambiguation + 0.004*resolution + 0.004*mentions
topic #89: 0.001*ci + 0.001*edus + 0.001*beverage + 0.001*intension + 0.001*hyponymy + 0.001*sandy + 0.001*mcnally + 0.001*hammer + 0.000*taboada + 0.000*dkl
topic #90: 0.006*rules + 0.005*koehn + 0.005*score + 0.004*rule + 0.004*mention + 0.004*moses + 0.004*rank + 0.004*decoder + 0.004*average + 0.003*mean
topic #91: 0.002*dtdp + 0.002*clinical + 0.001*dual + 0.001*learner + 0.001*opposite + 0.001*instantiation + 0.001*suggestion + 0.001*collocation + 0.001*probable + 0.001*mainichi
topic #92: 0.002*excitation + 0.002*nuclei + 0.002*aid + 0.002*hbms + 0.002*cdpmm + 0.001*bbam + 0.001*hbm + 0.001*overgeneralization + 0.001*dod + 0.001*nucleus
topic #93: 0.003*thwarting + 0.003*universals + 0.002*suggestion + 0.001*kj + 0.001*greenberg + 0.001*learner + 0.001*dryer + 0.001*pgm + 0.001*thwarted + 0.001*campbell
topic #94: 0.002*phone + 0.002*telugu + 0.001*tts + 0.001*synthesized + 0.001*phones + 0.001*epsilon + 0.001*polyglot + 0.001*perceptual + 0.001*synthesis + 0.001*phoneset
topic #95: 0.007*translation + 0.007*target + 0.006*smt + 0.005*parallel + 0.005*features + 0.005*bleu + 0.005*translations + 0.004*mt + 0.004*system + 0.004*senses
topic #96: 0.001*testimony + 0.001*weighed + 0.001*broaden + 0.001*visweswariah + 0.000*constituent + 0.000*mccord + 0.000*permutations + 0.000*preordering + 0.000*headwords + 0.000*watson
topic #97: 0.002*mdbn + 0.002*clir + 0.001*equivalency + 0.001*rbm + 0.001*munteanu + 0.001*pruning + 0.001*dbpp + 0.001*alternation + 0.001*plex + 0.001*cells
topic #98: 0.002*amortized + 0.002*subjective + 0.001*amortization + 0.001*eurovoc + 0.001*cognate + 0.001*titov + 0.001*jakob + 0.001*srikumar + 0.001*span + 0.001*klementiev
topic #99: 0.002*gen + 0.002*aggregation + 0.002*impar + 0.002*deps + 0.002*facts + 0.001*gp + 0.001*filler + 0.001*ilpnlg + 0.001*gps + 0.001*owl
Latent Dirichlet allocation
In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003.
Topics in LDA
In LDA, each document may be viewed as a mixture of various topics. This is similar to probabilistic latent semantic analysis (pLSA), except that in LDA the topic distribution is assumed to have a Dirichlet prior. In practice, this results in more reasonable mixtures of topics in a document. It has been noted, however, that the pLSA model is equivalent to the LDA model under a uniform Dirichlet prior distribution.
For example, an LDA model might have topics that can be classified as CAT_related and DOG_related. A topic has probabilities of generating various words, such as milk, meow, and kitten, which can be classified and interpreted by the viewer as "CAT_related". Naturally, the word cat itself will have high probability given this topic. The DOG_related topic likewise has probabilities of generating each word: puppy, bark, and bone might have high probability. Words without special relevance, such as the (see function word), will have roughly even probability between classes (or can be placed into a separate category). A topic is not strongly defined, neither semantically nor epistemologically. It is identified on the basis of supervised labeling and (manual) pruning on the basis of their likelihood of co-occurrence. A lexical word may occur in several topics with a different probability, however, with a different typical set of neighboring words in each topic.
Each document is assumed to be characterized by a particular set of topics. This is akin to the standard bag of words model assumption, and makes the individual words exchangeable.
Model
With plate notation, the dependencies among the many variables can be captured concisely. The boxes are “plates” representing replicates. The outer plate represents documents, while the inner plate represents the repeated choice of topics and words within a document. M denotes the number of documents, N the number of words in a document. Thus:
α is the parameter of the Dirichlet prior on the per-document topic distributions,
β is the parameter of the Dirichlet prior on the per-topic word distribution,
\theta_i is the topic distribution for document i,
\phi_k is the word distribution for topic k,
z_{ij} is the topic for the jth word in document i, and
w_{ij} is the specific word.
The w_{ij} are the only observable variables, and the other variables are latent variables. Mostly, the basic LDA model will be extended to a smoothed version to gain better results .[citation needed] The plate notation is shown on the right, where K denotes the number of topics considered in the model and:
\phi is a K*V (V is the dimension of the vocabulary) Markov matrix each row of which denotes the word distribution of a topic.
The generative process behind is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words. LDA assumes the following generative process for a corpus D consisting of M documents each of length N_i:
1. Choose \theta_i \, \sim \, \mathrm{Dir}(\alpha) , where i \in \{ 1,\dots,M \} and \mathrm{Dir}(\alpha) is the Dirichlet distribution for parameter \alpha
2. Choose \phi_k \, \sim \, \mathrm{Dir}(\beta) , where k \in \{ 1,\dots,K \}
3. For each of the word positions i, j, where j \in \{ 1,\dots,N_i \} , and i \in \{ 1,\dots,M \}
(a) Choose a topic z_{i,j} \,\sim\, \mathrm{Multinomial}(\theta_i).
(b) Choose a word w_{i,j} \,\sim\, \mathrm{Multinomial}( \phi_{z_{i,j}}) .
(Note that the Multinomial distribution here refers to the Multinomial with only one trial. It is formally equivalent to the categorical distribution.)
The lengths N_i are treated as independent of all the other data generating variables (q and z). The subscript is often dropped, as in the plate diagrams shown here.
Inference
Learning the various distributions (the set of topics, their associated word probabilities, the topic of each word, and the particular topic mixture of each document) is a problem of Bayesian inference. The original paper used a variational Bayes approximation of the posterior distribution;[1] alternative inference techniques use Gibbs sampling and expectation propagation.
Following is the derivation of the equations for collapsed Gibbs sampling, which means \varphis and \thetas will be integrated out. For simplicity, in this derivation the documents are all assumed to have the same length N_{}. The derivation is equally valid if the document lengths vary.
Applications, extensions and similar techniques
Topic modeling is a classic problem in information retrieval. Related models and techniques are, among others, latent semantic indexing, independent component analysis, probabilistic latent semantic indexing, non-negative matrix factorization, and Gamma-Poisson distribution.
The LDA model is highly modular and can therefore be easily extended. The main field of interest is modeling relations between topics. This is achieved by using another distribution on the simplex instead of the Dirichlet. The Correlated Topic Model follows this approach, inducing a correlation structure between topics by using the logistic normal distribution instead of the Dirichlet. Another extension is the hierarchical LDA (hLDA), where topics are joined together in a hierarchy by using the nested Chinese restaurant process. LDA can also be extended to a corpus in which a document includes two types of information (e.g., words and names), as in the LDA-dual model. Nonparametric extensions of LDA include the Hierarchical Dirichlet process mixture model, which allows the number of topics to be unbounded and learnt from data and the Nested Chinese Restaurant Process which allows topics to be arranged in a hierarchy whose structure is learnt from data.
As noted earlier, PLSA is similar to LDA. The LDA model is essentially the Bayesian version of PLSA model. The Bayesian formulation tends to perform better on small datasets because Bayesian methods can avoid overfitting the data. In a very large dataset, the results are probably the same. One difference is that PLSA uses a variable d to represent a document in the training set. So in PLSA, when presented with a document the model hasn't seen before, we fix \Pr(w \mid z)—the probability of words under topics—to be that learned from the training set and use the same EM algorithm to infer \Pr(z \mid d)—the topic distribution under d. Blei argues that this step is cheating because you are essentially refitting the model to the new data.
Variations on LDA have been used to automatically put natural images into categories, such as "bedroom" or "forest", by treating an image as a document, and small patches of the image as words; one of the variations is called Spatial Latent Dirichlet Allocation.
from wiki http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation


The ifgen
Interface Generation tool parses interface definition (.api
) files, and generates include files and IPC code (as needed).
ifgen
can be run manually, but it's usually run automatically by the mktools
.
ifgen
usage details are displayed using the -h
or -
-help
options:
Related info about ifgen: API Files.
Copyright (C) Sierra Wireless Inc. Use of this work is subject to license.


Preparation time
less than 30 mins
Cooking time
less than 10 mins
Serves
Serves 6-8
This summery dish works well on a barbecue, and is quick to throw together - especially with herbs from your garden.
First prepare your barbecue: light the barbecue so that it flares up and dies down, then wait until the coals are glowing but have not yet turned white. Alternatively, heat a griddle pan over a high heat until very hot.
Zest two of the lemons into a large bowl, then squeeze in the juice. Add a few of the sprigs of mint, parsley, dill, chervil and tarragon, and the halved garlic bulb. Pour in 150ml/5fl oz of the extra virgin olive oil, mix until well combined and set aside.
Sandwich the veal slices between two long sheets of cling film. Bash them with a rolling pin until they are about 1cm/½in thick, then add them to the herby oil and stir to coat. Set aside.
Finely chop the remaining herbs together and add them to a bowl with the shallot. Zest over two of the lemons, then chop again. Stir in the anchovies and chop again, then stir in the Dijon mustard and capers and chop until fine. Season, to taste, with salt and freshly ground black pepper, then make a well in the centre of the mixture.
Lift the veal slices out of the marinade and lay them on the barbecue or griddle pan. Cook for 2-3 minutes on one side, then 2 minutes on the other side. Transfer to a serving platter.
Meanwhile, heat 50ml/2fl oz of the remaining oil to a frying pan. Add the spinach leaves and cook until just wilted, then lay them on top of the veal slices.
Cut the remaining lemon into wedges. Drizzle the remaining oil into the well in the centre of the salsa verde mixture, then mix together to form a thick paste. Spoon the salsa verde on top of the veal and spinach. Garnish the platter with the lemon wedges.


PY Technology was founded in April 2002, we are an independent distributor specialized in IC components. Over these years PY-Tech has been providing service to customers of different scales, and has established a direct, convenient, highly efficient and low costly business relationships between end manufacturers and traders. We have our advantages both on channel and price aspect.
We mainly focuses on the sales of electronic parts of apparatus, and industrial parts. The main brands include:
FPGA ：XILINX, ALTERA/INTEL
Corporation principle:
Sparing no efforts to build up an outsourcing purchase center for the clients, and providing a perfect purchase solution plan. Optimizing the management of clients’ supply chain, and reducing the production and operation costs for the clients! “Till good is better, But better best”, this is the belief of all our staff.


A Keyman keyboard to allow users to type the English language in the Shavian alphabet. The Shavian alphabet is an alternative, phonemic way to write English. The alphabet takes its name from Irish writer Bernard Shaw who inspired and posthumously funded it. Shaw believed there should be a simpler way to write English. His wish was fulfilled in 1962 following a worldwide competition. The winner of that competition, Kingsley Read, developed an alphabet designed especially for English, with a unique letter for each of the 40 or so basic sounds we all use today.
Shaw QWERTY is a transitional keyboard layout which maps Shavian letters to similar sounding letters on the QWERTY layout, where possible. This keyboard evolved from the standard used for representing Shavian in ASCII, before the alphabet was added to Unicode.


test
. Så enkel flyttning av filer så. 🙂Känns dock som jag borde städa bort alla väldigt gamla email etc först..
For routine: if a few of us put on Google calendar, both on our own and our new group calendar, to export once every half a year?
We do have a shared calendar now. https://calendar.google.com/calendar/embed?src=stockholmblues%40gmail.com&ctz=Europe/Stockholm
I can add a reminder to that. 😄
Slarck - A place to keep your valuable conversations
Store your chat archives and get your entire conversation history back under your control, with powerful search and sharing features.
Jag tror jag har mindre koll än jag borde nuförtiden dock, men fixar de jag känner till!
International Blues Dancers gruppen verkar ha tappat bort sitt dokument för liknande.. 😕
Det är de 2 viktiga ställena jag känner till / kommer ihåg.
Think it would be best if they were composed as images showing all threads from a certain time-interval, with all comments opened and readable.
And I guess that's not what an automatic export-backup would look like, right? Or would it?
Maybe we could simply make pdf.s of it all (with one responsible person for each channel?) -- at certain intervalls (e.g. every 3rd month), which our calendars can remind us of.
And put these pdf.s in special "Slack-archive"-folders on Drive.


See this page with CSS
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Andre's Restaurant
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Bruce Page
301.730.9365
e-mail<EMAIL_ADDRESS>website: http://www.bigideasupply.com
vcf: Download
SUMMARY:
Front-end creative expert with 13+ years building E-commerce & promotional websites. Regular use of latest HTML5/CSS3/JS coding methods and thorough understanding of all relevant software packages. Growing online businesses through creative thinking, strategic planning and execution, strong, relevant and non-intrusive user experiences, and close partnerships with key business and team leaders. Proven track record of managing many successful website launches that have resulted in millions in online business growth.
WORK HISTORY:
The Big Idea Supply Company
Hagerstown, MD | 2/08 – current
Creative Director, Principal
- Develop compelling user-centric interfaces that are easy to both understand and use.
- Partner with clients to create engaging and unique concepts and strategies.
- Provide consultation to those needing help getting their creative, regardless of medium, to the next level.
- Help define and strengthen the perception of a product and/or brand's identity through thoughtful design.
- Create engaging sales creative that communicate and steer the user, but get out of the way and encourages interaction.
Healthy Directions, LLC
Potomac, MD | 4/04 – current
Executive Director, Web Design
- Oversee all aspects of HD’s internal creative web design agency.
- Design, and lead a team of designers, in the development of 200 online creative projects per month.
- Collaborate with internal and external stakeholders to develop best-in-class online collateral including: E-commerce and promotional websites, E-newsletters, landing pages, rich media banner placement, broadcast emails, Social marketing efforts and online streaming media through all possible Internet communication channels including PPC campaigns and affiliate relationships.
- Ensure implementations are timely and effective per brand guidelines, statistical analysis and user experience best practices.
- Implement state-of-the art content management tools + work with ECP vendors to ensure smooth platform migrations. Create requirements and test plans to verify platform is optimized for performance and results.
- Partner with other business leaders to brainstorm, develop, and execute creative business strategies.
- QA websites to verify coding best practices are being applied and maintained. Facilitate SEO optimization, accessibility standards compliance and full browser support in all desktop and mobile environments. Provide recommendations and manage updates if modifications are required.
- Oversee development of SOPs to maintain transparent communication of current processes and provide reference materials to design teams.
- Create cohesive team environment & facilitate growth through mentoring, regular internal training sessions, 1:1 weeklies with individual team members, benchmarking exercises and critiquing, off-site courses and conferences, and goal setting with regular progress check-ins.
- Provide creative consultation to Executive management as it pertains to ways we might leverage the Internet to expand business opportunities. Recommendations are regularly implemented and successful.
Kaplan University
Hagerstown, MD | 7/02 – 4/04
Full-Time Instructor
- Instructed a variety of classes on software packages including Adobe Photoshop, Premier, Illustrator and Flash.
- Instructed several courses on principals of graphic design, digital video production and color theory.
- Responsible for recommending software, installing and maintaining on 45+ Apple computers and file servers across the college’s internal network.
- Enhanced management skills through regular team building exercises and inherent student management responsibilities.
- Recognized as a top instructor in the Graphics department, based on end-of-semester student evaluation feedback for 3 consecutive semesters.
Ellipsis Design
Hagerstown, MD | 12/02 – 4/04
Creative Director | Partner
- Responsible for all creative executions presented to clients. Managed junior designer.
- Specked out projects and provided LOE assessments and quotes based on project requirements.
- Managed, built and retained relationships working 1:1 with clients.
- Worked with external vendors to deliver print and online-based collateral in various formats, always on time and within budget.
- Active member of the deign community. Joined AIGA and JAYCEES. Participated in numerous events.
- Managed all aspects of the business, including: taxes, payroll, legal and accounting consultation, business marketing and networking, Business incorporation.
- Aimed to exceed client expectations. Regularly received accolades from customers. Me being easy to work with was a regular complement, and reason why I was requested on projects often.
mediumFUSION, LLC Graphic Arts Group
Indianapolis, IN | 2/01 – 5/02
Creative Director | Partner
- Responsible for creative executions presented to clients.
- Specked out projects and provided LOE assessments and quotes based on project requirements.
- Managed, built and retained relationships working 1:1 with clients.
- Worked with external vendors to deliver print and online-based collateral in various formats, always on time and within budget.
- Designed and developed all online and video creative.
Career Note: Additional experience: Adjunct Professor, John Herron School of Art | Indianapolis, IN: Fall 1998, 2001 – 2002 • Art Director, Robert Feldman and Associates | Bethesda, MD: 2000 – 2001
INTERNSHIPS:
Indiana Pacers | Conseco Fieldhouse: Interior and Display Design | designer, 1998–1999
Iris Centre for Digital Arts: Advertising and Marketing | designer, 1999
North Street Media: Advertising and Marketing | web designer, 2000
EDUCATION:
BFA – Visual Communication – John Herron School of Art and Design, Indiana University.
Deans list 1997–2000.
SOFTWARE:
Adobe Creative Suite, Coda, Transmit, Fetch or any FTP app, MS Office Suite, Dropbox
CMS:
Ektron 400, Joomla!, Magento, OpenWeb, Wordpress, Drupal, WebLinc Direct, StrongMail
CODE & SUCH:
Hand-coded HTML(5), CSS(3), JS, jQuery, a little PHP, a little ASP, responsive coding methods, E-commerce design, HTML E-mail best practices including ESP delivery, Return Path testing and data mining, creative team management, Illustration, photography, printing processes... almost anything tied to visual communication.


TabStrip / Header Buttons
{{tab.name}} Content
An example on how to create a custom tabs with checkbox and a button in its header. Each tab has a checkbox, label and close button in header.
The tab header is fully customizable, you can add any custom content using HTML elements or other directives.


{% extends "base.html" %} {% block headscripts %} {{super()}} {% endblock headscripts %} {% block content %}
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| Prefix | Strengths |
| Anchoring | Warp Scrambling |
| Blinding | Sensor Dampening |
| Ghosting | Tracking/Missile Disruption |
| Harrowing | Target Painting |
| Raznaborg, Striking | Increased Damage Application |
| Renewing | Increased Remote Armor Repair |
| Starving | Energy Neutralization |


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Step 1
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Make sure it's public!
{% if error %}
The playlist URI you entered was invalid. Could you double check that it is valid and a public playlist?
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Click the ...
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When $a \ne 0$, there are two solutions to $\(ax^2 + bx + c = 0\)$ and they are $$x = {-b \pm \sqrt{b^2-4ac} \over 2a}.$$ $$\sum_{i=0}^n x_i^i $$ $$ x = \frac{-b - \sqrt{b^2-4ac}}{2a} $$ $$\dfrac{d}{dx} \left [[\int_a^x f(t)~dt \right ] = f(x)$$


LITTLE VOICE
Reviewed by Harvey Karten, Ph.D. Miramax Films Director: Mark Herman Writer: Mark Herman, play "The Rise and Fall of Little Voice" by Jim Cartwright Cast: Brenda Blethyn, Jane Horrocks, Michael Caine, Jim Broadbent, Ewan McGregor, Annette Badland, Philip Jackson
Here is yet another example of a play which has not been successfully transported to the screen. In this case, though, the drama itself--which unaccountably had a run on Broadway--is so minor that one questions why the adaptation was attmepted at all. It features a shy, agoraphobic young woman who rarely leaves her neat but downscale home in a small English town, who has a fine voice but can do little more than imitate some of the greats she listens to from her record collection. Brenda Blethyn, who is cast as the girl's mother and who turned in an Oscar-nomination performance in the multi-textured, award-winning film "Secrets and Lies," is a motormouth whose personality this time is so irritating that it's a wonder that half the audience does not walk out in disgust and embarrassment. It's even more amazing that Mark Herman, who directs the insignificant fiasco, expects anyone to believe that even a failed talent agent is willing to be seen in his pimpmobile, much less to apply his lips to hers. While Laura, the Little Voice of the title (so named because when she is not singing you can scarcely hear a sound from her mouth), is not unattractive, we wonder why a telephone repairman named Bill has romantic feelings toward her--unless this pigeon-fancier imagines himself to be a Mother Theresa getting his kicks from nursing a sick bird like the eponymous L.V.
The chief disaster of the movie is Mari Hoff (Brenda Blethyn), a widow who looks like hell and who has a mouth that should repel anyone with a pulse and two legs to get himself out of her way. While she cares only about booze and bonking, her daughter Laura (Jane Horrocks, of TV fame), spends all her time in the attic listening to her late father's record favorites--the songs of Billie Holiday, Shirley Bassey, Marilyn Monroe and Judy Garland). When a night club owner known as Boo (Jim Broadbent) hears her sing, he agrees to sign her into his two-bit club, while Little Voice's new agent, Ray Say (Michael Caine) sees her as the discovery that will re-launch his failed career.
The movie features one scene designed as a show- stopper. Laura, seeing her father's ghost in the audience of the night club, belts out valid imitations of the great singers who were her dad's favorites, only to fade away in a succeeding act like a Virgil Adamson losing his regained sight or Algernon's adult friend Charly's losing his gray matter. While some critics have interpreted Michael Caine's role as that of a man who is exploiting the young chanteuse, he is guilty only of bringing the wallflower out into the open as though she were one of telephone repairman Bill's birds flying out for exercise. James Berardinelli, a noted internet critic, concluded his review by saying that the movie offers "opportunities to laugh, cry and cheer." Yes, it's a movie to laugh at, to cry that you're not elsewhere, and to cheer the conclusion which comes at a mercifully reasonable time.
Rated R. Running Time: 99 minutes. (C) 1999 Harvey Karten
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Ocala events
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Wendy's Products, Calories and Nutritional Information
Find calorie and nutrition information for Wendy's foods, including popular items and . Food database and calorie counter, Brand Type: Restaurant/Fast Food .
http://www.fatsecret.com/calories-nutrition/wendysWendys Jr Bacon Cheeseburger - MyFitnessPal.com
Find calories, carbs, and nutritional contents for Wendys Jr Bacon . Largest food database - over 2,000,000 entries; Track all major nutrients - calories, fat, .
http://www.myfitnesspal.com/nutrition-facts-calories/wendys-jr-bacon-cheeseburger
whuppin stick fishing rods Upcoming events in Ocala
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vegetable garden circle mat
Apr 6 - Downtown Disney
john manning real estate appraiser paul newman spaghetti sauce
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Calories in Wendy's - Crispy Chicken Sandwich ... - Calorie Count
Curious about how many calories are in Crispy Chicken Sandwich, Sandwiches? Get nutrition information and sign up for a free online diet program at .
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Wendy's Nutrition, Wendy's Calories, Nutritional Facts ...
Wendy's Nutrition Facts. Find calories, carbs and other nutritional information for your favorite Wendy's food or drink items including healthy alternatives.
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Wendy's Breakfast Nutrition Facts, Wendy's Breakfast Calories ...
Find calories, carbs, and nutritional contents for Wendy's Breakfast and over . food database - over 2,000,000 entries; Track all major nutrients - calories, fat, .
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Wendy's Small Frosty Calorie Counter (CalorieLab)
Calories: 320 | Protein: 9 g | Carbs: 52 g | Fat: 8 g | Fiber: 0 g | Serving: 227.0000 g | Full data here on CalorieLab.
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Conway's Game of Life
Start
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SECTION: 200-General TITLE: CalledJetty QUESTION: Why is it called Jetty? Jetty was originally developed by Greg Wilkins of Mort Bay Consulting.
Mort Bay is at the heart of Balmain, Sydney NSW, Australia. A former working class suburb, now yuppieville in Sydney's close inner-west.
Greg was living by Mort Bay when the company was formed, hence the company name.
Jetty was originally called IssueTracker (its original application) and then MBServler (Mort Bay SERVLet servER). Neither of these were much liked, so Jetty was finally picked as:


Shakhtar score twice in stoppage time in 3-3 draw at Zagreb
Shakhtar Donetsk scored twice in injury time, the second a penalty converted in the 98th minute, to force a dramatic 3-3 draw away to Dinamo Zagreb after both teams finished with 10 men in the Champions League on Wednesday.
ZAGREB: Shakhtar Donetsk scored twice in injury time, the second a penalty converted in the 98th minute, to force a dramatic 3-3 draw away to Dinamo Zagreb after both teams finished with 10 men in the Champions League on Wednesday.
Alan Patrick gave Shakhtar a 13th minute lead in the Group C game and Bruno Petkovic replied to make it 1-1 at halftime but the real drama came in the second half.
Dinamo had Nikola Moro sent off for a second bookable offence in the 73rd minute but, instead of exploiting that advantage, their Ukrainian opponents then had Marlos dismissed, also for two yellow cards, six minutes later.
Petkovic cleverly laid the ball off for Luka Ivanusec to put Dinamo ahead in the 83rd minute and the Croatian champions thought they had won it when Arijan Ademi tapped in Dani Olmo's cross six minutes later.
The visitors had other ideas and Junior Moraes headed in Viktor Kovalenko's cross in the third minute of stoppage time before a penalty was awarded for a foul by Kevin Theophile-Catherine on Shakhtar goalkeeper Andriy Pyatov, who had come up for a corner.
Tete converted to complete an extraordinary finish and leave the two sides level with five points from four games. Manchester City lead with 10 points and Atalanta are bottom with one.
(Writing by Brian Homewood, editing by Pritha Sarkar)


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Struct CLIOptionTuple¶
Defined in File cli_wrapper.h
Struct Documentation¶
-
struct
CLIOptionTuple
¶ Helper tuple storing an option object, the associated variable and creation index.
Note: bare pointers are used for CLI::Option objects as this comes from the CLI11 library. Removing it would require deep modifications in the 3rd party library, what we want to avoid.
Public Members
-
CLI::Option *
opt
¶
-
size_t
idx
= {0}¶
-
OptionPriority
priority
= {cli::OptionPriority::DefaultValue}¶
-
CLI::Option *


What are my future prospects once I’ve successfully qualified?
Once you’ve successfully finished, you have plenty of choices. You could apply for another apprenticeship or find a career that uses your new skills.
Your apprenticeship level will depend on your current qualifications. This means you can start an apprenticeship at the right level for you, to put you on the right path for success and beyond.
Some of the top reasons for becoming an apprentice:
- earn while you learn: have a competitive salary paid
- your training comes free
- choose from hundreds of different apprentice jobs in thousands of organisations
- get high quality training and hands-on experience
- develop the skills that are transferrable to a range of exciting careers
- continue to study for qualifications - up to and including a degree, if you wish
- doesn't matter what age you are or background, an apprenticeship will give you the skills you need to succeed
And lastly:
"90% of apprentices stay on in their place of work after completing their apprenticeship" (Source: The Nursery: Research and planning)
How much can you earn?

Many employers choose to pay more than the apprenticeship minimum wage of £3.70 per hour if you’re aged 16 to 18, rising to £3.90 per hour from April 2019.
If you’re aged 19 or over and have completed the first year of your apprenticeship, you are entitled to the minimum wage for your age.
Remember, if you complete a higher or degree level apprenticeship, your training costs will be paid for and you’ll be earning for the duration of your apprenticeship.
What will my apprenticeship cost me?
When you become an apprentice, you’ll need to cover the cost of your day-to-day expenses, such as lunch and travel.
If you’re a care leaver aged 16-24, you’ll receive a £1,000 bursary payment to support you in the first year of your apprenticeship.


The first thing you’ll need to know is how we go about doing things here.
The bioinformatics service at TSL exists to provide scientific computing infrastructure, bioinformatics training, expert guidance and advice in performing bioinformatics experiments and analyses and personnel to carry out bioinformatics analyses for and with TSL staff.
We fully understand that bioinformatics is a really new field for most biologists and we will do everything we can to help you do the science you need to.
The simplest way we provide support is by doing the bioinformatics job for you: you drop by the office, we have a chat about what you’re trying to achieve and what data you have and we set up and run analyses for you. Usually it goes like this…
This is a great way to get things done, but only to a point. With lots of people using the service there gets to be a long wait for the results and when (like all science) the bioinformatics needs to be tweaked and re-run you get a bit of a recursive situation that is quitefrustrating for you because of the waiting it requires.
So if you are going to be doing a lot of bioinformatics and it will be core to your project, it makes sense for us to help you to help yourself do your analyses when you need them doing. We call the varied ways that we achieve this ‘training’. But its not like going back to school, or Army boot-camp or anything, so don’t panic.
We do training in lots of ways. Most noticeably we do training courses. In these we prepare a lot of slides and a handbook and exercises in the topic we’re presenting on, kind of like a computer lab back when you were an undergraduate at university or college (except we won’t be marking anything or handing out grades). We appreciate that when new people start at TSL some will have lots of experience in some things and less in others and we try to embrace this by providing training courses at lots of levels for lots of topics. For example, for bioinformatics newbies we’ll cover things like using Galaxy, or getting started with running programs on the command-line. For someone looking to expand their skillset we’ll do a course in advanced script writing in Python or something like that. We run courses on cutting edge techniques like RNA-seq too for someone seeking a grounding in a particular suite of tools.
Training courses like these are great, but they’re not the whole story, in fact they’re usually just the beginning. Learning any new skill takes time and we know you’ll get to a point where you can’t see the next step to take or feel there is a gap in your knowledge. We expect you to come back to us time and again to ask about the things that escaped us all before, even after you’ve been on a training course. In practice most of our training gets done in one-to-one chats on very specific issues, we don’t expect the training courses to be able to teach you every tiny aspect of a topic so feel free to come and follow up whenever you need to. Try to think of us as computer-based analogues of the friendly Post-Doc leading you through a new lab procedure for the first time.
We also provide a lot of on-line training resources, for you to look at and work through in your own time, should you enjoy that sort of learning. The hub for these is right here, at this training website!
All these scenarios start and progress through discussion. The big message here is that the bioinformatics team are ALWAYS happy to talk through any aspect of what you’re doing and find the right path for you. Whatever you want to get done, we want to get done too, just come and get us involved!
Ok, we’re sorry you think that, we don’t mean to give that impression although it is a logical conclusion given the way we work. We do care about your career and skill set and your overall productivity and want you to be able to get as much work done as possible. All the while we’re conscious that you should be able to include as much of this important skill as possible into your CV or resume or life mission statement manifesto or whatever. But if you feel stuck with the task at hand or haven’t got time or just aren’t happy with the way it’s going, please tell us. We can find another way to get the job done . We’ll never say no (we can’t, we’d get fired).
That’s brilliant, and it warms our hearts to hear it, but do drop by anyway for a chat when you’re starting with new bioinformatics experiments. We’re pretty good at bioinformatics down here in the dedicated bioinformatics team (if I do say so myself!) and I’m pretty certain that in lots of circumstances we can add something that can make your tasks more efficient or easier or just make your bioinformatics life more pleasurable.
Naturally, you should come by to talk to the bioinformatics team about your project and we’ll work out what we can do. Don’t be overwhelmed by the first chat, in fact it’s likely there won’t be anything to do immediately (you might be waiting on new data) and we think it works best if we do things step-by-step rather than hit you with a million new things right up front, we don’t ever want it to feel like this…
So, that’s it for now, welcome to TSL from the bioinformatics team and we look forward to working with you soon.


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After working on several client projects involving DynamicEntities, I have a few things to get written up from my notes. Since DynamicEntities, by definition, are not strongly-typed (in terms of CRM entities, of course they are concrete .NET types themselves) they require a bit of casting-and-cajoling in order to work with.
The first thing to note is that if a field is empty in CRM, DynamicEntity objects that are passed back from the CRM service won’t have this field in the properties collection at all. This means that we have to check for the field before we even start. Accessing the collection via its string indexer will throw an exception if the property doesn’t exist. In code:
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The second big thing is that, while we need an actual Property instance when adding properties to a DynamicEntity, they are not returned when read back from the collection. The DynamicEntity doesn’t even keep a reference to the Property instance under the hood – it basically throws it away. In code:
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I’ll post a few more things later and hopefully some code to keep this kind of complexity in check.
Update: I just wanted to add a little tidbit brought up by someone about iterating over a PropertyCollection. When performing an iteration over a collection of properties, there is an implicit conversion to Property that happens in the iteration statement. Take this sample code:
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I’ve bolded the conversion to Property as well as the cast back to KeyProperty. We still start out with a Key object, but it apparently GetEnumerator() wraps things up into a Property object behind the scenes.
Here is the disassembled code for PropertyCollection.InternalGetEnumerator():
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Here we see that internally there is a PropertyFactory that wraps the raw value up into a Property.
Hopefully this sheds some light on this confusing behavior.


--- layout: post title: Salted Caramel Brownies date: 2014-05-13 20:48:31.000000000 -04:00 type: post published: true status: publish categories: - brownies - recipes tags: - brownies - salted caramel meta: _edit_last: '1' author: login: sophie email<EMAIL_ADDRESS>display_name: Pika first_name: '' last_name: '' ---
Hello, old friend-- long time, no see! I promise I'll get back to writing to you more soon. But for now, a recipe-- both salty & sweet, just like my feelings about leaving my Alma Mater.
Salted Caramel Brownies
Makes 1 8" pan, cut into 16 squares (they're very rich!)
Fudge brownies are pretty much perfect as is but the addition of salted caramel makes them heavenly. My suitemates have been clamoring for the recipe for a week now, so I caved and am back to blogging. These are especially good chilled-- really like candy, almost.
Mark Bittman is one of my favorite chefs and his formula for brownies is my go-to-recipe: so simple it's child-proof, fudgy and sweet with crackly tops. I also love that they don't use cocoa powder, which I never seem to have lying around.
Nota bene: best friend's mother, who is an amazing cook, made this huge bowl of homemade dulce de leche-- pretty much the most addictive substance on earth. If you don't have such luck in friends, and are short on time, you can buy it premade-- or make it yourself.
Ingredients:
8 tablespoons (1 stick) butter, plus a little more for greasing the pan
3 ounces unsweetened chocolate, roughly chopped
1 cup sugar
2 eggs
1/2 cup all-purpose flour
salt
1/2 teaspoon vanilla extract, optional
about 1 cup dulce de leche, or however much 1 12-oz can of sweetened condensed milk makes
1. Heat the oven to 350°F. These are sticky, so either line the pan with aluminium foil and grease it or nonstick parchment paper (or else serving will be a nightmare!).
2. Combine the stick of butter and the chocolate in a small saucepan over very low heat, stirring occasionally. (Or microwave them in a large microwave-safe bowl on medium for 10-second intervals, stirring after each.) When the chocolate is just about melted, remove the saucepan from the heat (or bowl from the microwave) and continue to stir until the mixture is smooth.
3. Transfer the mixture to a large bowl (or use the bowl you put in the microwave) and stir in the sugar. Then beat in the eggs, one at a time. Gently stir in the flour, salt, and the vanilla if you’re using it.
4.Spread half the batter into the prepared pan. Carefully pour caramel over the top and gently spread with a spatula. Sprinkle with sea salt, then gently pour the rest of the batter over the top. Sprinkle again with a bit of sea salt over the top.
5. Bake 35-40 min, or until center is set (these take a lot longer than regular brownies but don't let them burn!).
This is the hard part-- cool for a good long time so that they're firm enough to cut. When brownies are cool, lift them out of the pan by the foil or parchment, place the whole thing on a cutting board, and cut into squares with a serrated knife, dipping in between cuts in hot water (they're super sticky, no joke!).
Enjoy :)


»Research Lines
Our group has stablished several research lines related with the use of biotechnological methods in plant breeding programs of species with high agronomical importance. The work includes the in vitro micro-propagation, tissue regeneration by both somatogenesis and embriogenesis, and the genetic modification. In this regards, we use a fundamentally empirical approach, like functional genomics of interesting genes, as well as an apply approach with genes that improve the agronomics characteristic of the crop. The main current research lines are described below:


An imageboard or image board is a type of Internet forum which operates mostly via posting images. The first imageboards were created in Japan, and inspired the creation of a number of English language imageboards. They are based on the textboard concept.
Imageboards are similar to bulletin board systems, are used for discussions of a variety of topics. Imageboards are also different from online galleries in that most of the works posted are not made by the poster, but instead are taken from other online sources: galleries, other imageboards, and edited pictures.
How to image board?
The basic stuff
You don't need to be registered (but you can), you can be anonymous. But we use cookies to assign special IDs to a post, this way you can see if you are talking with the same person or not
In the navigation bar (top of the page) you can find the Threads button to list the actual threads on the board.
Each thread can have multiple posts discussing a topic.
The list of threads is ordered from newest to oldest. In the list the threads are shown 6 prer page, use the pagination at the end of the list of threads
Threads
A thread can have a especific topic, but it's optional.
To create a thread you need to make a post, the thread post need to have a file attached to them and a comment.
The person that makes a thread (and the first post) is called OP (Original Poster).
This is a coment This is a reply link to another post in the thread (No. 20): >>20 This is a reply to the Original poster: >>30(OP)
This is a reply to a post that I made earlier: >>33(you) This is a reply to a post that have been deleted or doesn't exist inside the thread: >>100
At the top of the post is the information section:
The name of the poster. Default is Anonymous. When creating a post there is a field to put a name if you want. If you are signed in the name would be preceded by two # characters (i.e. ##username).
The special ID. You would have an unique ID for each thread. This IDs are tracked with cookies.
The dete and time when the post was made. The format of the date is Day/month/year.
Number of the post. If you click the number you can reply to that post. This numbers are global on the page, that means that a thread doesn't start with No. 1, it continues from the last post in the database.
The reply button. If you find hard to clic the number of the post to reply, you can just clic the arrow button and it would work the same.
The reply links. This are links to post that have replied to that post, you can click on them and they will scroll to the replies.
The next section is the file information:
The name and type of the file. Posts accepts formats: .jpg, .png, .gif and video .webm.
The size in B, KB or MB. Posts accepts 5MB or less.
The width and height of the file, if it's a video this isn't shown.
If there is no file attached the file information section is not shown.
The last section is the content:
In the left, the thumbnail of the file. If the file is larget than 125 px in width or height is going to be resized. You can clic on it to enlarge it. Clic again to make it smaller again. Files smaller than 125 px will not be resized nor can be enlarged.
If there is no file attached this section is not going to appear.
In the right, the comment of the post. A normal post (Not OP) must have either a file or a comment, can have both, can not be empty.
A comment can have up to 2000 characters.
Comments can have links to other posts inside the thread. This links allways need to have the >> characters to be identified as post links. If you clic on them they will scroll you to that comment.
A link that ends with (OP) means that if you clic on it, it will sroll to the first post of the thread.
A link that ends with (you) means that it's a reply to one of your post inside the thread.
A link that is corssed by a line is an invalid link. It's either a deleted post or that number doesn't exist inside the thread.
Editing or deleting posts is not posible, only Administrators can do it. It's the same for threads.
This is a coment
This is a reply link to another post in the thread (No. 20):
>>20
This is a reply to the Original poster:
>>30(OP)
This is a reply to a post that I made earlier:
>>33(you)
This is a reply to a post that have been deleted or doesn't exist inside the thread:
>>100


Domestic Violence
Baltimore Crime Safety “Domestic Violence” scores vs. percentage of population in Baltimore County.


WITNESS LIST Financial Institutions Committee February 15, 1999-3:00P Ofc of Consumer Credit Commissioner On: Pettijohn, Leslie (Ofc of Consumer Credit Commissioner) Texas Credit Union Department On: Feeney, Harold (Texas Credit Union Department) Texas Department of Banking On: Ghiglieri, Catherine (Texas Department of Banking) Texas Public Finance Authority On: Edwards, Kimberly (Texas Public Finance Authority) Texas Savings & Loan Department On: Pledger, Jim (Texas Savings & Loan Department)


@(page: model.Page)(implicit request: RequestHeader) @import views.support.EmailHelpers._ @import model.EmailAddons.EmailContentType @import common.{CanonicalLink, LinkTo} @fullRow(Seq("email__links")) { Manage your emails | Unsubscribe | Trouble viewing? } @fullRow(Seq("disclaimer")) {
@page.email.map { email => You are receiving this email because you are a subscriber to @{email.name}. } Guardian News & Media Limited - a member of Guardian Media Group PLC. Registered Office: @page.address
}