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{% extends 'base.html' %} {% load staticfiles %} {% block navpadding %}{% endblock %} {% block sticky %}{% endblock %} {% block content %} The Team A project this big can't be done alone. Here's just some of the great people that made this whole endeavor possible. Ali Torkamani, PhD Director, Genome Informatics Contact Me Nathan Wineinger, PhD Assistant Professor & Director, Biostatistics Emily Spencer, PhD Assistant Professor & Director, Clinical Genomics Research Program Evan Muse, MD, PhD Assistant Professor, Cardiologist Brian Schrader Software Engineer Contact Me Bhuvan Molparia, PhD Bioinformatics Scientist Contact Me Yunyue (Paris) Zhang Research Data Scientist Contact Me Will (Shuchen) Liu Software Engineer Contact Me Alexander Lippman Applications Systems Administrator Jennifer Wagner, RN, CCRC Study Coordinator {% endblock %}
A project this big can't be done alone. Here's just some of the great people that made this whole endeavor possible.


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In Pictures: Love Island winner Greg O'Shea launches the Just Eat National Takeaway Awards 20 Nov 2019, 16:55 by Mikie O'Loughlin Popular breakfast broadcaster and professional rugby 7s player Greg OShea joined fellow presenter Cassie Stokes in Dublin today to celebrate the launch of the Just Eat National Takeaway Awards. Pic Brian McEvoy Popular breakfast broadcaster and professional rugby 7s player Greg OShea celebrates the launch of the Just Eat National Takeaway Awards. Pic Brian McEvoy Popular breakfast broadcaster and professional rugby 7s player Greg OShea celebrates the launch of the Just Eat National Takeaway Awards. Pic Brian McEvoy Popular breakfast broadcaster and professional rugby 7s player Greg OShea joined fellow presenter Cassie Stokes in Dublin today to celebrate the launch of the Just Eat National Takeaway Awards. Now in its sixth year Irelands leading online food ordering and delivery app, is calling on the nation to visit just-eat.ie/takeawayawards2019 and back their favourite local takeaway, with the chance to win FREE takeaway for a year on Just Eat. Voting is open to all takeaway restaurants throughout Ireland until Sunday 15th December 2019, with the shortlist of nominees set to be announced on Tuesday 7thJanuary 2020. Pic Brian McEvoy No Repro fee for one use


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11.1. Chapter Introduction: Graphs (WORK IN PROGRESS)¶
11.1.1. Graph Terminology and Implementation¶
Graphs provide the ultimate in data structure flexibility. A graph consists of a set of nodes, and a set of edges where an edge connects two nodes. Trees and lists can be viewed as special cases of graphs.
Graphs are used to model both real-world systems and abstract problems, and are the data structure of choice in many applications. Here is a small sampling of the types of problems that graphs are routinely used for.
Modeling connectivity in computer and communications networks.
Representing an abstract map as a set of locations with distances between locations. This can be used to compute shortest routes between locations such as in a GPS routefinder.
Modeling flow capacities in transportation networks to find which links create the bottlenecks.
Finding a path from a starting condition to a goal condition. This is a common way to model problems in artificial intelligence applications and computerized game players.
Modeling computer algorithms, to show transitions from one program state to another.
Finding an acceptable order for finishing subtasks in a complex activity, such as constructing large buildings.
Modeling relationships such as family trees, business or military organizations, and scientific taxonomies.
The rest of this module covers some basic graph terminology. The following modules will describe fundamental representations for graphs, provide a reference implementation, and cover core graph algorithms including traversal, topological sort, shortest paths algorithms, and algorithms to find the minimal-cost spanning tree. Besides being useful and interesting in their own right, these algorithms illustrate the use of many other data structures presented throughout the course.
A graph \(\mathbf{G} = (\mathbf{V}, \mathbf{E})\) consists of a set of vertices \(\mathbf{V}\) and a set of edges \(\mathbf{E}\), such that each edge in \(\mathbf{E}\) is a connection between a pair of vertices in \(\mathbf{V}\). 1 The number of vertices is written \(|\mathbf{V}|\), and the number of edges is written \(|\mathbf{E}|\). \(|\mathbf{E}|\) can range from zero to a maximum of \(|\mathbf{V}|^2 - |\mathbf{V}|\).
- 1
Some graph applications require that a given pair of vertices can have multiple or parallel edges connecting them, or that a vertex can have an edge to itself. However, the applications discussed here do not require either of these special cases. To simplify our graph API, we will assume that there are no dupicate edges, and no edges that connect a node to itself.
A graph whose edges are not directed is called an undirected graph, as shown in part (a) of the following figure. A graph with edges directed from one vertex to another (as in (b)) is called a directed graph or digraph. A graph with labels associated with its vertices (as in (c)) is called a labeled graph. Associated with each edge may be a cost or weight. A graph whose edges have weights (as in (c)) is said to be a weighted graph.
An edge connecting Vertices \(a\) and \(b\) is written \((a, b)\). Such an edge is said to be incident with Vertices \(a\) and \(b\). The two vertices are said to be adjacent. If the edge is directed from \(a\) to \(b\), then we say that \(a\) is adjacent to \(b\), and \(b\) is adjacent from \(a\). The degree of a vertex is the number of edges it is incident with. For example, Vertex \(e\) below has a degree of three.
In a directed graph, the out degree for a vertex is the number of neighbors adjacent from it (or the number of edges going out from it), while the in degree is the number of neighbors adjacent to it (or the number of edges coming in to it). In (c) above, the in degree of Vertex 1 is two, and its out degree is one.
A sequence of vertices \(v_1, v_2, ..., v_n\) forms a path of length \(n-1\) if there exist edges from \(v_i\) to \(v_{i+1}\) for \(1 \leq i < n\). A path is a simple path if all vertices on the path are distinct. The length of a path is the number of edges it contains. A cycle is a path of length three or more that connects some vertex \(v_1\) to itself. A cycle is a simple cycle if the path is simple, except for the first and last vertices being the same.
An undirected graph is a connected graph if there is at least one path from any vertex to any other. The maximally connected subgraphs of an undirected graph are called connected components. For example, this figure shows an undirected graph with three connected components.
A graph with relatively few edges is called a sparse graph, while a graph with many edges is called a dense graph.  A subgraph \(\mathbf{S}\) is formed from graph \(\mathbf{G}\) by selecting a subset \(\mathbf{V}_s\) of \(\mathbf{G}\)’s vertices and a subset \(\mathbf{E}_s\) of \(\mathbf{G}\) ‘s edges such that for every edge \(e \in \mathbf{E}_s\), both vertices of \(e\) are in \(\mathbf{V}_s\). Any subgraph of \(V\) where all vertices in the graph connect to all other vertices in the subgraph is called a clique.
A graph without cycles is called an acyclic graph. Thus, a directed graph without cycles is called a directed acyclic graph or DAG.
A free tree is a connected, undirected graph with no simple cycles. An equivalent definition is that a free tree is connected and has \(|\mathbf{V}| - 1\) edges.
<IP_ADDRESS>. Graph Representations¶
There are two commonly used methods for representing graphs. The adjacency matrix for a graph is a \(|\mathbf{V}| \times |\mathbf{V}|\) array. We typically label the vertices from \(v_0\) through \(v_{|\mathbf{V}|-1}\). Row \(i\) of the adjacency matrix contains entries for Vertex \(v_i\). Column \(j\) in row \(i\) is marked if there is an edge from \(v_i\) to \(v_j\) and is not marked otherwise. The space requirements for the adjacency matrix are \(\Theta(|\mathbf{V}|^2)\).
The second common representation for graphs is the adjacency list. The adjacency list is an array of linked lists. The array is \(|\mathbf{V}|\) items long, with position \(i\) storing a pointer to the linked list of edges for Vertex \(v_i\). This linked list represents the edges by the vertices that are adjacent to Vertex \(v_i\).
Here is an example of the two representations on a directed graph. The entry for Vertex 0 stores 1 and 4 because there are two edges in the graph leaving Vertex 0, with one going to Vertex 1 and one going to Vertex 4. The list for Vertex 2 stores an entry for Vertex 4 because there is an edge from Vertex 2 to Vertex 4, but no entry for Vertex 3 because this edge comes into Vertex 2 rather than going out.
Both the adjacency matrix and the adjacency list can be used to store directed or undirected graphs. Each edge of an undirected graph connecting Vertices \(u\) and \(v\) is represented by two directed edges: one from \(u\) to \(v\) and one from \(v\) to \(u\). Here is an example of the two representations on an undirected graph. We see that there are twice as many edge entries in both the adjacency matrix and the adjacency list. For example, for the undirected graph, the list for Vertex 2 stores an entry for both Vertex 3 and Vertex 4.
The storage requirements for the adjacency list depend on both the number of edges and the number of vertices in the graph. There must be an array entry for each vertex (even if the vertex is not adjacent to any other vertex and thus has no elements on its linked list), and each edge must appear on one of the lists. Thus, the cost is \(\Theta(|\mathbf{V}| + |\mathbf{E}|)\).
Sometimes we want to store weights or distances with each each edge, such as in Figure 11.2.1 (c). This is easy with the adjacency matrix, where we will just store values for the weights in the matrix. In Figures 11.2.7 and 11.2.8 we store a value of “1” at each position just to show that the edge exists. That could have been done using a single bit, but since bit manipulation is typically complicated in most programming languages, an implementation might store a byte or an integer at each matrix position. For a weighted graph, we would need to store at each position in the matrix enough space to represent the weight, which might typically be an integer.
The adjacency list needs to explicitly store a weight with each edge. In the adjacency list shown below, each linked list node is shown storing two values. The first is the index for the neighbor at the end of the associated edge. The second is the value for the weight. As with the adjacency matrix, this value requires space to represent, typically an integer.
Which graph representation is more space efficient depends on the number of edges in the graph. The adjacency list stores information only for those edges that actually appear in the graph, while the adjacency matrix requires space for each potential edge, whether it exists or not. However, the adjacency matrix requires no overhead for pointers, which can be a substantial cost, especially if the only information stored for an edge is one bit to indicate its existence. As the graph becomes denser, the adjacency matrix becomes relatively more space efficient. Sparse graphs are likely to have their adjacency list representation be more space efficient.
Example 11.2.1
Assume that a vertex index requires two bytes, a pointer requires four bytes, and an edge weight requires two bytes. Then, each link node in the adjacency list needs \(2 + 2 + 4 = 8\) bytes. The adjacency matrix for the directed graph above requires \(2 |\mathbf{V}^2| = 50\) bytes while the adjacency list requires \(4 |\mathbf{V}| + 8 |\mathbf{E}| = 68\) bytes. For the undirected version of the graph above, the adjacency matrix requires the same space as before, while the adjacency list requires \(4 |\mathbf{V}| + 8 |\mathbf{E}| = 116\) bytes (because there are now 12 edges represented instead of 6).
The adjacency matrix often requires a higher asymptotic cost for an algorithm than would result if the adjacency list were used. The reason is that it is common for a graph algorithm to visit each neighbor of each vertex. Using the adjacency list, only the actual edges connecting a vertex to its neighbors are examined. However, the adjacency matrix must look at each of its \(|\mathbf{V}|\) potential edges, yielding a total cost of \(\Theta(|\mathbf{V}^2|)\) time when the algorithm might otherwise require only \(\Theta(|\mathbf{V}| + |\mathbf{E}|)\) time. This is a considerable disadvantage when the graph is sparse, but not when the graph is closer to full.


§385-5 Maximum benefits payable. A claimant's maximum potential additional unemployment benefits shall be thirteen times the claimant's weekly benefit amount, and shall be determined in the week in which the claimant first claims for additional unemployment benefits and shall remain unchanged for the period specified in section 385-16. [L 1961, c 157, pt of §1; Supp, §93B-6; HRS §385-5; gen ch 1985]
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Getting Started
Download and Installation
Download the
source distribution and unpack the archive.
Alternatively, you can also clone the repository by running
git clone git://github.com/c2s/C2Serve.git
.
The directory which contains the build and source will be referred in the following as
C2Serve_ROOT_DIR
.
The build is realized using
CMake, so you will need
CMake installed on your build machine.
Open a terminal and create a build directory.
CMake will need it to create its build environment and to build the binaries there.
Next step is to create the build environment.
Change into the build directory and run
cmake C2Serve_ROOT_DIR
.
By default, the libraries, headers and test executables are built and installed into the directory preferred by
CMake.
You can specify another directory by defining the install prefix:
cmake -DINSTALL_PREFIX=MY_INSTALL_DIR
.
Now you can build the binaries by running
make install
.
In the destination directory, a
lib
, an
include
and a
bin
directory is created.
You will find all components that are needed to use C2Serve there.
To build the unit tests, you have to install the
Boost libraries or specify the Boost root directory when initializing the build environment:
-DBOOST_ROOT=MY_BOOST_ROOT_DIR
.
Integration
In the C2Serve root directory you will find a script named
FindC2Serve.cmake
.
This script is a
CMake module script to find components of the C2Serve framework.
Use this script to integrate C2Serve into your
CMake project.
Usage:
set( C2Serve_ROOT_DIR "path/to/c2s/root/dir" )
find_package( C2Serve )
if ( C2Serve_FOUND )
include_directories( ${C2Serve_INCLUDE_DIRS} )
add_executable( my_c2s_server MyC2SServerMain.cpp )
target_link_libraries( my_c2s_server ${C2Serve_LIBRARIES} )
endif ( C2Serve_FOUND )
Alternatively, you can also easily add the
CMakeLists.txt
in the C2Serve root directory to your build system:
set( C2Serve_DISABLE_SYSTEM_SETTINGS ON )
add_subdirectory( ${C2Serve_ROOT_DIR} )
First of all you should disable the C2Serve system scripts.
Afterwards you can add the
C2Serve_ROOT_DIR
to your build paths.
If this directory is placed outside of your source tree,
CMake needs a binary directory defined to know where to build the binaries.
Check the
CMake documendation for more information.


SCROOGED A film review by Randy Parker Copyright 1996 Randy Parker
RATING: **1/2 (out of ****)
(Review written in 1988)
What do you get when you cross A CHRISTMAS CAROL with GHOSTBUSTERS? The answer: a confused and overblown comedy with an identity crisis, such as director Richard Donner's SCROOGED. Although SCROOGED aspires to be a modern day version of Charles Dickens' classic tale, A CHRISTMAS CAROL, the film seems as if it would rather be a GHOSTBUSTERS sequel. The movie is overwrought with big-budget special effects that look good but add nothing to the story.
SCROOGED stars Bill Murray as Frank Cross, the youngest network president in television history. Cross is a real S.O.B.; he is greedy, mean-spirited, and merciless. Following the general story line of Dickens' novel, Cross encounters the Ghosts of Christmas Past, Present, and Future, who give him a chance to reevaluate his life and change his ways.
Murray's performance in SCROOGED is adequate, but nothing special. He is fine when his role calls for humor. In the film's dramatic scenes, however, Murray fails to give his character emotional conviction. Perhaps Donner is at fault for not pushing SCROOGED far enough in any one direction. The movie never really decides whether it wants to be a dramatic, yet comic, re-telling of A CHRISTMAS CAROL or an all-out slapstick parody of it. The film's ambivalence prevents it from completely succeeding either dramatically or comically. Although SCROOGED is fairly entertaining and funny, it is also extremely uneven. The drama, for the most part, is unconvincing and never catches fire. And throughout the film, the extravagant (but unnecessary) special effects distract from the story.
While Murray may be somewhat disappointing in his comeback role, the rest of the cast in SCROOGED is magnificent; in fact, the supporting performances and the unending string of cameos are almost enough by themselves to make the film worthwhile. Robert Mitchum, Bobcat Goldthwait, and David Johansen are among the many actors who help to enliven SCROOGED. Carol Kane almost steals the entire movie in her 25 minute sequence as the petite, but physically abusive, Ghost of Christmas Present. The cameos in SCROOGED come fast and furious--so stay on your toes. SCROOGED is also worthwhile for its witty parody of television. The programming on Murray's network yields some of the funniest moments in the entire movie.
Overall, SCROOGED is a mixed bag. It does contain several rewarding performances and genuine laughs, but you have to pay for them by sitting through the movie's failed drama, low humor, and pacing problems. SCROOGED came close to winning me over, but ultimately its rewards just weren't enough to compensate for its frustrations.
--- Randy Parker<EMAIL_ADDRESS>http://www.shoestring.org
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
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


The test.spin routine is what we want to run. In order to run this best, we recommend you read this entire document.
Copy sysdep_demo_ser.spin to sysdep.spin, and edit it for your configuration. Pay particular attention to the clock frequency, where the SD card is attached, and how you want to supply input and output. If you do not have an input mechanism, make the rx method return " " the first time it is called and make it sit in an infinite loop all subsequent times (so the test is run exactly once).
If at all possible, reformat your secure digital card using 32K clusters (if it is 256MB or larger); for other sizes refer to the formatting instructions. This step is not necessary but it will help us get consistent results. Note that the cluster size has the biggest effect on the pwrite speed (smaller clusters = slower writing).
Be aware the test may destroy any data you have on your card; in particular, it destructively smashes a range of blocks starting at 8MB (you can modify this in test.spin if you want, to be closer to the end of your card, or nearer the beginning if your card is smaller than 16MB). If you do not want to take the risk of losing your data, we recommend you do not run this test.
Download test.spin to your system, insert the card, and run the program. By default the program waits for an input character to be typed (to give you a chance to set up PropTerm or whatever terminal program you are using.) Any character should start the test.
The test is rather chatty; if it fails, it will fail pretty loudly. Don't worry about writing things down as they scroll by; performance numbers will be collected and then printed again at the end of the test.
What we would like to get is the following:


Statistics for {{ time_description }}
Cases & Tests
In {{ time_description }}, there were:
- {{ num_pos }} positive cases
{% if num_pos >= prev_num_pos %}▲{% else %}▼{% endif %}
{{ num_pos|sub:prev_num_pos }}
- {{ num_tests }} tests performed
{% if num_tests >= prev_num_tests %}▲{% else %}▼{% endif %}
{{ num_tests|sub:prev_num_tests }}
- a {{ pos_rate|floatformat:2 }}% positivity rate
{% if pos_rate >= prev_pos_rate %}▲{% else %}▼{% endif %}
{{ pos_rate|sub:prev_pos_rate|floatformat:2 }}%
Across different areas of the UK:
Across different age ranges:
Contacts & Tracing
In {{ time_description }},
- Unknown{# { cases_contacted|floatformat:2 }} #}% of positive cases were contacted by tracers {% if cases_contacted >= prev_cases_contacted %}▲{% else %}▼{% endif %} {{ cases_contacted|sub:prev_cases_contacted|floatformat:2 }}
- {{ avg_contacts|floatformat:2 }} was the average number of contacts per positive case {% if avg_contacts >= prev_avg_contacts %}▲{% else %}▼{% endif %} {{ avg_contacts|sub:prev_avg_contacts|floatformat:2 }}
- {{ max_contacts }} was the largest number of contacts per positive case {% if max_contacts >= prev_max_contacts %}▲{% else %}▼{% endif %} {{ max_contacts|sub:prev_max_contacts }}
These contacts occurred in several clusters across the country, where a cluster is made up of at least {{ min_cases_in_cluster }} cases within {{ cluster_radius }}m of each other.
Cluster map here


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US Dollar Index Technical Analysis: DXY approaching critical resistance at the 98.50 level
by Flavio Tosti- DXY is grinding higher on the first day of the week.
- The level to beat for buyers is the 98.50 price level.
DXY daily chart
DXY (US Dollar Index) is trading in an uptrend above the main daily simple moving averages (DMAs). This Monday, DXY is trading mixed hanging near the November highs. The market is nearing an important resistance at the 98.50 level.
DXY 4-hour chart
DXY is evolving above the main SMAs, suggesting a bullish bias in the medium term. If the buyers overcome the 98.50 level, the market is set to accelerate towards the 99.20 level and possibly to the 99.67 level (2019 high).
DXY 30-minute chart
The index is trading above the main SMAs, suggesting a bullish bias in the near term. Support is seen at the 98.20 and 98.00 price levels.
Additional key levels
Dollar Index Spot
| Overview | |
|---|---|
| Today last price | 98.3 |
| Today Daily Change | 0.05 |
| Today Daily Change % | 0.05 |
| Today daily open | 98.25 |
| Trends | |
|---|---|
| Daily SMA20 | 97.91 |
| Daily SMA50 | 98.24 |
| Daily SMA100 | 98.08 |
| Daily SMA200 | 97.59 |
| Levels | |
|---|---|
| Previous Daily High | 98.31 |
| Previous Daily Low | 97.82 |
| Previous Weekly High | 98.31 |
| Previous Weekly Low | 97.68 |
| Previous Monthly High | 99.67 |
| Previous Monthly Low | 97.15 |
| Daily Fibonacci 38.2% | 98.12 |
| Daily Fibonacci 61.8% | 98.01 |
| Daily Pivot Point S1 | 97.94 |
| Daily Pivot Point S2 | 97.64 |
| Daily Pivot Point S3 | 97.45 |
| Daily Pivot Point R1 | 98.43 |
| Daily Pivot Point R2 | 98.62 |
| Daily Pivot Point R3 | 98.92 |


{% trans 'Filter' %}
{% for spec in cl.filter_specs %}{% admin_list_filter cl spec %}{% endfor %}{% blocktrans with module_name_plural as name %}No {{ name }} have been created yet. One of the following must be added to your site before any {{ name }} can be added.{% endblocktrans %}
-
{% for type in required_parent_types %}
- {{ type|title }} {% endfor %}
{% blocktrans with module_name_plural as name %}No {{ name }} have been created yet.{% endblocktrans %} {% if has_add_permission %} {% blocktrans with add_url as url %} Why not add one? {% endblocktrans %} {% endif %}
{% endif %}


Green values are mathematically possible while red values are impossible. Your value is circled.
Note that this plot is lenient for sample sizes that are multiples of 8.
Plot may take some time to generate.
Mean:{{mean}} Size:{{size}}


This blog post will focus on my experience thus far with pair programming during Phase 0 of DBC. So far in my career as a student at DBC, I've had the opportunity to pair program at least 10 times. So I've had the chance to solve problems with 10 different people of all different experiences and personalities, and get feedback from each one of them.
Solving challenges via pair programming can be a little bit tricky. I consider myself a good problem solver and I have a method of breaking problems down in order to solve them. After doing this, I like to visualize each step I will take to solve said problem. This process may not be the most time-effective, but it allows me to prepare myself for any unintended challenges that may arise throughout the process. Understandably, other people have their own ways of solving problems as well. Often, I find my pair trying to jump into the problem before I am mentally ready. This can get a little frustrating for both of us, so I like to discuss our problem-solving preferances before we get into the challenge at hand. However, when I have clear, open, and flowing conversation with a pair, it is VERY fun to run through a problem and finish in a half hour and say "That's it?" That has only happened twice, but it's a great feeling.
After each pairing session, DBC requires us to leave feedback for the person we paired with. Reading what people thought of working with me was a very eye-opeing experience. It was neat because all of the reviews were pretty similar. Specifically, almost every person complimented my patience and solid communication. I'm not sure if everyone says that about everyone, but it was good to hear becuase those were two things that I was specifically working on. For the future, I'm going to try to improve even further on these two things. I am also going to focus on writing better feedback. Telling it straight is actually the nicest thing you can do, because it allows people to improve on themselves. That has been tough for me because I don't want to be rude sometimes.


---
layout: en/default
class: press
title: Press
permalink: /press
redirect_from: /press/
theme: dark
---
Rocket.Chat is the leading free and open source team communication platform. Rocket.Chat Press
Founded in 2015 by Gabriel Engel, Rocket.Chat began life as a Live Chat support tool built on Meteor. It has come a long way since then, received funding from US-based New Enterprise Associates in 2016 worth $5,000,000 USD and won multiple prizes such as a 2016 Bossie Award for Best Open Source Application and first prize in the 2017 edition of All Things Open’s Startup Competition.
Today, Rocket.Chat is an open source team collaboration platform that enables banks, NGOs, startups, and governmental organizations to have their own chat tool, customize its look and feel, choose their users, and securely manage data.
Users can set up Rocket.Chat on cloud or by hosting their own servers on-premises. Rocket.Chat can be forked on Github and become a new platform altogether, thanks to its code being open source and thus openly available to the community. Key features include free audio and video conferencing, guest access, screen and file sharing, LiveChat, LDAP Group Sync, two-factor authentication (2FA), E2E encryption, SSO, dozens of OAuth providers and unlimited users, guests, channels, messages, searches and files. Customization is enabled with the option of adding or removing features and selecting your own integrations, plugins, themes, mobile and desktop apps.
Rocket.Chat is a fast-growing platform that is now installed on over 180k servers and counts over 10m users worldwide as well as having an active, passionate community of over 700 developer-contributors who help Rocket.Chat’s core team of developers to constantly improve the product. Rocket.Chat’s long-term vision is to replace email with a real-time federated communications platform and offer services to enable businesses to be built using Rocket.Chat.
Our Founder
Gabriel founded Rocket.Chat in 2015 and is passionate about collaboration tools, open source and new tech. Prior to Rocket.Chat, Gabriel co-founded Konecty, and worked as Product Manager for Business Innovation and Mobile Applications at Vodafone Global Enterprise.
Gabriel is always keen to speak with members of the press about his experiences and success with the company thus far.
For more artwork, check out our Artwork Repository
For comments, interviews, image and video files, or to speak with our media liaison, please contact us at:


I guess you could call this a followup to my last post about deploying
Meteor applications to Heroku (found
here).
I ran into this little error today while trying to get a new Meteor
application up and running via Heroku. Basically, my app was crashing,
and running:
1
heroku logs
yielded the following:
123456789101112
app/packages/meteor/url_common.js:14
2012-10-26T22:08:39+00:00 app[web.1]: throw new Error("Must pass options.rootUrl or set ROOT_URL in the server
2012-10-26T22:08:39+00:00 app[web.1]: ^
2012-10-26T22:08:39+00:00 app[web.1]: Error: Must pass options.rootUrl or set ROOT_URL in the server environment
2012-10-26T22:08:39+00:00 app[web.1]: at Object.Meteor.absoluteUrl (app/packages/meteor/url_common.js:14:13)
2012-10-26T22:08:39+00:00 app[web.1]: at app/packages/accounts-password/email_templates.js:3:20
2012-10-26T22:08:39+00:00 app[web.1]: at /app/.meteor/local/build/server/server.js:107:21
2012-10-26T22:08:39+00:00 app[web.1]: at Array.forEach (native)
2012-10-26T22:08:39+00:00 app[web.1]: at run (/app/.meteor/local/build/server/server.js:93:7)
2012-10-26T22:08:39+00:00 app[web.1]: at Function._.each._.forEach (/app/.meteor/local/build/server/underscore.js:76:11)
2012-10-26T22:08:40+00:00 heroku[web.1]: Process exited with status 1
2012-10-26T22:08:40+00:00 heroku[web.1]: State changed from starting to crashed
Not overly helpful, as it didn’t seem to be a Meteor specific error. And
it turns out it wasn’t, at least not wholly. I found that Heroku has a
config command where you can set things like the ROOT_URL, and for some
reason, in this case it needed it set manually (I’ve never had to use
this before, so maybe I’m just doing something wrong). Anyways, I ran:
1
heroku config:add ROOT_URL=[Heroku instance URL]
And, lo and behold, my app stopped crashing and started working.
I would like to know why I had to manually set this in this case, so if
anyone knows, give a shout in the comments. I’ll try and figure it out
on my own in the meantime.


章节目录 - Table of Content
下面罗列了一些Gradle的特性。
Here is a list of some of Gradle's features.
Gradle的核心是基于Groovy的DSL(领域特定语言)。Gradle通过提供声明式的语言元素来将声明式构建提高到一个新的水平，您可以根据自己的喜好组装。 这些元素还为Java，Groovy，OSGi，Web和Scala项目提供按惯例支持。 甚至，这种声明式的语言是可扩展的。 添加您自己的新语言元素或增强现有语言元素，从而提供简洁，可维护和易理解的构建。
At the heart of Gradle lies a rich extensible Domain Specific Language (DSL) based on Groovy. Gradle pushes declarative builds to the next level by providing declarative language elements that you can assemble as you like. Those elements also provide build-by-convention support for Java, Groovy, OSGi, Web and Scala projects. Even more, this declarative language is extensible. Add your own new language elements or enhance the existing ones, thus providing concise, maintainable and comprehensible builds.
声明式的语言描述在通用任务图中具有更高优先级，可以用来更加灵活地支持你的构建，可以最大程度的满足构建中的特殊需求。
The declarative language lies on top of a general purpose task graph, which you can fully leverage in your builds. It provides utmost flexibility to adapt Gradle to your unique needs.
Gradle强大的灵活性和通用性可以使你直接为你构建应用一些通用的原则。例如，你可以很容易的应用一些组件来完成你的构建。
The suppleness and richness of Gradle finally allows you to apply common design principles to your build. For example, it is very easy to compose your build from reusable pieces of build logic. Inline stuff where unnecessary indirections would be inappropriate.  Avoid smells like shotgun changes or divergent change that turn your build into a maintenance nightmare. At last you can create a well structured, easily maintained, comprehensible build.
你会非常乐意在整个构建执行的生命周期中使用 Gradle, 因为Gradle允许你管理和定制它的配置和执行行为。
From being a pleasure to be used embedded to its many hooks over the whole lifecycle of build execution, Gradle allows you to monitor and customize its configuration and execution behavior to its very core.
Gradle拥有良好的伸缩性，从简单的单一项目构建到企业级的多项目构建，这能显著提高您得生产效率。对于结构化构建而言这是实实在在的特性。藉由最先进的增量构建方法，可以切实解决大型企业级项目构建时的性能之痛。
Gradle scales very well. It significantly increases your productivity, from simple single project builds up to huge enterprise multi-project builds. This is true for structuring the build. With the state-of-art incremental build function, this is also true for tackling the performance pain many large enterprise builds suffer from.
Gradle对多项目构建的支持十分优秀。项目的依赖是一等公民，您可以在多项目构建中自行组织项目之间的关系，Gralde将遵循您的配置。
同时，Gradle提供了局部构建的功能.如果你构建一个单独的子项目, Gradle 会构建这个子项目依赖的所有子项目。 你也可以选择依赖于另一个特别的子项目重新构建这些子项目。 这样在一些大型项目里就可以节省非常多的时间。
Gradle's support for multi-project build is outstanding. Project dependencies are first class citizens. We allow you to model the project relationships in a multi-project build as they really are for your problem domain. Gradle follows your layout not vice versa.
Gradle provides partial builds. If you build a single subproject Gradle takes care of building all the subprojects that subproject depends on. You can also choose to rebuild the subprojects that depend on a particular subproject. Together with incremental builds this is a big time saver for larger builds.
每个团队都有管理外部依赖的方式。从远程Maven仓库或者Ivy仓库的依赖管理到本地文件系统中的依赖，Gradle针对各种方式提供了便利的支持。
Different teams prefer different ways to manage their external dependencies. Gradle provides convenient support for any strategy. From transitive dependency management with remote Maven and Ivy repositories to jars or directories on the local file system.
Ant 中的task是 Ant 中的核心构成。而更有趣的是，Ant projects 也同样是核心构成。 Gradle 对任意的 Ant 项目提供了深度导入，并在运行时将 Ant 目标(target)转换为原生的 Gradle 任务(task)。 你可以从 Gradle 上依赖它们(Ant targets)，增强它们，甚至在你的 build.xml 上定义对 Gradle tasks 的依赖。Gradle 为属性、路径等等提供了同样的整合。 Gradle 完全支持用于发布或检索依赖的 Maven 或 Ivy 仓库。 Gradle 同样提供了一个转换器，用于将一个 Maven pom.xml 文件转换为一个 Gradle 脚本。Maven 项目的运行时导入的功能将很快会有。
Ant tasks are first class citizens. Even more interesting, Ant projects are first class citizens as well. Gradle provides a deep import for any Ant project, turning Ant targets into native Gradle tasks at runtime. You can depend on them from Gradle, you can enhance them from Gradle, you can even declare dependencies on Gradle tasks in your build.xml. The same integration is provided for properties, paths, etc ...
Gradle fully supports your existing Maven or Ivy repository infrastructure for publishing and retrieving dependencies. Gradle also provides a converter for turning a Maven pom.xml
into a Gradle script. Runtime imports of Maven projects will come soon.
Gradle可以适应任何你有的结构。 因此，您可以随时在同一个分支中开发您的Gradle构建，您的生产构建存在，并且可以并行演化。 我们通常建议编写测试，确保生成的工件是相似的。 这样迁移的破坏性越小，越可靠越好。 这是遵循通过应用婴儿步骤重构的最佳实践。
Gradle can adapt to any structure you have. Therefore you can always develop your Gradle build in the same branch where your production build lives and both can evolve in parallel. We usually recommend to write tests that make sure that the produced artifacts are similar. That way migration is as less disruptive and as reliable as possible. This is following the best-practices for refactoring by applying baby steps.
Gradle 的构建脚本是采用 Groovy 写的，而不是用 XML。 但与其他方法不同，它并不只是展示了由一种动态语言编写的原始脚本的强大。 那样将导致维护构建变得很困难。 Gradle 的整体设计是面向被作为一门语言，而不是一个僵化的框架。 并且 Groovy 是我们允许你通过抽象的 Gradle 描述你个人的 story 的黏合剂。 Gradle 提供了一些标准通用的 story。这是我们相比其他声明性构建系统的主要特点。 我们的 Groovy 支持也不是简单的糖衣层，整个 Gradle 的 API 都是完全 groovy 化的。只有通过 Groovy才能去运用它并对它提高效率。
Gradle's build scripts are written in Groovy, not XML. But unlike other approaches this is not for simply exposing the raw scripting power of a dynamic language. That would just lead to a very difficult to maintain build. The whole design of Gradle is oriented towards being used as a language, not as a rigid framework. And Groovy is our glue that allows you to tell your individual story with the abstractions Gradle (or you) provide. Gradle provides some standard stories but they are not privileged in any form. This is for us a major distinguishing feature compared to other declarative build systems. Our Groovy support is not just sugar coating. The whole Gradle API is fully Groovy-ized. Adding Groovy results in an enjoyable and productive experience.
Gradle Wrapper可以使你在没有安装Gradle的机器上执行构建。这对一些持续集成服务器来说是非常有用的。对于开源项目而言，这也会降低采用Gradle进行构建的入门门槛。对企业而言，采用Gradle Wrapper无需为客户机提供相应的管理方法。采用它可以轻易地采用统一Gradle版本进行构建。
The Gradle Wrapper allows you to execute Gradle builds on machines where Gradle is not installed. This is useful for example for some continuous integration servers. It is also useful for an open source project to keep the barrier low for building it. The wrapper is also very interesting for the enterprise. It is a zero administration approach for the client machines. It also enforces the usage of a particular Gradle version thus minimizing support issues.
译者注：有人把Wrapper称作包装器，有人称之为封装。简而言之，言而简之 ：Gradle Wrapper 就是一个打包好了的Gradle，不需要你进行额外的配置，它会自己去下载Gradle安装包，调用原始的Gradle命令。
Gradle 是开源项目, 遵循 ASL许可。
Gradle is an open source project, and is licensed under the ASL.
我们认为采用基于动态语言的DSL作为脚本语言相对XML具有更大的优势。为何我们在众多动态语言中选中了Groovy呢？原因在于Gradle上下文的操作性。虽然Gradle核心是一个通用的构建工具，但它更主要的还是聚焦于JAVA项目的构建。在Java项目中团队成员对Java都非常熟悉，所以我们尽可能的保证构建过程对所有的团队成员都是透明的。
We think the advantages of an internal DSL (based on a dynamic language) over XML are tremendous when used in build scripts. There are a couple of dynamic languages out there. Why Groovy? The answer lies in the context Gradle is operating in. Although Gradle is a general purpose build tool at its core, its main focus are Java projects. In such projects the team members will be very familiar with Java. We think a build should be as transparent as possible to all team members.
或许你可能会疑问为何不直接用 Java 作为构建脚本语言，这里有一个问题，就是对于团队的最高的透明度和最低的学习曲线，但是由于 Java 的一些限制，作为构建脚本并不理想。相较而言，Python、Groovy、Rudy都更能胜任此项工作。我们选择 Groovy 是因为它较其他语言而言对 Java 开发者更加透明。它的基本语法与 Java 类似，包括 本文系统，包结构和其他方面。Groovy 提供了最重要内容但都是符合 Java 基础功能的。
In that case, you might argue why we don't just use Java as the language for build scripts. We think this is a valid question. It would have the highest transparency for your team and the lowest learning curve, but because of the limitations of Java, such a build language would not be as nice, expressive and powerful as it could be. [1] Languages like Python, Groovy or Ruby do a much better job here. We have chosen Groovy as it offers by far the greatest transparency for Java people. Its base syntax is the same as Java's as well as its type system, its package structure and other things. Groovy provides much more on top of that, but with the common foundation of Java.
对于对 Python 或 Ruby 知识拥有强烈的学习欲望的 Java 开发者来说，上述论点不适用。该工具的设计非常适合于创建另一个建立在 JRuby 和Jython 脚本引擎。对于我们来说暂时它只是不具有最高优先级。我们高兴地支持任何社区的努力来创建额外的构建脚本引擎。
For Java developers with Python or Ruby knowledge or the desire to learn them, the above arguments don't apply. The Gradle design is well-suited for creating another build script engine in JRuby or Jython. It just doesn't have the highest priority for us at the moment. We happily support any community effort to create additional build script engines.
[1] At http://www.defmacro.org/ramblings/lisp.html you find an interesting article comparing Ant, XML, Java and Lisp. It's funny that the 'if Java had that syntax' syntax in this article is actually the Groovy syntax.


--- permalink: /publications/ author_profile: true redirect_from: - /md/ - /markdown.html ---
Learning Discriminative Prototypes with Dynamic Time Warping.
Xiaobin Chang, Frederick Tung, Greg Mori.
CVPR, 2021. [PDF] [Code] [Bibtex]
Disjoint Label Space Transfer Learning with Common Factorised Space.
Xiaobin Chang, Yongxin Yang, Tao Xiang, Timothy M. Hospedales.
AAAI, 2019. [PDF] [Bibtex]
Multi-Level Factorisation Net for Person Re-Identification.
Xiaobin Chang, Timothy M. Hospedales, Tao Xiang.
CVPR, 2018. [PDF] [Bibtex]
Scalable and Effective Deep CCA via Soft Decorrelation.
Xiaobin Chang, Tao Xiang, Timothy M Hospedales.
CVPR, 2018. [PDF] [Bibtex]
The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching.
Qian Yu, Xiaobin Chang, Yi-Zhe Song, Tao Xiang, Timothy M Hospedales.
arXiv, 2017. [PDF] [Bibtex]
Deep Multi-View Learning with Stochastic Decorrelation Loss.
Xiaobin Chang, Tao Xiang, Timothy M Hospedales.
arXiv, 2017. [PDF] [Bibtex]
L1 Graph Based Sparse Model for Label De-noising.
Xiaobin Chang, Tao Xiang, Timothy M Hospedales.
BMVC Oral, 2016. [PDF] [Bibtex]
Facial skin beautification via sparse representation over learned layer dictionary.
Xi Lu, Xiaobin Chang, Xiaohua Xie, Jian-Fang Hu, Wei-Shi Zheng.
IJCNN, 2016. [PDF] [Bibtex]
Learning person–person interaction in collective activity recognition.
Xiaobin Chang, Wei-Shi Zheng, Jianguo Zhang.
IEEE Transactions on Image Processing, 2015. [PDF] [Bibtex]


None
Comparison
Comparison
Below are the summary comparison plots
comoving_distance
The figures below show the summary plots for the run
H1_matched_filter_snr
The figures below show the summary plots for the run
V1_matched_filter_abs_snr
The figures below show the summary plots for the run
recalib_V1_frequency_6
The figures below show the summary plots for the run
V1_optimal_snr
The figures below show the summary plots for the run
cos_theta_jn
The figures below show the summary plots for the run
recalib_L1_frequency_9
The figures below show the summary plots for the run
spin_1z
The figures below show the summary plots for the run
L1_matched_filter_snr
The figures below show the summary plots for the run
recalib_V1_frequency_5
The figures below show the summary plots for the run
recalib_H1_phase_8
The figures below show the summary plots for the run
recalib_V1_phase_1
The figures below show the summary plots for the run
recalib_H1_phase_6
The figures below show the summary plots for the run
recalib_V1_amplitude_1
The figures below show the summary plots for the run
chi_1_in_plane
The figures below show the summary plots for the run
recalib_H1_frequency_7
The figures below show the summary plots for the run
phi_1
The figures below show the summary plots for the run
final_spin_non_evolved
The figures below show the summary plots for the run
phi_12
The figures below show the summary plots for the run
total_mass_source
The figures below show the summary plots for the run
recalib_H1_amplitude_4
The figures below show the summary plots for the run
recalib_H1_frequency_6
The figures below show the summary plots for the run
theta_jn
The figures below show the summary plots for the run
recalib_H1_frequency_2
The figures below show the summary plots for the run
recalib_H1_amplitude_1
The figures below show the summary plots for the run
final_mass_source_non_evolved
The figures below show the summary plots for the run
final_mass_non_evolved
The figures below show the summary plots for the run
recalib_H1_frequency_8
The figures below show the summary plots for the run
network_matched_filter_snr
The figures below show the summary plots for the run
recalib_H1_phase_1
The figures below show the summary plots for the run
recalib_L1_phase_7
The figures below show the summary plots for the run
recalib_H1_amplitude_2
The figures below show the summary plots for the run
radiated_energy_non_evolved
The figures below show the summary plots for the run
recalib_V1_frequency_7
The figures below show the summary plots for the run
recalib_L1_frequency_0
The figures below show the summary plots for the run
recalib_H1_frequency_5
The figures below show the summary plots for the run
recalib_H1_amplitude_5
The figures below show the summary plots for the run
psi
The figures below show the summary plots for the run
recalib_L1_phase_6
The figures below show the summary plots for the run
recalib_V1_amplitude_8
The figures below show the summary plots for the run
chi_2_in_plane
The figures below show the summary plots for the run
recalib_H1_frequency_4
The figures below show the summary plots for the run
recalib_L1_phase_9
The figures below show the summary plots for the run
phi_jl
The figures below show the summary plots for the run
recalib_V1_phase_5
The figures below show the summary plots for the run
recalib_L1_frequency_2
The figures below show the summary plots for the run
recalib_H1_amplitude_3
The figures below show the summary plots for the run
spin_2x
The figures below show the summary plots for the run
recalib_V1_amplitude_0
The figures below show the summary plots for the run
spin_2y
The figures below show the summary plots for the run
recalib_L1_amplitude_3
The figures below show the summary plots for the run
final_mass_source
The figures below show the summary plots for the run
recalib_V1_phase_3
The figures below show the summary plots for the run
recalib_L1_amplitude_5
The figures below show the summary plots for the run
recalib_V1_frequency_1
The figures below show the summary plots for the run
chirp_mass_source
The figures below show the summary plots for the run
H1_matched_filter_snr_angle
The figures below show the summary plots for the run
recalib_L1_frequency_4
The figures below show the summary plots for the run
phi_2
The figures below show the summary plots for the run
tilt_1
The figures below show the summary plots for the run
recalib_H1_frequency_3
The figures below show the summary plots for the run
recalib_L1_frequency_5
The figures below show the summary plots for the run
recalib_L1_phase_0
The figures below show the summary plots for the run
recalib_H1_frequency_0
The figures below show the summary plots for the run
recalib_V1_amplitude_5
The figures below show the summary plots for the run
final_spin
The figures below show the summary plots for the run
recalib_V1_amplitude_7
The figures below show the summary plots for the run
H1_time
The figures below show the summary plots for the run
recalib_V1_amplitude_4
The figures below show the summary plots for the run
final_mass
The figures below show the summary plots for the run
recalib_L1_amplitude_0
The figures below show the summary plots for the run
L1_matched_filter_snr_angle
The figures below show the summary plots for the run
V1_matched_filter_snr
The figures below show the summary plots for the run
recalib_L1_frequency_1
The figures below show the summary plots for the run
H1_matched_filter_abs_snr
The figures below show the summary plots for the run
peak_luminosity
The figures below show the summary plots for the run
spin_2z
The figures below show the summary plots for the run
recalib_V1_frequency_9
The figures below show the summary plots for the run
recalib_L1_amplitude_4
The figures below show the summary plots for the run
V1_time
The figures below show the summary plots for the run
recalib_H1_phase_7
The figures below show the summary plots for the run
recalib_V1_phase_4
The figures below show the summary plots for the run
log_likelihood
The figures below show the summary plots for the run
recalib_L1_amplitude_9
The figures below show the summary plots for the run
recalib_H1_amplitude_7
The figures below show the summary plots for the run
recalib_V1_phase_2
The figures below show the summary plots for the run
recalib_L1_frequency_3
The figures below show the summary plots for the run
mass_1
The figures below show the summary plots for the run
spin_1y
The figures below show the summary plots for the run
recalib_L1_amplitude_2
The figures below show the summary plots for the run
recalib_L1_phase_3
The figures below show the summary plots for the run
recalib_H1_phase_9
The figures below show the summary plots for the run
recalib_V1_phase_7
The figures below show the summary plots for the run
network_optimal_snr
The figures below show the summary plots for the run
recalib_L1_amplitude_6
The figures below show the summary plots for the run
recalib_L1_amplitude_1
The figures below show the summary plots for the run
recalib_V1_phase_9
The figures below show the summary plots for the run
recalib_H1_amplitude_8
The figures below show the summary plots for the run
V1_matched_filter_snr_angle
The figures below show the summary plots for the run
radiated_energy
The figures below show the summary plots for the run
mass_2
The figures below show the summary plots for the run
recalib_V1_amplitude_3
The figures below show the summary plots for the run
chi_p
The figures below show the summary plots for the run
L1_matched_filter_abs_snr
The figures below show the summary plots for the run
recalib_V1_phase_0
The figures below show the summary plots for the run
recalib_V1_frequency_0
The figures below show the summary plots for the run
recalib_V1_phase_6
The figures below show the summary plots for the run
recalib_V1_phase_8
The figures below show the summary plots for the run
inverted_mass_ratio
The figures below show the summary plots for the run
ra
The figures below show the summary plots for the run
symmetric_mass_ratio
The figures below show the summary plots for the run
H1_optimal_snr
The figures below show the summary plots for the run
dec
The figures below show the summary plots for the run
recalib_L1_frequency_7
The figures below show the summary plots for the run
total_mass
The figures below show the summary plots for the run
a_2
The figures below show the summary plots for the run
mass_ratio
The figures below show the summary plots for the run
recalib_V1_amplitude_9
The figures below show the summary plots for the run
recalib_V1_amplitude_6
The figures below show the summary plots for the run
spin_1x
The figures below show the summary plots for the run
recalib_H1_phase_2
The figures below show the summary plots for the run
recalib_V1_frequency_8
The figures below show the summary plots for the run
recalib_V1_frequency_2
The figures below show the summary plots for the run
time_jitter
The figures below show the summary plots for the run
tilt_2
The figures below show the summary plots for the run
recalib_L1_amplitude_7
The figures below show the summary plots for the run
a_1
The figures below show the summary plots for the run
recalib_L1_phase_8
The figures below show the summary plots for the run
recalib_L1_phase_4
The figures below show the summary plots for the run
recalib_H1_phase_5
The figures below show the summary plots for the run
recalib_L1_phase_2
The figures below show the summary plots for the run
mass_1_source
The figures below show the summary plots for the run
recalib_L1_phase_5
The figures below show the summary plots for the run
recalib_H1_phase_3
The figures below show the summary plots for the run
recalib_L1_frequency_8
The figures below show the summary plots for the run
chi_eff
The figures below show the summary plots for the run
recalib_H1_frequency_9
The figures below show the summary plots for the run
recalib_L1_frequency_6
The figures below show the summary plots for the run
L1_time
The figures below show the summary plots for the run
L1_optimal_snr
The figures below show the summary plots for the run
recalib_H1_phase_4
The figures below show the summary plots for the run
recalib_H1_amplitude_6
The figures below show the summary plots for the run
recalib_H1_amplitude_9
The figures below show the summary plots for the run
cos_iota
The figures below show the summary plots for the run
luminosity_distance
The figures below show the summary plots for the run
redshift
The figures below show the summary plots for the run
peak_luminosity_non_evolved
The figures below show the summary plots for the run
recalib_H1_frequency_1
The figures below show the summary plots for the run
recalib_V1_frequency_4
The figures below show the summary plots for the run
recalib_V1_amplitude_2
The figures below show the summary plots for the run
iota
The figures below show the summary plots for the run
mass_2_source
The figures below show the summary plots for the run
chirp_mass
The figures below show the summary plots for the run
recalib_L1_phase_1
The figures below show the summary plots for the run
cos_tilt_1
The figures below show the summary plots for the run
recalib_V1_frequency_3
The figures below show the summary plots for the run
recalib_H1_amplitude_0
The figures below show the summary plots for the run
geocent_time
The figures below show the summary plots for the run
recalib_L1_amplitude_8
The figures below show the summary plots for the run
recalib_H1_phase_0
The figures below show the summary plots for the run
cos_tilt_2
The figures below show the summary plots for the run
phase
The figures below show the summary plots for the run