July 22, 1999
IUGG General Assembly, Birmingham, U.K.
Attending: Raymond Arritt, Jens
Hesselbjerg Christensen, Michael Fox-Rabinovitz, Filippo
Giorgi, Richard Jones, Jack Katzfey, Brent Lofgren, Daniel
Luethi, Bennert Machenhauer, John McGregor, John Roads,
Markku Rummukainen, Huqiang Zhang.
After introductions and a buffet dinner (courtesy of EPRI)
Arritt gave an overview of the agenda including brief plans
for analysis and publications from Experiment 1b. It was
agreed that while consistency of the regional models with
observed large-scale fields is a necessary point, the
analysis should focus mainly on other issues.
Some preliminary comments were made on possible followup
studies. Machehauer pointed out the need to address questions
of predictability, requiring true "climate" runs
(as opposed to specific episodes) or ensembles. Jones
suggested a second integration with different initial
conditions. It was generally agreed that followup studies
such as sensitivity experiments should be done as individual
efforts and not as a part of the PIRCS intercomparison.
Analysis of Experiment 1b
Discussion then turned to analysis of the 1993
case. Most
of the group felt that the analysis should focus on the
hydrologic cycle. Katzfey suggested examination of the
frequency distribution of precipitation, for which we could
get some information from the 3 hourly data. Following from
this suggestion, Roads recommended looking at extremes and
distributions. Fox-Rabinovitz suggested looking at
diagnostics related to the hydrologic cycle such as cloud
fraction, precipitable water and vertically integrated
fluxes. Katzfey pointed out the utility of satellite data
(such as ISCCP) to provide cloud optical depth and cloud
water for comparison to the models. Comparison of surface
fluxes also was recommended. Jones suggested looking at the
question of moisture recycling; i.e., the relative roles of
evaporation versus horizontal moisture flux in producing
precipitation. Roads suggested looking at the diurnal cycle
of precipitation and its association with the low-level jet.
Rummakainen pointed out the importance of critical
combinations of variables and variable extremes. It could be
instructive to see whether regional models can produce these.
Machenhauer noted that surface drying that has often been
found in climate scenarios and suggested this could be
examined.
Proposed PIRCS Experiment 2
The second major topic of discussion centered
around
possibilities for a future experiment or experiments. Roads
presented a proposal for simulating the El Nino episode of
1997-99. Impacts on Florida and California are of particular
interest. The regional models could be embedded within a
prediction instead of analysis. Ideally resolution should be
25 km in order for the models to represent terrain effects in
California, but this would almost certainly require a domain
smaller than the continental U.S. Fox-Rabinovitz asked
whether the models should use a free running land surface
scheme or prescribed soil moisture. Christensen asked what
criteria will be used for validation. The ENSO cycle is
driven by the large scale, but are there additional aspects
to be considered? How do we quantify how accurate regional
models have to be?
Rummukainen asked about the source of driving data. Roads
responded that this could be taken from analyses or from
forecasts, e.g., short term seasonal predictions. Christensen
asked whether an ensemble approach might be useful, while
Fox-Rabinovitz asked what is meant by prediction mode. Roads
responded that there are many ways of driving the regional
models. We can do "pure" prediction as well as pure
analysis, or any range between. Examples include analyses
with observed SST, or predicted SST, etc. We could use
prescribed SST and NCEP analysis, then compare with a
prediction having some skill (e.g., IRI). McGregor asked
whether an AMIP style approach could be useful. Giorgi pointed out the need to clarify the objective of
the experiment and that Roads' proposal would fit an
objective of seasonal prediction. We need to consider what is
a natural followon from Experiment 1. The group could do a
multiyear run, and look at other mesoscale effects (e.g.,
topography, western U.S., winter) in more of a natural
extension than seasonal prediction. The possibility of an
enhanced CO2 scenario was briefly discussed but it
was decided that meaningful runs would have to be too long to
be practical.
The topic of verification data for the 1993 experiment was
briefly revisited. Fox-Rabinovitz mentioned that his group
has run the 1993 experiment using their stretched grid model
in data assimilation mode with 60 km resolution over the U.S.
These results include comprehensive diagnostics and are
available for use as verification. Roads agreed that this
could be useful for the second paper and asked whether it is
it better than the NCEP reanalysis. Fox-Rabinovitz replied
that the stretched grid analyses are a bit better than the
standard (coarser resolution) GEOS (not NCEP) analyses,
according to his evaluation for summers of 1988 and 1993.
Giorgi suggested comparing the 1988 and 1993 model-predicted
precipitation to reanalysis precipitation. There was brief
discussion of the ways in which reanalysis precipitation is
affected by the "spinup" problem.
Roads said we need to ask what are people willing to do.
Roads suggested reconsidering what data should be archived as
the data volume can be a burden on some participants; for
example, whether it might be sufficient to save monthly
averages. Fox-Rabinovitz felt there is a clear need to save 6
hourly values in order to resolve the diurnal cycle and
mesoscale features. Arritt pointed out that data volume could
be reduced by saving only certain levels and pre-computing
some of the required quantities such as heights of standard
pressure surfaces. Katzfey recommended we save and examine
cloud fields (fraction, optical depth).
Stretched grid modeling
Fox-Rabinovitz presented an update on stretched grid modeling. His group is working on developing the stretched grid version of the new NASA/NCAR model. This version will be used for regional climate studies. Several groups are doing stretched grid modeling with about 3-4 groups aggressively involved. This could be a new component for PIRCS. Fox-Rabinovitz is running the model both in "simulation mode" and "assimilation mode." For the PIRCS experiment, they are running in assimilation mode with data withheld over the U.S. He will submit the 1993 results for the PIRCS comparison.
NCAR Community Regional Climate Model
In the absence of Ruby Leung, Arritt briefly discussed plans for a possible regional modeling option within the NCAR MM5, presented at a meeting on 24 June 1999 at NCAR. The modified OSU land-surface scheme (similar to the Eta model) is being added to make MM5 more suitable for extended runs. Giorgi mentioned that the relatively simple standard MM5 radiation scheme could be a problem as well as the land surface.
Geographical regions for Experiment 2
The group returned to recommendations for PIRCS
Experiment
2, focusing on the region or regions to be simulated. This
was motivated by comments by Christensen that while the
original PIRCS goals addressed aspects of interest to all,
not just U.S. participants, the project seems to have
progressed to a U.S. focus. This could discourage
participation from groups outside of the U.S. Fox-Rabinovitz
pointed out that modelers once were preoccupied with Indian
monsoon and that now ENSO over the U.S. is considered an
interesting event.
Christensen pointed out that the program needs overall
scientific objectives and must also be of benefit to
individual groups; do we learn anything new? We need to
revisit what is the fundamental reason for doing
intercomparisons and stress the new science. Giorgi felt that
a natural next step is to do multi-year perfect boundary
condition experiments and for this reason likes the 1997-99
ENSO idea.
Arritt brought up the possibility of doing the ENSO for
two regions. North America could be run as an extension of
Experiment 1 with a second region being simulated for
contrast. Giorgi asked what this region might be, and the
possibility of Brazil was mentioned. Other regions were also
mentioned. It was pointed out that there already lots of
people doing regional climate simulations for East Asia.
Africa was brought up as a possibility. After discussion, a
consensus emerged that South America would probably be the
most appropriate region to simulate in addition to (or
instead of) North America. This is partly because there are
some similarities to North America (e.g., the low-level jet
as a frequent mesoscale feature) and data from other studies
could be used for validation. Christensen reiterated the need
to keep in mind the ultimate goal of the project and that it
is hard to use separate simulations as an ensemble. We could
approach the two regions as 2a and 2b. Giorgi pointed out the
need to make contacts with other investigators and
institutions; e.g, Carlos Nobre, PACS, IAI. Roads mentioned
funding and suggested we collaborate more closely with AMIP.
Arritt volunteered to summarize the discussions into a
plan for PIRCS Experiment 2, with feedback from the rest of
the Iowa State group and from the PIRCS community at large.
Next PIRCS meeting
The topic of the next PIRCS meeting was brought up. The most likely venues are the AGU Fall Meeting in San Francisco (December 1999) or the AMS annual meeting in Long Beach (January 2000). Some felt we should meet at both places.
| Copyright/Trademark Legal Notice |


What's New in CiphSafe 1.3
Note: 1.3.1 is a very simple update, adding 64bit support (no new options or features).
Several by-popular-request items have been added to CiphSafe for 1.3.
Export
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The ability to export comma-separated value (CSV) and XML files
has been added. Now you can get data out of CiphSafe whenever
needed.
Exporting
|
Drag and drop handles moving and copying
|
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|
Prior versions only did a copy when dragging entries. Now,
per the Mac OS standard key combination of Command to move or
Option to copy, entries can be moved or copied as needed. Note
that moving only works between CiphSafe documents, you can
only copy to other applications.
Drag and drop
|
Alternate row color is selectable
|
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|
The main view of entries has had the alternating color option
for some time, but 1.3 now allows the alternate color to be
changed in case the standard light blue is too boring. (Yes,
bright red is really ugly for this.)
|
Additional new features
|
|
Some other, minor features include:
-
Searching—With Mac OS X 10.3, the rounded search field became
available to non-Apple developers; hence CiphSafe now uses
one instead of the uglier set of pop-up menu, rounded text
field, and reset button. The option of searching all
fields is now also available.
-
Help—CiphSafe's help has been moved to the
task-based style popular with Apple's apps, as well as vastly expanded.
Thanks to
Andy Matuschak for the documentation and toolkit.
-
Main window—the spacebar, when the main table has
focus, now pages down.
-
Links—Point things to
SourceForge instead of withay.com.
-
Preferences—The "warn on short passphrase" has been
removed, CiphSafe will now always warn when it's short.
-
Preferences—The "confirm entry deletion" has been removed,
as CiphSafe is capable of undo if an entry is accidentally deleted.
-
File Saving—Older version of CiphSafe didn't do anything
with the saved file's permission, which resulted in them being
readable to anyone else on the computer. Version 1.3 now makes sure
they are saved as readable only to the owner.
-
File Opening—Previously, if you had an auto-open document
selected and started CiphSafe by either double-clicking or dragging
a document, CiphSafe would open that as well as the auto-open
document. Now, the auto-open is not opened when another document is requested.
-
Entry Contextual Menu—The contextual menu (via right- or Ctrl-clicking)
for entries now disables any options for fields which are not present in the
given entry.
-
Quitting—An issue where having changes in a change window then quitting
CiphSafe would not warn about unsaved changes has been fixed.
-
Performance—Performance for really large documents containing complicated
notes fields has been improved.
-
Code—Not something the average user will see, but a number
of changes at the source level have been made to improve
things.
|


What is Apache Flink? — Operations
Apache Flink is a framework for stateful computations over unbounded and bounded data streams. Since many streaming applications are designed to run continuously with minimal downtime, a stream processor must provide excellent failure recovery, as well as, tooling to monitor and maintain applications while they are running.
Apache Flink puts a strong focus on the operational aspects of stream processing. Here, we explain Flink’s failure recovery mechanism and present its features to manage and supervise running applications.
Run Your Applications Non-Stop 24/7
Machine and process failures are ubiquitous in distributed systems. A distributed stream processors like Flink must recover from failures in order to be able to run streaming applications 24/7. Obviously, this does not only mean to restart an application after a failure but also to ensure that its internal state remains consistent, such that the application can continue processing as if the failure had never happened.
Flink provides several features to ensure that applications keep running and remain consistent:
- Consistent Checkpoints: Flink’s recovery mechanism is based on consistent checkpoints of an application’s state. In case of a failure, the application is restarted and its state is loaded from the latest checkpoint. In combination with resettable stream sources, this feature can guarantee exactly-once state consistency.
- Efficient Checkpoints: Checkpointing the state of an application can be quite expensive if the application maintains terabytes of state. Flink’s can perform asynchronous and incremental checkpoints, in order to keep the impact of checkpoints on the application’s latency SLAs very small.
- End-to-End Exactly-Once: Flink features transactional sinks for specific storage systems that guarantee that data is only written out exactly once, even in case of failures.
- Integration with Cluster Managers: Flink is tightly integrated with cluster managers, such as Hadoop YARN, Mesos, or Kubernetes. When a process fails, a new process is automatically started to take over its work.
- High-Availability Setup: Flink feature a high-availability mode that eliminates all single-points-of-failure. The HA-mode is based on Apache ZooKeeper, a battle-proven service for reliable distributed coordination.
Update, Migrate, Suspend, and Resume Your Applications
Streaming applications that power business-critical services need to be maintained. Bugs need to be fixed and improvements or new features need to be implemented. However, updating a stateful streaming application is not trivial. Often one cannot simply stop the applications and restart a fixed or improved version because one cannot afford to lose the state of the application.
Flink’s Savepoints are a unique and powerful feature that solves the issue of updating stateful applications and many other related challenges. A savepoint is a consistent snapshot of an application’s state and therefore very similar to a checkpoint. However in contrast to checkpoints, savepoints need to be manually triggered and are not automatically removed when an application is stopped. A savepoint can be used to start a state-compatible application and initialize its state. Savepoints enable the following features:
- Application Evolution: Savepoints can be used to evolve applications. A fixed or improved version of an application can be restarted from a savepoint that was taken from a previous version of the application. It is also possible to start the application from an earlier point in time (given such a savepoint exists) to repair incorrect results produced by the flawed version.
- Cluster Migration: Using savepoints, applications can be migrated (or cloned) to different clusters.
- Flink Version Updates: An application can be migrated to run on a new Flink version using a savepoint.
- Application Scaling: Savepoints can be used to increase or decrease the parallelism of an application.
- A/B Tests and What-If Scenarios: The performance or quality of two (or more) different versions of an application can be compared by starting all versions from the same savepoint.
- Pause and Resume: An application can be paused by taking a savepoint and stopping it. At any later point in time, the application can be resumed from the savepoint.
- Archiving: Savepoints can be archived to be able to reset the state of an application to an earlier point in time.
Monitor and Control Your Applications
Just like any other service, continuously running streaming applications need to be supervised and integrated into the operations infrastructure, i.e., monitoring and logging services, of an organization. Monitoring helps to anticipate problems and react ahead of time. Logging enables root-cause analysis to investigate failures. Finally, easily accessible interfaces to control running applications are an important feature.
Flink integrates nicely with many common logging and monitoring services and provides a REST API to control applications and query information.
- Web UI: Flink features a web UI to inspect, monitor, and debug running applications. It can also be used to submit executions for execution or cancel them.
- Logging: Flink implements the popular slf4j logging interface and integrates with the logging frameworks log4j or logback.
- Metrics: Flink features a sophisticated metrics system to collect and report system and user-defined metrics. Metrics can be exported to several reporters, including JMX, Ganglia, Graphite, Prometheus, StatsD, Datadog, and Slf4j.
- REST API: Flink exposes a REST API to submit a new application, take a savepoint of a running application, or cancel an application. The REST API also exposes meta data and collected metrics of running or completed applications.


{{ partial "header.html" .}} {{partial "nav.html" . }} {{ partial "index_hero.html" .}} {{partial "scripts.html" .}}


In this example, create a gym
environment to initialize the training environment, agent, and reward. Then, create a reinforcement learning model by a single-asset deep Q-network learning algorithm using the following observations and rewards:
| Data Category | Description |
|---|---|
| Observations | The 5-day open, high, low, close, and volume (OHLCV) of the SPY |
| Rewards | Maximum portfolio return |
Follow these steps to create a method to build the model:
gym
environment class.In this example, create a custom environment with the previous 5 OHLCV log-return data as observation and the highest portfolio value as reward.
class TradingEnv(gym.Env): FLAT = 0 LONG = 1 SHORT = 2 def __init__(self, ohlcv, ret): super(TradingEnv, self).__init__() self.ohlcv = ohlcv self.ret = ret self.trading_cost = 0.01 self.reward = 1 # The number of step the training has taken, starts at 5 since we're using the previous 5 data for observation. self.current_step = 5 # The last action self.last_action = 0 # Define action and observation space # Example when using discrete actions, we have 3: LONG, SHORT and FLAT. n_actions = 3 self.action_space = gym.spaces.Discrete(n_actions) # The observation will be the coordinate of the agent, shape for (5 previous data poionts, OHLCV) self.observation_space = gym.spaces.Box(low=-2, high=2, shape=(5, 5, 5), dtype=np.float64) def reset(self): # Reset the number of step the training has taken self.current_step = 5 # Reset the last action self.last_action = 0 # must return np.array type return self.ohlcv[self.current_step-5:self.current_step].astype(np.float32) def step(self, action): if action == self.LONG: self.reward *= 1 + self.ret[self.current_step] - (self.trading_cost if self.last_action != action else 0) elif action == self.SHORT: self.reward *= 1 + -1 * self.ret[self.current_step] - (self.trading_cost if self.last_action != action else 0) elif action == self.FLAT: self.reward *= 1 - (self.trading_cost if self.last_action != action else 0) else: raise ValueError("Received invalid action={} which is not part of the action space".format(action)) self.last_action = action self.current_step += 1 # Have we iterate all data points? done = (self.current_step == self.ret.shape[0]-1) # Reward as return return self.ohlcv[self.current_step-5:self.current_step].astype(np.float32), self.reward, done, {}
obs, rewards = self.get_observations_and_rewards()
self.env = TradingEnv(obs, rewards)
DQN
constructor with the learning policy and the gym
environment.self.model = DQN(MlpPolicy, env)


| 7:00 |
9:00 |
10:00 |
15:00 |
16:00 |
22:00 |
|
Welcome |
| 7:05 |
9:05 |
10:05 |
15:05 |
16:05 |
22:05 |
Jesus Arroyo |
INVITED: Spectral methods for overlapping community detection |
| 7:30 |
9:30 |
10:30 |
15:30 |
16:30 |
22:30 |
Alec Kirkley |
Multimodal Community Structure in Networks |
| 7:42 |
9:42 |
10:42 |
15:42 |
16:42 |
22:42 |
Mark He |
Community Detection in Weighted Multilayer Networks with Ambient Noise |
| 7:54 |
9:54 |
10:54 |
15:54 |
16:54 |
22:54 |
Subhadeep Paul and Yuguo Chen |
A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging |
| 8:07 |
10:07 |
11:07 |
16:07 |
17:07 |
23:07 |
|
Break |
| 8:17 |
10:17 |
11:17 |
16:17 |
17:17 |
23:17 |
Eleanor Power |
INVITED: How networks form: an anthropologist’s view on measuring and understanding social networks |
| 8:42 |
10:42 |
11:42 |
16:42 |
17:42 |
23:42 |
Thi Kim Nhung Dang, Doina Bucur, Berk Atıl, Guillaume Pitel, Frank Ruis and Nelly Litvak |
Prediction of new outlinks in the World Wide Web |
| 8:54 |
10:54 |
11:54 |
16:54 |
17:54 |
23:54 |
Catherine A. Calder |
INVITED: The Geometry of Continuous Latent Space Models for Network Data. |
| 9:19 |
11:19 |
12:19 |
17:19 |
18:19 |
0:19 |
|
Lunch / dinner break |
| 10:10 |
12:10 |
13:10 |
18:10 |
19:10 |
1:10 |
C. Seshadhri |
INVITED: Studying the (in)effectiveness of low dimensional graph embeddings |
| 10:35 |
12:35 |
13:35 |
18:35 |
19:35 |
1:35 |
Naomi Arnold |
Likelihood based method for fitting network growth models that change in time |
| 10:47 |
12:47 |
13:47 |
18:47 |
19:47 |
1:47 |
Joshua Loyal |
An Eigenmodel for Dynamic Multilayer Networks |
| 11:00 |
13:00 |
14:00 |
19:00 |
20:00 |
2:00 |
Izabel Aguiar, Dane Taylor and Johan Ugander |
Nonnegative Tucker Decomposition as a Multilayer SBM |
| 11:12 |
13:12 |
14:12 |
19:12 |
20:12 |
2:12 |
|
Break |
| 11:23 |
13:23 |
14:23 |
19:23 |
20:23 |
2:23 |
Eric D. Kolaczyk |
INVITED: Accounting for Network Noise: Counting, Experimenting, and Epidemic Control |
| 11:48 |
13:48 |
14:48 |
19:48 |
20:48 |
2:48 |
Anatol Wegner and Sofia Olhede |
Infering basic building blocks of networks |
| 12:00 |
14:00 |
15:00 |
20:00 |
21:00 |
3:00 |
Katherine Van Koevering, Austin Benson and Jon Kleinberg |
Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis |
| 12:12 |
14:12 |
15:12 |
20:12 |
21:12 |
3:12 |
Philip Chodrow, Nate Veldt and Austin Benson |
Generative Hypergraph Clustering: From Blockmodels to Modularity |
| 12:24 |
14:24 |
15:24 |
20:24 |
21:24 |
3:24 |
|
Break |
| 12:34 |
14:34 |
15:34 |
20:34 |
21:34 |
3:34 |
All invited speakers |
PANEL |
| 12:59 |
14:59 |
15:59 |
20:59 |
21:59 |
3:59 |
|
Closing |


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create_runner(command,
runner_path,
template=' #!/bin/bash -l\n# Name of the job - Must be unique\n#SBATCH - ... ,
job_name=' job ' ,
stdio_path=' stdio.txt ' ,
stderr_path=' stderr.txt ' ,
partition_name=' debug ' ,
time_limit=' 00-06:00:00 ' ,
memory_limit=512)
| source code
|
Create a runner (a BASH script) to run the specified job.
- Parameters:
command - The command to execute. This should be the full path to the file
that is being executed along with any command line arguments.
runner_path - The full path to create the runner.
template - The template BASH script to use. Refer to the included template,
"runner_template", for more details.
job_name - The name of the job (should be unique).
stdio_path - The full path to the standard out file.
stderr_path - The full path to the standard error file.
partition_name - The name of the partition.
time_limit - The maximum time limit.
memory_limit - The maximum memory requirements in MB.
- Returns:
- A string containing the new BASH script.
|


 Experiments can be deployed locally, either individually or via a psiturk battery to Amazon Mechanical Turk All of our experiments, deployments, and controllers for the two are publicly available, and we welcome contributions.About the Experiment Factory
Experiments
Deployments
Open Source
FAQ
We are a team of researchers in the Poldrack Lab at Stanford University. We like to build tools, and have a vision for reproducible science.


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gruut¶
A tokenizer and IPA phonemizer for multiple human languages.
from gruut import text_to_phonemes
text = 'He wound it around the wound, saying "I read it was $10 to read."'
for sent_idx, word, word_phonemes in text_to_phonemes(text, lang="en-us"):
print(word, *word_phonemes)
Output:
he h ˈi
wound w ˈaʊ n d
it ˈɪ t
around ɚ ˈaʊ n d
the ð ə
wound w ˈu n d
, |
saying s ˈeɪ ɪ ŋ
i ˈaɪ
read ɹ ˈɛ d
it ˈɪ t
was w ə z
ten t ˈɛ n
dollars d ˈɑ l ɚ z
to t ə
read ɹ ˈi d
. ‖
Note that “wound” and “read” have different pronunciations in different contexts (see Features for more details).
Installation¶
To install gruut with U.S. English support only:
pip install gruut
Additional languages can be added during installation. For example, with French and Italian support:
pip install gruut[fr,it]
Supported Languages¶
Arabic (
ar
)Czech (
cs
)German (
de
)English (
en
)Spanish (
es
)French (
fr
)Italian (
it
)Dutch (
nl
)Portuguese (
pt
)Russian (
ru
)Swedish (
sv
)Swahili (
sw
)
Credit to Michelle K. Hansen (translator) for help with German and French language support.
Usage¶
gruut performs two main functions: tokenization and phonemization.
The gruut.text_to_phonemes()
method does everything for you. See the TextToPhonemesReturn
enum for ways to adjust the return_format
.
If you need more control, see the language-specific classes in gruut.lang
as well as RegexTokenizer
and SqlitePhonemizer
.
Tokenziation operates on text and does the following:
Splits text into words by whitespace
Expands user-defined abbreviations
Breaks apart words and sentences further by punctuation (periods, commas, etc.)
Drops empty/non-word tokens
Expands numbers into words (100 -> one hundred)
Applies upper/lower case filter
Predicts part of speech tags (see
gruut.pos
)
Once tokenized, phonemization predicts the phonetic pronunciation for each word by:
Looking up each word in an SQLite database
Guessing the pronunciation with a pre-trained model (see
gruut.g2p
)
In cases where more than one pronunciation is possible for a word, the “best” pronunciation is:
Specified by the user with word indexes enabled and a word of the form “word_N” where N is the 1-based pronunciation index
Whichever pronunciation has the most compatible Features.
The first pronunciation
Command-Line¶
gruut tokenization and phonemization can be done externally with a command-line interface.
gruut en-us tokenize 'This is a test.' | gruut en-us phonemize | jq -r .pronunciation_text
ð ˈɪ s ˈɪ z ə t ˈɛ s t ‖
The combined text2phonemes
command will perform tokenization/phonemization in one step:
gruut en-us text2phonemes 'This is a test.' | jq -r .pronunciation_text
ð ˈɪ s ˈɪ z ə t ˈɛ s t ‖
See gruut <LANG> <COMMAND> --help
for more options.
Features¶
gruut tokens can contain arbitrary features. For now, only part of speech tags are implemented for English and French.
When determining the “best” pronunciation for a word, a phonemizer may consult these features. In English, for example, some word pronunciations in the lexicon contain “preferred” parts of speech. Words like “wind” may be pronounced differently depending on their use as a verb or noun. If a token “wind” is predicted to be a noun during tokenization, then the pronunciation “w ˈɪ n d” is selected instead of “w ˈaɪ n d”.
French uses part of speech tags differently. During the post-processing phase of phonemization, these features are help to add liasons between words. For example, in the sentence “J’ai des petites oreilles.”, “petites” will be pronounced “p ə t i t z” instead of “p ə t i t”.
Inline Pronunciations¶
If you want more control over a word’s pronunciation, you can include inline pronunciations in your sentences (enable with inline_pronunciations=True
or --inline
on the command-line). There are two different syntaxes, with different purposes:
Brackets -
[[ p h o n e m e s ]]
Curly Braces -
{{ words with s{eg}m{ent}s }}
The “brackets” syntax allows you to directly insert phonemes for a word. See gruut-ipa for the list of phonemes in your desired language. Some substitutions are automatically made for you:
Primary and secondary stress can be given with the apostrophe (
'
) and comma (,
)Elongation can be given with a colon (
:
)Ties will be added, if necessary (e.g.,
tʃ
becomest͡ʃ
)
The “curly brackets” syntax lets you sound out a word using other words (or segments of other words). For example, “Beyoncé” could be written as {{ bee yawn say }}
. From the curly brackets, gruut will look up each word’s pronunciation in the lexicon (or guess it), and combine all of the resulting phonemes. You may include phonemes inside the curly brackets as well with the syntax /p h o n e m e s/
alongside other words.
An even more useful aspect of the “curly brackets” syntax is using word segments. For most words in the lexicon, gruut has an alignment between its graphemes and phonemes. This enables you do insert partial pronunciations of words, such as the “zure” in “azure”, with a{zure}
. You can even have multiple segments from a single word! For example, {{ {mic}roph{one} }}
will produce phonemes sounding like “mike own”.
# raxacoricofallipatorius
gruut en-us text2phonemes --inline \
'{{ racks uh core {i}t {co}de {fall}{i}ble {pu}n tore s{ee} us }}' | jq -r .pronunciation_text
ɹ ˈæ k s ˈʌ k ˈɔ ɹ ˈɪ k ˈoʊ f ˈæ l ə p ˈʌ t ˈɔ ɹ ˈi ˈʌ s
Database¶
Word pronunciations and other metadata are stored in SQLite databases with the following tables:
- word_phonemes - word pronunciations
id INTEGER - primary key
word TEXT - word text
pron_order INTEGER - priority of pronunciation (lowest is first)
phonemes TEXT - whitespace-separated phonemes
- feature_names - names of extra pronunciation features (like part of speech)
id INTEGER - primary key
feature_id INTEGER - id of feature (linked to pron_features)
feature TEXT - name of feature (e.g., “pos”)
- pron_features - extra pronunciation features
id INTEGER - primary key
pron_id INTEGER - id from word_phonemes
feature_id INTEGER - feature_id from feature_names
- g2p_alignments - grapheme/phoneme alignments from Phonetisaurus
id INTEGER - primary key
word TEXT - word from lexicon
alignment TEXT - grapheme/phoneme alignment string
You can generate your own lexicon databases from a text file with the format:
word phoneme ...
word phoneme phoneme ...
...
by simply running:
python3 -m gruut.lexicon2db --casing lower --lexicon lexicon.txt --database lexicon.db
If your lexicon has part of speech (POS) tags, you can add the --pos
flag. In this case, your lexicon must have the following format:
word POS phoneme ...
word POS phoneme phoneme ...
G2P Models¶
Pre-trained g2p (grapheme to phoneme) models are available for all supported languages. These models guess pronunciations for unknown words, and are trained on the included lexicon databases using python-crfsuite.
To train your own model, you will need a lexicon with the format:
word phoneme ...
word phoneme phoneme ...
...
The first step is to use Phonetisaurus to generate an alignment corpus. If you use the phonetisaurus Python package, this can be generated with:
phonetisaurus train --corpus g2p.corpus --model g2p.fst lexicon.txt
The g2p.corpus
file contains aligned graphemes and phonemes, and is used to train a g2p CRF model with gruut:
python3 -m gruut.g2p train --corpus g2p.corpus --output g2p/model.crf
You can add the grapheme/phoneme alignments from g2p.corpus
to your lexicon database with:
python3 -m gruut.corpus2db --corpus g2p.corpus --output lexicon.db
See gruut.g2p
for more details.
POS Taggers¶
Pre-trained pos (part of speech) taggers are available for English and French. These models predict the part of speech for each word during tokenization, and are trained from the Universal Dependencies using python-crfsuite.
To train your own model, first download files in CoNLL-U format from the Universal Dependencies treebanks and install the conllu Python package.
Next, run the training script to generate a CRF model:
python3 -m gruut.pos train --conllu treebank.conllu --output pos/model.crf
You can change the predicted field with --label <FIELD>
, which defaults to xpos.
See gruut.pos
for more details.
eSpeak Phonemes¶
Most languages include an additional lexicon and pre-trained grapheme to phoneme model with IPA generated from espeak-ng.
These additional lexicons can be accessed via the model_prefix
argument to gruut.get_phonemizer()
or the --model-prefix
command-line argument to gruut <LANG> phonemize
.
from gruut import text_to_phonemes
text = uHe wound it around the wound, saying "I read it was $10 to read."u
for sent_idx, word, word_phonemes in text_to_phonemes(
text, lang="en-us", phonemizer_args={"model_prefix": "espeak"}
):
print(word, *word_phonemes)
Output:
he h ˈiː
wound w ˈaʊ n d
it ˈɪ t
around ɚ ɹ ˈaʊ n d
the ð ˈə
wound w ˈuː n d
, |
saying s ˈeɪ ɪ ŋ
i ˈaɪ
read ɹ ˈɛ d
it ˈɪ t
was w ʌ z
ten t ˈɛ n
dollars d ˈɑː l ɚ z
to t ˈuː
read ɹ ˈiː d
. ‖
To generate your own eSpeak lexicon, first gather a list of words in your target language into a text file (one word per line). Next, use the bin/espeak_word.sh
script to generate the lexicon:
bin/espeak_word.sh <VOICE> < words.txt > lexicon.txt
where <VOICE>
is the voice name you would pass to espeak-ng -v <VOICE>
. After generating the lexicon, see the instructions in Database and G2P Models for creating your own lexicon database and g2p models.
Adding a New Language¶
If you’d like to add a new language to gruut, please follow these steps:
- Add an IPA phoneme set to gruut-ipa
I usually follow Wikipedia’s phonology pages
Fewer phonemes is better, as long as you can properly represent word pronunciations
Try to find a Kaldi speech to text model for your language and match its phonemes to IPA
Try running to
espeak-ng -v <VOICE> -q --ipa <TEXT>
to see what eSpeak thinks
- Create or adapt an IPA pronunciation lexicon
Check for an existing IPA lexicon from ipa-dict
Try to find a Kaldi speech to text model for your language and use the bin/map_lexicon.py script to re-map phonemes to IPA
Extend wiktionary2dict and create a lexicon from Wiktionary
- Convert your text lexicon to a database by following the instructions in Database
Put in data/<language>/lexicon.db
- Create a grapheme to phoneme (g2p) model by following the instructions in G2P Models
Put in data/<language>/g2p/model.crf
- Edit
lang.py
and: Add your language to
LANG_ALIASES
Add a tokenizer and phonemizer sub-class for your language
Return your tokenizer/phonemizer in
get_tokenizer
andget_phonemizer
respectively
- Edit


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List my car
Available cars {{myTxt}}
{{marker.title}}
Selected Car informations
Car Info :
{{car_brand}} {{car_model}} {{car_year}}
Price ($CAN):
{{car_price}}
Odometer(KM) :
{{car_odometer}}
Address :
{{car_address}}
Book This Car!


Guess the computer generated random number between 1 and 1000.

Yes! That's it.
No of guesses : {{noOfTries}}


BY Michal Bacon
How should New Yorkers be recycling their trash?
What do they really do?
Each year, New Yorkers generate over 3 billion tons of trash. In face, NYC generates more trash than any other city in the world. Mayor DeBlasio has pledged that by 2030, New York will send ZERO Waste to landfills. To see if and how this is possible, we must first take a closer look at what's in our trash bins.
NYC trash is divided into three main categories, recognized by the color of the garbage bin: brown/black for refuse sent to landfills; green for metal, glass and plastic sent for recycling; and blue for paper, sent for recycling as well. Click below to learn what is in your trash.
According to the 2013 Waste Characterization Study, over 30% of refuse sent to landfills could have been recycled. However, in order to get to ZERO WASTE, New Yorkers are going to have to learn not just to recycle better, but to reduce their overall production of trash. Explore below to see the worse offenders.
Over 30% of all trash ends up in the wrong stream.
ALL PROPERLY CATEGORIZED IMPROPERLY CATEGORIZED


Frequently Asked Questions
Why shouldn't we trust atoms?
They make up everything
What do you call someone with no body and no nose?
Nobody knows
What's the object-oriented way to become wealthy?
Inheritance
How many tickles does it take to tickle an octopus?
Ten-tickles!
What is: 1 + 1?
Depends on who you are asking.


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--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/metaprogramming.20and.20structure.20inheritance.html ---
I'm trying to write a tactic that explores the inheritance structure. Given the name of a structure type, can I discover what are its ancestors?
Only for the new structure command. You need to reimplement get_parent_structures, but that shouldn't be too hard.
tl;dr: subobject fields start with "_"
How does one use the new structure command? Also, is get_parent_structures
exposed to Lean?
you're using the new structure command by default, I think
as opposed to the old structure command which is accessed with set option
yep
Thanks! And is it possible that structure_fields
by default does not list those symbols starting with _
?
Sorry, that was ambiguous. It's only in the inductive constructor parameters that they are encoded with _
All structure_fields
does is get the parameter names and strip any leading _
Let's say that I'm constructing an instance of group
(but I don't know in the tactic that it is specifically group). I'd like to know that monoid
is a direct parent so that I can use mk_instance
and use the result as a source in pexpr.mk_structure_instance
. How would you do it?
Last updated: Apr 24 2020 at 19:02 UTC


Loads all the plugins defined in the plugins
array.
An private list of the currently instantiated plugins.
A List of plugins that will be instantiated when plugins are loaded.
Sends a plugin event to all initialized plugins, calling the method defined in the event
parameter if it exists.
The name of the event to send
Optional arguments that the event method accepts
Generated using TypeDoc
Loads Plugins from a statically defined list of plugins that is built using the
@Plugin
annotation.


Members constants Change the constants properties to customize the operation of the TEETH Smart Toothbrush Timer Source: main.js, line 59 timers These values hold the setTimeout and setInterval handles so they can be cleared as part of a timer interuption Source: main.js, line 113


A secretive $120 billion hedge fund has bested Warren Buffett again and again. These are its 10 biggest holdings.
by Theron Mohamed- A secretive $120 billion hedge fund founded by a Cold War codebreaker has vastly outperformed Warren Buffett over the past three decades.
- A dollar invested in Renaissance Technologies’ flagship Medallion fund in 1988 would reportedly be worth $27,000 net of fees in 2018, dwarfing a $107 return from Buffett’s Berkshire Hathaway over the same period.
- Scroll down to see Renaissance’s 10 biggest holdings.
- View Business Insider’s homepage for more stories.
A secretive $120 billion hedge fund, founded by a Cold War codebreaker and math professor, vastly outperformed Warren Buffett over the past 30 years.
Renaissance Technologies, which was created by Jim Simons, counts Chipotle and Facebook among its largest investments. It recorded an annualized, post-fee return of 39% at its flagship Medallion fund between 1988 and 2018, according to “The Man Who Solved The Market: How Jim Simons launched the Quant Revolution,” a new book by Greg Zuckerman.
The 39% gain – 66% before fees – trumps returns of about 16% at Buffett’s Berkshire Hathaway and 10% from the S&P 500 over the same period.
Medallion’s remarkable performance means a dollar invested in the fund in 1988 would have grown to about $27,000 net of fees by the end of 2018, dwarfing a $107 return from Berkshire Hathaway and a $20 total return from the benchmark index.
While the Medallion fund is restricted to Renaissance employees, the hedge fund’s remarkable track record means its trading patterns are worth noting. Its 10 biggest investments, based on a SEC filing for the quarter ended 30 September, are listed below.
1. Bristol-Myers Squibb
Renaissance’s biggest holding is its $1.66 billion stake in Bristol-Myers Squibb, a pharmaceuticals giant that makes medicines to treat cancer, HIV/AIDS, cardiovascular disease, diabetes, and other ailments.
Federal regulators approved the company’s proposed $74 billion takeover of biotech firm Celgene this month.
2. Chipotle Mexican Grill
Renaissance has a $1.61 billion stake in Chipotle Mexican Grill. The fast-casual Mexican restaurant chain, which has more than 2,500 outlets, has largely recovered from a series of health scares a few years ago.
3. Verisign
Simons and his team have a $1.56 billion stake in Verisign, a network infrastructure group that operates the registry for domains including .com and .net.
4. Celgene
Renaissance has amassed a $1.54 billion stake in Celgene, a biotech firm that develops medicines to treat cancers and inflammatory disorders. Celgene is set to be acquired by Bristol Myers-Squibb, the hedge fund’s largest holding.
5. Novo Nordisk
Novo Nordisk is a Danish pharmaceutical company that makes medicines for diabetes, hemophilia, growth disorders, obesity, and other chronic conditions.
Renaissance holds a $1.28 billion stake in the company.
6. Palo Alto Networks
Renaissance holds a $1.19 billion stake in Palo Alto Networks, a cybersecurity group offering firewalls, network security, cloud-based threat analysis, and other software tools to businesses.
7. Biogen
Biogen develops treatments for Parkinson’s, Alzheimer’s, multiple sclerosis, and other neurological diseases. Renaissance has $983 million worth of Biogen shares.
8. Vertex Pharmaceuticals
Renaissance has a $982 million stake in Vertex Pharmaceuticals, a biotech firm that makes drugs to treat cystic fibrosis, HIV, hepatitis C, cancer, and other ailments.
9. Gilead Sciences
Gilead Sciences makes antivirus drugs to treat HIV, hepatitis, influenza, and other maladies. Renaissance has a $934 million stake in the biopharmaceuticals group.
10. Facebook
Renaissance has amassed $880 million worth of Facebook stock. The social-media titan owns WhatsApp and Instagram as well as Oculus, which makes virtual-reality headsets.


mssqlclient.py
An MSSQL client, supporting SQL and Windows Authentications (hashes too). It also supports TLS
# Description: [MS-TDS] & [MC-SQLR] example.
usage: mssqlclient.py [-h] [-port PORT] [-db DB] [-windows-auth] [-debug]
[-file FILE] [-hashes LMHASH:NTHASH] [-no-pass] [-k]
[-aesKey hex key] [-dc-ip ip address]
target
TDS client implementation (SSL supported).
positional arguments:
target [[domain/]username[:password]@]<targetName or address>
optional arguments:
-h, --help show this help message and exit
-port PORT target MSSQL port (default 1433)
-db DB MSSQL database instance (default None)
-windows-auth whether or not to use Windows Authentication (default
False)
-debug Turn DEBUG output ON
-file FILE input file with commands to execute in the SQL shell
authentication:
-hashes LMHASH:NTHASH
NTLM hashes, format is LMHASH:NTHASH
-no-pass don't ask for password (useful for -k)
-k Use Kerberos authentication. Grabs credentials from
ccache file (KRB5CCNAME) based on target parameters.
If valid credentials cannot be found, it will use the
ones specified in the command line
-aesKey hex key AES key to use for Kerberos Authentication (128 or 256
bits)
-dc-ip ip address IP Address of the domain controller. If ommited it use
the domain part (FQDN) specified in the target
parameter


An integrated experimental and computational approach is to study the biomechanics, dynamics, aerodynamics and flow physics underlying complex biological locomotion: flapping flight
Enjoy the slide show:
An integrated experimental and computational approach is to study the biomechanics and hydrodynamics of underwater propulsion.
An integrated experimental and computational approach is to study the aerodynamics of one of the 5 organisms capabale of gliding; Flying snakes
Pan Han, Ayodeji T. Bode-Oke, Tyler Van Buren, Daniel Floryan, Alexander Smits Comparison of Geometric Parameterization Methods for Optimal Shape Design in Efficient Flapping Propulsion AIAA Aviation 2019 Forum; DOI: https://doi.org/10.2514/6.2019-3339. Published 15 June 2019
Tyler Van Buren, Daniel Floryan Ayodeji T. Bode-Oke, Pan Han, Alexander Smits Optimizing foil shape for eﬃcient unsteady propulsion AIAA Scitech 2019 Forum; DOI: https://doi.org/10.2514/6.2019-1379. Published 6 January 2019
Ayodeji T. Bode-Oke, Samane Zeyghami, Haibo Dong Flying in reverse: kinematics and aerodynamics of a dragonfly in backward free flight J. R. Soc. Interface 2018 15 20180102; DOI: 10.1098/rsif.2018.0102. Published 27 June 2018
Ayodeji T. Bode-Oke, Haibo Dong, Justin King, Rajeev Kumar, Melissa Green Computational Study of the Three-Dimensional Wake and Performance of a Trapezoidal Pitching Panel In 2018 AIAA Aerospace Sciences Meeting, p. 0814. 2018. Published 30 January 2018
Haibo Dong,Ayodeji T. Bode-Oke, Chengyu Li Learning from Nature: Unsteady Flow Physics in Bioinspired Flapping Flight. Flight Physics-Models, Techniques and Technologies. InTech, 2018. Published 14 February 2018
Ayodeji T. Bode-Oke, Samane Zeyghami, Haibo Dong Aerodynamic Force and Flow Features of a Damselfly in Takeoff Flight Bioinspiration & biomimetics (2017): 056006; DOI: https://doi.org/10.1088/1748-3190/aa7f52. Published 25 September 2017
Samane Zeyghami, Ayodeji T. Bode-Oke, Haibo Dong Effect of body configuration and deformation on the dynamic stability of flapping flight AIAA Atmospheric Flight Mechanics Conference. DOI: https://doi.org/10.2514/6.2016-3540 Published 10 June 2016
Samane Zeyghami, Ayodeji T. Bode-Oke, Haibo Dong Quantification of wing and body kinematics in connection to torque generation during damselfly yaw turn Science China Physics, Mechanics & Astronomy, 60(1), p.014711.DOI: https://link.springer.com/article/10.1007/s11433-016-0302-5. Published 10 November 2016
Ayodeji T. Bode-Oke, Haibo Dong, “On the Backward Flight of a Cicada: Kinematics and Aerodynamics.”.American Physical Society – Division of Fluid Dynamics Meeting, November 18-20, 2018, Atlanta, GA.
Ayodeji T. Bode-Oke, Justin King, Rajeev Kumar, Melissa Green, Haibo Dong, “Computational Study of the Three-Dimensional Wake and Performance of a Trapezoidal Pitching Panel”.AIAA Aerospace Sciences Meeting, January 8-12, 2018, Kissimmee, FL.
Ayodeji T. Bode-Oke, Yan Ren, Frank Fish, Haibo Dong, “Cetacean Swimming with Prosthetic Limbs”,American Physical Society – Division of Fluid Dynamics Meeting , November 20-22, 2016, Portland, OR.
Ayodeji T. Bode-Oke, Samane Zeyghami, Haibo Dong, “Strategies for Achieving Backward Flight by a Dragonfly”,30th Congress of the International Council of the Aeronautical Sciences, September 25-30, 2016, Daejeon, South Korea.
Ayodeji T. Bode-Oke, Samane Zeyghami, Haibo Dong, “Kinematics and Aerodynamics of Backward Flying Dragonflies”.American Physical Society – Division of Fluid Dynamics Meeting , November 22-24, 2015, Boston, MA.
Ayodeji T. Bode-Oke, Samane Zeyghami, Haibo Dong, “How Insects Initiate Flight: Computational Analysis of a Damselfly in Takeoff Flight”.American Physical Society – Division of Fluid Dynamics Meeting , November 19-21, 2017, Denver, CO.


Coldplay respond to Robbie Williams' admission that he would be "crushed" if Coldplay beat him to Number 1 on the Official Albums Chart
Robbie admits he will be "crushed" if Coldplay beat him to Number 1 on this week's Official Albums Chart.
by Rob CopseyThe battle for this week's Number 1 on the UK's Official Albums Chart is set to be a fierce one, with Robbie Williams and Coldplay going head-to-head for the top spot.
Last week, Robbie said he would be "crushed" if his new album The Christmas Present was beaten by Coldplay's Everyday Life.
"If that happens, I will be really happy," Robbie told The Sun of reaching Number 1. "And if not, I will be crushed. I don’t mind saying I want nice things to happen to me."
Coldplay's Chris Martin has responded to the comments, insisting the band "don't mind" if they lose out on the Number 1 spot to Robbie.
"Robbie Williams, I don’t mind… we don’t mind," he told NME. "Honestly, we’re cool with that. He can have it, all power to Robbie. Really he was around first, too."
Robbie currently has 12 Number 1 albums in the UK, and topping the Official Albums Chart this week would put him equal with Elvis Presley, who has the most chart-toppers for a solo act.
Meanwhile, all seven of Coldplay's studio albums have reached Number 1 on the Official Chart; their last was 2015's A Head Full Of Dreams.
Article image: Shutterstock


Can cook things up starting from HTML,CSS TO MEAN/MERN/LAMP stack.
Dishes in Data Analysis,LSTM,AI chatbots(NLP).
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Nov 2019 : Winter Internship from Voltuswave,Hyderabad
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May 2019: Summer Internship from AVOPA,AP
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{% extends 'email.server.view.html' %}
{% block content %}
{{name}},
We haven't heard from you since you signed up to Trustroots.
Your profile will not be visible to others if you don't confirm your email address ({{email}}).
It's easy — just click on the link below.
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I'm a 5th (final) year Ph.D. candidate at Human-Computer Interaction Institute (HCII), School of Computer Science, Carnegie Mellon University, advised by Prof. Chris Harrison. I'm also a Qualcomm Innovation Fellow.
My research goal is to bring computing and interactivity closer to users through enabling computing devices with the knowledge of the immediate physical world around them. Specifically, I have built sensors on mobile devices to enable natural interactions beyond touchscreens. I have also invented deployed sensing technologies to create sensor feeds such as state, count, and rate of activities and events, which enable a broad range of applications such as personal and environment informatics, assistive and autonomous technologies, context-aware applications, and beyond.
I publish at ACM CHI and ACM UIST, and have received 2 Best Paper and 4 Honorable Mention Awards. Taxonomies of my completed research can be found below. More projects are on the way!
C Fang, Y Zhang, M Dworman, C Harrison (CHI 2020)
Best Paper Award
Wireality is a self-contained worn system that allows for individual joints on the hands to be accurately arrested in 3D space through the use of retractable wires that can be programmatically locked. This allows for convincing tangible interactions with complex geometries, such as wrapping fingers around a railing.
T Cheng, K Narumi, Y Do, Y Zhang, T Ta, T Sasatani, E Markvicka, Y Kawahara, L Yao, G Abowd, H Oh (IMWUT 2020)
Silver Tape is a simple yet novel fabrication technique to transfer inkjet-printed silver traces from paper onto versatile substrates, without time-/space- consuming processes such as screen printing or heat sintering. This allows users to quickly implement silver traces with a variety of properties by exploiting a wide range of substrates such as Scotch tape, PDMS, heat-resistant, and water-soluble tape.
Y Zhang, Y Iravantchi, H Jin, S Kumar and C Harrison (UIST 2019)
[Video]
[DOI]
[PDF]
[Code]
Sozu is a low-cost sensing system that can detect a wide range of events wirelessly, through walls and without line of sight, at whole-building scale. Instead of using batteries, Sozu tags convert energy from activities that they sense into RF broadcasts, acting like miniature self-powered radio stations.
Y Zhang, G Laput and C Harrison (UIST 2018)
[Video]
[DOI]
[PDF]
[Code]
Honorable Mention Award
Vibrosight senses activities across entire rooms using long-range laser vibrometry. Unlike a microphone, our approach can sense physical vibrations at one specific point, making it robust to interference from other activities and noisy environments. This property enables detection of simultaneous activities, which has proven challenging in prior work.
Y Zhang, C Yang, S E. Hudson, C Harrison and A Sample (CHI 2018)
[Video]
[DOI]
[PDF]
Best Paper Award
Wall++ is a low-cost sensing approach that allows walls to become a smart infrastructure. Our wall treatment and sensing hardware can track users' touch and gestures, as well as estimate body pose if they are close. By capturing airborne electromagnetic noise, we can also detect what appliances are active and where they are located.
G Laput, Y Zhang and C Harrison (CHI 2017)
[Video]
[DOI]
[PDF]
In this work, we explore the notion of general-purpose sensing, wherein a single, highly capable sensor can indirectly monitor a large context, without direct instrumentation of objects. Further, through what we call Synthetic Sensors, we can virtualize raw sensor data into actionable feeds, whilst simultaneously mitigating immediate privacy issues.
Y Zhang, G Laput and C Harrison (CHI 2017)
[Video]
[DOI]
[PDF]
Electrick is a low-cost and versatile sensing technique that enables touch input on a wide variety of objects and surfaces, whether small or large, flat or irregular. This is achieved by using electric field tomography in concert with an electrically conductive material, which can be easily and cheaply added to objects and surfaces.
Y Zhang and C Harrison (UIST 2015)
[Video]
[DOI]
[PDF]
Tomo recovers the interior impedance geometry of a user's arm by measuring the cross-sectional impedances from surface electrodes resting on the skin. We integrated the technology into a prototype wristband, which can classify gestures in real-time. Our approach is sufficiently compact and low-powered that we envision this technique being integrated into future smartwatches to allow hand gestures to work together with touchscreens.
Y Zhang, R Xiao and C Harrison (UIST 2016)
[Video]
[DOI]
[PDF]
We improved our prior work on wearable Electrical Impedance Tomography with higher sampling speed and resolution. In turn, this enables superior interior reconstruction and gesture recognition. More importantly, we use our new system as a vehicle for experimentation -- we compare two EIT sensing methods and three different electrode resolutions.
Y Zhang and C Harrison (CHI 2018)
[Video]
[DOI]
[PDF]
We developed a sensing technique for paper to track finger input and also drawn input with writing implements. Importantly, for paper to still be considered paper, our method had to be very low cost. This necessitated research into materials, fabrication methods and sensing techniques. We describe the outcome of our investigations and show that our method can be sufficiently low-cost and accurate to enable new interactive opportunities with this pervasive and venerable material.
Y Zhang, W Kienzle, Y Ma, S S. Ng, H Benko, C Harrison (UIST 2019)
[Video]
[DOI]
[PDF]
ActiTouch allows users to use their hands and arms as readily available touch input surfaces for AR and VR, opening a new interaction opportunity beyond conventional controllers and in-air gestures. We invented a powerful sensor fusion method which combines an electrical method with computer vision. This enables precise on-skin touch segmentations, which uniquely enables many fine-grained touch interactions such as scrolling and swiping.
Y Iravantchi, Y Zhang, E Bernitsas, M Goel, and C Harrison. (CHI 2019)
[Video]
[DOI]
[PDF]
Honorable Mention Award
Interferi is an on-body gesture sensing technique using acoustic interferometry. We use ultrasonic transducers resting on the skin to create acoustic interference patterns inside the wearer’s body, which interact with anatomical features in complex, yet characteristic ways. We focus on two areas of the body with great expressive power: the hands and face.
Y Zhang, M Pahud, C Holz, H Xia, G Laput, M McGuffin, X Tu, A Mittereder, F Su, W Buxton
and K Hinckley (CHI 2019)
[Video]
[DOI]
[PDF]
Honorable Mention Award
The mobility of tablets affords interaction from various user-centric postures including shifting hand grips, varying screen angle and orientation, planting the palm while writing or sketching. We propose Posture-Aware Interface which morphs to a suitable frame of reference, at the right time, and for the right (or left) hand.
Y Zhang, J Zhou, G Laput and C Harrison (CHI 2016)
[Video]
[DOI]
[PDF]
Honorable Mention Award
SkinTrack is a wearable system that enables continuous touch tracking on the skin. It consists of a signal-emitting ring and a sensing wristband with multiple electrodes. Due to the phase delay inherent in a high-frequency AC signal propagating through the body, a phase difference can be observed between pairs of electrodes, which we use to compute a 2D finger touch coordinate.
J Zhou, Y Zhang, G Laput and C Harrison (UIST 2016)
[Video]
[DOI]
[PDF]
AuraSense enhances smartwatches with Electric Field Sensing to support multiple interaction modalities. We identified four electrode configurations that can support six well-known modalities of particular interest and utility, including gestures above the watchface and touchscreen-like finger tracking on the skin.
J Gong, Y Zhang, X Zhou, XD Yang (UIST 2017)
[Video]
[DOI]
[PDF]
Pyro is a micro thumb-tip gesture recognition technique based on thermal infrared signals radiating from the fingers. Pyro uses a compact, low-power passive sensor, making it suitable for wearable and mobile applications. To demonstrate the feasibility of Pyro, we developed a self-contained prototype consisting of the infrared pyroelectric sensor, a custom sensing circuit, and software for signal processing and machine earning.
R Xiao, G Laput, Y Zhang and C Harrison (CHI 2017)
[Video]
[DOI]
[PDF]
We propose an approach where users simply tap a smartphone to an appliance to discover and rapidly utilize contextual functionality. To achieve this, our prototype smartphone recognizes physical contact with uninstrumented appliances through EMI sensing, and summons appliance-specific interfaces.
R Xiao, T Cao, N Guo, J Zhuo, Y Zhang and C Harrison (CHI 2018)
[Video]
[DOI]
[PDF]
LumiWatch is the first, fully-functional and self-contained projection smartwatch implementation, containing the requisite compute, power, projection and touch-sensing capabilities. Our watch offers more than five times that of a typical smartwatch display. We demonstrate continuous 2D finger tracking with interactive, rectified graphics, transforming the arm into a touchscreen.
Program Committee: CHI'20, MobileHCI'19, CHI'18
Conference Review: CHI'16 - '20, UIST'16 - '19, ISS'18 - '19, TEI'18 - '19, MobileHCI'19
Journal Review: IMWUT'19


 Gently fold in feta.
In a small bowl, make dressing: Combine vinegar, lemon juice, and oregano and season with salt and pepper. Slowly add olive oil, whisking to combine.
Drizzle dressing over salad.


WARNING: Registration is one of the rough spots in GSF which will need to be changed. This section describes the current way to do it, which is definitely not a great solution.
GSF is based on mime types. You register mime services for a particular mime type,
such as text/x-ruby
or text/javascript
. When you are
adding a new language, you should go and see if there is a common mime type
that is associated with your language, and if so use it. It doesn't really matter
what you choose, since GSF doesn't interpret it in any way, but by picking something
standard, other modules have a chance of using your editor services. For example,
in the property sheet code, used by the Visual Web Pack, some properties are associated
with JavaScript onclick handlers. This code just creates a JEditorPane
and sets the mime type to text/javascript
. "Magically" this makes
syntax highlighting etc. in these customizers for JavaScript attributes work,
since GSF has registered editing services for this mimetype.
The first think you'll want to do is make sure there is a mime resolver for files of the type you're trying to edit. This is described in detail in the Mime Resolver document. There is nothing GSF specific about this, but if you're trying to start writing a language support, you'll want to begin there. Registering a mime resolver will tell the IDE how to figure out the mime type of a file, but it doesn't create NetBeans DataLoaders or DataObjects for these files. GSF does that, as soon as you register the mime type with GSF.
To register your mime type with GSF, you'll want to modify your layer as follows:
<folder name="GsfPlugins"> <folder name="text"> <folder name="javascript"> <file name="language.instance"> <attr name="instanceClass" stringvalue="org.netbeans.modules.javascript.editing.JsLanguage"/> </file> </folder> </folder> </folder>This tells GSF that it should "own" the mime type "text/javascript". (NOTE: When you're describing mime types in the layer system, you must use nested folders for the mimetype. You can NOT create a
name="text/javascript"
item; it must
be a folder named text
with a folder named javascript
inside it!).
Once you've done this, GSF will claim files of this mime type and register its own
editor kit with it, and so on. (There is a way to use custom loaders with GSF if you
for example are trying to mix and match GSF with Schliemann or you have existing code
you are trying to integrate; that will be described later in this document.)
In addition, you also need to add the following target to your project's build file, such that at build time, GSF gets a chance to interject some things into your layer file. In earlier version, GSF would do this at runtime, by modifying your user directory such that it would merge content into the layer file, but this has had several disadvantages. An extra build step avoids this problem. It does however mean that when GSF changes incompatibly, it cannot easily account for older module registrations. Until this issue is resolved, you may have to regenerate your module when versions change.
<target name="jar" depends="init,compile,jar-prep" unless="is.jar.uptodate"> <taskdef name="gsfjar" classname="org.netbeans.modules.gsf.GsfJar" classpath="${nb_all}/gsf.api/anttask/gsfanttask.jar:${nb_all}/nbbuild/nbantext.jar"/> <gsfjar jarfile="${cluster}/${module.jar}" compress="${build.package.compress}" index="${build.package.index}" manifest="${manifest.mf}" stamp="${cluster}/.lastModified"> <fileset dir="${build.classes.dir}"/> </gsfjar> </target>
In the above registration, we have named the JsLanguage
class as the language
configuration object for this class. This JsLanguage
object is just an instance
of the GsfLanguage
interface. It's actually a subclass of the
DefaultLanguageConfig
class in the GSF SPI package, which implements this
interface.
The GSF language object defines key characteristics about your language. The most important method you have to implement is
@NonNull Language getLexerLanguage();This is referring to the Lexer API's Language class. GSF is built on top of the Lexer API. You must create a Lexer for your language. The Lexer API is already quite well documented so I won't repeat it here (though I should include a documentation reference link).
The first step is implementing a Lexer for your language, and returning its language
from your language configuration subclass.  You can now start the IDE, double on source files in
the file navigator, which should open them in the editor - and they should be properly
syntax highlighted! There is more information about the lexer aspects in the
lexing document.
You also need to register the lexer language with NetBeans. This is described in the lexing document in more detail.
There are some other methods in the language configuration class you can override.
| Method | Purpose |
|---|---|
String getLineCommentPrefix() |
If your language supports line comments, return the comment string here.
For example, for Java and JavaScript it's // , and for Ruby it's # .
If you return non null from this method, GSF will enable the comment, uncomment
and toggle comment actions, add them to your toolbar and implement an editor
action which commments, uncomments and toggles comments in your source files.
|
boolean isIdentifierChar(char c) |
This method lets you return whether a character is an identifier character
in your language. This method will be used to for example automatically
handle the case where the user double clicks in your editor; this should
select a complete identifiers. By default in NetBeans you get the Java behavior,
but in a language like JavaScript for example, $ should also be
included in the double click selection.(Unfortunately, the current design doesn't make it easy to handle more complicated scenarios like Ruby, where a $ should only be accepted as a prefix of the identifier, and
a ! should only be accepted as a suffix. I plan to refine this
approach a bit to handle this.
|
If you extend the DefaultLanguageConfig
class, there are additional
methods you can subclass. I'll describe these after I introduce the other services
you can register with NetBeans.
Most features in GSF will require parse information. This relies upon calling
your own implementation of the
Parser interface.
If you are subclassing the DefaultLanguageConfig
class, just override
the getParser()
method:
@Override public Parser getParser() { return new JsParser(); }Alternatively, you can register this service directly in the layer:
<folder name="GsfPlugins"> <folder name="text"> <folder name="javascript"> ... <file name="parser.instance"> <attr name="instanceClass" stringvalue="org.netbeans.modules.javascript.editing.JsParser"/> </file> </folder> </folder> </folder>
This is just like the parser registration. You need to implement the
Indexer interface, and
then register it, either via subclassing DefaultLanguageConfig
and
overriding getIndexer()
:
@Override public Parser getIndexer() { return new JsIndexer(); }Alternatively, you can register this service directly in the layer:
<folder name="GsfPlugins"> <folder name="text"> <folder name="javascript"> ... <file name="indexer.instance"> <attr name="instanceClass" stringvalue="org.netbeans.modules.javascript.editing.JsIndexer"/> </file> </folder> </folder> </folder>
You get the idea. You can override configuration functiosn in DefaultLanguageConfig
,
or register services directly in the layer. Sometimes you can do a combination of things.
For example, in the Ruby plugin, most editing related services are implemented by and registered
in the Ruby Editing module. These are instantiated directly by the RubyLanguage
configuration class. However, Ruby Quickfixes are implemented in a separate module, and therefore
the HintsProvider
implementation for Ruby is registered via the layer in the
hint module.
 Two attributes must be initialized
via the layer instead:
customEditorKit
attributecustomEditorKit
is false.
Well, if GSF were to look for these attributes in the langauge configuration objects,
it would have to load and instantiate all the language configuration objects at startup!
 (I in fact DID try this, and ended
up failing the blacklisted class commit validation test, so I had to revert initialization
of these two attributes to being layer-only).
Normally when you register a mime type with GSF, GSF will automatically handle file type handling for you, by creating NetBeans implementations like DataObjects, creating an EditorKit for these DataObjects, and so on.
However, there are cases where you have an existing EditorKit or DataLoader, and
you want to add GSF support selectively. To do that, you need to tell GSF
to back off. You do that with the useCustomEditorKit
attribute,
which you add as an attribute to the mime folder in the GsfPlugins
folder in the layer:
<folder name="GsfPlugins"> <folder name="application"> <folder name="x-httpd-eruby"> <attr name="useCustomEditorKit" boolvalue="true"/> ... </folder> </folder> </folder>When GSF sees this, it will not add ANY editing services for you. You are now free to add these in yourself in your layer. This is done by the HTML module for example, which had a lot of existing legacy code we didn't want to rewrite (at least not yet). HTML already has DataObjects (which were subclassed in other modules), it had custom code to handle formatting and code completion, and we wanted to keep these classes using the old code for now. So, the HTML module specifies
useCustomEditorKit
.
With the useCustomEditorKit
attribute you can use your own
EditorKit implementation. There is a price to pay though. When you do
this, you need to manually register all the GSF implementations you do
want to use. For example, if you're providing a semantic highlighter, you
have to register the GSF highlighting factory in the Editors folder:
<folder name="Editors"> <folder name="text"> <folder name="html"> Required for semantic highlighting: <file name="org-netbeans-modules-gsfret-editor-semantic-HighlightsLayerFactoryImpl.instance"/> Required for code folding: <folder name="FoldManager"> <file name="org-netbeans-modules-gsfret-editor-fold-GsfFoldManagerFactory.instance"/> </folder> <folder name="SideBar"> <file name="org-netbeans-modules-editor-gsfret-GsfCodeFoldingSideBarFactory.instance"> <attr name="position" intvalue="1200"/> </file> </folder> Required for GSF go to declaration: <folder name="HyperlinkProviders"> <file name="GsfHyperlinkProvider.instance"> <attr name="instanceClass" stringvalue="org.netbeans.modules.gsfret.editor.hyperlink.GsfHyperlinkProvider"/> <attr name="instanceOf" stringvalue="org.netbeans.lib.editor.hyperlink.spi.HyperlinkProviderExt"/> </file> </folder> Required for error status: <folder name="UpToDateStatusProvider"> <file name="org-netbeans-modules-gsfret-hints-GsfUpToDateStateProviderFactory.instance"/> </folder> Required for code completion: <folder name="CompletionProviders"> <file name="org-netbeans-modules-gsfret-editor-completion-GsfCompletionProvider.instance"/> </folder> </folder> </folder> </folder>I may be missing some services here - like live code templates and mark occurrences. If you do go this route, you should look at the
LanguageRegistry
class
in the GSF implementation to make sure you're picking up everything you intend to, to
make sure you have the right class names, with all the right attributes, etc.
instanceClass
attribute instead of the filename to designate the class to be instantiated.
You are free to use the NetBeans APIs directly. For example, in addition to relying on GSF's code completion provider, you can add an additional direct implementation of NetBeans' code completion API if you should want to. You don't need to switch to a custom editor kit to do this.
In NetBeans 6.5, there is a new API which lets you register editor actions (along with keybindings)
for arbitrary mime types. This means that you can extend the available actions for an editor type
from your modules. GSF used to have an API to help with this, but it's no longer necessary. To do this,
you just have to implement the editor BaseAction
class, and register it in the Editors mime
folder:
<folder name="Editors"> <folder name="text"> <folder name="x-ruby"> ... <folder name="Actions"> <file name="org-netbeans-modules-ruby-ReflowParagraphAction.instance"/> </folder> </folder> </folder> </folder>From here, you can proceed to also create a
Popup
folder to add it to the context
menu for this file type, and/or register a keybinding for it via an XML file
referenced from the Keybindings
folder.


Before proceeding, please note that all materials currently available are still works in progress.
The Armory SDK is released as an early preview. Any kind of support & feedback is greatly welcome!
Armory is an open-source 3D game engine with full Blender integration, turning it into a complete game development tool. The result is a unified workflow from start to finish, making you work faster. No more jumping between different applications to constantly export data from one to another.
Behind the scenes, Armory is powered by open-source technology. Utilizing Kha - multimedia framework and Haxe - cross platform toolkit, to provide top class performance and portability.
Serious effort has been put into the render system. A render path is fully scriptable with deferred and forward paths supported out of the box. To define materials, Armory is based on the Cycles nodes. Materials are pre-compiled into shaders suitable for real-time rendering.
Armory is oriented towards nodes. For materials, a subset of standard Cycles nodes is used. Every scene created in Armory is renderable as-is in Cycles using path-tracing. This makes it possible to use Cycles for light baking with no separate setup. Performance remains the priority. To define game behaviour, a system of logic nodes is developed.
Everything is bundled to provide the ultimate game development tool. As a starting point, you can choose one of the available game prototype templates. Code editor with debugging support is integrated. For logic, use nodes, write scripts in Haxe, or embed WebAssembly code.
Create live scenes and export them to desktop, web, mobile and consoles, so everyone can experience them. Binary data format, asset compression and efficient build tools are developed to prevent bloated package sizes.
A robust animation system is employed, with support for GPU skinning, action blending and events. On top of that, each node can be animated on timeline using keyframes, just like you are used to.


Of course, many borrowers end up renewing their loans for much more than a month. Unlike a borrower who pays back their loan quickly, who may just have a temporary emergency need for money, these borrowers struggle to ever pay back their loan, racking up more fees each time they renew the loan.
in-context