In Your_first_neural_network.ipynb
I began running benchmarks
across a a number of different hyperparameter permutations.
I saved these results to a pickle dump and CSV which I will read and analyze within this notebook in order to explore some questions about picking good hyperparameters for this neural net using the benchmarks I generated.
These benchmarks were only trained and evaluated once per hyperparameter. Training produces non-deterministic results so there will be slight variance from run-to-run.
I have moved forward with this approach because a given hyperparameter configuration should yield loss benchmarks that are fairly close in performance.
The data suggests definite trends that I will be opining about here but a good secondary step would be further sampling of benchmarks for each respective hyperparameter permutation to gain confidence of the loss benchmarks calculated
Furthermore, in the worst of cases, my results might be bogus if I have incorrectly configured my neural net. If I have built my network incorrectly many or all of my findings would need to be amended.
from operator import itemgetter
import pickle
# in "Your_first_neural_network.ipynb" we generated a pickle dump of
# the results of various training sessions on a variety of different
# hyperparameters
with open('results.pik', 'rb') as file_obj:
results = pickle.loads(file_obj.read())
results[0]
len(results)
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
After manually trying parameters I suspected that we get diminishing returns after a while as we optimize the nets further and further. I wanted to see if the data bore this out.
Note: I picked a good smattering of values for the various hyperparameters. They were not chosen linearly at all (if anything closer to exponential). This will affect the overall shape of the curve.
sorted_results = sorted(results, key=lambda x: x[-2], reverse=True)
plt.ylabel('training loss')
plt.xlabel('iteration number (sorted by descending training loss)')
plt.title("training loss for all results with loss < 1.")
plt.plot([x[-2] for x in sorted_results if x[-2] < 1.])
sorted_results = sorted(results, key=lambda x: x[-1], reverse=True)
plt.ylabel('validation loss')
plt.title("validation loss for all results with loss < 1.")
plt.xlabel('iteration number (sorted by descending training loss)')
plt.plot([x[-1] for x in sorted_results if x[-1] < 1.])
sorted_results = sorted(results, key=lambda x: sum(x[-2:]), reverse=True)
plt.ylabel('combined loss')
plt.title("combined loss for all results with loss < 1.")
plt.xlabel('iteration number (sorted by descending training loss)')
plt.plot([sum(x[-2:]) for x in sorted_results if sum(x[-2:]) < 1.])
Conclusion: Even though our hyperparameter brackets aren't linear these plots suggest that as training gets more effective we experience diminishing returns after a while.
Note: I'm focusing on measuring validation loss in the 2nd part of this analysis because focusing solely on training loss could run the risk of overfitting. As our validation set wasn't visible in the training data it serves as a good metric for how effective a job at learning our neural net is doing.
Arguably, combined loss (training loss + validation loss) would have been another way to go.
I also made a .csv
file of the same results and put it into a sqlite database called results.sqlite
:
sqlite> create table benchmark (hidden_nodes, output_nodes, learning_rate, iterations, train_loss, val_loss);
sqlite> .seperator ","
Error: unknown command or invalid arguments: "seperator". Enter ".help" for help
sqlite> .separator ","
sqlite> .import results.csv benchmark
In this section going to use SQL queries to hone in on data queries about hyperparameters
import sqlite3
from pprint import pprint
def plot_bar_chart(query_results, title):
values = []
labels = []
for value, label in query_results:
labels.append(label)
values.append(value)
#pprint(sorted(learning_rates, key=lambda x: x[0]), width=5)
ind = np.arange(len(labels))
fig, ax = plt.subplots()
ax.set_title(title)
ax.bar(ind, values, align='edge', width=.35)
ax.set_xticks(ind + 0.35/2)
ax.set_xticklabels(labels)
plt.show()
conn = sqlite3.connect('results.sqlite')
c = conn.cursor()
c.execute(
"select count(learning_rate), learning_rate from benchmark "
"where val_loss < .225 "
"group by learning_rate"
)
learning_rates = c.fetchall()
title = "learning rates for nets with validation loss < .225"
plot_bar_chart(learning_rates, title)
I picked 0.225
for the validation loss cutoff after playing around with
the above plots as there seemed to be a slight inflection point where values
leveled off below that.
I also noticed that when training was effective, it was not uncommon to get
validation loss <= 0.15
or so, so I also plotted that bar chart:
c.execute(
"select count(learning_rate), learning_rate from benchmark "
"where val_loss < .13 "
"group by learning_rate"
)
learning_rates = c.fetchall()
title = "learning rates for nets with validation loss < .13"
plot_bar_chart(learning_rates, title)
Conclusion: higher learning rates occur more frequently among nets that
experienced good validation loss within the benchmark data until we reach
a learning rate of 0.4
, which becomes both the lowest value and most
common when we set the validation loss cutoff down to 0.13
0.1
is the lowest possible learning rate value
for nets with validation loss below .225
, but it is not represented
nearly as much as higher valued learning rates. A learning rate
of 0.1
was not even visible in validation losses below 0.15
;
0.2
was the lowest.
However, by the time we get to near the upper bound of
effective validation training at 0.13
or lower we observe
that 0.4
dominates the greater learning rates.
I was very surprised by this result. I expected much lower values closer
to the default suggested 0.1
. Further analysis is available in the
"Conclusions" section.
In the HW notebook the assignment guidelines suggest ""
I wanted to see if any neural nets with extremely low learning rates had managed to get effective scores.
I ran the following query:
sqlite> select min(val_loss) from benchmark where learning_rate = 0.01;
0.432506563623
sqlite> select min(train_loss) from benchmark where learning_rate = 0.01;
0.268160070205
The minimum validation loss at this learning rate is not much better than
a coin flip. Moving to 0.05
fares better, but it is still not very
effective:
sqlite> select min(train_loss) from benchmark where learning_rate = 0.05;
0.123311698551
sqlite> select min(val_loss) from benchmark where learning_rate = 0.05;
0.24343148997
It's once we get to a learning rate of 0.1
that we start to get effective results:
sqlite> select min(train_loss) from benchmark where learning_rate = 0.1;
0.0742342265709
sqlite> select min(val_loss) from benchmark where learning_rate = 0.1;
0.152336873571
sqlite>
Conclusion: In general, no! This surprised me and makes me wonder if my neural net might be built incorrectly.
Again, this was a somewhat surprising and troubling result. I believed I'd find values much loser (more on this available in the "Conclusions" section).
I'd be curious if anyone else has evidence suggesting otherwise!
c = conn.cursor()
c.execute(
"select count(iterations), iterations from benchmark "
"where val_loss < .225 "
"group by iterations order by iterations"
)
iterations = c.fetchall()
title = "number of epochs for nets with validation loss < .225"
plot_bar_chart(iterations, title)
Neural nets trained 16,000 times were by far the most dense group across nets with validation loss < 0.225. This drops off more or less exponentially as the number of epochs decrease at this validation efficacy.
c = conn.cursor()
c.execute(
"select count(iterations), iterations from benchmark "
"where val_loss < .15 "
"group by iterations order by iterations"
)
iterations = c.fetchall()
title = "number of epochs for nets with validation loss < .15"
plot_bar_chart(iterations, title)
The same trend continues when we restrict to neural nets with efficacy under 0.15. Nets trained 2,000 times almost drop out of the set entirely.
What about even more effective neural nets at 0.13
or greater?
c = conn.cursor()
c.execute(
"select count(iterations), iterations from benchmark "
"where val_loss < .13 "
"group by iterations order by iterations"
)
iterations = c.fetchall()
title = "number of epochs for nets with validation loss < .13"
plot_bar_chart(iterations, title)
Conclusion: Lower validation losses suggest higher number of epochs, in general.
Unlike learning rate (which seemed centered around 0.4), this trend seems consistent as we increase validation efficacy cutoff
It is however possible to build effective nets with 4,000
samples or even less if you're willing accept validation
losses as high as 0.225
.
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .225 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "number of hidden nodes for nets with validation loss < .225"
plot_bar_chart(iterations, title)
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .15 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "number of hidden nodes for nets with validation loss < .15"
plot_bar_chart(iterations, title)
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .14 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "number of hidden nodes for nets with validation loss < .14"
plot_bar_chart(iterations, title)
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .13 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "number of hidden nodes for nets with validation loss < .13"
plot_bar_chart(iterations, title)
This isn't as obvious. Really effective nets (< 0.13 validation loss) seem to have a bimodal distribution centered around 4 and 32 nodes.
As we get less effective the bimodal distribution sort of distorts into a gaussian around 4-32 nodes cetered between 6-8 nodes.
This leads me to wonder for really effective nets, how are hidden nodes affected by learning rate and number of epochs?
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .13 and iterations <= 8000 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "number of hidden nodes for nets with validation loss < .13 and iterations <= 8k"
plot_bar_chart(iterations, title)
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .13 and iterations > 8000 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "number of hidden nodes for nets with validation loss < .13 and iterations > 8k"
plot_bar_chart(iterations, title)
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .13 and learning_rate > 0.4 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "number of hidden nodes for nets with validation loss < .13"
plot_bar_chart(iterations, title)
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .13 and learning_rate = 0.4 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "number of hidden nodes for nets with validation loss < .13"
plot_bar_chart(iterations, title)
note: there are no nets with val_loss < 0.13 with learning rates < 0.4 in my benchmark set.
Conclusion: the bimodal distribution around 4 and 32 nodes seems to hold across a number of learning rates. I believe this suggests that learning rate does not significantly change how the number of hidden nodes affects validation loss
However, when we look at number of epochs we see that lower number of epochs results in nets with 32 totally dominating the total density. I believe this suggests a possible tradeoff between number of hidden nodes and iterations. We seem to need more hidden nodes if we have less number of epochs, and need less hidden nodes if we have a higher number of epochs.
There seems to be a fundamental a tradeoff between number of epochs and learning rate.
Higher learning rates mean we can get effective training with fewer number of epochs.
Conversely, we can also get effective training on even the lowest tested learning rate, but we need epochs in excess of 16,000 in order to do so.
Possible implicit tradeoff between number of hidden nodes and number of epochs
This was a lot less clear, but number of epochs affected the distribution of hidden nodes more strongly than learning rate. Look look two cells up for more information on this possible finding.
My data suggests that learning rates < 0.1 are ineffective
As I mentioned earlier, this runs counter to what at least one peer mentioned was the suggested feedback by a mentor. I'd like to understand this further.
A learning rate of 0.4 seems most effective.
This value became the most dominant when we restricted ourselves to the lowest validation
losses we experienced in the benchmark set (0.13
or lower).
Effective learning < 4,000 iterations is definitely possible
However, as mentioned above, it seems to require a higher learning rate. This could probably be bad in situations where
On the Stanford CS 231 website they mention:
Good intuition to have in mind is that with a high learning rate, the system contains too much kinetic energy and the parameter vector bounces around chaotically, unable to settle down into deeper, but narrower parts of the loss function.
I believe that this high of a learning rate is probably really bad for a lot of problems and happens to work well with the spikey function we're predicting. The neural nets consistently has some issues predicting adequate seasonal demand around Dec 22-Dec 31. This could be because that part of the season is somewhat outlier from the rest of the data or possibly because of how the validation set was constructed.
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .13 and iterations <= 4000 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "# of hidden nodes for nets with validation loss < .13 and iterations <= 4k"
plot_bar_chart(iterations, title)
c = conn.cursor()
c.execute(
"select count(learning_rate), learning_rate from benchmark "
"where val_loss < .13 and iterations <= 4000 "
"group by learning_rate order by learning_rate"
)
iterations = c.fetchall()
title = "learning rate for nets with validation loss < .13 and iterations <= 4k"
plot_bar_chart(iterations, title)
Given there's only 2 x 2 values, I thought I'd see which one of the 4 possible states yielded the lowest validation loss:
sqlite> select min(val_loss) from benchmark where learning_rate = 0.8 and hidden_nodes = 6;
0.121990855737
sqlite> select min(val_loss) from benchmark where learning_rate = 0.8 and hidden_nodes = 6;
0.121990855737
sqlite> select min(val_loss) from benchmark where learning_rate = 0.8 and hidden_nodes = 8;
0.124737204954
sqlite> select min(val_loss) from benchmark where learning_rate = 0.9 and hidden_nodes = 6;
0.120635762511
sqlite> select min(val_loss) from benchmark where learning_rate = 0.9 and hidden_nodes = 8;
0.124901754971
The results are somewhat anecdotal since the val_loss of each training session is going to be non-deterministic and the variance between these values is extremely small.
Picking a learning rate of 0.8 or 0.9 and using 6 or 8 hidden nodes would be ideal.
Note: I had to move up to a validation loss of 0.16
to get results with a learning rate as low as 0.1
.
c = conn.cursor()
c.execute(
"select count(hidden_nodes), hidden_nodes from benchmark "
"where val_loss < .16 and learning_rate = 0.1 "
"group by hidden_nodes order by hidden_nodes"
)
iterations = c.fetchall()
title = "# of hidden nodes for nets with validation loss < .16 and iterations <= 4k"
plot_bar_chart(iterations, title)
c = conn.cursor()
c.execute(
"select count(iterations), iterations from benchmark "
"where val_loss < .16 and learning_rate = 0.1 "
"group by iterations order by iterations"
)
iterations = c.fetchall()
title = "# of epochs for nets with validation loss < .16 and learning rate = 0.1"
plot_bar_chart(iterations, title)
First of all, the results yielded by a lower learning rate are both less effective and require an unreasonable number of iterations than the results preferring a lower number of iterations.
I believe the lower learning rate means the weights are adjusted far less drastically than with a higher learning rate, meaning a far greater number of iterations are needed to get effective learning. The benefit as mentioned in the conclusion section above is that a lower learning rate will ultimately find "deeper, but narrower parts of the loss function."
A lower loss function could probably help us reduce the validation errors caused by the seasonal peak at the end of December, but we'd need an unreasonable number of iterations and would likely get diminishing returns.
If you had to pick a lower learning rate, I'd reccomend setting the number of hidden nodes to 6 and using as high of a number of epochs as you're willing to run.


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I’m Dinolin YP Job currently pursuing my undergraduation in Computer Science and Technology from Karunya Institute of Technology and Sciences.
I’m an insatiable learner,technology enthusiast and a passionate programmer with proficient technical skills.I love contributing towards Open Source and I'll be always a part of Open Source Community.
I have a fascination towards financial knowledge and I believe that making money work for one's own is more important than making money.
Developing a website builder, which helps users to build websites faster and smoother.
Frontend and Backend development using HTML,CSS,Javascript and PHP.
Signing up deals with the clients and coordinating the team to perform the required tasks. As an App developer I have created a Web application for the website grafic.biz
CGPA: 8.06/10
Percentage: 77.80%
CGPA: 9.20/10
If you would like to work with me or you have an idea but you don't know how to implement it.
Just ping me @ Mail : dinolinypjob@karunya.edu.in


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Maestro Nikola:
Yes, we have finally obtained Maphr's Tablet Fragment! This is amazing. let me see... Hmm... Could it be?
Knowing the characters is not enough to decipher this language. It will be impossible for us to interpret the tablet without the help of the Elders...
Very well, then! Now deliver this to Warehouse Keeper Parman. This is all we can do now. I plan to return to Shootguart after looking around the tower a bit more...


Books and Book Chapters
- P. Maragos, A. Potamianos and P. Gros (eds),
Multimodal Processing and Interaction: Audio, Video, Text
,
Springer-Verlag, 2008.
- A. Potamianos and M. Perakakis, "
Human-Computer Interafaces to Multimedia Content: A Review
,'' in Multimodal Processing and Interaction: Audio, Video, Text,
Springer-Verlag, 2008.
- A. Potamianos and M. Perakakis, "
Design Principles for Multimodal Spoken Dialogue Systems
,'' in Multimodal Processing and Interaction: Audio, Video, Text,
Springer-Verlag, 2008.
- G. Evangelopoulos, K. Rapatzikos, P. Maragos,
Y. Avrithis, A. Potamianos,
"Audiovisual Attention Modeling and Salient Event Detection",
in Multimodal Processing and Interaction: Audio, Video, Text,
Springer-Verlag, 2008.
Journal Publications
-
G. Evangelopoulos, A. Zlatintsi, A. Potamianos, P. Maragos, K. Rapantzikos,
G. Skoumas, and Y. Avrithis, "Multimodal saliency and fusion for movie
summarization based on aural, visual, and textual attention," IEEE
Transactions on Multimedia, vol. 15, pp. 1553--1568, Nov. 2013.
-
N. Malandrakis, A. Potamianos, I. Elias, and S. S. Narayanan, "Distributional
semantic models for affective text analysis," IEEE Transactions on
Audio, Speech and Language Processing, vol. 21, pp. 2379--2392, Nov. 2013.
-
T. Moschopoulos, E. Iosif, L. Demetropoulou, A. Potamianos, and S. Narayanan,
"Toward the automatic extraction of policy networks using web links and
documents," IEEE Transactions on Knowledge and Data Engineering,
vol. 25, pp. 2404--2417, Oct. 2013.
-
E. Iosif and A. Potamianos, "Similarity computation using semantic networks
created from web-harvested data," Natural Language Engineering,
vol. FirstView, pp. 1--31, July 2013.
- D. Dimitriadis, P. Maragos, and A. Potamianos,
"
On the Effects of Filterbank Design and Energy Computation on Robust Speech Recognition
,"
IEEE Transactions on Audio, Speech and Language Processing,
vol. 19, no. 6, pp. 1504-1516, Aug. 2011.
- S. Yildirim, S. Narayanan, and A. Potamianos,
"
Detecting Emotional State of a Child in a Conversational Computer Game
,"
Computer, Speech and Language , vol. 25, no. 1, pp. 29-44, Jan. 2011.
- E. Iosif, and A. Potamianos,
"
Unsupervised Semantic Similarity Computation Between Terms Using Web Documents
,''
IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 11 , pp. 1637-1647, Nov. 2010.
- D. Nion, K. Mokios, N. Sidiropoulos, A. Potamianos,
"
Batch and Adaptive PARAFAC-Based Blind Separation of Convolutive Speech
Mixtures ,"
IEEE Transactions on Audio, Speech
and Language Processing, vol. 18, no. 6, pp. 1193 - 1207, Aug. 2010.
- P. Tsiakoulis, A. Potamianos, and D. Dimitriadis,
"
Spectral Moment Features Augmented by Low Order Cepstral Coefficients for Robust ASR
," IEEE Signal Processing Letters, vol. 17, no. 6, pp. 551-554, June 2010.
- D. Dimitriadis, A. Potamianos, and P. Maragos,
"
A Comparison of the Squared Energy and Teager-Kaiser Operators for Short-Time
Energy Estimation in Noise
,''
IEEE Transactions on Signal Processing , vol. 57, no. 7, pp. 2569-2581, July 2009.
- E. Sanchez-Soto, A. Potamianos, and K. Daoudi, "
Unsupervised stream weight computation in classification and recognition tasks
,'' IEEE Transactions on Audio, Speech
and Language Processing, vol. 17, no. 3, pp. 436-445, Mar. 2009.
- M. Perakakis and A. Potamianos, "
A study in efficiency and modality usage in multimodal form filling systems
,'' IEEE Transactions on Audio, Speech
and Language Processing,
Vol. 16, pp. 1194 - 1206, Aug. 2008.
- E. Ammicht, E. Fosler-Lussier, and A. Potamianos, "
Information seeking spoken dialogue systems - Part I: Semantics and pragmatics
,'' IEEE
Transactions on Multimedia, vol. 9, no.3, Apr. 2007.
Vol. 9, pp. 532 - 549, April 2007.
- A. Potamianos, E. Fosler-Lussier, E. Ammicht, and M. Perakakis, "
Information seeking spoken dialogue systems - Part II: Multimodal dialogue
,'' IEEE Transactions on Multimedia,
Vol. 9, pp. 550 - 566, April 2007.
- D. Dimitriadis, P. Maragos, and A. Potamianos, "
Robust AM-FM features for speech recognition
,'' IEEE Signal Processing Letters, vol. 12,
pp. 621-624, Sept. 2005.
- A. Potamianos, S. Narayanan, and G. Riccardi, "
Adaptive categorical understanding for spoken dialogue systems
,'' IEEE Transactions on Speech
and Audio Processing,
Vol. 13, pp. 321 - 329, May 2005.
- A. Pargellis, E. Fosler-Lussier, C.-H. Lee, A. Potamianos, and A. Tsai,
"Auto-induced semantic classes,'' Speech Communication, vol. 43,
pp. 183-203, Aug. 2004.
- A. Potamianos and S. Narayanan, "
Robust recognition of children's speech
,''
IEEE Transactions on Speech and Audio Processing, vol. 11,
pp. 603-616, Nov. 2003.
- V. Weerackody, W. Reichl, and A. Potamianos, "
An error-protected speech recognition system for wireless communications
,'' IEEE Transactions on
Wireless Communications, vol. 1, pp. 282-291, Apr. 2002.
- S. Narayanan and A. Potamianos, "
Creating conversational interfaces for children
,'' IEEE Transactions on Speech and Audio Processing, vol. 10,
pp. 65-78, Feb. 2002. [IEEE Signal Processing Society Best Paper Award 2005]
- D. Dimitriadis, V. Pitsikalis, P. Maragos, and A. Potamianos, "
Modulation and chaotic features for speech recognition
,'' invited paper to the Journal
of Control and Intelligent Systems, Special Issue on Nonlinear Speech
Processing, vol. 30, pp. 19-26, Jan. 2002.
- A. Potamianos and P. Maragos, "
Time-frequency distributions for automatic speech recognition
,'' IEEE Transactions on Speech and Audio Processing,
vol. 9, pp. 196-200, Mar. 2001.
- A. Potamianos and P. Maragos, "
Speech analysis and synthesis using an AM-FM modulation model
,'' Speech Communication, vol. 28,
pp. 195-209, July 1999.
- S. Lee, A. Potamianos, and S. Narayanan, "
Acoustics of children's speech: Developmental changes of temporal and spectral parameters
,'' Journal of
the Acoustical Society of America, pp. 1455-1468, Mar. 1999. [Selected Research Article by JASA]
- P.Maragos and A. Potamianos, "
Fractal dimensions of speech sounds: Computation
and application to automatic speech recognition
,'' Journal of the
Acoustical Society of America, pp. 1925-1932, Mar. 1999.
- A. Potamianos and P. Maragos, "
Speech formant frequency and bandwidth tracking using multiband energy demodulation
,'' Journal of the Acoustical Society
of America, vol. 99, pp. 3795-3806, June 1996.
- P. Maragos and A. Potamianos, "
Higher-order differential energy operators
,''
IEEE Signal Processing Letters, vol. 2, Aug. 1995.
- H. M. Hanson, P. Maragos, and A. Potamianos, "
A system for finding speech formants and modulations via energy separation
,'' IEEE Transactions on
Speech and Audio Processing, vol. 2, pp. 436-443, July 1994.
- A. Potamianos and P. Maragos, "
A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation
,'' Signal
Processing, vol. 37, pp. 95-120, May 1994.
Conference Publications
-
I. Klasinas, A. Potamianos, E. Iosif, S. Georgiladakis, and G. Mameli, "Web
data harvesting for speech understanding grammar induction," in Proc.
Interspeech, Lyon, France, Aug. 2013.
-
V. Kouloumenta, M. Perakakis, and A. Potamianos, "Affective evaluation of
multimodal dialogue games for preschoolers using physiological signals," in
Proc. Interspeech, Lyon, France, Aug. 2013.
-
N. Malandrakis, S. Sundaram, and A. Potamianos, "Affective classification of
generic audio clips using regression models," in Proc. Interspeech,
Lyon, France, Aug. 2013.
-
A. Tsiartas, T. Chaspari, A. Katsamanis, P. Ghosh, M. Li, M. V. Segbroeck,
A. Potamianos, and S. Narayanan, "Multi-band long-term signal variability
features for robust voice activity detection," in Proc. Interspeech,
Lyon, France, Aug. 2013.
-
N. Malandrakis, I. Elias, V. Prokopi, A. Potamianos, and S. S. Narayanan,
"Deeppurple: Lexical, string and affective feature fusion for sentence-level
semantic similarity estimation," in Proc. 2nd Joint Conference on
Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main
Conference and the Shared Task: Semantic Textual Similarity, pp. 103--108,
Association for Computational Linguistics, June 2013.
-
N. Malandrakis, A. Kazemzadeh, A. Potamianos, and S. S. Narayanan, "Sail: A
hybrid approach to sentiment analysis," in Proc. 2nd Joint Conference
on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the
Seventh International Workshop on Semantic Evaluation (SemEval 2013),
pp. 438--442, Association for Computational Linguistics, June 2013.
-
G. Evgeneiadis, V. Kouloumenta, and A. Potamianos, "Analyzing exploration and
exploitation patterns in multimodal dialogue games for preschoolers," in
Proc. Games for Learning Workshop - Foundations of Digital Games
Conference, Chania, Greece, May 2013.
-
N. Malandrakis, A. Potamianos, and S. Narayanan, "Continuous models of affect
from text using n-grams," in Proc. Internat. Conf. on Acoust., Speech,
and Signal Process., Vancouver, Canada, May 2013.
-
P. Tsiakoulis, A. Potamianos, and D. Dimitriadis, "Instantaneous frequency and
bandwidth estimation using filterbank arrays," in Proc. Internat. Conf.
on Acoust., Speech, and Signal Process., Vancouver, Canada, May 2013.
-
E. Iosif, A. Potamianos, M. Giannoudaki, and K. Zervanou, "Semantic similarity
computation for abstract and concrete nouns using network-based
distributional semantic models," in Proc. 10th International Conference
on Computational Semantics (IWCS), Potsdam, Germany, pp. 328--334, Mar.
2013.
-
M. Perakakis and A. Potamianos, "An affective evaluation tool using brain
signals," in Proc. Intl. Conf. on Intelligent User Interfaces
(companion volume), Santa Monica, CA, pp. 105--106, ACM, Mar. 2013.
-
M. Perakakis and A. Potamianos, "Affective evaluation of a mobile multimodal
dialogue system using brain signals," in Proc. IEEE/ACM Workshop on
Spoken Language Technology, Miami, Florida, Dec. 2012.
-
A. Zlatintsi, P. Maragos, A. Potamianos, and G. Evangelopoulos, "A
saliency-based approach to audio event detection and summarization," in
Proc. European Signal Process. Conf., Aug. 2012.
-
N. Malandrakis, E. Iosif, and A. Potamianos, "Deeppurple: Estimating sentence
semantic similarity using n-gram regression models and web snippets," in
*SEM 2012: The 1st Joint Conference on Lexical and Computational
Semantics, Montreal, Canada, June 2012.
-
G. Riccardi, P. Cimiano, A. Potamianos, and C. Unger, "Up from limited dialog
systems!," in Proc. NAACL-HLT Workshop on future directions and needs
in the SDS community (position paper), Montreal, Canada, June 2012.
- N. Malandrakis, A. Potamianos, E. Iosif and S. Narayanan,
"
EmotiWord: Affective Lexicon Creation with Application to Interaction and Multimedia Data
,"
in Proc. MUSCLE International Workshop on Computational Intelligence for Multimedia Understanding ,
Pisa, Italy, Dec. 2011.
- N. Malandrakis, A. Potamianos, E. Iosif and S. Narayanan,
"
Kernel models for affective lexicon creation ,"
in Proc. Interspeech , Florence, Italy, Aug. 2011.
- N. Malandrakis, A. Potamianos, G. Evangelopoulos and A. Zlatintsi,
"
A supervised approach to movie emotion tracking ,"
in Proc. Intl. Conf. on Acoustics, Speech and Signal Processing
(ICASSP-2011), Prague, Czech Republic, Apr. 2011.
- P. Tsiakoulis, and A. Potamianos,
"
On the Effect of Fundamental Frequency on Amplitude and Frequency Modulation Patterns in Speech Resonances ,"
in Proc. Interspeech , Makuhari, Japan, Sept. 2010.
- M. Poesio, M. Baroni, O. Lanz, A. Lenci, A. Potamianos, H. Schutze,
S. Schulte im Walde, and L. Surian,
"
BabyExp: Constructing a huge multimodal resource to acquire commonsense knowledge like children do ,"
in Proc. LREC , Malta, May. 2010.
- S. Dimopoulos, C.H. Lee, E. Fosler-Lussier, and A. Potamianos, "
Transition features for CRF-based recognition and boundary detection
,"
in Proc. Automatic Speech Recogn. and Underst. Workshop (ASRU-2009),
Merano, Italy, Dec. 2009.
- P. Tsiakoulis, A. Potamianos, and D. Dimitriadis, "
Short-time instantaneous frequency and bandwidth features for speech recognition
,"
in Proc. Automatic Speech Recogn. and Underst. Workshop (ASRU-2009),
Merano, Italy, Dec. 2009.
- T. Kannetis, and A. Potamianos, "
Towards Adapting Fantasy, Curiosity and Challenge in Multimodal Dialogue
Systems for Preschoolers
,"
in Proc. Int'l Conf. on Multimodal Interfaces (ICMI-2009), Boston, MA, Nov. 2009.
- T. Kannetis, A. Potamianos, and G.N. Yannakakis, "
Fantasy, Curiosity and Challenge as Adaptation Indicators in Multimodal Dialog
Systems for Preschoolers
,"
in Proc. Workshop of Child, Computer and Interaction (WOCCI-2009), Boston, MA, Nov. 2009.
- M. Gerosa, D. Giuliani, S. Narayanan, and A. Potamianos, "
A Review of ASR Technologies for Children's Speech
,"
in Proc. Workshop of Child, Computer and Interaction (WOCCI-2009), Boston, MA, Nov. 2009.
- P. Tsiakoulis, and A. Potamianos, "
Statistical Analysis of Amplitude Modulation in Speech Signals using an AM-FM Model
,"
in Proc. Intl. Conf. on Acoustics, Speech and Signal Processing
(ICASSP-2009), Taipei, Taiwan, Apr. 2009.
-
G. Evangelopoulos, A. Zlatintsi, G. Skoumas, K. Rapantzikos, A. Potamianos, P. Maragos, and Y. Avrithis, "
Video Event Detection and Summarization Using Audio, Visual and Text Saliency
,"
in Proc. Intl. Conf. on Acoustics, Speech and Signal Processing
(ICASSP-2009), Taipei, Taiwan, Apr. 2009.
-
S. Dimopoulos, A. Potamianos, E. Fosler-Lussier, and C.-H. Lee, "
Multiple Time Resolution Analysis of Speech Signals Using MCE Training with Application to Speech Recognition
,"
in Proc. Intl. Conf. on Acoustics, Speech and Signal Processing
(ICASSP-2009), Taipei, Taiwan, Apr. 2009.
- V. Farantouri, A. Potamianos, and S. Narayanan, "
Linguistic Analysis of Spontaneous Children Speech
",
in Proc. Workshop of Child, Computer and Interaction (WOCCI-2008), Chania, Greece, Oct. 2008.
- M. Perakakis and A. Potamianos, "
Multimodal System Evaluation using Modality Efficiency and Synergy Metrics
",
in Proc. Int'l Conf. on Multimodal Interfaces (ICMI-2008), Chania, Greece, Oct. 2008.
- G. Evangelopoulos, K. Rapantzikos, A. Potamianos, P. Maragos, A. Zlatintsi and Y. Avrithis, "
Movie Summarization Based On Audio-Visual Saliency Detection
",
in Proc. Intl Conference on Image Processing (ICIP-2008),
San Diego, California, Oct. 2008.
- A. Tegos, V. Karkaletsis, and A. Potamianos, "
Learning of Semantic Relations Between Ontology Concepts Using Statistical Techniques
",
in Proc. HLIE Workshop , Antwerp, Belgium, Sept. 2008.
- M. Maragakis and A. Potamianos, "
Region-Based Vocal Tract Length Normalization for ASR
",
in Proc. Interspeech , Brisbane, Australia, Sept. 2008.
- K. Mokios, A. Potamianos, and N.  Sidiropoulos, "
On the Effectiveness of PARAFAC-Based Estimation in Blind Speech Separation
",
in Proc. Intl. Conf. on Acoustics, Speech and Signal Processing
(ICASSP-2008), Las Vegas, Nevada, Apr. 2008.
- E. Iosif and A. Potamianos, "
Unsupervised semantic similarity computation using web search engines
,'' in Proc. Intern. Conf. on Web Intelligence,
(Silicon Valley, USA), Nov. 2007.
- E. Sanchez-Soto, K. Daoudi, and A. Potamianos, "
Unsupervised stream weight computation in a segmentation task: Application to audio-visual speech
recognition
,'' in Proc. Intern. Conf. on Signal Proc. and
Communications, (Dubai, UAE), Nov. 2007.
- M. Perakakis and A. Potamianos, "
The effect of input mode on inactivity and interaction times of multimodal systems
,'' in Internat. Conf. on
Multimodal Interfaces, (Nagoya, Japan), Nov. 2007.
- A. Potamianos and S. Narayanan, "
A review of the acoustic and linguistic
properties of children's speech
,'' in Proc. Intern. Workshop on
Multimedia Signal Processing, (Chania, Greece), Oct. 2007.
- S. Siltanen et al, "
Multimodal user interface for augmented assembly
,'' in
Proc. Intern. Workshop on Multimedia Signal Processing, (Chania,
Greece), Oct. 2007.
- E. Iosif and A. Potamianos, "
A soft-clustering algorithm for automatic induction of semantic classes
,'' in Proc. Interspeech, (Antwerp,
Belgium), Aug. 2007.
- D. Dimitriadis, J. Segura, L. Garcia, A. Potamianos, P. Maragos, and
V. Pitsikalis, "
Advanced front-end for robust speech recognition in
extremely adverse environments
,'' in Proc. Interspeech, ( Antwerp, Belgium), Aug. 2007.
- A. Katsamanis, P. Tsiakoulis, P. Maragos, and A. Potamianos, "
Investigations in articulatory synthesis
,'' in Proc. Intenat. Conf. on Phonetics,
(Saarbrucken, Germany), Aug. 2007.
- E. Sanchez-Soto, A. Potamianos, and K. Daoudi, "
Unsupervised stream weight computation using anti-models
,'' in Proc. Internat. Conf. on Acoust.,
Speech, and Signal Process., (Hawaii, USA), Apr. 2007.
- E. Iosif, A. Tegos, A. Pangos, E. Fosler-Lussier, and A. Potamianos, "
Unsupervised combination of metrics for semantic class induction
,'' in IEEE/ACM Workshop on Spoken Language Technology, (Aruba), Dec. 2006.
- M. Perakakis, M. Toutoudakis, and A. Potamianos, "
Blending speech and visual input in multimodal dialogue systems
,'' in IEEE/ACM Workshop on Spoken
Language Technology, (Aruba), Dec. 2006.
- A. Potamianos et al, "
Towards speaker and enviromental robustness in ASR: the HIWIRE project
,'' in ITRW Workshop on Speech Recognition and
Intrinsic Variation, (Toulouse, France), May 2006.
- A. Potamianos, E. Sanchez-Soto, and K. Daoudi, "
Stream weight computation for multi-stream classifiers
,'' in Proc. Internat. Conf. on Acoust., Speech,
and Signal Process., (Toulouse, France), May 2006.
- K. Mokios, N. Sidiropoulos, and A. Potamianos, "
Blind speech separation algorithm using PARAFAC and integer least squares
,'' in Proc.
Internat. Conf. on Acoust., Speech, and Signal Process., (Toulouse, France),
May 2006.
- P. Karageorgakis, A. Potamianos, and I. Klasinas, "
Towards incorporating language morphology into statistical machine translation systems
,'' in Proc. Automatic Speech Recogn. and Underst. Workshop, (Cancun, Mexico), Dec.
2005.
- A. Pangos, E. Iosif, A. Potamianos, and E. Fosler-Lussier,
Combining statistical similarity measures for automatic induction of semantic
classes
,'' in Proc. Automatic Speech Recogn. and Underst. Workshop,
(Cancun, Mexico), Dec. 2005.
-
D. Dimitriadis, P. Maragos, and A. Potamianos, "
Auditory Teager energy cepstrum coefficients for robust speech recognition
,'' in Proc. European
Conf. on Speech Communication and Technology, (Lisbon, Portugal), Sept.
2005.
-
S. Yildirim, C. Lee, S. Lee, A. Potamianos, and S. Narayanan, "
Detecting politeness and frustration state of a child in a conversational computer game
,'' in Proc. European Conf. on Speech Communication and Technology,
(Lisbon, Portugal), Sept. 2005.
- A. Potamianos, E. Ammicht, and E. Fosler-Lussier, "
Modality tracking in the multimodal Bell Labs Communicator
,'' in Proc. Automatic Speech
Recogn. and Underst. Workshop, (St. Thomas, U.S. Virgin Islands), Dec. 2003.
- A. Potamianos, "Novel features for robust speech recognition,'' in invited presentation to the Conf. of the Acoustical Society of America,
(Cancun, Mexico), Dec. 2002.
- M. Walker et al, "
DARPA Communicator: Cross-system results for the 2001
evaluation
,'' in Internat. Conf. Speech Language Processing,
(Colorado), Sept. 2002.
- M. Walker et al, "DARPA Communicator evaluation: Progress from 2000 to
20001,'' in Internat. Conf. Speech Language Processing, (Colorado),
Sept. 2002.
- R. Argiles-Solsona, E. Fosler-Lussier, J. Kuo, A. Potamianos, and I. Zitouni, "
Adaptive language models for spoken dialogue systems
,'' in Proc.
Internat. Conf. on Acoust., Speech, and Signal Process., (Orlando, Florida),
May 2002.
- D. Dimitriadis, P. Maragos, and A. Potamianos, "
Modulation features for speech
recognition
,'' in Proc. Internat. Conf. on Acoust., Speech, and Signal
Process., (Orlando, Florida), May 2002.
- S. Lee, E. Ammicht, E. Fosler-Lussier, J. Kuo, and A. Potamianos, "
Spoken dialogue evaluation for the Bell Labs Communicator system
,'' in Proc. Human Language Technology Conf., (San Diego, California), Mar. 2002.
- M. Tsangaris and A. Potamianos, "
AGORA: A GUI approach to multimodal user interfaces
,'' in Proc. Human Language Technology Conf., (San Diego,
California), Mar. 2002.
- E. Ammicht, A. Potamianos, and E. Fosler-Lussier, "
Ambiguity representation and resolution in spoken dialogue systems
,'' in Proc. European Conf. on
Speech Communication and Technology, (Aalborg, Denmark), Oct. 2001.
- M. Galley, E. Fosler-Lussier, and A. Potamianos, "
Hybrid natural language generation for spoken dialogue systems
,'' in Proc. European Conf. on
Speech Communication and Technology, (Aalborg, Denmark), Oct. 2001.
- A. Pargellis, E. Fosler-Lussier, A. Potamianos, and C.-H. Lee, "
Metrics for measuring domain-independence of semantic classes
,'' in Proc. European
Conf. on Speech Communication and Technology, (Aalborg, Denmark), Oct. 2001.
- M. Walker et al, "DARPA Communicator dialog travel planning systems: the
June 2000 evaluation,'' in Proc. European Conf. on Speech
Communication and Technology, (Aalborg, Denmark), Oct. 2001.
- A. Potamianos and V. Weerackody, "
Soft-feature decoding for speech recognition
over wireless channels
,'' in Proc. Internat. Conf. on Acoust., Speech,
and Signal Process., (Salt Lake City, Utah), May 2001.
- A. Pargellis and A. Potamianos, "
Cross-domain classification using generalized
domain acts
,'' in Internat. Conf. Speech Language Processing, (Beijing,
China), Oct. 2000.
- A. Potamianos, E. Ammicht, and H.-K. Kuo, "
Dialogue management in the Bell Labs communicator system
,'' in Internat. Conf. Speech Language
Processing, (Beijing, China), Oct. 2000.
- A. Potamianos and H.-K. Kuo, "
Speech understanding using finite state transducers
,'' in Internat. Conf. Speech Language Processing, (Beijing,
China), Oct. 2000.
- W. Reichl, V. Weerackody, and A. Potamianos, "A codec for speech recognition
in a wireless system,'' in Proc. EUROCOMM, (Munich, Germany), May 2000.
- A. Potamianos and P. Maragos, "
Time-frequency distributions for automatic speech recognition
,'' in Proc. Workshop on Automatic Speech Recognition
and Understanding, (Keystone, Colorado), Dec. 1999.
- S. Narayanan, A. Potamianos, and H. Wang, "Multimodal systems for children:
Building a prototype,'' in Proc. European Conf. on Speech Communication
and Technology, (Budapest, Hungary), Sept. 1999.
- G. Potamianos and A. Potamianos, "Speaker adaptation for audio-visual speech
recognition,'' in Proc. European Conf. on Speech Communication and
Technology, (Budapest, Hungary), Sept. 1999.
- A. Potamianos, G. Riccardi, and S. Narayanan, "Categorical understanding using
statistical n-gram models,'' in Proc. European Conf. on Speech
Communication and Technology, (Budapest, Hungary), Sept. 1999.
- A. Potamianos et al, "
Design principles and tools for multimodal dialog
systems
,'' in Proc. ESCA Workshop Interact. Dialog. Multi-Modal Syst.,
(Kloster Irsee, Germany), June 1999.
- G. Riccardi, A. Potamianos, and S. Narayanan, "
Language model adaptation for spoken dialog systems
,'' in Internat. Conf. Speech Language Processing,
(Australia), Oct. 1998.
- S. Okawa, E. Brocchieri, and A. Potamianos, "
Multi-band speech recognition in noisy environments
,'' in Proc. Internat. Conf. on Acoust., Speech, and
Signal Process., (Seattle, Washington), May 1998.
- A. Potamianos and S. Narayanan, "
Spoken dialog systems for children
,'' in Proc. Internat. Conf. on Acoust., Speech, and Signal Process.,
(Seattle,Washington), pp. 197-201, May 1998.
- S. Lee, A. Potamianos, and S. Narayanan, "
Analysis of children's speech:
Duration, pitch and formants,'' in Proc. European Conf. on Speech
Communication and Technology, (Rhodes, Greece), pp. 473-476, Sept. 1997.
- P. Maragos and A. Potamianos, "On using fractal features of speech sounds in
automatic speech recognition,'' in Proc. European Conf. on Speech
Communication and Technology, (Rhodes, Greece), pp. 2531-2534, Sept. 1997.
- A. Potamianos and P. Maragos, "Speech analysis and synthesis using an
AM-FM modulation model,'' in Proc. European Conf. on Speech
Communication and Technology, (Rhodes, Greece), pp. 1355-1358, Sept. 1997.
- A. Potamianos, S. Narayanan, and S. Lee, "Automatic speech recognition for
children,'' in Proc. European Conf. on Speech Communication and
Technology, (Rhodes, Greece), pp. 2371-2374, Sept. 1997.
- I. Zeljkovic, S. Narayanan, and A. Potamianos, "Unsupervised HMM
adaptation based on speech-silence discrimination,'' in Proc. European
Conf. on Speech Communication and Technology, (Rhodes, Greece),
pp. 2055-2058, Sept. 1997.
- A. Potamianos and R. C. Rose, "
On combining frequency warping and spectral
shaping in HMM-based speech recognition
,'' in Proc. Internat. Conf. on
Acoust., Speech, and Signal Process., (Munich, Germany), Apr. 1997.
- R. C. Rose and A. Potamianos, "Improving robustness in HMM based speech
recognition through simultaneous frequency warping and spectral shaping,'' in
ESCA-NATO Workshop on Robust Speech Recognition, (Pont-a-Mousson,
France), Apr. 1997.
- A. Potamianos and R. C. Rose, "A feature-space transformation for telephone
based speech recognition,'' in Proc. European Conf. on Speech
Communication and Technology, (Madrid, Spain), Sept. 1995.
- P. Maragos, A. Potamianos, and B. Santhanam, "
Instantaneous energy operators:
Applications to speech processing and communications
,'' in IEEE Workshop
on Nonlinear Signal and Image Processing, (Thessaloniki, Greece), June 1995.
- A. Potamianos and P. Maragos, "
Speech formant frequency and bandwidth tracking
using multiband energy demodulation
,'' in Proc. Internat. Conf. on
Acoust., Speech, and Signal Process., (Detroit, MI), May 1995.
- A. Potamianos and P. Maragos, "
Applications of speech processing using an
AM-FM modulation model and energy operators
,'' in Proc. European
Signal Process. Conf., (Edinburgh, Scotland), pp. III: 1669-1672, Sept.
1994.
- P. Maragos, T. F. Quatieri, J. F. Kaiser, and A. Potamianos, "Demodulation of
AM-FM resonances in speech using energy separation,'' in presentation to the Conf. of the Acoustical Society of America, (Boston,
MA), June 1994.
- H. M. Hanson, P. Maragos, and A. Potamianos, "Finding speech formants and
modulations via energy separation: With application to a vocoder,'' in Proc. Internat. Conf. on Acoust., Speech, and Signal Process., (Minneapolis,
MN), Apr. 1993.
- J. Diamesis and A. Potamianos, "Tridiagonal state-space realization of a class
of 2-D transfer functions,'' in Proc. of the 25th Conf. on Information
Sciences and Systems, (Baltimore, MD), pp. 249-253, Mar. 1991.
Thesis
A. Potamianos,
Speech Processing Applications Using an AM-FM Modulation Model,
Harvard University, 1996
Back to Home Page


Carmelo Anthony explains what Trail Blazers opportunity means to him
by TSD StaffVeteran forward Carmelo Anthony is back in the NBA, and it appears he may get his swan song after all.
Anthony was signed and released by the Rockets just a few weeks later last November, in what appeared to be a bit of a curious move. And the fact that the Rockets parted ways with him so quickly scared other NBA teams away from signing him, which led some to believe that would spell the end of his NBA career.
Ending on that note would’ve been a brutal way for one of the best scorers in NBA history to go out, and we couldn’t be happier for Melo. He appears excited about the opportunity, and shared his thoughts for the first time on his YouTube channel.
“I always kept my eye on Portland,” he said. “It just didn’t work out at other times, but now it seems like it’s a perfect opportunity.
We can’t wait to see Melo suit up and play his first minutes with the Blazers — as he hasn’t played in a game since Nov. 8, 2018 — and hope that he gets to finish out the season. The league and its fans deserve one more year of Melo.


In graph theory, centrality is any measure that determines how structurally important a node is within a graph and distance is measured by the number of hops it takes from get from one point to another. The shortest path distance between two nodes is path that takes the minimum number of hops.
Closeness centrality is a measure of a node’s importance determined by the average shortest path distance from that node to every other node on the graph. The lower the average distance to all other nodes, the more central the position in the network.


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Best Finish
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Regionals
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To face global threats, scientists need to change
May 28, 2017
Diseases, energy crisis, climate change, antibiotic resistance, pollution etc.. You name it ! How can we deal with these global threats?
The simple answer is “together”.
Lets face it, no single person or policy maker or law can make this happen. Only when people around the globe make these challenges a personal issue, there is even a bleak chance of a change being brought about. It might seem impossible. Well is it?
Many of us stop learning once we pass out of school or college. We are no longer aware of the latest advancements in science and their implications. For example, how many of you knew that diamonds are no longer the hardest material on the planet? What I am trying to get at is times are changing and we are no longer aware of the evidence based conclusions or solutions anymore. It is time for scientists who have been studying these issues or parts of these issues for years to step up and educate the public.
Here is an open letter to all the scientists and researchers out there.
Hello there, I can imagine how fascinated and dedicated you are about your research, you might have spent years planning and carefully working on it, day in and day out. You might have also spent several months writing up your work to share it with other scientists in your field but have you ever taken a day or two to share it with the public who matter the most?
I guess the first question that crossed your mind was “who cares apart from someone from my field”? Scientists should know better that everything in this world is interconnected. From energy crisis to climate change, everything is interconnected in someway or the other and every bit of knowledge is necessary for people to put together and see the entire picture.
I know that you have done your years of research and your stats works out to be perfect but people don’t like to be told what to do. Shoving your results on their face is not the way to move forward. If you do so, most of them are not going to deny the facts but rather will cherry pick facts that align with their beliefs.
A change is possible when people are given a chance to solve the puzzle themselves.
Also lets face it, a one day of “science march” is not going to change the world or lead to evidence-based policy making. It’s a process and it takes time. Scientists need to establish a long-term relationship with the public before we can start to see a change and such a change is more likely to be adapted and sustained.
I hope I have convinced you enough, for you to acknowledge that a long-term relationship is needed to seed the change of tomorrow.
So how can you do this? A number of funding agencies have recognised this and now require scientists to get involved in public engagement. Of course public engagement can be done in a number of ways. What I propose is writing a blog post accompanying every publication, describing the study in a wider context and showing people where it fits in the larger puzzle. Write it in a simple way without jargons that people can’t understand.
Well thats the job of the media isn’t it?
To be honest people want to hear from the scientists themselves rather than through some news agency with clickbait titles. Scientists need to connect with the people on a publication level (every paper you publish) so that when it matters the most, they might be willing to listen. Describe what we have learned and are learning everyday to everyone from school children to policy makers. Don’t let the sleepless nights and extra hours of your tireless work go in vane. Share your science with every one. When the bubble between science, scientists and public are broken great things can happen.
Not convinced yet? Look at this tweet that I had made asking people on what they prefer?
What do you prefer?
— Ajit Johnson (@ajitjohnson_n) May 23, 2017
1. Scientists writing about their research in simple terms for the public or
2. Journalists writing about it
Most people clearly wanted scientists to connect with them directly. Science is one of those fields where trust and personal connection matter the most. (here are a few cherry picked replies)
Scientists.  Scientific journalism is a specialised job.
— Glenda Charles (@LadyStarChilde) May 23, 2017
Without hesitation, #1. Journalists have caused enough trouble with their click-bait headlines for too long.
— ©🌊 Stronger Together (@TerriDF) May 23, 2017
Of course, the first choice. This goes without saying, I have learned so much already from the books that scientists wrote in the past.
— Gundamwen (@Gundamwen2017) May 23, 2017
When I read a new journal, I want to read the authors interpretation not a second version through a journalist.
— Paul O'Connor (@PaulOCo121) May 24, 2017
Not journalists. A fundamental mechanism of cancer regulation in mice found; news screams "Researchers Found Cure for Cancer"
— Henry Parker (@hparker48) May 24, 2017
Some said a combination of both scientists and journalists because scientists seem to clearly get journalism wrong.
In theory, both; in practice, I've spent hours w/ journos explaining, then never heard from them again, saw totally diff. spin published
— radu ioviță (@iovitza) May 23, 2017
As often as I've seen journalism get science wrong, I've seen scientists get journalism wrong. Both sides need the other as editors.
— grantimatter (@grantimatter) May 24, 2017
Scientists; I want to hear it from the source but shouldn't need at PhD to understand
— Cameron Bennett (@cameronbennette) May 23, 2017
Anyway you get the point. People would like to hear from you in an easy and understandable format.
If you are convinced to blog about your research, great! But the problem is not solved yet. There is sadly no “one go-to place” on the internet where people can read articles written by the authors of the research themselves. As much as it is important for researchers to write about their research it is also important that they are all in one reliable place.
To solve this, I had established a platform called “The Surg” solely in an effort to provide a destination for people to read about the latest research from the scientists themselves.
I have been working hard, trying to convince scientists to write about their research here and also to making short videos explaining their latest research. A few more amazing students have joined me in his venture now. A small group like us will not be able to do everything but if you join us, together we can do greater things. If we can work together and get your research and your colleagues research blogged, may be more researchers will start writing. It starts with YOU !!
Together we can change the world.


Magic Trader Cema:
Thank you for collecting the samples for us, but we still need more if we are to conduct our experiments successfully. Could you possibly work a little longer for us?
"I will continue to work."
Quit


[gecode-users] compiling send-more-money
Mailing List Email
mailinglistx at gmail.com
Mon May 27 03:03:06 CEST 2013
That's because those include paths only take affect IF you compile from
within the IDE. If you compile from the command prompt, then you have to
add those include paths to the command line arguments to the compiler
manually.
Any reason why you simply don't build from the IDE itself?
On 27 May 2013 03:00, manel askri <askri.manel1 at gmail.com> wrote:
> hello evry one !
>
> To be honnest with you , i have read the Modeling and Programming with
> Gecode <http://www.gecode.org/doc-latest/MPG.pdf>. Also i searsh in the Gecode's
> documentation <http://www.gecode.org/documentation.html>, and the archive
> of the Gecode users mailing list<http://news.gmane.org/gmane.comp.lib.gecode.user> for
> solution to my problem but i didn't find one . i use windows 7 , VS2010 C++
> and gecode 4.0.0, when i want to compile send- more-money; i get this
> erreur "impossible d'ouvrir le fichier include : gecode/int.hh : no such
> file or directory"??
>
> I use the visual studio command prompt to compile, and i add C:\program
> files\gecode\include and C:\program files\gecode\lib to the properties of
> the project to garanti that it works but it doesn't work
> please if anyone could help me with this because its the main subject of
> my graduation project
>
> thanks verry much
>
> ASKRI Manel
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
>
>
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Hey, you asked for my feedback, it's brilliant. Damn, I envy you I didn't come up with something this good :D Keep it up, man, it's going to be very popular. Trust me !
Dennis Novac
5:16 PM
Wow, thanks. You'll be my main template tester from now on :)
Evan Williams
5:21 PM
I'm all in, as long as you continue to make such great templates.
Hello, Dennis. I wanted to let you know we reviewed your proposal and decided you'll start working together with our UI/UX team on Tuesday, January 10th, 2017. Congratulations and thank you!
Dennis Novac
1:51 PM
Hello, great news. I'm sure we're going to make something amazing together!


{% extends "layouts/_block_content.html" %} {% from "components/breadcrumb/_macro.njk" import onsBreadcrumb %} {% from "components/button/_macro.njk" import onsButton %} {% set page_title = "Share surveys overview" %} {% set breadcrumbsData = [ { "text": "Surveys", "url": "/surveys/todo", "id": "b-item-1" }, { "text": "Account", "url": "/my-account", "id": "b-item-2" }, { "text": "Share Surveys", "url": "/my-account/share-surveys", "id": "b-item-3" }, { "text": "Select business", "url": "/my-account/share-surveys/business-selection", "id": "b-item-4" }, { "text": "Select survey", "url": "/my-account/share-surveys/survey-selection", "id": "b-item-5" }, { "text": "Enter email address", "url": "/my-account/share-surveys/recipient-email-address", "id": "b-item-6" } ] %} {% block breadcrumb %} {{ onsBreadcrumb({ "ariaLabel": "Breadcrumbs", "id": "breadcrumbs", "itemsList": breadcrumbsData }) }} {% endblock breadcrumb %} {% block main %}
We have sent an email to the new respondent.
Once they have received it, they need to follow the link in the email to confirm their email address and finish setting up their account.
Email not arrived? It might be in their spam folder.
If it doesn’t arrive in the next 15 minutes, please call 0300 1234 931.
{% endblock main %}


G54.4−0.3
(HC40)
- Right Ascension: 19 33 20
- Declination: +18 56
- Size (/arcmin): 40
- Type: S
- Flux density at 1 GHz (/Jy): 28
- Spectral Index: 0.5
Notes: Has been called G54.5−0.3.
- Radio: Shell, in complex region.
- Optical: Faint filaments.
- Point sources: Pulsar outside NW rim.
- Distance: HI and CO observations suggest 6.6 kpc.
References:
- Velusamy & Kundu 1974, A&A, 32, 375. NRAO 300-ft at 2.7 GHz (5': S=34.4±5.0 Jy).
ADS
- Altenhoff et al. 1979, A&AS, 35, 23. Effelsberg 100-m at 4.9 GHz (2.'6).
ADS
- Caswell 1985, AJ, 90, 1224. DRAO at 1.4 GHz (1.'3×2.'6: S=18±4 Jy).
ADS
- Velusamy et al. 1986, JApA, 7, 105. WSRT at 609 MHz (50''×191'' smoothed to 100''×200'').
ADS
- Junkes et al. 1992, A&AS, 96, 1. Surrounding CO.
ADS
- Junkes et al. 1992, A&A, 261, 289. Nearby IRAS sources.
ADS
- Boumis et al. 2005, A&A, 443, 175. Optical observations.
ADS
- Reach et al. 2006, AJ, 131, 1479. Spitzer observations.
ADS
- Kang & Koo 2007, ApJS, 173, 85. SGPS of high velocity HI.
ADS
- Andersen et al. 2011, ApJ, 742, 7. Spitzer observations.
ADS
- Park et al. 2013, ApJ, 777, 14. Arecibo of HI in region.
ADS
- Froebrich et al. 2015, MNRAS, 454, 2586. H2 IR observations.
ADS
- Karpova et al. 2017, MNRAS, 466, 1757. X-ray observations of pulsar.
ADS
- Ranasinghe & Leahy 2017, ApJ, 843, 119. HI and CO observations.
ADS
Last updated: 9 Apr 2019
home |
documentation |
detailed listings |
summary listings
Dave Green/Cambridge, UK/


Rocket is the most unique moile app, designedfor managing startups small business projects andsupporting modern companies.
Short description for the ones who look for something new. Awesome!
Due to Extremely responsive and retin ready technology built in our app you can launch it with any device or mobile gadget.


Consider the following grammars:
| Grammar 1: | <s> ::= a <s> | b <s> | ε |
| Grammar 2: | <s> ::= a | b | <s> <s> | ε |
| Grammar 3: | <s> ::= a <s> b | ε |
| Grammar 4: | <s> ::= <s> a | <s> b | a |
| Grammar 5: | <s> ::= a <r> | <r> |
| <r> ::= b <s> | <s> | ε |
How many of these grammars are ambiguous?
Review the definition of an ambiguous grammar.
For each grammar, when looking for strings with multiple parse trees, start with the shortest strings that the grammar generates.
Common sources of grammatical ambiguity include recursive patterns such as double recursion and single, indirect recursion (i.e., from one non-terminal back to itself via one or more other non-terminals).
Grammar 1 is not ambiguous because every string in its
language is either empty or starts with an a
or a b
. This grammar handles each case once
and generates each string in only one way, namely from
left to right.
Grammar 2 is ambiguous because of the double recursion
in its third production. For example, the strings
ε and a
have more than one parse
tree.
Grammar 3 is not ambiguous because the only production that can produce a non-empty string is the first one.
Grammar 4 is not ambiguous because every string in its
language is either empty or ends with an a
or
a b
. This grammar handles each case once and
generates each string in only one way, namely from right
to left.
Grammar 5 is ambiguous because, for example, <s> can generate <r>, which in turn can re-generate <s>, leading to infinitely many, arbitrarily deep parse trees for all strings, including ε.


Here is a list of my projects. They're in varying levels of completion. I probably won't touch most of these again, so
whatever state they are in is the state they'll most likely remain for the rest of their life. Each project has it's
own Github repository with instructions on how to use the given project.
Each project starts with the project name, then under that is the percentage complete. These are rough estimates, and
if a project says it's 100% complete that does not mean it's flawless, it just means I'm probably never going to touch
it again. Then, there's a picture that shows a little bit of what the project looks like.
If you are not a programmer I will try to include less programmer friendly ways to try these projects so you can still
use them too :)
I decided to expand upon my learning from the Geometry Dash game I made, and made an even more complicated
engine. I switched to LWJGL so that I could handle the
rendering myself, which resulted in a 20% increase in CPU speed and about 1% increase in GPU speed. I was really
proud of the improvements, and the engine got some
massive upgrades as well. I also made a Youtube series documenting this
here: Mario
Maker in Java
You can also download the game
here: Mario Maker in Java
This repository is the first big game I coded. I wanted to make the entire engine from scratch and really
formalize my game-making skills, so I decided to code Geometry Dash in Java. I began to implement an AI for the
game using the NEAT algorithm, however I never quite got it working right. I made a Youtube series explaining how
to do it yourself also, documented
here: Geometry Dash in Java
You can also download the game
here: Geometry Dash in Java
This repository includes the link to the first representation of the programming language I am creating, called
Dymond. It looks a bit like Javascript, but behaves more like Python, and it is incredibly slow right now. I have
it coded in Java right now, and it just walks an AST to interpret the code you write. I am in the process of
speeding it up by coding it in C :)
I am very proud of this game. I am in the process of creating a Minecraft clone called... Tradecraft (Fanfare).
This is the first major 3D game I have made, and I plan on writing a book on the process. It is
coded in C++ using the Unreal Engine for some amazing graphics. Actually, check out the next project on
this list for a more in depth look at the book.
This game is literally one of the first games I ever developed. It was coded in Javascript and HTML/CSS, and it
was built from the ground up by following a tutorial from a book that introduced me to programming. It's a fun
little game and you can play it directly by following the directions on this link. Enjoy!
This is a remake of the classic, World's Hardest Game. I coded this from the ground up in Java, and it was
real fun to make. It was one of my first games from scratch, so please be gracious on the poor coding ;) The
instructions on how to download it and play can be found on the Github repository.
This is my take on the classic laser defender. I followed a tutorial for this, but added in several of my own
components. It's a pretty fun game, but it only works on Windows right now. I may eventually add support for Mac
and Linux. It was built in Unity and I used open source assets for all the images. Check it out here.
This is a game I created after following an extensive tutorial on Unity. I decided to put everything I had learned
into practice and release an App available on the Google Play Store. I used Unity Ads to try and monetize it. The
premise of the game is simple, tap the screen to flip the character and go for as long as possible.
I was perusing problems on Project Euler one day and
stumbled across a problem that asked to determine who wins a game of Poker. This will be fun I thought... and it
was :). I finished the problem which simply fed in a feed of Poker hands in a .txt file and asked you to determine
who won. After I finished I realized I had a Poker game in the making. So I went the next step and made it a game.
This one is a project I had to complete for a class at school. We were instructed to create a simple database and
build a form to fill out, the form would then be inserted into the database. This was my take on the project, I
decided to use Django, and I used MySql for the database.
Let's face it. This is like the "Hello world!" of game programming. I don't know how many different versions of
this I have made. This particular version was made in Java using Swing and AWT. I have a simple AI that tracks the
ball and has a certain percentage of error that it will have depending on the difficulty you chose. The paddles
initialize as random colors and the ball changes to the color of whichever paddle it hits.
Quadtrees are an interesting data structure used in some 2D game engines to help with the collision detection. The
premise is that it is implausible to test collisions against every single object with every other object in a
game. So what Quadtrees do is break up the gameplay space into several different sections so that you only have to
test the objects nearest to each other for collision. Unfortunately I never implemented this in any of my game
engines.


--- title: ANCOVA when the covariate is a mediator affected by treatment author: Jeff Walker date: '2020-07-12' slug: ancova-when-the-covariate-is-a-mediator-affected-by-treatment categories: - stats 101 tags: - ancova - causal graph - linear model - Wright style path analysis keywords: - tech ---
This is fake data that simulates an experiment to measure effect of treatment on fat weight in mice. The treatment is “diet” with two levels: “control” (blue dots) and “treated” (gold dots). Diet has a large effect on total body weight. The simulated data are in the plot above - these look very much like the real data.
The question is, what are problems with using an “ancova” linear model to estimate the direct effect of treatment on fat weight? By ancova linear model I mean
fat ~ body_weight + diet
A common assumption of ANCOVA is no treatment effect on the covariate. Here is how I think about the generating model for these data – or a series of generating models – encoded by causal diagrams. The effect we’re trying to estimate is the direct effect of diet on fat (\(\beta_2\) in model 1, or \(\beta\) in model 5). Model 1 is a typical “mediation” model. I don’t care about the mediator, except as a nuissance. Model 2 is a bit more realistic in that body weight is a proxy for “size”. Model 3 is thinking like Sewell Wright in his series of papers on “size factors” - which is the set of foundational papers for causal modeling and DAGs. Model 4 is just collapsing model 3 and model 5 is collapsing it a bit more (the special size factors aren’t measured)
The fake data were generated using model 1 and the ancova linear model estimates the direct effect without bias.
Two issues:
What is the best practice for this problem?


{% extends 'layout.html' %} {% block content %} {% with errors = get_flashed_messages(category_filter=["error"]) %} {% if errors %}
This is a simple hero unit, a simple jumbotron-style component for calling extra attention to featured content or information.
It uses utility classes for typography and spacing to space content out within the larger container.
Learn more


Overview
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I
IndustrySector
- Class in
com.ibm.webest.persistence.model
Entity implementation class for Entity: IndustrySector
Industry sectors are grouped by industry sector groups.
IndustrySector()
- Constructor for class com.ibm.webest.persistence.model.
IndustrySector
IndustrySectorGroup
- Class in
com.ibm.webest.persistence.model
Entity implementation class for Entity: IndustryGroup
Groups industry sectors.
IndustrySectorGroup()
- Constructor for class com.ibm.webest.persistence.model.
IndustrySectorGroup
industrySectorGroupChanged(ValueChangeEvent)
- Method in class com.ibm.webest.gui.action.
ProjectDescriptionAction
Action listener for changes of industry sector group field.
info(Object, Object, Throwable)
- Method in interface com.ibm.webest.processing.administration.
LoggingServiceLocal
Log a message at the INFO level for the given caller.
info(Object, Object)
- Method in interface com.ibm.webest.processing.administration.
LoggingServiceLocal
Log a message at the INFO level for the given caller.
info(Object, Object, Throwable)
- Method in class com.ibm.webest.processing.impl.administration.
LoggingService
Log a message at the INFO level for the given caller.
info(Object, Object)
- Method in class com.ibm.webest.processing.impl.administration.
LoggingService
Log a message at the INFO level for the given caller.
init(FilterConfig)
- Method in class com.ibm.webest.gui.utils.
MyFacesAdfResourcesFilter
initialize(OneUseCaseInScope)
- Method in class com.ibm.webest.persistence.validation.
OneUseCaseInScopeValidator
initialize(UniqueMilestoneIds)
- Method in class com.ibm.webest.persistence.validation.
UniqueMilestoneIdsValidator
initialize(UniquePhaseNumbers)
- Method in class com.ibm.webest.persistence.validation.
UniquePhaseNumbersValidator
initialize(ValidPhasePercentages)
- Method in class com.ibm.webest.persistence.validation.
ValidPhasePercentagesValidator
initialize(ValidPiPpSelection)
- Method in class com.ibm.webest.persistence.validation.
ValidPiPpSelectionValidator
initiate(boolean)
- Method in class com.ibm.webest.gui.action.
PiHistoryAction
initiate the selected value
isAdministrator()
- Method in class com.ibm.webest.gui.action.
AuthenticationAction
isAdministrator()
- Method in interface com.ibm.webest.processing.administration.
AuthenticationServiceRemote
isAdministrator()
- Method in class com.ibm.webest.processing.impl.administration.
AuthenticationService
isAlreadyRedefined()
- Method in class org.apache.openjpa.enhance.
PCEnhancer
Whether the type that this instance is enhancing has already been redefined.
isAlreadySubclassed()
- Method in class org.apache.openjpa.enhance.
PCEnhancer
Whether the type that this instance is enhancing has already been subclassed in this instance's environment classloader.
isDeletePopupVisible()
- Method in class com.ibm.webest.gui.action.
EstimateAction
getter pi-history-popup visibility
isDeletePopupVisible()
- Method in class com.ibm.webest.gui.action.
SolutionAction
getter for deletion popup
ISDETACHEDSTATEDEFINITIVE
- Static variable in class org.apache.openjpa.enhance.
PCEnhancer
isDivisionSelectable()
- Method in class com.ibm.webest.gui.action.
EstimateAction
isEditConstraint()
- Method in class com.ibm.webest.gui.action.
SolutionAction
isEstimateEditable()
- Method in class com.ibm.webest.gui.action.
AuthenticationAction
isEstimator()
- Method in class com.ibm.webest.gui.action.
AuthenticationAction
isEstimator()
- Method in interface com.ibm.webest.processing.administration.
AuthenticationServiceRemote
isEstimator()
- Method in class com.ibm.webest.processing.impl.administration.
AuthenticationService
isIncluded()
- Method in class com.ibm.webest.persistence.model.
DefectCategory
Indicates, if the defect category should be included into the project.
isIncluded()
- Method in class com.ibm.webest.persistence.model.
Milestone
isInScope()
- Method in class com.ibm.webest.persistence.model.
UseCase
isIosBrowser()
- Method in class com.ibm.webest.gui.action.
EstimateAction
checks if the used browser is an mobile safari or not
isIosBrowser()
- Static method in class com.ibm.webest.gui.utils.
ManagedBeanUtils
Evaluates, if the browser is an mobile version of safari
isManager()
- Method in class com.ibm.webest.gui.action.
AuthenticationAction
isManager()
- Method in interface com.ibm.webest.processing.administration.
AuthenticationServiceRemote
isManager()
- Method in class com.ibm.webest.processing.impl.administration.
AuthenticationService
isNewEstimatePopupVisible()
- Method in class com.ibm.webest.gui.action.
EstimateAction
getter delete estimate popup visibility
isPCSubclassName(String)
- Static method in class org.apache.openjpa.enhance.
PCEnhancer
Whether or not
className
is the name for a dynamically-created persistence-capable subclass.
isPiHistoryPopupVisible()
- Method in class com.ibm.webest.gui.action.
PiHistoryAction
getter pi-history-popup visibility
isPiSelected()
- Method in class com.ibm.webest.gui.action.
PiHistoryAction
Getter for selectedPI
isPpSelected()
- Method in class com.ibm.webest.gui.action.
PiHistoryAction
Getter for selectedPP
isReference()
- Method in class com.ibm.webest.persistence.model.
MeasuringUnit
Indicates, if the unit is the reference (base) unit.
isSaveTemplatePopupVisible()
- Method in class com.ibm.webest.gui.action.
EstimateAction
isSelected()
- Method in class com.ibm.webest.gui.model.
SelectableObject
Indicates whether the the object is marked as selected.
isSelected()
- Method in class com.ibm.webest.persistence.model.
SolutionStaffingShape
Required.
isValid(Solution, ConstraintValidatorContext)
- Method in class com.ibm.webest.persistence.validation.
OneUseCaseInScopeValidator
isValid(TemplateValues, ConstraintValidatorContext)
- Method in class com.ibm.webest.persistence.validation.
UniqueMilestoneIdsValidator
isValid(TemplateValues, ConstraintValidatorContext)
- Method in class com.ibm.webest.persistence.validation.
UniquePhaseNumbersValidator
isValid(TemplateValues, ConstraintValidatorContext)
- Method in class com.ibm.webest.persistence.validation.
ValidPhasePercentagesValidator
isValid(Solution, ConstraintValidatorContext)
- Method in class com.ibm.webest.persistence.validation.
ValidPiPpSelectionValidator
Overview
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--- layout: post audience: facilitators extraStyles: ["/js/bundled/register-wizard.css"] ---
As every year, you can list your event on coderetreat.org to make it easy for someone to find and attend. Fill out this form and send us a Pull Request to our repo.
To get your event listed on coderetreat.org, you need to fork our repository at github.com/coderetreat/coderetreat
_data/events/
such as berlin-swkberlin.json
.npm test


Hosomaki - sushi rolls. Special Makimono - house specialty sushi rolls. Complete dinner sets to include miso soup, cucumber and shrimp salad and steamed rice for an additional cost. Prices subject to change.
Surimi, avocado & cucumber
Grilled freshwater eel, avocado & cucumber
Big roll (5 pcs.) choice of shrimp, surimi or eel
Yasai Maki-Vegetarian
$8.95
Choice of fresh, cooked, or pickeled vegetables
Sushi Moriawase - Regular
$26.75
Chef's choice - 5 pcs, nigiri, 1 tekka maki
Sushi Moriawase - Large
$39.95
Chef's choice - 8 pcs, nigiri, 1 California roll
Chef's choice of sashimi over rice
Chef's choice deluxe combination
House specialty sushi rolls
Large tempura prawn roll topped with grilled freshwater eel
Avocado & tobiko roll, topped with salmon and sliced lemon
King crab & avocado roll, topped with assorted sashimi, & shrimp
Crispy soft shell crab, avocado, cucumber & tobiko roll
Tempura prawn & tobiko roll, topped with grilled eel and avocado


Banglore Palace
An epitome of great architecture and beauty, The Majestic Bangalore Palace preserves in it the spice of old regal opulence. Currently the central attraction in Bangalore, the palace was built in the year 1878. Chamarajendra Wadiyar's British Guardians bought the original property in 1873 from the principal of Bangalore Central High School, Rev, J Garret, from his funds.
Bannerghatta National Park
Housing the country's first butterfly park, this park has a huge variety of fauna, making it one of the most enjoyable tourist attractions in the city. Tourists can also opt for Safari rides through the park. One can spot Lions, Tigers, Crocodiles amongst numerous others in the park
Lal Bagh
Lalbagh Botanical Garden is located in Bangalore and is nationally and internationally renowned centre for botanical artwork, scientific study of plants and also conservation of plants. A haven for all nature lovers, Lal Bagh covers an area 240 acres in the heart of the city and has nearly 1,854 species of plants. It was commissioned by Hyder Ali in 1760 and completed by his son Tipu Sultan.


Angular Devtools only supports Angular versions 9 and above with ivy enabled.
We detected an application built with production configuration. Angular DevTools only supports development builds.
i Angular application not detected.


|
|
| PurleyPlatform (PCM *m, bool csv, bool bandwidth, bool verbose, uint32 delay) |
| |
|
| LegacyPlatform (initializer_list< string > events, initializer_list< eventGroup_t > eventCodes, PCM *m, bool csv, bool bandwidth, bool verbose, uint32 delay) |
| |
|
| IPlatform (PCM *m, bool csv, bool bandwidth, bool verbose) |
| |
|
|
static IPlatform * | getPlatform (PCM *m, bool csv, bool bandwidth, bool verbose, uint32 delay) |
| |
| enum | eventFilter { TOTAL,
MISS,
HIT,
fltLast
} |
| |
|
vector< vector< uint64 > > | eventSample |
| |
|
PCM * | m_pcm |
| |
|
bool | m_csv |
| |
|
bool | m_bandwidth |
| |
|
bool | m_verbose |
| |
|
uint | m_socketCount |
| |
|
vector< string > | filterNames |
| |
|
vector< string > | bwNames |
| |
The documentation for this class was generated from the following file:


Together with some traditional inference algorithms the reformulation algorithms accessible from the below portals are the beginning of the system Jack. Jack is an approach to logical inference that analyzes a given inference problem, chooses the automated reasoning tool that is most appropriate for solving that problem, transforms the problem into the language that tool requires, and finally invokes that tool to solve the inference problem.
The premise is that each automated reasoning tool was developed to address a unique class of problems, and if a system could automatically detect that class and reformulate the problem as appropriate, it would exhibit better performance properties than any one of the individual systems. So far, there is just one nontrivial reformulation algorithm, what-to-how, that transforms classical logic (Finite Herbrand Logic) queries into Logic Programming (nonrecursive datalog with negation) queries.


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I'm no stranger to drugs. I've used all kinds of drugs: alcohol, marijuana, cocaine (crack & powder), meth, benzodiazepines, opiates, mushrooms, and so on. So far Adderall is the only one that I lost complete touch with reality over. It completely changed who I was and taught me everything I know about addiction. I always thought what they taught us in health class was a scare tactic to keep us from using drugs. Boy was I wrong.
My amphetamine use started when I discovered my mom's phentermine that she was prescribed for weight loss.  I decided to try 2 one morning. As soon as they kicked in I was in heaven. It felt just like when I tried cocaine except I was still high after an hour. Everything was better, especially cigarettes. I could do tedious tasks that normally I wouldn't even think about doing. I was superman and totally unstoppable. Alcohol didn't get me drunk and the best weed around didn't get me stoned. One day at work (I used to work at a fast food restaurant) I was the only person making sandwiches on a very busy day. Working was FUN for the first time ever. I had every sandwich out in less than a couple minutes. It was like I had 4 other people helping me. At the end of the day my boss told me I was 'fucking phenominal.'
I used phentermine quite a few times, usually waiting a couple weeks or more inbetween. After a while I was taking up to 6 pills in a day, and they were the highest dose they came in (37.5 mg). I was talking about Adderall one day, which I had not yet tried, and a friend told me her little brother was prescribed 20 mg Adderall XR. I was beyond excited. We went right over to where they were living at the time and she gave me 3 capsules free of charge. I started off with one to see how it would affect me. I felt a little speedy, but not what I had expected. About an hour after I started feeling the effects of the first pill I took another. I didn't feel much different. Sometime early in the morning I took the third pill with not much of a difference.
The second time I was given these pills a month later, I took 2 at the same time. This is what I was looking for. I sat around waiting for the effects. The first thing I noticed was I got really hot and could feel my pulse in my face. A few minutes later I was zooming. I raked the yard and talked with my friend. I played a zoo game on my Nintendo DS and it was somehow the best gaming experience ever. I was more detailed with how I built things and I was the best zookeeper in the world. As usual with stimulants, the comedown sucked but nothing I couldn't handle. When my friend's brother's dosage was upped to 30mg I didn't hesitate in buying the 20 that were left.
I decided I was going to try getting my own prescription. I had to try Wellbutrin, Ritalin, Concerta and Vyvanse to keep from looking suspicious to my doctor. I really did, and still do, have trouble concentrating but I didn't think it was ADD until I found out I could have my own bottle of amphetamines if I went to the doctor for it. The doctor seemed to be determined to let me try everything EXCEPT Adderall. Now it makes sense to me because he's the owner of the treatment center there. I decided I needed a different doctor.
The first appointment I had I decided I wasn't leaving that room without an Adderall prescription. We discussed my situation, the medications I had previously been prescribed and so on. He wanted to give me Straterra because he thought I had ADD and depression. It came down to me literally asking him if I could try Adderall and it worked. I told my mom 'I like this doctor.'
I started off with 20 mg Adderall XR. I abused those for a couple of months when I found out they made pills that aren't extended release. I convinced the doctor that it would be more convenient for me to take a second dose when I needed it and it worked. I was given 60 10 mg Adderall to be taken twice a day. A month later my dose was increased to 15 mg twice a day, or 90 pills a month. Needless to say I thought I was in heaven. In reality I was on the path to hell. I don't remember when things went bad but a handful of Adderall every day is just asking for problems. People were hanging out with me just because I had a prescription and also quite generous. I'd hand out 5 pills at a time to people just to make them like 'me.' School was getting better but I was getting worse. I'd take up to 100 mg in a day and not accomplish a single thing.  Every little problem I had was magnified and seemed 10 times worse than it was.
This whole time I was on probation for posession of marijuana so I couldn't smoke to help me feel better. I was running out of pills within 2 weeks and started buying or borrowing them from a friend who was prescribed the same dose except he had (and still has) 120 a month. The only time I ever took a 'break' was when I needed to get some sleep, attempted to lower my tolerance, or just didn't have any.
I was given a saliva drug test at school by my probation officer. I hadn't been given a drug test in over 5 months so I started smoking weed again. I had been smoking all weekend so I knew I was screwed. This was at least my 4th dirty drug test so I began preparing to be sent away to an inpatient treatment. That's exactly what happened. I went to a treatment center for teens 2 hours away from home. I didn't want to bring my Adderall prescription with me because what's the point of taking the prescribed dose when I had such a ridiculously high tolerance? My mom called my doctor to get my prescription OK'd he said he wasn't going to trade one addiction (marijuana) for another (amphetamines) and refused to be my doctor. I still disagree with the way he handled that but in the end it was for the better.
Although I was in treatment I was still abusing Adderall almost every weekend. I was allowed to have home visits on the weekends and I'd put 10 or so pills in a little ziplock baggy and put them in my underwear before I went back. I'd take all 10 right after my drug test and become all anti-social as I usually did and draw or doodle for hours. I never slept the first night I came back from a home visit. Eventually I ran out of pills so I didn't have a choice but to stop. I told my mom that I had thrown them away.
After a while of being off Adderall I started to realize things that were so obvious before but I didn't want to admit to myself. I really did have an Adderall problem. I became happy and content without a stomach full of amphetamines. I always thought that if I ever quit taking it I would feel like I was coming down for the rest of my life. The only thing that happened was I began to like who I was instead of hating everything about myself. I was in treatment for 45 days and was discharged successfully. I turned 18 the next day and didn't hesitate to get drunk and stoned - I knew I was going to smoke weed again before I even got out.
I went to a party one night where I found a girl who was prescribed the 20mg XR's. We discussed it and agreed that I would buy some from her the next time I had money. I think it was the next day that I called her. I bought up to 10 at a time from her on a few occasions. For some reason I still wasn't getting that euphoric rush that I had gotten in the beginning, even after a couple months of not taking any. Everything was the same as it was before and I decided I was better off without it. I still haven't called her for more because I know what damage I'm capable of doing to myself, and that was 3 months ago. I'm happier now than ever and I have much bigger issues going on in my life than I ever have.
This past weekend the friend I mentioned earlier who was prescribed the same pills I was gave me a single 10 mg pill. Rejecting his offer crossed my mind for a split second but I didn't. I came home and put it in an empty Melatonin bottle and went to bed. I took it this morning with a vitamin B12 and waited about 45 minutes. I started feeling some small effects and thought that was all I was going to get. I told my friend that 10 mg was just a tease. I got to school and felt the same effects I did before I started abusing the hell out of it. I drank some coffee and wrote a 5-page essay on my theories of substance use, abuse and addiction.
I'm just glad that I was only given one and not enough to keep me up for days at a time and come down like a jet that just had an engine failure. I feel great that I was able to take Adderall and not feel completely miserable. I'm not taking it again any time soon. I'm glad I went through all of this because now I can see my drug use from a different perspective. I know when I'm getting out of control and what's a good idea and what's not. I'm just now coming down from that one pill almost 6 hours later, and I know my life isn't ending. I know that I'm going to be okay and happiness doesn't come in a pill. Thank you, Adderall, for making my life so miserable that I had no choice but to change for the better.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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What's New in the Orc Eclipse Plug-in
Version 2.0
- Various parts of the Orc Eclipse Plug-in, such as the outline view and configuration settings, are updated to correspond to the Orc language version 2.0.
- Orc projects and programs are explicitly set to use the UTF-8 character encoding.
- In the console view of Orc output, Orc error messages with source locations are links to the Orc program.
- In the console view of Orc output, http URIs are hyperlinks.
- The bundled Orc programming language implementation and User Guide are updated to version 2.0.
Version 1.1
- The outline view distinguishes between function definition clauses and function signatures.
- The New Orc file wizard is improved.
- Orc program run configurations now have an Alternative Orc Runtime Engine tab, to allow
programs to run with an Orc Runtime Engine other than the built-in one. This permits
experimenting with and debugging of the Orc language runtime.
- If not specified, the working directory of a program is now the directory containing the program.
- JavaSE 6 (or newer) is now required.
- The bundled Orc programming language implementation and User Guide are updated to version 1.1.
What's New in the Orc Programming Language
For Orc programming language release notes, see the Orc Wiki News page.


Stephen Kobourov
Stephen Kobourov is a Professor of Computer Science at the University of Arizona.
He works on graph drawing and information visualization, geometric algorithms and data
structures, human computer interaction, and pervasive computing.


XSETROOT(1) XSETROOT(1) NAME xsetroot - root window parameter setting utility for X SYNOPSIS xsetroot [-help] [-def] [-display display] [-cursor cursorfile maskfile] [-cursor_name cursorname] [-xcf cursorfile cursorsize] [-bitmap filename] [-mod x y] [-gray] [-grey] [-fg color] [-bg color] [-rv] [-solid color] [-name string] DESCRIPTION The xsetroot program allows you to tailor the appearance of the back‐ ground ("root") window on a workstation display running X. Normally, you experiment with xsetroot until you find a personalized look that you like, then put the xsetroot command that produces it into your X startup file. If no options are specified, or if -def is specified, the window is reset to its default state. The -def option can be specified along with other options and only the non-specified characteristics will be reset to the default state. Only one of the background color/tiling changing options (-solid, -gray, -grey, -bitmap, and -mod) may be specified at a time. OPTIONS The various options are as follows: -help Print a usage message and exit. -def Reset unspecified attributes to the default values. (Restores the background to the familiar gray mesh and the cursor to the hollow x shape.) -cursor cursorfile maskfile This lets you change the pointer cursor to whatever you want when the pointer cursor is outside of any window. Cursor and mask files are bitmaps (little pictures), and can be made with the bit‐ map(1) program. You probably want the mask file to be all black until you get used to the way masks work. -cursor_name cursorname This lets you change the pointer cursor to one of the standard cursors from the cursor font. Refer to appendix B of the X proto‐ col for the names (except that the XC_ prefix is elided for this option). -xcf cursorfile cursorsize This lets you change the pointer cursor to one loaded from an Xcursor file as defined by libXcursor, at the specified size. -bitmap filename Use the bitmap specified in the file to set the window pattern. You can make your own bitmap files (little pictures) using the bitmap(1) program. The entire background will be made up of repeated "tiles" of the bitmap. -mod x y This is used if you want a plaid-like grid pattern on your screen. x and y are integers ranging from 1 to 16. Try the different com‐ binations. Zero and negative numbers are taken as 1. -gray Make the entire background gray. (Easier on the eyes.) -grey Make the entire background grey. -fg color Use ``color'' as the foreground color. Foreground and background colors are meaningful only in combination with -cursor, -bitmap, or -mod. -bg color Use ``color'' as the background color. -rv This exchanges the foreground and background colors. Normally the foreground color is black and the background color is white. -solid color This sets the background of the root window to the specified color. This option is only useful on color servers. -name string Set the name of the root window to ``string''. There is no default value. Usually a name is assigned to a window so that the window manager can use a text representation when the window is iconified. This option is unused since you can't iconify the background. -display display Specifies the server to connect to; see X(7). SEE ALSO X(7), xset(1), xrdb(1), Xcursor(3) AUTHOR Mark Lillibridge, MIT Project Athena X Version 11 xsetroot 1.1.0 XSETROOT(1)