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BOZEMAN- As Sweet Pea comes to a close Wake Up Montana wants to show off a painting done by artist Juliene Sinclair.
Sinclair joined the crew out in the park Friday and spent time working on a Wake Up Montana work of art.
Sinclair has painted murals across town and across the world.
Recently she painted the one on the back of the fly-fishing shop in downtown Bozeman.
Her interpretation of the Wake Up logo shows the many different colors of the sun showcasing the many ways people wake up.
Sinclair's art will hang in the Wake Up studios for many years to come, if you would like to read more about Juliene Sinclair's work you can click here.


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55 BUSH CRESCENT, Wasaga Beach, Ontario L9Z1N1
- Quick Stats
- MLS Number: 204914
- Beds: 2
- Baths: 3
- Property Type: Single Family
- Year Built: 1999
- Appliances: Washer, Stove, Dryer, Freezer, Refrigerator
- Building Type: House
The perfect home in a perfect neighborhood. Large spacious home with 2 full and 1 half baths. The upper level boast a large open concept with 2 bedrooms (each with walk-in closets), 2 full baths (one being master en-suite), beautiful bright living/dining area, a kitchen with breakfast nook leading to a comfortable size deck with stairs down to large private back yard. Split staircase leads to lower level open concept provides a cozy finished family room with gas fireplace, an office/hobby area and 2-pce bath that can easily upgrade to 3/4 pce bath, 2 doors lead to areas that could quickly become 1/2 more bedrooms with large windows. The full spacious 2-car garage with a high ceiling craves a mezzanine. Inside entry from garage to large landing area with closet and laundry room. From landing – just a few steps to kitchen or down to family room. Loads of storage areas. Located on a truly beautiful, quiet, low traffic street with quick access to everything. Great value, Bring all Offer!! (id:23536)
Virtual Tour

subset: commoncrawl


text:
5'3 and 205, 20 weeks and I feel like I'm not really showing and I think I used to feel some flutters.. last heart beat was at 16 weeks and now I'm worried something happened or maybe it's normal cause of my size? Any other moms??
5'3 and 205, 20 weeks and I feel like I'm not really showing and I think I used to feel some flutters.. last heart beat was at 16 weeks and now I'm worried something happened or maybe it's normal cause of my size? Any other moms??
I feel like I just look fatter lol but I think I'm just self conscious. A lot of people I know say that you can definitely tell that I'm pregnant now. I wear my pants up high so I feel like I have a double roll instead of a bump but if I wear my pants lower you can see my bump lol. I feel random flutters and I have felt a few jabs here and there that felt like muscle spasms. I have an anterior placenta and the ultrasound tech said I may not feel as many kicks until she's bigger.
Hi I'm 5'9, 250 lbs and I also feel like I'm not showing just looking fatter. I thought I felt the baby from around 17 weeks then now I realized that was something else because now at 21 weeks I can definitely feel her and it's a different feeling. FTM (first time mom) I'm sure everything is going well, let's just wait for that bump... I'm desperate to have the bump I thought by this time I'd have it.
Im plus sized, 200lb and 18wks 2days just look fatter. Don't look pregnant. I think I won't show for awhile.
As for feeling, I think I feel him... But who knows if it's actually the baby.
Thanks all for sharing what about movement for u 18 weekers?
I am 20 weeks and don't feel baby. If you google its very common for plus size ladies or thise with an anterior placenta to not feel baby til 22 weeks plus. It makes me nervous too. I am larger than you are. I look rounder higher up and am guessing it is due to everything being pushed upwards and out of the way.
I'm 21 weeks 5 days, my "bump" is more prevalent after I've ate. It makes me feel fat, but people around me at work always say that they can see my bump. My belly is starting to round out and my fat pouch is slowly getting smaller and smaller. I've been feeling some movement, like a jab here and there and even a weird feeling when something inside gets hit. I was freaked out during weeks 17 to 20 because I was used to the flutters and then didn't feel anything as the baby moved up more into my belly.
I am at about 200 now but I do have a belly popping out. My first appt when she did a cervical check she said my uterus was tipped back a little so I think that's why I am not feeling much right now. I can feel a ball and once in a while at night I can feel a tiny movement of a roll but not any kicks or pokes yet. 18.2wks here. I can't wait to find out what we are having on dec 4th. I've been dying
I'm not plus sized, but I'm 19 weeks now with my 4th and just now feeling movement, although it's still pretty sporadic. I didn't feel anything with my last baby until I was almost 21 weeks.
5'3 203 and 18 weeks. I've just started to feel more consistent movement this is also my 3rd pregnancy which could make a difference. I only feel like you can see my belly after I eat a lot, but not because of baby.


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Telecoms tycoon Niel to sell mobile towers to Cellnex for $3bn
Businessman's company Iliad burned through €1.44bn in cash in 2018
Two French firms controlled by tycoon Xavier Niel bought 64.5 per cent of Eir last year. Photograph: Lionel Bonaventure/AFP/Getty Images)
French business tycoon Xavier Niel, who has a controlling interest in Eir, has agreed a €2.7 billion deal to sell mobile towers in France, Italy and Switzerland to Cellnex as he seeks to bolster the finances of his telecoms group Iliad.
Iliad burned through €1.44 billion in cash in 2018, twice as much as in 2017, and has lost half its market value over the last year.
The deal by billionaire Niel, considered a maverick in France's telecoms sector, follows a similar one by French rival Bouygues Telecom in 2017 under which it sold about 3,000 mobile sites to Cellnex for €900 million.
For Spanish towers group Cellnex, the acquisitions of thousands of new sites in the three countries allow it to extend its reach in Europe, where it has grown considerably over the past few years and is now seen as a key player in a potential consolidation of the telecoms infrastructure market.
Iliad shares were 5.6 per cent in midday trading, while Cellnex's were 6.6 per cent higher.
"This is quite a sensible move for them for two reasons: one is that it gives Iliad the financing to roll out in Italy, second ... Cellnex will end up having much better utilisation of the infrastructure," said Karen Egan, an analyst at Enders.
Iliad, which has shaken up the French mobile market since 2012 with low-cost services, is facing aggressive fixed and mobile discounts from rivals in its home country.
Part of the proceeds from the towers sale will help to cut its debt and invest in networks in both France and Italy, it said. The deal was announced hours before Iliad's investor day in Paris on Tuesday.
Analysts say the €2.7 billion investment is transformational for Cellnex, increasing by 75 per cent earnings before interest, tax, depreciation and amortisation (ebitda) and doubling its recurrent leveraged free cash flow.
It would allow the Spanish group to take control of Iliad's 5,700 sites in France and the 2,200 in Italy. It would also take control of the mobile towers of Salt, the Swiss telecom operator privately-owned by Mr Niel.
Cellnex also intends to spend an additional €1.35 billion on the roll-out of 2,500 new sites in France, 1,000 sites in Italy and 500 in Switzerland between 2020 and 2027.
"This is a big deal for Cellnex and materially increases the scale. We further believe there remains further opportunities for Cellnex to continue to do deals," said Credit Suisse in a note to clients.
Cellnex chief executive Tobias Martinez said on a conference call Britain's CTIL would be an appealing target but reiterated it could not afford to invest in France's TDF, which Spanish newspaper Expansion said it was considering.
Iliad, whose first-quarter sales grew by 7.7 per cent to €1.29 billion, said it targeted a return to revenue growth in France in 2019 and an acceleration of its core operating profit growth in the second-half of the year.
Mr Niel used Illiad and his private NJJ investment vehicle to buy a combined 64.5 per cent stake in Eir last year. Iliad also has an option to increase its 31.6 per cent stake in the Irish telco to almost 59 per cent in 2024 by buying out most of NJJ's shares.
- Reuters


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The annual student health fair was hosted at the Bluebonnet Ballroom in the University Center. The Student Health Advisory Committee assured students practiced having a safe spring break trip by giving important tips. The fair focused on nutrition, physical fitness, disease education and other health related topics. Free screenings such as chiropractic, blood pressure, STD, and HIV were provided. Health Educator, Alexis Longoria, says the goal for events like this is to provide these services for students rather than them going out of their way to find them.
-Jose Delgado


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Video Analysis
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Video analysis preformed by EOMAC Head Coach, Luis Escobar. You will receive 5-10 mins of pool space where Luis will film you for your preferred stroke. Corrections and guidance from Luis will be sent to you by email with your completed video attached.
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It's not an original case, but if you're going to have free labor working for you, consider its implications.Read More
A guest blog from Addie Fisher of Old World New on how we can impact climate change as individuals.Read More
My take on growing the pie and how that helps us all get things done and change the world.Read More
A few notes on our role as donors - now, and always.Read More
My favorite e-magazines and blogs related to social impact, for both business owners/leaders and individuals.Read More
An Impact Boss™ interview with my business school friend Evan Sharp about how he addresses black issues in corporate America and advice for all of us to do so as well, both at work and at home.Read More
Are we always looking beyond what's easy when it comes to driving forward social change? This is a note about opportunity . . . and music videos.Read More
If you want to be more intentional about influencing social impact results at home, at work, and in your day-to-day life, this is for you. I'm not a life coach, I'm not a career coach, but I am your perfect partner if your priority right now is to leave a positive impact and social impact footprint on the world however you are able.Read More
"Buy less, choose well, make it last." - Vivienne Westwood. Yes, this is a note about fashion.
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This is the third of my series of Impact Boss™ interviews showcasing various case studies of how passionate individuals have found opportunity to integrate social impact into both their personal and professional lives. See how losing hair and a PhD reality check ignited a new community for Dr. Tiffany Johnson.Read More
I get asked this question a lot, especially from young men and women who have a background and undergraduate education similar to mine. Here is my response. This is part one of a two part discussion on business school and social impact. In my next Blog (hannahgay.com/blog/7) I will discuss how to get into social impact if you have an MBA and are feeling a bit stuck.
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Is it our role to police language people use to empower themselves, even if we don't love it? I can't claim ownership to much more other than the language women are using to empower themselves, but I have some thoughts.
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We present improvements in automatic speech recognition (ASR) for Somali, acurrently extremely under-resourced language. This forms part of a continuingUnited Nations (UN) effort to employ ASR-based keyword spotting systems tosupport humanitarian relief programmes in rural Africa. Using just 1.57 hoursof annotated speech data as a seed corpus, we increase the pool of trainingdata by applying semi-supervised training to 17.55 hours of untranscribedspeech. We make use of factorised time-delay neural networks (TDNN-F) foracoustic modelling, since these have recently been shown to be effective inresource-scarce situations. Three semi-supervised training passes wereperformed, where the decoded output from each pass was used for acoustic modeltraining in the subsequent pass. The automatic transcriptions from the bestperforming pass were used for language model augmentation. To ensure thequality of automatic transcriptions, decoder confidence is used as a threshold.The acoustic and language models obtained from the semi-supervised approachshow significant improvement in terms of WER and perplexity compared to thebaseline. Incorporating the automatically generated transcriptions yields a6.55\% improvement in language model perplexity. The use of 17.55 hour ofSomali acoustic data in semi-supervised training shows an improvement of 7.74\%relative over the baseline.
Quick Read (beta)
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training
We present improvements in automatic speech recognition (ASR) for Somali, a currently extremely under-resourced language. This forms part of a continuing United Nations (UN) effort to employ ASR-based keyword spotting systems to support humanitarian relief programmes in rural Africa. Using just 1.57 hours of annotated speech data as a seed corpus, we increase the pool of training data by applying semi-supervised training to 17.55 hours of untranscribed speech. We make use of factorised time-delay neural networks (TDNN-F) for acoustic modelling, since these have recently been shown to be effective in resource-scarce situations. Three semi-supervised training passes were performed, where the decoded output from each pass was used for acoustic model training in the subsequent pass. The automatic transcriptions from the best performing pass were used for language model augmentation. To ensure the quality of automatic transcriptions, decoder confidence is used as a threshold. The acoustic and language models obtained from the semi-supervised approach show significant improvement in terms of WER and perplexity compared to the baseline. Incorporating the automatically generated transcriptions yields a 6.55% improvement in language model perplexity. The use of 17.55 hour of Somali acoustic data in semi-supervised training shows an improvement of 7.74% relative over the baseline.
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training
Astik Biswas, Raghav Menon, Ewald van der Westhuizen, Thomas Niesler
Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa
[email protected], [email protected], [email protected], [email protected]
Index Terms: speech recognition, Somali, semi-supervised, TDNN-F, under-resourced language
1 Introduction
In countries with a well established internet infrastructure, social media has become an accepted platform for sharing opinions and concerns [1, 2, 3]. Surveys conducted by the United Nations (UN) in places lacking sufficient internet infrastructure indicate that this function is fulfilled by radio phone-in shows [4, 5, 6]. Therefore, to support its humanitarian relief efforts in rural and under-developed parts of Uganda, the UN has piloted radio browsing systems in three of the country's languages [7, 8].
The success in Uganda served as a motivator for the development of a corresponding system for Somalia, an African country where the UN is also currently engaged. However, the Somali language is extremely under-resourced and the difficulty of compiling speech data resulted in an available training set of just 1.57 hours [9]. By leveraging available resources from better-resourced but unrelated languages [10, 11, 8], a system using a hybrid neural network acoustic model was able to achieve a word error rate (WER) of 53.75% [9]. While the additional language resources were beneficial to the acoustic model, we found that care had to be taken when deciding on the composition of the multilingual training set. To our knowledge, only one other study on Somali automatic speech recognition (ASR) has so far been described in the literature [12].
Somali is an Afroasiatic language. It is the official language of Somalia and widely used its neighbouring countries.11 1 https://www.alsintl.com/resources/languages/Somali/ There are an estimated 7 million native Somali speakers in Somalia and 10 to 16 million worldwide. Somali is written using the Latin alphabet, and has a phoneme inventory of 23 consonants and five vowels. Somali is an agglutinative language that is characterised by a high number of morphemes per word.
The multilingual Somali acoustic model described in [9] uses a hybrid neural network that contains several million parameters. This complexity increases the computational demand of the decoding process. Since the computational resources available in the target setting are very limited, it is important to reduce the required computation, for example by using an acoustic model with fewer parameters. The recently-introduced factorised time-delay neural networks (TDNN-F) [13] utilise half the number of parameters than the hybrid networks with comparable performance, in particular in a low-resource setting. This motivated us to consider this neural network architecture for acoustic modelling using our extremely small Somali training corpus.
In the following, we present the results of our most recent efforts to improve our Somali ASR system. We make use of TDNN-F acoustic models and experiment with the incorporation of additional but unannotated Somali speech data by semi-supervised training, an approach which has been applied successfully in some other low-resource settings [14, 15, 8, 16].
2 Radio browsing system
Figure 1 [8] shows the components of the radio browsing system. The preprocessed audio stream is passed to the ASR system which generates lattices which are subsequently searched for predefined keywords. Human analysts further process the data which aid in humanitarian decision making and situational awareness. This system is currently successfully deployed by the UN in Uganda.22 2 Examples of system output are available at http://radio.unglobalpulse.net
3 Acoustic and text data
3.1 Manually transcribed acoustic data
The Somali acoustic training and test data used in our experiments is described in Table 1. This small dataset of speech captured from broadcast Somali radio phone-in programmes, contains only 1.57 hours of transcribed speech that is available for training and 10 minutes for testing.
|Speech dataset
|Training set
|Test set
Besides the Somali data, the larger datasets used to train the Ugandan systems were available for acoustic modelling, as shown in Table 2. Luganda and Acholi are indigenous Ugandan languages, while Ugandan English is highly accented. In addition to the Ugandan data, a 20-hour dataset of South African English Broadcast News was available. While the Ugandan data was drawn exclusively from radio phone-in talk shows, the South African data was compiled from national radio news bulletins and consists of a mix of prepared and spontaneous speech [17]. The total transcribed multilingual speech data available for acoustic model training (ManT), comprising Somali and these additional languages, adds up to 46.37 hours.
|Speech dataset
|Ugandan English
|South African English
3.2 Untranscribed acoustic data
Approximately 17.55 hours of untranscribed Somali speech data, also collected from phone-in programmes, was available for experimentation. No information regarding speaker identity or other characteristics of the speech was available. As a simple and naïve first approach, the speech files were simply divided into fixed-length segments before being passed to the recogniser for transcription and semi-supervised retraining. In future, the effect of more sophisticated segmentation, based for example on diarisation, will be investigated. The automatically transcribed data, or specific subset thereof, that is output by the transcriptions systems will be referred to as 'AutoT.'
3.3 Text data
Table 3 summarises Somali text corpora that were available for language modelling. Besides the 15k words in the transcriptions of the Somali speech corpus training set (Table 1), a number of additional resources were available. Approximately 2M words were harvested from publicly-accessible news websites. A further 1.6M words were gathered from public Facebook posts and 3.5M words from selected Facebook comments. Comments were replies to the posts and were generally less well edited. While the Somali news text and Facebook posts were carefully manually cleaned and filtered, the Facebook comments consist of raw, unfiltered text [9]. Furthermore, two corpora taken from the Leipzig Corpora Collection (LCC) [18] were included in our language model (LM) data collection. Finally, it has been shown by some researchers that text generated artificially using a long short-term memory (LSTM) neural network can lower language model perplexity when incorporated as additional training data [14]. Hence we trained an LSTM network on the Somali acoustic training transcriptions and generated an 11M word corpus of artificial text.
|Word tokens
|Word types
|Somali transcriptions
|Somali news text
|Facebook posts
|Facebook comments
|LCC newspaper text
|LCC Wikipedia text
|LSTM generated text
4 Semi-supervised training
It has been shown that semi-supervised training can improve ASR performance in an under-resourced scenario [14, 15]. As we only have less than two hours of transcribed Somali acoustic data, increasing the pool of in-domain data by semi-supervised training was an attractive option. To test this, we used a recently-acquired corpus comprising 17.55 hours of untranscribed Somali radio speech, as described in Section 3.2. Since no speaker information was available, each utterance was considered to originate from a unique speaker.
Our semi-supervised training strategy is shown in Figure 2. The figure shows that we implemented three iterations of semi-supervised training, in each case re-transcribing the untranscribed data. To start the process, we used our best previously-available Somali acoustic model [9] labelled ASR1 in Figure 2.
Because Somali is an agglutinative language, the number of unique word tokens is large, which poses challenges to ASR while decoding the utterances. Thus, to improve the quality of the automatic transcriptions that are added to the training set, we applied a confidence threshold during each iteration. The threshold was calculated as the average decoder confidence score across all automatically transcribed utterances.
By applying this procedure, 9.11 hours of the available 17.55 hours of untranscribed data was added to the training set in the first pass to develop ASR2. In this second pass, 9.58 hours of automatically transcribed data was above the threshold and included in the training set to train ASR3.
Each training pass took approximately three days to complete on the limited available computational resources. Hence, given this computational restriction and time constraints, we were not able to perform an exhaustive search for an optimum threshold in the earlier passes. Nevertheless, in the final pass, we analysed the semi-supervised system performance for two different configurations:
Threshold = 0, meaning that the full 17.55 hours of semi-supervised data is considered (ASR4);
Threshold = average decoder confidence, leading to 9.86 hours of semi-supervised data (ASR5).
Table 5 describes the training strategies pass by pass for better insight.
5 Language modelling
All language models were built using the SRILM toolkit [19]. The vocabulary of the ASR system was drawn from the pool of T1, T2 and T3 texts in Table 3 by retaining all word types occurring at least four times. The resulting vocabulary consisted of 41.7k word types.
The language model used in [9] was used as the baseline (LMbase). This model was trained on the Somali training set transcriptions (T1) and further interpolated with language models trained on the additional sources T2, T3, T4 and T7. Sources T5 and T6 were not available at the time. The interpolation weights were optimised on the Somali corpus test set (Table 1).
Four additional language models were trained and evaluated. The training and interpolation of these models was accomplished in much the same way as LMbase. LM2 was trained on T1 and further interpolated with LMs trained on T2 to T7. The interpolation weights were optimised on the test set. LM3 was also trained on T1 and interpolated with LMs trained on T2 to T7, but interpolation weights were optimised on a held-out validation set extracted from the Somali acoustic training set transcriptions. LM4 was trained on T1, interpolated with the LMs trained on T2 to T7 and further interpolated with the LM trained on the automatic transcriptions obtained at the output of ASR2 after the confidence threshold was applied. The interpolation weights were optimised on the test set. LM5 was also trained on T1, interpolated with the LMs trained on T2 to T7 and interpolated with the LM trained on the automatic transcriptions obtained at the output of ASR2 after the confidence threshold was applied. However, in this case the interpolation weights were optimised on the held-out validation set. The language model perplexities are shown in Table 4. The reason for using the AutoT transcriptions from the output of ASR2 is discussed later in Section 7.
|Language model
|AM training data size (h)
6 Acoustic modelling
The Kaldi speech recognition toolkit was used for all ASR experiments [20]. All experiments were performed using a PC with an 8-core Intel i7 CPU, 32GB of RAM and a 12GB NVIDIA Tesla GPU. In our previous work, we found multilingual training to improve ASR performance substantially [9]. For multilingual training, the training sets of the four languages in Tables 1 and 2 were combined. The lexica were concatenated and the phoneme labels were left unaltered, resulting in a combined, language-dependent phoneme set. All ASR experiments used a closed vocabulary, i.e. no out-of-vocabulary words occur in the test data. First, a context-dependent GMM-HMM acoustic model with 25k Gaussians was trained using all the multilingual ManT data. 39-dimensional Mel-frequency cepstral coefficients (MFCC) with deltas and delta-deltas were used as features. This GMM-HMM provided the alignments required for neural network training. The same multilingual training data was used to compute acoustic features for neural network training. However, in this case three-fold data augmentation was applied prior to feature extraction [21] and the acoustic features comprised 40-dimensional MFCCs (without derivatives), 3-dimensional pitch features and 100-dimensional i-vectors for speaker adaptation.
It has recently been shown that, when semi-orthogonal low-rank matrix factorisation is applied to the parameter matrices of TDNN layers, ASR performance can be improved in low-resource situations [13]. Consequently, a TDNN-F acoustic model (10 time-delay layers followed by a rank reduction layer) was trained using the Librispeech recipe for Kaldi (version 5.2.164). The factorisation decomposes the high-dimensional parameter matrix into two factor matrices, one of which is constrained to be semi-orthogonal. This results in an intermediate bottleneck layer in the TDNN layer that has a lower dimension than the hidden layer. This factorisation allows the TDNN-F model to use fewer parameters than hybrid architectures such as TDNN-LSTM and TDNN-BLSTM (bidirectional LSTM). Hence, the TDNN-F models are faster to train.
Our TDNN-F was trained using the lattice-free maximum mutual information objective criterion [22]. No parameter tuning was performed during neural network training and the default recipe parameters were used.
7 Results and discussion
The ASR performance is reported in Table 5 in terms of the word error rate (WER) for the various training approaches. In comparison with our previous ASR system [9], the improvement afforded by TDNN-F is clear (rows 1 and 2). Even though TDNN-F uses only half the number of parameters as CNN-TDNN-BLSTM, it is able to offer better performance.
Next we consider the performance of semi-supervised training. Comparing ASR1 and ASR2, we see that the first pass of decoding and retraining decreases the WER by 3.30% relative to the baseline. Due to the agglutination property, Somali has a large vocabulary and more semi-supervised training data helps to improve performance. The neural network is able to learn from the larger training set afforded by the additional semi-supervised data. In the second pass, ASR3 yields a further relative improvement of 1.85% over ASR2. The third pass (ASR4 and ASR5) did not show any further WER improvement. ASR5, that was trained on the thresholded automatically transcribed data, was not able to outperform its counterpart from the previous pass (ASR3). However, it was able to perform better than ASR4 which was trained using the full set of automatically transcribed data. The results show that acoustic model training does not gain from the semi-supervised data after the second pass, and that the inclusion of poorly transcribed data leads to a deterioration in recognition performance.
The best performing ASR system so far is ASR3, which was trained on the semi-supervised data produced by ASR2. Next, we evaluate the language models described in Section 5 using the acoustic models of ASR3. LM2 and LM3 were trained on the sources in Table 3, while LM4 and LM5 included the automatically transcribed text from ASR2 (AutoTASR2) as additional training data. Figure 3 gives a representation of the semi-supervised experimental framework for the language model evaluations. LM2, which was optimised on the text set, did not show any significant improvement over the baseline. However, LM3, which was optimised on the validation set, showed an improvement of 1.86% relative to the baseline. The results in Table 6 show that the language models that included automatically transcribed text (LM4 and LM5) improve ASR performance. The perplexity of LM5 was higher than the baseline, but it was optimised on the validation set and provided the better ASR performance, decreasing the WER by 3.32%. Overall, we achieved a 7.74% relative improvement over our supervised baseline Somali ASR system, despite taking a simple approach to segmentation of the untranscribed speech.
|Acoustic Model
8 Conclusion
We have presented our initial efforts to increase the pool of Somali acoustic and language model data in a semi-supervised manner in an effort to improve automatic speech recognition for Somali. A training corpus of only 1.57 hours of in-domain segmented and transcribed Somali radio broadcast speech data was available. A further 17.55 hours of unannotated Somali speech was segmented using a straightforward approach. In addition, approximately 44.8 hours of annotated speech in three unrelated languages was available for multilingual modelling. A baseline TDNN-F acoustic model was trained using this multilingual data and semi-supervised training was carried out in three passes. In each pass, a threshold was applied to the confidence score of the decoded output, discarding utterances below the threshold. We found that only the first two such passes of semi-supervised learning improved performance. Although the segmentation approach for the untranscribed data was extremely simple, a 7.74% relative improvement over the baseline system was achieved. Ongoing work is exploring the effects of more sophisticated segmentation and diarisation approaches, as well as a more careful optimisation of the confidence threshold.
9 Acknowledgements
We thank the NVIDIA corporation for the donation of GPU equipment used for this research. We also gratefully acknowledge the support of Telkom South Africa.
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- [16] S. Thomas, M. L. Seltzer, K. Church, and H. Hermansky, "Deep neural network features and semi-supervised training for low resource speech recognition," in in Proc. ICASSP, 2013, pp. 6704–6708.
- [17] H. Kamper, F. de Wet, T. Hain, and T. Niesler, "Capitalising on North American speech resources for the development of a South African English large vocabulary speech recognition system," Computer Speech and Language, vol. 28, no. 6, pp. 1255–1268, 2014.
- [18] D. Goldhahn, T. Eckart, and U. Quasthoff, "Building large monolingual dictionaries at the Leipzig Corpora Collection: From 100 to 200 languages." in Proc. LREC, vol. 29, 2012, pp. 31–43.
- [19] A. Stolcke, "SRILM-an extensible language modeling toolkit," in Proc. ICSLP, 2002.
- [20] D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz et al., "The Kaldi speech recognition toolkit," in Proc. ASRU, 2011.
- [21] T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, "Audio augmentation for speech recognition," in Proc. Interspeech, 2015.
- [22] D. Povey, V. Peddinti, D. Galvez, P. Ghahremani, V. Manohar, X. Na, Y. Wang, and S. Khudanpur, "Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI." in Proc. Interspeech, 2016, pp. 2751–2755.


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Fun in sun over as Obama gets serious about second term
President Obama returns to work today bidding to defy White House history with a reforming second term that would save him from three years of lame duck status.
With Mr Obama's personal approval ratings at their lowest point following the accident-prone launch of his healthcare reforms, his Administration nontheless enters 2014 marked by cautious optimism.
The President's inner circle is buoyed by the end-of-year budget deal struck with senior Republicans, believing that it may herald sufficient cross-party momentum to push through comprehensive immigration reform, the top priority of hissecond term.
And the arrival of Bill Clinton's former chief of staff, John Podesta, as a senior counsellor is intended to give some impetus to his agenda on tackling climate change. Mr Podesta, a veteran of dealing…


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review by joey fanthom
Diego Maradona is a documentary feature directed by Asif Kapadia, who previously helmed the brilliant Senna (2010) and the Academy Award winning Amy (2015). The film follows the life and career of the eponymous former Argentine international footballer (deemed by many to be the greatest player the game has ever seen) through the use of meticulously arranged archive footage. The film focuses particularly on his eight-year stay at the Italian club Napoli from the rousing welcome he received upon his arrival from tens of thousands of Napoli fans, to his acrimonious departure amid controversy over his drug use.
As with Senna and Amy, which are about Formula One driver Ayrton Senna and singer Amy Winehouse respectively, Kapadia here has chosen an extremely compelling subject for his film. It is another fascinating study of the detrimental effect that public scrutiny and celebrity status can have on young talent. The on-field footage is a joy to watch, capturing Maradona at his best, gliding past defenders with a pace and control that is so rarely seen, a showcase of natural genius and ability. In the world of football, Maradona is an almost mythic presence at this point, but the film does a great job of dispelling some of that and getting to the root of the man at the heart of the myth who is a deeply complex character.
Throughout the film, voiceovers from different people who were close to the man himself, such as his former Napoli fitness coach and friend Fernando Signorini, explain the duality of his personality: a divide between the private 'Diego' and the public persona of 'Maradona' which manifests itself in myriad ways. There is the loving partner playing tennis with wife Claudia versus the party boy flirting and dancing with an array of women, and the dedicated trainer running on a treadmill versus the wild man going on three-day coke benders after matches. Signorini says he would walk forever with Diego but not move one step with Maradona. The film never tries to criticise nor glorify any aspect of his off-field behaviour, but merely to give a candid account of a conflicted human being.
There is an interesting parallel between his own marginalised status growing up in the slums of Buenos Aires and the way that the Neapolitans are marginalised by other Italians. There are clips from away games in the northern cities like Turin and Milan with chants telling them to wash and that they have cholera. As a result, Maradona, a man of the people, is adored there to the point of deification, but the weight of expectation is too much for the young man a long way from home and the city swallows him up. There is an increasing involvement with the Camorra crime family the Giulianos, starting with attendance at business openings but escalating to cocaine dependence when his request to leave the club for a less stressful environment is denied by its president. Despite the alarming regularity of his drug use, Maradona still led Napoli to a title that year. He was just that good.
The film is essential viewing for football fans but also offers a deeply engaging and thought-provoking character study for the casual viewer. It runs out of steam a little towards the end as the pacing drops significantly in the last half-hour, in contrast to the relentless intensity of Senna, though this could be down to the lack of a definitive conclusion. The tragically early ending of a great career is still apparent here, but Maradona lives on while Senna and Winehouse both died, and the film fizzles out after he leaves Napoli. That should not take away, however, from the admirable job that Kapadia does in providing a compelling and intimate insight into this complex personality, deconstructing the myth surrounding him to showcase the man, flaws and all.
Diego Maradona will screen in the Irish Film Institute from June 11th.


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Ronald Stolberg | Assistant Professor
California School of Professional Psychology
- Clinical Psychology (PsyD)
San Diego Campus
- email@example.com
Research Interests
Personality assessment instruments with an emphasis on the MMPI-2/MMPI-A; assessment instruments use among practicing clinicians; standard of care practices; suicide; clinical work with adolescents and families, and sport psychology
Dr. Stolberg is a native Californian and was born right here in San Diego. Despite San Diego being one of the most amazing places to live he has always had the travel bug and routinely finds himself in foreign countries either for pleasure or to work. Dr. Stolberg is a licensed clinical psychologist with a clinical specialty of working with acting out teenagers and their families. He is a strong proponent of family therapy and sees progress only when the entire system changes not just one family member. In addition, Dr. Stolberg has specialized in other high-risk groups including suicide, self-harm, and substance use disorders.
Dr. Stolberg recently published Teaching Kids to Think (Sourcebooks) with his wife, also a psychologist. Teaching Kids to Think has won several awards and been named too numerous top parenting book lists. It has also been adapted and translated into nearly 10 languages and can be found in Russia, Italy, China, Korea, and Romania just to name a few.
Dr. Stolberg regularly teaches psychological assessment courses and is a Fellow of the Society for Personality Assessment. He has published and presented numerous papers on psychological assessment and is particularly interested in how culture, acculturation, and language preference manifest on psychological assessment instruments.
Finally, because of his expertise in high-risk behavior, substance use and psychological assessment Dr. Stolberg routinely finds himself working with sports leagues, television shows, and high-profile entertainers and athletes. He was the psychologist for the hit reality television show Survivor and traveled on location with the show to ensure the mental health of the contestants and has had numerous appearances on news programs as an expert on sport psychology, peak performance, and mental toughness.
- PhD, Pacific Graduate School of Psychology
Sweetland, D. & Stolberg, R. (2017, July). Teach Your Children to Grow Smart, Confident, and Able to Think for Himself. (Russian Translation of Teaching Kids to Think). Moscow: Alpina Publishers. Bucky, S, & Stolberg, R. (2017). Multiple Relationships in Sports Psychology. In Multiple Relationships in Psychotherapy and Counseling. In A. Zur. (Ed.), New York, NY: Routledge. Blume-Marcovici, A., Stolberg, R., Khademi, M., Mackie, A.& Lam, C. (2017). Tracking our Tears: research on therapist Crying in Therapy. In A. Blume-Marcovici (Ed.), When therapists cry: Reflections on therapists' tears in therapy (pp. 23-42). New York, NY: Routledge. Blume-Marcovici, A., Schruafnagel, K, Khademi, & Stolberg, R. (2017). Supervising our tears: A guide for supervisors and trainees. In A. Blume-Marcovici (Ed.), When therapists cry: Reflections on therapists' tears in therapy (pp. 199-218). New York, NY: Routledge. Sweetland, D. & Stolberg, R. (2016, December). The Overscheduled Child: 5 Tips to Avoid the Pressure Trap. Ann Arbor Family Magazine. Sweetland, D. & Stolberg, R. (2016, May). Do Not Rush to Eat Sugar: First Teach Children Thinking (Complex Chinese Translation of Teaching Kids to Think). Taiwan: Heliopolis Culture Group. Sweetland, D. & Stolberg, R. (2016, May). Teaching Growing Children to Use Their Head (Italian Translation of Teaching Kids to Think). Rome, Italy: Urra-Feltrinelli. Sweetland, D. & Stolberg, R. (2015, November). Raise Confident and Thoughtful Children: Avoid 5 Common Parenting Traps. Boston Parents Paper, Stolberg, R. & Kauffman, C. (2015). The Utility of the MCMI-III in Child Custody Evaluations: The Impact of Clinical Norms, Journal of Child Custody, 12:3-4,325-336, DOI: 10.1080/15379418.2015.1120169 Sweetland, D & Stolberg, R. (2015, March). Teaching Kids to Think: Raising Confident, Independent, and Thoughtful Children in an Age of Instant Gratification. Chicago, IL: Sorcebooks. Kauffman, C., Stolberg, R. & Madero, J. (2015). An Examination of the MMPI-2-RF (Restructured Form) with the MMPI-2 and MCMI-III of Child Custody Litigants. Journal of Child Custody, 12:2, 129-151. Stolberg, R. (2015, June 19). Tips for Tackling Changing Role of Fatherhood. The San Diego Union-Tribune. Sweetland, D. & Stolberg, R. (2015, May 7). New Apps Encourage Brutal Cyberbullying. The San Diego Union-Tribune. Blume-Marcovici, A., Stolberg, R., & Khademi, M. (2015). Examining our Tears: Therapists' Accounts of Crying in Therapy. American Journal of Psychotherapy. 69(4): 399-421(23). Blume-Marcovici, A., Stolberg, R., & Khademi, M. (2015). When Therapist Cry: Implications for Supervision and Training. The Clinical Supervisor. Stolberg, R. (2013). Ride Across California: A family ritual. In Ritual and Healing: Stories of Ordinary and Extraordinary Transformation. (D. Eulert Editor). Motivational Press: Carlsbad, CA. Blume-Marcovici, A., Stolberg, R., & Khademi, M. (2013). Do Therapists Cry in Therapy: The role of experience and other factors in Therapist's tears. Psychotherapy. doi: 10.1037/a0031384 King, K. & Stolberg, R. (2012). So You Practice Sport Psychology: What exactly does that mean? The San Diego Psychologist. Vol. 27, No 6, pg 18-19. Levak, R., Siegel, L, Nichols, D. with Stolberg, R (2011). Therapeutic Assessment with the MMPI-2: A positive psychology approach. New York: Routledge Groth-Marnat, G., Stolberg, R., Bongar, B. & Burke, K. (2011). Integrative Personality Assessment in Special Settings. In T. M. Harwood, L. E. Beutler & G. Groth-Marnat (Eds.). Integrative Assessment of Adult Personality: Third Edition. New York: Guilford Press. Poizner, J., Stolberg, R., Lawson, G, & Bucky, S. (2010) A Cross-Cultural Examination of the MMPI-2-RF Substance Abuse Scale: Comparing Samples from Mexico and the United States. The Addictions Newsletter (TAN), Summer 2010. Bongar, B. & Stolberg, R. (2009) Risk Management with the Suicidal Patient The Register Report, Fall 2009. Stolberg, R. & Bongar, B. (2009). Assessment of Suicide Risk. In J. N. Butcher (Editor). Oxford Handbook of Personality Assessment. New York. Oxford University Press Stolberg, R., Murphy and the AUI Ethics Committee (2008). Maintaining Professionalism within Online Communities. San Diego Psychologist, Vol.23, No.3, 1-8. Stolberg, R., Bongar, B. & Groth-Marnat, G. (2003). Personality Assessment in Special Settings. In L. E. Beutler and G. Groth-Marnat (Eds.). Integrative Assessment of Adult Personality. New York: Guilford Press. Glassmire, D.M., Greene, R. L., Stolberg, R., Cripe, L. & Berry, D. (2003). Sensitivity and specificity of MMPI-2 neurologic correction factors: Receiver operating characteristics analysis. Assessment, Vol. 10, No. 3, 299-309 Stolberg, R. & Bongar, B. (2002). Assessment of Suicide Risk. In J. N. Butcher (Editor). Clinical Personality Assessment: Practical Approaches, Second Edition. New York. Oxford University Press Stolberg, R., Clark, D. C. & Bongar, B. (2002). Epidemiology, Assessment and Management of Suicide in Depressed Patients. In I. Gotlieb and C. Hammen (Eds.) The Handbook of Depression. New York: Guilford Press. Glassmire, D. M., Stolberg, R., Greene, R. L. & Bongar, B. (2001). The utility of MMPI-2 suicide items for assessing suicidal potential: Development of a suicidal potential scale. Assessment, Vol. 8, No. 3, 281-290.


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Day[9] Learns Dota - The Return–
After what seems like an eternity of not playing with Kevin, I'm FINALLY BACK! I fully expect him to catch me up on the meta, how to play, how to succeed, how to not throw, and what the right items to build are. Thanks kevin!


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By Anonymous | April 26, 2011 - 1:23 pm | Posted in AnonyNews
Anonymous are running tweets and news for Bahrain's. We now have word that doctors and students are being attacked. Magnanimous has launched Operation Lightning Rod in support of this and other ops. Operation Gun Shy continues. Dear Anonymous, Please copy this image and flood the target pages for targeting Bahrain doctors. Though revolutions may open Read more »


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Linn Grove Growth Funds Announces First Montana Company Investment
MATR Sponsor
After a year of hard work with several companies, Linn Grove is pleased to announce its first early stage investment in Montana. Actually a company founding,with 80% Montanans, see here on Restoration Beef Company:
Linn Grove Growth Funds Announces First Montana Company Investment(Fargo, ND) Linn Grove Growth Funds announces aseed investment in Restoration Beef Company (RBC,) headquartered in Bozeman, MT.
Terms of the investment were not disclosed.Dennis O'Brien, a managing director of Linn Grove shared, "As a Montana resident myself, I have long desired to find Montana companies that fit our fund's interest in early-stage companies in our target industries and our region. As RBC completes somemarket research, asset designs and partner agreements, we will share more of our vision with the public."
Founded in 2010, Linn Grove Growth Funds (also, "Linn Grove Venture Partners, LLC,) focuses on equity investment in early stage agricultural, food and health companies. The fund has a deep network of investors in Minnesota, North Dakota,Montana and the Canadian Prairie Provinces. It seeks to find inventors and entrepreneurs in the northern Great Plain region with superior solutions for significant unmet needs.The company is currently raising and investing capital from Linn Grove Angel Fund 3
A second project is taking shape with a Montana-based agriculture biotech. Adding to their existing technology, we are bringing an additional license, creating a company with superior biological controls for diseases and pests in Northern Great Plain crop production.
Other New Projects
Elsewhere we are co-founding a food animal health company, specifically designed to aid non-antibiotic animal husbandry. Backing a first-time entrepreneur with deep expertise in animal health, this woman pitched our investment committee to a "yes" vote with a brilliant solution to a major unmet need. Now to the hard work.
New Partners
Finally, we are pleased to be close to agreements with several investors who bring current and future commitments allowing us to complete our vision for Linn Grove Angel Fund 3.It was always part of our vision to have larger partners, but we have been gratified to receive their interest and support.
Our Humble Thanks
Some of you have been involved it making this work possible this past year, as investors, advisors and connectors. We are deeply and humbly thankful for your help, not only for us, but for the committed, creative, energetic people our work supports. It is a great time to build companies. And build them in Montana
MATR Supporters


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Andrea Tufted Velvet Settee (Assorted Colors)
Product Details
Why We Love This
Showcasing elegant tufted details, silver nailhead trim and tapered wooden legs, this cozy velvet settee invites comfort and style into any interior space.
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Green Tank Technologies Becomes the Cannabis Industry's Very First Vape Maker to Release Certified Child-Resistant Hardware
March 6, 2019 – TORONTO, CANADA – Green Tank Technologies ("Green Tank" or the "Company"), a manufacturer that is leading of, high-performance vaporization hardware announced that its GT Spectrum CR cartridge has today been discovered to fulfill rigorous united states standards and protocols for child-resistant official certification, making Green Tank the initial cannabis vape manufacturer to completely conform to such consumer item security criteria.
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By Tuba Sahin
ANKARA (AA) - Turkey's benchmark stock index rose by 0.06% or 59.91 points to open at 92,309.71 points Tuesday.
On Monday, Borsa Istanbul's BIST 100 index increased by 1.61% to close at 92,249.80 points with a daily trading volume of 5.9 billion Turkish liras ($1 billion).
The U.S. dollar/Turkish lira exchange rate rose to 5.8730 as of 9.30 a.m. local time (0630GMT), compared to Monday's closing rate of 5.8660.
The euro/Turkish lira exchange rate stood at 6.6010, versus 6.5970.
One British pound traded for 7.3770 Turkish liras, as the GBP/TRY exchange rate was 7.3980 at market close on Monday.
The price of Brent oil was around $60.70 per barrel as of 10.00 a.m. local time (0700GMT) on Tuesday.


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Central American Export Strengthening Fund
Fondo para el Fortalecimiento de las Exportaciones de Centroamérica (FOEXCA)
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following an agreement of Central American Bank for Economic Integration (CABEI) and the Central American countries of Costa Rica, El Salvador, Guatemala (Guatemala), Honduras and Nicaragua with European Union (EU). Also referred to in English as Fund for the Strengthening of the Central American Exports. Ceased to exist.
Reinforce intra- and extra-regional export capacity of Central American countries by increasing investment in the export sector; increase the attraction of income generated through export of non-traditional products, particularly from industrial manufacturing and agricultural sectors; award credit for circulating and fixed capital to economically feasible and financially healthy projects and exporting companies which contribute to expansion, updating and rehabilitating export capacity of Central American countries and to better adaptation of exports to conditions of regional and international competition; contribute to restructuring, reactivation and reinforcement of Central American economic integration.Available with paid subscription only.
As part of an integrated program of credit, training and technical assistance together with with Special Fund for the Promotion of Exports (FEPEX), provides financial and technical support to productive sectors of countries in the Central American region. Loans may be awarded directly to the final borrower or through commercial or fostering banks or eligible financial institutions or (exceptionally) private development organizations proposing the project. Loans may be for fixed capital investment or (with long term loans) expenditure in currency. They may also cover pre-investment studies, working capital, cost of pre- and post-shipping operations and consultancy.
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Relations with 1 inter-governmental organizations.
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- Commerce
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- Agriculture, Fisheries
- Agriculture
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Now we are looking on the crossword clue: Intelligence explanation, perhaps (Part 1).
it's A 42 letters crossword puzzle definition. See the possibilities below.
Did you find what you needed?
We hope you did!. If you are still unsure with some definitions, don't hesitate to search them here with our crossword puzzle solver.


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Gliding camps to be held from Sep 4
Chandigarh, Aug 23 (UNI) The Haryana Sports and Youth Welfare Department has decided to organise gliding camps for youth of the state at Civil Aviation Club, Basola-Panchkula from September 4 to 30.
The camps would be organized to encourage the youth to take up such activities so as to channelise their energy towards positive direction, a spokesman of the department said.
As many as 60 youth, including 30 women, would take part in the camp from each division of the state. As per the schedule, the Girls Gliding Camp for Ambala division would be organized from September 4 to 7, for Rohtak division from September 7 to 10, for Hisar divison from September 10 to 13 and for Gurgaon division from September 13 to 16.
The Boys Gliding Camp will be organized from September 18 to 21 for Ambala division, from September 21 to 24 for Rohtak division, from September 24 to 27 for Hisar division and September 27 to 30 for Gurgaon division.
The spokesman said that all the District Sports and Youth Welfare Officers had been issued directions to send the names of the interested candidates for these gliding camps according to the standard of the adventurous activities.


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Transform your
team's season with
planned sessions
Use our expert plans or build your own using our library of over 1000+ drills, and easy-to-use tools.
Hockey Coaching Q & A
Use our expert plans or build your own using our library of over 1000+ drills, and easy-to-use tools.
Hi Tim
Here is a session which you can tweak to make it suited to focus on marking, however there are drills within this plan that implement marking in 1v1 situations and dispossessing the player with the ball - Defending the break plan.
Plus, here are some focused marking drills that you could you also try out on your players:
Match-up-1v1 - marking using a fun game to develop skills.
Marking - from what you said your players seem to have already grasped the idea of marking but you can use this to sharpen their tools once more if needed.
Marking and Chanelling - have your players following their player making it difficult to receive the pass and taking it towards their goal.
I hope this helps.
Sportplan Team
Tennis Search and hit enter


text: Hello world! By Alex Neil Posted Dezembro 14, 2015 In Uncategorized Hello world!2015-12-142015-12-14https://checkin.pt/wp-content/uploads/2019/04/logo.gifCheckin - Agência de Viagenshttps://checkin.pt/wp-content/uploads/2019/04/logo.gif200px200px 1 Welcome to Jupiter Demos Sites. This is your first post. Edit or delete it, then start blogging! Alex Neil Comments Anónimo Dezembro 14, 2015 Responder Leave a Comment Cancelar resposta Save my name, email, and website in this browser for the next time I comment.


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For twice the amount of time, Kaiser's Room is proud to present it's 2nd KAMP KR. This summer, over the span of two weeks, our Kampers will take Acting, Dance, and Music to create an original Musical Theatre piece to perform for you, with the help of our amazing KR Teaching Artists!
Sign up now and let the musical juices start flowing!
We believe that imagination and connection are key to happy development, and we'd like to invite you and your family to participate! Each day will consist of two, 30-minute performance classes, a 15-minute break, and a 45-minute "rehearsal" where together, we will create a brand new show to perform for you and your loved ones. A professional photographer will be present to capture and provide photos and a commemorative video of the performance as a memento of the experience.
We are very excited to see what our Kampers come up with and can't wait to see you all at Kamp KR!
August 19th – 23rd
Drama League Studio Theatre
32 Avenue of the Americas
$150 per week
Self-Direction receipts and some financial assistance available upon request
To attend, please click below to fill out our Registration Form for each individual attending.
Kamp Day At-a-glance
9:50 - Participant Arrival
10:00 - Dance Class
10:30 - Acting Class
11:00 - Break
11:15 - Show Development/Rehearsal
12:00 - Pick-up


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The Temora Shire Driver Reviver has been relocated from the Temora township to a new site, 20km east in the village of Springdale, to encourage tir...
Business startups across the Cootamundra electorate that are developing new products are invited to apply for development grants of up to $25,000 f...
Member for Cootamundra Steph Cooke today encouraged young people across the Cootamundra electorate to get in their applications to join NSW's first...
Member for Cootamundra Steph Cooke has announced Round Three of the Stronger Country Communities Fund, which will continue the delivery of much-che...
From July 1st, 2019, anyone who advertises dogs or cats for sale, including those for free, will be required to include an identifying number in ...
Noise pollution is defined as 'the emission of offensive noise, which means noise that by reason of its level, nature, character or quality, of the time at which it is made, is harmful to or interferes with the comfort of a person outside the premises from which the noise is emitted".
Below is a list of domestic noise sources and the times during which special restrictions apply.
Environmental Department
Phone: 02 6980 1100
Fax: 02 6980 1138
Phone (02) 6980 1100
Fax (02) 6980 1138
Email: email@example.com
105 Loftus Street
PO Box 262
DX Address
The Shire of Temora offers all the benefits of country living with a comprehensive range of quality affordable services.
Temora is situated approximately 80 km north of Wagga Wagga in the South West (Riverina) of New South Wales. Canberra is two hours drive to the east, with Griffith a 1.5 hour drive to the west.
Temora has a huge advantage as it is centrally located between the cities of Sydney and Melbourne, as well as being situated within South West Slopes/Riverina area, making it an ideal base from which to visit the region.
Temora Shire includes the town of Temora, and villages of Ariah Park and Springdale, but outreaches to other small communities outside the Shire boundary. With a strong retail trade sector and an active business community, many visitors are surprised at the variety of shopping within the town.


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Republicans Clash In Ninth Debate
One week before the South Carolina Primary, the remaining Republican candidates for President clashed in a headed debate.
Republicans clashed in their ninth debate of the election cycle last night and, with the GOP field cleared of nearly all the also-ran candidates, the clashes seem to have become more fierce as the stakes have become higher:
Former Gov. Jeb Bush of Florida blasted Donald J. Trump for insulting the Bush family and ridiculed the idea that Mr. Trump could be commander in chief during a contentious and sometimes nasty Republican presidential debate in Greenville, S.C., on Saturday, a week before a crucial primary in the state.
With Mr. Trump leading in the polls in South Carolina and elsewhere after his victory in the New Hampshire primary on Tuesday, he was a ripe target for his Republican rivals, especially Mr. Bush and Senator Ted Cruz of Texas, who are under intense pressure to halt his political momentum. But the vitriol was so intense that it seemed to surprise even Mr. Trump, a combative figure who had not been so roundly pummeled at a debate before.
After a somber opening to the debate that focused on the death of Justice Antonin Scalia, Mr. Bush aggressively took on Mr. Trump — an imperative for the former governor, given his poor showings in New Hampshire and the Iowa caucuses this month. Having helped raise more than $155 million, and with his family's political reputation on the line, Mr. Bush had the look and feel of a man taking his last, best shot to rescue his candidacy and destroy Mr. Trump's.
Mr. Bush bore into Mr. Trump's knowledge and readiness to confront national security threats, noting that Mr. Trump had made positive remarks about Russia's role in fighting the Islamic State in Syria.
"It is absolutely ludicrous to suggest that Russia could be a positive partner in this," Mr. Bush said.
"Jeb is so wrong; Jeb is absolutely," Mr. Trump said, before being cut off by boos from the audience. "You've got to fight ISIS first," he added. "You have to knock them off strong."
Mr. Trump drew more boos by attacking a South Carolina Republican, Senator Lindsey Graham, who is a Bush ally. Mr. Trump dismissed the catcalls from the audience by saying they were from Bush supporters, donors and Washington insiders. "I tell the truth, lobbyists," Mr. Trump said.
Moments later, Mr. Trump stood by his past remarks that he would have supported the impeachment of President George W. Bush over the war in Iraq, saying that his administration misled the country about weapons of mass destruction. "They lied. They said there were weapons of mass destruction, and there were none," Mr. Trump said.
His remarks drew a slashing attack from Jeb Bush, who is well aware that many South Carolina Republicans hold high opinions of President Bush as well as of their parents. "I'm sick and tired of him going after my family," Mr. Bush said. "My dad is the greatest man alive, in my mind," he added. "My mom is the strongest woman I know."
"She should be running," Mr. Trump said tartly.
The debate was also critical for Senator Marco Rubio of Florida after his disastrous turn in a face-off last weekend in New Hampshire, where Gov. Chris Christie of New Jersey savaged him as scripted and callow after Mr. Rubio kept repeating the same stock attack on President Obama.
Mr. Rubio's political momentum slowed after that, and he finished fifth in the primary there. In his concession speech, he promised supporters that he would not make the same mistakes in the debate on Saturday night. If he did not embarrass himself, he did not appreciably improve. As before, he spoke very quickly, in long sentences, rattling off national security challenges in Asia, the Middle East and Russia without pausing, a furious rush akin to spitting out words.
Mr. Rubio also briefly pounced on Mr. Trump during the exchange over President George W. Bush, not only on the subject of Iraq, but also on the Sept. 11, 2001, attacks. Mr. Trump disputed Mr. Rubio's assertion that President Bush had shown leadership before the attacks.
"The World Trade Center came down during the reign of George Bush," Mr. Trump said. "That is not safe, Marco."
"The World Trade Center came down because Bill Clinton did not kill Osama Bin Laden when he had the chance to kill him," Mr. Rubio said.
While all of the candidates seemed more animated during the forum, the dynamic seemed to revert to the early days of the campaign, when Mr. Trump's presence, personality and blunt insults dominated the action.
Mr. Kasich, who has sought to portray himself as the most reasonable and centrist of the candidates, told the group at one point, "I think we're fixing to lose the election to Hillary Clinton if we don't stop this."
The candidates also clashed sharply over immigration, which has emerged as the major policy flash point in the South Carolina Republican primary. Mr. Rubio, who sees Mr. Cruz and to a lesser extent Jeb Bush as his chief threats in South Carolina, attacked Mr. Cruz with the same talking points he had used all week, insisting that the Texas senator had held lenient positions on illegal immigration.
"I would note not only that Marco has a long record when it comes to amnesty," Mr. Cruz said. "In the state of Florida, as speaker of the house, he supported in-state tuition for illegal immigrants. In addition to that, Marco went on Univision in Spanish and said he would not rescind Mr. Obama's illegal exclusive amnesty on his first day in office."
Mr. Rubio shot back, "I don't know how he knows what I said on Univision because he doesn't speak Spanish," which prompted a brief reply in Spanish from Mr. Cruz.
While Mr. Trump was repeatedly dismissive of Mr. Bush's political skills, portraying him as a loser whose supporters had spent more than $20 million on attack ads to little gain, he was even more scathing toward Mr. Cruz, who is regarded by the Trump campaign as its ultimate opponent. Mr. Trump appeared on the verge of losing his cool as Mr. Cruz invoked the death of Justice Scalia to warn that Mr. Trump would appoint "liberals" to the Supreme Court and brought up Mr. Trump's past support for abortion rights.
"You are the single biggest liar," Mr. Trump said, glaring at Mr. Cruz. "This guy will say anything. Nasty guy. Now I know why he doesn't have one endorsement from any of his colleagues" in the Senate.
Yet when Mr. Trump went on to attack Mr. Cruz over his campaign's attempt to undermine Ben Carson, a retired neurosurgeon, in Iowa, Mr. Cruz saw an opening and seized it.
"I will say it is fairly remarkable to see Donald defending Ben," Mr. Cruz said, "after he called him pathological and compared him to a child molester, both of which were offensive and wrong."
The Washington Post's summary also emphasizes the fierceness of this debate compared to those in the past:
GREENVILLE, S.C. — The six remaining Republican presidential candidates sparred with ferocity over U.S. foreign policy in a debate here Saturday night, with front-runner Donald Trump savaging former president George W. Bush's intervention in Iraq, which helped spawn more than a decade of instability in the Middle East.
In the most impassioned argument yet this year over the Bush legacy, Trump effectively set the past generation of Republican foreign policy ablaze by assailing the doctrine of forceful intervention, echoing views long held by many Democrats and drawing a fiery rebuke from Bush's brother, Jeb.
Trump, using visceral and vitriolic language, blamed the 43rd president for not keeping the country safe from the terrorist attacks of Sept. 11, 2001, and alleged that he lied to the nation about the existence of weapons of mass destruction in Iraq.
"Obviously the war in Iraq was a big, fat mistake," Trump thundered. "George Bush made a mistake. We can make mistakes, but that one was a beauty. We should have never been in Iraq. We have destabilized the Middle East."
Trump's assault came as the former president is scheduled to return to the political arena Monday in North Charleston, S.C., for a campaign rally with his brother. Trump's offensive sparked an electric series of exchanges with Jeb Bush, who condemned the billionaire mogul as an empty showman who lacks a serious worldview.
Bush accused Trump of mocking and tormenting his family as "blood sport."
"I am sick and tired of him going after my family," said Bush, a former Florida governor. "While Donald Trump was building a reality TV show, my brother was building a security apparatus to keep us safe."
Bush's rejoinder drew an endorsement from Sen. Marco Rubio (Fla.), who has an otherwise icy relationship with his onetime Florida mentor. "I thank God all the time that it was George W. Bush in the White House on 9/11 and not Al Gore," Rubio said. "He kept us safe."
Trump jumped in: "The World Trade Center came down during the reign of George Bush. He kept us safe? That is not safe."
The crowd, dominated by hundreds of South Carolina Republican activists and donors, loudly booed Trump at various points. The front-runner pooh-poohed the audience, dismissing them as "Jeb's special interests and lobbyists."
Trump tried to channel both the frustrations of many Americans who are weary of more than a decade of war and the fears that people have about the threats posed by Islamic State terrorists.
"You have to knock out ISIS. They're chopping off heads. These are animals. You have to knock 'em out. You have to knock them off strong," he said. But, Trump added, "We've been in the Middle East for 15 years and we haven't won anything."
Given the stakes in play for many of these candidates, it's not surprising that the battle on the debate stage would be a fierce as it was. Jeb Bush in particular faces a huge test in South Carolina after largely disappointing finishes in both Iowa and New Hampshire. This is one reason why, for example, why Bush is bringing his brother into the state to campaign for him. In nearly any other consequential primary state in the union, George W. Bush would arguably be as much of an albatross as he would a help even in a Republican primary. South Carolina is a state that Bush won quite handily in his nomination fight with John McCain in 2000, as did George H.W. Bush in his nomination battle with Bob Dole in 1988 and there is apparently still a lot of goodwill toward the Bush family in the state Additionally, South Carolina has a significant population of active duty and retired military among whom the 43rd President remains quite popular. Given that, bringing his brother in to campaign for him is overall a smart move for Bush given the fact that he needs a strong finish, or preferably a win, in the Palmetto State in order for his campaign to be able to credibly continue forward into March.
Trump, on the other hand, is playing what seems like the risky game of attacking a former President who remains relatively popular among Republicans, but there is arguably a logic to his attacks as well that could end up securing him a win that would make him seem like the near-inevitable Republican nominee for President. Like New Hampshire before, South Carolina is an open primary. This means that anyone can vote next Saturday. We already know that Trump draws a lot of his support from disaffected former Republicans and Independents, as well as members of the GOP base who largely have disdain for the establishment that the Bush family represents. Pushing Jeb Bush's buttons in the debate in the manner, even to the extent of bringing up Iraq and the 9/11 attacks, could end up helping Trump far more than it hurts him. In either case, it was vintage Trump, and if there's one thing we know about vintage Trump is that predictions that the way Trump acts in public are going to harm him should be immediately discounted.
In addition to the Trump-Bush clash, the clash between Ted Cruz and Marco Rubio that has been going on since November seems to be continuing in full force. Once again, the point of contention between the two candidates who stand closely matched in second and third place, albeit still far behind Donald Trump, in current polling. Unlike last week when Chris Christie managed to use his skills as a litigator to take Rubio down to such an extent that he likely influenced the outcome of the New Hampshire Primary, it does not appear that either Senator scored any significant points against the other, but the fact that both seem to realize that a solid performance in the Palmetto State is crucial to the future of their respective campaigns it is likely that these clashes will continue right up until the time voters go to the polls on Saturday.
As for the other candidate, John Kasich seems to be taking it upon himself to be the adult in the room, in that he once again spent much of the debate urging candidates to focus on the issues rather than on attacking each other In many respects, though, it seems clear that Kasich is looking beyond South Carolina toward the Midwestern primaries in the middle of March where his campaign seems to believe he can perform well based on his ties to the region and the fact that more divisive candidates like Ted Cruz are unlikely to resonate there in the same way they do in the south. Ben Carson, meanwhile, is clearly the candidate who doesn't belong on the stage at this point. When he'll realize that is an open question.
Going forward, of course, the real question is where the race in South Carolina is headed,and so far it still seems to be all about Donald Trump. Two polls released in advance of the debate — one that was commissioned by the South Carolina House Republican Caucus and the other from ARG — continued to show Trump with a decisive lead over his opponents, although the first poll showed John Kasich moving into second place ahead of Cruz and Rubio thanks to a substantial jump in the polls. Whether that number is an outlier or indicative of a significant post New Hampshire bump for the Governor of Ohio remains to be seen. With these polls factored in, RealClearPolitics now gives Trump a 19.3 point lead, with Cruz in second place with an average of 16% of the vote, Marco Rubio at 14% in third place, and Jeb Bush and John Kasich close together in fourth and fifth place at 11.3% and 11.0% respectively. We'll likely see in coming days what impact the debate and other events, such as the death of Justice Scalia, have had on the race.


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Chargement en cours...
HomeAccommodationAn unusual overnight experience
Unusual overnight experience1 results
For an unusual stay in Combloux. Enjoy an unforgettable experience in a preserved nature and amazing landscape.
Overnight in an igloo
Share a unique experience with the nature! First it's a snowshoes walk under the stars. Then, under a tipi heated by wood stove we will share a fondue. And finally a cozy night in the igloo, illuminated by the soft light candles.


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2017-12-19 · A Guide for Fire-tube Boiler Operation Boilers and Pressure Vessels Authority and volume of hot water and steam content is large. boiler pressure will press the water level in the glass higher than the real water level of the boiler. The water level …Get a Quote
2013-11-27 · Boiler Construction, Pressure Parts & Drum Internals. Mittwoch, 4. September Economizers and water wall sections are usually constructed with a mild or medium. carbon steel, whereas performance. Alloys for use as boiler tube materials in the United States are identified by an ASME. designation (SA -xxx) or an essentially equivalent.Get a Quote
PDF A steam boiler is a closed vessel, generally made of steel, in which water is heated by some source of heat produced by combustion of fuel and ultimately to generate steam. The steamGet a Quote
2019-6-3 · A high pressure watertube boiler[1] (also spelled water-tube and water tube) is a type of boiler in which water circulates in tubes heated externally by the fire. Fuel is burned inside the furnace, creating hot gas which heats water in the steam-generating tubes. In smaller boilers, additional generating tubes are separate in the furnaceGet a Quote
2019-5-29 · A fire-tube boiler is a type of boiler in which hot gases pass from a fire through one or (many) more tubes running through a sealed container of water. The heat of the gases is transferred through the walls of the tubes by thermal conduction, heating the water and ultimately creating steam.. The fire-tube boiler developed as the third of the four major historical types of boilers: lowGet a Quote
Water Tube Boiler Parts and Functions - Boilersinfo. main Water tube boiler parts and functions.typical Water tube boiler design and working and types.steam drum water drum Super heater economizer air heater. What is a water tube boiler? Water tube boilers were …Get a Quote
We will learn about fire tube boiler parts in the sections below. Types of Firetube boilers The main feature of the boiler is that the furnace of the boiler is surrounded by water and therefore there is no requirement of refractory material. It is a single phase pass boiler, which means that the hot gases pass just through the horizontalGet a Quote
2019-4-26 · Water Tube Boiler Parts And Their Functions 2018-02-27 13:38:34. There are many pressure parts and non pressure parts in a water tube boiler, we list the main components of the water tube boiler and their functions. Steam drum. Steam drum is a collection vessel for steam & water. Here water & steam is separated. It has steam separators.Get a Quote
What is a water tube boiler? Water tube boilers were developed for a variety of reasons, including the need for higher steam pressures, higher steam generation rate, superheated steam. these types of boilers have quicker response to load changes. We briefly discuss about Water tube boiler parts and functions.Get a Quote
The boiler is the part of the system that heats the water to be distributed. The key elements of a boiler include the burner, combustion chamber, heat exchanger, exhaust stack, and controls. Boiler accessories including the flue gas economizer are also commonly used as an effective method to recover heat from a boiler. Key Components of BoilersGet a Quote


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Seven Evergreen S All-road (Gravel bike) Demo 54cm
Seven Evergreen S All-road (Gravel bike) Demo 54cm
5,422.00 6,379.00
This Seven Evergreen S has very low miles on it.
This bike features the following:
Seven Cycles Evergreen S frame with Seven Matador Fork
Seat tube length -46.5cm center to center.
Top tube length - 54cm effective
Head tube angle - 71.5 degrees
Seat tube angle - 74 degrees
BB Drop - 7.6cm
Chainstay length - 44.2cm
Estimated front center - 58.1 cm
Head tube length - 13.5 cm
Head tube diameter -44mm
Seatpost diameter - 27.2mm
Bottom Bracket - 68mm English
Estimated standover - 77.8 cm
Groupset - Shimano Ultegra R8000 mechanical disc
Cranks - 172.5mm
Chainrings - 50/34
Cassette - 11/32
Any length stem can be ordered (must specify)
Fizik R5 seatpost
Seven Aluminum Bars 42cm
HED Ardenes alloy wheelset
Clement X-Plor MSO 700x40 Tubeless
TRP Spire Brakes