Fortunately for Hortensia, the woods where the four faeries had landed were full of thriving plant life. Drawing more energy from the plants around her, she recovered quickly. She awoke to feel sunlight dancing across her face as it streamed in through a break in the trees. Feeling refreshed, she sat up and noticed that she had been lying beside a large tree; its energy had undoubtedly fueled her recovery.
Her friends were not so lucky. Though Clarisse was also recovering quickly, thanks to the fresh air, Victoria and Bernadette didn't look much better than they had before. Hortensia guessed that being in contact with the elements that gave them magic would help them just as it had helped her.
She was digging around in her bag, looking for water, when she heard Clarisse stir behind her.
After the two faeries had gently splashed Victoria's hand with water, she began to recover almost instantly. Within a few minutes, she was well enough to sit up and take a drink, which sped her recovery still further.
"Not to mention the fact that we used magic for hours trying to break it," added Hortensia.
"How are we going to help Dette?" Clarisse asked, casting a worried glance at the dark faerie, who had not stirred.
"Let's see if we can move her out of the sun," suggested Hortensia. The three faeries carried Bernadette over to the shade of two trees, where it was considerably darker. Still, Bernadette showed no signs of recovery. The other three felt their strength rapidly returning, until they felt stronger every minute, but Bernadette did not stir.
Looking at the position of the sun, Hortensia judged that it had been mid-morning when they had awoken. It was mid-afternoon before Bernadette began to stir.
"Dette! You're awake!" cried Victoria joyfully when Bernadette finally opened her eyes.
"Yeah," she muttered shakily, closing her eyes again.
"Go without me," Bernadette muttered.
The three faeries exchanged worried glances. If Bernadette couldn't fly, they had nowhere to go.
"What?" said Clarisse, Victoria, and Hortensia all at once.
"And we should be safe inside the Castle," added Clarisse.
Together, the faeries helped Bernadette to her feet and set off. At first, they walked in the opposite direction of the inn, knowing only that they wanted to put as much distance between them and the inn as possible. Eventually, they emerged at the opposite end of the forest. Ahead, they could see the pinnacles atop the parapets of Brightvale Castle, rising above the landscape. As they drew closer, they could see clusters of buildings, with smoke rising from chimneys into the air.
The journey seemed to take forever, for Bernadette moved at an agonizing pace, occasionally relying on one of the others for support. Night was falling when the faeries reached the main streets of Brightvale.
They traveled through alleyways and dark passageways, cramming themselves single-file between thatch-roofed houses, avoiding the cobblestone streets in favor of the unpaved, muddy alleys.
At long last, they arrived at the castle. The battlements of the castle loomed before them, made even more intimidating by the deep shadows they cast in the moonlight. The faeries made their way to the back gate of the castle. The gatekeeper took one look at the four exhausted, grimy faeries, and grew instantly suspicious.
"We came into Brightvale before that," said Victoria quickly.
"Anyway, we really don't have time for this," said Bernadette. "We've come to visit an old friend, Professor Solon." She recited the name from memory, without needing to consult the list.
"Tell him we've been sent by Professor Hentoff," said Clarisse quickly.
Grumbling to himself, the gatekeeper dispatched a messenger to Professor Solon's room in the far wing of the castle. A few moments later, the messenger returned to announce that Professor Solon had requested to see his guests at once. Looking vaguely surprised, the gatekeeper allowed the faeries to enter.
The messenger led them through the twists and turns of the castle corridors until they arrived at a small wing of rooms off the library.
"Professor Solon lives here," said the messenger, indicating one of the doors. He left promptly, casting curious glances over his shoulder at the strange faeries.
Clarisse knocked hesitantly upon the door.
"Come in," said a gruff voice from within.
The faeries entered the small room. It was filled with books. Books were stacked floor to ceiling, stuffed on shelves, protruding from every nook and cranny, overflowing from every inch of the room. After a moment, the faeries managed to locate the small sofa in the center of the room. Upon it sat a thin, wiry blue Lenny with crooked glasses perched upon his nose. His face wrinkled with age.
"Who in Neopia are you?" he demanded as Hortensia shut the door behind them.
"Well, Hentoff was right," said Victoria matter-of-factly.
"That's a possibility," said Bernadette, feigning indifference.
The faeries exchanged meaningful looks, imagining Hentoff in his hospital bed.
"Yeah, five lifetimes ought to do it," muttered Bernadette, smiling to herself.
"Is he still teaching at the Academy?" Solon continued conversationally.
Solon looked up from his tea to find that all four faeries had fallen asleep almost instantly.
Fyora, accompanied by several guards and attendants, arrived at the inn just after nightfall. Cautiously, she approached the building. Almost immediately after entering, she sensed magic. Quickly, she cast protective spells around herself, so that any magic in the inn would not harm her. Then she rounded the corner and examined the magic lurking beyond it.
After examining the spell, Fyora was able to piece together what had happened. Clearly, the spell had been laid as a trap for the faeries. What surprised her was that the faeries were no longer here. As strong as they were, it was doubtful that the faeries would have been able to escape; the spell was a particularly malicious one that drained faerie magic, in addition to holding one captive.
The most likely explanation was that they had been moved somewhere else, most likely closer to Linisa and the Shining Sun. There was no chance of finding the faeries now; the only option remaining was to plan an attack on Linisa. Of course, Linisa would be prepared for such an attack, and with the Shining Sun as her weapon, she might be impossible to defeat.
All they could do now was head to Brightvale Castle to inform Hagan of what had happened. If she could gather support from all of Faerieland's allies, she might be able to mount an attack against Linisa. Without the Prophecy Faeries or the Shining Sun, it would be difficult, indeed, but the Faerie Queen was not one to give up without a fight.
"Where to next, Your Majesty?" asked Lyna anxiously when Fyora emerged from the inn.


See entries that contain "force"
definition 1: power, energy, or physical strength.
The force of the wind knocked down the trees.
definition 2: the use of such power, energy, or strength.
It took force to open the jar.
definition 3: someone or something with the power to influence or cause change.
She was the main force behind the city's smoking ban.
definition 4: a group of people with a common goal or activity.
She is a member of the police force.
definition 1: to make or cause to do something by using strength or power.
Ivan forced her to tell the truth.
definition 2: to cause to do something despite resistance or hesitation.
The accident forced her to make a difficult decision.
Force comes from fortis, a Latin word that means "strong." This Latin word is also the source of our words fort and fortify.


The American Foundation for the Blind (AFB)'s Directory of Services for Blind and Visually Impaired Persons in the United States and Canada was first published in 1926, only five years after the inception of AFB. That a national service directory was produced so quickly after the establishment of the organization indicates both the great need that existed for information on available services and the desire of AFB to support and respond promptly to blind and visually impaired persons, their families, and professionals who worked with them.
AFB continued to publish the Directory in print until 2005 and launched a fully accessible and searchable version in 2001 to meet the same pressing need and with the same critical purpose: to provide useful, comprehensive information that helps persons who are visually impaired and their families find the assistance they need and to help professionals identify, coordinate, and deliver services effectively.
Today, the AFB Directory database is available as an accessible app, and powers the Find Services sections on AFB's family of websites—AFB's main website, which features comprehensive profiles of agencies and descriptions of all the services they offer; FamilyConnect, a website for families of children with visual impairments sponsored by AFB and the National Association for Parents of Children with Visual Impairments (NAPVI), which lists agencies that offer services for children and descriptions of those services; VisionAware, AFB's website for adults and seniors with visual impairments, which lists agencies that offer services for adults and seniors and descriptions of those services.
The Directory database contains information on more than 2,000 organizations and agencies that serve people who are blind or visually impaired. It compiles listings of schools, agencies, organizations, and programs in the governmental and private, nonprofit sectors that provide a wide variety of direct and indirect services, information, and other assistance to blind and visually impaired children and adults, their families, and professionals who work with them. Information is also included on producers and distributors of braille, large-print publications, audiotape, and other alternate media; specialized computer hardware and software and other assistive technology and adapted devices; and other products used by people who are visually impaired.
or by e-mail to afbdirectory@afb.net.


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It's the little things in life that we take for granted and being able to open a double glazed window within the home is one of them. Therefore, having complete access to a functioning window equipped with a lock not only provides a living area with a sense of security, it also offers control over comfort.
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text:
\section{Introduction}
\label{sec.intro}

Nowadays deep learning techniques outperform the other conventional methods in most of the speech-related tasks. Training robust deep neural networks for each task depends on the availability of powerful processing GPUs, as well as standard and large scale datasets. In text-independent speaker verification, large-scale datasets are available, thanks to the NIST SRE evaluations and other data collection projects such as VoxCeleb~\cite{nagrani2017voxceleb}.

In text-dependent speaker recognition, experiments with end-to-end architectures conducted on large proprietary databases have demonstrated their superiority over traditional approaches~\cite{heigold2015end}. Yet, contrary to text-independent speaker recognition, text-dependent speaker recognition lacks large-scale publicly available databases. The two most well-known datasets are probably RSR2015~\cite{larcher2014text} and RedDots~\cite{lee2015reddots}. The former contains speech data collected from 300 individuals in a controlled manner, while the latter is used primarily for evaluation rather than training, due to its small number of speakers (only 64). Motivated by this lack of large-scale dataset for text-dependent speaker verification, we chose to proceed with the collection of the DeepMine dataset, which we expect to become a standard benchmark for the task.

Apart from speaker recognition, large amounts of training data are required also for training automatic speech recognition (ASR) systems. Such datasets should not only be large in size, they should also be characterized by high variability with respect to speakers, age and dialects. While several datasets with these properties are available for languages like English, Mandarin, French, this is not the case for several other languages, such as Persian. To this end, we proceeded with collecting a large-scale dataset, suitable for building robust ASR models in Persian.

The main goal of the DeepMine project was to collect speech from at least a few thousand speakers, enabling research and development of deep learning methods. The project started at the beginning of 2017, and after designing the database and the developing Android and server applications, the data collection began in the middle of 2017. The project finished at the end of 2018 and the cleaned-up and final version of the database was released at the beginning of 2019.
In~\cite{zeinali2018deepmine}, the running project and its data collection scenarios were described, alongside with some preliminary results and statistics. In this paper, we announce the final and cleaned-up version of the database, describe its different parts and provide various evaluation setups for each part. Finally, since the database was designed mainly for text-dependent speaker verification purposes, some baseline results are reported for this task on the official evaluation setups. Additional baseline results are also reported for Persian speech recognition. However, due to the space limitation in this paper, the baseline results are not reported for all the database parts and conditions. They will be defined and reported in the database technical documentation and in a future journal paper.

\section{Data Collection}
\label{sect:Data_Collection}

DeepMine is publicly available for everybody with a variety of licenses for different users. It was collected using crowdsourcing~\cite{zeinali2018deepmine}. The data collection was done using an Android application. Each respondent installed the application on his/her personal device and recorded several phrases in different sessions. The Android application did various checks on each utterance and if it passed all of them, the respondent was directed to the next phrase. For more information about data collection scenario, please refer to~\cite{zeinali2018deepmine}.

\subsection{Post-Processing}

In order to clean-up the database, the main post-processing step was to filter out problematic utterances. Possible problems include speaker word insertions (e.g. repeating some part of a phrase), deletions, substitutions, and involuntary disfluencies. To detect these, we implemented an alignment stage, similar to the second alignment stage in the LibriSpeech project~\cite{panayotov2015librispeech}. In this method, a custom decoding graph was generated for each phrase. The decoding graph allows for word skipping and word insertion in the phrase.

For text-dependent and text-prompted parts of the database, such errors are not allowed. Hence, any utterances with errors were removed from the enrollment and test lists. For the speech recognition part, a sub-part of the utterance which is correctly aligned to the corresponding transcription is kept. After the cleaning step, around 190 thousand utterances with full transcription and 10 thousand with sub-part alignment have remained in the database.

\subsection{Statistics}

After processing the database and removing problematic respondents and utterances, 1969 respondents\footnote{By ``respondent'', we mean a unique combination of speaker and mobile phone. Hence, one speaker with several different mobile devices is considered as several respondents.} remained in the database, with 1149 of them being male and 820 female. 297 of the respondents could not read English and have therefore read only the Persian prompts. About 13200 sessions were recorded by females and similarly, about 9500 sessions by males, i.e. women are over-represented in terms of sessions, even though their number is 17\:\% smaller than that of males. Other useful statistics related to the database are shown in Table~\ref{tbl.stats}.

\begin{table}[t]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.stats} Statistics of the DeepMine database.}
	\vspace{2mm}
	\centering{
		\setlength\tabcolsep{10pt}
		\begin{tabular}{ l c r }
			\toprule
			\midrule
			& & Count \\
			\midrule
			Finished sessions			        		& & 22,741 \\
			Finished sessions containing English	    & & 18,916 \\
			Recorded utterances						    & & 544,533 \\
			Unique speakers							    & & 1,858 \\
			Respondents with at least 5 utterances		& & 1,969 \\
			Respondents with at least 1 session		    & & 1,796 \\
			Respondents with at least 4 sessions		& & 1,104 \\
			Respondents with at least 8 sessions		& & 813 \\
			Respondents with at least 16 sessions		& & 607 \\
			Respondents with at least 60 sessions		& & 98 \\
			\midrule
			\bottomrule
		\end{tabular}
	}

\end{table}

The last status of the database, as well as other related and useful information about its availability can be found on its website, together with a limited number of samples\footnote{\url{http://data.deepmine.ir/en/}}.

\section{DeepMine Database Parts}

The DeepMine database consists of three parts. The first one contains fixed common phrases to perform text-dependent speaker verification. The second part consists of random sequences of words useful for text-prompted speaker verification, and the last part includes phrases with word- and phoneme-level transcription, useful for text-independent speaker verification using a random phrase (similar to Part4 of RedDots). This part can also serve for Persian ASR training. Each part is described in more details below. Table~\ref{tbl.phrase_counts} shows the number of unique phrases in each part of the database. For the English text-dependent part, the following phrases were selected from part1 of the RedDots database, hence the RedDots can be used as an additional training set for this part:
\begin{enumerate}
    \item ``My voice is my password.''
    \item ``OK Google.''
    \item ``Artificial intelligence is for real.''
    \item ``Actions speak louder than words.''
    \item ``There is no such thing as a free lunch.''
\end{enumerate}

\begin{table}[t]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.phrase_counts} Numbers of unique phrases in each phrase type. ``3-months/4-digits'' means a random subset of months/digits used as test phrase. ``Persian name and family'' means a sequence of several Persian full names.}
	\vspace{2mm}
	\centering{
		\setlength\tabcolsep{8pt}
		\begin{tabular}{ l c r c }
			\toprule
			\midrule
			& & Count & Part \\
			\midrule
			Persian text-dependent				& & 5           & 1 \\
			English text-dependent	        	& & 5           & 1 \\
			Persian months-name, 3-months		& & 1,320       & 2 \\
			Persian months-name, 12-months		& & 11,620      & 2 \\
			English digits, 4-digits			& & 5,040       & 2 \\
			English digits, 10-digits			& & 6,448       & 2 \\
			Persian name and family				& & 8,305       & 3 \\
			Persian transcribed phrases			& & 166,838     & 3 \\
			\midrule
			\bottomrule
		\end{tabular}
	}
\end{table}

\subsection{Part1 - Text-dependent (TD)}

This part contains a set of fixed phrases which are used to verify speakers in text-dependent mode. Each speaker utters 5 Persian phrases, and if the speaker can read English, 5 phrases selected from Part1 of the RedDots database are also recorded.

We have created three experimental setups with different numbers of speakers in the evaluation set. For each setup, speakers with more recording sessions are included in the evaluation set and the rest of the speakers are used for training in the background set (in the database, all background sets are basically training data). The rows in Table~\ref{tbl.td_sets} corresponds to the different experimental setups and shows the numbers of speakers in each set. Note that, for English, we have filtered the (Persian native) speakers by the ability to read English. Therefore, there are fewer speakers in each set for English than for Persian. There is a small ``dev'' set in each setup which can be used for parameter tuning to prevent over-tuning on the evaluation set.

\begin{table}[b]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.td_sets} Number of speakers in different evaluation sets of text-dependent part.}
	\vspace{2mm}
	\centering{
		\setlength\tabcolsep{4pt}
		\begin{tabular}{ l l c c c c c c c c }
			\toprule
			\midrule
						&	& & \multicolumn{3}{c}{Male} & & \multicolumn{3}{c}{Female} \\
							\cmidrule{4-6} \cmidrule{8-10}
			Lang		& Set name		& & back & dev & eval & & back & dev & eval \\
			\midrule
			\multirow{3}{*}{Persian}
			& 100-spk		& & 851 & 10 & 100 & & 607 & 10 & 100 \\
			& 200-spk		& & 741 & 20 & 200 & & 497 & 20 & 200 \\
			& 300-spk		& & 631 & 30 & 300 & & 387 & 30 & 300 \\
			\midrule
			\multirow{3}{*}{English}
			& 100-spk		& & 722 & 8  & 85  & & 510 & 8  & 88 \\
			& 200-spk		& & 620 & 18 & 175 & & 415 & 17 & 174 \\
			& 300-spk		& & 538 & 29 & 245 & & 318 & 27 & 261 \\
			\midrule
			\bottomrule
		\end{tabular}
	}
\end{table}

For each experimental setup, we have defined several official trial lists with different numbers of enrollment utterances per trial in order to investigate the effects of having different amounts of enrollment data. All trials in one trial list have the same number of enrollment utterances (3 to 6) and only one test utterance. All enrollment utterances in a trial are taken from different consecutive sessions and the test utterance is taken from yet another session. From all the setups and conditions, the 100-spk with 3-session enrollment (3-sess) is considered as the main evaluation condition. In Table~\ref{tbl.td_trials_count}, the number of trials for Persian 3-sess are shown for the different types of trial in the text-dependent speaker verification (SV). Note that for Imposter-Wrong (IW) trials (i.e. imposter speaker pronouncing wrong phrase), we merely create one wrong trial for each Imposter-Correct (IC) trial to limit the huge number of possible trials for this case. So, the number of trials for IC and IW cases are the same.

\begin{table}[t]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.td_trials_count} Numbers of trials in each different evaluation set for all possible trial types in text-dependent SV for 3-sess enrollment. TC: Target-Correct, TW: Target-Wrong, IC: Imposter-Correct, IW: Imposter-Wrong. K stands for thousand and M stands for million trials.}
	\vspace{2mm}
	\centering{
		\setlength\tabcolsep{2pt}
		\begin{adjustbox}{width=1.0\columnwidth}
		\begin{tabular}{ l c c c c c c c c c c}
			\toprule
			\midrule
					    & & \multicolumn{4}{c}{Male} & & \multicolumn{4}{c}{Female} \\
							\cmidrule{3-6} \cmidrule{8-11}
			Set name	& & TC & TW & IC & IW & & TC & TW & IC & IW \\
			\midrule
			100-spk		& & 409K & 1.63M & 30.7M & 30.7M & & 667K & 2.67M & 60.0M & 60.0M \\
			200-spk		& & 452K & 1.81M & 58.0M & 58.0M & & 792K & 3.17M & 126M  & 126M  \\
			300-spk		& & 480K & 1.92M & 85.6M & 85.6M & & 835K & 3.34M & 176M  & 176M  \\
			\midrule
			\bottomrule
		\end{tabular}
		\end{adjustbox}
	}
\end{table}

\subsection{Part2 - Text-prompted (TP)}

For this part, in each session, 3 random sequences of Persian month names are shown to the respondent in two modes: In the first mode, the sequence consists of all 12 months, which will be used for speaker enrollment. The second mode contains a sequence of 3 month names that will be used as a test utterance. In each 8 sessions received by a respondent from the server, there are 3 enrollment phrases of all 12 months (all in just one session), and $7 \times 3$ other test phrases, containing fewer words. For a respondent who can read English, 3 random sequences of English digits are also recorded in each session. In one of the sessions, these sequences contain all digits and the remaining ones contain only 4 digits.

Similar to the text-dependent case, three experimental setups with different number of speaker in the evaluation set are defined (corresponding to the rows in Table~\ref{tbl.tp_trials_count}). However, different strategy is used for defining trials: Depending on the enrollment condition (1- to 3-sess), trials are enrolled on utterances of all words from 1 to 3 different sessions (i.e. 3 to 9 utterances). Further, we consider two conditions for test utterances: \textbf{seq} test utterance with only 3 or 4 words and \textbf{full} test utterances with all words (i.e. same words as in enrollment but in different order). From all setups an all conditions, the 100-spk with 1-session enrolment (1-sess) is considered as the main evaluation condition for the text-prompted case. In Table~\ref{tbl.tp_trials_count}, the numbers of trials (sum for both seq and full conditions) for Persian 1-sess are shown for the different types of trials in the text-prompted SV. Again, we just create one IW trial for each IC trial.

\vspace{-1mm}
\begin{table}[b]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.tp_trials_count} Numbers of trials in each different evaluation sets for all possible trial types in text-prompted SV for 1-sess enrollment.}
	\vspace{2mm}
	\centering{
		\setlength\tabcolsep{2pt}
		\begin{adjustbox}{width=1.0\columnwidth}
		\begin{tabular}{ l c c c c c c c c c c}
			\toprule
			\midrule
					    & & \multicolumn{4}{c}{Male} & & \multicolumn{4}{c}{Female} \\
							\cmidrule{3-6} \cmidrule{8-11}
			Set name	& & TC & TW & IC & IW & & TC & TW & IC & IW \\
			\midrule
			100-spk		& & 98.5K & 394K & 7.61M & 7.61M & & 155K & 621K & 14.2M & 14.2M \\
			200-spk		& & 111K  & 446K & 15.2M & 15.2M & & 187K & 749K & 30.5M & 30.5M \\
			300-spk		& & 119K  & 474K & 22.5M & 22.5M & & 199K & 798K & 43.9M & 43.9M \\
			\midrule
			\bottomrule
		\end{tabular}
		\end{adjustbox}
	}
\end{table}

\subsection{Part3 - Text-independent (TI)}

In this part, 8 Persian phrases that have already been transcribed on the phone level are displayed to the respondent. These phrases are chosen mostly from news and Persian Wikipedia. If the respondent is unable to read English, instead of 5 fixed phrases and 3 random digit strings, 8 other Persian phrases are also prompted to the respondent to have exactly 24 phrases in each recording session.

This part can be useful at least for three potential applications. First, it can be used for text-independent speaker verification. The second application of this part (same as Part4 of RedDots) is text-prompted speaker verification using random text (instead of a random sequence of words). Finally, the third application is large vocabulary speech recognition in Persian (explained in the next sub-section).

Based on the recording sessions, we created two experimental setups for speaker verification. In the first one, respondents with at least 17 recording sessions are included to the evaluation set, respondents with 16 sessions to the development and the rest of respondents to the background set (can be used as training data). In the second setup, respondents with at least 8 sessions are included to the evaluation set, respondents with 6 or 7 sessions to the development and the rest of respondents to the background set. Table~\ref{tbl.ti_sets} shows numbers of speakers in each set of the database for text-independent SV case.

\begin{table}[t]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.ti_sets} Different evaluation sets of text-independent part of the DeepMine database.}
	\vspace{2mm}
	\centering{
		\setlength\tabcolsep{6pt}
		\begin{tabular}{ l c c c c c c c c }
			\toprule
			\midrule
						& & \multicolumn{3}{c}{Male} & & \multicolumn{3}{c}{Female} \\
							\cmidrule{3-5} \cmidrule{7-9}
			Set name	& & back & dev & eval & & back & dev & eval \\
			\midrule
			438-spk	    & & 689 & 91 & 181 & & 376 & 85 & 257 \\
			798-spk	    & & 540 & 59 & 365 & & 220 & 65 & 433 \\
			\midrule
			\bottomrule
		\end{tabular}
	}
\end{table}

For text-independent SV, we have considered 4 scenarios for enrollment and 4 scenarios for test. The speaker can be enrolled using utterances from 1, 2 or 3 consecutive sessions (1sess to 3sess) or using 8 utterances from 8 different sessions. The test speech can be one utterance (1utt) for short duration scenario or all utterances in one session (1sess) for long duration case. In addition, test speech can be selected from 5 English phrases for cross-language testing (enrollment using Persian utterances and test using English utterances). From all setups, 1sess-1utt and 1sess-1sess for 438-spk set are considered as the main evaluation setups for text-independent case. Table~\ref{tbl.ti_trials_count} shows number of trials for these setups.

\begin{table}[b]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.ti_trials_count} Numbers of trials in each different evaluation sets for all possible trial types in text-dependent SV for 3-sess enrollment. ``cross'' means enrollment using Persian utterances and test using 5 English utterances. K stands for thousand and M stands for million trials.}
	\vspace{2mm}
	\centering{
		\setlength\tabcolsep{4pt}
		\begin{tabular}{ l c c c c c c }
			\toprule
			\midrule
					            & & \multicolumn{2}{c}{Male} & & \multicolumn{2}{c}{Female} \\
						    	\cmidrule{3-4} \cmidrule{6-7}
			Set name    	    & & Target & Imposter & & Target & Imposter \\
			\midrule
			1sess-1utt		    & & 537K & 61.7M & & 1013K & 193.1M \\
			1sess-1utt-cross	& & 459K & 51.3M & & 890K  & 164.4M \\
			1sess-1sess		    & & 135K & 15.6M & & 254K  & 48.7M  \\
			1sess-1sess-cross	& & 229K & 25.6M & & 445K  & 82.2M  \\
			\midrule
			\bottomrule
		\end{tabular}
	}
\end{table}

For text-prompted SV with random text, the same setup as text-independent case together with corresponding utterance transcriptions can be used.

\subsection{Part3 - Speech Recognition}

As explained before, Part3 of the DeepMine database can be used for Persian read speech recognition. There are only a few databases for speech recognition in Persian~\cite{bijankhan1994farsdat, bijankhan1994tfarsdat}. Hence, this part can at least partly address this problem and enable robust speech recognition applications in Persian. Additionally, it can be used for speaker recognition applications, such as training deep neural networks (DNNs) for extracting bottleneck features~\cite{zeinali2016csl}, or for collecting sufficient statistics using DNNs for i-vector training.

We have randomly selected 50 speakers (25 for each gender) from the all speakers in the database which have net speech (without silence parts) between 25 minutes to 50 minutes as test speakers. For each speaker, the utterances in the first 5 sessions are included to (small) test-set and the other utterances of test speakers are considered as a large-test-set. The remaining utterances of the other speakers are included in the training set. The test-set, large-test-set and train-set contain 5.9, 28.5 and 450 hours of speech respectively.

There are about 8300 utterances in Part3 which contain only Persian full names (i.e. first and family name pairs). Each phrase consists of several full names and their phoneme transcriptions were extracted automatically using a trained Grapheme-to-Phoneme (G2P). These utterances can be used to evaluate the performance of a systems for name recognition, which is usually more difficult than the normal speech recognition because of the lack of a reliable language model.

\section{Experiments and Results}
\label{sec:results}

Due to the space limitation, we present results only for the Persian text-dependent speaker verification and speech recognition.

\subsection{Speaker Verification Experiments}

We conducted an experiment on text-dependent speaker verification part of the database, using the i-vector based method proposed in \cite{zeinali2016deep, zeinali2016trans} and applied it to the Persian portion of Part1. In this experiment, 20-dimensional MFCC features along with first and second derivatives are extracted from 16\,kHz signals using HTK~\cite{young1997htk} with 25\,ms Hamming windowed frames with 15\,ms overlap.

The reported results are obtained with a 400-dimensional gender independent i-vector based system. The i-vectors are first length-normalized and are further normalized using phrase- and gender-dependent Regularized Within-Class Covariance Normalization (RWCCN)~\cite{zeinali2016trans}. Cosine distance is used to obtain speaker verification scores and phrase- and gender-dependent s-norm is used for normalizing the scores. For aligning speech frames to Gaussian components, monophone HMMs with 3 states and 8 Gaussian components in each state are used~\cite{zeinali2016trans}. We only model the phonemes which appear in the 5 Persian text-dependent phrases.

For speaker verification experiments, the results were reported in terms of Equal Error Rate (EER) and Normalized Detection Cost Function as defined for NIST SRE08 ($\mathrm{NDCF_{0.01}^{min}}$) and NIST SRE10 ($\mathrm{NDCF_{0.001}^{min}}$). As shown in Table~\ref{tbl.trial_types}, in text-dependent SV there are 4 types of trials: {\em Target-Correct} and {\em Imposter-Correct} refer to trials when the pass-phrase is uttered correctly by target and imposter speakers respectively, and in same manner, {\em Target-Wrong} and {\em Imposter-Wrong} refer to trials when speakers uttered a wrong pass-phrase. In this paper, only the correct trials (i.e. Target-Correct as target trials vs Imposter-Correct as non-target trials) are considered for evaluating systems as it has been proved that these are the most challenging trials in text-dependent SV~\cite{zeinali2016csl, zeinali2016ivector}.

\begin{table}[t]
  \renewcommand{\arraystretch}{1.2}
  \caption{\label{tbl.trial_types} Trial types in text-dependent speaker verification~\cite{larcher2014text}.}
  \vspace{2mm}
  \centerline
  {
    \begin{tabular}{l | c c}
      \toprule
       & \bfseries{Target Speaker} & \bfseries{Imposter Speaker} \\
      \midrule
      \bfseries{Correct Pass-Phrase}	& Target-Correct	& Imposter-Correct \\
      \bfseries{Wrong Pass-Phrase}		& Target-Wrong		& Imposter-Wrong \\
      \bottomrule
    \end{tabular}
  }
\end{table}

\begin{table*}[tb!]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.text_dependent_results} i-vector/HMM results on text-dependent part (i.e. Part1) on 100-spk, 3-sess setup of Persian language. The results are only for correct trials (i.e. Target-Correct vs Imposter-Corrects). The first column shows filtering of the trials in target:imposter format. The first Y letter on each side of the colon shows the condition where the same mobile brand and the second Y shows exactly the same device models were used for recording the enrollment and test utterances.}
	\vspace{2mm}
	\centerline
	{
		\setlength\tabcolsep{4.5pt}
		\begin{tabular}{l | c c c c c | c c c c c }
			\toprule
			\midrule
			       & \multicolumn{5}{c}{\textbf{Male}} & \multicolumn{5}{c}{\textbf{Female}} \\
			       \cmidrule{2-11}
			Filter & \# Target & \# Imposter & EER [\%] & $\mathrm{NDCF_{0.01}^{min}}$ & $\mathrm{NDCF_{0.001}^{min}}$ & \# Target & \# Imposter & EER [\%] & $\mathrm{NDCF_{0.01}^{min}}$ & $\mathrm{NDCF_{0.001}^{min}}$ \\
			\midrule
			-- -- : -- --	& 408951 & 30742420 & 1.54 & 0.189 & 0.633 & 666946 & 59953740	& 3.01  & 0.163 & 0.318 \\
			NN : YY			& 55950	 & 233560	& 4.61 & 0.297 & 0.432 & 71459 	& 434464	& 15.54 & 0.562 & 0.701 \\
			YY : -- --		& 346799 & 30742420	& 1.09 & 0.159 & 0.568 & 560827	& 59953740	& 1.28	& 0.107 & 0.247 \\
			YY : YY			& 346799 & 233560	& 1.48 & 0.098 & 0.153 & 560827	& 434464	& 1.79	& 0.138 & 0.217 \\
			YY : YN			& 346799 & 8216620	& 1.13 & 0.084 & 0.142 & 560827	& 20902608	& 1.33	& 0.097 & 0.158 \\
			Y -- : -- --	& 353001 & 30742420	& 1.11 & 0.163 & 0.578 & 595487	& 59953740	& 1.49	& 0.123 & 0.271 \\
			-- N : Y --		& 62152 & 8450180	& 3.49 & 0.258 & 0.408 & 106119	& 21337072	& 10.45 & 0.439 & 0.590 \\
			\midrule
			\bottomrule
		\end{tabular}
	}
	\vspace{-1mm}
\end{table*}

Table~\ref{tbl.text_dependent_results} shows the results of text-dependent experiments using Persian 100-spk and 3-sess setup. For filtering trials, the respondents' mobile brand and model were used in this experiment. In the table, the first two letters in the filter notation relate to the target trials and the second two letters (i.e. right side of the colon) relate for non-target trials. For target trials, the first Y means the enrolment and test utterances were recorded using a device with the same brand by the target speaker. The second Y letter means both recordings were done using exactly the same device model. Similarly, the first Y for non-target trials means that the devices of target and imposter speakers are from the same brand (i.e. manufacturer). The second Y means that, in addition to the same brand, both devices have the same model. So, the most difficult target trials are ``NN'', where the speaker has used different a device at the test time. In the same manner, the most difficult non-target trials which should be rejected by the system are ``YY'' where the imposter speaker has used the same device model as the target speaker (note that it does not mean physically the same device because each speaker participated in the project using a personal mobile device). Hence, the similarity in the recording channel makes rejection more difficult.

The first row in Table~\ref{tbl.text_dependent_results} shows the results for all trials. By comparing the results with the best published results on RSR2015 and RedDots~\cite{zeinali2016trans, zeinali2016csl, zeinali2016ivector}, it is clear that the DeepMine database is more challenging than both RSR2015 and RedDots databases. For RSR2015, the same i-vector/HMM-based method with both RWCCN and s-norm has achieved EER less than 0.3\:\% for both genders (Table VI in~\cite{zeinali2016trans}). The conventional Relevance MAP adaptation with HMM alignment without applying any channel-compensation techniques (i.e. without applying RWCCN and s-norm due to the lack of suitable training data) on RedDots Part1 for the male has achieved EER around 1.5\:\% (Table XI in~\cite{zeinali2016trans}). It is worth noting that EERs for DeepMine database without any channel-compensation techniques are 2.1 and 3.7\:\% for males and females respectively.

One interesting advantage of the DeepMine database compared to both RSR2015 and RedDots is having several target speakers with more than one mobile device. This is allows us to analyse the effects of channel compensation methods. The second row in Table~\ref{tbl.text_dependent_results} corresponds to the most difficult trials where the target trials come from mobile devices with different models while imposter trials come from the same device models. It is clear that severe degradation was caused by this kind of channel effects (i.e. decreasing within-speaker similarities while increasing between-speaker similarities), especially for females.

The results in the third row show the condition when target speakers at the test time use exactly the same device that was used for enrollment. Comparing this row with the results in the first row proves how much improvement can be achieved when exactly the same device is used by the target speaker.

The results in the fourth row show the condition when imposter speakers also use the same device model at test time to fool the system. So, in this case, there is no device mismatch in all trials. By comparing the results with the third row, we can see how much degradation is caused if we only consider the non-target trials with the same device.

The fifth row shows similar results when the imposter speakers use device of the same brand as the target speaker but with a different model. Surprisingly, in this case, the degradation is negligible and it means that mobiles from a specific brand (manufacturer) have different recording channel properties.

The degraded female results in the sixth row as compared to the third row show the effect of using a different device model from the same brand for target trials. For males, the filters brings almost the same subsets of trials, which explains the very similar results in this case.

Looking at the first two and the last row of Table~\ref{tbl.text_dependent_results}, one can notice the significantly worse performance obtained for the female trials as compared to males. Note that these three rows include target trials where the devices used for  enrollment do not necessarily match the devices used for recording test utterances. On the other hand, in rows 3 to 6, which exclude such mismatched trials, the performance for males and females is comparable. This suggest that the degraded results for females are caused by some problematic trials with device mismatch. The exact reason for this degradation is so far unclear and needs a further investigation.

In the last row of the table, the condition of the second row is relaxed: the target device should have different model possibly from the same brand and imposter device only needs to be from the same brand. In this case, as was expected, the performance degradation is smaller than in the second row.

\subsection{Speech Recognition Experiments}

In addition to speaker verification, we present several speech recognition experiments on Part3. The experiments were performed with the Kaldi toolkit~\cite{povey2011kaldi}. For training HMM-based MonoPhone model, only 20 thousands of shortest utterances are used and for other models the whole training data is used. The DNN based acoustic model is a time-delay DNN with low-rank factorized layers and skip connections without i-vector adaptation (a modified network from one of the best performing LibriSpeech recipes). The network is shown in Table~\ref{tbl.ftdnn}: there are 16 F-TDNN layers, with dimension 1536 and linear bottleneck layers of dimension 256. The acoustic model is trained for 10 epochs using lattice-free maximum mutual information (LF-MMI) with cross-entropy regularization~\cite{povey2016purely}. Re-scoring is done using a pruned trigram language model and the size of the dictionary is around 90,000 words.

Table~\ref{tbl.ars_results} shows the results in terms of word error rate (WER) for different evaluated methods. As can be seen, the created database can be used to train well performing and practically usable Persian ASR models.

\begin{table}[t]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.ftdnn} Factorized TDNN architecture for acoustic modeling using Kaldi toolkit~\cite{povey2011kaldi}. Note that Batch-Norm applied after each ReLU is omitted for brevity.}
	\vspace{2mm}
	\centerline
	{
	    \setlength\tabcolsep{3pt}
	    \begin{adjustbox}{width=1.0\columnwidth}
		\begin{tabular}{l c c c c c c}
			\toprule
			\midrule
		    Layer & Layer type & Context & Context & Skip conn. & Size & Inner \\
		          &            & factor1 & factor2 & from layer &      & size \\
		    \midrule
		    1  & TDNN-ReLU   & t      &        &           & 1536 &     \\
		    2  & F-TDNN-ReLU & t-1, t & t, t+1 & 0 (input) & 1536 & 256 \\
		    3  & F-TDNN-ReLU & t-1, t & t, t+1 & 1         & 1536 & 256 \\
		    4  & F-TDNN-ReLU & t-1, t & t, t+1 & 2         & 1536 & 256 \\
		    5  & F-TDNN-ReLU & t      & t      & 3         & 1536 & 256 \\
		    6  & F-TDNN-ReLU & t-3, t & t, t+3 & 4         & 1536 & 256 \\
		    7  & F-TDNN-ReLU & t-3, t & t, t+3 & 5         & 1536 & 256 \\
		    8  & F-TDNN-ReLU & t-3, t & t, t+3 & 6         & 1536 & 256 \\
		    9  & F-TDNN-ReLU & t-3, t & t, t+3 & 7         & 1536 & 256 \\
		    10 & F-TDNN-ReLU & t-3, t & t, t+3 & 8         & 1536 & 256 \\
		    11 & F-TDNN-ReLU & t-3, t & t, t+3 & 9         & 1536 & 256 \\
		    12 & F-TDNN-ReLU & t-3, t & t, t+3 & 10        & 1536 & 256 \\
		    13 & F-TDNN-ReLU & t-3, t & t, t+3 & 11        & 1536 & 256 \\
		    14 & F-TDNN-ReLU & t-3, t & t, t+3 & 12        & 1536 & 256 \\
		    15 & F-TDNN-ReLU & t-3, t & t, t+3 & 13        & 1536 & 256 \\
		    16 & F-TDNN-ReLU & t-3, t & t, t+3 & 14        & 1536 & 256 \\
		    17 & F-TDNN-ReLU & t-3, t & t, t+3 & 15        & 1536 & 256 \\
		    \midrule
		    18 & Linear      &        &        &           & 256  &     \\
		    19 & Dense-ReLU-Linear  & &        &           & 256  & 1536 \\
		    20 & Dense       &        &        &           & \# Tar. &     \\
		    \midrule
            \bottomrule
            \end{tabular}
	    \end{adjustbox}
	}
\end{table}

\begin{table}[tb!]
	\renewcommand{\arraystretch}{1.1}
	\caption{\label{tbl.ars_results} WER [\%] obtained with different ASR systemss trained on DeepMine database. Test-set has 5.9 hours of speech and Large-Test-set contains  28.5 hours of speech.}
	\vspace{2mm}
	\centering{
		\setlength\tabcolsep{4pt}
		\begin{tabular}{ l | c | c }
			\toprule
			\midrule
			& Test-set & Large-Test-set \\
			\midrule
			MonoPhone			        			& 41.31 & 40.70 \\
			TriPhone + Deltas + Delta-Deltas	   	& 17.30 & 16.50 \\
			TriPhone + LDA + MLLT					& 14.08 & 13.32 \\
			TriPhone + LDA + MLLT + SAT				& 11.52 & 10.95 \\
			\midrule
			Kaldi F-TDNN							&  4.44 &  4.09 \\
			\midrule
			\bottomrule
		\end{tabular}
	}
\end{table}

\section{Conclusions}
\label{sec.conc}

In this paper, we have described the final version of a large speech corpus, the DeepMine database. It has been collected using crowdsourcing and, according to the best of our knowledge, it is the largest public text-dependent and text-prompted speaker verification database in two languages: Persian and English. In addition, it is the largest text-independent speaker verification evaluation database, making it suitable to robustly evaluate state-of-the-art methods on different conditions. Alongside these appealing properties, it comes with phone-level transcription, making it suitable to train deep neural network models for Persian speech recognition.

We provided several evaluation protocols for each part of the database. The protocols allow researchers to investigate the performance of different methods in various scenarios and study the effects of channels, duration and phrase text on the performance. We also provide two test sets for speech recognition: One normal test set with a few minutes of speech for each speaker and one large test set with more (30 minutes on average) speech that can be used for any speaker adaptation method.

As baseline results, we reported the performance of an i-vector/HMM based method on Persian text-dependent part. Moreover, we conducted speech recognition experiments using conventional HMM-based methods, as well as state-of-the-art deep neural network based method using Kaldi toolkit with promising performance. Text-dependent results have shown that the DeepMine database is more challenging than RSR2015 and RedDots databases.

\section{Acknowledgments}
The data collection project was mainly supported by Sharif DeepMine company. The work on the paper was supported by Czech National Science Foundation (GACR) project "NEUREM3" No. 19-26934X and the National Programme of Sustainability (NPU II) project "IT4Innovations excellence in science - LQ1602".

\vspace{-1mm}
\bibliographystyle{IEEEbib}

redpajama_set_name: RedPajamaArXiv


Royal Dutch Shell has accused Anadarko Petroleum of holding it and the Mozambican government hostage by refusing to supply enough domestic gas in the first phase of a giant liquefied natural gas project.
Anadarko of the US and Eni of Italy have discovered about 150tn cubic feet (4.5tn cubic metres) of gas in deep waters off the coast of Mozambique and have made initial investments in parallel projects expected to cost up to $40bn in total to develop. Most of the gas in what is being called "Africa's Qatar" will be pumped onshore to two LNG trains from where it will be exported to Asia and Europe.
As part of efforts to develop the local economy, Shell plans to build a 38,000-barrel-a-day gas-to-liquid plant that will produce kerosene, diesel and naphtha. But Moon Hussain, director of the project, said Anadarko was obstructing its plans by refusing to supply gas until the second phase of the LNG project, which is not expected to start until 2031.
"If we don't get the gas for our fuels," Mr Hussain told an FT Summit in Maputo last week, "the benefits of that get pushed out well beyond 2030 and the development of the local industries on the back of that gets pushed out beyond". Anadarko, he said, was "holding everybody to hostage" and could easily certify more gas to supply Shell's project.
Steve Wilson, Anadarko's vice-president and Mozambique country manager, questioned the economics of the project, which he said had only a third of the capacity of Shell's $19bn Pearl gas-to-liquid plant in Qatar. "I'm scratching my head, thinking this is a bloody fairy tale," he said.
Mr Wilson added that the Anadarko-led consortium would eventually supply 400m cubic metres of gas domestically, but that it could spare only 100m cubic metres in the first phase, expected to begin around 2025. Lenders, he said, were unwilling to allow Anadarko to certify additional reserves until production began.
Shell said its project was not only commercially feasible but would also make a valuable contribution to Mozambique's development.
In addition to earning royalties from gas exports, Maputo wants to develop local industry and improve power supply. Only about a quarter of 30m Mozambicans have access to electricity. There are additional plans for a $2bn gas fertiliser plant to be built by Norway's Yara.
Augusto Fernando, deputy minister of mineral resources and energy, said Maputo was urging Anadarko to "try to speed up" supply. "The Mozambican government would like to get these programmes moving," he said.
Maputo is trying to avoid the fate of countries such as Angola and Nigeria, where hydrocarbons have destroyed — rather than developed — large swaths of the economy. It has already made one mis-step, borrowing — and then defaulting — on more than $2bn of loans made on the back of anticipated gas receipts.


Each iPhone 7 comes with a Lightning-to-3.5-mm adapter. Apple sells the adapter independently as well. Apple also unveiled several Bluetooth wireless headphones ostensibly intended for use with iPhone 7, including AirPods, wireless in-ear headphones, and three new Beats headphone products. All four products utilize an in-house wireless chip known as Apple W1, which is designed to provide low-power Bluetooth operation and integration with iOS and macOS products (though they are still compatible with other Bluetooth-supported devices).
↑ ၁၀.၀ ၁၀.၁ ၁၀.၂ ၁၀.၃ "iPhone 7 and 7 Plus announced with water resistance, dual cameras, and no headphone jack".
↑ "How Apple Made Its Jet Black iPhone None More Black".
↑ ၁၂.၀ ၁၂.၁ Stein, Scott; Dolcourt, Jessica (September 8, 2016).
↑ "iPhone 7 home button doesn't work with most gloves".
↑ ၁၄.၀ ၁၄.၁ "Apple will sell replacement iPhone 7 headphone adapters for $9 each".
↑ ၁၅.၀ ၁၅.၁ Benjamin, Jeff (September 8, 2016).
↑ "Apple's new A10 Fusion processor is 120 times faster than the original iPhone".
↑ "iPhone 7 vs iPhone 7 Plus (Comparison)".
↑ "Improving Color on the Web".
↑ "The iPhone 7: More Camera, More Colors, Less Headphone Jack".
↑ Slivka, Eric (September 16, 2016). "iPhone 7 Teardown Reveals 'Incredibly Thin' A10 Chip With 2 GB RAM, Intel Modem".
↑ "With the iPhone 7, Apple Makes Dual Cameras the New Normal".
↑ "The iPhone 7 Plus has two cameras that work as one".


She was one of the first female art historians in Germany to receive a doctorate, directed the Wallraf-Richartz-Museum during the First World War, was one of the Dadaists who stirred up the established art scene in Cologne, and for eleven years was on the run from the Nazis before they finally murdered her in 1944 in Auschwitz. Yet Luise Straus-Ernst, this resolute woman who led a remarkable life, is often simply known as Max Ernst's first wife. The Wallraf is now countering this one-sided view of her with this dedicated exhibition. To this end, the museum is reconstructing the special exhibition Alte Kriegsdarstellungen – Graphik des 15. bis 18. [Old Representations of War – Prints from the 15th to the 18th century] which Straus-Ernst mounted in 1917 at her erstwhile centre of work, the "Wallraf-Richartz-Museum". With around 120 works from masters such as Dürer, Goltzius and Callot, she turned her attention to what then as now was a charged topic and promised the visitors in the accompanying catalogue "a brief panorama of representations of war in the graphic arts". But this was by no means a gleeful celebration of armed conflict aimed at rekindling the German people's dwindling enthusiasm for war. On the contrary: the silent works showed and show war as a human catastrophe – emphatically so, without pulling any punches. The Wallraf is reconstructing Luise Straus-Ernst's exhibition in its Print Room with a selection of 30 prints that were presented in 1917. At the same time the show focuses on Straus-Ernst's motivations for directing her attention to the topic of war in art. The global political situation today makes this critical enquiry seem more relevant than ever. Through her design for the reconstructed exhibition, the artist Louisa Clement (*1987 Bonn) will weave additional links between then and now.


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New drug hope for 1.5 million diabetic Britons at risk of kidney disease
By Oliver Last updated Dec 3, 2022
Diabetics should be offered a daily pill that drastically reduces the risk of developing life-threatening kidney and heart complications.
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Last week, NHS Scotland's spending monitors approved a tablet, finerenone, for patients with type 2 diabetes living with kidney disease. And experts say health chiefs will offer the drug to type 2 diabetics in England and Wales.
A decision on NHS funding of finerenone, which costs £17 a pill, is due next month, and charities say approval would benefit up to 1.5 million diabetic Britons, living with kidney disease. or are expected to develop it in the future.
"This drug has the exciting potential to substantially improve the care of patients with chronic kidney disease due to diabetes," says Dr Graham Lipkin, Member of Kidney Care UK. "It should have considerable benefit for patients and help them avoid the need for more intensive hospital treatments."
Around 4.4 million Britons have type 2 diabetes, a condition that occurs when blood sugar is too high, often due to genetics, obesity and lack of exercise.
This imbalance can trigger a number of complications, including damage to the blood vessels in the kidneys. This damages the kidneys, but it can also cause them to produce a hormone called aldosterone. Studies show that excessive levels of this hormone can scar the kidneys, causing irreversible damage, and increase the risk of heart disease.
Approximately 40 percent of type 2 diabetics will develop kidney disease at some point.
In a worldwide trial of more than 5,500 adults with kidney disease associated with type 2 diabetes, participants took the drug every day. The results, published last year in the New England Journal of Medicine, found that those who took finerenone were one-fifth less likely to die of kidney failure compared with patients with type 2 diabetes who did not take the drug.
"These conditions go hand in hand," says kidney doctor Dr Kieran McCafferty, a consultant nephrologist at Barts Health NHS Trust in London. "I spend most of my time trying to prevent heart disease. If you have diabetic kidney disease, you are more likely to die of heart disease than kidney failure. So it's very important that we have effective treatments for these patients that can treat both.'
Drugs capable of inhibiting aldosterone have been available on the NHS for two decades, but have been used sparingly as they can trigger irregular heartbeat, which in turn can lead to heart failure.
Finerenone, also known as Kerendia, also limits the amount of aldosterone the body makes.
However, studies show that it is much safer than previous medications, which means that you are unlikely to have heart complications.
The trial also found that finerenone reduced the risk of death or hospitalization for heart failure by 14 percent after two and a half years. The researchers believe that the long-term benefit of finerenone could be even greater. They estimate that taking the pill every day reduces the risk of kidney failure by up to 40 percent, which will reduce hospitalizations and deaths.
"This is a really exciting drug," says Dr. McCafferty. 'It's safe and keeps patients out of the hospital. Let's hope this evidence is enough for NICE [the National Institute for Health and Care Excellence] to finance the drug.
He adds: "The next step will be to offer the treatment to patients with kidney disease who do not have diabetes, which means that many more Britons could one day benefit."
One patient who benefits from finerenone is Anthony Price, 67, of Birmingham. The retired businessman has lived with type 2 diabetes for more than 30 years and was diagnosed with early-stage kidney disease just over a decade ago.
Her advisor offered her the opportunity to participate in the finerenone trial. "If my kidneys got worse, I could end up on dialysis or need a transplant, and I didn't want to do either," Anthony says.
"This medication was an added level of safety against kidney disease, along with the important task of paying attention to my blood sugar levels to keep my diabetes under control."
For three years, Anthony took the pill every morning with breakfast. He says that he did not experience any side effects. In addition, he has not suffered kidney or heart complications.
"I hope they return it to me when it's approved," he says.
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text:
Taiwan export orders down after tech demand shrinks
A fisherman navigates a raft near boats filling the Patoutzu Fishing Harbour in Keelung, Taiwan. Reuters
Taiwan's export orders fell for the fifth straight month in March, and at a much sharper pace than expected, as the island's manufacturers kept struggling with a drop in global tech demand.
Orders in March dropped 9 per cent from a year earlier to $38.59 billion, Ministry of Economic Affairs data showed on Monday.
For January-March, export orders slid 8.4 per cent, the biggest annual drop for a first quarter since 2009 when they tumbled 30 per cent, the ministry said.
March orders fell at almost twice the 5.45 per cent forecast in a Reuters poll, though the pace was less than February's 10.9 per cent, the sharpest fall in nearly three years.
Taiwan's hi-tech factories are major suppliers for global tech heavyweights such as Apple Inc and Qualcomm, and the continued drop in orders suggests global electronic could remain soft for some time.
The ministry said the March shrinkage was also due to declining machinery orders due to business caution in China as the US-Sino trade dispute continues.
However, launches of smartphone models and demand for new technology such as artificial intelligence and 5G could support a rebound of orders for electronics in coming months, it said.
The faster 5G networks are coming on line in the United States, China, South Korea and other places this year, but probably will not be widespread until 2020. The telecommunications industry is expected to spend $275 billion over seven years in the United States alone, according to Accenture estimates.
Carl Liu, an analyst at KGI Securities, said Taiwan export orders "are unlikely to return to growth until in June, thanks to re-stocking of electronics ahead of the peak season in the third quarter".
The ministry said it expects April export orders to decline 6.7-9.2 per cent, and that it could be July before orders rebound.
"We remain cautious about orders in the first half and expect a year-on-year decline for the second quarter," ministry official Lin Lee-jen said.
The slowing tech demand has taken a toll on Taiwan's supply chain manufacturers. Chipmaker TSMC last week posted its steepest quarterly profit drop in more than seven years partly due to sluggish smartphone sales.
But the company gave an upbeat forecast for the coming months, betting on rising chip demand thanks to a rollout of 5G mobile network. In March, weak export demand was seen from all Taiwan's major markets. Orders from the United States fell 9.7 per cent on-year, on top of February's 5.5 per cent decline.
Those from China dropped 13.7 per cent, compared with a fall of 14.3 per cent the previous month, while orders from Europe slipped 2.4 per cent. Japan orders declined 5.8 per cent.
Taiwan's government in February trimmed its 2019 economic growth forecast to 2.27 per cent, citing growing uncertainties over global growth. In February, Taiwan's export orders fell 10.9 per cent, the most in nearly three years due to cautious machinery orders from China as the United States-China trade dispute wears on, pointing to a further slowdown for the island's economy.
Taiwan export orders are an indicator of demand for Asia's hi-tech gadgets, and typically lead actual exports by two to three months.
The US-China trade war has hit Taiwan's manufacturing, exports and the demand for new smartphones, and fiscal and monetary policy can only do so much to help drive the recovery. 5G equipment production could provide some support, but before that happens weaker corporate earnings may lead to capital outflows and a softer currency Taiwan's economy is a small and open one, which is why the export sector is important to its manufacturing and related service industries such as ports and logistics. But since mid-2018, the external environment has turned negative for Taiwan.
Latest figures show export orders have contracted by 10.94 per cent in February 2019, while exports have contracted by 8.8 per cent since November 2018.
The government has taken up the main role in supporting the economy and will probably increase spending by 2.8 per cent to TWD 2.022 trillion in 2019, which will be around 11 per cent of nominal GDP. This includes raising infrastructure spending and giving rebates to consumers on energy-saving automobiles.
As you can see in the chart below, historically, fiscal spending can only support the economy for so long. For example, government spending in 1Q16 couldn't prevent GDP growth from falling below zero in annual terms, and it wasn't until export growth rebounded in 3Q16, the economy picked up momentum. So in light of that, we believe, the economy still needs to see a bounce back in exports to see faster economic growth.
Taiwan export orders fall in 8th consecutive month in June
Taiwan's export orders contracted for an eighth straight month in June, with global companies increasingly hesitant to make new investments in machinery as the China-US trade war wears on.
Balloon 'Tank Man' in Taiwan marks 30 years since Tiananmen crackdown
A Taiwanese artist has created giant inflatables of a tank and "tank man" - the lone protester who stood in front of a convoy of tanks on Beijing's Tiananmen Square - to mark 30 years since China's bloody crackdown on the pro-democracy movement.
India to keep sugar export subsidies despite complaints
India will keep its sugar export subsidies despite complaints to the World Trade Organization (WTO) from rival producers Brazil and Australia, though it will tweak how it provides them, four sources directly involved in the matter said.
India's merchandise exports down by 9.71 per cent in June
India's merchandise exports in June declined 9.71 per cent, on a year-on-year basis, to $25.01 billion from $27.70 billion reported for the corresponding month of last year, official data showed.
Asian shares, European stocks gain
Investors are hopeful for Biden's planned stimulus to aid in boosting the regional markets.
Indian share rise following gains in IT
The blue-chip NSE Nifty 50 index rose 0.4% to 14,574 and the benchmark S&P BSE Sensex was up 0.3% at 49,544.75. The Nifty and Sensex rose 1.7% on Tuesday.
Alibaba's shares record gains as founder reappears
Speculations have been going on for weeks about his whereabouts after he disappeared from the public following an October speech criticizing China's financial system.
Oil climbs on US stimulus hope
US West Texas Intermediate (WTI) crude futures climbed 37 cents, or 0.7%, to $53.35 a barrel while Brent crude futures rose 35 cents, or 0.6%, to $56.25 a barrel.


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20 Hilarious Advertisement Fails You Must See
20 Hilarious Quotes That Will Crack You Up
10 Strange And Funny Cake Toppers
20 Hilarious Foreign Phrases English Needs To Borrow
18 Hilarious And Bizarre Stock Photos
10 Cancelled TV Shows That Came Back With A Vengeance
10 Of The Funniest Wine Brand Names
10 Inappropriate T-Shirts That You Should Not Wear To School
10 Extremely Expensive And Incredible Wedding Cakes
20 Of The Deadliest Women Who Ever Lived
10 Oddly Elaborate Final Meals Of Death Row Inmates
15 Shockingly Expensive Desserts From Around The World
10 Modern Technologies That Are Older Than You Think
10 Weird Items You Can Purchase On Amazon
10 Products Created Due To The World Wars
10 Hilarious Photos Showing Pets Have No Concept Of Personal Space
20 Of The Worst Celebrity Baby Names Ever
10 Hilarious And Inappropriate Street Names
10 Cheaters Who Finally Got A Visit From Karma
10 Geeky Facts To Impress Your Friends
10 Hidden Images Discovered Beneath Famous Paintings
20 Of The Strangest Names For Animal Groupings
10 Incredible Rides From The World's Most Dangerous Amusement Park
10 Shocking Earthquake Facts That Will Make You Tremble
10 Amazing Wonders In The Natural World
10 Pet Portraits Inspired By How Their Owners Describe Them
20 Words That Should Be Added To The Dictionary
These 20 Amazing Facts About The World Will Blow Your Mind
10 Hilarious Photos Of Celebrities Without Teeth
10 Of The Strangest Insect Behaviors
10 Famous Quotes Everyone Always Gets Wrong
Quotes are one of the most memorable things that people encounter throughout their lives. A strong and interesting quote can live on in the memory long after a movie, novel or television has ended, giving fans a quick and easy way to remember their favorite characters and entertainment products. Unfortunately, the truth is that we aren't quite as good at remembering quotes as we would like to think. In fact, some of the most famous and most frequently quoted examples are ones that we get wrong almost all the time.
"Elementary, my dear Watson" has become a staple of Sherlock Holmes, with fans constantly quoting the phrase when discussing the stories. Holmes though, never uttered those words in any of the official novels by Arthur Conan Doyle and has instead become well known from various adaptations.
"Fasten your seatbelts, it's going to be a bumpy ride" is one of the most famous movie quotes of all time and yet the real quote was really "Fasten your seatbelts, it's going to be a bumpy night."
Although most people believe that that the film Dirty Harry features the quote "Do you feel lucky, punk" it isn't completely accurate. The movie actually has a longer version "Do I feel lucky?' Well, do ya punk?"
Ask anyone to say a famous phrase that Michael Caine has uttered and they are likely to say "Not a lot of people know that" despite the fact that he has never actually said it. The quote came from Peter Sellers while he was doing an impression of the actor and it has simply stuck in the public's eye ever since.
After Dorothy arrives in Oz from her home town of Kansas she says the immortal line "I don't think we're in Kansas anymore, Toto." Only she doesn't as the character actually says the line "Toto, I've got a feeling we're not in Kansas anymore" instead.
Many people quote the phrase from Wall Street is "Greed is good" when that is actually just a shortening of the sentiment that was meant by the much longer phrase "The point is, ladies and gentleman, that greed, for lack of a better word, is good. Greed is right, greed works."
Almost everyone knows the words that Uncle Ben says to Peter Parker in Spider-Man to teach him about being careful with his powers. The phrase is often quoted as "With great power comes great responsibility." However, none of those words were spoken by Ben or any other character in the comic and have now just become part of the mythos of the character.
"Mirror, mirror, on the wall, who is the fairest of them all?" is the quote most often attributed to the dastardly Queen says when looking in her mirror. It has become so well known that many people have forgotten entirely that the real quote is "Magic mirror on the wall, who is the fairest one of all?"
The Graduate
Almost everyone believes that the quote from The Graduate said by Dustin Hoffman is "Mrs Robinson, are you trying to seduce me?" when it is truthfully "Mrs Robinson, you're trying to seduce me. Aren't you?"
Star Wars Episode V: The Empire Strikes Back
The shocking twist at the end of The Empire Strikes Back reveals that Darth Vader is actually Luke Skywalker's dad with the apparent quote of "Luke, I am your father." The truth is though that this is a complete misquote that is repeated by even the most ardent fans of the franchise. The actual quote is "No, I am your father."


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Did Rush Limbaugh Accuse Michael J. Fox of Exaggerating His Parkinson's Symptoms?
A 2006 controversy resurfaced in 2020 after the conservative radio host was diagnosed with lung cancer.
Published Feb 5, 2020
Image Via Roy Rochlin/WireImage
Radio host Rush Limbaugh once claimed that actor Michael J. Fox was "exaggerating" his Parkinson's disease symptoms in a political ad.
In February 2020, conservative radio host Rush Limbaugh announced that he had been diagnosed with lung cancer. While U.S. President Donald Trump honored the controversial commentator with the Presidential Medal of Freedom, some social media users noted that Limbaugh had a history of making demeaning comments on his program.
For instance, one widely shared video clip supposedly showed Limbaugh mocking actor Michael J. Fox and claiming that he was "exaggerating" symptoms of his Parkinson's disease.
This is a genuine clip of Limbaugh describing Fox on his program. Media Matters archived the full audio of this segment, which originally aired during an Oct. 23, 2006, episode of Limbaugh's radio show.
Here's a partial transcript of the segment:
In this commercial he is exaggerating the effects of the disease. He is moving all around and shaking and it's purely an act. This is the only time I've ever seen Michael J. Fox portray any of the symptoms of the disease he has. He can control himself to keep himself in the frame of the picture. And he can control himself enough to keep his eyes right on the lens, the teleprompter. But his head and shoulders are moving all over the place. This is really shameless, folks. This is really shameless of Michael J. Fox. Either he didn't take his medication or he's acting.
Fox, who is probably best known for playing young conservative Alex P. Keaton on the show "Family Ties" and the time-traveling high school student Marty McFly in the "Back to the Future" series, was diagnosed with Parkinson's disease in 1991. In 2006, he filmed a 30-second ad for former Democratic U.S. Sen. Claire McCaskill and her support of stem cell research:
Limbaugh was commenting on this advertisement in the above-displayed clip. The segment stirred considerable controversy at the time and even drew an apology from Limbaugh.
The Guardian reported: "Hours later the talkshow host was forced to retract. 'I will bigly, hugely admit that I was wrong.'"
While Limbaugh initially apologized, he would later say on his show that he never made fun of Fox and that this controversy was created by the "drive-by" media. Limbaugh also renewed attacks on Fox and said that the actor was "allowing his illness to be exploited" and was "shilling for a Democratic politician."
The Washington Post reported:
After his apology, Limbaugh shifted his ground and renewed his attack on Fox.
"Now people are telling me they have seen Michael J. Fox in interviews and he does appear the same way in the interviews as he does in this commercial," Limbaugh said, according to a transcript on his Web site. "All right then, I stand corrected. . . . So I will bigly, hugely admit that I was wrong, and I will apologize to Michael J. Fox, if I am wrong in characterizing his behavior on this commercial as an act."
Then Limbaugh pivoted to a different critique: "Michael J. Fox is allowing his illness to be exploited and in the process is shilling for a Democratic politician."
Fox responded to the criticism a few times over the years. Here he is talking to reporter Katie Couric a few days after Limbaugh's segment:
While Limbaugh accused Fox of either "acting" or forgetting to take his medication, the actor told Couric that he was actually "too medicated" in this ad:
The irony of it is that I was too medicated and was dyskinesic. Because the thing about, the thing about being symptomatic is that it's not comfortable. Nobody wants to be symptomatic; it's like you want to hit yourself with a hammer. You want at all times to be as comfortable as you can be. At this point now, if I didn't take medication, I wouldn't be able to speak.
Limbaugh has made several controversial statements during his career. However, it should be noted that some quotes attributed to him have been presented without proper context or sourcing.
Ryan, Joal. "Michael J. Fox: I Have Parkinson's Disease." E! News. 25 November 1998.
Collins, Kaitlan. "Rush Limbaugh Awarded Medal of Freedom in Surprise State of the Union Move." CNN. 4 February 2020.
Montgomery, David. "Rush Limbaugh On the Offensive Against Ad with Michael J. Fox." The Washington Post. 25 October 2006.
Fishel, Ben. "Limbaugh on Michael J. Fox Ad for MO Dem: 'Either He Didn't Take His Medication or He's Acting.'" Media Matters. 23 October 2006.
Pilkington, Ed. "Radio Host Apologises For Claim Michael J Fox Faked Symptoms." The Guardian. 26 October 2006.
Rutenberg, Jim. "Michael J. Fox, Parkinson's and Stem Cells." The New York Times. 25 October 2006.
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text:
Bogart & Dietrich
Cornell Capa Letter
Mayor of Jerusalem Letter
Howard M. Squadron Letter
Beth Hatefutsoth Letter
Steve Kaufman
Melody Brown
Micha Bar-Am was born in Berlin in 1930 and immigrated with his parents to Palestine when he was six years old. He was active in the pre-State underground and later served in a Palmach (Elite Infantry Corps) Unit during Israel's 1948 War of Independence. It was during this war that Bar-Am witnessed a series of events that altered the face of his homeland.
In 1949, he helped to found Kibbutz Malkiya on the Lebanese border and later worked as a locksmith, mounted guard, and youth instructor at the Kibbutz Gesher-Haziv in Western Galilee. Never merely a detached witness of his country's history, people, and landscapes, Bar-Am began to photograph seriously in the early fifties, although his interest in photography started as a teenager.
Using borrowed cameras until he bought himself a vintage Leica, Bar-Am recorded in a small notebook the exposure times and lighting conditions of every photograph he took. Slowly his work began to be published. In 1957, he became a photographer-reporter for the Israeli Army magazine Ba-Mahaneh and was on its editorial staff for ten years. He has photographed the Israeli Army ever since, covering the Six-Day War in 1967 with Cornell Capa, recording the war in 1973 at the Suez Canal and documenting the Israeli invasion of Lebanon. He served as a photographic correspondent for the New York Times in Israel from 1968 to 1992.
Bar-Am has called Israel his "lifetime assignment" and his pictures are visual statements about the fascinating complexity of Israeli life. Rooted in photojournalism but shaped by an artist's eye, his work belongs to the tradition of such photographers as Robert Capa, Walker Evans and W. Eugene Smith.
Many are images of daily life during wartime, which do not depict direct combat as much as they do the traces of conflict. These include a crowd of people looking for names on the surface of a Holocaust Memorial wall and a fashion show held on an army base for the entertainment of a crowd of soldiers. The works in the exhibition are not so much about the nation's history as it is about the traces of history in everything from daily life to echoes of Bar-Am's own struggle, the rewards and frustrations of attempting the improbable task of observing and participating in the events as they are taking place.
Since assisting Cornell Capa in 1968 to create the landmark exhibition Israel the Reality at The Jewish Museum in New York City, Bar-Am has been heavily involved in the work of other photographers as a historian, and since 1977, as curator and founding director of the Department of Photographs at the Tel Aviv Museum of Art, a position he held until 1993. He has since returned to his own photography. See also, letters from friends and associates:
Mayor of Jerusalem
Howard M. Squadron
Beth Hatefutsoth
We guarantee each work sold to be as described. When available, catalogue references are included with the work. Works with certification by foundations will have this paper work included with the art. For original prints, the printing information will be included.
Copyright © 2017 Ultimate Art. All Rights Reserved.
We Promise To Beat Any Legitimate Competitors Price; We cannot Be Undersold. We Are Ultimateart.com
Steve Kaufman Pop Art
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text:
10 hours ago Police in Naivasha recover a vehicle stolen in Bahati after a 10-min
10 hours ago A betting shop owner urges the Government to reconsider lifting the
10 hours ago Acting KPA, MD Dr Manduku speaks about the upgrades on the container
US Olympic moves to disband USA Gymnastics body
Tuesday November 6 2018
Former Michigan State University and USA Gymnastics doctor Larry Nassar arrives for impact statements during the sentencing phase in Ingham County Circuit Court on January 24, 2018 in Lansing, Michigan. PHOTO | JEFF KOWALSKY | AFP
USOC chief executive Sarah Hirshland said in a statement Olympic chiefs had started formal proceedings to revoke USA Gymnastics' status as the governing body for the sport in the United States.
In an open letter addressed to US gymnasts, Hirshland said USA Gymnastics had failed to "change its culture, to rebuild its leadership, and to effectively serve its membership."
The United States Olympic Committee said Monday it had begun moves to disband the country's governing body for gymnastics, accusing the sport's rulers of failing to grapple with the aftermath of the Larry Nassar sexual abuse scandal.
"You deserve better," Hirshland told gymnasts in the letter.
USA Gymnastics has lurched from one self-inflicted debacle to the next as it struggles to rebuild in the aftermath of the Nassar abuse scandal, the worst scandal in US Olympic history.
Froome wins 2011 Vuelta after eight-year wait
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Former US team doctor Nassar was jailed for life earlier this year after abusing more than 250 athletes, including several stars of the United States' gold medal-winning teams at the 2012 and 2016 Olympics.
The beleaguered governing body, which had been accused of covering up Nassar's crimes, has been in a state of perpetual crisis ever since the scandal erupted.
Last month, newly installed chief executive Mary Bono resigned after just four days following an outcry by Olympic stars Simone Biles and Alexandra Raisman.
Bono had taken over from chief executive Kerry Perry, who had resigned after just nine months after criticism of her performance by USOC.
Perry's departure came after the appointment and swift resignation of elite coach Mary Lee Tracy - who had made comments supportive of Nassar in 2016 when allegations of abuse against him had begun to emerge.
USOC had warned it was prepared to take the ultimate sanction of revoking USA Gymnastics' status as a governing body unless it put its house in order.
In January, USA Gymnastics board of directors resigned en masse at the behest of USOC.
In her letter to US gymnasts on Monday, Hirshland said Olympic chiefs had given up hoping that USA Gymnastics was capable of reforming itself in the post-Nassar era.
Hirshland said USOC had concluded that "the challenges facing the organization are simply more than it is capable of overcoming in its current form."
"We have worked closely with the new USAG board over recent months to support them, but despite diligent effort, the NGB continues to struggle," Hirshland said.
"And that's not fair to gymnasts around the country. Even weeks ago, I hoped there was a different way forward. But we now believe that is no longer possible."
USOC's complaint seeking to dissolve USA Gymnastics in its present form will now go before a review panel which will convene a hearing on the case.
The review panel will issue a report and give a recommendation which will then go before the USOC board of directors, who will determine whether or not to formally strip USAG of its governing body status.
Hirshland meanwhile stressed that US gymnasts would continue to compete in events as normal, with the full support and backing of the Olympic committee.
"In the short-term, we have to work to ensure that USAG gymnasts have the support necessary to excel on and off the field of play.
"We are building plans to do just that. In the long-term, it will be the critically important responsibility of the recognized Gymnastics NGB, whether the existing organization or a new one, to lead gymnastics in the United States and build on the supportive community of athletes and clubs that can carry the sport forward for decades to come.
"We are prepared to identify and help build such an organisation."
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4 2019 Africa Cup of Nations Algeria win 2019 Africa Cup of Nations - PHOTOS
5 Golf Home boy Rory McIlroy misses Open cut at Portrush
6 2019 Africa Cup of Nations Senegal and Algeria line-ups for Afcon final


think! is a partnership between Martin Kapell, Ernesto Vela and Jack Esterson.
Previous experience: Studio A, TEK Architects, Kapell and Kostow Architects. Education: MArch, Yale University; BA, MFA in Theater Directing, Scenic and Lighting Design, New York University.
Marty loves adventure. Whether it's checking out a metal fabricator for a new material, cracking open a book of new global architecture, or getting in a car to explore a new neighborhood, for Marty it's about the excitement and the discovery of something unknown, unusual and fresh. And it's this love of adventure that informs the way he leads our clients and staff in search of unexpected design with unexpectedly delightful results. A consummate collaborator, Marty knows the best ideas can come from anywhere and anyone. Our 15 years working together has been a truly fulfilling creative partnership. With his generosity of spirit, Marty embraces the world and is committed to making it better, one design idea at a time.
Marty is a thinker as much as he is an artist. He always risks to dream. He is constantly searching and questioning not only architecture but, most importantly, its significance and enrichment in everyday life. Marty comes to architecture via the theater, a world that informed much of his creative approach to the design process. The evidence of this synergy is in beautiful and ingenious solutions that reach out and engage the emotional side of its users. A loyal leader and tireless cheerleader, Marty inspires respect. He honors the creative essence of each person, inspiring the people he works with to bring their best to each project.
Previous experience: Dattner Architects, Perkins Eastman, Kapell and Kostow Architects. Education: BA in Environmental Studies, University of Manitoba; BArch, Cornell University.
Ernesto is a quiet force within the office—a rigorous seeker who inspires his partners and staff with his devotion to the art of architecture. In addition to his solid technical skill as an architect, Ernesto is the guardian of think!'s design integrity and our adherence to our basic principles of practice. He is unflappable and firm. Truly, when the going gets tough, Ernesto gets calm and focused, a treasured skill that allows him to effectively lead, mentor and motivate the staff. To the extent that think! remains on track with regard to its mission and philosophy, it owes a great deal to Ernesto's perseverance and dedication to architecture and our firm.
Ernesto operates from a strong focused center. His contemplative nature is a reflection of his willingness to listen and consider carefully before putting forth design ideas that are as much about the client's aspirations as about his own considerable artistic ambitions. Those ambitions originate from a humanist world-view and an inherent empathy for people, the way they live, work and find enjoyment. Ernesto is also a teacher, a behind-the-scenes motivator who is masterful at understanding design's impact on the world and its inhabitants. His wealth of knowledge is considerably enhanced by his collaborative nature, pushing the think! team to a place of greater authenticity.
Previous experience: Studio A, TEK Architects, Thanhauser & Esterson Architects, Kliment & Halsband Architects, Skidmore Owings & Merrill. Education: BArch, Pratt Institute. Polytechnic of Central London.
Jack truly operates from the belief that architecture can improve the lives of all people and, by extension, make cities more livable and vital. Jack's entire career has been an example of how buildings can be made functional, accessible and beautiful regardless of the project type, the project's budget, its constituency or all of the practical and cultural challenges that conspire on an almost daily basis against vision and quality. In a career that has already spanned more than 40 years (with no end in sight) Jack has developed the type of hard won wisdom that allows him to always go straight to the heart of the matter and create designs of enormous clarity – always buildable and compelling.
Passion. This quality that best defines Jack comes with an open-minded approach to architecture refined over a long dynamic career. His sincere commitment to improving our city's civic life through thoughtful architecture is evident in every project he designs. In his work there is an unabashed belief that the beautiful in architecture is as essential as any other functional requirement. His passion for beauty and its rewards infuses his designs with integrity and timelessness. His design approach is always positive, encouraging and self-critical, bringing clients and collaborators closer to his vision of a sustainable and humanistic urban life.
When we started think! four years ago, we contacted our favorite former employees and collaborators and asked them to join us in our new endeavor—a surprising number of them did. Over this same time we have added to this core staff with seasoned experienced professionals, well rounded practitioners and enthusiastic recent graduates all of whom share our commitment to design, our values and our pursuit of quality in all areas of our practice.
The design of the think! office reflects the firm's attitude toward design and it fuels our creative process. Its pragmatism defines its aesthetic, its intelligence makes our lives easier, it leveraged the maximum design impact from a challenging budget and, most importantly, it promotes collaboration and vanquishes hierarchies. It looks like us and we love working here because it's just right for who we are, what we do and what we value.


This recording of the New Testament in Blackfoot is limited to the Gospel of John only. The recording was digitized directly from the original cassette tapes.
Blackfoot is also known as Blackfeet, Pied Noir, and Pikanii. Listen here for free to the Bible in Blackfoot.


Enforcement Directorate (ED) on Tuesday filed a chargesheet in Sterling Biotech Ltd (SBL) Group case before a special Prevention of Money Laundering Act (PMLA) court in Delhi. ED had registered a case of money laundering against SBL Group in August 2017.
Investigation by the ED has revealed that Sterling Biotech promoters — the Gujarat-based Sandesara family — and others hatched a criminal conspiracy for cheating banks. They manipulated figures in the balance sheets of their flagship companies and induced banks to sanction higher loans.


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Ethiopian songs are based on five-note scales called kignits.
Ambassel, Tezita, Anchihoye and Bati are the four main kignits and the basis of many popular songs.
Note that while these kignits can be represented on a piano keyboard they will not sound exactly the same. This is because Ethiopian music uses a different system of tuning (pure temperament) than the Western equal temperament.
Some modern synthesizers allow the modification of temperament and this allows a closer approximation to the true sound of the Ethiopian kignits.
Click the numbers to hear variations of the main ethiopian kignits. The dots indicate representative notes on a C major scale in pure temperament.
The traditional and recommended way to tune the krar or begena is by ear. Listen to songs by accomplished players and intimately learn the melody. Then attempt to play the melody on your instrument and tune the strings till the melody sounds true. You should choose one favorite song for each kignit. Start by tuning the 6th string to the highest note you can comfortably sing. This will ensure that the instrument is tuned properly for your vocal range.
You can also tune your instrument using a piano or keyboard. Please note that western keyboards are tuned using equal temperament while Ethiopian music uses pure temperament. This will cause slight discrepancies in your tuning but it will be acceptable to most.