text:
The 1999 NCAA Division I men's basketball tournament involved 64 schools playing in single-elimination play to determine the national champion of men's  NCAA Division I college basketball. It began on March 11, 1999, and ended with the championship game on March 29 at Tropicana Field in St. Petersburg, Florida. A total of 63 games were played. This Final Four was the first—and so far, only—to be held in a baseball-specific facility, as Tropicana Field is home to the Tampa Bay Rays (then known as the Devil Rays).

The Final Four consisted of Connecticut, making their first ever Final Four appearance; Ohio State, making their ninth Final Four appearance and first since 1968; Michigan State, making their third Final Four appearance and first since their 1979 national championship; and Duke, the overall number one seed and making their first Final Four appearance since losing the national championship game in 1994.

In the national championship game, Connecticut defeated Duke 77–74 to win their first ever national championship, snapping Duke's 32-game winning streak, and scoring the biggest point-spread upset in Championship Game history. Duke nonetheless tied the record for most games won during a single season, with 37, which they co-held until Kentucky's 38-win seasons in 2011–12 and 2014–15. The 2007–08 Memphis team actually broke this record first, but the team was later forced to vacate their entire season due to eligibility issues surrounding the team.

Richard "Rip" Hamilton of Connecticut was named the tournament's Most Outstanding Player. This was a significant victory for the program, as it cemented Connecticut's reputation as a true basketball power after a decade of barely missing the Final Four.

This tournament is also historically notable as the coming-out party for Gonzaga as a rising mid-major power. Gonzaga has made every NCAA tournament since then, and is now generally considered to be a high-major program despite its mid-major conference affiliation.

Due to violations committed by Ohio State head coach Jim O'Brien, the Buckeyes were forced to vacate their appearance in the 1999 Final Four.

Schedule and venues

The following are the sites that were selected to host each round of the 1999 tournament:

First and Second Rounds
 March 11 and 13
 South Region
 RCA Dome, Indianapolis, Indiana (Hosts: Butler University, Midwestern Collegiate Conference)
 Orlando Arena, Orlando, Florida (Host: Stetson University)
 West Region
 McNichols Sports Arena, Denver, Colorado (Hosts: Western Athletic Conference, Colorado State University)
 KeyArena, Seattle, Washington (Host: University of Washington)
 March 12 and 14
 East Region
 FleetCenter, Boston, Massachusetts (Host: Boston College)
 Charlotte Coliseum, Charlotte, North Carolina (Host: University of North Carolina at Charlotte)
 Midwest Region
 Bradley Center, Milwaukee, Wisconsin (Host: Marquette University)
 Louisiana Superdome, New Orleans, Louisiana (Host: Tulane University)
Regional semifinals and finals (Sweet Sixteen and Elite Eight)
 March 18 and 20
 South Regional, Thompson–Boling Arena, Knoxville, Tennessee (Host: University of Tennessee)
 West Regional, America West Arena, Phoenix, Arizona (Host: Arizona State University)
 March 19 and 21
 East Regional, Continental Airlines Arena, East Rutherford, New Jersey (Hosts: Seton Hall University, Big East Conference)
 Midwest Regional, Trans World Dome, St. Louis, Missouri (Host: Missouri Valley Conference)

National semifinals and championship (Final Four and championship)
 March 27 and 29
 Tropicana Field, St. Petersburg, Florida (Hosts: University of South Florida, Conference USA)

St. Petersburg became the 27th host city, and Tropicana Field the 32nd host venue, for the Final Four. Tropicana Field, the home of baseball's Tampa Bay Rays, was the sixth different domed stadium to host a Final Four, and the only one designed specifically for baseball; given its inability to be converted to a center court arena, it is unlikely to host one again. There were four new venues in the 1999 tournament, two in completely new host cities for the tournament. For the first time, the tournament came to Boston at the FleetCenter, which had replaced the Boston Garden in 1995. Despite the original Garden's rich college and NBA history, it never had hosted any NCAA tournament games. The tournament also came to downtown Phoenix for the first time, at the NBA home of the Phoenix Suns. Previous games in the metropolitan area were played in suburban Tempe at Arizona State University. For the second straight year, the Midwest Regional games were held in a new venue in St. Louis, this time at the Trans World Dome, then home to the NFL's St. Louis Rams. The tournament also returned to Seattle, this time at KeyArena, the downtown home to the Seattle SuperSonics. This tournament would be the last held at McNichols Sports Arena, which was scheduled to close later in the year and is now the site of parking for Empower Field at Mile High; subsequent games in Denver have moved to the Ball Arena. It is also the last tournament to date to include Tropicana Field and Thompson–Boling Arena, as neither has hosted since. Any future tournament games to be held in Tampa would be played at the Amalie Arena.

Teams

Bids by conference

Bracket
* Denotes overtime period

East Regional – East Rutherford, New Jersey

Regional Final summary

Midwest Regional – St. Louis, Missouri

Regional Final summary

South Regional – Knoxville, Tennessee

Regional Final summary

West Regional – Phoenix, Arizona

Game summaries

First Round

Second Round

Regional Semifinals

Regional Final

Final Four

St. Petersburg, Florida

Game summaries

Final four

National Championship

Media coverage

Television
CBS Sports

Studio host

Studio analysts

Commentary teams
 Jim Nantz/Billy Packer/Bonnie Bernstein First & Second Round at New Orleans, Louisiana; Midwest Regional at St. Louis; Final Four at St. Petersburg, Florida
 Sean McDonough and Bill Raftery First & Second Round at Indianapolis, Indiana; South Regional at Knoxville, Tennessee
 Verne Lundquist/Al McGuire/Armen Keteyian First & Second Round at Charlotte, North Carolina; East Regional at East Rutherford, New Jersey
 Gus Johnson/Dan Bonner/Barry Booker First & Second Round at Orlando, Florida; West Regional at Phoenix, Arizona
 Tim Brando/James Worthy/Beth Mowins First & Second Round at Milwaukee, Wisconsin
 Kevin Harlan/Jon Sundvold/Mike Harris First & Second Round at Seattle, Washington
 Ian Eagle and Jim Spanarkel First & Second Round at Denver, Colorado
 Craig Bolerjack and Rolando Blackman First & Second Round at Boston, Massachusetts

Radio
Westwood One

First and Second Rounds
 – East Region First and Second Rounds at Charlotte, North Carolina
 – East Region First and Second Rounds at Boston, Massachusetts
 – Midwest Region First and Second Rounds at Milwaukee, Wisconsin
 – Midwest Region First and Second Rounds at New Orleans, Louisiana
 – South Region First and Second Rounds at Indianapolis, Indiana
 – South Region First and Second Rounds at Orlando, Florida
 – West Region First and Second Rounds at Denver, Colorado
 – West Region First and Second Rounds at Seattle, Washington

Regionals
 – East Regional at East Rutherford, New Jersey
 – Midwest Regional at St. Louis, Missouri
 – South Regional at Knoxville, Tennessee
 – West Regional at Phoenix, Arizona

Final Four and National Championship
 Marty Brennaman and Ron Franklin – (Connecticut–Ohio State) Final Four at St. Petersburg, Florida
 John Rooney and Bill Raftery – (Duke–Michigan State) Final Four and National Championship Game at St. Petersburg, Florida

Local Radio

Additional notes
 Despite their loss in the finals to Connecticut, the 1998–1999 Duke team won 37 games.  This tied them with Duke's 1985–86 team, UNLV's 1986–87 squad, and later, Illinois' 2004–05 team and Kansas's 2007–08 team, for the most wins in a season, until their record was broken by the 38-win Memphis team in 2007–08.  However, as the NCAA vacated Memphis' 2007–2008 season due to the ineligibility of Derrick Rose, they reclaimed the 37-win record. The mark would once again be raised to 38 wins after Kentucky's dominant title run in 2012, which then tied with Kentucky's 2014–15 team.  Only one of the first 5 teams to be the winningest single-season teams won a national championship; UNLV's squad lost in the national semifinal to Indiana, and the other teams lost in the finals, to Louisville, UConn, and North Carolina, while Kansas defeated Memphis in the 2008 national championship game.  Kentucky's 2014–15 squad suffered their only loss that season in the national semifinal to Wisconsin.
 Connecticut's victory in the finals marks the biggest upset in Championship Game history in the NCAA tournament, as they were 9.5-point underdogs in the contest despite having compiled a 33–2 record going into the Championship game, including a 14–2 record in the tough Big East Conference.  In fact, Connecticut had spent more weeks as the number 1 team in the country, according to the AP Top 25 Poll, than had Duke. The previous record was held by Villanova, who defeated Georgetown as 9-point underdogs in 1985.
 The 1999 Final Four would be the last time Tropicana Field would host NCAA tournament games. For Duke, they had 2 straight promising seasons end on the Tropicana Field floor, with an 86–84 loss to Kentucky in the 1998 South Regional final, and then the 1999 National Championship game.
 North Carolina lost to Weber State which marked the first time the Tar Heels had lost in the first round of the expanded field era with 64 or more teams.
 This is the only tournament in which all four 7-seeds lost in the first round to their 10-seeded opponents.

Notes

See also
 1999 NCAA Division II men's basketball tournament
 1999 NCAA Division III men's basketball tournament
 1999 NCAA Division I women's basketball tournament
 1999 NCAA Division II women's basketball tournament
 1999 NCAA Division III women's basketball tournament
 1999 National Invitation Tournament
 1999 Women's National Invitation Tournament
 1999 NAIA Division I men's basketball tournament
 1999 NAIA Division II men's basketball tournament
 1999 NAIA Division I women's basketball tournament
 1999 NAIA Division II women's basketball tournament

NCAA Division I men's basketball tournament
Ncaa
Sports competitions in St. Petersburg, Florida
NCAA Division I men's basketball tournament
NCAA Division I men's basketball tournament
Basketball competitions in Florida


Friday, May 3rd marks the eighth semi-annual RAISE (Retention and Advanced Investment for the Southeast at Emory) Forum, presented by Emory University's Goizueta Business School. This invitation-only event, now accepting applications, brings together startups and investors from across the Southeast with the goal of closing the region's funding gap at higher levels of funding.
The Forum is accepting applications from post-revenue startups based in the Southeast (Georgia, Alabama, Florida, Tennessee, North Carolina and South Carolina) seeking funding between $750 thousand and $5 million. Eight startups will be selected to present to active, accredited investors at the May event. To be considered as a presenting startup, a company must have some revenue, as well as participate in a short training afternoon which will prepare applicants for investor presentations. Applications will close Friday, March 29th.
Roughly one in five presenting companies go into funding through the Forum's efforts, and each remains in the Southeast. Startups in any field are encouraged to apply to present at the Forum by visiting www.raiseforum.com/entrepreneurs.
Current investor support for the Forum comes from across the region, with investors from Alder Asset Management, BIP, VentureSouth, AIM Group, Fulcrum Equity, 11-11 Ventures and many more.
Additional support comes from Georgia Bio and other influential Southeastern startup incubators and influencers, as well as sponsors. Active investors or regional organizations interested in supporting the Forum, should visit raiseforum.com/investors or raiseforum.com/sponsors to learn more about sponsorship opportunities and registration.
RAISE Forum was founded in 2015 by Barry Etra as a vehicle for bringing together startups and investors from across the Southeast —fostering collaboration among the region's entrepreneurial ecosystem. The Forum, now an integral piece of the Entrepreneurship program at Goizueta, will be held next on May 3rd, 2019 at Emory University. To learn more about the event, visit www.raiseforum.com.


High demand for club level seating has prompted the creation of additional upper 100 level seats above the south baseline student section at State Farm Center. Enjoy easy access to the Legacy Club which features premium amenities and an exciting gameday experience.
** * Prices reflect annual commitments due each year by June 1. After establishing a campaign gift commitment, the first annual lease payment is due June 1. Prices will increase every commitment year by 2.5%. Discounts for prepayment of entire contract term are available. An annual I FUND commitment is required of all Legacy Club patrons for the life of their contract. By maintaining an I FUND account, Legacy Club patrons will have the opportunity to purchase special single-game tickets and receive an I FUND season parking pass. Single-game tickets and the I FUND parking pass are allocated based on I FUND club level and priority point totals. The minimum required I FUND membership for the Legacy Club is the Champion level.


text:
Fr. Michael E. Gaitley
Fr. Benedict Groeschel
Fr. Jacques Phillippe
Catechetical
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6"- 9" Crucifixes
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Outdoor Statuary
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The Bones of St. Peter
The first full account of the search for the Apostle's body
In the year A.D. 68, St. Peter was put to death in Rome as an enemy of the state. Roman law forbade him a proper burial and denied his friends even the right to recover his body, so few souls were able to learn his final resting place.
For centuries, tradition held that to honor and preserve the mortal remains of this true Prince of the Apostles, St. Peter's Basilica had been built right over St. Peter's grave.
Not, however, until 1939, did researchers, scholars, and scientists begin sustained, systematic efforts to discover the truth, an archaeological task that spanned 30 years. Carefully studying every known clue while literally unearthing others as they tunneled their way through a tangle of ancient structures beneath the magnificent high altar of St. Peter's, they slowly unraveled the secrets surrounding the burial of St. Peter.
The Bones of Saint Peter is the engrossing true story of how these determined researchers finally solved the puzzle of St. Peter's burial and rescued his bodily remains from centuries of oblivion.
Paperback. 256 Pages
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text:
Ted Lasso's Coach Beard covers the World Cup in new Apple podcast
By David Snow • 8:17 am, November 14, 2022
The new After the Whistle podcast covering the World Cup kicks off November 17.
Photo: Apple News
An amusing audio teaser from Apple News on Monday introduced the limited podcast After the Whistle, in which Coach Beard from the Apple TV+ hit Ted Lasso — aka actor Brendan Hunt — and veteran English sportscaster Rebecca Lowe will explain everything about the world's biggest sporting event, the World Cup, as it happens.
As you may know, the World Cup is a massive football tournament (to use the world's sensible term for soccer). It kicks off Sunday in Qatar and will be broadcast globally. Apple News boots up the podcast on Thursday in anticipation of it.
Official BBC Sport App Now Supports iPad & Android-Powered Tablets
By Cult of Android • 6:53 am, December 13, 2013
The BBC has updated its official BBC Sport app, introducing support for the iPad and Android-powered tablets. The new release also makes usability and stability improvements, and allows those on Android to listen to live radio — including Premier League commentaries.
FIFA 14 Now Available On iOS For Free
By Killian Bell • 2:30 am, September 24, 2013
EA Sports has brought the controversial free-to-play game model to the world's biggest soccer franchise in FIFA 14 for iOS, which is now available to download from the App Store — three days before the big console release.
The title boasts a whole heap of improvements, including a new and intuitive control system, online multiplayer, a game of the week feature, and the hugely popular FIFA Ultimate Team mode.
Vodafone U.K. Confirms 4G Rollout Will Begin August 29
By Cult of Android • 5:05 am, August 7, 2013
Vodafone U.K. has today announced that it will begin rolling out its new 4G network on August 29 — the same day rival O2 will launch its own 4G service. The company will reach 12 major cities across the country initially, with further expansion planned before the end of this year.
That means that by the end of this month, EE will no longer be the U.K.'s only 4G provider.
Sky Sports For iPad Now Lets You Choose Your Own Camera Angles During Replays
By Killian Bell • 5:13 am, May 20, 2013
When you're watching the game, wouldn't it be awesome if you could replay key moments from any camera angle you choose?
With the latest Sky Sports update for iPad, you can. Launching just in time for the upcoming UEFA Champions League final, Sky Sports 360 is a brand new feature that lets viewers select and replay exciting moments from over 20 different camera angles.
EA Sports Brings FIFA 13 To The App Store With Online Multiplayer
FIFA 13 promises "groundbreaking visuals" and the most responsive control system yet.
As a massive soccer fan, I love EA Sports's FIFA Soccer series. So imagine my delight when I opened up the App Store this morning to find FIFA 13 staring back at me. This is the FIFA game I've been waiting for for iOS. Why? Because it's the first to boast online multiplayer, allowing you to play against your friends over the Internet.
Apple To Bid For English Premier League Rights For Apple TV [Rumor]
By Killian Bell • 3:55 am, January 4, 2012
Apple is reportedly gearing up to bid for English Premier League streaming rights that would allow it to show live matches through its Apple TV and iOS devices. The Cupertino company hopes the content will boost sales of its set-top box and the iPad in the U.K.


Game Day 2017 is on like Donkey Kong!!!
Come to the Oswald Manor and join us for the 5th Annual Games Day on Saturday August 5th.
Dark Refuge Games and Windmill Game Co come together for the best camping and rpg gaming experience you can find.
Remember to bring your camping gear for the overnight!
I'm so excited for this I'll even come back to NY.
Things to keep in mind is water, sun screen, bug repellent, and food.
Nearest place to buy things is 15 miles ish away.


There dwelt once in the neighborhood of Grunberg Castle in Bohemia two brothers - Staglow and Chrudis, of the distinguished family of Klemowita - and these two had fallen into a fierce dispute over the inheritance of their father's lands. The older son Chrudis thought that he should inherit all of the estate - and that is the custom in some countries, you know - while the younger son, Staglow, declared that the property should be equally divided.
Now it happened that a sister of the princess Libuscha Vyched lived at the court. She entreated the princess to settle the quarrel according to law.
The princess yielded to her wish, and decided that the brothers should either inherit their father's estate jointly or divide it into equal shares.
All the lords of the country assembled to hear the rendering of the decision - brave knights from far and near. Chrudis and Staglow, of course, were present, very curious to hear what their princess would decide. Pungel of Hadio, proclaimed far and wide as the bravest of all the knights of Bohemia, was also among the company.
The princess herself rendered the decision, standing in white robes before her people. The two brothers stood near, and scarcely had the last word been uttered when the knight Chrudis, who, as first-born, claimed the estate for himself, sprang excitedly to his feet, mocking and insulting the princess. "Poor people," he said, addressing the assembly, "I am sorry for you who have to be ruled over by a girl."
Deeply grieved, the maiden-princess Libuscha rose, explaining that she would no longer rule alone. She commanded the people to choose her a husband.
"No matter whom you choose," she declared, "I will abide by your decision."
Thereupon the assembled subjects cried out that they would have Pungel of Hadio as prince; and Libuscha, stepping toward him, extended her hand to him in token of her agreement.
Thus did Pungel become the liege lord of the Bohemian nobles.
No one knows how long ago all this happened, for the manuscript that tells the story was very old when it was discovered in the year 1817. It had lain for many, many years among other old documents in the great chests that lined the walls of the courtroom in the ancient Castle Grunberg in Bohemia. The manuscript is now in a great museum in Prague, and perhaps, some day, when you go there, you will see it for yourself.
If you use Facebook or Google+ & enjoyed The Decision Of Libuscha, please tell your friends and let us know to find more like it for you!


text:
\section*{Results}
\subsection*{Multiplex network construction and analysis}

\begin{figure*}[ht]
\centering
\includegraphics[width=17cm]{Fig1}
\caption{(a) Visualisation of part of the multiplex network representing a toddler's lexicon. (b) Visualisation of an example of the MLN as an edge coloured network. Both (a) and (b) include only 24 of the 529 nodes/words in the whole MLN. (c) Degree correlations across layers quantified by the Kendall Tau. (d) Multiplexity or edge overlap among different layers relative to expectations from configuration models. (e) Reducibility dendrogram for the MLN. The MLN is irreducible: its layers are so different from each other that aggregating any of them would lead to a loss of information about topological patterns.}
\label{fig:visua}
\end{figure*}

We first construct a multiplex lexical network composed of four layers capturing i) free associations from the South Florida association norms \cite{nelson2004university}, (ii) feature sharing from the McRae et al. dataset \cite{mcrae2005semantic}, (iii) co-occurrence in child-directed speech from  the CHILDES dataset \cite{macwhinney2000childes}, and (iv) phonological similarities from WordNet 3.0 \cite{vitevitch2008} (cf. Methods and SI Sect. 2). Hence the resulting MLN properties emerge from the assembly of independent measures rather than from a multiplex network design. Figures~\ref{fig:visua} (a) and (b)  provide a visualisation of the links in each of the four MLN layers. Panel (a) treats each layer as separate whereas panel (b) describes the multiplex as an edge-coloured graph \cite{wasserman1994social}, in which different colours denote different relationships. As evident from (a) and (b), even if words might be disconnected on one layer, they could be connected on the  MLN structure.

We report single-layer summary statistics and comparison to configuration models (i.e. random graphs preserving the degrees of an empirical network \cite{newman2010}) in Table~\ref{tab:1}. We focus on network features already analysed in previous works \cite{motter2002,sigman2002,stey2005large,vitevitch2008}: degree, clustering, degree mixing, connectivity ratio and mean shortest path length. For instance, the degree of a node counts its connections \cite{newman2010} and on the phonological layer, it coincides with the size of its phonological neighbourhood \cite{vitevitch2008,vitevitch2012}. See Methods and SI Sect. S3 for precise definitions and interpretations.

Tab.~\ref{tab:1} shows that the phonological layer has a smaller mean degree compared to other layers. Furthermore, apart from the association layer all other layers are highly fragmented. Their largest connected components include between 24.2\% and 62.4\% of the 529 words. These percentages quantify how many words could be reached from each other by navigating through links on individual layers. In contrast, the \textit{multiplex network} is connected \cite{dedomenico2014navigability} -- every pair of words is connected by a path that potentially exploits different relationship types. Compared to configuration models, except for the co-occurrence layer all other layers display higher clustering, non-zero assortativity and slightly larger path length in their largest connected component. In agreement with previous work \cite{gravino2012complex,sigman2002,vitevitch2014,beckage2015cognition} these patterns suggest that the MLN layers display a core-periphery structure. From a cognitive perspective this feature might possibly facilitate navigation from word to word within cores (see SI Sect. S3 for further discussion).  Layers also differ in their degree distributions, see SI Fig. S2: they are exponential-like for the synonym and phonological layers but much more heavy-tailed for the co-occurrence and association layers. Heavy tails indicate the presence of network hubs, i.e. words which are significantly more connected than average. These hub words play a central role for navigation through concept space \cite{sigman2002,motter2002,gravino2012complex} and could therefore be good candidates for earlier acquisition.

Is it appropriate to represent the ML in terms of four separate layers or
can we get the same type of topological information with fewer layers? Structural reducibility analysis \cite{de2015structural} can test if multiplex layers can be aggregated without losing information (see SI for details). Results reported in Fig.~\ref{fig:visua} (e) show that aggregation cannot be performed without information loss, demonstrating that the chosen 4-layer representation is in fact \textit{irreducible} \cite{de2015structural}. This is not to say that each layer plays a role in language acquisition, just that the information encoded in each layer is different from all other layers.

Before turning to modeling word acquisition, we investigate the MLN structure in more detail. Specifically, Fig.~\ref{fig:visua} (c) analyses the similarity between degrees of a word on different layers. We find that words in the feature and co-occurrence layers tend to have negative degree correlations (Kendall Tau $\kappa \approx -0.16, p<0.0001$), indicating that hubs in one layer tend to have lower degrees in the other layers. This may suggest a type of semantic differentiation \cite{stey2005large} in English in which words that have similar features tend to not be used in close proximity of each other, at least when speaking to
young children. In contrast, the co-occurrence and phonological layers display positive degree correlations (Kendall Tau $\kappa \approx 0.27, p<0.0001$): in the children's lexicon, words having many co-occurrences also have larger phonological neighbourhoods, further supporting the idea that phonological similarities and co-occurrences in child directed speech
influence each other \cite{macwhinney2000childes,wiethan2014early}.

Another question of interest is to what extent links overlap across layers. This can be quantified by edge overlap (or multiplexity
\cite{gemmetto2015multiplexity}). Results are reported in
Fig.~\ref{fig:visua} (d). As expected, we find some overlap between
connections in the feature, co-occurrence and association layers; these layers capture semantic aspects of the ML. Beyond the overlap between these layers,
links tend not to overlap significantly more than one would expect in configuration model across the other layers. This suggests that different MLN layers tend to capture
different relational aspects of the ML, again emphasising that the MLN is a more complete model than single network representations. We now explore the importance of individual layers and of the MLN  as a whole in explaining acquisition trajectories.

\begin{table*}
\footnotesize
\caption{Metrics for the MLN layers with $N=529$ nodes, listing mean degree $\left\langle k \right\rangle$, mean clustering coefficient $CC$, assortativity coefficient $a$, percentage of nodes in the largest connected component $Conn.$, and mean shortest path length of the largest connected component $\left\langle
d\right\rangle$. The quantities are measured for the layers of the MLN and for randomized equivalents with the same degree sequences (denoted with CM). Error bars represent standard deviations and are reported in brackets behind the last significant digit.}
\label{tab:1}
\begin{tabular}{p{3cm}p{0.5cm}p{0.5cm}p{0.8cm}p{0.8cm}p{0.8cm}|p{0.9cm}p{1.1cm}p{1.4cm}p{1.1cm}}
\hline\noalign{\smallskip}
Empirical Network & $\left\langle k \right\rangle$ & $CC$ & $a$ & $Conn.$ & $\left\langle d\right\rangle$ & $CC\,(CM)$ & $a\,(CM)$ & \textit{Conn.} (CM) & $\left\langle d\right\rangle \,(CM)$\\
\hline\noalign{\smallskip}
\rule{0pt}{1em}Associations (Asso.) & 9.3  & 0.20  & -0.1 & 99.6\% & 3.2 &  0.03(1) &  $-0.01(6)$ &  99.6\% & 3.00(1)\\
\rule{0pt}{1em}Feature Norms (Feat.)    & 9.0 & 0.63 & -0.01 & 24.2\% & 1.8 &  0.38(2) & $-0.07(6)$  &  24.2\% & 1.72(1) \\
\rule{0pt}{1em}Co-occurrences (Co-occ.) & 8.1 & 0.69 & -0.44 & 62.4\% & 2.2 & 0.75(3)&  $-0.40(2)$ & 62.4\% & 2.19(1) \\
\rule{0pt}{1em}Phonological Sim (Phon.) & 1.31 & 0.37  &  0.48 & 33.1\% & 7.7 & 0.02(1) & $0.03(2)$ & 45.5\%(8) & 5.31(7) \\
\rule{0pt}{1em}Multiplex Aggregate      & 26.5 & 0.33 & -0.07 & 100\% & 2.4 & 0.18(5) &  $-0.10(8)$ & 100\% & 2.25(1) \\
\noalign{\smallskip}\hline\noalign{\smallskip}
\end{tabular}
\end{table*}

\subsection*{Multiplex Orderings: Results and Discussion}

Here we explore the process of word learning through the MLN assuming a
preferential acquisition scenario \cite{hills2009longitudinal},
where words are learned earlier if they are central in the
language environment. We assume that relevance of the language environment
can be summarised by connectivity in a language network \cite{sigman2002,motter2002,baronchelli2013,beckage2015cognition} rather than making assumptions on the statistics of relationships to infer relevance, as is more standard in a Bayesian approach \cite{xu2007word,frank2009using}.

We generate a word acquisition ordering $\tau$ by ranking words according to measures computed on the MLN. We assume that if one ranking is predictive, it would indicate that the feature generating it is relevant to early language learning. We compare MLN-based orderings to those based on non-relational, word-specific information such as word frequency and word length. For consistency with the literature, we investigated features previously considered in linguistic networks such as word degree \cite{stey2005large,beckage2011,vitevitch2014}, closeness \cite{motter2002,sigman2002}, betweenness \cite{borge2010,motter2002,sigman2002} and PageRank \cite{griffiths2007google}. For more details see Methods and SI Sect. S6.

We compare our orderings against an ensemble of empirical age of acquisition orderings, obtained from a probabilistic interpretation of the normative age of acquisition of words based on the MacArthur-Bates Communicative Development Inventory\cite{macwhinney2000childes} (CDI) and over a population of roughly 1000 toddlers (see Methods). The comparison is performed by computing the average word overlap $O(\tau,t)$ of ordering $\tau$ with normative orderings, namely how many words $\tau$ correctly predicts as learned until the inventory includes $t$ words. Word \textit{gains} are then obtained by subtracting $O(\tau,t)$ from the expected number of correctly predicted words by random guessing (see Methods and SI Sect. S6). Word gains are normalised by the inventory size $t$ and their Z-scores relative to statistics from random guesses are estimated. Positive \textit{vocabulary normalised word gain} indicates better than random performance of ordering $\tau$ and negative values indicate performance inferior to random guessing.

Fig.~\ref{fig:orde} reports normalised word gains (a) and their Z-scores (b) for the best performing orderings from our analysis. No ordering always outperforms the others: we notice shifts in which word features are the most relevant for different periods of word learning. This agrees with the idea that the progression of acquisition changes over the course of development: children learn some initial words and then tend to generalise those words based on network connectivity later, i.e. preferential acquisition \cite{hills2009longitudinal}. This cognitive interpretation of the observed shifts at different acquisition stages allow us to distinguish at least three distinct learning phases. In a first stage, comprising months 19 and 20, which we call the \textit{very early learning stage} (VELS), we are trying to predict the first 40 learned words (grey overlays in Fig.~\ref{fig:orde}). Direct sampling \cite{grimmett2001probability} for this period indicates that our results are below the 2.5\% threshold for statistical significance -- the network appears to contain little information relevant for predicting acquisition.
This phase is followed by an
\textit{early learning stage} (ELS) covering an age range between 20 and 23 months. This phase is characterised by \textit{closeness centrality outmatching} every other ordering (see also SI Sect. S6). Last we discriminate a \textit{late learning stage} (LLS) comprising ages between 23 and 28 months. During this stage the dominant hub words have already been learned and more localized structure is likely affecting learning. LLS is characterised by degrees in the association layer and frequency performing equally to or slightly better than closeness and other word features (cf. SI Sect. S6).

Interestingly, while previous work has found frequency to be a good predictor for normative age of acquisition \cite{kuperman2012age}, results in Fig.~\ref{fig:orde} suggest that word length and some topological network information are more predictive of early acquisition ordering. Good performance is achieved by word length in ELS, possibly because of a \textit{least effort effect} \cite{icancho2003} that suggests that short words are easier to memorise and learn. This hypothesis is also supported by the observation that word length loses its predictive
power at later learning stages (word gain Z-scores are compatible with random fluctuations after 300 words have been learned). We interpret this happens when more sophisticated learning
strategies are being used. One of them might be mediated by degree orderings. Among the four layers, ordering words according to their degree in the association layer gives the best performance and it can be up to 100\% more predictive than random guessing (cf. SI Fig. S7). This confirms previous results suggesting that association norms are generally good predictive models for early language learning \cite{hills2009longitudinal,stey2005large,beckage2015modeling}. We
conjecture associations perform well in our case because they are strongly related with semantic memory
\cite{nelson2004university,zock2015words,dedeyne2008network}.

Previous work has pointed out that in early learning, during VELS and ELS, it is very difficult to outperform random guessing because children may not have a clear strategy for learning new words early in language acquisition \cite{beckage2015modeling}. Instead here we find that better than random prediction is possible even in very early learning stages, at least for normative acquisition. Using the multiplex closeness of words it is possible to predict up to 160\% words more than at random (cf. SI Fig. S7) and up to 25\% more than by using degrees in the association layer (cf. SI Fig. S8). As reported in SI Sect. S6, we checked that multiplex closeness consistently outperforms every single-layer orderings during early learning stages. This is a promising result in that multiplex approaches may allow us to quantify learning strategies in toddlers. Also other multiplex measures such as versatile PageRank \cite{dedomenico2015ranking} perform up to 10\% better than degrees in the association layer (cf. SI Fig. S8). The enhanced prediction ability of multiplex features indicates that the multiplex representation of the ML captures meaningful influences of multiple types of feature representations. Altogether, we can conclude that early learning is strongly related to the multiplex structure, specifically the closeness of words considering \emph{all} layers that form the MLN.

Results for the LLS show a reversal of trends. Here we find
that a single layer based on association degree ordering gives best predictability. Global and local multiplex measures, as well as word specific features such as word frequency, perform similarly or slightly worse until 400 words have been learned (see Fig~\ref{fig:orde} and SI Fig. S8). This indicates that preferential acquisition is very important, but at this point in development the dominant
relevant relationships are those captured by the association layer. This is
compatible with the observed emergence of semantic learning in children
\cite{wiethan2014early}. However it has to be noticed that statistical significance deteriorates markedly during later stages of learning  making comparisons of performance in the LLS difficult.

\begin{figure*}[ht]
\centering
\includegraphics[height=4.9cm]{Fig2}
\caption{Left: Vocabulary normalised word gains for different orderings: shorter words first (Short Len.), words with higher degree in the association layer first (Deg. Asso.), closer words on the whole MLN first (Clos. Multiplex), more frequent words first (Freq. Child.) and higher multiplex Page Rank first (PageR. Multiplex). A normalised word gain of $0.20$ means that 20\% of words in the vocabulary have been predicted by the ordering strategy on top of expectations from random guessing. Higher word gains indicate better predictability. Right: Statistical significance indicated by word gain Z-score of the respective orderings. The distribution of the random overlaps can be approximated by a Gaussian after 60 words have been acquired (see SI Sect. S5). In this range, represented by the black dashed line, a score $Z\geq 1.96$ denotes word acquisition patterns that are different from random fluctuations with a 2.5\% significance level.
In both plots, error bars are the same size as the dots. Best performing orderings are
highlighted with different overlays, which identify different learning
stages: VELS (in grey), ELS (in light purple) and LLS (in white). Direct sampling was used in VELS for testing the statistical significance of results.}
\label{fig:orde}
\end{figure*}

So far, we evaluated orderings based on single network layers and the combined
multiplex, showing that prediction performance can benefit from taking
account of information from multiple layers. In this analysis we assumed that each layer plays an equal role. In the next section we evaluate the changing
influence of layers during the evolution of the lexicon.

\subsection*{Optimisation of layer influences}

\begin{figure*}[ht!]
\centering

\includegraphics[width=14.8cm]{Fig3}
\caption{Top (a-d): Optimisation results for the original MLN. (a,b): Vocabulary normalised word gains corresponding to the optimal layer influences at the end of VELS (black), middle of ELS (blue), and middle of LLS (pink) for degree optimisation (a) and closeness optimisation (b). Error margins represent standard deviations over randomised ensembles. In (b) no optimisation of degree outperforms multiplex closeness during ELS. (c,d): Average optimal layer weights indicating the influence or importance of layers over different learning stages obtained from Monte Carlo experiments with degree (c) and closeness (d) optimisation. The phonological layer resulted in influences around $10^-3$. The same optimisation trends were obtained when the phonological layer was excluded from the optimisation. Bottom (e,f): For comparison, optimal layer weights for the MLN where rather than the phonological layer with 529 words an extended one with 30,000 words is used, instead. Considering the extended phonological layer leads to word gains at least 6\% better than (a) and (b) (cf. SI Table 3).}
\label{fig:dego}
\end{figure*}

To explore the influence of different layers on word acquisition, we consider linear combinations of word features on each layer in order to obtain weighted multiplex measures (cf. Methods section). For a given network metric, we optimise the coefficients of the linear combinations to maximise vocabulary normalised word gains. The resulting layer weights, that maximise predictability, indicate the influence of the respective layers on word acquisition. We explored several types of optimisation, based on degree, closeness, betweenness and local clustering (cf. SI Sect. S7 and SI Tab. S3). We focus on the two best performing quantities from the ordering experiments, namely degree and closeness.

In order to avoid over-fitting, we perform a Monte Carlo robustness analysis: we optimise over subsets of word trajectories sampled uniformly at random, consisting of  only 80\% of the original words. Averages are computed over these trajectories and over different realisations of the age of acquisition trajectories from the normative age of acquisition ordering. We also confirmed that the improvement in performance of optimised multiplex parameters is strongly dependent on the structure and overlap of layers, as experiments on randomised multiplex networks result in far smaller word gains (see SI Sect. S8).

Optimisation results (cf. Fig. \ref{fig:dego}) indicated negligible contribution of the phonological layer, with weights ($\approx ~ 10^{-3}$). Degree and closeness orderings based only on the phonological layer perform poorly as well, yielding word gains compatible with random fluctuations at a 5\% significance level (see SI Sect. S6.1 and SI Fig. S5). We retrieved the same optimisation results for the other layers when the phonological layer was excluded. In addition, we considered phonological word scores coming from an "extended" phonological layer for adults, already analysed by Stella and Brede \cite{stella2015phonological} and including 30000 words (see also SI Sect. S6.1.1). Assessing the influence of adults' phonology over word acquisition in toddlers matches previous approaches in the relevant literature \cite{carlson2014children} and we present results in our MLN framework in Fig. \ref{fig:dego} but also in SI Sect. S7.

Optimal layer influences are displayed as a function of age for the MLN layers in Fig. \ref{fig:dego} (c) and (d). Similar patterns of layer influence are found for rankings based on both degree and closeness. We see a clear distinction between different learning stages. When degree is considered, cf. Fig. \ref{fig:dego} (c), in VELS the main layer
contributions stem from the association and the feature norm layers. Instead, when optimising closeness, cf. Fig. \ref{fig:dego} (d), in VELS all layers are found to contribute equally. In both cases the ELS, the next learning stage, marks  a transition region between the VELS (before) and the LLS (after) in which only the association layer dominates. This transition is what motivated our choice of distinguishing between VELS and ELS. For degree-based optimisations, contributions from the co-occurrence layer become very small in the LLS, while associations contribute 80\% of weight. An analogous transition is observed in the ELS phase when closeness is optimised and after that associations make up 80\% of the total layer influence. The dominant influence of associations in later stages of learning is indicative of the emergence of preferential acquisition \cite{hills2009longitudinal} that we already observed in ordering experiments.

When extended phonology is considered, in Fig. \ref{fig:dego} (e) and (f), the influences of semantic layers remain consistent, however, phonology has a higher impact when phonological neighbourhood size/degree is considered rather than closeness. In VELS the extended phonology has importances similar to co-occurrences but the influence of both approaches 0 later in development.

In Fig. \ref{fig:dego} (a) and (b) we compare vocabulary normalised word gains of trajectories based on optimal layer influences against our ordering experiments (cf. SI Fig. S9 for the respective Z-scores and previous section). On the one hand, when predicting only words learned in VELS, the optimal influence orderings outmatch both degree in the association layer and multiplex closeness during VELS but perform worse at later stages. Direct sampling indicates that these results are significant with a p-value of $0.01$. Optimal influences up to the middle of ELS and LLS provide similar word gains, which are both up to 25\% higher than the optimal word gains from VELS. This suggests that the \textit{same learning strategies} may be adopted \textit{after} VELS, starting in ELS and lasting throughout LLS. On the other hand, results in Fig.~\ref{fig:orde} (a) highlight the
importance of closeness centrality for predicting word acquisition at early
stages. In fact, in ELS even the optimal linear combination of degrees results in \textit{smaller word gains} compared to the unweighted multiplex closeness centrality. Optimal linear combinations of closeness perform even better than the unweighted multiplex closeness, see Fig.~\ref{fig:orde} (b). Notice that closeness is a \textit{global} network feature, accounting for the position of a node relative to all other nodes. This is distinct from \textit{local} features, such as degree, which only measure a node's relationship to its first neighbours. Our findings give support to the hypothesis that, particularly in the early learning stages, word learning is influenced by the global structure of the multiplex lexical network. This global multiplex structure is able to capture some important word patterns that influence early lexicon development.

\section*{Conclusions and future work}

Introducing the framework of \textit{multiplex lexical networks} (MLN), we have described and analysed normative word acquisition patterns of children between the ages of 19 and 28 months. Our approach considers semantic, phonological and syntactic contexts and captures more linguistic information than separate analysis of any single layer network previously could \cite{beckage2011,beckage2015modeling,dedeyne2008network,hills2009longitudinal}. We go beyond a purely topological description by exploiting network
properties to predict the order of normative word learning in children.
Interestingly, we find that the best topological feature in predicting word learning changes through the course of development. This allows us to use network information to distinguish three stages of learning: (i) a very early learning stage where all but the phonological layer contribute substantially to prediction, (ii) an early
learning stage which marks a transition period, and (iii) a late learning
stage in which contribution from word associations dominates word learning. We believe the last point is due to free associations serving as a much better proxy for detecting superordinate words compared to other network features (cf. SI Sect. 6.3 for a quantitative discussion). Further assessment of the extent to which the taxonomic organization serves as a mediator variable influencing word prediction is an interesting direction for future work.

Comparing the predictive power of various MLN features over time
confirms: (i) the superiority of some multiplex network characteristics
relative to single layer networks and word specific measures such as frequency or word length, and (ii) the special role closeness centrality might play in early word learning. This is emphasised in two ways: (i) strong performance of multiplex closeness, outperforming all combinations of
local network features in the early learning phase, and (ii) the best overall predictions of the order words are learned is related to a weighted multiplex version of single-layer closeness centralities. We thus find strong indications that, particularly at early stages of learning, word acquisition in children is  driven by minimising relational distances of learned words relative to other words in both the semantic and syntactic space of the mental lexicon.

With the MLN, we explored questions about normative lexical acquisition,
uncovering developmental learning stages and quantifying the influence of
certain types of linguistic information on early acquisition. Nevertheless, our MLN is limited in important ways. It is important to bear in mind that the MLN
representation is only a projection of an individual child's full ML. Additionally, the results presented in this paper consider normative lexical acquisition. That is to say, a specific child may not learn words in the same order as the normative orderings. Instead normative order is obtained by averaging over roughly 1000 productive vocabulary reports. We attempt to address this shortcoming by sampling multiple orders probabilistically from these norms. Extending the model and testing the universality of the learning strategies in VELS and ELS on longitudinal data for individual children would be an interesting future research direction \cite{aitchison2012}.

Another limitation is the use of a relatively small vocabulary. While the CDI is a commonly used check list of words a child produces, there are many words that a child may know which are not on the CDI. By expanding our model to the full vocabulary of a child, and for longer periods of development, we can further increase our understanding of word learning.

While the present study focuses on word learning in English, results should be corroborated by considering word learning in different languages. Future work can extend our analysis by considering correlations in word learning across other cognitive and linguistic domains to build an even richer picture of language organisation and learning than presented here. A stream of work in psycholinguistics has explored detailed mechanisms of word learning \cite{xu2007word}. Such work typically explores how words are memorised when clues are presented in different order and focuses on the underlying mechanisms of inferring meaning-object mappings, pointing out that learning is strongly dependent on context. Integrating context-dependent information in our MLN framework would surely be an exciting future research direction for investigating individual children's learning trajectories.

The marginal influence of the original phonological layer found in this study can be reconciled with previous findings. Dautriche \textit{et al.}\cite{dautriche2015learning} showed that 18 months old toddlers failed to learn an object label when it was a phonological neighbour of a noun they already knew. In our dataset 80\% of words learned by toddlers within the first two months are nouns. This noun-richness is in opposition with orderings extracted from phonological degree and closeness, which identify mainly non-noun words, verbs in particular, as learned initially. This trend is not surprising, as having learned a verb earlier makes it easier for toddlers to learn its phonological neighbours \cite{dautriche2015learning}. However, this contrast in noun-richness at early development stages reflects in the poor performance of the original phonological layer in predicting normative acquisition ordering. Nonetheless, we show that phonology influences word acquisition by considering a phonological network extended to the adults' lexicon, like in \cite{carlson2014children}. This extended layer identifies nouns as early candidates for learning and provides significant prediction results. This interplay between phonology and acquisition is in agreement with previous findings in the relevant literature \cite{carlson2014children,stamer2012,vitevitch2012,vitevitch2014}.

We have shown strong evidence that multiplex lexical networks
capture a richer picture of the mental lexicon than previous works using only single-layer networks, in spite of the above limitations. More importantly, this work provides novel formalised methods for exploring and explaining the influence of linguistic features on early acquisition.

\section*{Methods}
\subsubsection*{Age of acquisition dataset}

Age of acquisition orderings are constructed based on the MacArthur-Bates
Communicative Development Inventory (CDI) norms \cite{Dale1996}. The CDI,
based on parent report of the productive vocabulary of children aged between
16 and 30 months, has been shown to be related to future language ability
in young children \cite{Dale1996,Fenson1994}. The norms are averaged over
more than 1000 vocabulary reports and indicate the percentage of children,
at a given age, that reportedly produce (and understand) a specific word.
From these norms, we sample orderings of word learning. For example, "mommy" and "ball"
are reported as produced by 93\% and 64\% of children by 16 months, respectively; these words are (usually) learned earlier than "chair" which is produced by only
14\% of 16 month olds. We assume a word is known once 50\% of children in a
given month produce that word. We then sample an order of words learned within a month assuming that higher rates of production probabilistically indicate earlier learning. Starting from position one of the ranking, we: (i) sample a learned word with probability proportional to the percentage of children which are able to produce it, conditioned on the overall production rate being over 50\%, (ii) erase it from the list of candidate words to be learned at later positions and (iii) proceed one position down in the ranking and start from (i). The sampling starts from month 16 and it stops when every word has been sampled. Note that a non-probabilistic version of this ordering has been used in other developmental modelling contexts \cite{hills2009longitudinal,beckage2015modeling}.

\subsubsection*{Construction of the multiplex network}

The four layers of the MLN were selected because of their use in previous single-layer
network studies investigating word learning of young children
\cite{beckage2011,beckage2015cognition,hills2009longitudinal,borge2010}. The
four layers we consider here are (i) a semantic layer based on the Florida
Free Association Norms \cite{nelson2004university} where a link exists from
word A to word B if word B is a free associate of the cue word A; (ii) a layer capturing feature similarity based on the McRae Feature Norms \cite{mcrae2005semantic} where
words A and B are connected if they share at least one semantic feature;
(iii) a layer based on word co-occurrences (measured in child-directed
speech \cite{macwhinney2000childes}) where words A and B are connected if
they co-occur more than 45 times, where the threshold 45 was chosen
approximately to match the link density of the other semantic layers; and a
(iv) layer capturing phonological similarities (based on IPA transcription
from  WordNet 3.0 \cite{miller1995wordnet}) where words A and B are connected if they have
IPA transcriptions with edit distance one. We treat the association
layer as undirected and edge weights in layers (i) to (iii) are ignored. The
resulting MLN is composed of 529 words which represent the intersection
between the CDI data and the set of words which have at
least one connection on any layer (see SI).

\subsubsection*{Cognitive interpretation of network metrics}

We investigate the same local and global features of words/nodes that were
investigated in previous works about network representations of
language: degree, closeness, betweenness and PageRank (see SI). In a given network, the degree of a node is the number of its links \cite{newman2010}. In our MLN, a word is characterised by one degree measure per layer. For instance, on the association layer a word with degree $k^{(Asso.)}$ has $k^{(Asso.)}$ associates. On the phonological layer the degree of a word coincides with its phonological neighbourhood size \cite{vitevitch2014}: a word with degree $k^{(Phon.)}$ has $k^{(Phon.)}$ similar sounding words. Higher degree words in semantic networks were found to have lower age of
acquisition norms \cite{stey2005large}, while degree on the phonological
layer correlated positively with word confusability \cite{vitevitch2012}. On the whole MLN, we can associate each word to its multidegree, namely the sum of its degrees on all the layers \cite{dedomenico2013math}. Fundamentally, the usefulness of measures such as degree would suggest that words with multiple associates, multiple phonological neighbors and multiple
shared features are those words that are learned earliest. The mechanisms
behind this phenomenon could be that words with many associates and shared
features are those words that are important in the language environment
because they play a central role or because they are important to the
parent or child. We remain agnostic as to the exact reason behind the
increased `importance' of a word, but suggest that it can be summarized by the degree.
Closeness relates to how fast information spreads from a node to others \cite{newman2010}. It is interesting for cognitive science since previous work showed that words closer on semantic topologies tend to be processed together in shorter time
\cite{collins1969retrieval,collins1975spreading,motter2002}. Betweenness captures the extent to which a node falls on the shortest path between pairs of words \cite{newman2010}. It captures centrality of words through a hypothetical navigation on the ML \cite{sigman2002,motter2002,borge2010} (cf. SI Sect. S6). PageRank identifies the likelihood of reaching a given node by a random walk in a network. High PageRank words on semantic networks were found easier to retrieve in fluency tasks \cite{griffiths2007google}. Closeness, betweenness and PageRank on multiplex networks exploit jumps through layers (cf. SI Sect. S6).

\subsubsection*{Overlap measures and word ranking}

A word trajectory is an ordered list $\tau=(w_1,w_2, ...,w_N)$ indicating
the exact order in which words are learned. We define ${\tau_{aoa}}$ to be the ensemble of
rankings derived from the CDI norms. We
sample each specific ordering $\tau_{aoa}$ based on the probability of production as reported in the age of acquisition norms.

Predicted word orderings are generated as follows. First, each word is
ranked according to a word score, $s_i$, which is derived from network
specific or extrinsic word features (e.g. frequency or word length). Words are
then ordered according to word score, starting with the largest score. Words
receive a position in the predicted trajectory according to their position
in the ordering of word scores. Ties in word orderings $s_i$ are taken into
account by averaging over all resolutions. For example, one might wish to
consider orderings based on degree in the association layer and would obtain
the following word scores $s_{food}=62$, $s_{water}=45$, $s_{eat}=20$. The
corresponding predicted acquisition trajectory would be
$\tau=(food,water,eat)$. Apart from the features mention in the main text of
the paper, we also considered many others (cf. SI Sect. 6).

To evaluate predictive performance of a word score, we first measure the
overlap $O(\tau,t)$ between the predicted learning trajectory
$\tau$ and the empirically known trajectory $\tau_{aoa}$ at time $t$ by
counting the number of words that co-occur in $\tau$ and $\tau_{aoa}$ up to
time $t$ and averaging over different $\tau_{aoa}$. We define the word gain $g(\tau,t)$ as the overlap minus random fluctuations, i.e.
\begin{equation}
\centering
g(\tau,t)=O(\tau,t) - \langle O(\tau_{ran},t) \rangle.
\end{equation}

The vocabulary size normalised word gains are computed as $G(\tau,t)=g(\tau,t)/t$ while the word gain Z-Scores consider deviations of overlaps from random guessing in terms of standard deviations $\sigma$, as $Z=g(\tau,t)/\sigma(O(\tau_{ran},t))$.

\subsubsection*{Calculating optimal combinations of layers}

To allow for varying influences of layers within the multiplex we construct word scores as convex
combinations of individual layer influence, i.e. a word score $s_w$ for
word $w$ is obtained as:
\begin{equation}
\centering
s_{w}=\alpha s_{w}^{(Asso)}+\beta s_{w}^{(Feat)}+ \gamma s_{w}^{(Co-occ)} + (1-\alpha-\beta-\gamma)s_{w}^{(Phon)},
\end{equation}
where $s_w^{(Asso)}$, $s_w^{(Feat)}$, $s_w^{(Co-occ)}$, and $s_w^{(Phon)}$
are word scores obtained from the respective single layer metrics
and the coefficients $\alpha$, $\beta$, $\gamma$ give the
influence of each layer on the overall word score $s_w$. This linear combination of
single-layer features is similar in spirit to previous decompositions of
multiplex centrality, see \cite{sola2013eigenvector}
and discussions in \cite{de2015structural}. The optimisation finds the influences $\alpha,\beta,\gamma$ that maximise vocabulary normalised word gain (either over the entire time period or over specific learning phases) after removing 20\% of the words
at random. 20$\%$ was chosen in order not to
remove too many words particularly in the very early word learning stages. Performance is evaluated on all words. Optimisation was performed using a differential evolution method \cite{price2006differential}. Averages are calculated over 50 configurations of left out words for 30 normative age of acquisition orderings.

redpajama_set_name: RedPajamaArXiv


text:
Synthesis of 1-oxidopyridinium-2-thiolato- and pyridine-2-thiolato-complexes of nickel, palladium, and platinum
Jack L. Davidson, Peter N. Preston, Maria V. Russo
Quinoline N-oxide reacts with K[PtCl3(CH2=CH2)] to give [PtCl2(C2H4)(ONC9H7)] whereas 1-oxidopyridinium-2-thiol (1a) gives [Pt(SONC5H4)2] (3a) and an insoluble oligomeric material containing at least four Pt atoms; complex (3a) and the oligomer can also be synthesised from K2[PtCl4]. The oligomeric material can be cleaved by PPh3 and PMe2Ph to give [PtCl(SONC5H4)(PPh3)] (5) and [PtCl(SONC5H4)(PMe2Ph)2] (6) respectively, and compound (3a) is converted by PPh3into the monophosphine complex [Pt(SONC5H4)2(PPh3)] (7a). The complex (7a) and an analogue [Pd(SONC5H4)2(PPh3)] (7b) have been prepared from [MCl2(PPh3)2] and the thallium salt of (1a), and related complexes [M(SC5H4N)2(PPh3)2] [M = Pt (8a) or Pd (8b)] have been similarly synthesised from the thallium salt of 2-mercaptopyridine. Evidence for intramolecular scrambling in (7) and (8) has been adduced from variable temperature 1H n.m.r. spectra.
Journal of the Chemical Society, Dalton Transactions
https://doi.org/10.1039/DT9830000783
10.1039/DT9830000783
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Davidson, J. L., Preston, P. N., & Russo, M. V. (1983). Synthesis of 1-oxidopyridinium-2-thiolato- and pyridine-2-thiolato-complexes of nickel, palladium, and platinum. Journal of the Chemical Society, Dalton Transactions, (4), 783-786. https://doi.org/10.1039/DT9830000783
Davidson, Jack L. ; Preston, Peter N. ; Russo, Maria V. / Synthesis of 1-oxidopyridinium-2-thiolato- and pyridine-2-thiolato-complexes of nickel, palladium, and platinum. In: Journal of the Chemical Society, Dalton Transactions. 1983 ; No. 4. pp. 783-786.
@article{253a8de4d39f4c35919390e114da3795,
title = "Synthesis of 1-oxidopyridinium-2-thiolato- and pyridine-2-thiolato-complexes of nickel, palladium, and platinum",
abstract = "Quinoline N-oxide reacts with K[PtCl3(CH2=CH2)] to give [PtCl2(C2H4)(ONC9H7)] whereas 1-oxidopyridinium-2-thiol (1a) gives [Pt(SONC5H4)2] (3a) and an insoluble oligomeric material containing at least four Pt atoms; complex (3a) and the oligomer can also be synthesised from K2[PtCl4]. The oligomeric material can be cleaved by PPh3 and PMe2Ph to give [PtCl(SONC5H4)(PPh3)] (5) and [PtCl(SONC5H4)(PMe2Ph)2] (6) respectively, and compound (3a) is converted by PPh3into the monophosphine complex [Pt(SONC5H4)2(PPh3)] (7a). The complex (7a) and an analogue [Pd(SONC5H4)2(PPh3)] (7b) have been prepared from [MCl2(PPh3)2] and the thallium salt of (1a), and related complexes [M(SC5H4N)2(PPh3)2] [M = Pt (8a) or Pd (8b)] have been similarly synthesised from the thallium salt of 2-mercaptopyridine. Evidence for intramolecular scrambling in (7) and (8) has been adduced from variable temperature 1H n.m.r. spectra.",
author = "Davidson, {Jack L.} and Preston, {Peter N.} and Russo, {Maria V.}",
doi = "10.1039/DT9830000783",
journal = "Journal of the Chemical Society, Dalton Transactions",
Davidson, JL, Preston, PN & Russo, MV 1983, 'Synthesis of 1-oxidopyridinium-2-thiolato- and pyridine-2-thiolato-complexes of nickel, palladium, and platinum', Journal of the Chemical Society, Dalton Transactions, no. 4, pp. 783-786. https://doi.org/10.1039/DT9830000783
Synthesis of 1-oxidopyridinium-2-thiolato- and pyridine-2-thiolato-complexes of nickel, palladium, and platinum. / Davidson, Jack L.; Preston, Peter N.; Russo, Maria V.
In: Journal of the Chemical Society, Dalton Transactions, No. 4, 1983, p. 783-786.
T1 - Synthesis of 1-oxidopyridinium-2-thiolato- and pyridine-2-thiolato-complexes of nickel, palladium, and platinum
AU - Davidson, Jack L.
AU - Preston, Peter N.
AU - Russo, Maria V.
N2 - Quinoline N-oxide reacts with K[PtCl3(CH2=CH2)] to give [PtCl2(C2H4)(ONC9H7)] whereas 1-oxidopyridinium-2-thiol (1a) gives [Pt(SONC5H4)2] (3a) and an insoluble oligomeric material containing at least four Pt atoms; complex (3a) and the oligomer can also be synthesised from K2[PtCl4]. The oligomeric material can be cleaved by PPh3 and PMe2Ph to give [PtCl(SONC5H4)(PPh3)] (5) and [PtCl(SONC5H4)(PMe2Ph)2] (6) respectively, and compound (3a) is converted by PPh3into the monophosphine complex [Pt(SONC5H4)2(PPh3)] (7a). The complex (7a) and an analogue [Pd(SONC5H4)2(PPh3)] (7b) have been prepared from [MCl2(PPh3)2] and the thallium salt of (1a), and related complexes [M(SC5H4N)2(PPh3)2] [M = Pt (8a) or Pd (8b)] have been similarly synthesised from the thallium salt of 2-mercaptopyridine. Evidence for intramolecular scrambling in (7) and (8) has been adduced from variable temperature 1H n.m.r. spectra.
AB - Quinoline N-oxide reacts with K[PtCl3(CH2=CH2)] to give [PtCl2(C2H4)(ONC9H7)] whereas 1-oxidopyridinium-2-thiol (1a) gives [Pt(SONC5H4)2] (3a) and an insoluble oligomeric material containing at least four Pt atoms; complex (3a) and the oligomer can also be synthesised from K2[PtCl4]. The oligomeric material can be cleaved by PPh3 and PMe2Ph to give [PtCl(SONC5H4)(PPh3)] (5) and [PtCl(SONC5H4)(PMe2Ph)2] (6) respectively, and compound (3a) is converted by PPh3into the monophosphine complex [Pt(SONC5H4)2(PPh3)] (7a). The complex (7a) and an analogue [Pd(SONC5H4)2(PPh3)] (7b) have been prepared from [MCl2(PPh3)2] and the thallium salt of (1a), and related complexes [M(SC5H4N)2(PPh3)2] [M = Pt (8a) or Pd (8b)] have been similarly synthesised from the thallium salt of 2-mercaptopyridine. Evidence for intramolecular scrambling in (7) and (8) has been adduced from variable temperature 1H n.m.r. spectra.
U2 - 10.1039/DT9830000783
DO - 10.1039/DT9830000783
JO - Journal of the Chemical Society, Dalton Transactions
JF - Journal of the Chemical Society, Dalton Transactions
Davidson JL, Preston PN, Russo MV. Synthesis of 1-oxidopyridinium-2-thiolato- and pyridine-2-thiolato-complexes of nickel, palladium, and platinum. Journal of the Chemical Society, Dalton Transactions. 1983;(4):783-786. https://doi.org/10.1039/DT9830000783


I might be feeling smug about my Beef Wellington but not so when handling ganache. Harriet's 21st birthday cake tasted great but it was so hard to get the icing smooth.
I let the ganache cool down a bit because I thought it would easier to spread. Not so. It tasted delicious but didn't look that flash. Lots more to learn with this.


text:
Key people to watch in 2009
Thursday, January 01, 2009 — updated on July 03, 2020
Top from left: President Mwai Kibaki, Prime Minister Raila Odinga, Justice minister Martha Karua and Agriculture minister William Ruto. Bottom from left: US President-elect Barack Obama, Former UN Secretary General Kofi Annan, Speaker Kenneth Marende and Attorney General Amos Wako. Photos/FILE
President Kibaki, Prime Minister Raila Odinga and Cabinet minister Martha Karua top a list of key politicians and public officials whose decisions, conduct and performance will shape the country's destiny this year.
As the country moves to implement Agenda Four of the peace deal agreed between President Kibaki and Mr Odinga last year, the three will have a major impact on whether the country carries out crucial legal, land and other reforms, among other things.
Other leaders whose performance will be closely watched this year include Cabinet ministers William Ruto and Kiraitu Murungi, Attorney General Amos Wako, National Assembly Speaker Kenneth Marende, and Mandera Central MP Mohamed Abdikadir.
City lawyer Carolyne Kamende joins race for LSK presidency
Kisii University dons boycott work
Education Jan 01, 2009
They will be charged with the responsibility of ensuring that Kenya finally gets a new constitution, the tribunal to try post election violence suspects is set up, a truth, justice and reconciliation commission starts work, citizens get affordable energy and food.
Supreme Council of Kenya Muslims (Supkem) Director General Latiff Shabaan said it was gratifying that President Kibaki and Mr Odinga had taken leadership on the country's reform agenda.
Therefore, he said, Kenyans must help them by ensuring that those given various responsibilities do deliver on their mandates.
Mr Shabaan added that the country's leadership must realise that Kenyans are demanding their human rights in terms of an affordable livelihood. He noted that the country was in need of policies that will keep issues such as housing, energy and food prices under check.
He also said that the country cannot "keep running around whenever these issues arise searching for solutions".
Deplorable
"The issue of IDPs is also another issue that we as a country must solve once and for all. It was very sad that as Kenyans celebrated the New Year there were some of us still in camps living in deplorable conditions," Mr Shabaan said.
National Civil Society Congress member Harun Ndumbi said that the issue of a new constitution was key but should not be dealt with in isolation.
He noted that reconstruction of the country through the truth, justice and reconciliation commission and making sure that those responsible for what was unearthed by the Kriegler and Waki commissions are held responsible were also key in 2009.
"In terms who should lead in these matters, I believe it is must be corporate where the government, Parliament, the civil society and the public work together to ensure that all these objectives are met," Mr Ndumbi told the Nation.
Mr Ndumbi noted that issues relating to the cost of living must be addressed, noting that the government had the responsibility to protect consumers against exploitation. It was possible to make the market profitable but also fair to the consumers, he noted.
"Treating unequal people equally is an injustice," Mr Ndumbi added. President Kibaki, Mr Odinga and Ms Karua will be particularly watched because of the roles they are expected to play in the reforms that the Grand Coalition Government is expected to implement in its second year and place the country firmly on the path of political stability and economic revival.
Recent polls have shown that Kenyans expect a new Constitution to be delivered this year, prices of basic commodities to come down, leaders to exercise greater political responsibility and internally displaced people to be resettled.
Other issues that rank highly in the public agenda are improved security, heightened fight against corruption and justice for suspects behind the post-election violence.
In their New Year messages, President Kibaki and Mr Odinga appeared to resonate with the public when they identified a new constitution, national healing and reconciliation, electoral reforms, high prices of basic commodities and fuel, justice, equality and land reforms as the challenges facing the country.
After bidding bye to a year that saw the fabric of the Kenya nation come under the severest test, it is expected that the two leaders who have the onerous task of steering the country back to stability and economic prosperity, will step forward and guide the public towards the desired goals.
The expected setting up of a special tribunal to try post-election violence suspects; confessions from the Truth, Justice and Reconciliation Commission on past misdeeds; the review process and reduction of prices of food and fuel will test the political will of the President and the PM.
At the end of the year, the two principals will either receive accolades from Kenyans for the successful accomplishment of the goals or face criticism for failing to meet their goals.
This is because it has always been argued that the reason behind the failure to meet those challenges is the lack of political will by the Government.
The President and the PM will, however, not be alone. They will depend heavily on the decisions of Cabinet ministers Karua, Ruto, Murungi, Attorney General Wako, Speaker Marende, and MP Abdikadir, among other key officials.
Ms Karua, the Justice minister, has the daunting task of presiding over the implementation of the reforms that were outlined in the National Accord, among them a new constitution.
Her ministry will determine the pace of the review process; it has already worked on the time-table for the establishment of the Special Tribunal for Kenya; and implementation of reports of commissions that were set up after the post-election violence.
Mr Ruto, the Agriculture minister, will be the man to ensure that food prices are affordable to Kenyans. The decisions he and his team at Kilimo House make this year in terms of investment in food production, farm input prices, credit facilities, food storage and distribution will either make the Government the pride of its citizens or erode it credibility.
Kenyans will be looking up to Mr Murungi, the Energy minister, to address the issue of high fuel and electricity prices that gripped the country last year.
He has already promised to enforce price controls in the oil industry starting this month to contain consumer exploitation.
Indeed the high fuel prices was one of the issues that President Kibaki promised to tackle this year in his address to the nation on New Year's eve.
All the legislations for key reforms will take place in Parliament and Speaker Marende will have the onerous task of making rulings that will pave the way for the changes.
On the other hand, Mr Wako, is expected to provide sound legal advice to the reform process while Mr Abdikadir, as chairman of the Parliamentary Select Committee on the Constitution, will be looked upon to firmly guide the pace of the review process.
Others closely linked to the reforms will be former UN secretary general Kofi Annan who has a key role in ensuring that targets of the National Accord are met by the Government while US President-elect Barrack Obama is expected to inspire the Kenyan leadership to the next level of political and economic prosperity.
It is widely expected that the new American leader, whose father was Kenyan, will have more than passing interest in events in Nairobi.
Indeed this was demonstrated during the post-election violence when Mr Obama disclosed that he had spoken to President Kibaki and Mr Odinga over the chaos.
In terms of business, Kenyans will be watching closely the performance of the Capital Markets Authority (CMA) and the Nairobi Stock Exchange (NSE). The two are key institutions in investment and wealth creation.
More glory is also expected to come from our sports men and women especially the performance of 800 metres track queen Pamela Jelimo.
Nation. Empower Africa.
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Football 4 hours ago
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The accused appeared before Kahawa Law Courts' Senior Principal Magistrate Boaz Ombewa.
Business 10 hours ago
Court frees businessman Jimi Wanjigi
The High Court on Wednesday issued an order barring Mr Jimi Wanjigi's prosecution for an alleged land fraud.
News 7 hours ago


text:
Robin Bates-Pualuan, MS, LMFT, LPCC – Marriage and Family Therapist
I'm a Licensed Marriage and Family Therapist (license #MFC52419) and Licensed Professional Clinical Counselor (license #LPC420), offering psychotherapy and counseling services in the south Bay Area. I focus on treating individual adults and parents of young children. In addition to my private practice, I am a Clinical Supervisor at Almaden Valley Counseling Service (AVCS) in south San Jose. As an intern, I worked with individuals, teens, children, couples, and families. I served as a school counselor at Leland High School, Castillero Middle School, Union Middle School, Guadalupe Elementary School and Los Alamitos Elementary School. I've taught parenting classes for parents of children of all age groups. I've also provided supportive telephone crisis counseling at Santa Clara County's Suicide and Crisis Hotline and at Contact Cares Crisis Hotline.
I was born and raised in the south Bay Area. I earned a Bachelor's degree in Psychology from UC Davis and a Master's degree in Clinical Psychology from San Jose State University. After working in non-profit fundraising for nearly 10 years, my fascination with people's life stories led me to choose a career helping others find new ways to tap into and fulfill their potential.
I am married and have two young children. As a child of divorce, and now a married person myself, I have a special interest in relationships and the hard work it sometimes takes to keep our relationships healthy and strong. As a mother, I also particularly enjoy helping moms and dads work through the struggles that accompany the joys of raising kids.
I use a variety of approaches to address my clients' unique needs. I work with individual adults utilizing cognitive behavioral (CBT) and existential therapy techniques to help you discover your genuine values, examine where you may be stuck in your life, and take action to move in the direction of what you truly desire. Who are you? Who do you want to be? Together we can look at these questions and generate new possibilities for you to try, moving you in the direction of your most fulfilling, authentic life.
When I work with couples I utilize principles from the Gottman method and Emotionally Focused Therapy to help partners get at the heart of their pain, learn to be able to share and receive emotions with their partner, and build strong communication strategies to improve the friendship and intimacy of their partnership.
"What lies behind us and what lies before us are tiny matters compared to what lies within us."
Education & Credentials:
B.A. in Psychology, UC Davis
M.S. in Clinical Psychology, San Jose State University
Licensed Marriage and Family Therapist (license #MFC52419)
Licensed Professional Clinical Counselor (license #LPC420)
California Association of Marriage and Family Therapists (CAMFT)
Santa Clara Valley chapter of CAMFT (SCV-CAMFT)
As an experienced marriage and family therapist, it is my honor to assist you in exploring your core issues and move toward a healthy, fulfilling life.
I offer counseling and therapy for individual adults and parents of young children in and around Campbell, California.
2155 S. Bascom Avenue, Suite 203
"I'd love to hear from you. Contact me to schedule a free phone consultation."
Why do therapists love "I statements" so much?
Want to Feel More In Love?
What are Your Values, and Why Should You Care?
Taking the Leap
Hope: The Essential Element
Robin Bates-Pualuan, MS, LMFT, LPCC | South Bay Psychotherapy
Find help for Anxiety or Fears, Codependency, Depression, Parenting, Postpartum Depression, Relationship Issues and Marriage Counseling
Self-esteem, Work & Career Counseling, Cognitive Behavioral Therapy (CBT).
Service Areas: Campbell, Los Gatos, Monte Sereno, Saratoga, Cupertino, Sunnyvale, Santa Clara, Los Altos, Mountain View, San Jose, Palo Alto, Milpitas, Willow Glen, Santa Cruz, Scotts Valley, Morgan Hill, Silicon Valley and the greater Bay Area.
Marriage and Family Therapist in Campbell, California, 95008
Check out Robin Bates - Pualuan LMFT LPCC on Yelp
© 2020 Robin Bates-Pualuan, MS, LMFT, LPCC
Websites for Therapists by TherapyTribe — Up ↑


text:
// ----------------------------------------------------------------------------------
//
// Copyright Microsoft Corporation
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// ----------------------------------------------------------------------------------

using Microsoft.AzureStack.Management.StorageAdmin;
using Microsoft.AzureStack.Management.StorageAdmin.Models;
using System;
using System.Globalization;
using System.Management.Automation;
using System.Net;
using System.Runtime.InteropServices;

namespace Microsoft.AzureStack.Commands.StorageAdmin
{
    /// <summary>
    ///
    /// </summary>
    [Cmdlet(VerbsData.Export, Nouns.Log, SupportsShouldProcess = true)]
    public sealed class InvokeLogCollect : AdminCmdlet
    {
        /// <summary>
        ///     Farm Identifier
        /// </summary>
        [Parameter(Mandatory = true, ValueFromPipelineByPropertyName = true)]
        [ValidateNotNullOrEmpty]
        public string FarmName { get; set; }

        /// <summary>
        /// StartTime
        /// </summary>
        [Parameter(Mandatory = true)]
        [ValidateNotNullOrEmpty]
        public DateTime StartTime { get; set; }

        /// <summary>
        ///
        /// </summary>
        [Parameter(Mandatory = true)]
        [ValidateNotNullOrEmpty]
        public DateTime EndTime { get; set; }

        /// <summary>
        /// Credential to run the log copy
        /// </summary>
        [Parameter(Mandatory = true)]
        [ValidateNotNullOrEmpty]
        public PSCredential Credential { get; set; }

        /// <summary>
        /// Blob Prefix for the uploaded logs
        /// </summary>
        [Parameter(Mandatory = false)]
        [ValidateNotNullOrEmpty]
        public string LogPrefix { get; set; }

        /// <summary>
        /// Azure blob name
        /// </summary>
        [Parameter(Mandatory = false)]
        [ValidateNotNullOrEmpty]
        public string AzureStorageContainer { get; set; }

        /// <summary>
        /// Azure Storage account name
        /// </summary>
        [Parameter(Mandatory = false)]
        [ValidateNotNullOrEmpty]
        public string AzureStorageAccountName { get; set; }

        /// <summary>
        /// Azure storage account key
        /// </summary>
        [Parameter(Mandatory = false)]
        [ValidateNotNullOrEmpty]
        public string AzureStorageAccountKey { get; set; }

        /// <summary>
        /// Azure SAS Token
        /// </summary>
        [Parameter(Mandatory = false)]
        [ValidateNotNullOrEmpty]
        public string AzureSasToken { get; set; }

        /// <summary>
        /// Target Share folder to save logs
        /// </summary>
        [Parameter(Mandatory = false)]
        [ValidateNotNullOrEmpty]
        public string TargetShareFolder { get; set; }

        protected override void Execute()
        {
            IntPtr valuePtr = IntPtr.Zero;
            string password;
            try
            {
                valuePtr = Marshal.SecureStringToGlobalAllocUnicode(Credential.Password);
                password = Marshal.PtrToStringUni(valuePtr);
            }
            finally
            {
                Marshal.ZeroFreeGlobalAllocUnicode(valuePtr);
            }

            LogCollectParameters parameters = new LogCollectParameters
            {
                AzureBlobContainer = AzureStorageContainer,
                AzureSasToken = AzureSasToken,
                AzureStorageAccountKey = AzureStorageAccountKey,
                AzureStorageAccountName = AzureStorageAccountName,
                EndTime = EndTime,
                StartTime = StartTime,
                LogPrefix = LogPrefix,
                UserName = Credential.UserName,
                PlainPassword = password,
                TargetShareFolder = TargetShareFolder
            };

            var logCollectTask = Client.Farms.CollectLogAsync(ResourceGroupName, FarmName, parameters);

            WriteVerbose("Sending request to start a log Collect job, it will take some time...");

            var response = logCollectTask.Result;

            if (response.StatusCode != HttpStatusCode.OK)
            {
                throw new AdminException(string.Format(CultureInfo.InvariantCulture, Resources.OperationFailedErrorMessage, response.StatusCode));
            }

            WriteObject(response);
        }
    }
}

redpajama_set_name: RedPajamaGithub


text:
Similarly, the country of origin specification category on the CPI checklist istested to see if the hedonic model for clothes dryers could be fitted with any of thesevariables. Country of origin represents the country in which the product in thiscase clothes dryers was constructed and is believed to serve as a proxy for the qualityof a good and service. In the sample used in this study, over 98 percent of the 341clothes dryers were constructed in the USA . The remaining clothes dryer models inthe sample were made in Canada .
This didn t sit well with the Williamses. John, who works for the Montana Department of Corrections, often interacts with people facing the reality of our finite time on Earth. If you re spending the rest of your life in prison, you know, it makes you think about the mortality of life, he says. What s important is not how long you live, but rather what you do with the life you re given. MaryMargret feels the same way. For years she has worked in a local dermatology office. She knows all too well the cultural pressures to stay young, and wishes more people would embrace the inevitability of getting older. You get wrinkles, you get old, that s part of the process, she says.
We elected to go for sunset because we are staying in Kapalua and did not want to get up at 2am for sunrise. We spent the day visiting other local attractions and headed up about 3pm. It was very cloudy and rainy going up and we had our doubts about if it was worth the trip. We had downloaded the Gypsy App for Haleakala which is a guided audio tour and it reassured us that the weather would be different at the top and it was! Sunny and clear, although the temp went from 80 down to 63 degrees at the top. The Visitors Center at the base is under construction and the one at the top is open from 10-3:30. There are restrooms at the summit. There are many switch backs and driving can be nerve racking but definitely worth the views.
You will be invoiced in Ringgit, the final BND price will depend on the exchange rate applied by your credit card/bank
Our after dark cocktail dresses , formal dresses, designer dresses and party dresses are by labels such as Motel, Bless'ed Are The Meek, August Street and Fairground, Toi et Moi.
De Magalhaes and Gladyshev are under no illusions about the arduous journey but they remain hopeful. History is full of claims by experts that certain advances are impossible, only to be proven wrong soon after, says de Magalhaes. Consider the state of medicine just 120 years ago, when some of today s bowhead whales were still in their infancy. Back then life-threatening infections were just a fact of life. Today, antibiotics are taken for granted as the most basic healthcare.


text:
Q: How to position an image using pixel coordinates in python? I'm currently working on a personal project that involves kivy GUI app maker in python.
And I would like to work with images and a FloatLayout. The thing is I'm currently stuck because the next step of this project is to position an image to the FloatLayout by using coordinates.
Here's an example of what I'd like to happen :

The blue square is the FloatLayout, the orange square is the image, by having at least two points of reference in the FloatLayout and inside the image (using pixels as coordinates), the image should go to the '1' and '2' points inside the blue square. kivy's widget scatter should do the job of reorienting and scaling the image.
I've thinking about using matrix to somehow solve this problem. I'd appreciate if you recommend me another way to help me achieve this, or link me to a python library that works with this specific kind of problems.

redpajama_set_name: RedPajamaStackExchange


It amazes me how many Christians do not realize the serious warfare we are in on planet Earth!
The truth is there is the natural realm that we see with our eyes and then there is the spiritual realm.
Ephesians 6 clearly says we wrestle not with flesh and blood but against spiritual wickedness in high places.
Furthermore, many do not know how to battle victoriously! Lord Jesus showed us how to do so in Luke 4:1-13 and Matthew 4:1-11.
We battle with God's Word! We give voice to His word! Even His angels pay heed to God's word as we speak it out. See Ps 103:20.
We enforce by our words and faith what Christ the Lord has done on the Cross and through His Holy powerful shed Blood! "And they overcame him by the blood of the Lamb and by the word of their testimony…" Revelations 12:11.
'The word of our testimony' refers to what we are saying and by God's Word and Grace believing we defeat our foes. All of them! Amen!
We speak God's Word to our situations and lives. This is a blessed key for us believers in Christ as Lord!
Shall I show you how? For example, if fear is troubling you, find a scripture on it and 'speak' it out often. A good one is 2 Timothy 1:6-7.
Submit your life and situation totally to God. Resist the devil in JESUS CHRIST Name, fear or whatever is troubling you and it WILL flee from you! We have God's word on it, according to James 4:7-8! Then you draw near to God and He will draw near to you. Hallelujah!
Then pray verses that enable 'Perfect Love' which casts out fear to be in your life, according to 1 John 4:18. Follow also by blessed Holy Spirit the instructions laid out in 1 John 2:5 and 1 John 4:13, 16-17. Be blessed and experience the Perfect Love that casts our fear and have your love made perfect according to God's promises. Amen.
I encourage you to fulfill God's word and daily deny yourself (the old sinful nature given to us by the first Adam). Pick up your Cross and follow Jesus. In Luke 9:23 Jesus instructs us to do that daily!
The Cross attained our Victory and we need to be walking in our new nature always, as shown in 2 Corinthians 5:17.
God's word say put on the whole Armour of God! A victorious instruction! All the Armour is for our defense except the "SWORD of the Spirit".
Then say, "I take the SWORD of the Spirit (which is God's Rhema Word) and pray." Using God's word in your prayers helps tremendously.
All this that I write, I do. God knows and I am so moved to help God's army move more strongly!
He has led me to put together a wee booklet of Scriptural Prayers and a DVD of messages on Spiritual Warfare that I have spoken on our Eternally Yours Telecast. Be encouraged!
We win indeed for greater is Christ in us than our enemy in the world, as shown in 1 John 4:4.
So in closing, yield to Jesus Christ Lordship and always do Spiritual Warfare mingled with Rich Praise so you can be Victorious!
It is every Christian's destiny to be hupernikao (Greek word for 'More than conquerors').
So be it Lord Jesus. Amen!


Tenancy fraud costs the public purse almost £1.8 billion each year and is recognised as the second largest area of annual fraud within the public sector.
With temporary accommodation costing an average of £18k per family, per year, - how well do you know your tenants, and how eligible are they for the home you are providing for them?
There are 4.5 million social housing tenants across the UK with a further 1.2million on the social housing waiting list in England alone.
Fully integrated with Opus Case Management, Datatank provides end-to-end tenant lifecycle services to help you confirm who is living in your properties, provide support and guidance to those who need it most and tackle tenancy fraud.
All tenants must be identity checked, but how do you know they are who they say they are?
Quickly bulk screen applicants to verify they are who they say they are before the tenancy life cycle begins.
Our fast and accurate tenant identity checks can be performed with in-situ tenants or at point of application for new tenants meaning you can free valuable staff to provide front line services.
Identifying tenancy misuse not only reduces tenancy fraud but also minimises the cost of providing temporary accommodation and lowers the waiting times for social housing.
Datatank provides Local Authorities and Social Housing providers with a simple and cost effective means of determining tenancy misuse.
Our service identifies tenancy fraud such as subletting, succession and right to buy allowing you to focus your resource where they are most needed.
More than often a tenant in rent arrears will leave the property and give no details of where they are moving to, meaning the debt is left unpaid.
It is time consuming and resource intensive to trace a tenant who has absconded. Even when the tenant is traced, further work is to be carried out to understand if they are able to pay the debt owed.
Datatank trace former tenants with outstanding rent arrears, where their whereabouts is not known. We will give insight into the movement of that tenant between addresses.
We understand that it is time consuming and resource intensive to investigate and prosecute tenancy fraud. You may also be a social housing provider who does not have the rights under the Prevention of Social Housing Fraud Act 2013 (PoSHFA 2013).
This is where we can help you, whether you are a local authority or social housing provider.
We are working with Counter Fraud Investigators who will work alongside your own staff to prevent, detect and investigate tenancy fraud on your behalf.


So when I was away recently, Holly – my sister, and Lettice went off to Claridge's one morning to have Letty's portrait taken for the campaign for the new Mulberry capsule collection of collars and leads in collaboration with luxury Dog & Cat Outfitters Mungo & Maud.
Of course Holly and I are insanely proud that she (and her new friend Jackson) are currently fronting the campaign. Go Letty!
These limited collars and leads are in new season Mulberry colours (a sneak preview of the colour palette for next season) and are offered in combinations of Hibiscus, Sea Blue, Jungle Green and Mandarin. The collars are designed to fit a variety of breeds from Dachshunds to Great Danes.
The Mungo & Maud for Mulberry capsule collection is available on mulberry.com and in Mulberry stores on New Bond Street in London, Madison Avenue and Bleecker Street in New York, Rue St Honore in Paris and in the Mulberry concession in Harrods.
Mulberry would like to say a huge thank you to the canine friends who modelled the collars and leads, including Stanley the Boston Terrier, Boris the Lurcher, Quincey the British Bulldog, Lettice and Jackson the Miniature Dachshunds, Stanley the Border Terrier, Wilbur and Wesley the Weimaraners, Lexi the Golden Doodle, Loki the German Spitz, Betty the Toy Poodle, Doris the Teacup Poodle and Eric the Great Dane.
For the behind the scenes story click here.
This is bloody brilliant! Can you do another post showing the dogs sniffing each other's butts, making love to the kit bags and getting their wet snouts all over the cameras etc (though I do love this glossy post too, natch)?
It's nice to see Lettice have her moments in the spotlight.
Oh gosh they're all adorable! Stanley's little expression melts my heart, what is he thinking?!


Discussion in 'Tech' started by hans5849, Jan 24, 2016.
This morning I started up my computer and for apparently no reason I found myself in a infinite boot loop. I opened up the computer, jiggled some cables and blew some dust out. Still infinite boot loop. I enter the bios, and change one setting that's not even related to the startup process and boom the computer starts. If I knew nothing about computers I would have been stuck; I would have had to take the computer to a repair shop and shell out $50 for a problem that took 5 minutes to fix.
I should add, this computer is 6/7 years old and running Windows 10.
I just checked and I ordered my computer January 9th 2009. Granted the only things from the original build are the case, ram, PSU, and DVD drive.
what you did there, that's how you mobo!
OMG! Binman rose from the dead!
The downfall to what Hans said: everyone and their brothers wants your tech support for free. They automatically think you'll bail them out.
I also managed to get my old router (now my parents router) to upgrade to DD-WRT today. It was stuck on a version from 2014. I had to use TFTP to flash the current version. It wasn't a big deal, but it was much easier because my dad wasn't home crying about the WiFi being down while I did it.
It's as easy as learning to mobo.


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