text:
Paris attack suicide bomber buried in unmarked grave as European capitals step up security
By Europe correspondent Lisa Millar, wires
Updated December 28, 2015 09:19:38
Photo: Suicide bomber Samy Amimour, one of the terrorists who massacred 90 people at the Bataclan Theatre in Paris. (AFP: Amimour Family)
Related Story: Ninth suspect arrested in connection with Paris attacks
Related Story: France plans to strip dual citizens' nationality in terrorism cases
Map: France
One of the suicide bombers involved in the terrorist attacks on Paris in November has been buried in an unmarked grave north of the city.
The family of Samy Amimour arranged the burial on Christmas Eve but it is only now being reported.
The coordinated Paris terror attacks on November 13 left 130 people dead, including 90 at the Bataclan Theatre music venue.
Amimour was one of the three men who attacked the theatre. The 28 year-old was buried in the suburb Seine Saint Denis, where he grew up and where his parents still live.
"There were very, very few people there," said a source in the local town hall.
French law gives individuals the right to a burial either in their area of residence, the area they died, or where their family has a collective plot.
Family members said Amimour was a bus driver and he spent about two years in Syria before the Paris terrorist attacks.
Three other terrorists blew themselves up outside the Stade de France stadium, and another exploded his suicide vest outside a bar not far from the Bataclan.
Meanwhile police in Vienna and other European capitals have stepped up security in the wake of an intelligence alert warning of a potential attack similar to that seen in Paris.
Vienna police released a statement saying they had been warned of a possible bomb or gun attack between Christmas and New Year's day.
"In the lead-up to Christmas, a friendly intelligence service sent a warning to numerous European capitals saying potential bomb or gun attacks could take place in areas with large crowds between Christmas and New Year," Vienna police said in a statement.
The statement said the warning included "several names of potential attackers" but investigations have so far yielded no concrete results.
In response to the alert, police officials said they would increase security at public events and tighten controls on roads in Vienna and elsewhere in Austria.
ABC/AFP
Topics: terrorism, france, austria
First posted December 28, 2015 08:57:44
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I wanted to see the drought for myself. I didn't expect to see this
This time the WA economy may actually be turning around. For real
Two disturbing images show how depressing our political debate has become

redpajama_set_name: RedPajamaCommonCrawl


Tan Sri loves to use symbols such as birds, wings and the act of flying represents creativity that has no limitations or boundaries. This is emphasized in the way he lives. acts and thinks. This is even evident in the way he designed his own residence.


text:
Album Of The Week: Bob Mould Blue Hearts
Album Of The Week September 22, 2020 12:34 PM By Ryan Leas
Just last year, Bob Mould was happy. He named a whole album Sunshine Rock, of all things. Can you imagine? A veteran purveyor of emotions ranging from pissed-off to angsty to deeply tortured was, after all these years, looking on the bright side. Of course, you could already get the sense from interviews that Mould had aged gracefully, finding some modicum of peace and exuding a warmer, more reflective persona than the pricklier reputation expected of him in past decades. And it's not like he was lost in the clouds at the same time — when I interviewed him last year, he talked about old Hüsker Dü material like "In A Free Land" feeling, sadly, just as relevant in Trump's America as it was in Reagan's. He had plenty to say about the precarious state of the world, and maybe you could sense he would soon do that in song.
That brings us to Blue Hearts, arriving a mere 17 months after Sunshine Rock and yet existing on the extreme opposite side of Mould's songwriting. Bob Mould is angry again. Righteously, volcanically angry. It's good to have that version of him back.
Not that Mould has been far from sight in some time. Over the last decade or so, he's undergone a great late-career renaissance. He's partially attributed this resurgence to revisiting the power-trio format that's always served him well, and the fact that he's landed on a stable band with Jason Narducy and Jon Wurster. After a middle era experimenting with electronica and other sounds — generally moving away from the raw rock format — Mould seemed to reclaim his old voice and aesthetic on 2009's Life And Times, which partially arose out of research conducted for his memoir. That sonic homecoming yielded a prolific streak, totaling six albums in 11 years, with highlights spread out evenly amongst them. Still, even by Mould's recent standards, the quick followup and simultaneous about-face after Sunshine Rock make Blue Hearts feel like a particularly urgent and fiery missive from his elder statesman years. He's been around, but not like this.
Mould introduced the album with the pointedly titled "American Crisis," which happened to come out during a week when nationwide protests erupted in the wake of the police killing of George Floyd. The song was actually originally written for Sunshine Rock, then left off for somewhat obvious reasons. When it did arrive, it seemed unfortunately perfect — Mould was talking about his own experience with a different kind of pandemic (AIDS in the '80s), but the song dropped into a year in which Americans were grappling with coronavirus and an entirely mishandled government response at the same time that they were protesting the frequent killing of innocent Black people. "American Crisis" became the genesis of Blue Hearts when, last fall, Mould found himself compelled to write a new collection of songs addressing all his frustrations and anxieties about this country in recent years. It's a bracing, seething track, with Mould's lyrics cataloguing an overflowing lineage of our society's broken parts. The album it birthed could have just as well been called American Crisis — the phrase, either way, is certainly its central theme.
But Blue Hearts starts in a slightly different place. "Heart On My Sleeve" is a bare, acoustic manifesto for the album — it's restrained compared to everything that follows, but you can still hear Mould shivering with ire, all his pent-up rage about to boil over across the rest of the album. In broad strokes, he manages to gesture at almost all the concerns that populate the album: "The Left Coast is covered in ash and flames/ Keep denying the winds of climate change," "Across the Plains are fields of rotting wheat/ Appalachian trail of pain relief," "And the cities are teeming/ Roiling over with tension and greed," "The rising tide of broken government… And we're going to war/ And we're going to die."
The climate and opioid crises, dead-end economic prospects, gentrification, late capitalism, more deeply entrenched divisions between different parts of this country, threats to American democracy from within — it's all on the table. "Don't know what I believe anymore," Mould sings towards the end, perhaps alluding to the distortion of facts that's become all too common in daily life (and conversation amongst ourselves), but also getting at the general headspace of Blue Hearts. Bob Mould is angry, but he's also lost and disoriented while staring down the apocalypse at a middle distance. The feeling is relatable, to say the least.
From there, it is clear Mould has everything on his mind. There is a lot to be furious about these days, and Mould is right there, railing against hypocritical conservative evangelicals in one chorus and mulling over the warping of concepts like "free speech" in another. Accordingly, he is not doing so over contemplative, half-measured music, nor does his big State Of The World album necessitate sprawl or complex arrangements. While the entire album might not match the runaway energy of "American Crisis," Blue Hearts is almost uniformly loud, Mould roaring or barking over caustic distortion and impatient, fed-up arrangements. From "Next Generation" to "Fireball" to "Siberian Butterfly" to "Racing To The End," Mould and Narducy and Wurster favor breakneck rhythms that mirror the desperation at the album's core — a desperation to understand how we can keep letting these things go on, and how in the hell we're going to escape this mess.
Nor is Blue Hearts always overly concerned with subtlety in its lyrics. These are, in basic form, protest songs, and Mould does a whole lot of his own sloganeering: "Here's the newest American Crisis/ Thanks to Evangelical ISIS" or "You're one of us/ Or one of them/ If you're one of them/ Don't come near me again." Elsewhere, he teases out interesting images. "Siberian Butterfly" approaches the climate crisis from the perspective of a rare creature and the people who would make the planet unlivable for the rest of us for the sake of their own profit. Meanwhile, "Password To My Soul" could be a first-person reckoning, or it could be a story about losing sight of someone in your own life as they fall down a rabbit hole of online manipulation and conspiracy theories, choosing the solace of bizarre fantasies in a period of time that itself feels illegible as reality.
This era appears as if it will, to some extent, confound artists for years to come. The threats looming in our lives are so significant, and our leadership so perverse, that the absurd death-drive timbre of recent years has already been well-established as almost impervious to fictionalization or satire. How are artists supposed to process it then? How can you work with real-life material that, if it originated from a book or movie, would regularly be decried as ridiculously on-the-nose? Maybe there's not much room for subtlety in that context.
Blue Hearts is one example of an artist reacting to these times, in these times, without much regard for tip-toeing around it. You pretty much can tell what "Baby Needs A Cookie" is about before you even listen to it, and maybe you don't really need a Trump-era song called "Baby Needs A Cookie" in your life. But then you hear Mould sneering his way around the words, relishing them, and you think: This is a guy who has been, on and off for nearly 40 years, making loud music about his own demons, but also society's ills. There's almost something comforting about hearing someone a few decades your senior being just as aghast and confused and exhausted as we've all felt these past several years.
That's the thing about Blue Hearts: Maybe a non-stop political screed from Mould would be, on some level, welcome on its own. And, sure enough, songs like "American Crisis" and "Forecast Of Rain" provide catharsis as much as they remind you that you should be very angry right now, that none of us can afford to become complacent. But ultimately, the album feels born from a place of compassion, the outpouring of an aging man who has seen cycles of trauma and history play out, but never quite like this before. "The last few years have been so frustrating I lose little pieces of myself each day/ The lines get deeper on my face each season/ Say I don't care as I weather away," he sings in "Little Pieces," towards the end of the album. For all the global and national levels on display across Blue Hearts, Mould's biggest gut-punches come when he articulates the personal loss that occurs in tumultuous times.
To that end, there are little detours, side stories that flesh out Blue Hearts. Songs like "When You Left" and "Leather Dreams" seem to allude to lost love and former dalliances. The fact that, in the span of all this, Mould writes about relationships as a gay man — a topic core to his life that he couldn't speak on for so many years — seems to only serve his broader points about the destructive cycles we're caught in and the way it all seems to slide further down into chaos. He has seen too much of this before; in a recent interview with American Songwriter, he reflected on the Trump presidency and his recollections of 1983, before Reagan's reelection, living as a closeted gay man watching a "televangelist presidency" that refused to acknowledge AIDS. Sometimes Mould makes all the parallels explicit, and sometimes he lets the thoughts fly around in the kind of frantic cloud all our minds occupy these days. But the overarching point is always there: The ongoing struggle against people in power who will never give a shit about making the world better for those beneath or different from them.
In that same interview, Mould acknowledged he didn't speak up as aggressively decades ago, and he sees himself in a position now where he had to — if he didn't, what else were all these years of accumulated experience and stature good for? "When I'm hoarse and deaf and feeble/ Nobody's going to listen to me/ Time to take one last stand/ Try to make you understand," he sings early on, in the rushing "Next Generation." The song looks back to Mould's own youth, but it also thinks of all the young people who haven't lived yet. "Life isn't a joke, it's so much more/ It's all we get, there's nothing else/ But what gets left for the next generation." Across Blue Hearts, Mould isn't just reacting to the latest headlines. He's fearful for our very future. In the year 2020, Mould's extended comeback has coalesced into one of the most dialed-in, vital collections he's put together in quite a while. It sounds like a rallying cry. It also sounds like a warning.
Blue Hearts is out 9/25 on Merge. Pre-order it here.
Blue Hearts by Bob Mould
Other albums of note out this week:
• Fleet Foxes' gorgeous, suddenly released new album Shore.
• Sufjan Stevens' The Ascension, which we'll talk about more soon.
• Deftones' heady, heavy, very Deftones-y Ohms.
• IDLES' Ultra Mono, which is certainly an IDLES album.
• Thurston Moore's latest solo outing, By The Fire.
• Sylvan Esso's reliably idiosyncratic Free Love.
• Lydia Loveless post-divorce, post-move, big-life-changes album Daughter.
• Will Butler's sophomore solo effort, Generations.
• Sadie Dupuis' latest Sad13 album, Haunted Painting.
• Yves Jarvis's enigmatic, genre-agnostic Sundry Rock Song Stock.
• Róisín Murphy's wonderfully titled Róisín Machine.
• The Menzingers' Hello Exile reimagining From Exile.
• A Certain Ratio's first new album in 11 years, ACR Loco.
• Svalbard beautifully heavy When I Die, Will I Get Better?.
• Dropdead's first album in over 20 years, Self Titled.
• Mint Field's heat-soaked, psychedelic Sentimiento Mundial.
• Ziemba hazy, emotive True Romantic.
• Marie Davidson & L'Œil Nu's rock/pop/band-oriented exploration Renegade Breakdown.
• Squirrel Nut Zippers' Andrew Bird-featuring Lost Songs Of Doc Souchon.
• Action Bronson's as-yet-unheard Only For Dolphins.
• 2 Chainz's also as-yet-unheard So Help Me God.
• Carrie Underwood's Christmas album My Gift.
• Elvin Bishop & Charlie Musselwhite's team-up 100 Years Of Blues.
• Beverly Glenn-Copeland's career-spanning compilation, Transmissions: The Music of Beverly Glenn-Copeland.
• Public Enemy's What You Gonna Do When The Grid Goes Down, which is stacked with collabs with fellow legendary artists.
• Blue Hawaii's awesome house mixtape, Under 1 House.
• Tim Heidecker's more sincere classicist rock (and Weyes Blood-featuring) collection, Fear Of Death.
• Prince's deluxe edition reissue of Sign O' The Times.
Ryan Leas Contributor ryanleas
more from Album Of The Week


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There are several alternative ways to get out into the High Country and the most atmospheric is on horseback, just like the mountain's early pioneers.
Saddle up and explore Mt Stirling like the Man from Snowy River, roaming freely along mountain ridges and through wide-open fields. You can even join a guided tour with one of the local horsemen. Horse riding is available outside of the winter period only.
Horse Riding activities on Mt Stirling are permitted between 1 November and 30 April each year. There are lots of fantastic rides for all ages and abilities, including trips to Craig's Hut and summit tours. Day trips and overnight horse rides are also available.
Please note: A number of trails on Mt Stirling are shared by walkers, mountain bikers and horses. Always give way to others - other trail users have the same rights as you. Anticipate other trail users around corners or in blind spots. When you do come across other trail users let them know you're coming – a friendly greeting is considerate and works well.
Tours operators conduct rides:
McCormacks Mountain Valley Trail Rides
www.mountainvalleytrailrides.com.au
Hidden Trails
www.hiddentrails.com.au
High Country Horses
www.highcountryhorses.com.au
Buckle Up Bush Rides
www.buckleupbushrides.com.au
Due to current health measures to keep Victorian's safe, we are still in the process of confirming our operations for the 2020-21 season. Check back here soon for updates.
Download the Horse Riding Code
Download Fact Sheet: Horse Trail Riding in the Mansfield High Country, Mt Stirling Area
The Department of Environment, Land, Water and Planning (DELWP) have created a Fact Sheet to assist you if you're planning on holding a club event or saddling up and bringing some friends to horse ride around Mt Stirling, Mansfield State Forest or the Alpine National Park.


text:
Sports Injury Phsyiotherapy in Sandhurst, Fleet and Yateley
Early management for Sporting Injuries is vital!
Our Physiotherapists proudly have experience of working with local Rugby, Basketball, American Football and Gymnastic clubs
Our Sports Physiotherapists have a broad sports injury knowledge and are trained to accurately diagnose and manage a full range of Sports Injuries.
It is our aim to assess you immediately after your sporting injury.
Call 01276 37670 or email admin@thorpesphysiotherapy.com for an appointment with of our fully qualified Sports Injury Physiotherapists now.
Jonathan has treated my daughter for several years as she is a competitive gymnast and has sustained injuries. He is always professional and has extensive specialised knowledge of sports injuries and the best way to get her back to full fitness. He has been amazing at managing her case every time and always provided advice and support in clinic and over the telephone. Working with children who are still growing can be difficult but he has always known exactly how to treat her and keep her motivated to do the exercises and get back to full fitness. I have recommended him to several other gymnasts and would have no hesitation in using Thorpes again.
Alix G
My daughter participates in competitive gymnastics and its fair to say that she has had her fair share of injuries! Jonathan has been fantastic in making sure that she has returned to full health as quickly as possible, yet in a safe way, so doesn't have to miss too much of the sport she loves. Jonathan is a true professional and we know that we are in safe hands under his care and guidance.
Gemma P
We highly recommend Jonathan and Thorpes Physiotherapy for their outstanding treatment and professionalism. We first visited Jonathon with my daughter in early 2012. As a young teenager she was having a growth spurt which was impacting on her gymnastics resulting in her being dropped from her team just before the British Championships. With frequent visits to Jonathon she was able to represent GB at the European Team Gym Championships later that year. She continues to see Jonathan for manipulation and acupuncture and is our first port of call following injury and he has her back on her feet in no time. Jonathon always explains to her what he is doing in an age appropriate manner and has been flexible with his appointments working around school and training hours. Without Jonathon's expert help she would not have such an impressive record of achievement.


text:
«Три жизни Томазины» () — британо-американский семейный фантастический фильм режиссёра . Релиз в США состоялся в 1963 году.

Фильм основан на романе Пола Гэллико «Томасина», вышедшем в 1957 году.

Сюжет
Томасина — кошка девочки по имени Мэри, дочери шотландского ветеринара Эндрю Макдуи. Когда кошка тяжело заболела, врач её усыпил. Это привело к серьёзной ссоре между отцом и дочерью. Спустя некоторое время местная «ведьма», Лори Макгрегор, оживила кошку.

В ролях

Отзывы
 из «The New York Times» назвал эту картину хорошим, сентиментальным и чрезвычайно нежным фильмом, который лучше всего подходит для маленьких девочек. Крейг Батлер из «AllMovie» пишет, что этот фильм — «прекрасная и нежная, но энергичная детская сказка, которая понравится и взрослым».

Примечания

Ссылки
 Официальный сайт

Фильмы на английском языке
Фильмы на гэльском языке
Фильмы-драмы США
Фильмы-драмы Великобритании
Фильмы США в жанре фэнтези
Фильмы Великобритании в жанре фэнтези
Фильмы о Шотландии
Фильмы о кошках
Фильмы о реинкарнации
Диснеевские фильмы
Экранизации произведений Пола Гэллико
Фильмы Дона Чеффи
Фильмы США 1963 года
Фильмы Великобритании 1963 года
Фильмы Walt Disney Pictures

redpajama_set_name: RedPajamaWikipedia


Hello! My name is Dena Eltouny and I am the Kindergarten teacher here at Saint Genevieve Catholic School. I graduated with a Bachelor of Arts Degree from Kean University. I am a certified teacher with 10 years of teaching experience. I spent several years overseas teaching English to children from all over the world. I love working with children and seeing them light up as they learn new things. I believe in keeping things fun, engaging, and always having a positive attitude.


Andrew is based in Long Island, NY and can be seen performing anywhere on the island or in New York City. Andrew is more than happy to travel outside of the NYC/Long Island area for bookings.
Does Andrew perform children's shows?
The short answer to this question would be No. Although Andrew's material is "clean", it is geared toward educated adults and would be most entertaining to audiences no younger than high school students.
What is the difference between Magic and Mentalism?
Magic focuses on achieving the impossible; Vanishing items and producing them, or changing one thing into another. Magic is of a much more visual and physical nature.
Mentalism focuses on powers of the mind; using methods of psychology, deception, suggestion and theatrics to seemingly read the minds of others, influence them to behave a certain way, or know things that nobody could possibly know.
There's no simple answer to this question as show prices vary with each event due to elements including audience size, amount of equipment needed, length of show, travel time, style of show and more. By using the "contact us" link and giving us a description of your event, we can give you a free quote and make sure you get the show that best suits your event.


The Cabarrus County Community Foundation's annual grant awards increased more than 100 percent from 2017 to 2018, representing some of the largest annual grants ever awarded in the organization's 30-year history. In total, nearly $115,000 was awarded to 19 nonprofits serving the community. This dramatic growth is thanks to the community meeting a generous match opportunity.
Nineteen local organizations received grants from the Cabarrus County Community Foundation, including Cabarrus Cooperative Christian Ministry. The grant will support the long-term needs of homeless families with children, granting them housing, basic needs and critical education for 3-5 years.
Amazing Grace Advocacy was another of the beneficiaries of CCCF's grant funding this year. The grant will support the family wellness program and provide comprehensive case management to families raising children with mental illness.
The Cabarrus County Community Foundation is hosting a reception in early May to celebrate the 2018 grantees and the successful match campaign. To learn more about the Cabarrus County Community Foundation, please contact Katie Russell at krussell@fftc.org.
For a full listing of the grants and projects awarded by the Cabarrus County Community Foundation, visit www.cabarruscounty-cf.org.


All or nothing; it's a fairly simple focus. Many teams and people have used this mentality as a means of motivation, and the UAA Volleyball team buys into it as well.
UAA's next road trip consists of matches against both Northwest Nazarene University and Seattle Pacific University. In the 2008 season, UAA lost both of these matches three sets to none.
From a coach's perspective, putting the best effort forward each game is the most one can ask for. But, only the players know what their best is.
"We are really strong offensively this year, whereas last year we were not quite as effective offensively but made up for it in our defense," Meisner said when asked about differences between the two seasons.
When comparing statistics, UAA has improved from last season. After their first 19 games last year, UAA's record was 11-8. This year, after 19 games, their record stands one better at 12-7.
Both their 2008 and progress 2009 season, so far, stand above their 2007 season, which ended with a record of 5-19.
What could inspire such a turnaround in seasons?
This increase in fundamentals has affected the team in many ways. They have improved their overall match consistency, which has allowed them to win big games. Though improving fundamentals has helped, the main factor in turning the volleyball team from a losing team to a winning one has been hard work and dedication by both the players and the staff.
In order to keep GNAC championship in sight, the Seawolves need to win on the road against both Seattle Pacific and Northwest Nazarene Universities on Oct. 15 and 17.


text:
Der Jüdische Friedhof Kaiserswerth ist ein jüdischer Friedhof im Düsseldorfer Stadtteil Kaiserswerth an der Ecke Alte Landstraße zum Zeppenheimer Weg. Der Friedhof wurde 1737 erstmals erwähnt. Belegt wurde er in den Jahren 1892 bis 1942. Elf Grabsteine sind erhalten. Der Friedhof ist nicht öffentlich zugänglich.

Siehe auch
 Juden in Düsseldorf

Weblinks

 Kaiserswerth. In: Übersicht über alle Projekte zur Dokumentation jüdischer Grabinschriften auf dem Gebiet der Bundesrepublik Deutschland. Nordrhein-Westfalen. Bearbeiterin: Claudia Pohl.

Einzelnachweise

Geschichte (Düsseldorf)
Friedhof in Düsseldorf
Baudenkmal in Düsseldorf
Kaiserswerth
Bauwerk in Kaiserswerth
Erbaut im 18. Jahrhundert
Denkmalgeschütztes Bauwerk in Düsseldorf
Geschlossener Friedhof


text:
\section{Introduction}
\label{sec:intro}
\input{1_intro}

\section{Related Work}
\label{sec:related}
\input{2_related}

\section{Stylized Co-Speech Gesture Animation}
\label{sec:problem}
\input{5_problem}

\input{z89_model}
\section{Mix-StAGE: \underline{Mix}ture-Model guided \underline{St}yle and \underline{A}udio for \underline{Ge}sture Generation}
\label{sec:model}
\input{6_model}

\section{Experiments}
\label{sec:exp}
\input{75_experiments.tex}

\section{Results and Discussion}
\label{sec:results}
\input{7_results}

\section{Conclusions}
\label{sec:conclusions}
\input{8_conclusions}
\subsubsection{Acknowledgements}
This material is based upon work partially supported by the National Science Foundation (Awards \#1750439 \#1722822), National Institutes of Health and the InMind project. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of National Science Foundation or National Institutes of Health, and no official endorsement should be inferred.

\bibliographystyle{splncs04}

\subsubsection{Disentanglement and Transfer of Style}:
Style extraction and transfer have been studied in context of image artistic style \cite{gatys2015neural,johnson2016perceptual}, factorizing foreground and background in videos\cite{denton2017unsupervised,villegas2017decomposing}, disentanglement in speech \cite{wang2018style,bian2019multi,gurunath2019disentangling}. These approaches were extended to translation between properties of style such as map edges and real photos using paired samples \cite{isola2017image}. Paired data limits the variety of attributes of source and target, which encouraged unsupervised domain translation for images \cite{zhu2017unpaired,zhu2017toward} and videos\cite{bansal2018recycle}. Style was disentangled from content using a shared latent space\cite{liu2017unsupervised}, a cycle consistency loss \cite{zhu2017unpaired} and contrastive learning \cite{nagrani2020disentangled}. Cycle consistency losses were shown to limit diversity in the generated outputs as opposed to a weak consistency loss \cite{huang2018multimodal} and shared content space \cite{lee2019drit++}. Cycle consistency in cross-domain translation assumes reversibility (i.e. domain A can be translated to domain B and vice-versa). These assumptions are violated in cross-modal translation \cite{ma2018neural} and style control \cite{wang2018style} tasks where information in modality B (e.g. pose) is a subset of that in modality B (e.g. audio).
Style transfer for pose has been studied in context of generating dance moves based on the content of the audio \cite{lee2019dancing} or walking styles \cite{smith2019efficient}. Generated dance moves are conditioned on both the style and content of the audio (i.e. kind of music like ballet or hip-hop), unlike co-speech gesture generation which requires only the content and not the style of the audio (i.e. speaker specific style like identity or fundamental frequency). Co-speech gesture styles have been studied in context of speaker personalities \cite{neff2008gesture}, but requires a long annotation process to create a profile for each speaker. To our knowledge, this is the first fully data-driven approach that learns gesture style transfer for multiple speakers in a co-speech gesture generation setting.

\subsection{Challenges of Jointly Modeling Human Pose, Speech and Text}

\subsection{M$^2$GS: Multi-mode Multimodal Gesture Space }
\label{ssec:cluster}
Humans perform different styles of gestures, where each style consists of  different kinds of gestures (i.e beat, metaphorical, emblematic, iconic and so on)\cite{McNeill1992}. Learning pose generators for multiple speakers, each with their own style of gestures, presents a distribution with multiple modes. These gestures have a tendency of switching from one mode to the other over time, which depends on style embeddings and content of the audio.

To prevent mode collapse \cite{arjovsky2017wasserstein} we propose the use of mixture-model guided sub-generators \cite{hao2018mixgan,arora2017generalization,hoang2018mgan}, each learning a different mode of $M^2$ gesture space $F_{\Phi|a,s}$.
\begin{equation}
    \mathbf{\hat{Y}}_p = \sum_{m=1}^M \mathbf{\phi}_m G_m(\mathbf{Z}) = G(\textbf{Z})\label{eq:mm_func}
\end{equation}
where $\textbf{Z} \in \lbrace \textbf{Z}_{a\rightarrow p}, \textbf{Z}_{p\rightarrow p}\rbrace$ are cross-modal and self-modal latent spaces respectively. They are defined as $\textbf{Z}_{a\rightarrow p} = \left[ E^c_a(\textbf{X}_a), E^s_p(\textbf{Y}_p)\bigotimes \textbf{S} \right]$ and $\textbf{Z}_{p\rightarrow p} = \left[ E^c_p(\textbf{Y}_p), E^s_p(\textbf{Y}_p)\bigotimes \textbf{S} \right]$ where $\textbf{S}$ is the style embedding matrix (See Section \ref{ssec:style}) and $\bigotimes$ is $\text{argmax}$ for style ID followed by matrix multiplication. Pose sequence $\hat{Y}_p \sim F_{p|\Phi}F_{\Phi|a,s}$ represents the pose probability distribution conditioned on audio and style. $G_m \sim F_{p|a, s}^m \hspace{0.1cm}\forall m \in \left[1, 2, \ldots M\right]$ are sub-generator functions with corresponding mixture-model priors $\Phi = \setLow{\mathbf{\phi}}{M}$. These mixture model priors represent the $M^2$ gesture space and are estimated at inference time conditioned on the input audio and style.

\noindent\textbf{Estimating Mixture Model Priors: }
During training, we partition poses $Y_p$ into $M$ clusters using an unsupervised approach, Lloyd's algorithm \cite{lloyd1982least}. While other unsupervised clustering methods \cite{reynolds2009gaussian} can also be used at this stage, we choose Lloyd's algorithm for its simplicity and speed. Each of these clusters represent samples from probability distributions $\set{F_{p|a, s}}{M}$. If a sample belongs to the $m^{th}$ cluster, $\phi_m = 1$, otherwise $\phi_m =0$, making $\Phi$ a sequence of one-hot vectors. While training the generator $G$ with loss function $\mathcal{L}_{\mbox{rec}}$, if a sample belongs to the distribution $F_{p|a, s}^m$, only parameters of sub-generator $G_m$ are updated. Hence, each sub-generator learns different components of the true distribution, which are combined using Equation \ref{eq:mm_func} to give the generated pose.

At inference time, we do not have the true values of mixture-model priors $\Phi$. As mixture model priors modulate based on the style of the speaker and audio content at any given moment, we jointly learn a classification network $H \sim F_{\Phi|a,s}$ to estimate values of $\Phi$ in form of a mode regularization loss function

\begin{equation}
    \mathcal{L}_{mix} = \mathbb{E}_{\Phi, \mathbf{Z}}\mbox{CCE}(\Phi, H(\mathbf{Z})) \label{eq:aux}
\end{equation}
where CCE is categorical cross-entropy.
\subsection{Joint Space of Style, Audio and Pose}
\label{ssec:recon}
A set of marginal distributions $F_a$ and $F_s$ are learnt by our content encoders $E_a^c$ and $E_p^c$, which together define the joint distribution of the generated poses: $F_{p,a,s}$. Since both cross-modal $\textbf{Z}_{a\rightarrow p}$ and self-modal $\textbf{Z}_{p\rightarrow p}$ latent spaces are designed to represent the same underlying content distribution, they should be consistent with each other. Using the same generator $G$ for decoding both of these embeddings\cite{liu2016coupled} yields content invariant generator. We enforce a reconstruction and joint loss \cite{ahuja2019language2pose} which encourages a reduction in distance between $\textbf{Z}_{a\rightarrow p}$ and $\textbf{Z}_{p\rightarrow p}$. As cross-modal translation is not reversible for this task (i.e. audio signal cannot be generated with pose input), a bi-directional reconstruction loss \cite{lee2019drit++} for latent distribution matching cannot be directly used. This joint loss achieves the same goal of latent distribution matching in a uni-modal translation task \cite{huang2018multimodal,rosca2017variational,royer2020xgan} but for a cross-modal translation task.

\begin{align}
    \mathcal{L}_{joint} &= \mathbb{E}_{\mathbf{Y}_p}\| \mathbf{Y}_p - G(\textbf{Z}_{p\rightarrow p})\|_1 \label{eq:joint} \\
    \mathcal{L}_{rec} &= \mathbb{E}_{\mathbf{Y}_p, \textbf{X}_a}\| \mathbf{Y}_p - G(\textbf{Z}_{a\rightarrow p})\|_1 \label{eq:reg}
\end{align}

\subsection{Style Embedding}
\label{ssec:style}
We represent style as a collection of embeddings $S(i) \in \textbf{S} \sim F_s$, where $S(i)$ is the style of the $i^{th}$ speaker in the style matrix $\textbf{S}$. Style space and embeddings are conceptually similar to the GST (Global Style Token) layer \cite{wang2018style} which decomposes the audio embedding space into a set of basis vectors or style tokens, but only one out of the two modalities in the stylized audio generation task \cite{wang2018style,ma2018neural} have both style and content. In our case, both audio and pose have style and content. To ensure that generator $G$ is attending only to style of pose while ignoring style of the audio, a style consistency loss is enforced on input $\textbf{Y}_p$ and generated $\hat{\textbf{Y}}_p$.

\begin{equation}
    \mathcal{L}_{id} = \mathbb{E}_{Y \in \lbrace\textbf{Y}_p, \hat{\textbf{Y}}_p\rbrace}\mbox{CCE}\left(\mbox{Softmax}\left(E^s_p(Y)\right), \textbf{ID}\right) \label{eq:style}
\end{equation}
where $\textbf{ID}$ is a one-hot vector denoting the speaker level style.

\subsection{Total Loss with Adversarial Training}
\label{ssec:adv}
To alleviate the challenge of overly smooth generation caused by L1 reconstruction and joint losses in Equation \ref{eq:joint},\ref{eq:reg}, we use the generated pose sequence $\mathbf{\hat{Y}}^p$ as a signal for the adversarial discriminator $D$ \cite{ginosar2019learning}. The discriminator tries to classify the true pose $\mathbf{Y}^p$ from the generated pose $\mathbf{\hat{Y}}^p$, while the generator learns to fool the discriminator by generating realistic poses. This adversarial loss\cite{goodfellow2014generative} is written as:
\begin{equation}
    \mathcal{L}_{adv} = \mathbb{E}_{\mathbf{Y}_p}\log D\left(\mathbf{Y}_p) + \mathbb{E}_{\textbf{X}_a, \textbf{Y}_p}\log\left(1- D(G\left(\left[E^c_a(\textbf{X}_a), E^s_p(\textbf{X}_p)\right]\right)\right)\right) \label{eq:adv}
\end{equation}

The model is jointly trained to optimize the overall loss function:
\begin{equation}
    \max_{D} \min_{E^c_a, E^c_p, E^s_p, G} \mathcal{L}_{mix} + \mathcal{L}_{joint} + \mathcal{L}_{rec} + \lambda_{id}\mathcal{L}_{id} + \mathcal{L}_{adv}
\end{equation}
where $\lambda_{id}$ controls the weight of the style consistency loss term.

\subsection{Network Architectures}
Our proposed approach can work with any temporal network, giving it the flexibility of incorporating domain dependent or pre-trained temporal models.

In our experiments we use a Temporal Convolution Network (TCN) module for both content and style encoders. The style space is a matrix $\mathbf{S}\in \mathbb{R}^{N \times D}$ where $N$ is the number of speakers and $D$ is the length of the style embeddings. The generator $G(.)$ consists of a 1D version of U-Net \cite{ronneberger2015u,ginosar2019learning} followed by $M$ TCNs as sub-generator functions. The discriminator is also a TCN module with lower capacity than the generators. A more detailed architecture can be found in the supplementary
\input{z81_table_main2}

\subsection{Implementation Details}

\subsection{Baseline Models}
\subsubsection{\underline{Single-Speaker Models}}
: These models are not designed to perform style transfer and hence are not included for those experiments.
\begin{itemize}
    \item \noindent\textbf{Speech2Gesture \cite{ginosar2019learning}}: The closest work to co-speech gesture generation is one that only generates individualistic styles. We use the pre-trained models available from their code-base to render the videos. For rest of the speakers in PATS, we replicate their model, hyper-parameters and train speaker specific models.
    \item \noindent\textbf{CMix-GAN }(variant of our model): As an ablation, we remove the style embedding module and style consistency losses from our model Mix-StAGE . Hence, a separate model is required to be trained for each speaker for style preservation experiments.
\end{itemize}

\subsubsection{\underline{Multi-speaker Models}}
\begin{itemize}
    \item \noindent\textbf{MUNIT \cite{huang2018multimodal}}: The closest work to our style-transfer task is MUNIT which takes multiple domains of images (i.e. uni-modal). We modify the encoders and decoders to domain specific architectures (i.e. 1D convolutions for audio instead of 2D convolutions for images) while retaining the loss functions.
    \item \noindent\textbf{StAGE }(variant of our model): As an ablation, we fix the number of sub-generators in our model Mix-StAGE  to one. This is equivalent to setting $M=1$ in equation \ref{eq:mm_func}.
\end{itemize}

\subsection{Evaluation Metrics}
\subsubsection{Human Perceptual Study:}
\label{ssec:human_study}
We conduct a human perceptual study on Amazon Mechanical Turk (AMT) for co-speech gesture generation (or style preservation) and style transfer (speaker-level and attribute-level) and measure preferences in two aspects of the generated animations, (1) \textbf{naturalness}, and (2) \textbf{style transfer correctness} for animation generation with content (i.e. audio) of speaker A and style of speaker B. We show a pair of videos with skeletal animations to the annotators. One of the animations is from the ground-truth set, while the other is generated using our proposed model. The generated animation could either have the same style or a different style as the original speaker. With unlimited time, the annotator has to answer two questions, (1) Which of the videos has more natural gestures? and (2) Do these videos have the same attribute-level style (or speaker-level style)?  The first question is a real vs. fake perceptual study against the ground truth, while the second question measures how often the algorithm is able to visually preserve or transfer style (attribute or individual level). We run this study for randomly selected 100 pairs of videos from the held-out set.

\subsubsection{Probability of Correct Keypoints (PCK):} To measure the accuracy of the gesture generation, PCK \cite{andriluka20142d,simon2017hand} is used to evaluate all models.
PCK values are averaged over $\alpha=0.1, 0.2$ as suggested in \cite{ginosar2019learning}.

\subsubsection{Mode Classification F1:}
Correctness of shape of a gesture can be quantified by measuring the number of times the model has sampled from the correct mode of the pose distribution. Formally, we use the true ($\textbf{Y}_p$) and generated ($\mathbf{\hat{Y}}^p$) pose to find the closest cluster $\hat{m}$ and $m$ respectively. If $m = \hat{m}$, the generated pose was sampled from the correct mode. F1 score of this $M$-class classification problem is defined as Mode Classification F1, or simply F1

\subsubsection{Inception Score (IS): } Generated pose sequences with the audio of speaker A and style of speaker B does not have a ground truth reference. To quantitatively measure the correctness and diversity of generated pose sequence we use the inception score \cite{salimans2016improved}.
For generative tasks such as image generation, this metric has been used with a pre-trained classification network such as Inception Model \cite{szegedy2016rethinking}. In our case, the generated samples are not images, but a set of 2D keypoints. Hence, we train a network which classifies a sequence of poses to its corresponding speaker which estimates the conditional likelihood to calculate IS scores.
\input{z77_human_study2}

\subsection{Pose-Audio-Transcript-Style (PATS) dataset }
\input{4_dataset}
\input{z80_table_style}

\subsection{Gesture Animation and Style Preservation}
To understand the impact of adding more speakers, we select a random sample of 8 speakers for the largest 8-speaker multi-speaker model, and train smaller 4-speaker and 2-speaker models where the speakers trained are always a subset of the speakers that were trained in a larger model. This allows to compare the performance on the same two initial speakers which are `\textit{Corden}' and `\textit{lec\_cosmic}' in our case\footnote{The complete set of speakers used in our experiments are listed in the supplementary.}. We also compare with single-speaker models trained and tested on one speaker at a time.

\subsubsection{Impact of training with Multiple Speakers}
Results from Table \ref{tab:main} show that multi-speaker models outperform single-speaker models especially for pose accuracy (i.e. PCK), shape and timing (i.e. F1). We find that increasing the number of speakers could sometimes reduce the performance of individual speakers but the overall performance generally shows improvement.

\subsubsection{Comparison with previous baselines}
To compare with prior baselines, we focus first on the subjective evaluation shown in Figure \ref{fig:human_study2}\makered{a}, since it is arguably the most important metric. The results show consistent improvements on the naturalness rating for our proposed model Mix-StAGE  and also our single-speaker variant CMix-GAN over the previous state of the art approach S2G \cite{ginosar2019learning}. We also observe that multi-speaker models perform better than single speaker-models. In Table \ref{tab:main}, we show similar quantitative improvements of Mix-StAGE  and CMix-GAN over S2G for both PCK and F1 scores.
\input{z78_human_study1}

\subsubsection{Impact of Multiple Generators for Decoding}
Mix-StAGE's gesture space models multiple modes, as seen in Figure \ref{fig:tsne}. Its importance is shown in Table \ref{tab:main} where models with single generators as the decoder (i.e. S2G, MUNIT and StAGE) showed lower F1 scores, most likely due to mode collapse while training. Multiple generators in CMix-GAN and Mix-StAGE boost F1 scores as compared to other models in the single-speaker and multi-speaker regimes respectively. A similar trend was observed in the perceptual study in Figure \ref{fig:human_study2}.

We also study the impact of the number of generators (hyperparameter M) in our Mix-StAGE  model. While for small number of speakers (i.e. 2 speakers) a single generator is good enough, the positive effect of multiple generators can be observed as the number of speakers increase (see Table \ref{tab:main}). We also vary  $M \in \lbrace 1, 2, 4, 8, 12 \rbrace$ and observe that improvements seem to plateau at $M=8$ with only marginal improvements for larger number of sub-generators. For the ablation study we refer the readers to the supplementary.
\subsubsection{Attribute-level Style Preservation in Multi-Speaker Models}
We also study style preservation for attributes in Section \ref{sec:exp} as a perceptual study in Figure \ref{fig:human_study3}. We observe that humans deem animations generated by Mix-StAGE   significantly more natural in most cases.  High scores ranging 60-90\% for style preservation correctness, with Mix-StAGE  outperforming others, are observed for pairs of speakers in Figure \ref{fig:human_study3}\makered{b}. This indicates that style preservation may be a relatively easy task as compared to style transfer for multi-speaker models. With this, we now shift our focus to style transfer.

\input{z73_human_study3}
\subsection{Style Transfer}
\subsubsection{Speaker-level Style Transfer}
To study our capability to transfer style of a specific speaker to a new speaker, we will compare the gesture spaces between the original speakers and the transferred speakers. Figure \ref{fig:tsne}\makered{a} shows that each original speaker occupies different regions in the $M^2$ gesture space. Using our Mix-StAGE  model to transfer style, we can see the new gesture space in Figure \ref{fig:tsne}\makered{b}. For the transferred speakers the 2 spaces look quite similar. For instance, `\textit{Corden}' style (a speaker in our dataset) is represented by the color blue in Figure \ref{fig:tsne}\makered{a} and occupies the lower region of the gesture space. When Mix-StAGE  generates co-speech gestures using audio of `\textit{Oliver}' and the style of `\textit{Corden}', it occupies a subset of `\textit{Corden's}' region in the gesture space, also represented by blue in Figure \ref{fig:tsne}\makered{b}. We see a similar trend for styles of `\textit{Oliver}` and `\textit{ytch\_prof}'. This is an indication of a successful style transfer across different speakers. We note the lack of clean separation in the gesture space among different styles as there could common gestures across multiple speakers.

For the perceptual study, we want to know if humans can distinguish the generated speaker styles. For this, we show human annotators two videos: a ground truth video in a specific style, and a generated video which is either from the style of the same speaker or a different speaker. Annotators have to decide if this is the same style or not. We use the 4-speaker model for this experiment. Figure \ref{fig:human_study2}\makered{b} shows naturalness preference and \ref{fig:human_study2}\makered{c} shows percentage of the time style was transferred correctly. Our model Mix-StAGE  performs best in both cases. This trend is corroborated with higher inception scores in Table \ref{tab:inception}.

\subsubsection{Impact of Number of Speakers for Style Transfer}
In Table \ref{tab:inception}, we observe that increasing the number of speakers used for training also increases the average inception score for the stylized gesture generations. This is a welcome effect as it indicates increases in the diversity and the accuracy of the generations.

\input{z79_attribute_heatmaps}
\subsubsection{Attribute-level Style Transfer in Multi-Speaker Models}
We study four common attributes of gesture style which are also visually distinguishable by humans: (1) sitting vs. standing, (2) high vs low gesture frequency, (3) left vs right body orientation and (4) left vs right primary arm. Speakers were selected carefully to represent each extremes of these four attributes. We run a perceptual study similar to the one for speaker-level styles. However, we ask the annotators to judge if the attribute is the same in both of the videos (e.g. are both the people gesturing with the same arm?). Results from Figure \ref{fig:human_study1} show that Mix-StAGE  generates more (or similar) number of natural gestures with the correct attribute-level style compared to the other baselines. We also observe that it is harder for humans to determine if a person is standing or sitting, which we suspect is due to the missing waistline in the animation.

For a visual understanding of the generated gestures and stylized gestures, we plot a style-content heatmap in Figure \ref{fig:att_heatmap}, where columns represent generations for a specific style, while rows represent different speaker's audio as input. These heatmaps show that gestures are consistent across audio inputs but different between styles. Accuracy and diversity of style transfer is corroborated by inception scores in Table \ref{tab:inception}.

\section{PATS dataset }
\subsection{Speaker List}
The list of speakers in the dataset are in Figure \ref{fig:dendrogram} as a dendrogram. This dendogram was created using text as the discriminating features. Speakers within the same cluster have a similar vocabulary. For the purposes of our experiments we use the speakers listed in Table \ref{tab:evil} and \ref{tab:main_complete}.
\input{z92_text_cluster.tex}

\subsection{Attributes}
We define 4 different attributes in Table \ref{fig:attrtable} and select pairs of speakers that demonstrate visually striking differences with respect to those attributes.
\input{z72_attrtable}

\section{Other Results, Discussions and Future Directions}
These results complement Section 6 of the main paper with a few more observations, explorations and ablation studies. This is followed by potential future directions.
\subsubsection{Attribute-Level Style Preservation}
Gesture generation for pairs of speakers shows improvements in PCK and F1 scores (see Table \ref{tab:evil}) following the trend of perceptual study in Figure 6 of the main paper.
\input{z83_table_evil}

\subsubsection{Speaker-Level Style Preservation}
Complete numerical results for speaker-level style preservation (for Table 1 in the main paper) are listed in Table \ref{tab:main_complete}. The PCK and F1 scores of the individual speakers show the same trend as the average score for each model.
\input{z82_table_main}

\subsubsection{Impact of value of $M$ on gesture generation}
We run an ablation study on the choice of $M$ for the pose decoder. We report the average of PCK and F1 scores in Table \ref{tab:mode_ablation} which were calculated for each speaker in single-speaker models. We find the the scores plateau with increasing values of $M$ for single speaker models unlike multi-speaker models like Mix-StAGE .
\input{sup_z89_table_mode_ablation_concise}

\subsubsection{Exploring Style Control}
As a preliminary experiment, we modified the style vector to $[0.5, 0.5]$ in order to mix the styles of two speakers with different Primary Arm Functions. The generated gesture space in Figure \ref{fig:mix} indicates that different speaker styles could be interpolated into a completely new style.
\input{z75_mix_heatmaps}

\subsubsection{Future Directions}
Our efforts were aimed at modeling, disentangling and transferring gesture style under the assumption that the emotional state of the speaker does not affect the gestures. While this is reasonable for speakers in PATS, which are mostly scripted monologues, it may not be true in general hence motivating an interesting future direction. Another direction, that might induce diversity in the generated gestures, is the inclusion of verbal information (i.e. natural language). This may not be trivial in context of style transfer as the difference in vocabulary of different speakers could create an unwanted bias - some words might get associated with certain styles of gesturing.

\section{Implementation Details}
This section gives more detail about the exact architectures used for our model also described in Section 4 of the main paper.
\subsection{Network Architectures}
Figure \ref{fig:encoder} and \ref{fig:decoder} consists of the visual representation of the architectures used for our model Mix-StAGE . For the decoder $\bigoplus$ is a weighted sum as described in Equation (2) of the main paper. Every operation is a \textbf{1D-convolution} followed by a \textbf{Batch-Norm} and finally \textbf{ReLU}. Each convolution uses a kernal size of 3 and hop length of 1, except for cases where temporal dimension is downsampled where the kernal size is 4 and hop length is 2.

\subsection{Training Details}
We use Adam \cite{kingma2014adam} to optimize the model with a exponentially decaying learning rate of $0.001$. We train each model for 60000 iterations while check-pointing every 3000 iterations. Finally, we choose the best model based on loss on the development set. We use $\lambda_{id} = 0.1$ to prevent the style consistency loss from stealing focus while training the pose gesture generator.
\input{sup_z91_encoder.tex}
\input{sup_z90_decoder.tex}

\subsection{Human study experiments}
We conducted our human studies on Amazon Mechanical Turk (or AMT), for which we used 100 random videos for each speaker which gave us 2400 pairs of comparisons per model for each study (out of 5). This is a significant number of comparisons and helps with reliability of the results. Each annotation task contained 20 videos and is performed by 3 different users; hence we had approximately (2400*3/20) = 360 participants.

To help filter unreliable annotators, we use two ground truth videos from the same speaker with the same style as control samples. If annotators tag these two videos as different styles, then we disregard this annotation set as unreliable.\\

\noindent \textbf{Sample study for style transfer (other studies also follow similar method)}\\
Two videos are shown to the user. One video is a ground truth(Speaker A Style A) and the other is generated by a model. The generated video could be either of (a) Speaker A Style A or (b) Speaker B Style A.
We ask two questions to measure correctness of style transfer and naturalness:
\begin{enumerate}
    \item Do the animations have different styles of gestures?
    \item Which of the videos 1 or 2 has more Natural gestures with respect to the audio?
\end{enumerate}

\section{Videos: Style Transfer and Preservation}
We refer the readers to \url{http://chahuja.com/mix-stage} for demo videos.

\section{Video Frames: Style Preservation Qualitative Results}
These results complement Figure 8 in the main paper. We plot some more animation figures generated by random audio samples in the test-set to provide some more samples for qualitative judgment in Figure \ref{fig:animations}.
\input{sup_z92_animations.tex}

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text:
Q: python3: extract IP address from compiled pattern I want to process every line in my log file, and extract IP address if line matches my pattern. There are several different types of messages, in example below I am using p1andp2`.
I could read the file line by line, and for each line match to each pattern. But
Since there can be many more patterns, I would like to do it as efficiently as possible. I was hoping to compile thos patterns into one object, and do the match only once for each line:
import re

IP = r'(?P<ip>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})'

p1 = 'Registration from' + IP + '- Wrong password'
p2 = 'Call from' + IP + 'rejected because extension not found'

c = re.compile(r'(?:' + p1 + '|' + p2 + ')')

for line in sys.stdin:
    match = re.search(c, line)
    if match:
        print(match['ip'])

but the above code does not work, it complains that ip is used twice.
What is the most elegant way to achieve my goal ?
EDIT:
I have modified my code based on answer from @Dev Khadka.
But I am still struggling with how to properly handle the multiple ip matches. The code below prints all IPs that matched p1:
for line in sys.stdin:
    match = c.search(line)
    if match:
        print(match['ip1'])

But some lines don't match p1. They match p2. ie, I get:
1.2.3.4
None
2.3.4.5
...

How do I print the matching ip, when I don't know wheter it was p1, p2, ... ? All I want is the IP. I don't care which pattern it matched.

A: You can consider installing the excellent regex module, which supports many advanced regex features, including branch reset groups, designed to solve exactly the problem you outlined in this question. Branch reset groups are denoted by (?|...). All capture groups of the same positions or names in different alternative patterns within a branch reset grouop share the same capture groups for output.
Notice that in the example below the matching capture group becomes the named capture group, so that you don't need to iterate over multiple groups searching for a non-empty group:
import regex

ip_pattern = r'(?P<ip>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})'
patterns = [
    'Registration from {ip} - Wrong password',
    'Call from {ip} rejected because extension not found'
]
pattern = regex.compile('(?|%s)' % '|'.join(patterns).format(ip=ip_pattern))
for line in sys.stdin:
    match = regex.search(pattern, line)
    if match:
        print(match['ip'])

Demo: https://repl.it/@blhsing/RegularEmbellishedBugs

A: why don't you check which regex matched?
if 'ip1' in match :
    print match['ip1']
if 'ip2' in match :
    print match['ip2']

or something like:
names = [ 'ip1', 'ip2', 'ip3' ]
for n in names :
    if n in match :
        print match[n]

or even
num = 1000   # can easily handle millions of patterns =)
for i in range(num) :
    name = 'ip%d' % i
    if name in match :
        print match[name]

A: thats because you are using same group name for two group
try this, this will give group names ip1 and ip2
import re

IP = r'(?P<ip%d>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})'

p1 = 'Registration from' + IP%1 + '- Wrong password'
p2 = 'Call from' + IP%2 + 'rejected because extension not found'

c = re.compile(r'(?:' + p1 + '|' + p2 + ')')

A: Named capture groups must have distinct names, but since all of your capture groups are meant to capture the same pattern, it's better not to use named capture groups in this case but instead simply use regular capture groups and iterate through the groups from the match object to print the first group that is not empty:
ip_pattern = r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})'
patterns = [
    'Registration from {ip} - Wrong password',
    'Call from {ip} rejected because extension not found'
]
pattern = re.compile('|'.join(patterns).format(ip=ip_pattern))
for line in sys.stdin:
    match = re.search(pattern, line)
    if match:
        print(next(filter(None, match.groups())))

Demo: https://repl.it/@blhsing/UnevenCheerfulLight

A: Adding ip address validity to already accepted answer.
Altho import ipaddress & import socket should be ideal ways, this code will parse-the-host,
import regex as re
from io import StringIO

def valid_ip(address):
    try:
        host_bytes = address.split('.')
        valid = [int(b) for b in host_bytes]
        valid = [b for b in valid if b >= 0 and b<=255]
        return len(host_bytes) == 4 and len(valid) == 4
    except:
        return False

ip_pattern = r'(?P<ip>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})'

patterns = patterns = [
    'Registration from {ip} - Wrong password',
    'Call from {ip} rejected because extension not found'
]

file = StringIO('''
Registration from 259.1.1.1 - Wrong password,
    Call from 1.1.2.2 rejected because extension not found
''')

pattern = re.compile('(?|%s)' % '|'.join(patterns).format(ip=ip_pattern))

list1 = []
list2 = []

for line in file:
    match = re.search(pattern, line)
    if match:
        list1.append(match['ip']) # List of ip address
        list2.append(valid_ip(match['ip'])) # Boolean results of valid_ip

for i in range(len(list1)):
        if list2[i] == False:
            print(f'{list1[i]} is invalid IP')
        else:
            print(list1[i])

259.1.1.1 is invalid IP
1.1.2.2

[Program finished]

redpajama_set_name: RedPajamaStackExchange