A circuit or device may be described by its transfer function, the mathematical relation of input to output. With analog circuits, composed of resistors, capacitors, and so on, only certain transfer functions are practical, while a digital device incorporating a computer could realize almost any conceivable transfer function. Yet before the 1960s few engineers even considered digital circuits for signal processing because computers were hardly available, they were extremely expensive, and they were not fast enough to do real..time signal processing. As computers became widely used for simulation in the 1960s, there arose the idea, in a number of places, that the signal processing itself might be done by computer. This article describes this and other developments for signal processing in the turbulent 1960s.
Frederik Nebeker, "Going Digital: The 1960s," in Signal Processing: The Emergence of a Discipline, 1948-1998 (Piscataway, NJ: IEEE Press, 1998), 55-87.
This page was last edited on 29 July 2014, at 10:05.


Macro bowls are a simplified way of eating nutrient-dense, whole foods with the added benefit that you don't need to follow an 'exact' recipe. You get to choose your favourite seasonal vegetables, your preferred carbohydrate and protein sources and you can select from lots of extra superfood add ons depending on what you fancy. It's a good idea to batch cook rice, other grains or sweet potatoes in advance and roast a whole chicken so you can use the leftovers for quick meals or packed lunches during the week.
Foundation: each macro bowl is a combination of grains, vegetables, beans, meat or fish; and seaweeds or fermented foods such as sauerkraut or kimchi. There is no correct breakdown of macronutrients but here are some rough guidelines (men can double the wholegrain and protein portions).
Wholegrain: 1 heaped handful of cooked grains such as brown rice, quinoa, millet or cooked sweet potato.
Vegetables: 1-2 fistfuls of raw or very lightly steamed vegetables such as kale, spring onions, broccoli, carrots, beetroot, green beans.
Protein: A palm-sized portion of cooked free-range chicken breast or baked wild salmon; 150g raw, hard tofu or a cup of beans such as cooked edamame or black beans.
Fermented Foods or Sea Vegetables: 1-2 tablespoons. Fermented foods like sauerkraut and kimchi support healthy digestion and gut health. Sea vegetables such as nori, wakame, pulse and kombu are a valuable source of bioavailable essential minerals such as iodine, magnesium and sulphur and support hormonal health.
Seeds, Nuts, Oils + Condiments: Black or white sesame seeds, pumpkin seeds and sunflower seeds can be toasted for extra flavour. Toasted sesame oil and coconut oil are great for Asian themed bowls. Try extra virgin olive oil infused with red chilli, herbs or garlic for a Mediterranean theme. For condiments I like tamari (a gluten free, non-GMO soy sauce), pickled ginger, pickled beetroot, tahini, basil or tomato pesto. Include 1-2 tbsp of seeds plus 1/2 to 1 tbsp oils or other oil based condiments.
(serves 1 – double / triple ingredients for additional servings).
Cook the rice according to packet instructions (generally 1 part rice to 1.75 parts water and simmer for 15 minutes).
Meanwhile prepare your choice of protein: shred the cooked chicken breast / place the eggs into a saucepan of water and simmer for 3 minutes, cool, remove the shell and halve / Drain the black beans and heat gently in a saucepan for 5 minutes / slice the tofu into cubes and stir into the rice.
In a bowl combine the rice, sesame seeds, sunflower seeds, tamari and coconut or sesame oil.
To build the macro-bowl spoon the rice into the bowl. Add the chicken/egg/beans/tofu. Tuck the carrot matchsticks between the chicken and rice and sprinkle with spring onion, nori and extra sesame seeds. Serve.


The squad of perfect soldiers in ranks of three by three.
Looking forward, waiting for orders, standing at ease.
Tallest on the right and shortest on the left, with rifles in hand.
Their medals and shoes shining, like the trumpets in their band.
The ensign waving in the wind, flying high and proud.
When they're called to attention, their feet stomp hard on the ground.
And laced around their shoulders are lanyards that will forever glow gold.
With their belt buckles around their thin waists always shining.
Accompanied by the glorious red trouser leg lining.
Just like his sword, that blinds with disciplined light.
While they keep their heads high within each height - ordered line.
While at the same time they lead their country to glory.


text:
Birmingham City Council and Birmingham and Solihull Women's Aid launch new hub to tackle domestic violence and homelessness
Published: Wednesday, 20th March 2019
Today Birmingham City Council in partnership with Birmingham & Solihull Women's Aid have opened a new domestic abuse hub that will provide support to those experiencing and fleeing domestic violence.
The centre will take a holistic approach to supporting women suffering from abuse that will include a range of both prevention and crisis support services. It has been designed to be more than just a physical place for women to take shelter. The hub will act as a specialist environment that will provide advice, information and support to women at risk of domestic abuse, who are as a result, at risk of homelessness. Birmingham and Solihull Women's Aid working in partnership with the Council's Housing Options team will ensure a tailored, specialist response leading to improved outcomes for women and children.
The pilot has been co-funded by the Council and Women's Aid and is expected to see up to 25 new cases a day.
Councillor Sharon Thompson, Cabinet Member for homes and neighbourhoods said, "I'm really proud to be involved in the launch of this new centre that will provide a safe haven, expert support and the start of the end to the suffering of women and their children right across this city.
"We can't continue putting plasters on major issues like domestic violence and homelessness, hoping that they just go away. They need to be faced head on with innovation, the right support and the right expertise. That's why we joined with our partner, Women's Aid, so that we can come together and put in place the best response to domestic violence and the risk of homelessness"
"In Birmingham, we've seen the number of people experiencing homelessness skyrocket over the last five years and until we start to address these underlying causes, we'll never eradicate homelessness from our city. Domestic violence is one of the leading causes for homelessness. By putting this specialist care and support in place, it means that those who are at risk of homelessness are identified and supported at an earlier stage meaning that they are less likely to become homeless."
In June of last year, Birmingham launched both its homelessness and domestic abuse prevention strategies which identified domestic violence as accounting for 20% of all of its homeless cases. The strategies were both led by more than 30 organisations from across the public, private and third sectors.
Maureen Connolly, Chief Executive at Birmingham and Solihull Women's Aid said, "This collaboration is a significant advance in the way we approach domestic violence in Birmingham. By understanding and responding to its impact upon women and children, the multi-disciplinary team offers a service, focused upon the needs of women and children affected by domestic violence. It takes into account all factors, in particular the risks that might lead to the need to leave behind everything they have, including their homes; belongings; family; friends; pets; as well as their employment and schools with a view to become safe.
"Our job is to be empathetic, informative and honest, offer a prevention approach as well as to support women and children through changes to make them free from the fear of abuse."
While today marks the official opening of the hub, it has been operational on a trial basis for two months and feedback has been well received by women using this service.
One woman commented, "I finally feel like I've made a massive step in the right direction and I can soon leave all the terrible experiences in the past to move on to a new life. This service is supportive and life-changing. It's given me an awesome sense of relief."
For an appointment at the Hub, please call the home options number on 0808 169 9604. For emergency help, the 24 hour Women's Aid helpline number is 0808 200 0247.

redpajama_set_name: RedPajamaCommonCrawl


text:
HomeAudiobooksContemporary Fiction
Tooth and Claw and Other Stories
Written by T.C. Boyle
Narrated by T.C. Boyle
The fourteen stories gathered here, which have appeared in The New Yorker, GQ, Harper's, McSweeney's, and Playboy, as well as in The O. Henry Prize Stories and Best American Stories volumes, display T. C. Boyle's imaginative muscle, emotional sensitivity and astonishing range. There are the whimsical tales for which Boyle is justly famous, including "Swept Away," which tells of a female ornithologist who falls in love on the blustery island of Unst, and "The Kind Assassin," about a bored and loveless radio shock jock who sets the world record for most continuous hours without sleep-and who may never sleep again.
Listeners will love the comedic drama and lyrical beauty of the title story, about a young man who must contend with a vicious feral cat from Africa that he has won in a bar bet. And who could resist the gripping power of "Dogology," about a young woman in suburban New England who becomes so obsessed with man's best friend that she begins to lose her own identity to a pack of strays, or "Chicxulub," a nerve-shattering tale of collision, whether it be that of a young woman with a car or of huge objects from outer space slamming into the planet. With these compelling and always entertaining stories, Boyle proves once again that he is "a writer who can take any topic and spin a yarn too good to put down" (Men's Journal).
Publisher: Penguin Random House AudioReleased: Sep 6, 2005ISBN: 9781415923085Format: audiobook
More Audiobooks By T.C. Boyle
Outside Looking In: A Novel
T.C. Boyle
The Terranauts: A Novel
The Relive Box and Other Stories
The Tortilla Curtain
Pets!
A Friend of the Earth
Baseball!
Rolfe Humphries
Without a Hero: Stories
Moral Disorder: And Other Stories
Dear Life: Stories
The David Foster Wallace Reader
The Golden House: A Novel
Against the Day
A Pale View of Hills: A Novel
Brief Interviews with Hideous Men
Pinball, 1973: Trilogy of the Rat
The Song of the Lark
Oblivion: Stories
This Is How You Lose Her
Junot Díaz
After the Quake: Stories
Signifying Rappers
Bagombo Snuff Box
What We Talk About When We Talk About Love
Salvation City: A Novel
Sigrid Nunez
Wind/Pinball: Two Novels
Lunar Park
Tenth of December: Stories
George Saunders
Zero K
The Whore's Child and Other Stories
Fierce Pajamas: Selected Humor Writing from The New Yorker
David Remnick
Fierce Invalids Home from Hot Climates
Tom Robbins
A German Requiem
Although of Course You End Up Becoming Yourself: A Road Trip with David Foster Wallace
The Body Artist
Two Years Eight Months and Twenty-Eight Nights: A Novel
Travels in the Scriptorium
Stories of Breece D'J Pancake
Breece D'J Pancake
T.C. Boyle's Fictions of Catastrophe
The Best Writing Advice of 2016
Suffering In Silence, And Out Loud
LISA HALLIDAY'S DEBUT novel, Asymmetry, begins with a lopsided affair—a perfect vehicle for a story of inexperience and advantage. This romance is between Alice, a young woman in publishing, and Ezra Blazer, a literary éminence grise, who resembles a
Match Book
Dear Match Book, I've been reading "The Norton Anthology of Short Fiction" over the last few years, but I can't seem to find satisfying contemporary literary fiction. I think the publishing world's obsession with New York City is pompous and a little
The Gradual Disappearance of Jane Ashland Casts an Unlikely Spell
A Year in Reading: Brandon Hobson
Like probably most people reading this, last year I read a great deal and obviously can't mention everything in this space. In terms of these books, I read some chilly motherfuckers and some funny motherfuckers. Here are a few standouts from 2018: Er
Good Writers Borrow, Great Writers Remix
Bowlaway
BY Elizabeth McCracken PAGES 371 | GENRE Historical Fiction REVI EW BY David Canfield @davidcanfield97 I THOUGHT I KNEW THE SPRAWLING family saga—intimate domestic dramas smacked against vast historical backdrops, decades spanned in the sprint to
Eileen Myles on the Most Interesting Nights of Their Life
In this video in support of the Festival Neue Literatur, which celebrates contemporary German-language and American fiction (this year's theme: Queer as Volk) and begins today in New York, Eileen Myles talks about the importance of translators and FN
Mamta Chaudhry on What Ghosts Can Access That the Rest of Us Can't
Celebrating Socially Engaged Fiction at the Aspen Words Literary Prize
By the Book: Richard Lloyd Parry
Q: What books are on your nightstand? A: The books beside my bed are like the expensive, and suspiciously unsullied, pair of running shoes in the cupboard: an aspiration, and a symbol of the man I would like to be rather than the one I truly am. I've
A New $10,000 Literary Award Determined by Online Popular Vote
Today, Albertine, the French- and English-language bookstore run by the Cultural Services of the French Embassy (named, I can only imagine, after Proust's Albertine Simonet), has announced the inaugural Albertine Prize, an annual $10,000 award ($8,00


The beautifully restored Assembly Rooms Edinburgh on George Street is proud to be hosting Brian Taylor's Big Debate – a live broadcast on BBC Radio Scotland – on Friday 25 October at 12 noon.
This is an hour-long live discussion hosted by Brian Taylor and covering topical issues along the lines of its television counterpart, Question Time. The event, to be held in the Assembly Rooms Music Hall, will feature a panel of politicians and prominent figures from the worlds of business, academia, arts and the media, and all are welcome to attend.
If you would like to take the opportunity to put a question on a burning issue to the panel, or just come along and be part of the audience, entry is completely free.
Details of the panel and the topics to be discussed are usually announced nearer the time, but tickets need to be booked in advance. You can secure your place at this lively and informative event by completing the application form on the BBC website.


text:
Q: How can I use redux in react typescript?
ERROR in [at-loader] ./src/app/components/partials/userPartial.tsx:101:33
TS2339: Property 'level' does not exist on type 'IntrinsicAttributes & IntrinsicClassAttributes<Component<{}, ComponentState>> & { children?: Reac...'.

ERROR in [at-loader] ./src/app/components/partials/userPartial.tsx:102:33
TS2339: Property 'medal' does not exist on type 'IntrinsicAttributes & IntrinsicClassAttributes<Component<{}, ComponentState>> & { children?: Reac...'.

But interfaces provided. Component where I getting errors:
<NotificationTypes
   level={item.level}
   medal={item.medal}
   referred={item.user}
   points={item.points}
   type={item.type}
/>
Component where intefaces provided

export interface IPoints {
    earned: number,
    total: number
}

export interface INotificationTypesProps {
    level: any,
    medal: any,
    points: IPoints,
    type: number,
}

class NotificationTypes extends React.Component<INotificationTypesProps , any> {
    constructor(props:any){
        super(props);
    }

    render(){

        const { type, level, points, medal} = this.props;

        switch(type) {
            case 132:
                return (

                        <Translate value="notification.earned"/>
{medal}

                );
            case 131:
                return (

                        <Translate value="notification.received"/> {points.earned} <Translate
                        value="notification.pointScored"/>
                         {points.total} <Translate value="notification.points"/>

                );
            default:
                return

        }
    }
}

const mapStateToProps = (state) => {
    return {
        _CONFIG: state._CONFIG.CDN.static_uri,
    };
};
export default connect(mapStateToProps, null)(NotificationTypes);

redpajama_set_name: RedPajamaStackExchange


One of our favourite things to talk about here at Kingstonist is food. This is probably because there is so much great cuisine to enjoy in ygk. Whether we're reviewing a restaurant or polling our readers on their favourites, food always brings up a good discussion. While we have looked at who serves up the best Chinese, Thai or Vietnamese, today we're getting more specific. Let's talk spring rolls.
Which restaurant makes the best spring rolls in Kingston?
A perfect spring roll can set off the whole tone of a meal. What makes a spring roll great in your eyes (or stomach)? Do you like the wrapper to be thick and bubbly or thin and crisp? The sauce is a major factor to consider as well. Peanut sauce, hoisin sauce, the sweet and common nuoc cham sauce…all of these can be equally as important as the roll itself. Tell us what makes your spring roll perfect, and if we missed your favourite spot, be sure to give them a shout out in the comments below.
Thanks to stu_spivack for today's mouthwatering photo.


text:
Mathias est un nom propre qui peut désigner :

Prénom
 Mathias est un prénom masculin.

Saints chrétiens
 Matthias ou Mathias (), apôtre qui remplaça Judas Iscariote après sa trahison et sa mort ; fêté le 14 mai en Occident et le 9 août en Orient.

Patronyme

 Émile Mathias (1861-1942), physicien français ;
 Maurice Mathias (1912-2006), peintre belge ;
 Georges Mathias (1826-1910), pianiste et compositeur français ;
 Jacques Mathias (1752-?), avocat et homme politique français ;
 Vincent Mathias, directeur de la photographie français.

Hydronyme
 La rivière Mathias, cours d'eau de l'Île du Sud de la Nouvelle-Zélande.

Interjection calédonienne
Le prénom Mathias, tout comme d'autres prénoms (Alice et Baptiste notamment), est également utilisé comme interjection de surprise en Nouvelle-Calédonie.

Voir aussi

 Lycée Mathias

Références
Prénom masculin
Prénom hébraïque

redpajama_set_name: RedPajamaWikipedia


As Soon As I was 17 I had work cutting a neighbor's garden. It wasn't a bad strategy to commit half an hour: I bought to generally be all alone and that i could daydream, which in those days was pretty much an entire time job.get-essay.com This task paid me 10 bucks every other 7-day period. Even in the summertime of 1982, that wasn't big money. However, it had been decent to get the hard cash, nonetheless I often didn't know how to deal with it. As Soon As I was completed enjoying it on computer games or McDonald's, I believed as though I had just tossed it away from. That exact twelve months the band Pinkish Floyd launched A Final Minimize . their very first recording following your Walls . The Retaining wall had converted me in to a devoted Pinkish Floyd fan. I'd purchased close to everything they'd unveiled, also the fairly as soon as possible formative goods. On the day The Final Cut back reached the racks, I raced onto a report retailer, happy to acquire a thing effective to do with this money. Though it wasn't as effective as The Divider . I have done not really feel just like I needed chucked your money aside. Whatever I gotten from songs and textbooks survived much longer in me than cuisine as well as short very high of computer games.
When I was 42, I started out a different position. I needed wasted the last 20 years hanging around kitchen tables and authoring fiction we got no good fortune advertising. I had come to be very thinking about what is routinely labeled as spirituality, especially the way it pertaining to creative imagination and writing. I was way interested in this field than I was in a of those novels I used to dispose of. I couldn't quit considering it. I thought with regards to it right after i happened to run, once i showered, and while I did the recipes. If you ask me it turned out for instance a problem as their answers at all times taken more desirable even more exciting thoughts. Basically, I loved it. The situation, I had to acknowledge, was that I possessed no clue how to earn money giving it. I had only ever produced moolah by cutting up timber, no matter whether mowing yards or covering steaks. I needed do not ever been paid for the things i would gladly do free. During those times, really being purchased what I treasured seemed forget about true than those daydreams during which I would float as a good teen. I realized creating, and I understood the right way to speak with sectors; now I will have to how to be bought it.
8 several years later I discovered , myself personally standing regarding a podium at a seminar hallway bringing a 40-minute keynote dialog to eight hundreds freelance writers. How have done I recieve there? I suppose it acquired something related to weblogs I had written, or interview I needed achieved, and groups I needed instructed, however in truth the most important thing I needed performed ended up being to place emphasis each day about a lot I treasured something i wished to distribute. Folks purchase the things they worth. A lot more distinctly I perceived the price of a few things i was writing about, the greater amount of definitely many others could perceive it too. If you desire to earn an income as an author, place all your awareness regarding how a great deal you benefits the story you're informing. If you need to study marketplaces, high-quality; if you desire to enhance your craft, good. But not any of which will help should you not perceive value of your history or poem or essay whilst it grows from a lawn no one else could see. Somewhere in existence are target audience who will be equally enthusiastic as I had been together with the Finalized Lower to industry their money for the purpose they will certainly get in your report. But first you must learn the value of anything you take pleasure in, separate of just about anyone else's view, are aware of it as you know what amount of you love the reviews you choose; and what may have one time appeared like an aspiration will mature definitely into real truth.


text:
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Home South Africa Sri Lanka T20 Series 2013 South Africa vs Sri Lanka: Some relief for Proteas after T20 series win
South Africa vs Sri Lanka: Some relief for Proteas after T20 series win
CricNama at August 05, 2013 South Africa, Sri Lanka, T20 Series 2013,
The ODI series against Sri Lanka was more of a nightmare for South Africa where they faced one defeat after the other and lost their 4th position in the ODI rankings. However, T20 series has been a silver lining for them. South Africa defeated the hosts in second T20 played at Hambantota by 22 runs, stamping a 2-0 lead in the series.
[caption id="attachment_2125" align="alignright" width="300"] The 36 on 21 balls from the 'Charged bat' of Miller made him Man of the Match.[/caption]
South African captain Faf du Plessis won the toss and set out to bat. Batsmen's performance was pretty ordinary in the beginning until the fireworks by David Miller.
South Africa lost Henry Davids at the score of 11. Quinton de Kock and du Plessis were also back in pavilion before the total could reach 50 . JP Duminy and AB de Villiers dragged the score to 86, and then came David Miller after de Viliers' dismissal. Miller led the team to a decent 145 total with his "charged bat". He scored 36 off just 21 with two sixes and one four. On the other end, Duminy scored 30 off 23.
Sri Lanka's Nuwan Kulasekara and Sachithra Senanayake took two wickets each and Lasith Malinga took one.
Sri Lanka looked completely helpless in the chase. Their batting line displayed total opposite picture as compared to ODIs. They lost three valuable wickets of Mahela Jayawardene, Dinesh Chandimal, and Kusal Perera at 48.
Kumar Sangakkara and Lahiru Thirimanne pushed the score to 85 but Sri Lanka lost Thirimanne, Angelo Mathews, and Kumar Sangakkara one after the other in the next three overs. These three scored 18, 1, and 39, respectively. Sri Lanka only managed to score 123 for seven wickets by the end of 20 overs.
The victory over ranked number one team will add a positive impact on the position of Proteas in the rankings. Miller was declared the man of the match. The next and final match of the series will be played on Thursday at Hambantota.
South Africa, Sri Lanka, T20 Series 2013
CricNama
PSL 2018: Peshawar Zalmi Vs Karachi Kings in Pictures
Pakistan Super League (PSL) Artist Press Conference
The press conference of Pakistan Super League 2018 (PSL 3) artist was held yesterday at the Dubai International Cricket Stadium. The backdr...
Facebook and Google fight to broadcast cricket in India
Facebook and Google have positioned themselves to obtain the rights to broadcast international cricket matches in India, which are to be a...
Chinese Cricketers Join Peshawar Zalmi
Chinese cricketers Jian Li and Yufei Zhang have reached Dubai as special guests of Peshawar Zalmi chairman Javed Afridi. Both players will t...
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text:
Qaleh-ye Ali Hoseyn Seljuki (, also Romanized as Qal'eh-ye 'Alī Ḩoseyn Seljūqī; also known as Qal'eh-ye 'Alī Ḩoseyn) is a village in Dasht-e Zarrin Rural District, in the Central District of Kuhrang County, Chaharmahal and Bakhtiari Province, Iran. At the 2006 census, its population was 111, in 18 families. The village is populated by Lurs.

References

Populated places in Kuhrang County
Luri settlements in Chaharmahal and Bakhtiari Province


NOTE: No Sunday Dharma Service at Manhattan Won Buddhist Temple on Sept. 16th.
Master Chwasan served as the Fourth Head Dharma Master of Won Buddhism, succeeding Master Daesan.
He entered the Won Buddhist tradition at the age of twenty and served as the head minister of the Chongno Temple in Seoul. He also served on the Supreme Council before being inaugurated as the Fourth Head Dharma Master in 1994.
Since stepping down as the Head Dharma Master in 2006, he has been practicing and devoting himself to realize three great vows: world peace, reunification of South and North Korea, and healing the mind and heart of the modern individual.
He has been a lifelong truth seeker using the wisdom he has attained through spiritual practices as a basis for providing specific solutions for many practical things from social issues to everyday mindful living.


text:
\section{Introduction}

\begin{figure}[htbp]
	\centering
	\includegraphics[width=6cm]{Figures/Fig1.pdf}

	\caption{Example conversations sampled from the benchmark dataset~\citep{poria2021recognizing}}\label{fig-1}
\end{figure}
With the recent proliferation of open conversational data on social media platforms, such as Twitter and Facebook, Emotion Analysis in Conversations (EAC) has become a popular research topic in the field of Natural Language Processing (NLP). Most of the existing works on EAC mainly focus on Emotion Recognition in Conversations (ERC), i.e., recognizing emotion labels of utterances (e.g., happy, sad, etc.) ~\citep{poria2017context,poria2019emotion,wang2020contextualized,zhang2020knowledge}. However,~\citet{poria2021recognizing} point out that these studies lack further reasoning about emotions, such as understanding the stimuli and the cause of the emotion. Since Recognizing Emotion Cause in Conversations (RECCON) holds the potential to improve the interpretability and performance of affect-based models,~\citet{poria2021recognizing} put forward a new promising task, named RECCON, which includes two different sub-tasks: Causal Span Extraction (CSE) at word/phrase level and Causal Emotion Entailment (CEE) at utterance level. Due to the simplicity and sufficiency describing emotion causes at the utterance level, we focus on the CEE task in this paper, whose goal is to predict which particular utterances in the conversational history contain the cause of non-neutral emotion in the target utterance.

Compared to the Emotion Cause Extraction (ECE) in news articles~\citep{gui2016event,xia2019emotion}, CEE is particularly challenging due to the informal expression style and the intermingling dynamic among interlocutors.
\citet{poria2021recognizing} consider CEE as a set of independent utterance pair classification problems and neglect the emotion and speaker information in the conversational history. Thus, they can neither capture the correlations between contextual utterances in a global view nor model the speaker's emotional influences, namely the intra-speaker and inter-speaker emotional influences.\footnote{The speaker's emotional influences are predominant types of emotion causes in the dataset as shown in Table~\ref{tab-1}.}
Intra-speaker emotional influences mean that the cause of the emotion is primarily due to the speaker's stable mood induced from previous dialogue turns. As shown in Figure~\ref{fig-1} (a), utterance 1 establishes the concept that Speaker A ($S_A$) likes winter, which triggers a happy mood for future utterances 3 and 5. Inter-speaker emotional influences mean that the emotion of the target speaker is induced from an event mentioned or emotion revealed by other speakers. As Figure~\ref{fig-1} (b) shows, $S_B$'s happy emotion may be triggered by the event ``getting married'' mentioned by $S_A$, or by the fact that $S_A$ is excited about getting married.

To remedy this defect, we tackle CEE in a joint framework. We classify multiple utterances synchronously to capture the correlations between contextual utterances and propose a TSAM to effectively model the speaker's emotional influences in the conversational history. Specifically, the TSAM contains three modules: EAN, SAN, and interaction module. The EAN provides utterance-to-emotion interactions to incorporate emotion information by performing attention over emotion embeddings. The SAN represents different speaker relations between utterances in a graph, which provides utterance-to-utterance interactions to incorporate speaker information by performing attention over the speaker relation graph. These two modules incorporate emotion and speaker information in parallel. Moreover, inspired by~\citep{li2021dual,tang2020dependency}, the interaction module interchanges relevant information between the EAN and SAN through a mutual BiAffine transformation. Finally, the entire TSAM can be stacked in multiple layers to refine iteratively and interchange emotion and speaker information.

\begin{itemize}
\item For the first time, we tackle CEE in a joint framework to capture the correlations between contextual utterances in a global view.
\item We propose a TSAM to model the speaker's emotional influences in the conversational history, which contains EAN, SAN, and interaction module to incorporate and interchange emotion and speaker information.
\item Experimental results on the benchmark dataset~\citep{poria2021recognizing} demonstrate the effectiveness of our proposed model, surpassing the SOTA model significantly.
\end{itemize}

\begin{figure*}[htbp]
	\centering

	\includegraphics[width=14cm]{Figures/A5.pdf}

	\caption{The top is the proposed model's entire architecture, and the bottom is the detailed architecture of model components: (a) EAN, (b) SAN. First, we obtain the contextual representation for each utterance with RoBERTa. Then, the TSAM is utilized to model the speaker's emotional influences in the conversational history. Finally, the cause prediction module is used to output the predictions.
	}\label{fig-2}

\end{figure*}

\section{Related Work}
\paragraph{ECE} Early works mainly exploit rule-based methods~\citep{lee2010text,lee2010emotion,chen2010emotion} to identify the potential causes for certain emotion expressions in the text.~\citet{gui2016event} first release a public annotated dataset for ECE, and based on which some feature based~\citep{gui2016emotion} and neural based methods~\citep{gui2017question,li2018co,ding2019independent,DBLP:conf/ijcai/XiaZD19,DBLP:conf/acl/Yan0PH20,li2021boundary} appear. To extract emotion and its corresponding cause jointly,~\citet{xia2019emotion} first put forward the Emotion-Cause Pair Extraction (ECPE) task and tackle it by a two-step method. Subsequently, many improved methods are proposed to tackle ECPE in an end2end manner~\citep{ding2020ecpe,ding2020end,yuan2020emotion,fan2020transition,wei2020effective,cheng2020symmetric,chen2020unified,chen2020end}. However, these works mentioned above use news articles as the target corpus for ECE, which largely reduces reasoning complexity. By contrast,
CEE is more challenging due to the intermingling dynamic among interlocutors and the informal expression style.

\paragraph{ERC} Recently, due to the proliferation of publicly available conversational datasets~\citep{zhou2018emotional,chen2019emotionlines,poria2019meld,chatterjee2019understanding,yuqiang2022}, there is a growing number of studies on ERC~\citep{hazarika2018icon,hazarika2018conversational,majumder2019dialoguernn,zhong2019knowledge,jiao2019higru,ghosal2020dialoguegcn,ishiwatari2020relation,ghosal2020cosmic,DBLP:conf/acl/ShenWYQ20,DBLP:conf/acl/ZhuP0ZH20,DBLP:conf/acl/HuWH20,guibon2021few,DBLP:conf/ijcai/ZhaoZL22,peng2022you}.
Although substantial progress has been made in ERC, these studies lack further reasoning about emotions, such as understanding the stimuli or the cause of an emotion expressed by a speaker~\citep{poria2021recognizing}.

\paragraph{RECCON} For further reasoning about emotions,~\citet{poria2021recognizing} propose a new task named RECCON, which contains two different sub-tasks: CSE at word/phrase level and CEE at utterance level.~\citet{poria2021recognizing} formalize CEE as a set of independent utterance pair classification problems, neglecting the emotion and speaker information in the conversational history. Specifically, they pair a target utterance with each utterance in its conversational history and determine whether the utterance contains the cause of emotion in the target utterance. Thus, they cannot capture the correlations between contextual utterances in a global view and fail to model the speaker's emotional influences in the conversational history. From a new perspective, we tackle CEE in a joint framework. We encode and classify multiple utterances synchronously to capture the correlations in a global view and propose a TSAM to model the speaker's emotional influences effectively.

\section{Task Definition}
We first define the task of CEE formally. For a target utterance $u_t$, i.e., the $t^{th}$ utterance in the conversation, the goal of CEE is to predict which particular utterances in the conversational history $L(u_t)=(u_1,u_2,...,u_t)$ are responsible for the non-neutral emotion in the target utterance. $u_i$ is set as a positive example if it contains the cause of non-neutral emotion in the target utterance and a negative example otherwise, where $i=1,...,t$.
The independent utterance pair classification framework~\citep{poria2021recognizing} performs $t$ independent classifications, each of which takes $(u_t, u_i)$ as input. Therefore, it fails to capture the correlations between contextual utterances in a global view.
On the contrary, the proposed joint classification framework only performs one joint classification with $L(u_t)$ as input,
which makes it possible to capture the correlations between contextual utterances.

\section{Method}
\input{methods.tex}

\section{Experimental Settings}
\subsection{Dataset and Evaluation Metrics}

\begin{table}[tbp]
\centering
\fontsize{10}{13}\selectfont
\renewcommand\tabcolsep{3.0pt}
\begin{tabular}{c|cc|c}
\hline
\multicolumn{3}{c|}{Statistics}                                                                                & RECCON-DD \\ \hline\hline
\multirow{6}{*}{\begin{tabular}[c]{@{}c@{}}Data\\ Distributions\end{tabular}}             & \multirow{2}{*}{Train} & Positive & 7269  \\
                                                                                &                        & Negative & 20646  \\ \cline{2-4}
                                                                                & \multirow{2}{*}{Dev}   & Positive & 347  \\
                                                                                &                        & Negative & 838  \\ \cline{2-4}
                                                                                & \multirow{2}{*}{Test}  & Positive & 1894  \\
                                                                                &                        & Negative & 5330  \\ \hline
\multirow{5}{*}{\begin{tabular}[c]{@{}c@{}}Cause\\ Type\\ Distributions\end{tabular}}     & \multicolumn{2}{c|}{No Context}   & 43\%  \\
                                                                                & \multicolumn{2}{c|}{Inter}        & 32\%  \\
                                                                                & \multicolumn{2}{c|}{Intra}        & 9\%  \\
                                                                                & \multicolumn{2}{c|}{Hybrid}       & 11\%  \\
                                                                                & \multicolumn{2}{c|}{Unmentioned}          & 5\%  \\ \hline
\end{tabular}

\caption{Statistics of the RECCON-DD dataset. \textit{No Context}: The cause is present within the target utterance itself; \textit{Inter}: Inter-speaker emotional influences; \textit{Intra}: Intra-speaker emotion influences (Self-Contagion); \textit{Hybrid}: Inter and Intra can jointly cause the emotion of an utterance; \textit{Unmentioned}: Some instances have no explicit emotion causes in the conversational history.}
\label{tab-1}
\end{table}

We evaluate the proposed model on a benchmark dataset for RECCON, named RECCON-DD~\citep{poria2021recognizing}, which is constructed based on DailyDialog dataset \cite{li2017dailydialog}.\footnote{DailyDialog is a natural human communication dataset which is usually used in ERC task. It contains utterance-level emotion labels and covers various topics related to daily lives.} Some statistics about RECCON-DD are reported in Table~\ref{tab-1}. Following~\citep{poria2021recognizing}, the macro-averaged F1 score is utilized as the evaluation metric in this paper. We also report the F1 score for both positive and negative samples, denoted as Pos. F1 and Neg. F1 respectively.

\begin{table*}[tbp]
\centering
\begin{tabular}{@{}c|c|ccccccc@{}}
\hline
\multirow{2}{*}{\#} & \multirow{2}{*}{Model} & \multicolumn{3}{c}{W/O CH}   &  & \multicolumn{3}{c}{W/ CH}  \\ \cmidrule(lr){3-5} \cmidrule(l){7-9}
                    &                        & Pos. F1 & Neg. F1 & macro F1 &  & Pos.F1 & Neg.F1 & macro F1 \\ \hline\hline
0                   & $\text{INDEP}_{base}$            &    56.64     &    85.13     &   70.88       &  &    64.28    &    88.74    &   76.51       \\
1                   & $\text{INDEP}_{large}$           &    50.48     &    87.35     &    68.91      &  &   66.23     &     87.89   &     77.06     \\ \hline
2                   & $\text{JOINT}_{base}$             & -       & -       & -        &  &   66.61     &    89.11    &   77.86       \\
3                   & $\text{JOINT}_{large}$           & -       & -       & -        &  &   68.30     &  89.16      &      78.73    \\ \hline
4                   & $\text{Ours}_{base}$              & -       & -       & -        &  &     68.59   &     89.75   &    79.17      \\
5                   & $\text{Ours}_{large}$           & -       & -       & -        &  &  $\textbf{70.00}^{\dagger}$       &    $\textbf{90.48}^{\dagger}$    &    $\textbf{80.24}^{\dagger}$      \\ \hline
\end{tabular}
\caption{Performance of our model and baselines on the test set of RECCON-DD. Bold font denotes the best performance.
``Ours'' denotes the proposed model without removing any module (``Ours'' = ``JOINT'' + TSAM). ``$^{\dagger}$'' denotes that $\text{Ours}_{large}$ is statistically significant~\citep{koehn2004statistical} better than $\text{INDEP}_{large}$ W/ CH ($p$-$\text{value} < 0.05$).}
\label{tab-2}
\end{table*}

\subsection{Baselines}
For a comprehensive performance evaluation, we compare our model with the following baselines:

\paragraph{(1) $\text{INDEP}_{base}$}\citep{poria2021recognizing} It tackles CEE in an independent classification framework (INDEP) and uses the RoBERTa-Base model~\citep{liu2019roberta} as the utterance encoder. The input is formated as "[CLS]$u_t$[SEP]$u_i$[SEP]" and the classification is performed from the final representation of the token "[CLS]".
\paragraph{(2) $\text{INDEP}_{large}$}\citep{poria2021recognizing} Compared to (1), it uses the RoBERTa-Large model as utterance encoder;
\paragraph{(3) $\text{JOINT}_{base}$} It's one of the variants of our model, where the TSAM is removed. It tackles RECCON in a joint classification framework (JOINT) and uses the RoBERTa-Base model as the utterance encoder. Moreover, its input format is "[CLS]$u_1$[SEP][CLS]$u2$[SEP],...,[CLS]$u_t$[SEP]" and the classifications are performed synchronously from the corresponding contextual utterance representations of the [CLS] tokens;
\paragraph{(4) $\text{JOINT}_{large}$} Compared to (3), it uses RoBERTa-Large model as the utterance encoder.

For \textbf{INDEP} baselines, there are two different settings: With Conversational History (W/ CH) and Without Conversational History (W/O CH). W/ CH means considering the conversational history. When performing utterance pair classification, the conversational history $L(u_t)$ is concatenated after the input to incorporate contextual information, while W/O CH means ignoring the history.

\subsection{Implementation Details}
Our model's base and large versions use pre-trained RoBERTa-Base and RoBERTa-Large models as the utterance encoders, respectively.\footnote{Our RoBERTa models are adapted from this implementation: \url{https://github.com/huggingface/transformers}} The binary cross-entropy loss along with L2-regularization is used during training, where the coefficient of L2 term is $0.01$ in the RoBERTa structure and $1e$-$5$ in other structures. We set the dropout rate to $0.1$. The learning rate and the batch size are set as $1e$-$5$ and $2$, respectively. Our model is trained with the Adam optimizer~\citep{kingma2015adam}. We set the dimensions of the contextual utterance representation $d_h$ as 768/1024 in the Base/Large version of the proposed model. We use $4$-$ $head attention in EAN, and the number of TSAM layers $L$ is set to $3$. We train the model for $40$ epochs in total and use the early stopping strategy based on the performance on the development set. Then, the model with the highest macro-averaged F1 score is used to evaluate the test set. Other hyper-parameters are selected according to the performance of the development set. All of the experiments are conducted on an NVIDIA V100 GPU with $32$GB of memory.

\section{Results and Discussions}

\subsection{Main Results}
Experimental results are reported in Table~\ref{tab-2}. We directly cite the results for the baselines reported in ~\citep{poria2021recognizing}. For the performance of each model we implemented, we report the average score of 5 runs. From Table~\ref{tab-2}, we can find that the proposed model (\#5) outperforms all of the baselines and surpasses the best model (\#1, W/ CH) in~\citep{poria2021recognizing} with more than 3 points of macro F1 score.

Further comparisons show that models with the large pre-trained utterance encoder are more likely to achieve better performance (about 1 point of macro F1 score) than the corresponding models with the base one, except for the models under W/O CH setting in the Table~\ref{tab-2}.
By comparing two different settings W/O CH and W/ CH in Table~\ref{tab-2}, we can find that the conversational history plays a significant role for $\text{INDEP}_{base}$ and $\text{INDEP}_{large}$ models. This is mainly because that the conversational history is able to provide the contextual information for prediction.
Due to the simultaneous classification of multiple utterances in the conversational history under the joint framework, $\text{JOINT}_{base}$ and $\text{JOINT}_{large}$ models can naturally incorporate the contextual information.
The $\text{JOINT}_{base}$ and $\text{JOINT}_{large}$ models significantly outperform the $\text{INDEP}_{base}$ W/ CH and $\text{INDEP}_{large}$ W/ CH models by about 1.5 points of macro F1 scores respectively (comparing \#0 with \#2, and \#1 with \#3 in Table 2). There may be two main factors: 1) Simply concatenating the conversational history after the utterance pair to be classified in INDEP W/ CH models may destroy the structure of the conversation; 2) Compared to INDEP W/ CH models, classification of multiple utterances synchronously in JOINT models will have more sufficient supervision signals and can more effectively model the correlations between contextual utterances in a global view, i.e., utterances with similar semantics are supposed to have similar chances being the emotion cause.
The comparison between \#2 and \#4 (or \#3 and \#5) in Table~\ref{tab-2} shows the effectiveness of the proposed TSAM. The model with TSAM (\#5) achieves an improvement up to 1.51 points of macro F1 score than the model without TSAM (\#3).

\subsection{Ablation Study}
In this subsection, we conduct ablation studies to analyze the effects of different components based on $\text{Ours}_{large}$ mentioned in Table~\ref{tab-2}.

\paragraph{Effect of Emotion Information}

We compare three different ways for incorporating the emotion information: no emotion information incorporated, incorporating emotion information with direct application emotional embedding, and incorporating emotion information with EAN. The results are shown in Table~\ref{tab-3}.
We can find that the performance of the proposed model degrades if the emotion information is not incorporated (comparing row 1 with 3 in Table~\ref{tab-3}). This result shows that the emotion information in the conversational history plays a significant role in the task of CEE. By comparing rows 2 with 3 in Table~\ref{tab-3}, the result shows that EAN achieves better performance than DAEE since EAN can extract richer emotion information and model the mutual impact among different emotions.

\begin{table}[tbp]
\centering
\resizebox{0.98\hsize}{!}{%
\begin{tabular}{c|ccc}
\hline
Emotion Information  & Pos. F1 & Neg. F1&macro F1 \\ \hline\hline
No                            & 68.40   & 89.80 & 79.10     \\
DAEE       &68.90  &  90.03	  &   79.47  \\
      EAN                      & \textbf{70.00} & \textbf{90.48} &   \textbf{80.24}    \\ \hline
\end{tabular}}
\caption{Comparison of different ways of incorporating emotion information. \textit{No}: no emotion information incorporated; \textit{DAEE}: incorporating the emotion information with direct application emotional embedding.}
\label{tab-3}
\end{table}

\paragraph{Effect of Speaker Information}
To evaluate the effects of speaker information, we remove the speaker relations in SAN, resulting in a single edge relation throughout the graph. As Table~\ref{tab-4} shows, the performance of our model decreases dramatically if not considering the speaker information. This result presents that modeling the speaker information in the conversational history is very important for the final performance.

\begin{table}[htbp]
\centering
\resizebox{0.98\hsize}{!}{%
\renewcommand\tabcolsep{3.0pt}
\begin{tabular}{c|ccc}
\hline
Speaker Information & Pos. F1 & Neg. F1&macro F1 \\ \hline\hline
Not Consider        & 67.99 &   89.42 &     78.71     \\
Consider        & \textbf{70.00} & \textbf{90.48} &   \textbf{80.24}       \\ \hline
\end{tabular}}
\caption{Results on experiments whether considering speaker information or not in SAN.}
\label{tab-4}
\end{table}

\paragraph{Effect of Interaction Module}
We remove the interaction module in each layer so that the EAN and SAN can't interact. As Table~\ref{tab-5} shows, the performance of our model decreases dramatically when the interaction module is removed. This result shows that the effective interchange of relevant information between EAN and SAN is conducive to the final performance.
\begin{table}[htbp]
\centering
\resizebox{0.98\hsize}{!}{%
\renewcommand\tabcolsep{3.0pt}
\begin{tabular}{c|ccc}
\hline
 & Pos. F1 & Neg. F1&macro F1 \\ \hline\hline
W/O Interaction         & 68.18 &   88.93 &     78.56     \\
W/ Interaction         & \textbf{70.00} & \textbf{90.48} &   \textbf{80.24}       \\ \hline
\end{tabular}}
\caption{Results on experiments whether removing interaction module or not in TSAM.}
\label{tab-5}
\end{table}

\paragraph{Ability on Modeling Emotional Influences}
To evaluate the proposed model's ability to model the speaker's emotional influences, we collect the positive examples from the test set where the causes are induced from the inter-speaker or intra-speaker emotional influences. And we test the prediction accuracy on the collected samples for the proposed $\text{Ours}_{large}$ with and without TSAM. As shown in Table~\ref{tab-6},  W/ TSAM outperforms W/O TSAM by around $2$ points on both cause types, which further verifies that the TSAM can effectively model the emotional influences between speakers.
\begin{table}[tbp]
\centering
\renewcommand\tabcolsep{8.0pt}
\begin{tabular}{c|cc}
\hline
Models & Intra & Inter \\ \hline\hline
W/O TSAM   & 62.06 & 72.67      \\
W/ TSAM  & \textbf{63.82} & \textbf{74.81}  \\ \hline
\end{tabular}
\caption{Accuracy on the collected samples. \textit{Intra}: Intra-speaker emotional influences;  \textit{Inter}: Inter-speaker emotional influences.}
\label{tab-6}
\end{table}

\subsection{Impact of the TSAM Layer Number}
Since TSAM for modeling speakers' emotional influences is the critical component of our model, we chose the number of TSAM layers $L$ (ranging from 1 to 5) on the development set of RECCON-DD. As shown in Figure~\ref{fig-3}, our model with three TSAM layers achieves the best performance. On the one hand, emotion and speaker information may not be refined and interchanged well when the number of layers is small. On the other hand, if the number of layers is excessive, the performance will decrease, possibly due to information redundancy.

\begin{figure}[htbp]
	\centering
	\includegraphics[width=7.5cm,height=5cm]{Figures/figure2.pdf}

	\caption{Results of $\text{Ours}_{large}$ with various TSAM layers on the development set of RECCON-DD.}\label{fig-3}
\end{figure}

\subsection{Error Analysis}
By analyzing our predicted emotion causes, we find that the following aspects mainly cause the predicted errors. Firstly, our model weakly gives the correct predictions for target utterances with three or more causes.\footnote{Utterances with 3 or more causes account for approximately 14\% of the RECCON-DD dataset}  Compared to the utterances with 1 or 2 causes, the proposed model dropped six macro F1 scores on utterances with multiple causes. Secondly, our model cannot predict well when the underlying emotional cause is latent. At this point, recognizing emotion causes may require complex reasoning steps, and commonsense knowledge is an integral part of this process. We take the case below as an example:
\begin{itemize}
\item $S_A$ (happy): Hello, thanks for calling 123 Tech Help. I'm Todd. How can I help you?

\item $S_B$ (fear): Hello? Can you help me? My computer! Oh, man...
\end{itemize}
In this case, $S_A$ is happy to help $S_B$. In this example, the cause of happy emotion is due to the event ``greeting'' or intention to provide help. On the other hand, $S_B$ is fearful because his or her computer is broken. Both speakers' causes of elicited emotions can only be inferred using commonsense knowledge, which our model does not explicitly consider.

\section{Conclusion and Future Work}
For the first time, we tackle CEE in a joint framework. We classify multiple utterances synchronously to capture the correlations between contextual utterances in a global view and propose a TSAM to effectively model the speaker's emotional influences.
Experimental results on the benchmark dataset show that our model significantly outperforms the SOTA model, and further analysis verifies the effectiveness of each component. This paper points out a new reliable route for follow-up works: incorporating the emotion and speaker information explicitly and modeling the speaker's emotional influences effectively can bring enormous benefits for the tasks similar to CEE.

In the future, we would explore three aspects: (1) Learn emotion recognition and emotion cause recognition in conversations jointly; (2) Apply our model to other similar tasks which need to incorporate the speaker and emotion information; (3) Incorporate commonsense knowledge into the model explicitly to address situations when the underlying emotion cause is latent.

\subsection{Contextual Utterance Representation}
The pre-trained RoBERTa is utilized as the utterance encoder, and we extract the contextual utterance representations by feeding the whole of the conversational history $L(u_t)$ into the RoBERTa \citep{liu2019roberta}.
Specifically, each utterance in $L(u_t)$ is expanded to start with the token ``[CLS]'' and end with the token ``[SEP]''. The input representation for each token is the sum of its corresponding token and position embeddings. The contextual representation $\bm{h}^u_i \in \mathbb{R}^{d_h}$ for utterance $u_i$ is the output of the corresponding ``[CLS]'' token, where $d_h$ denotes the dimension of the utterance representation. The contextual representation for all utterances is denoted as $\bm{H}^{u} \in \mathbb{R}^{t\times d_h}$. The RoBERTa we utilized is fine-tuned with the training process.

\subsection{TSAM}
The TSAM models the speaker's emotional influences with three modules: EAN, SAN, and Interaction. We first illustrate the calculation process of each module in one-layer TSAM and then generalize it to multiple successive layers.

\subsubsection{EAN}
The EAN provides utterance-to-emotion interactions to explicitly incorporate emotion information by performing attention over emotion embeddings.

\paragraph{Emotion Representation} Given the set of candidate emotion labels $\mathcal{E}=\{e_1,...,e_{|\mathcal{E}|}\}$, each emotion label $e_k$ is represented using an embedding vector \citep{cui2019hierarchically}:
\begin{equation}
       \bm{x}^e_k=\text{Embed}(e_k)\in \mathbb{R}^{d_h}
\end{equation}
where $k=1,...,|\mathcal{E}|$, $d_h$ denotes the dimension of the emotion embedding. $\text{Embed}$ represents an emotion embedding lookup table, which is initialized by contextual embeddings from RoBERTa and tuned during model training. The embedding for the set of the whole emotion labels is denoted as $\bm{X}^e \in \mathbb{R}^{|\mathcal{E}|\times d_h}$.

\paragraph{EAN Inference} With the emotion labels represented as embeddings, we extract the emotion information $\bm{H}^e\in \mathbb{R}^{t\times d_h}$ by performing dot-product attention over contextual utterance representations and emotion embeddings, which is calculated as:
\begin{align}
    \bm{H}^e &= \textit{attention}(\bm{Q},\bm{K},\bm{V}) = \bm{\alpha}\bm{V}\\
    \bm{\alpha} &= \textit{softmax}(\frac{\bm{Q}\bm{K}^T}{\sqrt{d_h}})
\end{align}
where $\bm{Q}=\bm{H}^u, \bm{K}=\bm{V}=\bm{X}^e$, $\bm{\alpha}\in\mathbb{R}^{t\times |\mathcal{E}|}$ is an attention matrix consisting of potential emotion distributions for all utterances. Compared to the standard attention mechanism above, it may be beneficial to use multi-head attention \citep{vaswani2017attention} to capture multiple emotion distributions in parallel and obtain richer emotion information:
\begin{align}
    \bm{H}^e &= \textit{concat}(head_1,...,head_m)\\
    head_j &= \textit{attention}(\bm{Q}\bm{W}^{Q}_j,\bm{K}\bm{W}^K_j,\bm{V}\bm{W}^V_j)
\end{align}
where $\bm{W}^Q_j,\bm{W}^K_j,\bm{W}^V_j\in\mathbb{R}^{d_h\times\frac{d_h}{m}}$ are learnable parameters and $m$ is the number of parallel heads.

Since the emotion labels of the utterances in the conversational history are known, we can also simply use the embedding of emotion label corresponding to the utterance as the extracted emotion information:
\begin{equation}
     \bm{H}^e = \Tilde{\bm{X}}^{e}
\end{equation}
where $\Tilde{\bm{X}}^{e}\in\mathbb{R}^{t\times d_h}$ is the embedding of emotion labels corresponding to all utterances in the history. We refer to the method as Direct Application Emotional Embedding (DAEE). Compared with DAEE, the potential advantages of the EAN are as follows: (1) The EAN can provide utterance-to-emotion interactions and capture multiple potential emotion distributions through multi-head attention to obtain more comprehensive and richer emotion information; (2) The soft emotion distributions can model the mutual impact among different emotions for further enhancement of emotion embeddings, while each emotion embedding is relatively independent of each other in DAEE; (3) The EAN can avoid emotion annotation errors to a certain extent. We apply EAN in our model to incorporate emotion information by default and compare the EAN and DAEE in the part of experiments.

\subsubsection{SAN}
The SAN provides utterance-to-utterance interactions to incorporate speaker information by performing attention over the speaker relation graph.

\paragraph{Graphical Structure} We define a conversational history with $t$ utterances as a graph $\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{R})$, with nodes (utterances) $v_i\in \mathcal{V}$ and labeled edges (relations) $(v_i,r,v_j)\in \mathcal{E}$, where $r\in \mathcal{R}$ is a relation type. We also add a self-loop edge to every node, as the cause may be present within the target utterance itself. The representation of node $v_i$ is initialized with the contextual utterance representation $\bm{h}^u_i\in \mathbb{R}^{d_h}$, i.e., the $i^{th}$ embedding in $\bm{H}^u$. There are two relation types of edges: (1) \textbf{Intra} relation type: how the utterance influences other utterances (including itself) expressed by the same speaker; (2) \textbf{Inter} relation type: how the utterance influences ones expressed by other speakers.

\paragraph{SAN Inference} The representation of a node $\bm{h}_i$ is updated by aggregating representations of its neighborhood $\mathcal{N}^r(i)$ under the relation type $r$. The graph attention mechanism \citep{velivckovic2018graph} is used to attend to the neighborhood's representations. The output of a node $\bm{h}^s_i\in \mathbb{R}^{d_h}$ is calculated as the sum of the hidden features $\bm{h}_{ir}\in \mathbb{R}^{d_h}$ under relation $r$. The propagation is defined as follows:
\begin{align}
    \alpha_{ijr} &=\textit{softmax}_i(\textit{LRL}(\bm{a}^T_r[\bm{W}_r\bm{h}^u_i;\bm{W}_r\bm{h}^u_j]))\\
    \bm{h}_{ir} &= \sum_{j\in\mathcal{N}^r(i)}\alpha_{ijr}\bm{W}_r\bm{h}^u_j\label{equ}\\
    \bm{h}^s_i &= \sum_{r\in \mathcal{R}}\bm{h}_{ir}
\end{align}
where $\alpha_{ijr}$ denotes the edge weight from utterance $u_i$ to its neighborhood $u_j$ under relation type $r$, $\bm{W}_r\in \mathbb{R}^{d_h\times d_h}$ and $\bm{a}_r\in\mathbb{R}^{d_h}$ denote a learnable weight matrix and a vector for each relation type $r$ respectively. $\textit{LRL}$ denotes $\textit{LeakyReLU}$ activation function. The updated representation of all nodes is denoted as $\bm{H}^{s} \in \mathbb{R}^{t\times d_h}$.

\subsubsection{Interaction Module}
To effectively interchange relevant information between the EAN and SAN, we apply a mutual BiAffine transformation as a bridge. The calculation process is formulated as follows:
\begin{align}
    \bm{A}_1 &= \textit{softmax}(\bm{H}^e\bm{W}_1(\bm{H}^s)^T)\\
    \bm{A}_2 &= \textit{softmax}(\bm{H}^s\bm{W}_2(\bm{H}^e)^T)\\
    \bm{H}^{e'} &= \bm{A}_1\bm{H}^s\\
    \bm{H}^{s'} &= \bm{A}_2\bm{H}^e
\end{align}
where $\bm{W}_1,\bm{W}_2\in \mathbb{R}^{d_h\times d_h}$ are trainable parameters and $\bm{A}_1, \bm{A}2\in \mathbb{R}^{t\times t}$ are temporary alignment matrices projecting from $\bm{H}^s$ to $\bm{H}^e$ and $\bm{H}^e$ to $\bm{H}^s$, respectively. Here, $\bm{H}^{e'}\in \mathbb{R}^{t\times d_h}$ can be viewed as a projection from $\bm{H}^s$ to $\bm{H}^e$ , and $\bm{H}^{s'}\in \mathbb{R}^{t\times d_h}$ follows the same principle.

\subsubsection{The Whole Process}
We generalize the TSAM to multiple successive layers to iteratively refine and interchange emotion and speaker information. The detailed procedures are as follows:
\begin{align}
   \bm{H}^e_l &= \textbf{EAN}(\bm{E}_l, \bm{X}^e) \\
   \bm{H}^s_l &= \textbf{SAN}(\bm{S}_l) \\
   \bm{H}^{e'}_l, \bm{H}^{s'}_l &= \textbf{Interaction}(\bm{H}^e_l,  \bm{H}^s_l)\\
   \bm{E}_{l+1}, \bm{S}_{l+1} &=  \bm{H}^{e'}_l, \bm{H}^{s'}_l

\end{align}
where $\bm{E}_0=\bm{S}_0=\bm{H}^u$. The TSAM can be stacked in $L$ layers and $l\in [0,L-1]$.

\subsection{Cause Prediction}
We obtain the final utterance representation for $u_i$ by concatenating the output $(\bm{E}_L,\bm{S}_L)$ of the L-layer TSAM. Finally, the concatenated vector is classified using a Fully-Connected Network (FCN):
\begin{align}
    \bm{l}_i &= \textit{ReLU}(\bm{W}_1[\bm{e}^L_i;\bm{s}^L_i]+\bm{b}_1)\\
    \hat{y}_i &= \textit{sigmoid}(\bm{W}_2\bm{l}_i+b_2)
\end{align}
where $\hat{y}_i$ is the probability for utterance $u_i$ containing the cause of emotion in the target utterance, $\bm{e}^L_i, \bm{s}^L_i\in \mathbb{R}^{d_h}$ denote the $i^{th}$ embedding in $\bm{E}_L$ and $\bm{S}_L$, respectively, $\bm{W}_1\in\mathbb{R}^{d_h\times 2d_h}, \bm{W}_2\in \mathbb{R}^{1\times d_h}, \bm{b}_1\in \mathbb{R}^{d_h}$ and $b_2$ are learnable parameters of FCN.

redpajama_set_name: RedPajamaArXiv


Mariachristiecoaching.com, empowered dating and Relationship coach.
Self Doubt is those sneaky, stubborn little thoughts that we all experience from time to time.
It's the opposite extreme to self-confidence, which we need to nurture and protect to feel content, and achieve our most meaningful goals in life.
It's natural to a degree. Our brains are wired to protect us to avoid "risky" behaviours, whether that's risk of failure, discomfort or uncertainty.
However, if self –doubt has made itself comfortable in your mind, it will be sabotaging your self-confidence to take the bold, brave actions you know you would love to take and need to.
Whether that's to begin dating again and attract a lovely new relationship, or to take your career and business to the next level, or to find your voice and express your opinions and needs with confidence.
It will be the underlying cause, preventing you from trusting your abilities, feeling worthy, being the most magnetic version of you, and living a life you love.
That's why it's essential to have a smart strategy to work through it when it arises, so that you take complete control of your self-doubt, instead of it controlling you.
I recommend thinking about it as a decision to embrace new rewarding habits to replace an outdated one that doesn't help you most of the time.
All it takes is a commitment to small steps every day to see and feel a significant effect.
While we all know self-doubt doesn't help us. It needs some understanding, before skipping straight to the solutions.
I believe it's useful to identify and challenge (respectfully), how you experience self-doubt in your daily life, to be able to find the best long-lasting method for you, to work through it effectively.
These are the most common types of self-doubt.
Hesitating is triggered by uncertainty.
If you find yourself waiting, over-thinking, prone to "perfectionism" and keeping busy to avoid dealing with a situation, this may be your default self-doubt behaviour.
What do you tend to tell yourself?
What activities do you to avoid acting?
What do you tend to over think about?
What are you waiting to be perfect?
Hiding is triggered by fear of others and overwhelm.
If you find yourself avoiding, procrastinating, shrinking back, playing small, staying behind the scenes and tend to be on the quieter side, because you fear being judged or disappointing others, this could be your default self-doubt behaviour.
When are you more prone to hide and procrastinate?
When you find yourself hiding and procrastinating, what's the fear that causes it?
Why does the thing you're afraid of matter?
Hypercritical is triggered by fear of being hurt, disappointed, or failing.
If you're likely to argue against yourself, be self-critical, complain, focus on flaws, why you can't do it and what can go wrong, this is your default self-doubt behaviour.
What are the reasons (and excuses) you're using to hold yourself back?
What are you afraid is going to happen?
Helplessness is triggered by insecurity or low self-worth and can be a fear of success.
If you tend to fall into "victim" mode, blaming, don't trust yourself, have a defeatist attitude you may fall into this version of self-doubt.
You may find yourself believing that "nothing ever goes my way" or tend to blame outside circumstances, and have a false sense of incompetence that you can't handle your problems, and perhaps an unwillingness to be vulnerable.
Perhaps you're dependent on others who fulfil one of these roles fixer, rescuer, advice giver, enabler, caretaker, guru.
What areas in your life do you feel helpless ?
What do you believe about yourself that makes you feel helpless?
Identify what you're afraid of that leads you not to want to take responsibility.
What are you afraid of losing if you were to be self-sufficient and accept personal responsibility for yourself?
Do you identify with one or more of these behaviours?
When you have self-awareness about your instinctive self-doubting habits, it's easier to chart a personal plan to move forward.
There isn't a cookie-cutter approach for every person with self-doubt. When I work with my clients, we dive deeper into some of the resistance and challenges they experience individually and each client is different.
However approaching your self-doubt, with compassion, some objectivity and a smart method will start the process of replacing it, with more action, and momentum which activates more self-confidence. Here are few habits you might like to try.
1. Make a decision that you're going to commit to your best efforts for a set period of time to start. Fully engage in it, to give yourself a chance to prove you can do this, which you can.
2. Remember your Why and what it means to you to push past your self-doubt, each day.
3. Remember what it's costing you, and your future reality NOT to overcome self-doubt. Will it have been worth not overcoming it?
4. Start with small steps.
Take the situation you're experiencing self-doubt about, from the big often daunting picture and the details, and what could go wrong. and break it down into tiny steps, and schedule those steps.
Only focus on the next step each day. All these actions matter, because they contribute towards your bigger heart goal. A Harvard study also found that making progress (not perfection) in one thing that matters to you most, motivates you to move you forward.
5. Plan how you will mitigate the avoidance and obstacles that will take you off track.
Work with your natural tendencies, and priorities to manage inevitable distractions and self-doubting behaviours.
Expect them in the beginning and plan for them.
Incorporate steps in your schedule that you'll take when these arise.
Perhaps give yourself a time limit for the avoidance, then commit to taking action when your time is up, or as soon as you can. Find the best solution for you but do take the action you committed to that day!
6. Use your Resources and Support Network.
Ensure you're giving yourself every opportunity to succeed, by using the support and resources that will help you. Whether it's sharing with a trusted friend or family member for extra support.
Or seeking professional support to guide you, keep on track, accountable and out of your comfort zone.
This isn't the time to remain timid about your needs, or asserting boundaries, if it makes the difference to you overcoming self-doubt.
7. Keep accomplishments you're proud of alive in your thoughts and visible to remind you of your superpowers, and that you can have and do what you're self-doubt is now holding you back from. i.e. photos and momentoes.
8. Self Love Rules each step of the way.
However small you feel the step you've made is, it's necessary to create a habit of celebrating your progress and mini-triumphs each step of the way.
Often it's in the smaller steps that the magic happens, so stay kind to yourself in the process.
Think of the little treats you will reward yourself with after reaching each milestone, and enjoy them.
You're building a new habit, and it will take some time to gain traction and find your rhythm and new routine. But keep going.
If you think about the Heart Goal, you have been delaying taking action on now, what one step are you committing to taking in the next week?
I'd love you join us and share with us in our closed facebook community here for ladies, where you'll receive daily support and advice.
If you would like personalised support to overcome self-doubt, and ignite your self-confidence to create a Relationship and Life you Love, via a power hour coaching session, or more in depth, 8 or 12 week one to one coaching programme, it'll be a pleasure to discuss your needs with you. It's your Time! Click the link below and let's begin!
Join me to receive your weekly free practical, tips, resources , and interviews directly to your inbox.


iOS app, we have always been surprised by the generosity of the development community around us.
Today Gabriel Rinaldi, the awesome developer behind our iOS app, topped his generosity by releasing the Cloud 66 iOS app source code on Github under the MIT open source license.
The Cloud 66 iOS app is a fine example of a great mobile app, backed by an API protected with OAuth 2 and now is open for everyone to inspect, learn from and contribute to.
We are ever so grateful to all of our supportive customers and especially Gabriel for their ongoing support and generosity and wish all of your a great year!


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Q: "Failed to set trace monitor for deviance There are no observed stochastic nodes" error when running JAGS model in R I'm trying to get an N-mixture model to run in JAGS for a stats class that I'm currently in. However, I keep getting the error "Failed to set trace monitor for deviance
There are no observed stochastic nodes" whenever I try to run the model. At the moment I'm just trying to get a very basic model with no covariates to run to make sure I have everything formatted correctly but am still unable to get the model to run. Any advice would be very much appreciated as I've been struggling with this for a while now and can't figure out where I've gone wrong.
Here's a reproducible example of the issue:
library(R2jags)

# Create example dataframe

years <- c(1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2)
sites <- c(1,1,1,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3)
months <- c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3)

# Detection data
day1 <- floor(runif(18,0,7))
day2 <- floor(runif(18,0,7))
day3 <- floor(runif(18,0,7))
day4 <- floor(runif(18,0,7))
day5 <- floor(runif(18,0,7))

df <- as.data.frame(cbind(years, sites, months, day1, day2, day3, day4, day5))

# Put data into array
y <- array(NA,dim=c(2,3,3,5))

for(m in 1:2){
  for(k in 1:3){
    sel.rows <- df$years == m &
      df$months==k
    y[m,k,,] <- as.matrix(df)[sel.rows,4:8]
  }
}

# JAGS model
sink("model1.txt")
cat("
    model {

    # PRIORS

     lambda ~ dunif(0,10)
     p ~ dunif(0,1)

    # LIKELIHOOD
    # ECOLOGICAL MODEL FOR TRUE ABUNDANCE
      for (m in 1:2) {                            # Loop over years (1-2)

        for (k in 1:3) {                          # Loop over months (1-3)

          for (i in 1:3) {                        # Loop over sites (1-3)

            N[m,k,i] ~ dpois(lambda)

            for (j in 1:5) {                     # Loop over days (1-5)

              y[m,k,i,j] ~ dbin(p, N[m,k,i])

            }#j
          }#i
        }#k
      }#m
  }#END", fill=TRUE)
sink()

win.data <- list(y <- y)

Nst <- apply(y,c(1,2,3),max)+1

inits <- function()list(N = Nst)

params <- c("lambda",
            "N")

nc <- 3
nt <- 1
ni <- 5000
nb <- 500

out <- jags(win.data, inits, params, "model1.txt",
             n.chains = nc, n.thin = nt, n.iter = ni, n.burnin = nb,
             working.directory = getwd())

A: Inside of list objects you cannot use the "<-" assignment operator to create a named list, which is what you need to provide to JAGS. Instead, you need to use =.
So if you change this line in your code:
# this line
win.data <- list(y <- y)

to:
win.data <- list(y = y)

the model compiles.


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Kiera O'Connor's summer was all planned when she finally showed a doctor a stubborn lump in her thigh.
The first doctor she saw referred her for an ultrasound that was going to take six months. But her step-mom had a hunch. She called the ultrasound clinic and was able to get O'Connor in within a week.
They weren't given the results but the look on the ultrasound clinician's face said it all, O'Connor said.
By mid-June, at 15 years of age, O'Connor was diagnosed with nodular lymphocyte-predominant Hodgkin lymphoma.
That was it for her summer plans. O'Connor cancelled out of the two-week school trip to Europe she'd been fundraising for since last year. And she opted out of a summer job working with international exchange students at Saint Michaels University School. Instead, she started chemotherapy and got used to bunking at Ronald McDonald House.
O'Connor is now 16 and is nearly finished her third and final round of chemotherapy and the results are positive. A PET scan suggested the cancer is shrinking and there is much hope she won't need further radiation.
Though the Gordon Head teen was extremely positive from the start, she wasn't without her doubts.
After each day of chemo session she's on a cocktail of meds to help control the extreme nausea and vomiting for seven to nine days after, said her step-mom Lisa. She is usually in bed for five days after as well.
There's been some bright spots, on her 16th birthday Oak Bay Beach Hotel provided O'Connor with a spa visit, pool visit and lunch.
Even the physical toll of chemotherapy and the cocktails of serious medications that are needed to help a patient through cancer treatment are not enough to keep her spirits down.
O'Connor's former school, Gordon Head middle, and her current school, Mount Douglas secondary, are both holding a series of fundraisers.
During a recent visit to Gordon Head Kiera felt like a rock star, giving out dozens of hugs while receiving positive well wishes and handmade cards.
This week at Gordon Head is Kaps for Kiera, where students can pay for the right to wear a hat, thereby superseding the school's no-hat policy. On Thursday, staff and students will provide coffee (by donation) during the morning drop-off. And on Oct. 31, Gordon Head will hold a head-shave assembly at which O'Connor will shave the head of teacher Alex de Medeiros. Her 19-year-old brother Kyle O'Connor is getting the chance to come back to school and shave the head of principal Kevin Luchies (who were recently student and principal at Lambrick Park secondary).
Mount Douglas also held Kaps for Kiera this week, in which students are wearing hats, and passing around a hat to collect money toward the campaign.
There is also a GoFundMe page, which is well over its $2,500 target, and offers a 2016 U.S. Open golf tournament flag signed by winner Dustin Johnson, and a $1,000 home alarm system from Beacon Security Systems.
The money is not for the O'Connor family, rather, O'Connor has asked it be split 50-50 between the B.C. Children's Hospital and Ronald McDonald House.


text:
IPL Team Squads Trending News
IPL 2022 Lucknow Player List : Squad of Lucknow Team 2022
Sanjiv Goenka's RPSG group is one of the winners of the 2 auctions for new IPL equipment organized by BCCI in Dubai on October 25, 2021.
Sanjiv Goenka's RPSG group won the auction war for 7,090 crore (over $ 932 million). RPSG's offer was 250% above BCCI's base price of INR 2000 crore for IPL equipment.
Lucknow IPL Team 2022 Owner
Sanjiv Goenka's RPSG group is the owner of the Lucknow IPL team. RPSG won the bidding war for 7,090 crore (over US $ 932 million) to own Lucknow's equipment.
RP – The Sanjiv Goenka Group, with an asset base of US $ 6 billion and revenues of US $ 4 billion, is one of the fastest growing conglomerates in India with a significant global presence.
The Group's businesses include electricity and energy, carbon black manufacturing, retail, IT services, consumer goods, media and entertainment, and agriculture.
The group has a turnover of 26,900 crore (US $ 3.6 billion) in 2020 with a total asset value of ₹ 47,405 crore (US $ 6.3 billion). The Group has more than 50,000 employees and more than 100,000 shareholders.
Sanjiv Goenka has served as President of the Confederation of Indian Industries (CII), as well as the Management Institute of India (AIMA) and a member of the Prime Minister's Council for Trade and Industry.
He is also the current Chairman of the Board of Directors of Woodlands Medical Center Ltd, Kolkata. Born in 1961, Sanjiv Goenka is currently Canada's Honorary Consul in Kolkata.
In April 2001, he assumed the post of youngest President of the Confederation of Indian Industry (CII), was appointed Chairman of the Board of Governors of the Indian Institute of Technology Kharagpur (IIT-KGP) and currently serves on the Board of Directors of the Indian Institute of Management, Kolkata (IIM-Kolkata).
Lucknow IPL 2022 team coach
The Lucknow IPL franchise has approached Ashish Nehra and Gary Kirsten to be their head coach and consultant for the 2022 season.
Lucknow IPL 2022 team captain
KL Rahul will likely lead the Lucknow team at IPL 2022. Lucknow's team captain will be announced after the 2022 IPL auction.
As for reports, Shreyas Iyer / KL Rahul will be the captains of the Lucknow squad. Both players have shown no interest in continuing to play for their old franchise.
Lucknow IPL Team 2022 Players
The list of Lucknow players will be listed below, right after IPL Mega Auction 2022. Stay tuned for regular updates.
According to the BCCI rules for the IPL 2022 auction, Lucknow can choose a maximum of 3 players from the player pool for the IPL 2022 auction before the auction date.
Lucknow IPL 2022 team logo
Each team will have a unique logo to represent them in this cash rich league. The Lucknow IPL team logo will be updated here once officially announced.
Lucknow IPL Team 2022 Jersey
IPL teams will have a unique jersey with unique color combinations to represent them in IPL matches. The official Lucknow jersey will be updated here once officially announced by the franchise.
IPL 2022 Auction date
The window for the 8 existing teams to retain players will be from November 1, 2021 to November 30, 2021.
The 2 new IPL teams will have the opportunity to retain players from December 1, 2021 to December 25, 2021.
Immediately after this retention period, BCCI will organize the IPL 2022 auction in late December or early January 2022.
IPL 2022 retention rules
It is established that a team can keep up to three Indian players (with / without limit) and two foreign players with a total number of catches not exceeding four.
He made it clear that a team cannot have more than two players without a cap.
Depending on the conditions, the team management may retain three Indian players and one foreign player or two Indian players and two foreign players.
Two new IPL teams can choose 2 Indian players and 1 foreign player from the Mega Auctions pool before the start of the 2022 auction.
Additionally, new teams can only choose one player with no limit before the auction. There will also be no Right to Match (RTM) cards for the 2022 IPL Mega Auction.
BCCI also explained the salary cap for retained players based on the team's decision on the number of retained players.
IPL Tickets Mumbai 2022 : Mumbai Indian Tickets 2022 Online Booking
IPL 2022 Online Tickets Trending News
IPL Tickets Rajasthan 2022 : Rajasthan Royals Tickets 2022 Online Booking
IPL 2022 Sunrisers Hyderabad Team List


We want to provide a safe, loving environment where children of all ages can learn about God and His amazing Love. Our desire is to make Him real and relevant in every child's life. We are excited about the opportunity to partner with parents as they seek to raise their children to know God in a personal way through a personal relationship with Jesus Christ.


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Table 3.23. Useful Thermal Output by Energy Source: Total Combined Heat and Power (All Sectors), 2008 - 2018
(Billion Btus)
2008 315,244 29,554 18,263 509,330 110,680 610,131 23,729 1,616,931
2009 281,557 32,591 20,308 513,002 99,556 546,974 33,287 1,527,276
2016 220,162 8,607 20,122 785,413 148,881 698,858 25,342 1,907,384
January 21,767 887 1,437 69,485 12,156 61,034 2,214 168,980
February 19,988 1,183 1,742 63,128 11,095 57,474 2,149 156,760
March 19,348 680 1,609 64,650 13,241 58,071 2,305 159,905
April 16,611 676 1,417 60,432 12,636 54,858 2,115 148,745
May 16,955 760 1,799 63,171 12,648 56,622 1,935 153,892
June 18,196 694 1,885 65,879 12,958 56,133 2,023 157,769
July 19,422 698 1,905 70,075 12,519 57,550 2,179 164,348
August 18,885 570 1,860 71,562 12,770 57,813 2,303 165,764
September 16,674 524 1,326 65,239 12,068 53,760 2,051 151,642
October 16,523 657 1,690 62,957 11,961 55,338 2,002 151,128
November 16,378 623 1,764 61,410 11,932 58,348 1,965 152,420
December 19,413 654 1,688 67,424 12,897 71,855 2,099 176,031
February 15,582 499 1,247 60,824 11,780 55,422 1,879 147,233
November 16,298 1,040 1,397 64,436 12,448 59,608 1,884 157,112
December 17,187 1,476 1,362 72,066 13,068 62,726 2,023 169,907
January 18,956 2,597 1,662 72,417 13,963 61,382 1,994 172,972
Coal includes anthracite, bituminous, subbituminous, lignite, and waste coal; synthetic coal and refined coal; and beginning in 2011, coal-derived synthesis gas. Prior to 2011 coal-derived synthesis gas was included in Other Gases.
Petroleum Liquids includes distillate and residual fuel oils, jet fuel, kerosene, waste oil, and beginning in 2011, propane. Prior to 2011 propane was included in Other Gases.
Petroleum Coke includes petroleum coke-derived synthesis gas. Prior to 2011, petroleum coke-derived synthesis gas was included in Other Gases.
Other Gas includes blast furnace gas and other manufactured and waste gases derived from fossil fuels. Prior to 2011, Other Gas included propane and synthesis gases.
See the Technical Notes for fuel conversion factors.
Renewable Sources include wood, black liquor, other wood waste, biogenic municipal solid waste, landfill gas, sludge waste, agriculture byproducts, other biomass, geothermal, and solar thermal.
Other includes non-biogenic municipal solid waste, batteries, hydrogen, purchased steam, sulfur, tire-derived fuel, and other miscellaneous energy sources.
Notes: Beginning with 2001 data, non-biogenic municipal solid waste and tire-derived fuels are reclassified as non-renewable energy sources and included in Other. Biogenic municipal solid waste is included in Other Renewable Sources.
Beginning with the collection of Form EIA-923 in January 2008, the methodology for separating the fuel used for electricity generation and useful thermal output from combined heat and power plants changed. The new methodology was retroactively applied to 2004-2007, as well as 2008-2015. Beginning with the 2016 Form EIA-923 data, the methodology for seperating the fuel used for electricity generation and useful thermal output from CHP plants was updated. This update will apply to the 2016 data and future data years. See the Technical Notes (Appendix C) for further information.
See Glossary for definitions. Values are final. See Technical Notes for a discussion of the sample design for the Form EIA-923 and predecessor forms.
Totals may not equal sum of components because of independent rounding. NM=Not meaningful due to large standard error. W=Withheld to avoid disclosure of individual company data.
Sources: U.S. Energy Information Administration, Form EIA-923, Power Plant Operations Report; U.S. Energy Information Administration, Form EIA-906, Power Plant Report; U.S. Energy Information Administration, Form EIA-920 Combined Heat and Power Plant Report; and predecessor forms.
Beginning with 2008 data, the Form EIA-923, Power Plant Operations Report, replaced the following: Form EIA-906, Power Plant Report; Form EIA-920, Combined Heat and Power Plant Report;
Form EIA-423, Monthly Cost and Quality of Fuels for Electric Plants Report; and Federal Energy Regulatory Commission, FERC Form 423, Monthly Report of Cost and Quality of Fuels for Electric Plants.


Sepia-toned illustrations complement the text of this story of a girl growing up in Germany during Hitler's rise to power. Readers are challenged to think about what they would have done in a similar situation by considering questions like: Would you salute to Hitler in school along with all of the other teachers and students? Would you watch as soldiers abuse an elderly Jewish man on the street? During the course of the war Margot's Jewish father commits suicide in order to save his wife and child from persecution. With many difficult issues to tackle, the book's picture book format is deceptive.


Upgrade one or both of the standard charm closures that comes on your Hourglass to a custom plain pewter heart. Comes in Pewter/Silver, Gold, or Rose Gold.
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Such a beautiful touch ?
Such a beautiful touch ??


text:
Religious wild cards
Donald Trump will be prayed into office by mavericks and mainstreamers
The president-elect has chosen six clerics to pray at his inauguration
Religion and public policy
Jan 1st 2017
by ERASMUS
AS ERASMUS has remarked before, there are countries (like England and Denmark) which have state churches but relatively little religious input in public policy; and there are other democracies, like America, which formally bar the establishment of any faith but nonetheless have a powerful "civil religion" which somehow invests important national events with an air of the transcendent. Presidential inaugurations are a locus of that generic form of faith.
That's why a president-elect's choice of clerics to say prayers at the inauguration is watched carefully. An incoming head of state can use the selection to send signals and set a new ideological and moral tone. The new incumbent can reward spiritual figures, and constituencies, who have served as political allies, and offer an olive branch to religious and social groups that view the election result with dismay. The latter tactic is a risky one. When, in 2009, Barack Obama offered a slot to Rick Warren, a preacher and best-selling author with conservative views on homosexuality, both men drew opprobrium from their respective constituencies.
In some ways, the choice was even harder for Donald Trump, a religiously illiterate figure who nonetheless won the votes of many religious people. To pray, speak or read scriptural verses at his big day on January 20th he has chosen six individuals, all of whom carry a certain edginess. Among them is his personal spiritual adviser Paula White, a Florida televangelist of the Pentecostal kind who urges followers to make generous contributions to the church and expect material bounty in return. Then there is Wayne T. Jackson, an African-American pastor from Detroit with a similar penchant for the prosperity gospel. He helped Mr Trump attract at least a few black votes by conducting a sympathetic interview with the candidate, and firmly believes the incoming president's wealth is a sign of divine blessing.
Another chosen speaker, Samuel Rodriguez is a leading figure among Hispanic evangelicals, a growing constituency which leans more to the right than Catholic Latinos do. Rabbi Marvin Hier will be the first Jewish participant in the ceremony since 1985; he runs the Simon Wiesenthal Center in Los Angeles which has just denounced a UN vote critical of Israel, which America declined to block, as the most egregious anti-Semitic act of 2016.
But the two highest-profile figures on Mr Trump's list are Franklin Graham, who is probably America's best-known evangelical preacher, and Cardinal Timothy Dolan, the champion of Catholicism in New York (pictured). Mr Graham is no stranger to high places; his emphatically Christian supplication at the inauguration of George W. Bush in 2001 prompted a law-suit from a secularist campaigner called Michael Newdow who believes that public references to God violate the constitution.
Mr Graham went on to embarrass the younger President Bush by saying, in the aftermath of the 9/11 terrorist attacks, that Islam was a "very evil and wicked religion"; at the time the administration was rallying a broad coalition, including Muslim countries, to a "war against terror" and it was stressing that it had no quarrel with Islam as such. On the face of things, Mr Graham's harsh views on Islam, and in particular his belief in curbing Muslim immigration, might sit more easily with the new president; but nobody should be surprised if at some point in the future Mr Trump is left uncomfortable by one of Mr Graham's rambunctious outbursts.
With Cardinal Dolan, meanwhile, immigration is more likely than any other issue to be a point of friction with the incoming head of state. The Catholic prelate will be happy to see the White House occupied by a man who holds (as of recently, anyway) conservative views on bioethical issues, especially abortion. But the cardinal follows the church's line in advocating a liberal and humane immigration policy, and in deploring selfish "nativism" among Americans whose recent forebears were members of struggling ethnic minorities. On that matter, he and the White House will part company.
Mr Trump's spiritual supporters may be carefully chosen, and for the most part they seem to be honoured by the invitation, but he should not count on their unconditional loyalty. If religion is worth the name, it has a propensity for slipping out of any earthly leader's control.
More from Erasmus
The future of the Catholic church
A high-noon moment for Pope Francis over the Amazon
Why American Muslims lean leftwards for 2020
Turbulent priests
Taking sides in the Orthodox Church's battles over Russia and Ukraine


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Several Upper Cape School Districts To Go Remote Next Week
By KATIE GOERS
Several Upper Cape school districts have announced plans to hold all classes remotely during the week following holiday vacation due to rising COVID-19 cases.
The public school systems in Sandwich, Bourne and Falmouth will all be holding remote learning weeks from Monday, January 4, through Friday, January 8.
Falmouth Public Schools Superintendent Lori S. Duerr sent a statement to families on Wednesday, December 30, announcing the decision. She said the decision was made after consulting with town Health Agent Scott McGann and town physician Dr. Gregory Parkinson.
"We resolved to move remote to ensure the continued health and safety of our staff and students," she said.
All athletics and extracurricular activities are also postponed in Falmouth until the week of January 11.
Dr. Duerr said that COVID-19 cases are on the rise in town and that the positivity rate has reached 3.93 percent. Additionally, she said the district has been receiving reports of gatherings, house parties and families traveling.
The Falmouth High School girl's basketball team is in quarantine, she said.
"While we do not want to interrupt our students' school routines, we have to take all necessary precautions to ensure the safest school community we can," Dr. Duerr said.
She pleaded with families to follow the travel and quarantine guidelines and to stay home if they are not feeling well.
Bourne Public Schools Superintendent Kerri Anne Quinlan-Zhou sent a letter to families on New Year's Eve with a similar message, saying that she had made the decision in consultation with health agent Terri Guarino and district pediatrician Dr. Roshann Hooshmand.
She said that while the district's ultimate goal is to continue with in-person learning, having one remote week will help keep staff and students safe and healthy.
"We determined that it is necessary to take proactive measures to temporarily pivot to remote learning for all students for the week of January 4 through 8," she said. "The one week of remote learning during the week of January 4 allows for a quarantine period post-holiday that will ensure that we can continue with in-person learning for the long term."
Sandwich Public Schools made the decision to have a remote week after vacation ahead of time. Superintendent Pamela A. Gould announced the decision to parents ahead of school break on December 14.
During the remote week, all district staff will have the opportunity be be tested for the virus with results expected to be returned before in-person learning resumes on January 11. All athletics and extracurriculars are on hold in Sandwich until that time as well.
Some of the highest needs students in Sandwich will be able to return to in-person learning next week and Dr. Gould said that those families will be contacted by the district.
Mashpee Public Schools have not announced any change to the district's schedule next week.
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