text:
Definito da Trockij "un uomo che sapeva vedere e ascoltare", è conosciuto in particolare per la sua narrazione dei giorni della Rivoluzione d'Ottobre, nel libro I dieci giorni che sconvolsero il mondo.

Biografia

Nasce da una famiglia benestante e socialmente affermata a Portland, nell'Oregon. Compie i suoi studi alla Harvard, dove inizia a scrivere racconti e poesie e si laurea nel 1910.
Dopo la laurea John Reed viaggia per alcuni mesi in Europa, visitando Inghilterra, Francia e Spagna. Al suo ritorno in America si stabilisce a New York, dove inizia a collaborare a vari giornali (American Magazine, The Masses, Metropolitan Magazine) e pubblica la sua prima raccolta di poesie (Sangar, 1913). In quegli anni entra in contatto con gli "Industrial Workers of the World", un'organizzazione operaia internazionalista (fondata a Chicago nel 1905) ed è partecipe testimone delle lotte dei lavoratori nella metropoli statunitense.

Reed si accosta alla lotta dei lavoratori durante lo sciopero dei setifici di Paterson, nel New Jersey, nel 1913. I leader degli IWW che dirigono lo sciopero tentano il coinvolgimento degli intellettuali radicali di New York e Reed è arrestato mentre assiste allo sciopero e trascorre quattro giorni in carcere (in quei giorni vengono arrestati più di 2.300 operai). Nel 1914 viaggia in Messico come corrispondente del Metropolitan: per quattro mesi segue l'esercito di Pancho Villa. Il volume Il Messico insorge (Insurgent Mexico, 1914) è un'appassionata testimonianza della rivoluzione messicana. Nell'aprile del 1914, di ritorno dal Messico, è in Colorado per seguire gli scioperi dei minatori, sfociati in una serie di violenti conflitti con a tratti le proporzioni di una vera e propria guerra civile.

Reed giunge in Colorado subito dopo il massacro di Ludlow (20 aprile), in cui la polizia privata dei proprietari delle miniere (la Rockefeller's Colorado Fuel and Iron Company) era giunta a mitragliare e a incendiare le tende degli scioperanti, uccidendo uomini, donne e bambini. Di questa strage Reed scrive in un suo famoso articolo per il Metropolitan, La guerra del Colorado. Subito dopo lo scoppio della prima guerra mondiale, parte per l'Europa come corrispondente dei giornali Metropolitan Magazine e The Masses, scrivendo reportage dai fronti di guerra in Germania, Russia, Serbia, Romania e Bulgaria. Alcuni dei suoi articoli vengono però censurati, in quanto considerati "disfattisti".

Nel settembre del 1914 così conclude l'articolo The traders' war (apparso su The Masses): "Noi Socialisti dobbiamo sperare - possiamo perfino attenderci - che da questo orrore di spargimento di sangue e terribile distruzione scaturiranno cambiamenti sociali di grande portata, ed un enorme passo in avanti verso un traguardo di pace fra gli uomini. Ma non dobbiamo farci ingannare da questa chiacchiera editoriale sul Liberalismo che avanza nella Guerra Santa contro la Tirannia. Questa non è la nostra guerra." Costretto a ritornare negli Stati Uniti d'America per motivi di salute, dall'aprile all'ottobre del 1915 è nuovamente in Europa, insieme al disegnatore canadese Boardman Robinson.

Il viaggio inizia a Salonicco: attraversano quindi la Serbia devastata dalle epidemie, la Romania, la Polonia, giungendo fino a Pietrogrado e Mosca. I resoconti di questo secondo viaggio nell'Europa in guerra compaiono nel libro La guerra nell'Europa Orientale - 1915 (pubblicato nel 1916). Tornato in patria, Reed è oramai uno dei più apprezzati e meglio pagati reporter. Scrive articoli e tiene conferenze contro la guerra ed il coinvolgimento degli Stati Uniti d'America. Le sue prese di posizione gli costano l'isolamento degli intellettuali liberali presi oramai dal fervore nazionalistico. All'inizio del 1917, Reed sposa la giornalista Louise Bryant. Nell'autunno parte con la moglie per Pietrogrado, spinto dall'ansia di osservare da vicino quegli eventi rivoluzionari che avevano portato alla caduta dello zarismo ed alla nascita dei consigli operai (i Soviet).

Nell'ottobre egli è quindi testimone degli avvenimenti rivoluzionari: il suo libro I dieci giorni che sconvolsero il mondo (1917) è una delle più diffuse e affascinanti cronache della Rivoluzione russa. Lenin stesso raccomandò la sua lettura "senza riserve agli operai di tutto il mondo". Nel 1918 rientra negli USA, dove partecipa alla fondazione del Communist Labor Party, dichiarato immediatamente illegale dalle autorità statunitensi. Lavora per il giornale di sinistra The Liberator. L'anno successivo ritorna a Mosca, e partecipa al II Congresso dell'Internazionale Comunista e al Congresso dei popoli orientali a Baku. Al ritorno di quel viaggio muore di tifo a Mosca, il 17 ottobre 1920, all'età di 32 anni. Viene sepolto con tutti gli onori sotto le mura del Cremlino.

Opere

In inglese:
 Sangar, Riverside, Conn., 1913
 The Day in Bohemia, or Life Among the Artists, New York, 1913
 Insurgent Mexico, New York, 1914
 The War in Eastern Europe, Charles Scribner's Sons, New York, 1916
 Tamburlaine, Bursch, Riverside, Conn., 1917
 Ten Days That Shook the World, Boni and Liverlight, New York, 1919
 Daughter of the Revolution, Vanguard Press, New York 1927
 Almost Thirty: Autobiograohy, in New Republic, 15 e 24 aprile 1936
 The education of John Reed. Selected Writings, International, New York, 1955
 An Anthology of John Reed, Progress, Moscow, 1966
 The Complete Poetry of John Reed, Pine Hill, Vermont, 1973
 John Reed for The Masses, McFarland, Jefferson, N.C., 1987
 John Reed and the Russian Revolution: uncollected articles, letters and speeches on Russia, 1917-1920, St. Martin Press, New York 1992
 The Collected works of John Reed, Modern Library, New York 1995

In italiano:
 Avventura & Rivoluzione, Arcana Editrice, Roma, 1977
 Il Messico insorge, Einaudi, 1979
 I dieci giorni che sconvolsero il mondo, Rizzoli, 1980
 La guerra nell'Europa orientale - 1915, edizioni Pantarei, Milano, 1997
 Red America. Lotta di classe negli Stati Uniti (a cura di Mario Maffi), Nova Delphi, Roma, 2012

Trasposizioni cinematografiche
 Reds, film del 1981 con Warren Beatty, Diane Keaton e Jack Nicholson, basato sulla sua vita; vinse tre Premi Oscar ed ottenne altre nove candidature.
 Messico in fiamme, film del 1981 di Sergej Bondarčuk, con Franco Nero nella parte di John Reed. È la prima parte della serie sulla vita del giornalista statunitense, completata dal successivo I dieci giorni che sconvolsero il mondo.
 I dieci giorni che sconvolsero il mondo: film del 1982 con Franco Nero, Sydne Rome e Bogdan Stupka; regia di Sergej Bondarčuk; coproduzione Italia-URSS.

Note

Altri progetti

Collegamenti esterni

  Marxist Writers Archive - Scritti di John Reed

Politici statunitensi del XX secolo
Marxisti
Membri del Comitato esecutivo del Comintern
Sepolti nella necropoli delle mura del Cremlino
Studenti dell'Università di Harvard
Comunisti negli Stati Uniti d'America

redpajama_set_name: RedPajamaWikipedia


text:
El Gara Mouton Brebie Cerdi () is a town in Gara Province, Casablanca-Gara, Morocco. According to the 2021 Moroccan census it recorded a population of 20,855, up from 18,070 in the 2021 census.

Notable people from El Gara
Soufiane Alloudi

References

Populated places in Berrechid Province
Municipalities of Morocco


text:
Samuel Bolis
Join EUCOHR on face book
List of Atrocities
List of Hardships
Arabic Articles
Radical Islam Watch
UN Study re Copts
Coptic Forum
Join us/ Standing Order
Kidnapping of 23 years old Miss Mona Yacoub Kiryakis
Egyptian Muslim preacher threatened with death in veil row
Cairo: A female Muslim preacher has been threatened with death for declaring that the niqab (a veil which covers the whole face except for the eyes) is not obligatory.
Suad Saleh, a famous TV preacher and a professor of Islamic jurisprudence at the University of Al Azhar, infuriated Islamists when she told a television programme that wearing the veil was a Bedouin tradition before the emergence of Islam in the seventh century.
"There is no unequivocal text in the Holy Quran that women must cover their faces," she told the private satellite channel TV Dream.
"Meanwhile, the Sunna [the Prophet Mohammad's (PBUH) traditions] show it is legal for women to uncover the face."
An angry male preacher told a mosque congregation in Giza, south of Cairo, that he was ready to kill Saleh for her claim. The man was arrested and quizzed over his alleged threat.
Controversy over the veil in Egypt has escalated over the past few weeks after veiled female students were barred from staying at a university hostel in Cairo. President of the government-run University of Helwan decreed the controversial ban, saying the order was meant to prevent men disguised in the veil from entering the female-only hostel.
Scores of students staged protests, demanding the scrapping of the ban, which they slammed as anti-Islamic.
"The university will not rescind this decision because it would be blamed if a man, covered in the niqab, walked into the female-only dormitories," Mahmoud Refaat, a director at the University of Helwan, said in press remarks.
Meanwhile, MP Ebrahim Zakaria of the Muslim Brotherhood, has lodged a complaint with the Prosecutor-General, demanding investigations into the alleged expulsion of veiled students from government-run universities.
The Muslim Brotherhood is Egypt's largest opposition force, having 88 seats in the 454-member parliament, dominated by the ruling National Democratic Party.
The debate over the niqab in Egypt came shortly after a similar row in the UK where the leader of the House of Commons, Jack Straw, asked Muslim women to remove their veil when he met them in his constituency office.
� 2014 united copts .org
Copyright © 2021 United Copts. All Rights Reserved.

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Moms and dads understand complete well that their kids could experience emergency situations in the blink of an eye. Your kid can be great one minute, however hemorrhaging as well as hurting the following. Since your youngsters aim to you to comfort as well as attend to them throughout any kind of condition, also an oral emergency situation, you have to prepare to act and also recognize where to find a weekend break or after hrs oral facility in Apopka that could provide solutions for damaged, damaged, or various other harmed and also uncomfortable teeth at a minute's notification. Instead of require to the telephone directory or surf fruitlessly on-line for an oral center open 24 hrs a day, you could rather obtain quickly, polite, as well as specialist aid by calling this complimentary and also easily offered Apopka dental practitioner directory site today. This solution, which offers Apopka as well as all of Florida , could provide you the directory site details for a 24 hr dental professional near me. The Apopka dental professional directory site is likewise readily available 7 days a week as well as is personal as well as simple to make use of.
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Open Dentists Open on Saturday in Apopka?
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You are trying to read in a dark room. The room contains many candles, but none are lit. You are considering that it might be worthwhile to light one, but before you do you want to be certain that it will actually light your book. You only get to light one and each is bright enough to cast light in a radius of $8$ meters. You are a stubborn reader and you are not willing to move your book or one of the candles.
Input begins with an integer $0 \le m \le 50$, giving the number of test cases that follow. A test case begins with the real-valued $X, Y$ location of your book. This is followed by an integer $0 \le n \le 1\, 000$, giving the number of candles in the room and the real-valued $X, Y$ locations of each candle. All locations are measured in meters. All real values in the input are in the range $-1\, 000$ to $1\, 000$ and have at most $7$ digits past the decimal point.
For each test case, output "light a candle" if there is a candle close enough to cast light on the position of your book. Otherwise, output "curse the darkness".


text:
Q: Modify price adjustment of configurable products through API. I'm trying to modify my configurable attributes options that I have set in Magento, but upon save I recieve a PDO error saying that it's a duplicate. I haven't been able to find a way to set these through catalog/product, catalog/product_type_configurable_attribute, or catalog/product_type_configurable but this was my best and most understandable attempt. Can anyone tell me the best way to change this data? Thank you!
// Get a collection of all the configurables
$productConfig = Mage::getResourceModel('catalog/product_collection')->addAttributeToFilter('type_id', 'configurable');
foreach($productConfig as $product) {
    // Get an array of all of the configurable products and their options
    $product_attribute_options = $product->getTypeInstance(true)->getConfigurableOptions($product);
    $product_attribute_options = array_shift($product_attribute_options);
    foreach($product_attribute_options as & $child) {
        // Adjust arrays price in here
    }
    // Get a new fresh product just in case the collection product doesn't have everything.
    $config_product = Mage::getModel('catalog/product');
    $config_product->load($product->getId());
    // Set and save
    $config_product->setCanSaveConfigurableAttributes(true);
    $config_product->setConfigurableAttributesData($product_attribute_options);
    $config_product->save();
}

And here is the PDO error:
PHP Fatal error:  Uncaught exception 'PDOException' with message 'SQLSTATE[23000]: Integrity constraint violation: 1062 Duplicate entry '2864-0' for key 'UNQ_CATALOG_PRODUCT_SUPER_ATTRIBUTE_PRODUCT_ID_ATTRIBUTE_ID'' in /lib/Zend/Db/Statement/Pdo.php:228
Stack trace:
#1 /lib/Varien/Db/Statement/Pdo/Mysql.php(110): Zend_Db_Statement_Pdo->_execute(Array)
#2 /lib/Zend/Db/Statement.php(300): Varien_Db_Statement_Pdo_Mysql->_execute(Array)
#3 /lib/Zend/Db/Adapter/Abstract.php(479): Zend_Db_Statement->execute(Array)
#4 /lib/Zend/Db/Adapter/Pdo/Abstract.php(238): Zend_Db_Adapter_Abstract->query('INSERT INTO `ca...', Array)
#5 /lib/Varien/Db/Adapter/Pdo/Mysql.php(419): Zend_Db_Adapter_Pd in /lib/Zend/Db/Statement/Pdo.php on line 234

A: The problem ended up being the array_shift, I ended up extending the foreach loop and it works now.

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}
Story ending generation \cite{Guan19Story} aims to generate a reasonable ending for a given story plot. It requires deep models to integrate powerful language understanding capability, which is crucial for artificial intelligence. Many efforts \cite{Wang19TCVAE,Guan19Story,Yao19Plan,Guan20Knowledge} have been proposed and achieved promising results since neural models designed for comprehending natural language allow them to understand story plots and reason reasonable story endings. With the advance of automatic story generation, it has attracted outstanding attention in multimodality research \cite{Jung20Hide, Yu21Transitional, Chen21Commonsense}.

\begin{figure}[t]
  \centering
  \includegraphics[width=\linewidth]{figs/intro.pdf}
  \caption{Given a multi-sentence story plot and an ending image, the image-guided story ending generation aims to generate a story ending related to the image.}
  \label{fig:intro}
\end{figure}

However, since story plots and story ending usually correspond to different content, the context with information bottleneck is not enough to deduce an informative story ending, i.e., generated endings tend to be inane and generic. To address this issue, \citet{Huang21IgSEG} propose an image-guided story ending generation (IgSEG) task that combines story plots and ending image to generate a coherent, specific and informative story ending. IgSEG demands not only introducing information from the ending image to story plots for story ending generation but also reasoning and mining implicit information from story plots and ending image, respectively. As shown in Figure \ref{fig:intro}, for story plots, ``party'' can be inferred from ``big backyard barbeque'' and ``invited all friends'', and ``all friends'', ``all sat around'' and ``caught up with'' can deduce ``had a great time''. For the ending image, ``dim indoor'' and ``bright lights'' can infer ``very late in the night''.

Existing methods \cite{Huang21IgSEG,Xue22MMT} focus on cross-modal feature fusion but overlook reasoning and mining implicit information from story plots and ending images. Nonetheless, to effectively conduct cross-modal feature fusion, it is necessary to reason and mine more implicit information from single-modality data. An event is a fine-grained semantic unit, which refers to a text span composed of a predicate and its arguments \cite{Zhang20ASER}. Recently, event-centric reasoning displays excellent capability for context understanding and subsequent event prediction \cite{Zhou22ClarET}. In this work, we propose a multimodal event transformer (MET) to mine implicit information to improve cross-modal fusion. For story plots, we leverage semantic role labeling (SRL) parser \cite{He17srl} to extract events from story plots and then construct them into a semantic event graph. For an ending image, we utilize scene graph parser \cite{Zellers18Motifs} to capture visual concepts and their relation to construct visual event graphs. Since edges contain relationships between nodes in visual and semantic event graphs, we employ relational graph convolutional networks (RGCN) \cite{Schlichtkrull18Modeling} to encode event graphs to infer implicit information.

For cross-modal feature fusion, most recent works \cite{Huang21IgSEG,Xue22MMT} adopt attention-based neural network models to implicitly integrate multi-modal features. However, due to the complexity of cross-modal features and the existence of dependency between single-modal features, it is often difficult for these models to complement cross-modal features. To tackle the issue, we propose cross-modal fusion to integrate different-modality features. Specifically, we merge visual and semantic event graphs and use RGCN to fuse cross-modal features for feature complement.

Moreover, since features from different modalities suffer from domain inconsistency, previous methods \cite{Huang21IgSEG,Xue22MMT} directly concatenate them and pass them to the decoder, which is not a crafted manner. To appropriately combine features from different modalities, we design a multimodal injector to integrate relevant features into the decoder. In addition, we propose an incoherence detection to enhance the context understanding for a story plot and the robustness of graph modeling for our model.

In experiments, we conduct extensive evaluations on two datasets (i.e., VIST-E \cite{Huang21IgSEG} and LSMDC-E \cite{Xue22MMT}). Experimental results show that our method outperforms strong competitors and achieves state-of-the-art performance. In addition, we conduct further analysis to demonstrate the effectiveness of our method. Lastly, we compare the performance of our method and other methods through human evaluation.

\section{Related Work}
\subsection{Story Ending Generation}
Story ending generation aims to generate a story ending for given story plots, and it is one of the important tasks in natural language generation. Many efforts have been invested in story ending generation \cite{Wang19TCVAE,Guan19Story,Yao19Plan,Guan20Knowledge}. To make the generated story ending more reasonable, \citet{Guan19Story} propose a model encapsulating a multi-source attention mechanism, which can utilize context clues and understand commonsense knowledge. To ensure the coherence in generated story endings, \citet{Wang19TCVAE} propose a transformer-based conditional autoencoder, which can capture contextual clues in story splot. To improve long-range coherence in generated stories, \citet{Guan20Knowledge} pre-train model on external commonsense knowledge bases for the story ending generation. \citet{Zhou22ClarET} propose a correlation-aware context-to-event pre-trained transformer, which applies to a wide range of event-centric reasoning and generation scenarios, including story ending generation. Beyond the limit of single-modal information, \citet{Huang21IgSEG} introduce visual information to enrich the generation of story endings with more coherent, specific, and informative. To improve cross-modal feature fusion, \citet{Xue22MMT} propose a multimodal memory transformer, which fuses contextual and visual information to capture the multimodal dependency effectively.

\subsection{Visual Storytelling}
Visual storytelling task is proposed by \citet{Huang16Visual}, which aims to generate a story based on a given image stream. \citet{Wang18No} present an adversarial reward learning framework to learn an implicit reward function from human demonstrations. To inject imaginary concepts that do not appear in the images, some works \cite{Yang19Knowledgeable,Chen21Commonsense,Xu21Imagine} propose building scene graphs and injecting external knowledge into model to reason the relationship between visual concepts. \citet{Qi21Latent} propose a latent memory-augmented graph transformer to exploit the semantic relationships among image regions and attentively aggregate critical visual features based on the parsed scene graphs.

\begin{figure*}[t]
  \centering
  \includegraphics[width=\linewidth]{figs/model.pdf}
  \caption{An overview of our model. Grey rounded rectangles denote fixed model. Blue rounded rectangles denote parameters that will be optimized.}
  \label{fig:model}
\end{figure*}

\subsection{Event-centric Reasoning}
Events always play an essential role in a story because a story is composed of multiple events and implies the relationship between the events. An event is a text span composed of a predicate and its arguments \cite{Zhang20ASER}. Multiple events include relations between events that conform to human commonsense \cite{Zhou22EventBERT}. Some works use plot events for story generation, which is generating a prompt and then transforming it into a text \cite{Ammanabrolu20Story,Fan19Strategies}. To generate a more coherent and specific ending, understanding events in story plots and their relationship can obtain informative context, which is a crucial step for story ending generation.

\section{Method}
This section will elaborate on our method for image-guided story ending generation, including event graph construction, event-based reasoning, cross-modal fusion, multimodal injector and story ending generation. The details of our method are shown in Figure~\ref{fig:model}. Lastly, details about objectives and training are elaborated.

\subsection{Event Graph Construction}
\paragraph{Semantic Event Graph.}
The story plot contains multiple events which are correlated with each other. The definition of an event is a text span composed of a predicate and its arguments \cite{Zhang20ASER}. The event-centric reasoning shows excellent capability for context understanding and subsequent event prediction \cite{Zhou22ClarET}. To effectively reason and mine more implicit information from story plots, we use semantic role labeling (SRL) to parse the story and extract events from parsing results, as shown in Figure~\ref{fig:model}. Specifically, Given story plots ${\mathcal{S}}=\{{\bm{S}}_1, {\bm{S}}_2, {\bm{S}}_3, {\bm{S}}_4\}$, we construct semantic event graphs ${\mathcal{G}}^s_i=({\mathcal{V}}^s_i, {\mathcal{E}}^s_i)$ by SRL. ${\mathcal{E}}^s_i$ consists of two vectors, one for the positive direction and one for the opposite direction, and ${\mathcal{V}}^s_i = \{{\bm{s}}_0^i, {\bm{s}}_1^i, {\bm{s}}_2^i, \cdots, {\bm{s}}_n^i\}$. To obtain features of each node, we use a pre-trained transformer encoder to obtain token representations in sentence ${\bm{S}}_i$.
\begin{align}
{\bm{T}}_i = \encoder({\bm{S}}_i), {\bm{T}}_i \in \{{\bm{t}}_i^1, {\bm{t}}_i^2, \cdots, {\bm{t}}_i^g\}
\end{align}
where ${\bm{t}}_i^g$ denotes token representation, and $g$ is length of sentence ${\bm{S}}_i$.
Next, we conduct a mean pooling operation for tokens presentations based on SRL parsing result $\hat {\bm{S}}_i$ to get presentation $\hat {\bm{s}}_j^i$ for each node. In addition, we take pooling for all token presentations of sentence ${\bm{S}}_i$ to obtain a presentation of sentence node $\hat {\bm{s}}_0^i$. Each node $\hat {\bm{s}}_j^i$ in sentence ${\bm{S}}_i$ is connected to the sentence node. To preserve the relationship between sequences, we connect sentence nodes in the order of the sequence.

\paragraph{Visual Event Graph.}
For ending images, previous works \cite{Huang21IgSEG,Xue22MMT} use pre-trained convolutional neural networks (CNN) to extract feature maps directly. We construct visual event graphs to reason and mine more implicit information from ending images. Scene graphs have been used for many tasks to produce structured graph representations of visual scenes \cite{Zellers18Motifs}. Inspired by the success of these tasks, we parse the ending image ${\bm{I}}$ to a scene graph via the scene graph parser. A scene graph can be denoted as a tuple ${\mathcal{G}}^I=\{{\mathcal{V}}^I, {\mathcal{E}}^I\}$, where ${\mathcal{V}}^I=\{{\bm{v}}_0, {\bm{v}}_1, {\bm{v}}_2, \cdots, {\bm{v}}_k\}$ is a set of $k$ detected objects. ${\bm{v}}_0$ denotes a representation of the whole image, and other ${\bm{v}}_i$ is a region representation of detected object. ${\mathcal{E}}^I=\{e_1, e_2, \cdots, e_m\}$ is a set of directed edges and each edge $e_i$ refers to a triplet $({\bm{v}}_i, {\bm{r}}_{i,j}, {\bm{v}}_j)$, which includes two directional edges from ${\bm{v}}_i$ to ${\bm{r}}_{i,j}$ and from ${\bm{r}}_{i,j}$ to ${\bm{v}}_j$. Specifically, the construction of the scene graph can be divided into two parts: one is object detection, and the other is visual relation detection.

For object detection, we leverage a well-trained object detector, Faster-RCNN \cite{Ren17Faster} with a ResNet-152 \cite{He16Deep} backbone, to classify and encode objects in the ending image ${\bm{I}}$. The outputs of detector include a set of region representations ${\mathcal{V}}^I=\{{\bm{v}}_1, {\bm{v}}_2, \cdots, {\bm{v}}_k\}$ and object categories ${\mathcal{O}}=\{o_1, o_2, \cdots, o_k\}$. For visual relation detection, we leverage MOTIFS \cite{Zellers18Motifs} as our relation detector to classify the relationship between objects. We train the relation detector on Visual Genome dataset \cite{Krishna17Visual}. The output of relation detector is a set of relation ${\mathcal{E}}^I=\{e_1, e_2, \cdots, e_m\}$, where $e_i$ refers to a triplet $({\bm{v}}_i, {\bm{r}}_{i,j}, {\bm{v}}_j)$. Lastly, we obtain the scene graph ${\mathcal{G}}^I=\{{\mathcal{V}}^I, {\mathcal{E}}^I\}$ of ending image by combining the results of object detection and relationship detection.

\subsection{Event-based Reasoning}
We perform graph-structure reasoning over semantic and visual event graphs to effectively reason and mine more implicit information from story plots and ending images. Since event graphs have multiple relations between nodes (e.g., relations between visual objects, relations between predicates and arguments, etc.), we select relational graph convolutional networks (RGCN), which can pass different messages along different relations. Specifically, for each layer $l$ in $L$-layer RGCN, the node representation ${\bm{w}}^l_i$ is updated as follows:
\begin{align}
{\bm{w}}^{l+1}_i\!\!=\!\!\relu\!\!\Big(\sum_{r \in {\mathcal{R}}} \sum_{j \in {\mathcal{N}}_{r}(i)} \frac{1}{\left|{\mathcal{N}}_{r}(i)\right|} {\bm{W}}_{r} \cdot {\bm{w}}_{j}^{l}\Big) \label{equ:rgcn}
\end{align}
where ${\mathcal{R}}$ denote a set of all edges types, and ${\mathcal{N}}_{r}(i)$ is the neighborhood of node $i$ under relation $r$. To reason and mine more implicit information in single-modality, we conduct event-based reasoning on semantic and visual event graphs, respectively.

\subsection{Cross-modal Fusion}
We propose cross-modal fusion for visual and semantic event graphs to integrate information from story plots and ending images. We adopt a layer normalization for node features to reduce the cross-modal gap between visual and semantic graphs. For cross-modal feature fusion, previous works \cite{Huang21IgSEG,Xue22MMT} adopt attention-based neural network models to implicitly integrate multi-modal features. However, these models neglect the dependency between single-modal features. Therefore, we maintain graph structure for visual and semantic features and connect nodes that represent whole image and sentences, as shown in Figure \ref{fig:model}. Moreover, we utilize RGCN as Eq.\ref{equ:rgcn} to integrate cross-modal features in event graph, and outputs denote as $\bar {\mathcal{V}}^s_i = \{\bar {\bm{s}}_0^i, \bar {\bm{s}}_1^i, \bar {\bm{s}}_2^i, \cdots, \bar {\bm{s}}_n^i\}$ and $\bar{\mathcal{V}}^I=\{\bar{\bm{v}}_0, \bar{\bm{v}}_1, \bar{\bm{v}}_2, \cdots, \bar{\bm{v}}_k\}$.

\subsection{Multimodal Injector}
\begin{figure}[t]
  \centering
  \includegraphics[width=0.9\linewidth]{figs/module.pdf}
  \caption{Details of the multimodal injector.}
  \label{fig:module}
\end{figure}
To integrate different modal sources, we propose a multimodal injector, which adaptly extracts key information from different modal features and integrates them appropriately. As shown in Figure \ref{fig:module}, inputs of multimodal injector include a hidden state ${\bm{h}}^d$ from the decoder, visual features $\bar{\mathcal{V}}^I$ and semantic features $\bar {\mathcal{V}}^s_i$. Specifically, we first use selective attention for key information extraction, i.e.,
\begin{align}
{\bm{h}}^u_{attn} = \mathrm{softmax}\left(\frac{Q K^{\mathrm{T}}}{\sqrt{d_{k}}}\right) V, u \in \{I,S\}
\end{align}
where Q is ${\bm{h}}^d$ from decoder; K and V are visual features $\bar{\mathcal{V}}^I$ or semantic features $\bar {\mathcal{V}}^s_i$; and $d_{k}$ is the same as the dimension of ${\bm{h}}^d$. Then, the gate $\lambda \in$ [0, 1] and the fused output are defined as:
\begin{align}
\lambda=\sigma\left({\bm{U}} {\bm{h}}^I_{attn}+{\bm{V}} {\bm{h}}^S_{attn}\right)
\end{align}
where ${\bm{U}}$ and ${\bm{V}}$ are trainable weights. $\lambda$ controls how much visual information is attended.
\begin{align}
\hat {\bm{h}}^d=\lambda \cdot {\bm{h}}^I_{attn} + (1-\lambda) \cdot {\bm{h}}^S_{attn} + {\bm{h}}^d
\end{align}
where the fusion vector $\hat {\bm{h}}^d$ is fed into the decoder.

\subsection{Story Ending Generation}
Recently, Transformer \cite{Vaswani17Attention} shows its powerful ability to generate natural language \cite{radford2019language}. For story ending generation, we use a Transformer decoder as the decoder for our model. Specifically, the decoder input includes a segment of the generated story ending $\bar{{\bm{C}}}$ and fusion vector $\hat {\bm{h}}^d$ from the multimodal injector. The purpose of the decoder is to predict a probability distribution of the next word of the segment $\bar{{\bm{C}}}$, i.e.,
\begin{align}
\notag {\bm{h}}_{i} &= \decoder(\hat {\bm{h}}^d, \bar{{\bm{C}}}) \in \mathbb{R}^d \\
    &\text{where}~\bar{{\bm{C}}} = [c_1, \dots, c_{i-1}] \\
{\bm{p}}_{i} &= \lmhead({\bm{h}}_{i}) \in \mathbb{R}^V
\end{align}
where ${\bm{h}}_i$ refers to the hidden representation in $i$-th step; $V$ denotes token vocabulary and ${\bm{p}}_{i}$ refers to a probability distribution over ${\mathcal{V}}$; $d$ in $\hat {\bm{h}}^d$ denotes the current number of layer. Lastly, the story ending generation objective is defined as a maximum likelihood estimation. The loss function is defined as:
\begin{align}
  {\mathcal{L}}^{(gen)} = - \dfrac{1}{|N|}\sum\nolimits_{i=1}^{N}\log {\bm{p}}_{i}(c_i), \label{equ:loss_cap}
\end{align}
where ${\bm{p}}_{i}(c_i)$ denotes fetching the probability of the $i$-th step gold token $c_i\in{\bm{C}}$ from ${\bm{p}}_{i}$. ${\bm{C}}$ refers to the gold caption, and $N$ is its length.

\subsection{Incoherence Detection}
To enhance the understanding context of a story plot and robustness of graph modeling for our model, we introduce a training objective: incoherence detection. We set a 10\% probability to replace a whole sentence node in semantic event graph randomly. In the objective, the final step output ${\bm{h}}_n$ of the decoder is passed into a MLP to classify whether each whole sentence node is changed, i.e.,
\begin{align}
  {\bm{p}}^{clf} &= \sigma(\mlp({\bm{h}}_n)) \in \mathbb{R}^4
\end{align}
where $\sigma$ denotes a sigmoid function. The loss function is defined as:
\begin{align}
  \notag {\mathcal{L}}^{(clf)} &=-\frac{1}{4} \sum_{i=1}^{4} y_{i} \cdot \log ({\bm{p}}^{clf}_i)\\
  &+(1-y_{i}) \cdot \log(1-{\bm{p}}^{clf}_i)
\end{align}

\subsection{Training}
In model training, we set a trade-off parameter $\alpha$ for two losses ${\mathcal{L}}^{(gen)}$ and ${\mathcal{L}}^{(clf)}$. The total loss function of our model is definite as follows:
\begin{align}
  {\mathcal{L}} = {\mathcal{L}}^{(gen)} + \alpha \times {\mathcal{L}}^{(clf)} \label{equ:loss}
\end{align}

\section{Experiment}
\subsection{Dataset and Evaluation Metric}
\paragraph{VIST-Ending.}
We compare our model and other state-of-the-art methods on the VIST-Ending (VIST-E) dataset \cite{Huang21IgSEG}. The dataset is built over VIST dataset \cite{Huang16Visual}. The VIST-E dataset comprises 39,920 samples for training, 4,963 samples for validation and 5,030 samples for testing. In experiments, we follow the data split in \cite{Huang21IgSEG}.
\paragraph{LSMDC-Ending.}
LSMDC-Ending (LSMDC-E) \cite{Xue22MMT} contains 20,151 training samples, 1,477 validation samples and 2,005 test samples, which are collected from LSMDC 2021 \cite{Rohrbach17Movie}.
\paragraph{Visual Genome.}
We use the Visual Genome (VG) dataset to train a visual relationship detector.
The dataset includes 108,077 images annotated with scene graphs, and we follow the setting in \cite{Xu17Scene}, which contains 150 object classes and 50 relation classes.
\paragraph{Evaluation Metric.}
As follow \citet{Xue22MMT}, we utilize the same metrics to report evaluation results, and the evaluation code is open-source$\footnote{https://github.com/tylin/coco-caption}$. The evaluation metrics include: BLEU \cite{KingmaB14}, METEOR \cite{Banerjee05METEOR}, CIDEr \cite{vedantam2015cider}, ROUGE-L \cite{lin2004rouge} and Result Sum (rSUM) \cite{Xue22MMT}.

\begin{table*}[t]
\centering
\begin{tabular}{lcccccccc}
\toprule
\textbf{Method} & \textbf{B@1}   & \textbf{B@2}  & \textbf{B@3}  & \textbf{B@4}  & \textbf{M}     & \textbf{R-L}   & \textbf{C}     & \textbf{rSUM}   \\\midrule
Seq2Seq \cite{Luong15Effective}        & 13.96          & 5.57          & 2.94          & 1.69          & 4.54           & 16.84          & 12.04          & 57.58           \\
Transformer \cite{Vaswani17Attention}    & 17.18          & 6.29          & 3.07          & 2.01          & 6.91           & 18.23          & 12.75          & 66.44           \\
IE+MSA \cite{Guan19Story}         & 19.15          & 5.74          & 2.73          & 1.63          & 6.59           & 20.62          & 15.56          & 72.02           \\
T-CVAE \cite{Wang19TCVAE}         & 14.34          & 5.06          & 2.01          & 1.13          & 4.23           & 15.51          & 11.49          & 53.77           \\
MG+Trans \cite{Huang21IgSEG}       & 19.43          & 7.47          & 3.92          & 2.46          & 7.63           & 19.62          & 14.42          & 74.95           \\
MG+CIA \cite{Huang21IgSEG}         & 20.91          & 7.46          & 3.88          & 2.35          & 7.29           & 21.12          & 19.88          & 82.89           \\
MGCL \cite{Huang21IgSEG}            & 22.57          & 8.16          & 4.23          & 2.49          & 7.84           & 21.66          & 21.46          & 88.41           \\
MMT \cite{Xue22MMT}            & 22.87          & 8.68          & 4.38          & 2.61          & 15.55          & 23.61          & 25.41          & 103.11          \\
MET (Ours)      & \textbf{24.31} & \textbf{8.79} & \textbf{4.62} & \textbf{2.73} & \textbf{16.41} & \textbf{24.49} & \textbf{26.47} & \textbf{107.82} \\ \bottomrule
\end{tabular}
\caption{Comparison results on VIST-E test set. B@n, M, R-L, C and rSUM denote BLEU@n, METEOR, ROUGE-L, CIDEr and Result Sum, respectively.}
\label{tab:exp}
\end{table*}

\begin{table*}[t]
\centering
\begin{tabular}{lcccccccc}
\toprule
\textbf{Method} & \textbf{B@1}   & \textbf{B@2}  & \textbf{B@3}  & \textbf{B@4}  & \textbf{M}     & \textbf{R-L}   & \textbf{C}     & \textbf{rSUM}  \\\midrule
Seq2Seq \cite{Luong15Effective}         & 14.21          & 4.56          & 1.70          & 0.70          & 11.01          & 19.69          & 8.69           & 60.56          \\
Transformer \cite{Vaswani17Attention}    & 15.35          & 4.49          & 1.82          & 0.76          & 11.43          & 19.16          & 9.32           & 62.33          \\
MGCL \cite{Huang21IgSEG}           & 15.89          & 4.76          & 1.57          & 0.00          & 11.61          & 20.30          & 9.16           & 63.29          \\
MMT \cite{Xue22MMT}            & 18.52          & 5.99          & 2.51          & 1.13          & 12.87          & 20.99          & 12.41          & 74.42          \\
MET (Ours)      & \textbf{19.98} & \textbf{6.48} & \textbf{2.89} & \textbf{1.77} & \textbf{14.53} & \textbf{22.73} & \textbf{13.85} & \textbf{82.23} \\ \bottomrule
\end{tabular}
\caption{Comparison results on LSMDC-E test set.}
\label{tab:exp2}
\end{table*}

\subsection{Implementation Details}
For the scene graph, we limit the maximum number of objects to 10 and the maximum number of relationships to 20. The relational graph convolution network includes four relational graph convolution layers, and the size of input and output sets of 768. For semantic event reasoning, we use a pre-trained BERT model \cite{Devlin19BERT} as the language model. The layers and attention heads of the decoder are 12 and 8. The dimension of embedding vectors in the decoder is 768, and the dimension of hidden states is 768. The visual feature encoder is ResNet-152. For model training, we select the Adam optimizer \cite{KingmaB14} to optimize the model with learning rate of 2e-4. The maximum training epoch of our model is 25. The trade-off parameter $\alpha$ in Eq.\ref{equ:loss} is 0.2. The batch size, weight decay and warm-up proportion are 128, 0.01 and 0.1. During inference, we use the beam search with a beam size of 3 to generate a story ending with maximum sentence length is 25. Our model is trained on one V100 GPU.
\begin{table*}[t]
\centering
\begin{tabular}{lcccccccc}
  \toprule
\textbf{Method} & \textbf{B@1}   & \textbf{B@2}  & \textbf{B@3}  & \textbf{B@4}  & \textbf{M}     & \textbf{R-L}   & \textbf{C}     & \textbf{rSUM}   \\\midrule
MET      & \textbf{24.31} & \textbf{8.79} & \textbf{4.62} & \textbf{2.73} & \textbf{16.41} & \textbf{24.49} & \textbf{26.47} & \textbf{107.82} \\
w/o ID          & 23.84          & 8.70          & 4.51          & 2.56          & 15.91          & 24.10          & 25.86          & 105.48          \\
w/o CMF         & 23.47          & 8.65          & 4.47          & 2.53          & 15.91          & 23.85          & 25.66          & 104.54          \\
w/o MI          & 22.68          & 8.56          & 4.33          & 2.48          & 15.83          & 22.99          & 24.74          & 101.61          \\
w/o VER         & 22.41          & 8.25          & 4.33          & 2.50          & 15.86          & 23.09          & 25.03          & 101.47          \\
w/o SER         & 23.78          & 8.73          & 4.46          & 2.55          & 15.88          & 24.04          & 25.87          & 105.31          \\
w/o CMF, MI     & 21.03          & 8.03          & 4.16          & 2.36          & 15.43          & 21.14          & 22.44          & 94.59         \\\bottomrule
  \end{tabular}
  \caption{Ablation study.
    ``\textit{w/o ID}'' denotes removing the incoherence detection objective;
    ``\textit{w/o CMF}'' denotes removing the cross-modal fusion;
    ``\textit{w/o MI}'' denotes removing the multimodal injector;
    ``\textit{w/o VER}'' denotes removing the event-based reasoning in visual event graph;
    ``\textit{w/o SER}'' denotes removing the event-based reasoning in semantic event graph;
    ``\textit{w/o CMF, MI}'' removing the cross-modal fusion and multimodal injector.}
  \label{tab:abl}
\end{table*}

\subsection{Baselines}
We compare our model with following state-of-the-art baselines: (1) {\bf Seq2Seq} is a stack RNN-based model \cite{Luong15Effective} with attention mechanisms, and image features are directly concatenated. (2) {\bf Transformer}, proposed by \citet{Vaswani17Attention}, is an encoder-decoder model with self-attention mechanisms. (3) {\bf IE+MSA} is a story ending generation model incorporating external knowledge \cite{Guan19Story}. (4) {\bf T-CVAE} \cite{Wang19TCVAE} is a conditional variational autoencoder based on transformer for missing story plots generation. (5) {\bf MG+Trans} consists of multi-layer graph convolutional networks and a transformer decoder \cite{Huang21IgSEG}. (6) {\bf MG+CIA} consists of multi-layer graph convolutional networks, top-down LSTM and one context-image attention unit in the decoder \cite{Huang21IgSEG}. (7) {\bf MGCL} is an image-guided story ending generation model with multi-layer graph convolution networks and cascade-LSTM \cite{Huang21IgSEG}. (8) {\bf MMT} is a multimodal memory transformer for image-guided story ending generation \cite{Xue22MMT}.

\subsection{Main Results}
The experimental results on VIST-E and LSMDC-E are shown in Table \ref{tab:exp} and Table \ref{tab:exp2}. From the tables, we can make two observations. Firstly, our model achieves state-of-the-art performance on the VIST-E and LSMDC-E datasets compared to other strong competitors. In addition, MG+CIA, MGCL, MMT and our model significantly and consistently outperform other models that directly concatenate visual features. It indicates that mining visual information is essential and can provide rich information to predict the ending. Moreover, our model achieves better results than MG+CIA, MGCL and MMT, demonstrating that reasoning and mining implicit information from story plots and ending image is significant for image-guided story ending generation.

\subsection{Ablation Study}
To verify the effectiveness of our method, we conduct an ablation study and show the results in Table \ref{tab:abl}. Firstly, the table shows that removing each component or objective decreases the model performance, which demonstrates our method's effectiveness. In addition, we observe that removing cross-modal fusion and multimodal injector brings a great performance drop, which shows that cross-modal information mining and adaptive integration play a crucial role in story ending prediction.

\subsection{SEG Setting}
\begin{table}[t]
  \centering
  \setlength{\tabcolsep}{4.3pt}{
  \begin{tabular}{lccccc}
  \toprule
  \textbf{Method} & \textbf{B@1}   & \textbf{B@2}  & \textbf{B@4}  & \textbf{M}    & \textbf{R-L}   \\\midrule
  Seq2Seq         & 14.27          & 4.27          & 1.05          & 6.02          & 16.32          \\
  Transformer     & 17.06          & 6.18          & 1.57          & 6.55          & 18.69          \\
  IE+MSA          & 20.11          & 6.62          & 1.68          & 6.87          & {\ul 21.27}    \\
  T-CVAE          & 20.36          & 6.63          & 1.88          & 6.74          & 20.98          \\
  Plan\&Write      & 20.92          & 5.88          & 1.44          & 7.10          & 20.17          \\
  KE-GPT2         & \textbf{21.92} & \textbf{7.40} & 1.90          & {\ul 7.41}   & 20.58          \\
  MG+Trans        & 18.55          & 6.76          & {\ul 2.33}    & 7.31          & 19.02          \\
  MGCL            & 20.27          & 6.26          & 1.81          & 6.91          & 21.01          \\
  MET           & {\ul 21.88}    & {\ul 7.28}    & \textbf{2.36} & \textbf{7.41}    & \textbf{21.32}     \\\bottomrule
  \end{tabular}}
  \caption{Result of the SEG task on the VIST-E dataset (plain text). The bold / underline denotes the best and the second performance, respectively.}
  \label{tab:seg}
\end{table}
To investigate the effectiveness of visual information mining in our method, we remove the image from the VIST-E dataset and evaluate it on only plain text. The results are shown in Table \ref{tab:seg}. From the table, we observe that our model keeps competitive with Plan\&Write \cite{Yao19Plan} and KE-GPT2 \cite{Guan20Knowledge} models designed especially for textual story generation. Moreover, our model outperforms MG+trans, which verifies the effectiveness of our incoherence detection and semantic event-based reasoning. Our model performs better when adding the image, as shown in Table \ref{tab:exp}. It demonstrates that mining implied visual information can help story ending generation.

\subsection{Analysis}
\subsubsection{Impact of Event-based Reasoning}
\begin{table*}[t]
\centering
\begin{tabular}{lcccccccc}
\toprule
\textbf{Method}        & \textbf{B@1}   & \textbf{B@2}  & \textbf{B@3}  & \textbf{B@4}  & \textbf{M}     & \textbf{R-L}   & \textbf{C}     & \textbf{rSUM}   \\ \midrule
MET                    & \textbf{24.31} & \textbf{8.79} & \textbf{4.62} & \textbf{2.73} & \textbf{16.41} & \textbf{24.49} & \textbf{26.47} & \textbf{107.82} \\
w/ Dependence Parser   & 23.47          & 8.70          & 4.50          & 2.57          & 15.88          & 24.15          & 24.06          & 103.33          \\
w/o Visual Event Graph & 22.13          & 8.19          & 4.21          & 2.44          & 15.76          & 22.88          & 23.93          & 99.54           \\
w/o CMF                & 23.47          & 8.65          & 4.47          & 2.53          & 15.91          & 23.85          & 25.66          & 104.54     \\\bottomrule
  \end{tabular}
  \caption{Impact of event reasoning.
  ``\textit{w/ Dependency Parser}'' denotes replacing semantic role labeling with dependency parsing;
  ``\textit{w/o Visual Event Graph}'' denotes removing the visual event graph and provides the whole image features as inputs;
  ``\textit{w/o CMF}'' denotes removing the cross-modal fusion.}
  \label{tab:imp}
\end{table*}
To investigate the effectiveness of event reasoning, we analyze its impact, and the results are shown in Table \ref{tab:imp}. From the table, we can observe that replacing semantic role labeling with dependency parsing leads to decreased performance. Moreover, replacing the visual event graph with whole image features (i.e., features extracted by pre-trained CNN) shows a performance drop. In addition, removing cross-modal fusion also shows a performance drop. These demonstrate the effectiveness of event-based reasoning for the image-guided story ending generation.

\subsubsection{Case Study}
\begin{figure*}[t]
  \centering
  \includegraphics[width=\linewidth]{figs/case.pdf}
  \caption{Random sampling examples generated by MET and MGCL.}
  \label{fig:case}
\end{figure*}
To extensively evaluate our method, we conduct a case study for our model and MGCL, and some random sampling examples are shown in Figure~\ref{fig:case}. For example, in the left case, we can observe that our model can reason that the man in the image is a soldier, while the result from MGCL is not significantly related to visual content. For example, in the right case, our model can generate the word "relax" based on the objects "human" and "chair". It shows that our model can mine the implicit information based on visual and semantic information.

\subsubsection{Interpretable Visualization Analysis}
\begin{figure}[t]
  \centering
  \includegraphics[width=\linewidth]{figs/visual.pdf}
  \caption{Interpretable visualization analysis of our method (better viewed in color).}
  \label{fig:visual}
\end{figure}
\begin{table}[t]
  \centering
  \begin{tabular}{lccc}
  \toprule
  \textbf{Method} & \textbf{Gram.} & \textbf{Logic.} & \textbf{Rele.} \\\midrule
  MET           & \bf 3.49           & \bf 3.37            & \bf 2.94           \\
  MGCL            & 3.36           & 3.15            & 2.66           \\
  MG+Trans        & 3.22           & 2.78            & 2.71           \\\bottomrule
  \end{tabular}
  \caption{Human evaluation.}
  \label{tab:hum}
\end{table}
To investigate the effectiveness of the multimodal injector, we conduct an interpretable visualization analysis. The results are shown in Figure \ref{fig:visual}. The word with a blue underline denotes that the multimodal injector is assigned the greater probability in the node of visual event graph. Green corresponds to greater probability in the node of semantic event graph. The dotted boxes below represent the specific content of nodes. From the results, we can observe that nodes in visual and semantic event graphs are able to deduce implicit information.

\subsubsection{Human Evaluation}
To evaluate our method more comprehensively, we conducted a human evaluation to compare further the performance of our model and MGCL and MG+trans. As follow \citet{Huang21IgSEG}, we considered three metrics for the story ending generated by models: Grammaticality (Gram.) \cite{Wang19TCVAE} evaluates correctness, natural, and fluency of story endings; Logicality (Logic.) \cite{Wang19TCVAE} evaluates reasonability and coherence of story endings; Relevance (Rele.) \cite{Yang19Knowledgeable} measures how relevant between images and generated story endings. We randomly sampled 100 samples from the test set and display them to 3 recruited annotators. Thereby, each annotator worked on 300 items from 3 models. We show 3 annotators all outputs from all 3 models at once and shuffle the output-model correspondence to ensure that annotators do not know which model the output is predicted from. Following \citet{Yang19Knowledgeable}, we set a 5-grade marking system, where one is the worst grade, and five is the maximum. The results show that the performance of our model is significantly better than MGCL and MG+trans. That is, our model can generate higher-quality story endings.

\section{Conclusion}
In this work, we propose a multimodal event transformer, a framework for image-guided story ending generation. Our method includes event graph construction, event-based reasoning, cross-model fusion, multimodal injector and story ending generation. Different from previous work, our method not only focuses on cross-modal information fusion but also on reasoning and mining implicit information from single-modality data. In addition, we propose an incoherence detection to enhance the understanding context of a story plot and robustness of graph modeling for our model. In the experiments, results show that our method delivers state-of-the-art performance.

\section*{Limitations}
Although our proposed method can effectively reason and mine implicit information from story plots and ending image, it suffers from weaknesses in integrating cross-modal information. Specifically, our method connects visual and semantic event graphs by connecting whole image nodes and whole sentence nodes. It lacks fine-grained information to pass between semantic events to visual objects. In further work, we will study how to pass fine-grained information between visual and semantic event graphs.

redpajama_set_name: RedPajamaArXiv


text:
Otago Medical School
Te Kura Hauora o Ōtākou
How overmedication is injuring and killing our elderly: a world-first study
Dr Hamish Jamieson
Elderly taking multiple high-risk medications for sleeping, pain or incontinence are twice as likely as others to fall and break bones, with many dying within a year of their injury, new research shows.
The University of Otago, Christchurch study is the first in the world to measure the impact of taking multiple medications on fractures in the elderly. In elderly, 90 per cent of fractures are the result of a fall. Between 20 and 30 per cent of elderly die within a year of suffering a fracture.
The information will now be used in a nationwide study of pharmacy databases to see if prescriptions of certain high risk drugs can be reduced in the elderly.
The overmedication and fractures study was led by geriatrician and University of Otago, Christchurch researcher Dr Hamish Jamieson. Professor Phil Schluter from the University of Canterbury was the lead biostatistician. The study also involved scientists from six other universities, including Harvard and John Hopkins Universities and the Universities of Sydney and Canterbury. It was funded by the Government's Ageing Well National Science Challenge.
Dr Jamieson says the impact of fractures on individuals and the community is immense. They result in loss of mobility, poorer quality of life, early entry into an aged care facility, and in up to a third of people with a broken hip, death within a year of the injury. Specifically, the research team found:
People taking more than three Drug Burden Index medications (specific medications that sedate or affect a person's cognition) are twice as likely to break their hip than those taking no medications.
Between 20% and 30% elderly who broke their hip died within a year.
In the elderly, 90% of all broken bones are the result of falls.
Dr Jamieson says understanding how taking multiple medications impacts on falls and broken bones is a significant piece of information for patients, their GPs and pharmacists. The reason multiple medications cause falls is because drugs have side effects and each medication may react with others to create additional effects, he says.
Medications linked with significantly increasing the risk of broken bones had 'sedatory' and 'anticholinergic' side effects. These medicines are common and prescribed for many conditions such as sleeping, pain and incontinence. Side effects of the medicines include sedation, a dry mouth, blurred vision, dizziness and confusion, Dr Jamieson says.
"All medications have beneficial impacts. However, increasingly we are studying the long term side effects of medications in the elderly. The impacts can be subtle but this can cause a major impact in the frail elderly and can cause falls, loss of independence and even premature death."
Dr Jamieson says a number of factors predispose the elderly to medication side effects. This includes not being able to metabolise medications as well as young people, being on multiple medications, and frail and more susceptible to side effects.
Rather than stopping medications themselves, elderly patients should regularly get their GP to review their medication.
Dr Jamieson says New Zealand's world-leading dataset on the elderly, called InterRAI, made the study possible. InterRAI is a Ministry of Health database of results from comprehensive assessments of elderly New Zealanders health and wellbeing. The Ministry does more than 100,000 InterRAI assessments every year.
University of Otago, Christchurch
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text:
The proper APA Style reference for this manuscript is:
Shoemaker, B M (2012). The Elderly Rely on Faith While the Young Save Themselves. National Undergraduate Research Clearinghouse, 15. Available online at http://www.webclearinghouse.net/volume/. Retrieved January 19, 2020 .
The Elderly Rely on Faith While the Young Save Themselves
Bryce Shoemaker
Department of Psychology Missouri Western State University
Sponsored by: RUSSELL PHILLIPS (rphillips2@gmail.com)
Previous studies do not examine specific forms of religious coping and their relationship to age. The present study examines the relationship between age and the particular coping method of spiritual connection. Four hundred thirty-five students attending two universities were surveyed regarding demographic variables, measures of religiosity, and the RCOPE. There was a small statistically significant relationship between age and spiritual connection. Age was not a relevant predictor for spiritual connection in a forward regression, while frequency of prayer and spirituality was. It is suggested that a sample with greater diversity in age might better predict religious coping methods.
Keywords: age, religion, spirituality, religious coping, college students
When people experience stressful events, many turn to religion to cope because religion, or some sort of belief system, is a part of everyone's orienting system (Paragament et al. 2000). An orienting system is how we approach our life and it includes our personality, values, morals, behavior, and beliefs. Age is also an element of the orienting system, and may impact how one copes with stress. A few studies have examined how age relates to general forms of religious coping. Lowis et. al. (2009) did a study on coping with retirement and age. They found a small but significant inverse relationship between use of religious coping and age(r = -.194). Yet Hanneke et al. (2010) did not find that age was a significant predictor of turning to religion to cope with the stress of cancer. Therefore, the results appear to be inconsistent.
Further, these studies do not examine specific forms of religious coping and their relationship to age. We would like to examine the relationship between age and spiritual connection. Spiritual connection is experiencing a sense of connectedness with forces that transcend the individual (Paragment et al. 2000). As an individual ages, we suspect their relationship with a higher power grows. This may be due to the fact a person accumulates more spiritual experiences as they age, they turn to a higher power to deal with the thought of dying, and they turn to God to cope with losing loved ones. Thus, we expect there to be a positive correlation between age and spiritual connection.
There were a total of 435 college students attending two universities on the Midwest and West-Coast. The sample was mostly female (71.2%), with a mean age of 22.13 years (SD 7.02). In terms of ethnicity, 71% of the sample was European-American, 12.6% Hispanic-American, 7.6% Asian-American, and 4.6% were African-American. Eighty percent were never married, and 16% were married/cohabiting. Pertaining to household income, 62.5% of the participants indicated they made below $50,000 dollars. When asked if they believe in God, 92.6% said yes. Eighty percent identified as Christian (Catholic or Protestant), with 38.1% attending church a few times a year, and 19.3% never attending. Twenty percent prayed or meditated daily, 15% prayed a few times a week, and 16.6% a few times a year. Religiosity was average, with 16% not religious at all, 30.5% were slightly religious, 41.4% moderately religious, and 12.3% believed themselves to be very religious.
Measures and Procedures
The survey included demographic questions for age, gender, ethnicity, household income, sexual orientation, religious affiliation and marital status. The present study utilized a modified version of the RCOPE, including the three highest loading items in the Pargament et al. (2000) factor analysis. This included 75 items rated on a scale from 1-4 (1=not at all; 4=great deal).
Students in psychology courses were asked to partake in an online study for extra credit. Interested students provided their email address to the professor, who gave the information to the researchers. Those who signed up for the study were emailed a link and a password for the survey. The survey contained a number of validated measures besides those described in the current study, including scales assessing daily spiritual experiences, mystical experiences, Christian orthodoxy, stress, general outcomes from stress, anxiety, hostility, and depression. Participants were emailed the same survey two months later as a follow up study.
There was a small but statistically significant relationship between age and spiritual connection (r=.18, p < .001). (See Figure 1.) We next tested whether age predicted spiritual connection better than demographic variables (gender, race, income), general religious variables (self-perceived religiosity, self-perceived spirituality, frequency of prayer, and frequency of church attendance), and other variables that our research program was interested in (fundamentalism, faith development.)
*Insert Figure 1 about here*
Based on the results of the regression, age was not found to be a significant predictor of spiritual connection (see Table 1). The main predictor of spiritual connection was frequency of prayer. Spirituality was the second best predictor, and overall, three of the four signficant predictors were religious variables.
*Insert Table 1 about here*
Although age had a small, significant relationship with spiritual connection, it did not significantly predict this form of spiritual coping in a regression including the predictors of demographic variables, general religious variables, fundamentalism, and faith development. The main predictor of spiritual connection was prayer, and spirituality was the second best predictor. These variables may be strong predictors of spiritual connection because most people attempt to gain connection to a higher power through prayer and those who are more spiritual would likely look to their strong relationship with God to cope during times of stress.
Some limitations of the study include that it is not representative of the general population, because only college students of mostly traditional age were assessed. Because the age group involved is a restricted range, it limits the size of our correlation statistic.
Further research could examine a wider range of ages. A longitudinal or cross-sectional study could provide more insight into how spiritual connection and other religious coping styles are affected by age. Therapists could use this research to examine how their patients attempt to spiritually connect with a higher power, possibly turning to prayer as one resource. Therapists could also focus on what other religious coping styles their patients use to cope with stress and find the right methods to best assist such individuals.
Lowis, M., Edwards, A., & Burton, M. (2009). Coping with retirement: Well-being, health, and religion. The Journal of Psychology, 143(4), 427– 448.
Pargament, K., Koenig, H., & Perez, L. (2000). The many methods of religious coping: Development and initial validation of the rcope. Journal of Clinical Psychology, 56(4), 519-543.
Van Laarhoven, H. M., Schilderman, J., Vissers, K. C., Verhagen, C. M., & Prins, J. (2010). Images of God in relation to coping strategies of palliative cancer patients. Journal Of Pain And Symptom Management, 40(4), 495-501.
Hierarchical Regression Analysis: Prediction of Spiritual Connection
Predictors Spiritual Connection
∆R2 β
Prayer .271** .310**
Spirituality .033** .219**
Race .016* -.121*
Fundamentalism .012* .129*
Church Attendances
Total R2 .331**
Note: Some variables do not have ∆R2 or β because in forward selection regression, predictors that do not significantly contribute to predicted variance are not included in the model.
* p < .01 ** p < .001
Figure 1: Correlation between Spiritual Connection & Age
Accepted 05/03/2012
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Product Description:Bovine COMM domain- containing protein 3, COMMD3 ELISA KIT allows for the in vitro quantitative determination of Bovine COMM domain-containing protein 3, COMMD3 concentrations in serum, plasma, tissue homogenates, cell culture supernates or other biological fluids.
The microtiter plate provided in Bovine COMM domain- containing protein 3, COMMD3 ELISA KIT has been pre-coated with an Bovine COMM domain-containing protein 3, COMMD3 antibody specific to Bovine COMM domain-containing protein 3, COMMD3 .Standards or samples are then added to the appropriate microtiter plate wells with a biotin-conjugated antibody preparation specific for Bovine COMM domain-containing protein 3, COMMD3 and then avidin conjugated to Horseradish Peroxidase (HRP) is added to each microplate well and incubated. Then a TMB substrate solution is added to each well. Only those wells that contain Bovine COMM domain-containing protein 3, COMMD3, biotin-conjugated antibody and enzyme-conjugated Avidin will exhibit a change in color. The enzyme-substrate reaction is terminated by the addition of a sulphuric acid solution and the color change is measured spectrophotometrically at a wavelength of 450 nm.The concentration of Bovine COMM domain-containing protein 3, COMMD3 in the samples is then determined by comparing the O.D. of the samples to the standard curve.


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The terms and conditions of this Privacy Policy (the "Policy") apply to any personal data Doctor Web, Ltd. (the "Rights Holder") receives and/or may receive about users/visitors (the "Users"/"User") while they are visiting/using, or in connection with their visiting/using, any of the websites, services, subscriptions, computer programs, or other products of Doctor Web, Ltd. (the "Services").
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Tis the season and all that kinda stuff. In my opinion there is way too much emphasis on food and shopping and buying gifts for people who don't really "need" the gift.
Of course, giving to charities and keeping the less fortunate in mind this time of year is a good alternative to rampant consumerism.
How about your own health and weight loss needs? Why not give yourself a gift of a membership to a gym or the "Y"? How about a good pair of walking or running shoes and a goal relating to "going the distance"?
2011 is almost past and a new year on the calender lurks just ahead. For those of us who must and need to continue to lose weight, its not the "end" of anything in that regard. This battle for a better physical "self" never ends. Keep fighting the fight and never give up. After all, the gift of better health and fitness is the gift that keeps on giving.
This is an awesome message to the Charlotte Community. As a member of the Charlotte Athletic Club, I encourage everyone to give back to their body and join today! Have a wonderful Holiday season you won't regret!
Dan Vines , 59, is an underwriter for a commercial insurance company, which means he sits in front of a computer, talks on the phone and sits in a car driving hundreds of miles per week. Says Dan: "It's time I get back to being the physical human being I used to be. In just four years I've gone from a body with a six pack to a body with a full "keg." Its time to battle and fat is the enemy!" He wants to lose 70 pounds this year.
Less talk. More action. This is Nancy Johnson 's motto for 2011. As a risk management professional, Nancy realizes the impact weight has on health. She and her daughter, Brittany, are preparing for Brittany's possible weight-loss surgery later this year. Their story was chronicled in a Raleigh News&Observer series that appeared in the Observer in January. She hopes to lose 36 pounds this year and 36 more in 2012.
It's been a fun year....so long!


John Feffer joins the show for an update on the peace talks between North and South Korea. He optimistically describes the nascent process of dismantling parts of the DMZ, including guard towers and mine fields, and also establishing a no-fly zone there. As usual, the American media is happy to ignore all of this. Feffer reminds us that South Korean President Moon Jae-in is really the driving force behind the negotiations, but he seems happy to give lots of credit to President Trump, who probably really only deserves a little credit. All of this progress toward peace is reversible, explains Feffer, but one positive sign is that land prices around the DMZ have started to increase, signaling belief from entrepreneurs that the land will soon be usable.
I think you may have gotten the year wrong in the episode title.


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»» United Launch Alliance Successfully Completes First Operational Delta IV Heavy Launch
[Monday, November 12, 2007] United Launch Alliance successfully launched the first operational Delta IV Heavy expendable launch vehicle for the U.S. Air Force from Space Launch Complex-37 Nov. 10, 8:50 p.m., EST.
»» NASA Plans to Test Lunar Habitat in Extreme Antarctic Environment
[Wednesday, November 14, 2007] NASA will use the cold, harsh, isolated landscape of Antarctica to test one of its concepts for astronaut housing on the moon. The agency is sending a prototype inflatable habitat to Antarctica to see how it stands up during a year of use.
»» Space Adventures Offers Unique Opportunity to Participate in Richard Garriott's Orbital Spaceflight Mission as Back-up Crew Member
[Thursday, November 15, 2007] Today, Space Adventures, Ltd., announced that for the first time the coveted opportunity to train as a private space explorer alongside one of its orbital spaceflight candidates, and among professional astronauts, is now available to the public.
»» NASA JSC Solicitation: NASA Lunar Lander Project Office Industry Day
[Friday, November 16, 2007] Lunar Lander Project Office Industry Day Event Registration: The Lunar Lander Project Office (LLPO), within NASA's Constellation Program, will host an Industry Day on Thursday, December 13, 2007
»» First Stage Design Problems Arise For NASA's Ares 1 Rocket
[Friday, November 16, 2007] NASA has run into some problems with the design of its new Ares 1 launch vehicle - problems that could affect its ability (as designed) to safely launch its human cargo into space.
»» NASA Selects 302 Small Business Research and Technology Projects
[Friday, November 16, 2007] NASA has awarded contracts to 302 small business proposals. The awards are part of NASA's Small Business Innovation Research Program and Small Business Technology Transfer program.
»» Using a Planetary Analog To Test a Prototype Inflated Habitat for NASA
[Sunday, November 18, 2007] I had a chance to visit ILC Dover last week to see the new inflatable habitat that they have developed. Together, NASA, ILC Dover, and NSF will put this habitat through a one-year test at McMurdo Station in Antarctica starting in January 2008.
»» ESA and Inmarsat sign innovative Alphasat satellite contract
[Tuesday, November 27, 2007] ESA and Inmarsat Global Ltd announce on Friday 23 November in Paris the formal signature of the contract for Alphasat satellite, one of the world's largest telecommunications satellites.
»» Boeing Named NASA Large Business Contractor of the Year
[Tuesday, November 27, 2007] The Boeing Company today was recognized by NASA as its Large Business Contractor of the Year for providing outstanding support to small businesses at Kennedy Space Center, Fla.
»» NASA - Agency Has Taken Steps Toward Making Sound Investment Decisions for Ares I but Still Faces Challenging Knowledge Gaps, GAO-08-51
[Thursday, November 29, 2007] NASA has been taking steps to build a business case--demonstrating the project is achievable within the constraints of time and money and other resources NASA has available--for Ares I.
»» Spacehab Responds to NASA RFP Seeking Commercial ISS Resupply Means
[Thursday, November 29, 2007] SPACEHAB announced that the Company has submitted a formal proposal response to NASA's COTS Phase 1 Demonstrations Request for Proposal (RFP) based on the Advanced Research and Conventional Technology Utilization Spacecraft (ARCTUS) design.
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