Titulus cardinalitius est titulus a Romano Pontifice cardinalibus datum Romanarum Ecclesiarum.
Secundum Codicem Iuris Canonici, Cardinalium Collegium in tres ordines distribuitur: episcopalem . . . ; presbyteralem et diaconalem.[1]
Cardinales episcopi titulum accipiunt Ecclesiae suburbicariae Romae; vel, si Patriarcha Orientalis ad cardinalatum elevatus est, non assumit titulum sed ipsum habet suae Ecclesiae. [2] Cardinales autem presbyteri et diaconi suus cuique titulus aut diaconia in Urbe assignatur a Romano Pontifice.[3]
Decanus vero Cardinalium Collegii titulum habet dioecesim Ostiensem, una cum alia Ecclesia quam in titulum iam habeat.[4]
Index
1Nonnulli Tituli Cardinalitii
1.1Cardinales Episcopi
1.2Cardinales Presbyteri
2Nexus interni
3Notae
Nonnulli Tituli Cardinalitii |
Hic est index imperfectus. Adde et comple si potes.
Cardinales Episcopi |
Albanensis
Tusculanus
Praenestinus
Ostiensis
Imperfectam listam suburbicariarum Ecclesiarum Romae, una cum aliis suffraganeis sedibus, vidi potest
"Dioecesis" e The Hierarchy of the Catholic Church (situs a Davide M. Cheney elaboratus) .mw-parser-output .existinglinksgray a,.mw-parser-output .existinglinksgray a:visited{color:gray}.mw-parser-output .existinglinksgray a.new{color:#ba0000}.mw-parser-output .existinglinksgray a.new:visited{color:#a55858}(Anglice) ((Anglice)).
Cardinales Presbyteri |
Basilica Beatissimae Virginis et Omnium Angelorum et Martyrum
Sanctus Petrus ad Vincula
Basilica Sancti Andreae Apostoli de Hortis
Basilica Sancti Sebastiani extra muros
Ecclesia Sancti Ludovici Francorum de Urbe
Ecclesia Trinitatis Montium
Aedes Sancti Sixti de Urbe
Ecclesia Sancti Bernardi ad Thermas
Basilica Sanctae Praxedis
Basilica Sanctae Sabinae in Monte Aventino
Sancta Maria Pacis
Sanctus Marcellinus et Petrus[5]
Sanctus Gregorius Magnus in Magliana Nova[6]
Nexus interni
Ecclesia Catholica
Notae |
↑Codex Iuris Canonici, 350, §1
↑Confer ibidem.
↑Ibidem §2.
↑Ibidem §4.
↑
"Dioecesis" e The Hierarchy of the Catholic Church (situs a Davide M. Cheney elaboratus) (Anglice) (Anglice)
↑
"Dioecesis" e The Hierarchy of the Catholic Church (situs a Davide M. Cheney elaboratus) (Anglice) (Anglice)
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
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This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
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I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...