Vide etiam paginam discretivam: Apuleius (discretiva)
Apuleius[1] est nomen fictum auctoris vel auctorum de rebus rusticis et medicis, Latine scribentium. Opus breve de herbis servatum est in multis libris manu scriptis sub nomine Apulei. Opus aliud exstat de religione aut philosophia Hermetica, nomine Asclepius, Apuleio attributum. Exstant etiam in Geoponicis Constantino Porphyrogenito dicatis, et in Fragmentis Anatolii de bubus in corpore Hippiatricorum servatis nonnulla excerpta operi Apulei adscripta.
Index
1Opera integre servata
2Index fragmentorum
3Notae
4Bibliographia
Opera integre servata |
Herbarius sive Herbarium sive De herbis
Asclepius
Index fragmentorum |
Fragmenta Anatolii de bubus in corpore Hippiatricorum servata[2]
Geoponica 1.5.3: Prognosticatio temporum.
Geoponica 1.14.10: de grandine.
Geoponica 2.8: de necessitate monticularum.
Geoponica 2.18: de seminibus post sationem.
Geoponica 2.39.3: de lupinis.
Geoponica 5.33.
Geoponica 6.11.
Geoponica 7.26.
Geoponica 8.38.
Geoponica 9.19.
Geoponica 10.21.
Geoponica 12.8.
Geoponica 13.5; Palladius, Opus agriculturae 1.35.9.
Geoponica 13.8.6.
Geoponica 13.9.5.
Notae |
↑Interdum Apuleius Platonicus aut Apuleius Barbarus.
↑E. Oder, K. Hoppe, Corpus hippiatricorum Graecorum vol. 2 (Lipsiae, 1927) vol. 2 pp. 330-336.
Bibliographia |
Ernestus Howald, Henricus E. Sigerist, edd., Antonii Musae De herba vettonica liber; Pseudoapulei Herbarius; Anonymi de taxone liber; Sexti Placiti liber medicinae ex animalibus etc. Lipsiae: Teubner, 1927. (Corpus medicorum latinorum, 4) Textus
H. J. de Vriend, ed., The Old English Herbarium and Medicina de quadrupedibus. Londinii: Oxford University Press, 1984. (Early English Text Society, original series, 286)
Anne McCabe, A Byzantine Encyclopaedia of Horse Medicine: the sources, compilation, and transmission of the "Hippiatrica". Oxonii: Oxford University Press, 2007. ISBN 978-0-19-927755-1
R. H. Rodgers, "The Apuleius of the Geoponica" in California Studies in Classical Antiquity vol. 11 (1978) pp. 197-207.
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...
