Chelicerata (nomen a Heymons anno 1901) sunt arthropoda cheliceris, pedipalpis et quattuor paribus cruribus praedita. Corpus in duabus partibus divisum (cephalothorax et abdomen), et appendices sunt birameae. Et mandibulis et antennis carent.
Index
1Taxinomia
1.1Classis Merostomat
2Bibliographia
3Nexus externi
Taxinomia |
Chelicerata in tribus classibus divisa: Merostomata, Pycnogonida Arachnidaque.
Classis Merostomat |
Si plus cognoscere vis, vide Merostomata
Merostomata animalia aquatica, appendicibus abdominalibus in branchiis mutatis sunt. Duas subclasses comprehendit: Eurypterida et Xiphosurida. Eurypterida duobus oculis compositis duobusque ocellis in cephalothorace; binis cheliceris ante os, et senis appendicibus post id. Telson magnum.
Xiphosurida corpus in cuticula calcarea, binas cheliceras et deni crura habent. Duo pares oculorum in cephalothorace eis sunt: bini compositi laterales, et bini, interdum singuli, ocelli mediani. Abdomen quinque paribus branchiarum laminarium.
Bibliographia |
Brusca, R. C., et G. J. Brusca. 1990. Invertebrates. Sunderlandiae Massachusettae: Sinauer Associates.
Latreille, P. A. 1829. Les Crustacés, les Arachnides et les Insectes, distribués en familles naturelles: Le Règne Animal, distribué d'après son organisation, pour servir de base à l'histoire naturelle des animaux et d'introduction à l'anatomie comparée. Ed. 2a. Vol. 4.
Pearse, V., J. Pearse, M. Buchsbaum, et R. Buchsbaum. 1987. Living Invertebrates. Palo Alto Californiae: Blackwell Scientific Publications.
Nexus externi |
Chelicerata apud Systema Natura
Vide Chelicerata apud Vicispecies.
Vicimedia Communia plura habent quae ad Chelicerata spectant.
Situs scientifici: • ITIS • NCBI • Encyclopedia of Life • Marine Species • Fossilworks
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...
Si plus cognoscere vis, vide Merostomata
