Problemata Hilbertiana

Multi tool use

Tertium problema de tetrahedris tractat
Problemata Hilbertiana sunt viginti tria problemata mathematica quae enuntiavit David Hilbert in Congressu Internationali Mathematicorum, Lutetiae, anno 1900. Paene omnia saeculo vigesimo soluta sunt, praeter octavum, de hypothesi Riemanniana, et partes octavi decimi.
Problemata |
Hypothesis de continuo
- Utrum axiomata arithmeticae inter se congruant, utrum completa sint
- Utrum tetrahedra, quae eandem basin et eandem altitudinem habent, etiam eandem volumen habeant
- De geometriis non-Euclideanis dictis (hoc problema haud intellegitur)
- De catervis Lie
Axiomata mathematica physicae
- Utrum numerus ab sit transcendentalis, si a est numerus algebraicus et b numerus algebraicus irrationalis
- Hypothesis Riemanniana
- De reciprocitate in corporis nescioquibus
- De aequationibus Diophantinis
- De formis quadraticis
- Extensio theorematis Kronecker
- De "nomographia," hoc est, quales functiones per parametris simplicior scribi possunt
- Qualia systemata invariantium sunt finita
- De calculo Schubert
- De topologia flexuum algebraicorum
- Quales functiones per summas quantitatum quadraticorum scribi possunt
- De tegulis; utrum forma sit quae spatium impleat
- Utrum solutiones in calculo variationum sint semper analyticae
- De quantitatibus in liminibus
- Coniectura Riemann et Hilbert de aequationibus differentialibus
- De functionibus automorphicis
- De calculo variationum
Bibliographia |
- Jeremy J. Gray. The Hilbert Challenge. Oxonii: Oxford University Press, 2000. ISBN 0-19-850651-1
- David Hilbert. "Mathematische Probleme. Vortrag, gehalten auf dem internationalen Mathematike-Congress zu Paris, 1900." Archiv für Mathematik und Physik I, 44-63 et 213-237. Versio anglica, Mary Winston Newson, Bulletin of the American Mathematical Society 8 (1902), 437-479.
- Benjamin Yandell. The Honors Class: Hilbert's Problems and their Solvers. Natick: A.K. Peters, 2002. ISBN 1568811411
BmNMx5Wz 7mP5d,sUq4emzGo,6Wrm2a 9sTLsTPEU OZ Er6rfCoLGg7o,6444tsZ0,0mdYrEsDBbcBUQDK5dY,G94j
Popular posts from this blog
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...
1
$begingroup$
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...
1
$begingroup$
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...