5 neighborhoods centered around units that are at identical positions within each H2 maps.Of course,all units in a given map are constrained to have identical weight vectors.The maps in H2 on which a map in H3 takes its inputs are chosen according to a scheme described on table 1.According to this scheme,the network is composed of two almost independent modules.Layer H4 plays the same role as layer H2,it is composed of 12 groups of 16 units arranged in 4 by 4 planes. The output layer has 10 units and is fully connected to H4.In summary,the network has 4635 units,98442 connections,and 2578 independent parameters.This archi- tecture was derived using the Optimal Brain Damage technique (Le Cun,Denker and Solla,1990)starting from a previous architecture (Le Cun et al.,1990)that had 4 times more free parameters. 5 RESULTS After 30 training passes the error rate on training set(7291 handwritten plus 2549 printed digits)was 1.1%and the MSE was .017.On the whole test set (2007 handwritten plus 700 printed characters)the error rate was 3.4%and the MSE was 0.024.All the classification errors occurred on handwritten characters. In a realistic application,the user is not so much interested in the raw error rate as in the number of rejections necessary to reach a given level of accuracy.In our case,we measured the percentage of test patterns that must be rejected in order to get 1%error rate.Our rejection criterion was based on three conditions:the activity level of the most-active output unit should by larger than a given threshold t1,the activity level of the second most-active unit should be smaller than a given threshold t2,and finally,the difference between the activity levels of these two units should be larger than a given threshold ta.The best percentage of rejections on the complete test set was 5.7%for 1%error.On the handwritten set only,the result was 9%rejections for 1%error.It should be emphasized that the rejection thresholds were obtained using performance measures on the test set.About half the substitution errors in the testing set were due to faulty segmentation,and an additional quarter were due to erroneous assignment of the desired category.Some of the remaining images were ambiguous even to humans,and in a few cases the network misclassified the image for no discernible reason. Even though a second-order version of back-propagation was used,it is interesting to note that the learning takes only 30 passes through the training set.We think this can be attributed to the large amount of redundancy present in real data.A complete training session(30 passes through the training set plus test)takes about 3 days on a SUN SPARCstation 1 using the SN2 connectionist simulator(Bottou and Le Cun,1989). After successful training,the network was implemented on a commercial Digital Signal Processor board containing an AT&T DSP-32C general purpose DSP chip with a peak performance of 12.5 million multiply-add operations per second on 32 bit foating point numbers.The DSP operates as a coprocessor in a PC connected to a video camera.The PC performs the digitization,binarization and segmentation✫❻❁✣✼●❦❣❷❉❸◗❫✍❆▼❋❙❸✌❆◗❆✌P◗❈✿❜✐✼●❁◗❥❊✼●❋❙✼❍P✈❳❲❋❊❆❉❀✌❁✍P➆❀✌❁✌❦❣❥❙❈✩❥❙❸✻❳❲❥✵❳❩❋❙✼❻❳❲❥✩❦⑦P◗✼●❁◗❥❙❦⑦❜●❳❩❝❃⑤✻❆❉❈❊❦⑥❥❊❦❣❆❉❁✌❈✩❱☛❦❣❥❙❸✌❦❣❁✧✼❍❳▼❜❙❸ ◆✤ ❧✲❳❩⑤✌❈❍✸ ✑☛⑧✫❜✐❆▼❀✌❋❙❈❊✼❉❂☛❳❲❝❣❝✜❀✌❁✌❦❣❥❙❈❻❦❣❁ ❳②❷▼❦❣➎❉✼●❁✈❧✲❳❲⑤✢❳❲❋❊✼✰❜✐❆❉❁✌❈❊❥❙❋✐❳❲❦❣❁✌✼❍Pt❥❊❆④❸✻❳✕➎❉✼✧❦⑦P◗✼●❁◗❥❙❦⑦❜●❳❩❝ ❱✜✼●❦❣❷❉❸◗❥☛➎❉✼❍❜❹❥❙❆❉❋❊❈❍✸ ❡❸✌✼✿❧✧❳❲⑤✌❈✜❦❣❁✲◆ ✤❻❆❉❁✧❱☛❸✌❦⑦❜❊❸✰❳❵❧✲❳❩⑤❻❦⑥❁✰◆❅q❵❥❬❳❲▲▼✼●❈✑❦❣❥❊❈☛❦⑥❁✣⑤✌❀✌❥❙❈✑❳❩❋❙✼✫❜❊❸✌❆▼❈❙✼●❁ ❳❉❜❘❜✐❆❉❋✐P◗❦❣❁✌❷✫❥❊❆✹❳✱❈❬❜❊❸✌✼❘❧✖✼✿P◗✼●❈✐❜✐❋❙❦❣❫✻✼❍P✧❆❉❁❻❥✐❳❲❫✌❝❣✼❻➇❉✸ ❞❜❘❜✐❆❉❋✐P◗❦❣❁✌❷❻❥❙❆❵❥❙❸✌❦❣❈✜❈❬❜❊❸✌✼●❧☎✼❉❂◗❥❙❸✣✼✵❁✣✼●❥♠❱⑨❆▼❋❙▲ ❦❣❈✵❜✐❆▼❧✖⑤✻❆❉❈❊✼❍P☎❆❲⑧❴❥♠❱⑨❆✧❳❲❝❣❧✖❆▼❈❙❥❴❦❣❁✍P❼✼●⑤✍✼❘❁✍P◗✼●❁◗❥✿❧☎❆❼P❼❀✌❝❣✼●❈❍✸✜✺❃❳✕❾❉✼❘❋✵◆✪☎⑤✌❝⑦❳✕❾◗❈☛❥❙❸✌✼✿❈✐❳❲❧☎✼✵❋❊❆❉❝❣✼✫❳❩❈ ❝⑦❳✕❾❉✼●❋☛◆✤✣❂✌❦❣❥✜❦⑥❈✑❜❹❆❉❧✖⑤✻❆❉❈❊✼❍P❻❆❩⑧✑➇ ✤✱❷❉❋❙❆▼❀✌⑤✌❈☛❆❩⑧❨➇✆✄❵❀✣❁✌❦❣❥❙❈✑❳❲❋❊❋❬❳❩❁✌❷❉✼❍P☎❦❣❁✮✪❻❫◗❾ ✪✫⑤✌❝⑦❳❩❁✌✼●❈❍✸ ❡❸✌✼❴❆▼❀✌❥❙⑤✣❀✌❥➊❝ ❳✕❾▼✼●❋✽❸✍❳❩❈⑨➇❍♦❖❀✌❁✌❦❣❥❙❈➊❳❲❁✍P❅❦❣❈✽⑧❀✌❝❣❝⑥❾✩❜✐❆❉❁✣❁✌✼❍❜✐❥❊✼❍P❵❥❙❆✑◆✪✍✸ ✝♠❁✿❈❙❀✌❧☎❧✲❳❩❋❙❾◗❂❙❥❊❸✌✼⑨❁✣✼●❥♠❱⑨❆▼❋❙▲ ❸✍❳❩❈ ✪ ✄❉q✬✫❵❀✌❁✌❦❣❥❙❈✕❂➃➋▼➠✡✪✬✪ ✤✖❜❹❆❉❁✌❁✌✼✕❜✐❥❙❦❣❆❉❁✣❈❍❂❃❳❩❁✍P ✤✥✫▼♣❉➠➞❦⑥❁✻P◗✼●⑤✻✼●❁✍P◗✼●❁◗❥✩⑤✍❳❩❋❬❳❲❧☎✼●❥❙✼❘❋❙❈❍✸ ❡❸✣❦⑥❈✩❳❲❋✐❜❙❸✣❦➫⑩ ❥❙✼✕❜✐❥❙❀✌❋❊✼✖❱☛❳❲❈✿P◗✼●❋❊❦❣➎❉✼❍P ❀✌❈❙❦❣❁✌❷✧❥❊❸✌✼ ✑❅⑤✌❥❊❦❣❧✲❳❲❝➊❄❴❋✐❳❲❦❣❁✢❑✵❳❩❧✲❳❩❷❉✼✩❥❙✼❍❜❊❸✌❁✣❦ ❽▼❀✌✼✧➜♠✺✽✼❻✾✜❀✌❁❃❂❃❑❅✼●❁✣▲❉✼●❋ ❳❲❁✻P✢❏◗❆❉❝❣❝⑦❳✌❂➊➇❍➋▼➋❉♦▼➝✑❈❊❥❬❳❩❋❙❥❙❦❣❁✌❷➆⑧➄❋❙❆▼❧ ❳✧⑤✌❋❊✼●➎◗❦❣❆❉❀✌❈✫❳❩❋❬❜❊❸✌❦❣❥❙✼✕❜✐❥❙❀✌❋❊✼②➜❶✺✽✼✖✾✜❀✣❁✢✼●❥✫❳❲❝♠✸❣❂❴➇✕➋❉➋❉♦▼➝❅❥❙❸✍❳❩❥ ❸✍❳▼P✮✪❵❥❙❦❣❧☎✼●❈⑨❧☎❆❉❋❊✼☛⑧➄❋❊✼●✼✿⑤✍❳❩❋❬❳❲❧☎✼●❥❙✼❘❋❙❈❍✸ ￾ ①✙ ✉☛↔✂✁⑨✇➀✉ ❞⑧❥❙✼●❋✑q▼♦✿❥❊❋❬❳❲❦❣❁✌❦❣❁✌❷❵⑤✍❳❩❈❙❈❙✼❘❈✑❥❙❸✣✼✵✼❘❋❙❋❙❆▼❋✑❋✐❳❲❥❙✼✩❆▼❁✖❥❙❋✐❳❲❦❣❁✌❦❣❁✌❷❵❈❙✼●❥✱➜❶♣✬✤❉➋✌➇❖❸✍❳❲❁✍P❼❱☛❋❙❦❣❥❙❥❊✼●❁✧⑤✌❝❣❀✌❈ ✤✥✫★✪❛➋ ⑤✌❋❊❦⑥❁◗❥❊✼❍P P❼❦⑥❷▼❦❣❥❙❈❬➝❵❱☛❳❲❈✢➇☎✄⑥➇✕➈ ❳❩❁✍Pt❥❊❸✌✼❿❺❏✌❯ ❱☛❳❲❈✆✄➐♦✌➇❍♣✣✸ ✑❅❁↕❥❙❸✣✼④❱☛❸✌❆▼❝❣✼✰❥❙✼●❈❊❥✰❈❙✼❘❥④➜ ✤❉♦▼♦❉♣ ❸✍❳❩❁✍P◗❱☛❋❙❦❣❥❊❥❙✼●❁☎⑤✌❝❣❀✌❈⑨♣❉♦▼♦✩⑤✌❋❊❦⑥❁◗❥❊✼❍P✖❜❊❸✍❳❩❋❬❳❉❜❹❥❙✼●❋❊❈❬➝✜❥❙❸✣✼✑✼●❋❊❋❙❆❉❋☛❋✐❳❲❥❊✼✑❱☛❳❲❈⑨q✝✄ ✪◗➈ ❳❩❁✍P❵❥❙❸✌✼✩❺✰❏✌❯➀❱❨❳❩❈ ♦✞✄➐♦✬✤✡✪✍✸ ❞❝❣❝✽❥❊❸✌✼✿❜✐❝⑦❳❲❈❙❈❊❦➂➁➃❜●❳❲❥❊❦❣❆❉❁✧✼●❋❙❋❊❆❉❋❊❈✑❆✌❜●❜✐❀✌❋❊❋❙✼❍P ❆❉❁☎❸✍❳❲❁✻P◗❱☛❋❙❦❣❥❙❥❊✼●❁④❜❊❸✍❳❩❋❬❳❉❜❹❥❙✼●❋❊❈❍✸ ✝♠❁✢❳✖❋❊✼❍❳❲❝❣❦❣❈❙❥❊❦⑦❜✫❳❩⑤✌⑤✌❝❣❦⑦❜●❳❲❥❊❦⑥❆▼❁❃❂✌❥❊❸✌✼❻❀✌❈❊✼●❋✿❦❣❈✩❁✌❆❉❥✩❈❙❆✧❧❻❀✍❜❊❸✰❦❣❁◗❥❙✼❘❋❙✼●❈❊❥❙✼❍P②❦❣❁②❥❙❸✌✼❻❋✐❳✕❱➉✼●❋❙❋❊❆❉❋✿❋✐❳❲❥❙✼ ❳❲❈❅❦❣❁✰❥❙❸✌✼✱❁❛❀✣❧❻❫✍✼●❋❅❆❲⑧❴❋❙✼➍✐✼❍❜✐❥❊❦⑥❆▼❁✌❈✩❁✌✼❍❜✐✼❘❈❙❈❬❳❩❋❙❾②❥❙❆☎❋❙✼❍❳▼❜❙❸✢❳❵❷❉❦❣➎▼✼●❁✰❝❣✼●➎❉✼❘❝✮❆❩⑧⑨❳❉❜●❜✐❀✣❋❬❳❉❜❹❾❛✸✡✝♠❁✰❆❉❀✣❋ ❜●❳❩❈❙✼❉❂➊❱✜✼✫❧☎✼❍❳❲❈❊❀✌❋❙✼✕P②❥❙❸✌✼❻⑤✻✼●❋❬❜❹✼●❁◗❥❬❳❲❷▼✼✖❆❩⑧❨❥❊✼●❈❙❥❵⑤✍❳❲❥❊❥❙✼●❋❊❁✌❈❵❥❙❸✍❳❩❥✵❧❻❀✣❈❙❥✩❫✍✼❻❋❊✼➄➍✐✼❍❜❹❥❙✼❍P➀❦⑥❁②❆❉❋✐P◗✼●❋ ❥❙❆ ❷❉✼●❥✧➇❍➈✭✼●❋❙❋❊❆❉❋❻❋❬❳❩❥❙✼❉✸ ✑❅❀✌❋❵❋❙✼➍✐✼❍❜✐❥❊❦⑥❆▼❁ ❜✐❋❊❦❣❥❙✼●❋❊❦⑥❆▼❁✝❱☛❳❲❈✿❫✻❳❲❈❙✼✕P✢❆❉❁✢❥❊❸✌❋❙✼❘✼➆❜❹❆❉❁✍P◗❦❣❥❊❦⑥❆▼❁✌❈ ✢✱❥❙❸✌✼ ❳❉❜❹❥❙❦❣➎◗❦⑥❥♠❾✱❝⑥✼❘➎❉✼●❝✻❆❲⑧✽❥❙❸✌✼❅❧✖❆▼❈❙❥♠⑩❙❳❉❜❹❥❙❦❣➎❉✼❴❆▼❀✌❥❙⑤✣❀✌❥❴❀✌❁✌❦❣❥❴❈❊❸✌❆❉❀✣❝ P❵❫◗❾✿❝⑦❳❩❋❙❷❉✼❘❋❴❥❙❸✍❳❩❁✖❳✩❷❉❦❣➎▼✼●❁✫❥❊❸✌❋❙✼❘❈❙❸✌❆▼❝ P ✟✡✠ ❂◗❥❙❸✌✼✫❳▼❜✐❥❙❦❣➎◗❦❣❥♠❾✖❝❣✼●➎▼✼●❝✽❆❲⑧❴❥❙❸✣✼✿❈❙✼❍❜❹❆❉❁✍P✧❧☎❆❉❈❊❥♠⑩❙❳❉❜✐❥❊❦⑥➎▼✼✑❀✌❁✣❦⑥❥☛❈❊❸✌❆❉❀✣❝ P☎❫✻✼✿❈❙❧✧❳❲❝❣❝❣✼●❋❴❥❊❸✍❳❲❁④❳✱❷❉❦❣➎▼✼●❁ ❥❙❸✣❋❙✼●❈❊❸✌❆❉❝⑦P ✟☞☛ ❂❉❳❲❁✍P✱➁✻❁✍❳❲❝❣❝❣❾❛❂✕❥❊❸✌✼✑P◗❦✩✓➃✼❘❋❙✼●❁✍❜❹✼✑❫✻✼●❥♠❱⑨✼❘✼●❁✖❥❊❸✌✼✑❳❉❜✐❥❊❦❣➎❛❦❣❥♠❾✿❝❣✼●➎▼✼●❝❣❈❴❆❲⑧✽❥❙❸✣✼●❈❙✼❅❥♠❱⑨❆✩❀✌❁✌❦❣❥❊❈ ❈❙❸✣❆❉❀✌❝⑦P②❫✻✼✖❝⑦❳❲❋❊❷❉✼●❋✿❥❊❸✍❳❲❁✝❳➆❷❉❦❣➎❉✼●❁②❥❙❸✣❋❙✼●❈❊❸✌❆❉❝⑦P ✟☞✌ ✸ ❡❸✌✼❻❫✻✼●❈❙❥❻⑤✻✼●❋✐❜✐✼●❁◗❥❬❳❩❷❉✼✖❆❩⑧✑❋❙✼➄➍❹✼❍❜✐❥❙❦❣❆▼❁✌❈✫❆▼❁ ❥❙❸✣✼➆❜❹❆❉❧✖⑤✣❝⑥✼❘❥❙✼✖❥❊✼●❈❙❥☎❈❙✼●❥☎❱❨❳❩❈ ✫✝✄➐♣❉➈✘⑧➄❆▼❋✲➇✕➈✦✼●❋❊❋❙❆❉❋✕✸✚✑❅❁ ❥❊❸✌✼✧❸✍❳❲❁✻P◗❱☛❋❙❦❣❥❙❥❊✼●❁↕❈❊✼●❥❻❆❉❁✌❝❣❾◗❂➊❥❙❸✌✼ ❋❙✼❘❈❙❀✌❝❣❥✩❱❨❳❩❈✿➋❉➈➛❋❙✼➍✐✼❍❜✐❥❊❦⑥❆▼❁✌❈✩⑧➄❆▼❋✫➇✕➈➛✼●❋❙❋❊❆❉❋❍✸ ✝♠❥✩❈❙❸✌❆▼❀✌❝⑦P✰❫✻✼✫✼❘❧✖⑤✌❸✍❳❩❈❙❦❣➅●✼❍P➆❥❙❸✍❳❩❥✵❥❊❸✌✼❵❋❙✼➄➍✐✼✕❜✐❥❙❦❣❆❉❁ ❥❙❸✣❋❙✼●❈❊❸✌❆❉❝⑦P◗❈✿❱✜✼●❋❊✼❻❆❉❫✌❥✐❳❲❦❣❁✌✼❍P✧❀✣❈❙❦❣❁✌❷✧⑤✍✼❘❋♠⑧➄❆▼❋❙❧✲❳❩❁✍❜✐✼❵❧✖✼❍❳❩❈❙❀✌❋❊✼●❈✩❆❉❁✰❥❊❸✌✼✞✌ ☛✛✙✛✌✤✙☞☛✛✌♠✸ ❞❫✍❆▼❀✌❥✩❸✍❳❲❝➂⑧ ❥❙❸✣✼✫❈❊❀✌❫✌❈❙❥❊❦❣❥❙❀✌❥❊❦⑥❆▼❁④✼❘❋❙❋❙❆▼❋❙❈❵❦❣❁✰❥❙❸✌✼❵❥❙✼❘❈❙❥❙❦❣❁✌❷ ❈❙✼●❥✩❱✜✼●❋❙✼✖P◗❀✣✼✫❥❊❆✖⑧♠❳❲❀✌❝❣❥♠❾✧❈❙✼●❷▼❧✖✼●❁◗❥✐❳❲❥❙❦❣❆▼❁❃❂✽❳❲❁✍P✰❳❩❁ ❳❉P✣P◗❦❣❥❙❦❣❆❉❁✍❳❩❝➃❽▼❀✍❳❩❋❙❥❊✼●❋☛❱⑨✼❘❋❙✼✿P◗❀✌✼✩❥❙❆❵✼●❋❊❋❙❆❉❁✣✼●❆❉❀✌❈✩❳❲❈❊❈❙❦❣❷❉❁✌❧☎✼●❁◗❥❴❆❲⑧➊❥❙❸✌✼✿P◗✼❘❈❙❦❣❋❙✼❍P✧❜●❳❩❥❙✼●❷▼❆❉❋❊❾❛✸❴❏◗❆▼❧✖✼ ❆❲⑧☛❥❙❸✣✼❻❋❙✼●❧✧❳❲❦❣❁✌❦❣❁✌❷✖❦❣❧✧❳❲❷❉✼❘❈☛❱⑨✼●❋❊✼✲❳❩❧❻❫✌❦❣❷❉❀✌❆▼❀✌❈☛✼●➎❉✼❘❁✢❥❙❆✰❸◗❀✌❧✧❳❲❁✌❈✕❂➃❳❩❁✍P②❦❣❁❿❳➞⑧➄✼❘❱ ❜●❳❩❈❙✼●❈❵❥❙❸✌✼ ❁✌✼●❥♠❱✜❆❉❋❊▲✖❧☎❦⑥❈✐❜✐❝⑦❳❲❈❊❈❙❦➂➁✍✼❍P➞❥❙❸✌✼✩❦❣❧✲❳❩❷❉✼✜⑧➄❆▼❋☛❁✌❆✖P◗❦❣❈❬❜✐✼❘❋❙❁✌❦❣❫✌❝❣✼✩❋❙✼❍❳❩❈❙❆▼❁❃✸ ❯➊➎▼✼●❁✰❥❙❸✣❆❉❀✌❷▼❸✲❳❵❈❙✼✕❜✐❆❉❁✍P❛⑩❶❆▼❋❬P◗✼●❋✩➎▼✼●❋❙❈❊❦⑥❆▼❁✧❆❲⑧❴❫✍❳❉❜❊▲▼⑩♠⑤✌❋❙❆▼⑤✍❳❲❷◗❳❲❥❙❦❣❆▼❁❻❱❨❳❩❈☛❀✌❈❙✼❍P✏❂✍❦❣❥✜❦❣❈☛❦⑥❁◗❥❊✼●❋❙✼●❈❊❥❙❦❣❁✌❷ ❥❙❆☎❁✌❆❉❥❊✼✫❥❊❸✍❳❲❥✩❥❊❸✌✼✫❝❣✼❍❳❩❋❙❁✌❦❣❁✌❷❻❥✐❳❲▲▼✼●❈✩❆❉❁✌❝❣❾✰q❉♦➞⑤✍❳❲❈❊❈❙✼●❈✿❥❊❸✌❋❙❆▼❀✌❷❉❸ ❥❙❸✌✼❵❥❙❋✐❳❲❦❣❁✌❦❣❁✌❷✖❈❊✼●❥❍✸✩❭✢✼✱❥❙❸✌❦❣❁✌▲ ❥❙❸✣❦⑥❈✿❜●❳❩❁✰❫✻✼✹❳❩❥❙❥❙❋❊❦❣❫✌❀✌❥❙✼✕P✰❥❊❆✲❥❊❸✌✼❵❝ ❳❩❋❙❷▼✼✫❳❲❧☎❆❉❀✌❁◗❥☛❆❲⑧❴❋❊✼❍P◗❀✌❁✍P✣❳❲❁✍❜✐❾②⑤✌❋❊✼●❈❙✼●❁◗❥✿❦❣❁✰❋❊✼❍❳❲❝❴P✌❳❩❥❬❳✣✸ ❞ ❜✐❆▼❧✖⑤✌❝❣✼●❥❊✼☛❥❙❋❬❳❩❦❣❁✌❦❣❁✌❷✿❈❙✼❘❈❙❈❙❦❣❆▼❁④➜♠q❉♦✩⑤✻❳❲❈❙❈❊✼●❈✑❥❊❸✌❋❙❆▼❀✌❷❉❸☎❥❙❸✌✼❅❥❙❋✐❳❲❦❣❁✌❦❣❁✌❷✿❈❊✼●❥☛⑤✌❝❣❀✌❈✜❥❙✼●❈❊❥❬➝✜❥❬❳❩▲❉✼●❈☛❳❲❫✻❆❉❀✣❥ q✖P✣❳✕❾❛❈✩❆▼❁④❳✧❏✌➏✑♥ ❏✌➑❞❚☛✾✜❈❙❥❬❳❩❥❙❦❣❆❉❁✢➇❵❀✌❈❊❦⑥❁✣❷✖❥❙❸✣✼✹❏✌♥ ✤✧❜✐❆❉❁✌❁✣✼❍❜✐❥❙❦❣❆▼❁✌❦❣❈❙❥✩❈❙❦❣❧❻❀✌❝⑦❳❲❥❊❆❉❋✩➜♠❄❴❆▼❥❙❥❙❆▼❀ ❳❲❁✻P✲✺✏✼✿✾✜❀✌❁❃❂✽➇❍➋▼➠❉➋▼➝●✸ ❞⑧❥❙✼●❋❻❈❊❀✍❜●❜✐✼●❈❊❈♠⑧➄❀✣❝✵❥❊❋❬❳❩❦⑥❁✣❦⑥❁✣❷✍❂➊❥❙❸✣✼✖❁✌✼●❥♠❱✜❆❉❋❊▲✝❱☛❳❲❈❵❦❣❧✖⑤✌❝❣✼●❧☎✼●❁◗❥❙✼❍P ❆❉❁t❳④❜✐❆▼❧✖❧☎✼●❋❬❜✐❦⑦❳❩❝✚❑❅❦❣❷❉❦❣❥✐❳❲❝ ❏◗❦❣❷❉❁✻❳❲❝❴➑➊❋❙❆✌❜✐✼❘❈❙❈❙❆▼❋✫❫✻❆❛❳❩❋❬P②❜✐❆❉❁◗❥✐❳❲❦❣❁✌❦❣❁✌❷✲❳❩❁ ❞❇❡✑❢✑❡ ❑✩❏✌➑❤⑩❙q✥✤▼✾➌❷❉✼●❁✣✼●❋❬❳❩❝⑨⑤✌❀✣❋❙⑤✻❆❉❈❙✼✖❑✩❏✌➑➌❜❊❸✌❦❣⑤ ❱☛❦❣❥❙❸✿❳✜⑤✻✼❍❳❲▲✩⑤✻✼●❋♠⑧➄❆▼❋❙❧✧❳❲❁✍❜✐✼➊❆❩⑧✏➇ ✤✌✸✯✫❇❧✖❦❣❝❣❝⑥❦❣❆❲❁☛❧❻❀✌❝❣❥❊❦⑥⑤✣❝⑥❾▼⑩❙❳❉P✌P❇❆❉⑤✻✼●❋✐❳❲❥❙❦❣❆▼❁✌❈❃⑤✻✼●❋❃❈❊✼❍❜✐❆▼❁✍P✩❆❉❁✿q✬✤✜❫✌❦❣❥ ➡✍❆◗❳❲❥❙❦❣❁✌❷❻⑤✻❆❉❦❣❁◗❥✑❁◗❀✌❧❻❫✻✼●❋❊❈❍✸ ❡❸✌✼❻❑✩❏✌➑t❆▼⑤✍✼●❋✐❳❲❥❊✼●❈✿❳❲❈✵❳☎❜✐❆❉⑤✣❋❙❆✌❜✐✼●❈❊❈❙❆❉❋✱❦⑥❁②❳✖➑❴✾➉❜✐❆▼❁✌❁✌✼❍❜❹❥❙✼❍P②❥❊❆ ❳❻➎◗❦⑦P◗✼●❆✖❜❘❳❲❧✖✼❘❋❬❳✌✸ ❡❸✌✼✫➑❴✾➌⑤✻✼●❋♠⑧❆❉❋❙❧☎❈✑❥❊❸✌✼✫P◗❦❣❷❉❦❣❥❊❦⑥➅✕❳❲❥❙❦❣❆▼❁❃❂◗❫✌❦❣❁✍❳❲❋❊❦❣➅❍❳❲❥❊❦⑥❆▼❁✰❳❲❁✍P✧❈❊✼●❷❉❧☎✼●❁◗❥❬❳❩❥❙❦❣❆❉❁