PROC.OF THE IEEE,NOVEMBER 1998 networks 21,and networks of heterogeneous modules 22 ferentiable,and therefore lends itself to the use of Gradient- A simple derivation for generic multi-layer systems is given Based Learning methods.Section V introduces the use of in Section I-E. directed acyclic graphs whose arcs carry numerical infor- The fact that local minima do not seem to be a problem mation as a way to represent the alternative hypotheses, for multi-layer neural networks is somewhat of a theoretical and introduces the idea of GTN. mystery.It is conPectured that if the network is oversized The second solution described in Section VII is to elim- for the task (as is usually the case in practice),the presence inate segmentation altogether.The idea is to sweep the of "extra dimensions"in parameter space reduces the risk recognizer over every possible location on the input image, of unattainable regions.Back-propagation is by far the and to rely on the "character spotting"property of the rec- most widely used neural-network learning algorithm,and ognizer,i.e.its ability to correctly recognize a well-centered probably the most widely used learning algorithm of any character in its input field,even in the presence of other form. characters besides it,while relecting images containing no centered characters [26],[27].The sequence of recognizer D.Learn Vig Vi F eal s andurig F ecogn on Systems outputs obtained by sweeping the recognizer over the in- Isolated handwritten character recognition has been ex- put is then fed to a Graph Transformer Network that takes tensively studied in the literature(see [23],[24]for reviews). linguistic constraints into account and finally extracts the and was one of the early successful applications of neural most likely interpretation.This GTN is somewhat similar networks [25].Comparative experiments on recognition of to Hidden Markov Models (HMM),which makes the ap- individual handwritten digits are reported in Section III. proach reminiscent of the classical speech recognition [28], They show that neural networks trained with Gradient- [29].While this technique would be quite expensive in Based Learning perform better than all other methods the general case,the use of Convolutional Neural Networks tested here on the same data.The best neural networks, makes it particularly attractive because it allows significant called Convolutional Networks,are designed to learn to savings in computational cost extract relevant features directly from pixel images (see Section II). 7.Glonally Travanle Systems One of the most difficult problems in handwriting recog- As stated earlier,most practical pattern recognition sys- nition,however,is not only to recognize individual charac- tems are composed of multiple modules.For example,a ters,but also to separate out characters from their neigh- document recognition system is composed of a field locator, bors within the word or sentence,a process known as seg- which extracts regions of interest,a field segmenter,which mentation.The technique for doing this that has become cuts the input image into images of candidate characters,a the "standard"is called eurVt Over-Segmentatin.It recognizer,which classifies and scores each candidate char- consists in generating a large number of potential cuts acter,and a contextual post-processor,generally based on between characters using heuristic image processing tech- a stochastic grammar,which selects the best grammatically niques,and subsequently selecting the best combination of correct answer from the hypotheses generated by the recog- cuts based on scores given for each candidate character by nizer.In most cases,the information carried from module the recognizer.In such a model,the accuracy of the sys- to module is best represented as graphs with numerical in- tem depends upon the quality of the cuts generated by the formation attached to the arcs.For example,the output heuristics,and on the ability of the recognizer to distin-of the recognizer module can be represented as an acyclic guish correctly segmented characters from pieces of char-graph where each arc contains the label and the score of acters,multiple characters,or otherwise incorrectly seg- a candidate character,and where each path represent a mented characters.Training a recognizer to perform this alternative interpretation of the input string.Typically, task poses a mapor challenge because of the difficulty in cre- each module is manually optimized,or sometimes trained, ating a labeled database of incorrectly segmented charac- outside of its context.For example,the character recog- ters.The simplest solution consists in running the images nizer would be trained on labeled images of pre-segmented of character strings through the segmenter,and then man- characters.Then the complete system is assembled,and ually labeling all the character hypotheses.Unfortunately,a subset of the parameters of the modules is manually ad- not only is this an extremely tedious and costly task,it is Pusted to maximize the overall performance.This last step also difficult to do the labeling consistently.For example, is extremely tedious,time-consuming,and almost certainly should the right half of a cut up 4 be labeled as a 1 or as suboptimal. a non-character2 should the right half of a cut up 8 be A better alternative would be to somehow train the en- labeled as a 32 tire system so as to minimize a global error measure such as The first solution,described in Section V consists in the probability of character misclassifications at the docu- training the system at the level of whole strings of char-ment level.Ideally,we would want to find a good minimum acters,rather than at the character level.The notion of of this global loss function with respect to all the param- Gradient-Based Learning can be used for this purpose.The eters in the system.If the loss function d measuring the system is trained to minimize an overall loss function which performance can be made differentiable with respect to the measures the probability of an erroneous answer.Section V system's tunable parameters W,we can find a local min- explores various ways to ensure that the loss function is dif- imum of d using Gradient-Based Learning.However,at￾✂✁☎✄✝✆✟✞✠✄☛✡✌☞✎✍✟✏✒✑✓✏✂✏✂✏✎✔✖✕☛✄☎✗☛✏✙✘✛✚✙✏✂✁✢✜✤✣✥✣✧✦ ￾ é✙ï➊è④ó✇ì❛ä✧ô✂ê❅✞✑❼➾✡✠❖➌➈å➄é✎ù✒é✙ï➊è④ó✇ì❛ä✧ô✂ê❯ì➄ë✟ç✙ï➊è✧ï➊ä✥ì✑✂❛ï➊é✙ï✖ì➈ÿ☎ê❇î➻ì✂ù✂ÿ✙û➀ï✖ê ✞✑✫✑ ✠♠ð ✕✻ê✧ø➑î➻æ✙û➀ï➔ù✂ï✖ä✧ø➀ú✠å✠è✧ø➀ì➈é❭ëíì❛ä❷✂➈ï➊é☎ï➊ä✥ø✠￾✛î❙ÿ✙û➩è✥ø➟✝⑥û✓å✪✘➈ï➊ä❣ê❺✘✂ê④è✥ï➊î➓ê❣ø✓ê❷✂➈ø➀ú➈ï✖é ø➀é✫þ✂ï✒￾↔è✥ø➑ì❛é✫➏❖✝✭➉ð ã✛ç✙ï➉ë⑨å➎￾↔è✇è✧ç☎å➄è✇û➀ì☛￾✖å➄û◆î➻ø➑é✙ø➀î➓å❙ù✙ì✒é✙ì➈è➵ê✧ï➊ï➊î✭è✥ì➉✡◆ï➳å✒æ✙ä✧ì➎✡✙û➀ï➊î ëíì➈ä❯î✒ÿ☎û➩è✥ø➟✝⑥û➀å✪✘❛ï➊ä❇é✙ï➊ÿ✙ä❿å➄û✐é✙ï➊è④ó✇ì❛ä✧ô✂ê✝ø✓ê❦ê✤ì❛î➻ï➊ó❨ç☎å➄è❇ì➄ë◆å❨è✧ç☎ï➊ì➈ä✥ï❶è✥ø✠￾✖å➄û î➉✘✂ê✤è✧ï✖ä❺✘❛ð✬➏⑥è♣ø✓ê✞￾❶ì➈é✬✓④ï✒￾↔è✥ÿ✙ä✥ï✖ù✺è✥ç☎å✠è➳ø➩ë➏è✥ç✙ï❭é✙ï❶è④ó➵ì➈ä✥ô✶ø✓ê➉ì✠ú❛ï➊ä❿ê✤ø✠➽➊ï❞ù ëíì➈ä❯è✥ç✙ï❫è❿å➈ê✧ô➣➪⑨å❛ê❇ø✓ê❯ÿ☎ê✧ÿ☎å➄û➀û✙✘➳è✥ç✙ï✩￾➊å➈ê✧ï❫ø➀é✒æ☎ä✥å➎￾↔è✧ø✁￾❶ï✪➶✹➌✠è✥ç✙ï➵æ✙ä✧ï❞ê✤ï✖é✗￾❶ï ì➄ë✹☛✧ï❯↔➇è✧ä❿å➻ù✂ø➑î➻ï✖é☎ê✤ø➀ì➈é✎ê❴✌➻ø➀é✫æ☎å➄ä❿å➄î➻ï➊è✧ï➊ä➔ê✧æ☎å➎￾❶ï✌ä✥ï✖ù✂ÿ✗￾➊ï✖ê✛è✥ç✙ï➞ä✥ø➀ê✧ô ì➄ë➞ÿ☎é☎å✠è✧è✥å➄ø➀é☎å✑✡✙û➑ï❺ä✧ï✒✂➈ø➀ì➈é☎ê✖ð②✚✛å✑￾❿ô❩✝⑥æ✙ä✧ì❛æ☎å❄✂✐å✠è✥ø➑ì❛é❤ø✓ê ✡☛✘❑ë⑨å➄ä✢è✧ç✙ï î➻ì❛ê✤è➞ó❨ø➀ù✙ï➊û✠✘❺ÿ☎ê✧ï✖ù é✙ï✖ÿ✙ä❿å➄û✙✝♠é✙ï➊è④ó✇ì❛ä✧ô✫û➀ï✖å➈ä✧é☎ø➑é❼✂✫å➈û✙✂❛ì➈ä✥ø➩è✥ç✙î✫➌❀å➄é☎ù æ✙ä✥ì✑✡☎å✑✡✙û✠✘➲è✧ç☎ï➓î❭ì✐ê④è❙ó❨ø➀ù✂ï✖û✙✘❺ÿ☎ê✧ï✖ù➷û➀ï✖å➄ä✥é✙ø➀é❼✂✫å➄û✠✂➈ì❛ä✧ø➑è✧ç✙î❂ì➄ë❨å➄é☛✘ ëíì➈ä✥î✺ð ✧✛❊✢➳✴➢♦➡✹➨✤✣●➨✦✥❵✣●➨✂✁✬➳✴➢✲☎✄➑➢♦➨ ✪♦➻❷➡▼✣●➺❩✣●➨✵✥✆✁✬➳❄✴➯❴✥✑➨✤✣●➺❩✣ ➯♦➨✞✝☎✟✪➩✹➺❖➳✄➲➑➩ ➏④ê✤ì❛û➀å➄è✧ï✖ù✿ç☎å➈é☎ù✂ó❨ä✥ø➩è✧è✧ï✖é✫￾❿ç✎å➄ä❿å✑￾↔è✥ï➊ä✛ä✥ï✒￾❶ì➎✂➈é✙ø➑è✧ø➀ì➈é✿ç☎å❛ê✔✡✎ï✖ï➊é✫ï❯↔☛✝ è✧ï✖é☎ê✧ø➑ú❛ï➊û✠✘♣ê✤è✧ÿ☎ù✂ø➀ï✖ù➤ø➀é➤è✧ç☎ï❫û➀ø➩è✥ï➊ä❿å✠è✧ÿ☎ä✧ï✩➪⑨ê✧ï➊ï✟✞✑✫❜✠❖➌✵✞✑❝✫✠➄ëíì➈ä✝ä✥ï➊ú➇ø➀ï➊ó➔ê✴➶✹➌ å➄é✎ù❖ó✛å➈ê♣ì➈é✙ï➻ì➈ë➏è✧ç✙ï➻ï❞å➄ä✥û✙✘➲ê✧ÿ✗￾✄￾➊ï✖ê✥ê④ëíÿ☎û❦å➈æ✙æ✙û➀ø✠￾✖å✠è✥ø➑ì❛é☎ê➳ì➄ë✇é☎ï➊ÿ✙ä❿å➄û é✙ï➊è④ó✇ì❛ä✧ô✂ê✹✞✑✫✙✆✠⑥ð✎☞✇ì❛î❭æ✎å➄ä❿å✠è✧ø➀ú➈ï➳ï❯↔✂æ◆ï➊ä✥ø➑î➻ï✖é❛è❿ê❨ì➈é✺ä✥ï✒￾➊ì✑✂➈é☎ø➩è✥ø➑ì❛é✢ì➈ë ø➀é☎ù✂ø➀ú✐ø✓ù✂ÿ☎å➈û✇ç✎å➄é☎ù✂ó❨ä✥ø➑è✤è✧ï✖é ù✂ø✠✂➈ø➑è✥ê✒å➈ä✧ï✶ä✧ï✖æ✎ì❛ä✤è✥ï✖ù➷ø➀é❤þ➇ï✒￾❶è✧ø➀ì➈é✢➏❺➏❇➏↔ð ã✛ç✙ï✒✘➘ê✧ç✙ì✠ó▼è✧ç✎å✠è✢é☎ï➊ÿ✙ä❿å➄û♣é✙ï❶è④ó➵ì➈ä✥ô✂ê➻è✧ä❿å➄ø➀é✙ï✖ù✻ó❨ø➩è✥ç✾â➳ä✥å❛ù✂ø➀ï➊é✐è❇✝ ✚✛å➈ê✧ï✖ù✷✗❀ï✖å➈ä✧é✙ø➀é❼✂✾æ✎ï✖ä✤ëíì❛ä✧î ✡◆ï❶è✤è✥ï➊ä è✧ç✎å➄é➶å➈û➑û➻ì➈è✧ç✙ï✖ä❺î➻ï❶è✥ç✙ì✂ù✙ê è✧ï❞ê④è✥ï✖ù ç✙ï✖ä✧ï➻ì➈é❺è✧ç☎ï➽ê✧å➈î❭ï➓ù✙å➄è✥å☎ð➻ã✛ç☎ï➚✡◆ï✖ê✤è➞é✙ï✖ÿ✙ä✥å➈û❣é✙ï❶è④ó➵ì➈ä✥ô✂ê✄➌ ￾➊å➈û➑û➀ï✖ù✟☞✇ì➈é➇ú➈ì❛û➑ÿ✙è✧ø➀ì➈é☎å➈û♣ñ➔ï➊è④ó✇ì❛ä✧ô✂ê✒➌➉å➄ä✥ï❺ù✙ï✖ê✧ø✙✂❛é✙ï✖ù✻è✧ì û➑ï❞å➄ä✥é❍è✧ì ï❯↔➇è✥ä✥å➎￾↔è✢ä✥ï➊û➀ï➊ú✠å➈é❛è✢ëíï✖å➄è✧ÿ✙ä✥ï✖ê✺ù✂ø➀ä✧ï★￾↔è✥û✙✘ ëíä✥ì➈î æ✙ø✙↔✂ï➊û➤ø➑î➓å✑✂➈ï✖ê➛➪⑨ê✧ï➊ï þ➇ï★￾↔è✧ø➀ì➈é✫➏❺➏❇➶↔ð ý♣é✙ï➔ì➄ë✟è✥ç✙ï➉î➻ì❛ê✤è✇ù✂ø✯➣￾❶ÿ✙û➑è✇æ☎ä✧ì➎✡✙û➑ï✖î➓ê➏ø➀é➓ç☎å➄é☎ù✙ó❨ä✧ø➑è✧ø➀é❼✂➞ä✥ï✒￾➊ì✑✂❄✝ é✙ø➑è✧ø➀ì➈é✏➌➇ç✙ì✠ó➵ï➊ú❛ï➊ä★➌➈ø✓ê❫é☎ì➄è✛ì➈é✙û✠✘❭è✧ì✒ä✥ï✒￾➊ì✑✂❛é✙ø✙➽✖ï➔ø➀é☎ù✂ø➀ú✐ø✓ù✂ÿ☎å➈û✈￾❿ç✎å➄ä❿å✑￾✹✝ è✧ï✖ä✥ê✒➌✈✡✙ÿ✂è✌å➄û✓ê✤ì➻è✥ì✢ê✧ï➊æ☎å➈ä✥å➄è✧ï✌ì➈ÿ✙è✞￾❿ç☎å➈ä✥å➎￾↔è✧ï✖ä✥ê❨ëíä✧ì❛î✴è✥ç✙ï➊ø➀ä♣é☎ï➊ø✠✂➈ç✦✝ ✡◆ì➈ä❿ê❨ó❨ø➩è✥ç✙ø➀é✺è✧ç✙ï✌ó➵ì➈ä❿ù✶ì❛ä➔ê✤ï✖é✐è✧ï➊é✥￾❶ï✑➌◆å❭æ✙ä✧ì✦￾➊ï✖ê✥ê✛ô✐é☎ì✠ó❨é✺å❛ê❨ê✤ï✒✂❄✝ î➻ï➊é✐è✥å➄è✧ø➀ì➈é✝ð✌ã✛ç✙ï✒è✧ï★￾❿ç✙é✙ø✁➍❛ÿ☎ï✒ëíì❛ä➳ù✂ì❛ø➑é✗✂➽è✧ç☎ø➀ê♣è✧ç✎å✠è♣ç✎å➈ê④✡✎ï★￾❶ì➈î➻ï è✧ç☎ï ☛✧ê✤è✥å➈é☎ù✙å➈ä✥ù✌✿ø✓ê➉￾✖å➄û➀û➑ï❞ù✠✄➑➳☛✡✦➡▼✣✁➩✹➺❩✣✂❄✌☞✎✍♦➳✄➡▼✭✏✝✇➳✿✥❄➲➵➳✄➨✗➺r➢♦➺❩✣ ➯♦➨☎ð✫➏⑥è ￾❶ì❛é☎ê✧ø➀ê✤è✥ê❖ø➑é②✂❛ï➊é✙ï✖ä✥å➄è✧ø➀é❼✂✻å❍û✓å➄ä❘✂➈ï é➇ÿ✙î➑✡✎ï✖ä❖ì➈ë➽æ✎ì➈è✧ï➊é✐è✥ø➀å➈û➑￾❶ÿ✙è✥ê ✡◆ï❶è④ó➵ï➊ï➊é ￾❿ç☎å➈ä✥å➎￾↔è✧ï✖ä✥ê➉ÿ☎ê✧ø➑é❼✂✺ç✙ï✖ÿ✙ä✥ø➀ê✤è✧ø✁￾✒ø➀î➓å❄✂❛ï❙æ✙ä✥ì✦￾❶ï❞ê✧ê✧ø➑é✗✂✶è✥ï✒￾❿ç✦✝ é✙ø✁➍✐ÿ✙ï✖ê✒➌✂å➄é☎ù➓ê✧ÿ❼✡☎ê✧ï✒➍✐ÿ✙ï✖é❛è✥û✙✘➓ê✧ï➊û➀ï✒￾❶è✧ø➀é❼✂✌è✧ç☎ï④✡◆ï✖ê✤è✎￾➊ì➈î➉✡☎ø➑é☎å➄è✧ø➀ì➈é➓ì➈ë ￾❶ÿ✙è✥ê✔✡☎å❛ê✤ï❞ù➽ì➈é✿ê❺￾➊ì➈ä✥ï✖ê➃✂➈ø➀ú➈ï✖é➽ëíì➈ä✛ï❞å✑￾❿ç➙￾✖å➄é☎ù✙ø➀ù✙å➄è✧ït￾❿ç☎å➄ä❿å✑￾❶è✧ï✖ä➃✡☛✘ è✧ç☎ï➓ä✧ï★￾❶ì✑✂❛é✙ø✠➽➊ï➊ä❞ð➓➏➠é➷ê✤ÿ✥￾❿ç❺å✿î➻ì➇ù✙ï➊û✶➌☛è✧ç✙ï➽å➎￾✄￾➊ÿ✙ä✥å➎￾❯✘✺ì➄ë✇è✧ç✙ï➓ê❺✘✂ê❅✝ è✧ï✖î ù✂ï➊æ◆ï➊é☎ù☎ê✇ÿ☎æ✎ì❛é➓è✧ç✙ï✞➍✐ÿ☎å➄û➀ø➩è❅✘❭ì➄ë☛è✥ç✙ï④￾➊ÿ✂è✥ê➃✂➈ï✖é✙ï➊ä❿å✠è✥ï✖ù➚✡☛✘❭è✧ç✙ï ç✙ï✖ÿ✙ä✧ø✓ê✤è✧ø✁￾➊ê✒➌❫å➄é☎ù❤ì➈é❤è✧ç✙ï✺å❄✡✙ø➀û➀ø➩è❅✘ ì➈ë➔è✧ç✙ï✺ä✥ï✒￾➊ì✑✂➈é☎ø✙➽✖ï➊ä➞è✥ì ù✙ø➀ê✤è✧ø➀é✦✝ ✂➈ÿ☎ø➀ê✧ç①￾❶ì➈ä✥ä✥ï✒￾↔è✥û✙✘❺ê✤ï✒✂➈î➻ï➊é✐è✧ï❞ù➛￾❿ç✎å➄ä❿å✑￾↔è✥ï➊ä❿ê♣ëíä✧ì❛î❋æ✙ø➀ï✒￾➊ï✖ê✌ì➈ë✔￾❿ç☎å➈ä❇✝ å✑￾❶è✧ï✖ä✥ê✒➌➔î✒ÿ☎û➩è✥ø➑æ✙û➀ï➛￾❿ç✎å➄ä❿å✑￾↔è✥ï➊ä❿ê✄➌❨ì❛ä✶ì➈è✧ç✙ï✖ä✧ó❨ø✓ê✤ï❖ø➑é✗￾➊ì➈ä✥ä✧ï★￾↔è✥û✙✘➘ê✤ï✒✂❄✝ î➻ï➊é✐è✧ï❞ù ￾❿ç☎å➄ä❿å✑￾❶è✧ï➊ä❿ê✖ð❭ã❇ä✥å➈ø➑é☎ø➑é❼✂✫å✿ä✥ï✒￾❶ì➎✂➈é✙ø✠➽➊ï✖ä➳è✧ì✫æ◆ï➊ä✧ëíì➈ä✥î è✧ç✙ø✓ê è✥å❛ê✤ô➤æ◆ì❛ê✧ï✖ê❇å➔î➓å✓④ì❛ä❳￾❿ç☎å➈û➑û➀ï➊é❼✂❛ï❷✡✎ï★￾➊å➈ÿ☎ê✤ï✇ì➄ë✂è✥ç✙ï✇ù✂ø✯➣￾❶ÿ✙û➑è❅✘✌ø➀é➉￾❶ä✥ï❯✝ å✠è✥ø➑é✗✂✿å✿û✓å❄✡◆ï➊û➀ï✖ù ù✙å✠è❿å❄✡☎å❛ê✤ï❙ì➄ë✇ø➀é✗￾➊ì➈ä✥ä✧ï★￾↔è✧û✠✘✫ê✤ï✒✂➈î➻ï➊é✐è✥ï✖ù➔￾❿ç✎å➄ä❿å✑￾✹✝ è✧ï✖ä✥ê✖ð✛ã✛ç✙ï➞ê✤ø➀î➻æ✙û➀ï✖ê✤è♣ê✤ì❛û➑ÿ✙è✧ø➀ì➈é✆￾❶ì❛é☎ê✤ø✓ê✤è✥ê❨ø➀é✫ä✧ÿ☎é✙é✙ø➀é❼✂➻è✧ç✙ï✒ø➑î➓å❄✂❛ï✖ê ì➄ë❀￾❿ç✎å➄ä❿å✑￾↔è✥ï➊ä➵ê④è✥ä✧ø➀é❼✂❛ê➏è✧ç✙ä✥ì➈ÿ✗✂➈ç➻è✧ç☎ï➳ê✧ï✄✂❛î❭ï✖é✐è✧ï➊ä★➌➇å➄é☎ù➻è✥ç✙ï➊é✢î➓å➄é✦✝ ÿ☎å➈û➑û✠✘➓û➀å✑✡✎ï✖û➑ø➀é❼✂➓å➄û➀û◆è✧ç✙ï✌￾❿ç☎å➈ä✥å➎￾↔è✥ï➊ä❫ç☛✘➇æ◆ì➄è✧ç☎ï✖ê✧ï✖ê✖ð❨→➉é✂ëíì➈ä✧è✧ÿ☎é☎å✠è✥ï➊û✠✘✑➌ é✙ì➈è➉ì➈é✙û✠✘✶ø✓ê❨è✥ç✙ø✓ê➉å➄é✫ï✄↔➇è✧ä✥ï➊î➻ï➊û✠✘➽è✥ï✖ù✂ø➀ì➈ÿ☎ê♣å➄é☎ù✫￾➊ì❛ê✤è✧û✠✘➽è✥å❛ê✤ô✇➌✎ø➩è➉ø✓ê å➄û✓ê✧ì✶ù✂ø✯➵￾➊ÿ✙û➑è♣è✥ì✶ù✂ì➽è✥ç✙ï✒û✓å❄✡◆ï➊û➀ø➀é❼✂➙￾➊ì➈é☎ê✧ø➀ê✤è✧ï✖é✐è✧û✠✘➈ð✬✜☎ì➈ä➳ï❯↔✙å➄î➻æ✙û➀ï✑➌ ê✧ç✙ì➈ÿ✙û✓ù✺è✥ç✙ï✒ä✥ø✙✂❛ç✐è➉ç☎å➄û➑ë➏ì➄ë❫å ￾❶ÿ✂è➳ÿ✙æ ❝➣✡◆ï✒û✓å❄✡◆ï➊û➀ï✖ù❖å➈ê➉å➔➾➞ì➈ä➳å➈ê å é✙ì➈é❼✝❖￾❿ç☎å➈ä✥å➎￾↔è✥ï➊ä✒✑ ê✤ç✙ì❛ÿ✙û✓ù è✥ç✙ï✺ä✧ø✠✂➈ç✐è➻ç☎å➄û➑ë♣ì➈ë➳å➒￾❶ÿ✂è➓ÿ✙æ ✺➒✡✎ï û✓å❄✡◆ï➊û➀ï✖ù✺å➈ê❨å❑❜✓✑ ã✛ç✙ï①➞☎ä❿ê④è❖ê✧ì➈û➀ÿ✂è✥ø➑ì❛é✏➌➞ù✂ï✖ê❘￾❶ä✥ø✠✡✎ï❞ù✾ø➀é✹þ➇ï★￾↔è✧ø➀ì➈é✟✣ ￾➊ì➈é☎ê✧ø✓ê④è❿ê✫ø➑é è✧ä❿å➄ø➀é✙ø➀é❼✂✫è✥ç✙ï✢ê❺✘✂ê④è✥ï➊î å✠è❙è✧ç✙ï✢û➑ï✖ú➈ï➊û✇ì➄ë➔ó❨ç✙ì❛û➑ï✢ê④è✥ä✧ø➀é❼✂❛ê✒ì➄ë✬￾❿ç☎å➈ä❇✝ å✑￾❶è✧ï✖ä✥ê✒➌❦ä✥å➄è✧ç✙ï✖ä➞è✧ç✎å➄é❤å➄è➞è✧ç✙ï✫￾❿ç☎å➈ä✥å➎￾↔è✧ï✖ä➞û➀ï➊ú➈ï✖ûüð❺ã✛ç✙ï✶é✙ì➈è✧ø➀ì➈é ì➈ë â➳ä❿å➈ù✂ø➀ï➊é✐è❇✝r✚✛å➈ê✧ï✖ù✒✗✝ï❞å➄ä✥é✙ø➀é❼✂✛￾➊å➈é➓✡✎ï✇ÿ☎ê✧ï✖ù➳ëíì➈ä❇è✧ç☎ø➀ê❀æ✙ÿ✙ä✥æ✎ì✐ê✤ï❛ð❇ã✛ç✙ï ê❺✘➇ê✤è✧ï✖î ø➀ê❀è✧ä❿å➄ø➀é✙ï❞ù➤è✧ì➳î➻ø➑é✙ø➀î➻ø✙➽✖ï➵å➈é✒ì✠ú❛ï➊ä❿å➄û➀û➄û➑ì✐ê✧ê❀ëíÿ✙é✗￾❶è✧ø➀ì➈é❙ó❨ç✙ø✠￾❿ç î➻ï✖å❛ê✤ÿ✙ä✥ï✖ê✝è✧ç✙ï✇æ✙ä✥ì✑✡☎å✑✡✙ø➑û➀ø➑è❅✘♣ì➈ë☎å➄é➞ï✖ä✧ä✥ì➈é✙ï✖ì➈ÿ☎ê❀å➄é☎ê✧ó✇ï✖ä✖ð❯þ➇ï✒￾❶è✧ø➀ì➈é➓✣ ï❯↔✂æ✙û➀ì➈ä✥ï✖ê❇ú✠å➄ä✥ø➑ì❛ÿ☎ê❇ó➵å✪✘✂ê☛è✥ì♣ï✖é☎ê✧ÿ✙ä✧ï❫è✧ç☎å➄è❦è✧ç✙ï➵û➑ì✐ê✧ê❀ëíÿ✙é✗￾❶è✧ø➀ì➈é❭ø➀ê❦ù✂ø➑ë●✝ ëíï➊ä✥ï➊é✐è✥ø➀å✑✡✙û➑ï➎➌✠å➄é☎ù➳è✥ç✙ï➊ä✥ï❶ëíì❛ä✧ï➏û➀ï➊é✎ù✙ê✝ø➑è✥ê✧ï➊û➑ë✂è✧ì❨è✥ç✙ï❫ÿ✎ê✤ï✇ì➄ë☎â➳ä✥å❛ù✂ø➀ï➊é✐è❇✝ ✚✛å➈ê✧ï✖ù❖✗❀ï✖å➄ä✥é✙ø➀é❼✂➓î➻ï❶è✥ç✙ì✂ù✙ê➊ð➳þ➇ï✒￾❶è✧ø➀ì➈é✆✣✹ø➀é✐è✧ä✥ì✂ù✂ÿ✗￾❶ï❞ê❨è✥ç✙ï✒ÿ☎ê✧ï➞ì➈ë ù✂ø➀ä✧ï★￾↔è✥ï✖ù å✑￾✄✘✦￾❶û➀ø✠￾➵✂❛ä✥å➈æ✙ç☎ê✌ó❨ç☎ì❛ê✧ï✶å➄ä✴￾➊ê➓￾✖å➄ä✥ä❺✘❺é➇ÿ✙î➻ï➊ä✥ø✠￾✖å➄û❫ø➀é✂ëíì❛ä❇✝ î➓å✠è✥ø➑ì❛é å➈ê✒å➲ó➵å✪✘❺è✧ì➲ä✥ï➊æ☎ä✧ï❞ê✤ï✖é❛è✌è✥ç✙ï✢å➈û➩è✥ï➊ä✥é☎å✠è✥ø➑ú❛ï➽ç☛✘➇æ✎ì➈è✧ç✙ï❞ê✤ï❞ê✄➌ å➄é✎ù✶ø➀é✐è✧ä✥ì➇ù✙ÿ✗￾❶ï❞ê➵è✧ç✙ï✌ø✓ù✂ï✖å➻ì➈ë❦â➤ã❨ñ✒ð ã✛ç✙ï➞ê✤ï★￾❶ì❛é☎ù✺ê✤ì❛û➑ÿ✂è✥ø➑ì❛é✫ù✂ï✖ê❘￾❶ä✥ø✙✡◆ï✖ù✿ø➀é➲þ➇ï✒￾❶è✧ø➀ì➈é✫✣✬➏❺➏➵ø✓ê✛è✥ì➓ï➊û➀ø➑î➚✝ ø➀é☎å✠è✥ï✺ê✧ï✄✂❛î➻ï➊é✐è✥å➄è✧ø➀ì➈é❤å➈û➩è✥ì✑✂➈ï➊è✧ç✙ï✖ä✖ð❑ã✛ç✙ï✺ø➀ù✂ï❞å❖ø✓ê❭è✧ì ê✧ó✇ï✖ï➊æ❤è✧ç✙ï ä✥ï✒￾❶ì➎✂➈é✙ø✠➽➊ï✖ä➏ì✠ú➈ï✖ä➏ï➊ú❛ï➊ä❘✘❙æ◆ì❛ê✥ê✧ø✙✡✙û➀ï➉û➀ì✦￾➊å➄è✧ø➀ì➈é➓ì➈é➽è✧ç✙ï➳ø➑é☎æ✙ÿ✂è✛ø➑î➓å❄✂❛ï✑➌ å➄é✎ù➞è✥ì➳ä✥ï➊û✠✘➞ì❛é➞è✧ç☎ï ☛❺￾❿ç☎å➈ä✥å➎￾↔è✧ï✖ä❯ê✧æ◆ì➄è✤è✥ø➑é✗✂✌♣æ✙ä✧ì❛æ✎ï✖ä✤è❅✘✌ì➈ë☎è✥ç✙ï❨ä✧ï★￾✹✝ ì✑✂❛é✙ø✠➽➊ï➊ä★➌❞øüð ï➈ð❇ø➩è❿ê❀å❄✡☎ø➑û➀ø➩è❅✘➳è✥ì✬￾❶ì❛ä✧ä✥ï✒￾❶è✧û✠✘➉ä✥ï✒￾❶ì➎✂➈é✙ø✠➽➊ï❣å❨ó➵ï➊û➀û✙✝❖￾➊ï➊é✐è✧ï✖ä✧ï❞ù ￾❿ç☎å➈ä✥å➎￾↔è✧ï✖ä✌ø➑é❤ø➩è❿ê➞ø➀é✙æ✙ÿ✂è➑➞☎ï➊û✓ù❢➌❦ï➊ú❛ï➊é ø➀é➷è✧ç✙ï✢æ✙ä✧ï❞ê✤ï✖é✗￾❶ï➓ì➈ë➔ì➄è✥ç✙ï➊ä ￾❿ç☎å➈ä✥å➎￾↔è✧ï✖ä✥ê✎✡◆ï✖ê✧ø➀ù✙ï✖ê➔ø➑è✒➌✎ó❨ç✙ø➀û➑ï➞ä✥ï✓④ï★￾↔è✥ø➑é❼✂➓ø➀î➓å❄✂❛ï✖ê✛￾➊ì➈é✐è✥å➈ø➑é✙ø➀é❼✂➻é✙ì ￾❶ï✖é✐è✧ï➊ä✥ï✖ù➒￾❿ç☎å➈ä✥å➎￾↔è✧ï✖ä✥ê❄✞✑❆✓✠❖➌ ✞✑✗✔✆✠♠ð➻ã✛ç✙ï➽ê✧ï✒➍✐ÿ✙ï➊é✥￾❶ï➻ì➄ë➵ä✧ï★￾❶ì✑✂❛é✙ø✠➽➊ï➊ä ì➈ÿ✙è✧æ✙ÿ✂è❿ê➞ì➎✡✂è✥å➈ø➑é✙ï❞ù ✡☛✘❺ê✧ó✇ï✖ï➊æ✙ø➀é❼✂✺è✧ç☎ï➽ä✧ï★￾❶ì➎✂➈é✙ø✠➽➊ï✖ä✌ì✠ú➈ï➊ä➤è✥ç✙ï➽ø➀é✦✝ æ✙ÿ✂è✇ø➀ê❯è✥ç✙ï➊é➻ëíï❞ù✒è✥ì➞å➞â➳ä✥å➈æ✙ç➻ã❀ä❿å➄é☎ê✤ëíì➈ä✥î➻ï➊ä❣ñ➔ï➊è④ó✇ì❛ä✧ô➞è✧ç☎å➄è❣è✥å➄ô❛ï✖ê û➀ø➑é❼✂❛ÿ✙ø✓ê④è✥ø✠￾➑￾❶ì➈é✎ê④è✥ä✥å➈ø➑é✐è✥ê➔ø➑é✐è✧ì✢å✑￾✄￾➊ì➈ÿ✙é✐è♣å➈é☎ù✫➞☎é✎å➄û➀û✙✘✿ï❯↔➇è✧ä❿å✑￾❶è✥ê➔è✧ç✙ï î➻ì❛ê✤è➔û➑ø➀ô➈ï✖û✙✘✢ø➑é✐è✥ï➊ä✥æ✙ä✧ï➊è✥å➄è✧ø➀ì➈é✝ð➵ã✛ç✙ø➀ê➳â➤ã❨ñ ø✓ê➔ê✤ì❛î➻ï➊ó❨ç☎å➄è➉ê✤ø➀î➻ø➑û✓å➄ä è✧ì õ➔ø✓ù✙ù✂ï✖é❑ö➲å➈ä✧ô❛ì✠ú❖ö✫ì✂ù✂ï➊û✓ê➐➪⑨õ➉ö❖ö➔➶✹➌❦ó❨ç✙ø✁￾❿ç î➓å➄ô❛ï✖ê✌è✥ç✙ï✺å➄æ✦✝ æ✙ä✥ì❛å➎￾❿ç✶ä✥ï➊î➻ø➑é☎ø➀ê❘￾❶ï✖é❛è❨ì➈ë❇è✥ç✙ï➓￾❶û✓å➈ê✥ê✤ø✁￾➊å➈û☛ê✤æ◆ï➊ï★￾❿ç✿ä✥ï✒￾➊ì✑✂❛é✙ø➩è✥ø➑ì❛é ✞✑❆✺✬✠✶➌ ✞✑❆❀✠⑥ð ✎ç✙ø➀û➀ï❖è✥ç✙ø➀ê✿è✥ï✒￾❿ç✙é☎ø✠➍✐ÿ✙ï➷ó➵ì➈ÿ✙û✓ù❭✡◆ï ➍✐ÿ✙ø➑è✧ï➷ï✄↔➇æ◆ï➊é✎ê✤ø➀ú➈ï ø➑é è✧ç☎ï✩✂➈ï➊é☎ï➊ä❿å➄û❼￾➊å❛ê✤ï➎➌✠è✧ç☎ï❨ÿ☎ê✤ï❨ì➈ë✏☞✇ì❛é➇ú➈ì➈û➀ÿ✂è✥ø➑ì❛é☎å➄û✂ñ➔ï✖ÿ✙ä❿å➄û☎ñ➉ï❶è④ó➵ì➈ä✥ô➇ê î➓å➄ô❛ï✖ê✝ø➑è❇æ✎å➄ä✧è✧ø✁￾❶ÿ✙û✓å➄ä✥û✙✘➤å✠è✧è✧ä❿å✑￾❶è✧ø➀ú➈ï❹✡◆ï✒￾✖å➄ÿ☎ê✧ï❫ø➑è❯å➈û➑û➀ì✠ó➔ê❀ê✧ø✙✂❛é✙ø✙➞✥￾➊å➈é❛è ê✥årú✐ø➀é❼✂✐ê✇ø➀é✆￾❶ì❛î❭æ☎ÿ✂è✥å➄è✧ø➀ì➈é☎å➈û✏￾❶ì❛ê✤è✖ð ✔✛ ➠❅✲✙➯✫✯✴➢✲ ✲✕✟ ➦❼➡❺➢✣●➨✇➢✫✯❪✲✙➳✖✝☎✟✪➩✹➺❖➳✄➲➑➩ ✕➉ê❫ê✤è✥å➄è✧ï❞ù➻ï✖å➄ä✥û➀ø➑ï✖ä✒➌➄î➻ì✐ê④è➏æ✙ä❿å✑￾❶è✧ø✁￾➊å➈û✙æ☎å✠è✧è✧ï✖ä✧é➓ä✥ï✒￾➊ì✑✂➈é☎ø➩è✥ø➑ì❛é❭ê❺✘✂ê❅✝ è✧ï✖î➓ê❙å➈ä✧ï➐￾❶ì➈î➻æ◆ì❛ê✧ï✖ù➷ì➈ë➔î✒ÿ✙û➑è✧ø➀æ✙û➀ï✢î➻ì✂ù✂ÿ✙û➀ï✖ê✖ð➒✜☎ì➈ä❭ï❯↔✙å➄î➻æ✙û➀ï✑➌❣å ù✂ì✦￾❶ÿ☎î❭ï✖é✐è❯ä✥ï✒￾➊ì✑✂➈é☎ø➩è✥ø➑ì❛é➞ê❇✘✂ê✤è✧ï➊î ø✓ê❳￾➊ì➈î➻æ◆ì❛ê✧ï✖ù✌ì➈ë✎å✩➞☎ï✖û➀ù➞û➑ì✦￾➊å➄è✧ì❛ä✒➌ ó❨ç✙ø✁￾❿ç✶ï✄↔✐è✥ä✥å➎￾↔è❿ê❫ä✥ï✄✂❛ø➑ì❛é☎ê❫ì➈ë❀ø➀é❛è✥ï➊ä✥ï✖ê✤è✒➌✂å✌➞✎ï➊û✓ù✢ê✧ï✄✂❛î❭ï✖é✐è✧ï➊ä★➌✐ó❨ç✙ø✠￾❿ç ￾❶ÿ✙è✥ê❦è✥ç✙ï➔ø➀é✙æ✙ÿ✂è➏ø➀î➓å❄✂➈ï❨ø➀é✐è✧ì➤ø➀î➻å✑✂➈ï❞ê❦ì➄ë❢￾➊å➄é✎ù✂ø➀ù☎å✠è✧ï✛￾❿ç✎å➄ä❿å✑￾↔è✥ï➊ä❿ê✄➌✠å ä✥ï✒￾❶ì➎✂➈é✙ø✠➽➊ï✖ä✒➌➈ó❨ç✙ø✠￾❿ç➣￾➊û➀å❛ê✧ê✧ø✙➞☎ï✖ê➏å➈é☎ù➓ê❘￾❶ì➈ä✥ï✖ê❦ï❞å✑￾❿ç➣￾✖å➄é☎ù✂ø✓ù✙å➄è✧ï✬￾❿ç☎å➈ä❇✝ å✑￾❶è✧ï✖ä✒➌☎å➈é☎ù✺å➝￾❶ì➈é✐è✥ï❯↔➇è✧ÿ☎å➈û✝æ◆ì❛ê✤è❇✝⑥æ✙ä✧ì✦￾➊ï✖ê✥ê✤ì❛ä✒➌☛✂➈ï✖é✙ï➊ä❿å➄û➀û✠✘➣✡☎å➈ê✧ï✖ù✢ì➈é å➔ê✤è✧ì✦￾❿ç☎å❛ê④è✥ø✠￾❷✂❛ä✥å➈î➻î➻å➈ä✒➌✖ó❨ç✙ø✁￾❿ç➞ê✤ï✖û➑ï★￾↔è✥ê✝è✧ç✙ï➃✡✎ï❞ê④è❜✂➈ä❿å➄î➻î➓å✠è✥ø✠￾✖å➄û➀û✙✘ ￾❶ì❛ä✧ä✥ï✒￾❶è❇å➄é✎ê✤ó➵ï➊ä❇ëíä✧ì❛î❲è✧ç✙ï➵ç☛✘✐æ◆ì➄è✥ç✙ï✖ê✧ï✖ê❳✂➈ï✖é✙ï➊ä❿å✠è✥ï✖ù✌✡☛✘➳è✧ç☎ï✇ä✥ï✒￾➊ì✑✂❄✝ é✙ø✠➽➊ï✖ä✖ð➃➏➠é✿î➻ì✐ê④è✬￾✖å➈ê✧ï✖ê✒➌✂è✧ç✙ï✌ø➀é✂ëíì❛ä✧î➓å✠è✥ø➑ì❛é✆￾➊å➄ä✥ä✥ø➑ï❞ù➽ëíä✥ì➈îPî❭ì✂ù✂ÿ☎û➑ï è✧ì❙î➻ì➇ù✙ÿ✙û➑ï➳ø✓ê➜✡◆ï✖ê✤è✇ä✥ï➊æ✙ä✥ï✖ê✧ï➊é✐è✥ï✖ù➽å❛ê❹✂➈ä❿å➄æ☎ç☎ê➏ó❨ø➑è✧ç✢é✐ÿ☎î❭ï✖ä✧ø✁￾➊å➈û☎ø➀é✦✝ ëíì➈ä✥î➓å✠è✥ø➑ì❛é➷å➄è✤è❿å✑￾❿ç✙ï❞ù❖è✥ì✺è✧ç✙ï✢å➄ä✴￾➊ê✖ð ✜✙ì❛ä➞ï❯↔✙å➄î➻æ✙û➀ï✑➌❇è✧ç☎ï➽ì➈ÿ✂è✥æ✙ÿ✂è ì➄ë❣è✥ç✙ï❭ä✧ï★￾❶ì✑✂❛é✙ø✠➽➊ï➊ä➉î❭ì✂ù✂ÿ☎û➑ï➝￾➊å➈é↕✡✎ï❙ä✧ï✖æ✙ä✥ï✖ê✧ï➊é✐è✧ï❞ù✫å➈ê➳å➄é❖å➎￾❯✘✦￾❶û➀ø✠￾ ✂➈ä❿å➄æ☎ç ó❨ç☎ï➊ä✥ï➓ï✖å✑￾❿ç å➈ä❘￾➵￾➊ì➈é✐è✥å➈ø➑é☎ê✌è✧ç✙ï✢û➀å✑✡✎ï✖û❫å➄é☎ù è✥ç✙ï✢ê❘￾❶ì❛ä✧ï➻ì➈ë å✢￾✖å➄é☎ù✂ø✓ù✙å➄è✧ï➔￾❿ç☎å➈ä✥å➎￾↔è✥ï➊ä★➌✛å➈é☎ù➘ó❨ç✙ï✖ä✧ï➲ï❞å✑￾❿ç❍æ☎å➄è✧ç❍ä✥ï➊æ✙ä✥ï✖ê✧ï➊é✐è✶å å➄û➑è✧ï✖ä✧é✎å✠è✧ø➀ú➈ï✺ø➀é✐è✧ï➊ä✥æ✙ä✥ï❶è❿å✠è✧ø➀ì➈é➘ì➈ë♣è✥ç✙ï➲ø➀é✙æ✙ÿ✂è✢ê✤è✧ä✥ø➀é❼✂☎ð❲ã➜✘➇æ✙ø✁￾➊å➈û➑û✠✘✑➌ ï✖å➎￾❿ç✢î➻ì✂ù✂ÿ✙û➀ï➤ø➀ê➔î➻å➈é➇ÿ☎å➄û➀û✙✘➓ì❛æ✂è✧ø➀î➻ø✙➽✖ï✖ù❢➌☎ì➈ä❨ê✤ì❛î➻ï❶è✧ø➀î➻ï✖ê➵è✥ä✥å➈ø➑é✙ï❞ù❢➌ ì➈ÿ✙è✥ê✧ø➀ù✂ï✶ì➄ë❨ø➩è❿ê➑￾➊ì➈é✐è✧ï✄↔➇è✖ð↕✜✙ì❛ä➞ï❯↔✙å➈î❭æ☎û➑ï➎➌❇è✥ç✙ï➙￾❿ç✎å➄ä❿å✑￾↔è✥ï➊ä✌ä✥ï✒￾➊ì✑✂❄✝ é✙ø✠➽➊ï✖ä✇ó➵ì➈ÿ✙û✓ù➵✡◆ï➉è✥ä✥å➈ø➑é☎ï✖ù➓ì❛é➽û➀å✑✡✎ï✖û➑ï❞ù➽ø➀î➓å❄✂➈ï❞ê➏ì➈ë✝æ✙ä✧ï✄✝⑥ê✧ï✄✂❛î➻ï➊é✐è✧ï❞ù ￾❿ç☎å➈ä✥å➎￾↔è✧ï✖ä✥ê✖ð➲ã✛ç✙ï✖é è✧ç✙ï✫￾❶ì❛î➻æ✙û➑ï➊è✧ï✶ê❺✘✂ê④è✥ï➊î÷ø✓ê❙å❛ê✧ê✧ï➊î➑✡✙û➑ï❞ù❢➌❣å➄é☎ù å➻ê✤ÿ✗✡☎ê✤ï➊è➔ì➄ë❇è✧ç✙ï✌æ☎å➈ä✥å➈î➻ï❶è✧ï✖ä✥ê➵ì➈ë❇è✥ç✙ï✌î➻ì➇ù✙ÿ✙û➑ï❞ê✛ø➀ê➔î➻å➈é➇ÿ☎å➄û➀û✙✘✢å➈ù☛✝ ✓④ÿ☎ê✤è✧ï❞ù➓è✧ì❙î➓å♦↔✂ø➑î➻ø✠➽➊ï➉è✧ç✙ï♣ì✠ú➈ï➊ä❿å➄û➀û✙æ◆ï➊ä✧ëíì➈ä✥î➓å➄é✗￾➊ï➈ð❦ã✛ç✙ø➀ê✇û✓å➈ê✤è➵ê✤è✧ï➊æ ø✓ê❦ï❯↔➇è✧ä✥ï➊î➻ï➊û✠✘➤è✧ï❞ù✂ø➀ì➈ÿ☎ê✒➌✠è✧ø➀î➻ï❯✝r￾❶ì❛é☎ê✤ÿ☎î❭ø➀é❼✂✥➌➈å➄é☎ù❭å➄û➀î➻ì❛ê✤è❨￾➊ï➊ä✧è✥å➈ø➑é✙û✠✘ ê✧ÿ❼✡✎ì❛æ✂è✧ø➀î➓å➄û♠ð ✕ ✡✎ï➊è✤è✥ï➊ä♣å➈û➩è✥ï➊ä✥é☎å✠è✥ø➑ú❛ï✌ó✇ì❛ÿ✙û✓ù➙✡◆ï➞è✥ì➽ê✤ì❛î➻ï➊ç✙ì✠ó✾è✧ä❿å➄ø➀é✿è✧ç✙ï➞ï✖é✦✝ è✧ø➀ä✥ï✇ê❺✘➇ê✤è✧ï✖îòê✤ì➳å➈ê✝è✧ì➳î❭ø➀é✙ø➀î➻ø✙➽✖ï➵å✬✂➈û➀ì✑✡☎å➈û➈ï➊ä✥ä✥ì➈ä❀î➻ï❞å➈ê✧ÿ✙ä✧ï➵ê✤ÿ✥￾❿ç✒å➈ê è✧ç☎ï✌æ✙ä✧ì➎✡☎å❄✡☎ø➑û➀ø➩è❅✘✶ì➄ë❜￾❿ç✎å➄ä❿å✑￾↔è✥ï➊ä✛î➻ø➀ê❘￾❶û✓å➈ê✥ê✧ø➟➞✥￾✖å✠è✥ø➑ì❛é☎ê✛å✠è✛è✧ç✙ï➞ù✙ì☛￾➊ÿ✦✝ î➻ï➊é✐è❣û➑ï✖ú➈ï✖ûüð❳➏④ù✂ï✖å➈û➑û✠✘✑➌✠ó➵ï✇ó➵ì➈ÿ☎û➀ù✒ó➵å➈é❛è❯è✧ì④➞☎é☎ù❭å④✂➈ì➇ì✂ù✌î➻ø➑é✙ø➀î✒ÿ☎î ì➄ë❫è✧ç✙ø✓êt✂➈û➀ì✑✡☎å➈û❇û➀ì❛ê✥ê♣ëíÿ✙é✗￾❶è✧ø➀ì➈é ó❨ø➩è✥ç❖ä✥ï✖ê✧æ✎ï★￾↔è➳è✧ì✫å➄û➀û❯è✧ç✙ï❭æ☎å➄ä❿å➄î➚✝ ï❶è✥ï➊ä❿ê➳ø➑é❺è✧ç✙ï➽ê❺✘✂ê④è✥ï➊î✺ð➉➏⑥ë✇è✧ç✙ï➓û➀ì❛ê✥ê♣ëíÿ✙é✗￾❶è✧ø➀ì➈é▲❉✴î➻ï✖å❛ê✤ÿ☎ä✧ø➀é❼✂✢è✧ç✙ï æ◆ï➊ä✧ëíì➈ä✥î➻å➈é✗￾❶ï✩￾✖å➄é➚✡✎ï➔î➓å❛ù✂ï❨ù✂ø➟➘✟ï➊ä✥ï➊é✐è✥ø➀å✑✡✙û➑ï❨ó❨ø➑è✧ç➻ä✥ï✖ê✧æ✎ï★￾↔è❣è✥ì➤è✧ç✙ï ê❺✘➇ê✤è✧ï✖î❁ ê♣è✧ÿ☎é☎å❄✡✙û➀ï➓æ☎å➈ä✥å➈î❭ï➊è✧ï✖ä✥ê✒❂➌✝ó➵ï➚￾✖å➄é➛➞☎é☎ù å✢û➀ì✦￾➊å➈û❯î➻ø➀é✦✝ ø➀î✒ÿ✙î ì➈ë❇❉ ÿ☎ê✧ø➑é❼✂➷â➳ä✥å❛ù✂ø➀ï➊é✐è❇✝r✚✛å➈ê✧ï✖ù▲✗✝ï❞å➄ä✥é✙ø➑é✗✂☎ð❺õ➉ì✠ó✇ï✖ú➈ï✖ä✒➌❦å✠è