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Something went wrong.The page you are looking for could not be found Students in Brazilians schools will no longer have full access to their smartphones following the implementation of a new law restricting their use (AP video shot by: Mayeron Abade and Thiago Mostazo) Students store their mobile phones in lockers as they head to class under a new law restricting their use on campus at Porto Seguro School in Sao Paulo Students play cards during recess in their first week at school under a new law that forbids the use of mobile phones on campus Students store their mobile phones in lockers at Porto Seguro School in Sao Paulo as they head to class under a new law that forbids their use in schools Students attend a class at Porto Seguro School on their first week at school under a new law that forbids the use of mobile phones on campus A student stores her mobile phone in a locker at Porto Seguro School in Sao Paulo before heading to class under a new law restricting their use in schools A sign reads in Portuguese " The use of cell phones in the school is prohibited” at Porto Seguro school in Sao Paulo Students return to class after recess during their first week in school under a new law restricting the use mobile phones in school classrooms at Porto Seguro School in Sao Paulo Phones are still allowed for educational purposes and when needed for the student’s accessibility and health such as whether students can keep phones in backpacks or store them in lockers or designated baskets most of Brazil’s 26 states — including Rio de Janeiro Maranhao and Goias — had already applied some restrictions to phone use in schools nearly two-thirds of Brazilian schools had some limitations according to a survey last year by the Brazilian Internet Steering Committee But rules varied between states and between schools and authorities and administrators struggled with enforcement a nearly 150-year-old private school in Sao Paulo prohibited smartphones in classrooms last year and encouraged students to disconnect completely once a week requiring students to keep their phones in lockers for the entire school day “Students were having trouble concentrating,” school principal Meire Nocito said in an interview Thursday “There was also the issue of social isolation Many students who used technology excessively would isolate themselves during breaks “Banning cellphone use has helped create a space for social interaction fostering relationships and teaching students to navigate conflicts which are a natural part of human interactions Brazil’s Ministry of Education said in a statement Monday that the restriction aims to protect students’ mental and physical health while promoting more rational use of technology said Brazil had more smartphones than people with 258 million devices for a population of 203 million Brazilians Local market researchers said last year that Brazilians spend 9 hours and 13 minutes per day on screens which is among one of the world’s highest rates of use She uses it to communicate with friends and family and to find entertainment on social media Being forced to stay away from her phone made her find new ways to interact with friends improved her focus and even strengthened her relationship with her family The restriction also helped “include people who didn’t have many friends and would use their phones to hide from making new friends or to avoid being out there,” she said in an interview These people end up playing board games or reading books.” AP videojournalist Thiago Mostazo contributed to this report O endereço abaixo não existe na globo.com Aston Martin Aramco Cognizant Formula One™ Team (AMF1) is pleased to announce a partnership with Brazilian insurance brand The team's cars will carry Porto Seguro logos at this week's Abu Dhabi Grand Prix as the insurance company becomes the latest Brazilian brand to partner with AMF1 and show its support for reserve driver Having announced Brazilian financial services company the partnership between AMF1 and Porto Seguro further underlines the shared ambition to capitalise on Brazil's love of motorsport and invest in one of the sport's young talents in Felipe Felipe is set to drive for the team during the first free practice session in Abu Dhabi this Friday – his first involvement in an official session during a Formula One Grand Prix weekend The Porto Seguro logos will appear on the front wing end plates of the AMR22 in Abu Dhabi continues for the 2023 season building on AMF1's push to elevate the profile of Brazilian motorsport "This is an exciting opportunity for Porto Seguro as we partner with Aston Martin Aramco Cognizant Formula One™ Team and support the development of Felipe Drugovich Brazil has a very strong tradition with F1 and auto racing that must be sustained in the future "This project – bringing together strong Brazilian companies – is building real momentum It will expand our potential to operate in this segment and reinforce our commitment to be a safe haven for people and their dreams transforming the realities of all Brazilians." "I am delighted to see Porto Seguro show their support for Aston Martin Aramco Cognizant Formula One™ Team and Felipe Drugovich They are Brazil's most successful insurance company and share our determination to capitalise on the popularity of the sport in South America and support one of Brazil's brightest talents in Felipe "Alongside our existing partnership with XP they will champion the cause to establish Brazil's place at the top of the sport in the years ahead." Driven by the core belief that energy is opportunity A sustainable mining champion with global presence Aston Martin Aramco Formula One Team is bringing new energy to F1 in its quest for world titles the team's talented driver squad includes double World Champion Fernando Alonso and Canada's Lance Stroll We use cookies on our website. By continuing to use this website you consent to the storing and accessing of cookies on your device in accordance with our Cookie Policy To learn more about cookies, how we use them on our site and how to change your settings please view our Cookie Policy. Text description provided by the architects. An invitation to the public is made through large entrances without physical barriers and with a welcoming character. The folds guide the route and encourage curiosity to discover a new space. An exposed concrete pure monolith, gives life to the new cultural center of the city of Sao Paulo, Brazil. Located in Campos Elíseos, central area of the city, in the corner between Alameda Barão de Piracicaba and Alameda Nothmann, the proposed architecture comes with a series of measures aimed towards the urban revitalization of the region. Courtesy of Yuri Vital TeamIn spaces where lighting and ventilation is necessary such as: management, museology, classrooms and bathrooms, was idealized a differential front where the glass facade is protected by a second skin, a separate element of concrete and wood, creating an unusual facade. Courtesy of São Paulo ArquiteturaThe Cultural Center used a reinforced concrete based system it had a great influence on modern Brazilian architecture The use of concrete was essential to get the plasticity that the architecture needed The malleability capacity of the material facilitated the construction of the shapes requested by the project the structural elements are components that gives the building You'll now receive updates based on what you follow Personalize your stream and start following your favorite authors If you have done all of this and still can't find the email The centre was built to encourage urban rejuvenation of this part of Sao Paulo, inviting creative opportunities to the area The concrete building is separated into 5 areas; administration Access to the back half of the centre is provided via a ramp which sits within a central gap in the building The galleries feature a number of ’folds’ which guide access to the rooms and also ensure a good interior acoustic All Espaço Cultural Porto Seguro interiors were designed with flexibility in mind as the space will be used to host from festivals to exhibitions For more information on the Espaço Cultural Porto Seguro, visit the website VIEW GOOGLE MAPS escapism and design stories from around the world direct to your inbox Porto Seguro holds a significant portion of Brazil’s nature and history: on April 22 Pedro Álvares Cabral and his Portuguese ships landed right there which would later be named Costa do Descobrimento (Discovery Coast) has become a major national tourist destination Between the colonization period constructions and the indigenous reserve that directly influenced local culture the coastline displays a diversity of mangroves coconut trees and a sea that is sometimes ​​light blue while national parks preserve even greener areas of Atlantic Forest besides housing the district of the same name combines three other tourist destinations that together form four in one are among the most delightful in the country: between Arraial d’Ajuda you will surely find your beach – or be enchanted by them all Porto Seguro’s colonial architecture the charming encounter of the river with the sea in Caraíva: although they are very close to one another each of the four districts have theirpeculiarities Searching for authentic experiences in Porto Seguro Airbnb Experiences are activities offered by people want to share their knowledge and turn their work into an extra source of income by connecting with travelers from around the world Click on the image below to learn about our Experiences in the region Learn more about our partnership with the county of Porto Seguro Airbnb signed a partnership with the county of Porto Seguro and pledged to cooperate with the city in strengthening sustainable tourism in the region we are developing actions to promote the city’s four destinations and to qualify tourism services by training local residents as well as sharing the platform’s aggregated data we are developing actions aimed at the popularization of the city’s four destinations and the qualification of the tourist services by training local residents as well as sharing the aggregated data obtained by the platform The agreement between Airbnb and Porto Seguro was the first initiative of the company’s Responsible Tourism Center The commitment made by the platform is that the local residents are the main beneficiary of authentic tourism focused on the development of the community itself as long as tourism activity grows and becomes relevant to the economy Also visit the official digital guide of Porto Seguro The Aston Martin Formula One team have signed a one-year deal with Brazilian insurance brand Porto Seguro which commenced at last weekend's Abu Dhabi Grand Prix Porto Seguro branding appeared on the front wing endplates at the 2022 season finale but there is no confirmation on further branding opportunities such as on driver overalls and in the team garage This deal follows Aston Martin partnering with XP another Brazilian company enticed to the British team following its signing of Formula Two champion Felipe Drugovich as a development driver Aston Martin F1 achieves top FIA environmental accreditation “This is an exciting opportunity for Porto Seguro as we partner with Aston Martin Aramco Cognizant Formula One Team and support the development of Felipe Drugovich Brazil has a very strong tradition with F1 and auto racing that must be sustained in the future,” said Roberto Santos “This project – bringing together strong Brazilian companies – is building real momentum transforming the realities of all Brazilians.” managing director of commercial and marketing at Aston Martin, added: “I am delighted to see Porto Seguro show their support for Aston Martin Aramco Cognizant Formula One Team and Felipe Drugovich “Alongside our existing partnership with XP they will champion the cause to establish Brazil's place at the top of the sport in the years ahead.” This website is using a security service to protect itself from online attacks The action you just performed triggered the security solution There are several actions that could trigger this block including submitting a certain word or phrase You can email the site owner to let them know you were blocked Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page The Haas F1 team has signed up Italian fashion brand Palm Angels in an agreement that will launch ahead of the 2023 Formula 1 season and include visibility on the team’s car ©2025 SBG Companies Limited or its affiliated companies Parece que a página que você está procurando não está disponível 43,000+ global companies doing business in the region 102,000+ key contacts related to companies and projects news and interviews about your industry in English Try finding what you're looking for in our search box ...or hit the homepage for the latest news updates Tell us which broken link brought you here if you really think the page you were looking for should be here © 2025 SurferToday.com | All Rights Reserved The world’s leading publication for data science My main point in this article is that in Business contexts we should not choose between machine learning models based on predictive performance alone I approach the problem of choosing models from the investment perspective: different models are alternative courses of action each with some kind of cost and benefit attached As with other investments facing businesses we need to try to measure if the benefits outweigh the costs for each alternative to choose wisely between them To make this point, I made a case study using Porto Seguro’s Safe Driver Prediction dataset on Kaggle I ran different models and measured their predictive performance against the time we need to fit them and make predictions I found out that a model like LightGBM could not compete with models like logistic regression or Gaussian Naïve Bayes (GNB) The improvement in predictive performance for LightGBM is not enough to compensate for the extra costs of fitting it and making predictions I used a simple analysis called cost-effectiveness analysis which is similar to the more popular cost-benefit analysis I briefly explain what these analyses are and how we can use them I think the best example of how cost-benefit analysis is present in our lives and makes much sense is in chess pieces have different values called points the Queen is the most powerful piece on the board and therefore worths 9 points It is very common in chess for two or more pieces to be under attack by the opponent’s protected pieces in such a way that a particular move will earn you a piece at the cost of losing another and in war the army with more soldiers always have the tactical advantage the relative value of pieces is fundamental to the game making it possible to tell who has the tactical advantage at any moment Because every piece is in a common unit of measurement we can just subtract the cost (the value of the piece I lose) from the benefit (the value of the piece I earn) for a given move one should rarely move the Queen in such a way that it gets captured by a Pawn because this move results in a loss of 8 points the cost of losing the Queen doesn’t compensate for the benefit of earning a Pawn so that the player making such a move ends up in a tactical disadvantage equivalent to 8 points By making such calculations for many different moves the chess players are able to choose wisely between different courses of action gaining a tactical advantage or compensating for the opponent’s This is all there is to know about cost-benefit analysis One of them is that cost-benefit analysis can be used only if we can measure things in the same unit we need to find a way to compare incommensurable things One of the most common applications of cost-effectiveness analysis is in the energy industry Getting the cost of providing energy from a given source is relatively simple because we can measure the costs along the chain and arrive at some estimate is a very tricky job because they change rather chaotically all the time So we have to measure energy in another way Now that we have costs in money and energy in kWh how we calculate the equivalent of the cost-benefit measure Because the two quantities are in different units we cannot subtract them: subtracting money from kWh doesn’t make any sense What we can do instead is to divide one by the other This ratio can be used to compare different sources of energy to determine which one is better let’s say we want to decide whether to use coal or wind to produce energy we can reason about that problem in the following terms: if I divide the amount of money spent to get the observed amounts of energy for both sources the result is how much money I need to spend to get 1 kWh of energy from that source The source with the lowest money/kWh ratio is the best because it is the one in which we spend less money to get the same amount of energy consider the completely hypothetical values in the table below There we can have an idea about why most countries in the world still use pollutant energy sources So eolic energy is twice more expensive in this scenario even after accounting for the environmental damage of coal energy Therefore it is more effective to use coal and then do damage control than to use eolic energy we could use the money earned from the 30% fee on coal energy producers to fund research in eolic energy eventually engineers will be able to bring the costs down enough to make eolic energy as efficient as coal energy (or bring the energy produced up) Now that you got the idea of cost-effectiveness analysis I have a classification problem on a dataset with 595,212 rows and 57 features But here what I’m trying to figure out is not the model that will win the Kaggle competition but what model Porto Seguro should adopt to earn the best financial return The business scenario I’m imagining is the one in which models will be trained and deployed in cloud environments, where the main cost is the fee on computing power Because those fees are charged by the hour time is the most convenient unit to measure costs raw predictive power is the most convenient unit for measuring the benefits (or effects) also known as positive predictive value (PPV) I prefer this metric to the more popular AUC-ROC because it has a nice business interpretation it means that 1 out of 100 clients expected to file an insurance claim will actually do so I will drop the percentage and just say that 1 PPV equals 1 true-positive out of 100 positives 0.1 PPV equals 1 true-positive out of 1,000 0.01 PPV equals 1 true-positive out of 10,000 False-negatives are already accounted for because of the way Scikit-Learn calculates the average precision (check it out here) For brevity, I will skip the details about what I did to arrive at my model results, but you can check out the full analysis and code in the project repository on GitHub All that you need to know is that this analysis requires that all models be ran in the same hardware (at least with the same amount of CPUs and RAM memory) the time measurements (our proxy for costs) won’t be fair because some models would have more computing power at their disposal than others One way to account for this is to multiply by the fee charged by the cloud provider to use the machine I produced the results discussed below after grid-searching So the measurements aren’t influenced by inefficient hyperparameter configuration I also incorporated all preprocessing into every model and all of them use the same preprocessing pipeline This way the cost of preprocessing is accounted for and there is no difference in preprocessing between models I computed averages and standard errors from 50 cross-validations of every model (specifically I measured the time each model took to fit and predict along with their respective average precision or PPV The standard errors on those averages are meant to give an idea of the degree of uncertainty in those measurements Here we confirm what I said in the introduction: LightGBM won’t return more money to the company than GNB meaning that it costs that much time to find 1 true-positive out of 100 positives (notice how the choice of an interpretable metric facilitates our analysis) which is about seven and a half times higher This corresponds to an increase in costs of about 650% Training and predicting with LightGBM is seven and a half times more expensive than training and predicting with GNB This means that LightGBM uses seven and a half more resources to find the same amount of true-positives as GNB even after accounting for the fact that its PPV is on average higher than GNB’s I can’t dispute the fact that LightGBM is better at predicting than GNB LightGBM has 5.43 PPV against 5.33 PPV for GNB this 0.1 PPV difference is statistically significant meaning it would probably be observed if we repeated this comparison many times with similar data Many data scientists don’t even bother to look at standard errors for doing such analysis it is important to also consider the magnitude of the difference observed This is because statistical significance is designed only to tell if results are reproducible The standard error always shrinks as the sample size grows ridiculously small differences would be highly statistically relevant This is the reason why a mere 0.1 PPV increase corresponds to five standard errors enough to run a test with less than 1% probability of a false-positive But this translates into only one more true-positive out of 1,000 Does this really make a difference to the business seven and a half times more expensive to find what is needed for LightGBM to be cost-effective Two things can happen to accomplish this here folks that developed LightGBM could find out a way of dropping both training and prediction time so that LightGBM trains and predicts as fast as GNB (and therefore as cheaply) the PPV of LightGBM should increase by over seven and a half times so that it is so much better at predicting that it compensates for the additional costs But LightGBM is only about 1.88% better than GNB at predicting and this is the reason it is the least cost-effective we can spot another interesting thing one should think about when doing this analysis Look at logistic regression: it is nearly twice more expensive to train the logistic regression will catch up with GNB by returning the extra training time in the form of prediction time saved during job execution Logistic regression costs about 1,633 ms/PPV to train and about 86 ms/PPV to predict every time we run the job GNB costs about 796 ms/PPV to train and about 111 ms/PPV to predict how should the cost evolve in a scenario where we trained each model one time The logistic regression (blue line) starts higher because of its higher training cost. But the cost grows slower than that of GNB (red line); therefore, sooner or later the cost curve of GNB will cross the cost curve of logistic regression, making it more expensive. I assumed here that there is no time value of money so that the discount rate is zero and the curves evolve linearly the logistic regression would catch up in the 34th time we ran the prediction job it will save us money in computational fees the longer it takes logistic regression to achieve this Hence assuming a zero discount rate both simplifies calculations and makes the analysis more conservative by giving an advantage to the challenger model This leads us to think about the expected lifetime of the model about the time we expect would have to pass before we need to retrain the model it would take nearly three years for logistic regression to catch up we would probably have retrained the model already So logistic regression would never invert the balance in a little over a month logistic regression will pay off for we probably won’t retrain in the next month Note that LightGBM will never pay off because it is more expensive both to train and to predict than those two alternatives There are two main takeaways from this study choosing between machine learning models transcends the evaluation of predictive performance The measurement of improvement in predictive performance may be very reliable (i.e. but it might not be enough to compensate for increased costs or even to make a significant impact on the business By studying Porto Seguro’s Safe Driver dataset I found out that using LightGBM would probably result in a loss of money for this model is about seven and a half times more expensive than GNB The difference in PPV between LightGBM and GNB is surely statistically significant but corresponds to only one more true-positive out of 1,000 This raises the question of whether LightGBM impacts the business in a manner worthy of its costs The second point is that cost-benefit and cost-effectiveness analyses are simple but powerful tools this does not mean we don’t need to be cautious in using them Cost-effectiveness analysis solves the problem of incommensurability It is therefore more general than cost-benefit analysis which requires costs and benefits to be in the same unit of measurement Yet both of them suffer the problem of inaccurate calculations and Some of these factors may be impossible to measure in any way at all Hence this kind of analysis is only fruitful if done with much critical reasoning Cost-effectiveness and cost-benefit analyses are guides to good decisions But good decisions don’t come from blindly following numbers Good chess players don’t blindly follow piece trade calculations but make carefully thought decisions using them So do good political leaders about money/kWh ratios It is perfectly fine for a chess player to trade the Queen for a Pawn if he or she is sure it will lead to a checkmate Winning the game brings much more benefit than it costs to lose 8 points even if this doesn’t enter into the calculation because the game itself can’t be measured in piece points a good political leader may choose to stick with eolic energy to make a strong political point about the importance of adopting less pollutant energy sources Every decision boils down to what you are trying to accomplish are we making good decisions about which model to adopt in business contexts The very simple case study I did here hints that we tend not to do so I have never seen anyone talk about how much it costs to have a sophisticated model in production in the way I did here I did this case study purely out of curiosity for I asked myself that many times recently and wanted to have some hint at the answer Businesses thrive by making fruitful investments Machine learning is a particularly expensive investment: it requires many highly capable professionals and a mindset very few companies actually have So data scientists and machine learning engineers should be very careful in deciding which model to adopt Looking only to raw predictive power may not be enough in such situations I’m not saying here that we should never consider sophisticated models but I’m a data-driven person and for this particular problem I would go with GNB Because I carefully thought about its pros and cons and systematically compared them to alternative models aiming at what really matters for the job I wanted to do That is perfectly fine: their authors simply didn’t want to address them choosing to focus on other matters they thought more important That is a very legitimate reason and I myself would choose to do so if I were in their place it is perfectly fine if facing this data you still want to go on with LightGBM (or any other model in this specific situation) But would you also pay seven and a half times more for a data scientist that is only 1.88% more productive you are consistent and that is all that matters It is fine if you want to have sophisticated models in production and hence choose to ignore their inefficiency (if it exists) it is fine if the mentioned data scientist is your nephew and you don’t care about nepotism (and neither if others say you have dubious moral values) either way you are still being more careful than blindly following higher predictive performance measures The purpose of such analyses is exactly that: to induce careful thinking As long as a decision is a carefully thought one Step-by-step code guide to building a Convolutional Neural Network A beginner’s guide to forecast reconciliation Here’s how to use Autoencoders to detect signals with anomalies in a few lines of… Solving the resource constrained project scheduling problem (RCPSP) with D-Wave’s hybrid constrained quadratic model (CQM) An illustrated guide on essential machine learning concepts Derivation and practical examples of this powerful concept The world’s leading publication for data science French designer Lucien Pellat-Finet, aged, 78 died after he drowned at a beach in the northeastern Brazilian state of Bahia on Monday, his family confirmed on his Instagram. Pellat-Finet, known as the 'King of Cashmere,' hosted a family barbecue at his home in Porto Seguro and then went out for a swim at Itaquena Beach. Two teenagers were surfing and saw him struggling to stay afloat and attempted to rescue him with their surf boards before he drowned, according to Brazilian news outlet G1. The Frenchman's body was recovered hours later near an area that is popular with surfers. 'It is with great sadness that I inform you of the accidental death of Lucien Pellat Finet, this afternoon in Trancoso,' his niece Camille Dauchez said. 'At the time, he was in the company of his family and dear friends. In due course, I will inform you of the details of the farewell ceremony.' Brazilian news outlet Globo reported Wednesday that Pellat-Finet's body will be cremated this week. Pellat-Finet made his home in Trancoso, a Porto Seguro tourist district, and celebrated his birthday February 11. He owned Casa Santa Rita, a luxurious residence constructed out of wood which he purchased 30 years ago and featured 10 rooms spread out across the garden in chalets. Pellat-Finet, who grew up in the French Riviera, founded his label in 1994 and earned the monicker 'King of Cashmere' due to his luxurious sweaters that were designed in Paris and handcrafted in London. The designer's website lauded him as 'a bon-vivant with a pioneering spirit, he often found himself at odds with the passing fads of his times. 'In the 1980s he devised the ideal outfit to wear on his transatlantic journeys: a cashmere pullover with no branding or label stitched on the collar, Knitted with the utmost care using only the finest, most sensual fibre,' the site says. 'Elegance in fabrication distilled with minimalistic styling in a pullover that is worn as a perennially fashionable tee-shirt. An 'anti-fashion' stance that set him up as the figurehead of a new elite in pursuit of comfort and rareness.' Pellat-Finet stepped down from the brand after it was acquired by Thierry Gillier, the French-born founder of Zadig & Voltaire, in 2019. Gillier praised the late designer as 'an agitator, a precursor, a creator.' Pellat-Finet published "Super F**king Lucky: Lucien Pellat-Finet - King of Cashmere and (Anti) Fashion" in March 2019 to commemorate his brand's 25th anniversary. The book, authored by Natasha Fraser-Cavassoni, looked back on Pellat-Finet's personal and career journey and how he became to be known as the "King of Cashmere." Major terror attack 'was just HOURS away' before it was foiled by the special forces and police:... Victim of acid attack 'plotted by his ex-partner who teamed up with a gang' dies in hospital six... Pub is forced to pay family £75,000 after wrongly accusing them of 'dine and dash' over £150... King, Queen, William and Kate honour selfless devotion of Britain's wartime heroes as they lead... 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Husband of British mother, 65, who was knifed to death in French village says her affair is a... British 'ringleader' of hacking group 'behind M&S cyber attack' fled his home after 'masked thugs... Lucien Pellat-Finet drowns at Brazilian beach after a family barbecueCommenting on this article has endedNewest{{#isModerationStatus}}{{moderationStatus}}