𝐇𝐚𝐩𝐩𝐲 𝐖𝐞𝐝𝐧𝐞𝐬𝐝𝐚𝐲. 𝐆𝐨𝐨𝐝 𝐃𝐚𝐲 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞! ✨✨
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GOOD ideas begin with brainstorming, but GREAT ideas start with coffee all the time for me! Do you know that my mornings' breakie is made better with 𝐊𝐎𝐏𝐈𝐊𝐎 𝟑𝐢𝐧𝐎𝐍𝐄 𝐊𝐚𝐰. There's nothing quite like that first cup of coffee in the morning. After a few sips of the aromatic magic drink, you're wide awake & ready to tackle the day. It's truly the best. ☕☕
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Introducing the 𝐋𝐞𝐛𝐢𝐡 𝐏𝐞𝐤𝐚𝐭 𝐋𝐞𝐛𝐢𝐡 𝐊𝐀𝐖 ~ 𝐊𝐎𝐏𝐈𝐊𝐎 𝟑𝐢𝐧𝐎𝐍𝐄 𝐊𝐚𝐰! Now everyone gets to enjoy & savour premium coffee right at the comfort of your home with the all-new improved formulation of 𝐊𝐎𝐏𝐈𝐊𝐎 𝟑𝐢𝐧𝐎𝐍𝐄 𝐊𝐚𝐰. 😍😍
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I like 𝐊𝐎𝐏𝐈𝐊𝐎 because of its richer taste with a distinct strong premium taste & robust aroma combining the finest coffee beans, top-grade creamer & sugar to concoct a perfect cup of coffee delighting my tastebuds. 😋😋
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Oh yes, do you know that has a 𝐍𝐄𝐖 𝐏𝐀𝐂𝐊𝐀𝐆𝐈𝐍𝐆 recently? Enjoy 𝐆𝐑𝐄𝐀𝐓𝐄𝐑 𝐕𝐀𝐋𝐔𝐄 𝐖𝐢𝐭𝐡 𝐀𝐝𝐝-𝐎𝐧 𝐒𝐚𝐜𝐡𝐞𝐭𝐬 𝐎𝐟 "𝟐𝟕 𝐒𝐚𝐜𝐡𝐞𝐭 + 𝟑 𝐒𝐚𝐜𝐡𝐞𝐭𝐬". Experience the more robust flavour & superior taste. Hurry, head to their Facebook at Kopiko Malaysia's bio to get your packet of Kopiko 3inONE Kaw! 👍🏻👍🏻
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#FollowMeToEatLa #kopiko #kopikokaw #Kopiko3inONEKaw #lebihpekatlebihkaw #kopikomalaysia
同時也有60部Youtube影片,追蹤數超過5萬的網紅Hunter 物理治療師,也在其Youtube影片中提到,【藉由燈的閃爍來訓練你的敏捷度與反應能力】 燈光反應訓練,是一個能有效訓練敏捷度、反應能力與平衡能力的方法。它的訓練概念很簡單,主要是將幾組燈具擺設在特定位置,讓使用者專注隨機閃爍的燈光。一旦燈光亮起,就要盡可能在最短的時間拍打燈具讓燈光熄滅。在過去因器材的限制,這類反應燈訓練大多被用在專業運動員上...
「robust to」的推薦目錄:
- 關於robust to 在 Follow Me To Eat La - Malaysian Food Blog Facebook 的最佳貼文
- 關於robust to 在 Engadget Facebook 的精選貼文
- 關於robust to 在 Taipei Ethereum Meetup Facebook 的最讚貼文
- 關於robust to 在 Hunter 物理治療師 Youtube 的最佳解答
- 關於robust to 在 Adam Lobo TV Youtube 的最讚貼文
- 關於robust to 在 PHOLFOODMAFIA Youtube 的最佳解答
- 關於robust to 在 Making Deep Neural Networks Robust to Label Noise: A Loss ... 的評價
robust to 在 Engadget Facebook 的精選貼文
Google has also added support for more Spanish language commands.
robust to 在 Taipei Ethereum Meetup Facebook 的最讚貼文
📜 [專欄新文章] Gas Efficient Card Drawing in Solidity
✍️ Ping Chen
📥 歡迎投稿: https://medium.com/taipei-ethereum-meetup #徵技術分享文 #使用心得 #教學文 #medium
Assign random numbers as the index of newly minted NFTs
Scenario
The fun of generative art NFT projects depends on randomness. The industry standard is “blind box”, where both the images’ serial number and the NFTs’ index are predetermined but will be shifted randomly when the selling period ends. (They call it “reveal”) This approach effectively solves the randomness issue. However, it also requires buyers to wait until the campaign terminates. What if buyers want to know the exact card right away? We’ll need a reliable onchain card drawing solution.
The creator of Astrogator🐊 isn’t a fan of blind boxes; instead, it thinks unpacking cards right after purchase is more interesting.
Spec
When initializing this NFT contract, the creator will determine the total supply of it. And there will be an iterable function that is randomly picking a number from the remaining pool. The number must be in range and must not collide with any existing ones.
Our top priority is accessibility/gas efficiency. Given that gas cost on Ethereum is damn high nowadays, we need an elegant algorithm to control gas expanse at an acceptable range.
Achieving robust randomness isn’t the primary goal here. We assume there’s no strong financial incentive to cheat, so the RNG isn’t specified. Implementers can bring their own source of randomness that they think is good enough.
Implementation
Overview
The implementation is pretty short and straightforward. Imagine there’s an array that contains all remaining(unsold) cards. When drawIndex() is called, it generates a (uniform) random seed to draw a card from the array, shortens the array, and returns the selected card.
Algorithm
Drawing X cards from a deck with the same X amount of cards is equal to shuffling the deck and dealing them sequentially. It’s not a surprise that our algorithm is similar to random shuffling, and the only difference is turning that classic algo into an interactive version.
A typical random shuffle looks like this: for an array with N elements, you randomly pick a number i in (0,N), swap array[0] and array[i], then choose another number i in (1,N), swap array[1] and array[i], and so on. Eventually, you’ll get a mathematically random array in O(N) time.
So, the concept of our random card dealing is the same. When a user mints a new card, the smart contract picks a number in the array as NFT index, then grabs a number from the tail to fill the vacancy, in order to keep the array continuous.
Tweak
Furthermore, as long as the space of the NFT index is known, we don’t need to declare/initialize an array(which is super gas-intensive). Instead, assume there’s such an array that the n-th element is n, we don’t actually initialize it (so it is an array only contains “0”) until the rule is broken.
For the convenience of explanation, let’s call that mapping cache. If cache[i] is empty, it should be interpreted as i instead of 0. On the other hand, when a number is chosen and used, we’ll need to fill it up with another unused number. An intuitive method is to pick a number from the end of the array, since the length of the array is going to decrease by 1.
By doing so, the gas cost in the worst-case scenario is bound to be constant.
Performance and limitation
Comparing with the normal ascending index NFT minting, our random NFT implementation requires two extra SSTORE and one extra SLOAD, which cost 12600 ~ 27600 (5000+20000+2600) excess gas per token minted.
Theoretically, any instantly generated onchain random number is vulnerable. We can restrict contract interaction to mitigate risk. The mitigation is far from perfect, but it is the tradeoff that we have to accept.
ping.eth
Gas Efficient Card Drawing in Solidity was originally published in Taipei Ethereum Meetup on Medium, where people are continuing the conversation by highlighting and responding to this story.
👏 歡迎轉載分享鼓掌
robust to 在 Hunter 物理治療師 Youtube 的最佳解答
【藉由燈的閃爍來訓練你的敏捷度與反應能力】
燈光反應訓練,是一個能有效訓練敏捷度、反應能力與平衡能力的方法。它的訓練概念很簡單,主要是將幾組燈具擺設在特定位置,讓使用者專注隨機閃爍的燈光。一旦燈光亮起,就要盡可能在最短的時間拍打燈具讓燈光熄滅。在過去因器材的限制,這類反應燈訓練大多被用在專業運動員上,一般民眾較難取得。但隨著科技的進步,現在我們只需要一隻智慧型手機就可以與反應燈做連結,在家也能使用這樣的訓練器材。今天的影片就要來開箱Blazepod的反應燈訓練組,並分享實際的操作方式。
Reaction light training is an effective way to robust agility, balance and reaction time. The concepts is simple, we place training devices at some specific points, and focus on those devices that flash randomly. Once the light is on, we need to tap the device as quickly as possible to make the light out. In the past, it’s mostly used for professional athletes. It’s hard for general public to approach this kind of training devices due to the inaccessibility of the devices. However, with the advances in technology, we can connect our smartphone to the reaction light and use it in our home now. In today’s video, I will unbox Blazepod flash light training devices and show you how it works in our training.
Blazepod粉絲專頁:
https://facebook.com/BlazePodTW
Blazepod官方網站:
https://www.blazepod.com.tw/
Blazepod反應燈訓練購買連結:
https://reurl.cc/YO5k2a
限時優惠活動時間7/31-8/15,在蝦皮輸入優惠碼「 CHUNPOD88」就可折500元!!
#Lightbasedreactivesystem #Blazepod #agility #balance #reactiontime #training #athlete #physiotherapy #hunterptworkout #燈光反應訓練 #敏捷度 #平衡 #反應速度 #訓練 #運動員 #物理治療
robust to 在 Adam Lobo TV Youtube 的最讚貼文
??? In this video, I will be giving you 6 REASONS why you should get the new 2021 HUAWEI MatePad Pro! I will share with you everything that you need to know about it and I will give you a definite answer on who the tablet is for and whether or not it is worth it.
If you find this video helpful and would love to watch more, you can SUBSCRIBE here:
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Timecode:
00:00 - Intro
00:23 - Unboxing
00:58 - #1. Redefined Viewing Experience
01:30 - #2. Immersive Entertainment on Demand
02:23 - #3. Exceptional and Seamless Connection Experience
04:06 - #4. Creativity Unleashed with the HUAWEI M-Pencil (2nd Gen)
05:23 - #5. Robust Performance
06:33 - #6. Amazing Freebies
Get the HUAWEI MatePad Pro At The Link Below:-
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??? My Desk Setup 2020
https://youtu.be/xK1QUClu-V0
??? Check out my other videos about HUAWEI PC/laptops/tablets:
HUAWEI MateBook X Pro - https://youtu.be/EMf8dCDzy0Q
HUAWEI MateStation S - https://youtu.be/LQqBuBeU4zs
HUAWEI MatePad 10.4 - https://youtu.be/nUZFBU38tpw
HUAWEI MateBook 14 - https://youtu.be/H5nlKzZEpWk
HUAWEI MediaPad M6 - https://youtu.be/XjIZg-q4pYw
??? Or check out my entire TABLETS & LAPTOPS playlist!
https://youtube.com/playlist?list=PLn02abmm5Ra5YviPjMBn35ukHnqW4cwnG
---
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My name is Adam Lobo, I'm a Tech YouTuber from Kuala Lumpur, Malaysia, who creates high-quality tech reviews on YouTube, Instagram & Facebook and I am currently the only Malaysian Tech YouTuber who produces 6K Resolution content.
My passion is to help everyone to make a purchase decision with all the tech items I get my hands on, where you'll find weekly smartphones, tablets, audio, smart home and other cool tech related videos as well. I produce these videos at least twice a week so do consider subscribing to my channel.
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robust to 在 PHOLFOODMAFIA Youtube 的最佳解答
@PHOLFOODMAFIA
For the full recipe, please visit Official website :
➤ https://www.pholfoodmafia.com
Subscribe for more easy and delicious recipes:
ติดตามได้ที่??
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#หมึกนึ่งมะนาว
?หมึกตัวใหญ่นึ่งให้สุกกำลังดี เสิร์ฟพร้อมซอสรสจัดจ้าน? ทานคู่กับกะหล่ำช่วยลดความเผ็ด
สูตรโดย : พล ตัณฑเสถียร
แนะนำให้เพื่อนๆ เข้าไปดูสูตรที่เว็บไซต์
https://bit.ly/34oXn2g
Steamed Squid with Spicy and Sour Sauce
?Springy and toothsome steamed squid in a spicy and robust sauce, served with cabbage to lessen the heat.
Recipe by Phol Tantasathien
Recipes is on the website
https://bit.ly/34oXn2g
#ตามสั่งตามนั้น #ตามสั่ง #อาหารตามสั่ง #กับข้าว #แจกสูตร
For English recipes, please go to the respective video's description on the link below
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robust to 在 Making Deep Neural Networks Robust to Label Noise: A Loss ... 的美食出口停車場
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