Caffeination的藝術發起Kickstarter運動以資助聰明的咖啡撲克牌

Caffeination的藝術發起Kickstarter運動以資助聰明的咖啡撲克牌

德克薩斯州聖安東尼奧市,2020年10月29日(SEND2PRESS NEWSWIRE)— Art of Caffeination公司正在醞釀一些新穎而令人興奮的事情,這是一家專注於所有咖啡產品的新設計業務。它的第一個產品發布-快速參考的咖啡撲克牌-專為咖啡愛好者,發燒友和遊戲玩家而設計,不僅可以帶來樂趣,而且可以學到一些東西。

“關於咖啡的隨便卡片”是一本方便製作的,快速製作的快速參考撲克牌,其中包含咖啡製作方法,飲料配方和有用的沖泡技巧。甲板肯定會踢出任何家庭常規咖啡或比賽之夜。

該製造商Vincent Lam充分利用了他的工業設計和圖形藝術的專業知識,創建了一套參考紙牌,從而消除了通過在線資源或成堆的筆記進行分類的麻煩和時間。撲克牌旨在通過周到的視覺圖形展示配料和沖泡參數,從而簡化沖泡咖啡和沖泡飲料的過程。

“每種方法卡都仔細研究了釀造參數和起點,並以易於理解的“釀造計量表”呈現。這些“釀酒儀”為用戶提供了說明性參考,以調整釀造值以適合他們的口味。” Lam說。

咖啡方法和飲料配方的全面收集將吸引咖啡新手和鑑賞家。這些卡片具有有關18種流行咖啡製作方法和18種飲料配方的重要釀造信息,以及12種設計精美的法院卡片,具有有關咖啡和釀造的有用指導和教育提示。

撲克大小的撲克牌經過精心設計和說明,以供快速參考,並包裝在以咖啡為靈感的塔克盒中。這些卡以優質的黑芯亞麻成品製成,適合令人興奮的遊戲玩法和收藏。

更多文章:真人娛樂場如何徹底改變了線上娛樂場遊戲產業

觸手可及的食譜上有咖啡沖泡技巧,“關於咖啡卡的直接購買”是任何咖啡生活方式的重要組成部分。

關於咖啡因藝術

咖啡因藝術總部位於德克薩斯州聖安東尼奧市,由擁有20年消費和醫療產品設計經驗的專業工業設計師Vincent Lam創立。他曾獲得多個設計獎,包括紅點獎。iF(產品設計獎,由設在德國漢諾威的iF國際論壇設計有限公司每年授予);和IDEA獎(國際設計卓越獎,由美國工業設計師協會(IDSA)舉辦的設計獎計劃)。咖啡因藝術將形式跟隨功能設計方法應用於所有含咖啡因的事物,從而為所有年齡和興趣的人們創造有用和令人愉悅的產品。

真人娛樂場如何徹底改變了線上娛樂場遊戲產業

真人娛樂場如何徹底改變了線上娛樂場遊戲產業

線上賭場的引入將撼動整個行業,因為玩家突然可以不用離開家就可以訪問自己喜歡的賭場遊戲。 隨後從陸上賭場向線上賭場的穩定轉移,不僅得益於更流暢的圖形和更好的界面,而且還得益於賭場線上支付的巨額頭獎。

儘管如此,即使有了重大的支出和先進的視覺效果,線上娛樂場網站也無法真正為玩家提供與任何陸基娛樂場相同的體驗。 人們認為線上賭場非常實用,甚至可以帶來非常豐厚的利潤,但與此同時,沒有什麼比實體賭場的美好舊氛圍了。

儘管近年來情況有所改變,因為線上真人娛樂場的確使將大部分基於陸地的娛樂場體驗帶到您的客廳成為可能。

借助實時攝像頭,流媒體播放器與真正的遊戲場地相連,其中包括真正的發牌人,他們歡迎他們參加他們喜歡的桌面遊戲。 正是由於這種人情味,真人娛樂場在玩家中變得非常受歡迎。 我們將告訴您有關現場體驗的所有信息。

現場經銷商帶來了不同

選擇任何線上賭場遊戲,您都需要面對電腦,並由隨機數生成器(RNG)生成隨機結果。 如果您更喜歡二十一點和輪盤賭之類的傳統賭場遊戲,那麼您可能想看看桌上發生了什麼,而不必希望軟件生成的結果對您有利。

真人娛樂場由真人荷官指導,給人一種非常個人化的感覺,使您可以觀察到發生了什麼。 這使您感覺自己在真正的賭場中,但是不必離開沙發(或是否喜歡玩遊戲)。 主持人往往非常熱情好客,以確保您在餐桌上感覺舒適。 現場經銷商帶來了不同。更多文章:針對現金賭博所使用的網路視頻製造商

真人娛樂場遊戲

有越來越多的真人娛樂場遊戲供您選擇,但讓我們指出您會在任何真人娛樂場中找到的最常見遊戲:

  • 輪盤賭:在可以說是世界上最受歡迎的娛樂場遊戲中,玩家需要對單個數字,不同的數字分組,紅色或黑色,數字是奇數還是偶數,或者數字低(1-18)進行投注 或較高(19–36)。
  • 21點:想法是用您的卡獲得21分,或至少接近獲得21分。 只要確保您不超過21分即可。 如果可以的話 你輸了。
  • 百家樂:也稱為Punto Banco。 在此遊戲中,punto(玩家)和banco(發牌人)會收到紙牌。 這個主意? 猜測誰都獲得最高分:玩家或莊家。
  • 其他真人娛樂場遊戲:傳統上,大多數真人娛樂場投資組合都是基於紙牌遊戲的,但是也增加了非常現代的運營商,這全都歸功於遊戲開發商Evolution Gaming。 我們所說的其他遊戲是什麼? 想一想像《命運之輪》和《 Live Monopoly》這樣的遊戲節目,這兩款遊戲都包括現場主持人。

針對現金賭博所使用的網路視頻製造商

賭場監控攝影拿破崙是英格蘭北部由A&S Leisure Group Ltd.擁有的五個賭場的連鎖。該公司尋求其曼徹斯特的賭場和餐廳的視頻安全,而無需使用多個提供商。必須保護娛樂場,不僅要防止欺詐或盜竊,而且要滿足英國監管機構,賭博委員會和地方當局設定的法律要求。Axis合作夥伴Brock業務支持指定了一組定制產品,包括來自網絡視頻產品製造商Axis Communications的產品。由Blackpool的AD Systems Limited安裝。

A&S Leisure Group Ltd.安全部門負責人Guy Hewson表示:“在考慮新站點的要求時,監視和訪問控制系統之間的集成非常重要,同時還要具有來自攝像機的高質量圖像和即時視頻播放回到音頻。我們求助於Brock業務支持,該業務支持通過與Axis Communications的緊密合作關係,可以指定一種可以根據我們的要求量身定制的系統。”

曼徹斯特市中心站點安裝了約100個攝像機,需要六個AXIS T8524 PoE +網絡交換機並在三台服務器上進行記錄,所有這些均由運行在四個觀察站上的AXIS Camera Station軟件驅動。員工入口處有一個AXIS A8004-VE網絡視頻門禁站;使用生物識別讀取器進行掃描後,工作人員將被錄取。在內部,AXIS M3065-V網絡微型球罩覆蓋了房屋的後部,提供了廣角視圖。

帶有雙向音頻的AXIS P3375-V網絡攝像機可監控遊戲桌,匯兌區,酒吧和老虎機。輪盤上的AXIS F1015傳感器單元位於桌子顯示器的內部;提供謹慎,完整的視圖。在外部,外圍由帶有AXIS Lightfinder的AXIS P3245-LVE固定球型網絡攝像機覆蓋,適合開發人員說,適用於變化的光照和天氣條件。

Brock業務支持董事總經理Dave Brock表示:“在整個Tw Casino站點上安裝許多攝像頭和傳感器的過程中,考慮了多個因素,這意味著要進行仔細的計劃,直到圍繞連接性進行考慮。例如,使用AXIS Zipstream技術意味著可以使用Cat5E電纜,這意味著無需升級。這代表了賭場所有者的立即節省成本。”

Axis表示,這些產品使用開放平台來允許使用API​​和標準IoT協議與其他系統集成。這意味著Axis可以利用合作夥伴的技術,將其與Axis組件融合在一起以構成系統。如果發生事件,該安裝程序可以為當局實時捕獲和導出實時視頻數據。A&S Leisure Group Ltd現在計劃在其其他四個站點上審查安全技術。

Facebook is experimenting with new technologies to turn flat photos into 3D photos

Facebook is experimenting with new technologies to turn flat photos into 3D photos

With the development of technology, people can now take pictures of their favorite moments with mobile phones and other devices. Many people may have thought, if there is a black technology that will make the flat 2D photos we take into three-dimensional 3D photos …

Facebook has long thought about this, and to improve the user experience, in 2018, Facebook introduced the 3D photo feature. This is a new immersive format that you can use to share photos with friends and family. However, this feature relies on the dual-lens “portrait mode” function found only on high-end smartphones, and cannot be used on ordinary mobile devices.

To allow more people to experience this new visual format, Facebook has developed a system using machine learning. This system can infer the 3D structure of any image, and any device and any time the image taken can be converted into 3D form, which can make people easily use 3D photo technology.

Not only that, it can also process family photos and other precious images from decades ago. Anyone with an iPhone 7 and above, or a mid-range or higher Android device can now try this feature in the Facebook app.

Building such enhanced 3D pictures requires overcoming many technical challenges, such as training a model that can correctly infer the 3D positions of various topics, and optimizing the system to execute on a typical mobile processor device in 1 second. To overcome these challenges, Facebook trained convolutional neural networks (CNNs) on millions of public 3D images and their accompanying depth maps, and leveraged various action optimization technologies previously developed by Facebook AI, such as FBNet and ChamNet. The team also recently discussed related research on 3D understanding .

This feature is now available to anyone using Facebook, so how exactly is it built? Let’s take a look at the technical details.

Delivering efficient performance on mobile devices

Given a standard RGB image, 3D Photos CNN (3D Photo Convolutional Neural Network) can estimate the distance of each pixel from the camera. Researchers achieve this goal in four ways:

  • Build a network architecture with a set of parameterizable, action-optimizable neural building blocks.
  • Automate architecture searches to find effective configurations of these modules, enabling the system to perform tasks on a variety of devices in less than a second.
  • Quantitative perception training, using high-performance INT8 quantization on mobile devices, while minimizing performance degradation during quantization.
  • Get a lot of training data from public 3D photos.

Neural Construction Module

Facebook’s architecture use is inspired by the building blocks of FBNet. FBNet is a framework for optimizing the ConvNet architecture for resource-constrained devices such as mobile devices. A building block consists of pointwise convolution, optional upsampling, kxk depth convolution, and additional point-by-point convolution. Facebook implemented a U-net-style architecture that has been modified to place FBNet building blocks along skip connections. The U-net encoder and decoder each contain 5 stages, each of which corresponds to a different spatial resolution.

Automated architecture search

In order to find an effective architecture configuration, the ChamNet algorithm developed by Facebook AI automates the search process. The ChamNet algorithm continuously extracts points from the search space to train precision predictors. The accuracy predictor is used to accelerate genetic search to find a model that maximizes prediction accuracy under the condition of meeting specific resource constraints.

A search space is used in this setup, which can change the channel expansion factor and the number of output channels of each module, thereby generating 3.4 × 1,022 possible architectures. Facebook then used the 800 Tesla V100 GPU to complete the search in approximately 3 days, setting and adjusting FLOP constraints on the model architecture to achieve different operating points.

Quantitative Perception Training

By default, its model is trained using single-precision floating-point weights and triggers, but researchers have found that quantizing weights and triggers to 8 bits has significant advantages. In particular, the int8 weight requires only a quarter of the storage required for the float32 weight, which reduces the number of bits that must be transferred to the device when first used.

The throughput of Int8-based operators is also much higher compared to float32-based operators, thanks to an optimized database such as QNNPACK of Facebook AI, which has been integrated into PyTorch. We use Quantitative Sensing Training (QAT) to avoid quality degradation caused by quantization. QAT is now part of PyTorch, which simulates quantization and supports back-propagation during training, thereby closing the gap between training and production performance.

Finding new ways to create 3D experiences

In addition to improving depth estimation algorithms, researchers are also working to provide high-quality depth estimates for images taken by mobile devices.

Since the depth of each frame must be consistent with the next frame, image processing technology is challenging, but it is also an opportunity to improve performance. Observing the same object multiple times can provide additional signals for highly accurate depth estimation. As the performance of Facebook’s neural network continues to improve, the team will also explore the use of technologies such as depth estimation, surface normal estimation, and spatial inference in real-time applications such as augmented reality.

In addition to these potential new experiences, this work will help researchers better understand the content of 2D images. A better understanding of 3D scenes can also help robots navigate and interact with the physical world. Facebook hopes to help the artificial intelligence community make progress in these areas by sharing the details of the 3D picture system and create new advanced 3D experiences.

More article: Beginner photography tips for better photos

Old photos from the 19th century. Why do the characters in the photos look like having poker face?

Old photos from the 19th century. Why do the characters in the photos look like having poker face?

If you look closely at the photos of people in the 19th century, you will find a “fun” phenomenon: the expressions of the characters in many photos are like the portrait expressions of having poker face  J, Q, and K. The expressions are almost the same, without smiles. Facial stiffness. The pictures below are one by one serious and feel like they are being coerced into taking pictures.

Why is this happening?

Although there are various reasons for this phenomenon, the following factors are the most likely:

At that time, the photographing technology was limited, the photographing cost was very high, and the wealthy people were able to take photographs. Therefore, when taking photos, photographers do not waste film as much as possible and save the film as much as possible, but they need to put as many pose as possible, but the problem comes: the more you want to pose, the more it is counterproductive and makes people feel bad. The expression is unnatural.

Because there are not many opportunities to take pictures. At the moment, the subject has almost no natural and relaxed experience in taking pictures. The more I want to take pictures, the more pretentious.

The most important thing is that the teeth of the people at that time were generally not good-looking. Therefore, try not to expose your teeth when taking pictures.

Beginner photography tips for better photos

Beginner photography tips for better photos

About off-camera lighting, or hence the name, flash. I just mentioned him to him, Ken Rockwell mentioned him. He is almost a Nikon fanatic and he talks about jokes on many different topics. He also has a lot of great information about Nikon camera equipment. Joshua Hoffine is probably one of my favorite photographers because the camera he sets is like a movie. He also has a great blog, so I suggest you follow him so you can see his latest amazing work and how he created it.

Now there is no brain…

Continue to press the shutter!

The practice is perfect, and taking pictures is no exception. If you want to be the best photographer in town or in the world (oh, this is possible!) You have to leave and start shooting more. For those who register for more than 60 hours a week and still strive to be a great photographer, you must take time out to shoot. I know that it is very difficult. I work only 50 hours a week in my retail job in Berkeley, Michigan, and I know firsthand how hard it is to take photos.

If you want to be better, you have to take the time to take a photo. When I first entered photography, my camera took no more than a few hours per month. It shows; my photos look the same as I did at the beginning. What I need to do makes me develop the habit of spending a few hours a week studying technology. Every other day, I finally develop into a few hours to learn how to take pictures. Soon I found myself taking pictures and learning a few hours a day, and I felt that my head got a few centimeters from all the information I collected!

Last words

If I try to be a better photographer last year, I found one thing, that is, attitude is related to photos. If you like, most of this article has nothing to do with the actual photography “technology”, because I feel that if you are told to take a picture in some way, you will be limited in a set of stupid rules, and you will never be able to Do your best to explore photography with your own feelings. Anyway, I hope that I can make some good points, and you will take action soon, because if you do, I know that you will soon find that you have a great photographer in your heart. You must have the motivation to continue and maintain the right attitude.

Do you want to know how to take better pictures? How to create great images with the help of Photoshop? I know that I have. Ok, I will tell you something; I will tell you some secrets to create amazing and memorable images, and how to make the most of your camera through these photography techniques, these techniques will definitely improve your photography. .