satopradhan Vision

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satopradhan Vision

What is APY Vision (VISION) | What is VISION token

What Is APY.Vision (VISION)?

APY.Vision is an analytics platform that provides clarity for liquidity providers contributing capital on Automated Market Making (AMM) protocols. Innovations in blockchain technology and Decentralized Finance (DeFi) have opened the gates to allow anyone, with any amount of spare capital, to contribute liquidity to markets and earn a fee from doing so.

We are a tool that tracks impermanent losses of a user’s pooled tokens and keeps track of the user’s financial analytics. In addition, we provide historical pool performance and actionable insights for liquidity providers.

VISION is the membership token that is used for accessing the PRO edition of the tool. We provide our PRO members with additional analytics. Furthermore, token holders can vote on new features to determine the roadmap of the product. In the future, when we expand to other DeFi verticals such as decentralized options and derivatives, VISION holders can gain access to those analytics modules.

We believe the future is DeFi, and we want to build the best tools and provide the best analytics to this new breed of investors.

How Many VISION tokens Are There in Circulation?

VISION launched the membership token on Nov 15, 2020. The max supply of the token is 5,000,000 and 15% of the tokens are reserved for the foundation, while 4% of the token supply is earmarked for marketing and promotions, while 1% of the tokens is reserved for giving back to the ecosystem.

Where Can I Buy APY.Vision Membership Tokens (VISION)?

You can acquire the membership tokens on our bonding curve or on Uniswap under the VISION/ETH pair.

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How APY Vision gives you 20/20 vision

Having been LPs ourselves, we experienced firsthand how there was a lack of visibility into an LP’s holdings and your profits and losses. We decided to solve this problem for ourselves by creating a tool that tracks impermanent gains and losses of your pooled tokens, allowing you to have all the analytics you need at your fingertips with actionable insights to ensure you’re in the best pools.

We truly believe AMMs are here to stay and we want to enable anyone, anywhere, who wants to be an LP to have the best information and knowledge they need to become successful in this fast moving, high stakes game of market making.

Beat the rest to be the best

We believe in a democratized world — after all, that’s why the ethos of blockchain appeals to us first and foremost. With that being said, the huge amount of work we’re doing needs support to continue to provide value to all our users. Our aim is that APY Vision will always be a free tool. For the more advanced LPs however, who require additional insights into the pools they are providing liquidity for, we provide a pro offering that unlocks additional features to give you a leg up over everyone else.

Our pro offering will enable:

  • Real-time price quotes (free members get refreshed quotes every hour)
  • Remembering previous addresses
  • Grouping wallet addresses into one single account view
  • Expedited query speeds (your queries will be prioritized)
  • Viewing historical gain/losses (free members can only see current liquidity pool positions) *
  • Tracking Total APY and returns with farming rewards included (a common use case for LPs that farm with staking contracts) *
  • Pool Insights advanced search (min 2000 VISION tokens)
  • Dark mode option
  • Daily summary emails *
  • Additional AMMs *
  • Vote for new features (min 2000 VISION tokens) *
  • Dedicated #gold channel on Discord (min 2000 VISION tokens) *

*Features will be released in subsequent releases

(At launch, we will be supporting a few of these features but we are working hard on rolling all the pro features out!)

Become a pro, hold a token bro

We’ve been inspired by the innovative products being born in the DeFi space and have modeled our pro membership on these projects. To become a pro member and unlock pro features, hold our membership tokens in your wallet.

Normally, a subscription service costs the same regardless of your level of usage.

However, with blockchain technology, we can be a bit more creative and innovative to ensure fair access for all.

To activate our pro features, you only need to hold 100 VISION membership tokens per $10,000 of USD tracked in your wallet(s). This ensures that people who are not big portfolio holders can benefit by holding just a small amount of VISION tokens in their wallet. As you provide more liquidity, you can add more VISION tokens to your wallet to activate the pro features — it’s that simple!

Tokens — not that big of a deal around here

First and foremost, we’d like to stress that the VISION tokens are not a security token. The token is designed to not hold value and does not have any inherent value. It is merely a way to unlock subscription access to our pro features. It is not meant to be speculated on. We are not an ICO or claim to return you any gains by acquiring the VISION token. This is simply a membership token and not an asset.

We will be launching our membership token based on a bonding curve. A bonding curve contract is one where the tokens being acquired cost more for each subsequent one. The initial cost of a VISION token is 0.0005 ETH, which means it will cost 0.05 ETH to track $10,000 USD worth in a portfolio (for life).

While we are working on delivering all the pro features, we want to enable our community to start supporting the project by being an early adopter. Thus, the cost of 0.0005 ETH per VISION token will stay that way until 250000 VISION tokens have been distributed.

Early bird gets the worm — initial phase

To ensure that there is product market fit for APY Vision, there is an option to exchange the VISION tokens back to ETH in the bonding curve contract in the beginning until the 250000th VISION tokens. In this phase, users can exchange VISION back to ETH at 100% of the price that they used to exchange the VISION tokens with in the first place (0.0005 ETH per VISION).

This ensures that if the project doesn’t gain any traction, early users can get their ETH back. That’s because we’re that committed to providing value to our community.

Also important to note is that in this phase, the foundation cannot sell tokens to the curve (in addition to the vesting terms below).

Normal Exchange Phase

After the 250000th VISION tokens have been exchanged, the token will be sold on the curve at the current price. A few days after the initial phase, we will be adding a Uniswap pool so that existing token holders can sell on the Uniswap pool and new users can choose to either buy or sell on the bonding curve.

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You get a token, everyone gets a token

There is a maximum cap of 5,000,000 VISION tokens.

The breakdown:

  • 7.5% (to the initial founding team, subject to a 36 month vesting period with 1/36 of the amount vests each month)
  • 7.5% going to a fund for contributors (subject to vesting)
  • 4% marketing / promotions / giveaways
  • 1% public goods projects
  • 80% bonding curve token contract

The master plan

At the heart of it, we’re nerds. We want to provide awesome tooling and analytics, especially since the tooling piece is sorely missing for Liquidity Providers today. That will always guide what we do.

The next phase of the Liquidity Network will be to enable monitoring and alerts to ensure Liquidity Providers can take action if there are any sudden pool movements.

Once we perfect the analytics and monitoring pieces, we want to enable a way for Liquidity Providers to automatically enter/exit liquidity pools based on alerts and parameters they set up. This will be done via smart contract wallets that only the users have access to and it will be non-custodial (because we don’t want to touch your funds with a nine foot pole, even if you paid us).

We will also be licensing our API for enterprise use. To access the API on a commercial basis, companies will need to pay for a monthly/yearly plan (in VISION tokens) and the tokens collected will be burned.

Finally, because Liquidity Provider tokens are currently held in a wallet (and not doing much), we will be looking at ways in which we can leverage them. Imagine being able to collateralize your LP tokens and borrow/lend against it to magnify your gains. Rest assured our valuable community members (you) will be able to vote on the final product.

Update — the bonding curve contract is LIVE

You can view the bonding curve contact here:

Contract: https://etherscan.io/address/0xf406f7a9046793267bc276908778b29563323996#code

Token Exchange Website:

https://curve.apy.vision/#/

Please do not acquire more than what you need. This is a membership token and it is inherently worthless. It costs 100 VISION tokens to track $10,000 USD worth. If you are unsatisfied you can return the VISION token for 100% of the ETH when less than 250000 VISION tokens have been sold. The contract has not been audited, so please use at your own risk.

FAQ (or the questions you’re too scared to ask)

Is the token bonding curve contract audited?

No, the contract has not been audited — please use it at your own risk. We will not be held responsible or liable for any losses that occur as a result of the contract. We did, however, base our contract off well-known and audited contracts and tweaked the parameters to our use.

Where is the contract address?

The contract address will be released in a subsequent blog post along with step by step instructions for acquiring the VISION tokens.

If I don’t like the service during the initial phase, can I cancel at any time?

You’ll really hurt our feelings but yes! You can simply exchange the VISION tokens back to ETH in the initial phase (where there are less than 250,000 VISION tokens sold). In that case, you get 100% back of the initial exchange rate. After the initial phase, you can sell it back on Uniswap after we create the pool.

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Dominic  Feeney

Dominic Feeney

1620458262

Computer Vision Using TensorFlow Keras - Analytics India Magazine

Computer Vision attempts to perform the tasks that a human brain does with the aid of human eyes. Computer Vision is a branch of Deep Learning that deals with images and videos. Computer Vision tasks can be roughly classified into two categories:

  1. Discriminative tasks
  2. Generative tasks

Discriminative tasks, in general, are about predicting the probability of occurrence (e.g. class of an image) given probability distribution (e.g. features of an image). Generative tasks, in general, are about generating the probability distribution (e.g. generating an image) given the probability of occurrence (e.g. class of an image) and/or other conditions.

Discriminative Computer Vision finds applications in image classificationobject detectionobject recognitionshape detectionpose estimationimage segmentation, etc. Generative Computer Vision finds applications in photo enhancementimage synthesisaugmentationdeepfake videos, etc.

This article aims to give a strong foundation to Computer Vision by exploring image classification tasks using Convolutional Neural Networks built with TensorFlow Keras. More importance has been given to both the coding part and the key concepts of theory and math behind each operation. Let’s start our Computer Vision journey!

Readers are expected to have a basic understanding of deep learning. This article, “Getting Started With Deep Learning Using TensorFlow Keras”, helps one grasp the fundamentals of deep learning.

#developers corner #computer vision #fashion mnist #image #image classification #keras #tensorflow #vision

Rusty  Shanahan

Rusty Shanahan

1595436840

3-D Reconstruction with Vision

Exactly a year back, before I started writing this article, I watched Andrej Karapathy, the director of AI at Tesla delivering a talk where he showed the world a glimpse of how a Tesla car perceives depth using the cameras hooked to the car in-order to reconstruct its surroundings in 3D and take decisions in real-time, everything(except the front radar for safety) was being computed just with vision. And that presentation blew my mind!

Of course, I knew 3-D reconstruction of an environment is possible through cameras, but I was in a mindset that why would anyone risk using a normal camera when we’ve got such highly accurate sensors like LiDAR, Radar, etc. that could give us an accurate presentation of the environment in 3-D with far less computation? And I started studying(trying to understand) papers related to this topic of Depth Perception and 3-D Reconstruction from Vision and came to the conclusion that we humans have never had rays coming out of our heads to perceive depth and environment around us, we are intelligent and aware of our surroundings just with the two eyes we’ve got, from driving our car or bike from office to work, or driving a formula 1 at 230 mph in the world’s most dangerous tracks, we never required lasers to make decisions in microseconds. The world around us was constructed by us for us, beings with vision and so as Elon said, ‘these costly sensors would become pointless once we solve vision’.

There’s huge research going on in this field of depth perception with vision, especially with the advancements in Machine Learning and Deep Learning we are now able to compute depth just from vision at high accuracy. So before we start learning the concepts and implementing these techniques, let us look at what stage this technology is currently in and what are the applications of it.

Robot Vision:

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Environment Perception with ZED camera

Creating HD Maps for autonomous driving:

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Depth Perception with Deep Learning

SfM(Structure from Motion) and SLAM(Simultaneous Localisation and Mapping) are one of the major techniques that make use of the concepts that I am going to introduce you to in this tutorial.

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Demonstration of an LSD-SLAM

Now that we’ve got enough inspiration to learn, I’ll start the tutorial. So first I’m going to teach you the basic concepts required to understand what’s happening behind the hood, and then apply them using the OpenCV library in C++. The question you might ask is why am I implementing these concepts in C++ while doing it in python would be far easier, and there’s reason behind it. The first reason is that python is not fast enough for these concepts to implement in real-time, and the second reason is that unlike python, using C++ would mandate our understanding of the concept without which one can’t implement.

In this tutorial we are going to write two programs, one is to get a depth map of a scene and another is to obtain a point cloud of the scene, both using stereo vision.

Before we dive right into the coding part, it is important for us to understand the concepts of camera geometry, which I am going to teach you now.

The Camera Model

The process used to produce images has not changed since the beginning of photography. The light coming from an observed scene is captured by a camera through a frontal aperture(a lens) that shoots the light onto an image plane located at the back of the camera lens. The process is illustrated in the figure below:

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From the above figure, do is the distance from the lens to the observed object, di is the distance between the lens and image plane. And f will hence become the focal length of the lens. These described quantities have a relation between them from the so-called “Thin Lens Equation” shown below:

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Now let us look into the process of how an object from the real-world that is 3-Dimensional, is projected onto a 2-Dimensional plane(a photograph). The best way for us to understand this is by taking a look into how a camera works.

A camera can be seen as the function that maps 3-D world to a 2-D image. Let us take the simplest model of a camera, that is the Pinhole Camera Model, the older photography mechanisms in human history. Below is a working diagram of a pinhole camera :

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From this diagram we can derive :

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Here it’s natural that the size hi of the image formed from the object will be inversely proportional to the distance do of the object from camera. And also that a 3-D scene point located at position (X, Y, Z) will be projected onto the image plane at (x,y) where (x,y) = (fX/Z, fY/Z). Where the Z coordinate refers to the depth of the point, which was done in the previous image. This entire camera configuration and notation can be described with a simple matrix using the homogeneous coordinate system.

When cameras generate a projected image of the world, projective geometry is used as an algebraic representation of the geometry of objects, rotations and transformations in the real world.

Homogeneous coordinates are a system of coordinates used in projective geometry. Even though we can represent the positions of objects(or any point in 3-D space) in real-world in Euclidean Space, any transformation or rotation that has to be performed must be performed in homogeneous coordinate space and then brought back. Let us look at the advantages of using Homogeneous coordinates:

  • Formulas involving Homogeneous Coordinates are often simpler than in the Cartesian world.
  • Points at infinity can be represented using finite coordinates.
  • A single matrix can represent all the possible protective transformations that can occur between a camera and the world.

In homogeneous coordinate space, 2-D points are represented by 3 vectors, and 3-D points are represented by 4 vectors.

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In the above equations, the first matrix with the f notation is called the intrinsic parameter matrix(or commonly known as the intrinsic matrix). Here the intrinsic matrix contains just the focal length(f) right now, we’ll look into more parameters of this matrix ahead of this tutorial.

The second matrix with the r and t notations is called the extrinsic parameter matrix(or commonly known as the Extrinsic Matrix). The elements within this matrix represent the rotation and translation parameters of the camera(that is where and how the camera is placed in real world).

Thus these intrinsic and extrinsic matrices together can give us a relation between the (x,y) point in image and (X, Y, Z) point in real world. This is how a 3-D scene point is projected onto a 2-D plane depending on the given camera’s intrinsic and extrinsic parameters.

#depth-perception #stereo #stereo-vision #deep-learning #computer-vision #deep learning

Deep Computer Vision for the Detection

Deep Computer Vision is capable of doing object detection and image classification task. In image classification tasks, the particular system receives some input image and the system is aware of some predetermined set of categories or labels. There are some fixed set of category labels and the job of the computer is to look at the picture and assign it a fixed category label. Convolutional Neural Network (CNN) has gained wide popularity in the field of pattern recognition and machine learning. In our present work, we have constructed a Convolutional Neural Network (CNN) for the identification of the presence of tantalum and niobium fragments in a High Entropy Alloy (HEA). The results showed 100 % accuracy while testing the given dataset.

Introduction

Vision is the most important sense that humans possess. In day to day life, people depend on vision for example identifying objects, picking objects, navigation, recognizing complex human emotions and behaviors. Deep computer vision is able to solve extraordinary complex tasks that were not able to be solved in the past. Facial detection and recognition and detection are an example of deep computer vision. Figure 1 shows the vision coming into a deep neural network in the form of images or pixels or videos and the output at the bottom is the depiction of a human face [1–4].

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Fig.1. Illustration of the working of Deep Computer Vision

The next thing should be worth answering to the question, how computer process an image or a video, and how do they process pixels coming from those? The images are just numbers and also the pixels have some numerical values. So our image can be represented by a two-dimensional matrix consisting of numbers. Let’s understand this with an example of image identification i.e. whether the image is of a boy or a girl or an animal. Figure 2 shows that the output variable takes a class label and can produce a probability of belonging to a particular class.

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Fig.2. Image Classification

In order to properly classify the image, our pipeline must correctly tell about what is unique about the particular picture. Convolutional Neural Network (CNN) finds application in the manufacturing and material science domain. Lee et al. [5] proposed a CNN model for fault diagnosis and classification in the manufacturing process of semiconductors. Weimer et al. [6] designed deep convolutional neural network architectures for automated feature extraction in industrial applications. Scime et al. [7] used the CNN model for the detection of in situ processing defects in laser powder bed fusion additive manufacturing. The results showed that the CNN architecture improved the classification accuracy and overall flexibility of the designed system.

In the present work, we have designed the CNN architecture for detecting the trace of tantalum and niobium in the microstructure of high entropy alloy (HEA). In 1995, Yeh et al. [8] firstly discovered the high entropy alloys, and in 2004 Cantor et al. [9] coined high entropy alloy as a multi-component system. HEAs are generally advanced alloys and novel alloys which are consist of 5–35 at.% where all the elements behave as principal elements. In comparison to their conventional alloys, they possess superior properties like high wear, corrosion resistance, high thermal stability, and high strength. Zhang et al. [10–11] listed down the various parameters for the parameters for fabrication of HEAs which are shown in the below equations:

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HEAs find application in various industries like aerospace, submarines, automobiles, and nuclear power plant industries [12–14]. HEAs are also used as a filler material for the micro-joining process [15]. Geanta et al. [16] carried out the testing and characterization of HEAs from AlCrFeCoNi System for Military Applications. It was observed that at the melt state, the microstructure of HEAs has frozen appearance as shown in Figure 3.

#convolutional-neural-net #computer-vision #machine-learning #machine-vision #high-entropy-alloys #deep learning

Chet  Lubowitz

Chet Lubowitz

1599048840

Machine Vision Recipes: Isolating a Color in an Image

Image segmentation is one of the key processes in machine vision applications for partitioning a digital image into a group of pixels. There are many interesting ways one can segment an image. Take a look at the below image of candies placed in a particular order to form a word. And, If a robot with vision were a task to count the number of candies by color, it would be important for it to understand the boundaries between the candies.

A direct thresholding to separate the foreground from its background will create overlapping candies making it hard for us to count them. Take a look the below grayscale image and the one after OTSU threshold.

Image in Grayscale

Image in Grayscale

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Image after Thresholding

As we can clearly see that its almost impossible for a robot to count the candies because there is no way for us to define the boundaries between the candies. I even tried a variant of watershed algorithm but because of the presence of bright coloured candies the thresholding always failed.

This led me to the only solution which is how even a human being would do this task. Let us make our robot color aware because Grayscale algorithms are clearly not useful here. The image we are dealing with here is an RGB image with millions of colors where each pixel is a set of Red, Green and Blue channel intensity ranging from 0–255. The colors R (Red), G (Green) and B (Blue) have a close color correlation level which makes it difficult to segment. The trick is to transform RGB to a different color space where the boundaries between the colors are well defined. For this experiment I will pick the CIE-Lab color space which closely resembles how humans see colors.

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#computer-vision #machine-vision