#online convex optimization

[convexstudiowp]

Best Digital Marketing Agency Kitchener: Elevating Your Business Growth

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Choosing the right digital marketing agency can significantly impact the success of your business. A top-tier agency will offer a range of services designed to increase your visibility, drive traffic, and enhance conversions. From SEO (Search Engine Optimization) to PPC (Pay-Per-Click) campaigns, social media management, and website design, the best agencies have the tools and expertise to implement strategies that align with your business objectives.

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Convex Studio: A Leader in Digital Marketing in Kitchener

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Convex Studio focuses on understanding your business and target market. This insight allows them to develop marketing strategies that are not only effective but sustainable in the long term. By utilizing an analytical approach, they measure the performance of campaigns and continuously optimize them to ensure the highest return on investment.

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[majid706]

online soft skills | online marketing | ueducate

in which the strategy chosen by one business has no bearing on the overall firm population's payoffs. We demonstrate that the Stage II

problem is a potential game under such an approximation. We also demonstrate how, at equilibrium, a higher DSF results in higher company participation and a better fit with customer desires. However, the stage I problem lacks a closed-form solution and is a non-convex optimization problem. In order to learn more,

[trafficsafetysblog]

The Ultimate Guide to Convex Mirrors for Easy Navigation and Safety

Welcome, readers! We are thrilled to delve into the fascinating world of convex mirrors in this comprehensive guide. Convex mirrors are unique in shape and function, offering a wide range of benefits for enhanced visibility and safety. 

In this guide, we will explore what convex mirrors are, why they are advantageous, how to choose the right one, installation steps, maintenance tips, and address some commonly asked questions. So, let's get started!

What are Convex Mirrors?

Convex Mirrors Online are curved mirrors with a bulging outward shape. Unlike flat or concave mirrors, convex mirrors cause light rays to diverge, resulting in a wider field of view. This unique shape allows for a greater range of visibility, making them ideal for areas with restricted views or blind spots.

Understanding the Benefits of Convex Mirrors:

Convex mirrors offer numerous advantages that make them an essential tool for improved safety and navigation. 

Firstly, their wide-angle visibility eliminates blind spots, reducing the risk of accidents and ensuring better awareness of surroundings. Whether in crowded parking lots, driveways, or tight corners, convex mirrors provide a broader view, allowing drivers or pedestrians to anticipate potential hazards.

Additionally, convex mirrors play a vital role in enhancing security. By strategically placing convex mirrors in areas prone to theft or vandalism, such as retail stores or warehouses, they act as a visual deterrent, discouraging potential criminals from engaging in illegal activities.

 Choosing the Right Convex Mirror:

Finding the right convex mirror for your specific needs requires careful consideration of size and placement. The size of the mirror should be proportionate to the area it will cover, ensuring optimal visibility. For instance, a larger convex mirror may be more suitable for a large parking lot, while a smaller one can be effective in a residential driveway.

When it comes to mounting options, there are two primary choices: wall-mounted and pole-mounted. Wall-mounted mirrors are secured directly onto a wall or building, while pole-mounted mirrors are attached to a pole for greater flexibility in placement. Factors such as location, available space, and the desired angle of visibility should be taken into account when deciding on the mounting option.

 Installation Steps:

Step 1: Planning

Before embarking on the installation process, it is crucial to plan ahead. Assess the location where the convex mirror will be installed and identify any potential obstacles that may hinder visibility. This includes considering the height, angle, and distance at which the mirror should be placed to provide the best view.

Step 2: Tools and Materials

Gather the necessary tools and materials to ensure a smooth installation process. This typically includes a drill, screws, brackets, and any additional hardware specific to your chosen mounting option.

Step 3: Mounting

Once the planning and preparation are complete, it's time to mount the convex mirror. Follow the manufacturer's instructions for your specific mirror model. If wall-mounted, mark the appropriate spots for drilling and secure the mirror using the provided brackets. If pole-mounted, attach the mirror to the pole using the designated mounting hardware.

Step 4: Adjustments

After mounting the convex mirror, it's essential to adjust the angle for optimal visibility. Stand in front of the mirror and ensure it provides a clear view of the intended area. Make any necessary adjustments to the angle until you are satisfied with the visibility it offers.

Maintenance Tips:

To maximize the longevity and performance of your convex mirror, regular maintenance is key. Here are some practical tips to keep in mind:

- Cleaning Techniques: Regularly clean the mirror surface using a non-abrasive cleaner and a soft cloth. Avoid using harsh chemicals that may damage the mirror or its protective coating.

- Inspection Frequency: Conduct routine inspections to ensure the mirror is free from dirt, debris, or any damage that may impair visibility. The frequency of inspections may vary depending on the location and environmental conditions.

- Minor Repairs: In the event of minor damages, such as scratches or cracks, consider repairing them promptly. Contact the manufacturer or a professional for guidance on suitable repair methods.

Frequently Asked Questions (FAQs):

Can convex mirrors be used for indoor purposes?

Yes, convex mirrors can be used indoors as well. They are particularly useful in areas such as warehouses, retail stores, and hospitals where enhanced visibility is crucial for safety and security.

Can I install a convex mirror myself, or should I hire a professional?

Installing a convex mirror is generally a straightforward process that can be done by yourself. However, if you are unsure or not confident in your abilities, it's always a good idea to seek professional assistance to ensure proper installation.

Are convex mirrors weather-resistant?

Most convex mirrors are designed to withstand various weather conditions. However, it's advisable to choose a mirror with a sturdy frame and a weather-resistant coating for long-lasting durability.

Conclusion:

In conclusion, convex mirrors are an invaluable tool for improved safety and navigation. Their wide-angle visibility helps eliminate blind spots and enhances security in various settings. By choosing the right convex mirror, following the installation steps, and practicing proper maintenance, you can maximize their benefits and ensure long-lasting performance. 

So, why wait? Start exploring the advantages of convex mirrors and experience safer and more convenient navigation today. Share your experiences and feedback with us in the comments section below. Safe travels!

Source By - https://tinyurl.com/2k58sfdh 

[bayesic-bitch]

So, this is fairly different from the motivation you'll see in other sources, but it's the motivation that works best for me and my area of research.

I assume the reader is familiar with gradient descent. You find the negative gradient of the function, which points in the direction of steepest descent. You take a step in that direction, and then repeat. But there's a couple problems here. Too large a step size, and the algorithm won't be stable, because the function we're approximating changes too quickly. Too small a step size, and we'll take ages to converge. Here's an alternative derivation that might shed some light on the issue:

Consider that we want to minimize a function f, using the derivative as a local linear approximation (for now we'll only consider a 1D function for simplicity). Additionally, we have an upper bound C on the second derivative. So we know that f(x) <=f^(x) = f'(x0)*(x - x0) + C*||x - x0||^2. Visually, that looks like this:

We might not be able to minimize the function in a single step, but we can find the value that minimizes the upper bound f^ easily. f^ is convex, so we can just set the derivative equal to zero and solve for the value that minimizes the bound.

So:

0 = d/dx f^(x) = f'(x0) + 2C (x - x0)

- f'(x0) = 2C (x - x0)

- f'(x0) = 2C x - 2C x0

- f'(x0) + 2C x0 = 2C x

x = - 1/(2C) f'(x0) + x0

This is exactly gradient descent with a step of 1/(2C)

This gives us some insights into gradient descent -- for instance, we probably want this bound to be as tight as possible to ensure fast convergence, while remaining a true upper bound. But more importantly it gives us an opportunity to generalize gradient descent to other domains. For instance, squared distance is not the only upper bound on the function that lets us do this. Any convex upper bound works. So let's change this a bit to instead have a "divergence" D(x, x0), with f^(x) = f'(x0)*x - f'(x0)*x0 + C D(x, x0). When D(x, x0) = ||x - x0||^2, we recover normal gradient descent. Another advantage of this formulation is that it allows us to generalize gradient descent beyond R^N. If we generalize f'(x0) to grad_x0 f(x0), then in our new definition, we only care that the space x lives in supports inner products between the gradient and x. You can do a lot of things with this, but the one we care about right now is generalizing to the space of probability distributions. That means we need a divergence for probability distributions, and the most obvious one is the KL divergence.

If you repeat the above derivation, finding the minimum, it gives an algorithm called Exponential Weights. EW gives the following update rule (keep in mind that x in this setting is a probability distribution over a discrete set, x(i)):

This algorithm was originally designed for a setting called online learning, where you take actions to minimize the sum of f over time, while an adversary actively chooses f to make your score as bad as possible, under certain constraints. EW is optimal at that problem. IN this form, the connection to Bayes rule is easy to see: when grad f is the log probability of the observation, we recover Bayes rule. In the last few years, there have been a lot of similar connections people have noticed, where a very wide range of algorithms can be derived by slight modifications to the divergence. A recent paper showed that the UCB algorithm used by MCTS and AlphaGo is basically mirror descent on the visitation frequency of actions in the search tree. Another recent work showed connections to Thompson Sampling. Another one showed that RRT (the robot motion planning algorithm) and count-based exploration can be derived from mirror descent. Most importantly, work on best-of-both-worlds algorithms have shown that mirror descent or its close cousin, Follow the Regularized Leader, is optimal at online learning, at bandit problems, and basically every setting in between.

There have been a few things in my grad student career that have made me feel like my third eye snapped open, but none of them quite as much as mirror descent. Did you know that you can show that Bayes rule is just gradient descent if you use the KL divergence as your measure of squared distance? And you can define other pseudo-bayesian inference rules for different definitions of information

[craigbrownphd]

If you did not already know

DeepShift Deep learning models, especially DCNN have obtained high accuracies in several computer vision applications. However, for deployment in mobile environments, the high computation and power budget proves to be a major bottleneck. Convolution layers and fully connected layers, because of their intense use of multiplications, are the dominant contributer to this computation budget. This paper, proposes to tackle this problem by introducing two new operations: convolutional shifts and fully-connected shifts, that replace multiplications all together and use bitwise shift and bitwise negation instead. This family of neural network architectures (that use convolutional shifts and fully-connected shifts) are referred to as DeepShift models. With such DeepShift models that can be implemented with no multiplications, the authors have obtained accuracies of up to 93.6% on CIFAR10 dataset, and Top-1/Top-5 accuracies of 70.9%/90.13% on Imagenet dataset. Extensive testing is made on various well-known CNN architectures after converting all their convolution layers and fully connected layers to their bitwise shift counterparts, and we show that in some architectures, the Top-1 accuracy drops by less than 4% and the Top-5 accuracy drops by less than 1.5%. The experiments have been conducted on PyTorch framework and the code for training and running is submitted along with the paper and will be made available online. … L1-Norm Batch Normalization (L1BN) Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs). However, BN brings additional computation, consumes more memory and generally slows down the training process by a large margin, which aggravates the training effort. Furthermore, the nonlinear square and root operations in BN also impede the low bit-width quantization techniques, which draws much attention in deep learning hardware community. In this work, we propose an L1-norm BN (L1BN) with only linear operations in both the forward and the backward propagations during training. L1BN is shown to be approximately equivalent to the original L2-norm BN (L2BN) by multiplying a scaling factor. Experiments on various convolutional neural networks (CNNs) and generative adversarial networks (GANs) reveal that L1BN maintains almost the same accuracies and convergence rates compared to L2BN but with higher computational efficiency. On FPGA platform, the proposed signum and absolute operations in L1BN can achieve 1.5$\times$ speedup and save 50\% power consumption, compared with the original costly square and root operations, respectively. This hardware-friendly normalization method not only surpasses L2BN in speed, but also simplify the hardware design of ASIC accelerators with higher energy efficiency. Last but not the least, L1BN promises a fully quantized training of DNNs, which is crucial to future adaptive terminal devices. … SuperSCS We present SuperSCS: a fast and accurate method for solving large-scale convex conic problems. SuperSCS combines the SuperMann algorithmic framework with the Douglas-Rachford splitting which is applied on the homogeneous self-dual embedding of conic optimization problems: a model for conic optimization problems which simultaneously encodes the optimality conditions and infeasibility/unboundedness certificates for the original problem. SuperMann allows the use of fast quasi-Newtonian directions such as a modified restarted Broyden-type direction and Anderson’s acceleration. … Time-Variant System A time-variant system is a system that is not time invariant (TIV). Roughly speaking, its output characteristics depend explicitly upon time. In other words, a system in which certain quantities governing the system’s behavior change with time, so that the system will respond differently to the same input at different times. … https://analytixon.com/2022/06/13/if-you-did-not-already-know-1743/?utm_source=dlvr.it&utm_medium=tumblr

[wsitho123]

Social Media Marketing In Canada

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In a technology where inbound marketing grows in popularity, generating leads through social media is of giant importance. Customers now anticipate to see and work together with brands in this area. In a visual world, picture and video is considered one of the most effective communication instruments out there to companies.

Beyond Facebook Ads to offer a extra diversified solution for contemporary brands looking for new ways to succeed in an ever evolving customer. Labworks engaged with TechWyse to rebuild its' web site and grow our web optimization. At TechWyse, I, the gross sales and marketing coordinator at Labworks, interfaced with Project Manager, who was answerable social media advertising services for overseeing all updates/ changes and back-end rebuilds to get us prepared for our web optimization. It’s not straightforward running a small enterprise and responding to evaluations — particularly the unfavorable ones. Let our staff at TechWyse take that off your to-do list, serving to you release valuable time.

This components in a quantity of conferences with our group, inventive development, edits/changes, ready for approvals, and so forth. However, we're identified to offer turn round times as quickly as 3-5 days for particular requests or urgent last-minute campaigns if we are ready to accommodate them. At BluCactus Social Media Agency we perceive that any delay from our finish could harm your every day efficiency. Accordingly, we're always obtainable to achieve and respond to your followers on time.

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Twitter is a popular social media channel that's the go-to platform for locating breaking news and connecting with small- and big-name influencers. At Convex Studio, we perceive that our customer base comes from an enormous geographic space. "We hired convex to make a short video for our enterprise. They did an excellent job and we had been very proud of the tip results. We shall be doing more. Thanks to the convex group."

The power of our online shops is as a result of of a number of elements, but one that can’t be missed is our gorgeous images and videography that we capture in house and use in our clients’ web sites. And all of this, ultimately, will allow you to beat your competition, become social marketing service the go-to native roofer, and generate you leads frequently. Best recommendation is to be taught from previous expertise and identical thing applies with social media. Targeting the best viewers utilizing our customer construct audience section will certainly improve your ROI.

Furthermore, efficient social media management additionally empowers your corporation to convey its content material to its goal market both rapidly and cost-effectively. Digital advertising is an effective mode of digital marketing for creating a significant connection between your model and your customers. As increasingly more individuals are searching for services and products on the internet, inserting yourself in front of these customers will help you build a stable online presence. We method digital advertising strategically, putting an emphasis on high-quality site visitors and better conversion. If your digital adverts aren’t leading to extra revenue, you need our assist.

[pbli33]

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Praise for the Third Edition . . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail. --MAA Reviews Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus. This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex

[craigbrownphd-blog-blog]

If you did not already know

Monotonic Classification Monotonic classification problems mean that both feature values and class labels are ordered and monotonicity relationships exist between some features and the decision label. Monotonic classification: an overview on algorithms, performance measures and data sets … SceneFlowFields++ State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense interpolation, we push the limits of scene flow estimation. Avoiding strong assumptions on the domain or the problem yields a more robust algorithm. This algorithm is fast because we avoid explicit regularization during matching, which allows an efficient computation. Using image information from multiple time steps and explicit visibility prediction based on previous results, we achieve competitive performances on different data sets. Our contributions and results are evaluated in comparative experiments. Overall, we present an accurate scene flow algorithm that is faster and more generic than any individual benchmark leader. … Multi-Task Graph Autoencoder We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local graph structure and available node features for the simultaneous multi-task learning of unsupervised link prediction and semi-supervised node classification. Our simple, yet effective and versatile model is efficiently trained end-to-end in a single stage, whereas previous related deep graph embedding methods require multiple training steps that are difficult to optimize. We provide an empirical evaluation of our model on five benchmark relational, graph-structured datasets and demonstrate significant improvement over three strong baselines for graph representation learning. Reference code and data are available at https://…/graph-representation-learning … ADAM We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm. ➘ “GENESYS” SAdam: A Variant of Adam for Strongly Convex Functions … https://analytixon.com/2022/08/06/if-you-did-not-already-know-1796/?utm_source=dlvr.it&utm_medium=tumblr

[craigbrownphd]

If you did not already know

Monotonic Classification Monotonic classification problems mean that both feature values and class labels are ordered and monotonicity relationships exist between some features and the decision label. Monotonic classification: an overview on algorithms, performance measures and data sets … SceneFlowFields++ State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense interpolation, we push the limits of scene flow estimation. Avoiding strong assumptions on the domain or the problem yields a more robust algorithm. This algorithm is fast because we avoid explicit regularization during matching, which allows an efficient computation. Using image information from multiple time steps and explicit visibility prediction based on previous results, we achieve competitive performances on different data sets. Our contributions and results are evaluated in comparative experiments. Overall, we present an accurate scene flow algorithm that is faster and more generic than any individual benchmark leader. … Multi-Task Graph Autoencoder We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local graph structure and available node features for the simultaneous multi-task learning of unsupervised link prediction and semi-supervised node classification. Our simple, yet effective and versatile model is efficiently trained end-to-end in a single stage, whereas previous related deep graph embedding methods require multiple training steps that are difficult to optimize. We provide an empirical evaluation of our model on five benchmark relational, graph-structured datasets and demonstrate significant improvement over three strong baselines for graph representation learning. Reference code and data are available at https://…/graph-representation-learning … ADAM We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm. ➘ “GENESYS” SAdam: A Variant of Adam for Strongly Convex Functions … https://analytixon.com/2022/08/06/if-you-did-not-already-know-1796/?utm_source=dlvr.it&utm_medium=tumblr

[wsitho123]

Video Marketing 101

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By hiring a video marketing firm, you'll have the ability to shoulder your obligations of designing and marketing video content strategically that enhances your call-to-action. We flavour your ideas with the trade video marketing solution trends & assist you to unleash the ability of video marketing for your choices on the internet. What is the second most visited search engine on the internet?

In right now's world, so much of an organization's marketing is designed to build trust. Video is the bridge that connects what you say to who you really are, allowing prospects to see backstage and learn about your brand. Video content shared online is a combine of unique manufacturing and edited variations. From crew to cast, we've manufacturing options for both big and small finances initiatives. With a group so numerous, Origin Films has the potential to create, movie, edit and market your commercial.

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I highly suggest them for Website Design & Development and Digital Marketing. Videos could be really effective, if we work on them in the proper method. They might help us to bring up the most effective video marketing solution a part of our product with ease. When we work well on movies, its graphics and other related stuff, video marketing becomes an efficient marketing device.

Convex Studio, for example, can create and promote videos optimized to generate the most results at least price. The video productions we produce will exceed your expectations and you could be astounded by what we are able to do. There aren't any different video production corporations that may match our commitment to quality, professionalism, and cutting-edge know-how.

A video is a perfect opportunity to speak your merchandise, your companies but also, what makes you stands out from the competition. With a well-produced video, your prospects and current customers will have the power to put a clear picture on the product or service they are seeking. Reach Digital has state-of-the-art equipment and an skilled video marketing solution group of videographers, editors, and producers to help you create high-quality video content and stand out from your competition. We can help you conceptualize, create, edit, and launch short & long-form content material to help elevate your brand. Video content material provides you a chance to talk directly to your viewers.

Video is in fact the finest way of the lengthy run but there are all kinds of issues that you are able to do to stand out within the crowd. Of course you possibly can have a tremendous description, but that is only one piece to the puzzle. Some different ways you can consider is having some superb thumbnails.

[textbooksolutionsmanuals]

Solve Your Geometry like A Pro with the Geometry Solution Manuals

Are the different lines, circles, and angles getting on your nerves? Have you been trying to solve them but still to no avail? There are these geometry textbook solution manuals that will help you solve every other sum with ease.

The geometry solution manuals will help the students understand and learn better. With the help of solution manuals, the students clarify all their doubts and improve their results.

A brief introduction on geometry and its branches

Geometry is one of the oldest branches of mathematics. It deals with the angles, sizes, shapes, and dimensions of the properties of space we see in our daily life.  

Some of the important concepts in geometry include axioms, points, lines, planes, angles, curves, surfaces, manifolds, length, area, volume, symmetry, dimensions, etc.

Geometry is divided into some major branches which include algebraic geometry, discrete geometry, differential geometry, Euclidean geometry, convex geometry, and topology.

Algebraic Geometry

It is the branch of geometry that deals with the zeros of the multivariate polynomials. The application of algebraic theory comprises cryptography, string theory, etc.

Discrete Geometry

It deals with the relative position of a simple geometric object. The application of this comprises using lines, triangles, points, circles, etc.

Differential Geometry

This type of geometry uses techniques of algebra and calculus for solving problems. The problems include general relativity in physics and the process of analysis of data on nonflat surfaces.

Euclidean Geometry

This type of geometry deals with the study of plane and solid figures based on axioms and theorems including points, lines, angles, planes, solid figures, etc.

Convex Geometry

This type of geometry uses the techniques of real analysis. It has uses in the application in optimization and functional analysis in number theory.

Topology

It deals with the properties of space under continuous mapping. It deals with metric spaces, proximal continuity, proximal spaces, initial and final structures, hyperspace topologies, separation axioms, etc.

Career Options

Students with a degree in geometry have a wide rand of career options. They can become architects, cartographers, drafters, mechanical engineers, surveyors, urban and regional planners, etc.

The Utility of Solution Manuals

With a good result and a great personality and character, any candidate will be hired by a good company. While the improvement of a person's personality and character depends on oneself; a student can improve their grades by taking help from solution manuals.

A solution manual is useful to students in many different ways.  

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[fengmingfang-research]

Annotated Bibliography

Numbers 1-5 are books.

1.  Schewe, J. (2015). The Digital Negative: Raw Image Processing in Lightroom, Camera Raw, and Photoshop. 2nd ed. [Berkeley, California]: Peachpit Press.

The author is not only an early adopter of digital imaging and Photoshop， but also participated in the early development of Camera Raw and Lightroom. In this book, he uses his photography as an example to show how to use Lightroom and Camera Raw plug-ins to process digital negatives into colour or more vibrant images, and then use Photoshop for advanced processing to optimize images.  As a result, a high-efficiency digital film processing process will follow.

The content of this book is related to the first assignment of my Media course. I also used Photoshop to perform special effects on selfies, which produced a magical effect.  Through the knowledge in the book, I got more professional learning and improvement，moreover I learned to use several software and plug-ins to process images at the same time.

2. Rettberg, J. (2016). Seeing Ourselves Through Technology: How We Use Selfies, Blogs and Wearable Devices to See and Shape Ourselves. London: Palgrave Macmillan.

In this book, the author mainly introduces the self-representation from the traditional use of Convex Mirror drawing and sculpting to the development of written, visual, quantitative three aspects. In the era of digital media, social platforms such as written diaries, photo albums, visuals represent social platforms such as blogs and smartphones. Quantitatively refers to the use of wearable devices to track human activity and even positioning; these three different ways are intertwined to make people better understand themselves, improve themselves, and express their emotions. On the other hand, it also provides a channel for governments and institutions to collect people's information and track people's locations, which helps to ensure life faster in emergencies.

I think that self-expression is essential for the shaping of a person. Through the understanding in the book, I will be able to use more ways to record and express myself. In the meanwhile, I can also get some monitoring and security guarantee, which makes me more at ease.

3. Roberts-Breslin, J. (2017). Making Media, 4th Edition. 4th ed. Focal Press.

The book details the process of making media from preparation for the development of an excellent finished product. The author divided it into 14 modules for rigorous description. They are the story development process, composition and framework, creating images, movement in different media, virtual, augmented and mixed reality, time and space, the combination of light, audio, sound and image, production collaboration, linear structure theory, post-production, non-linear structure, exhibition and distribution. At the same time, the author mentioned that expressing the work and understanding the needs of the audience and adapting to the current market, only with all the above conditions can a high-quality production media work be produced.

Making media is a work of art that I have been researching and producing as a digital media student. The detailed analysis and some excellent examples given by the author in this book are a particularly comprehensive learning process for me, which will allow me to be more rigorous and meticulous in my future creations.

4. Tricart, C. (2017). Virtual reality filmmaking. Focal Press.

This book first introduces the theoretical basis and current technical level of VR motion capture, game engine-based VR and head-mounted VR devices. It thoroughly explores and compares the differences and advantages between two significant blocks of VR movie production-Cinematic VR and Interactive VR. The author proposes that interactive VR can be manipulated and has a stronger sense of experience. Finally, the author details how to make a movie with VR from beginning to end, including preparation and use of technology such as Unity.

This book not only comprehensively understands the development history and current technology level of VR but also knows in detail how to make a movie with VR in terms of technology. For me, it will undoubtedly help my future exposure to VR. I will not only be a participant but more likely to become a producer.

5. Bloom, G. (2018). Gaming the stage. Playable media and the rise of English commercial theater. Ann Arbor: The University of Michigan Press.

In a broad sense, the author relies on feedback from academics of different professions to study how interactive games, theatres, and performances are combined and developed. On the one hand, she analyzed how the theatre, as one of the earliest media technologies used for interactive games, could enhance the interactivity and participation of performances to become a commercial activity to attract consumers to participate. Among them, the commercial gamification drama that prevailed in London in the early 17th century was used as an example to illustrate the work of Punchdrunk's Sleep No More. On the other hand, she took a series of interactive games such as Microsoft Kinect as examples to analyze the enhancement of stories in interactive games. Dramatic performance brings greater appeal and experience. The combination of the two can bring apparent advantages, and also benefit their future development.

Since I am very interested in media and performance, I will also try to develop in this field in the future. This book has inspired me to make better meaningful performing artworks.

6.  Sutherland-Cohen, R. (2017). Introduction to Production: Creating Theatre Onstage, Backstage, & Offstage. Focal Press, p.Chapter 5: The Creative Team.

The author is Robert Sutherland-Cohen, a professor with extensive work experience on Broadway and some regional theatres. His series of books describe in detail the organization and operation of commercial theatres. The author has made a broad overview of this book. It describes how all the staff behind and behind the theatre stage work together to complete a drama.  It focuses on narrating the organization of the theatre, the scope of personnel and the entire theatre planning process. These contents provide introductions and help for different positions for those who want to enter the theatre.

At present, I am very concerned about the internal art design of the theatre and the special effects design in conjunction with the theatre, so I plan to find work in this area in the future. The fifth chapter of the book about the creative team inspired me. There are many examples and detailed explanations about the working status and how to take responsibility for each creative team member. These contents gave me a deeper understanding and what kind of design is appropriate.

7. Bailenson, J. & Yee, N. (2007). Virtual interpersonal touch: Haptic interaction and copresence in collaborative virtual environments. Multimedia Tools and Applications, [online] 37(1), pp.5-14. Available at: https://link.springer.com/article/10.1007/s11042-007-0171-2#aboutcontent.

This journal explores the comparative experiments of human interaction and comparative experiments between human and digital models, which have an impact on the development of haptic communication systems and measurement methods in virtual environments. The researchers proposed the concept of virtual interpersonal touch (VIT) and studied the collaborative virtual environment (CVES). A certain number of people were selected to arrange haptic devices randomly, so they directly observed the behaviour of experimental participants in this process. They found that in this virtual environment interaction, different body parts produce different forces. In this way, environments in which users can touch each other themselves may increase coexistence. So in the future, researchers need to explore the interpersonal relationship in CVE further to improve the touch communication system.

Since I am interested in the future development of interaction, it can inspire me about the user experience of tactile interaction. When I need to create interaction artworks in the future, it helps me.

8. Anthropy, A. (2019). Make your own Twine games!. No Starch Press.

Author Anthropy is a game designer who takes readers into a Twine world. The book is a tutorial on game development with Twine, a free online web story interactive tool. The author first mentioned the need to determine the initial design reason and game settings, and then started to create an introduction through the initial design. Additionally, the book details how to use links, add scripts and effects to make the game more vivid, and finally encourage readers to try more. No doubt for me, this is an excellent way for beginners to understand and learn Twine. It is bright and logical. I can follow this book step by step and finally complete a complete interactive game.

9. Lee, J., Lee, S., Kim, Y. and Noh, J. (2017). ScreenX: Public Immersive Theatres with Uniform Movie Viewing Experiences. IEEE Transactions on Visualization and Computer Graphics, [online] 23(2), pp.1124-1138. Available at: https://ieeexplore-ieee-org.ezproxy.herts.ac.uk/document/7414506.

This article focuses on analyzing ScreenX, a novel immersive movie viewing platform. Comparing it with similar products, such as IMAX, Cinerama, and illumiRoom, which are now widespread, found that ScreenX is more suitable for large scenes such as theatres, but there is a significant disadvantage that the surrounding walls will have image distortion, which depends on the position of the viewer. Through research on its principle and technology, and using analytical experiments, the author proposes how to minimize distortion and summarizes related technical issues. As a student studying digital media, I am delighted to see the combination of film viewing experience and immersion. The future development of film and television is becoming more and more scientific and technological, which will not only create more possibilities for us in the technical field but also make the user experience better.

10. Lesaffre, M., Van Dyck, E. and Leman, M. (2019). Expressive interaction with music. Musicae Scientiae, [online] 23(3), pp.281-284. Available at: https://journals.sagepub.com/doi/full/10.1177/1029864919851367.

This special issue focuses on the relationship and interaction between music and Expressive Interaction. With the development of Expressive Interaction, unlike the application software and robotics fields it involved in the past, the area of art music science has begun to develop. It mentions that the emergence and development of this fascinating new research field affect the concept of music, which not only increases the enthusiasm of related professionals but also has an essential impact on the control and treatment of physical diseases. Reveals its potential to enhance awareness and broaden the horizons of music science. As for me, I can't live without music in my daily life, whether it's as a hobby or when I do my professional work. The special issue allows me to understand that technological interaction can also be combined with music and produce specific effects, which will bring more rigorous and in-depth thinking and use to my future creations.

[craigbrownphd-blog-blog]

If you did not already know

DeepShift Deep learning models, especially DCNN have obtained high accuracies in several computer vision applications. However, for deployment in mobile environments, the high computation and power budget proves to be a major bottleneck. Convolution layers and fully connected layers, because of their intense use of multiplications, are the dominant contributer to this computation budget. This paper, proposes to tackle this problem by introducing two new operations: convolutional shifts and fully-connected shifts, that replace multiplications all together and use bitwise shift and bitwise negation instead. This family of neural network architectures (that use convolutional shifts and fully-connected shifts) are referred to as DeepShift models. With such DeepShift models that can be implemented with no multiplications, the authors have obtained accuracies of up to 93.6% on CIFAR10 dataset, and Top-1/Top-5 accuracies of 70.9%/90.13% on Imagenet dataset. Extensive testing is made on various well-known CNN architectures after converting all their convolution layers and fully connected layers to their bitwise shift counterparts, and we show that in some architectures, the Top-1 accuracy drops by less than 4% and the Top-5 accuracy drops by less than 1.5%. The experiments have been conducted on PyTorch framework and the code for training and running is submitted along with the paper and will be made available online. … L1-Norm Batch Normalization (L1BN) Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs). However, BN brings additional computation, consumes more memory and generally slows down the training process by a large margin, which aggravates the training effort. Furthermore, the nonlinear square and root operations in BN also impede the low bit-width quantization techniques, which draws much attention in deep learning hardware community. In this work, we propose an L1-norm BN (L1BN) with only linear operations in both the forward and the backward propagations during training. L1BN is shown to be approximately equivalent to the original L2-norm BN (L2BN) by multiplying a scaling factor. Experiments on various convolutional neural networks (CNNs) and generative adversarial networks (GANs) reveal that L1BN maintains almost the same accuracies and convergence rates compared to L2BN but with higher computational efficiency. On FPGA platform, the proposed signum and absolute operations in L1BN can achieve 1.5$\times$ speedup and save 50\% power consumption, compared with the original costly square and root operations, respectively. This hardware-friendly normalization method not only surpasses L2BN in speed, but also simplify the hardware design of ASIC accelerators with higher energy efficiency. Last but not the least, L1BN promises a fully quantized training of DNNs, which is crucial to future adaptive terminal devices. … SuperSCS We present SuperSCS: a fast and accurate method for solving large-scale convex conic problems. SuperSCS combines the SuperMann algorithmic framework with the Douglas-Rachford splitting which is applied on the homogeneous self-dual embedding of conic optimization problems: a model for conic optimization problems which simultaneously encodes the optimality conditions and infeasibility/unboundedness certificates for the original problem. SuperMann allows the use of fast quasi-Newtonian directions such as a modified restarted Broyden-type direction and Anderson’s acceleration. … Time-Variant System A time-variant system is a system that is not time invariant (TIV). Roughly speaking, its output characteristics depend explicitly upon time. In other words, a system in which certain quantities governing the system’s behavior change with time, so that the system will respond differently to the same input at different times. … https://analytixon.com/2022/06/13/if-you-did-not-already-know-1743/?utm_source=dlvr.it&utm_medium=tumblr

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[kino97-me]

Stanford CS PhD Info

[Disclaimer] All materials are publicly accessible (Jun 2019); compiled based on my personal interest; for learning purpose only.

Graduation requirement

CS300 seminar (autumn only, attend >=2/3)

First-year research rotation: 3 groups, each for 1 quarter

Courses + breadth requirements* by Spring Quarter of year 2

QE by Spring Quarter of year 3

Teaching: 4 units, 1=10h for one quarter

Dissertation/Oral

*Breadth Requirements: 2 subareas within each of the 3 areas

Area A: Mathematical and Theoretical Foundations

A.  Analysis of Algorithms:

CS161 Design and Analysis of Algorithms. 3-5 Units.

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 103 or 103B; 109 or STATS 116.

CS168 The Modern Algorithmic Toolbox. 3-4 Units.

This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Prerequisites: CS107 and CS161, or permission from the instructor.

CS261 Optimization and Algorithmic Paradigms. 3 Units.

Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to sub-linear algorithms and decision making under uncertainty. Prerequisite: 161 or equivalent.

CS265 Randomized Algorithms and Probabilistic Analysis. 3 Units.

Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms. Prerequisites: CS 161 and STAT 116, or equivalents and instructor consent.

or CS361 Engineering Design Optimization. 3-4 Units.

Design of engineering systems within a formal optimization framework. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. Topics will also include quantitative methodologies for addressing various challenges, such as accommodating multiple objectives, automating differentiation, handling uncertainty in evaluations, selecting design points for experimentation, and principled methods for optimization when evaluations are expensive. Applications range from the design of aircraft to automated vehicles. Prerequisites: some familiarity with probability, programming, and multivariable calculus.

B.  Theory of Computation and Complexity Theory:

CS154 Introduction to Automata and Complexity Theory. 3-4 Units.

This course provides a mathematical introduction to the following questions: What is computation? Given a computational model, what problems can we hope to solve in principle with this model? Besides those solvable in principle, what problems can we hope to efficiently solve? In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. By the end of this course, students will be able to classify computational problems in terms of their computational complexity (Is the problem regular? Not regular? Decidable? Recognizable? Neither? Solvable in P? NP-complete? PSPACE-complete? etc.). Students will gain a deeper appreciation for some of the fundamental issues in computing that are independent of trends of technology, such as the Church-Turing Thesis and the P versus NP problem. Prerequisites: CS 103 or 103B.

or CS254 Computational Complexity. 3 Units.

An introduction to computational complexity theory. Topics include the P versus NP problem; diagonalization; space complexity: PSPACE, Savitch's theorem, and NL=coNL; counting problems and #P-completeness; circuit complexity; pseudo-randomness and de-randomization; complexity of approximation; quantum computing; complexity barriers. Prerequisites: 154 or equivalent; mathematical maturity.

C.  Numerical Analysis and  Convex Optimization:

CS 205L Continuous Mathematical Methods with an Emphasis on Machine Learning. 3 Units. (replaces CS205a)

A survey of numerical approaches to the continuous mathematics used in computer vision and robotics with emphasis on machine and deep learning. Although motivated from the standpoint of machine learning, the course will focus on the underlying mathematical methods including computational linear algebra and optimization, as well as special topics such as automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, RNNs, etc. (Replaces CS205A, and satisfies all similar requirements.)

CS334a Convex Optimization I. 3 Units. (same as EE364a, or EE364b)

D.  Logic:

CS157 Computational Logic. 3 Units.

Rigorous introduction to Symbolic Logic from a computational perspective. Encoding information in the form of logical sentences. Reasoning with information in this form. Overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth. Topics include the syntax and semantics of Propositional Logic, Relational Logic, and Herbrand Logic, validity, contingency, unsatisfiability, logical equivalence, entailment, consistency, natural deduction (Fitch), mathematical induction, resolution, compactness, soundness, completeness.

Phil 251 (=Phil 151 Metalogic)

or CS258 Introduction to Programming Language Theory

Area B: Computer Systems

A.  Computer Architecture

B.  Compilers

C.  Networks

D.  Programming Languages

E.  Software Systems

Area C: Artificial Intelligence and Applications

A.  Artificial Intelligence:

CS121 Introduction to Artificial Intelligence. 3 Units.

CS221 Artificial Intelligence: Principles and Techniques. 3-4 Units

     OR any TWO of the following:

CS222 Rational Agency and Intelligent Interaction. 3 Units.

For advanced undergraduates, and M.S. and beginning Ph.D. students. Logic-based methods for knowledge representation, information change, and games in artificial intelligence and philosophy. Topics: knowledge, certainty, and belief; time and action; belief dynamics; preference and social choice; games; and desire and intention. Prerequisite: propositional and first-order logic. (Same as PHIL 358)

CS223a Introduction to Robotics. 3 Units.

CS224m Multi-Agent Systems.  3 Units. 2014

CS224N

CS224w Analysis of Networks. 3-4 Units.

CS224U

CS227b General Game Playing. 3 Units.

CS228 Probabilistic Graphical Models: Principles and Techniques. 3-4 Units.

CS229 Machine Learning. 3-4 Units.

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics. (Same as: STATS 229)

CS229t Statistical Learning Theory. 3 Units.

How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. We will present various learning algorithms and prove theoretical guarantees about them. Topics include generalization bounds, implicit regularization, the theory of deep learning, spectral methods, and online learning and bandits problems. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning (STATS 315A or CS 229). (Same as STATS 231)

CS231a Computer Vision: From 3D Reconstruction to Recognition. 3-4 Units. (Formerly 223B)

or CS237a Numerical Linear Algebra. 3 Units.

B.  Computational Biology

C.  Computer Network and Security

D.  Databases

E.  Graphics

F.  HCI

Academic Calendar

Spring quarter: Apr 1 - June 7

Summer quarter: June 25 - August 16

Autumn quarter: September 23 - December 8

Winter quarter: January 7 - March 15

Online Courses

stanford@Youtube

CS230 | Autumn 2018: DL

CS224N | Winter 2019: NLP with DL

CS224U | Spring 2019: NLU

CS234 | Winter 2019: Reinforcement learning

[rita8960-blog]

What skills do I need to have to get a job in Machine Learning?

It has become a common trend of interest towards Machine learning course these days. You are not alone! More individuals are attracted to Machine Learning every day. In fact, It would be hard-pressed to find a field generating more buzz these days rather than this one.  Machine Learning’s inroads into our communal understanding have been both history making and hysterical but regardless of how you discovered it, one thing is clear: Machine Learning has arrived.

That said, it’s one thing to get interested in Machine Learning, it’s another thing in total to actually start working in the field. This post will help you recognize both the overall mindset and the specific skills you’ll need to start working as a Machine Learning engineer.

To begin, there are two very important things that you need to understand if you’re considering a career as a Machine Learning engineer. Fundamentally you don’t have to do research or have an academic background. Secondly, it is not enough to have either software engineering or data science experience. You ideally need both the courses.

  The Data Mining process is similar to that of Machine Learning. As both search out patterns of data. However, as an alternate of extracting data for human understanding — as is the case in data mining applications — machine learning uses its data to improve the program’s own understanding. Machine Learning programs detect data patterns and adjust accordingly.

Here is a list of key skill sets

1. Python/C++/R/Java: If you want a job in Machine Learning, you will possibly have to learn all these languages at some point.  As C++ helps in speeding up the code. R Programming course works great in statistics and plots, and Hadoop is a Java-based language, so you probably need mappers and reducers in Java as implementers.

2. Probability and Statistics: Theories help individuals to learn about algorithms. Great samples are of Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. You need to have an understanding firm of Probability and Stats to understand these models. Go nuts and study measure theory. Using statistics as a model evaluation metric: confusion matrices, receiver-operator curves, p-values, etc.

3. One must have knowledge of Applied Math and Algorithms: Having a firm understanding of algorithm theory and knowing how the algorithm works, you can also discriminate models such as SVMs. You will require to understand subjects such as gradient descent, convex optimization, Lagrange, quadratic programming, partial differential equations and alike. Also, get used to looking at summations.

4. Divided Computing: Mostly, machine learning jobs involve working of large data sets these days. One cannot process data using a single machine, one needs to distribute it using an entire cluster. Projects of Apache Hadoop and cloud services such as Amazon’s EC2 makes it more easy and cost-effective.

5. Expanding the Expertising Tools in Unix: You need to master using great UNIX tools that were designed for a cat, grep, find, awk, sed, sort, cut, tr, and more. The processing will most likely be on the Linux-based machine, such that one needs to access these tools. Learn their functioning and utilize them well. They certainly have made the industries life a lot easier.

6. One need to learn more about Advanced Signal Processing techniques:  Extraction is one of the most important parts of the machine-learning course. Different problems need various solutions, you may be able to make use of really cool advance signal processing algorithms such as wavelets, shearlets, curvelets, contourlets, bandlets. This will help you learn about time-frequency analysis, and will help you apply it to your problems. If you have not read about Fourier Analysis and Convolution, you will require to learn about this stuff too.

7. Other skills: (a) Update oneself: One needs to be up to date with any upcoming changes. It also means to be responsive to the news regarding the growth of the tools, theory, and algorithms. Imagine and nurture this change. (b) Start Reading a lot: Read papers like Google Map-Reduce, Google File System, Google Big Table, and the irrational efficiency of Data as there are great free machine learning books online and you should read those as well as they are useful.