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Artificial Intelligence, Operating Systems, User Experience, Choreographyworkflow, Swarm Intelligence.
Collaboration Spaces to integrating conversational interfaces and diverse agents, each aspect of AssistOS has been thoughtfully designed with the user in mind. As it evolves, AssistOS is set to lead the charge in the future of operating systems, demonstrating the immense potential of AI in | medium | 5,629 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
Abstract: The field of synthetic biology seeks to harness the complexity of biological systems through the redesign of gene regulatory networks (GRNs), aiming to produce novel, beneficial phenotypes. Given the non-linear and high-dimensional nature of these networks, classical computational models | medium | 5,631 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
often fall short in navigating the intricate energy landscapes inherent to these systems. In this paper, I delve into a quantum optimization framework leveraging Variational Quantum Algorithms (VQA) and Quantum Annealing (QA) to address these challenges, elucidating the application through | medium | 5,632 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
mathematical formulations and quantum mechanical principles. Introduction to Complex Dynamics in Gene Regulatory Networks Gene regulatory networks define the regulatory interactions between genes, proteins, and other molecules within a cell. The dynamic behavior of these networks is influenced by a | medium | 5,633 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
myriad of factors including genetic variations, epigenetic modifications, and environmental inputs. Mathematically, GRNs can be modeled as dynamical systems governed by a set of differential equations: where 𝑥 represents the vector of gene expression levels, 𝑢 denotes external inputs or control | medium | 5,634 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
variables, and 𝑓encapsulates the nonlinear interactions within the network. Quantum Representation of Gene Expression States To utilize quantum computing for GRNs, we encode the state of the network into a quantum state ∣𝜓⟩ within a Hilbert space. Each basis vector ∣𝑥𝑖⟩ of the Hilbert space | medium | 5,635 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
corresponds to a specific gene expression profile, with the overall state given by: The coefficients 𝑐𝑖 are complex numbers whose moduli square ∣𝑐𝑖∣^2 represent the probability of the network being in state ∣𝑥𝑖⟩. Hamiltonian Formulation of the Optimization Problem The optimization problem in GRNs | medium | 5,636 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
is to find a state that minimizes a cost function representing the network’s energy, formulated as the expectation value of a Hamiltonian operator 𝐻: The Hamiltonian 𝐻 is designed to reflect the energy landscape of the GRNs, incorporating terms that penalize undesirable gene interactions and reward | medium | 5,637 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
favorable ones. Variational Quantum Eigensolver for GRN Optimization The VQE utilizes a parameterized quantum circuit to prepare states ∣𝜓(𝜃)⟩, optimizing the parameters 𝜃 to minimize the Hamiltonian: The circuit parameters are updated using gradient descent or other optimization techniques: where | medium | 5,638 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
𝜂 is the learning rate. Quantum Annealing for Exploring Energy Landscapes Quantum Annealing is used to find the global minimum of the Hamiltonian by exploiting quantum tunneling. The process starts from a superposition state and gradually modifies the system’s Hamiltonian: This technique is | medium | 5,639 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
particularly useful for navigating through the rugged, multi-dimensional landscapes typical of GRNs. Integration with Classical Computational Models The quantum solutions are integrated with classical simulation models that predict the broader biological impacts. This involves iteratively refining | medium | 5,640 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
the quantum results with classical feedback, enhancing both the accuracy and biological relevance of the solutions. Future Perspectives and Theoretical Implications The integration of quantum computing with synthetic biology opens new avenues for the control and design of biological systems at a | medium | 5,641 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
level of precision previously unattainable. Mathematical frameworks and quantum mechanical principles presented herein provide a very high level foundation for future explorations and practical implementations in this interdisciplinary field. This investigation highlights the potential of quantum | medium | 5,642 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
computational technologies to revolutionize the optimization of gene regulatory networks, offering a deeper understanding and enhanced control of biological systems. The mathematical and quantum mechanical formulations presented provide a rigorous basis for further research and application in | medium | 5,643 |
Physics, Quantum Computing, Quantum Physics, Mathematics, Biology.
synthetic biology and beyond. Invitation for Discussion I encourage an open dialogue among researchers in the fields of quantum computing, synthetic biology, and mathematical modeling to explore these concepts further, aiming to harness quantum technologies for the advancement of life sciences. | medium | 5,644 |
Deep Dives, AI, Drug Development, Drug Discovery.
Up until recently Alban de La Sablière was Sanofi’s chief dealmaker, reporting directly to CEO Paul Hudson. In October he left to become Chief Business Officer at Owkin, a French AI scale-up focused on drug development and delivery. For Dr. Thomas Clozel, Owkin’s CEO, the move is evidence of a | medium | 5,645 |
Deep Dives, AI, Drug Development, Drug Discovery.
paradigm shift in the industry, with power moving to smaller, more nimble companies. The six-year-old company, which Clozel co-founded with computer science researcher Gilles Wainrib, specializes in AI and federated learning for medical research. It has inked deals with both Sanofi and Bristol | medium | 5,646 |
Deep Dives, AI, Drug Development, Drug Discovery.
Myers Squibb to help those companies improve their odds in drug discovery and clinical trials respectively. Now Owkin plans to evolve from licensing its research and technology to developing its own intellectual property. The company is a unicorn (firms valued at $1 billion or more) and its big | medium | 5,647 |
Deep Dives, AI, Drug Development, Drug Discovery.
ambitions are also helping it attract talent from Big Tech. Jean-Philippe Vert, a former research scientist at Google Brain, became Owkin’s Chief Research and Development Officer in September. Just as his parents Jean-Paul and Martine Clozel, founders of Switzerland’s Actelion, helped pioneer | medium | 5,648 |
Deep Dives, AI, Drug Development, Drug Discovery.
Europe’s biotech industry to research and develop medicines for unmet medical needs, Clozel, a trained oncologist, is intent on using AI to usher in a new era of drug development. The excitement around AI’s potential to transform healthcare will be evident at an international gathering of 1,500 | medium | 5,649 |
Deep Dives, AI, Drug Development, Drug Discovery.
startups, investors, and medtech and pharma companies at the AI For Health Summit in Paris on November 16. Owkin will be among them. But with the high risks involved in creating feasible treatments, and the limited history of the technology platforms involved, investors, who have poured billions of | medium | 5,650 |
Deep Dives, AI, Drug Development, Drug Discovery.
dollars into the application of AI and machine learning in healthcare in recent years, will need to see solid evidence that the technology will live up to its promise. Medical regulators have not yet approved a drug that was designed using AI. But, says Clozel, the French | medium | 5,651 |
Deep Dives, AI, Drug Development, Drug Discovery.
doctor-turned-entrepreneur, the time is getting closer, and the results could be transformative for medical providers and patients suffering from hard-to-treat diseases. A $50 Billion Opportunity Much of the traditional process of discovering new drugs is costly guesswork. But a new wave of drug | medium | 5,652 |
Deep Dives, AI, Drug Development, Drug Discovery.
development platforms like Owkin’s are helping companies use vast data sets to speed up the process of identifying patient response markers and developing viable drug targets more cheaply and efficiently. In a recent report Morgan Stanley Research said it believes that modest improvements in | medium | 5,653 |
Deep Dives, AI, Drug Development, Drug Discovery.
early-stage drug development success rates enabled by artificial intelligence and machine learning could lead to an additional 50 novel therapies over a 10-year period, which could translate to a more than $50 billion opportunity. For biotech firms, it can take a blockbuster drug discovery just to | medium | 5,654 |
Deep Dives, AI, Drug Development, Drug Discovery.
break even. The median investment required to bring a new drug to market is estimated to be nearly $1 billion, while the true cost of research and development may be as high as $2.5 billion per marketed therapy, when factoring in abandoned trials and clinical failures, notes the Morgan Stanley | medium | 5,655 |
Deep Dives, AI, Drug Development, Drug Discovery.
report. Savings from AI could offer significant value. That promise is helping Owkin and other young companies like CytoReason and Exscientia to raise money and strike partnership deals. For example, in June of this year Owkin secured $80 million from Bristol Myers Squibb; this was on top of the | medium | 5,656 |
Deep Dives, AI, Drug Development, Drug Discovery.
$180 million it raised from Sanofi in 2021, as part of a new strategic collaboration with that company focused on discovery and development programs in four types of cancer. Pfizer recently announced that it will pour up to $110 million into Tel Aviv-based CytoReason to fund their work together and | medium | 5,657 |
Deep Dives, AI, Drug Development, Drug Discovery.
to cover potential licensing deals for CytoReason’s AI platform and disease models. Meanwhile Bristol Myers Squibb agreed to pay up to $1.2 billion to enter a drug discovery collaboration with Exscientia, a U.K.-based pharmatech company with an AI platform that has designed two drugs that are in | medium | 5,658 |
Deep Dives, AI, Drug Development, Drug Discovery.
Phase 1 human clinical trials This week the U.K. AI company announced that Bristol Myers Squibb has elected to in-license an immune-modulating drug candidate created by Exscientia. The in-licensed candidate targets a critical immunological kinase that historically has proven difficult to target due | medium | 5,659 |
Deep Dives, AI, Drug Development, Drug Discovery.
to the need for a combination of potency, selectivity, and overall drug-like properties. To design a novel candidate that overcame these issues, Exscientia said it utilized its end-to-end platform to drive the discovery process, including AI-driven design, structural biology, chemistry, and | medium | 5,660 |
Deep Dives, AI, Drug Development, Drug Discovery.
pharmacology, as well as late-stage pre-clinical studies. The molecule was found within 11 months of beginning drug design. Some other partnerships between young companies and established pharma firms have also begun to bear fruit. AstraZeneca’s long-standing collaboration with U.K.-based | medium | 5,661 |
Deep Dives, AI, Drug Development, Drug Discovery.
BenevolentAI, an AI-enabled drug discovery company resulted in the identification of multiple new targets in idiopathic pulmonary fibrosis, a serious lung disease, with subsequent broadening of the scope to other therapeutic areas, notes McKinsey. And in January 2021, the companies announced a new | medium | 5,662 |
Deep Dives, AI, Drug Development, Drug Discovery.
drug target for chronic kidney disease. BenevolentAI’s computers predicted the target, and AstraZeneca’s experiments validated it. Another example is Japan’s Sumitomo Dainippon Pharma, which worked with Exscientia to identify DSP-1181 for obsessive compulsive disorder in less than a quarter of the | medium | 5,663 |
Deep Dives, AI, Drug Development, Drug Discovery.
time typically taken for drug discovery processes (under 12 months versus four and a half years) — with ambitions to enter the molecule into Phase I trials. Unprecedented Collaboration In addition to partnerships on specific drug development and delivery programs, Owkin has been working | medium | 5,664 |
Deep Dives, AI, Drug Development, Drug Discovery.
cross-industry, using AI and federated learning to underpin unprecedented collaboration in academic research and drug discovery. Its technology makes it possible to extract insights, while still preserving each participating company’s intellectual property, privacy, and control on data. Owkin’s | medium | 5,665 |
Deep Dives, AI, Drug Development, Drug Discovery.
technology powered the blockchain-based Machine Learning Ledger Orchestration for Drug Discovery (MELLODDY), an EU-grant funded project which trained machine learning on chemical libraries from ten pharma companies including Amgen, AstraZeneca, GlaxoSmithKline, Janssen, Merck and Novartis. The | medium | 5,666 |
Deep Dives, AI, Drug Development, Drug Discovery.
French company’s platform is additionally used by HealthChain, a collaborative project between four French academic medical centers excellence, to build some predictive analytics in a federated way on patients data and build predictions of patient outcomes in melanoma and breast cancer. The | medium | 5,667 |
Deep Dives, AI, Drug Development, Drug Discovery.
company, which has operations in both France and the U.S., is also applying its technology to empower researchers in hospitals, universities, and pharmaceutical companies to understand why drug efficacy varies from patient to patient, enhance the drug development process, and identify patients in a | medium | 5,668 |
Deep Dives, AI, Drug Development, Drug Discovery.
center with a predicted rare mutation or biomarker. Its proprietary technology platform has two main modules: Owkin Studio, which uses machine learning technology to integrate biomedical images, genomics and clinical data to discover biomarkers and mechanisms associated with diseases and treatment | medium | 5,669 |
Deep Dives, AI, Drug Development, Drug Discovery.
outcomes and Owkin Connect which allows participants to remotely connect in a privacy preserving setting to datasets and launch federated or non federated analytics. It is completed by a federated data platform of the best academic research centers in Europe. Owkin is currently working on many | medium | 5,670 |
Deep Dives, AI, Drug Development, Drug Discovery.
research projects with these centers: from federated learning to derive and share insights about everything from breast cancer and melanoma, to construction of predictive models in lung cancer to improving early clinical trials or creation of signatures to stratify patients with autoimmune diseases | medium | 5,671 |
Deep Dives, AI, Drug Development, Drug Discovery.
in clinical trials. For example, in a study conducted with French cancer research institute Gustave Roussy, Owkin analyzed digital tumor slides alongside the patient’s medical history and other clinical data, including age at surgery and surgery type, to classify a breast cancer patient’s risk of a | medium | 5,672 |
Deep Dives, AI, Drug Development, Drug Discovery.
metastatic relapse over the next five years. The results, showing accuracy of about 81%, were presented at the annual meeting of the European Society of Medical Oncology. Before that, Owkin demonstrated it could help predict treatment responses in mesothelioma, a deadly, aggressive form of cancer, | medium | 5,673 |
Deep Dives, AI, Drug Development, Drug Discovery.
based on scans of tumor biopsy samples. That program, dubbed MesoNet, which was detailed in a 2019 publication in Nature, could also visually highlight the areas of the tissue slide that drove its predictions to help pathologists better classify patients. And last month Owkin announced that two of | medium | 5,674 |
Deep Dives, AI, Drug Development, Drug Discovery.
its AI models — one to predict if breast cancer patients will relapse after treatment, and another that can determine whether cases of colorectal cancer contain microsatellite instability — have now received CE mark approval, allowing them to be used on cancer patients across Europe. It is also | medium | 5,675 |
Deep Dives, AI, Drug Development, Drug Discovery.
currently participating in the launch of a landmark National Institutes of Health-funded academic project, along with 12 leading research institutions, that may establish voice as a biomarker used in clinical care. NEXT STEPS Owkin’s success to date is just the beginning, promises Clozel. “The | medium | 5,676 |
Deep Dives, AI, Drug Development, Drug Discovery.
models used by pharma and biotech are broken,” he says. “You need one platform connecting academics, researchers, and hospitals with federated access to patient data, with a layer of AI, to build effective new medications. That is the platform we are building.” An AI-led drug discovery and | medium | 5,677 |
Deep Dives, AI, Drug Development, Drug Discovery.
development company is in a much better position to discover new medicines than big pharma, he says. “Many of their chief digital officers have left. They don’t have the right data sets and no clear AI strategy,” he says. Owkin has access to 10x more data through its federated data network. “We are | medium | 5,678 |
Deep Dives, AI, Drug Development, Drug Discovery.
going to start by licensing compounds we find interesting,” he says, and “we are aiming to start our first AI-first phase drug within a year.” Other AI scale-ups have made similar promises. A pitch deck from 2017 describes BenevolentAI as having 24 “validated hypotheses in clinical and preclinical | medium | 5,679 |
Deep Dives, AI, Drug Development, Drug Discovery.
development” but, as an article in Sifted recently pointed out, five years on, it’s yet to bring a newly discovered drug to market. Clozel acknowledges that AI has not yet fully delivered on its promise to cure diseases and usher in an age of personalized medicine but if Owkin and other companies | medium | 5,680 |
Deep Dives, AI, Drug Development, Drug Discovery.
like it succeed in developing their own, significantly more efficient drug discovery pipelines, the world may not have to wait much longer. This article is content that would normally only be available to subscribers. Sign up for a four-week free trial to see what you have been missing. To read | medium | 5,681 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
In the world of business, the chase for leads can often feel like a race against time. But what’s the value of speed without accuracy? Imagine having a bucket full of gems, only to find out that most of them are just glittering stones. That’s exactly why the quality of leads matters more than their | medium | 5,683 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
quantity. However, distinguishing the genuine gems from the mere stones isn’t always straightforward. Many businesses grapple with the challenge of verifying the authenticity of their leads. This blog aims to demystify the process, providing you with practical steps to ensure your leads are not | medium | 5,684 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
just numerous, but also true gold. Comparing Information Across Sources The cornerstone of verifying lead authenticity is gathering information from a variety of sources. Why? Imagine you’re hearing a story. If you hear it from just one person, you have only one perspective. But if multiple people | medium | 5,685 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
tell you the same story, with the details aligning closely, the story begins to hold more weight. Comparing Data for Consistency Start by collecting data from different platforms — social media profiles, company websites, and public records, to name a few. Lay this information side by side and look | medium | 5,686 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
for consistency in details like names, job titles, company affiliations, and contact information. Consistency is a sign of legitimacy. Red Flags to Watch Out For Be on the lookout for discrepancies that could signal a lead might not be genuine. These include mismatched details across sources, | medium | 5,687 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
outdated information, and profiles that lack depth or substance. Steps for Discrepancies If you spot inconsistencies, don’t write off the lead immediately. Instead, use this as an opportunity to engage. Reach out to clarify the discrepancies. This not only helps you verify the lead but also | medium | 5,688 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
demonstrates your diligence and interest. Using Email Verification Tools https://hunter.io/email-verifier Email communication remains a staple in business interactions, making email verification a crucial step in lead validation. Tools like Hunter.io, NeverBounce, and ZeroBounce offer a | medium | 5,689 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
straightforward solution without needing you to be a tech wizard. How These Tools Work In simple terms, these tools check an email against several criteria to ensure it’s valid and won’t bounce. They look at whether the email syntax is correct, if the domain exists, and if the email server can | medium | 5,690 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
receive messages. Benefits of Email Verification The primary benefit is certainty. Confirming an email address is valid reduces the risk of wasting resources on dead-end leads. Plus, it helps maintain a clean, reputable email list for your marketing campaigns. Checking Company Websites A lead’s | medium | 5,691 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
company website is a treasure trove of information and an essential checkpoint for verification. Ensuring Legitimacy A legitimate company website should have a professional appearance, clear information about the business, and contact details. Look for signs of regular updates and activity that | medium | 5,692 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
indicate the company is operational. Operational Status and Relevance Verify that the company’s offerings align with the information your lead has provided. Check for press releases, blog posts, or news sections to assess the company’s current status and relevance in its industry. Reviewing Social | medium | 5,693 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
Media Profiles Social media, especially LinkedIn for professional connections, plays a significant role in lead verification. Gauging Authenticity A genuine profile often shows regular activity, interactions with other users, and consistency in information provided elsewhere. An active presence | medium | 5,694 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
suggests the person is engaged in their professional community. Interpreting Social Media Signals Beyond activity, look at the quality of connections and endorsements. These can provide additional layers of confirmation regarding the lead’s standing in their industry. Conclusion Verifying the | medium | 5,695 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
accuracy and authenticity of leads is a multi-step process that demands attention to detail and a bit of detective work. By comparing information across sources, using email verification tools, checking company websites, and reviewing social media profiles, you can significantly increase the | medium | 5,696 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
reliability of your leads. Remember, diligence in this process not only saves you time and resources but also sets the stage for more fruitful business relationships. Now that you’re armed with strategies to vet your leads, I’d love to hear about your experiences or any additional tips you might | medium | 5,697 |
Sales, Marketing, Entrepreneurship, Business Development, Social Media.
have. Share your stories in the comments below. And if you’re looking for more insights into refining your business strategies, consider subscribing or reaching out for further assistance. Together, let’s turn lead generation into a process of discovering genuine opportunities. | medium | 5,698 |
Ukraine War, Ukraine, Russia, War.
The war in Donbas has been raging for a long time now, and the situation on the ground is constantly changing. One of the most contested areas is the Avdiivka chemical plant, a strategic asset for both sides. In this blog post, I will give you an overview of the latest developments and the | medium | 5,699 |
Ukraine War, Ukraine, Russia, War.
challenges faced by the Ukrainian forces defending the plant. The Avdiivka chemical plant is one of the largest coke producing facilities in Europe, and it provides fuel for steelmaking furnaces across Ukraine. It is also located near the Donetsk airport, another key point of contention between the | medium | 5,700 |
Ukraine War, Ukraine, Russia, War.
Ukrainian army and the Russian-backed separatists. The plant has been under Ukrainian control since 2014, but it has been repeatedly attacked and damaged by the Russian forces. The latest offensive by the Russians began in late November, when they launched a massive assault on the northern flank of | medium | 5,701 |
Ukraine War, Ukraine, Russia, War.
Avdiivka, aiming to encircle the plant and cut off its supply lines. The Ukrainian forces, however, managed to repel the attack and inflict heavy casualties on the enemy. The Russians then adjusted their plan and focused on improving their tactical position before making another attempt. They | medium | 5,702 |
Ukraine War, Ukraine, Russia, War.
accumulated forces in the nearby tree lines, using personnel carriers to transport their soldiers to the outskirts of Krasnohorivka, and then moving by foot to their positions. The Ukrainians, meanwhile, used drones and artillery to strike the Russian shelters and reinforcements, trying to prevent | medium | 5,703 |
Ukraine War, Ukraine, Russia, War.
them from reaching the contact line. They also prepared for a counterattack, using their firing point north of the chemical plant to cover their advance. On December 3, the Russians launched a second assault, concentrating their forces on the most problematic region for them: the Ukrainian firing | medium | 5,704 |
Ukraine War, Ukraine, Russia, War.
point. They left their flanks exposed and overwhelmed the Ukrainian defenders with sheer numbers. The Ukrainians fought back fiercely, but they ran out of ammunition and had to surrender. The Russians, however, showed no mercy and killed the two Ukrainian soldiers who surrendered. The Ukrainians, | medium | 5,705 |
Ukraine War, Ukraine, Russia, War.
enraged by this act of brutality, launched a swift and decisive counterattack, supported by artillery and drones. They stormed the Russian positions and eliminated all the enemy soldiers. They also regained control of the firing point and secured their supply lines. At the same time, the Russians | medium | 5,706 |
Ukraine War, Ukraine, Russia, War.
tried to attack the settlement of Stepove, south of the chemical plant, hoping to distract the Ukrainians and create a breach in their defenses. However, they met with fierce resistance from the Ukrainian artillery, which coordinated with the drone operator and annihilated one Russian unit after | medium | 5,707 |
Ukraine War, Ukraine, Russia, War.
another. The Russians eventually retreated, leaving behind many dead and wounded. The battle for the Avdiivka chemical plant was a major victory for the Ukrainian forces, who demonstrated their courage, skill, and determination. They inflicted heavy losses on the Russians, who failed to achieve | medium | 5,708 |
Ukraine War, Ukraine, Russia, War.
their objectives and suffered a humiliating defeat. The Ukrainians also proved that they are not alone in this fight, as they received support and solidarity from their allies and the international community. The war in Donbas is not over yet, and the situation remains tense and volatile. The | medium | 5,709 |
.
Conducting Actuarial Studies — Part 8: Basic Ideas of Linear regression: The Two-Variable Model Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using | medium | 5,711 |
.
(OLS). It is important to know how to apply the concepts of simple linear regression and how sample data can be used to estimate population regression parameters. 2. Regression Analysis Regression analysis attempts to measure the relationship between a dependent variable and one or more independent | medium | 5,714 |
.
variables. 3. Scattergram A scatter plot (or scattergram) is a collection of points on a graph where each point represents the values of two variables (i.e., an X/Y pair). 4. Population Regression Function A population regression line indicates the expected value of a dependent variable conditional | medium | 5,715 |
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specializes in advanced analysis and model development. Over more than 17 years, he has conduct actuarial studies which include: constructing discrete and continuous probability distributions, applying population and sample statistics, making statistical inference and hypothesis testing, estimating | medium | 5,722 |
.
Actuaries (IAVFA). Mr. Polanitzer is the Founder of the IAVFA and currently serves as its chairman. Mr. Polanitzer’s professional recognitions include being designated a Financial Risk Manager (FRM) by the Global Association of Risk Professionals (GARP), a Certified Risk Manager (CRM) by the Israel | medium | 5,726 |
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Association of Valuators and Financial Actuaries, and frequently speaks on business valuation at professional meetings and conferences in Israel. He also developed IAVFA’s certification programs in the field of valuation and he is responsible for writing the IAVFA’s statement of financial valuation | medium | 5,729 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
Reading: EDCNN — Enhanced Deep Convolutional Neural Network (Codec Filtering) Using ResNeXt Block, 6.45% BDBR Reduction, Outperforms SRResNet and RHCNN EDCNN is proposed to replace the original in-loop filter (DF & SAO) in HEVC In this story, Enhanced Deep Convolutional Neural Network (EDCNN), by | medium | 5,731 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
anjing University of Information Science and Technology, Chinese Academy of Sciences, Sungkyunkwan University, and City University of Hong Kong, is described. In this paper, a CNN-based in-loop filter is proposed to replace the original in-loop filter (DF and SAO) in the conventional HEVC. This is | medium | 5,732 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
a paper in 2020 TIP where TIP has a high impact factor of 6.79. (Sik-Ho Tsang @ Medium) Outline EDCNN Network Architecture Weight Normalization Feature Information Fusion Block Mixed MSE and MAE Loss Function Experimental Results 1. EDCNN Network Architecture EDCNN Network Architecture The input is | medium | 5,733 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
the picture before filtering, the output is the picture after filtering which has higher image quality. EDCNN consists of 7 blocks, each fusion block contains 4 convolution and ReLU layers. Each convolution layer has an operation of weight normalization. (The fusion block and weight normalization | medium | 5,734 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
will be mentioned later.) The overall proposed network has 16 layers. The detailed network parameters are as follows: EDCNN Network Parameters 2. Weight Normalization For batch normalization (BN), the output of each neuron (before application of the nonlinearity) is normalized by the mean and | medium | 5,735 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
standard deviation of the outputs calculated over the examples in the minibatch. However, noise is added to the gradient. Weight normalization is to normalize the weight: Thus, the dependencies of mini-batch will not be introduced. And weight normalization can be viewed as a cheaper and less noisy | medium | 5,736 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
approximation to BN. (If interested, please the paper about weight normalization: Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. This is a paper in 2016 NIPS.) Loss of Weight Normalization and Batch Normalization It is found that the loss obtained | medium | 5,737 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
by the weight normalization is lower than the BN one. Thus, weight normalization is adopted. 3. Feature Information Fusion Block 3.1. 1×1 Conv Then 3×3 Conv Fusion Block A ResNeXt block is used as the feature information fusion block. (Please feel free to read my story about ResNeXt.) Number of | medium | 5,738 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
branches α where α is the number of branch tested. It is found that α=4 obtains the highest PSNR, as shown in the table above. 3.2. 3×3 Conv Fusion Block Another Fusion Block Variant Another fusion block variant is also tried which does not have 1×1 to reduce the dimensionality. Instead, the 3×3 | medium | 5,739 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
conv alone performs both dimensionality reduction and feature extraction altogether using a larger stride. And it is found that the fusion block using 1×1 conv plus 3×3 conv has the better result. 3.3. With or Without Fusion Block NF: Network fusion block. NWF: Network without fusion block. | medium | 5,740 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
(However, it is not clear in the paper that whether the whole fusion block is removed from the network, or it is replaced by a 3×3 conv, or just α=1.) Of course, as shown above, NF is better than NWF. 4. Mixed MSE and MAE Loss Function 4.1. Mean Square Error (MSE) loss However, MSE will over | medium | 5,741 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
penalize the errors by the square, and it has been proved that the MSE cannot capture the intricate characteristics of the HVS. 4.2. Mean Absolute Error (MAE) loss The network is easier to obtain the precise results due to the MAE is not sensitive to the outlier. However, the MAE is hard to | medium | 5,742 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
descend. 4.3. Mixed MSE and MAE Loss where δ is an adaptive parameter according to loss convergence. where N is the number of continuous epoch, and it equals to 3; c represents the number of current epoch; L is the loss value; ξ is the threshold, which is used to control the performance of the loss | medium | 5,743 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
function. To come up with the optimal ξ, a group of ξ values from 0.009 to 0.018 are tested as below: It is found that ξ=0.015 has the best performance. Among MSE, MAE and the proposed mixed loss, the proposed one obtains the highest PSNR. (a) Ground Truth, (b) MSE, (c) MAE, (d) Proposed Mixed MSE | medium | 5,744 |
Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
and MAE Loss The zoomed regions of proposed loss function has the best performances with less artifacts. 4. Experimental Results 4.1. BDBR (BD-Rate) BDBR (BD-Rate) (%) and BDPSNR (dB) Using Low Delay Configuration BDBR (BD-Rate) (%) and BDPSNR (dB) Using Random Access Configuration The BDBR | medium | 5,745 |
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