Block-T

Russia invaded Ukraine on the 24th of February 2022. Belarus offers logistic support to the Russian army. Our company website until the invasion stops. We don't intend to punish our readers from Russia and Belarus but to show dissatisfaction and disapproval of the political and philosophical stance of the Russian government. won't be available in Russia and Belarus Our company and I are in . In addition to being against war, we strongly oppose evil and destructive ideas that support autocracies. Make no mistake, , but a joint effort of individuals who believe it is not you who should be deciding how to live your life. strong supporters and believers individual liberty, free trade, and free association this is not the doing of one man We will continue doing as much as possible to show our support for the Ukrainian people and invite you all to do the same. If you use CloudFront as your CDN, the following tutorial will help you to and block traffic from Russia and Belarus. enable CloudFront Geographic Restrictions If you are using any other CDN or a different provider, send us an email at . I will personally assist you in blocking traffic. hello@crocoder.dev I will show you three ways to block traffic depending on how your CloudFront distribution is provisioned. Manually / AWS Console CloudFormation Terraform Manually / AWS Console Open and choose the distribution. CloudFront Distributions Click on the tab. Click on edit button. Geographic restrictions Check and select from the dropdown Russia and Belarus. Click Save changes. Restriction type - Block list You have just blocked all traffic from Russia and Belarus. All visitors from those locations will get status code. 403 (Forrbidden) CloudFormation If you are deploying the CloudFront via CloudFormation add this code to in CloudFrontDistribution Properties. Restrictions Use the following code snippet: CloudFrontDistribution:
  Type: 'AWS::CloudFront::Distribution'
  Properties:
    DistributionConfig:
      ...
      Restrictions:
        GeoRestriction:
          Locations:
          - RU
          - BY
          RestrictionType: blacklist Terraform You can easily add restrictions objects to your . aws_cloudfront_distributions Use the following code snippet: resource "aws_cloudfront_distribution" "website_distribution" {
  ...
  restrictions {
    geo_restriction {
      restriction_type = "blacklist"
      locations        = ["RU", "BY"]
    }
  }
} Thank you for reading. ❤️🇺🇦 Also published on . CroCoder

How to Block Traffic to Your Website from Russia and Belarus

: #NEWS 20 June 2018 Stellar in Talks to Acquire Blockchain Startup Chain . The $500 million acquisition is now public knowledge. So it is confirmed that Ari David Paul tipped non-public ('Insider’) info. to Kazonomics on 5 May 2018. How and when did you learn about the acquisition of Chain by Stellar? You tipped Kazonomics with insider information about the acquisition on 5 May 2018. Who else did you tip? Has BlockTower Capital traded XLM in 2018? How much? Please supply all trade details. Have you personally traded XLM in 2018? How much? Please supply all trade details. Resources for Insider Trading Whistleblowers Blow the Whistle on Pump and Dump Schemers (CFTC) Submit a Tip (CFTC) Whistleblower Program (CFTC) Background: DM Conversation Between Ari David Paul and Kazonomics On the pseudonymous Twitter account posted this enigmatic tweet. It raised little attention. Then he went quiet. 2 June 2018 @Kazonomics Open the link in Twitter and read full thread On Kaz published this redacted version of a DM exchange between and himself from . (The date format is US: M/D/Y). 10 June Ari David Paul 5 May and 6 May Ari David Paul is CIO of , a crypto fund with $150 million under management. Paul co-founded the firm with Matthew Goetz. BlockTower Capital Note that this DM conversation took place before Kaz’s mysterious Tweet of 2 June. Redacted version of DM conversation. It is hard to make sense of that exchange without a little background. What is ‘ ’ and all this talk of racism? On 5 May Paul had Tweeted recommending Technical Analysts. ugh that old TA thread At this stage someone called Zissou weighed. He and Kaz obviously have an old beef. However, all of that is irrelevant. The only statement of significance in the entire redacted exchange is this. But it was impossible to make sense of it at first because it referred to the previous redacted paragraphs. There has been no Chain announcement at 5 May 2018. The news of the acquisition became public on 20 June 2018. Paul means XLM is a great buy (for insiders) before imminent announcement. On 12 June Kaz published the full, unredacted conversation: 1.2 is the smoking gun Paul states “ ” Stellar will announce an acquisition of Adam Ludwin’s Chain sometime in the next 2 weeks. This is not widely known, but at least some funds know. Who are these entities? : Stellar is a platform that connects banks, payments systems, and people. It claims to ‘Integrate to move money quickly, reliably, and at almost no cost.’ The Stellar token is XLM. (Paul: “ .”) XLM is a cryptoasset, but is it a security? This has great significance in the eventual judgement of Ari David Paul. Stellar For example, if I thought XLM was a great buy because of the Chain announcement, I would first buy it then tell you Details of the , Developers, Board Members, and Advisors. Stellar Team : Adam Ludwin CEO at Chain. on Twitter. **@**adamludwin : Chain is a San Francisco based technology company on a mission to enable a smarter and more connected economy.They build cryptographic ledgers that underpin breakthrough financial products and services. Chain Insider Trading What is ? Insider Trading From Investopedia: . Insider trading is the buying or selling of a security by someone who has access to material nonpublic information about the security Insider trading can be illegal or legal depending on when the makes the trade. It is illegal when the material information is still nonpublic. insider Legal insider trading happens when directors of the company purchase or sell shares, but they disclose their transactions legally. Illegal insider trading includes tipping others when you have any sort of nonpublic information. The Martha Stewart Case Directors of companies are not the only people who have the potential to be convicted of insider trading. In 2003, Martha Stewart was by the SEC with obstruction of justice and securities fraud — including insider trading — for her part in the 2001 ImClone case. charged Stewart sold 4,000 shares of biopharmaceutical company ImClone Systems based on information received from Peter Bacanovic, a broker at Merrill Lynch. Bacanovic’s tip came after ImClone Systems’ chief executive officer (CEO), Samuel Waksal, sold all his shares of the company. This came around the time ImClone was waiting on the Food and Drug Administration (FDA) for a decision on its cancer treatment, Erbitux. Shortly after these sales, the FDA rejected ImClone’s drug, causing shares to fall 16% in one day. The early sale by Stewart saved her a loss of $45,673. However, the sale was done based on a tip she received about Waksal selling his shares, which was not public information. After a 2004 case, Stewart was charged with lesser crimes of obstruction of a proceeding, conspiracy, and making false statements to federal investigators. Stewart served five months in a federal corrections facility. To summarise the chain of events in the Martha Stewart case: ImClone Systems’ chief executive officer (CEO), Samuel Waksal, sold all his shares of the company when he learned the FDA’s decision. Peter Bacanovic, a broker at Merrill Lynch, tipped this info. to Martha Stewart. She sold. Food and Drug Administration (FDA) announces its negative decision on ImClone cancer treatment. Shares crash. Martha prosecuted and imprisoned. Bacanovic imprisoned. Waksal imprisoned. The question about Paul is whether these events transpired. Stellar and Chain privately agree terms to an acquisition. This can be expected to be positive news for the XLM price when it is announced. Someone passes this information to Paul. Did Paul buy XLM? Did Blocktower buy XLM? On 5 May 2018 Paul DMs Kazonomics and, totally unsolicited, tips the inside information, to Kazonomics. Kazonomics understands that he has been placed in a dangerous position and blows the whistle. On 20 June 2018 of the acquisition of Chain by Stellar. Fortune breaks the news Was Ari David Paul Insider Trading? If Paul had access to insider information about an imminent acquisition of Chain by Stellar and divulged that information to third parties with a view to them buying Stellar XLM, would that be insider trading? The answer surely depends on two things. Did Paul have insider information? Yes. I have searched Google, the websites of Stellar and Chain, Twitter, the Twitter accounts of Stellar, Chain, Adam Ludwin, and Ari Paul, and there is no information about an imminent acquisition of Chain by Stellar until the Fortune story on 20 June 2018. 2**. Is Stellar a security?** The whole matter of whether certain cryptoassets are securities or not is the subject of public debate. The SEC seems to have ruled that . Bitcoin and Ethereum are not securities The jury is still out on Stellar XLM and Ripple XRP, which are tokens of a very similar nature. However the consensus is forming that XRP (and most likely XLM) will be defined as securities. That is bad news for Ari David Paul. But until that matter is settled, whether XLM is a security, it seems that Ari Paul has a ‘get out of jail’ card. There was no increase in the price of XLM when Fortune broke the Stellar/Chain story. Is this a possible defence for Paul? It seems not: Ten [Original] Questions for Ari David Paul of BlockTower Capital [ .] These questions were written before the news of the Stellar/Chain deal became public on 20 June 2018 Are the screenshots by Kazonomics a true impression of the DM conversation you had with him on 5 and 6 May 2018? [ .] Paul has acknowledged on Twitter they are genuine Is there an imminent acquisition of Chain by Stellar? [ .] The acquisition became public news on 20 June If there is an imminent acquisition, how and when did you learn about it? Is this public information or insider information? [ .] It became public on 20 June If it is public information, please give a link to an internet site showing that information. If there is no imminent acquisition of Chain by Stellar, why did you inform Kazonomics that there is an imminent acquisition? Who else have you informed privately of an imminent acquisition of Chain by Stellar? Does BlockTower Capital hold XLM? How much? When did it buy it? Do you personally hold XLM? How much? When did you buy it? You stated on 5 May 2018 that “ Six weeks have now passed at time of writing (15 June 2018) and this has not happened. Has the acquisition been cancelled? [ .] Stellar will announce an acquisition of Adam Ludwin’s Chain sometime in the next 2 weeks.” No

Four (Revised) Questions for Ari David Paul of BlockTower Capital

Efficient Neural Network Approaches for Conditional Optimal Transport: Numerical Experiments

ArrayPool<T>, a mechanism to recycle temporary buffers and optimize performance by reducing garbage collection cycles. Introduction Frequent allocation and deallocation of bigger buffers can impact performance due to the increased work on the garbage collector. The recommendation is to use ArrayPool<T>, a mechanism to recycle temporary buffers and optimize performance by reducing garbage collection cycles. Learning Objectives Understanding the Problem with Traditional Buffer Allocation The Approach Using ArrayPool<T> Best Practices for Using ArrayPool<T> Prerequisites for Developers Basic understanding of C# programming language. 30 Day .Net Challenge Getting Started Understanding the Problem with Traditional Buffer Allocation A common approach developers use involves directly allocating a new buffer // Allocating a new large buffer
byte[] buffer = new byte[4096]; While the aforementioned code snippet looks clean and straightforward, it has significant drawbacks regarding performance, particularly in applications that frequently request large temporary buffers. Each allocation will increase the heap size, leading to more frequent garbage collection. The Approach Using ArrayPool<T> ArrayPool<T> is part of the System.Buffers namespace and provides temporary arrays, thereby reducing the need for frequent memory allocations and GC. // Using ArrayPool<T> to recycle large buffers
var pool = ArrayPool<byte>.Shared;
byte[] buffer = pool.Rent(4096);
try
{
    // Work with the buffer
}
finally
{
    pool.Return(buffer);
} Best Practices for Using ArrayPool<T> While ArrayPool<T> can significantly enhance performance, there are best practices to ensure its effective use: Always return the rented buffers to the pool in a final block to ensure that they are returned even if an exception occurs. Properly Return Buffers: If sensitive data is stored in the buffer, use the overload of the Return method that takes a boolean parameter indicating whether the buffer should be cleared. Clear Buffers When Necessary: Rent buffers for the shortest time necessary to keep the pool efficient and avoid exhausting the pool. Avoid Holding Buffers for Long Periods: Complete Code Create another class named ArrayPoolExample and add the following code snippet. public static class ArrayPoolExample
{
    public static void BadMethod()
    {
        // Allocating a new large buffer
        byte[] buffer = new byte[4096];
        // Simulate work with the buffer
        FillBuffer(buffer, 0xAA); // Example operation
        Console.WriteLine("Buffer used and will be discarded after method execution.");
    }

    public static void GoodMethod()
    {
        var pool = ArrayPool<byte>.Shared;
        byte[] buffer = pool.Rent(4096);
        try
        {
            // Work with the buffer
            FillBuffer(buffer, 0xBB); // Example operation
            Console.WriteLine("Buffer rented from the pool and returned after use.");
        }
        finally
        {
            pool.Return(buffer);
        }
    }

    public static void FillBuffer(byte[] buffer, byte value)
    {
        for (int i = 0; i < buffer.Length; i++)
        {
            buffer[i] = value;
        }
        // Just an example to simulate buffer usage
        Console.WriteLine($"Buffer filled with value: {value}");
    }
} Execute from the main method as follows. #region Day 22: Array Pool
static string ExecuteDay22()
{

    Console.WriteLine("Demonstrating BAD Method:");
    ArrayPoolExample.BadMethod();
    Console.WriteLine("\nDemonstrating GOOD Method:");
    ArrayPoolExample.GoodMethod();
    return "Executed Day 22 successfully..!!";
}

#endregion Console Output Demonstrating BAD Method:
Buffer filled with value: 170
Buffer used and will be discarded after method execution.

Demonstrating GOOD Method:
Buffer filled with value: 187
Buffer rented from the pool and returned after use. Complete Code on GitHub GitHub — ssukhpinder/30DayChallenge.Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: Follow us: | | | Youtube X LinkedIn Dev.to Visit our other platforms: GitHub More content at C# Programming

The 30-Day .NET Challenge - Day 22: Use Array Pool

TOPTAL, LLC v. DENIS GROSZ Court Filing, retrieved on February 26, 2024, is part of . You can jump to any part in this filing . This part is 5 of 7. HackerNoon’s Legal PDF Series here B. Repaying Matured Debt is not a Breach The Court agrees with Toptal's interpretation that based on the unambiguous language of the Note and the NPA, principal and accrued interest were due and payable any time after the May 14, 2014, Maturity Date. The Note itself states that “Unless earlier converted into Conversion Shares pursuant to Section 2.2 of [the NPA] ... the principal and accrued interest shall be due and payable by the Company on demand by the Lender at any time after the Maturity Date.” Consistent with that provision, the NPA provides that Mr. Grosz could have sued Toptal for principal and interest after the Maturity Date without first demanding payment before suing, as Mr. Grosz has admitted in briefs filed with the Court. Grosz's Reply in Supp. of Mot. to Dismiss at 5 n.3. Toptal thus did not breach by attempting to pay Mr. Grosz an amount equal to the principal and accrued interest on March 26, 2020, nearly six years after the Note's Maturity Date. E.g., The Note's use of the phrase “on demand by the Lender” does not change this conclusion. California courts have held for over 150 years that a “note payable on demand[] is payable immediately, and demand need not be made.” 38 Cal. 407, 407 (1869); demand accrues with the inception of the obligation and without the necessity for any demand hardly requires the citation of authority.).[4] Mr. Grosz does not cite any authority—binding or persuasive—to the contrary.[5] Thus, Toptal did not breach by attempting to repay after the Maturity Date even though Mr. Grosz had not made a demand. , 54 Cal. App. 485, 488-89 (1921) (applying “the well-settled principle that an obligation for the payment of money on demand is due immediately, no actual demand is necessary, “and an “express waiver" of demand “is equivalent to a contract that the debt shall be payable without any previous demand“). E.g., Bell v. Sackett, see See Rottman v. Hevener Toptal’s reading that the Note became due and payable on the Maturity Date also is consistent with the meaning of the term “Maturity Date.“ As court have observed, “maturity dates generally rank as among the most staid of contract terms. While one can muster adventurous arguments and debate about all sort of other terms, what is there to contest about the typical maturity date in a promissory note? Before the date, there is merely an obligation. On or after the date, principal and any itnerest are owed.” 2014 WL 1494370, at *1 (N.D. Cal. Apr. 16, 2014). As noted above, Mr. Grosz has admitted that he could have sued Toptal for failure to repay after the Maturity Date without first making a demand, and “[i]t is settled that an obligation ‘matures‘ when the holder of the note has a legal right to bring ana ction to force payment.” Jessup Farms v. Baldwin, 33 Cal. 3d 639 (1983). Fennie v. E-Fuel Corp., The Court rejects Mr. Grosz's argument that the Note prohibits Toptal from paying the principal and accrued interest without his consent after the Maturity Date. The provision to which Mr. Grosz points is a prohibition on “prepayment”—not “repayment”—and it simply stands for the uncontested fact that Toptal was prohibited from paying Mr. Grosz the principal and accrued interest prior to when it became due and payable on the Maturity Date. Moreover, Section 7.1(a) of the NPA authorizes Mr. Grosz to declare an event of default and sue Toptal if Toptal defaulted in the payment of any part of the principal and interest more than thirty days after the Maturity Date. Mr. Grosz's argument that he could block Toptal from returning the principal and interest on the matured Note by withholding demand but at the same time declare an event of default and sue Toptal for failure to repay introduces an “unnecessary internal inconsistency.” 229 Cal. App. 4th 549, 568 (2014); 2010 WL 19295666, at *4 (N.D. Cal. May 12, 2010). The only reasonable interpretation, then, is that Toptal had an obligation to repay Mr. Grosz after the Maturity Date; otherwise, there would be no rational reason why Mr. Grosz could declare an event of default and sue Toptal for failing to repay thirty days after the Maturity Date. Cal. Civ. Code § 1641 (“The whole of a contract is to be taken together, so as to give effect to every part, if reasonably practicable, each clause helping to interpret the other.”); § 1643 (“A contract must receive such an interpretation as will make it ... reasonable.”); 3542 (“Interpretation must be reasonable.”). See Dameron Hosp. Assn v. AAA N. California, Nevada & Utah Ins. xch., Harrison Ventures, LLC v. Alta Mira Treatment Ctr., LLC, See id. id. § The Court thus finds that the terms of the NPA and Note, as well as settled California law, establish that Mr. Grosz's Note became due and payable on the Maturity Date and that Toptal was within its rights to return an amount equal to Mr. Grosz's principal and accrued interest in March 2020. Summary judgment is granted to Toptal on this theory of breach. Continue Reading . Here [4] The Court disagrees with Mr. Grosz's argument that cases interpreting the phrase “on demand” in promissory notes are irrelevant because his Note is “a modern convertible promissory note.” It is neither uncommon nor inappropriate to review case law from the past. In any event, as Toptal observes, California courts acknowledge that convertible debt instruments have long been issued in the United States, including its initial industrial expansion and the building of railroads, see Kessler v. Gen. Cable Corp., 92 Cal. App. 3d 531, 537-39 (1979), and the California case law interpreting the “on demand” language in convertible notes is as recent as 2019, see t’Bear v. Forman, 359 F. Supp. 3d 882 (N.D. Cal. 2019). [5] Mr. Grosz cites to the unpublished decision in Vigdor v. Super Lucky Casino, Inc., 2018 WL 6069944 (N.D. Cal. Nov. 20, 2018), but that case did not overturn a century and a half of California case law interpreting “on demand“ contracts. Vigdor did not address the interpretation of the words “on demand“ or “maturity date“ under California law (and the briefing in that case shows that none of the cases cited above were brought to the Vigdor court’s attention by either side). Further, the Vigdor note purchase agreement did not provide for default and the ability to sue for repayment if the principal and interest were not repaid within 30 days of the maturity date. Moreover, the Vigdor note purchase agreement involved multiple lenders, and the complaining noteholders only had a minority in interest who did not have the contractual ability to act absent the consent and approval of other third parties who represented a majority in interest. In this case, Mr. Grosz, as the single lender under the NPA, was at all times able to unilaterally act under his contract with Toptal without recourse to the consent of any third parties. Notably, the result in Vigdor was repayment of the principal and interest after the maturity date over the objection of certain noteholders who preferred to receive equity, just as Mr. Grosz does here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on October 11, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. media.licdn.com

Toptal's Debt Repayment Strategy Evaluated in Legal Context

Introduction Would you like to see Mona Lisa smile like a witch? Or would you like the girl with the pearl earring to wink and smile? Google has just dropped a video generation model named Lumiere [1] that's capable of doing all of it for you. Though it's primarily a text-to-video model, it's able to do much more than that. Given a reference image with a prompt, it can stylize your videos by copying the style of the reference image to your videos. You can even edit your videos with just a single prompt. The model is Lumiaire. It's even able to animate objects within a user-specified region in an image, a technique called Cinemagraphs. When it comes to inpainting, Lumiere is even able to reason about a completely missing object such as a cake in this example. It all boils down to a diffusion model with a novel space-time U-Net architecture [3]. It's a customization of the U-Net architecture to solve the problem of temporal consistency which is quite prevalent in video generation models. Visual Explanation A visual explanation of the Lumiere paper, the model architecture, and the results are available. https://youtu.be/QcYa6xxsMTU?si=IBpUqjPtj8lJDHo0&embedable=true So, What Is Temporal Consistency? We all know that videos are a sequence of images. So, let's take a sequence of images shown in the top row in the above figure. If we narrow down just one row in the image, which is indicated by the green line going from left to right, we need to see a smooth transition in the pixel values between images in the sequence. If the transition is smooth, then we won’t see a jumping effect while we are watching the video. For example, if we take Stable Video Diffusion [2], and see the video of an astronaut walking on the moon (above), we can see that his hands just disappear between frames. In other words, the temporal consistency between frames is lacking. This kind of temporal inconsistency between time and the intensity in the x direction can be plotted as the X-T Slice as highlighted in the figure above. And if there is temporal inconsistency, it is highlighted in the plot of the X-T slice. Lumiere addresses this problem by introducing a space-time diffusion model and a modified U-Net architecture present in the diffusion model. Pipeline of a Text to Video Before looking into the details, let's start with the typical pipeline of a Text-to-video generation model. These pipelines sample every 5th frame as the keyframes from the input video sequence and train a base model that can generate these keyframes at a resolution as low as 128 by 128 at just 3 frames per second. Then temporal Super Resolution is used to increase the frame rate by predicting the intermediate frames. So the frame rate now becomes 16 frames per second. The spatial resolution of these frames is then increased to say 1024 by 1024 by a spatial superresolution network (SSR) which finally leads to our generated video. The base model used in this framework is usually a diffusion model which in turn has a U-Net inside it. The Proposed Pipeline of Lumiere The proposed pipeline of Lumiere on the other hand processes all the frames at once without dropping frames. To cope with the computational cost of processing all the frames, the architecture of the base diffusion model is modified to come up with the space-time UNet architecture or STUNet. As STUNet deals with all the input frames, the need for Temporal Super Resolution or TSR is eliminated. So, the pipeline still has the spatial super-resolution or SSR. But the novelty is the introduction of MultiDiffusion. From U-Net to STUNet Let's quickly review the U-Net before looking into the space-time U-Net. The input to the U-Net is a 3 3-dimensional image with Width W, Height H, and channels RGB. After every double convolution stage of the U-Net, we apply max pooling to downsample or reduce the spatial dimension of the features. This spatial dimension reduction step is indicated by the red arrows. Similarly, during the decoder stage, there are up convolutions to increase or upsample the resolution back to the size of the input. When it comes to videos, we have an additional dimension in the input which is time. so space-time U-Net proposes to downsample and up-sample the video not only in the spatial dimension but also in the dimension of time T. This is the main idea of temporal resizing and is the main contribution of this Lumiere paper. For resizing, they use 3D pooling instead of 2D pooling as the input now has an additional dimension. Like me, you may be surprised by the simplicity of the idea. The authors themselves have mentioned in the paper: Surprisingly, this design choice has been overlooked by previous T2V models, which follows the convention to include only spatial down and up-sampling operations in the architecture, and maintain a fixed temporal resolution across the network. Implementation Let's get to some of the nuances of the implementation. They use factorized convolution introduced in this paper called Video Diffusion Models. The idea is to change each 2D convolution into a space-only 3D convolution, for instance, by changing each 3x3 convolution into a 1x3x3 convolution. For the attention, after each spatial attention block, we insert a temporal attention block that performs attention over the first axis and treats the spatial axes as batch axes. With those two changes, the factorized convolution blocks are added to the pre-trained model, and only the additional layers are trained with the pre-trained layer weights fixed. The second novelty of the paper is the MultiDiffusion introduced during the spatial superresolution. If you take the video generation models before lumiere, the spatial superresolution model takes a sequence of frames. However, the sequences were not overlapping. For example, the first 8 frames and the next 8 frames taken as input by the SSR module are separate without any overlap. But when it comes to Lumiere, the first 8 frames and the second 8 frames have an overlap of two frames. By doing this, the spatial super-resolution model seems to achieve smooth transitions between the temporal segments. This is what is referred to as multiDiffusion in the paper. Applications Coupling the two proposed techniques along with the absence of a cascaded diffusion model architecture which is prevalent in prior architectures such as imagen video, leads to quite a few varied applications. For example: The model can convert text to videos with prompts such as “an astronaut walking on planet Mars making a detour around his base” or “a dog driving a car wearing funny sunglasses.” It can convert images to videos along with a text prompt such as “A girl winking and smiling.” It can stylize generation with a reference image and a text prompt such as “a bear dancing.” Talking of Cinemagraphs, it can animate regions selected by the user such as fire or steam. It can even edit dresses that people are wearing with just a single prompt. Evaluation To evaluate the model quantitatively, the model was run through a user study where the users compared the results of the proposed model against some state-of-the-art models such as Pika, ZeroScope, or stable video diffusion. The results indicate that the users preferred the Lumiere model both in the case of text to video and image to video. Conclusion So, to conclude, other than all the promotional video stunts, such as the smiling Monalisa, the contribution of this paper is fairly simple. Put in one line, the paper introduces a downsampling of the temporal channel. This coupled with MultiDiffusion, which is nothing but having overlapped frames fed into the superresolution model, generates high-fidelity videos that are temporally consistent. What I would have liked to see is some ablation studies in the paper showing the results with and without the multiDiffusion process. That brings us to the end of this article. The next time someone talks to you about Lumiere, you know what to say in one line. I hope that provided some insight into the Lumiere model. I will see you in my next, until then, take care… References [1] Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Herrmann, Roni Paiss, Shiran Zada, Ariel Ephrat, Junhwa Hur, Yuanzhen Li, Tomer Michaeli, Oliver Wang, Deqing Sun, Tali Dekel, Inbar Mosseri, (2024), arXiv preprint. A Space-Time Diffusion Model for Video Generation [2] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, Varun Jampani, Robin Rombach, (2023), arXiv preprint. Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets [3] Olaf Ronneberger, Philipp Fischer and Thomas Brox, (2015), International Conference on Medical Image Computing and Computer-Assisted Intervention. U-Net: Convolutional Networks for Biomedical Image Segmentation Also published here

Google Unveils Its Most Promising Text-to-Video Model Yet: Lumiere

গুগল এখনও পর্যন্ত তার সবচেয়ে প্রতিশ্রুতিশীল পাঠ্য-টু-ভিডিও মডেল উন্মোচন করেছে: লুমিয়ের

Google stellt sein bisher vielversprechendstes Text-to-Video-Modell vor: Lumiere

Google presenta su modelo de texto a video más prometedor hasta el momento: Lumiere

Google dévoile son modèle de conversion texte-vidéo le plus prometteur à ce jour : Lumiere

Google ने अपना अब तक का सबसे आशाजनक टेक्स्ट-टू-वीडियो मॉडल लॉन्च किया: लुमिएरे

Google、これまでで最も有望なテキストからビデオへのモデル「Lumiere」を発表

Google은 지금까지 가장 유망한 텍스트-비디오 모델인 Lumiere를 공개했습니다.

Google revela seu modelo de conversão de texto em vídeo mais promissor: Lumiere

Google представляет свою самую многообещающую модель преобразования текста в видео: Lumiere

Google Şimdiye Kadarki En Umut Verici Metinden Videoya Modelini Açıkladı: Lumiere

Google công bố mô hình chuyển văn bản thành video hứa hẹn nhất: Lumiere

谷歌推出迄今为止最有前途的文本转视频模型：Lumiere

**PANAMA, February 5th, 2024/Chainwire/--**The Protocol Foundation has launched a to drive growth in Taraxa’s unique EVM-compatible blockDAG Layer-1 ecosystem, after an overwhelmingly successful on-chain governance approval. Taraxa $10 million Grant Program The Taraxa Grant Program is open to any individual, team, and organizations of any type. Funds will be granted specifically for technical development with an emphasis on driving usage and growth within the Taraxa ecosystem, and are disbursed according to quantifiable milestones. Building on Taraxa presents a unique opportunity for developers to experience the benefits of the world’s first and only native EVM-compatible decentralized network built with the blockDAG architecture. Here are just a few of the reasons why builders should consider building the next big idea on Taraxa. An unique opportunity to Build on blockDAG: blockDAG is the next evolutionary step up from block-chain, able to achieve stunning throughput and low latency without sacrificing security and decentralization by enabling parallel block processing on the network instead of just one block at a time. True EVM Compatibility: any dApp that works on Ethereum just works on Taraxa, no need to learn new languages, frameworks, or tooling. Fast & Low-Cost: with peak throughput of 5k TPS, sub-second block times, instant block inclusion, and under 4-second finalization, working on Taraxa is fast & smooth, with each transaction costing next to nothing. True Finality: zero risk of transactions being reversed, Taraxa’s True Finality guarantees that when a transaction is finalized (under 4 seconds), it is finalized forever. Front-Running Prevention: Taraxa’s architecture and speed minimize and fragment the network’s mem pool, making it almost impossible and definitively unprofitable for DeFi transactions to be targeted for front-running. Supportive Community: Taraxa’s community is highly engaged and the development team is helpful to a fault. Not only will the user be building on a network backed by the best technology in the world, but he will also receive the full support, attention, and participation from Taraxa’s community! The upcoming Ficus Root Bridge in mid-2024 further strengthens the Taraxa ecosystem’s utility, asset diversity, and cross-chain liquidity. DeFi builders don’t have to worry about fragmented assets or liquidity, since they flow seamlessly between Ethereum and Taraxa. The bridge, coupled with Taraxa’s fast, low-cost and natively EVM-compatibility, make Taraxa a natural Layer-2 solution for dApps or consensus layer for rollups. Developers can today and pitch their ideas on how to creatively leverage decentralization to solve problems! apply to the Taraxa Grant Program About Taraxa Founded by two Stanford engineers in 2018, Taraxa is an EVM-compatible smart contract platform based on t-Graph consensus utilizing blockDAG, with many world-first technical innovations on top of the blockDAG architecture. After launching its mainnet in 2023, Taraxa represents the next evolutionary step in decentralized networks. Contact Taraxa Protocol Foundation foundation@taraxa.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program . here

Taraxa Launches $10 Million Grant Program For Its BlockDAG Ecosystem

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Carlos Efrain Quintero-Narvaez, School of Science and Engineering Tecnologico de Monterrey; (2) Raul Monroy-Borja, School of Science and Engineering Tecnologico de Monterrey. TABLE OF LINKS Abstract and Introduction Background Design Discussion Conclusions and References II. BACKGROUND A. Blockchain and Smart Contracts Blockchain technologies are being increasingly adopted in different domains for their features regarding transparency and decentralization. Bitcoin [8] was the first proposed protocol based on this architecture, combining in its functioning techniques such as Proof-of-Work (PoW) [3], SHA-256 hashing, Merkle Trees, and the Elliptic Curve Digital Signature Algorithm (ECDSA). It is specially powerful because of its decentralized nature, provided through the Proof-of-Work validation done with each block of transactions added to a public registry known as the Blockchain. In the following years, variations on the protocol proposed by Bitcoin appeared, collectively known as cryptocurrencies, each having their own blockchain where the transactions are registered. Ethereum [4], Monero [12], Polkadot [13], Polygon [6], NEAR, Solana [14] and Avalanche [11] are some examples of these derivate protocols. Of the protocols we just mentioned, Ethereum is a critical one to talk about as it was the first to work with the Ethereum Virtual Machine (EVM) [4] architecture, an essential feature on which many other blockchains are built upon. The EVM allows protocols to execute code in a decentralized manner and register all of its execution steps on the blockchain, making the results of that code transparent and unbiased. This code is then deployed to an address stored in the blockchain and referred to as a Smart Contract, i.e. a contract that enforces itself automatically. A simple example of a task that can be performed using a Smart Contract is that of currency exchange. Usually, when two entities, Alice and Bob, each holding a different digital currency, want to exchange one for the other at a certain rate, a level of trust is needed. Indeed, either Alice has to transfer its currency to Bob first or vice versa, making the first entity that transfers vulnerable to the other not fulfilling their part of the deal. One way to balance this trust is by introducing a third-party entity, Charly, to which both parties transfer their funds so that Charly is tasked with ensuring that the deal is fulfilled. However, this only changes the protocol so that now both Alice and Bob have to trust Charly to act correctly. Here is where Smart Contracts come in, instead of having the parties transfer to a third one, transfer to a Smart Contract entity that automatically verifies that the deal is fulfilled without Alice and Bob having to trust in it, as it is executed on a decentralized network. This is illustrated in Figure 1. These trustless features of Smart Contracts then lead the way to a variety of applications to be built. Decentralized Finance (DeFi) [1], Automated Market Makers [2], and decentralized social media [10], are among the many uses of Smart Contracts that have developed in recent years. Once again, the decentralized and trustless nature in the execution of the code for each of these applications is what allows them to provide an enhanced trustless performance in comparison to their traditional counterparts. As we have said before, the essential base on which the Smart Contract infrastructure is built is the Blockchain. Let us note some of the implications of one particular attribute of the Blockchain, its functioning as a public registry. Every time a new block of valid transactions appears, it needs to be “mined”, i.e. finding a valid “nonce” value that when added at the end of the transaction block makes its SHA256 hash have certain characteristics. These characteristics are often that the hash has a number of zeros at the beginning, a number determined by the “difficulty” imposed by the network. This mining process is non-trivial by design, so that “work” needs to be performed before adding any transaction to the public ledger, hence the Proof-of-Work name. Notice that in order for the network to reach consensus that the work has been performed and the transactions validated accordingly, it is necessary for everyone to have complete access to each new transaction block. This makes any transfer of funds in the Blockchain public, including its receivers and senders. For this reason, it would be convenient to have a method through which the network could validate transactions without receiving compromising knowledge about its users, this is where Zero-Knowledge Proofs come in to play. B. Zero-Knowledge Proofs In the context of Blockchain transactions, one problem that arises is that, due to the public consensus nature of the protocol, all transactions are public. It is straightforward to check the movements of an entities funds by just checking the Blockchain registry. Although this can be partially avoided due to the easiness with which one can create new addresses (if an entity’s address is disclosed, it can just send its funds to a new one), it is still possible to just trace back the origins of any funds. A possible solution to this could work if there was a way for the network to verify the validity of transactions without them knowing the sender or the receiver, i.e. if there was a way to prove that a transaction is valid without disclosing any additional knowledge. Indeed, Zero-Knowledge Proofs are capable of doing this, as we will explaint next. A Zero-Knowledge Proof (ZKP) is a method through which one entity can prove a statement to another one without disclosing more information than the fact that particular statement is true. For example, given a fixed function f : X −→ Y and a fixed value y ∈ Y . If we wanted to prove to a third party that we know a value x ∈ X such that f(x) = y, a trivial solution would be to just disclose the value of x we know. However, that shares more information than the fact that the statement is true. A ZKP would entail generating a proof generator function P and a proof verifier V . Such that we can generate a witness w = P(x) that does not disclose the value x, and that another entity can then evaluate with the verifier function V (w) and obtain a true or false value that determines whether the witness was created from a valid solution or not. This way, we can prove to another entity that we know a value of x such that f(x) = y, while disclosing zero knowledge about which specific value of x we know. For a more intuitive example, consider a hashing function hash : I −→ H from an input space I to a hash space H, this function could be SHA-256, SHA-1, etc. Also, consider two entities, Alice A and Bob B. Imagine that Alice guards an entrance to a treasure cave, to which one can only enter by having a password ρ that is verified using its hash, i.e. Alice has the hash of the password h = hash(ρ). However, Alice herself does not have the password and cannot enter the cave. Now consider that Bob does have the password ρ and wants to enter the cave, but he does not want to disclose the password to Alice as to not have to share it with her. For most use cases, this can be achieved by having Bob compute the hash of ρ, and show it to Alice, having her verify that it is the same as h, the one she has. However, say that h is not disclosed to Alice through a private channel but through some public method as to ensure that she verifies passwords correctly and does not commit any dishonesty. This renders just showing the hash value h of the password not a valid method for verification. Usually, the only method left would be for Bob to disclose ρ to Alice and have her compute its hash, but there is another way to do this, which involves Zero-Knowledge Proofs. Groth16[5] is a Zero-Knowledge Proof system designed to generate a proof-verifier pairing schema for satisfiability of any arithmetic circuit. What is important about the arithmetic circuit satisfiability problem is the fact that it is NP-Complete and thus allows for a wide arrange of statements to be translated into that form, including those which are often of interest in the context of ZKPs. Note that, arithmetic circuits are comprised of gates that compute arithmetic operations (addition and multiplication) on a field F, with wires connecting the gates to represent results from one going to another. Thus, recall the SAT problem, which is NP-Complete and formulated as “given a statement with boolean variables, is it possible to find an assignment for them such that the statement is true?”. Similarly, the arithmetic circuit satisfiability problem is NPComplete too and formulated as “given a statement with variables with values from a field F, is it possible to find an assignment for them such that the statement is true?”. This makes Groth16 an ideal system for working when generating ZKPs, being used for ZK languages like Circom 2 [7]. Coming back to our conundrum with Alice and Bob in the treasure cave. As it happens, hashing functions such as SHA256 can be expressed in terms of arithmetic circuit satisfiability and can thus have a corresponding Zero-Knowledge Proof generated. Therefore, in the situation we had, it is enough for Alice to have a SHA-256 ZK proof verifier V and for Bob to use the prover function P to generate a witness w = P(ρ). Bob then shares w with Alice, so she evaluates it with the verifier V (w) and determines that the witness is valid. Thus, Alice can be sure that Bob does have the password without receiving any information about what is its exact value. Finally, it is straightforward to see how this can be applied to Blockchain transaction verification. Suppose that we have a transaction t represented in some space T, along with a validation function v that tells whether t is valid with the current state of the Blockchain, having 0 or 1 as output. Then for the transaction to be added by a miner into the registry, it would suffice to send a proof that v(t) = 1. Then, we could translate the computation of v into the Groth16 schema by using a compiler such as Circom 2.This would then allow us to generate a proof-verifier pair P and V . And finally, as was done with the Alice and Bob case, the sender would just generate a witness of the transaction w = P(t) and send it to the miners which would verify it by evaluating V (w). This schema is used by cryptocurrencies such as Monero and Zcash. Now, see that in the context that concerns this paper, a ZKP could be generated for the validity of the evaluation of a questionnaire according to some set of constraints. This would allow the user or any third party to get a convincing proof of the evaluation having been performed fairly, without revealing any information about the answers that give certain results. This paper is under CC 1.0 license. available on arxiv

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