Mills believes that modern technology can help reduce friction and barriers within small business lending. The way she sees it, the two challenges in small business lending boils down to information opacity, since it's "nearly impossible" to see inside a ...

Former SBA Chief Believes That APIs Can Revolutionize Small-Business Lending

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Welcome to this issue of the Activation Function Newsletter. For those of you who are new, it’s pretty simple - Every other week, I introduce you to a new and exciting open-source backend technology (that you’ve probably only kind of heard about ) and explain it to you in 5 minutes or less so you can make better technical decisions moving forward. In this issue, we’ll explore K8sGPT, an open-source tool that combines expert knowledge and Generative AI to help you identify, understand, and troubleshoot your Kubernetes cluster in plain English. Before you dismiss it as “just another ChatGPT wrapper,” let me assure you that it’s not. It’s much more than that. I think it’s a thoughtful implementation of GenAI that has the potential to bring tangible benefits and make your life a bit easier. Alright, Let’s dig in! Quick Facts: K8sGPT was launched at KubeCon Amsterdam 2023. K8sGPT is open-sourced under an Apache-2.0 license. K8sGPT was accepted to CNCF on December 19, 2023, at the Sandbox maturity level. K8sGPT now has over 4K GitHub stars and 50 contributors. Introduction For years, we’ve used ML for APM and Observability to identify patterns, trends, and anomalies (e.g., behavioral changes in applications or infrastructure), anticipate performance issues, generate early warnings, etc. But so far, it hasn’t been great at actually helping us understand and fix issues. The story goes something like this. You get an alert from your ML-enabled monitoring tool and then spend a lot of time looking through and interpreting complex error messages, trying to communicate those to your team, and iterating through remediation steps until you fix the problem. Enters K8sGPT. Its primary function is to help you quickly triage and remedy issues by giving hints and shortcuts in plain English. It does this by leveraging expert knowledge, Natural Language Processing (NLP), and Generative AI. How K8sGPT Works K8sGPT uses contain codified knowledge, which is essentially a series of rules/checks that an experienced Site Reliability Engineer (SRE) might use to diagnose Kubernetes issues such as pod crashes, service failures, and ingress misconfigurations, etc. Analyzers that It was a good decision to avoid using GenAI at this stage to prevent speculative guesses, hallucinations, and, ultimately, a lot of noise. Thought: Once K8sGpT is connected to your Kubernetes cluster through the API Server, the will start scanning your cluster and looking at logs, error messages, configurations, and pod status to identify potential issues. Analyzers : K8sGPT has a set of , but you will be able to write your own analyzers. Technical Note analyzers that are built in It then tries to correlate issues and run additional checks if necessary to add more context and figure out the root cause before packaging the data and sending it to the AI backend provider to get a simple and descriptive text of the issue and potential remediation steps. As you can see, the use of Gen AI is targeted and practical. Of course, the AI can hallucinate and provide incorrect remediation steps, but that’s a minor inconvenience you can spot and ignore easily. Technical Note: Getting Started with K8sGPT The easiest way to get started is using a by running a brew command on Linux/Mac or downloading the Windows Bianary if you’re on Windows. CLI installation Once installed, you’ll need to , and within a few minutes, you’ll be ready to run your first scan. authenticate your chosen AI backend K8sGPT is designed to work with various AI providers, including , , , , , , and . Technical Note: OpenAI Cohere Amazon Bedrock Amazon SageMaker Azure OpenAI Google Gemini LocalAI You can find how to configure different backends . here Once everything is configured, you can start using K8sGPT directly from your CLI using the following set of commands:‍ Usage:
  k8sgpt [command]

Available Commands:
  analyze     This command will find problems within your Kubernetes cluster
  auth        Authenticate with your chosen backend
  cache       For working with the cache the results of an analysis
  completion  Generate the autocompletion script for the specified shell
  filters     Manage filters for analyzing Kubernetes resources
  generate    Generate Key for your chosen backend (opens browser)
  help        Help about any command
  integration Integrate another tool into K8sGPT
  serve       Runs k8sgpt as a server
  version     Print the version number of k8sgpt

Flags:
      --config string        Default config file (default is $HOME/.k8sgpt.yaml)
  -h, --help                 help for k8sgpt
      --kubeconfig string    Path to a kubeconfig. Only required if out-of-cluster.
      --kubecontext string   Kubernetes context to use. Only required if out-of-cluster.

Use "k8sgpt [command] --help" for more information about a command. You’ll soon be able to debug interactively by asking K8sGPT questions about any issue directly in your CLI. As for writing, this feature isn’t live. [Technical Note] K8sGPT Operator K8sGPT can also be installed as an Operator within your Kubernetes Cluster. By using the K8sGPT operator, you’ll be able to scan your cluster for errors continuously. The works by leveraging Kubernetes Custom Resources and producing reports that it stores in your cluster as YAML manifests. K8sGPT Operator You can cache your results locally or like AWS S3. [Technical Note] offload them to a remote location You can also easily customize the analysis and output of the operator to fit your needs and workflow. For example, you specify a certain namespace or resource and a specific output format like JSON. This makes K8sGPT easy to integrate into your CI/CD pipelines and other workflows. Integrations K8sGPT integrates with observability tools like Grafana and Prometheus, but the really cool thing is that it offers an that allows you to extend scanning and troubleshooting capabilities by writing your own plugins that connect it to other tools. integration feature The first one, contributed by the core team, is the plugging, which enables you to generate vulnerability scans as part of your analysis and leverage AI to help you understand and triage Common Vulnerabilities and Exposures (CVEs). Trivy Security & Privacy Alright! I’m sure this is the first or second thing that came to your mind when hearing that K8sGPT sends your data to an external AI-API. Well, the K8sGPT team thought of this and developed a nifty little feature called anonymize, which masks sensitive data like Kubernetes object names and labels before sending it to the AI backend for analysis. All you need to do is use the anonymize flag: k8sgpt analyze --explain --anonymize During the analysis, k8sGPT retrieves sensitive data and masks it before sending it to the AI backend. Once the solution is returned, the masked data is replaced with the actual Kubernetes object names and labels. Where to go from here? K8sGPT is still a nascent project and is quickly evolving thanks to an active community of contributors. As of writing, an exciting new feature on the horizon is the interactive debugging mode that lets you ask questions directly inside your terminal. Other things on the horizon include Kubernetes auto-remediation with K8sGPT, Karpenter, deep integration into AWS services, and new backends into services such as Hugging Face. If you want to dig deeper into K8sGPT, here are a few resources for you: Documentation & Repos - The main code repository k8sgpt - Documentation Website ( ) docs https://docs.k8sgpt.ai - Community-related information community Tutorials K8SGPT - Kubernetes SRE SUPERPOWERS Using AI with Kubernetes | K8sGPT Operator | DEMO Full Tutorial: K8sGPT -- SRE superpowers through AI Talks AI for Kubernetes with ChatGPT and k8sgpt Unleashing the Power of AI in Kubernetes through K8sGPT | Alex Jones Communities Slack: https://k8sgpt.slack.com Who to Follow Alex Jones (Creator of K8sGPT) on and Linkedin X K8sGPT on and LinkedIn X Thomas Schuetz (Maintainer) on and Linkedin X Matthis Holleville (Maintainer) on and Linkedin X It’s a wrap! I hope this gave you a good overview of K8sGPT and how Gen AI might make your life a bit easier when troubleshooting your Kubernetes clusters. If you want to take K8sGPT for a quick spin, I suggest you check out this . Sandbox tutorial Until next time! Also published . here

A Gentle Intro to K8sGPT: Your Kubernetes Debugging Sidekick

Mr. Skelmersdale In Fairyland

Enterprise Blockchain Technologies is a flexible, extensible, open-source university course, part of Hyperledger Labs🎓 This course aims at providing a reliable basis for university students to get started in enterprise blockchain. This course contains the practical part of a university course on enterprise blockchain technologies. The first module: The second module: At the end of this course, I will: ✅ Understand theory on blockchain: what is it ✅ Get to know Hyperledeger Fabric’s components in detail, such as architecture and transactional model, chaincode network, and how to develop an enterprise full-stack blockchain decentralized application ✅ Understand advanced concepts on the area of consensus, using Hyperledger Umbra Course Organization The course is currently divided into two modules: * Module I introduces enterprise blockchain (labs 1–4) * Module II focuses on Hyperledger Fabric (labs 5–8). For universities using trimesters, a course on enterprise technologies uses 1 module. For universities using semesters, a course on enterprise technologies uses 2 modules. This course is flexible because you can pick the laboratories you wish to complete across modules. Currently, this course supports Hyperledger Fabric and Hyperledger Umbra but can be easily extended by adding modules. Get the course at: https://github.com/hyperledger-labs/university-course A 0.2-preview release is available at: https://github.com/hyperledger-labs/university-course/releases/tag/v0.2-preview This release contains the PDFs on the laboratory guides and respective solutions. The support code aids the compression of the topics discussed. A huge thanks to the contributors of the course 🙏 * JRafaelSoares * CatarinaPedreira * Maramih * Dhuseby * Raphaelvrosa * banoris

Enterprise Blockchain Technologies — A University-level Open-Source Course

Updated & Revised to include over 400 Banks & Financial Institutions using blockchain technology in 2020 ________________________ The below article is from: . The list has been updated, however, to include over double the banks originally listed. At the time of publishing, the list contains 414 banks and Financial Institutes from around the world that have invested in blockchain technology to some degree. Comprehensive List of Banks Using Blockchain Technology One of the industries which have been the most opposed to blockchain technology is the finance industry, but specifically banks. Initially, they saw the concept of blockchain as direct competition, actively removing the control they have over the funds and accounts of their customers. However, major players in the finance industry have increasingly begun to warm up to blockchain. Financial institutions and other major companies are inherently slow in adopting any new technology, but have actually been exploring various use cases for some time now. So let’s take a look at which institutes have already implemented the change or are looking into it as we speak. According to CEBNet in early 2019, of the 26 publicly listed banks in China, 12 have already adopted blockchain technology for various use cases within their systems. These banks vary from state owned ones like the Bank of China, China Construction Bank and the Agriculture Bank of China, to privately owned ones, including China Merchants bank. This is just China, before the strong public drive for blockchain adoption in late 2019, but the sentiment remains positive throughout the rest of the world. ALFA Bank from Russia, Yes Bank from India, UOB (United Overseas Bank) from Singapore, CommonWealth Bank from Australia, and LatiPay from New Zealand are a few banks that have collaborated with a US based blockchain company. The Federal Bank from India & Lulu Exchange have partnered with a Bengaluru based fin-tech company. Axis Bank, RAK Bank, & Standard Charter Bank have connected with another blockchain network. Kotak Bank, which enables blockchain based trade finance operation, partnered with Deloitte & JP Morgan Singapore. Other use-cases within the financial sector which have been developed include simplying and speeding up cross-border payments, improving trading accuracy, reliability and shorter settlement processes. Smart contracts have created benefits with the processing of transactions by removing the need of the middle man, as well as being used to develop loyalty and rewards systems. There are a lot more banks working within multiple technology & service providers to integrate blockchain in their Use Cases. This just demonstrates the demand in all industries for the adoption of blockchain technology. is making this possible with the ecosystem and the aelf Innovation Alliance which will enable blockchain technology to be adopted and integrates into mainstream organizations large and small. This has enabled the alliance to be successful with partnerships from experts and influencers like and , , and . Not only do organizations now have a platform to build upon, but they have resources, capital and expert advisors at their fingertips, which is all made possible through the this revolutionary initiative. Aelf aelf Michael Arrington Arrington XRP Capital Huobi Labs FBG Capital aelf ________________________ Below is a list of over 400 banks and financial institutes using or soon to be using blockchain technology: Banks 77 Bank ABN AMRO AEON Bank AIB Akbank Akita Bank Al Rajhi Banking & Investment Corporation Alawwal Bank Alfa Bank Allied Irish Bank ANZ (Australian and New Zealand Bank) Aomori Bank Al Rajhi Bank Ashikaga Bank ATB Financial Axis Bank Awa Bank Alipay Ant Financial B3 Banco Bilbao Vizcaya Argentaria (BBVA) Banca Intesa Sanpaolo Banca Mediolanum Banca Mifel Banco BICE Banco Bisa Banco Bradesco Banco de Crédito del Perú Banco de Investment Global Banco de Sabadell Banco de Galicia y Buenos Aires Banco Industrial Banco Mercantil del Norte Banco Mercantil Santa Cruz Banco Nacional de Bolivia Banco Popular Dominicano Banco Regional Banco Santander Bangkok Bank Bank ABC Bank Al Habib Limited Bank Busan Bank Dhofar Bank Frick & Co Bank Of America|Merrill Lynch Bank of Cyprus Bank Of England Bank of France Bank Of Indonesia Bank Of Iwate Bank Of Leumi Bank of Lithuania Bank of Montreal Bank Of Nagoya Bank Of Okinawa Bank Of Thailand Bank Of The Ryukyus Bank Of Tokyo-Mitsubishi UFJ Bank Of Yokohama Bankinter Banorte Banreservas Banque Internationale de Commerce — BRED (Suisse) Banque Thaler Barclays’ investment bank BCI Bexs Banco De Cambio BMO Financial Group (Bank of Montreal) BNP Paribas BNY Mellon BRAC Bank Limited Banco BTG Pactual Bank Mellat Bank Melli Iran Bank Pasargad BBVA CBW Bank (Citizens Bank Of Weir) Central Bank of Brazil Chiba Bank Chiba Kogyo Bank China Banking Association China Merchant Bank China CITIC Bank International Limited China Guangfa Bank Chong Hing Bank Limited Chugoku Bank (Bank of China) CIBC (Canadian Imperial commercial banks) CIM Banque S.A Geneve Citi Coast Capital Savings Commercial Bank of Africa Limited Commerzbank Commonwealth Bank Of Australia Cooperatieve Rabobank Credicorp Bank Credit Agricole Credit Libanais SAL Credit Suisse Cross River Bank CTBC Bank Cuallix Daishi Bank Daiwa Next Bank Danske Bank DBS Bank (Development Bank of Singapore) Deloitte DenizBank A.S. Deutsche Börse DNB Deutsche Bank Ehime Bank Eastern Caribbean Central Bank EQI Bank First America Bank Federal Bank of India Federal Reserve System Fidor Bank Fukui Bank Gazprombank Gunma Bank Goldman Sachs Habib Metropolitan Bank Limited Hachijuni Bank Hiroshima Bank HOKURIKU BANK Hong Kong Monetary Authority HSBC (Hong Kong Shanghai Bank) HYAKUGO BANK ICICI Bank Indovina Bank Ltd IndusInd Bank (Indus India Bank) Intesa Sanpaolo (Intesa Sanpaolo) Itau Unibanco Iyo Bank ING Japan Post Bank Joint Stock Commercial Bank for Investment and Development of Vietnam Joint-Stock Company BCS-Investment Bank JPMorgan JS Bank Limited Juroku Bank KASIKORNBANK Public Company Limited KEB Hana Bank Keiyo Bank Kotak Mahindra Bank Krungsri (Bank Of Ayudhya / Bank of Ayudhya) Kuveyt Turk Katilim Bankasi Kuwait Finance House Macquarie Group MauBank Limited Michinoku Bank Mitsubishi UFJ Trust And Banking Corporation Mizuho Financial Group Monetary Authority Of Singapore Morgan Stanley Musashino Bank National Australia Bank National Bank Of Abu Dhabi (NBAD) National Bank of Cambodia National Bank of Canada National Bank of Egypt National Bank of Kenya Limited National Bank Of Kuwait Natixis NISHI-NIPPON CITY BANK Nomura Trust And Banking Company Norddeutsche Landesbank Girozentrale Nordea Bank Norinchukin Bank North Pacific Bank NPCI (National Payments Corporation Of India) Oita Bank ORIX Bank Oversea-Chinese Banking Corporation PKO BP Prime Bank Ltd. Privatbank IHAG Zurich PT Bank Central Asia Tbk PT Bank CIMB Pakistani Central Bank Rabobank Nederland Raiffeisen Bank RAKBANK (National Bank Of Ras Al-Khaimah PJSC) RBL Bank RCBC RCI Bank & Services ReiseBank Reserve Bank Of India Resona Bank Royal Bank Of Canada Royal Bank Of Scotland San-In Godo Bank Santander Bank Saudi Arabian Monetary Authority (SAMA) SBI Sumishin Net Bank Scotiabank (Nova Scotia Bank) SEB Senshu Ikeda Bank Seven Bank Shanghai Huarui Bank (SHRB / Shanghai HanaMizuho Bank) Shiga Bank Shikoku Bank Shimizu Bank Shinhan Bank Shinkin Central Bank Shinsei Bank Shoko Chukin Bank Siam Commercial Bank Skandinaviska Enskilda Banken (SEB) SMBC Societe General Sony Bank South African Reserve Bank South Indian Bank Standard Chartered Bank Star One Credit Union Sumitomo Mitsui Banking Corporation Sumitomo Mitsui Trust Bank Suruga Bank Sveriges Riksbank Switzerland’s UBS Synchrony Bank Signature Bank Silvergate Bank The First International Bank of Israel Ltd Tochigi Bank Toho Bank Tokyo Star Bank Toronto-Dominion Bank Towa Bank Tsukuba Bank Turkiye Finans Katilim Bankasi Telenor Microfinance Bank UBS Union Bank United Overseas Bank UniCredit Union Bank Of The Philippines US Bancorp Vietnam Joint Stock Commercial Bank for Industry and Trade Volksbank Mittweida Wells Fargo Westpac Bank Woori Bank WEG Bank Yachiyo Bank Yamagata Bank Yamaguchi Bank Yes Bank __________________ Other institutes related to Financial Services Accenture Ainslie Bullion AirWallex ALFAcashier American Express AstroPay Australian Stock Exchange and Depository Trust Banquecorp Physical Gold BCS Information Systems BeeTech Bill & Melinda Gates Foundation BitcoinIRA BitGo Bladex Bluzelle BNY Mellon Bolsa de Madrid BPCE Bradesco Bloomberg BNP Paribas Boerse Stuttgart Digital Ventures BP PLC Cambridge Global Payments CBH Compagnie Bancaire Helvétique CGI CIBC Clearing Corporation [DTCC] CLS Group Coil Crypto Facilities Currencies Direct Daiwa Securities Group Davivienda DAYLI Financial Group Deloitte Touche Tohmatsu DH Corporation Dianrong Digiledge Dinex DLocal DBS DTCC Earthport Ernst & Young Everis European Central Securities Depositories Association Exchange4Free Expanse Expertus Technologies EZforex Employees’ Retirement System eToro FairFX FlashFX Fulton Financial Corporation Fidelity Investments Global ID Gold Bullion International GSR Markets Ho Chi Minh City Development JS Commercial HQLA IBM IDT Corporation Infocomm Development Authority Of Singapore Infosys InstaReM IntellectEU International Foreign Exchange (IFX) Intesa Sanpaolo Itaú International Chamber of Commerce International Securities Services Association Jaccoo Jamica Stock Exchange Julius Baer KB Financial Group LatiPay Lianlian Pay Lulu Exchange Linx Partners London Metal Exchange London Stock Exchange Luxembourg Stock Exchange Marqeta Mastercard MIT (Massachusetts Institute of Technology) MoneyMatch Mercury FX MoneyGram Morgan Creek MUFG Natixis New Forest Capital Niaga Tbk Nordea Northern Trust NASDAQ Nomura OANDA Omidyar Network Omni OP Financial Group Paris Europlace Paycase Financial Persistent Systems PolySign Portal Finance Paypal Proof of Capital Qiwi R3 CEV RationalFX Reyl & Cie SA RIALTO.AI Revolut Saldo.Mx SAP Satoshi Systems SBI Holdings SBI Remit SBI Ripple Asia SBI Virtual Currencies Scopus SEB Group Shuttle Holdings SIA Singapore Exchange (SGX) Smart Token Chain SnapCheck Sparro SRI Capital Stock Exchange of Thailand SuMi TRUST SunTrust SWIFT (Society for Worldwide Interbank Financial Telecommunication) SGX SIX Group Société Générale SoFi Square Swissquote TAS Group Tata Consultancy Services (TCS) Telindus Temasek Holdings Temenos The PAR Foundation The Royal Mint TradeCloud TradeIX TradeWind TransferGo Thai Stock Exchange The National Payments Corporation of India Thomson Reuters Eikon Tora UAE Exchange (UAE Exchange) Union FX UniPAY Viamericas Corporation Volante Technologies Virginia’s Police Officer’s Retirement System Visa Western Union Yantra Financial Technologies Zip Remit If you find a bank or financial institute not on this list please comment or message me directly so I can add to this list. At the time of writing this was the most comprehensive list available to the public

Comprehensive List of Banks using Blockchain Technology in 2020  [Updated]

At VIM AEC we process extremely large architectural models and import meshes and BIM data into game engines (such as Unity), 3D editing tools (such as 3ds Max), and applications running on different devices (such as the Magic Leap). When using FBX as a format for representing large meshes it takes close to four minutes to import a 28 million polygon model in Unity. As an alternative we developed an open format for representing 3D geometry called G3D which reduced import time to just under 6 seconds. Overview The G3D format can represent triangular and quadrilateral meshes, point clouds, line segments, and polygonal meshes along with arbitrary attributes (e.g. normals, UVs, colors, smoothing groups, etc.) associated with different sub-elements of the mesh (vertices, faces, face-corners, polygon groups, or whole object). Our G3D loader is over an order of magnitude faster in our tests than reading OBJ, FBX, or PLY files with the Assimp model import library or importing into Unity. The specification and reference implementation is also considerably simpler than other binary formats such as glTF while supporting a wider range of data attributes. On the G3D Github repo we provide a reference implementation in C# using .NET Standard 2.0 (making it cross-platform) and a Unity test project for importing and exporting G3D meshes. We also have released a Nuget package which we use in our production code. Motivation The common data formats for representing 3D geometry (e.g. OBJ, FBX, Collada, glTF, etc.) generally have one or more shortcomings: poor serialization and deserialization performanceplatform or language specificlack of extensibilitycomplex to implementlimits on number and types of attributes stored Our goal was to design a format that would enable us to quickly and easily implement importers and exporters as plug-ins to different editing tools, game engines, and applications running on different platforms from desktop to WebGL to spatial computing devices like the Magic Leap. We also wanted this format to be able to transport a wide variety of data from different sources without loss. How is G3D so fast? The reason the G3D format is so fast is that it doesn’t have to pre-process data before getting it into a renderable, GPU-friendly, state. Text-based mesh formats such as OBJ, PLY, Collada, etc. require the computer to spend a significant amount of time converting from a text-representation to a binary representation consumable by the converter. Similarly for some binary formats, such as FBX, the mesh vertex data is organized as a vectors of 4 double-precision floating point values. Most rendering contexts expect the vertex position data to be encoded as vectors of 3 single-precision floats, which means that a pre-process step is required to truncate the data and pack it in more convenient format. G3D data buffers are strictly aligned, so that once the entire file is loaded into memory, the individual data buffers can be passed to the GPU as-is with no additional processing or memory allocations. A Primer on 3D Meshes Commonly a 3D mesh consists of a series of points in space (called vertices) and a list of faces that specify how those points are connected to make a faceted surface in 3 dimensional space. Image from Wikipedia licensed under CC-SA-3.0 by RChoetzlien The list of points is often called a vertex buffer, and the list of faces is represented as an index buffer. If the size of each face is fixed (e.g. 3 for triangles or 4 for quads), then every N indices are the indices of the vertices of the corners for a different face. Given this observation it follows naturally that point clouds and line segments are degenerate cases of faces where there are only one or two points per face respectively. Meshes supporting mixed size polygons can be encoded using an additional data array for the sizes of each face. Edges There are two types of edges to consider: undirected edges, and directed edges (also called half-edges). In G3D, like in 3ds Max, the edge refers to the half-edge. Half-edges are so named because two adjacent faces each have a directed half-edge that both share the same vertices, but flow in different directions, completing the full edge that separates the two faces. Enumerating half-edges is simpler, because every face has N half-edges, where N = the number of points in the face. So the half-edge index buffer is exactly the same as the index buffer. Surface or Polygon Group A surface or polygon group is a sequence of contiguous faces which make a continuous surface. Often this surface will share a common material. Consider the case of a cylinder, the curved section can be thought of as one curved face, while the end caps are both surfaces. Having polygon groups enables more compact data representation (e.g. we can have a shared material ID per group, or a single shared normal in the case of a planar surface). Polygon groups can also be used to encode “sub-mesh” data from Unity or a Revit API Face. Attributes The G3D mesh format is based on representing geometry as a collection of strictly aligned binary arrays called attributes. An attribute is an array of scalars or vectors associated with vertices, face corners, faces, or polygon groups. In some cases an attribute is a single value associated with the entire object. Some common examples of attributes include: Vertex colorsUV coordinatesFace or vertex normalsSmoothing group IDsMaterial IdsPer-vertex data (e.g. soft-selection or crease weights)Edge visibilityTangents and binormals Attribute Descriptor String Each attribute in a G3D is associated with a descriptor, which is encoded as a string in the following format: g3d:<association>:<semantic>:<data_type>:<data_arity> The descriptor consists of the following components: association — point, corner, edge, face, group, allprimitive data type — int8, int16, int32, int64, float32, float64arity — number of primitives per data elementsemantic — an identifier that defines the role or purpose of the attribute Multiple attributes may share the same descriptor string, such as the multiple UV channels stored in a Unity mesh. Implementation Details The underlying binary layout of a G3D file conforms to the BFAST serialization format, simple and efficient open binary format for serializing collections of named byte arrays. (See this article on Hackernoon) The first named buffer in the BFAST container is reserved for meta-information about the file encoded in JSON format. It has the name “meta”. There are currently no restrictions or requirements on what data is encoded in the meta JSON object. Each subsequent data buffer stores attribute data, and uses the attribute descriptor string as a name. As per the BFAST specification attribute data is stored in 64-byte aligned data-buffers. Semantic Attributes also have a “semantic” tag which is used to identify what role the attribute has for rendering or processing. An application may use any string it chooses to represent the semantic, however the G3D specification recommends a number of semantics with predefined meanings (see the Github readme.md for the full list). This is intended to make it possible for different applications to exchange, process, and render G3D data in a common manner. Further Reading If you are interested in understanding the design decisions that lead up to the G3D format, it can be useful to read up on how other mesh representations are designed: Assimp Mesh class documentationFBX Mesh class documentation3ds Max Mesh class documentationGLTF SpecificationPaul Bourke’s collection of reference to graphics data formats Final Words At VIM AEC we are very interested in helping others adopt the G3D format, as we believe it can help improve the quality of graphics software for everyone. Please reach out to me via email or the Github issues to ask questions or make suggestions. Disclaimer: I work at VIM AEC as Head of Research. At VIM AEC we process extremely large architectural models and import meshes and BIM data into game engines (such as Unity), 3D editing tools (such as 3ds Max), and applications running on different devices (such as the Magic Leap). VIM AEC When using FBX as a format for representing large meshes it takes close to four minutes to import a 28 million polygon model in Unity. As an alternative we developed an open format for representing 3D geometry called G3D which reduced import time to just under 6 seconds. G3D G3D Overview The G3D format can represent triangular and quadrilateral meshes, point clouds, line segments, and polygonal meshes along with arbitrary attributes (e.g. normals, UVs, colors, smoothing groups, etc.) associated with different sub-elements of the mesh (vertices, faces, face-corners, polygon groups, or whole object). Our G3D loader is over an order of magnitude faster in our tests than reading OBJ, FBX, or PLY files with the Assimp model import library or importing into Unity. The specification and reference implementation is also considerably simpler than other binary formats such as glTF while supporting a wider range of data attributes. Assimp glTF On the G3D Github repo we provide a reference implementation in C# using .NET Standard 2.0 (making it cross-platform) and a Unity test project for importing and exporting G3D meshes. We also have released a Nuget package which we use in our production code. G3D Github repo Nuget package Motivation Motivation The common data formats for representing 3D geometry (e.g. OBJ, FBX, Collada, glTF, etc.) generally have one or more shortcomings: poor serialization and deserialization performance platform or language specific lack of extensibility complex to implement limits on number and types of attributes stored poor serialization and deserialization performance platform or language specific lack of extensibility complex to implement limits on number and types of attributes stored Our goal was to design a format that would enable us to quickly and easily implement importers and exporters as plug-ins to different editing tools, game engines, and applications running on different platforms from desktop to WebGL to spatial computing devices like the Magic Leap. We also wanted this format to be able to transport a wide variety of data from different sources without loss. How is G3D so fast? The reason the G3D format is so fast is that it doesn’t have to pre-process data before getting it into a renderable, GPU-friendly, state. Text-based mesh formats such as OBJ, PLY, Collada, etc. require the computer to spend a significant amount of time converting from a text-representation to a binary representation consumable by the converter. Similarly for some binary formats, such as FBX, the mesh vertex data is organized as a vectors of 4 double-precision floating point values. Most rendering contexts expect the vertex position data to be encoded as vectors of 3 single-precision floats, which means that a pre-process step is required to truncate the data and pack it in more convenient format. G3D data buffers are strictly aligned, so that once the entire file is loaded into memory, the individual data buffers can be passed to the GPU as-is with no additional processing or memory allocations. A Primer on 3D Meshes A Primer on 3D Meshes Commonly a 3D mesh consists of a series of points in space (called vertices) and a list of faces that specify how those points are connected to make a faceted surface in 3 dimensional space. Image from Wikipedia licensed under CC-SA-3.0 by RChoetzlien Wikipedia RChoetzlien The list of points is often called a vertex buffer, and the list of faces is represented as an index buffer. If the size of each face is fixed (e.g. 3 for triangles or 4 for quads), then every N indices are the indices of the vertices of the corners for a different face. Given this observation it follows naturally that point clouds and line segments are degenerate cases of faces where there are only one or two points per face respectively. Meshes supporting mixed size polygons can be encoded using an additional data array for the sizes of each face. Edges There are two types of edges to consider: undirected edges, and directed edges (also called half-edges). In G3D, like in 3ds Max, the edge refers to the half-edge. Half-edges are so named because two adjacent faces each have a directed half-edge that both share the same vertices, but flow in different directions, completing the full edge that separates the two faces. Enumerating half-edges is simpler, because every face has N half-edges, where N = the number of points in the face. So the half-edge index buffer is exactly the same as the index buffer. Surface or Polygon Group A surface or polygon group is a sequence of contiguous faces which make a continuous surface. Often this surface will share a common material. Consider the case of a cylinder, the curved section can be thought of as one curved face, while the end caps are both surfaces. Having polygon groups enables more compact data representation (e.g. we can have a shared material ID per group, or a single shared normal in the case of a planar surface). Polygon groups can also be used to encode “sub-mesh” data from Unity or a Revit API Face . Revit API Face Attributes The G3D mesh format is based on representing geometry as a collection of strictly aligned binary arrays called attributes. An attribute is an array of scalars or vectors associated with vertices, face corners, faces, or polygon groups. In some cases an attribute is a single value associated with the entire object. Some common examples of attributes include: Vertex colors UV coordinates Face or vertex normals Smoothing group IDs Material Ids Per-vertex data (e.g. soft-selection or crease weights) Edge visibility Tangents and binormals Vertex colors UV coordinates Face or vertex normals Smoothing group IDs Material Ids Per-vertex data (e.g. soft-selection or crease weights) Edge visibility Tangents and binormals Attribute Descriptor String Each attribute in a G3D is associated with a descriptor, which is encoded as a string in the following format: g3d:<association>:<semantic>:<data_type>:<data_arity> g3d:<association>:<semantic>:<data_type>:<data_arity> g3d: <association> :<semantic> :<data_type> :<data_arity> g3d: <association> :<semantic> :<data_type> :<data_arity> The descriptor consists of the following components: association — point, corner, edge, face, group, all primitive data type — int8, int16, int32, int64, float32, float64 arity — number of primitives per data element semantic — an identifier that defines the role or purpose of the attribute association — point, corner, edge, face, group, all primitive data type — int8, int16, int32, int64, float32, float64 arity — number of primitives per data element semantic — an identifier that defines the role or purpose of the attribute Multiple attributes may share the same descriptor string, such as the multiple UV channels stored in a Unity mesh. Implementation Details The underlying binary layout of a G3D file conforms to the BFAST serialization format , simple and efficient open binary format for serializing collections of named byte arrays. (See this article on Hackernoon ) BFAST serialization format BFAST serialization format this article on Hackernoon The first named buffer in the BFAST container is reserved for meta-information about the file encoded in JSON format. It has the name “meta”. There are currently no restrictions or requirements on what data is encoded in the meta JSON object. Each subsequent data buffer stores attribute data, and uses the attribute descriptor string as a name. As per the BFAST specification attribute data is stored in 64-byte aligned data-buffers. Semantic Semantic Attributes also have a “semantic” tag which is used to identify what role the attribute has for rendering or processing. An application may use any string it chooses to represent the semantic, however the G3D specification recommends a number of semantics with predefined meanings (see the Github readme.md for the full list). This is intended to make it possible for different applications to exchange, process, and render G3D data in a common manner. the Github readme.md Further Reading If you are interested in understanding the design decisions that lead up to the G3D format, it can be useful to read up on how other mesh representations are designed: Assimp Mesh class documentation FBX Mesh class documentation 3ds Max Mesh class documentation GLTF Specification Paul Bourke’s collection of reference to graphics data formats Assimp Mesh class documentation Assimp Mesh class documentation FBX Mesh class documentation FBX Mesh class documentation 3ds Max Mesh class documentation 3ds Max Mesh class documentation GLTF Specification GLTF Specification Paul Bourke’s collection of reference to graphics data formats Paul Bourke’s collection of reference to graphics data formats Final Words At VIM AEC we are very interested in helping others adopt the G3D format, as we believe it can help improve the quality of graphics software for everyone. Please reach out to me via email or the Github issues to ask questions or make suggestions. me via email Github issues Disclaimer : I work at VIM AEC as Head of Research . Disclaimer Disclaimer : I work at VIM AEC as Head of Research

[Deep Dive] What is the G3D Geometry Exchange Format?

In the Go community, the size of application binaries is always a hot topic. In the pursuit of extreme performance, every byte counts. Go is known for its simplicity and efficiency, but as projects grow in size, so can the binaries. In this article, I will show you how to use go-size-analyzer and how to interpret the results. Installation The project is hosted on GitHub. You can visit https://github.com/Zxilly/go-size-analyzer to read the full documentation. go-size-analyzer provides precompiled versions which you can download from GitHub Release. It is also available through some package managers: Homebrew (MacOS/Linux):
brew install go-size-analyzer



Scoop (Windows):
scoop install go-size-analyzer



Go Build and Install:
go install github.com/Zxilly/go-size-analyzer/cmd/gsa@latest

The compiled version needs to download the required resource files from GitHub at runtime. These files are embedded in the precompiled versions. Usage Run gsa --version to ensure go-size-analyzer is installed correctly. Locate the binary you want to analyze. Here, we choose the Linux x86_64 version of CockroachDB. gsa --web cockroach-linux-amd64 Wait for the command line to display: Use your browser to visit http://localhost:8080, and you will see: If this is your project, you can click on the package name on the left to see specific details. Is there a rarely used dependency taking up a lot of space? Has embed included extra files? A simpler way is to check the sections on the right. We can see that the .debug_* segments take up a lot of space. Using go tool link --help to view the specific parameters of Go's linker, we find: -s    disable symbol table
  -strictdups int
        sanity check duplicate symbol contents during object file reading (1=warn 2=err).
  -tmpdir directory
        use directory for temporary files
  -v    print link trace
  -w    disable DWARF generation This means using the -s -w parameters can remove the .symtab and .debug_* segments, saving about 40M of space in this binary. To pass parameters to the linker during compilation, you can use the -ldflags parameter. The complete command would be go build -ldflags="-s -w". Summary In this article, we introduced how to use the go-size-analyzer tool to analyze the size of Go application binaries. This tool allows you to visualize the space occupied by different parts of your program and identify areas for optimization. We found that in many cases, the .debug_* segments take up a considerable amount of space, and using the -s -w parameters of the Go linker can significantly reduce the binary size. If you find this tool helpful, consider visiting my GitHub repository at https://github.com/Zxilly/go-size-analyzer and giving it a star. Your support is very important to me, as it not only motivates me to continue improving this tool but also helps more people discover this useful resource. In the Go community, the size of application binaries is always a hot topic. In the pursuit of extreme performance, every byte counts. Go is known for its simplicity and efficiency, but as projects grow in size, so can the binaries. In this article, I will show you how to use go-size-analyzer and how to interpret the results. go-size-analyzer Installation The project is hosted on GitHub. You can visit https://github.com/Zxilly/go-size-analyzer to read the full documentation. https://github.com/Zxilly/go-size-analyzer go-size-analyzer provides precompiled versions which you can download from GitHub Release . It is also available through some package managers: go-size-analyzer GitHub Release Homebrew (MacOS/Linux):
brew install go-size-analyzer Scoop (Windows):
scoop install go-size-analyzer Go Build and Install:
go install github.com/Zxilly/go-size-analyzer/cmd/gsa@latest

The compiled version needs to download the required resource files from GitHub at runtime. These files are embedded in the precompiled versions. Homebrew (MacOS/Linux): brew install go-size-analyzer Homebrew (MacOS/Linux): Homebrew brew install go-size-analyzer brew install go-size-analyzer Scoop (Windows): scoop install go-size-analyzer Scoop (Windows): Scoop scoop install go-size-analyzer scoop install go-size-analyzer Go Build and Install: go install github.com/Zxilly/go-size-analyzer/cmd/gsa@latest The compiled version needs to download the required resource files from GitHub at runtime. These files are embedded in the precompiled versions. Go Build and Install : Go Build and Install go install github.com/Zxilly/go-size-analyzer/cmd/gsa@latest go install github.com/Zxilly/go-size-analyzer/cmd/gsa@latest The compiled version needs to download the required resource files from GitHub at runtime. These files are embedded in the precompiled versions. Usage Run gsa --version to ensure go-size-analyzer is installed correctly. gsa --version go-size-analyzer Locate the binary you want to analyze. Here, we choose the Linux x86_64 version of CockroachDB. gsa --web cockroach-linux-amd64 gsa --web cockroach-linux-amd64 Wait for the command line to display: Use your browser to visit http://localhost:8080 , and you will see: http://localhost:8080 If this is your project, you can click on the package name on the left to see specific details. Is there a rarely used dependency taking up a lot of space? Has embed included extra files? embed A simpler way is to check the sections on the right. We can see that the .debug_* segments take up a lot of space. .debug_* Using go tool link --help to view the specific parameters of Go's linker, we find: go tool link --help -s    disable symbol table
  -strictdups int
        sanity check duplicate symbol contents during object file reading (1=warn 2=err).
  -tmpdir directory
        use directory for temporary files
  -v    print link trace
  -w    disable DWARF generation -s    disable symbol table
  -strictdups int
        sanity check duplicate symbol contents during object file reading (1=warn 2=err).
  -tmpdir directory
        use directory for temporary files
  -v    print link trace
  -w    disable DWARF generation This means using the -s -w parameters can remove the .symtab and .debug_* segments, saving about 40M of space in this binary. -s -w .symtab .debug_* 40M To pass parameters to the linker during compilation, you can use the -ldflags parameter. The complete command would be go build -ldflags="-s -w" . -ldflags go build -ldflags="-s -w" Summary In this article, we introduced how to use the go-size-analyzer tool to analyze the size of Go application binaries. This tool allows you to visualize the space occupied by different parts of your program and identify areas for optimization. We found that in many cases, the .debug_* segments take up a considerable amount of space, and using the -s -w parameters of the Go linker can significantly reduce the binary size. go-size-analyzer .debug_* -s -w If you find this tool helpful, consider visiting my GitHub repository at https://github.com/Zxilly/go-size-analyzer and giving it a star. Your support is very important to me, as it not only motivates me to continue improving this tool but also helps more people discover this useful resource. https://github.com/Zxilly/go-size-analyzer

Binary Size in Go Applications: How to Use Go-size-analyze

DISCLAIMER: The code included in this tutorial belongs to TJ Tsai, the creator of Bootleg Score. This tutorial is designed to facilitate the initial approach for new researchers. If you have any questions, do not hesitate to contact him: ttsai@g.hmc.edu. Without Optical Music Recognition (OMR), computers can not be able to interpret a music score, and a lot of tasks related with Music Information Retrieval can not be achieved. In simple words, OMR transforms the data from music scores into a format that machines can understand, with some famous music representations such as MIDI, MusicXML or PianoRoll. In this short tutorial, I aim to introduce you to Bootleg Score, a mid-level music representation that facilitates music sheet processing without requiring significant computational power or extensive libraries and dependencies. Since I have been working with this representation lately, I think its possibilities should be known by more people. This representation was created by TJ Tsai and his lab from Harvey Mudd College, which seeks to represent the position of filled notes on a staff using zeros and ones, explicitly encoding the rules of Western Music Notation for piano sheet music. Some of their articles showing applications and results obtained by performing tasks with Bootleg Score representation include the following: MIDI Passage Retrieval Using Cell Phone Pictures Of Sheet Music
Composer Style Classification Of Piano Sheet Music Images Using Language Model Pretraining
Camera-Based Piano Sheet Music Identification
MIDI-Sheet Music Alignment Using Bootleg Score Synthesis In order to facilitate the first approach to this representation, all the necessary files to get the Bootleg Score representation of a PDF sheet music are available in the following github repository: How To Use Bootleg Score INSTALLMENT All requirements are found in the requirements.txt file of the repository. I recommend working in a Linux system such as Ubuntu. Once you create a Conda Environment, installing all the dependencies is as easy as writing the following command in the terminal. pip install -r requirements.txt Some of the most important libraries are OpenCV, frequently used for computer vision, and Scikit-Learn, used for data analysis. HOW DOES BOOTLEG SCORE WORKS To get this low resource optical recognition system, it is necessary to focus on three things on the sheet music: filled noteheads, staff lines and bar lines. All the functions mentioned in the next paragraphs are included in the ExtractBootlegFeatures1.py file from the repository. NOTEHEAD DETECTION The filled noteheads are selected by using a simple blob detector and estimating the noteheads that fill in a notehead template. The function adaptiveNoteheadDetect() performs the adaptive detection of noteheads from our image. Finally, with the notes list data (nhlocs), we can obtain the required information of all the noteheads detected. Another function takes a list of bounding boxes representing noteheads, calculates the central coordinates of each one of them, and provides an average estimation of the width and length of the noteheads in that list. STAFF LINE DETECTION The staff lines location and spacing are estimated using every detected notehead from the previous step. The function isolateStaffLines() uses morphological operations to isolate the staff lines in an image. First, the horizontal lines are identified and then the note bars are removed to obtain only the staff lines. With the getEstStaffLineLocs() function, we have a list of predictions where each prediction is a tuple containing the initial and final position of a staff line, the associated column and row, and the corresponding index in the featmap. The next step consists of assigning the noteheads to the staff that corresponds to them.The final result will be similar to what is shown in the following image. BAR LINE DETECTION The Bar Line detection helps to cluster the staff line into grand staff systems. It is important to remember that the Bootleg Score representation is taught to be used with Piano Sheet Music. Bounding boxes are predicted around the bar lines in the image. Bar lines are necessary to properly group the staff lines into clusters of grand staff systems, each one containing a staff corresponding to the right hand and another corresponding to the left hand. BOOTLEG SCORE GENERATION Using the information from the noteheads, staff lines, and bar lines, the Bootleg Score is generated as an array of 0’s and 1’s. The vertical axis represents the positions of the right and left hand staves, while the notes are located along the horizontal axis. For each single bootleg line, collapseSimultaneousEvents() groups the noteheads that are close to each other in the same column, assuming that these notes sound simultaneously. The function constructBootlegScore encodes the previous information into the Bootleg Score representation. The Bootleg representation is a matrix with dimensions of 62×N, where 62 represents the possible staff positions and N represents the number of simultaneous events. An example of how a Bootleg Score can look is shown in the next image: Once the final representation is done, it is time for you to try different tasks involving music information retrieval taking advantage of the possibilities that a low resource representation like this one provides to users. A sheet music PDF is included in the repository for testing. Once everything is correctly configured, the only thing you need to do is execute the Test_Bootleg.py file. Do not hesitate to take a deep look at the code; it is a good opportunity to learn new methods for analyzing sheet music, and I found it very funny.. CONCLUSIONS In conclusion, TJ Tsai’s Bootleg Score representation represents a significant progress in Optical Music Recognition, where computational costs and extensive processing times have been a problem for many years. The articles mentioned in previous sections demonstrate that using the Bootleg Score can achieve good results in various tasks, so I encourage you to try it and give it an opportunity. DISCLAIMER: The code included in this tutorial belongs to TJ Tsai, the creator of Bootleg Score. This tutorial is designed to facilitate the initial approach for new researchers. If you have any questions, do not hesitate to contact him: ttsai@g.hmc.edu. DISCLAIMER : The code included in this tutorial belongs to TJ Tsai, the creator of Bootleg Score. This tutorial is designed to facilitate the initial approach for new researchers. If you have any questions, do not hesitate to contact him: ttsai@g.hmc.edu . DISCLAIMER DISCLAIMER ttsai@g.hmc.edu ttsai@g.hmc.edu Without Optical Music Recognition (OMR), computers can not be able to interpret a music score, and a lot of tasks related with Music Information Retrieval can not be achieved. In simple words, OMR transforms the data from music scores into a format that machines can understand, with some famous music representations such as MIDI, MusicXML or PianoRoll. In this short tutorial, I aim to introduce you to Bootleg Score, a mid-level music representation that facilitates music sheet processing without requiring significant computational power or extensive libraries and dependencies. Since I have been working with this representation lately, I think its possibilities should be known by more people. This representation was created by TJ Tsai and his lab from Harvey Mudd College , which seeks to represent the position of filled notes on a staff using zeros and ones, explicitly encoding the rules of Western Music Notation for piano sheet music. Some of their articles showing applications and results obtained by performing tasks with Bootleg Score representation include the following: Harvey Mudd College Harvey Mudd College MIDI Passage Retrieval Using Cell Phone Pictures Of Sheet Music Composer Style Classification Of Piano Sheet Music Images Using Language Model Pretraining Camera-Based Piano Sheet Music Identification MIDI-Sheet Music Alignment Using Bootleg Score Synthesis MIDI Passage Retrieval Using Cell Phone Pictures Of Sheet Music MIDI Passage Retrieval Using Cell Phone Pictures Of Sheet Music MIDI Passage Retrieval Using Cell Phone Pictures Of Sheet Music MIDI Passage Retrieval Using Cell Phone Pictures Of Sheet Music Composer Style Classification Of Piano Sheet Music Images Using Language Model Pretraining Composer Style Classification Of Piano Sheet Music Images Using Language Model Pretraining Composer Style Classification Of Piano Sheet Music Images Using Language Model Pretraining Composer Style Classification Of Piano Sheet Music Images Using Language Model Pretraining Camera-Based Piano Sheet Music Identification Camera-Based Piano Sheet Music Identification Camera-Based Piano Sheet Music Identification Camera-Based Piano Sheet Music Identification MIDI-Sheet Music Alignment Using Bootleg Score Synthesis MIDI-Sheet Music Alignment Using Bootleg Score Synthesis MIDI-Sheet Music Alignment Using Bootleg Score Synthesis MIDI-Sheet Music Alignment Using Bootleg Score Synthesis In order to facilitate the first approach to this representation, all the necessary files to get the Bootleg Score representation of a PDF sheet music are available in the following github repository: How To Use Bootleg Score How To Use Bootleg Score How To Use Bootleg Score How To Use Bootleg Score INSTALLMENT INSTALLMENT All requirements are found in the requirements.txt file of the repository. I recommend working in a Linux system such as Ubuntu. Once you create a Conda Environment, installing all the dependencies is as easy as writing the following command in the terminal. requirements.txt pip install -r requirements.txt pip install -r requirements.txt Some of the most important libraries are OpenCV , frequently used for computer vision, and Scikit-Learn , used for data analysis. OpenCV OpenCV Scikit-Learn Scikit-Learn HOW DOES BOOTLEG SCORE WORKS HOW DOES BOOTLEG SCORE WORKS To get this low resource optical recognition system, it is necessary to focus on three things on the sheet music: filled noteheads, staff lines and bar lines. All the functions mentioned in the next paragraphs are included in the ExtractBootlegFeatures1.py file from the repository. NOTEHEAD DETECTION NOTEHEAD DETECTION The filled noteheads are selected by using a simple blob detector and estimating the noteheads that fill in a notehead template. The function adaptiveNoteheadDetect() performs the adaptive detection of noteheads from our image. Finally, with the notes list data (nhlocs), we can obtain the required information of all the noteheads detected. Another function takes a list of bounding boxes representing noteheads, calculates the central coordinates of each one of them, and provides an average estimation of the width and length of the noteheads in that list. STAFF LINE DETECTION STAFF LINE DETECTION The staff lines location and spacing are estimated using every detected notehead from the previous step. The function isolateStaffLines() uses morphological operations to isolate the staff lines in an image. First, the horizontal lines are identified and then the note bars are removed to obtain only the staff lines. With the getEstStaffLineLocs() function, we have a list of predictions where each prediction is a tuple containing the initial and final position of a staff line, the associated column and row, and the corresponding index in the featmap. The next step consists of assigning the noteheads to the staff that corresponds to them.The final result will be similar to what is shown in the following image. BAR LINE DETECTION BAR LINE DETECTION The Bar Line detection helps to cluster the staff line into grand staff systems. It is important to remember that the Bootleg Score representation is taught to be used with Piano Sheet Music. Bounding boxes are predicted around the bar lines in the image. Bar lines are necessary to properly group the staff lines into clusters of grand staff systems, each one containing a staff corresponding to the right hand and another corresponding to the left hand. BOOTLEG SCORE GENERATION BOOTLEG SCORE GENERATION Using the information from the noteheads, staff lines, and bar lines, the Bootleg Score is generated as an array of 0’s and 1’s. The vertical axis represents the positions of the right and left hand staves, while the notes are located along the horizontal axis. For each single bootleg line, collapseSimultaneousEvents() groups the noteheads that are close to each other in the same column, assuming that these notes sound simultaneously. The function constructBootlegScore encodes the previous information into the Bootleg Score representation. The Bootleg representation is a matrix with dimensions of 62×N, where 62 represents the possible staff positions and N represents the number of simultaneous events. An example of how a Bootleg Score can look is shown in the next image: Once the final representation is done, it is time for you to try different tasks involving music information retrieval taking advantage of the possibilities that a low resource representation like this one provides to users. A sheet music PDF is included in the repository for testing. Once everything is correctly configured, the only thing you need to do is execute the Test_Bootleg.py file. Do not hesitate to take a deep look at the code; it is a good opportunity to learn new methods for analyzing sheet music, and I found it very funny.. CONCLUSIONS CONCLUSIONS In conclusion, TJ Tsai’s Bootleg Score representation represents a significant progress in Optical Music Recognition, where computational costs and extensive processing times have been a problem for many years. The articles mentioned in previous sections demonstrate that using the Bootleg Score can achieve good results in various tasks, so I encourage you to try it and give it an opportunity.

How to Use Bootleg Score: A Low Resource Optical Music Recognition System

November 26, 2017 is definitely a historical day in this technological era. According to , it was on that day that the whole world watched Bitcoin’s monetary value reach the 10,000 USD mark. On December 16, 2017 it went on to reach its all-time high of 19,192.15 USD. Bitcoin.com I knew it was rising… and I refused to buy I remember these weeks particularly well since I was also watching closely the most valuable rising to new heights. Although I couldn’t have predicted the peaks in value of Bitcoin or (another rising cryptocurrency at the time), I had been observing their growth in real time for all three months preceding their high performance. As a matter of fact, ever since I installed the on my iPhone a few months earlier, I was scrutinizing their fluctuations on a daily basis. I probably knew earlier than a lot of folks that ‘something was up’ with these cryptocurrencies. cryptocurrency Ethereum Coinbase App I wasn’t part of the happy crowd though. For three months, as I watched these cryptocurrencies rise higher and higher, I made the same decision every single day; I chose not to invest in any of them. I chose so because I wasn’t convinced Bitcoin was an actual legitimate investment vehicle. And while I assume many crypto-investors were counting their 50, 75, 100 and 200 percent return on investment, I was seriously wondering whether I would regret that choice in the future. I was skeptical To be honest, I am the last person I would have imagined getting involved with cryptocurrencies. Although I heard about cryptocurrencies from my roommate back in 2014, it never inspired me anything other than skepticism. I often ignored the topic when brought up. And when I did give it some thought, I seriously questioned the moral value of a device that was used either for uneducated speculation or as a means of transactions for . Being the kind of guy who appreciates staying out of trouble, these facts were enough to keep me away. drug dealers Besides, no one I knew could answer my question: how are cryptocurrencies helpful? Transacting using Bitcoin not only looked unnecessary to me, but it was also impractical. If you wanted to acquire Bitcoins, you had to buy them. Even though one could argue (with difficulty) that obtaining cryptocurrencies is feasible for everyone, very few stores and retailers accepted them. Our good old traditional fiat currencies were much better for buying groceries, shopping, and keeping life simple. So Bitcoin and other crypto stuff were a no-no for me. Please. No. It wasn’t until after I graduated from University in 2017 and started working full-time that I revised my position — at least on some aspects. I met colleagues then who were and who also worked on projects related to the blockchain. I intend to explain the blockchain as clearly as I can in my upcoming articles but, until then, my short explanation will be “the technology underlying cryptocurrencies”. That is at least how my new colleagues first defined it to me. Before I had time to decide I would never work on that “sketchy blockchain stuff”, they also mentioned that the blockchain could accomplish much more than operating Bitcoin. crypto-investors And then I looked into it I was told for the first time that the blockchain was apparently a network with the unmatched ability to enable transactions between its participants without a third-party while preserving the security and transparency of these transactions. Different ideas came to mind as I contemplated this claim: money transfers without going through a bank or a financial agency, business operations like purchases, sales, and commissions without a middleman. Little room for corruption and privacy breaches. No institution, no corporation, just people connected together through a network. Utopian, I thought, but worth looking into. I still had questions. “How is the absence of a bank helpful or even reassuring? Isn’t this technology undermining the value of trust upon which society is built? How easy is it to build such a complex system”? I also still had difficulties explaining to others how and why the blockchain could be helpful. That made me see how limited my understanding of its applications was and it was obvious I had a lot of learning to do. The risks of wasted time seemed high but the rewards of discovering a potentially successful technology were motivating. Therefore, I chose to investigate the blockchain technology. “A book with a magnifying glass on top of it, next to a pen, and globes on a desk in Cianorte” by on João Silas Unsplash I intend to share more details about my research in my next article. The least I can say is that the more I learned about the blockchain, the more I was fascinated by its potential. I was comforted in my decision to learn about it, and it had nothing to do with Bitcoin and crypto-investments. Conclusion Back in December 2017, as Bitcoin hit its highest value, I was wondering whether not buying Bitcoins was a choice I would regret for a long time. I finally concluded that it did not matter: I knew there was much more to the blockchain than Bitcoin, Ethereum, ICOs, etc. And I was ultimately more interested in finding out how the blockchain could really help do more than gambling and facilitating crime. I already believed then that it had good chances to be a technology for tomorrow, beyond the buzz of today. Photo by on Reuben Juarez Unsplash

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