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Yous

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This story is a part of Hacker Noon's series of interviews. The series is intended for tech professionals contributing the most insightful Hacker Noon stories to share more about their writing habits, ideas, and professional background (and maybe a hobby or two). If you too would like to start contributing to Hacker Noon, . Meet the Writer you can do so here So let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. My name is Bader - how you pronounce it (batter, bayder, butter), I don’t care all too much.  I’ve heard it all at this point 😒. I’m a full-stack web3 architect and developer, with a side of tech writing whenever I can fit it in.  I write about web3, blockchain use cases, and general projections of what I think society is going to / is becoming. I’ve been involved with, and may have founded my own startups since I was in high school - all centered around blockchain or DLT.  I also am an avid cat lover, coffee enthusiast, and bedroom musician. In fact, next to tech - music is probably my biggest passion. Interesting! What was your latest Hackernoon Top story about? Ironically, despite all of my work mostly going to promoting web3, blockchain, NFTs, etc - I actually covered why I think most NFTs today are actually While that may seem harsh, I end up providing examples on how to provide logic and utility to them to make them useful instead. useless. Do you usually write on similar topics? If not, what do you usually write about? I do! I usually cover extensive use cases with how blockchain or web3 tech can improve existing solutions - or make entirely new ones.  For example an edge learning, hivemind of package delivery drones that uses a ledger to settle all interactions. Great! What is your usual writing routine like (if you have one?) At the moment, it’s pretty sporadic, however, I’d like to write at least weekly. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? I found balancing opinion with fact is pretty difficult, especially in tech.  Most of the time - there is no clear right or wrong way to do something. Therefore, ensuring you back up your viewpoint with concrete evidence is even more crucial than other types of writing. What is the next thing you hope to achieve in your career? Create the best possible tools and docs to enable web3 for anyone to build their own company, adopt the tech to make their lives better, as well as create more opportunities in the space in general.   Web3 should be accessible and easy to build on, save costs, and give 10x the benefits of web2.0. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? I’m not going to lie, I have a terrible sweet tooth, my specific weakness being watermelon sour patch kids (I’m determined to somehow work web3 into this one day). Do you have a non-tech-related hobby? If yes, what is it? I play a few instruments, namely guitar, uke, bass, and piano. What can the Hacker Noon community expect to read from you next? I have a few drafts - I think we’re going to see some pretty exciting use cases, how-to guides, and maybe even some video content in the next months around how to build on web3! Thanks for taking time to join our “Meet the writer” series. It was a pleasure. Do you have any closing words? Build to learn, don’t learn to build ! Also, I have a lot of fun doing it, it’s not worth it otherwise.

Meet the Writer: HackerNoon's Contributor Bader Youssef, Web3 Developer

HackerNoon Reporter: Please tell us briefly about your  background. Having worked for more than 4 years in the corporate venture capital and treasury departments at an F500 company, I realized the need for an alternative financial risk management solution, one which really meets the needs of the customers. Therefore I started Mount Wish in 2016 with the aim to replace derivatives-based hedging with something simpler and more efficient. What's your startup called? And in a sentence or two, what does it do? Mount Wish is the company behind RiskPool, a quasi-mutual insurance which as a side effect (and I am quite passionate about that) also achieves the joint mission of the International Monetary Fund (IMF) and Bank for International Settlements (BIS) with a higher degree of efficiency and efficacy. This said, our customers benefit from Significant cost savings resulting in an up to 12% increase of their EBT Lean and clean insurance processes, i.e. no MiFID II, EMIR or other compliance hassles anymore A seamless integration of RiskPool with their ERP system and complementary products (e.g. our independent financial services marketplace fincato allowing easy cross-border financings) Automated counterparty risk and overlay-effect optimization No longer being dependent on financial forecasts in their risk management, a fact clients really loved during the unpredictable corona pandemic What is the origin story? Managing FX, interest rate, and commodity price risks (FICC risks) is becoming more and more cumbersome due to ever-increasing complexity (e.g. unforeseeable geopolitical or other events like Brexit or the coronavirus disrupting global supply chains and their impact on FICC markets), regulation (e.g. EMIR, MiFID II,…), and finally treasury costs, having reached an average of 2.87 USD per 100 USD in revenues as per a recent KPMG study. Thus, an insurance-like risk pooling solution which drastically increases the efficiency in handling these risks in a VUCA world while also cutting costs is what customers really want. Hence the interest in RiskPool was huge right from the very start. Though there were three big challenges which we had to address first: The first challenge was that the product only works at scale and on the global level and thus we had to make sure to grow almost equally in all parts of the world at the same time. This is why we came up with complementary solutions such as fincato, or our automated audit products, which again allowed us to win sales partners with compelling barter deals. These barter deals also included the initial outsourcing of parts of our business model inherent insurance asset management. The second big challenge was and now is even more the political risk in a world where international financial organizations are increasingly subject to political discussions. Hence a lot of diplomacy is required which is why it is so vital for us to work with strong and respected partners such as the IMF or BIS and international organizations like the World Economic Forum. The third and last challenge was a bit more technical and relates to making the product work for both fiat and crypto currencies. This said, we believe that all these different currencies will continue to coexist side-by-side. Hence bitcoin is important yet - in our opinion - will not dominate the world as big tech companies like facebook, Microsoft, or Amazon will more likely start their own crypto currency projects rather than jumping onto a third-party one. For that reason even when crypto grows in importance our solution will remain relevant not just for managing commodity and interest risks but also currency ones. The latter is especially true as fiat currencies (though likely digitized one way or another) will remain important given their economic functions and certain interest groups. What do you love about your team, and why are you the ones to solve this problem? Well, it’s a very diverse team with decades of experience in finance, tech, law and sales. And with regards to diversity I even dare to say that we are one of the most diverse teams in the industry. This said about 55% of all leadership positions are currently held by female colleagues and the top level is comprised of 4 ethnicities. We generally welcome minorities and people with different views in our team; be it people from different cultural backgrounds, the LGBTQI community or people with disabilities. This kind of diversity makes work pretty exciting and more important from a business perspective allows us to quickly adapt our product to different customer groups. Speaking of the latter, RiskPool not only works worldwide but now even caters to customers asking for a risk management product which is compliant with Islamic finance rules. Another fun part is our geographic flexibility which allows people to work from any of our locations (i.e. partner offices) worldwide as long as they join the big team meeting in one of our regional hubs at least once a month. This approach allowed us to establish follow-the-sun processes very early on and to gain a strong presence globally despite an initial lack of resources. The latter aspects have been key when it comes to time-to-market and growth. If you weren’t building your startup, what would you be doing? I would work on other compelling (impact) startup ideas… well I am much more of a company builder, strategist, relationship and vision person anyway rather than a “corporat-ish” CEO/manager type. At the moment, how do you measure success? What are your core metrics? One of the most important metrics is the netting quota, referring to the amount of positions and volume which can be offset in the risk pool. Other key metrics include gross and net revenues as well as Opex in relation to assets. We also track politically-induced provisions given the rise of trade wars. What’s most exciting about your traction to date? The growth rate and fact that we were pre-financed by our customers and later also by key business partners as many VCs deemed the risk too high. That said, there are quite a few off balance sheet positions to be aware of, mostly for work completed by business partners (customers and third parties) in return for promised future discounts, future business or other forms of compensation often subject to our own business performance again (thus income-contingent payback and interest terms apply). We are also very excited about the strong network effects which we achieved due to the very nature of RiskPool and which make it very hard for competitors to attack us directly. We are also proud to see a considerable number of very well-known brands among our customers. What technologies are you currently most excited about, and most worried about? And why? Julia is our preferred programming language and we are really looking forward to see how the language will evolve over the next years as it combines the ease of use and familiar syntax of Python and R with the speed of C, C++ or Java. So programmers no longer need to estimate models in one language and reproduce them in a faster production language. So that’s really exciting. The other technology we are excited about - though we are only using it to a small degree right now - is the blockchain. This said, we are currently looking forward to combing traditional database approaches on micro-level (local operations and customer data) with the blockchain on macro-level (for aggregated local data used to make global/regional decisions). What advice would you give to the 21-year-old version of yourself? Also take care of your health without compromising the need for hard work. The trick is to find balance at work. So about 20% of the time I’ll work from home, which in my case means working from a small sailing boat circumnavigating the world. This way I keep working but get to recharge my batteries quickly for the other 80% of my time spent in the office. Plus, the created opportunities on the docks - golfing was yesterday, nowadays your meeting the real power brokers in the harbor - and benefits from offshore-closings are very real too. Stay open-minded and constantly challenge your own views as the world has become to complex for simple answers. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Be ready to fire yourself. What I mean by that is to be aware of your personality (I am for example an ENTP person with the following Big Five scores: Openness 92%, Conscientiousness: 72%, Extraversion: 80%, Agreeableness 25%, Neuroticism 21%) and always review your performance and ability against the very needs of the company. In the end it’s all about finding out how to best leverage each ones strengths and where necessary hiring the people who can help you achieve your goals. Given the nature of Mount Wish’s core business, growing concerns about the stability of the financial services industry and an increasingly hard to manage political landscape (rising trade wars, countries accusing each other of currency manipulations, outrageous antitrust actions against tech companies, etc.) we not only got under more scrutiny but also dragged into a lot of politically-motivated legal battles and thus have been facing more and more potential black-swan risks and therefore in my case it was best to take a step back, hand the CEO role over to someone more experienced, more “corporat-ish”, and someone a bit more diplomatic. Therefore, I fired myself as CEO and switched to the newly created role of Executive Chairman which accounts better for my current responsibilities, background, capabilities, future plans (well I am following a bit more of a Virgin-Group-like management approach here) and some personal aspects so that we are now combining the strengths and experience of all of us in an optimal way. Today, I focus on strategy, corporate and business development, and product vision while the new CEO manages and leads the company. It’s great and most importantly it strengthened Mount Wish once more. Mount Wish was in Startups of the Year hosted by HackerNoon. nominated as one of the best startups in New York City

"Be ready to fire youself," advised Markus Wunsch, Executive Chairman at Mount Wish

In this article, we are going to go over the main points of collecting Customer Feedback, why it matters to your business to hone in your customer satisfaction, and finally, answer our main question: why most of you are not doing it properly. A bit of context I want anything but swamping you with data and statistics, but we all agree that some figures to introduce our topic won’t hurt. So, did you know that.. A typical business hears from . 4% of it’s dissatisfied customers Source: “Understanding Customers” by Ruby Newell-Legner have bailed on a transaction or not made an intended purchase because of a poor service experience. 78% of consumers Source: American Express Survey, 2011 It takes to make up for experience. 12 positive experiences one unresolved negative Source: “Understanding Customers” by Ruby Newell-Legner News of as many ears as praise for a good service experience. bad customer service reaches more than twice Source: White House Office of Consumer Affairs It is more to acquire a new customer than it is to keep a current one. 6–7 times expensive Source: White House Office of Consumer Affairs It takes 12 positive experiences to make up for one unresolved negative experience. That was a really quick overview, we won’t go into the nitty-gritty here. Our topic is therefore completely backed by real data, let’s get down to work now. Understand your customers’ needs The barebones of any business? Get a grasp over what your customers need, and why they need it. Whether you’re product is a pain-killer (solving an obvious need) or a vitamine (appeal to users’ emotional), you have to adopt a stance, and make sure you have that hyped product-market fit. lean For the most sharp-sighted, we are referring here to this book: Hooked: How to Build Habit-Forming Products — by Nir Eyal . Then, once you got those initial feedback and those first customers, what happens if you fail to provide the right customer experience? Simple. . And the data speaks for itself…🙃 Customers will be upset However, by measuring your customer satisfaction and strengthening the relationships, you get happier customers that will simply talk about your product and help your business thrive. Beyond that, those feedback will also give you access to new hidden and nifty features or improvements you haven’t thought of. Effective , both positive and negative, is very helpful. Feedback is valuable information that will be used to make important decisions. Top performing companies top performing companies because they consistently search for ways to make their best even better. feedback are Are you proud of your overall answers ratio? Have you ever wondered why you’re not getting that much results? Why are some customers just dropping off your survey and not making it through? Well, there is one simple answer: 🧐 they most often don’t feel the value and what they would get in return. That’s why you must make sure to value your customers. They give you time (and energy) to provide feedback, but what are you giving them back? That’s where our recommendations come into play: implement your own reward program and show your customers how much they’re worth to you. You will simply get more results by luring the customers with a catchy reward. , as simple as that! You win, they win Feedier widget demo That’s why we built . Craft an enjoyable experience for your customers, and start understanding them with valuable and actionable feedback now. 🚀 Feedier, the next generation feedback software Whether you want to reach out by Email, SMS, Facebook or a widget on your website, we have crafted the integration and it’s ready to be consumed. Feedier - Next generation feedback But, that’s not all… How do you go about re-engaging with them? They made it all the way through the survey and finally got a reward — staggering. The customer is just mind-blown and thrilled. 😁 How about engaging further with him? You could simply urge him to give you a nice review on your marketplace, drop you a Google review, or simply just ask for his email address for further marketing actions. And, what other critics would you made regarding the current paradigm? First of all, there is clearly a lack of engagement and follow-up after the feedback has been given by the customer. You might collect email addresses, but it’s really scarce to see company engaging with the feedback giver. There is no real relationship that is being created. Is the customer having a blast filling in his feedback? Clearly not... if anything, most surveys are old-school, lacking information about the different steps and timing. And, that’s the point, your customer have to be delighted and empowered if you want to get valuable feedback. Finally, as the marketing guru, you then have to fend for yourself to pull out some data and draw conclusions. That’s what we stand for with , giving you access to a top-notch reporting dashboard, giving you access to a keywords explorer, user analytics, timing data, and much more. Again, we want your business to thrive, and not you striving to get an idea of what your customers are expecting! Feedier Sounds like a plan, doesn’t it? Join our ship now at — and get yourself a free plan at . We are still in beta, looking for early-adopters, helping us improve the product. Feedier feedier.com We hope this article has been helpful to anyone struggling to collect and rally up real feedback from real customers. Let’s discover the details of Feedier Any question or feedback? Please leave a comment below, we will be delighted to chat up! Don’t forget to clap our article and to get more amazing articles if you liked it👏. You can also find us on . subscribe Twitter

Why do you s*ck at collecting Customer Feedback?

Hello, writers! We love fall, but the last thing we want is for you to fall out of your writing habits lol! One of the best ways to create content that truly resonates with your audience is to ask them directly what they want to see. You can ensure your content aligns with their interests and needs by gathering feedback through surveys or social media polls. Here's how you can do just that: 1. Analyze Your HackerNoon Profile Your profile page is the perfect place to begin your analysis. Head to your profile, and check out your most popular stories—these are the ones with the highest readership. Dive into your story stats, and think back to when you wrote each piece. Consider whether external factors might have influenced those numbers: Did you write about a trending topic at the time? Were you covering a breaking development in tech? Look for patterns. Do certain topics consistently perform better? Maybe your audience loves your unique approach to a specific subject. Take notes: Pay attention to your tags and structure. What tags did you use in your most popular stories? Are they popular tags on HackerNoon? You can check this by searching them and seeing the number of published stories and their average engagement. Also, think about the structure of your high-performing articles: Did you format them differently? Was there a specific writing style or hook that worked better? This analysis will help you cogitate theories on what’s working (and what isn’t). 2. Reach Out to Your Subscribers As a HackerNoon writer, you have exclusive access to your followers—readers who love your work enough to subscribe to your updates. You can view your followers on the stats page and download a CSV list with their emails, handles, and profile links. Start by browsing your subscribers’ profiles to gain a better understanding of your audience. Take note of whether they are professionals in your field, fellow writers, or tech enthusiasts. This will give you valuable insights into who’s consuming your content and how to tailor future pieces. 3. Craft a Compelling Email Once you’ve done your background research, it’s time to engage your audience. Craft a compelling email, and send it to your subscribers asking for their feedback. Here’s what to include: Introduce yourself and explain why you’re reaching out.
Ask them what topics or formats they enjoy most in your work.
Inquire about areas for improvement and what they'd love to see more of.
Keep it brief and engaging to encourage responses.
If you're including a questionnaire, aim for just a few questions to avoid overwhelming your audience. To sweeten the deal, consider offering a sneak peek of 2 or 3 upcoming stories you're planning to publish, and let your subscribers vote on their favorite. Pro-tip: Don’t forget to thank your subscribers in your next article for their valuable feedback! Need an example? Use this email copy as inspiration: Feel free to add your style and personality! If you prefer to include your questions in a questionnaire format, there are plenty of tools available such as Google Forms, Typeform, SurveyMonkey, JotForm, Microsoft Forms, Paperform, and others. Here’s a potential format you can use for your questionnaire: Share Your Findings: As you go down this rabbit hole, why not share your findings in your next HackerNoon story? Use this writing template. 4. Take It to Your Socials The final step to gauge your audience’s interests is to take the conversation to social media. Choose the platform(s) where you have a strong following or where it makes the most sense to engage your audience about your writing. Then, start a poll. Ask your followers questions like: What’s your favorite story of yours?
What topics are they most interested in reading?
Have they read any of your stories? If not, give them compelling reasons to start. Just like with your email outreach, you can share a short questionnaire and snippets of your upcoming stories. Be sure to highlight the best quotes to grab their attention. Here’s an example of a poll HackerNoon ran to gauge our users’ interests: As you can see, there’s no need to overcomplicate things. Keep it simple and direct. Your goal is to get a sense of what topics resonate with your audience while also promoting yourself and your work 😉 Try these tips and let us know how it goes—we’d love to hear your feedback. P.S.: Don’t be discouraged if you don’t get a high response rate initially. Any feedback is valuable, and you can refine your strategies over time. Auto Posting Via API On Threads Now, every published HackerNoon story is automatically shared on Threads and X, giving your content double the exposure across platforms. Each post includes your story's meta description, uses the first tag as a hashtag, and, if you provide it, tags your Threads/X handle. It's a great way to maximize your reach and gain more visibility with minimal effort! Hello, writers! We love fall, but the last thing we want is for you to fall out of your writing habits lol! fall One of the best ways to create content that truly resonates with your audience is to ask them directly what they want to see. You can ensure your content aligns with their interests and needs by gathering feedback through surveys or social media polls. Here's how you can do just that: 1. Analyze Your HackerNoon Profile 1. Analyze Your HackerNoon Profile Your profile page is the perfect place to begin your analysis. Head to your profile, and check out your most popular stories—these are the ones with the highest readership. Dive into your story stats , and think back to when you wrote each piece. Consider whether external factors might have influenced those numbers: story stats Did you write about a trending topic at the time? Did you write about a trending topic at the time? Were you covering a breaking development in tech? Were you covering a breaking development in tech? Look for patterns. Do certain topics consistently perform better? Maybe your audience loves your unique approach to a specific subject. Take notes: Pay attention to your tags and structure. What tags did you use in your most popular stories? Are they popular tags on HackerNoon? You can check this by searching them and seeing the number of published stories and their average engagement. Take notes : Take notes popular tags Also, think about the structure of your high-performing articles: Did you format them differently? Did you format them differently? Was there a specific writing style or hook that worked better? Was there a specific writing style or hook that worked better? Was there a specific writing style or hook that worked better? This analysis will help you cogitate theories on what’s working (and what isn’t). 2. Reach Out to Your Subscribers As a HackerNoon writer, you have exclusive access to your followers—readers who love your work enough to subscribe to your updates . You can view your followers on the stats page and download a CSV list with their emails, handles, and profile links. subscribe to your updates stats page Start by browsing your subscribers’ profiles to gain a better understanding of your audience. Take note of whether they are professionals in your field, fellow writers, or tech enthusiasts. This will give you valuable insights into who’s consuming your content and how to tailor future pieces. 3. Craft a Compelling Email Once you’ve done your background research, it’s time to engage your audience. Craft a compelling email, and send it to your subscribers asking for their feedback. Here’s what to include: Here’s what to include: Introduce yourself and explain why you’re reaching out. Ask them what topics or formats they enjoy most in your work. Inquire about areas for improvement and what they'd love to see more of. Keep it brief and engaging to encourage responses. If you're including a questionnaire, aim for just a few questions to avoid overwhelming your audience. Introduce yourself and explain why you’re reaching out. Ask them what topics or formats they enjoy most in your work. Inquire about areas for improvement and what they'd love to see more of. Keep it brief and engaging to encourage responses. If you're including a questionnaire, aim for just a few questions to avoid overwhelming your audience. To sweeten the deal, consider offering a sneak peek of 2 or 3 upcoming stories you're planning to publish, and let your subscribers vote on their favorite. Pro-tip: Don’t forget to thank your subscribers in your next article for their valuable feedback! Pro-tip: Don’t forget to thank your subscribers in your next article for their valuable feedback! Need an example? Use this email copy as inspiration: Need an example? Use this email copy as inspiration: Feel free to add your style and personality! If you prefer to include your questions in a questionnaire format, there are plenty of tools available such as Google Forms, Typeform, SurveyMonkey, JotForm, Microsoft Forms, Paperform, and others. Here’s a potential format you can use for your questionnaire: Share Your Findings: As you go down this rabbit hole, why not share your findings in your next HackerNoon story? Use this writing template. Share Your Findings : As you go down this rabbit hole, why not share your findings in your next HackerNoon story? Use this writing template . Share Your Findings this writing template 4. Take It to Your Socials 4. Take It to Your Socials The final step to gauge your audience’s interests is to take the conversation to social media. Choose the platform(s) where you have a strong following or where it makes the most sense to engage your audience about your writing. Then, start a poll. Ask your followers questions like: What’s your favorite story of yours? What topics are they most interested in reading? Have they read any of your stories? If not, give them compelling reasons to start. What’s your favorite story of yours? What topics are they most interested in reading? Have they read any of your stories? If not, give them compelling reasons to start. Just like with your email outreach, you can share a short questionnaire and snippets of your upcoming stories. Be sure to highlight the best quotes to grab their attention. Here’s an example of a poll HackerNoon ran to gauge our users’ interests: example of a poll As you can see, there’s no need to overcomplicate things. Keep it simple and direct. Your goal is to get a sense of what topics resonate with your audience while also promoting yourself and your work 😉 Try these tips and let us know how it goes—we’d love to hear your feedback. P.S.: Don’t be discouraged if you don’t get a high response rate initially. Any feedback is valuable, and you can refine your strategies over time. P.S.: Don’t be discouraged if you don’t get a high response rate initially. Any feedback is valuable, and you can refine your strategies over time. P.S.: Auto Posting Via API On Threads Now, every published HackerNoon story is automatically shared on Threads and X , giving your content double the exposure across platforms. Each post includes your story's meta description, uses the first tag as a hashtag, and, if you provide it, tags your Threads/X handle. It's a great way to maximize your reach and gain more visibility with minimal effort! every published HackerNoon story is automatically shared on Threads and X

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📢 O'quvchilaringizni jalb qiling: so'rovlar va ijtimoiy media orqali kontent g'oyalarini qanday olish mumkin

📢 Bandakanya Abafundi Bakho: Ungayithola Kanjani Imibono Yokuqukethwe Ngocwaningo Nezokuxhumana

Content marketing. Everyone talks about it, but it feels like something only consumer brands truly crack. Put a glossy video of photogenic influencers in shiny shoes on TikTok, and watch the hearts build up. But for tech startups—especially if you’re building something niche or complex—content marketing feels like scaling Everest with nothing but flip-flops. (Yeah, automation isn’t the sexiest thing to slap on Instagram.) Approaching content marketing the wrong way means diving headfirst into some big, nebulous time-suck-spaghetti-mess of blog posts, emails, and social media updates that make a lot of noise but get you nowhere. BUT good content marketing is the thing that can turn a scrappy startup into a force of nature. Ask Figma how they got on the map. Or Robinhood—Market Snacks (now TBOY), anyone? What you need is a strategy that doesn’t just fill space but fills your sales pipeline. I’ll break down my 4-STAGE FRAMEWORK that’ll turn your content from the digital equivalent of a tumbleweed into a traffic-driving, lead-generating, conversion-inducing machine. (I’ll cover each step in detail with examples in my next posts. And yes, this framework has helped real humans in the past.) The I.D.E.A. Framework Stage 1: Identify & Map Your Audience’s Beliefs Humans don’t act based on logic. If they did, we’d all have six-pack abs, perfectly organized inboxes, and zero impulse buys on our credit cards. Unfortunately (or fortunately?) we act based on beliefs—and that’s where your content needs to start. Your audience isn’t buying your SaaS tool because you’ve dazzled them with advanced features and polished webpages. They’re buying it because they believe those features will make their life easier, their work more productive, or their future brighter. Consumer tech is a great place to look for examples of highly advanced tech falling on deaf ears due to belief misalignment. (Remember a certain pair of glasses?) So, here’s how to get people’s beliefs on your side. Identify existing beliefs: What does your audience think right now? What are the misconceptions or myths floating around? You need to pinpoint both the beliefs that work in your favor and the ones that hold people back.
Determine what they need to believe: For every step in your funnel, there’s at least one belief your audience needs to have in order to move to the next stage. It could be as simple as “I need to be more efficient” to “this product will make me look like a genius at work.” It’s your job to map that belief journey and strategically nudge them forward.
Make the map: Easy. Simple table. First row – stage of funnel. Second row – beliefs needed at each stage. Third row – platforms you’re likely to engage your audience on. (You’ll need to use these platforms in Stage 3).
Validate your map: Don’t assume you know everything about your audience (even if you’ve got a cool job title). Use focus groups, surveys, or just talk to people. Validate your belief map before you start writing articles or producing videos that no one will care about. Stage 2: Distill Themes From Beliefs Once you’ve got the belief map, you’re not just going to throw out random blog posts and hope someone cares. That’s like hosting a dinner party and serving your guests lukewarm pizza—you can do better. But here’s where most companies get stuck—they focus too narrowly on the surface-level objections. If your audience believes “automation is for the big guns only,” your instinct might be to counter that with a list of case studies, or (even worse) spam them with posts about your affordable pricing plans. Wrong approach. All that does is trap you in an endless cycle of defensiveness. (And there are only so many case studies you can write before being diagnosed with creative block.) Instead, look all those beliefs you’ve pinpointed and ask yourself: what’s connecting these beliefs? Maybe it’s a fear of change, maybe it’s a desire for efficiency. From there, you can distill bigger themes that turn your content from a scattered trail of breadcrumbs into a full-blown, seven-course feast—complete with dessert that nobody saw coming. Taking the automation example: shift the conversation from “small companies can automate, too” to something much bigger, like “Automation & The Future of Work.” Now you’re not arguing with your audience, but opening the door to a much broader discussion, and endless content permutations. Stage 3: Establish Your Content Plan This is where the urge to rush into brainstorming hits—usually ends with the team locked in a room with a whiteboard and lots of takeout. But don’t confuse activity with progress. Before you dive into the brainstorming frenzy, focus on these three steps. Context is king: Forget “content is king”—it’s not. Context is. You don’t want your content performing Shakespeare in a fast-food joint—misplaced brilliance helps no one. This is why adding platforms in your belief map is crucial. Each stage of the funnel needs content tailored to both the belief(s) and the right platform. Go to your belief map and zero in on the top 1-2 platforms where your audience is spending time.
Research what works: Look at content that’s killing it. Not just at your competitors, not just in your industry—anyone who’s nailing it with your target audience. If they’re a fan of Uncle Roger or something, go investigate. What formats, tones, styles, camera angles are grabbing attention? Also, algorithms don’t just vary by platform. They vary within platforms—what flies on YouTube search won’t work in the feed. Know the game you’re playing.
Now go brainstorm: But don’t go wild—keep your ideas tight and focused on those themes you established earlier. And here’s where AI can give you a hand—don’t be afraid to use tools to spark your creativity. We’re all secretly cyborgs anyway at this point. (Maybe a post on AI later). Stage 4: Act, Adapt, Iterate—Fast You don’t need to be perfect. As a startup, your superpower is that you can move fast and forget about apologizing. Your content doesn’t need to look like it was produced by a full creative agency with drones and 4K cameras. Be scrappy. People love scrappy. Test, iterate, and repeat. Throw content out there, see what gets people clicking, learn what the algorithms have an appetite for, and rapidly adjust from there. If you’re waiting for the perfect strategy before hitting “publish,” you’ll be waiting forever. Besides, no one EVER nails the strategy from the get-go. If you’re not surprised by the initial results, then you’re probably doing something wrong. But have at least a month’s worth of content ready to go before you launch. Consistency is what’s going to keep you top of mind—not one brilliant blog post every three months. The more regularly you show up, the more likely people will start to pay attention. Wrapping It Up Content marketing is less about filling space and more about filling minds with the right beliefs. You’ve got to shift your audience from skeptical to sold, and this 4-stage framework will get you there. Map your audience’s beliefs. Extract compelling themes. Create content that fits where they are. And move fast enough to leave your competition wondering what just happened. Do that, and you’ll not only have their attention—you’ll have their business. Content marketing . Everyone talks about it, but it feels like something only consumer brands truly crack. Put a glossy video of photogenic influencers in shiny shoes on TikTok, and watch the hearts build up. But for tech startups—especially if you’re building something niche or complex— content marketing feels like scaling Everest with nothing but flip-flops . (Yeah, automation isn’t the sexiest thing to slap on Instagram.) Content marketing content marketing feels like scaling Everest with nothing but flip-flops Approaching content marketing the wrong way means diving headfirst into some big, nebulous time-suck-spaghetti-mess of blog posts, emails, and social media updates that make a lot of noise but get you nowhere. BUT good content marketing is the thing that can turn a scrappy startup into a force of nature . Ask Figma how they got on the map. Or Robinhood—Market Snacks (now TBOY), anyone? BUT good content marketing is the thing that can turn a scrappy startup into a force of nature a force of nature What you need is a strategy that doesn’t just fill space but fills your sales pipeline. I’ll break down my 4-STAGE FRAMEWORK that’ll turn your content from the digital equivalent of a tumbleweed into a traffic-driving, lead-generating, conversion-inducing machine. (I’ll cover each step in detail with examples in my next posts. And yes, this framework has helped real humans in the past.) What you need is a strategy that doesn’t just fill space but fills your sales pipeline. I’ll break down my 4-STAGE FRAMEWORK that’ll turn your content from the digital equivalent of a tumbleweed into a traffic-driving, lead-generating, conversion-inducing machine. (I’ll cover each step in detail with examples in my next posts. And yes, this framework has helped real humans in the past.) What you need is a strategy that doesn’t just fill space but fills your sales pipeline. 4-STAGE FRAMEWORK 4-STAGE FRAMEWORK traffic-driving, lead-generating, conversion-inducing machine. traffic-driving, lead-generating, conversion-inducing machine. The I.D.E.A. Framework The I.D.E.A. Framework The I.D.E.A. Framework Stage 1: Identify & Map Your Audience’s Beliefs Stage 1: Identify & Map Your Audience’s Beliefs Humans don’t act based on logic. If they did, we’d all have six-pack abs, perfectly organized inboxes, and zero impulse buys on our credit cards. Unfortunately (or fortunately?) we act based on beliefs—and that’s where your content needs to start. Your audience isn’t buying your SaaS tool because you’ve dazzled them with advanced features and polished webpages. They’re buying it because they believe those features will make their life easier, their work more productive, or their future brighter. Consumer tech is a great place to look for examples of highly advanced tech falling on deaf ears due to belief misalignment . (Remember a certain pair of glasses?) believe believe believe belief misalignment So, here’s how to get people’s beliefs on your side. So, here’s how to get people’s beliefs on your side. Identify existing beliefs: What does your audience think right now? What are the misconceptions or myths floating around? You need to pinpoint both the beliefs that work in your favor and the ones that hold people back. Determine what they need to believe: For every step in your funnel, there’s at least one belief your audience needs to have in order to move to the next stage. It could be as simple as “I need to be more efficient” to “this product will make me look like a genius at work.” It’s your job to map that belief journey and strategically nudge them forward. Make the map: Easy. Simple table. First row – stage of funnel. Second row – beliefs needed at each stage. Third row – platforms you’re likely to engage your audience on. (You’ll need to use these platforms in Stage 3). Validate your map: Don’t assume you know everything about your audience (even if you’ve got a cool job title). Use focus groups, surveys, or just talk to people. Validate your belief map before you start writing articles or producing videos that no one will care about. Identify existing beliefs: What does your audience think right now? What are the misconceptions or myths floating around? You need to pinpoint both the beliefs that work in your favor and the ones that hold people back. Identify existing beliefs: Determine what they need to believe: For every step in your funnel, there’s at least one belief your audience needs to have in order to move to the next stage. It could be as simple as “I need to be more efficient” to “this product will make me look like a genius at work.” It’s your job to map that belief journey and strategically nudge them forward. Determine what they need to believe: Make the map: Easy. Simple table. First row – stage of funnel. Second row – beliefs needed at each stage. Third row – platforms you’re likely to engage your audience on. (You’ll need to use these platforms in Stage 3). Make the map: platforms Validate your map: Don’t assume you know everything about your audience (even if you’ve got a cool job title). Use focus groups, surveys, or just talk to people. Validate your belief map before you start writing articles or producing videos that no one will care about. Validate your map: Stage 2: Distill Themes From Beliefs Stage 2: Distill Themes From Beliefs Stage 2: Distill Themes From Beliefs Once you’ve got the belief map, you’re not just going to throw out random blog posts and hope someone cares. That’s like hosting a dinner party and serving your guests lukewarm pizza—you can do better. But here’s where most companies get stuck— they focus too narrowly on the surface-level objections . If your audience believes “automation is for the big guns only,” your instinct might be to counter that with a list of case studies, or (even worse) spam them with posts about your affordable pricing plans. Wrong approach . All that does is trap you in an endless cycle of defensiveness. (And there are only so many case studies you can write before being diagnosed with creative block.) they focus too narrowly on the surface-level objections Wrong approach Instead, look all those beliefs you’ve pinpointed and ask yourself: what’s connecting these beliefs? Maybe it’s a fear of change, maybe it’s a desire for efficiency. From there, you can distill bigger themes that turn your content from a scattered trail of breadcrumbs into a full-blown, seven-course feast—complete with dessert that nobody saw coming . Taking the automation example: shift the conversation from “small companies can automate, too” to something much bigger, like “Automation & The Future of Work.” Now you’re not arguing with your audience, but opening the door to a much broader discussion, and endless content permutations. what’s connecting these beliefs? what’s connecting these beliefs? distill bigger themes that turn your content from a scattered trail of breadcrumbs into a full-blown, seven-course feast—complete with dessert that nobody saw coming Stage 3: Establish Your Content Plan Stage 3: Establish Your Content Plan Stage 3: Establish Your Content Plan This is where the urge to rush into brainstorming hits—usually ends with the team locked in a room with a whiteboard and lots of takeout. But don’t confuse activity with progress. Before you dive into the brainstorming frenzy, focus on these three steps . Before you dive into the brainstorming frenzy, focus on these three steps Context is king: Forget “content is king”—it’s not. Context is. You don’t want your content performing Shakespeare in a fast-food joint—misplaced brilliance helps no one. This is why adding platforms in your belief map is crucial. Each stage of the funnel needs content tailored to both the belief(s) and the right platform. Go to your belief map and zero in on the top 1-2 platforms where your audience is spending time. Research what works: Look at content that’s killing it. Not just at your competitors, not just in your industry—anyone who’s nailing it with your target audience. If they’re a fan of Uncle Roger or something, go investigate. What formats, tones, styles, camera angles are grabbing attention? Also, algorithms don’t just vary by platform. They vary within platforms—what flies on YouTube search won’t work in the feed. Know the game you’re playing. Now go brainstorm: But don’t go wild—keep your ideas tight and focused on those themes you established earlier. And here’s where AI can give you a hand—don’t be afraid to use tools to spark your creativity. We’re all secretly cyborgs anyway at this point. (Maybe a post on AI later). Context is king: Forget “content is king”—it’s not. Context is. You don’t want your content performing Shakespeare in a fast-food joint—misplaced brilliance helps no one. This is why adding platforms in your belief map is crucial. Each stage of the funnel needs content tailored to both the belief(s) and the right platform. Go to your belief map and zero in on the top 1-2 platforms where your audience is spending time. Context is king: Forget “content is king”—it’s not. Context is. Research what works: Look at content that’s killing it. Not just at your competitors, not just in your industry— anyone who’s nailing it with your target audience . If they’re a fan of Uncle Roger or something, go investigate. What formats, tones, styles, camera angles are grabbing attention? Also, algorithms don’t just vary by platform. They vary within platforms —what flies on YouTube search won’t work in the feed. Know the game you’re playing. Research what works: anyone who’s nailing it with your target audience vary within platforms Now go brainstorm: But don’t go wild—keep your ideas tight and focused on those themes you established earlier. And here’s where AI can give you a hand—don’t be afraid to use tools to spark your creativity. We’re all secretly cyborgs anyway at this point. (Maybe a post on AI later). Now go brainstorm: tight and focused Stage 4: Act, Adapt, Iterate—Fast Stage 4: Act, Adapt, Iterate—Fast Stage 4: Act, Adapt, Iterate—Fast You don’t need to be perfect. As a startup, your superpower is that you can move fast and forget about apologizing . Your content doesn’t need to look like it was produced by a full creative agency with drones and 4K cameras. Be scrappy. People love scrappy. move fast and forget about apologizing Test, iterate, and repeat. Throw content out there, see what gets people clicking, learn what the algorithms have an appetite for, and rapidly adjust from there. If you’re waiting for the perfect strategy before hitting “publish,” you’ll be waiting forever. Besides, no one EVER nails the strategy from the get-go. If you’re not surprised by the initial results, then you’re probably doing something wrong . If you’re not surprised by the initial results, then you’re probably doing something wrong But have at least a month’s worth of content ready to go before you launch. Consistency is what’s going to keep you top of mind—not one brilliant blog post every three months. The more regularly you show up, the more likely people will start to pay attention . at least a month’s worth of content ready The more regularly you show up, the more likely people will start to pay attention Wrapping It Up Wrapping It Up Content marketing is less about filling space and more about filling minds with the right beliefs. You’ve got to shift your audience from skeptical to sold, and this 4-stage framework will get you there. Content marketing is less about filling space and more about filling minds with the right beliefs. You’ve got to shift your audience from skeptical to sold, and this 4-stage framework will get you there. Map your audience’s beliefs. Extract compelling themes. Create content that fits where they are. And move fast enough to leave your competition wondering what just happened. Do that, and you’ll not only have their attention—you’ll have their business. Do that, and you’ll not only have their attention—you’ll have their business.

Skeptical To Sold: The 4-Stage Content Marketing Hack No Tech Startup Should Ignore

من المتشكك إلى المبيع: حيلة تسويق المحتوى المكونة من أربع مراحل والتي لا ينبغي لأي شركة ناشئة في مجال التكنولوجيا أن تتجاهلها

De escéptico a vendido: el truco de marketing de contenidos en cuatro etapas que ninguna startup tecnológica debería ignorar

Saltzeko eszeptikoa: 4-etapako edukien marketina Hack No Tech Startup-ek alde batera utzi behar

Da scettico a venduto: il trucco del content marketing in 4 fasi che nessuna startup tecnologica dovrebbe ignorare

懐疑から納得へ: テクノロジー系スタートアップが無視してはいけない 4 段階のコンテンツ マーケティング ハック

სკეპტიკურად გასაყიდად: 4-ეტაპიანი კონტენტის მარკეტინგის ჰაკი, რომელიც ტექნიკურმა დამწყებმა არ უნდა იგნორირება

ບໍ່ຄ່ອຍເຊື່ອງ່າຍໆທີ່ຈະຂາຍ: ການ Hack ການຕະຫຼາດເນື້ອຫາ 4 ຂັ້ນຕອນທີ່ບໍ່ມີ Tech Startup ຄວນບໍ່ສົນໃຈ

Sceptyczny wobec sprzedanych: 4-etapowy hack marketingu treści, którego żaden startup technologiczny nie powinien ignorować

Скептически настроены к продаже: 4-этапный контент-маркетинговый прием, который не должен игнорировать ни один технологический стартап

Satylmagyna şübheli: 4 basgançakly mazmun marketingi Hack ýok Tehniki başlangyç äsgermezlik etmeli

Skeptical To Sold: The 4-Stage Content Marketing Hack Ku hava Tech Startup leyi Faneleke Ku Honisa

从怀疑到销售：科技初创公司不应忽视的四阶段内容营销技巧

The Systemic Impact of Deplatforming on Social Media: Acknowledgements, Data, and References

COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 23 of 42. 21. OpenOffice 2.4 document (Reliance Document) {ID_000260} / {L2/294/1} 400. The document purports to be a precursor to the Bitcoin White Paper and is presented as if written by Dr Wright. It is dated 8-9 March 2008 in its metadata and Dr Wright states it to be from 2008 in his evidence in these proceedings. [Exhibit CSW-14] (a) COPA’s Reasons for Alleging Forgery 401. The document contains language taken from the Bitcoin White Paper, but the document has been backdated as if to make it appear earlier than it is. [PM23 [41-45]]. 402. The document purports to have been created on 8 March 2008 and last saved on 9 March 2008 using OpenOffice.org version 2.4 with internal version number 680m12$Build9286. However, this version of OpenOffice.org was not uploaded for use until 16 March 2008 and was not generally released until 27 March 2008, both of which post-date the purported date of {ID_000260}. [PM23 [3, 6, 10, 24-34, 44]]. 403. Further, in addition to not being uploaded until 16 March 2008, the software in question (OpenOffice.org 2.4 680m12$Build-9286) was not even in development until after 14 March 2008. [PM23 [35-40]]. 404. The document contains content imported from an external document or documents. No external document has been disclosed. [PM23 [41c]]. 405. The document has an implausible edit time, matching precisely the time between its created and last saved timestamp. [PM23 [14-18]]. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 406. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (suggesting a precursor document to the Bitcoin White Paper, supposedly prepared in early 2008), contrary to fact. 407. Further, the document has been created using the same software (OpenOffice.org) and same version (version 2.4) as used by Satoshi Nakamoto to write all versions of the Bitcoin White Paper, indicating an intention to create a document within Dr Wright’s possession with the appearance of being a predecessor. [PM3 [23, 40, and 47]]. 408. Dr Wright has positively asserted that this is a document on which he primarily relies as supporting his claim to be Satoshi Nakamoto. 409. In his first witness statement in these proceedings, Dr Wright lists this document as a document to which he has been referred when preparing his evidence. (c) Dr Wright’s Explanations and COPA’s Rebuttal 410. In Appendix B to Wright11, Dr Wright sought to explain away the fact of this document being created with a version of OpenOffice that was issued after its supposed creation date by saying that he created the document in LATEX on 8/9 March 2008 but set the metadata using LATEX “so that it looks like I wrote this document in the future.” He explained this odd behaviour by saying that he had “taught the process of using versioning in this way to demonstrate aspects of making a determination about a document difficult to students in 2008 to 2012”. See {CSW/2/33}. 411. He sought to explain away the indications that the document had imported content (including from the Bitcoin White Paper, supposedly written a year later) by saying that it was a function of the document having been created in LATEX and then recompiled later. See {CSW/2/34}. 412. He sought to explain away the implausible editing time by saying that it was a result of him using the Sweave program with LATEX, which (as he believed) caused metadata time fields to be updated with re-compilations. See {CSW/2/34}. 413. This explanation should be rejected as dishonest for the following reasons: 413.1. Dr Wright has disclosed no LATEX original of this document. It is inherently implausible that he would recall the production of such an original 15 years on without having a copy. 413.2. His story of setting the metadata to make the document misleadingly appear to have been created later is at odds with his evidence in the Kleiman proceedings that: “I do not manipulate metadata on things for any purpose”: {L15/131/71}. 413.3. His story of using this document in 2008 as a demonstration tool for students is one which beggars belief. It presupposes that, having made substantial efforts to remain anonymous, the real Satoshi used a document relating to his prospective Bitcoin project as a demonstration tool for students, when he could have used any other document to make the point. 413.4. In his oral evidence, Dr Wright was driven to answer this point by claiming that he had been quite open about his identity as Satoshi, revealing that identity to many, many people, including the tax office in Australia, government officials, “individuals at companies I knew” as well as “my students” (all unnamed, save for reference to Shoaib Yousuf and David Bridges, who were clear that Dr Wright had not revealed himself as Satoshi to them before the public revelation in 2015/16): {Day3/43:19} to {Day3/45:8}. This was not only implausible, but also at odds with his evidence in the Kleiman proceedings, where he told the Court that only Don Lynam and Gareth Williams knew his identity as Satoshi by early 2011: {L15/125/102}. 413.5. Dr Wright’s story also requires his explanation of the signs of imported content and of the implausible edit time to be accepted, despite their being unsupported by any independent expert evidence. (d) Conclusion 414. Dr Wright’s explanations show how he builds lie upon lie in his attempt to explain away Mr Madden’s convincing analysis which wholly supports COPA’s submissions and case. I find this document was plainly forged by Dr Wright. Continue Reading 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 January 29, 2024, judiciary.uk 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. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 23 of 42. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 23 of 42. HackerNoon’s Legal PDF Series HackerNoon’s Legal PDF Series here here 21. OpenOffice 2.4 document (Reliance Document) {ID_000260} / {L2/294/1} 400. The document purports to be a precursor to the Bitcoin White Paper and is presented as if written by Dr Wright. It is dated 8-9 March 2008 in its metadata and Dr Wright states it to be from 2008 in his evidence in these proceedings. [Exhibit CSW-14] (a) COPA’s Reasons for Alleging Forgery (a) COPA’s Reasons for Alleging Forgery 401. The document contains language taken from the Bitcoin White Paper, but the document has been backdated as if to make it appear earlier than it is. [PM23 [41-45]]. 402. The document purports to have been created on 8 March 2008 and last saved on 9 March 2008 using OpenOffice.org version 2.4 with internal version number 680m12$Build9286. However, this version of OpenOffice.org was not uploaded for use until 16 March 2008 and was not generally released until 27 March 2008, both of which post-date the purported date of {ID_000260}. [PM23 [3, 6, 10, 24-34, 44]]. 403. Further, in addition to not being uploaded until 16 March 2008, the software in question (OpenOffice.org 2.4 680m12$Build-9286) was not even in development until after 14 March 2008. [PM23 [35-40]]. 404. The document contains content imported from an external document or documents. No external document has been disclosed. [PM23 [41c]]. 405. The document has an implausible edit time, matching precisely the time between its created and last saved timestamp. [PM23 [14-18]]. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 406. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto (suggesting a precursor document to the Bitcoin White Paper, supposedly prepared in early 2008), contrary to fact. 407. Further, the document has been created using the same software (OpenOffice.org) and same version (version 2.4) as used by Satoshi Nakamoto to write all versions of the Bitcoin White Paper, indicating an intention to create a document within Dr Wright’s possession with the appearance of being a predecessor. [PM3 [23, 40, and 47]]. 408. Dr Wright has positively asserted that this is a document on which he primarily relies as supporting his claim to be Satoshi Nakamoto. 409. In his first witness statement in these proceedings, Dr Wright lists this document as a document to which he has been referred when preparing his evidence. (c) Dr Wright’s Explanations and COPA’s Rebuttal (c) Dr Wright’s Explanations and COPA’s Rebuttal 410. In Appendix B to Wright11, Dr Wright sought to explain away the fact of this document being created with a version of OpenOffice that was issued after its supposed creation date by saying that he created the document in LATEX on 8/9 March 2008 but set the metadata using LATEX “so that it looks like I wrote this document in the future.” He explained this odd behaviour by saying that he had “taught the process of using versioning in this way to demonstrate aspects of making a determination about a document difficult to students in 2008 to 2012”. See {CSW/2/33}. 411. He sought to explain away the indications that the document had imported content (including from the Bitcoin White Paper, supposedly written a year later) by saying that it was a function of the document having been created in LATEX and then recompiled later. See {CSW/2/34}. 412. He sought to explain away the implausible editing time by saying that it was a result of him using the Sweave program with LATEX, which (as he believed) caused metadata time fields to be updated with re-compilations. See {CSW/2/34}. 413. This explanation should be rejected as dishonest for the following reasons: 413.1. Dr Wright has disclosed no LATEX original of this document. It is inherently implausible that he would recall the production of such an original 15 years on without having a copy. 413.2. His story of setting the metadata to make the document misleadingly appear to have been created later is at odds with his evidence in the Kleiman proceedings that: “I do not manipulate metadata on things for any purpose”: {L15/131/71}. 413.3. His story of using this document in 2008 as a demonstration tool for students is one which beggars belief. It presupposes that, having made substantial efforts to remain anonymous, the real Satoshi used a document relating to his prospective Bitcoin project as a demonstration tool for students, when he could have used any other document to make the point. 413.4. In his oral evidence, Dr Wright was driven to answer this point by claiming that he had been quite open about his identity as Satoshi, revealing that identity to many, many people, including the tax office in Australia, government officials, “individuals at companies I knew” as well as “my students” (all unnamed, save for reference to Shoaib Yousuf and David Bridges, who were clear that Dr Wright had not revealed himself as Satoshi to them before the public revelation in 2015/16): {Day3/43:19} to {Day3/45:8}. This was not only implausible, but also at odds with his evidence in the Kleiman proceedings, where he told the Court that only Don Lynam and Gareth Williams knew his identity as Satoshi by early 2011: {L15/125/102}. 413.5. Dr Wright’s story also requires his explanation of the signs of imported content and of the implausible edit time to be accepted, despite their being unsupported by any independent expert evidence. (d) Conclusion (d) Conclusion 414. Dr Wright’s explanations show how he builds lie upon lie in his attempt to explain away Mr Madden’s convincing analysis which wholly supports COPA’s submissions and case. I find this document was plainly forged by Dr Wright. Continue Reading Here. Continue Reading Here . 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 January 29, 2024, judiciary.uk 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. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk 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. judiciary.uk judiciary.uk

Craig Wright Accused of Forging Early Bitcoin Document with Modern Software

Deep Neural Networks to Detect and Quantify Lymphoma Lesions: Results

ডিপ নিউরাল নেটওয়ার্ক লিম্ফোমা ক্ষত সনাক্ত এবং পরিমাপ করতে: ফলাফল

Tiefe neuronale Netzwerke zur Erkennung und Quantifizierung von Lymphomläsionen: Ergebnisse

Redes neuronales profundas para detectar y cuantificar lesiones de linfoma: resultados

Réseaux de neurones profonds pour détecter et quantifier les lésions de lymphome : résultats

लिम्फोमा घावों का पता लगाने और मात्रा निर्धारित करने के लिए डीप न्यूरल नेटवर्क: परिणाम

リンパ腫病変を検出し定量化するディープニューラルネットワーク：結果

림프종 병변을 탐지하고 정량화하기 위한 심층 신경망: 결과

Redes Neurais Profundas para Detectar e Quantificar Lesões de Linfoma: Resultados

Глубокие нейронные сети для обнаружения и количественной оценки поражений лимфомой: результаты

Lenfoma Lezyonlarını Tespit Etmek ve Ölçmek İçin Derin Sinir Ağları: Sonuçlar

Mạng lưới thần kinh sâu để phát hiện và định lượng tổn thương ung thư hạch: Kết quả

深度神经网络检测和量化淋巴瘤病变：结果

Deep Neural Networks to Detect and Quantify Lymphoma Lesions: Materials and Methods

ডিপ নিউরাল নেটওয়ার্ক লিম্ফোমা ক্ষত সনাক্ত এবং পরিমাপ করার জন্য: উপাদান এবং পদ্ধতি

Tiefe neuronale Netzwerke zur Erkennung und Quantifizierung von Lymphomläsionen: Materialien und Methoden

Redes neuronales profundas para detectar y cuantificar lesiones de linfoma: materiales y métodos

Réseaux de neurones profonds pour détecter et quantifier les lésions de lymphome : matériels et méthodes

लिम्फोमा घावों का पता लगाने और मात्रा निर्धारित करने के लिए डीप न्यूरल नेटवर्क: सामग्री और विधियाँ

リンパ腫病変を検出し定量化するディープニューラルネットワーク：材料と方法

림프종 병변을 검출하고 정량화하기 위한 심층 신경망: 재료 및 방법

Redes Neurais Profundas para Detectar e Quantificar Lesões de Linfoma: Materiais e Métodos

Глубокие нейронные сети для обнаружения и количественной оценки поражений лимфомой: материалы и методы

Lenfoma Lezyonlarını Tespit Etmek ve Ölçmek İçin Derin Sinir Ağları: Malzemeler ve Yöntemler

Mạng lưới thần kinh sâu để phát hiện và định lượng tổn thương ung thư hạch: Vật liệu và phương pháp

深度神经网络检测和量化淋巴瘤病变：材料和方法

Authors:
(1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: shadabahamed1996@gmail.com);
(2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA;
(3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada;
(4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea;
(5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada;
(6) Don Wilson, BC Cancer, Vancouver, BC, Canada;
(7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada;
(8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada;
(9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada;
(10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA;
(11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA;
(12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA;
(13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada;
(14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada. Table of Links Abstract and Intro
Related Work
Materials and Methods
Results
Discussion
Conclusion and References VI. CONCLUSION In this study, we assessed various neural network architectures for automating lymphoma lesion segmentation in PET/CT images across multiple datasets. We examined the reproducibility of lesion measures, revealing differences among networks, highlighting their suitability for specific clinical uses. Additionally, we introduced three lesion detection criteria to assess network performance at a per-lesion level, emphasizing their clinical relevance. Lastly, we discussed challenges related to ground truth consistency and stressed the importance of having well-defined protocol for segmentation. This work provides valuable insights into deep learning’s potentials and limitations in lymphoma lesion segmentation and emphasizes the need for standardized annotation practices to enhance research validity and clinical applications. REFERENCES [1] S. F. Barrington et al. “FDG PET for therapy monitoring in Hodgkin and non-Hodgkin lymphomas”. In: European Journal of Nuclear Medicine and Molecular Imaging 44.1 (Aug. 2017), pp. 97–110. ISSN: 1619- 7089. [2] K. Okuyucu et al. “Prognosis estimation under the light of metabolic tumor parameters on initial FDG-PET/CT in patients with primary extranodal lymphoma”. en. In: Radiol. Oncol. 50.4 (Dec. 2016), pp. 360–369. [3] N. Wu et al. “Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening”. In: IEEE Transactions on Medical Imaging 39.4 (2020), pp. 1184–1194. [4] C. Yuan et al. “Diffuse large B-cell lymphoma segmentation in PET-CT images via hybrid learning for feature fusion”. In: Medical Physics 48.7 (2021), pp. 3665– 3678. [5] H. Hu et al. “Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy”. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020, pp. 1197–1200. [6] H. Li et al. “DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images”. In: IEEE Access 8 (2020), pp. 8004–8018. [7] L. Liu et al. “Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans”. In: Multiscale Multimodal Medical Imaging. Ed. by X. Li et al. Cham: Springer Nature Switzerland, 2022, pp. 121–129. ISBN: 978-3-031-18814-5. [8] C. S. Constantino et al. “Evaluation of Semiautomatic and Deep Learning–Based Fully Automatic Segmentation Methods on [18F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization”. In: Journal of Digital Imaging 36.4 (Aug. 2023), pp. 1864–1876. ISSN: 1618-727X. [9] A. J. Weisman et al. “Comparison of 11 automated PET segmentation methods in lymphoma”. English. In: Physics in medicine & biology 65.23 (2020), pp. 235019–235019. [10] A. J. Weisman et al. “Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma”. In: Radiology: Artificial Intelligence 2.5 (2020), e200016. [11] C. Jiang et al. “Deep learning–based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images”. In: European Radiology 32.7 (July 2022), pp. 4801–4812. ISSN: 1432-1084. [12] P. Blanc-Durand et al. “Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDGPET/CT for total metabolic tumour volume prediction using a convolutional neural network.” In: European Journal of Nuclear Medicine and Molecular Imaging 48.5 (May 2021), pp. 1362–1370. ISSN: 1619-7089. [13] S. Ahamed et al. “A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors and Lymph Nodes from Head and Neck Cancer PET/CT Images”. In: Head and Neck Tumor Segmentation and Outcome Prediction. Ed. by V. Andrearczyk et al. Cham: Springer Nature Switzerland, 2023, pp. 94–106. ISBN: 978-3-031-27420-6. [14] S. Gatidis et al. “A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions”. In: Scientific Data 9.1 (Oct. 2022), p. 601. ISSN: 2052-4463. [15] M. Pop et al. “Left-Ventricle Quantification Using Residual U-Net”. English. In: vol. 11395. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Switzerland: Springer International Publishing AG, 2019, pp. 371–380. ISBN: 0302-9743. [16] A. Myronenko. “3D MRI Brain Tumor Segmentation Using Autoencoder Regularization”. English. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer International Publishing, 2019, pp. 311–320. ISBN: 0302-9743. [17] F. Isensee et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation”. In: Nature Methods 18.2 (Dec. 2020), pp. 203–211. [18] A. Hatamizadeh et al. “Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images”. English. In: (2022). [19] M. J. Cardoso et al. MONAI: An open-source framework for deep learning in healthcare. 2022. arXiv: 2211. 02701 [cs.LG]. [20] S. Ahamed et al. “Towards enhanced lesion segmentation using a 3D neural network trained on multiresolution cropped patches of lymphoma PET images”. In: Journal of Nuclear Medicine 64.supplement 1 (2023), P1360–P1360. ISSN: 0161-5505. [21] J. L. Fleiss. “Measuring nominal scale agreement among many raters”. English. In: Psychological bulletin 76.5 (1971), pp. 378–382. [22] A. K. Jha et al. “Nuclear Medicine and artificial intelligence: Best practices for evaluation (the RELAINCE guidelines)”. en. In: J. Nucl. Med. 63.9 (Sept. 2022), pp. 1288–1299. [23] N. Hasani et al. “Artificial Intelligence in Lymphoma PET Imaging: A Scoping Review (Current Trends and Future Directions)”. In: PET clinics 17.1 (Jan. 1), pp. 145–174. ISSN: 1556-8598. [24] S. K. Warfield et al. “Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation”. en. In: IEEE Trans. Med. Imaging 23.7 (July 2004), pp. 903–921. This paper is available on arxiv under CC 4.0 license. Authors: (1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: shadabahamed1996@gmail.com); (2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA; (3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada; (4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea; (5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada; (6) Don Wilson, BC Cancer, Vancouver, BC, Canada; (7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada; (8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada; (9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada; (10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA; (11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA; (12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA; (13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada; (14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada. Authors: Authors: (1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: shadabahamed1996@gmail.com); (2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA; (3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada; (4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea; (5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada; (6) Don Wilson, BC Cancer, Vancouver, BC, Canada; (7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada; (8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada; (9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada; (10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA; (11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA; (12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA; (13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada; (14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada. Table of Links Abstract and Intro Related Work Materials and Methods Results Discussion Conclusion and References Abstract and Intro Abstract and Intro Related Work Related Work Materials and Methods Materials and Methods Results Results Discussion Discussion Conclusion and References Conclusion and References VI. CONCLUSION In this study, we assessed various neural network architectures for automating lymphoma lesion segmentation in PET/CT images across multiple datasets. We examined the reproducibility of lesion measures, revealing differences among networks, highlighting their suitability for specific clinical uses. Additionally, we introduced three lesion detection criteria to assess network performance at a per-lesion level, emphasizing their clinical relevance. Lastly, we discussed challenges related to ground truth consistency and stressed the importance of having well-defined protocol for segmentation. This work provides valuable insights into deep learning’s potentials and limitations in lymphoma lesion segmentation and emphasizes the need for standardized annotation practices to enhance research validity and clinical applications. REFERENCES [1] S. F. Barrington et al. “FDG PET for therapy monitoring in Hodgkin and non-Hodgkin lymphomas”. In: European Journal of Nuclear Medicine and Molecular Imaging 44.1 (Aug. 2017), pp. 97–110. ISSN: 1619- 7089. [2] K. Okuyucu et al. “Prognosis estimation under the light of metabolic tumor parameters on initial FDG-PET/CT in patients with primary extranodal lymphoma”. en. In: Radiol. Oncol. 50.4 (Dec. 2016), pp. 360–369. [3] N. Wu et al. “Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening”. In: IEEE Transactions on Medical Imaging 39.4 (2020), pp. 1184–1194. [4] C. Yuan et al. “Diffuse large B-cell lymphoma segmentation in PET-CT images via hybrid learning for feature fusion”. In: Medical Physics 48.7 (2021), pp. 3665– 3678. [5] H. Hu et al. “Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy”. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020, pp. 1197–1200. [6] H. Li et al. “DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images”. In: IEEE Access 8 (2020), pp. 8004–8018. [7] L. Liu et al. “Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans”. In: Multiscale Multimodal Medical Imaging. Ed. by X. Li et al. Cham: Springer Nature Switzerland, 2022, pp. 121–129. ISBN: 978-3-031-18814-5. [8] C. S. Constantino et al. “Evaluation of Semiautomatic and Deep Learning–Based Fully Automatic Segmentation Methods on [18F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization”. In: Journal of Digital Imaging 36.4 (Aug. 2023), pp. 1864–1876. ISSN: 1618-727X. [9] A. J. Weisman et al. “Comparison of 11 automated PET segmentation methods in lymphoma”. English. In: Physics in medicine & biology 65.23 (2020), pp. 235019–235019. [10] A. J. Weisman et al. “Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma”. In: Radiology: Artificial Intelligence 2.5 (2020), e200016. [11] C. Jiang et al. “Deep learning–based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images”. In: European Radiology 32.7 (July 2022), pp. 4801–4812. ISSN: 1432-1084. [12] P. Blanc-Durand et al. “Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDGPET/CT for total metabolic tumour volume prediction using a convolutional neural network.” In: European Journal of Nuclear Medicine and Molecular Imaging 48.5 (May 2021), pp. 1362–1370. ISSN: 1619-7089. [13] S. Ahamed et al. “A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors and Lymph Nodes from Head and Neck Cancer PET/CT Images”. In: Head and Neck Tumor Segmentation and Outcome Prediction. Ed. by V. Andrearczyk et al. Cham: Springer Nature Switzerland, 2023, pp. 94–106. ISBN: 978-3-031-27420-6. [14] S. Gatidis et al. “A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions”. In: Scientific Data 9.1 (Oct. 2022), p. 601. ISSN: 2052-4463. [15] M. Pop et al. “Left-Ventricle Quantification Using Residual U-Net”. English. In: vol. 11395. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Switzerland: Springer International Publishing AG, 2019, pp. 371–380. ISBN: 0302-9743. [16] A. Myronenko. “3D MRI Brain Tumor Segmentation Using Autoencoder Regularization”. English. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer International Publishing, 2019, pp. 311–320. ISBN: 0302-9743. [17] F. Isensee et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation”. In: Nature Methods 18.2 (Dec. 2020), pp. 203–211. [18] A. Hatamizadeh et al. “Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images”. English. In: (2022). [19] M. J. Cardoso et al. MONAI: An open-source framework for deep learning in healthcare. 2022. arXiv: 2211. 02701 [cs.LG]. [20] S. Ahamed et al. “Towards enhanced lesion segmentation using a 3D neural network trained on multiresolution cropped patches of lymphoma PET images”. In: Journal of Nuclear Medicine 64.supplement 1 (2023), P1360–P1360. ISSN: 0161-5505. [21] J. L. Fleiss. “Measuring nominal scale agreement among many raters”. English. In: Psychological bulletin 76.5 (1971), pp. 378–382. [22] A. K. Jha et al. “Nuclear Medicine and artificial intelligence: Best practices for evaluation (the RELAINCE guidelines)”. en. In: J. Nucl. Med. 63.9 (Sept. 2022), pp. 1288–1299. [23] N. Hasani et al. “Artificial Intelligence in Lymphoma PET Imaging: A Scoping Review (Current Trends and Future Directions)”. In: PET clinics 17.1 (Jan. 1), pp. 145–174. ISSN: 1556-8598. [24] S. K. Warfield et al. “Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation”. en. In: IEEE Trans. Med. Imaging 23.7 (July 2004), pp. 903–921. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv available on arxiv

Deep Neural Networks to Detect and Quantify Lymphoma Lesions: Conclusion and References

ডিপ নিউরাল নেটওয়ার্ক লিম্ফোমা ক্ষত সনাক্ত এবং পরিমাপ করার জন্য: উপসংহার এবং রেফারেন্স

Tiefe neuronale Netzwerke zur Erkennung und Quantifizierung von Lymphomläsionen: Schlussfolgerung und Referenzen

Redes neuronales profundas para detectar y cuantificar lesiones de linfoma: conclusiones y referencias

Réseaux de neurones profonds pour détecter et quantifier les lésions de lymphome : conclusion et références

लिम्फोमा घावों का पता लगाने और मात्रा निर्धारित करने के लिए डीप न्यूरल नेटवर्क: निष्कर्ष और संदर्भ

リンパ腫病変の検出と定量化のためのディープニューラルネットワーク：結論と参考文献

림프종 병변을 검출하고 정량화하기 위한 심층 신경망: 결론 및 참고 문헌

Redes Neurais Profundas para Detectar e Quantificar Lesões de Linfoma: Conclusão e Referências

Глубокие нейронные сети для обнаружения и количественной оценки поражений лимфомой: заключение и ссылки

Lenfoma Lezyonlarını Tespit Etmek ve Ölçmek İçin Derin Sinir Ağları: Sonuç ve Referanslar

Mạng lưới thần kinh sâu để phát hiện và định lượng tổn thương ung thư hạch: Kết luận và tài liệu tham khảo

深度神经网络检测和量化淋巴瘤病变：结论和参考文献

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