Staple develops technology that reads, interprets and extracts data from documents faster, more affordably and more accurately than any human can, at scale

Staple

The future of patient care depends on digitization. Forward-thinking skilled nursing facilities and other long-term care centers are on the hunt for technology that won't only serve their residents but will enhance their overall operations as well. play a critical role in patient safety and comfort. Smart beds more accurately track patient data and help to better inform patient diagnosis and care. Wearables And thanks to the recent pandemic, has expanded into the long-term care sector and is giving residents access to virtual care options they've never had before. telehealth We're already pretty familiar with these technologies and their impact on the healthcare industry, but there's a lesser-known innovation that's working to combat a dangerous, and often deadly, side of senior care: senior isolation. The Senior Isolation Crisis We know that feelings of isolation and loneliness begin to intensify with age and as a consequence of worsening physical ailments, like hearing loss or a recently diagnosed chronic illness. But senior isolation is even more critical in elderly patients at skilled nursing facilities and other long-term care centers. This has been an issue that's plagued the long-term care sector for a while now, and it can be a fatal problem. Loneliness and isolation aren't only associated with higher levels of depression and anxiety in the elderly patient population, but shows they put seniors at a 50 percent higher risk of dementia and a 29 percent higher risk of heart disease, while also increasing their susceptibility to infectious diseases and, worse, premature death. research SNFs and other long-term care centers have taken measures to curtail senior isolation, like organizing social events and encouraging family visitation hours. However, these measures aren't enough to beget deep-rooted change, and the pandemic proved they aren't possible to do in the midst of a crisis because social activities only add to the risk of spreading infectious diseases, like COVID-19. The loneliness epidemic, which was , has shown healthcare executives that combating senior isolation is just as important as updating their medical equipment or implementing digital tools that make patient data collection easier and more accurate. only made worse by the recent pandemic Notice what those last two things have in common? They're only possible because of technological advancements. If digitization can help enhance a patient's medical care, shouldn't it also be used to better all-around care as well? This takes us back to the tech that is working to combat senior isolation. Introducing: virtual pets. A Virtual Companion to Help With Longevity We're no strangers to the benefits of animal companionship. If you've ever had a dog or cat, you know the level of happiness and comfort they bring is unmatched to human solace. Except when it comes to long-term care environments, these furry friends aren't welcome — unless they're robots. The New York State Office for the Aging was the first state to test how virtual pets impact senior isolation. After just one year of their trial, they found that reported feeling less isolated. 70 percent of participants Other clinical studies established the same findings; robotic pets can help enhance a patient's well-being and quality of life, especially for those . living with dementia and Alzheimer's Virtual pets weren't initially designed for the elderly, but Hasbro's Vice President, Ted Fischer, soon realized the need for senior-friendly interactive pets after discovering that , which were intended for young children, were being purchased for older adults. 10 to 15 percent of their FurReal Friends So, they launched their first virtual cat around three years ago, followed by a robotic dog one year later. To best serve elderly patients, these smart pets must offer both aesthetics and functionality. The goal isn't to create a 'super' pet that can talk or do anything outside the norm, as it's important they still look and feel like a real animal. Hasbro's Joy for All cats and dogs feature built-in sensors, brushable fur, and 'vibrapurr' and 'barkback' technology that makes their pets look and sound remarkably realistic. The goal is to use the tactile touch of an animal, along with their movements and sounds, to stimulate a patient's brain and eliminate the loneliness and social isolation of being away from loved ones. Additionally, virtual pets also help to alleviate the stress on caregivers by helping to make their jobs a bit easier. It not only gives patients something to do to keep their minds occupied, but these pets will soon be able to participate in a patient's care as well. Similar to the technology that makes wearables so important to a patient's diagnosis and treatment, virtual pets may also be able to be programmed to notice a problem and wirelessly communicate an alert to a caregiver's phone or computer one day. While the idea of virtual pets may seem like a fun addition to a long-term care center, it's more than just entertainment for residents. These robotic companions are just as critical to patient care as any other digital component. And just like the other technologies that help to enhance your operations, virtual pets can also be used by your caregivers to support their daily tasks and responsibilities. But most importantly, these lifelike pets can prevent the cognitive decline and premature death that is running rampant among isolated seniors. The future of healthcare and patient care is digital, whether that's smart beds, wearables, telehealth, or virtual pets. They all play an important role in longevity.

Why Virtual Pets Are Becoming a Staple in Senior Care

The Hound of the Baskervilles: Chapter 7 - The Stapletons of Merripit House

HackerNoon Reporter: Please tell us briefly about your  background. I’m born in Dublin but grew up in Kildare, Ireland. I loved all sports as a kid and played rugby to a semi-professional level. Set up my first tech brand (project) at 19 while studying for my BA and MSc in university. I moved to London in 2011 to start a career in sales as an FX Broker. What's your startup called? And in a sentence or two, what does it do? BUA FIT, it means victory in Gaelic. BUA is a platform for group outdoor and online fitness classes. We connect fitness professionals to consumers for live classes but we do it with social, e-commerce technology. What is the origin story? I was very unhappy in financial sales and at times miserable in my career. Exercising in a gym was the only thing that got me through my day. There I saw the gap in the market. Trainers were self-employed and paid crazy rents. Consumer struggled to book indoor classes due to limited space, gyms can be a very intimidating environment for a lot of people which is why active members are low. Lastly, being a lover of all outdoor sports, I couldn’t find a variety of outdoor classes in London anywhere to book into as a consumer. What do you love about your team, and why are you the ones to solve this problem? At our stage, we have to hustle every day and spin several plates. I love this about our team and how transparent we are. We’re obsessed with helping trainers succeed while inspiring people to live a happier and healthier life. I’m using my connecting skills as a broker and sales experience in finance while following my passion in fitness so I feel nothing can stop me. If you weren’t building your start-up, what would you be doing? Building another start-up. At the moment, how do you measure success? What are your core metrics? As a business, ‘classes serviced’ is our North Star KPI. Our economics for connecting is working and our unit economics for growth is looking exciting. We’ve successfully connected supply and demand well over 3,000 times by raising very little money. Personally, working on a problem you are obsessed with solving. It’s a great feeling! What’s most exciting about your traction to date? Our social technology has 275,000 engagement pieces with supply and demand making connections. It’s all about scaling our engagement right now. What technologies are you currently most excited about, and most worried about? And why? I’m not really worried about the competition that much as we are playing the infinite game. I’m most excited about seeing VR develop in the fitness and wellness space. What drew you to get published on HackerNoon? What do you like most about our platform? A cool brand with a great competition. What advice would you give to the 21-year-old version of yourself? Encourage failing more to learn more. Work on yourself everyday. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Thankfully, we survived the crisis. As a start-up fitness brand, it’s been a super tough time to stay in business. We have been successful at growing quarter on quarter since our first funding round. BUA FIT was in Startups of the Year hosted by HackerNoon nominated as one of the best startups in London

"Fail more", advised David Stapleton, BUA FIT's Founder & CEO

The Power of Community in Memecoin Success

Intro How often have you heard that the golden era for VC startups is coming to an end? More and more voices, from Steve Blank to VC investors and accelerator representatives, are emphasizing that startup founders need to show financial performance rather than simply relying on groundbreaking ideas and technological innovations. As a result, founders are faced with a crucial question: do they have the skills to model and utilize unit economics? To gain a deeper understanding of this framework, I had a conversation with Daniil Khanin, a Barcelona (Spain) - based entrepreneur known as the guru of Unit Economics and the Data-Driven Decisions approach. Daniil is the founder and CEO of the startup ueCalc and the author of the book "Unit Economics: Data-Driven Decisions for Business and Startups" featuring a foreword by Ash Maurya, bestselling author of "Running Lean" and "Scaling Lean". Over the years, we've collaborated with startups through The Internet Initiatives Development Fund accelerator, and as part of my special project, "Behind the Startup: Conversations with Founders & VC Visionaries", I extended an invitation to Daniil to participate in an interview. Unit economics offers a rapid and accessible means to answer critical questions about the effectiveness of a chosen business model, the required investment for a startup and its duration, the projected depletion of funds, and the prioritization of business processes. Q&A Victor Markov: "Let's begin with the most common question: what is unit economics, and why is it so crucial?" Daniil Khanin: "Unit economics serves as a universal framework that integrates the financial aspects of a business with the product competencies of the team driving that business. In the early stages of its journey, an innovative startup operates in a realm of complete uncertainty. It's unclear who the customer is, what their true needs are, how much time it will take to develop a sought-after product, and the associated costs for the team. Nonetheless, decisions regarding product creation must be made. While various frameworks exist for this purpose, such as Lean Canvas developed by Ash Maurya, unit economics stands out as a tool for evaluating finances at the outset. Additionally, this tool aids in identifying growth opportunities within an already expanding startup." Victor Markov: "What makes unit economics so indispensable?" Daniil Khanin: "The significance of unit economics lies in its ability to conserve the scarce resources of a startup. It guides startups in correctly allocating resources over time, directing them toward the most crucial processes. Furthermore, it can lead to the unpopular decision of not launching a startup at all if it lacks economic viability. While this may seem counterintuitive - who can predict the future? - unit economics is not solely about forecasting; it's about assessing the team's readiness to shape that future." Victor Markov: "I recall numerous instances where teams opted to abandon their business models and shut down their startups after analyzing their financial models and unit economics. Can you share your experiences working with such teams?" Daniil Khanin: "While I can't disclose specific details due to confidentiality, I can provide a general overview. It began when a team set out to develop IT solutions for large public facilities like stadiums and major shopping centers. The team harbored expectations regarding the project's profitability. However, upon assembling their financial model, they were dismayed by the dismal financial outlook. It became evident that the projected returns were insufficient to justify pursuing the venture. At this juncture, conventional wisdom might dictate tweaking the model parameters to yield favorable outcomes. However, with a unit economics-based model, altering several key parameters implies adjusting corresponding aspects of the business model, each overseen by a team member. This realization prompted the team to acknowledge their lack of requisite competencies to enter the market and navigate subsequent business development." Victor Markov: "Why were the teams unable to recognize this themselves?" Daniil Khanin: "Often, our unwavering belief in an idea blinds us to rational and critical evaluation. No set criteria can circumvent this tendency. I've made similar mistakes countless times. The beauty of unit economics lies in its transparency. By constructing a financial model based on unit economics, you control a limited set of parameters, each directly linked to your team and its expertise. You can deceive yourself all you want, but the numbers lay bare any underlying issues, illuminating the true cost involved." Victor Markov: "To what extent does this approach align with traditional methods? Financial modeling has been a staple of business schools and online courses for quite some time." Daniil Khanin: "This is a pivotal question. Formally, a financial model crafted using my templates mirrors one crafted by seasoned financiers. However, the challenge lies in finding someone within a startup capable of creating such a document and adeptly managing all the parameters therein. My approach represents a fundamental shift - a new philosophy of document creation. I encapsulate it as 'being accountable only for what you can control.'" Victor Markov: "Intriguing. Can you go into more detail about this philosophy?" Daniil Khanin: "It all circles back to unit economics. As unit economics intertwines business processes with financial metrics, it facilitates the construction of financial documents like profit and loss statements, cash flows, and balance sheets. However, the model's author only manages a select set of metrics comprising the unit economics model. Thus, to achieve specified targets - say, EBITDA or Revenue - the model's author adjusts unit economics metrics, which, in turn, directly influence business processes. Whether the team possesses the means to impact these processes becomes readily apparent. Most importantly, this approach is easily verifiable. If a team claims a 15% conversion rate significantly higher than the market average, they must elucidate their strategy for achieving this feat. Do they possess relevant experience or a concrete plan? If not, surpassing the market average seems improbable." Victor Markov: "It seems like a win-win situation, benefiting both startup founders and potential investors. You recently published a book on this topic. Could you give more details about it?" Daniil Khanin: "Certainly. While advocating for unit economics, I fielded inquiries about further reading on the subject. Surprisingly, I couldn't identify any books despite accumulating a wealth of material. Consequently, I compiled a book elucidating the approach, philosophy, and underscoring how competencies influence business's financial performance. Essentially, I distilled my decade-long experience with unit economics, examining myriad examples of its application in addressing various business inquiries - from identifying growth opportunities to crafting financial models." Victor Markov: "Okay, while startups leverage unit economics for modeling, those at later funding stages with impressive revenue figures also employ this framework for decision-making. How is this approach utilized in such scenarios?" Daniil Khanin: "Indeed, unit economics alone proves insufficient. While it offers insight into the current state of a business, informed decision-making requires identifying processes ripe for improvement, thereby enhancing business performance while minimizing resource expenditure. This task isn't straightforward, but it represents the essence of business focus. By pinpointing the process that currently offers the most potential for growth, you can achieve substantial growth with minimal resource outlay - a critical consideration for resource-constrained startups." Victor Markov: "That sounds intriguing. Can you shed light on how startups can identify these growth opportunities? What tools or solutions are required?" Daniil Khanin: "You can start with just a napkin and pen, but effective tools are indispensable. While you can construct a model using any spreadsheet editor or even pen and paper, pinpointing growth opportunities and crafting financial models is no easy feat. It all boils down to cohorts - the bedrock of unit economics data. Managing and analyzing cohorts isn't straightforward, even with Excel. Hence, I developed ueCalc. It streamlines the creation of unit economics models for most business models and facilitates the rapid construction of corresponding financial models. The service handles the heavy lifting, requiring the team merely to delineate their fundamental capabilities in influencing their model's unit economics metrics. Even though creating a high-quality model necessitates some adjustments and a serious commitment, the process remains far more streamlined and expedited than Excel-based modeling." Outro Cohort analysis in unit economics presents a multifaceted challenge deserving of a dedicated article. In this interview, Daniil Khanin touched upon critical issues in today's world and the state of the venture capital market, such as profitability, resource conservation, and decision-making based on data-driven decisions. While implementing the framework requires time, the return on investment is swift. Crucially, all the necessary elements are present: market demand, accessible solutions, and a plethora of articles, books, and resources. Such models yield benefits not only for startups and investors but also for the market at large. Intro How often have you heard that the golden era for VC startups is coming to an end? More and more voices, from Steve Blank to VC investors and accelerator representatives, are emphasizing that startup founders need to show financial performance rather than simply relying on groundbreaking ideas and technological innovations. As a result, founders are faced with a crucial question: do they have the skills to model and utilize unit economics? Steve Blank To gain a deeper understanding of this framework, I had a conversation with Daniil Khanin , a Barcelona (Spain) - based entrepreneur known as the guru of Unit Economics and the Data-Driven Decisions approach. Daniil is the founder and CEO of the startup ueCalc and the author of the book "Unit Economics: Data-Driven Decisions for Business and Startups" featuring a foreword by Ash Maurya , bestselling author of "Running Lean" and "Scaling Lean". Daniil Khanin Barcelona (Spain) ueCalc "Unit Economics: Data-Driven Decisions for Business and Startups" Ash Maurya Over the years, we've collaborated with startups through The Internet Initiatives Development Fund accelerator , and as part of my special project, "Behind the Startup: Conversations with Founders & VC Visionaries" , I extended an invitation to Daniil to participate in an interview. The Internet Initiatives Development Fund accelerator "Behind the Startup: Conversations with Founders & VC Visionaries" Unit economics offers a rapid and accessible means to answer critical questions about the effectiveness of a chosen business model, the required investment for a startup and its duration, the projected depletion of funds, and the prioritization of business processes. Q&A Victor Markov: "Let's begin with the most common question: what is unit economics, and why is it so crucial?" Victor Markov: Daniil Khanin: "Unit economics serves as a universal framework that integrates the financial aspects of a business with the product competencies of the team driving that business. In the early stages of its journey, an innovative startup operates in a realm of complete uncertainty. It's unclear who the customer is, what their true needs are, how much time it will take to develop a sought-after product, and the associated costs for the team. Nonetheless, decisions regarding product creation must be made. While various frameworks exist for this purpose, such as Lean Canvas developed by Ash Maurya , unit economics stands out as a tool for evaluating finances at the outset. Additionally, this tool aids in identifying growth opportunities within an already expanding startup." Daniil Khanin: Ash Maurya Victor Markov: "What makes unit economics so indispensable?" Victor Markov: Daniil Khanin: "The significance of unit economics lies in its ability to conserve the scarce resources of a startup. It guides startups in correctly allocating resources over time, directing them toward the most crucial processes. Furthermore, it can lead to the unpopular decision of not launching a startup at all if it lacks economic viability. While this may seem counterintuitive - who can predict the future? - unit economics is not solely about forecasting; it's about assessing the team's readiness to shape that future." Daniil Khanin: Victor Markov: "I recall numerous instances where teams opted to abandon their business models and shut down their startups after analyzing their financial models and unit economics. Can you share your experiences working with such teams?" Victor Markov: Daniil Khanin: "While I can't disclose specific details due to confidentiality, I can provide a general overview. It began when a team set out to develop IT solutions for large public facilities like stadiums and major shopping centers. The team harbored expectations regarding the project's profitability. However, upon assembling their financial model, they were dismayed by the dismal financial outlook. It became evident that the projected returns were insufficient to justify pursuing the venture. Daniil Khanin: At this juncture, conventional wisdom might dictate tweaking the model parameters to yield favorable outcomes. However, with a unit economics-based model, altering several key parameters implies adjusting corresponding aspects of the business model, each overseen by a team member. This realization prompted the team to acknowledge their lack of requisite competencies to enter the market and navigate subsequent business development." Victor Markov: "Why were the teams unable to recognize this themselves?" Victor Markov: Daniil Khanin: "Often, our unwavering belief in an idea blinds us to rational and critical evaluation. No set criteria can circumvent this tendency. I've made similar mistakes countless times. The beauty of unit economics lies in its transparency. By constructing a financial model based on unit economics, you control a limited set of parameters, each directly linked to your team and its expertise. You can deceive yourself all you want, but the numbers lay bare any underlying issues, illuminating the true cost involved." Daniil Khanin: Victor Markov: "To what extent does this approach align with traditional methods? Financial modeling has been a staple of business schools and online courses for quite some time." Victor Markov: Daniil Khanin: "This is a pivotal question. Formally, a financial model crafted using my templates mirrors one crafted by seasoned financiers. However, the challenge lies in finding someone within a startup capable of creating such a document and adeptly managing all the parameters therein. My approach represents a fundamental shift - a new philosophy of document creation. I encapsulate it as 'being accountable only for what you can control.'" Daniil Khanin: Victor Markov: "Intriguing. Can you go into more detail about this philosophy?" Victor Markov: Daniil Khanin: "It all circles back to unit economics. As unit economics intertwines business processes with financial metrics, it facilitates the construction of financial documents like profit and loss statements, cash flows, and balance sheets. However, the model's author only manages a select set of metrics comprising the unit economics model. Thus, to achieve specified targets - say, EBITDA or Revenue - the model's author adjusts unit economics metrics, which, in turn, directly influence business processes. Daniil Khanin: Whether the team possesses the means to impact these processes becomes readily apparent. Most importantly, this approach is easily verifiable. If a team claims a 15% conversion rate significantly higher than the market average, they must elucidate their strategy for achieving this feat. Do they possess relevant experience or a concrete plan? If not, surpassing the market average seems improbable." Victor Markov: "It seems like a win-win situation, benefiting both startup founders and potential investors. You recently published a book on this topic. Could you give more details about it?" Victor Markov: Daniil Khanin: "Certainly. While advocating for unit economics, I fielded inquiries about further reading on the subject. Surprisingly, I couldn't identify any books despite accumulating a wealth of material. Consequently, I compiled a book elucidating the approach, philosophy, and underscoring how competencies influence business's financial performance. Essentially, I distilled my decade-long experience with unit economics, examining myriad examples of its application in addressing various business inquiries - from identifying growth opportunities to crafting financial models." Daniil Khanin: Victor Markov: "Okay, while startups leverage unit economics for modeling, those at later funding stages with impressive revenue figures also employ this framework for decision-making. How is this approach utilized in such scenarios?" Victor Markov: Daniil Khanin: "Indeed, unit economics alone proves insufficient. While it offers insight into the current state of a business, informed decision-making requires identifying processes ripe for improvement, thereby enhancing business performance while minimizing resource expenditure. This task isn't straightforward, but it represents the essence of business focus. By pinpointing the process that currently offers the most potential for growth, you can achieve substantial growth with minimal resource outlay - a critical consideration for resource-constrained startups." Daniil Khanin: Victor Markov: "That sounds intriguing. Can you shed light on how startups can identify these growth opportunities? What tools or solutions are required?" Victor Markov: Daniil Khanin: "You can start with just a napkin and pen, but effective tools are indispensable. While you can construct a model using any spreadsheet editor or even pen and paper, pinpointing growth opportunities and crafting financial models is no easy feat. It all boils down to cohorts - the bedrock of unit economics data. Managing and analyzing cohorts isn't straightforward, even with Excel. Daniil Khanin: Hence, I developed ueCalc . It streamlines the creation of unit economics models for most business models and facilitates the rapid construction of corresponding financial models. The service handles the heavy lifting, requiring the team merely to delineate their fundamental capabilities in influencing their model's unit economics metrics. Even though creating a high-quality model necessitates some adjustments and a serious commitment, the process remains far more streamlined and expedited than Excel-based modeling." ueCalc Outro Outro Cohort analysis in unit economics presents a multifaceted challenge deserving of a dedicated article. In this interview, Daniil Khanin touched upon critical issues in today's world and the state of the venture capital market, such as profitability, resource conservation, and decision-making based on data-driven decisions. Daniil Khanin While implementing the framework requires time, the return on investment is swift. Crucially, all the necessary elements are present: market demand, accessible solutions, and a plethora of articles, books, and resources. Such models yield benefits not only for startups and investors but also for the market at large.

Interview With UeCalc Founder Daniil Khanin: Unlocking the Power of Unit Economics for Startups 

UeCalc প্রতিষ্ঠাতা ড্যানিল খানিনের সাথে সাক্ষাৎকার: স্টার্টআপের জন্য ইউনিট ইকোনমিক্সের পাওয়ার আনলক করা

Interview mit UeCalc-Gründer Daniil Khanin: Die Kraft der Stückkostenrechnung für Startups freisetzen

Entrevista con el fundador de UeCalc, Daniil Khanin: Liberando el poder de la economía unitaria para las empresas emergentes

Entretien avec le fondateur de l'UeCalc, Daniil Khanin : Libérer la puissance de l'économie unitaire pour les startups

यूईकैल्क के संस्थापक डेनियल खानिन के साथ साक्षात्कार: स्टार्टअप्स के लिए यूनिट इकोनॉमिक्स की शक्ति को अनलॉक करना

UeCalc 創設者 Daniil Khanin 氏へのインタビュー: スタートアップ企業のためのユニット エコノミクスの力を引き出す

UeCalc 창립자 Daniil Khanin과의 인터뷰: 스타트업을 위한 단위 경제학의 힘 활용

Entrevista com o fundador da UeCalc, Daniil Khanin: Desbloqueando o poder da economia unitária para startups

Интервью с основателем UeCalc Даниилом Ханиным: раскрываем возможности Unit Economics для стартапов

UeCalc Kurucusu Daniil Khanin ile Röportaj: Yeni Başlayanlar için Birim Ekonomisinin Gücünün Kilidini Açmak

Phỏng vấn Người sáng lập UeCalc, Daniil Khanin: Khai phá sức mạnh của Kinh tế học Đơn vị cho các công ty khởi nghiệp

专访 UeCalc 创始人 Daniil Khanin：为初创企业释放单位经济学的力量

The AI Odyssey: A Journey Through its History and Philosophical Implications

Navigating the Nuances of Median Statistics in Sliding Windows Following the whirlwind success of our into the algorithmic enigmas of coding challenges, it's time to delve into another intriguing puzzle that has garnered attention in coding interviews and beyond. Today, we're shifting our focus from the tangible geometry of cube surfaces to the abstract realms of statistics, specifically, the calculation of medians within a sliding window. But what exactly is a median, and why does it hold more significance than the average in certain scenarios? previous exploration A median represents the middle value in a dataset when arranged in ascending order. Unlike the mean, which sums all values and divides by their count, the median is less susceptible to outliers—a property that renders it invaluable in datasets with high variance and a low signal-to-noise ratio, such as those found in financial markets. The formula for the median in a sorted list of numbers depends on the count being odd or even: for an odd count, it's the middle number; for an even count, it's the average of the two middle numbers. Let's be real: when it comes to slicing and dicing data over time, window-based stats are pretty much the superhero of the story. , understanding the dynamic median of a dataset over a specified window offers insights that static analysis could miss. Whether in high-frequency trading (HFT), machine learning, time series analysis, or monitoring systems The Problem: Median in a Sliding Window Consider a sliding window moving across a series of numerical data points. Within this window, we aim to calculate the median of the data points at each step, providing a continuous view of the dataset's central tendency as the window progresses. This task is not only a staple of coding interviews but also finds practical application in fields as diverse as HFT, machine learning, and real-time data analysis. Example: Given the input series , with a window size of , the medians are [ -7, -7, -6, 1, -6, -5, -10, 4, 6, 8] 3 [-7.0, -6.0, -6.0, -5.0, -6.0, -5.0, 4.0, 6.0] This task's relevance extends far beyond the confines of coding challenges, impacting various domains such as HFT, where accurate and timely data analysis can yield significant financial outcomes, machine learning models that depend on robust feature extraction from noisy datasets, and the critical evaluation of trends in time series data, vital for forecasting and anomaly detection. The application of such an algorithm in real-world scenarios underscores the overarching importance of median statistics in sliding windows, offering a more reliable measure of central tendency amidst data fluctuations. As we proceed, this article will unravel the intricacies of calculating medians in sliding windows, a testament to the ongoing need for adept problem-solving and algorithmic prowess in the ever-evolving field of software development and data analysis. Stay tuned as we dissect the problem, outline a solution, and delve into the practical applications of this fascinating statistical challenge. Essential Data Structures: Binary Heaps, Queues, and Lazy Deletion Dictionaries To address the median in a sliding window challenge, we deploy an arsenal of sophisticated data structures, each serving a unique role in the algorithm's design and functionality. Here's a closer look at these components: : At the core of our solution are two types of binary heaps: the min-heap and the max-heap. A binary heap is a complete binary tree where each node is smaller (min-heap) or larger (max-heap) than its children, ensuring the smallest or largest element is always at the root. Picture a binary heap as a family tree where each parent is either the runt (in a min-heap) or the giant (in a max-heap) compared to their kids. This property is key to efficiently managing the data within the sliding window, allowing quick access to the median. The max-heap is used to store the lower half of the numbers in the window, ensuring quick access to the smaller values, while the min-heap holds the upper half, managing the larger values. This division enables us to efficiently locate the median value by looking at the roots of these heaps. Binary Heaps : This data structure operates on a first-in, first-out (FIFO) principle, making it perfect for tracking the order of elements as they enter and exit the sliding window. As new data points arrive, they're enqueued (added to the rear), and as the window moves, the oldest data points are dequeued (removed from the front). This mechanism ensures we always know which element to remove from our binary heaps when it exits the window. Queue : Lazy deletion is an optimization technique used to handle the removal of elements from our heaps. Instead of immediately removing an element from a heap when it exits the window, we mark it for deletion in a dictionary. This approach allows us to defer the actual removal until necessary (i.e., when the element reaches the root of the heap), reducing the overhead associated with heap operations. The dictionary tracks the count of how many times each element is marked for deletion, enabling efficient management of duplicates and ensuring that our heaps always reflect the current state of the sliding window. Dictionary for Lazy Deletions By integrating these data structures, we construct a robust framework capable of dynamically calculating medians in a sliding window, balancing efficiency with accuracy. This strategic assembly not only facilitates the median calculation but also exemplifies the power of data structure synergy in solving complex algorithmic problems. The crux of the median calculation lies in maintaining a balanced view of the data as it changes dynamically. By keeping half of the elements in a max-heap and the other half in a min-heap, we can efficiently calculate the median by examining the tops of these heaps. This approach adapts well to sliding window scenarios, where data points continuously enter and exit the window, necessitating real-time updates to the median calculation. Algorithmic Approach: Building a Composite Data Structure Our solution architecture combines the aforementioned data structures to form a composite structure capable of efficiently solving our median calculation problem. This setup involves: : When a new element arrives, we determine which heap to add it to based on its value relative to the current median. This step might ruffle some feathers or, well, disturb the heap harmony for a sec. Adding Elements : To ensure the heaps remain balanced (i.e., have the same number of elements or differ by at most one), we may need to move elements between them after adding a new element or removing an old one. Rebalancing Heaps : As the window slides, the oldest element (the one that exits the window) must be removed. We use the lazy deletion strategy here, marking elements for deletion in our dictionary and physically removing them from the heaps only when they reach the top, thereby avoiding unnecessary reheapifications. Removing Old Elements : This step involves removing the elements marked for deletion from the heaps, ensuring that our median calculation remains accurate and our data structure does not grow unnecessarily large. Lazy Cleaning Working with the Composite Structure Implementing this composite structure involves careful management of each component: : Setting up the min-heap, max-heap, queue, and lazy deletion dictionary. Initialization An important aspect of our approach that readers should note is the use of a simple yet effective trick for converting between max heaps and min heaps. We achieve this by applying a unary minus to the elements before adding them to the heap. This allows us to use Python's standard min-heap provided by as a max heap, by adding and removing elements with their sign inverted. This approach significantly simplifies managing the two heaps to maintain the median in a sliding window, facilitating the process of adding new elements, balancing the heaps, and adjusting the median as elements are added or removed. heapq : Populating our data structure with an initial set of elements to start the median calculation process. Warm-up : Efficiently determining the median based on the current state of the min-heap and max-heap. Calculating the Median : Adjusting the heaps to maintain balance after adding or removing elements. Rebalancing : Adding new elements to the appropriate heap based on their value relative to the median, and removing elements from the queue (and marking them for lazy deletion) as they exit the window. Element Addition and Removal : Periodically executing lazy deletion to remove marked elements from the heaps, ensuring the efficiency and accuracy of the median calculation. Lazy Cleanup Through this comprehensive approach, we address the challenges of calculating the median in a dynamic, sliding window environment, showcasing the power of combining simple data structures to solve complex algorithmic problems. As we continue, we will delve deeper into the specifics of each component within the class, providing a detailed guide to its implementation and usage. MedianWindow The Code: MedianWindow Class Implementation Having explored the theoretical underpinnings and the essential data structures that make our solution viable, it's time to present the implementation of the class. This class is the cornerstone of our approach, embodying the algorithmic strategy we've outlined to dynamically calculate medians within a sliding window of data points. MedianWindow import heapq

class MedianWindow:
    def __init__(self, win_size):
        self.win_size = win_size
        self.min_heap = []
        self.max_heap = []
        self.queue = deque([])
        self.heap_dict = defaultdict(int)
        self.balance = 0

    def warmup(self, batch):
        for x in batch:
            self.queue.append(x)
            heapq.heappush(
                self.max_heap, 
                -x
            )
            heapq.heappush(
                self.min_heap, 
                -heapq.heappop(self.max_heap)
            )
            if len(self.min_heap) > len(self.max_heap):
                heapq.heappush(
                    self.max_heap, 
                    -heapq.heappop(self.min_heap)
                )

    @property
    def median(self):
        if self.win_size % 2 == 1:
            return -self.max_heap[0]
        else:
            return (-self.max_heap[0] + self.min_heap[0]) / 2

    def rebalance(self):
        if self.balance < 0:
            heapq.heappush(
                self.max_heap, 
                -heapq.heappop(self.min_heap)
            )
        elif self.balance > 0:
            heapq.heappush(
                self.min_heap, 
                -heapq.heappop(self.max_heap)
            )

    def push(self, x):
        self.queue.append(x)
        oldest = self.queue[0]
        self.balance = -1 if oldest <= self.median else 1
        if x <= self.median:
            self.balance += 1
            heapq.heappush(
                self.max_heap, 
                -x
            )
        else:
            self.balance -= 1
            heapq.heappush(
                self.min_heap, 
                x
            )
        self.rebalance()

    def should_remove_max_heap_top(self):
        h = self.max_heap
        return h and self.heap_dict[-self.max_heap[0]] > 0

    def should_remove_min_heap_top(self):
        h = self.min_heap
        return h and self.heap_dict[self.min_heap[0]] > 0

    def lazy_clean(self):
        while self.should_remove_max_heap_top():
            self.heap_dict[-self.max_heap[0]] -= 1
            heapq.heappop(self.max_heap)

        while self.should_remove_min_heap_top():
            self.heap_dict[self.min_heap[0]] -= 1
            heapq.heappop(self.min_heap)

    def pop(self):
        prev_num = self.queue.popleft()
        self.heap_dict[prev_num] += 1
        self.lazy_clean()
        return prev_num Example of usage: w = MedianWindow(k)
w.warmup(data[:k])
result = [w.median]
for i in range(k, len(data)):
    w.push(data[i])
    old_elem = w.pop()
    result.append(w.median) This implementation encapsulates the complexity of managing a sliding window of data points and efficiently calculating the median. The class utilizes binary heaps to maintain a running median, a queue to track the sliding window, and a dictionary for lazy deletions, illustrating a practical application of these data structures in solving a non-trivial algorithmic challenge. Analyzing Time Complexity Understanding the time complexity of our solution is crucial for assessing its efficiency and suitability for real-world applications. The MedianWindow class's operations can be analyzed as follows: ): The constructor has a constant time complexity, O(1), as it merely sets up the initial state without processing any data. Initialization ( __init__ ): This method has a time complexity of O(k log k), where k is the size of the sliding window. Each insertion into a heap is O(log k), and we perform k such insertions. Warm-up ( warmup ): Retrieving the median has a constant time complexity, O(1), since it involves returning the top element from one or both heaps, operations that do not depend on the size of the window. Median Calculation ( median ): The rebalance operation, invoked after adding or removing elements, maintains the heaps in a balanced state. This operation has a logarithmic time complexity, O(log k), due to the heap insertions and deletions. Rebalancing ( rebalance ): Adding a new element involves inserting it into one of the heaps and possibly rebalancing. The overall time complexity is O(log k) due to these heap operations. Adding Elements ( push ): The removal process has a logarithmic time complexity, O(log k), for similar reasons to the addition process. However, because of lazy deletion, the actual removal from the heap is deferred, potentially reducing the frequency of heap reorganizations. Removing Elements ( pop ): This operation's time complexity can vary. In the worst case, if many elements are marked for deletion, cleaning them out when they reach the top of the heap can take O(log k) per element. However, this cost is amortized over the operations that caused the elements to be marked, mitigating the impact on performance. Lazy Cleanup ( lazy_clean The efficiency of the MedianWindow class hinges on the logarithmic time complexity of heap operations, making it well-suited for handling sliding windows of data where the window size is significantly smaller than the total number of data points. This property is particularly beneficial in applications like high-frequency trading and real-time data analysis, where performance and timely data processing are paramount. In the next section, we'll delve into the practical challenges one might encounter when implementing this algorithm and suggest potential optimizations and real-world applications, underscoring the practical relevance of mastering data structures and algorithms through the lens of the sliding window median problem. Factors Affecting Performance The efficiency of the MedianWindow algorithm hinges on several critical factors: : The core of our performance lies in the heap operations, primarily insertions and deletions, each with a logarithmic time complexity of O(log k), where k is the window size. Efficient management of these operations is key to maintaining the algorithm's overall performance. Heap Operations : Our approach to lazy deletion significantly impacts performance by deferring the removal of elements from the heaps until absolutely necessary. While this strategy reduces immediate computational overhead, its effectiveness is contingent on the frequency and distribution of elements marked for deletion. Lazy Deletion Strategy : The size of the sliding window and the distribution of input data can affect performance. Larger windows increase the complexity of heap operations, and skewed data distributions may lead to unbalanced heaps, necessitating more frequent rebalancing operations. Window Size and Data Distribution Practical Challenges To solidify understanding and encourage hands-on experimentation, here are some practical challenges for readers: : Enrich the algorithm's capabilities by enabling the calculation of any percentile, not just the median. This enhancement involves introducing a new parameter to adjust the algorithm accordingly. To tackle this challenge, consider how the current structure, based on min and max heaps, could be generalized or modified to provide efficient access to arbitrary percentiles. This extension not only broadens the algorithm's applicability but also challenges the reader to think about data structures in a more flexible and adaptable way. Percentile Calculation : Modify the MedianWindow class to improve memory efficiency when dealing with large datasets. Explore strategies to minimize the memory footprint of heap structures. Memory Efficiency : Extend the algorithm to support median calculations on datasets with non-numeric types, such as timestamps or custom objects, ensuring type consistency and appropriate comparison mechanisms. Diverse Data Types Algorithmic Improvements and Optimization While the MedianWindow algorithm demonstrates robust capabilities, there's always room for optimization: : Implement a heuristic approach to rebalancing the heaps based on anticipated data patterns or historical distributions. This could reduce the number of rebalancing operations needed, enhancing performance. Heuristic Rebalancing : For datasets where real-time processing is not critical, modify the algorithm to process data in batches. This can significantly reduce the computational overhead associated with heap operations. Batch Processing : Explore the feasibility of parallelizing heap operations or the processing of incoming data points. While the inherently sequential nature of median calculation may limit parallelism, certain preprocessing tasks or the lazy cleanup operation might be parallelized to improve throughput. Parallel Processing : Investigate the use of alternative data structures, such as Fibonacci heaps or soft heaps, which might offer better amortized time complexities for certain operations, albeit at the cost of increased implementation complexity or specific trade-offs. Alternative Data Structures In concluding our exploration of the Dynamic Median Calculation within the Sliding Windows algorithm, we've not just tackled a complex problem but have also highlighted the critical thinking skills vital in modern data analysis. This endeavor goes beyond mere computation; it is an exercise in enhancing our algorithmic agility and intellectual flexibility. Stay engaged as we continue to explore the vast frontiers of algorithmic development, each step bringing us closer to mastering the art of problem-solving in an ever-evolving digital world.

Coding Interview Prep: Mastering Median Statistics in Sliding Windows

Every hardcore VR user knows there are two important things when it comes to daily VR gaming: comfort, and battery. One accessory that can improve both of those things is a headstrap that houses a built-in mobile battery, or a “battery strap”. Let’s face it, the stock strap that comes with the Quest 3 is a complete joke. It almost offends me that Meta raises their prices every year and continues to find materials from the dollar store to make their stock strap. Thus, users have no option but to buy a new one, and Meta bets on that. That’s why they keep doing it and they make it simple to plug and play new straps into the headset; . When it comes to VR accessories, I’ve reviewed almost every major thing on the market. they’re too cheap and lazy to make a quality one of their own For the past month, I've tested KIWI's latest offering – the Now, if you're anything like me, the thrill of diving into VR worlds comes with the nagging dread of seeing that low battery warning pop up just as you're getting into the groove. Single-Point-Charging (SPC) Battery Head Strap for the Quest 3. Battery straps aim to solve these problems. With a bunch of cheap options on the market, I want to show you want $79.99 can get you in today’s VR landscape. What We’ll Cover: Specs Design Features Cons Final Verdict I’ve not been compensated by Kiwi in anyway to write this review. However, they did send me their item for free to test for this article. Disclaimer: Specs 6400mAh lithium polymer battery Provides extra 2-4 hours of playtime 1.89 pounds 10.04 x 9.45 x 2.76 inches Memory foam cushion Cable-holding attachment This strap was manufactured by KIWI Design, a China-based company that's been carving its path in the VR scene since 2015. The company is a culmination of years of VR innovation, backed by over 100 patents. What stands out is its claim to balance comfort with extended playtime, addressing a fundamental flaw in VR headsets: their notorious front-heaviness. This strap aims to redistribute that weight, making long sessions in virtual worlds less of a strain and more of an immersive joy. Design Now, let’s talk about the design. The first thing you'll notice is the memory foam padding. It's like a gentle hug for your head, soft yet supportive, a far cry from the standard straps that are $10-20 on AliExpress or Wish. The overall aesthetic is sleek and modern, a white and black strap that has a fair amount of polish. But it's not just about looks; the functionality is where this design truly shines. The balance achieved with this strap significantly reduces the front-heavy drag, showing that KIWI has not only listened to user feedback but acted on it in a meaningful way. With a closer look at this strap, you can tell it’s a blend of thoughtful ergonomics and subtle aesthetics. The smooth, matte finish coupled with the sleek, curving lines gives the strap a futuristic vibe without trying to stand out too much. Form Factor The contours appear to follow the natural shape of the head, suggesting that comfort was at the forefront of KIWI's design philosophy. The integration of the battery itself is seamless, maintaining the headset’s clean lines—a stark contrast to the often bulky add-ons that can feel tacked on rather than part of a unified whole. And let's talk about that battery housing for a second. The way it's positioned and padded, it’s going to give you that counterbalance for the front-heavy feel of most VR headsets, which is exactly what you want for those marathon sessions. In my opinion, this isn't just another clunky power bank slapped onto a strap. It's meticulously crafted. It’s easy to buy a velcro band and strap an external battery back to a standard Quest 3 strap. I’ve done that for years. However, with this strap, I no longer felt much need to add more counterweights on my own. I may dabble in that just to see how it feels, but this is honestly enough for me in terms of counterweight. Features Focusing on features, KIWI’s SPC Battery Head Strap ticks a lot of the boxes we need for long VR gaming sessions. With a 6400mAh battery, KIWI fulfills the promise of doubling or even tripling your playtime. Built-in Power: It’s a plug-and-play system that allows simultaneous charging of both the headset and the battery strap with a single cable. This convenience is the kind of detail that shows KIWI isn’t just manufacturing accessories; they’re crafting seamless user experiences. Single-Point Charging: VR gamers know that the full adjustability of the strap isn’t just about tightness —it’s about angle and positioning. The freedom to position the back cushion precisely where it feels best is a huge plus. Whether you like it high up near the crown of your head or low for a traditional feel, this strap adapts to your preference. Ergonomic Design: It’s been designed to snuggle right into the official carrying case, and by extension, most aftermarket cases. KIWI recognizes that VR enthusiasts are on the move, and they’ve made sure your power-packed head strap doesn’t tie you down. Compatibility and Portability: Cons Even the best-designed products have their drawbacks, and the SPC Battery Head Strap is no exception. It's a bummer that the SPC's innovative charging feature is limited to the . If you're not on board with this specific dock, you're out of luck, missing out on one of the strap's key conveniences. I guess it could work with another dock that uses a similar magnetic pin connector, but there’s a very low chance you’ll find a non-KIWI dock that works with this. External Compatibility: KIWI RGB Vertical Charging Stand : Although the counterweight design is a step in the right direction, it could do with a bit more heft for an even better balance. Some users might find themselves needing to add an extra counterweight for optimal comfort during those longer sessions. Weight Distribution While the seamless integration with KIWI's charging stand is a plus, it also means you're buying into a specific ecosystem. If you decide to switch to another brand's accessories or docks in the future, this head strap's unique charging feature becomes redundant. Exclusive Ecosystem: , the sticker shock is real. Sure, we're getting a head strap that doubles as a lifeline for our VR sessions, thanks to the integrated battery pack. And when you consider that this strap itself becomes literally half of the headset, the price tag starts to seem more reasonable. However, it’s not just VR. If you want to buy a new PS5 controller, the price is around that $80 mark, so this is standard for the gaming industry. Pricing: At $79.99 I always think VR accessories are too damn expensive. It's perfect for those willing to invest in their , but for budget-conscious gamers, it might be a stretch too far, which is likely why Meta has become criminally cheap with their stock headstraps - they know it costs a lot to make a quality one. But let's not sugarcoat it—if your wallet's feeling light, or you're trying to keep your VR spending in check, this head strap is a luxury rather than a necessity. VR experience Despite these points, it's important to consider them in the context of your specific VR setup and how you use it. These cons may not be dealbreakers for everyone, and for those who are fully invested in the KIWI ecosystem, the advantages might far outweigh the limitations. Final Verdict: 8.5/10 Taking all of this into account, the SPC Battery Head Strap from KIWI is a strong contender in the Quest 3 headstraps market. It's thoughtfully designed with user comfort and convenience in mind, backed by a powerful battery to keep you immersed for hours. The single-point charging capability and the ergonomic design make it stand out from the crowd. . It makes the entire setup more cohesive and user-friendly, turning what could be a tangled mess of cables and charging times into a streamlined process. If you're already using or planning to get the KIWI RGB Vertical Charging Stand, this head strap is practically a must-have Overall, for those committed to the KIWI ecosystem, the SPC Battery Head Strap is a wise investment that enhances your VR experience significantly. It offers a comfortable, long-lasting, and hassle-free solution to one of VR gaming's most persistent issues — battery life. My recommendation? If you're looking for a top-tier VR head strap and you're cool with the KIWI universe, go for it. It's an 8.5/10 from me, earning its place as a staple in my daily VR sessions on the Quest 3.

This Quest 3 Battery Strap by Kiwi Design Embarrasses Meta’s Stock Strap

কিউই ডিজাইনের এই কোয়েস্ট 3 ব্যাটারি স্ট্র্যাপটি মেটার স্টক স্ট্র্যাপকে বিব্রত করে

Dieser Quest 3-Batteriegurt von Kiwi Design bringt Metas Schaftgurt in Verlegenheit

Esta correa de batería Quest 3 de Kiwi Design avergüenza a la correa original de Meta

Cette sangle de batterie Quest 3 de Kiwi Design embarrasse la sangle d'origine de Meta

कीवी डिज़ाइन का यह क्वेस्ट 3 बैटरी स्ट्रैप मेटा के स्टॉक स्ट्रैप को शर्मिंदा करता है

Kiwi Design のこの Quest 3 バッテリー ストラップは Meta のストック ストラップを当惑させます

Kiwi Design의 이 Quest 3 배터리 스트랩은 Meta의 스톡 스트랩을 당혹스럽게 합니다.

Esta pulseira de bateria Quest 3 da Kiwi Design embaraça a pulseira de estoque da Meta

Этот ремешок для аккумулятора Quest 3 от Kiwi Design смущает стандартный ремешок Meta

Kiwi Design'ın Bu Quest 3 Pil Kayışı, Meta'nın Stok Kayışını Utandırıyor

Dây đeo pin Quest 3 này của Kiwi Design khiến dây đeo cổ phiếu của Meta xấu hổ

Kiwi Design 设计的这款 Quest 3 电池带让 Meta 的库存表带尴尬不已

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