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have been all the rage for the past decade, but in the last year or two, the hype around AI has grown to unimaginable levels. Artificial intelligence and machine learning While artificial intelligence is capable of performing a wide variety of tasks, like recognizing and tracking objects in photos and videos, generating human-like text, extracting information and extrapolating from large amounts of data, and much more, it’s not the magic pill for every ailment. AI systems, while being widespread, may still not be the perfect fit for solving a given problem. One of the main reasons behind companies rejecting AI is cost. Artificial systems may seem omnipresent, thus inducing the feeling of them being easy and affordable to create, but this is far from the truth. Even the most simple AI-powered app of the system requires a significant time and money investment, and it’s not always obvious just how much of a commitment artificial systems really are. In this article, I aim to help businesses assess the cost of AI development more accurately as well as evaluate whether AI is truly going to save money in the long run. The Cost Of AI Development One of the main misconceptions about AI is that due to its popularity, it’s affordable and easily attainable. Artificial intelligence software, despite being widely used in many business operations, is complex to develop and takes a lot of time and, consequently, money. It’s crucial to calculate as precisely as possible the cost of AI development beforehand to avoid wasting time looking for an AI development team and disappointment when the total project cost doesn’t fit your budget. Calculating the development project is not the main step in the process of assessing the cost of AI development. In my opinion, calculating how much money it takes to complete a task you’re looking to automate now, using manual labor, is far more important. On the surface, it may seem like automating a certain task is the most sensible approach, when in reality, implementing an AI system to replace human labor is more often than not too expensive and not as effective compared to human labor. Calculate The Current Cost Before looking for AI developers, you must understand your business processes and have a clear look at how much it costs to complete a task you are looking to automate with AI. For example, a complex AI surveillance system that counts how many clients visit a store in a day may seem like a good idea on the surface: the counting process is automatic, and AI can not only count the people in the store but also track their purchases or general behavior. Falling for the AI hype, a business owner may consider getting such a system developed to get insights into their business operations. However, the cost of developing a real-time video processing AI for object detection is likely to be far more expensive, especially if your business is on a smaller side, than hiring an additional employee who will complete the same tasks for much less money. On the other hand, certain tasks may require a lot more human labor. For example, a company working with a lot of paperwork may find itself with 50+ employees sorting through hundreds of paper documents daily, looking for relevant information, and punching it into the systems manually. The cost of labor for over 50 employees, in the long run, is considerably more than the cost of developing an AI-powered OCR system for automatic document processing. Even with the upfront cost of system development, the AI solution remains the more sensible option for the business money-wise. This step may seem obvious, but many business owners seem to skip it entirely, falling for the AI hype. AI remains a complex technology that is rather expensive to develop and requires lots of data, lots of testing, and lots of time. While AI is superior to humans in completing certain tasks and may seem like the ideal choice for a certain business scenario, it may not make sense economically even in the long run. Calculating the cost of labor a certain task requires is the first step in developing any AI project for a business. Estimate Project Scope Some AI systems are smaller and less complex than others, and understanding where your project lies on this spectrum is the best way to get a precise project cost estimation. Here are a few questions to ask yourself to understand the size of the project at hand: Define The Project Goal Define a precise goal of the AI system, whether it’s looking for a specific piece of information within a document, detecting forest fires, or facial recognition for an onboarding process. It’s better to define the goal as precisely as possible, e.g. “an AI for detecting if a document has been signed and by who” instead of “an AI for processing documents automatically”. Define The AI Type Object recognition and classification, OCR, natural language processing, data analysis are all different in what type of AI models are used and how complex they are. Defining what type of an AI model your project requires will help with estimating the total project complexity and, therefore, its cost. Assess The Dataset A dataset is the cornerstone of any AI project. A dataset is used for training and testing machine learning models, and is often one of the most time-consuming and challenging parts of a project. Datasets are not small collections of images: to get accurate results, you need to include hundreds, better thousands of images, videos, audio recordings, scanned documents, etc. Estimate if you already have a dataset, for example, an archive of scanned paper documents or videos from surveillance cameras, and if not, estimate the time it would take to gather the needed data. Calculate Project Cost A precise cost calculation of any AI project can only be done by a professional AI development company that will assess your project and your business needs, test and choose ML models to get the best accuracy and provide you with a breakdown of different development stages. There are certain guidelines when it comes to AI project costs. Here’s how much time a project requires depending on its complexity: A tiny AI project with very limited functionality and a predictive AI, e.g. a model that identifies patterns in past events and makes predictions about future events, takes , at least 100 hours to develop A small AI project, including object recognition and classification, , starts at 2000 to 2500 hours Larger AI projects can easily . span across multiple years The hourly rate of any project is the main metric when it comes to calculating how much money the project will require. By multiplying this number by the amount of hours a project takes, you can get an assessment of the project cost. The rate of AI developers varies greatly based on the region, the developer’s qualification and their experience. Make sure to research the rates of AI projects in your area (or in the area where you are planning to outsource the development) to get a more accurate cost assessment for your project. For the sake of example, I will calculate the approximate AI project cost based on an average AI developer rate. According to Salary.com, the average hourly rate of an AI developer is around $50. Let’s look at the hours I’ve listed earlier for different AI projects. For example, a very small AI project with a simple pattern detection and event prediction model takes at least 100 hours. Multiplying this number by the average developer’s rate, we get $5000 — the approximate cost of this type of project. Performing this very simple calculation with the rest of the hourly estimates I’ve listed earlier results in the following numbers: A very small AI project with a simple pattern detection and event prediction model costs at least $5000, A small AI project with an object detection model starts at $100.000, A large AI project with multiple starts at $200.000. There are rough estimates, but they can be a start at assessing the cost of your AI project. Keep in mind that the hourly rate of AI developers depends in the region and county and can vary, significantly affecting the total cost of development. Additional costs, like the hourly rate of a project manager, servers, etc. also add up to the total project cost. Assess Additional Costs While additional costs are not the main chunk of the total investment, they are important to keep in mind when assessing whether an AI system is worth it for your business. A lot of custom software runs in-cloud, meaning you need to pay a monthly or yearly fee for renting servers. These costs are usually small enough to not have a large impact on the overall cost of development, but AI systems are different in this regard. AI systems require servers with powerful GPUs to run efficiently, and those tend to be more expensive, thus increasing the cost of monthly system upkeep. If you’re looking to develop a complex AI system or an AI system that will be used by dozens of employees, you need to account for tech support to guide users through the system and fix any bugs or malfunctions a system may experience. Another important cost to consider is the cost of additional development. During the system operation you may develop a need for additional functionality. For example, you may need to start processing new document types, or detect more objects on a video. These additions can be costly, so you need to keep them in mind when calculating the total project budget. The Cost Of AI vs Human Labor After you roughly estimate the cost of the AI system in question, compare it to the money you now spend on completing the same task. You may be surprised, but more often than not the price of human labor is much cheaper than the price of a custom-made AI system. Even with all the hype around AI, it’s important to keep your head cool and hype-free when looking into having an AI developed. Tempting as it is, implementing AI is not always a sensible idea due to economics.

AI Is More Expensive Than You Think

AI আপনার ধারণার চেয়ে বেশি ব্যয়বহুল

KI ist teurer als Sie denken

La IA es más cara de lo que crees

L’IA coûte plus cher que vous ne le pensez

AI आपके विचार से कहीं अधिक महंगा है

AI は思っているよりも高価です

AI는 생각보다 비싸다

IA é mais cara do que você pensa

ИИ дороже, чем вы думаете

Yapay Zeka Düşündüğünüzden Daha Pahalı

AI đắt hơn bạn nghĩ

人工智能比你想象的更昂贵

GPT-Like LLM With No GPU? Yes! Legislation Analysis With LLMWare

There is no doubt that AI and Robotics are making human life better. The way these technologies are helping mankind is beyond our imagination. Here are some insights on the impact of AI and Robotics in the healthcare industry. Early Diagnosis AI is being used to diagnose the diseases such as different kinds of cancers. For example. The impressive IBM Watson has an accuracy rate of 99% in diagnosing cancer. (Source: Futurism ) And another endoscopic system from Japan can detect colon cancer, although the accuracy rate is 86%. (Source: Futurism ) Besides these robots, AI systems in the shape of wearables and other devices are also helpful in managing and analyzing early heart diseases. This enables the chances for doctors and caregivers to provide better monitoring and treating the life-threatening conditions soon and in treatable stages. Improved healthcare As we all are aware that Robots don’t have any feelings. They can’t get distracted and don’t know what tiredness is. That makes it more potential for healthcare. The Waldo robots are being used by many of the top hospitals to perform surgeries. They can perform the task with excellent precision. The robots also have more strength with no quivers of the knife. The main thing here to notice is that the program must be set correctly for the undergoing procedure. And throughout the surgery, the human surgeon helps the process as the secondary supervisor. Medical Research AI and Robotics both improved the research field of healthcare too. As per the , the drugs take 12 years to reach the patient from the research lab. It is also remarked that only 5 from the quantity of 5000 makes it to the preclinical testing, and just 1 out of these five is approved for human use. CBRA The estimated amount of $359 million will cost the company to develop a new drug and pass the process. The latest growth and development in AI modernize the discovery of drugs and repurposing the method to cut the time to market the new drug and its cost too. Better decision making These AI tools are helping too much in recording data and using it for better analysis. Now because of this upgrade, the healthcare experts can align big data with accuracy and make a decision precisely on time. This also helps in extrapolative analytics and can support clinical assumptions. This also helps to import the best administrative tasks. AI is now used to identify patients that are at the risk of developing a condition by using the pattern. AI is beginning to take over the grasp in healthcare. Helping hands Secondary robots are the helpers. Hospitals run 24/7. There is no doubt that the nurses, ward boys, and other staff have too much to do. That is why secondary robots were invented. The robots such as the Moxi robot by Diligent Robotics help the hospital staff restock, bring the items, clean, and do other works. This enables the human staff to spend more time with the patient to offer a friendly environment while leaving the never-ending work to the robots. You can book a demo of Moxi Robot directly on their . website Another excellent example of those secondary robots is the UV light disinfection robot. It is stated that the robot doesn’t leave the room until it is totally germs-free. AI Assistants This might surprise you (or maybe not), but some robots are made to assist patients. This is a great help for medical staff. Robots like exoskeleton help the patients who are paralyzed. They assist them to walk again. AI technology like smart prostheses makes a great impact in . The limbs are reactive and more exact than the original body parts. It adds the possibility to cover them with skin and connect them to muscles. healthcare Some medical robots in the healthcare industry The Xenex Germ-Zapping Robot The TUG The da Vinci Surgical Robot The CyberKnife The PARO Therapeutic Robot These are the five famous robots that are being used in the healthcare field most. Wrap Up From surgical operation room to inventory, the tools and robots are being used. This not only helps the doctors but the patients too, and as we discussed above, some robots help them regain their strength. There is more to come in healthcare because of AI and Robotics.

AI, Robotics, and the Future of Healthcare

Why AI Will Bring an Explosion of New Jobs

Biometric authentication technology has been an important industry trend for years, especially in 2021 due to the latest AI innovations available on the market. According to IBM, are caused by compromised credentials. Worse, it can take an average of 287 days to identify and respond to a data breach. 20% of breaches AI-based security is increasing in usage and will be necessary to remain competitive in any industry. IBM reports that as of 2021, 25% of businesses have completed deployment of AI-based security, while 40% are partially deployed. The remaining 35% have not begun this process, and if your business falls into this category, you may be placing your clients at great risk for dangerous data breaches. Investing in AI-based security can save a business up to $3.81 million in 2021. Being able to use artificial intelligence to identify and automatically respond to data breaches is incredibly important for protecting the data and privacy of a company and its customers. AI-based biometrics authentication provides yet another safeguard against a data breach that is essential for businesses of any scale. Not only that, but IBM’s study shows that correct use of AI security platforms will cost businesses less. It is extremely important to understand and consult experts in the field to develop the solution that is best for your business. With more advanced biometric authentication, verification, and identification technology at our disposal in 2021 than ever before, we can make industry systems more secure while maintaining efficiency and accessibility. Let’s talk about how biometric authentication works and the most important new trends, such as artificial intelligence biometrics. Biometrics Technology Overview At first glance, biometrics may appear to simply be a way of accessing a cell phone more quickly while retaining security, but there are many more applications than this. However, at its core, the objective of biometric security technology is to improve security using parts of our bodies that cannot be easily replicated. Although machine learning and quantum computing may be able to , biometric information like fingerprints, faces, and irises are not so easily determined or emulated. guess a password According to Statista, the biometric technology market will value at before 2027. $55.42 billion Over the range of 10 years from 2017 to 2027, worldwide spending in the identity verification market is forecasted to grow by more than 13 billion U.S. dollars, increasing from 4.93 billion in 2017 to over in 2027. Identity verification is an important aspect of information security, as it ensures that only the people with legitimate authority to access information are able to do so, thereby preventing harmful or malicious intrusion to protected information. $18 billion Types of Biometrics Biometrics technology can be split into three domains: identification, verification, and authentication. Identification is used when the system wants to know who the user is. Verification is about using that biometric information to determine if any other information is associated with the user. Finally, the goal of authentication is to understand if the identity the user claims to be is correct and authorized to access the services and data they are requesting. An example of biometric identification may be used to determine the identity of a user against a database of faces. Law enforcement facial recognition databases use this technology to a great degree and can help investigate security camera evidence footage with better accuracy than a human. Verification is helpful for ensuring that the user and the information stored in the database about them match. Authentication is perhaps the most critical of the three, enabling authorized users to access sensitive information while restricting unauthorized persons. The table below shows the various characteristics of each type of biometric technology. One of the most important takeaways for any business considering biometric security methods is that it is not always wise to rely on only one form of biometric technology, i.e., unimodal. Instead, a multimodal approach that uses more than one type of biometrics is much more secure. Facial Recognition Technology The facial recognition market was estimated at in 2020. The market is projected to grow, reaching 8.5 billion U.S. dollars by 2025. $3.8 billion Biometric authentication technology that uses facial recognition has been a valued feature for security for years, making its way onto millions of iPhone and Android smartphones. With advances in augmented reality and machine learning aiding computer recognition of human faces, facial recognition technology has become more advanced than ever before in 2021. One of the reasons why facial recognition technology has improved for identification and authorization this year is because of the necessities of the pandemic. Due to the increased usage of face coverings like masks, facial recognition has become less accessible. However, many facial recognition solutions now can account for obstacles like face coverings. This is not only good for consumer convenience, but also helpful for law enforcement identification of criminals. By matching the face of the subject with a database, users can be identified even if they are wearing a mask. Since 3D cameras can get much more information about a human face than a two-dimensional camera would, facial recognition is much more secure than before as a biometric authentication technology. Previously, it was much easier for computers to be fooled by a picture of someone’s face or a similar-looking face. However, machine learning has helped narrow the margin of error to prevent hackers from exploiting facial recognition-based security, and various anti-spoofing methods now exist. Voice Recognition Technology The global voice recognition market size was to grow from $10.7 billion in 2020 to $27.16 billion by 2026. forecasted Hands-free communication has been enriched greatly by voice recognition biometrics technology. However, voice recognition has another purpose: identification. Smart home device ecosystems like Google Home, when trained, can identify members of a household-based on their voice. This is very helpful for displaying information pertinent to that individual, such as their calendar and notifications. Voice recognition has other applications in authentication and verification. Being able to identify someone and verify their access based on their voice can save a lot of time, but we are getting to the point where voices may not be as secure as other biometric methods. Deep learning has made it possible to realistically mimic a voice using a computer, a technique called vocal synthesis. ‘Deep fake’ voices have been used in successful that, in one case, cost an energy firm in the United Kingdom $250,000 in 2019. fraudulent activity As deep learning technology improves, the more realistic vocal synthesis will become. Unless technology improves to better recognize legitimate vs. fake voices, voice recognition biometrics technology is better suited for non-security applications, particularly identification. This may be for consumer-based systems like smart homes or enterprise solutions such as automatic transcriptions of meeting recordings that can attribute speakers by name. Iris Scanning Technology This form of biometrics technology is often seen in science fiction. It is similar to facial recognition in that it is contactless but focuses only on one particular feature, the eye. One advantage is that there is less complexity to account for. Advanced facial recognition systems are not needed, only something that can recognize the features of the iris. You don’t necessarily have to be close to an eye to scan the iris. At 40 feet away, tests at Carnegie Mellon University . can scan an iris One advantage of this technology is that because it uses infrared sensors to analyze the iris, scanning the eye can be done in low light conditions. Law enforcement applications of this technology for the identification of suspects are definitely one of the first things that may come to mind. However, this can also be a great authentication and verification method for both consumer and enterprise applications. Samsung’s Galaxy S9 and Note 9 smartphones both have iris scanning technology, demonstrating that this biometric method can be . This can be useful not only for consumers but also for enterprise-level device security. used on portable devices When comparing against a single authorized user, an iris scanner can authenticate a user nearly instantaneously. When identifying or verifying a user against a large database of hundreds of thousands of subjects, less than is sufficient to authenticate. 20 seconds Fingerprints Authentication Technology Fingerprints have been in use for for identification. They have particularly been useful in forensics technology, but recently they have become extremely useful for consumer and enterprise security solutions. In 2003, the pioneered the very first cell phone with a fingerprint sensor. It would later be popularized with Touch ID on Apple iPhones and various scanners on Android phones. thousands of years Fujitsu F505i Fingerprint authentication technology works just like any other biometric method. By comparing the grooves and valleys of an individual’s finger against a standard subject or a database of subjects, they can be identified, verified, and/or authenticated. This can be done very quickly by the user without disrupting their experience. However, one downside to fingerprint scanning is that it most often is not contactless. The user has to press their finger against the sensor in order for it to register. In some circumstances, this may not be desirable due to health concerns, especially with the global spread of the SARS-CoV-2 virus. However, it is possible to scan a fingerprint or entire hand via contactless methods. The smartphone uses a time of flight (ToF) sensor and infrared camera to detect the blood vessels in the hand of the user, all without requiring the user to press their hand against a surface. LG G8 ThinQ Another downside to fingerprinting is that it can be inaccessible for some groups of people with disabilities. Because of this limitation, it may be wise to invest in multiple forms of biometric authentication to maintain accessibility. How AI Can Improve Biometric Authentication Systems When it comes to security, improving the accuracy and efficiency of biometric authentication systems cannot always be done with human programming. Artificial intelligence and machine learning can help us make our systems more secure and efficient. Biometric technologies can be split into two domains, physical and behavioral. Physical Biometric Technology Physical biometrics include everything that we have already discussed in this article. These are objective characteristics of a person like their face or fingerprints, DNA, and more. This must be converted into data that can be analyzed by the AI system and compared against a database for authentication purposes. One of the cases where AI and ML prove most helpful is with facial recognition. Used extensively with augmented reality solutions, AI can help make facial recognition by computers much easier by analyzing facial features and matching them with a database. Behavioral Biometric Technology One of the most interesting trends that artificial intelligence makes possible is . This uses unique behavioral characteristics of how they interact with the world, things that the user may not even realize about themselves. This is one of the most effective lines of defense against deepfake fraud attempts. Some of the more popular ways to measure behavioral biometrics are: behavioral biometric technology Mouse activity Keystroke movement Touch screen press size, area, and pressure Mobile device motion The Biometrics and Data Pattern Analytics Lab from the Autonomous University of Madrid , a system for bot detection that uses behavioral biometrics. It’s entirely possible that behavioral biometrics can be used without the user’s knowledge, eliminating the need for annoying tests of the user’s humanity as CAPTCHA challenges have done for years. In the next few years, users may never have to click on pictures of crosswalks and stop lights to prove they are ‘not a robot’ again while browsing the Internet. developed BeCAPTCHA Importantly, behavioral biometrics can assist in ensuring security throughout an entire session. For example, if a user authenticates themselves and then leaves the room, unwittingly enabling an unauthorized user to gain access to their computer, this would be a serious security risk. However, behavioral biometrics can detect the inconsistent behavior of the second user and restrict their access dynamically. How Multimodal Biometric Authentication Solutions Work Every form of biometrics technology has pros and cons. They each are also not universally effective. To improve the accuracy and effectiveness of biometric authentication technology, it’s important to layer security with multimodal biometric recognition solutions. We can see this in action in the following diagram: *Neural networks on mobile applications can be challenging to develop and implement. Neural networks can be integrated into applications using TensorFlow Lite; however, there are various limitations. Training your neural network models with the TensorFlow library can help. You will need to thoughtfully design the architecture of the application beforehand and put those requirements into consideration. ** If a native application is not possible, this process can be offloaded to the cloud to process the data like Rest API. However, this will incur additional networking resources and require an Internet connection. There is an Nvidia Docker that can simplify the deployment of the system, while a service provider (such as AWS) can provide an uninterrupted communication channel, computing power for neural networks, and a convenient interface for scaling your system. Test No. 1 - Facial Recognition The user creates a photo-imprint that’s stored on the device using a camera. This biometric imprint is converted and normalized using the OpenCV library. The face is identified using a photo, and each of the 64 landmarks detected by OpenCV are highlighted. A biometric verification landmark includes things like a distance from the bridge of the nose to the eye and other facial features. These landmarks and a cutout image of the face are transferred to a deep neural network, which is trained using the TensorFlow library. An eDNA feature vector is formed after neural network processing is completed. The feature vector collects the biometric characteristics of a particular person. The length of the vector is usually 2048 bits. The lengths depend on the DNN architecture. During verification, eDNA is issued and compared with the anchor record that was formed earlier. Reverse engineering is impossible because there is no access to the vector. The biometric system will periodically update this anchor record to match an individual’s changing looks. Test No. 2 - Voice Verification The user provides a voice sample through a microphone that gets processed by the Librosa library. The library reads the audio, transforms and converts it, and then transmits biometrics to the neural network (DNN). An eDNA feature vector (2048 bits) is formed, which takes into account such biometrics as timbre, intonation, tempo, pitch, and other characteristics that the neural network was trained to respond to. Case Study: AI-based Biometric Authentication Solution By combining a microservice-based architecture, WebRTC, and machine learning-powered biometric recognition, we developed a single sign-on (SSO) biometric authentication solution for a US-based firm. Utilizing voice and facial recognition, we developed an enterprise verification as-a-service (EVaas) solution that uses the technologies and trends discussed earlier. This product proved that biometric authentication systems can be highly customizable and easy to use, powering a very simple user interface from behind the scenes. In addition, this example was able to integrate with existing systems via API. Biometric Authentication Solutions Development Securing systems is not a matter to take lightly. When sensitive information is on the line, like protected health information or institutional data is on the line, it is extremely important to take the measures necessary to prevent data breaches. Failure to maintain a high level of security surrounding this information can result in fraud that can cost businesses tens or even hundreds of millions. Written by , AI/ML Solution Architect at Evgeniy Krasnokutsky MobiDev

AI Biometric Authentication is Changing

Learning AI If You Suck at Math - Part Eight - The Musician in the Machine

A revolution in communication between people and computers If you’ve ever attended an conference, I bet you passed under the placid gaze of a chrome-plated humanoid, lovingly selected from an that marketing teams can’t resist pasting on every billboard these days. AI ocean of creepy robot stock images Clearly, I’m personally guilty of using -blue sci-fi art to lure weary travelers to my . It certainly works, which is why it’s a pity that those images have to do with . octarine blog next to nothing AI Robots are very exciting but mostly useless. Today’s AI is mostly boring but very useful. You’d think we’d all be more ashamed of ourselves, but don’t worry, is to go away, no matter how much we all cry wolf. Here’s why. AI too useful AI gives programmers an alternative way to tell computers what to do. Marketing folk run around trying to get your attention with sci-fi gimmicks, but the reason you’ll stick around long enough to buy into is entirely different. The real story is about communication with machines. AI To understand why this new way to talk to machines is so useful and why it’s a technological revolution, let’s forget machines for a moment and talk about people. I’ll lay the cards flat on the table so there’s no more purple mystery about what AI is for. AI gives programmers an alternative way to tell computers what to do. How people talk to one another We express our wishes to other people in two ways. One is with explicit instructions, the other is with examples. If you wanted to learn how to predict my Starbucks order, you could follow me around on my travels and you’d probably notice that the quad espresso I order in US airports becomes a latte in Taipei, Mumbai, and Nairobi. What’s up with that? Given a few more examples, you’d probably figure out the rule yourself. That’s what AI does — . There’s no way you’d figure it out if you only saw me order Starbucks only once or twice (not enough ) or if you only observed 50 counts of me ordering my usual cappuccino at the place on my street ( , since that place is not Starbucks). Same goes for AI. turns examples into instructions data irrelevant data Of course, I could also have just told you my Starbucks rule explicitly since I can express it easily: (Don’t judge me!) “If they have half-and-half, order 4 shots of espresso in a tall cup, then fill ‘er up with half-and-half. If they don’t, order a tall latte with an extra shot.” The point here is that if I’m teaching a human travel companion, it’s awfully nice to have access to both modes of communication. When explicit instructions are easy to come up with and express, I can program a friend the way people have been talking to computers for decades: . if this, do that But what if I don’t even know why I order a cappuccino on some New York days and flat white on others? I can’t give you the formula because . But I can ask you to watch me and see if can figure out the pattern. Maybe there is one, maybe there isn’t, but it’s awesome that you could at least work it out. Without , a computer can’t to . It’s explicit instructions or bust. even I don’t know it you try ML/AI try find a pattern AI is about human self-expression. Maybe you’d realize that some places have a smell that does it. You might not know why that works (perhaps the smell triggers a feeling related to drinking cappuccinos with my father after the theatre, but you don’t have access to those information) but you’ll realize that you’re able to predict what I’ll do accurately. Eventually, you’ll feel confident enough to say, I’d be standing there with my jaw dropped because I don’t know how you know that. After a while I won’t worry about it, I’ll just . And as long as my preferences don’t change, you’ll keep getting it right, even if . “Flat white this time? Yeah, I got this.” trust you neither of us knows why My ability to give you my explicit instructions is . My ability to ask you to learn from relevant examples is the . traditional programming essence of machine learning and AI So here’s why : in real life, there’s no way I’m giving up my ability to fall back on teaching with examples if I’m not clever enough to come up with the instructions. Absolutely not! I’m pretty sure I use examples more than instructions to communicate with other humans when I stumble around the real world. AI is not a fad AI means I can communicate with computers that second way — via examples — not only by instructions, are you seriously asking me to suddenly gag my own mouth? Remember, in the old days we had to rely primarily on instructions only because we couldn’t do it the other way, in part because processing all those examples would . strain the meager CPUs of last century’s poor desktops But now that humanity has unlocked its ability to express itself to machines via examples, why would we suddenly give that option up entirely? A second way of talking to computers is too important to drop like yesterday’s shoulderpads. What we should drop is our expectation that there’s a one-size-fits-all way of communicating with computers about every problem. Say what you mean and say it the way that works best. Sometimes you want to give instructions and sometimes you want to feed in a bunch of examples instead. Some tasks are so complicated that you can’t hold their instructions in your memory. Because AI allows you to automate the ineffable, it’s our only option for those situations where you can’t fathom the instructions. Where you’re not smart enough to work out what those patterns mean yourself or where the instructions are so complicated that you forgot the first line by the time you got to the seven thousandth one. Want to memorize all this? Me neither. Computers don’t mind, though. Computers don’t mind memorizing long boring or instruction manuals. They can churn through those examples even though that’s a task you wouldn’t want to touch with a bargepole. When all the are automated with straightforward explicit instructions, progress will demand working on the complicated ones. In that zone, it’ll be AI or nothing. example sets Some tasks are so complicated that you can’t hold their instructions in your memory. low hanging fruit tasks If those tasks are very complicated, you’ll probably not be able to automate them flawlessly, but with AI you still might do better than nothing. (Don’t forget to .) If you do get flawless performance, my first instinct is to wonder whether your task . Don’t convert between dollars and cents with AI… seriously, what are you doing?! It’s where the task is too hard the old way that you might turn to AI. That’s also why the is to start with the task and double-check that you can’t solve it without AI first. build safety nets might be so simple that you really have solved it the traditional way instead *should* first step in AI If you’re keen to get started with letting AI make itself useful to you, here’s a guide that decision-makers should read before anyone even thinks about data or technical nitty-gritty.

Why AI is here to stay

The Role of Classic and Quantum Prime Patterning in Understanding Simplexity

Metaphysics and Mathematics 2: The Complex Space of Prime Numbers

How I Use AI While Working As A Food Delivery Worker

The , although derived from axioms and ground-set rules, has a quite peculiar interplay that dances within itself. The quantum realm recently discovered has opened avenues not only for a new science branch but also for science itself. world of science As empires are built and crumble, we fail to see that not only the wideness of the wall secures them, but also the inside aspects such as economy and faith. And of course, there’s a lot more to take into account when going towards the central figures. But for the sake of science, we will assume that there is a specific pattern. A green line in which, if the central figures of a city are found, the city is growing. Just like the habitable zone that allows our planet to sustain life. Further, I would like to explain my view of faith as the 100% certainty that all odds are in favor. However, to transcend faith, we would require tangible proof which would let our reasoning take the steps from 100% possible to absolute truthfulness. A state in which, every time we stumble under the same starting conditions, we can fast-forward to the past result and instantly acknowledge it as true or false, or even in some cases, use the same reasoning or formulas for different starting conditions, but under the same conceptual background to skip some math. Maybe the world of quantum represents a world in which we are forced to either find the true statement or keep looking for it. Further, as I lack the studies of a university, I will use ChatGPT to help me formalize and bring those ideas to a whole new level of understanding. Therefore, please keep in mind that all that comes next (and what I write after all) is purely theoretical and no previous research or ground-based testing has been done. I strongly believe however that the new road of science is only in its infant state, despite the results we have so far achieved. And what I find so great about it, is that and rule. reason logic The most fundamental aspects of logic and reasoning which we have used with both algebra and geometry could be revived as insights into this domain. Now, however, they might be used only as starting arguments that allow for action/reaction/movement/dimensionality and many more to take shape in a new form. Remember that the magic of storytelling should not be in the words, but in the story itself and how we decide to visualize it. Part 1: Fuzzy Logic Try to envision mathematical concepts as a background set of rules. Arrangements in which this fuzzy logic makes sense. Quantum mechanics is the whole of this world. A world that has segments that are either ordered by those rules or not. When a rule is not followed, the truthfulness of information kind of transcends into the realm of possibilities where they fade until they will eventually reappear. Higher-dimensional aspects are those that show us insights into where the state of the probability is. More clouds suggest a higher chance of rain. The mathematical rule has not been achieved yet. It's at a point where it is in the process of being completed. The full background shows us that it is not raining yet, even if it’s close. Let us see how our little AI helper sums these up. (Of course, I will lend my understanding to each point with italicized text.) Mathematics provides a set of rules and structures that help us model and understand the world. These rules can range from simple arithmetic to complex algebraic equations and beyond. The consistency and universality of mathematical principles make them a powerful tool for describing various phenomena. Mathematical Concepts as Background Set Rules: Fuzzy logic deals with degrees of truth rather than absolute truth values. It's often employed in situations where information is imprecise or uncertain. Quantum mechanics, on the other hand, is a branch of physics that deals with the behavior of particles at the quantum level. The probabilistic nature of quantum mechanics introduces an element of uncertainty, and the concept of superposition allows particles to exist in multiple states simultaneously. Fuzzy Logic and Quantum Mechanics: Here, we could say that those clouds are seen by 2 cities. In one city it’s raining, while in the other, it’s not. Even if the clouds are the central figure of the statement, the space marks the difference between where this is true and where it isn’t. Just like 2 particles can influence each other despite the distance, but when change is made, how can we know who made the change if it wasn’t us? In mathematics, higher-dimensional spaces extend beyond our familiar three-dimensional space. These spaces are often used to model complex systems or describe relationships involving more than three variables. The idea of higher dimensions is also explored in physics and string theory. Higher-Dimensional Spaces: Higher dimensions could simply be the newly opened avenues in which the probability fluctuates. Maybe dimensions are created and destroyed just like cells. Cells may depend on both internal and external variables when it comes to their destruction. Whereas dimensions are created and destroyed only by rules that result from a greater whole, the whole of summed parts. We could see them as being only externally dependent. Simply representing the “external” concept. Something beyond. Quantum mechanics introduces the concept of probability into the description of physical systems. The behavior of particles is often described by wave functions that provide probabilities for different outcomes. Observations collapse these probabilities into definite states. Probability and Quantum States: By observing, you see the “present” which was “present” 0.001, no, 2 seconds, no, 5 seconds ago. I believe there is no known way yet that allows us to see the real slope of growth as it is dependent on multiple factors. Focusing on just some of them will never allow us to see the full picture of the past, but we can use it to gather information for the potential full picture of the future. Just like a pirate using his spyglass to view a castle. He can never be sure that the castle is 100% the same as when he spies at the door since he cannot see the whole castle. The notion that certain mathematical concepts are not yet complete suggests ongoing exploration and discovery. In fields like pure mathematics, researchers are continuously working on extending existing theories, proving new theorems, and exploring the boundaries of mathematical knowledge. Incomplete Mathematical Concepts: In the end, we might get to a new science, a new understanding which we will call “true science” and for those that come next will be just… science. Part 2: A State of Chaos There is so much information. So many weights and counterweights to take into consideration. What has to remain and what has to be scratched off? Oh God… I feel like my head is going to explode—theoretically, of course. The point of no return, when the force of rejection will simply become stronger than the pull, is too far ahead. But then… we wouldn’t wish for a blackhole to appear either. How can we balance this play? What does it MEAN to be in balance yet, not be dead? We know (or theorize) that life could resemble the turning gears of change. Without change, everything is static. No good, no bad could happen further. But then, if life is always pulling up the rope of change, balance is surely broken. If we were to counterweight life, we would arrive at death. What else could happen here so that we will ever grow? Could life be its own domain? Could life be so inextricably different from death that it never touches it? Could life and death, and their impossibility to touch each other, be both enveloped into a both-enveloping sphere? So many questions, so few answers. “But how? And why?” - and this just makes it worse. Let’s turn our glare towards the prime numbers and something interesting ChatGPT explained to me. ”The distribution of prime numbers is a fascinating topic in number theory. Primes are often considered the "building blocks" of natural numbers, and their distribution on the number line has been a subject of extensive study. While primes themselves may seem somewhat irregular (as they have no divisors other than 1 and themselves), the overall pattern of their distribution exhibits some regularities. There are well-known results and conjectures related to the distribution of primes, such as the Prime Number Theorem, which describes the asymptotic distribution of primes among the natural numbers.” - ChatGPT. Part 3: When will this stop? “The overall pattern… exhibits some regularities.” Down below, I would like to place some patterns I randomly found a while ago when playing with primes. Now if you are to ask me what is going on in those pictures, I will ask you in return for better equipment. I will check the patterns and think that I see something. After days, weeks, or months, I would simply take a break from all the patterning and turn back to rational thinking in the hope that this time, the patterns that come into my mind support my claims. And this is how I deal with primes in a nutshell. No fuzzy logic. No fuzzy math (only to the point where logic and reason seem to hold tight to the primes). Only admiring and thinking about what could this great picture of art mean. Could we have discovered order itself? If yes, then how would we explain it? How can we understand its meaning? How can we communicate with/using it? Sadly, my job is not a full-time researcher. No matter how much I involve myself in the field there will always be someone ahead of me. Someone who will say: The Riemann zeta function is a mathematical function that plays a crucial role in number theory and complex analysis. It is named after the German mathematician Bernhard Riemann, who first defined and studied it. The Riemann zeta function is defined for complex numbers �(kind of S and C combined symbol)s with real part greater than 1 by the infinite series: �(�)=1�+2−�+3−�+4−�+…ζ(s)=1s+2−s+3−s+4−s+… This series converges when the real part of �s is greater than 1. The series can be analytically continued to other values of �s (except for �=1s=1, where it has a simple pole), providing a way to define the zeta function in a larger domain. One of the most famous properties of the Riemann zeta function is its connection to the distribution of prime numbers. In particular, the zeta function is closely related to the Prime Number Theorem, which describes the asymptotic distribution of prime numbers. - ChatGPT You are telling me that you found a function that correctly jumps at each prime number occurrence, yet, you have no idea why it does that. Now you try looking into those domains and dimensions that you had no idea even existed. Exploring a land of the unknown where all your beliefs shatter, only to force you to connect them once again if you want it to make sense. And that is… MARVELOUS. We, as humankind, are insignificant. Yet, here we are. Closer and closer to the power of understanding dimensionality. This pursuit of knowledge seems like it must be our happiest years. When in the meantime, some place their ideas into new smart ways for crypto. Others pray that today will be our happy day. Others are simply lost due to psychological or physical warfare. If knowledge is what we say defines us, it sure seems we never met our parents. I hope you all stay safe and healthy. It is a very foggy world out there. And our lanterns seem to shine only ourselves. Take care of your loved ones, of yourself, and of course, of your knowledge. “If she is cherished, one is exalted; if she is embraced, one is honored. But if you refuse to listen to her when she calls, when you pay no attention when she stretches out her hand, so too, will she ignore you when you most need her, in times of distress and trouble. How much better to get wisdom than gold, to get insight rather than silver!” - Eternalised Lead image by Manuel on Unsplash

Quantum Realms, Fuzzy Logic, and the Enigma of Prime Patterns

Plunging Into Data: Unraveling Time Series Patterns

Author:
(1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Simulating the Exposure Map Acknowledgment and References Appendix APPENDIX A. SOFTWARE ACKNOWLEDGEMENT This research was done with the following free software programs and libraries: 1.23, Bzip2 1.0.8, CFITSIO 4.1.0, CMake 3.24.0, cURL 7.84.0, Dash 0.5.11-057cd65, Discoteq flock 0.4.0, Expat 2.4.1, File 5.42, Fontconfig 2.14.0, FreeType 2.11.0, Git 2.37.1, GNU Astronomy Utilities 0.20.72-08b0 (Akhlaghi & Ichikawa 2015; ?), GNU Autoconf 2.71, GNU Automake 1.16.5, GNU AWK 5.1.1, GNU Bash 5.2-rc2, GNU Binutils 2.39, GNU Bison 3.8.2, GNU Compiler Collection (GCC) 12.1.0, GNU Coreutils 9.1, GNU Diffutils 3.8, GNU Findutils 4.9.0, GNU gettext 0.21, GNU gperf 3.1, GNU Grep 3.7, GNU Gzip 1.12, GNU Integer Set Library 0.24, GNU libiconv 1.17, GNU Libtool 2.4.7, GNU libunistring 1.0, GNU M4 1.4.19, GNU Make 4.3, GNU Multiple Precision Arithmetic Library 6.2.1, GNU Multiple Precision Complex library, GNU Multiple Precision Floating-Point Reliably 4.1.0, GNU Nano 6.4, GNU NCURSES 6.3, GNU Readline 8.2- rc2, GNU Scientific Library 2.7, GNU Sed 4.8, GNU Tar 1.34, GNU Texinfo 6.8, GNU Wget 1.21.2, GNU Which 2.21, GPL Ghostscript 9.56.1, Help2man , Less 590, Libffi 3.4.2, Libgit2 1.3.0, libICE 1.0.10, Libidn 1.38, Libjpeg 9e, Libpaper 1.1.28, Libpng 1.6.37, libpthread-stubs (Xorg) 0.4, libSM 1.2.3, Libtiff 4.4.0, libXau (Xorg) 1.0.9, libxcb (Xorg) 1.15, libXdmcp (Xorg) 1.1.3, libXext 1.3.4, Libxml2 2.9.12, libXt 1.2.1, Lzip 1.23, OpenSSL 3.0.5, PatchELF 0.13, Perl 5.36.0, pkgconfig 0.29.2, podlators 4.14, Python 3.10.6, utilLinux 2.38.1, util-macros (Xorg) 1.19.3, WCSLIB 7.11, X11 library 1.8, XCB-proto (Xorg) 1.15, xorgproto 2022.1, xtrans (Xorg) 1.4.0, XZ Utils 5.2.5 and Zlib 1.2.11. The LATEX source of the paper was compiled to make the PDF using the following packages: courier 61719 (revision), epsf 2.7.4, etoolbox 2.5k, helvetic 61719 (revision), lineno 5.3, pgf 3.1.10, pgfplots 1.18.1, revtex4- 1 4.1s, tex 3.141592653, textcase 1.04 and ulem 53365 (revision). We are very grateful to all their creators for freely providing this necessary infrastructure. This research (and many other projects) would not be possible without them. This paper is available on arxiv under CC 4.0 DEED license. Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Author: Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Abstract and Intro Simulating the Exposure Map Simulating the Exposure Map Acknowledgment and References Acknowledgment and References Appendix Appendix APPENDIX A. SOFTWARE ACKNOWLEDGEMENT This research was done with the following free software programs and libraries: 1.23, Bzip2 1.0.8, CFITSIO 4.1.0, CMake 3.24.0, cURL 7.84.0, Dash 0.5.11-057cd65, Discoteq flock 0.4.0, Expat 2.4.1, File 5.42, Fontconfig 2.14.0, FreeType 2.11.0, Git 2.37.1, GNU Astronomy Utilities 0.20.72-08b0 (Akhlaghi & Ichikawa 2015; ?), GNU Autoconf 2.71, GNU Automake 1.16.5, GNU AWK 5.1.1, GNU Bash 5.2-rc2, GNU Binutils 2.39, GNU Bison 3.8.2, GNU Compiler Collection (GCC) 12.1.0, GNU Coreutils 9.1, GNU Diffutils 3.8, GNU Findutils 4.9.0, GNU gettext 0.21, GNU gperf 3.1, GNU Grep 3.7, GNU Gzip 1.12, GNU Integer Set Library 0.24, GNU libiconv 1.17, GNU Libtool 2.4.7, GNU libunistring 1.0, GNU M4 1.4.19, GNU Make 4.3, GNU Multiple Precision Arithmetic Library 6.2.1, GNU Multiple Precision Complex library, GNU Multiple Precision Floating-Point Reliably 4.1.0, GNU Nano 6.4, GNU NCURSES 6.3, GNU Readline 8.2- rc2, GNU Scientific Library 2.7, GNU Sed 4.8, GNU Tar 1.34, GNU Texinfo 6.8, GNU Wget 1.21.2, GNU Which 2.21, GPL Ghostscript 9.56.1, Help2man , Less 590, Libffi 3.4.2, Libgit2 1.3.0, libICE 1.0.10, Libidn 1.38, Libjpeg 9e, Libpaper 1.1.28, Libpng 1.6.37, libpthread-stubs (Xorg) 0.4, libSM 1.2.3, Libtiff 4.4.0, libXau (Xorg) 1.0.9, libxcb (Xorg) 1.15, libXdmcp (Xorg) 1.1.3, libXext 1.3.4, Libxml2 2.9.12, libXt 1.2.1, Lzip 1.23, OpenSSL 3.0.5, PatchELF 0.13, Perl 5.36.0, pkgconfig 0.29.2, podlators 4.14, Python 3.10.6, utilLinux 2.38.1, util-macros (Xorg) 1.19.3, WCSLIB 7.11, X11 library 1.8, XCB-proto (Xorg) 1.15, xorgproto 2022.1, xtrans (Xorg) 1.4.0, XZ Utils 5.2.5 and Zlib 1.2.11. The LATEX source of the paper was compiled to make the PDF using the following packages: courier 61719 (revision), epsf 2.7.4, etoolbox 2.5k, helvetic 61719 (revision), lineno 5.3, pgf 3.1.10, pgfplots 1.18.1, revtex4- 1 4.1s, tex 3.141592653, textcase 1.04 and ulem 53365 (revision). We are very grateful to all their creators for freely providing this necessary infrastructure. This research (and many other projects) would not be possible without them. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Gnuastro: Simulating The Exposure Map of a Pointing Pattern - Appendix

Author:
(1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Simulating the Exposure Map Acknowledgment and References Appendix 3. ACKNOWLEDGEMENT The workflow of this research note was developed in the reproducible framework of Maneage (Managing data lineage, Akhlaghi et al. 2021, latest Maneage commit 8161194, from 22 May 2023). This note is created from the Git commit 4176d29 that is hosted on Codeberg[4] and is archived on SoftwareHeritage for longevity. The analysis of this research note was done using GNU Astronomy Utilities (Gnuastro, ascl.net/1801.009) version 0.20.72-08b0. Work on Gnuastro has been funded by the Japanese Ministry of Education, Culture, Sports, Science, and Technology (MEXT) scholarship and its Grant-in-Aid for Scientific Research (21244012, 24253003), the European Research Council (ERC) advanced grant 339659-MUSICOS, the Spanish Ministry of Economy and Competitiveness (MINECO, grant number AYA2016-76219-P) and the NextGenerationEU grant through the Recovery and Resilience Facility project ICTS-MRR-2021- 03-CEFCA. I also acknowledge the financial support provided by the Governments of Spain and Aragon through their general budgets and ´ the Fondo de Inversiones de Teruel, and the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033 y FEDER, Una manera de hacer Europa) with grant PID2021- 124918NA-C43. REFERENCES Akhlaghi, M., & Ichikawa, T. 2015, ApJS, 220, 1, doi: 10.1088/0067-0049/220/1/1 Akhlaghi, M., Infante-Sainz, R., Roukema, B. F., et al. 2021, CiSE, 23, 82, doi: 10.1109/MCSE.2021.3072860 Calabretta, M. R., & Greisen, E. W. 2002, A&A, 395, 1077, doi: 10.1051/0004-6361:20021327 Illingworth, G. D., Magee, D., Oesch, P. A., et al. 2013, ApJS, 209, 6, doi: 10.1088/0067-0049/209/1/6 Trujillo, I., D’Onofrio, M., Zaritsky, D., et al. 2021, A&A, 654, A40, doi: 10.1051/0004-6361/202141603 This paper is available on arxiv under CC 4.0 DEED license. [4] https://codeberg.org/gnuastro/paper-pointing-simulate Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Author: Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Abstract and Intro Simulating the Exposure Map Simulating the Exposure Map Acknowledgment and References Acknowledgment and References Appendix Appendix 3. ACKNOWLEDGEMENT The workflow of this research note was developed in the reproducible framework of Maneage (Managing data lineage, Akhlaghi et al. 2021, latest Maneage commit 8161194, from 22 May 2023). This note is created from the Git commit 4176d29 that is hosted on Codeberg[4] and is archived on SoftwareHeritage for longevity. The analysis of this research note was done using GNU Astronomy Utilities (Gnuastro, ascl.net/1801.009) version 0.20.72-08b0. Work on Gnuastro has been funded by the Japanese Ministry of Education, Culture, Sports, Science, and Technology (MEXT) scholarship and its Grant-in-Aid for Scientific Research (21244012, 24253003), the European Research Council (ERC) advanced grant 339659-MUSICOS, the Spanish Ministry of Economy and Competitiveness (MINECO, grant number AYA2016-76219-P) and the NextGenerationEU grant through the Recovery and Resilience Facility project ICTS-MRR-2021- 03-CEFCA. I also acknowledge the financial support provided by the Governments of Spain and Aragon through their general budgets and ´ the Fondo de Inversiones de Teruel, and the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033 y FEDER, Una manera de hacer Europa) with grant PID2021- 124918NA-C43. REFERENCES Akhlaghi, M., & Ichikawa, T. 2015, ApJS, 220, 1, doi: 10.1088/0067-0049/220/1/1 Akhlaghi, M., Infante-Sainz, R., Roukema, B. F., et al. 2021, CiSE, 23, 82, doi: 10.1109/MCSE.2021.3072860 Calabretta, M. R., & Greisen, E. W. 2002, A&A, 395, 1077, doi: 10.1051/0004-6361:20021327 Illingworth, G. D., Magee, D., Oesch, P. A., et al. 2013, ApJS, 209, 6, doi: 10.1088/0067-0049/209/1/6 Trujillo, I., D’Onofrio, M., Zaritsky, D., et al. 2021, A&A, 654, A40, doi: 10.1051/0004-6361/202141603 This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv [4] https://codeberg.org/gnuastro/paper-pointing-simulate

Gnuastro: Simulating The Exposure Map of a Pointing Pattern - Acknowledgment and References

Author:
(1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Simulating the Exposure Map Acknowledgment and References Appendix 2. SIMULATING THE EXPOSURE MAP Within Gnuastro, the executable in charge of simulating the exposure map of a given list of pointings is called astscript-pointing- -simulate. As with all Gnuastro features, it has a complete documentation in Gnuastro’s manual[2] which is available in many formats (on the command-line, as web pages, PDF and etc). A complete and dedicated tutorial is also available within the tutorials chapter[3] of the manual. Three of the images produced in the tutorial (as of version 0.21) are displayed in Figure 1. The left image shows the exposure map that is produced from a five-point, “+”-shaped set of pointings on the sky, with one pointing in the center and four on the outer extents. The middle image uses one of the hooks that are available within the script to produce a noisy image and not an exposure map. This hook can also be used to insert simulated/real objects. The right image shows how another hook can be used to account for trimming of the outer edges (which sometimes do not get exposed enough due to vignetting). This hook can also be used to insert any random area of bad pixels or to simulate persistence in near infra-red detectors (these can be large). For its processing, this script needs two input files: 1) a table containing the RA and Dec of each pointing, 2) a FITS image from the camera that the dither pattern is meant for. The image has to contain the celestial world coordinate system (WCS, see Calabretta & Greisen 2002) headers. Having a WCS is very important, especially for cameras with a wide field of view, because distortions can become visible/significant. The tutorial and dedicated manual for this script are available in the Gnuastro manual and will always be up to date with the running version; therefore for the practical details, we encourage readers to follow the Gnuastro manual. The future changes in the script that break with this research note will be clearly designated in a “changes after publication” subsection of the section describing this script. This paper is available on arxiv under CC 4.0 DEED license. [2] https://www.gnu.org/software/gnuastro/manual [3] https://www.gnu.org/software/gnuastro/manual/html node/Tutorials.html Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Author: Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Abstract and Intro Simulating the Exposure Map Simulating the Exposure Map Acknowledgment and References Acknowledgment and References Appendix Appendix 2. SIMULATING THE EXPOSURE MAP Within Gnuastro, the executable in charge of simulating the exposure map of a given list of pointings is called astscript-pointing- -simulate. As with all Gnuastro features, it has a complete documentation in Gnuastro’s manual[2] which is available in many formats (on the command-line, as web pages, PDF and etc). A complete and dedicated tutorial is also available within the tutorials chapter[3] of the manual. Three of the images produced in the tutorial (as of version 0.21) are displayed in Figure 1. The left image shows the exposure map that is produced from a five-point, “+”-shaped set of pointings on the sky, with one pointing in the center and four on the outer extents. The middle image uses one of the hooks that are available within the script to produce a noisy image and not an exposure map. This hook can also be used to insert simulated/real objects. The right image shows how another hook can be used to account for trimming of the outer edges (which sometimes do not get exposed enough due to vignetting). This hook can also be used to insert any random area of bad pixels or to simulate persistence in near infra-red detectors (these can be large). For its processing, this script needs two input files: 1) a table containing the RA and Dec of each pointing, 2) a FITS image from the camera that the dither pattern is meant for. The image has to contain the celestial world coordinate system (WCS, see Calabretta & Greisen 2002) headers. Having a WCS is very important, especially for cameras with a wide field of view, because distortions can become visible/significant. The tutorial and dedicated manual for this script are available in the Gnuastro manual and will always be up to date with the running version; therefore for the practical details, we encourage readers to follow the Gnuastro manual. The future changes in the script that break with this research note will be clearly designated in a “changes after publication” subsection of the section describing this script. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv [2] https://www.gnu.org/software/gnuastro/manual https://www.gnu.org/software/gnuastro/manual [3] https://www.gnu.org/software/gnuastro/manual/html node/Tutorials.html https://www.gnu.org/software/gnuastro/manual/html

Gnuastro: Simulating The Exposure Map of a Pointing Pattern - Simulating the Exposure Map

Author:
(1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Simulating the Exposure Map Acknowledgment and References Appendix ABSTRACT The pointing pattern is an integral part of designing one’s observation strategy for a certain scientific goal. But accounting for the particular science case or instrument artifacts (like distortion, vignetting or large areas of bad pixels) can make it hard to predict how the exposure map of the final stack will be. To help address this problem, Gnuastro 0.21 includes a new executable program called astscript-pointing-simulate that is fully described in the Gnuastro manual and comes with a complete tutorial. The figures of this research note are reproducible with Maneage, on the Git commit 4176d29. Keywords: Astronomical methods (1043), Field of view (534), Direct imaging (387), Astronomical techniques (1684), Astronomy software (1855) 1. INTRODUCTION Astronomical images are often composed of many single exposures. After completing the observations, when the science topic does not depend on the time of observation, we stack those single exposures into one deep stacked image (also known as coadd). Designing the strategy to take those single exposures is therefore a very important aspect of planning an astronomical observation. Each exposure has a pointing (the location on the sky that it was targeting). Subsequent exposures are usually taken with different pointings. When the next pointing is ver near (a small fraction of the field of view) to the previous one, it is known as a dither (literally/generally meaning trembling or vibration). But when the distance between subsequent pointings is large (and issues like re-focusing become necessary), the pointing is known as an offset. These terms are sometimes used interchangably by some. When we only have dithers, most of the central part of the final stack has a fixed depth. Only a thin border becomes shallower (conveying a sense of vibration!). For example see Figures 3 and 4 of Illingworth et al. (2013) which show the exposures that went into the XDF survey. These types of images (where each pixel contains the number of exposures, or time, that were used in it) are known as exposure maps. However, it can happen that only offsets are used. For example see Figure 1 of Trujillo et al. (2021), which shows the exposure map for the LIGHTS survey. The dithering pattern therefore is strongly defined by the science case (high-level purpose of the observation) and your telescope’s field of view. For example in the HUDF, CANDELS and other proposals (that constitute the XDF), the scientific target was high redshift (distant) galaxies. When counting the pixels that they cover in relation to the number of pixels in each exposure, these targets are very small objects. Such that within that small footprint (of just 1 arcminute!) we have thousands of such small objects (which may also be stars or nearby galaxies of similar observed size to the high redshift galaxies). Therefore for the scientific goal of the XDF, the very small dithers were sufficient to avoid instrument artifacts. However, the LIGHTS survey is focused on the halos of large nearby galaxies (that can be more than 10 arcminutes wide); thus covering a large fraction of the field of view of the Large Binocular Telescope that was used. In order to do robust calibration of the images (for example to estimate the flat field pattern, or the sky background level) it was necessary to have offsets. This enabled the galaxy and its halo to cover very different pixels from one exposure to the next. In other cases, the pointings include both offsets and dithers (around each offset). To find the ideal dither pattern for the particular scientific goal, it therefore helps to be able to simulate various strategies. In the next section, an installed script is introduced for this purpose (to simulate the exposure map of a series of pointings) in GNU Astronomy Utilities (Gnuastro Akhlaghi & Ichikawa 2015) version[1] 0.21 and later. This paper is available on arxiv under CC 4.0 DEED license. [1] This paper is published shortly before the release of Gnuastro 0.21. If Gnuastro 0.21 is not yet available, please use the latest test (alpha) release. Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Author: Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Abstract and Intro Simulating the Exposure Map Simulating the Exposure Map Acknowledgment and References Acknowledgment and References Appendix Appendix ABSTRACT The pointing pattern is an integral part of designing one’s observation strategy for a certain scientific goal. But accounting for the particular science case or instrument artifacts (like distortion, vignetting or large areas of bad pixels) can make it hard to predict how the exposure map of the final stack will be. To help address this problem, Gnuastro 0.21 includes a new executable program called astscript-pointing-simulate that is fully described in the Gnuastro manual and comes with a complete tutorial. The figures of this research note are reproducible with Maneage, on the Git commit 4176d29. Keywords : Astronomical methods (1043), Field of view (534), Direct imaging (387), Astronomical techniques (1684), Astronomy software (1855) Keywords 1. INTRODUCTION Astronomical images are often composed of many single exposures. After completing the observations, when the science topic does not depend on the time of observation, we stack those single exposures into one deep stacked image (also known as coadd). Designing the strategy to take those single exposures is therefore a very important aspect of planning an astronomical observation. Each exposure has a pointing (the location on the sky that it was targeting). Subsequent exposures are usually taken with different pointings. When the next pointing is ver near (a small fraction of the field of view) to the previous one, it is known as a dither (literally/generally meaning trembling or vibration). But when the distance between subsequent pointings is large (and issues like re-focusing become necessary), the pointing is known as an offset. These terms are sometimes used interchangably by some. When we only have dithers, most of the central part of the final stack has a fixed depth. Only a thin border becomes shallower (conveying a sense of vibration!). For example see Figures 3 and 4 of Illingworth et al. (2013) which show the exposures that went into the XDF survey. These types of images (where each pixel contains the number of exposures, or time, that were used in it) are known as exposure maps. However, it can happen that only offsets are used. For example see Figure 1 of Trujillo et al. (2021), which shows the exposure map for the LIGHTS survey. The dithering pattern therefore is strongly defined by the science case (high-level purpose of the observation) and your telescope’s field of view. For example in the HUDF, CANDELS and other proposals (that constitute the XDF), the scientific target was high redshift (distant) galaxies. When counting the pixels that they cover in relation to the number of pixels in each exposure, these targets are very small objects. Such that within that small footprint (of just 1 arcminute!) we have thousands of such small objects (which may also be stars or nearby galaxies of similar observed size to the high redshift galaxies). Therefore for the scientific goal of the XDF, the very small dithers were sufficient to avoid instrument artifacts. However, the LIGHTS survey is focused on the halos of large nearby galaxies (that can be more than 10 arcminutes wide); thus covering a large fraction of the field of view of the Large Binocular Telescope that was used. In order to do robust calibration of the images (for example to estimate the flat field pattern, or the sky background level) it was necessary to have offsets. This enabled the galaxy and its halo to cover very different pixels from one exposure to the next. In other cases, the pointings include both offsets and dithers (around each offset). To find the ideal dither pattern for the particular scientific goal, it therefore helps to be able to simulate various strategies. In the next section, an installed script is introduced for this purpose (to simulate the exposure map of a series of pointings) in GNU Astronomy Utilities (Gnuastro Akhlaghi & Ichikawa 2015) version[1] 0.21 and later. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv [1] This paper is published shortly before the release of Gnuastro 0.21. If Gnuastro 0.21 is not yet available, please use the latest test (alpha) release.

Gnuastro: Simulating The Exposure Map of a Pointing Pattern - Abstract and Intro

Building Resilient Systems With Retry Patterns

Authors:
(1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de);
(2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de);
(3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de);
(4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Table of Links Abstract and 1 Introduction 2 Background 3 Design and Implementation of Licma and 3.1 Design 3.2 Implementation 4 Methodology and 4.1 Searching and Downloading Python Apps 4.2 Comparison with Previous Studies 5 Evaluation and 5.1 GitHub Python Projects 5.2 MicroPython 6 Comparison with previous studies 7 Threats to Validity 8 Related Work 9 Conclusion, Acknowledgments, and References 4.2 Comparison with Previous Studies To understand the differences between crypto misuses for §1 to §6, cf. Table 1, in Python and previous studies in Java, with the analysis CryptoLint, [4] and C/C++, with the analysis CryptoREX, [13], we compared the reported results. As we concentrated on the same rule set, we only need a few adjustments to compare the results. First, for our meta-analysis we exclude §6 since the 5 analyzed Python modules avoid this misuse by design. Second, we merge the results for §1 of Egele et al. [4] as they split their result into two different cases: The explicit use of the block mode ECB on one side and the implicit use of this block mode due to the API design on the other. Third, due to the design of our analysis, we only consider definite findings. CryptoLint and CryptoREX do not distinguish between potential misuses and definite ones. Fourth, to enable a fair comparison, we compare only percentages rather than absolute numbers, as we are interested in the general distribution and the influence of API design on crypto misuses. We choose to compare the studies on the percentage of applications using crypto and having at least one misuse of a respective rule as introduced by Egele et al. [4]. Unfortunately, Zhang et al. [13] only reports details for the successfully unpacked firmware images before filtering for crypto usages. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de); (2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de); (3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de); (4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Authors: Authors: (1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de); (2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de); (3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de); (4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Design and Implementation of Licma and 3.1 Design 3 Design and Implementation of Licma and 3.1 Design 3.2 Implementation 3.2 Implementation 4 Methodology and 4.1 Searching and Downloading Python Apps 4 Methodology and 4.1 Searching and Downloading Python Apps 4.2 Comparison with Previous Studies 4.2 Comparison with Previous Studies 5 Evaluation and 5.1 GitHub Python Projects 5 Evaluation and 5.1 GitHub Python Projects 5.2 MicroPython 5.2 MicroPython 6 Comparison with previous studies 6 Comparison with previous studies 7 Threats to Validity 7 Threats to Validity 8 Related Work 8 Related Work 9 Conclusion, Acknowledgments, and References 9 Conclusion, Acknowledgments, and References 4.2 Comparison with Previous Studies To understand the differences between crypto misuses for §1 to §6, cf. Table 1, in Python and previous studies in Java, with the analysis CryptoLint, [4] and C/C++, with the analysis CryptoREX, [13], we compared the reported results. As we concentrated on the same rule set, we only need a few adjustments to compare the results. First, for our meta-analysis we exclude §6 since the 5 analyzed Python modules avoid this misuse by design. Second, we merge the results for §1 of Egele et al. [4] as they split their result into two different cases: The explicit use of the block mode ECB on one side and the implicit use of this block mode due to the API design on the other. Third, due to the design of our analysis, we only consider definite findings. CryptoLint and CryptoREX do not distinguish between potential misuses and definite ones. Fourth, to enable a fair comparison, we compare only percentages rather than absolute numbers, as we are interested in the general distribution and the influence of API design on crypto misuses. We choose to compare the studies on the percentage of applications using crypto and having at least one misuse of a respective rule as introduced by Egele et al. [4]. Unfortunately, Zhang et al. [13] only reports details for the successfully unpacked firmware images before filtering for crypto usages. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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