Simula is on a mission to build the world's first Linux VR Computer, a portable VR headset dedicated to office computing and knowledge work

SimulaVR

AVR Microcontroller Testing Procedures

I heard about AI-generated art for the first time in a Wall Street Journal podcast in 2021. In the podcast, the host was playing AI-generated pieces of classical music. These pieces emulated the style of classical composers, and the precision with which they did that was rather impressive. A listener uninitiated to classical music would probably have a hard time differentiating them from the actual works of those composers. This was also the first time I took seriously. Listening to those pieces made me wonder – what if AI gets so good at making music that those renowned composers eventually get buried under piles of AI-generated classical music? I have always believed in judging a work of art for what it is instead of finding its aesthetic validity in the legacy of a musician or how critics received a piece of music. If AI-generated music is going to get better, I will most likely say, Sorry, Chopin. generative AI (GenAI) Last week, a colleague showed me OpenAI Sora, a text-to-video model that generates remarkably detailed, realistic videos from text prompts. The first video they showcased on the website featured a woman (with five fingers, not four) walking through the streets of Tokyo. The texture of her skin, the blemishes and spots on her face, the wrinkles, and her hair – everything about this woman in focus is more realistic than (or at least, as much as) subjects in those videos that were once ‘really shot’ by professional videographers. Are we entering the era of the hyperreal? In his book Simulacra and Simulation, Jean Baudrillard first defined the concept of hyperreal as the blurring of the lines that separate reality from representation. This blurring is caused by the creation of representations (signs) that do not refer to anything that exists in reality. Hyperreality results in our inability to distinguish what is simulated from what is real. In his text, Baudrillard was alluding to the process by which value gets created in consumerist paradigms. But with GenAI, his idea is being stretched to its literal limits. On the OpenAI Sora page are over 30 video clips, each containing chunks of time that never happened, all in great detail, ready for us to witness with our eyes. What do we see when we see ‘historical footage of California during the gold rush’ generated by Sora? In truth, there is nothing historical about this footage, and neither is it footage in the true sense. The historical effect is rendered by employing visual aesthetics that elicit a sense of historicity in films and documentaries – warm tones, vintage overlay, grainy effects, and a lower frame rate. Yet, the prompt results in a very believable "footage." I am no historian, so I cannot say if the clip gets historical details right in how it renders the look of the buildings and the attire of the people on horses. But the geographical features look rather compelling. But this is not the crux of the issue here. Such visual effects have been achieved by studios using CGI for decades now. But these endeavors were applied to a specific end and within particular contexts – like creating a realistic game, a movie sequence, or making a documentary more tangible, if you will. The content was vetted by experts who could verify its correctness and authenticity. But now that this technology (text-to-video models) will democratize access to such capabilities, the consequences will be very different. Content will be affected by this democratization of GenAI long before it starts weaving symphonies that draw tears from our eyes or write Nobel-winning novels. A new distinction between content and art Content and art have coexisted ever since the advent of media – writing and painting, followed by print and photography, then audio and video. Content was usually short-lived and created for a specific function, such as entertaining, delivering news, capturing memories, recording events, or even selling things. Art served a purpose in the human being’s socio-cultural experience, and artworks survived well beyond their time. However, both content and art had to be created before GenAI entered the picture. higher Until now, art – or at least, what is deemed art in ‘high culture’ has remained largely untouched by GenAI capabilities. While GenAI may be able to emulate Mozart’s musical style, the aesthetic and cultural value of that style was rooted in the time and context in which Mozart was creating music. The same goes for literary works, which, in effect, created a paradigm shift in the ways of thinking about the world, human beings, society, and the small and big questions underpinning our existence during a period in history. The story of the content is somewhat different. Until some point in the 2000s, content creation required specialized apparatus – sophisticated video cameras or DSLRs and teams of people were needed to create audio-visual content, for example. But with social media and the proliferation of smartphones, content creation took a new turn. Everyone was now a creator, snipping pictures (or videos) of their dogs and cats doing funny stuff, creating video memes, or making comic faces with Instagram filters. Compilations of these snippets of human existence made their way to YouTube in droves, and content flooded depthless Instagram, Facebook, and TikTok feeds. Yet, few pieces of content warranted the need to question the reality of the subject of these videos. With the democratization of GenAI, we will find ourselves asking this question more often. In fact, while scrolling through the comments section of the Apple Airpods Pro 2 launch live stream in 2023, I found a user wondering whether the woman on screen, who was presenting the features of the new Airpods, was a real person. However, the industry is attempting to solve this problem with standards like the C2PA, which will embed details like the creator and mode of creation within media files. This will make it possible to differentiate AI-generated content from ‘real’ content. Nonetheless, AI-generated content will coexist with the content that is created by people. This will add another level of distinction between content and art – which is that art be created, but content be generated. must can The ability to generate content will undoubtedly have far-reaching economic consequences and leave a profound impact on many professions. But it will also alter our relationship with content and how it informs our worldview. The desert of the real? What if generated content is more compelling, more convincing, more entertaining, and more appealing to the viewer? Given how most societies today are inclined to promote value-creating systems, generated content will likely overtake content created by people in such a scenario – which means that content platforms will be saturated with AI-generated content that will show places that never existed, people who never walked on the planet, events that never occurred, or cute dog stuff that no real dog ever did. This is quite likely to happen – AI thought leader Nina Schick says 90% of online content could be AI-generated. But the proliferation of AI-generated content could go two ways. According to neuroscientists, knowing an artwork as authentic activates reward pathways in the brain of viewers. Forged or fake art, on the other hand, does not lead to the same effect. If this result is extrapolated to AI-generated content, one would be led to conclude that AI-generated content will not be as fulfilling as that which is created by real people recording real things – given that content platforms enforce rules that enable viewers to identify AI-generated content. Will AI-generated dog videos be as effective in reducing our stress levels as the of real dogs doing real things? If not, then the adoption of text-to-video models will be limited to certain areas, like making clips for purchasable stock video collections. Or generating elements of videos, like backgrounds or animated overlays. footage In this scenario, we will see the return of content that is created rather than generated – and social media platforms would be the first sites where this shift will unfold. But there is another factor that must be taken into account here. Research on Instagram usage patterns has shown that upward comparison (comparing oneself to those who people perceive as superior to them) through content consumption has detrimental effects on users' mental health. If AI-generated content breaks through the barriers of creative possibilities, it will present a version of the world that is to its real counterpart. If this happens, will AI-generated content immerse us in a hyperreal world, framing our existing one into a dull and dry reality? superior Probably not – at least not right away. Current text-to-video models still have limitations. For instance, the wolf pups in Sora-generated clips sometimes emerge out of nothing, and bodies sometimes magically pass through other bodies. In another clip, a plastic chair sometimes behaves like a sheet of paper and changes shape through the clip. OpenAI claims that giving the model foresight into multiple frames enables them to retain the visual identity of a subject even when it goes out of view for a few moments. But this doesn't make the model infallible, as is evident in the videos on OpenAI’s Sora page. Moreover, the generated clips are still only 60 seconds long, and it will probably be a while before we see full-length feature films and documentaries generated by such models. However, these limitations will likely be eliminated in upcoming generations of multimodal models – and soon enough, considering that the technology has been in development for a little over half a decade. When that time comes, the experience of consuming content or simply navigating through cyberspace will take a new turn. For the better or worse? Only time will tell. Looking towards a brighter future Generation and creation remain very distinct ideas, even in the era of GenAI. This distinction is rooted in the etymology of these words – create comes from the Latin , which means to form out of nothing. On the other hand, generate comes from the Latin (meaning to produce), which in turn comes from (meaning a type or a family). In both mathematics and linguistics, generation pertains to a process of applying a set of rules to an input to produce something. This is precisely what Generative Pre-Trained Transformers (GPTs) do. creare generō genus As far-fetched as the meaning of creation may seem in the era of content, GenAI is compelling us to exercise our higher cognitive abilities as it abstracts the heavy lifting of content generation from the creators. Creation, at its heart, remains a process of making something out of nothing. It is this idea of creation that has ushered era-defining epochs in the course of our cultural, social, and technological history. generated With GenAI solutions undertaking the less cognitive aspects of content creation, each of us will be empowered to create more sophisticated things, just better and faster. But where this paradigm of GenAI-augmented creation takes us will be determined collectively by content-powered platforms, regulators, content provenance standards and their endorsement, and our inevitable neurological responses to new forms of content.

Democratization of Text-to-video Models Will Launch Us Into a Hyperreal Cyberspace. Are We Ready?

I'd like to share my experiences using evilginx2 and gophish for red teaming phishing simulations. While there are numerous guides available, I aim to highlight the nuances and practical insights that aren't typically covered in documentation and tutorials. Approaches When setting up a phishing campaign for red team purposes, you have three options: you can clone the page and only collect data
the next level is not only cloning the page but also verifying the captured data against a legitimate page
The last and most sophisticated method involves a man-in-the-middle attack, where your page is a proxy between the victim and the legitimate site. Evilginx2 The third method is done by evilginx2, which, despite its name, does not have nginx built in, but the functionality is the same, except that evilginx2 has built-in filters and functions like inject_js that make the attacker's life more comfortable. Gophish Gophish is a tool that allows you to manage groups and users, dynamically create emails, and send them at will. Integration A few weeks ago, Evilginx got an update 3.3, which introduced integration with Gophish, a framework for phishing campaigns. There have been several such projects in the past, such as gophish[1], but in this text, I will focus on the official evilginx by K. Gretzky. More info about the update with the demo can be found here: https://breakdev.org/evilginx-3-3-go-phish/ Both tools are in Golang, which is a relatively easy-to-understand language. Experience with Evilginx2 Malicious proxy with evilginx has several drawbacks compared to just cloning the page. Firstly, the setup is much more complicated. When you clone a page, you download the necessary files via developer tools and add the password-storing feature via some backend. Evilginx setup is more complicated. Setup needs a configuration file and a phishlet that works as a template for the phishing proxy. Setting up and running evilginx2 in the production version took me over a week compared to just cloning. The advantage is that evilginx scales much better. Once you know how to create them, you can change phishing scenarios at will because adding new phishlet is trivial. I should mention the state of the project in which evilginx is. The documentation is concise and not very useful if you have no experience with evilginx2 already. Some features and behaviors are not documented, and you need to read and debug the source code to find the behavior. The project has special financing where you can pay 400$ for a course of excellent quality, but it replaces the documentation to some extent, which doesn't seem like a good way. Either it's an open-source project with all the parts, or it's not open-source; this way, it's open-code, but you are forced into buying the expensive course. It doesn't help that the project has many open issues and bugs that nobody addresses. If you're serious about phishing, you'll probably have to edit the source code and use your version because the author doesn't put much effort into finding new PRs. For example, the PR request I made to fix the POST of a GET request three weeks[2] ago has still not been merged. The project is plagued by old libraries that are long unused. Of course, I don't want the author to handle everything himself, but sometimes, you have to give up control of the project to benefit the project. And more moderators and maintainers who want to help would certainly be found. But not to be only negative, this project is still the best of its kind. If you learn all its quirks and modify it to your purpose, it will become a tool allowing you to do campaigns of all kinds, with a level of automation that is impossible if you write the tooling yourself. Experience with Gophish Gophish isn't updated very often either, but I haven't encountered any bugs, so it doesn't really bother me. Although gophish has an API, it lacks some functionality that would be useful, so K.Gretzky had to add this API[3] to make the integration with evilginx work, anyway this approach is not sustainable and I think gophish would benefit from an API that would allow event reporting. The Gophish email template community is active, and you don't need to invest much time in a replica of the o365 email because there is a good chance that someone has already created the template. The UI is very good, and many features, like a timeline of events, will be appreciated by clients in the report. Bypassing anti measures If you send emails within a known service such as o365 or Gmail, it is good to first build an instance where you collect crawler IP addresses which you then block on the production instance and domain, thus minimizing the risk of being blacklisted, for example, outlook. Of course, this is the easier part. The harder part is to have an SMPT server with a good reputation that allows you to send phishing emails in bulk, so I do not recommend building your own SMTP server but rather use some email as a service with its own domain that has a good server reputation by default, for example, Gmail business. Summary While evilginx2 offers powerful features and scalability, its complex setup and poor documentation are significant hurdles.
Gophish provides robust campaign management tools but also suffers from limited API functionality.
Both tools require considerable customization and understanding to effectively use in red team operations.
Mastering both tools offers significant rewards. Thank you for reading. Feedback is appreciated! References [1]: https://github.com/fin3ss3g0d/evilgophish [2]: https://github.com/kgretzky/evilginx2/pull/1047 [3]: https://github.com/kgretzky/gophish I'd like to share my experiences using evilginx2 and gophish for red teaming phishing simulations. While there are numerous guides available, I aim to highlight the nuances and practical insights that aren't typically covered in documentation and tutorials. Approaches When setting up a phishing campaign for red team purposes, you have three options: you can clone the page and only collect data the next level is not only cloning the page but also verifying the captured data against a legitimate page The last and most sophisticated method involves a man-in-the-middle attack, where your page is a proxy between the victim and the legitimate site. you can clone the page and only collect data the next level is not only cloning the page but also verifying the captured data against a legitimate page The last and most sophisticated method involves a man-in-the-middle attack, where your page is a proxy between the victim and the legitimate site. Evilginx2 The third method is done by evilginx2 , which, despite its name, does not have nginx built in, but the functionality is the same, except that evilginx2 has built-in filters and functions like inject_js that make the attacker's life more comfortable. evilginx2 evilginx2 nginx evilginx2 inject_js Gophish Gophish is a tool that allows you to manage groups and users, dynamically create emails, and send them at will. Gophish Gophish Integration A few weeks ago, Evilginx got an update 3.3 , which introduced integration with Gophish, a framework for phishing campaigns. There have been several such projects in the past, such as gophish[1], but in this text, I will focus on the official evilginx by K. Gretzky. 3.3 More info about the update with the demo can be found here: https://breakdev.org/evilginx-3-3-go-phish/ https://breakdev.org/evilginx-3-3-go-phish/ Both tools are in Golang, which is a relatively easy-to-understand language. Experience with Evilginx2 Malicious proxy with evilginx has several drawbacks compared to just cloning the page. Firstly, the setup is much more complicated. When you clone a page, you download the necessary files via developer tools and add the password-storing feature via some backend. Evilginx setup is more complicated. Setup needs a configuration file and a phishlet that works as a template for the phishing proxy. Setting up and running evilginx2 in the production version took me over a week compared to just cloning. The advantage is that evilginx scales much better. Once you know how to create them, you can change phishing scenarios at will because adding new phishlet is trivial. I should mention the state of the project in which evilginx is. The documentation is concise and not very useful if you have no experience with evilginx2 already. Some features and behaviors are not documented, and you need to read and debug the source code to find the behavior. The project has special financing where you can pay 400$ for a course of excellent quality, but it replaces the documentation to some extent, which doesn't seem like a good way. Either it's an open-source project with all the parts, or it's not open-source; this way, it's open-code, but you are forced into buying the expensive course. It doesn't help that the project has many open issues and bugs that nobody addresses. If you're serious about phishing, you'll probably have to edit the source code and use your version because the author doesn't put much effort into finding new PRs. For example, the PR request I made to fix the POST of a GET request three weeks[2] ago has still not been merged. The project is plagued by old libraries that are long unused. Of course, I don't want the author to handle everything himself, but sometimes, you have to give up control of the project to benefit the project. And more moderators and maintainers who want to help would certainly be found. But not to be only negative, this project is still the best of its kind. If you learn all its quirks and modify it to your purpose, it will become a tool allowing you to do campaigns of all kinds, with a level of automation that is impossible if you write the tooling yourself. Experience with Gophish Gophish isn't updated very often either, but I haven't encountered any bugs, so it doesn't really bother me. Although gophish has an API, it lacks some functionality that would be useful, so K.Gretzky had to add this API[3] to make the integration with evilginx work, anyway this approach is not sustainable and I think gophish would benefit from an API that would allow event reporting. The Gophish email template community is active, and you don't need to invest much time in a replica of the o365 email because there is a good chance that someone has already created the template. The UI is very good, and many features, like a timeline of events, will be appreciated by clients in the report. Bypassing anti measures If you send emails within a known service such as o365 or Gmail, it is good to first build an instance where you collect crawler IP addresses which you then block on the production instance and domain, thus minimizing the risk of being blacklisted, for example, outlook. Of course, this is the easier part. The harder part is to have an SMPT server with a good reputation that allows you to send phishing emails in bulk, so I do not recommend building your own SMTP server but rather use some email as a service with its own domain that has a good server reputation by default, for example, Gmail business. Summary While evilginx2 offers powerful features and scalability, its complex setup and poor documentation are significant hurdles. Gophish provides robust campaign management tools but also suffers from limited API functionality. Both tools require considerable customization and understanding to effectively use in red team operations. Mastering both tools offers significant rewards. While evilginx2 offers powerful features and scalability, its complex setup and poor documentation are significant hurdles. Gophish provides robust campaign management tools but also suffers from limited API functionality. Both tools require considerable customization and understanding to effectively use in red team operations. Mastering both tools offers significant rewards. Thank you for reading. Feedback is appreciated! References [1]: https://github.com/fin3ss3g0d/evilgophish https://github.com/fin3ss3g0d/evilgophish [2]: https://github.com/kgretzky/evilginx2/pull/1047 https://github.com/kgretzky/evilginx2/pull/1047 [3]: https://github.com/kgretzky/gophish https://github.com/kgretzky/gophish

Red Team Phishing Simulations: Using Evilginx2 and GoPhish

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References V. REAL-WORLD SIMULATION CASE STUDIES An assembly operation from an actual auto-body assembly process is used as a real-world case study. Fig. 5 describes a car’s floor pan, which is the assembled product from this process. The assembled product consists of four parts, including the right bracket, left bracket, right floor pan, and left floor pan. Fig. 6 shows the process assembly procedure consisting of three stations. During the assembly process, the parts are held by fixtures, which are the KCCs in this process [7]. KPCs are measured from four points, namely, M1, M2, M3, and M4, respectively, as shown in Fig. 5. Every part has a designated location for measuring the KPCs. For instance, part 1 has M1, part 2 has M2, part 3 has M3, and part 4 has M4. These measurements can be taken at each station once the relevant part has been assembled in the preceding stations. For example, M3 on part 3 cannot be measured in station 1 because part 3 has not yet been assembled at station 1 [7]. In the designated location, KPCs are measured in three directions (X, Y, and Z). In this assembly process, there exists a total of 33 KCCs, which are dimensional errors of fixture locators [8]. The fault pattern matrix Φ is established based on the literature [7], [11], [47] and provided in Appendix F. Since the number of measurements (12) is less than that of KCCs (33), it causes an underdetermined system in the fault quality linear model. Therefore, sparse estimation is required to identify process faults. In this assembly process, two lists of correlated KCCs exist; the rest are assumed to be independent [8]. The first list comprises six KCCs, including KCC8, KCC9, KCC10, KCC11, KCC12, and KCC13. The second list is composed of KCC31, KCC32, and KCC33. The correlation structures of the two groups are described (for simplification, the lower triangular terms are removed) as follows [8]: To provide nonstationary process faults along the KPCs samples collected over time, two groups are provided. In the first group, the first correlated list is provided as process faults. For the process faults of the second group, all the correlated lists and one independent KCCs are included. Therefore, the case study represents the situation when the process faults evolve over time since the variation of certain KCCs will be propagated to other KCCs if the process faults are not mitigated immediately during the process. 50 KPCs samples are generated from each group. The variances of process faults, non-process faults, and noise of KPCs are determined to be the same value as in Section IV. In addition, performance evaluation measures and benchmark methods used in Section IV are still utilized in Section V. Table III shows the performance comparison in AUC and NMSE by varying the correlation coefficients. For convenience, the correlation coefficients k1 and k2 are equal and are selected from {0.1, 0.3, 0.6, 0.9, 0.95} in the case studies. Specifically, the AUC of the proposed method is close to 1.0 at every correlations levels, representing the perfect classification between the process faults and non-process faults in terms of estimated variance. The effectiveness of the proposed method is significant in NMSE when a high correlation exists. Specifically, the NMSE of the proposed method is less than 10% of that of all the benchmark methods when the correlation coefficient is 0.9 and 0.95. To demonstrate the generalizability of the proposed method, the correlated KCCs are randomly selected in further case studies. Assuming there exist two correlated lists with the size of three, respectively. Therefore, six KCCs are randomly selected from 33 KCCs for two correlated lists in each trial. As in the previous study, one correlated list is provided as process faults in the first group, while two correlated lists and two independent KCCs are used as process faults in the remaining second group. Fig. 7 shows the performances of all the methods in AUC and NMSE results in ten different spatial correlations (i.e., k1 = k2). From Fig. 7 (a), it is prominent that the proposed method successfully differentiates between process faults and non-process faults. Specifically, the AUC of the proposed method is nearly 1.0, while those of most benchmark methods are below 0.9 in the highly correlated case studies. In addition, Fig. 7 (b) represents that the proposed method provides valuable information to the practitioners to determine when to stop the operations to maintain the process when a high correlation exists between KCCs. All the benchmark methods illustrate similar trends compared to Section IV since the methods cannot consider both the spatial correlation of KCCs and the nonstationary process faults. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References V. REAL-WORLD SIMULATION CASE STUDIES An assembly operation from an actual auto-body assembly process is used as a real-world case study. Fig. 5 describes a car’s floor pan, which is the assembled product from this process. The assembled product consists of four parts, including the right bracket, left bracket, right floor pan, and left floor pan. Fig. 6 shows the process assembly procedure consisting of three stations. During the assembly process, the parts are held by fixtures, which are the KCCs in this process [7]. KPCs are measured from four points, namely, M1, M2, M3, and M4, respectively, as shown in Fig. 5. Every part has a designated location for measuring the KPCs. For instance, part 1 has M1, part 2 has M2, part 3 has M3, and part 4 has M4. These measurements can be taken at each station once the relevant part has been assembled in the preceding stations. For example, M3 on part 3 cannot be measured in station 1 because part 3 has not yet been assembled at station 1 [7]. In the designated location, KPCs are measured in three directions (X, Y, and Z). In this assembly process, there exists a total of 33 KCCs, which are dimensional errors of fixture locators [8]. The fault pattern matrix Φ is established based on the literature [7], [11], [47] and provided in Appendix F. Since the number of measurements (12) is less than that of KCCs (33), it causes an underdetermined system in the fault quality linear model. Therefore, sparse estimation is required to identify process faults. In this assembly process, two lists of correlated KCCs exist; the rest are assumed to be independent [8]. The first list comprises six KCCs, including KCC8, KCC9, KCC10, KCC11, KCC12, and KCC13. The second list is composed of KCC31, KCC32, and KCC33. The correlation structures of the two groups are described (for simplification, the lower triangular terms are removed) as follows [8]: To provide nonstationary process faults along the KPCs samples collected over time, two groups are provided. In the first group, the first correlated list is provided as process faults. For the process faults of the second group, all the correlated lists and one independent KCCs are included. Therefore, the case study represents the situation when the process faults evolve over time since the variation of certain KCCs will be propagated to other KCCs if the process faults are not mitigated immediately during the process. 50 KPCs samples are generated from each group. The variances of process faults, non-process faults, and noise of KPCs are determined to be the same value as in Section IV. In addition, performance evaluation measures and benchmark methods used in Section IV are still utilized in Section V. Table III shows the performance comparison in AUC and NMSE by varying the correlation coefficients. For convenience, the correlation coefficients k1 and k2 are equal and are selected from {0.1, 0.3, 0.6, 0.9, 0.95} in the case studies. Specifically, the AUC of the proposed method is close to 1.0 at every correlations levels, representing the perfect classification between the process faults and non-process faults in terms of estimated variance. The effectiveness of the proposed method is significant in NMSE when a high correlation exists. Specifically, the NMSE of the proposed method is less than 10% of that of all the benchmark methods when the correlation coefficient is 0.9 and 0.95. To demonstrate the generalizability of the proposed method, the correlated KCCs are randomly selected in further case studies. Assuming there exist two correlated lists with the size of three, respectively. Therefore, six KCCs are randomly selected from 33 KCCs for two correlated lists in each trial. As in the previous study, one correlated list is provided as process faults in the first group, while two correlated lists and two independent KCCs are used as process faults in the remaining second group. Fig. 7 shows the performances of all the methods in AUC and NMSE results in ten different spatial correlations (i.e., k1 = k2). From Fig. 7 (a), it is prominent that the proposed method successfully differentiates between process faults and non-process faults. Specifically, the AUC of the proposed method is nearly 1.0, while those of most benchmark methods are below 0.9 in the highly correlated case studies. In addition, Fig. 7 (b) represents that the proposed method provides valuable information to the practitioners to determine when to stop the operations to maintain the process when a high correlation exists between KCCs. All the benchmark methods illustrate similar trends compared to Section IV since the methods cannot consider both the spatial correlation of KCCs and the nonstationary process faults.

Sparse Bayesian Learning: Real-World Simulation Case Studies

The Noonification: Using pgvector To Locate Similarities In Enterprise Data (3/22/2024)

Using pgvector To Locate Similarities In Enterprise Data

এন্টারপ্রাইজ ডেটাতে মিল খুঁজে পেতে pgvector ব্যবহার করা

Verwenden von pgvector zum Auffinden von Ähnlichkeiten in Unternehmensdaten

Uso de pgvector para localizar similitudes en datos empresariales

Utilisation de pgvector pour localiser les similitudes dans les données d'entreprise

एंटरप्राइज़ डेटा में समानताएँ खोजने के लिए pgvector का उपयोग करना

pgvector を使用して企業データ内の類似点を特定する

pgVector를 사용하여 기업 데이터의 유사점 찾기

Usando pgvector para localizar semelhanças em dados corporativos

Использование pgvector для поиска сходств в корпоративных данных

Kurumsal Verilerdeki Benzerlikleri Bulmak İçin pgvector'u Kullanma

Sử dụng pgvector để xác định điểm tương đồng trong dữ liệu doanh nghiệp

使用 pgvector 查找企业数据中的相似之处

Comprehending the laws and boundaries of nature enhances our skills as programmers. In recent years, a fascinating analogy has emerged, drawing parallels between the universe and a concept familiar to computer scientists: the discrete event simulator. The universe operates much like a massive simulation coordinated without a central clock ticking. Let's dive in... Understanding the Universe as a Discrete Event Simulator Reality operates in discrete increments of time and space. Rather than a continuous flow, events unfold at specific points in time, triggering state transitions within the cosmic fabric. This concept aligns with the principles of , a computational method used to model systems where events occur at distinct moments. discrete-event simulation Time advances in discrete steps, with events driving changes in system states with discrete units of time. The universe appears to operate on a fundamental level where time and space are quantized, meaning they consist of indivisible units. indivisible Researchers think that Planck units encapsulate this discretization of spacetime. Physicists named Planck units after the physicist . Max Planck Planck Units and the Fabric of Reality represent the smallest possible length, time, mass, and other physical quantities within the framework of quantum mechanics. Planck units Units are derived from fundamental constants such as the , Planck's constant, and the . speed of light gravitational constant The represents the smallest possible length scale, approximately . Planck length 1.616 × 10^-35 meters The , approximately , marks the smallest possible time interval, beyond which time loses its meaning. Planck time 5.391 × 10^-44 seconds Planck time is the time it takes for light to travel a distance equal to the Planck length in a vacuum. This minimal duration represents the shortest meaningful interval of time that can be defined within our current understanding of physics. At durations shorter than the Planck time, the concept of time itself becomes ambiguous, and the laws of physics as we know them break down. You can't break plank units in the same way you cannot break . fundamental particles Ancient Greeks created the notion of indivisible particles, which they termed "atoms." The term "atom" originates from the Greek word "atomos," where "a-" means "not" or "un-" and "tomos" means "cut" or "divided." Atoms are not the least divisible particle but the brilliant idea remains. Implications for Computer Simulations The analogy between the universe and a discrete-event simulator has profound implications for computer simulations and scientific inquiry. https://hackernoon.com/what-is-wrong-with-software-uh8j3y7k?embedable=true Once you acknowledge the between the two realms, you can gain new insights into the nature of reality and develop more efficient simulation techniques. parallels The concept of the universe as a discrete event simulator challenges traditional notions of computation. In the past, computer scientists had a lot of trouble dealing with infinities, problems, , and dealing with divisible units. precision floats For example, comparing two dates was always a problem since we usually represent them as arbitrary divisible points in time. Navigating non-infinitely divisible money amounts, distances, and other measurements has sparked numerous renowned challenges like the , the , the , etc. Genesis mission Mars Climate Orbiter Ariane 5 Flight 501 Conclusion The universe as a discrete event simulator offers a compelling framework for understanding the fundamental principles governing reality. Planck units set a barrier on where to stop diving the measurements. I am not telling you to model timestamps with plank units precision. I am asking you to change your framework and acknowledge we are not dealing with arbitrary precision problems but with finite precision ones. We should leave problems related to infinities only to math domains, not real-world simulators.

Warning: The Universe's Event Simulator Is a Fierce Adversary for Coders!

Authors: (1) Maggie D. Bailey, Colorado School of Mines and National Renewable Energy Lab; (2) Douglas Nychka, Colorado School of Mines; (3) Manajit Sengupta, National Renewable Energy Lab; (4) Aron Habte, National Renewable Energy Lab; (5) Yu Xie, National Renewable Energy Lab; (6) Soutir Bandyopadhyay, Colorado School of Mines. Table of Links Abstract and Intro Data Bayesian Hierarchical Model (BHM) Solar Radiation Example Results Conclusion Appendix A: Simulation Study Appendix B: Regridding Coefficient Estimates References Appendix A: Simulation Study Here we implement a short simulation study which highlights some of the main differences, as well as similarities, between the naive method and the BHM. A1 Simulation Study Setup For this simulation study, we utilize the same grids from the original regional climate models. The full grids used in the original study are shown in the top row of Figure A1 with the RCM grids in grey and NSRDB grid over California in blue. The magenta points represent the subsetted area used in the regridding study, which will be also be used in this short simulation study. The bottom row of Figure A1 shows the grids used for this simulation study. The grey and blue dots show the “true" grids which include both the RCM and the NSRDB grid. The magenta points are again the simulation study area on the NSRDB grid. This will be explained more fully in the next section. We added a 2 degree buffer around the final study domain in magenta to avoid any edge effects in the simulation and regridding steps. A2 Simulation Study Steps The simulation study follows the steps: 2. Subset the grey points in the bottom row of Figure A1 to only the points that fall on the original RCM grid, in grey in the top row of Figure A1. The “true” NSRDB observations are generated by weighting each RCM grid in a linear model by location: 3. Regridding and model fitting is done for each method in the following way: (a) Conditionally simulate each day, or field, 100 times onto the NSRDB Study Area from the RCM grids. For each simulation, run the BHM as described in Section 3.2 and take 50 draws from the posterior distribution for each coefficient resulting in a posterior distribution for each location and for each coefficient. Bayesian Regridding: (b) Regrid once from the RCM grid to the NSRDB grid using Kriging. Then estimate a linear model for each location across the 100 days, or fields. Naive Regridding: 4. Compare the coefficient estimates and standard errors of the coefficients from the naive regridding and the BHM regridding method. A3 True Coefficients In step 2, the true response, representing the observations from the NSRDB, are generated by weighting each RCM at each location in the study area across all 100 days. These coefficients are set by taking the average βˆ from the BHM in the original manuscript study. The true values for the intercept and each RCM at each location are shown in Figure A2. The weights correspond to the weights estimated for the CanRCM4, CRCM5-UQAM and WRF. The WRF had the highest weight between the three RCMs in the original study. A4 Results The raw difference relative to the RMSE between the estimated βˆ from the linear model and mean of the posterior distribution for the BHM are shown in Figure A3. Generally, relative to the overall variation of the error, the difference between the true and estimate coefficients is smaller for the BHM and higher for the naive method. This indicates that the regridding uncertainty decreases the raw error in the estimated weights. We also see that for the largest weight (RCM3) the BHM slightly underestimates β3 but the naive method sees larger errors. In general we can say that the bias between the estimates from the BHM is lower than that of the naive regridding method. We can also look that raw error without normalizing by the RMSE, seen in Figure A3. This figure shows similar errors between the two methods compared to the true coefficients. The exception is seen in the coefficient estimate for RCM3, which is slightly overestimated by the BHM when considering the raw difference compared to the RCM, which slightly underestimates or is closer to the true coefficient. One interesting finding is that the coefficient standard error is larger for the posterior distribution of the BHM estimates than it is for the naive coefficient estimates. This is seen in Figure A4, where the top row shows the standard error for the naive regridding coefficient estimates and the bottom row shows the posterior standard deviations of the BHM estimates. The coefficient standard error for the BHM estimates is on average 46% larger than it is for the naive method. This signifies that incorporating the regridding uncertainty results in a slightly wider range of potential weights for the RCMs in predicting the observed values. However, the difference in the standard error is not large and because the posterior mean of the BHM coefficient estimates and the naive regridding estimates are very similar, this result may not have much of an effect in this application. A5 Simulation Study Conclusions This simulation study highlights some important differences between the naive regridding and BHM regridding methods. In particular, the biases between the true coefficients (weights) and the estimated weights is smaller relative to the error variance for the BHM and larger for the naive method. This indicates that when regridding uncertainty is taken into account, the BHM is more precise in estimating the RCM weights. This finding is further emphasized in the standard error of the coefficients for the two methods. This value is lower for the BHM than it is in the naive method, though the difference in standard error between the two methods is small. Overall we can conclude that taking regridding uncertainty into account results in more precise estimates for model weights when predicting observed values based on climate model output. This precision provides increased understanding in which climate model may have higher importance in predicting observed outcomes and, therefore, future projections for solar radiation. This paper is under CC 4.0 license. available on arxiv

SIMULAVR

simulavr.com

-- employees

Since n.d.

COMPANY RANKING

SIMULAVR

EVERGREEN INDEX #10019

Representing this company? Click here to claim and customize this page!

SimulaVR WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

SIMULAVR

simulavr.com

-- employees

Since n.d.

COMPANY RANKING

SIMULAVR

EVERGREEN INDEX #10019

RELATED COMPANIESRANK

STORIES ABOUT SIMULAVR

Representing this company? Click here to claim and customize this page!

SimulaVR WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!