Parkable, the leading Parking Management Software, has announced an integration with Engrain, a recognized leader in next-generation touring technology and map-based data visualization.

Parkable partners with Engrain to provide parking efficiencies across multi-tenanted buildings

Market Overview of Global Parking Management market: According to our latest research, the global Parking Management market looks promising in the next 5 years. As of 2022, the global Parking ...

Parking Management Market Dynamics till 2031: Business Development Analysis with Latest Dominating Growth Strategies

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The Conservatives have left themselves with an electoral liability, a property market in crisis, tenants searching around in vain for a place to live “Bidding wars, tenant CVs, queues outside properties – these phenomena have become common in the ...

COMMENT: Government has got itself in a muddle over the rental market

"Pave paradise and put up a parking lot." Santa Clara County should render the spaces along Arastradero Road non-parkable by placing signage and ticketing. Possibly the placement of bullards would Help sustain the local news you depend on. Your ...

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A kind-hearted landlord has been left destitute and suicidal after being tricked out of £212,000 by one of his tenants. Cruel conman Derek Conway (main pic, inset) preyed on Eugene Schofield’s good nature by pretending he was terminally ill but that he ...

Tenant jailed after conning kind-hearted landlord out of £212,000

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A big council in the South of England is to crack down on HMO landlords when its new additional licencing scheme goes live in three weeks. Although larger HMOs in Portmsouth with five or more people who form two or more households are already covered by ...

Council reveals HMO licencing crackdown as it fines two landlords £39,000

Bolton council has revealed its intention to begin a coordinated campaign to rein-in the creation of more HMOs within its boundary including via planning and parking rules and, in addition, a focus on council tax re-banding. Landlords seeking to convert ...

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What Are the Top Startups in Oceania?

There are many qualities of a great software engineer that will make you stand out. Among them, these 3 are crucial for you to become a great software engineer and leave the competition behind. Surely technical qualities matter a lot to be counted as a superior software engineer. On the other hand, this post is geared towards the non-technical aspects of software engineering. Software engineer qualities to work on for your career development With no doubt, a great software engineer writes working, testable and maintainable code. Numerous technical skills will be required to differentiate yourself as a great software engineer. Writing clean code, debugging bugs, and following standard are some of these technical skills. Some opine as must-have qualities of a great software engineer. Another points to curiosity and a love of learning to be essential to be an efficient software engineer. You can find a in Eastern Europe too if you are looking for one. positive attitude and communication skills post great software engineer For this post, we are more concerned than just the hard skills needed to prove yourself as a valuable software engineer. Let’s proceed to the software engineer qualities you need to work on to set you up for success: Good business acumen Simply put, is an understanding of how a company makes money and meets its goals and objectives. I have written in a too: business acumen previous talk Solutions and value to the business is much more important than the latest language/framework. As a software engineer, you must not worship a particular language or framework. Similarly, you should not apply the latest trends and fads to your day to day work just because it is trendy. Always think of what value does it add to the business. Also, analyze, how does it help make/save money for the business. Think of the trade-offs and the cost to benefit ratio too. For instance, once we became aware of a bug. It was affecting ~0.25% of the customers. The cost of fixing the issue at that time was far more than not fixing it. We did eventually fix the small bug but it was only in the next sprint. This is an example of good business acumen as the business was not losing any money. Yes, a fraction of the customers were facing a minor inconvenience but that was also fixed in about a week. Product managers/owners must have great business acumen. It is also important for software engineers to have good business acumen. I have never seen a software engineer who was promoted and had bad business acumen. Maybe their technical skill was a bit rusty but it was easily covered by good business acumen. If you work as a software engineer for an e-commerce company, you must know what the is. Similarly, it is better to know things like and . In case you are working for a SAAS business, you much understand what is all about to sharpen your business acumen. You can learn more about how investors look at numbers in a fun way by watching . conversion rate Customer acquisition cost (CAC) Customer Lifetime Value(CLTV) churn rate Shark Tank It sums up to having a great sense of how the business works and accumulate valuable domain knowledge over time are super crucial qualities of a great software engineer. Highly responsible Great software engineers are highly responsible. If they take up a task, they make sure that it is working well on production. They will even go above and beyond to establish more clarity around the task by building reports for better visibility. I have never met a great software engineer who will throw his/her work over the wall. By that, I mean saying things like “It is done from my end now it is QA’s responsibility” or “I have written the code, it is approved. Now it is DevOps’ headache to get it to production”. A quality of a great software engineer is that they follow up the task on hand very well. Even after the task is done they will come back to it after days or even weeks to check that is it working fine. They will evaluate numbers and take pride in the great work they have shipped. For an efficient and highly responsible software engineer the Quality Assurance (QA) team is more like a gate to pass by not a group of testers. The engineer would have already thoroughly tested their work in both automated and manual ways. Finding obvious bugs in the work done by an amazing engineer would be difficult. As they would be already highly responsible to think about that edge case or that probable use-case and tackled it in the code. They might have gone ahead to write tests for such scenarios so that it doesn’t come back and bite the team in the future. Following true DevOps and SRE philosophy, a great software engineer will own their work and be accountable for it. If things break in something they have developed and deployed, they will be the first to respond. The response can be as quick as a rollback or even write a quick fix to patch the issue temporarily. They will come back to the issue and release a permanent solution to the issues introduced by the code they have added. Basically, great software engineers truly live by the philosophy of . They also make great engineers. you build it you run it on-call Ace team players Being an ace team player is another crucial quality of a great software engineer. Engineering resilient software is a team sport and efficient software engineers know this fact very well. A great software engineer is an excellent mentor and a coachable mentee too. A great software engineer will always look out for the whole team. They will help engineers junior to him/her and not hesitate to learn from everyone. A valuable software engineer will logically help another teammate. This does not mean that software engineer will always leave their talk on hand, context switch, and help another engineer. This means given the task on hand is at a parkable state they will park it, then go and help the junior team member get unblocked. I did a talk some years back about things a junior software engineer should know. Great software engineers have this innate ability to translate technical jargon and requirements into simple layman language. This in turn helps all team members understand what is being done. For instance, a non-technical product owner can easily explain to the executive why this feature will take more time. A valuable software engineer would go the extra mile to explain the need to refactor and then only do the main task. This will make it faster in the long run and make the feature more reliable too. A great software engineer is an impact multiplier. Let’s say if s/he is somewhat free and they see that the QA person has a lot to do in this sprint. They will not think twice about helping the QA person and help the QA person. A great software engineer knows and values the team output. S/he contributes to the team’s flow and outcome to be better not only their own. Imagine this, the sprint is near to an end but nothing is deployed to production even though pull requests have been approved. A software engineer who is a valuable team player would assist in things that would help code reach production. They will be able to do it without overstepping on anyone’s responsibilities. These software engineer qualities are found in great software engineers. Conclusion All of the above will only foster if the Software engineering culture of the company is on the right path. As Dr. Willam Tate - “Rarely do managers focus on the quality of the fish tank & what surrounds the fish: they mostly notice individual fish and become fixated on them. But if the water is toxic, the fish suffer. If there is no movement in the water, it will be deprived of life-giving oxygen” mentioned To get the best qualities applied even out of great software engineers the whole software engineering culture needs to support it. Also published here.

3 Non-Technical Qualities of a Great Software Engineer

Good things aren’t as good as you think. Bad things aren’t as bad as you think. The operational definition of good is: “A measure of how many favorable future outcomes are perceived from something.” But your perception isn’t accurate at all. You can only predict so much. Good things can lead to unfavorable outcomes. Bad things can also lead to favorable outcomes. What matters is how you control the outcomes. Make the most of your mistakes. Rotten milk is waste, but when you control the rot, you get yogurt — which costs more than milk. Rotten grapes are waste, but when you control the rot, you get wine — which costs more than grapes. Controlled mistakes bring growth. If you can reflect on your mistakes, extract lessons, generate ideas, and solve problems for yourself and others, instead of hating yourself for them, you’ll become grateful for them. You can’t really forget your mistakes; you need to utilize them to do good. You’re haunted by your mistakes because you haven’t made the most of them. Once you extract value from them, they’re no longer mistakes — they become resources. Your mistakes aren’t “bad” or “good.” You determine that. There’s no absolute good or bad; it’s all about timing and perspective. The parable of the boy and the horse: A rich man buys his son a horse. The villagers say, “Good for him!” The father says, “We’ll see.” The boy breaks his leg riding the horse. The villagers say, “Bad for him!” The father says, “We’ll see.” Then a war starts, and soldiers take all the young men, except the boy — his leg is broken. The villagers go back to saying, “Lucky him.” Sometimes, it was a good thing he got the horse, sometimes it was bad. In the end, it doesn’t matter. It just is. Accept it. If something happens, ignore it. If you can’t ignore it, accept it. If you can’t accept it, change it. Anything beyond that is just noise. Want to see more concepts through a new, practical lens that makes life easier? You’ll love Redefyne. I’ve compiled 100+ operational definitions for daily success, inspired by O.G.s like Naval Ravikant, Alex Hormozi, and much more. The directory is open and free: redefyne.onrender.com Check out this article to learn more about redefyne: https://hackernoon.com/dont-let-dictionaries-keep-you-stuck-understand-and-conquer-the-world-with-redefyne Good things aren’t as good as you think. Bad things aren’t as bad as you think. Good things aren’t as good as you think. Bad things aren’t as bad as you think. The operational definition of good is: “A measure of how many favorable future outcomes are perceived from something.” good “A measure of how many favorable future outcomes are perceived from something.” But your perception isn’t accurate at all. You can only predict so much. Good things can lead to unfavorable outcomes. Bad things can also lead to favorable outcomes. Good things can lead to unfavorable outcomes. Bad things can also lead to favorable outcomes. What matters is how you control the outcomes. control Make the most of your mistakes. Make the most of your mistakes. Rotten milk is waste, but when you control the rot, you get yogurt — which costs more than milk. which costs more than milk. Rotten grapes are waste, but when you control the rot, you get wine — which costs more than grapes. which costs more than grapes. Controlled mistakes bring growth. If you can reflect on your mistakes, extract lessons, generate ideas, and solve problems for yourself and others, instead of hating yourself for them, you’ll become grateful for them. grateful You can’t really forget your mistakes; you need to utilize them to do good. utilize You’re haunted by your mistakes because you haven’t made the most of them. Once you extract value from them, they’re no longer mistakes — they become resources. Your mistakes aren’t “bad” or “good.” You determine that. Your mistakes aren’t “bad” or “good.” There’s no absolute good or bad; it’s all about timing and perspective. The parable of the boy and the horse: A rich man buys his son a horse. The villagers say, “Good for him!” The father says, “We’ll see.” The boy breaks his leg riding the horse. The villagers say, “Bad for him!” The father says, “We’ll see.” Then a war starts, and soldiers take all the young men, except the boy — his leg is broken. The villagers go back to saying, “Lucky him.” Sometimes, it was a good thing he got the horse, sometimes it was bad. In the end, it doesn’t matter. It just is. Accept it. It just is. Accept it. If something happens, ignore it . If you can’t ignore it, accept it . If you can’t accept it, change it . Anything beyond that is just noise. ignore it accept it change it Want to see more concepts through a new, practical lens that makes life easier? You’ll love Redefyne. Want to see more concepts through a new, practical lens that makes life easier? You’ll love Redefyne. I’ve compiled 100+ operational definitions for daily success, inspired by O.G.s like Naval Ravikant, Alex Hormozi, and much more. The directory is open and free : redefyne.onrender.com open and free redefyne.onrender.com Check out this article to learn more about redefyne: https://hackernoon.com/dont-let-dictionaries-keep-you-stuck-understand-and-conquer-the-world-with-redefyne https://hackernoon.com/dont-let-dictionaries-keep-you-stuck-understand-and-conquer-the-world-with-redefyne

Here's Why It Is What It Is

Understanding Parallel Programming: A Guide for Beginners

Authors:
(1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google;
(2) Dawei Yang, Google Research;
(3) Tyler Zhu, Google Research;
(4) Fitsum Reda, Google Research;
(5) William Chan, Google Research;
(6) Chitwan Saharia, Google Research;
(7) Mohammad Norouzi, Google Research;
(8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction 2. Related Work 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 4. Experiments 5. Summary and Future Work and References Appendix A. Implementation Details B. Additional Results A. Implementation Details A.1. Parallel-UNet A.2. Training and Inference TryOnDiffusion was implemented in JAX [4]. All three diffusion models are trained on 32 TPU-v4 chips for 500K iterations (around 3 days for each diffusion model). After trained, we run the inference of the whole pipeline on 4 TPU-v4 chips with batch size 4, which takes around 18 seconds for one batch. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Authors: Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. Method 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 3.2. Parallel-UNet 4. Experiments 4. Experiments 5. Summary and Future Work and References 5. Summary and Future Work and References Appendix Appendix A. Implementation Details A. Implementation Details B. Additional Results B. Additional Results A. Implementation Details A.1. Parallel-UNet A.2. Training and Inference TryOnDiffusion was implemented in JAX [4]. All three diffusion models are trained on 32 TPU-v4 chips for 500K iterations (around 3 days for each diffusion model). After trained, we run the inference of the whole pipeline on 4 TPU-v4 chips with batch size 4, which takes around 18 seconds for one batch. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

The Nuts and Bolts of Parallel-UNet: Implementation Details

Authors:
(1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google;
(2) Dawei Yang, Google Research;
(3) Tyler Zhu, Google Research;
(4) Fitsum Reda, Google Research;
(5) William Chan, Google Research;
(6) Chitwan Saharia, Google Research;
(7) Mohammad Norouzi, Google Research;
(8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction 2. Related Work 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 4. Experiments 5. Summary and Future Work and References Appendix A. Implementation Details B. Additional Results 3.2. Parallel-UNet The 128×128 Parallel-UNet can be represented as Combining warp and blend in a single pass. Instead of warping the garment to the target body and then blending with the target person as done by prior works, we combine the two operations into a single pass. As shown in Fig. 2, we achieve it via two UNets that handle the garment and the person respectively. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Authors: Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. Method 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 3.2. Parallel-UNet 4. Experiments 4. Experiments 5. Summary and Future Work and References 5. Summary and Future Work and References Appendix Appendix A. Implementation Details A. Implementation Details B. Additional Results B. Additional Results 3.2. Parallel-UNet The 128×128 Parallel-UNet can be represented as Combining warp and blend in a single pass. Instead of warping the garment to the target body and then blending with the target person as done by prior works, we combine the two operations into a single pass. As shown in Fig. 2, we achieve it via two UNets that handle the garment and the person respectively. Combining warp and blend in a single pass. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

How Parallel-UNet Transforms Virtual Try-On with Implicit Warping and Unified Operations

Vara vs Aptos, Monad, Sui, and Sei: A Technical Deep Dive into Data Parallelism

AIDataDoc: ChatGPT + Web Technologies - One File to Rule Them All (Open Source, Simple, Portable)

AIDataDoc: ChatGPT + ওয়েব টেকনোলজিস - তাদের সকলকে শাসন করার জন্য একটি ফাইল (ওপেন সোর্স, সহজ, পোর্টেবল)

AIDataDoc: ChatGPT + Webtechnologien – eine Datei, sie alle zu beherrschen (Open Source, einfach, portabel)

AIDataDoc: ChatGPT + Tecnologías web: un archivo para gobernarlos a todos (código abierto, simple, portátil)

AIDataDoc : ChatGPT + Web Technologies - Un fichier pour les gouverner tous (Open Source, simple, portable)

AIDataDoc: ChatGPT + वेब टेक्नोलॉजीज - सब पर राज करने वाली एक फ़ाइल (ओपन सोर्स, सरल, पोर्टेबल)

AIDataDoc: ChatGPT + Web テクノロジー - すべてを統括する 1 つのファイル (オープン ソース、シンプル、ポータブル)

AIDataDoc: ChatGPT + 웹 기술 - 모든 것을 지배하는 하나의 파일(오픈 소스, 간단, 휴대 가능)

AIDataDoc: ChatGPT + Tecnologias da Web - Um arquivo para governar todos eles (código aberto, simples, portátil)

AIDataDoc: ChatGPT + Web Technologies — один файл, чтобы управлять всем (открытый исходный код, простой, портативный)

AIDataDoc: ChatGPT + Web Teknolojileri - Hepsinin Üstesinden Gelmek İçin Tek Bir Dosya (Açık Kaynak, Basit, Taşınabilir)

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AIDataDoc：ChatGPT + Web 技术 - 一个文件统治一切（开源、简单、便携）

Mass can give energy and energy can give mass—only in the presence of the speed of light, squared—is a principle of fusion in particle physics. Or, simply, energy and mass are interchangeable. However, can this be remotely used to explain an aspect of the human mind, for rough similarities, towards how the brain works? Although the human brain does not have [anything moving at] the speed of light, electrical signals have conduction velocity. While both are not equivalent, might a parallel partly apply, to explaining a mechanism of the human mind? In the brain, there are two common elements, electrical and chemical signals. Electrical signals are in relay and chemical signals are mostly stationed at synapses. Though chemical signals are in transport as well, they often move within a smaller perimeter in comparison to electrical signals that span the axonal length. It can, therefore, be assumed that given these differences, electrical signals are in an extensive motion, while chemical signals are in marginal motion. However, because chemical signals are mostly confined around synapses—which can be near stationary while active, at times—chemical signals can be said to be a comparatively slower mass, with some velocity as well, such that their total momentum [sets of electrical and chemical signals] is conserved, normally, without [say] a traumatic brain injury or something else. Neurons are often in clusters across circuits in the brain. It is theorized that these clusters allow electrical and chemical signals to work in loops or as sets. This means that when a function is mechanized in a certain cluster of neurons, it is the enterprise of the signals. The chemical signals [in set] are postulated to provide rations that make up a configuration or formation for the specificity of that function. Simply, because smells are different and smell is different from touch or vision, it means that the configuration of a set of chemical signals that specifies one is different from the other. These chemical signals are located within the range of the synapse, from the vesicles to the cleft to the receptors. Their configuration [in a set] for a function, conceptually, may be due [as established in brain science] to their volumes in the cleft or their exit sequence—through receptors, or getting taken back up, or cleared out by enzymes. Simply, chemical signals in a set, provide rations that make up the specificity of functions. Chemical signals are molecules, mostly dominated by neurotransmitters, though others like neurohormones and so forth are involved. Electrical signals are established in neuroscience to be ions, moving through the length of the axon, due to action potentials, from the voltage-gated channels. It is also established that ions have mass and molecules have mass. It is theorized that in a set, these ions carry the summaries of functions and features, from one destination—or set of chemical signals—to the next. Electrical signals deliver what they bear [to sets of electrical signals] and access what is available there, then proceed again, conceptually. A Human Mind Phase of Matter It is theorized that in a set, electrical signals strike to fuse briefly with chemical signals, to access the configuration in that set. Simply, chemical signals have rations that make up configurations, but in order to have those rations provided, they have to be struck by the mass of incoming ions. It is postulated that when [a set of] electrical signals strike, they fuse briefly, resulting in a mix that is partly ions, partly molecules, and a third transient phase, which is different from both. It is this phase, postulated to be a new state of matter, that keeps some of the configuration information and gives off the volumes of chemical signals needed. It is also what collects—or contains—some of the energy that is given off, preventing several concurrent interactions in the brain from causing disturbing experiences. Simply, for functions of the mind, it is postulated that [sets of] electrical and chemical signals have to interact. This interaction is the strike and fusion of both, giving off a new mix, with a part of it, as a different phase from both, which holds some configuration and collects the energy, conceptually. Sets of electrical and chemical signals both have mass and velocity. When they strike and fuse, they give off energy. Electrical signals have their conduction velocity, which is enough to produce a new phase of matter—within the biological environment. There are grades to the interaction or how they are qualified, resulting in features like attention, awareness, self and intent, or free will. Though the speed of light is necessary for energy and mass interchange, the interactions of [sets of] electrical and chemical signals of the mind let the conduction velocity act as a conversion factor of sorts. The velocity of [sets of] chemical signals may also play a role—or be a tad negligible in other instances. This implies that a parallel of the energy-momentum relation could be developed for sets of electrical and chemical signals, in a theoretical neurobiophysics equation. Sets of electrical signals with mass, incoming, striking to fuse with [sets of] chemical signals, which are masses too—near static—giving off energy which is collected in another phase and used also to access configurations for functions and features of the human mind, conceptually. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features, in sets, in clusters of neurons, across the central and peripheral nervous systems. There is a recent paper in Nature Communications Physics, Unveiling universal aspects of the cellular anatomy of the brain, where the authors wrote, "Here, we propose that statistical physics can provide a guiding framework for determining and quantifying additional structural features in the cellular complexity of the brain. By analyzing properties related to cell size, as well as pairwise and higher-order correlations in cellular-level volumetric partial brain reconstructions from multiple organisms, we show that the cellular structure of the brain displays signatures associated with collective phenomena close to criticality. Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions." There is a recent article in The Conversation, Could quantum physics be the key that unlocks the secrets of human behaviour?, stating that, "Yet research has shown that human behaviour can’t be fully captured by these traditional or “classical” laws of probability. Could it instead be explained by the way probability works in the more mysterious world of quantum mechanics? Mathematical probability is also a vital component of quantum mechanics, the branch of physics that describes how nature behaves at the scale of atoms or sub-atomic particles. However, as we’ll see, in the quantum world, probabilities follow very different rules." Mass can give energy and energy can give mass—only in the presence of the speed of light, squared—is a principle of fusion in particle physics. Or, simply, energy and mass are interchangeable. However, can this be remotely used to explain an aspect of the human mind, for rough similarities, towards how the brain works? Although the human brain does not have [anything moving at] the speed of light, electrical signals have conduction velocity. While both are not equivalent, might a parallel partly apply, to explaining a mechanism of the human mind? In the brain, there are two common elements, electrical and chemical signals. Electrical signals are in relay and chemical signals are mostly stationed at synapses. Though chemical signals are in transport as well, they often move within a smaller perimeter in comparison to electrical signals that span the axonal length. It can, therefore, be assumed that given these differences, electrical signals are in an extensive motion, while chemical signals are in marginal motion. However, because chemical signals are mostly confined around synapses—which can be near stationary while active, at times —chemical signals can be said to be a comparatively slower mass, with some velocity as well, such that their total momentum [sets of electrical and chemical signals] is conserved, normally, without [say] a traumatic brain injury or something else. near stationary while active, at times Neurons are often in clusters across circuits in the brain. It is theorized that these clusters allow electrical and chemical signals to work in loops or as sets. This means that when a function is mechanized in a certain cluster of neurons, it is the enterprise of the signals . it is the enterprise of the signals The chemical signals [in set] are postulated to provide rations that make up a configuration or formation for the specificity of that function. Simply, because smells are different and smell is different from touch or vision, it means that the configuration of a set of chemical signals that specifies one is different from the other. These chemical signals are located within the range of the synapse, from the vesicles to the cleft to the receptors. Their configuration [in a set] for a function, conceptually, may be due [as established in brain science] to their volumes in the cleft or their exit sequence— through receptors, or getting taken back up, or cleared out by enzymes . through receptors, or getting taken back up, or cleared out by enzymes Simply, chemical signals in a set, provide rations that make up the specificity of functions. Chemical signals are molecules, mostly dominated by neurotransmitters, though others like neurohormones and so forth are involved. Electrical signals are established in neuroscience to be ions, moving through the length of the axon, due to action potentials, from the voltage-gated channels. It is also established that ions have mass and molecules have mass. It is theorized that in a set , these ions carry the summaries of functions and features, from one destination—or set of chemical signals—to the next. Electrical signals deliver what they bear [to sets of electrical signals] and access what is available there, then proceed again, conceptually. It is theorized that in a set A Human Mind Phase of Matter A Human Mind Phase of Matter It is theorized that in a set, electrical signals strike to fuse briefly with chemical signals, to access the configuration in that set. Simply, chemical signals have rations that make up configurations, but in order to have those rations provided, they have to be struck by the mass of incoming ions. It is postulated that when [a set of] electrical signals strike, they fuse briefly, resulting in a mix that is partly ions, partly molecules, and a third transient phase, which is different from both. It is this phase, postulated to be a new state of matter , that keeps some of the configuration information and gives off the volumes of chemical signals needed. It is also what collects—or contains—some of the energy that is given off, preventing several concurrent interactions in the brain from causing disturbing experiences. postulated to be a new state of matter Simply, for functions of the mind, it is postulated that [sets of] electrical and chemical signals have to interact. This interaction is the strike and fusion of both, giving off a new mix, with a part of it, as a different phase from both, which holds some configuration and collects the energy, conceptually. Sets of electrical and chemical signals both have mass and velocity. When they strike and fuse, they give off energy. Electrical signals have their conduction velocity, which is enough to produce a new phase of matter—within the biological environment. There are grades to the interaction or how they are qualified, resulting in features like attention, awareness , self and intent, or free will. attention, awareness Though the speed of light is necessary for energy and mass interchange, the interactions of [sets of] electrical and chemical signals of the mind let the conduction velocity act as a conversion factor of sorts. The velocity of [sets of] chemical signals may also play a role—or be a tad negligible in other instances. This implies that a parallel of the energy-momentum relation could be developed for sets of electrical and chemical signals, in a theoretical neurobiophysics equation. tad Sets of electrical signals with mass, incoming, striking to fuse with [sets of] chemical signals, which are masses too—near static—giving off energy which is collected in another phase and used also to access configurations for functions and features of the human mind, conceptually. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features, in sets , in clusters of neurons, across the central and peripheral nervous systems. their interactions and features, in sets There is a recent paper in Nature Communications Physics , Unveiling universal aspects of the cellular anatomy of the brain , where the authors wrote, "Here, we propose that statistical physics can provide a guiding framework for determining and quantifying additional structural features in the cellular complexity of the brain. By analyzing properties related to cell size, as well as pairwise and higher-order correlations in cellular-level volumetric partial brain reconstructions from multiple organisms, we show that the cellular structure of the brain displays signatures associated with collective phenomena close to criticality. Our results indicate that the cellular structure of the brains of multiple organisms show signatures of being at or close to a structural phase transition and that the corresponding critical exponents are consistent between organisms. If these structural properties of the brain are indeed critical and universal between organisms, we would expect to observe consistent exponents in the cellular structures of other organisms and brain regions." Nature Communications Physics Unveiling universal aspects of the cellular anatomy of the brain There is a recent article in The Conversation , Could quantum physics be the key that unlocks the secrets of human behaviour? , stating that, "Yet research has shown that human behaviour can’t be fully captured by these traditional or “classical” laws of probability. Could it instead be explained by the way probability works in the more mysterious world of quantum mechanics? Mathematical probability is also a vital component of quantum mechanics, the branch of physics that describes how nature behaves at the scale of atoms or sub-atomic particles. However, as we’ll see, in the quantum world, probabilities follow very different rules." The Conversation Could quantum physics be the key that unlocks the secrets of human behaviour?

Neuroscience, Particle Physics: Does e = mc^2 Have a Parallel in the Human Mind?

Double Trouble: The Curse of Redundant Structures TL;DR: Parallel hierarchies lead to duplication and tight coupling. Problems Increased complexity
DRY / Code Duplication
Maintenance Nightmare
Coupling
Ripple Effect
Potential for inconsistencies across different hierarchies Solutions Merge hierarchies
Use composition
Extract Common Functionality Refactorings https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true Context Parallel hierarchies occur when you must make a counterpart every time you create a domain class. The counterpart might be persistence, UI, Controller, tests, Serialization, etc. This leads to duplicate structures and tight coupling. Changes in the domain model require changes in the parallel classes, making the system more brittle and harder to manage. Sample Code Wrong // Domain classes
abstract class Transaction {
    private String id;
    private double amount;
}

class BankTransaction extends Transaction {
    private String bankName;
}

class CreditCardTransaction extends Transaction {
    private String cardNumber;
}

// Persistence classes
abstract class TransactionDAO {
    private String id;
    private double amount;
}

class BankTransactionDAO extends TransactionDAO {
    private String bankName;
}

class CreditCardTransactionDAO extends TransactionDAO {
    private String cardNumber;
} Right public class TransactionService {
    private EntityManager entityManager;

    public TransactionService(EntityManager entityManager) {
        this.entityManager = entityManager;
    }

    public void saveTransaction(Transaction transaction) {
        entityManager.getTransaction().begin();
        entityManager.persist(transaction);
        entityManager.getTransaction().commit();
    }

    public Transaction loadTransaction(
        Long id, Class<? extends Transaction> transactionClass) {
        return entityManager.find(transactionClass, id);
    }
} Detection [x]Semi-Automatic You can detect this smell by traversing the hierarchies Exceptions Some frameworks force you to extend your domain using this technique Tags Hierarchies Level [x]Intermediate AI Generation AI generators often create this smell by mirroring domain models in persistence layers without understanding the implications, leading to unnecessary duplication. AI Detection AI Assistants can fix this smell with instructions to consolidate hierarchies and use composition, reducing duplication and improving maintainability. ChatGPT offered a solution using 'Instanceof' which is an even worse code smell Conclusion Parallel hierarchies create unnecessary complexity and make the codebase harder to maintain. They bring deep hierarchies, which is a symptom of subclassification for code reuse You can merge the hierarchies and use composition to simplify the design and improve the system's robustness. You can use Metaprogramming to manage the persistence or the unit tests. Metaprogramming is also a code smell when you use it for domain problems, but persistence and testing are orthogonal domains. Relations Code Smell 137 - Inheritance Tree Too Deep Code Smell 11 - Subclassification for Code Reuse Code Smell 58 - Yo-yo Problem More Info https://hackernoon.com/laziness-chapter-i-meta-programming-6s4l300e?embedable=true Disclaimer Code Smells are my opinion. Inheritance is surely a good answer but who knows the questions? Michel Gauthier Software Engineering Great Quotes This article is part of the CodeSmell Series. How to Find the Stinky Parts of your Code Double Trouble: The Curse of Redundant Structures Double Trouble: The Curse of Redundant Structures TL;DR: Parallel hierarchies lead to duplication and tight coupling. TL;DR: Parallel hierarchies lead to duplication and tight coupling. Problems Increased complexity DRY / Code Duplication Maintenance Nightmare Coupling Ripple Effect Potential for inconsistencies across different hierarchies Increased complexity DRY / Code Duplication Maintenance Nightmare Coupling Ripple Effect Potential for inconsistencies across different hierarchies Solutions Merge hierarchies Use composition Extract Common Functionality Merge hierarchies Use composition Extract Common Functionality Refactorings https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true Context Parallel hierarchies occur when you must make a counterpart every time you create a domain class. The counterpart might be persistence, UI, Controller, tests, Serialization, etc. This leads to duplicate structures and tight coupling. Changes in the domain model require changes in the parallel classes, making the system more brittle and harder to manage. Sample Code Wrong // Domain classes
abstract class Transaction {
    private String id;
    private double amount;
}

class BankTransaction extends Transaction {
    private String bankName;
}

class CreditCardTransaction extends Transaction {
    private String cardNumber;
}

// Persistence classes
abstract class TransactionDAO {
    private String id;
    private double amount;
}

class BankTransactionDAO extends TransactionDAO {
    private String bankName;
}

class CreditCardTransactionDAO extends TransactionDAO {
    private String cardNumber;
} // Domain classes
abstract class Transaction {
    private String id;
    private double amount;
}

class BankTransaction extends Transaction {
    private String bankName;
}

class CreditCardTransaction extends Transaction {
    private String cardNumber;
}

// Persistence classes
abstract class TransactionDAO {
    private String id;
    private double amount;
}

class BankTransactionDAO extends TransactionDAO {
    private String bankName;
}

class CreditCardTransactionDAO extends TransactionDAO {
    private String cardNumber;
} Right public class TransactionService {
    private EntityManager entityManager;

    public TransactionService(EntityManager entityManager) {
        this.entityManager = entityManager;
    }

    public void saveTransaction(Transaction transaction) {
        entityManager.getTransaction().begin();
        entityManager.persist(transaction);
        entityManager.getTransaction().commit();
    }

    public Transaction loadTransaction(
        Long id, Class<? extends Transaction> transactionClass) {
        return entityManager.find(transactionClass, id);
    }
} public class TransactionService {
    private EntityManager entityManager;

    public TransactionService(EntityManager entityManager) {
        this.entityManager = entityManager;
    }

    public void saveTransaction(Transaction transaction) {
        entityManager.getTransaction().begin();
        entityManager.persist(transaction);
        entityManager.getTransaction().commit();
    }

    public Transaction loadTransaction(
        Long id, Class<? extends Transaction> transactionClass) {
        return entityManager.find(transactionClass, id);
    }
} Detection [x]Semi-Automatic [x] Semi-Automatic [x] You can detect this smell by traversing the hierarchies Exceptions Some frameworks force you to extend your domain using this technique Some frameworks force you to extend your domain using this technique Tags Hierarchies Hierarchies Level [x]Intermediate [x] Intermediate [x] AI Generation AI generators often create this smell by mirroring domain models in persistence layers without understanding the implications, leading to unnecessary duplication. AI Detection AI Assistants can fix this smell with instructions to consolidate hierarchies and use composition, reducing duplication and improving maintainability. ChatGPT offered a solution using 'Instanceof' which is an even worse code smell 'Instanceof' Conclusion Parallel hierarchies create unnecessary complexity and make the codebase harder to maintain. They bring deep hierarchies , which is a symptom of subclassification for code reuse deep hierarchies subclassification for code reuse You can merge the hierarchies and use composition to simplify the design and improve the system's robustness. You can use Metaprogramming to manage the persistence or the unit tests. Metaprogramming is also a code smell when you use it for domain problems, but persistence and testing are orthogonal domains. Metaprogramming Relations Code Smell 137 - Inheritance Tree Too Deep Code Smell 137 - Inheritance Tree Too Deep Code Smell 11 - Subclassification for Code Reuse Code Smell 11 - Subclassification for Code Reuse Code Smell 58 - Yo-yo Problem Code Smell 58 - Yo-yo Problem More Info https://hackernoon.com/laziness-chapter-i-meta-programming-6s4l300e?embedable=true https://hackernoon.com/laziness-chapter-i-meta-programming-6s4l300e?embedable=true Disclaimer Code Smells are my opinion . opinion Inheritance is surely a good answer but who knows the questions? Inheritance is surely a good answer but who knows the questions? Michel Gauthier Michel Gauthier Software Engineering Great Quotes Software Engineering Great Quotes This article is part of the CodeSmell Series. How to Find the Stinky Parts of your Code How to Find the Stinky Parts of your Code

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