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Propl

propl

Honey Moons and All Things Come to an End

by Richard Kallos is an Erlang library I wrote for concurrently executing task dependency graphs. It’s intended purpose is to run a set of pre-defined tasks that have a partial ordering between them. In this post, I explain why I wrote wrek, what it can be used for, and how to use it. Wrek Motivation Wrek emerged as the result of two distinct forces. First, my amateurish enjoyment of graph theory made me try to see graphs wherever I could, laying the conceptual foundation for this library. Second, I realized that a project I was working on at Adgear would benefit from such a library, causing me to finally start writing Wrek. Conceptual The graph is a pervasive data structure in computing. When I learned about graph algorithms in school, I was amazed by their broad applications. Graphs play essential roles in compilers and build systems. Various graph algorithms form the backbone of how we communicate with each other over the internet. Our everyday lives tend to be filled with lists. We have to-do lists, shopping lists, recipes, checklists, instructions, and much, much more. One day, I realized that some of these lists are deceiving. Some of these lists are actually graphs hiding in list’s clothing; these lists are of . Two of the more obvious cases of this are to-do lists and recipes. You can’t send a letter that you haven’t written yet, nor can you cook spaghetti if you haven’t boiled a pot of water. topological orderings directed acyclic graphs Once the sneaky graphs were discovered, I spent some time thinking about how to benefit from treating various lists like the DAGs that they secretly are. The clearest analogies between these lists and DAGs were to-do lists and recipes. These very closely resembled . Unlike in their list representation, these dependency graphs show which vertices (individual tasks) can be executed concurrently. Sometimes this is obvious (e.g: I can chop vegetables while I wait for the pot of water to boil! I can begin preparing a second baking sheet of cookies while the first batch is in the oven!), but I had my hopes that the act of reformulating lists as dependency graphs could expose more opportunities for concurrency. dependency graphs Boil water -- Add pasta -- Cook pasta --.  
                                                     \\ Purée tomatoes --.                                      \\                                      \Chop vegetables -- Combine in saucepan -- Simmer sauce -- Combine pasta and sauce -- Serve/Add spices --' Edges between vertices in the above graph express a partial ordering. Vertices with no path between them can be executed concurrently. The relationship between vertices in the graph reflects the truth in the kitchen. We can cook the pasta while our sauce is simmering. It doesn’t matter whether we chop vegetables or purée tomatoes first; they both need to be done if we’re going to make any sauce. Despite my best efforts, I am still a disaster in the kitchen. As a means of changing this inconvenient fact, I thought it would be fun to try representing recipes as directed graphs, with extra data like how long each step can be expected to take. This sat in my list of ‘someday projects’ for a long time, and I was only reminded of it when I begin to think about a project I was working on at $JOB. Practical One of the projects I accomplished last year at Adgear was removing an expensive computation that was taking place on each of our already maxed-out edge servers, and replaced it with a system that performed the expensive computation on a single machine, then distributed the result. The system worked when nobody touched it, but it was the programming equivalent of twigs and duct tape; cron jobs and shell scripts. This system continued to be painful to use, and more examples of expensive computations being done on CPU-starved machines were cropping up. At this point, it made sense start writing a more robust system. These expensive computations decomposed nicely into lists of steps to be performed. Fetch this data, transform it, transform it some more, send some of the data to this group of servers, send this other data to some other group of servers. Shell scripts are pretty good at encoding these pipelines, so it was an acceptable choice at the time of implementation. After a while, it dawned on me that these lists of steps were not really lists; they were dependency graphs. I tried to expose the latent concurrency in the shell scripts by spicing them up with some background jobs and waitpid , but it was decided that it would make more sense to switch to Erlang and benefit from all that OTP has to offer. Let’s Get ‘Wrek’ ed (I’m sorry. I couldn’t resist.) Wrek accepts dependency graphs like the above spaghetti recipe as input, and executes each vertex. The nature of concurrently executing actions as soon as they are able to execute is to any problem that can be represented by a dependency graph. The structure of a dependency graph, as well as the tasks involved in each of the graph’s vertices are to the user’s wishes. Following the same generic/specific split as OTP; the generic portion of this class of problems is solved by and modules. The specific portion is provided by the user in the form of an Erlang map describing each vertex in the graph, and a set of callback modules that implement the behaviour. generic specific wrek wrek_vert wrek_vert The behaviour consists of a single callback , where the first argument is a list of arguments to be sent to the callback function, and the second argument is the ID of a process which can provide information generated by other vertices. The expected result of this callback function is either wrek_vert run/2 or . If the callback function succeeds, will be taken by wrek and made available to other processes. If the callback function crashes or returns an error, the entire graph will be shut down. {ok, Any :: any()} {error, Reason :: any()} Any wrek_vert Making Erlang Pasta Following the contrived example above, let’s set about using wrek to make pasta. Of course, our program won’t really make pasta, but it’s output ought to fool somebody. Looking at the above graph, each step seems to do one of three things: Add an ingredient Combine ingredients in a vessel Do something to ingredients in a vessel Let’s go ahead write some s for each of those actions. If you don’t feel like following along in a text editor, the full code can be found . wrek_vert here -module(cook_add). -behaviour(wrek_vert).-export([run/2]). run([Ingredient, Quantity], _Pid) ->io:format(“adding ~s. amount: ~s.~n”, [Ingredient, Quantity]),{ok, #{added => [{Ingredient, Quantity}]}}. That’s all for . It prints a message, then produces a map with a key added whose value is a proplist with a single pair. cook_add -module(cook_heat). -behaviour(wrek_vert).-export([run/2]). run([Verb, Noun], _Pid) ->io:format("~ping ~p.~n", [Verb, Noun]),{ok, #{}}. is also pretty short. It’s also very abstract. It could be used to print a message about ing any , not just cooking ingredients! cook_heat Verb Noun Our final callback module is a little longer, because it does a little bit more than printing a message. -module(cook_combine). -behaviour(wrek_vert).-export([run/2]). run([Ingredients, Vessel], Pid) ->Fun = fun(Step, Acc) ->Stuff = wrek_vert:get(Pid, Step, added),io:format("combining ~p with ~p in ~p.~n", [Stuff, Acc, Vessel]),Stuff ++ Accend,Stuff = lists:foldl(Fun, [], Ingredients),io:format("~p now contains: ~p.~n", [Vessel, Stuff]),{ok, #{added => Stuff}}. is expected to be a list of vertex names. We finally use our parent process’s Pid as an argument to . This lets us consume the data produced by the callback module. After combining everything, we returns a new collection of ingredients. Ingredients wrek_vert:get/3 cook_add Alright! We’re nearly done describing the specific portion of our problem! The last step is to represent our dependency graph in terms of these callback modules and the arguments we want to pass to them. -module(wrek_example). -export([make_pasta/0]). make_pasta() ->application:ensure_all_started(wrek),Graph = #{tomatoes => #{module => cook_add,args => ["pureed tomatoes", "1 can"],deps => []},vegetables => #{module => cook_add,args => ["chopped vegetables", "lots"],deps => []},spices => #{module => cook_add,args => ["spices", "to taste"],deps => []},saucepan => #{module => cook_combine,args => [[tomatoes, vegetables, spices], saucepan],deps => [tomatoes, vegetables, spices]},simmer_sauce => #{module => cook_heat,args => [simmer, sauce],deps => [saucepan]},boil_water => #{module => cook_heat,args => [boil, water],deps => []},add_pasta => #{module => cook_add,args => ["pasta", "1 handful"],deps => [boil_water]},cook_pasta => #{module => cook_heat,args => [cook, pasta],deps => [add_pasta]},mix_pasta_with_sauce => #{module => cook_combine,args => [[saucepan, add_pasta], saucepan],deps => [simmer_sauce, cook_pasta]}},wrek:start(Graph). That’s an eyeful! What we’ve done here is created an Erlang map whose keys represent the names of vertices in our original dependency graph, and whose values are maps that specify a callback module, arguments to pass to the callback module, as well as any dependencies the vertex may have. I congratulate those of you who noticed that we are never straining the pasta; I did confess to being a disaster in the kitchen. I promise I learned my lesson. Alright, we’re done coding! Let’s start up a shell and make some pasta! Read the rest of this blog post on codesync.global Code BEAM SF 2018: 15–16 March 2018 Join Richard Kallos, developer at AdGear, at CodeBEAM SF where he will be giving a talk on . wrek Originally published at codesync.global .

Introducing Wrek — A Miniature Erlang Graph Engine

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 5 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 5 Experiments We sought to evaluate and refine the performance of our proposed latent diffusion architecture in our experimental analysis. To this end, we employed automatic metrics, specifically FID-CLIP curves on the COCO-30K dataset, to obtain the optimal guidance-scale value and compare Kandinsky with competitors (cf. Figure 4). Furthermore, we conducted investigations with various image prior setups, exploring the impact of different configurations on the performance. These setups included: no prior, utilizing text embeddings directly; linear prior, implementing one linear layer; ResNet prior, consisting of 18 residual MLP blocks; and transformer diffusion prior. An essential aspect of our experiments was the exploration of the effect of latent quantization within the MoVQ autoencoder. We examined the outputs with latent quantization, both enabled and disabled, to better comprehend its influence on image generation quality. To ensure a comprehensive evaluation, we also included an assessment of the IF model [12], which is the closest open-source competitor to our proposed model. For this purpose, we computed FID scores for the IF model [13] (Table 1). However, we acknowledged the limitations of automatic metrics that become obvious when it comes to capturing user experience nuances. Hence, in addition to the FID-CLIP curves, we conducted a blind human evaluation to obtain insightful feed-back and validate the quality of the generated images from the perspective of human perception based on the DrawBench dataset (Saharia et al., 2022b). The combination of automatic metrics and human evaluation provides a comprehensive assessment of Kandinsky performance, enabling us to make informed decisions about the effectiveness and usability of our proposed image prior to design. This paper is available on arxiv under CC BY 4.0 DEED license. [12] https://github.com/deep-floyd/IF [13] https://github.com/mseitzer/pytorch-fid Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 5 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 5 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 5 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 5 Experiments We sought to evaluate and refine the performance of our proposed latent diffusion architecture in our experimental analysis. To this end, we employed automatic metrics, specifically FID-CLIP curves on the COCO-30K dataset, to obtain the optimal guidance-scale value and compare Kandinsky with competitors (cf. Figure 4). Furthermore, we conducted investigations with various image prior setups, exploring the impact of different configurations on the performance. These setups included: no prior, utilizing text embeddings directly; linear prior, implementing one linear layer; ResNet prior, consisting of 18 residual MLP blocks; and transformer diffusion prior. An essential aspect of our experiments was the exploration of the effect of latent quantization within the MoVQ autoencoder. We examined the outputs with latent quantization, both enabled and disabled, to better comprehend its influence on image generation quality. To ensure a comprehensive evaluation, we also included an assessment of the IF model [12], which is the closest open-source competitor to our proposed model. For this purpose, we computed FID scores for the IF model [13] (Table 1). However, we acknowledged the limitations of automatic metrics that become obvious when it comes to capturing user experience nuances. Hence, in addition to the FID-CLIP curves, we conducted a blind human evaluation to obtain insightful feed-back and validate the quality of the generated images from the perspective of human perception based on the DrawBench dataset (Saharia et al., 2022b). The combination of automatic metrics and human evaluation provides a comprehensive assessment of Kandinsky performance, enabling us to make informed decisions about the effectiveness and usability of our proposed image prior to design. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [12] https://github.com/deep-floyd/IF [13] https://github.com/mseitzer/pytorch-fid

Russian Scientists Create AI That Generates Images People Actually Love

Table of Links Abstract and 1. Introduction 2 Related Work 3 Preliminaries 4 Method 4.1 Key Sample and Joint Editing 4.2 Edit Propagation Via TokenFlow 5 Results 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.3 Ablation Study 6 Discussion 7 Acknowledgement and References A Implementation Details 4.2 EDIT PROPAGATION VIA TOKENFLOW This paper is available on arxiv under CC BY 4.0 DEED DEED license. Authors:
(1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution;
(2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution;
(3) Shai Bagon, Weizmann Institute of Science;
(4) Tali Dekel, Weizmann Institute of Science. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Method 4 Method 4.1 Key Sample and Joint Editing 4.1 Key Sample and Joint Editing 4.2 Edit Propagation Via TokenFlow 4.2 Edit Propagation Via TokenFlow 5 Results 5 Results 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.3 Ablation Study 5.3 Ablation Study 6 Discussion 6 Discussion 7 Acknowledgement and References 7 Acknowledgement and References A Implementation Details A Implementation Details 4.2 EDIT PROPAGATION VIA TOKENFLOW This paper is available on arxiv under CC BY 4.0 DEED DEED license. This paper is available on arxiv under CC BY 4.0 DEED DEED license. available on arxiv Authors: (1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution; (2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution; (3) Shai Bagon, Weizmann Institute of Science; (4) Tali Dekel, Weizmann Institute of Science. Authors: Authors: (1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution; (2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution; (3) Shai Bagon, Weizmann Institute of Science; (4) Tali Dekel, Weizmann Institute of Science.

How to Edit Propagation Via TokenFlow

Crypto investors are often seen as tech-savvy trailblazers who are unafraid to dive into complex tech and even more complicated topics like decentralised finance (DeFi) and blockchain. The reality, however, couldn’t be further from the truth. Soaring crypto prices have attracted millions of new participants, but many are still novices. Although financial inclusion and freedom lie at the heart of crypto, the current experience often does exactly the opposite - leaving users hamstrung with complexity and too confused to participate. This presents a unique and urgent challenge for the crypto community: designing tech that works for beginners. Just how many new users are we talking? Spoiler alert: it’s more than you think. The crypto market isn’t just growing; it’s expanding rapidly. According to Statista, the number of cryptocurrency users globally surged by over 40 million in the first half of 2024 alone. Meanwhile, cryptonomist reports that the number of active addresses has 400+ million people - further evidence that a significant portion of the global population is engaging with crypto. What is their experience of crypto? Spoiler alert: It’s not all sunshine and smooth sailing. Despite these increasing numbers, many potential crypto investors encounter frustration when trying to invest. A staggering 72% of people say they find the process confusing and “do not know where to start.” Common concerns include that information is difficult to understand; and they don’t know who to ask for help. These barriers are mirrored among those who have invested, showing that the problem transcends novice users and affects the entire community. Bottom line: the crypto industry need to do better - we can’t afford to keep making new investors feel like they’re navigating a labyrinth of jargon and technicalities. If the industry wants to scale effectively, it must prioritise making crypto a lot more approachable and accessible. Education needs to become our focus It’s time to stop speaking in code and start speaking human. If we’re serious about mass adoption, we need to shift our mindset from designing ‘crypto solutions for the tech-savvy' to ‘crypto for humans’. This means prioritising simple, plain-language communication across all platforms and tools. Resources shouldn’t just be informative but welcoming - a place where investors can get the answers they need without having to wade through a swamp of confusing terminology. Exchanges, in particular, need to do more than just host transactions - they need to help users make informed decisions, equip them with up-to-date knowledge and be a source of trusted information. Make the user experience (UX) simpler and more inclusive It shouldn’t take a PhD to secure your wallet. Crypto can be intimidating, especially when it comes to securing your assets. Using long seed phrases to protect crypto wallets, for example, has scared off many potential users. To gain trust, we need to simplify the user experience and ensure that people can manage and recover their wallets more easily, without fear of losing access. Self custody doesn't have to mean using a seed phrase. You can own your own funds while taking advantage of the backup and protection of third party software that not only enables you to sign transactions but, importantly, gives you simpler options around how you secure and protect your funds. Crypto friendly doesn’t equal scam savvy! Just because they’re ready to invest doesn’t mean they’re ready to defend. Being ‘crypto friendly’ (keen to invest) doesn’t automatically mean being scam-savvy. Fraudsters are continuously evolving their tactics and there has been an increase in scams targeting beginners who are often more vulnerable due to their lack of experience. Without better safeguards and education, this risk is only going to grow. Scammers are becoming more sophisticated, using tactics like phishing, Ponzi schemes and fake exchanges to trick newcomers. Crypto teams should always assume that their users may not be aware of these threats. The blockchain is about enabling financial transparency - yet tech providers often fail to be upfront about how they’re helping keep users safe. Educating users about the most common scams and teaching them practical security tips - such as enabling multi-factor authentication (MFA), using strong passwords, and spotting phishing attempts - is crucial. Importantly, this education shouldn’t just be the job of exchanges; it should be an industry-wide standard. Tap into tech for better protection Fraud prevention should be multifaceted, combining technological innovation with regulatory oversight. For instance, verifying users’ identities to prevent the creation of fraudulent accounts is essential. Crypto platforms should employ both automated and manual monitoring systems to detect and respond to suspicious activities swiftly. Advanced tech, such as transaction monitoring and behavioral analytics, can flag unusual behavior and trigger real-time intervention. What’s more, security measures like transaction limits should be standard to mitigate potential losses in case of compromised accounts and users should expect these strict security measures and verify that they’re in place before trusting a platform. People should be able to appreciate what crypto delivers without needing to be deeply technically savvy to use it. Think of it this way: you don’t need to understand the inner workings or physics of how an airplane flies to enjoy the benefits of being able to fly across the world. Similarly, the crypto industry needs to focus on making the experience seamless and intuitive, so that users can benefit from its potential without being overwhelmed by the technical details. It’s time to set a new standard for technology that’s not just innovative, but inclusive, secure and welcoming for everyone. Crypto investors are often seen as tech-savvy trailblazers who are unafraid to dive into complex tech and even more complicated topics like decentralised finance (DeFi) and blockchain. The reality, however, couldn’t be further from the truth. Soaring crypto prices have attracted millions of new participants, but many are still novices. Although financial inclusion and freedom lie at the heart of crypto, the current experience often does exactly the opposite - leaving users hamstrung with complexity and too confused to participate. This presents a unique and urgent challenge for the crypto community: designing tech that works for beginners. Crypto investors are often seen as tech-savvy trailblazers who are unafraid to dive into complex tech and even more complicated topics like decentralised finance (DeFi) and blockchain. The reality, however, couldn’t be further from the truth. Soaring crypto prices have attracted millions of new participants, but many are still novices. Although financial inclusion and freedom lie at the heart of crypto, the current experience often does exactly the opposite - leaving users hamstrung with complexity and too confused to participate. This presents a unique and urgent challenge for the crypto community: designing tech that works for beginners. Just how many new users are we talking? Just how many new users are we talking? Spoiler alert: it’s more than you think. Spoiler alert: it’s more than you think. The crypto market isn’t just growing; it’s expanding rapidly. According to Statista, the number of cryptocurrency users globally surged by over 40 million in the first half of 2024 alone. Meanwhile, cryptonomist reports that the number of active addresses has 400+ million people - further evidence that a significant portion of the global population is engaging with crypto. cryptonomist 400+ million people What is their experience of crypto? What is their experience of crypto? Spoiler alert: It’s not all sunshine and smooth sailing. Spoiler alert: It’s not all sunshine and smooth sailing. Despite these increasing numbers, many potential crypto investors encounter frustration when trying to invest. A staggering 72% of people say they find the process confusing and “do not know where to start.” Common concerns include that information is difficult to understand; and they don’t know who to ask for help. These barriers are mirrored among those who have invested, showing that the problem transcends novice users and affects the entire community. Bottom line: the crypto industry need to do better - we can’t afford to keep making new investors feel like they’re navigating a labyrinth of jargon and technicalities. If the industry wants to scale effectively, it must prioritise making crypto a lot more approachable and accessible. Education needs to become our focus It’s time to stop speaking in code and start speaking human. It’s time to stop speaking in code and start speaking human. If we’re serious about mass adoption, we need to shift our mindset from designing ‘crypto solutions for the tech-savvy' to ‘crypto for humans’. This means prioritising simple, plain-language communication across all platforms and tools. Resources shouldn’t just be informative but welcoming - a place where investors can get the answers they need without having to wade through a swamp of confusing terminology. Exchanges, in particular, need to do more than just host transactions - they need to help users make informed decisions, equip them with up-to-date knowledge and be a source of trusted information. Make the user experience (UX) simpler and more inclusive It shouldn’t take a PhD to secure your wallet. It shouldn’t take a PhD to secure your wallet. Crypto can be intimidating, especially when it comes to securing your assets. Using long seed phrases to protect crypto wallets, for example, has scared off many potential users. To gain trust, we need to simplify the user experience and ensure that people can manage and recover their wallets more easily, without fear of losing access. Self custody doesn't have to mean using a seed phrase. You can own your own funds while taking advantage of the backup and protection of third party software that not only enables you to sign transactions but, importantly, gives you simpler options around how you secure and protect your funds. have to Crypto friendly doesn’t equal scam savvy! Just because they’re ready to invest doesn’t mean they’re ready to defend. Just because they’re ready to invest doesn’t mean they’re ready to defend. Being ‘crypto friendly’ (keen to invest) doesn’t automatically mean being scam-savvy. Fraudsters are continuously evolving their tactics and there has been an increase in scams targeting beginners who are often more vulnerable due to their lack of experience. Without better safeguards and education, this risk is only going to grow. Scammers are becoming more sophisticated, using tactics like phishing, Ponzi schemes and fake exchanges to trick newcomers. Crypto teams should always assume that their users may not be aware of these threats. The blockchain is about enabling financial transparency - yet tech providers often fail to be upfront about how they’re helping keep users safe. Educating users about the most common scams and teaching them practical security tips - such as enabling multi-factor authentication (MFA), using strong passwords, and spotting phishing attempts - is crucial. Importantly, this education shouldn’t just be the job of exchanges; it should be an industry-wide standard. Tap into tech for better protection Tap into tech for better protection Fraud prevention should be multifaceted, combining technological innovation with regulatory oversight. For instance, verifying users’ identities to prevent the creation of fraudulent accounts is essential. Crypto platforms should employ both automated and manual monitoring systems to detect and respond to suspicious activities swiftly. Advanced tech, such as transaction monitoring and behavioral analytics, can flag unusual behavior and trigger real-time intervention. What’s more, security measures like transaction limits should be standard to mitigate potential losses in case of compromised accounts and users should expect these strict security measures and verify that they’re in place before trusting a platform. People should be able to appreciate what crypto delivers without needing to be deeply technically savvy to use it. Think of it this way: you don’t need to understand the inner workings or physics of how an airplane flies to enjoy the benefits of being able to fly across the world. Similarly, the crypto industry needs to focus on making the experience seamless and intuitive, so that users can benefit from its potential without being overwhelmed by the technical details. It’s time to set a new standard for technology that’s not just innovative, but inclusive, secure and welcoming for everyone.

Crypto Tech is More Anti-people Than You Think

How Effective Are AI Models at Biomedical Problem Solving?

Author:
(1) David Staines. Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility 9.2 Algebraic Aspects (I) Singularities and Covers At the end of this subsection, I precisely describe the behavior of the non-linear system local to the ZINSS. Beforehand, I develop the requisite material from algebraic topology. Definition 8. Let Y be a topological space. A covering space of Y is a topological space X with a surjective (onto) map Definition 9. A ramification point is where pairs of branches of a covering map meet. Finally, it helps to have some shorthand notation for all the removable singularities The last ingredient is De Rham’s Theorem (Tu [2011]), which extends differential calculus to manifolds. The idea is that the fundamental theorem of calculus and its immediate implications, hold when appropriately accounting for singular surfaces. All that is required is a differential structure, roughly a formulation of the overall system that is continuously differentiable. It is now possible to reveal the theorem of this subsection. The construction for the demand shocks is identical, except the cover comes from the non-linear expansion corresponding to (167) and (168) around ZINSS. The second part justifies the calculations made in 7.2.2. What is happening is that the second order approximations are dropping off, which ensures that the coefficients from past and present shocks will have opposite signs in the first order approximation. The singularities cause them to be conflated and cancel out. This is an exceptional mathematical pathology. This problem is avoided with price dispersion because its covering by inflation is not inter-temporal (there are no lags in its recursion (48)). This symmetry will prove a universal feature of dynamic responses to shocks affecting efficient resource allocation around the non-stochastic steady state of models with only static market failure, including other Keynesian models. This contrasts with ad hoc shocks to expectations or mark-ups that should not move the efficient allocation and will lack dynamical structure. This symmetry will break down around non-degenerate stochastic equilibrium because there will be differential exposure to uncertainty in other variables. This promises to be a major theme in modern macroeconomics. Remark 21. The assumption β → 1 is a requirement for the analysis, to ensure there is an inter-temporal covering by a choice variable, as opposed to a shock. When discounting is introduced the covering interpretation breaks down but the singularity will remain and smoothly approach the covering case, as will be demonstrated in the next subsection. This paper is available on arxiv under CC 4.0 license. [69] This motivates the cover terminology. The homeomorphic copies in C of an evenly covered neighborhood N form sheets over N. One can think of X as "hovering above" Y, with the horizontal sheets piled up on top of N and the cover C pointing down to the base. Thus the fibers over y consist of those points in X that lie directly above y. Author: (1) David Staines. Author: Author: (1) David Staines. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility 9.2 Algebraic Aspects (I) Singularities and Covers At the end of this subsection, I precisely describe the behavior of the non-linear system local to the ZINSS. Beforehand, I develop the requisite material from algebraic topology. Definition 8. Let Y be a topological space. A covering space of Y is a topological space X with a surjective (onto) map Definition 8. Definition 9. A ramification point is where pairs of branches of a covering map meet. Definition 9. Finally, it helps to have some shorthand notation for all the removable singularities The last ingredient is De Rham’s Theorem (Tu [2011]), which extends differential calculus to manifolds. The idea is that the fundamental theorem of calculus and its immediate implications, hold when appropriately accounting for singular surfaces. All that is required is a differential structure, roughly a formulation of the overall system that is continuously differentiable. It is now possible to reveal the theorem of this subsection. De Rham’s Theorem The construction for the demand shocks is identical, except the cover comes from the non-linear expansion corresponding to (167) and (168) around ZINSS. The second part justifies the calculations made in 7.2.2. What is happening is that the second order approximations are dropping off, which ensures that the coefficients from past and present shocks will have opposite signs in the first order approximation. The singularities cause them to be conflated and cancel out. This is an exceptional mathematical pathology. This problem is avoided with price dispersion because its covering by inflation is not inter-temporal (there are no lags in its recursion (48)). This symmetry will prove a universal feature of dynamic responses to shocks affecting efficient resource allocation around the non-stochastic steady state of models with only static market failure, including other Keynesian models. This contrasts with ad hoc shocks to expectations or mark-ups that should not move the efficient allocation and will lack dynamical structure. This symmetry will break down around non-degenerate stochastic equilibrium because there will be differential exposure to uncertainty in other variables. This promises to be a major theme in modern macroeconomics. Remark 21. The assumption β → 1 is a requirement for the analysis, to ensure there is an inter-temporal covering by a choice variable, as opposed to a shock. When discounting is introduced the covering interpretation breaks down but the singularity will remain and smoothly approach the covering case, as will be demonstrated in the next subsection. Remark 21. The assumption β → 1 is a requirement for the analysis, to ensure there is an inter-temporal covering by a choice variable, as opposed to a shock. When discounting is introduced the covering interpretation breaks down but the singularity will remain and smoothly approach the covering case, as will be demonstrated in the next subsection. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [69] This motivates the cover terminology. The homeomorphic copies in C of an evenly covered neighborhood N form sheets over N. One can think of X as "hovering above" Y, with the horizontal sheets piled up on top of N and the cover C pointing down to the base. Thus the fibers over y consist of those points in X that lie directly above y.

How Small Changes in Economic Model Assumptions Can Lead to Big Problems

Prop trading is increasingly attracting younger traders, with over 60% of prop firms’ clients now belonging to Gen Z and Millennial demographics. Recent prop firm statistics have shown this shift is due to changing priorities, with the younger generations favoring flexible, innovative options over traditional forex brokers and CFD trading models. The Appeal of Prop Trading for Younger Generations With features like firm-funded accounts and low one-off challenge fees, prop trading resonates with younger generations' appetite for high-reward opportunities that minimize personal financial risk. This age group is also drawn to prop trading's adaptability, enabling them to engage with markets on their terms, supported by advanced tools and tech-driven solutions. These features align closely with the values and trading preferences of Gen Z and Millennials, who prioritize instant access, cost-efficiency, and tech-driven solutions. Instant Funding Options The demand for instant funding has skyrocketed, with searches increasing by 13,070% between 2020 and 2024. This feature eliminates lengthy evaluation processes, providing traders with immediate access to capital and allowing them to quickly participate in market opportunities. Prop firms offering instant funding have gained a competitive edge by catering to younger traders’ need for speed and simplicity. Affordability and Accessibility Budget-friendly options are a significant driver of interest, with searches for cheap prop firms growing by 15,805% over four years. Compared to traditional trading models, prop trading offers lower upfront costs, making it accessible for traders starting with limited funds. This affordability appeals particularly to Gen Z and Millennials, who often seek financially flexible entry points into trading. Technological Advancements and Tools The rise of automated trading tools has been a game-changer, with search volumes for Expert Advisors (EAs) increasing by 382%. Prop firms integrating these tools provide younger traders with a technological advantage, enabling them to optimize strategies and improve efficiency. While not yet widely offered, the ability to use EAs sets some firms apart, with others introducing these tools as add-on services to attract tech-savvy traders. Gamification of Trading Challenges Some prop firms, such as The Funded Trader, are incorporating gamification into their evaluation processes and marketing challenges in a way that resembles video game competitions. Features like leaderboards, performance-based rewards, and tiered progression systems aim to make trading more engaging, particularly for younger audiences. Industry expert Noam Korbl, from Prop-Firms.com, comments on this trend: "Gamification appeals to the competitive instincts of younger traders, offering structured challenges and clear milestones that make the process more engaging. While this approach helps to reduce intimidation and attract new participants to financial markets, it's crucial to balance the fun aspects with education, as successful trading still requires discipline and a deep understanding of market risks." While gamification encourages engagement, it runs a high risk of oversimplifying the challenges of trading. Financial regulators are increasingly scrutinizing such practices, and the industry is anticipating stricter regulations to be introduced soon. The Influence of Social Media and Content Creators on Prop Trading Social media platforms such as TikTok, YouTube, and Instagram have become powerful tools in promoting prop trading, particularly among Gen Z and Millennials. While these trading communities make concepts more accessible for beginners, the unregulated nature of proprietary trading and the marketing tactics firms use introduces both opportunities and challenges. Making Prop Trading Accessible Content creators simplify complex trading concepts, making them relatable and engaging for younger audiences. Short, easily digestible videos and tutorials demystify trading strategies, funding models, and prop firm challenges, encouraging new traders to explore these opportunities. These influencers often share their personal experiences, providing aspirational content that resonates with younger demographics. Marketing Strategies of Prop Firms Prop firms use social media as a key marketing channel, with influencers playing a central role in promoting their services to younger traders. These collaborations often involve influencers sharing personal trading experiences, highlighting specific firms, and showcasing the benefits of prop trading through engaging, aspirational content. While such influencers can make trading concepts more accessible, they often blend education with marketing, potentially creating unrealistic expectations. Balancing Education and Oversight Social media's role in prop trading growth is undeniable, offering unprecedented accessibility and engagement. However, traders should approach these platforms with caution, critically evaluating the claims made by content creators and prop firms. Greater transparency and clear communication of risks are essential to ensure that social media-driven interest in prop trading leads to informed decisions rather than unrealistic expectations. Asset Classes That Attract Young Traders Younger traders are driving demand for specific financial instruments within prop trading, with data highlighting rapid growth across several asset classes. These preferences reflect the priorities of a tech-savvy generation drawn to liquidity, volatility, and innovation. Forex - remains the most popular choice, with search volumes increasing by 863% between 2020 and 2024.
Futures trading - has seen unprecedented growth, with a 5,556% increase in searches during the same period.
Cryptocurrency - continues to gain traction, with search volumes rising by 5,156%. Younger traders are particularly drawn to crypto’s high-risk, high-reward potential, leveraging their tech familiarity to navigate the market’s volatility. Challenges and Opportunities in Prop Trading Despite its appeal, prop trading presents significant challenges. Only 7% of prop traders achieve profit payouts, with the average trader spending $4,270 on challenges before becoming profitable. However, diversification offers a path to success. Research shows that 90% of traders use 2–5 prop firms to mitigate risks and increase their chances of passing challenges. By adopting disciplined strategies and leveraging the variety of opportunities offered by different firms, younger traders can navigate these challenges more effectively. The Future of Prop Trading for Younger Generations Prop trading is set to see continued growth among Gen Z and Millennials, driven by evolving technology and innovative funding models tailored to their needs. Features like instant funding, accessible challenges, and performance-based rewards are expected to remain central to its appeal. If prop firms refine their marketing approaches and implement greater trader protections, younger traders can benefit from greater market access and reduced financial risks. Prop trading is increasingly attracting younger traders, with over 60% of prop firms’ clients now belonging to Gen Z and Millennial demographics. Recent prop firm statistics have shown this shift is due to changing priorities, with the younger generations favoring flexible, innovative options over traditional forex brokers and CFD trading models. prop firm statistics prop firm statistics The Appeal of Prop Trading for Younger Generations With features like firm-funded accounts and low one-off challenge fees, prop trading resonates with younger generations' appetite for high-reward opportunities that minimize personal financial risk. This age group is also drawn to prop trading's adaptability, enabling them to engage with markets on their terms, supported by advanced tools and tech-driven solutions. These features align closely with the values and trading preferences of Gen Z and Millennials, who prioritize instant access, cost-efficiency, and tech-driven solutions. Instant Funding Options The demand for instant funding has skyrocketed, with searches increasing by 13,070% between 2020 and 2024. This feature eliminates lengthy evaluation processes, providing traders with immediate access to capital and allowing them to quickly participate in market opportunities. Prop firms offering instant funding have gained a competitive edge by catering to younger traders’ need for speed and simplicity. Affordability and Accessibility Budget-friendly options are a significant driver of interest, with searches for cheap prop firms growing by 15,805% over four years. Compared to traditional trading models, prop trading offers lower upfront costs, making it accessible for traders starting with limited funds. This affordability appeals particularly to Gen Z and Millennials, who often seek financially flexible entry points into trading. cheap prop firms Technological Advancements and Tools The rise of automated trading tools has been a game-changer, with search volumes for Expert Advisors (EAs) increasing by 382%. Prop firms integrating these tools provide younger traders with a technological advantage, enabling them to optimize strategies and improve efficiency. While not yet widely offered, the ability to use EAs sets some firms apart, with others introducing these tools as add-on services to attract tech-savvy traders. Gamification of Trading Challenges Some prop firms, such as The Funded Trader, are incorporating gamification into their evaluation processes and marketing challenges in a way that resembles video game competitions. Features like leaderboards, performance-based rewards, and tiered progression systems aim to make trading more engaging, particularly for younger audiences. Industry expert Noam Korbl, from Prop-Firms.com, comments on this trend: "Gamification appeals to the competitive instincts of younger traders, offering structured challenges and clear milestones that make the process more engaging. While this approach helps to reduce intimidation and attract new participants to financial markets, it's crucial to balance the fun aspects with education, as successful trading still requires discipline and a deep understanding of market risks." "Gamification appeals to the competitive instincts of younger traders, offering structured challenges and clear milestones that make the process more engaging. While this approach helps to reduce intimidation and attract new participants to financial markets, it's crucial to balance the fun aspects with education, as successful trading still requires discipline and a deep understanding of market risks." While gamification encourages engagement, it runs a high risk of oversimplifying the challenges of trading. Financial regulators are increasingly scrutinizing such practices, and the industry is anticipating stricter regulations to be introduced soon. The Influence of Social Media and Content Creators on Prop Trading Social media platforms such as TikTok, YouTube, and Instagram have become powerful tools in promoting prop trading, particularly among Gen Z and Millennials. While these trading communities make concepts more accessible for beginners, the unregulated nature of proprietary trading and the marketing tactics firms use introduces both opportunities and challenges. proprietary trading proprietary trading Making Prop Trading Accessible Content creators simplify complex trading concepts, making them relatable and engaging for younger audiences. Short, easily digestible videos and tutorials demystify trading strategies, funding models, and prop firm challenges, encouraging new traders to explore these opportunities. These influencers often share their personal experiences, providing aspirational content that resonates with younger demographics. Marketing Strategies of Prop Firms Prop firms use social media as a key marketing channel, with influencers playing a central role in promoting their services to younger traders. These collaborations often involve influencers sharing personal trading experiences, highlighting specific firms, and showcasing the benefits of prop trading through engaging, aspirational content. While such influencers can make trading concepts more accessible, they often blend education with marketing, potentially creating unrealistic expectations. Balancing Education and Oversight Social media's role in prop trading growth is undeniable, offering unprecedented accessibility and engagement. However, traders should approach these platforms with caution, critically evaluating the claims made by content creators and prop firms. Greater transparency and clear communication of risks are essential to ensure that social media-driven interest in prop trading leads to informed decisions rather than unrealistic expectations. Asset Classes That Attract Young Traders Younger traders are driving demand for specific financial instruments within prop trading, with data highlighting rapid growth across several asset classes. These preferences reflect the priorities of a tech-savvy generation drawn to liquidity, volatility, and innovation. Forex - remains the most popular choice, with search volumes increasing by 863% between 2020 and 2024. Futures trading - has seen unprecedented growth, with a 5,556% increase in searches during the same period. Cryptocurrency - continues to gain traction, with search volumes rising by 5,156%. Forex - remains the most popular choice, with search volumes increasing by 863% between 2020 and 2024. Futures trading - has seen unprecedented growth, with a 5,556% increase in searches during the same period. Cryptocurrency - continues to gain traction, with search volumes rising by 5,156%. Younger traders are particularly drawn to crypto’s high-risk, high-reward potential, leveraging their tech familiarity to navigate the market’s volatility. crypto’s crypto’s Challenges and Opportunities in Prop Trading Despite its appeal, prop trading presents significant challenges. Only 7% of prop traders achieve profit payouts, with the average trader spending $4,270 on challenges before becoming profitable. However, diversification offers a path to success. Research shows that 90% of traders use 2–5 prop firms to mitigate risks and increase their chances of passing challenges. By adopting disciplined strategies and leveraging the variety of opportunities offered by different firms, younger traders can navigate these challenges more effectively. The Future of Prop Trading for Younger Generations Prop trading is set to see continued growth among Gen Z and Millennials, driven by evolving technology and innovative funding models tailored to their needs. Features like instant funding, accessible challenges, and performance-based rewards are expected to remain central to its appeal. If prop firms refine their marketing approaches and implement greater trader protections, younger traders can benefit from greater market access and reduced financial risks.

Why Gen Z and Millennials are Choosing Prop Trading Firms

Proč si generace Z a mileniálové vybírají obchodní firmy

Hvorfor Gen Z og Millennials vælger prop-handelsfirmaer

Por qué la Generación Z y los Millennials están eligiendo empresas de prop trading

מדוע דור Z ו-Millennials בוחרים בחברות מסחר בנכסים

Z世代とミレニアル世代がプロップトレーディング会社を選ぶ理由

რატომ ირჩევენ Gen Z და Millennials Prop სავაჭრო ფირმებს

ເປັນຫຍັງ Gen Z ແລະ Millennials ຈຶ່ງເລືອກບໍລິສັດການຄ້າ Prop

Kodėl Z kartos ir tūkstantmečiai renkasi butų prekybos įmones

Зошто Gen Z и Millennials избираат реквизити за трговски фирми

Gen Z болон Millennials яагаад тулгуур худалдааны пүүсүүдийг сонгож байна вэ?

Ke ka lebaka la eng Gen Z le Millennials ba Kgetha Prop Trading Firms

Kungani uGen Z kanye neMillennials bekhetha amafemu okuhweba eProp

This is part 2 of a series. If you are reading this and it feels whoa out of context I will kindly direct you back to part 1 click here: After the long, hard journey of scrolling through a never ending feed of network requests, painstakingly extracting all the juicy, mouthwatering data and generating my totally *slay* graph, I did what every normal person would do- post it on Instagram, where my epic journey all started. I know, its a very circular narrative structure, for the English nerds out there ;) And when I posted it and it reached its terminal like count (by guessing, around 30), my friends told me that they liked my post when I talked to them the next day. That was a great ego boost but I was wondering => did they really like it? And as you can see from the graph, I have 10 posts. Every time I post, the next day, people would say "omg that was so good i totally fr fr liked all ur posts" or something like that (memories can be faulty). But did they really though? As you can see from the crystal clear data, my likes were fluctuating, just like a normal teenage mood swing. Step 8: But wait......................................................................................................... there's more If my likes are not consistent, but people say that my posts were good, does this mean that it must be the same people saying that it was good? (I cant remember if it were the same people. I post posts with some time apart and it is hard to remember who said what lol). But that's not the point. The new idea The point was, I had a new idea, one that I could expand on, which is why you are seeing a part 2 of this mini series. And if I have another crazy idea, you would see a part 3. But that's for next time, sometime in the near or far future. What if I could get the number of times each person in my friends/followers list had liked my posts? I could do this manually of course, if I were to click on more details of each post, and scrolled down, I would see who exactly liked my post. Let the Liking Games begin, where the winner has pressed that juicy like button the most. What a good way to incite friendly competition amongst friends. Instead of a potentially heated argument or a fierce running race where I would certainly be out of breath after a few futile minutes, I could simply flash the undeniable, concrete data to prove who was the real winner. Step 9: MORE NETWORK REQUESTS In order to find out which people *specifically* liked my posts, I had to follow the holy grail of data collecting, again. Scrolling through network requests to find out the source of the giant stream of data which powers everything. I'm going to decrease the level of yap for the moment, so you can enjoy this giant list of pictures showing the process of gathering data ---for the second time if you have already viewed the first article--- The old saying -- "A picture is a thousand words" is very much relevant here, so I hope you enjoyed reading the 3x1000 word academic essay. (cue round of applause). Yap machine is rebooting => beep => beep => beep Anyway Now I have all the data, I can finally arrange them into a desired format, to, ya know, easier convey the big flex across. Step 10: Roleplaying as instagram (again -_-) Same old process of using postman to test requests 😃 Then repeating the same process in python. Well somewhat similar. The feature of getting how many times a friend liked my total posts is not exactly provided in the obvious way unlike just getting likes and comments. I could just go onto a post and check the like number but in order to find who liked it, and calculating (adding it up) for all posts, is a whole different story. It chucks a request at a nicely named url endpoint 'likers', conveniently indicating what I was looking for- who liked my posts


By using the request to get likes and comments counts (from the previous article) I could get the 'pk' of each of the posts; although I like to think it means 'pookie' used in 'pookie bear' it more likely means 'primary key', used to identify record in databases. BORING but necessary.   


When looking at the data, you can see everyone that liked the post, and their usernames.


Going through the /likers endpoint for different posts (by using the ‘pk’) there are some similarities of who appears in the list. This means that one person liked multiple posts.   5.  Thus going through the all the /likers endpoint for each of the, and gathering data from the response of each endpoint and storing it in a dictionary format (key:value structure) was a good option.   6.  And thus, the graphing. Step 11: Making another graphing monstrosity that math teachers would hate with a burning passion Adding necessary axis labels, letting the computer know what data to put where etc... (necessary admin stuff). And now the monstrosity Zoom in if you don't want to spend the day looking at the numbers 😅 Step 12: Possible use cases/consequences? Well all these steps were used on my account but if I wanted to, I could also use it on other people's accounts and grab some visualised data, not offered natively in Instagram. But why, do you seriously have to graph every single piece of data there is? I don’t have a clue but like → It looks cool 😎 (undeniably)


Its a weird flex 💪 -- while everyone else is showing cool pics of their dog on instagram, their rave times on parties or tropical cool beaches I’m just standing awkwardly like ==> "umm I don’t exactly go anywhere 'fun' cuz im a couchworm but here check out this messy, barely legible graph I made of your instagram data 📊 🤷🤷🤷"


Also it can start a friendly rivalry -- heres a short roleplay
Me: Hey person a, check out this graph on how much times others liked your posts
Person A: Thanks bro, interesting (looks intently at the graph, making sure to squint eyes due to the tiny font size) ...... OMG PERSON B ONLY LIKED MY POSTS 8 OUT OF 10 TIMES!!!!!@#!@$#$# I THOUGHT THEY WERE MY BEST FRIEND!@#!@$@##!@$#%% (cue intense crying noises)
Person B: Omg bro are you ok?
Person A: OMG YOU SAID YOU LIKED ALL MY INSTAGRAM POSTS BUT YOU DIDN’T YOU GODDAMN LIAR!!!
Person C: (silently appearing from nowhere ) hey would you look at that, I liked your posts 10 times :)
Person A: Thanks buddy, you’re my new best friend now, just ignore the fact that I knew person b for like 10 years while I only know u for like 10 days or something
And here, new rivalries are born, wars are started and injustices are created
hehehehehe "knowledge is power" But as you can see, what is more powerful is: Visualising knowledge in a (somewhat) readable way, so you can flex on your friends and show your data collection skills. This is part 2 of a series. If you are reading this and it feels whoa out of context I will kindly direct you back to part 1 click here: This is part 2 of a series. If you are reading this and it feels whoa out of context I will kindly direct you back to part 1 click here : here After the long, hard journey of scrolling through a never ending feed of network requests, painstakingly extracting all the juicy, mouthwatering data and generating my totally *slay* graph, I did what every normal person would do- post it on Instagram, where my epic journey all started. I know, its a very circular narrative structure, for the English nerds out there ;) And when I posted it and it reached its terminal like count (by guessing, around 30), my friends told me that they liked my post when I talked to them the next day. That was a great ego boost but I was wondering => did they really like it? And as you can see from the graph, I have 10 posts. Every time I post, the next day, people would say "omg that was so good i totally fr fr liked all ur posts" or something like that (memories can be faulty). But did they really though? As you can see from the crystal clear data, my likes were fluctuating, just like a normal teenage mood swing. something like that Step 8: But wait......................................................................................................... there's more If my likes are not consistent, but people say that my posts were good, does this mean that it must be the same people saying that it was good? (I cant remember if it were the same people. I post posts with some time apart and it is hard to remember who said what lol). But that's not the point. The new idea The point was, I had a new idea, one that I could expand on, which is why you are seeing a part 2 of this mini series. And if I have another crazy idea, you would see a part 3. But that's for next time, sometime in the near or far future. What if I could get the number of times each person in my friends/followers list had liked my posts? I could do this manually of course, if I were to click on more details of each post, and scrolled down, I would see who exactly liked my post. Let the Liking Games begin, where the winner has pressed that juicy like button the most. What a good way to incite friendly competition amongst friends. Instead of a potentially heated argument or a fierce running race where I would certainly be out of breath after a few futile minutes, I could simply flash the undeniable, concrete data to prove who was the real winner. Step 9: MORE NETWORK REQUESTS In order to find out which people *specifically* liked my posts, I had to follow the holy grail of data collecting, again. Scrolling through network requests to find out the source of the giant stream of data which powers everything. I'm going to decrease the level of yap for the moment, so you can enjoy this giant list of pictures showing the process of gathering data ---for the second time if you have already viewed the first article--- The old saying -- "A picture is a thousand words" is very much relevant here, so I hope you enjoyed reading the 3x1000 word academic essay. (cue round of applause). Yap machine is rebooting => beep => beep => beep Anyway Now I have all the data, I can finally arrange them into a desired format, to, ya know, easier convey the big flex across. Step 10: Roleplaying as instagram (again -_-) Same old process of using postman to test requests 😃 Then repeating the same process in python. Well somewhat similar. The feature of getting how many times a friend liked my total posts is not exactly provided in the obvious way unlike just getting likes and comments. I could just go onto a post and check the like number but in order to find who liked it, and calculating (adding it up) for all posts, is a whole different story. It chucks a request at a nicely named url endpoint 'likers', conveniently indicating what I was looking for- who liked my posts By using the request to get likes and comments counts (from the previous article) I could get the 'pk' of each of the posts; although I like to think it means 'pookie' used in 'pookie bear' it more likely means 'primary key', used to identify record in databases. BORING but necessary. When looking at the data, you can see everyone that liked the post, and their usernames. Going through the /likers endpoint for different posts (by using the ‘pk’) there are some similarities of who appears in the list. This means that one person liked multiple posts.   5.  Thus going through the all the /likers endpoint for each of the, and gathering data from the response of each endpoint and storing it in a dictionary format (key:value structure) was a good option.   6.  And thus, the graphing. It chucks a request at a nicely named url endpoint 'likers', conveniently indicating what I was looking for- who liked my posts It chucks a request at a nicely named url endpoint 'likers', conveniently indicating what I was looking for- who liked my posts By using the request to get likes and comments counts (from the previous article) I could get the 'pk' of each of the posts; although I like to think it means 'pookie' used in 'pookie bear' it more likely means 'primary key', used to identify record in databases. BORING but necessary. By using the request to get likes and comments counts (from the previous article) I could get the 'pk' of each of the posts; although I like to think it means 'pookie' used in 'pookie bear' it more likely means 'primary key', used to identify record in databases. BORING but necessary. When looking at the data, you can see everyone that liked the post, and their usernames. When looking at the data, you can see everyone that liked the post, and their usernames. Going through the /likers endpoint for different posts (by using the ‘pk’) there are some similarities of who appears in the list. This means that one person liked multiple posts.   5.  Thus going through the all the /likers endpoint for each of the, and gathering data from the response of each endpoint and storing it in a dictionary format (key:value structure) was a good option.   6.  And thus, the graphing. Going through the /likers endpoint for different posts (by using the ‘pk’) there are some similarities of who appears in the list. This means that one person liked multiple posts. 5.  Thus going through the all the /likers endpoint for each of the, and gathering data from the response of each endpoint and storing it in a dictionary format (key:value structure) was a good option. 6.  And thus, the graphing. Step 11: Making another graphing monstrosity that math teachers would hate with a burning passion Adding necessary axis labels, letting the computer know what data to put where etc... (necessary admin stuff). Adding necessary axis labels, letting the computer know what data to put where etc... (necessary admin stuff). And now the monstrosity Zoom in if you don't want to spend the day looking at the numbers 😅 Step 12: Possible use cases/consequences? Well all these steps were used on my account but if I wanted to, I could also use it on other people's accounts and grab some visualised data, not offered natively in Instagram. But why, do you seriously have to graph every single piece of data there is? I don’t have a clue but like → It looks cool 😎 (undeniably) Its a weird flex 💪 -- while everyone else is showing cool pics of their dog on instagram, their rave times on parties or tropical cool beaches I’m just standing awkwardly like ==> "umm I don’t exactly go anywhere 'fun' cuz im a couchworm but here check out this messy, barely legible graph I made of your instagram data 📊 🤷🤷🤷" Also it can start a friendly rivalry -- heres a short roleplay
Me: Hey person a, check out this graph on how much times others liked your posts
Person A: Thanks bro, interesting (looks intently at the graph, making sure to squint eyes due to the tiny font size) ...... OMG PERSON B ONLY LIKED MY POSTS 8 OUT OF 10 TIMES!!!!!@#!@$#$# I THOUGHT THEY WERE MY BEST FRIEND!@#!@$@##!@$#%% (cue intense crying noises)
Person B: Omg bro are you ok?
Person A: OMG YOU SAID YOU LIKED ALL MY INSTAGRAM POSTS BUT YOU DIDN’T YOU GODDAMN LIAR!!!
Person C: (silently appearing from nowhere ) hey would you look at that, I liked your posts 10 times :)
Person A: Thanks buddy, you’re my new best friend now, just ignore the fact that I knew person b for like 10 years while I only know u for like 10 days or something
And here, new rivalries are born, wars are started and injustices are created
hehehehehe It looks cool 😎 (undeniably) It looks cool 😎 (undeniably) Its a weird flex 💪 -- while everyone else is showing cool pics of their dog on instagram, their rave times on parties or tropical cool beaches I’m just standing awkwardly like ==> "umm I don’t exactly go anywhere 'fun' cuz im a couchworm but here check out this messy, barely legible graph I made of your instagram data 📊 🤷🤷🤷" Its a weird flex 💪 -- while everyone else is showing cool pics of their dog on instagram, their rave times on parties or tropical cool beaches I’m just standing awkwardly like ==> "umm I don’t exactly go anywhere 'fun' cuz im a couchworm but here check out this messy, barely legible graph I made of your instagram data 📊 🤷🤷🤷" Also it can start a friendly rivalry -- heres a short roleplay Me: Hey person a, check out this graph on how much times others liked your posts Person A: Thanks bro, interesting (looks intently at the graph, making sure to squint eyes due to the tiny font size) ...... OMG PERSON B ONLY LIKED MY POSTS 8 OUT OF 10 TIMES!!!!!@#!@$#$# I THOUGHT THEY WERE MY BEST FRIEND!@#!@$@##!@$#%% (cue intense crying noises) Person B: Omg bro are you ok? Person A: OMG YOU SAID YOU LIKED ALL MY INSTAGRAM POSTS BUT YOU DIDN’T YOU GODDAMN LIAR!!! Person C: (silently appearing from nowhere ) hey would you look at that, I liked your posts 10 times :) Person A: Thanks buddy, you’re my new best friend now, just ignore the fact that I knew person b for like 10 years while I only know u for like 10 days or something And here, new rivalries are born, wars are started and injustices are created hehehehehe Also it can start a friendly rivalry -- heres a short roleplay Me: Hey person a, check out this graph on how much times others liked your posts Person A: Thanks bro, interesting ( looks intently at the graph, making sure to squint eyes due to the tiny font size ) ...... OMG PERSON B ONLY LIKED MY POSTS 8 OUT OF 10 TIMES!!!!!@#!@$#$# I THOUGHT THEY WERE MY BEST FRIEND!@#!@$@##!@$#%% ( cue intense crying noises ) looks intently at the graph, making sure to squint eyes due to the tiny font size cue intense crying noises Person B: Omg bro are you ok? Person A: OMG YOU SAID YOU LIKED ALL MY INSTAGRAM POSTS BUT YOU DIDN’T YOU GODDAMN LIAR!!! Person C: ( silently appearing from nowhere ) hey would you look at that, I liked your posts 10 times :) silently silently appearing from nowhere Person A: Thanks buddy, you’re my new best friend now, just ignore the fact that I knew person b for like 10 years while I only know u for like 10 days or something And here, new rivalries are born, wars are started and injustices are created hehehehehe "knowledge is power" But as you can see, what is more powerful is: Visualising knowledge in a (somewhat) readable way, so you can flex on your friends and show your data collection skills.

Graphing How Many Times People Liked my Posts on Instagram

Table of Links Abstract and Introduction
Related Work
Problem Definition
Method
Experiments
Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference 4 METHOD Our proposed method, In-Context Preference Learning (ICPL), integrates LLMs with human preferences to synthesize reward functions. The LLM receives environmental context and a task description to generate an initial set of K executable reward functions. ICPL then iteratively refines these functions. In each iteration, the LLM-generated reward functions are used to train agents within the environment, producing a set of agents; we use these agents to generate videos of their behavior. A ranking is formed over the videos, from which we retrieve the best and worst reward functions corresponding to the top and bottom videos in the ranking. These selections serve as examples of positive and negative preferences. The preferences, along with additional contextual information, such as reward traces and differences from previous good reward functions, are provided as feedback prompts to the LLM. The LLM takes in this context and is asked to generate a new set of rewards. Algo. 1 presents the pseudocode, and Fig. 1 illustrates the overall process of ICPL. 4.1 REWARD FUNCTION INITIALIZATION To enable the LLM to synthesize effective reward functions, it is essential to provide task-specific information, which consists of two key components: a description of the environment, including the observation and action space, and a description of the task objectives. At each iteration, ICPL ensures that K executable reward functions are generated by resampling until there are K executable reward functions. 4.2 SEARCH REWARD FUNCTIONS BY HUMAN PREFERENCES For tasks without reward functions, the traditional preference-based approach typically involves constructing a reward model, which often demands substantial human feedback. Our approach, ICPL, aims to enhance efficiency by leveraging LLMs to directly search for optimal reward functions without the need to learn a reward model. To expedite this search process, we use an LLM-guided search to find well-performing reward functions. Specifically, we generate K = 6 executable reward functions per iteration across N = 5 iterations. In each iteration, humans select the most preferred and least preferred videos, resulting in a good reward function and a bad one. These are used as a context for the LLM to use to synthesize a new set of K reward functions. These reward functions are then used in a PPO (Schulman et al., 2017) training loop, and videos are rendered of the final trained agents. 4.3 AUTOMATIC FEEDBACK In each iteration, the LLM not only incorporates human preferences but also receives automatically synthesized feedback. This feedback is composed of three elements: the evaluation of selected reward functions, the differences between historical good reward functions, and the reward trace of these historical reward functions. Evaluation of reward functions: The component values that make up the good and bad reward functions are obtained from the environment during training and provided to the LLM. This helps the LLM assess the usefulness of different parts of the reward function by comparing the two. Differences between historical reward functions: The best reward functions selected by humans from each iteration are taken out, and for any two consecutive good reward functions, their differences are analyzed by another LLM. These differences are supplied to the primary LLM to assist in adjusting the reward function. Reward trace of historical reward functions: The reward trace, consisting of the values of the good reward functions during training from all prior iterations, is provided to the LLM. This reward trace enables the LLM to evaluate how well the agent is actually able to optimize those reward components. Authors:
(1) Chao Yu, Tsinghua University;
(2) Hong Lu, Tsinghua University;
(3) Jiaxuan Gao, Tsinghua University;
(4) Qixin Tan, Tsinghua University;
(5) Xinting Yang, Tsinghua University;
(6) Yu Wang, with equal advising from Tsinghua University;
(7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute;
(8) Eugene Vinitsky, with equal advising from New York University (zoeyuchao@gmail.com). This paper is available on arxiv under CC 4.0 license. Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References Abstract and Introduction Abstract and Introduction Related Work Related Work Problem Definition Problem Definition Method Method Experiments Experiments Conclusion and References Conclusion and References A. Appendix A. Appendix A.1. Full Prompts and A.2 ICPL Details A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A. 3 Baseline Details A.4 Environment Details A.4 Environment Details A.5 Proxy Human Preference A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A.6 Human-in-the-Loop Preference 4 METHOD Our proposed method, In-Context Preference Learning (ICPL), integrates LLMs with human preferences to synthesize reward functions. The LLM receives environmental context and a task description to generate an initial set of K executable reward functions. ICPL then iteratively refines these functions. In each iteration, the LLM-generated reward functions are used to train agents within the environment, producing a set of agents; we use these agents to generate videos of their behavior. A ranking is formed over the videos, from which we retrieve the best and worst reward functions corresponding to the top and bottom videos in the ranking. These selections serve as examples of positive and negative preferences. The preferences, along with additional contextual information, such as reward traces and differences from previous good reward functions, are provided as feedback prompts to the LLM. The LLM takes in this context and is asked to generate a new set of rewards. Algo. 1 presents the pseudocode, and Fig. 1 illustrates the overall process of ICPL. 4.1 REWARD FUNCTION INITIALIZATION To enable the LLM to synthesize effective reward functions, it is essential to provide task-specific information, which consists of two key components: a description of the environment, including the observation and action space, and a description of the task objectives. At each iteration, ICPL ensures that K executable reward functions are generated by resampling until there are K executable reward functions. 4.2 SEARCH REWARD FUNCTIONS BY HUMAN PREFERENCES For tasks without reward functions, the traditional preference-based approach typically involves constructing a reward model, which often demands substantial human feedback. Our approach, ICPL, aims to enhance efficiency by leveraging LLMs to directly search for optimal reward functions without the need to learn a reward model. To expedite this search process, we use an LLM-guided search to find well-performing reward functions. Specifically, we generate K = 6 executable reward functions per iteration across N = 5 iterations. In each iteration, humans select the most preferred and least preferred videos, resulting in a good reward function and a bad one. These are used as a context for the LLM to use to synthesize a new set of K reward functions. These reward functions are then used in a PPO (Schulman et al., 2017) training loop, and videos are rendered of the final trained agents. 4.3 AUTOMATIC FEEDBACK In each iteration, the LLM not only incorporates human preferences but also receives automatically synthesized feedback. This feedback is composed of three elements: the evaluation of selected reward functions, the differences between historical good reward functions, and the reward trace of these historical reward functions. Evaluation of reward functions : The component values that make up the good and bad reward functions are obtained from the environment during training and provided to the LLM. This helps the LLM assess the usefulness of different parts of the reward function by comparing the two. Evaluation of reward functions Differences between historical reward functions : The best reward functions selected by humans from each iteration are taken out, and for any two consecutive good reward functions, their differences are analyzed by another LLM. These differences are supplied to the primary LLM to assist in adjusting the reward function. Differences between historical reward functions Reward trace of historical reward functions : The reward trace, consisting of the values of the good reward functions during training from all prior iterations, is provided to the LLM. This reward trace enables the LLM to evaluate how well the agent is actually able to optimize those reward components. Reward trace of historical reward functions Authors: (1) Chao Yu, Tsinghua University; (2) Hong Lu, Tsinghua University; (3) Jiaxuan Gao, Tsinghua University; (4) Qixin Tan, Tsinghua University; (5) Xinting Yang, Tsinghua University; (6) Yu Wang, with equal advising from Tsinghua University; (7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute; (8) Eugene Vinitsky, with equal advising from New York University (zoeyuchao@gmail.com). Authors: Authors: (1) Chao Yu, Tsinghua University; (2) Hong Lu, Tsinghua University; (3) Jiaxuan Gao, Tsinghua University; (4) Qixin Tan, Tsinghua University; (5) Xinting Yang, Tsinghua University; (6) Yu Wang, with equal advising from Tsinghua University; (7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute; (8) Eugene Vinitsky, with equal advising from New York University ( zoeyuchao@gmail.com ). zoeyuchao@gmail.com This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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