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A cool concept in topological data analysis is the simplicial complex. It connects points with edges, triangles, and higher order analogs. By gradually increasing each point's neighborhood size it captures data's topological structure (e.g. voids, holes) at different scales.

Alec Helbling

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54,432 views • 8 months ago •via X (Twitter)

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Big step forward for root cause analysis in real-world applications! There’s a new method that will help identify the causes of a problem or event. It uses causal discovery, boosting trees together with TDA. This is crucial to enable root cause analysis in tasks like the following: • Fraud detection • Drug discovery • Customer behavior analysis • Energy and sustainability • Financial analysis and risk management • Failure analysis in engineering systems Topological data analysis (TDA), on the other hand, studies the topological properties of data sets. You can use TDA for clustering, classification, and anomaly detection. The team DataRefiner developed a new approach to integrating causal discovery with TDA segmentation, and they are getting the best results from anything in the market right now. For the first time, there's a tool where users can choose clusters and get a focused causal dependency graph. They use boosting trees to estimate causal effects in complex systems. They complement this approach with TDA by offering insights into potential causal pathways. With TDA, users can visualize and understand the relationship between variables. Most open-source systems struggle with categorical parameters or values with different scales. This new approach doesn’t have those problems. Look at the attached video to understand what you can do with this. Here is a link to a post with all of the details. It contains three detailed examples that will drive the idea home: Thanks to DataRefiner for sponsoring this post.

Big step forward for root cause analysis in real-world applications! There’s a new method that will help identify the causes of a problem or event. It uses causal discovery, boosting trees together with TDA. This is crucial to enable root cause analysis in tasks like the following: • Fraud detection • Drug discovery • Customer behavior analysis • Energy and sustainability • Financial analysis and risk management • Failure analysis in engineering systems Topological data analysis (TDA), on the other hand, studies the topological properties of data sets. You can use TDA for clustering, classification, and anomaly detection. The team DataRefiner developed a new approach to integrating causal discovery with TDA segmentation, and they are getting the best results from anything in the market right now. For the first time, there's a tool where users can choose clusters and get a focused causal dependency graph. They use boosting trees to estimate causal effects in complex systems. They complement this approach with TDA by offering insights into potential causal pathways. With TDA, users can visualize and understand the relationship between variables. Most open-source systems struggle with categorical parameters or values with different scales. This new approach doesn’t have those problems. Look at the attached video to understand what you can do with this. Here is a link to a post with all of the details. It contains three detailed examples that will drive the idea home: Thanks to DataRefiner for sponsoring this post.

Santiago

234,796 views • 2 years ago

Why would a UFO use bismuth? It’s about electromagnetism.. Magnesium bismuth is as a high-k dielectric: it can store a lot of electromagnetic charge and discharge it fast. Look at the Biefeld–Brown effect idea: put a high-k dielectric between a negative and positive electrode, and the “thrust” scales with higher dielectric constant. Key detail: it’s has ultra-thin “micron layers,” thinner than a human hair. Layered materials like this (including topological insulators) can produce weird, large scale electrical effects when energized. Even aerospace is exploring them! Episode with Clas drops today.

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American Alchemy

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Sark - OSU defense is elite at disguising coverages and it's a sophisticated scheme. Jedd Fisch on the difficulties of OSU defense - It's an NFL-like complexity with their disguises. PJ Fleck - it's the schematic pieces that make it so complex, where every single play is a different front structure, blitz structure, coverage structure. Nick Saban - The multiples they present to a QB in a game, it makes it difficult for a QB to have success against all the looks. Michigan media - I WaTcHeD the film their defense is basic and simple.

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JBook.

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Faheem Ullah

11,959 views • 4 months ago

Elon may have engineered the cleanest IPO support structure ever built Insiders cannot sell unless the stock is already 30% above the IPO price That means selling pressure only arrives at $175 or higher Then Nasdaq index funds step in as forced buyers Limited supply. Forced demand. Supply faucet controlled by one person This is not a normal lockup. It was designed Most people are debating whether SpaceX is overpriced at $135 Almost nobody is looking at the structure underneath it. Check the Claude article with the full IPO analysis.

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plutos

658,391 views • 1 month ago

$Wow Sora is so amazing, I wanted to really push it to the limits and see how it would handle a very complex prompt: “A 45-45-90 right triangle divides in half within itself repeatedly to create an organized fractal structure with self similarity, increasing in size from small at the top left to large at the bottom right, the whole structure is gradually growing from the top left point as the origin to create a seamless loop over 8 seconds, the individual triangles form platforms like a staircase descending downwards and have small rounded corners, as they move towards the bottom of the screen they each divide in half and one half moves downwards slightly to create another step and then the halves should divide in half again and one moves downwards to create one more step, after the second division curved cut-outs inside the triangles emerge upwards in a mechanical motion to form a continuous winding curved path towards the top of the structure, the edges of the path cutouts should be visible in the floor before they emerge so the viewer can anticipate the path being formed, after the path pieces move into place three Adélie penguins hop upwards along the path chasing a bright turquoise butterfly, the penguins should be hopping like the penguins in the following videos [ and should be progressing up the path while staying visually centered onscreen, the animation of the triangle platforms dividing and path forming and penguins hopping should be visually clear from top to bottom and arranged in such a way that you can look anywhere in the image to focus on a certain part of the story yet also be able to follow along from top to bottom to watch how the story unfolds throughout the image, the platforms should all be made of an elegant slightly reflective light wooden floor and white walls like a contemporary art museum, as each path segment moves upwards into place it is revealed that it is built on reflective chrome columns, lighting in the room is dark except for a single spotlight following the penguins and thin neon tube lighting around the edge of each triangle platform placed just below the floor level so that when the platforms divide and descend it reveals the neon light from the platform behind it which lights up the edges of the newly formed triangles and makes their form clear to the viewer's eye, some more neon lights inside the edge of the gap left by the path segments rising to form the path, the large dark triangle that makes up the upper right corner should be divided repeatedly by subtly glowing lines in a similar structure to the main layout but moving in the opposite direction as if to suggest that the 45-45-90 triangle on which the whole structure is based is being gradually disintegrated over time into a series of smaller identical triangles and emotionally relatable architecture as a frame for the penguins at the center to make them seem like the protagonist of the piece even though the true protagonist is the triangle itself, in the style of shapiro500, isometric 3d, beautiful lighting, best quality, masterpiece, hyperdetailed 4k, octane render” but I don't have Sora access so I made it by hand with Cinema 4D instead$

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Gavin Shapiro 🐧

176,839 views • 2 years ago

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Weaver Labs

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gemchanger

174,961 views • 5 months ago

The Fraud Industrial Complex has crossed into RICO territory... AI is now pulling the entire structure into view by linking stolen identities, foreign call centers, compromised providers, shell companies, banks, cryptocurrency, synthetic consent, Medicare, Medicaid, and billions in extracted public money into one coordinated enterprise... The fraud was spread across institutions, jurisdictions, and borders to keep the machine hidden, yet every claim, payment, provider, account, transfer, digital signature, front company, recruiter, doctor, broker, banker, data thief, and money launderer now lands on the same conspiracy map... This is the shift... the government is moving upstream, seeing the network while it operates, stopping payments before they clear, tracing the money as it moves, and building the full enterprise from the ground up... watch the video below... AI connects the pattern... RICO captures the structure... and the Fraud Industrial Complex is being dismantled as one living system...

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Observing Consciousness

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Autowelt

114,267 views • 6 months ago

🚨🚨🚨 BREAKING NEWS PRESS CONFERENCE FROM JAPAN Japan's Real World Data shows vaccinated individuals had a higher probability of infection "We collected data from 1,000 people, divided into about 500 vaccinated and 500 unvaccinated individuals. In that case, there is a paper available now, so I will show it to you. Once it was carefully filtered, when comparing vaccinated and unvaccinated individuals, the vaccinated individuals had a higher probability of infection. It increases with each dose administered. There is a statistically significant difference."

🚨🚨🚨 BREAKING NEWS PRESS CONFERENCE FROM JAPAN Japan's Real World Data shows vaccinated individuals had a higher probability of infection "We collected data from 1,000 people, divided into about 500 vaccinated and 500 unvaccinated individuals. In that case, there is a paper available now, so I will show it to you. Once it was carefully filtered, when comparing vaccinated and unvaccinated individuals, the vaccinated individuals had a higher probability of infection. It increases with each dose administered. There is a statistically significant difference."

aussie17

171,737 views • 1 year ago

We often hear that Machine Learning models learn patterns in data. But what does that look like in geometry? Picture dropping an elastic mesh into a cloud of points and letting it adapt. How would it bend, stretch, and settle so it matches the shape hiding in the data? In this scene we watch a self-organizing map(SOM)...a classic unsupervised neural model...learn a 2D dataset arranged like a spiral arm. Over the points we place a square grid of neurons whose weights live in the same plane. At the start it’s just a flat net drifting across the cloud. No clue what the structure is. Fir the SOM, learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, then nudge that neuron and its neighbours toward the point. Repeat, repeat, repeat...while gradually shrinking how wide the neighbourhood influence spreads. The result is satisfying...the grid stops being a grid and turns into a coordinate sheet wrapped onto the spiral. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML #SelfOrganizingMap #Topology

We often hear that Machine Learning models learn patterns in data. But what does that look like in geometry? Picture dropping an elastic mesh into a cloud of points and letting it adapt. How would it bend, stretch, and settle so it matches the shape hiding in the data? In this scene we watch a self-organizing map(SOM)...a classic unsupervised neural model...learn a 2D dataset arranged like a spiral arm. Over the points we place a square grid of neurons whose weights live in the same plane. At the start it’s just a flat net drifting across the cloud. No clue what the structure is. Fir the SOM, learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, then nudge that neuron and its neighbours toward the point. Repeat, repeat, repeat...while gradually shrinking how wide the neighbourhood influence spreads. The result is satisfying...the grid stops being a grid and turns into a coordinate sheet wrapped onto the spiral. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML #SelfOrganizingMap #Topology

Mathelirium

23,021 views • 6 months ago

.Jolie says the Hantavirus is unlikely to cause a pandemic because it would have to mutate three times — and we've had data about the virus for 30 years. "For Andes virus to become pandemic capable... it needs to essentially change on three different points." "It's not a single point mutation, and make complex changes to its own surface proteins in order to become transmissible by airway, and that would be what we have to see to increase transmission." "The virus has had three decades to mutate. And given it was obviously mostly from rodent to human, and it was mostly contained in certain places but it has not mutated in certain super spreader events and in 30 years." "I understand the point of the skepticism. But I do wanna emphasize the point and the baseline is that we've had 30 years of data."

.Jolie says the Hantavirus is unlikely to cause a pandemic because it would have to mutate three times — and we've had data about the virus for 30 years. "For Andes virus to become pandemic capable... it needs to essentially change on three different points." "It's not a single point mutation, and make complex changes to its own surface proteins in order to become transmissible by airway, and that would be what we have to see to increase transmission." "The virus has had three decades to mutate. And given it was obviously mostly from rodent to human, and it was mostly contained in certain places but it has not mutated in certain super spreader events and in 30 years." "I understand the point of the skepticism. But I do wanna emphasize the point and the baseline is that we've had 30 years of data."

MTS

48,449 views • 3 months ago

🇺🇸 MICROSOFT BUILDS QUANTUM CHIP WITH NEW STATE OF MATTER Microsoft just unveiled a quantum chip called Mirana One, and it’s powered by a material that doesn’t naturally exist. To make it work, scientists created a new state of matter that’s not solid, liquid, or gas. It controls quantum particles using atom-by-atom topo conductors, enabling stable topological qubits…100x smaller than a human hair. Eight qubits already fit on a chip the size of your palm. The goal? A million. Processing power beyond anything on Earth. Forget supercomputers. This fits in your hand. Source: Rowan Cheung

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Mario Nawfal

194,733 views • 1 year ago

E-commerce has always been rich in data. But it’s been poor in intelligence. Until now. Today we’re announcing a major expansion of StordAI with the launch of Chat, Search and Feed. Chat is live in beta today and is embedded directly into our platform. It connects data across OMS, WMS, CRM, and fulfillment systems, and turns it into instant, actionable answers. Ask a question. Get insight Take action. Read the Forbes article: Get started at:

E-commerce has always been rich in data. But it’s been poor in intelligence. Until now. Today we’re announcing a major expansion of StordAI with the launch of Chat, Search and Feed. Chat is live in beta today and is embedded directly into our platform. It connects data across OMS, WMS, CRM, and fulfillment systems, and turns it into instant, actionable answers. Ask a question. Get insight Take action. Read the Forbes article: Get started at:

Sean Henry

793,479 views • 5 months ago

made a big breakthrough on tinynodes! this is a huge .blend file with 679 nodes, running 100% in the browser... and it's fully dynamic! at the end of the video, you can see the raw geometry and the node graph that describes how to turn it into this complex wooden structure

made a big breakthrough on tinynodes! this is a huge .blend file with 679 nodes, running 100% in the browser... and it's fully dynamic! at the end of the video, you can see the raw geometry and the node graph that describes how to turn it into this complex wooden structure

Seth Thompson

36,271 views • 2 months ago

Trump has made it clear. This is a regime change mission. But you don't achieve regime change by replacing Khamenei with another ayatollah. In order for regime change to happen, the entire structure of the ayatollahs and the Revolutionary Guard must be swept away.

Trump has made it clear. This is a regime change mission. But you don't achieve regime change by replacing Khamenei with another ayatollah. In order for regime change to happen, the entire structure of the ayatollahs and the Revolutionary Guard must be swept away.

John Bolton

252,090 views • 5 months ago

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

delost

20,790 views • 22 days ago