#universality — Public Fediverse posts

Explore the intersection of data mining and many-body physics with Dr. Marcello Dalmonte from the Abdus Salam International Centre for Theoretical Physics (ICTP) and his first lecture of the 7-part series 'Data Mining the Many-body Problem.' Marcello is eager to answer your questions on Enabla, so don’t miss the chance to deepen your understanding through direct interaction!

🔗 Watch here: https://enabla.com/pub/702/about

Abstract: In this lecture, we focus on the intersection of data mining and the many-body problem, within the framework of statistical mechanics. We outline our motivations for leveraging advances in computational techniques to analyze large datasets generated by complex physical systems. We discuss concepts of universality across diverse fields and how they can inform our understanding of data structures. By exploring lattice models such as the two-dimensional Ising one, we aim to uncover universal behaviors that bridge physics and data science through innovative methodologies like unsupervised learning. This course seeks to equip participants with tools that highlight shared characteristics among seemingly disparate phenomena and are interested in understanding the many-body problem in terms of high-dimensional geometry concepts.

#DataScience #ManyBodyPhysics #StatisticalMechanics #Universality #OpenAccess

Rules are artificial so unnatural, patterns are within nature and us so they are natural.

#thougts #philosohy #science #discusion #nature #spirit #rebellion #think #pattern #universe #universality

A Systematic Approach to Universal Random Features in Graph Neural Networks

Billy Joe Franks, Markus Anders, Marius Kloft, Pascal Schweitzer

Action editor: Rémi Flamary.

https://openreview.net/forum?id=AXUtAIX0Fn

#universality #features #urf

@science_quotes @jcastroarnaud
I tried to make it general enough that anyone could respond, so no worries there.

[Here's a post I just happened to see that talks about the importance of measuring principles, in the context of domestic abuse: https://c.im/@[email protected]e/109523214049642663 ]

The thing I could have emphasized more was the dual nature of the process. When we're stuck in one, the other will suffer.

So 'letting go' cuts both ways. As you point out, often times our models run into problems, and we have to make changes. The disagreement between #experiment and #theory is a big red flag.

The default seems to be to alter the theory in some way. This is not always bad, nor is it always right. The impetus for this is our confidence in the model, fueled by various factors. The farther back the model goes, the less likely people are to challenge it, or even go back to the start for a complete review.

At the #fundamental layer of maths are simply the concept of numbers & quantity. If you're going to use them other than the way they come, the set is probably the next option.

OTOH, not doing this is also creating #HiddenAssumptions that do pass along to the higher levels. The problem with problems is that we don't always know where they're coming from!

The idea of 'continuous' reality, measured by a continuous number line populated by integers, and then modified by 'infinitely divisible units' between them in order to gain accuracy or change #scale, is the status quo that few ever bother to question.

IMO, the time to do that re-examination is exactly in circumstances like you brought up. Looking only at GR, or only at QM, we might walk away with the impression of their correctness & #universality. But when they cannot be brought to terms with each other, then the next (or at least after a century of tinkering & failing) logical area to question is in the models & structures prior to them (rinse and repeat until resolved).

Our notion of making single, #continuous measurements and being able to determine things like #simultaneity or mass-energy relations has failed in these areas.

The similarity between the theory / #measurement relationship, and between practice / 'live' performance is what I was aiming to highlight.

When not performing, all masters practice /revisit the basics on a regular basis. A good practice session 'lets go' of the conditions of a performance, and vice versa.

@science_quotes @jcastroarnaud
I tried to make it general enough that anyone could respond, so no worries there.

[Here's a post I just happened to see that talks about the importance of measuring principles, in the context of domestic abuse: https://c.im/@[email protected]e/109523214049642663 ]

The thing I could have emphasized more was the dual nature of the process. When we're stuck in one, the other will suffer.

So 'letting go' cuts both ways. As you point out, often times our models run into problems, and we have to make changes. The disagreement between #experiment and #theory is a big red flag.

The default seems to be to alter the theory in some way. This is not always bad, nor is it always right. The impetus for this is our confidence in the model, fueled by various factors. The farther back the model goes, the less likely people are to challenge it, or even go back to the start for a complete review.

At the #fundamental layer of maths are simply the concept of numbers & quantity. If you're going to use them other than the way they come, the set is probably the next option.

OTOH, not doing this is also creating #HiddenAssumptions that do pass along to the higher levels. The problem with problems is that we don't always know where they're coming from!

The idea of 'continuous' reality, measured by a continuous number line populated by integers, and then modified by 'infinitely divisible units' between them in order to gain accuracy or change #scale, is the status quo that few ever bother to question.

IMO, the time to do that re-examination is exactly in circumstances like you brought up. Looking only at GR, or only at QM, we might walk away with the impression of their correctness & #universality. But when they cannot be brought to terms with each other, then the next (or at least after a century of tinkering & failing) logical area to question is in the models & structures prior to them (rinse and repeat until resolved).

Our notion of making single, #continuous measurements and being able to determine things like #simultaneity or mass-energy relations has failed in these areas.

The similarity between the theory / #measurement relationship, and between practice / 'live' performance is what I was aiming to highlight.

When not performing, all masters practice /revisit the basics on a regular basis. A good practice session 'lets go' of the conditions of a performance, and vice versa.

@science_quotes @jcastroarnaud
I tried to make it general enough that anyone could respond, so no worries there.

[Here's a post I just happened to see that talks about the importance of measuring principles, in the context of domestic abuse: https://c.im/@[email protected]e/109523214049642663 ]

The thing I could have emphasized more was the dual nature of the process. When we're stuck in one, the other will suffer.

So 'letting go' cuts both ways. As you point out, often times our models run into problems, and we have to make changes. The disagreement between #experiment and #theory is a big red flag.

The default seems to be to alter the theory in some way. This is not always bad, nor is it always right. The impetus for this is our confidence in the model, fueled by various factors. The farther back the model goes, the less likely people are to challenge it, or even go back to the start for a complete review.

At the #fundamental layer of maths are simply the concept of numbers & quantity. If you're going to use them other than the way they come, the set is probably the next option.

OTOH, not doing this is also creating #HiddenAssumptions that do pass along to the higher levels. The problem with problems is that we don't always know where they're coming from!

The idea of 'continuous' reality, measured by a continuous number line populated by integers, and then modified by 'infinitely divisible units' between them in order to gain accuracy or change #scale, is the status quo that few ever bother to question.

IMO, the time to do that re-examination is exactly in circumstances like you brought up. Looking only at GR, or only at QM, we might walk away with the impression of their correctness & #universality. But when they cannot be brought to terms with each other, then the next (or at least after a century of tinkering & failing) logical area to question is in the models & structures prior to them (rinse and repeat until resolved).

Our notion of making single, #continuous measurements and being able to determine things like #simultaneity or mass-energy relations has failed in these areas.

The similarity between the theory / #measurement relationship, and between practice / 'live' performance is what I was aiming to highlight.

When not performing, all masters practice /revisit the basics on a regular basis. A good practice session 'lets go' of the conditions of a performance, and vice versa.

@science_quotes @jcastroarnaud
I tried to make it general enough that anyone could respond, so no worries there.

[Here's a post I just happened to see that talks about the importance of measuring principles, in the context of domestic abuse: https://c.im/@[email protected]e/109523214049642663 ]

The thing I could have emphasized more was the dual nature of the process. When we're stuck in one, the other will suffer.

So 'letting go' cuts both ways. As you point out, often times our models run into problems, and we have to make changes. The disagreement between #experiment and #theory is a big red flag.

The default seems to be to alter the theory in some way. This is not always bad, nor is it always right. The impetus for this is our confidence in the model, fueled by various factors. The farther back the model goes, the less likely people are to challenge it, or even go back to the start for a complete review.

At the #fundamental layer of maths are simply the concept of numbers & quantity. If you're going to use them other than the way they come, the set is probably the next option.

OTOH, not doing this is also creating #HiddenAssumptions that do pass along to the higher levels. The problem with problems is that we don't always know where they're coming from!

The idea of 'continuous' reality, measured by a continuous number line populated by integers, and then modified by 'infinitely divisible units' between them in order to gain accuracy or change #scale, is the status quo that few ever bother to question.

IMO, the time to do that re-examination is exactly in circumstances like you brought up. Looking only at GR, or only at QM, we might walk away with the impression of their correctness & #universality. But when they cannot be brought to terms with each other, then the next (or at least after a century of tinkering & failing) logical area to question is in the models & structures prior to them (rinse and repeat until resolved).

Our notion of making single, #continuous measurements and being able to determine things like #simultaneity or mass-energy relations has failed in these areas.

The similarity between the theory / #measurement relationship, and between practice / 'live' performance is what I was aiming to highlight.

When not performing, all masters practice /revisit the basics on a regular basis. A good practice session 'lets go' of the conditions of a performance, and vice versa.

@science_quotes @jcastroarnaud
I tried to make it general enough that anyone could respond, so no worries there.

[Here's a post I just happened to see that talks about the importance of measuring principles, in the context of domestic abuse: https://c.im/@[email protected]e/109523214049642663 ]

The thing I could have emphasized more was the dual nature of the process. When we're stuck in one, the other will suffer.

So 'letting go' cuts both ways. As you point out, often times our models run into problems, and we have to make changes. The disagreement between #experiment and #theory is a big red flag.

The default seems to be to alter the theory in some way. This is not always bad, nor is it always right. The impetus for this is our confidence in the model, fueled by various factors. The farther back the model goes, the less likely people are to challenge it, or even go back to the start for a complete review.

At the #fundamental layer of maths are simply the concept of numbers & quantity. If you're going to use them other than the way they come, the set is probably the next option.

OTOH, not doing this is also creating #HiddenAssumptions that do pass along to the higher levels. The problem with problems is that we don't always know where they're coming from!

The idea of 'continuous' reality, measured by a continuous number line populated by integers, and then modified by 'infinitely divisible units' between them in order to gain accuracy or change #scale, is the status quo that few ever bother to question.

IMO, the time to do that re-examination is exactly in circumstances like you brought up. Looking only at GR, or only at QM, we might walk away with the impression of their correctness & #universality. But when they cannot be brought to terms with each other, then the next (or at least after a century of tinkering & failing) logical area to question is in the models & structures prior to them (rinse and repeat until resolved).

Our notion of making single, #continuous measurements and being able to determine things like #simultaneity or mass-energy relations has failed in these areas.

The similarity between the theory / #measurement relationship, and between practice / 'live' performance is what I was aiming to highlight.

When not performing, all masters practice /revisit the basics on a regular basis. A good practice session 'lets go' of the conditions of a performance, and vice versa.

Renato Quagliani #EPFL shows in #CERN #LHC seminar the impressive new #LHCb measurement of of R(K) and R(K∗) with the full Run 1 and 2 data based on lepton #universality

Showing results fully compatible with the #StandardModel

無 #mu /# wu

Simplified: 无

Non presence

a race for superiority is never ending. when we run the other way, towards #nothingness, the 無, the wu, we go back to the basics, the zero. at zero, we are one. equality is possible when I am #nothing. this is the principle of #minimalism. a simplification of existence comes from #universality. the radical of 無 is fire. #nondoing is active, not passive. to exist through the subtle is to move out of the gross, into the #intangible.

#mindfulness #zen

"[...] as a pedagogic project, I find #Processing actively uninterested in its own underlying materiality, aspiring instead to a kind of disembodied and bland #universality. Students are encouraged to explore the “world at large” by adding additional layers of #technology in the form of sensors, rather than considering all the ways the technologies they use are already engaged with the world. The project’s “neutral” aesthetics, while dimly echoing a once-radical #Bauhaus #aesthetic, ignore the larger pedagogic program of the historical Bauhaus’s engagement and #experimentation with the materials of its contemporary, technical production."
Welcomed critique (finally!) of Processing, #CaseyReas
#JohnMaeda, Design by Numbers (#DBN) with the perspectives given by the vernacular #ImageMagick and #SeymourPapert #LOGO.
"Torn at the seams: #vernacular approaches to #teaching with #computational tools" by #MichaelMurtaugh in the publication "Vernaculars come to #matter, (re)orienting #language and #technology" by #vvvvvvaria @manetta @ccl and #JulieBoschatThorez
https://vltk.vvvvvvaria.org/w/Torn_at_the_seams:_vernacular_approaches_to_teaching_with_computational_tools
#bricolage #pedagogy

"[...] as a pedagogic project, I find #Processing actively uninterested in its own underlying materiality, aspiring instead to a kind of disembodied and bland #universality. Students are encouraged to explore the “world at large” by adding additional layers of #technology in the form of sensors, rather than considering all the ways the technologies they use are already engaged with the world. The project’s “neutral” aesthetics, while dimly echoing a once-radical #Bauhaus #aesthetic, ignore the larger pedagogic program of the historical Bauhaus’s engagement and #experimentation with the materials of its contemporary, technical production."
Welcomed critique (finally!) of Processing, #CaseyReas
#JohnMaeda, Design by Numbers (#DBN) with the perspectives given by the vernacular #ImageMagick and #SeymourPapert #LOGO.
"Torn at the seams: #vernacular approaches to #teaching with #computational tools" by #MichaelMurtaugh in the publication "Vernaculars come to #matter, (re)orienting #language and #technology" by #vvvvvvaria @manetta @ccl and #JulieBoschatThorez
https://vltk.vvvvvvaria.org/w/Torn_at_the_seams:_vernacular_approaches_to_teaching_with_computational_tools
#bricolage #pedagogy

"[...] as a pedagogic project, I find #Processing actively uninterested in its own underlying materiality, aspiring instead to a kind of disembodied and bland #universality. Students are encouraged to explore the “world at large” by adding additional layers of #technology in the form of sensors, rather than considering all the ways the technologies they use are already engaged with the world. The project’s “neutral” aesthetics, while dimly echoing a once-radical #Bauhaus #aesthetic, ignore the larger pedagogic program of the historical Bauhaus’s engagement and #experimentation with the materials of its contemporary, technical production."
Welcomed critique (finally!) of Processing, #CaseyReas
#JohnMaeda, Design by Numbers (#DBN) with the perspectives given by the vernacular #ImageMagick and #SeymourPapert #LOGO.
"Torn at the seams: #vernacular approaches to #teaching with #computational tools" by #MichaelMurtaugh in the publication "Vernaculars come to #matter, (re)orienting #language and #technology" by #vvvvvvaria @manetta @ccl and #JulieBoschatThorez
https://vltk.vvvvvvaria.org/w/Torn_at_the_seams:_vernacular_approaches_to_teaching_with_computational_tools
#bricolage #pedagogy