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1000 results for “quantixed”
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💪 En cette période de mobilisation, voici un tirage de luxe de saison : Le cri du Peuple tome 3 grand format numéroté/signé à 900 exemplaires par l'immense Tardi, accompagné d'une superbe lithographie (signée aussi).
Cette petite merveille est disponible en quantité limitée dans les deux Bdnet. Contactez-nous pour les mettre de côté.
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#LeCriDuPeuple #Tardi #JeanVautrin #Casterman #drawing #bd #bandedessinee #bdnet #bdnetbastille #bdnetnation #bookworm #booklover #bookstore -
A nice discussion on the "best human cell line" in the Quantixed blog: https://quantixed.org/2022/10/01/line-up-recommendations-for-the-best-human-cell-line/
#cellline #biology -
Les nuages un réservoir de #pesticides bien plus vaste qu’escompté, et, chaque jour,d’importantes quantités de substances actives ( #herbicides #insecticides #fongicides ) sont précipitées sur terre avec la pluie https://www.slobodenpecat.mk/fr/istrazhuvanje-desetici-toni-pesticidi-vkluchuvajkji-gi-i-zabranetite-prisutni-vo-oblacite-nad-francija/ Angelica Bianco,chercheuse au Labo de météorologie physique de l'Université de Clermont-Auvergne, a déclaré qu'elle s'attendait à trouver «seulement quelques kilogrammes» de pesticides dans les nuages au-dessus de la #France https://www.uca.fr/recherche/presentation/lactualite-scientifique/institut-des-sciences/les-nuages-sont-ils-un-reservoir-de-pesticides
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In 1900, Max Planck managed to convince himself that heat is quantized. Later, Albert Einstein showed that light is also bundled into tiny quanta, which we call photons. This was the beginning of Quantum Theory. #Poetry #Science #History #Quantum_mechanics #Planck (https://sharpgiving.com/thebookofscience/items/p1900.html)
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Une puce créée au Québec transfère une quantité gigantesque de données en un temps record
https://ici.radio-canada.ca/nouvelle/2178397/puce-optique-creation-universite-laval
#COPL #ULaval #recherche #tech #IA #optique #lumière #puce #électronique #science #communication #électricité #Québec #Canada #données -
There is a pull request by Robert Haase that introduces #ChatGPT to the #FijiSc Script Editor:
https://github.com/scijava/script-editor/pull/67
Caveat emptor!
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Idosos com memória excepcional têm grande quantidade de neurônios jovens
- cijnusp
https://jornal.usp.br/radio-usp/idosos-com-memoria-excepcional-tem-grande-quantidade-de-neuronios-jovens/
#MayanaZatz #RdioUSP #Alzheimer #Memria #Neurognese -
@gogmagog
Right. How to unpack?Midi humanizer is one thing, but what's the threshold setting for Larsanization?
And then that got me thinking, about the opposite side... if they have midi triggers on his studio kit to allow them to adjust the mix later, is he close enough for quantize to even work? In some respects, it's like spellcheck... you have to be close enough for it to recognize the word it needs to correct.
And who really deserves the title Floppotron? The machine or #Lars?
🤘 😂
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Angariação de Fundos - Covas do Barroso
📍 Casa do Comun 📅 sábado, 5 abril (15:00) Malta de Lisboa e arredores, amanhã há benefit solidário para a luta em Covas do Barroso na Casa do Comum, no Bairro Alto! Venham todxs, vai ser fixe! Conversas, concertos, dj sets, jantarada! Há uns tempos houve uma conversa aqui neste grupo sobre a necessidade de se mercantilizarem os filmes que se fazem sobre esta luta para se poder pagar aos “artistas” que os fizeram. Acho que este tipo de benefits é um exemplo interessante para se pensar na quantidade de pessoas (“artistas” e não só) que trabalham e fazem as suas merdas de “borla” em prol da resistência. E a malta de Covas e de todos os lugares onde se luta tb não recebe salários para estar a lutar, né? Vamos lá, camaradas, menos escadinha no sistema e mais corpo ao manifesto. Estamos juntxs. Até amanhã!https://eventos.coletivos.org/event/angariacao-de-fundos-covas-do-barroso
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Relatório pós-refeição:
Acabei de comer. E não foi pouco. Foi na escala "Luffy depois do Gear Five". O cardápio? Arroz branco e batata doré.A sensação é de que expandi meus limites estomacais para um novo gear. Agora entendo o protagonista: poder vem de comida boa e simples, em quantidades heroicas.
Preciso de um cochilo de rei dos piratas. Ou pelo menos de um sofá. #OnePiece #Luffy #GearFive #FomeEpica #ArrozComBatata #CronicaGourmet
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Very thin galaxies
The stability of spiral galaxies was a foundational motivation to invoke dark matter: a thin disk of self-gravitating stars is unstable unless embedded in a dark matter halo. Modified dynamics can also stabilize galactic disks. A related test is provided by how thin such galaxies can be.
Thin galaxies exist
Spiral galaxies seen edge-on are thin. They have a typical thickness – their short-to-long axis ratio – of q ≈ 0.2. Sometimes they’re thicker, sometimes they’re thinner, but this is often what we assume when building mass models of the stellar disk of galaxies that are not seen exactly* edge-on. One can employ more elaborate estimators, but the results are not particularly sensitive to the exact thickness so long as it isn’t the limit of either razor thin (q = 0) or a spherical cow (q = 1).
Sometimes galaxies are very thin. Behold the “superthin” galaxy UGC 7321:
UGC 7321 as seen in optical colors by the Sloan Digital Sky Survey.It also looks very thin in the infrared, which is the better tracer of stellar mass:
Fig. 1 from Matthews et al (1999): H-band (1.6 micron) image of UGC 7321. Matthews (2000) finds a near-IR axis ratio of 14:1. That’s super thin (q = 0.07)!UGC 7321 is very thin, would be low surface brightness if seen face-on (Matthews estimates a central B-band surface brightness of 23.4 mag arcsec-2), has no bulge component thickening the central region, and contains roughly as much mass in gas as stars. All of these properties dispose a disk to be fragile (to perturbations like mergers and subhalo crossings) and unstable, yet there it is. There are enough similar examples to build a flat galaxy catalog, so somehow the universe has figured out a way for galaxy disks to remain thin and dynamically cold# for the better part of a Hubble time.
We see spiral galaxies at various inclinations to our line of sight. Some will appear face on, others edge-on, and everything in between. If we observe enough of them, we can work out what the intrinsic distribution is based on the projected version we see.
First, some definitions. A 3D object has three principle axes of lengths a, b, and c. By convention, a is the longest and c the shortest. An oblate model imagines a galaxy like a frisbee: it is perfectly round seen face-on (a = b); seen edge-on q = c/a. More generally, an object can be triaxial, with a ≠ b ≠ c. In this case, a galaxy would not appear perfectly round even when seen perfectly face-on^ because it is intrinsically oval (with similar axis lengths a ≈ b but not exactly equal). I expect this is fairly common among dwarf Irregular galaxies.
The observed and intrinsic distribution of disk thicknesses
Benevides et al. (2025) find that the distribution of observed axis ratios q is pretty flat. This is a consequence of most galaxies being seen at some intermediate viewing angle. One can posit an intrinsic distribution, model what one would see at a bunch of random viewing angles, and iterate to extract the true distribution in nature, which they do:
Figure 6 from Benevides et al. (2025): Comparison between the observed (projected) distribution and the inferred intrinsic 3D axis ratios for a subsample of dwarfs in the GAMA survey with –. The observed shapes are shown with the solid black line and are used to derive an intrinsic (long-dashed) and (dotted) distribution when projected. Solid color lines in each panel corresponds to the values obtained from the 3D model after random projections. Note that a wide distribution of values is generated by a much narrower intrinsic distribution. For example, the blue shaded region in the left panel shows that an observed of galaxies with requires of galaxies to have an intrinsic for an oblate model. Similarly, for a triaxal model (right panel, red curve) of galaxies are required to be thinner than . The additional freedom of in the triaxial model helps to obtain a better fit to the projected distribution, but the changes mostly affect large values and changes little the frequency derived from highly elongated objects.That we see some thin galaxies implies that they they have to be common, as most of them are not seen edge-on. For dwarf$ galaxies of a specific mass range, which happens to include UGC 7321, Benevides et al. (2025) infer a lot% of thin galaxies, at least 40% with q < 0.2. They also infer a little bit of triaxiality, a ≈ b.
The existence and numbers of thin dwarfs seems to come as a surprise to many astronomers. This is perhaps driven in part by theoretical expectations for dwarf galaxies to be thick: a low surface brightness disk has little self-gravity to hold stars in a narrow plane. This expectation is so strong that Benevides et al. (2025) feel compelled to provide some observed examples, as if to say look, really:
Figure 8 – images of real galaxies from Benevides et al. (2025): Examples of highly elongated dwarf galaxies with and – . They resemble thin edge-on disks and can be found even among the faintest dwarfs in our sample. Legends in each panel quote the stellar mass, the shape parameter , as well as the GAMA identifier. Objects are sorted by increasing , left to right.As an empiricist who has spent a career looking at low mass and low surface brightness galaxies, this does not come as a surprise to me. These galaxies look normal. That’s what the universe of late type dwarf$ galaxies looks like.
Edge-on galaxies in LCDM simulations
Thin galaxies do not occur naturally in the hierarchical mergers of LCDM, where one would expect a steady bombardment by merging masses to mess things up. The picture above is not what galaxy-like objects in LCDM simulations look like. Scraping through a few simulations to find the flattest galaxies, Benevides et al. (2025) find only a handful of examples:
Figure 11 – images of simulated galaxies from Benevides et al. (2025): Edge-on projection of examples of the flattest galaxies in the TNG50 simulation, in different bins of stellar mass.Note that only the four images on the left here occupy the same stellar mass range as the images of reality above. These are as close as it gets. Not terrible, but also not representative&. The fraction of galaxies this thin is a tiny fraction of the simulated population whereas they are quite common in reality. Here the two are compared: three different surveys (solid lines) vs. three different simulations (dashed lines).
Figure 9 from Benevides et al. (2025): Fraction of galaxies that are derived to be intrinsically thinner than as a function of stellar mass. Thick solid lines correspond to our observational samples while dashed lines are used to display the results of cosmological simulations. Different colors highlight the specific survey or simulation name, as quoted in the legend. In all observational surveys, the frequency of thin galaxies peaks for dwarfs with , almost doubling the frequency observed on the scale of MW-mass galaxies. Thin galaxies do not disappear at lower masses: we infer a significant fraction of dwarf galaxies with to have . This is in stark contrast with the negligible production of thin dwarf galaxies in all numerical simulations analyzed here.Note that the thinnest galaxies in nature are dwarfs of mass comparable to UGC 7321. Thin disks aren’t just for bright spirals like the Milky Way with log(M*) > 10.5. They are also common*$ for dwarfs with log(M*) = 9 and even log(M*) = 8, which are often gas dominated. In contrast, the simulations produce almost no galaxies that are thin at these lower masses.
The simulations simply do not look like reality. Again. And again, etc., etc., ad nauseam. It’s almost as if the old adage applies: garbage in, garbage out. Maybe it’s not the resolution or the implementation of the simulations that’s the problem. One could get all that right, but it wouldn’t matter if the starting assumption of a universe dominated by cold dark matter was the input garbage.
Galaxy thickness in Newton and MOND
Thick disks are not merely a product of simulations, they are endemic to Newtonian dynamics. As stars orbit around and around a galaxy’s center, they also oscillate up and down, bobbing in and out of the plane. How far up they get depends on how fast they’re going (the dynamical temperature of the stellar population) and how strong the restoring force to the plane of the disk is.
In the traditional picture of a thin spiral galaxy embedded in a quasi-spherical dark matter halo, the restoring force is provided by the stars in the disk. The dark matter halo is there to boost the radial force to make the rotation curve flat, and to stabilize the disk, for which it needs to be approximately spherical. The dark matter halo does not contribute much to the vertical restoring force because it adds little mass near the disk plane. In order to do that, the halo would have to be very squashed (small q) like the disk, in which case we revive the stability problem the halo was put there to solve.
This is why we expect low surface brightness disks to be thick. Their stars are spread thin, the surface mass density is low, so the restoring force to the disk should be small. Disks as thin as UGC 7321 shouldn’t be possible unless they are extremely cold*# dynamically – a situation that is unlikely to persist in a cosmogony built by hierarchical merging. The simulations discussed above corroborate this expectation.
In MOND, there is no dark matter halo, but the modified force should boost the vertical restoring force as well as the radial force. One thus expects thinner disks in MOND than in Newton.
I pointed this out in McGaugh & de Blok (1998) along with pretty much everything else in the universe that people tell me I should consider without bothering to check if I’ve already considered. Here is the plot I published at the time:
Figure 9 of McGaugh & de Blok (1998): Thickness q = z0/h expected for disks of various central surface densities 0. Shown along the top axis is the equivalent B-band central surface brightness 0 for * = 2. Parameters chosen for illustration are noted in the figure (a typical scale length h and two choices of central vertical velocity dispersion z). Other plausible values give similar results. The solid lines are the Newtonian expectation and the dashed lines that of MOND. The Newtonian and MOND cases are similar at high surface densities but differ enormously at low surface densities. Newtonian disks become very thick at low surface brightness. In contrast, MOND disks can remain reasonably thin to low surface density.There are many approximations that have to be made in constructing the figure above. I assumed disks were plane-parallel slabs of constant velocity dispersion, which they are not. But this suffices to illustrate the basic point, that disks should remain thinner&% in MOND than in Newton as surface density decreases: as one sinks further into the MOND regime, there is relatively more restoring force keep disks thin. To duplicate this effect in Newton, one must invent two kinds of dark matter: a dissipational kind of dark matter that forms a dark matter disk in addition to the usual dissipationless cold dark matter that makes a quasi-spherical dark matter halo.
The idea of the plot above was to illustrate the trend of expected thickness for galaxies of different central surface brightness. One can also build a model to illustrate the expected thickness as a function of radius for a pair of galaxies, one high surface brightness (so it starts in the Newtonian regime at small radii) and one of low surface brightness (in the MOND regime everywhere). I have chosen numbers** resembling the Milky Way for the high surface brightness galaxy model, and scaled the velocity dispersion of the low surface brightness model so it has very nearly the same thickness in the Newtonian regime. In MOND, both disks remain thin as a function of radius (they flare a lot in Newton) and the lower surface brightness disk model is thinner thanks to the relatively stronger restoring force that follows from being deeper in the MOND regime.
The thickness of two model disks, one high surface brightness (solid lines) and the other low surface brightness (dashed lines), as a function of radius. The two are similar in Newton (black), but differ in MOND (blue). The restoring force to the disk is stronger in MOND, so there is less flaring with increasing radius. The low surface brightness galaxy is further in the MOND regime, leading naturally to a thinner disk.These are not realistic disk models, but they again suffice to illustrate the point: thin disks occur naturally in MOND. Low surface brightness disks should be thick in LCDM (and in Newtonian dynamics in general), but can be as thin as UGC 7321 in MOND. I didn’t aim to make q ≈ 0.1 in the model low surface brightness disk; it just came out that way for numbers chosen to be reasonable representations of the genre.
What the distribution of thicknesses is depends on the accretion and heating history of each individual disk. I don’t claim to understand that. But the mere existence of dwarf galaxies with thin disks is a natural outcome in MOND that we once again struggle to comprehend in terms of dark matter.
*Seeing a galaxy highly inclined minimizes the inclination correction to the kinematic observations [Vrot = Vobs/sin(i)] but to build a mass model we also need to know the face-on surface density profile of the stars, the correction for which depends on 1/cos(i). So as a practical matter, the competition between sin(i) and cos(i) makes it difficult to analyze galaxies at either extreme.
#Dynamically cold means the random motions (quantified by the velocity dispersion of stars σ) are small compared to ordered rotation (V) in the disk, something like V/σ ≈ 10. As a disk heats (higher σ) it thickens, as some of that random motion goes in the vertical direction perpendicular to the disk. Mergers heat disks because they bring kinetic energy in from random directions. Even after an object is absorbed, the splash it made is preserved in the vertical distribution of the stars which, once displaced, never settle back into a thin disk. (Gas can settle through dissipation, but point masses like stars cannot.)
^Oval distortions are a major source of systematic error in galaxy inclination estimates, especially for dwarf Irregulars. It is an asymmetric error: a galaxy with a mild oval distortion can be inferred to have an inclination (i > 0) even when seen face-on (i = 0), but it can never have an inclination more face-on (i < 0) than exactly face-on. This is one of the common drivers of claims that low mass galaxies fall off the Tully-Fisher relation. (Other common problems include a failure to account for gas mass, bad distance estimates, or not measuring Vflat.)
$In a field with abominable terminology, what is meant by a “dwarf” galaxy is one of the worst offenders. One of my first conference contributions thirty years ago griped about the [mis]use of this term, and matters have not improved. For this particular figure, Benevides et al. (2025) define it to mean galaxies with stellar masses in the range 9 < log(M*) < 9.5, which seems big to me, but at least it is below the mass of a typical L* spiral, which has log(M*) ~ 10.5. For comparison, see Fig. 6 of the review of Bullock & Boylan-Kolchin (2017), who define “bright dwarfs” to have 7 < log(M*) < 9, and go lower from there, but not higher into the regime that we’re calling dwarf right now. So what a dwarf galaxy is depends on context.
%Note that the intrinsic distribution peaks below q = 0.2, so arguably one should perhaps adopt as typical the mode of the distribution (q ≈ 0.17).
&Another way in which even the thin simulated objects are not representative of reality is that they are dynamically hot, as indicated by the κrot parameter printed with the image. This is the fraction of kinetic energy in rotation. One of the more favorable cases with κrot = 0.67 corresponds to V/σ = 2.5. That happens in reality, but higher values are common. Of course, thin disks and dynamical coldness go hand in hand. Since the simulations involve a lot of mergers, the fraction of kinetic energy in rotation is naturally small. So I’m not saying the simulations are wrong in what they predict given the input physics that they assume, but I am saying that this prediction does not match reality.
*$The fraction of thin galaxies observed by DESI is slightly higher than found in the other surveys. Having looked at all these data, I am inclined to suspect the culprit is image quality: that of DESI is better. Regardless of the culprit for this small discrepancy between surveys, thin disks are much more common in reality than in the current generation of simulations.
*#There seems to be a limit to how cold disks get, with a minimum velocity dispersion around ~7 km/s observed in face-on dwarfs when the appropriate number, according to Newton, would be more like 2 km/s, tops. I remember this number from observations in the ’80s and ’90s, along with lots of discussion then to the effect of how can it be so? but it is the new year and I’m feeling too lazy to hunt down all the citations so you get a meme instead.
&%In an absolute sense, all other things being equal, which they’re not, disks do become thicker to lower surface brightness in both Newton and MOND. There is less restoring force for less surface mass density. It is the relative decline in restoring force and consequent thickening of the disk that is much more precipitous in Newton.
**For the numerically curious, these models are exponential disks with surface density profiles Σ(R) = Σ0 e-R/Rd. Both models have a scale length Rd = 3 kpc. The HSB has Σ0 = 866 M☉ pc-2; this is a good match to the Eilers et al. (2019) Milky Way disk; see McGaugh (2019). The LSB has Σ0 = 100 M☉ pc-2, which corresponds roughly to what I consider the boundary of low surface brightness, a central B-band surface brightness of ~23 mag. arcsec-2. For the velocity dispersion profile I also assume an exponential with scale length 2Rd (that’s what supposed to happen). The central velocity dispersion of the HSB is 100 km/s (an educated guess that gets us in the right ballpark) and that of the LSB is 33 km/s – the mass is down by a factor of ~9 so the velocity dispersion should be lower by a factor of $\sqrt{9}$. (I let it be inexact so the solid and dashed Newtonian lines wouldn’t exactly overlap.)
These models are crude, being single-population (there can be multiple stellar populations each with their own velocity dispersion and vertical scale height) and lacking both a bulge and gas. The velocity dispersion profile sometimes falls with a scale length twice the disk scale length as expected, sometimes not. In the Milky Way, Rd ≈ 2.5 or 3 kpc, but the velocity dispersion falls off with a scale length that is not 5 or 6 kpc but rather 21 or 25 kpc. I have also seen the velocity dispersion profile flatten out rather than continue to fall with radius. That might itself be a hint of MOND, but there are lots of different aspects of the problem to consider.
#Dynamically -
Here's my #PatchOfTheWeek in video form. This week, we're looking at #quantizers, and using them to tame the #MakeNoise Easel.
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The body turns into data, its essence quantified within the frameworks of digital culture.
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In 2024, the average cell biology preprint on bioRxiv gets ~50% of its views in the first 4-5 days after posting. And 75% of all views in its first month.
#ScientificPublishing #PowerToThePreprint #ReadAllAboutIt #CellBiology
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Mon partenariat avec @ANABF5 fonctionne bien. Les pavés, c'est l'idéal pour dissuader mes victimes d'un gonflage suffisant pour éviter le pincement. Longue vie aux ABF !
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RT @cyclocube
A l’atelier c’est fou la quantité de vélos aux pneus sous gonflés qu’on voit. Ne sous-estimez pas l’importance d’un pneu bien gonflé !!! #CyclistesGonflez #efficacite #anticrevaison #confort #protegetajante #rendement
https://twitter.com/cyclocube/status/1623957279585234944 -
terminology question: exact vs approximate #kNN
HNSW is approximate, brute-force exact. but what about quantized brute-force? it's both exact (brute-force) and approximate to some degree (quantized). how do you differentiate between algorithm and precision? it should be called... -
#Communiqué 🗞️ Des scientifiques montrent que l’âge, le sexe biologique et le patrimoine génétique modulent la quantité d’anticorps produits, mais aussi les régions précises du virus vers lesquelles les anticorps sont dirigés.
👉 https://www.cnrs.fr/fr/presse/comment-lage-le-sexe-et-la-genetique-faconnent-les-anticorps -
Des pneus par milliers ont été brûlés sur des ronds-ronds de Bretagne par des agriculteurs lors d'actions menées par les FDSEA et les JA, ces dernières semaines. Des quantités telles que Morgan Large a eu quelques doutes sur leur provenance. 🤔
Elle en a parlé avec Antoine Chao sur France Inter.
#agriculture #bretagne #politique #social #manifestation #pollution #environnement #biodiversite #fnsea #franceinter #interview #splann #morganlarge #alimentation #radio
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#JCRR RESEARCH ARTICLE NOTIFICATION: Our latest peer-reviewed research is now live!
In this article, authors Ruth Petrie, Dongwon Han, Ludovico Nicotina, Adam Alvarez, and Tyler Cox (Inigo) present a frequency-aware quantile mapping (FAQM) approach for assessing climate change impacts in #hurricane catastrophe models.
As a #DiamondOpenAccess article, this paper is free to read and download: https://journalofcrr.com/research/04-02-petrie-et-al/
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#JCRR RESEARCH ARTICLE NOTIFICATION: Our latest peer-reviewed research is now live!
In this article, authors Ruth Petrie, Dongwon Han, Ludovico Nicotina, Adam Alvarez, and Tyler Cox (Inigo) present a frequency-aware quantile mapping (FAQM) approach for assessing climate change impacts in #hurricane catastrophe models.
As a #DiamondOpenAccess article, this paper is free to read and download: https://journalofcrr.com/research/04-02-petrie-et-al/
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#JCRR RESEARCH ARTICLE NOTIFICATION: Our latest peer-reviewed research is now live!
In this article, authors Ruth Petrie, Dongwon Han, Ludovico Nicotina, Adam Alvarez, and Tyler Cox (Inigo) present a frequency-aware quantile mapping (FAQM) approach for assessing climate change impacts in #hurricane catastrophe models.
As a #DiamondOpenAccess article, this paper is free to read and download: https://journalofcrr.com/research/04-02-petrie-et-al/
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#JCRR RESEARCH ARTICLE NOTIFICATION: Our latest peer-reviewed research is now live!
In this article, authors Ruth Petrie, Dongwon Han, Ludovico Nicotina, Adam Alvarez, and Tyler Cox (Inigo) present a frequency-aware quantile mapping (FAQM) approach for assessing climate change impacts in #hurricane catastrophe models.
As a #DiamondOpenAccess article, this paper is free to read and download: https://journalofcrr.com/research/04-02-petrie-et-al/
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I AM NOT A NUMBER!
I am an individual
A member of the human race
Amazingly unique
Who the Hell am I kidding?
To the powers that be
I'm just a blip
on a data base screenA stat gnat
on a spread sheet
to be #measured
and quantifiedTotally marginalized
into the homogenized
numerical soup
of high tech
21st Century inhumanity -
Loop quantum gravity suggests spacetime is not continuous but quantised, leading to new insights into black holes and the early universe. In the article, explore how far this idea goes and where it falls short.
#cosymorg #science #LQG #BigBounce #SpinFoam #SpinNetworks
https://cosym.org/2026/04/11/loop-quantum-gravity/?utm_source=mastodon&utm_medium=jetpack_social
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Po latach wróciłem do #Fahrenheit (znanego w USA jako #IndigoProphecy), czyli drugiej gry #QuanticDream. Ależ to była przejażdżka...
Dziś wszyscy już doskonale znamy ambicje Davida Cage'a, w Fahrenheicie wyraźne sugestie dawało menu główne, w którym zamiast "nowej gry" rozpoczynamy "nowy film", czy też zatrudnienie Angelo Badalamentiego, stałego współpracownika Davida Lyncha, do stworzenia soundtracku. Muzyka zresztą jest w tej grze całkiem niezła ("Sandpaper Kisses" Martiny Topley-Bird od paru dni nie może się ode mnie odczepić), historia początkowo też całkiem intryguje...
No bo jak tu nie dać się zauroczyć scenerii obsypanego śniegiem Nowego Jorku? Jak nie zainteresować się, czemu nasz główny bohater rozpoczyna swoją historię od zamordowania obcego mężczyzny w toalecie zapyziałej restauracji? Jak pozbyć się chęci prowadzenia śledztwa na jego temat i odkrycia, co go - prawdopodobnie dość dosłownie - opętało? Niestety, jak to często z takim budowaniem oczekiwań bywa, prawdopodobnie nie było możliwości, by uniknąć rozczarowania odbiorcy. A na pewno nie w przypadku takiego storytellera jak #DavidCage.
Fahrenheit to łatwy chłopiec do bicia: elementów, nad którymi można się w nim znęcać, jest co niemiara. Od niekończącego się festiwalu wyjątkowo niewygodnych QTE (w tym cringe'owych scen erotycznych), przez niewiarygodne zwroty akcji, aż po sceny z udziałem czarnoskórego detektywa Tylera Milesa, przy których obowiązkowo musi grać najbardziej stereotypowy funk. Może nieco temu tytułowi brakuje do tytułu "The Room" świata gier, ale porównania do filmografii Uwego Bolla byłyby już chyba całkiem celne.
Mimo to - a może właśnie dlatego - bawiłem się przy Fahrenheicie świetnie. Aż trudno uwierzyć, że to samo studio dało nam później w gruncie rzeczy całkiem niezłe (choć wcale niepozbawione podobnych bzdur) Heavy Rain i Detroit. Zabawne też, ile błędów Fahrenheita powielało Beyond. David Cage pozostaje jednym z najbardziej charakterystycznych twórców tej branży. Z wszystkimi tego plusami i minusami.
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DC Live! Detroit: Become Human – Cross challenge, part 3 (final) livestream tonight at 8pm ET
Will Connor and Markus cross paths this time?
https://www.dcgameblog.com/2023/11/dc-live-detroit-become-human-cross-challenge-part-3-final-livestream-tonight-at-8pm-et/
#Livestream #Videogames #DavidCage #DetroitBecomeHuman #QuanticDream -
DC Live! Detroit: Become Human – Cross challenge, part 2 livestream tonight at 8pm ET
There ain't no Jasoning this X we're on
https://www.dcgameblog.com/2023/11/dc-live-detroit-become-human-cross-challenge-part-2-livestream-tonight-at-8pm-et/
#Livestream #Videogames #DavidCage #DetroitBecomeHuman #QuanticDream -
DC Live! Detroit: Become Human – Circle challenge livestream, part 4 tonight at 8 ET
Well, we lost an android last time, but at least it wasn't a protagonist.But things are getting real now. Opportunities to die or otherwise get bad ends will be coming fast and furious the whole wa
https://www.dcgameblog.com/2022/08/dc-live-detroit-become-human-circle-challenge-livestream-part-4-tonight-at-8-et/
#Livestream #Videogames #DavidCage #Detroit:BecomeHuman #Drew #livestream #QuanticDream -
DC Live! Detroit: Become Human – Circle challenge livestream, part 3 tonight at 9 ET
Chance of Circle: 100%
https://www.dcgameblog.com/2022/07/dc-live-detroit-become-human-circle-challenge-livestream-part-3-tonight-at-9-et/
#Livestream #Videogames #DavidCage #Detroit:BecomeHuman #Drew #livestream #QuanticDream