241 karma · joined February 14, 2017
josephlemaitre.com
> “What is happening right now is extremely, extremely unusual. Seeing a cloud this massive and reaching such an altitude above a wildfire is exceptionally rare,” the researcher explains.
“During the Pedrógão fire [which claimed around 60 lives in Portugal in 2017], there were two episodes of fire-induced thunderstorms. Here, we are already at eight or ten, and the forecast is not encouraging at all.”
Although unprecedented in both intensity and frequency, the phenomenon has been observed in France before, according to the expert: during the Fontainebleau wildfire (Seine-et-Marne) in July, the Ribaute fire (Aude) in 2025, and the Générac fire (Gard) in 2019.
It seems that this cloud and the attached storms have been seen before, even recently, in France, but this one is one of a kind for intensity, by far.
[1] https://www.lemonde.fr/planete/article/2026/07/27/le-pyrocum...
A better question is would you settle for o3 now or pay 20$ or 200$/month for fable ? Because o3 quality is available OSS.
It is like the new IPhone, in some sort. At some point come a feature many would like to have, despite diminishing returns.
We will see how long labs can keep up and what the scaling curve look like, but I would be more worried into losing sota status to Chinese companies than letting them take the open non-sota approach.
It's a very important fundamental for modern data-science, to give one intuition about stochastic gradient descent, high-dimensional models, ... And this book starts with just that. I'm hooked. Thanks for sharing.
See this older hacker news thread as well: https://news.ycombinator.com/item?id=45116849 A Random Walk in 10 Dimensions (2021)
you may replace "my model is wrong" by e.g " my certainty that no one is using cloud-activation technology".
Now, I'm not native in English and I see how someone (especially with a math background) might find this sentence confusing:
> you are predicting something (e.g rain tomorrow), then if you believe it will rain with probability 0.7 but you are 80% sure of your belief, you won’t say 0.7; you will say something else: 0.8 × 0.7 + 0.2 × something_you_believe = 0.58. Coherence forces you to collapse your uncertainty into your probability at each forecast.
Turns out it's very hard about math non-formally for generalist audience and might not have done the best job about it. It should read
> you are predicting something (e.g rain tomorrow), then your model believe it will rain with probability 0.7 but you are 80% sure of your model, then you won’t report 0.7; you will report : 0.8 × 0.7 + 0.2 × something_you_believe = 0.58. Coherence forces you to collapse your uncertainty into your probability at each forecast.
I make an intermediate step explicit in this derivation because it is important for the understanding of the problem.
This kind of intermediate step happens a lot in forecasting competitions, where participants are asked for their forecasts and their confidence in their forecasts. I want to show here that you need to include that in your forecast (all you belief) and not keep it separated.
You are making the same point that I did, if you read the rest. 0.7 at 80% is an intermediate step in formulating a forecast, and the point is to show that you cannot stop there and have to include everything in your number, like you have said. In your words, the intermediate steps are what you list (trust in weather forecasts, past experience,…).
So everything in the paper is distribution and when you forecast for a binary event, you give a number which is the expectation of that distribution. This is a probabilistic forecast.
If you were to give a probabilistic forecast for a continuous quantity, then yes you would give in a distribution, as in section 4.2
A calibrated forecast means that if you say there is a 20% chance of rain, then it actually rains 20% of the time. It’s a desired feature, but not the only one (e.g. you could be calibrated by stating: Chick-fil-A is open every day except Monday, but your forecast will always be wrong on Sunday and Monday).
So if
1. you are Bayesian (you state your beliefs)
2. and coherent (the laws of probability apply, so e.g. if P(A) = 0.4, then P(not A) cannot be anything other than 0.6):
and you are predicting something (e.g rain tomorrow), then if you believe it will rain with probability 0.7 but you are 80% sure of your belief, you won’t say 0.7; you will say something else: 0.8 × 0.7 + 0.2 × something_you_believe = 0.58. Coherence forces you to collapse your uncertainty into your probability at each forecast.
This theorem shows that, over many forecasts, in your belief system you are certain to be producing a calibrated forecast: your current beliefs assign probability 1 to the proposition that your future forecasts will be calibrated.
But that can’t be, which is the paradox. So Bayesianism is too strong compared to how scientists reason, because scientists always think their model can have an error.
this thing is better disabled because it's not ready.
EDIT: your message is unclear if your friend use your chat or his, in the later, I don't know
It's open-source, really well designed, local, you can even self-host sync...
But: the devs make questionable decisions that makes the development roadmap quite bumpy. It should ease up.
I do version control by exporting the .edn (a serialized file that contains all nodes) and using git.
All of that is very alpha (to be honest, I don't understand them releasing a beta now). You need to hang in the discord from time to time to make sure you do not miss a thing. I think my note app being open-source is pretty important to me that I still deal with that.
Though because of this tagging thing, it seems very "AI-ready" in the sense that queries are naturals as some block have an identity.
An example: I have a tag called job-application which has a status (like a checked box) for applied, in-process, awaiting-input, discarded, ... and I have a tag for the pages that corresponds with my research projects, with status (published, chased, forgotten, ...) and some information (GitHub, collaborators, ...).
there are views that summarize this (all projects, all jobs application)
When I mention a person in my journal, it's very easy to see how my last meeting with them went and all.
I don't use sync, I've been told it works really well.
EDIT: I forgot to say you can enable a markdown-mirror and have one way sync (DB->MD) which is very convenient for agent, or if you like markdown.
I switched to it from Apple Notes + Obsidian (I've used logseq MD in the distant past). I have to say though that there are still some rough edges in the current developments and many concepts are still half-baked (Assets, Library).
I still use it because with it, I take more notes and retrieve them better, which is really convenient. The barrier to jotting something down is very low. I think the dev have really hit a sweet spot so I hope they can polish this application as it should be.
* Does a comparison with last year help (flu activity is 10% more than last year)? * Should we remove the median from the display? * How hard should it be to check multiple models?
Otherwise, this is a preset link to see the ensemble (the consensus of most models) and our model (influpaint, a diffusion model) and flusion (avery good model by UMass) performance last year:
https://www.respilens.com/?view=flu_projs&flu_dates=2024-11-...
[1] https://www.census.gov/programs-surveys/geography/guidance/g... [2] https://www2.census.gov/geo/docs/reference/codes/files/natio...
(EDIT I see the other comment says something similar and you have replied)
Zotero is an amazing tool and I could not imagine writing papers without it. For collaborative writing, it syncs the bibliography with overleaf[2] and google docs. The browser add-on allows to build your library with one click while browsing, and it parses wonderfully meta-data from all (most) publishers and pre-print servers. Then my unique bibfile (symlinked in every project) is continuously updated to reflect new items or manual changes.
But I use it even for non-publishing side of research: one can save webpages (with snapshots) and write notes with formatting around them. My personal research workflow involves note-taking with the markdown app Zettlr[2], from which I can cite my Zotero library using simply `@[Author:Title:Year]`. I can then build notes from a twitter thread and stats.stackexchange.com answers, then connect it with papers and blog posts that way, and a specific chapter of a book with my annotation on the cloud. It not always straightforward as a workflow but it works wonders.
And the team is very responsive on the forum and on twitter. If one of the dev is ready this, thank you so much for making researchers life way easier !
EDIT: I just realised I can share my libraries online. A bit ashamed because it is very messy, but in case you are wondering what a Zotero library looks like, here is the link to mine [4] (without the notes and the embedded pdf and data files because I cannot share all of them. Moreover, I don't use the folder structure anymore so its very messy).
[1] https://www.zotero.org/support/pdf_reader_preview
[2] I recommend the BetterBibTex extension for anything related to tex, it'll save you a lot of time
[3] https://zettlr.com, another great app.
Indeed there are some wonderful contributed papers and some members use it responsibly. I also concur with with everything you said, just adding that even in laudable cases (struggling grad, ...), there is some injustice and it'll always be misleading.
Thanks for the precisions and the mention of eLife.
[1] https://twitter.com/arambaut/status/1248387395201847296?s=19
The paper[1] was contributed in PNAS, the Proceedings of the National Academy of Sciences of the United States of America. PNAS is a very well regarded, and high impact journal.
Members of National Academy of Science, usually proeminent professors, have the possibility to "contribute" their work. They can choose reviewers, and the paper will be accepted by the editors (an important step for such a journal). When you are in this position, finding reviewers that will not reject you is really easy: academia is a small world with a few really powerful individuals.
I think the reason for this is that NAS members performs a lot of editorial works for the Academy, and they are very good scientist hence their contribution should be valued, and fast.
This has been the source of a lot of outrage (I was maintaining a list but I can't find it). Some NAS members used this process to rapidly publish when timing was important (such as covid-19 research), scooping the work of researchers that don't have such a convenient outlet. It is especially problematic when a professor is a member of the NAS for his work in some field, but uses "contributed" papers to push mediocre or naive work in another field (s)he doesn't know at all (such as physicist in epidemiological modeling). A lot of very bad quality work ended up in the news because it was in PNAS.
To mitigate these issues, I think it's now limited to two papers per year. Moreover, the paper mentions "contributed by" and the name of the reviewers. But if you go and check some google scholar page, some people made a career out of it. For any idea, you get an audience and tons of credibility for free.
The same has been found by other groups on France.
Some findings HN readers might find interesting, I'm referring to figures in the manuscript[1] and it's appendix.
- R0 has started to decrease before the government measures (Fig 2). It even reaches 1 simultaneously to the main "lockdown" measure.
- Mobility (from google mobility reports) also started to decrease before the measures (Fig. 3), but R0 also started to decrease before even mobility (Fig. 3).
- People awareness seemed to rise before government measures and mobility decrease, consistent with google trends (Appendix Fig. 14). Might explain why R0 starts to decrease so early.
- The appendix contains an interesting data analysis of hospitalization processes, with data on env. 1'000 patients. The length of stay in ICU are incredibly long. To answer the question: How long ?, we performed a survival analysis. It's necessary as estimates (such as the mean) are biased towards shorter stays.
- A serology study is conducted at the moment in Geneva. It seems that our estimates of seroprevalence (Fig. 5: only 3% country wide by April 24) are consistent with the study (Appendix Fig. 7). We were quite proud of that, as these results were unknown to us at the time.
- Method wise: There is different way of estimating R0 given hospitalization, death, cases:
(i) Most estimates are done with methods "deconvoluting" the data using the distributions (Cori et al, Wallinga and Teunis, implemented in the EpiEstim R package). It can works very well, but it's tricky to have unbiased estimates (see [2]).
(ii) Other methods involve choosing a breakpoint and calibrating to R0: before and after breakpoint. Variations of this method involve calibrating the breakpoint date, choosing a shape (e.g spline) and calibrating all the parameters. These method rely on some assumptions on the decrease. A incorrect assumption leads to biased estimates. E.g it would seem reasonable to assume R0 to decrease on the day of the government measures. But from what we estimated it wasn't the case in Switzerland. So your estimate of R0 post-measure would be lower than what it is really (to catch-up).
The method used here uses the full timeserie and no assumptions. First we built an hidden-markov model of COVID-19 transmission and hospitalization (see diagram Appendix Fig. 4). We performed frequentist inference (using [3]) of relevant parameters. Last, we "filter" R0 as a state of our model: R0 is a random walk, with calibrated variance and using particle filter we keep only R0 timeseries that support the underlying data. Therefore we impose no assumption on R0.
[1] https://smw.ch/article/doi/smw.2020.20295 and Appendix