FWHM

Planck's radiation law

Since the volume of the inert gas is much lager than the volume of the sample it provides not only an isobaric environment

wire sample (typically 0.5 mm diameter and 40 mm in length) is clamped between two sets of brass jaws and resistively heated in an inert-gas-filled discharge chamber while the heating current, the sample voltage drop, and the surface radiance are recorded

The apparatus itself and the specific details on the data reduction used in these measurements have been extensively described elsewhere [10], [11], wherefore a detailed description is omitted in this paper.

the use of an ellipsometric approach

containerless conditions

ohmic pulse-heating apparatus

isobaric heat capacity

At the subsecond thermophysics laboratory in Graz thermophysical properties determinations are performed for many years and vanadium was one of the pure metals which have been investigated recently.

Table 1. Values for the melting temperature Tm of pure vanadium from different sources [2], [3], [4], [5], [6], [7], [8], [9]Source of melting temperatureTm (K)Goodfellow Cambridge Limited (supplier) [2]2163Desai [3]2202 (IPTS 68)McClure and Cezairliyan [4]2201 (ITS 90)Aesar [5]2183Storms and McNeal [6]2161Oriani and Jones2192Hultgren et al. [8]2199Kocherzhinskii et al. [9]2223Note: The value given by McClure and Cezairliyan [4] is the value from [3] (2202 K) adapted to the ITS 90.

Pure vanadium is a bright white metal with good corrosion resistance to, i.e., alkalis and salt water and is therefore commonly used as an additive in producing rust resistance in springs, and highspeed tool steels, since it is an important carbide stabilizer in making steels

Summarizing, the following results for thermophysical properties at the melting point have been obtained: radiance temperature at melting (650 nm) Tr,m = 1993 K, melting temperature Tm = 2199 K, normal spectral emissivity at melting (684.5 nm) ɛ = 0.353. An observed feature of all measured data and results is, that a much better agreement with literature references exists for the liquid phase than in the solid state, thus we have restricted the presentation to liquid vanadium.

normal spectral emissivity

The aim was to obtain another full dataset of properties (enthalpy, heat of fusion, electrical resistivity, thermal conductivity, emissivity) of liquid vanadium to either confirm existing recommendations for certain properties or presenting newer measurements for comparison leading towards such recommendations.

This recent work presents the results of thermophysical measurements on vanadium including normal spectral emissivity at 684.5 nm

Although vanadium is commonly used as an additive in the steel production, literature data for thermophysical properties of vanadium around the melting point are sparse and show, where available a variation over a wide range. This manifests especially in the melting temperature (variation of ±30 K), heat of fusion, or specific enthalpy.

What does Microsoft Word look like with a Photoshop-like palette on the side?

Text is becoming something new,

So the camera doesn’t just observe and record, it changes us.

Could you dynamically change the register or tone of text depending on audience, or the reading age, or dial up the formality or subjective examples or mentions of wildlife, depending on the psychological fingerprint of the reader or listener?

to take a chapter of a book and edit it, not by changing words, but by scrubbing it with the semantic equivalent of the burn and dodge tools in Photoshop.

But this is super early technology. vec2text will improve.

Again I don’t know what that means, to have associations and contextualisations always present with a text, a structuralist’s dream, but… it’s different.

All text will be auto-glossed

nce, for me, is that two thresholds have been crossed: speed and automation.

Sean Graves at the Autonomy Institute has developed a tool called GERM. We used GERM to build a dataset of risks mentioned by the 266,989 UK companies who filed their accounts throughout March 2024. – Sean Graves (Autonomy Data Unit), GERM (Geopolitical & Environmental Risk Monitor) (2024)

What if the difference between statements that are simply speculative and statement that mislead are as obvious as, I don’t know, the difference between a photo and a hand-drawn sketch?

What would it mean to listen to a politician speak on TV, and in real-time see a rhetorical manoeuvre that masks a persuasive bait and switch?

hermeneutics

For example, here’s a sequence of episode titles that transitions smoothly from geology to history: Vulcanology 1816, the Year Without a Summer Climate Change Meterology Voyages of James Cook Astronomy and Empire Longitude …and so on.

So I tried it, and yes you can.

There is something there! New information to be interpreted!

, I feel like I’m peering into the world’s first microscope and spying bacteria, or through a blurry, early telescope, and spotting invisible dots that turn out to be the previously unknown moons of Jupiter…

can individual authors be fingerprinted by how they construct text?

embeddings also change our relationship with text, and what we can do with text

January on the PartyKit blog: Using Vectorize to build an unreasonably good search engine in 160 lines of code (2024). (That post has diagrams and source code.) But what I want to emphasise is how little code there is.

Search for main roman god – this is also Jupiter, but a different one: this Jupiter is the king of the gods in the Roman pantheon. The top result is an episode about Rome and European civilisation, not the episode about the planet Jupiter, showing that embeddings can distinguish concepts even when similarly named.

Search for the biggest planet – again, the episode about the planet Jupiter is at the top. There is no synonyms database here, which is how a traditional search engine would work. The phrase the biggest planet has been translated into its “coordinates” and the search engine has looked for “nearby” coordinates representing episodes.

Function calling Some API providers already provide function calling APIs, but these are extremely ad-hoc, and require manual type inference to extract a e.g OpenAPI compatible schema. Similar to Vercel's automatic deployment of lambda functions specified in a api/ directory, a compelling feature here would be automatically making registered functions available to LLM calls. An additional problem to solve here is function search - which functions should you expose based on the prompt/query?

A Watermark for Large Language Models Undetectable Watermarks for Language Models On the Reliability of Watermarks for Large Language Models

th tools which produce GBNF grammars from typescript interfaces,

"wedge",

these companies typically focus on APIs and deployment rather than developer experience.

This involves a more substantial investment (time and code) than calling an API, which gives Mistral an edge in retaining users,

, as well as filtering low value users, as they will likely take the easier route of API usage. Whether they can find services or new models which those users would pay for is another question.

run them themselves

will proliferate,

This

LLMs have the simplest IO of any new technology in history, and they are also (currently) completely stateless.

users discover alternatives via API aggregators, and then switch to the cheapest/best one.

wheras

LLM serving is completely commoditized

Because LLM serving costs are dominated by GPU access currently,

use

A comparison with Vercel

Why will the big language model serving company not be a cloud provider?

The cloud providers still get their piece of the pie.

Is LLM serving any different?

this

SSR

In particular,

value-add

and then makes

that specialize

If

The Langage Model Serving Companyself.__wrap_b=(e,t,r)=>{let n=(r=r||document.querySelector(`[data-br="${e}"]`)).parentElement,o=e=>r.style.maxWidth=e+"px";r.style.maxWidth="";let l=n.clientWidth,a=n.clientHeight,i=l/2-.25,u=l+.5,s;if(l){for(;i+1<u;)o(s=Math.round((i+u)/2)),n.clientHeight===a?u=s:i=s;o(u*t+l*(1-t))}r.__wrap_o||"undefined"!=typeof ResizeObserver&&(r.__wrap_o=new ResizeObserver(()=>{self.__wrap_b(0,+r.dataset.brr,r)})).observe(n)};self.__wrap_b(":r0:",1)

Box 1: Human Intelligence and Prediction

A teenager learns to drive with tens or hundreds of miles of practice.

computer scientists flatly reject his emphasis on the cortex as a model for prediction machines

Between July and December 2015, object detection in a set of difficult-to-recognize images (the KITTI vision benchmark) improved from 39% to 87% success.

An approach called “deep learning” has been particularly important to the changes of the past five years.

humans can do them so easily.

“machine learning.

What we will argue here is that AI — in its modern incarnation — is all about advances in prediction

what is this reducing the cost of?

Although deep neural networks havein theory much higher representational power thanshallow models, it is not clear if simple text classi-fication problems such as sentiment analysis are theright ones to evaluate them.

Unlike unsupervisedly trained wordvectors from word2vec, our word features can beaveraged together to form good sentence represen-tations.

Our model is trainedasynchronously on multiple CPUs.

different tasks

We show that by incorporatingadditional statistics such as using bag of n-grams,we reduce the gap in accuracy between linear anddeep models, while being many orders of magnitudefaster.

At the same time, simple linear models have alsoshown impressive performance while being verycomputationally efficient