Announcing the general availability of #Newton 1.0, the open-source, extensible physics engine for robot learning.
It supports articulated mechanism simulation, hydroelastic contact modeling, deformable bodies, and scalable robot learning.
Learn more: bit.ly/4cMm39x
#NVIDIAGTC
What I discovered by letting my normie relatives use my laptop is that if you use Linux with i3 window manager you don't need a screen locker at all.
Standard methods for detecting discontinuities in conditional means are not applicable to outcomes that are complex, non-Euclidean objects like distributions, networks, or covariance matrices. This article develops a nonparametric test for jumps in conditional means when outcomes lie in a non-Euclidean metric space. Using local Fr\'echet regression$\unicode{x2014}$which generalizes standard regression to metric-space valued data$\unicode{x2014}$the method estimates a mean path on either side of a candidate cutoff, extending existing k-sample tests to a flexible regression setting. Key theoretical contributions include a central limit theorem for the local estimator of the conditional Fr\'echet variance and the asymptotic validity and consistency of the proposed test. Simulations confirm nominal size control and robust power in finite samples. Two applications demonstrate the method's value by revealing effects invisible to scalar-based tests. First, I detect a sharp change in work-from-home compositions at Washington State's income threshold for non-compete enforceability during COVID-19, highlighting remote work's role as a bargaining margin. Second, I find that countries restructure their input-output networks after losing preferential US trade access. These findings underscore that analyzing regression functions within their native metric spaces can reveal structural discontinuities that scalar summaries would miss.
arXiv📈🤖
A Test for Jumps in Metric-Space Conditional Means
By Dijcke
A/B testing has become a gold standard for modern technological companies to conduct policy evaluation. Yet, its application to time series experiments, where policies are sequentially assigned over time, remains challenging. Existing designs suffer from two limitations: (i) they do not fully leverage the entire history for treatment allocation; (ii) they rely on strong assumptions to approximate the objective function (e.g., the mean squared error of the estimated treatment effect) for optimizing the design. We first establish an impossibility theorem showing that failure to condition on the full history leads to suboptimal designs, due to the dynamic dependencies in time series experiments. To address both limitations simultaneously, we next propose a transformer reinforcement learning (RL) approach which leverages transformers to condition allocation on the entire history and employs RL to directly optimize the MSE without relying on restrictive assumptions. Empirical evaluations on synthetic data, a publicly available dispatch simulator, and a real-world ridesharing dataset demonstrate that our proposal consistently outperforms existing designs.
arXiv📈🤖
Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning
By Wu, Wen, Zhang et al
Did this sketch recently. Still haven't found any motivation to color it or whatever. Maybe one day I will. Posting it here just in case.
At some point we gonna have a generation of programmers who doesn't remember Python 2 to Python 3 transition and they totally gonna repeat it.
Nature research paper: Solid phase transitions as a solution to the genome folding paradox
https://go.nature.com/3S8gJSq
The Lorentz Plasma Cannon is back! www.youtube.com/watch?v=lix-...
Proof that dogs are actually human beings
Both our dogs are super empathetic
Building an Operating System for the Raspberry Pi
- great tutorial, almost covering everything to build a os for raspberry pi
- if you are interested in making an operating system, you should definitely check this out
jsandler18.github.io
apparently, you cannot post a gif in bs 😕
it's been a while... refreshing my brain once again, soon n-body problem 😁
🔴 **LIVE ON TWITCH** Implementing Scientific Paper in C twitch.tv/tsoding
📦 lobehub / lobe-chat
⭐ 43,204 (+24)
🗒 TypeScript
🤯 Lobe Chat - an open-source, modern-design AI chat framework. Supports Multi AI Providers( OpenAI / Claude 3 / Gemini / Ollama / Azure / DeepSeek), Knowledge Base (file upload / knowledge management / RAG ), Multi-Modals (Vision/TTS) a...
I'm immensely disappointed with Linus Torvalds.
Just want to state this out loud: All the people from Finland are welcome in my community regardless of what their government is doing.
yeesssssss
Hello lots of new followers! If you just joined Bluesky, welcome. In case you’re interested how it works under the hood, I helped write this architecture description: arxiv.org/abs/2402.03239 (updated with new content just a few days ago)
🔥 Hot Repo！ 🔥 (100+ new stars)
📦 chrislgarry / Apollo-11
⭐ 57,976 (+110)
🗒 Assembly
Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.
Hello World!
