Claude Fable worries about extinction and forbids my research. So I used it for something safer - creating a game. Link: https://t.co/MoLmw6In4J . Source: https://t.co/6YGqpngJ2g .
Uber and Lyft drivers in Massachusetts have officially unionized, a first-in-the-nation move that now sets up what could be the final stage in a years-long organizing effort. https://t.co/RvqLkJUt76
Chinese researchers have developed the best shortest-path algorithm in 41 years!
Dijkstra’s Algorithm has been the undefeated king of the shortest path for over 40 years.
Whether you’re using Google Maps, booking a flight, or routing internet packets, Dijkstra is the engine running in the background.
Since 1984, textbooks have taught that its efficiency was hit by a "sorting barrier."
To find the shortest path, you have to sort the points by distance. And sorting has a mathematical floor you can’t cross.
Until now.
A research team from Tsinghua University just published a paper that shatters the 41-year-old record.
They proved that Dijkstra is not optimal.
By combining the logic of the Bellman-Ford algorithm with a revolutionary "recursive partial ordering" method, they figured out how to find the path without fully sorting the nodes.
The results are a massive shift in theoretical computer science:
- The first deterministic improvement to the Single-Source Shortest Path (SSSP) problem since 1984.
- A new time complexity of $ O(m \log^{2/3} n)$, officially beating the long-standing $ O(m + n \log n)$ limit.
- On massive sparse graphs (like the web or global logistics), this means finding the best route significantly faster than previously thought possible.
For four decades, the greatest minds in algorithms believed this limit was absolute.
Last year, even the legendary Robert Tarjan won an award proving Dijkstra was "optimally efficient" at sorting distances.
Tsinghua’s answer? Stop sorting.
The world’s most settled problem is suddenly wide open again.
If we can break a 40-year-old law in basic graph theory, what other "impossible" speed limits are waiting to be crushed?
Decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference and #MachineLearning as a #postdoc! No biomed bg needed.
Postdoc in Single-Cell and Spatial Multi-Omic Gene Regulatory Networks @__wang__
UMass Chan Medical School
See the full job description on jobRxiv: https://t.co/CZzF5cpZxD
#causalinference #generegulatorynetwork #machinelearning #multiomics #singlecellomics
#ScienceJobs
https://t.co/Y1KiwMhlFu
Our lab's first paper is officially published in Nature Communications! AirQTL maps single-cell eQTL (sceQTL) 1E8 times faster, enabling the first cell state-specific causal gene regulatory network (GRN) inference! 1/n
Having an independent lab was finally the right moment to finish this project. I'm so grateful that Matthew and Yuhe joined and carried this project to the finishing line together! /fin
This is brilliant.
A professor noticed take home assignments coming back suspiciously good. Like McKinsey memos.
So he started cold calling the students asking why they made certain choices in their submissions. They couldn't explain even basic choices! Clear copy/past from LLMs.
So he fought AI with AI -- an oral final exam run by a voice agent and evaluated by a council of LLM graders.
> 36 students examined in 9 days
> ~25 min avg per exam
> $15 total all‑in (≈ $0.42/student)
> Full transcripts, audit trail, and super actionable feedback
This works because you can paste into ChatGPT and copy the output, but you can’t fake coherent, real‑time reasoning about your project when someone keeps drilling.
Interesting that the LLM grading committee actually converged after deliberation and exposed a teaching gap (A/B testing was the weak spot across the class).
Students using AI killed take home exams. Very clever to fight fire with fire and use AI to bring back oral exams. Perhaps not surprising, only 13% of students preferred the AI oral format 😂
Oral exams used to be the gold standard in education but were replaced by more scalable written exams. With AI, oral exams are scalable again. Will be interesting to see how this changes education.
Also thanks to @StabilityAI@EMostaque@openbioml for providing an open research environment and some initial resources to make possible training our DNA-Diffusion model!
How does a stem cell "decide" its fate?
Development requires both reliability (consistent cell types) AND flexibility (diverse outcomes from identical progenitors). Cells achieve this by dynamically tuning deterministic drift and stochastic diffusion.
New in @NatMachIntell: scDiffEq models state-dependent drift AND diffusion, improving fate prediction by ~8% over SOTA. scDiffEq also enables genome-wide in silico perturbation screens and reveals temporal gene dynamics.
🧵https://t.co/sKkGQApHVJ
Very (un)surprised to see quite some people expressing sadness for civilians on both sides, and only condemning one side for military actions. Curious what are they thinking really? “I’m sorry for your loss, but you deserved it”? Or maybe just the gravity of power…
The Onion speaks with more courage, insight and moral clarity than the leaders of every academic institution put together. I wish there were a @TheOnion university.
https://t.co/R8gufC9opJ