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@davidaugust@mastodon.online
2025-08-22 23:51:22

If all of bordering states followed illegal orders from potus to invade Chicago, it’d be:
~45,000 invaders vs. ~32,000 defenders.
(Indiana, Iowa, Missouri and Wisconsin National Guard against Illinois National Guard, State Police, Chicago Police, County Sheriffs)
Plus, it would take more than 53,000 soldiers to even begin to think one could hold Chicago (population ~2.6 million).
#USpol

A bar chart titled "Troop Density Ratios for Occupation/Counterinsurgency (per 1,000 residents)" comparing troop densities for various military doctrines and historical operations, with a highlighted U.S. doctrinal benchmark.
@cowboys@darktundra.xyz
2025-09-15 03:19:20

Cowboys' trio of RB, WR and TE had more tackles than all the Dallas defensive ends vs. Giants sportingnews.com/us/nfl/dallas

Cowboys' trio of RB, WR and TE had more tackles than all the Dallas defensive ends vs. Giants
This is one way to get the job done.

@pbloem@sigmoid.social
2025-07-18 09:25:22

Now out in #TMLR:
🍇 GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks 🍇
There's lots of work on sampling subgraphs for GNNs, but relatively little on making this sampling process _adaptive_. That is, learning to select the data from the graph that is relevant for your task.
We introduce an RL-based and a GFLowNet-based sampler and show that the approach perf…

A diagram of the GRAPES pipeline. It shows a subgraph being sampled in two steps and being fed to a GNN, with a blue line showing the learning signal. The caption reads Figure 1: Overview of GRAPES. First, GRAPES processes a target node (green) by computing node inclusion probabilities on its 1-hop neighbors (shown by node color shade) with a sampling GNN. Given these probabilities, GRAPES samples k nodes. Then, GRAPES repeats this process over nodes in the 2-hop neighborhood. We pass the sampl…
A results table for node classification on heterophilious graphs. Table 2: F1-scores (%) for different sampling methods trained on heterophilous graphs for a batch size of 256, and a sample size of 256 per layer. We report the mean and standard deviation over 10 runs. The best values among the sampling baselines (all except GAS) are in bold, and the second best are underlined. MC stands for multi-class and ML stands for multi-label classification. OOM indicates out of memory.
Performance of samples vs sampling size showing that GRAPES generally performs well across sample sizes, while other samplers often show more variance across sample sizes. The caption reads Figure 4: Comparative analysis of classification accuracy across different sampling sizes for sampling baseline and GRAPES. We repeated each experiment five times: The shaded regions show the 95% confidence intervals.
A diagrammatic illustration of a graph classification task used in one of the theorems. The caption reads Figure 9: An example of a graph for Theorem 1 with eight nodes. Red edges belong to E1, features xi and labels yi are shown beside every node. For nodes v1 and v2 we show the edge e12 as an example. As shown, the label of each node is the second feature of its neighbor, where a red edge connects them. The edge homophily ratio is h=12/28 = 0.43.
@jamie@boothcomputing.social
2025-09-06 19:57:24

So, we came early to the #baseball game for the Chipper Jones Bobblehead. We were successful, but we'll see how the game looks come the 6:05pm start time. The game is Stripers vs Durham Bulls.
#milb

picture of the front of a blue box for a Chipper Jones Bobblehead with an action shot of Chipper swinging.
back of a blue Chipper Jones Bobblehead box. the text reads: An Atlanta Brave for his entire 19-year career, Jones is regarded as one of the best switch hitters in baseball history (he hit over .300 from both sides of the plate). An offensive force from his rookie season in 1995 through his retirement in 2012, he batted .303 with 468 home runs, 1,623 RBI, and a .930 OPS in 2,499 games. Jones racked up eight All-Star selections, two Silver Slugger awards, one World Series title (1995), one Nat…
picture of a baseball field with a white tarp covering the infield. the skies are grey and the seats are mostly empty.
@cowboys@darktundra.xyz
2025-08-09 17:20:23

3 Dallas Cowboys' wide receiver battles to watch for vs. Rams insidethestar.com/3-dallas-cow

3 Dallas Cowboys' wide receiver battles to watch for vs. Rams
As Brian Schottenheimer prepares for his first "game" as head coach, all eyes should be on one position group in his offense. The Dallas Cowboys' wide

@teledyn@mstdn.ca
2025-06-30 21:11:02

Reverse Physics Rap Battle: String Theory vs Loop Quantum Gravity - 😆
Take the top expert in each of two competing theories, and assign each to defend the other's theory. I can't recall the last time I laughed so hard. Best of all, it's all true and they both know it, but yes, one has had 'promising' results for more decades than the other. 🤣
youtube.com/watch?v=jEr038WOKFI

@brichapman@mastodon.social
2025-08-25 11:24:01

"I want to declare once and for all: There is no limit to what I am willing to feel in order to stay with my world." -- more @ #activism

Hopium vs. Doomerism
Two ways to leave. One reason to stay.

@cowboys@darktundra.xyz
2025-09-01 22:13:04

Pickens on debut for Cowboys vs. Eagles: 'I'm ready' dallascowboys.com/news/pickens

Pickens on debut for Cowboys vs. Eagles: 'I'm ready'
In one of the biggest trades of the NFL offseason, the Dallas Cowboys acquired George Pickens via trade, and all of the anticipation comes to a head when he faces the Eagles in Week 1.