Tootfinder

Meanwhile, in a blue state, VT Gov. Phil Scott: “From what I’m seeing, I just think it’s unnecessary. It further divides and threatens people. We need stability right now in this country — we don’t need more unrest. **I don’t think our guards should be used against our own people. I don’t think the military should be used against our own people. In fact, it’s unconstitutional… [u]nless, of course, there’s an insurrection, much like we saw Jan. 6 a few years ago.**”

scellop: A Scalable Redesign of Cell Population Plots for Single-Cell Data
Thomas C. Smits, Nikolay Akhmetov, Tiffany S. Liaw, Mark S. Keller, Eric M\"orth, Nils Gehlenborg
https://arxiv.org/abs/2510.09554

scellop: A Scalable Redesign of Cell Population Plots for Single-Cell Data
Summary: Cell population plots are visualizations showing cell population distributions in biological samples with single-cell data, traditionally shown with stacked bar charts. Here, we address issues with this approach, particularly its limited scalability with increasing number of cell types and samples, and present scellop, a novel interactive cell population viewer combining visual encodings optimized for common user tasks in studying populations of cells across samples or conditions. Av…

In der polnischen Sprache fällt auf, dass die MÄNNLICHE Form von Angehörigen eines Volkes ausschließlich den Menschen bezeichnet (z.B. Niemiec = Deutscher), die WEIBLICHE daneben aber zusätzlich auch einen Gegenstand o. ä.:
Die Polin ist ein Tanz (polka).
Die Ungarin eine Pflaumensorte (węgierka).
Die Kanadierin ein Boot (kanadyjka).
Die Schwedin eine Jacke (szwedka).
Und dies sind nur einige Beispiele.

Paired by the Teacher: Turning Unpaired Data into High-Fidelity Pairs for Low-Resource Text Generation
Yen-Ju Lu, Thomas Thebaud, Laureano Moro-Velazquez, Najim Dehak, Jesus Villalba
https://arxiv.org/abs/2509.25144

Paired by the Teacher: Turning Unpaired Data into High-Fidelity Pairs for Low-Resource Text Generation
We present Paired by the Teacher (PbT), a two-stage teacher-student pipeline that synthesizes accurate input-output pairs without human labels or parallel data. In many low-resource natural language generation (NLG) scenarios, practitioners may have only raw outputs, like highlights, recaps, or questions, or only raw inputs, such as articles, dialogues, or paragraphs, but seldom both. This mismatch forces small models to learn from very few examples or rely on costly, broad-scope synthetic exam…