2026-03-17 17:26:36
Just a little one and a half hour walk nearby before it was supposed to be raining (and rugby starts).
It was a nice walk and good to be out. Strangely, my old GPS device refused to get a location despite having lots of satellites. Just 10min before coming back (after a couple of restarts and taking the batteries out and back in) it worked.
Fortunately, I only had it with me to test if my heart rate monitor also connects to it.
Very few of us remember a company named Palantir.
No I don't mean that satanic data gathering atrocity that we read about in the daily news-of-the-awful.
No, I mean the company in the mid 1980's that built networked image scanners (sort of like a network of "office" ink jet printers.)
One of the cool aspects of their products is that they had a test program that would exercise the pieces of the machine - it would drive the various stepper motors and make i…
Another AI depth mapping test, applying The vOICe for Android monocular AI depth view to the stereo vision test image <scene1.row3.col1.png> from the web page http://stereo--vision.com/kiji1e.html AI wrongly "thinks" the lamp is behind the bust, but otherwise it's not too bad.
So, got the server in place but discovered that the Docker image that I created that has the Sisyphus client and all of the binaries like `ffmpeg`, `av1an` was not very happy. Ffmpeg crashed because it couldn't find the `libSvtAvcEnc.so.4` library which was because I had a custom version of it installed (`svt-av1-psyex`).
Got the Dockerfile fixed by installing `svt-av1-psyex` and then compiling `ffmpeg` against those libraries, then installing both `svt-av1-psyex` and `ffmpeg` int…
Quick test of recording from the mill on the $40 amazon USB microscope/endoscope/whatever camera.
Endmill is 250 μm diameter for scale, overall camera FOV a couple of mm. The QFN lands around the perimeter are 500 μm pitch.
Vibration degrades the image quite a bit when the motor is running due to the rather unstable mount I have right now. You can see in the last few seconds when I turn the spindle off, the image quality improves a lot.
Audio is not great, but with a mill s…
Series D, Episode 03 - Traitor
AVON: So Tarrant, this is your big moment.
TARRANT: If the teleport works.
VILA: It's working perfectly now, I checked it myself.
DAYNA: [Laughs] Yes, but would you use it yourself Vila? That's the real test.
https://blake.torpidity.net/m/403/176…
Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[5/5]:
- AppellateGen: A Benchmark for Appellate Legal Judgment Generation
Yang, Wang, Fan, Hu, Wang, Liu, Zeng, Fu, Gong, Zhang, Li, Zheng, Xu
https://arxiv.org/abs/2601.01331 https://mastoxiv.page/@arXiv_csCY_bot/115847038572575387
- Vision-Language Agents for Interactive Forest Change Analysis
James Brock, Ce Zhang, Nantheera Anantrasirichai
https://arxiv.org/abs/2601.04497 https://mastoxiv.page/@arXiv_csCV_bot/115864542639529766
- FigEx2: Visual-Conditioned Panel Detection and Captioning for Scientific Compound Figures
Jifeng Song, Arun Das, Pan Wang, Hui Ji, Kun Zhao, Yufei Huang
https://arxiv.org/abs/2601.08026 https://mastoxiv.page/@arXiv_csCV_bot/115892719657942341
- Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts
Luo, Zhang, Hu, Zhang, Wang, Su, Sun, Liang, Zhang
https://arxiv.org/abs/2601.10079 https://mastoxiv.page/@arXiv_csLG_bot/115904206341755873
- Compounding Disadvantage: Auditing Intersectional Bias in LLM-Generated Explanations Across India...
Amogh Gupta (Neil), Niharika Patil (Neil), Sourojit Ghosh (Neil), SnehalKumar (Neil), S Gaikwad
https://arxiv.org/abs/2601.14506 https://mastoxiv.page/@arXiv_csCY_bot/115937624654783353
- Measuring Complexity at the Requirements Stage: Spectral Metrics as Development Effort Predictors
Vierlboeck, Pugliese, Nilchian, Grogan, Babu
https://arxiv.org/abs/2602.07182 https://mastoxiv.page/@arXiv_csSE_bot/116045826365214235
- CoPE-VideoLM: Leveraging Codec Primitives For Efficient Video Language Modeling
Sarkar, Pautrat, Miksik, Pollefeys, Armeni, Rad, Dusmanu
https://arxiv.org/abs/2602.13191 https://mastoxiv.page/@arXiv_csCV_bot/116079824094529198
- MoD-DPO: Towards Mitigating Cross-modal Hallucinations in Omni LLMs using Modality Decoupled Pref...
Ashutosh Chaubey, Jiacheng Pang, Mohammad Soleymani
https://arxiv.org/abs/2603.03192 https://mastoxiv.page/@arXiv_csCV_bot/116170511143131333
- Image Generation Models: A Technical History
Rouzbeh Shirvani
https://arxiv.org/abs/2603.07455 https://mastoxiv.page/@arXiv_csCV_bot/116204960613280699
- Rethinking Attention Output Projection: Structured Hadamard Transforms for Efficient Transformers
Shubham Aggarwal, Lokendra Kumar
https://arxiv.org/abs/2603.08343 https://mastoxiv.page/@arXiv_csLG_bot/116205064359384079
- FGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoning
Chaojie Sun, Bin Cao, Tiantian Li, Chenyu Hou, Ruizhe Li, Jing Fan
https://arxiv.org/abs/2603.12702 https://mastoxiv.page/@arXiv_csIR_bot/116237827836520478
- CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad
Yongqiang Chen, Chenxi Liu, Zhenhao Chen, Tongliang Liu, Bo Han, Kun Zhang
https://arxiv.org/abs/2603.14575 https://mastoxiv.page/@arXiv_csLG_bot/116243782215605653
- Silicon Bureaucracy and AI Test-Oriented Education: Contamination Sensitivity and Score Confidenc...
Yiliang Song, Hongjun An, Jiangan Chen, Xuanchen Yan, Huan Song, Jiawei Shao, Xuelong Li
https://arxiv.org/abs/2603.21636 https://mastoxiv.page/@arXiv_csAI_bot/116283590092117172
- Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits
Eric Czech, Zhiwei Xu, Yael Elmatad, Yixin Wang, William Held
https://arxiv.org/abs/2603.22339 https://mastoxiv.page/@arXiv_csLG_bot/116288991182888131
- X-OPD: Cross-Modal On-Policy Distillation for Capability Alignment in Speech LLMs
Di Cao, Dongjie Fu, Hai Yu, Siqi Zheng, Xu Tan, Tao Jin
https://arxiv.org/abs/2603.24596 https://mastoxiv.page/@arXiv_eessAS_bot/116300009464853696
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