Tootfinder

If you are not an expert on how our DNA works and how it forms who we are: read this masterpiece of Philip Ball:
https://www.marginaliareviewofbooks.com/post/alpha-genome

AlphaGenome: On the Promise and Limits of AI in Science
PHILIP BALL | According to the old story, we once thought that all this other DNA was just “junk,” a term coined in this context in the 1970s. It was accumulated over the course of evolution, for example by viruses inserting their genetic material into ours, and it did nothing useful but just cluttered up our genome the way all our old detritus of a lifetime clutters up our attic.

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
toXiv_bot_toot

Philip Ball:
"There’s a broader issue at stake here too that pertains to the place of AI in scientific research. Too often now, we hear claims of how AI will replace human scientists and, in doing so, will solve our most pressing problems: curing all disease, solving climate change, figuring out how to conduct nuclear fusion. Such claims are never supported by experts in those fields—and that’s not because they want to convince us that they are indispensable after all. Rather, it i…

AlphaGenome: On the Promise and Limits of AI in Science
PHILIP BALL | According to the old story, we once thought that all this other DNA was just “junk,” a term coined in this context in the 1970s. It was accumulated over the course of evolution, for example by viruses inserting their genetic material into ours, and it did nothing useful but just cluttered up our genome the way all our old detritus of a lifetime clutters up our attic.

Just in case you’re afraid AI is taking over: Ask any of them to play a round of #wordle 🤣😵‍💫

My loose process for evaluating an ACR (VPAT®):
https://adrianroselli.com/2026/01/how-i-evaluate-an-acr-vpat.html
You may have your own thoughts / ideas. Add them to the comments.
I was also torn between the meat grinder and a Plinko metaphor.

How I Evaluate an ACR (VPAT®)
ACRs are Accessibility Conformance Reports, which are the output of a VPAT, or Voluntary Product Accessibility Template maintained by ITIC, or the Information Technology Industry Council (which is why VPAT often has a ® symbol hanging off it). An organization may fill out the template to indicate how or if…

OK, I think I have a reasonably efficient table driven implementation of the 100baseTX scrambler LFSR, capable of predicting future scrambler states with offsets of up to 2^30-1 in constant time.
I still don't grok the math. I ended up doing this by essentially bruteforcing the algebraic solution, creating a class representing a 11th-order polynomial with boolean coefficients symbolically and evaluating the LFSR in a loop 1, 2, 4, 8, 16, etc. times.
The table generator logic …

scopehal/scopeprotocols/Ethernet100BaseTXDecoder.cpp at 693d0d958b96e9c7a85754fc0742ba2c83e31d54 · ngscopeclient/scopehal
Test and measurement hardware abstraction library and protocol decodes. This is the library only. Most users should use scopehal-apps. - ngscopeclient/scopehal

Application of RFSoC Technology for Beam Position Monitors at the SuperKEKB Storage Rings Injection Points
B. Urbschat, G. Mitsuka, L. Ruckman
https://arxiv.org/abs/2602.14870 https://arxiv.org/pdf/2602.14870 https://arxiv.org/html/2602.14870
arXiv:2602.14870v1 Announce Type: new
Abstract: In order to achieve its ambitious luminosity target, the SuperKEKB collider must achieve and sustain high beam currents on the order of Ampere in its storage rings. This requires continuous top-up injection and operation with a two-bunch injection scheme, injecting two 96 ns spaced bunches in a single injection cycle. An important input for tuning the injection beam is the position reading from a dedicated beam position monitor (BPM), located after the septum magnets, slightly upstream of where the injected and stored beams converge. Previously, the readout electronics used for these special BPMs were not capable of independent measurement of both bunches in the two-bunch injection mode and modification of the concerned devices and their firmware was not feasible. The opportunity was taken to develop a new readout device based on the AMD/Xilinx RF System on a Chip (RFSoC) platform with the goal of not only providing a sufficiently flexible and performant readout solution for the concerned BPMs, but also to evaluate and gain experience with the platform for beam monitor electronics applications. This paper is concerned with the details of this development as well as evaluation and operation of the developed device.
toXiv_bot_toot

Replaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- Cumulative Games: Who is the current player?
Urban Larsson, Reshef Meir, Yair Zick
https://arxiv.org/abs/2005.06326
- Contest Design with Threshold Objectives
Edith Elkind, Abheek Ghosh, Paul W. Goldberg
https://arxiv.org/abs/2109.03179
- Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications
V. Udaya Sankar, Vishisht Srihari Rao, Y. Narahari
https://arxiv.org/abs/2401.05683 https://mastoxiv.page/@arXiv_csGT_bot/111741115483021453
- Charting the Shapes of Stories with Game Theory
Daskalakis, Gemp, Jiang, Leme, Papadimitriou, Piliouras
https://arxiv.org/abs/2412.05747 https://mastoxiv.page/@arXiv_csGT_bot/113627246220336424
- Computing Evolutionarily Stable Strategies in Multiplayer Games
Sam Ganzfried
https://arxiv.org/abs/2511.20859 https://mastoxiv.page/@arXiv_csGT_bot/115620508246637361
- Autodeleveraging: Impossibilities and Optimization
Tarun Chitra
https://arxiv.org/abs/2512.01112 https://mastoxiv.page/@arXiv_csGT_bot/115649040881525135
- Static Pricing Guarantees for Queueing Systems
Jacob Bergquist, Adam N. Elmachtoub
https://arxiv.org/abs/2305.09168 https://mastoxiv.page/@arXiv_csDS_bot/110382625621173269
- Game of arrivals at a two queue network with heterogeneous customer routes
Agniv Bandyopadhyay, Sandeep Juneja
https://arxiv.org/abs/2310.18149 https://mastoxiv.page/@arXiv_csPF_bot/111322112226936579
- Characterization of Priority-Neutral Matching Lattices
Clayton Thomas
https://arxiv.org/abs/2404.02142 https://mastoxiv.page/@arXiv_econTH_bot/112205968984928881
- Seven kinds of equivalent models for generalized coalition logics
Zixuan Chen, Fengkui Ju
https://arxiv.org/abs/2501.05466 https://mastoxiv.page/@arXiv_csLO_bot/113819715349259373
- Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences
Hadi Hosseini, Samarth Khanna, Ronak Singh
https://arxiv.org/abs/2506.04478 https://mastoxiv.page/@arXiv_csAI_bot/114635186215388479
toXiv_bot_toot