Tootfinder

IBM-Chef hält aktuellen KI-Bauwahn für nicht tragbar
Das Wettrüsten zwischen Hyperscalern verschlingt Billionen US-Dollar. IBM-CEO Arvind Krishna hinterfragt, wie wirtschaftlich das ist.
https://www.

Workout of the day.

collins_yeast: Collins yeast interactome (2007)
Network of protein-protein interactions in Saccharomyces cerevisiae (budding yeast), measured by co-complex associations identified by high-throughput affinity purification and mass spectrometry (AP/MS).
This network has 1622 nodes and 9070 edges.
Tags: Biological, Protein interactions, Unweighted

Madrid – tragaperras
#kamafotos

this #RaspberryPi 500 is cute but misses the #trackpoint :(

MMGaP: Multi-User MIMO Detection and Precoding using GPU-assisted Physics-inspired Computation
Abhishek Kumar Singh, Kyle Jamieson
https://arxiv.org/abs/2510.01579 https://

MMGaP: Multi-User MIMO Detection and Precoding using GPU-assisted Physics-inspired Computation
Physics-inspired and quantum compute based methods for processing in the physical layer of next-generation cellular radio access networks have demonstrated theoretical advances in spectral efficiency in recent years, but have stopped short of practical realization on commodity processors, leaving a gap between the throughput practical systems can achieve and the projected throughput the state-of-the-art should achieve. To fill this gap, this paper proposes MMGaP, an uplink multi-user MIMO detec…

New work laptop. I don't want to sign it into my #Apple account for obvious reasons.
Connect my Apple trackpad to the new work machine and it works fine.
Try to reconnect the trackpad to my personal Mac so I can do some homelab stuff over the weekend, not so fine. It shows up in the BT menu, but won't actually connect until I have the laptop forget the device.
Ok, I think, I'll just connect it wired since I only use the trackpad at my desk at home anyway.
That doesn't work because why would you want to be able to plug in a device with a wire and have it Just Work when instead you could get to fuck around with Bluetooth.
And of course I get to do the forget device bullshit again in order to use it with my work laptop today

From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Fan Yang, Zhiyang Chen, Yousong Zhu, Xin Li, Jinqiao Wang
https://arxiv.org/abs/2510.00806

From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Current video generation models produce physically inconsistent motion that violates real-world dynamics. We propose TrajVLM-Gen, a two-stage framework for physics-aware image-to-video generation. First, we employ a Vision Language Model to predict coarse-grained motion trajectories that maintain consistency with real-world physics. Second, these trajectories guide video generation through attention-based mechanisms for fine-grained motion refinement. We build a trajectory prediction dataset ba…

Reinforcement Learning with Action-Triggered Observations
Alexander Ryabchenko, Wenlong Mou
https://arxiv.org/abs/2510.02149 https://arxiv.org/pdf/2510.021…

Reinforcement Learning with Action-Triggered Observations
We study reinforcement learning problems where state observations are stochastically triggered by actions, a constraint common in many real-world applications. This framework is formulated as Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs), where each action has a specified probability of triggering a state observation. We derive tailored Bellman optimality equations for this framework and introduce the action-sequence learning paradigm in which agents commit to ex…

How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu, Sastry Kompella
https://arxiv.org/abs/2510.02265

How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmissions. The transmitter-receiver pair learns to avoid jamming and optimize throughput over time (without prior knowledge of channel conditions or jamming strategies) by using reinforcement learning (RL) to adapt transmit power, modulation, and channel selection. Q-learning is employed for discrete jamming-event states…