Tootfinder

Hat jemand die neue Identitätsprüfung von Intrum beim SwissPass schon ausprobiert und die Datenschutz-Regeln angeschaut?
Wäre interessiert zu wissen, was Intrum mit den Ausweiskopien und Selfies machen darf, ob da z.B. eine "konzerninterne Weiterverwendung" enthalten ist...

SwissPass setzt auf digitale Identitätsprüfung von Intrum
SwissPass nutzt neu eine digitale Identitätsprüfung von Intrum. Die Lösung sorgt für eine sichere, benutzerfreundliche Verifikation von Ausweisen – datenschutzkonform und effizient. Sie ist Teil einer langfristigen Partnerschaft im Bereich Digital Onboarding.

Even if “AI” worked (it doesn’t), there’s many reasons why you shouldn’t use it:
1. It’s destroying Internet sites that you love as you use chat bots instead of actually going to sources of information—this will cause them to be less active and eventually shut down.
2. Pollution and water use from server farms cause immediate harm; often—just like other heavy industry—these are built in underprivileged communities and harming poor people. Without any benefits as the big tech companies get tax breaks and don’t pay for power, while workers aren’t from the community but commute in.
3. The basic underlying models of any LLM rely on stolen data, even when specific extra data is obtained legally. Chatbots can’t learn to speak English just by reading open source code.
4. You’re fueling a speculation bubble that is costing many people their jobs—because the illusion of “efficiency” is kept up by firing people and counting that as profit.
5. Whenever you use the great cheat machine in the cloud you’re robbing yourself from doing real research, writing or coding—literally atrophying your brain and making you stupider.
It’s a grift, through and through.

Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions
Shirui Zhou, Shiteng Zheng, Junfang Tian, Rui Jiang, and H. M. Zhang
https://arxiv.org/abs/2506.05909

Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions
The Intelligent Driver Model (IDM), proposed in 2000, has become a foundational tool in traffic flow modeling, renowned for its simplicity, computational efficiency, and ability to capture diverse traffic dynamics. Over the past 25 years, IDM has significantly advanced car-following theory and found extensive application in intelligent transportation systems, including driver assistance systems and autonomous vehicle control. However, IDM's deterministic framework and simplified assumptions fac…

Symbiosis: Multi-Adapter Inference and Fine-Tuning
Saransh Gupta, Umesh Deshpande, Travis Janssen, Swami Sundararaman
https://arxiv.org/abs/2507.03220 http…

Symbiosis: Multi-Adapter Inference and Fine-Tuning
Parameter-efficient fine-tuning (PEFT) allows model builders to capture the task specific parameters into adapters, which are a fraction of the size of the original base model. Popularity of PEFT technique for fine-tuning has led to creation of a large number of adapters for popular Large Language Models (LLMs). However, existing frameworks fall short in supporting inference or fine-tuning with multiple adapters in the following ways. 1) For fine-tuning, each job needs to deploy its dedicated b…

This https://arxiv.org/abs/2408.11978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Optimized Kalman Filter based State Estimation and Height Control in Hopping Robots
Rotor-based hopping locomotion significantly improves efficiency and operation time as compared to purely flying systems; where most hopping robots use the liftoff states and an assumed ballistic trajectory to determine the hopping height. However, significant aerial phase force (e.g., thrust and drag) can invalidate this assumption and lead to poor estimation performance. To combat this issue, a group has implemented multiple sensors (active and passive optical, inertial, and contact) and sign…

ReasonBridge: Efficient Reasoning Transfer from Closed to Open-Source Language Models
Ziqi Zhong, Xunzhu Tang
https://arxiv.org/abs/2506.22865 https://

ReasonBridge: Efficient Reasoning Transfer from Closed to Open-Source Language Models
Recent advancements in Large Language Models (LLMs) have revealed a significant performance gap between closed-source and open-source models, particularly in tasks requiring complex reasoning and precise instruction following. This paper introduces ReasonBridge, a methodology that efficiently transfers reasoning capabilities from powerful closed-source to open-source models through a novel hierarchical knowledge distillation framework. We develop a tailored dataset Reason1K with only 1,000 care…

'Dinge die mit "S" beginnen oder so aussehen ...' #FotoVorschlag (Things starting with S).
#Schnee / #Snow
I took this

Unified Interference-Aware Water-Filling for QoS-Constrained Communication, Sensing, and JRC
Ahmed Naeem, Anastassia Gharib, H\"useyin Arslan
https://arxiv.org/abs/2506.01400

Unified Interference-Aware Water-Filling for QoS-Constrained Communication, Sensing, and JRC
Water-filling (WF) algorithms are pivotal in maximizing capacity and spectral efficiency in multiple-input and multiple-output (MIMO) systems. However, traditional WF approaches cater solely to communication requirements, neglecting the emerging heterogeneity of 6G, including sensing and joint radar-communication (JRC). As these diverse demands grow in importance and have different Quality of Service (QoS) constraints, traditional WF becomes inadequate. Therefore, in this paper, we propose a un…

Spatiotemporal Mapping of Anisotropic Thermal Transport in GaN Thin Films via Ultrafast X-ray Diffraction
Thanh Nguyen, Chuliang Fu, Mouyang Cheng, Buxuan Li, Tyra E. Espedal, Zhantao Chen, Kuan Qiao, Kumar Neeraj, Abhijatmedhi Chotrattanapituk, Denisse Cordova Carrizales, Eunbi Rha, Tongtong Liu, Shivam N. Kajale, Deblina Sarkar, Donald A. Walko, Haidan Wen, Svetlana V. Boriskina, Gang Chen, Jeehwan Kim, Mingda Li

Spatiotemporal Mapping of Anisotropic Thermal Transport in GaN Thin Films via Ultrafast X-ray Diffraction
Efficient thermal management is essential for the reliability of modern power electronics, where increasing device density leads to severe heat dissipation challenges. However, in thin-film systems, thermal transport is often compromised by interfacial resistance and microscale defects introduced during synthesis or transfer, which are difficult to characterize using conventional techniques. Here we present a non-contact, spatiotemporal-resolved ultrafast x-ray diffraction method to extract in-…

This https://arxiv.org/abs/2503.07792 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Efficient Neural Clause-Selection Reinforcement
Clause selection is arguably the most important choice point in saturation-based theorem proving. Framing it as a reinforcement learning (RL) task is a way to challenge the human-designed heuristics of state-of-the-art provers and to instead automatically evolve -- just from prover experiences -- their potentially optimal replacement. In this work, we present a neural network architecture for scoring clauses for clause selection that is powerful yet efficient to evaluate. Following RL principle…