Tootfinder

The European Commission has published new requirements for EV charging points. These requirements will make hardware more expensive and add to the complexity of the already opaque pricing landscape for public EV charging. They also lack a focus on true interoperability from a user's perspective.

The delegated act amending the European Alternative Fuels Infrastructure Regulation (AFIR) has now been officially published by the European Commission and will apply from 8 January 2026 onwards. | Jaap Burger
The delegated act amending the European Alternative Fuels Infrastructure Regulation (AFIR) has now been officially published by the European Commission and will apply from 8 January 2026 onwards. From this date onwards, all newly installed public EV charging stations will be required to support the outdated EN ISO 15118-2 standard, without specifying the functionality. From January 2027 onwards, all new public and private EV charging points will be required to support EN ISO 15118-20, again wit…

Robust and efficient multiple-unit switchback experimentation
Paul Missault, Lorenzo Masoero, Christian Delb\'e, Thomas Richardson, Guido Imbens
https://arxiv.org/abs/2506.12654

Robust and efficient multiple-unit switchback experimentation
User-randomized A/B testing has emerged as the gold standard for online experimentation. However, when this kind of approach is not feasible due to legal, ethical or practical considerations, experimenters have to consider alternatives like item-randomization. Item-randomization is often met with skepticism due to its poor empirical performance. To fill this gap, in this paper we introduce a novel and rich class of experimental designs, "Regular Balanced Switchback Designs" (RBSDs). At their co…

User Altruism in Recommendation Systems
Ekaterina Fedorova, Madeline Kitch, Chara Podimata
https://arxiv.org/abs/2506.04525 https://a…

User Altruism in Recommendation Systems
Users of social media platforms based on recommendation systems (RecSys) (e.g. TikTok, X, YouTube) strategically interact with platform content to influence future recommendations. On some such platforms, users have been documented to form large-scale grassroots movements encouraging others to purposefully interact with algorithmically suppressed content in order to "boost" its recommendation; we term this behavior user altruism. To capture this behavior, we study a game between users and a Rec…

Improving AI-generated music with user-guided training
Vishwa Mohan Singh, Sai Anirudh Aryasomayajula, Ahan Chatterjee, Beste Aydemir, Rifat Mehreen Amin
https://arxiv.org/abs/2506.04852

Improving AI-generated music with user-guided training
AI music generation has advanced rapidly, with models like diffusion and autoregressive algorithms enabling high-fidelity outputs. These tools can alter styles, mix instruments, or isolate them. Since sound can be visualized as spectrograms, image-generation algorithms can be applied to generate novel music. However, these algorithms are typically trained on fixed datasets, which makes it challenging for them to interpret and respond to user input accurately. This is especially problematic beca…

This https://arxiv.org/abs/2505.23655 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Keyed Chaotic Tensor Transformations for Secure And Attributable Neural Inference
This work introduces a novel framework for secure and privacy-preserving neural network inference based on keyed chaotic dynamical transformations. The proposed method applies a deterministic, cryptographically seeded chaotic system to tensors, producing non-invertible, user-specific transformations that enable authenticated inference, tensor-level watermarking, and data attribution. This framework offers a scalable and lightweight alternative to conventional cryptographic techniques, and estab…

WANDER: An Explainable Decision-Support Framework for HPC
Ankur Lahiry, Banooqa Banday, Tanzima Z. Islam
https://arxiv.org/abs/2506.04049 https://

WANDER: An Explainable Decision-Support Framework for HPC
High-performance computing (HPC) systems expose many interdependent configuration knobs that impact runtime, resource usage, power, and variability. Existing predictive tools model these outcomes, but do not support structured exploration, explanation, or guided reconfiguration. We present WANDER, a decision-support framework that synthesizes alternate configurations using counterfactual analysis aligned with user goals and constraints. We introduce a composite trade-off score that ranks sugges…

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Christoph Brunken, Olivier Peltre, Heloise Chomet, Lucien Walewski, Manus McAuliffe, Valentin Heyraud, Solal Attias, Martin Maarand, Yessine Khanfir, Edan Toledo, Fabio Falcioni, Marie Bluntzer, Silvia Acosta-Guti\'errez, Jules Tilly

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT). In this white paper, we present our MLIP library which was created with two core aims: (1) provide to industry experts without machine learning background a user-friendly and computationally efficient set of tools to experiment with MLIP models, (2) provide m…

This https://arxiv.org/abs/2505.23655 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Keyed Chaotic Tensor Transformations for Secure And Attributable Neural Inference
This work introduces a novel framework for secure and privacy-preserving neural network inference based on keyed chaotic dynamical transformations. The proposed method applies a deterministic, cryptographically seeded chaotic system to tensors, producing non-invertible, user-specific transformations that enable authenticated inference, tensor-level watermarking, and data attribution. This framework offers a scalable and lightweight alternative to conventional cryptographic techniques, and estab…

Keyed Chaotic Tensor Transformations for Secure And Attributable Neural Inference
Peter David Fagan
https://arxiv.org/abs/2505.23655 https://

Keyed Chaotic Tensor Transformations for Secure And Attributable Neural Inference
This work introduces a novel framework for secure and privacy-preserving neural network inference based on keyed chaotic dynamical transformations. The proposed method applies a deterministic, cryptographically seeded chaotic system to tensors, producing non-invertible, user-specific transformations that enable authenticated inference, tensor-level watermarking, and data attribution. This framework offers a scalable and lightweight alternative to conventional cryptographic techniques, and estab…