Tootfinder

Binary Mixtures of Intelligent Active Brownian Particles with Visual Perception
Rajendra Singh Negi, Roland G. Winkler, Gerhard Gompper
https://arxiv.org/abs/2506.09698

Binary Mixtures of Intelligent Active Brownian Particles with Visual Perception
The collective properties of a binary mixture of A- and B-type self-steering particles endowed with visual perception are studied by computer simulations. Active Brownian particles are employed with an additional steering mechanism, which enables them to adjust their propulsion direction relative to the instantaneous positions of neighboring particles, depending on the species, either steering toward or away from them. Steering can be nonreciprocal between the A- and B-type particles. The under…

Strong squeezing and perfect one-way EPR steering in electro-optomechanical system
Qing-Min Zeng, A-Peng Liu, Qi Guo
https://arxiv.org/abs/2507.07697 https…

Strong squeezing and perfect one-way EPR steering in electro-optomechanical system
We consider a three-mode electro-optomechanical system in which a mechanical oscillator is coupled to an optical cavity and a LC circuit through radiation pressure and capacitive coupling, respectively. By controlling the two-tone driving of the optical cavity and the microwave driving of the LC circuit, the strong squeezing of both the microwave field of the LC circuit and the mechanical mode will be obtained. Moreover, by further altering the driving power of the system, the perfect one-way E…

Ukraine's security service says AI allowed its drones to keep flying "along a pre-planned route" even after losing signal in Russia during "Operation Spiderweb" (Fabrice Deprez/Financial Times)

How AI guided Ukraine’s drones to hit Russian airfields
Artificial intelligence took over the steering of unmanned quadcopters after losing signal

SoK: Machine Unlearning for Large Language Models
Jie Ren, Yue Xing, Yingqian Cui, Charu C. Aggarwal, Hui Liu
https://arxiv.org/abs/2506.09227 https://

SoK: Machine Unlearning for Large Language Models
Large language model (LLM) unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of techniques have been proposed, including Gradient Ascent, model editing, and re-steering hidden representations. While existing surveys often organize these methods by their technical characteristics, such classifications tend to overlook a more fundamental dimension: the underlying…

@… @… I would like to add “if the steering wheel behaves differently every day, that’s not your fault either”.
Of course, Larry probably wrote this before that particular issue became such a big deal.

Dynamics and multi-stability of a rotor-actuated Twistcar robot with passive steering joint
Anna Zigelman, Zitao Yu, Rom Levy, Yizhar Or
https://arxiv.org/abs/2507.04846

Dynamics and multi-stability of a rotor-actuated Twistcar robot with passive steering joint
The nonlinear dynamics of many under-actuated wheeled platforms are governed by nonholonomic constraints of no-skid for passively rolling wheels, coupled with momentum balance. In most of theoretical models, the shape variables, i.e. joint angles, are directly prescribed as periodic inputs, such as steering angle of the Twistcar. In this work, we study a variant of the Twistcar model where the actuation input is periodic oscillations of an inertial rotor attached to the main body, while the ste…

InfoSteer: Steering Information Utility in Language Model Post-Training
Chunyuan Deng, Ruidi Chang, Hanjie Chen
https://arxiv.org/abs/2507.05158 https://…

InfoSteer: Steering Information Utility in Language Model Post-Training
Recent advancements in language models (LMs) gradually ushered in an era where post-training is crucial. Yet, post-training approaches such as supervised fine-tuning (SFT) do not guarantee effective use of knowledge acquired during pretraining. We therefore present \ours, a lightweight method that encourages parametric information utilization in LMs during post-training. This is achieved via treating FFN layer as associate key-value memory, and promotes the use of stored memory vectors via forw…

Apple says it is appealing the EU's €500M DMA fine, levied in April 2025 over App Store steering rules, and claims it goes "far beyond what the law requires" (Juli Clover/MacRumors)
https://www.macrumors.com/2025/07/07/apple-appeals-eu-500m-euro…

Apple Challenges 'Unprecedented' €500M EU Fine Over App Store Steering Rules
Apple is appealing the 500 million euro ($570 million) fine that it is facing in the European Union for allegedly violating the Digital Markets...

This https://arxiv.org/abs/2502.01907 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Robust Cislunar Low-Thrust Trajectory Optimization under Uncertainties via Sequential Covariance Steering
Spacecraft operations are influenced by uncertainties such as dynamics modeling, navigation, and maneuver execution errors. Although mission design has traditionally incorporated heuristic safety margins to mitigate the effect of uncertainties, particularly before/after crucial events, it is yet unclear whether this practice will scale in the cislunar region, which features locally chaotic nonlinear dynamics and involves frequent lunar flybys. This paper applies chance-constrained covariance st…

PII Jailbreaking in LLMs via Activation Steering Reveals Personal Information Leakage
Krishna Kanth Nakka, Xue Jiang, Xuebing Zhou
https://arxiv.org/abs/2507.02332

PII Jailbreaking in LLMs via Activation Steering Reveals Personal Information Leakage
This paper investigates privacy jailbreaking in LLMs via steering, focusing on whether manipulating activations can bypass LLM alignment and alter response behaviors to privacy related queries (e.g., a certain public figure's sexual orientation). We begin by identifying attention heads predictive of refusal behavior for private attributes (e.g., sexual orientation) using lightweight linear probes trained with privacy evaluator labels. Next, we steer the activations of a small subset of these at…

How to craft a deep reinforcement learning policy for wind farm flow control
Elie Kadoche, Pascal Bianchi, Florence Carton, Philippe Ciblat, Damien Ernst
https://arxiv.org/abs/2506.06204

How to craft a deep reinforcement learning policy for wind farm flow control
Within wind farms, wake effects between turbines can significantly reduce overall energy production. Wind farm flow control encompasses methods designed to mitigate these effects through coordinated turbine control. Wake steering, for example, consists in intentionally misaligning certain turbines with the wind to optimize airflow and increase power output. However, designing a robust wake steering controller remains challenging, and existing machine learning approaches are limited to quasi-sta…

#AI impact on work force: killing jobs, enhancing jobs or what?
Interesting read: https://www.ineteconomics.org/perspectives/b…

Ha good luck with that https://geeknews.chat/@theregister/114813445350914205

CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
Hang Lv, Sheng Liang, Hao Wang, Hongchao Gu, Yaxiong Wu, Wei Guo, Defu Lian, Yong Liu, Enhong Chen
https://arxiv.org/abs/2507.04756

CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
Personalized text generation has become crucial for adapting language models to diverse and evolving users' personal context across cultural, temporal, and contextual dimensions. While existing methods often rely on centralized fine-tuning or static preference alignment, they struggle to achieve real-time adaptation under resource constraints inherent to personal devices. This limitation creates a dilemma: large cloud-based models lack access to localized user-specific information, while small …

Quantum correlations in molecular cavity optomechanics
E. Kongkui Berinyuy, D. R. Kenigoule Massembele, P. Djorwe, R. Altuijri, M. R. Eid, A. -H. Abdel-Aty, S. G. Nana Engo
https://arxiv.org/abs/2506.07277

Quantum correlations in molecular cavity optomechanics
In quantum information, quantum correlations--particularly entanglement, EPR steering, and quantum discord--stand as interesting resources, powering breakthroughs from secure communication and quantum teleportation to efficient quantum computation. Here, we unveil a theoretical framework for generating and controlling these vital quantum phenomena within a double-cavity molecular optomechanical (McOM) system. This innovative approach leverages strong interactions between confined optical fields…

Exciton transport driven by spin excitations in an antiferromagnet
Florian Dirnberger, Sophia Terres, Zakhar A. Iakovlev, Kseniia Mosina, Zdenek Sofer, Akashdeep Kamra, Mikhail M. Glazov, Alexey Chernikov
https://arxiv.org/abs/2507.07071

Exciton transport driven by spin excitations in an antiferromagnet
A new class of optical quasiparticles called magnetic excitons recently emerged in magnetic van der Waals materials. Akin to the highly effective strategies developed for electrons, the strong interactions of these excitons with the spin degree of freedom may provide innovative solutions for long-standing challenges in optics, such as steering the flow of energy and information. Here, we demonstrate transport of excitons by spin excitations in the van der Waals antiferromagnetic semiconductor C…

So apparently the Switch 2 has a hollow kickstand that technically allows you to do this in your car, and while hilarious, please do not actually do this in your car (while driving).
(via https://www.reddit.com/r/NintendoSwitch2/c

Importance of User Control in Data-Centric Steering for Healthcare Experts
Aditya Bhattacharya, Simone Stumpf, Katrien Verbert
https://arxiv.org/abs/2506.18770

Importance of User Control in Data-Centric Steering for Healthcare Experts
As Artificial Intelligence (AI) becomes increasingly integrated into high-stakes domains like healthcare, effective collaboration between healthcare experts and AI systems is critical. Data-centric steering, which involves fine-tuning prediction models by improving training data quality, plays a key role in this process. However, little research has explored how varying levels of user control affect healthcare experts during data-centric steering. We address this gap by examining manual and aut…

Narrow beam and low-sidelobe two-dimensional beam steering on thin-film lithium niobate optical phased array
Yang Li, Shiyao Deng, Xiao Ma, Ziliang Fang, Shufeng Li, Weikang Xu, Fangheng Fu, Xu Ouyang, Yuming Wei, Tiefeng Yang, Heyuan Guan, Huihui Lu
https://arxiv.org/abs/2506.22124

Narrow beam and low-sidelobe two-dimensional beam steering on thin-film lithium niobate optical phased array
Optical beam steering has become indispensable in free-space optical communications, light detection and ranging (LiDAR), mapping, and projection. Optical phased array (OPA) leads this field, yet conventional versions still suffer from a narrow steering field of view (FOV), insufficient sidelobe suppression, and limited angular resolution. Thin-film lithium niobate (LN), with its strong Pockels electro-optic (EO) effect, offers a powerful integrated-photonics platform to overcome these limitati…

Goal-oriented optimal sensor placement for PDE-constrained inverse problems in crisis management
Marco Mattuschka, Noah An der Lan, Max von Danwitz, Daniel Wolff, Alexander Popp
https://arxiv.org/abs/2507.02500

Goal-oriented optimal sensor placement for PDE-constrained inverse problems in crisis management
This paper presents a novel framework for goal-oriented optimal static sensor placement and dynamic sensor steering in PDE-constrained inverse problems, utilizing a Bayesian approach accelerated by low-rank approximations. The framework is applied to airborne contaminant tracking, extending recent dynamic sensor steering methods to complex geometries for computational efficiency. A C-optimal design criterion is employed to strategically place sensors, minimizing uncertainty in predictions. Nume…

Happy Jacob Geller Book day AND Nintendo Switch 2 day to all who celebrate! 🎉
What a day!

Logit-Gap Steering: Efficient Short-Suffix Jailbreaks for Aligned Large Language Models
Tung-Ling Li, Hongliang Liu
https://arxiv.org/abs/2506.24056 https:…

Logit-Gap Steering: Efficient Short-Suffix Jailbreaks for Aligned Large Language Models
We introduce logit-gap steering, a fast jailbreak framework that casts the refusal-affirmation gap of RLHF-aligned language models as a single pass over the vocabulary. A forward-computable score blends gap reduction with lightweight proxies for KL penalty and reward shift, allowing a "sort-sum-stop" sweep to complete in under a second and return a short suffix--two orders of magnitude fewer model calls than beam or gradient attacks. The same suffix generalises to unseen prompts and scales from…

Evaluation of "As-Intended" Vehicle Dynamics using the Active Inference Framework
Kazuharu Kidera, Takuma Miyaguchi, Hideyoshi Yanagisawa
https://arxiv.org/abs/2506.00035

Evaluation of "As-Intended" Vehicle Dynamics using the Active Inference Framework
We constructed a computational model of the driver's brain for steering tasks using the active inference framework, grounded in the free energy principle - a theory from computational neuroscience. This model enables quantitative estimation of how accurately the brain learns vehicle dynamics and performs appropriate steering, using a measure called variational free energy. Through driving simulator experiments, we observed strong correlations between variational free energy and both expert driv…

Steering Conceptual Bias via Transformer Latent-Subspace Activation
Vansh Sharma, Venkat Raman
https://arxiv.org/abs/2506.18887 https://

Steering Conceptual Bias via Transformer Latent-Subspace Activation
This work examines whether activating latent subspaces in language models (LLMs) can steer scientific code generation toward a specific programming language. Five causal LLMs were first evaluated on scientific coding prompts to quantify their baseline bias among four programming languages. A static neuron-attribution method, perturbing the highest activated MLP weight for a C++ or CPP token, proved brittle and exhibited limited generalization across prompt styles and model scales. To address th…

This https://arxiv.org/abs/2410.01050 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Steering Elongate Multi-legged Robots By Modulating Body Undulation Waves
Centipedes exhibit great maneuverability in diverse environments due to their many legs and body-driven control. By leveraging similar morphologies and control strategies, their robotic counterparts also demonstrate effective terrestrial locomotion. However, the success of these multi-legged robots is largely limited to forward locomotion; steering is substantially less studied, in part because of the difficulty in coordinating a high degree-of-freedom robot to follow predictable, planar trajec…

Self-Steering Deep Non-Linear Spatially Selective Filters for Efficient Extraction of Moving Speakers under Weak Guidance
Jakob Kienegger, Alina Mannanova, Huajian Fang, Timo Gerkmann
https://arxiv.org/abs/2507.02791

Self-Steering Deep Non-Linear Spatially Selective Filters for Efficient Extraction of Moving Speakers under Weak Guidance
Recent works on deep non-linear spatially selective filters demonstrate exceptional enhancement performance with computationally lightweight architectures for stationary speakers of known directions. However, to maintain this performance in dynamic scenarios, resource-intensive data-driven tracking algorithms become necessary to provide precise spatial guidance conditioned on the initial direction of a target speaker. As this additional computational overhead hinders application in resource-con…

Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning
Yuheng Lei, Sitong Mao, Shunbo Zhou, Hongyuan Zhang, Xuelong Li, Ping Luo
https://arxiv.org/abs/2506.05985

Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning
A generalist agent must continuously learn and adapt throughout its lifetime, achieving efficient forward transfer while minimizing catastrophic forgetting. Previous work within the dominant pretrain-then-finetune paradigm has explored parameter-efficient fine-tuning for single-task adaptation, effectively steering a frozen pretrained model with a small number of parameters. However, in the context of lifelong learning, these methods rely on the impractical assumption of a test-time task identi…

Fine-Grained control over Music Generation with Activation Steering
Dipanshu Panda, Jayden Koshy Joe, Harshith M R, Swathi Narashiman, Pranay Mathur, Anish Veerakumar, Aniruddh Krishna, Keerthiharan A
https://arxiv.org/abs/2506.10225

Fine-Grained control over Music Generation with Activation Steering
We present a method for fine-grained control over music generation through inference-time interventions on an autoregressive generative music transformer called MusicGen. Our approach enables timbre transfer, style transfer, and genre fusion by steering the residual stream using weights of linear probes trained on it, or by steering the attention layer activations in a similar manner. We observe that modelling this as a regression task provides improved performance, hypothesizing that the mean-…

When you are a swift developer, every day is Xmas!
https://forums.swift.org/t/swift-sdks-for-webassembly-now-available-on-swift-org/80405

Swift SDKs for WebAssembly now available on swift.org
As the Platform Steering Group accepted the vision document for WebAssembly support, we’re excited to announce that Swift SDKs for Wasm are now available at swift.org/install. The website now also hosts a getting started guide for Wasm with installation and build instructions. You may notice "Swift SDKs" is in plural. The reason is that artifact bundles for Wasm distributed from swift.org include two Swift SDKs: one with the full feature set of the Swift standard library and Swift Testing, a…

Another change proposal was submitted to the Fedora Engineering Steering Committee (FESCo) for approval[1]:
Replace the #Xorg #Xserver with the X11Libre Xserver for #Fedora 43-

Multi-Functional Metasurfaces with M-Type Ferrites: Shaping the Future of mmWave Absorption and Beam Steering
Nohgyeom Ha, Horim Lee, Min Jang, Gyoungdeuk Kim, Hoyong Kim, Byeongjin Park, Manos M. Tentzeris, Sangkil Kim
https://arxiv.org/abs/2506.23240

Multi-Functional Metasurfaces with M-Type Ferrites: Shaping the Future of mmWave Absorption and Beam Steering
This paper presents a comprehensive review and tutorial on multi-functional metasurfaces integrated with M-type ferrite materials for millimeter-wave (mmWave) absorption and beam control. As wireless communication systems transition toward beyond-5G architectures, including non-terrestrial networks (NTNs), the demand for adaptive, low-profile electromagnetic surfaces that can manage interference while enabling beam reconfiguration becomes increasingly critical. Conventional metasurfaces often s…

Wasserstein Distributionally Robust Adaptive Beamforming
Kiarash Hassas Irani, Sergiy A. Vorobyov, Yongwei Huang
https://arxiv.org/abs/2506.01154 https://

Wasserstein Distributionally Robust Adaptive Beamforming
Distributionally robust optimization (DRO)-based robust adaptive beamforming (RAB) enables enhanced robustness against model uncertainties, such as steering vector mismatches and interference-plus-noise covariance matrix estimation errors. Existing DRO-based RAB methods primarily rely on uncertainty sets characterized by the first- and second-order moments. In this work, we propose a novel Wasserstein DRO-based beamformer, using the worst-case signal-to-interference-plus-noise ratio maximizatio…

Contactless Precision Steering of Particles in a Fluid inside a Cube with Rotating Walls
Lucas Amoudruz, Petr Karnakov, Petros Koumoutsakos
https://arxiv.org/abs/2506.15958

Contactless Precision Steering of Particles in a Fluid inside a Cube with Rotating Walls
Contactless manipulation of small objects is essential for biomedical and chemical applications, such as cell analysis, assisted fertilisation, and precision chemistry. Established methods, including optical, acoustic, and magnetic tweezers, are now complemented by flow control techniques that use flow-induced motion to enable precise and versatile manipulation. However, trapping multiple particles in fluid remains a challenge. This study introduces a novel control algorithm capable of steering…

"cybernetics" comes from kybernētikos, which is Greek for "good at steering a boat"
Therefore John Blackthorne is a cyborg, but Robocop is not.

Directional Flow of Confined Polaritons in CrSBr
Pratap Chandra Adak, Sichao Yu, Jaime Abad-Arredondo, Biswajit Datta, Andy Cruz, Sorah Fischer, Kseniia Mosina, Zden\v{e}k Sofer, Antonio I. Fern\'andez-Dom\'inguez, Francisco J. Garc\'ia-Vidal, Vinod M. Menon
https://arxiv.org/abs/2507.04367

Directional Flow of Confined Polaritons in CrSBr
Nanoscale control of energy transport is a central challenge in modern photonics. Utilization of exciton-polaritons hybrid light-matter quasiparticles is one viable approach, but it typically demands complex device engineering to enable directional transport. Here, we demonstrate that the van der Waals magnet CrSBr offers an inherent avenue for steering polariton transport leveraging a unique combination of intrinsic optical anisotropy, high refractive index, and excitons dressed by photons. Th…

Replaced the front wheel bearing on my car today... I am a mechanic by necessity, not by choice. Can't afford garage prices, so I buy the parts and do the job myself.

Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm
Baixiang Huang, Zhen Tan, Haoran Wang, Zijie Liu, Dawei Li, Ali Payani, Huan Liu, Tianlong Chen, Kai Shu
https://arxiv.org/abs/2506.20606

Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm
Agents based on Large Language Models (LLMs) have demonstrated strong capabilities across a wide range of tasks. However, deploying LLM-based agents in high-stakes domains comes with significant safety and ethical risks. Unethical behavior by these agents can directly result in serious real-world consequences, including physical harm and financial loss. To efficiently steer the ethical behavior of agents, we frame agent behavior steering as a model editing task, which we term Behavior Editing. …

Can boundary configuration be tuned to optimize directional quantum steering harvesting?
Xiao-Li Huang, Xiao-Ying Jiang, Yu-Xuan Wang, Si-Yu Liu, Zejun Wang, Shu-Min Wu
https://arxiv.org/abs/2506.18734

Can boundary configuration be tuned to optimize directional quantum steering harvesting?
We investigate the harvesting of quantum steering and its asymmetry between two static detectors locally interacting with a vacuum massless scalar field near an infinite, perfectly reflecting boundary. The detectors are arranged either parallel or orthogonal to the boundary, with detector $B$ assumed to have an energy gap greater than or equal to that of detector $A$. It is interesting to observe that, with increasing distance between the detectors and the boundary, the boundary tends to suppre…

DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Maximilian Du, Shuran Song
https://arxiv.org/abs/2506.13922 https://

DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Deploying large, complex policies in the real world requires the ability to steer them to fit the needs of a situation. Most common steering approaches, like goal-conditioning, require training the robot policy with a distribution of test-time objectives in mind. To overcome this limitation, we present DynaGuide, a steering method for diffusion policies using guidance from an external dynamics model during the diffusion denoising process. DynaGuide separates the dynamics model from the base pol…

Navigating with Annealing Guidance Scale in Diffusion Space
Shai Yehezkel, Omer Dahary, Andrey Voynov, Daniel Cohen-Or
https://arxiv.org/abs/2506.24108 htt…

Navigating with Annealing Guidance Scale in Diffusion Space
Denoising diffusion models excel at generating high-quality images conditioned on text prompts, yet their effectiveness heavily relies on careful guidance during the sampling process. Classifier-Free Guidance (CFG) provides a widely used mechanism for steering generation by setting the guidance scale, which balances image quality and prompt alignment. However, the choice of the guidance scale has a critical impact on the convergence toward a visually appealing and prompt-adherent image. In this…

SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer
Diego Di Carlo (RIKEN AIP), Mathieu Fontaine (LTCI, IP Paris), Aditya Arie Nugraha (RIKEN AIP), Yoshiaki Bando (RIKEN AIP), Kazuyoshi Yoshii
https://arxiv.org/abs/2506.18954

SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer
This paper describes a sound source localization (SSL) technique that combines an $α$-stable model for the observed signal with a neural network-based approach for modeling steering vectors. Specifically, a physics-informed neural network, referred to as Neural Steerer, is used to interpolate measured steering vectors (SVs) on a fixed microphone array. This allows for a more robust estimation of the so-called $α$-stable spatial measure, which represents the most plausible direction of arrival…

Reducing Motion Sickness in Passengers of Autonomous Personal Mobility Vehicles by Presenting a Driving Path
Yuya Ide, Hailong Liu, Takahiro Wada
https://arxiv.org/abs/2506.23457 …

Reducing Motion Sickness in Passengers of Autonomous Personal Mobility Vehicles by Presenting a Driving Path
Autonomous personal mobility vehicles (APMVs) are small mobility devices designed for individual automated transportation in shared spaces. In such environments, frequent pedestrian avoidance maneuvers may cause rapid steering adjustments and passive postural responses from passengers, thereby increasing the risk of motion sickness. This study investigated the effects of providing path information on 16 passengers' head movement behavior and motion sickness while riding an APMV. Through a contr…

Beam Squint Mitigation in Wideband Hybrid Beamformers: Full-TTD, Sparse-TTD, or Non-TTD?
Mehdi Monemi, Mehdi Rasti, Omid Yazdani, Onel Lopez, Matti Latva-aho
https://arxiv.org/abs/2506.19381

Beam Squint Mitigation in Wideband Hybrid Beamformers: Full-TTD, Sparse-TTD, or Non-TTD?
Beam squint poses a fundamental challenge in wideband hybrid beamforming, particularly for mmWave and THz systems that demand both ultra-wide bandwidth and high directional beams. While conventional phase shifter-based beamformers may offer partial mitigation, True Time Delay (TTD) units provide a fundamentally more effective solution by enabling frequency-independent beam steering. However, the high cost of TTD units has recently driven much interest in Sparse-TTD architectures, which combine …

Numerical Techniques for the Maximum Likelihood Toeplitz Covariance Matrix Estimation: Part I. Symmetric Toeplitz Matrices
Yuri Abramovich, Victor Abramovich, Tanit Pongsiri
https://arxiv.org/abs/2507.01230

Numerical Techniques for the Maximum Likelihood Toeplitz Covariance Matrix Estimation: Part I. Symmetric Toeplitz Matrices
In several applications, one must estimate a real-valued (symmetric) Toeplitz covariance matrix, typically shifted by the conjugated diagonal matrices of phase progression and phase "calibration" errors. Unlike the Hermitian Toeplitz covariance matrices, these symmetric matrices have a unique potential capability of being estimated regardless of these beam-steering phase progression and/or phase "calibration" errors. This unique capability is the primary motivation of this paper.

Sources: Apple is locked in last-minute EU negotiations over App Store changes to avoid fines set for this week, and is expected to offer "steering" concessions (Barbara Moens/Financial Times)
https://www.ft.com/content/b5d51870-e864-4aa5-b998-c0d2994a7e2…

Apple locked in last-minute App Store negotiations to avoid Brussels fines
Tech group must make concessions to comply with Digital Markets Act or be hit by escalating levies from Thursday

This https://arxiv.org/abs/2505.24502 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Predictability of quantum observables: Applications in QKD and top quarks
We quantify and optimize the predictability of local observables in bipartite quantum systems by employing the Bayes risk and the inference variance, two measures rooted in statistical learning theory. Specifically, we minimized these quantities when the prediction is improved by an additional quantum system, providing analytical expressions for arbitrary two-qubit states, and showcasing a connection with Einstein-Podolsky-Rosen steering criteria. Then, we embed our Bayes risk minimization into…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- Understanding Reasoning in Thinking Language Models via Steering Vectors
Constantin Venhoff, Iv\'an Arcuschin, Philip Torr, Arthur Conmy, Neel Nanda

Faster Fixed-Point Methods for Multichain MDPs
Matthew Zurek, Yudong Chen
https://arxiv.org/abs/2506.20910 https://arxiv.org/pdf/2506…

Faster Fixed-Point Methods for Multichain MDPs
We study value-iteration (VI) algorithms for solving general (a.k.a. multichain) Markov decision processes (MDPs) under the average-reward criterion, a fundamental but theoretically challenging setting. Beyond the difficulties inherent to all average-reward problems posed by the lack of contractivity and non-uniqueness of solutions to the Bellman operator, in the multichain setting an optimal policy must solve the navigation subproblem of steering towards the best connected component, in additi…

Steering Robots with Inference-Time Interactions
Yanwei Wang
https://arxiv.org/abs/2506.14287 https://arxiv.org/pdf/2506.14287…

Steering Robots with Inference-Time Interactions
Imitation learning has driven the development of generalist policies capable of autonomously solving multiple tasks. However, when a pretrained policy makes errors during deployment, there are limited mechanisms for users to correct its behavior. While collecting additional data for finetuning can address such issues, doing so for each downstream use case is inefficient at deployment. My research proposes an alternative: keeping pretrained policies frozen as a fixed skill repertoire while allow…

Selling Certification, Content Moderation, and Attention
Heski Bar-Isaac, Rahul Deb, Matthew Mitchell
https://arxiv.org/abs/2506.12604 https://

Selling Certification, Content Moderation, and Attention
We introduce a model of content moderation for sale, where a platform can channel attention in two ways: direct steering that makes content visible to consumers and certification that controls what consumers know about the content. The platform optimally price discriminates using both instruments. Content from higher willingness-to-pay providers enjoys higher quality certification and more views. The platform cross-subsidizes content: the same certificate is assigned to content from low willing…

Shaping Ultrafast Pulses for Enhanced Resonant Nonlinear Interactions
Omri Meron, Snir Nehemia, Uri Arieli, Haim Suchowski
https://arxiv.org/abs/2507.00568

Shaping Ultrafast Pulses for Enhanced Resonant Nonlinear Interactions
Coherent control with shaped ultrafast pulses is a powerful approach for steering nonlinear light-matter interactions. Previous studies in quantum control have shown that, beyond transform-limited pulses, those with antisymmetric spectral phases can drive nonresonant multiphoton transitions with comparable efficiency. However, in resonant multiphoton transitions, the material's spectral-phase response introduces dispersion that degrades nonlinear efficiency. Pre-shaping the pulse to compensate …

Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
Andrew Mole, Max Weissenbacher, Georgios Rigas, Sylvain Laizet
https://arxiv.org/abs/2506.20554

Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
Traditional wind farm control operates each turbine independently to maximize individual power output. However, coordinated wake steering across the entire farm can substantially increase the combined wind farm energy production. Although dynamic closed-loop control has proven effective in flow control applications, wind farm optimization has relied primarily on static, low-fidelity simulators that ignore critical turbulent flow dynamics. In this work, we present the first reinforcement learnin…

Predictability of quantum observables: Applications in QKD and top quarks
Dennis I. Mart\'inez-Moreno, Miguel Castillo-Celeita, Diego G. Bussandri
https://arxiv.org/abs/2505.24502

Predictability of quantum observables: Applications in QKD and top quarks
We quantify and optimize the predictability of local observables in bipartite quantum systems by employing the Bayes risk and the inference variance, two measures rooted in statistical learning theory. Specifically, we minimized these quantities when the prediction is improved by an additional quantum system, providing analytical expressions for arbitrary two-qubit states, and showcasing a connection with Einstein-Podolsky-Rosen steering criteria. Then, we embed our Bayes risk minimization into…

Replaced article(s) found for physics.acc-ph. https://arxiv.org/list/physics.acc-ph/new
[1/1]:
- Strategy for Bayesian Optimized Beam Steering at TRIUMF-ISAC's MEBT and HEBT Beamlines
O. Hassan, O. Shelbaya, W. Fedorko, T. Planche, O. Kester

Real-time Terrain Analysis for Off-road Autonomous Vehicles
Edwina Lewis, Aditya Parameshwaran, Laura Redmond, Yue Wang
https://arxiv.org/abs/2506.21347 ht…

Real-time Terrain Analysis for Off-road Autonomous Vehicles
This research addresses critical autonomous vehicle control challenges arising from road roughness variation, which induces course deviations and potential loss of road contact during steering operations. We present a novel real-time road roughness estimation system employing Bayesian calibration methodology that processes axle accelerations to predict terrain roughness with quantifiable confidence measures. The technical framework integrates a Gaussian process surrogate model with a simulated …

Analyzing URA Geometry for Enhanced Spatial Multiplexing and Extended Near-Field Coverage
Ahmed Hussain, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil
https://arxiv.org/abs/2506.15470

Analyzing URA Geometry for Enhanced Spatial Multiplexing and Extended Near-Field Coverage
With the deployment of large antenna arrays at high frequency bands, future wireless communication systems are likely to operate in the radiative near-field. Unlike far-field beam steering, near-field beams can be focused within a spatial region of finite depth, enabling spatial multiplexing in both the angular and range dimensions. This paper derives the beamdepth for a generalized uniform rectangular array (URA) and investigates how array geometry influences the near-field beamdepth and the l…

Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach
Fernando Galaz Prieto, Antti Lassila, Maryam Samavaki, Sampsa Pursiainen
https://arxiv.org/abs/2506.13452

Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach
As DBS technology advances toward directional leads and optimization-based current steering, this study aims to improve the selection of electrode contact configurations using the recently developed L1-norm regularized L1-norm fitting (L1L1) method. The focus is in particular on L1L1's capability to incorporate a priori lead field uncertainty, offering a potential advantage over conventional approaches that do not account for such variability. Our optimization framework incorporates uncertainty…

Replaced article(s) found for physics.app-ph. https://arxiv.org/list/physics.app-ph/new
[1/1]:
Strategy for Bayesian Optimized Beam Steering at TRIUMF-ISAC's MEBT and HEBT Beamlines

TUM Teleoperation: Open Source Software for Remote Driving and Assistance of Automated Vehicles
Tobias Kerbl, David Brecht, Nils Gehrke, Nijinshan Karunainayagam, Niklas Krauss, Florian Pfab, Richard Taupitz, Ines Trautmannsheimer, Xiyan Su, Maria-Magdalena Wolf, Frank Diermeyer
https://arxiv.org/abs/2506.13933

TUM Teleoperation: Open Source Software for Remote Driving and Assistance of Automated Vehicles
Teleoperation is a key enabler for future mobility, supporting Automated Vehicles in rare and complex scenarios beyond the capabilities of their automation. Despite ongoing research, no open source software currently combines Remote Driving, e.g., via steering wheel and pedals, Remote Assistance through high-level interaction with automated driving software modules, and integration with a real-world vehicle for practical testing. To address this gap, we present a modular, open source teleoperat…

Replaced article(s) found for physics.acc-ph. https://arxiv.org/list/physics.acc-ph/new
[1/1]:
Strategy for Bayesian Optimized Beam Steering at TRIUMF-ISAC's MEBT and HEBT Beamlines

Saturable nonlinearity induced quantum correlations in optomechanics
D. R. Kenigoule Massembele, E. Kongkui Berinyuy, P. Djorwe, A. -H. Abdel-Aty, M. R. Eid, R. Altuijri, S. G. Nana Engo
https://arxiv.org/abs/2506.10709

Saturable nonlinearity induced quantum correlations in optomechanics
We propose a scheme that induces quantum correlations in optomtomechanical systems. Our benchmark system consists of two optically coupled optical cavities which interact with a common mechanical resonator. The optical cavities host saturable nonlinearity which triggers either gain or losses in each cavity. Without these nonlinearities, there are no quantum correlations, i.e., entanglement and steering, generated in the system. By turning on the nonlinearities, gain and losses are switched on, …