Tootfinder

The optical constants and grain sizes of interstellar dust measured directly using the dust scattered x-ray halo of GRB 221009A
Albert Sneppen, Darach Watson
https://arxiv.org/abs/2506.12125

The optical constants and grain sizes of interstellar dust measured directly using the dust scattered x-ray halo of GRB 221009A
X-ray scattering is a powerful probe of the optical constants and grain size distribution of interstellar dust. Bright, transient sources are excellent tools for this, since they fade rapidly, leaving only the expanding scattered x-ray halo. Here, we analyse the dust-scattered x-ray halo data of the unprecedentedly bright $γ$-ray burst, GRB 221009A, using anomalous diffraction theory to measure the grain size distribution of dust in the Galaxy as well as the complex refractive index, $m$, and …

Social Media Reactions to Open Source Promotions: AI-Powered GitHub Projects on Hacker News
Prachnachai Meakpaiboonwattana, Warittha Tarntong, Thai Mekratanavorakul, Chaiyong Ragkhitwetsagul, Pattaraporn Sangaroonsilp, Raula Kula, Morakot Choetkiertikul, Kenichi Matsumoto, Thanwadee Sunetnanta
https://arxiv.org/abs/2506.12643

Social Media Reactions to Open Source Promotions: AI-Powered GitHub Projects on Hacker News
Social media platforms have become more influential than traditional news sources, shaping public discourse and accelerating the spread of information. With the rapid advancement of artificial intelligence (AI), open-source software (OSS) projects can leverage these platforms to gain visibility and attract contributors. In this study, we investigate the relationship between Hacker News, a social news site focused on computer science and entrepreneurship, and the extent to which it influences de…

Data Driven Approach to Input Shaping for Vibration Suppression in a Flexible Robot Arm
Jarkko Kotaniemi, Janne Saukkoriipi, Shuai Li, Markku Suomalainen
https://arxiv.org/abs/2506.14405

Data Driven Approach to Input Shaping for Vibration Suppression in a Flexible Robot Arm
This paper presents a simple and effective method for setting parameters for an input shaper to suppress the residual vibrations in flexible robot arms using a data-driven approach. The parameters are adaptively tuned in the workspace of the robot by interpolating previously measured data of the robot's residual vibrations. Input shaping is a simple and robust technique to generate vibration-reduced shaped commands by a convolution of an impulse sequence with the desired input command. The gene…

Nucleation feedback can drive establishment and maintenance of biased microtubule polarity in neurites
Hannah G. Scanlon, Gibarni Mahata, Anna C. Nelson, Scott A. McKinley, Melissa M. Rolls, Maria-Veronica Ciocanel
https://arxiv.org/abs/2506.12209

Nucleation feedback can drive establishment and maintenance of biased microtubule polarity in neurites
The microtubule cytoskeleton is comprised of dynamic, polarized filaments that facilitate transport within the cell. Polarized microtubule arrays are key to facilitating cargo transport in long cells such as neurons. Microtubules also undergo dynamic instability, where the plus and minus ends of the filaments switch between growth and shrinking phases, leading to frequent microtubule turnover. Although microtubules often completely disassemble and new filaments nucleate, microtubule arrays have…

The Holography of Spread Complexity: A Story of Observers
Zhehan Li, Jia Tian
https://arxiv.org/abs/2506.13481 https://arxiv.org/pdf/…

The Holography of Spread Complexity: A Story of Observers
We propose a holographic description of spread complexity and its rate in 2D CFTs, building on the pioneering work \cite{Caputa:2024sux}. By exploiting the $SL(2,\mathbb{R})$ symmetry, we construct the Krylov basis and demonstrate that its non-uniqueness gives rise to ambiguities analogous to those in quantum complexity. Within the AdS/CFT correspondence, we identify the spread complexity as the energy measured by a bulk observer, with its rate corresponding to the radial momentum. These result…

Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
Georgios Koutroumpas, Sebastian Idesis, Mireia Masias Bruns, Carlos Segura, Joemon M. Jose, Sergi Abadal, Ioannis Arapakis
https://arxiv.org/abs/2506.13409

Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
Traditionally, Recommender Systems (RS) have primarily measured performance based on the accuracy and relevance of their recommendations. However, this algorithmic-centric approach overlooks how different types of recommendations impact user engagement and shape the overall quality of experience. In this paper, we shift the focus to the user and address for the first time the challenge of decoding the neural and behavioural variability across distinct recommendation categories, considering more…

Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
Xiao Wang, Junru Yu, Jun Huang, Qiong Wu, Ljubo Vacic, Changyin Sun
https://arxiv.org/abs/2506.14502

Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
Despite the recent advancements in artificial intelligence technologies have shown great potential in improving transport efficiency and safety, autonomous vehicles(AVs) still face great challenge of driving in time-varying traffic flow, especially in dense and interactive situations. Meanwhile, human have free wills and usually do not make the same decisions even situate in the exactly same scenarios, leading to the data-driven methods suffer from poor migratability and high search cost proble…

Accelerating Cloud-Based Transcriptomics: Performance Analysis and Optimization of the STAR Aligner Workflow
Piotr Kica, Sabina Licho{\l}ai, Micha{\l} Orzechowski, Maciej Malawski
https://arxiv.org/abs/2506.12611

Accelerating Cloud-Based Transcriptomics: Performance Analysis and Optimization of the STAR Aligner Workflow
In this work, we explore the Transcriptomics Atlas pipeline adapted for cost-efficient and high-throughput computing in the cloud. We propose a scalable, cloud-native architecture designed for running a resource-intensive aligner -- STAR -- and processing tens or hundreds of terabytes of RNA-sequencing data. We implement multiple optimization techniques that give significant execution time and cost reduction. The impact of particular optimizations is measured in medium-scale experiments followe…

Measurement of the $\Omega_c^0$ and $\Xi_c^0$ baryon lifetimes using hadronic $b$-baryon decays
LHCb collaboration, et al.
https://arxiv.org/abs/2506.13334

Measurement of the $Ω_c^0$ and $Ξ_c^0$ baryon lifetimes using hadronic $b$-baryon decays
The lifetimes of the $Ω_c^0$ and $Ξ_c^0$ baryons are measured using a $pp$ collision dataset collected by the LHCb experiment, corresponding to an integrated luminosity of $9~\rm{fb^{-1}}$. The charm baryons are produced in the fully reconstructed decay chains $Ω_b^- \rightarrow Ω_c^0 (\rightarrow pK^-K^-π^+)~π^-$ and $Ξ_b^- \rightarrow Ξ_c^0 (\rightarrow pK^-K^-π^+)~π^-$. The measurement uses topologically and kinematically similar $B^- \rightarrow D^0(\rightarrow K^-K^+π^-π^+)~π^…

Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder
Ioannis Christoforos Koune, Alice Cicirello
https://arxiv.org/abs/2506.13658

Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder
Inference and prediction under partial knowledge of a physical system is challenging, particularly when multiple confounding sources influence the measured response. Explicitly accounting for these influences in physics-based models is often infeasible due to epistemic uncertainty, cost, or time constraints, resulting in models that fail to accurately describe the behavior of the system. On the other hand, data-driven machine learning models such as variational autoencoders are not guaranteed t…