Tootfinder

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Xiaodong Wang, Ping Wang, Zijun He, Mengjie Qin, Xin Yuan
https://arxiv.org/abs/2509.12079 https://

Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
Coded aperture snapshot spectral imaging (CASSI) retrieves a 3D hyperspectral image (HSI) from a single 2D compressed measurement, which is a highly challenging reconstruction task. Recent deep unfolding networks (DUNs), empowered by explicit data-fidelity updates and implicit deep denoisers, have achieved the state of the art in CASSI reconstruction. However, existing unfolding approaches suffer from uncontrollable reconstruction trajectories, leading to abrupt quality jumps and non-gradual re…

On the Feasibility of Inter-Flow Service Degradation Detection
Balint Bicski, Adrian Pekar
https://arxiv.org/abs/2509.11140 https://arxiv.org/pdf/2509.1114…

On the Feasibility of Inter-Flow Service Degradation Detection
Hardware acceleration in modern networks creates monitoring blind spots by offloading flows to a non-observable state, hindering real-time service degradation (SD) detection. To address this, we propose and formalize a novel inter-flow correlation framework, built on the hypothesis that observable flows can act as environmental sensors for concurrent, non-observable flows. We conduct a comprehensive statistical analysis of this inter-flow landscape, revealing a fundamental trade-off: while the …

Nonlinear instability for the 3D MHD equations around the Taylor-Couette flow
V\'ictor Navarro-Fern\'andez, David Villringer
https://arxiv.org/abs/2510.12707 https://

Nonlinear instability for the 3D MHD equations around the Taylor-Couette flow
We study the 3D magnetohydrodynamics (MHD) equations in an annular cylinder, perturbed around the explicit steady state given by the 3D Taylor-Couette velocity field and zero magnetic field. Combining a recent linear instability result for the magnetic field with the framework of Friedlander, Pavlović and Shvydkoy [Comm. Math. Phys. 264 (2006), no. 2, 335-347], we prove nonlinear instability of the solution around this steady state in $L^p$, for any $p>1$. In particular, our results are, to th…

State-Dependent X-ray Variability in Cygnus X-1: A 12-Year NuSTAR Timing Study of Accretion Flow Geometry
Kshitij Duraphe, Kartik Mandar, Chooda Khanal, Abha Pareek, Tejaswi Kondhiya, V Sree Suswara, Deeksha Dinesh, Vidyasagar Bhat, Gopal Bhatta
https://arxiv.org/abs/2510.10746

State-Dependent X-ray Variability in Cygnus X-1: A 12-Year NuSTAR Timing Study of Accretion Flow Geometry
We present a comprehensive timing analysis of the black hole X-ray binary Cygnus X-1 using 26 NuSTAR observations spanning 2012-2024, providing the most detailed characterization to date of its accretion flow variability across spectral states. Our analysis reveals fundamental insights into the physics governing state transitions in stellar-mass black holes. We discover distinct bimodal flux distributions in the 8-79 keV band with well-separated peaks, contrasting with overlapping distributions…

Viscous flow in glass-forming liquids: The twice-activation analysis and the bond wave model
Elena A. Chechetkina
https://arxiv.org/abs/2509.11211 https://…

Viscous flow in glass-forming liquids: The twice-activation analysis and the bond wave model
The twice-activation method for analysis of experimental viscosity-temperature data reveals a set of interconnected parameters describing the state of the flowing glass-forming liquid in terms of convergation point which describes an infinite set of viscosity-temperature relations for the liquid considered. The observed uncertainty in the viscosity-temperature behavior permits to consider glass-forming liquid as the self-organizing system realizing by the bonds wave as dissipative pattern. The …

Collective motion in the massive Schwinger model via Tensor Network
Haiyang Shao, Shile Chen, Shuzhe Shi
https://arxiv.org/abs/2509.10835 https://arxiv.org…

Collective motion in the massive Schwinger model via Tensor Network
We simulate the real-time dynamics of a massive Schwinger model using the Time-Evolving Block Decimation tensor network algorithm. Starting from a non-equilibrium initial state with localized energy excitation on top of vacuum, we track the subsequent evolution to investigate two distinct physical phenomena. First, by analyzing the system's energy-momentum tensor, we show that this system exhibits hydrodynamic behavior analogous to Bjorken flow at large coupling-to-mass ratio, a signature that …

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
Alberto Solera-Rico, Carlos Sanmiguel Vila, Stefano Discetti
https://arxiv.org/abs/2510.11600

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
We present an efficient and realisable active flow control framework with few non-intrusive sensors. The method builds upon data-driven, reduced-order predictive models based on Long-Short-Term Memory (LSTM) networks and efficient gradient-based Model Predictive Control (MPC). The model uses only surface-mounted pressure probes to infer the wake state, and is trained entirely offline on a dataset built with open-loop actuations, thus avoiding the complexities of online learning. Sparsification …

Geometry-aware Policy Imitation
Yiming Li, Nael Darwiche, Amirreza Razmjoo, Sichao Liu, Yilun Du, Auke Ijspeert, Sylvain Calinon
https://arxiv.org/abs/2510.08787 https://…

Geometry-aware Policy Imitation
We propose a Geometry-aware Policy Imitation (GPI) approach that rethinks imitation learning by treating demonstrations as geometric curves rather than collections of state-action samples. From these curves, GPI derives distance fields that give rise to two complementary control primitives: a progression flow that advances along expert trajectories and an attraction flow that corrects deviations. Their combination defines a controllable, non-parametric vector field that directly guides robot be…

A Comparative Analysis of Contextual Representation Flow in State-Space and Transformer Architectures
Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen, Min-Yen Kan, Luu Anh Tuan
https://arxiv.org/abs/2510.06640

A Comparative Analysis of Contextual Representation Flow in State-Space and Transformer Architectures
State Space Models (SSMs) have recently emerged as efficient alternatives to Transformer-Based Models (TBMs) for long-sequence processing, offering linear scaling and lower memory use. Yet, how contextual information flows across layers and tokens in these architectures remains understudied. We present the first unified, token- and layer-level analysis of representation propagation in SSMs and TBMs. Using centered kernel alignment, stability metrics, and probing, we characterize how representat…

Critical States Identiffcation in Power System via Lattice Partition and Its Application in Reliability Assessment
Han Hu, Wenjie Wan, Feiyu Chen, Xiaoyu Liu, Bo Yu, Kequan Zhao
https://arxiv.org/abs/2510.09420

Critical States Identiffcation in Power System via Lattice Partition and Its Application in Reliability Assessment
With the increasing complexity of power systems,accurately identifying critical states (the states corresponding to minimal cut sets) and assessing system reliability have become crucial tasks. In this paper, a mathematical lattice structure is employed to represent and partition the state space of power system. Based on this structure, a novel recursive method is proposed to efffciently identify critical states by leveraging lattice partitioning and Optimal Power Flow(OPF) calculations. This m…

Tennessee just turned exhausted farmland into something remarkable. Middle Fork Bottoms State Park now teems with wildlife and draws 15,000 visitors monthly.
The secret? Letting floodplains do what they do best—control floods, filter water, recharge groundwater naturally.
It's becoming a model for climate solutions across neighboring states.

Depleted Tennessee Farmland Is Now Teeming With Wildlife
Middle Fork Bottoms State Park demonstrates the benefits that flow when ecology is left to do what it does naturally.

Typestate via Revocable Capabilities
Songlin Jia, Craig Liu, Siyuan He, Haotian Deng, Yuyan Bao, Tiark Rompf
https://arxiv.org/abs/2510.08889 https://arxiv…

Typestate via Revocable Capabilities
Managing stateful resources safely and expressively is a longstanding challenge in programming languages, especially in the presence of aliasing. While scope-based constructs such as Java's synchronized blocks offer ease of reasoning, they restrict expressiveness and parallelism. Conversely, imperative, flow-sensitive management enables fine-grained control but demands sophisticated typestate analyses and often burdens programmers with explicit state tracking. In this work, we present a novel…

Zero-Shot Forecasting of Network Dynamics through Weight Flow Matching
Shihe Zhou, Ruikun Li, Huandong Wang, Yong Li
https://arxiv.org/abs/2510.07957 https://

Zero-Shot Forecasting of Network Dynamics through Weight Flow Matching
Forecasting state evolution of network systems, such as the spread of information on social networks, is significant for effective policy interventions and resource management. However, the underlying propagation dynamics constantly shift with new topics or events, which are modeled as changing coefficients of the underlying dynamics. Deep learning models struggle to adapt to these out-of-distribution shifts without extensive new data and retraining. To address this, we present Zero-Shot Foreca…

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilib...
Ken Ishihara, Hideaki Shimazaki
https://arxiv.org/abs/2502.15440 https://mastoxiv.page/@arXiv_qbioNC_bot/114057779012161849
- Mechanisms for anesthesia, unawareness, respiratory depression, memory replay and sleep: MHb > IP...
Karin Vadovi\v{c}ov\'a
https://arxiv.org/abs/2509.04454 https://mastoxiv.page/@arXiv_qbioNC_bot/115167812677714466
- Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback
Dimitra Maoutsa
https://arxiv.org/abs/2512.09366 https://mastoxiv.page/@arXiv_qbioNC_bot/115699940165988688
- Prefrontal scaling of reward prediction error readout gates reinforcement-derived adaptive behavi...
Sang, Huang, Zhong, Wang, Yu, Li, Feng, Wang, Chai, Menon, Wang, Fang, Wang
https://arxiv.org/abs/2512.09761 https://mastoxiv.page/@arXiv_qbioNC_bot/115700046994546552
- Proof of a perfect platonic representation hypothesis
Liu Ziyin, Isaac Chuang
https://arxiv.org/abs/2507.01098 https://mastoxiv.page/@arXiv_csLG_bot/114788750477759162
toXiv_bot_toot

Shortcut Flow Matching for Speech Enhancement: Step-Invariant flows via single stage training
Naisong Zhou, Saisamarth Rajesh Phaye, Milos Cernak, Tijana Stojkovic, Andy Pearce, Andrea Cavallaro, Andy Harper
https://arxiv.org/abs/2509.21522

Shortcut Flow Matching for Speech Enhancement: Step-Invariant flows via single stage training
Diffusion-based generative models have achieved state-of-the-art performance for perceptual quality in speech enhancement (SE). However, their iterative nature requires numerous Neural Function Evaluations (NFEs), posing a challenge for real-time applications. On the contrary, flow matching offers a more efficient alternative by learning a direct vector field, enabling high-quality synthesis in just a few steps using deterministic ordinary differential equation~(ODE) solvers. We thus introduce …

DynaFlow: Dynamics-embedded Flow Matching for Physically Consistent Motion Generation from State-only Demonstrations
Sowoo Lee, Dongyun Kang, Jaehyun Park, Hae-Won Park
https://arxiv.org/abs/2509.19804

DynaFlow: Dynamics-embedded Flow Matching for Physically Consistent Motion Generation from State-only Demonstrations
This paper introduces DynaFlow, a novel framework that embeds a differentiable simulator directly into a flow matching model. By generating trajectories in the action space and mapping them to dynamically feasible state trajectories via the simulator, DynaFlow ensures all outputs are physically consistent by construction. This end-to-end differentiable architecture enables training on state-only demonstrations, allowing the model to simultaneously generate physically consistent state trajectori…

Error Analysis of Discrete Flow with Generator Matching
Zhengyan Wan, Yidong Ouyang, Qiang Yao, Liyan Xie, Fang Fang, Hongyuan Zha, Guang Cheng
https://arxiv.org/abs/2509.21906 …

Error Analysis of Discrete Flow with Generator Matching
Discrete flow models offer a powerful framework for learning distributions over discrete state spaces and have demonstrated superior performance compared to the discrete diffusion model. However, their convergence properties and error analysis remain largely unexplored. In this work, we develop a unified framework grounded in stochastic calculus theory to systematically investigate the theoretical properties of discrete flow. Specifically, we derive the KL divergence of two path measures regard…

Ah! That rush of energy when you hit the flow state in a writing session and end up, after 2,000 words, saying, "Well, that's not bad."

Imaging shapes of ground-state uranium-238 nuclei in high-energy nuclear collisions at RHIC
Chunjian Zhang (for the STAR Collaboration)
https://arxiv.org/abs/2510.02689 https://…

Imaging shapes of ground-state uranium-238 nuclei in high-energy nuclear collisions at RHIC
The shape and orientation of colliding nuclei play a crucial role in determining the initial conditions of the quark-gluon plasma (QGP), which influence key observables such as anisotropic and radial flow. In these proceedings, we present the measurements of $v_2$, $p_{\rm T}$ fluctuations and $v_2-p_{\rm T}$ correlations in $^{238}$U + $^{238}$U and $^{197}$Au + $^{197}$Au collisions at center of mass energies $\sqrt{s_{\rm NN}}=$ 193 and 200 GeV, respectively. Our results reveal significant d…

Quantum Lattice Boltzmann Method for Multiple Time Steps Without Reinitialization for Linear Advection-Diffusion Problems
Aaron Nagel, Johannes L\"owe
https://arxiv.org/abs/2510.05965

Quantum Lattice Boltzmann Method for Multiple Time Steps Without Reinitialization for Linear Advection-Diffusion Problems
To simulate highly-resolved flow fields, we extend the Quantum Lattice Boltzmann Method (QLBM) to be able to simulate multiple time steps without state extraction or reinitialization. We adjust and extend given QLBM approaches from the literature to completely remove the need to measure or reinitialize the flow field in between the simulation time steps. Therefore, our algorithm does not require to sample the entire flow field at any time. We solve the linear advection-diffusion problem and der…

On the sensitivity of different galaxy properties to warm dark matter
Bel\'en Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
https://arxiv.org/abs/2510.05037

On the sensitivity of different galaxy properties to warm dark matter
We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1,024 state-of-the-art cosmological hydrodynamical simulations from the DREAMS project. We begin by using a Multilayer Perceptron (MLP) coupled with a normalizing flow to explore global statistical descriptors of galaxy populations, such as the mean, standard deviation, and histograms of 14 galaxy properties. We find that subhalo gas mass is the most informative feature for constraining the WDM mass, achievin…

My brain seems to have two modes:
1. time-unaware, that slightly chaotic relaxed flow state where I just do the next thing that feels right
2. time-aware, which is always a bit tense and stressful because I need to force myself to keep to a calendar, and execute tasks in specific times.

How Far Do Time Series Foundation Models Paint the Landscape of Real-World Benchmarks ?
Lujun Li, Lama Sleem, Yiqun Wang, Yangjie Xu, Niccol\`o Gentile, Radu State
https://arxiv.org/abs/2509.26347

How Far Do Time Series Foundation Models Paint the Landscape of Real-World Benchmarks ?
Recent evaluations of time-series foundation models (TSFMs) have emphasized synthetic benchmarks, leaving real-world generalization less thoroughly examined. This work proposes a novel benchmarking approach that bridges synthetic and realistic data by extracting temporal signals from real-world video using optical flow and curating datasets reflecting everyday temporal dynamics. Building upon this pipeline, we introduce REAL-V-TSFM, a novel dataset designed to capture rich and diverse time seri…

Transport Based Mean Flows for Generative Modeling
Elaheh Akbari, Ping He, Ahmadreza Moradipari, Yikun Bai, Soheil Kolouri
https://arxiv.org/abs/2509.22592 https://

Transport Based Mean Flows for Generative Modeling
Flow-matching generative models have emerged as a powerful paradigm for continuous data generation, achieving state-of-the-art results across domains such as images, 3D shapes, and point clouds. Despite their success, these models suffer from slow inference due to the requirement of numerous sequential sampling steps. Recent work has sought to accelerate inference by reducing the number of sampling steps. In particular, Mean Flows offer a one-step generation approach that delivers substantial s…

GRMHD modelling of accretion flow around Sagittarius A$^*$ constrained by EHT measurements
Gargi Sen (IITG), Debaprasad Maity (IITG), Santabrata Das (IITG)
https://arxiv.org/abs/2510.03602

GRMHD modelling of accretion flow around Sagittarius A$^*$ constrained by EHT measurements
We study low angular momentum, advective accretion flows around a Kerr black hole within the framework of general relativistic magnetohydrodynamics (GRMHD) in the steady state. By solving the full set of GRMHD equations, we aim to provide a comprehensive understanding of the behavior of magnetized plasma in the strong gravity regime near a rotating black hole. The accretion solutions are obtained for a set of input parameters, namely energy (${\cal E}$), angular momentum (${\cal L}$), magnetic …

Charge, heat, and spin transport phenomena in metallic conductors
Nynke Vlietstra, Sebastian T. B. Goennenwein, Rudolf Gross, Hans Huebl
https://arxiv.org/abs/2509.21635 https:/…

Charge, heat, and spin transport phenomena in metallic conductors
In solid state materials, gradients of the electro-chemical potential, the temperature, or the spin-chemical potential drive the flow of charge, heat, and spin angular momentum, resulting in a net transport of energy. Beyond the primary transport processes - such as the flow of charge, heat, and spin angular momentum driven by gradients in their respective potentials - a wide range of coupled or cross-linked transport responses can occur, giving rise to a rich variety of transport phenomena. Th…

Supersonic flow and hydraulic jump in an electronic de Laval nozzle
Johannes Geurs, Tatiana A. Webb, Yinjie Guo, Itai Keren, Jack H. Farrell, Jikai Xu, Kenji Watanabe, Takashi Taniguchi, Dmitri N. Basov, James Hone, Andrew Lucas, Abhay Pasupathy, Cory R. Dean
https://arxiv.org/abs/2509.16321

Supersonic flow and hydraulic jump in an electronic de Laval nozzle
In very clean solid-state systems, where carrier-carrier interactions dominate over any other scattering mechanisms, the flow of electrons can be described within a hydrodynamic framework. In these cases, analogues of viscous fluid phenomena have been experimentally observed. However, experimental studies of electron hydrodynamics have so far been limited to the low velocity, linear response regime. At velocities approaching the speed of sound, the electronic fluid is expected to exhibit compre…

Probing Initial State Clustering through Photon Anisotropic Flow in 7A TeV $^{16}$O $^{16}$O Collisions at the LHC
Sanchari Thakur, Pingal Dasgupta, Rupa Chatterjee, Sinjini Chandra, Sidharth K. Prasad
https://arxiv.org/abs/2509.20954

Probing Initial State Clustering through Photon Anisotropic Flow in 7A TeV $^{16}$O+$^{16}$O Collisions at the LHC
The presence of $α$ clustered structures in light nuclei can enhance the initial spatial anisotropies in relativistic nuclear collisions relative to those arising from nuclei with uniform density distributions. Thus, observables that are strongly sensitive to the initial geometry can be a more efficient probe of the clustered structures than observables dominated by final state dynamics. We investigate the collisions of $α$ clustered oxygen nuclei at $\sqrt{s_{NN}}=7$A TeV at the LHC using th…

Flow Development in the Entrance Region of Slender Converging Pipes
Vinicius Maron Sauer
https://arxiv.org/abs/2510.05313 https://arxiv.org/pdf/2510.05313

Flow Development in the Entrance Region of Slender Converging Pipes
The hydrodynamic entrance region in internal conduits, where the velocity profile evolves to a fully developed state, is a classical problem in fluid mechanics. Although many fundamental studies involve developing flows in circular ducts with constant area -- for which several theoretical descriptions exist -- converging pipe sections are often employed, demanding the extension of the analysis to pipes with slowly varying areas. This work presents an analytical investigation of the hydrodynamic…

MedVSR: Medical Video Super-Resolution with Cross State-Space Propagation
Xinyu Liu, Guolei Sun, Cheng Wang, Yixuan Yuan, Ender Konukoglu
https://arxiv.org/abs/2509.21265 https:…

MedVSR: Medical Video Super-Resolution with Cross State-Space Propagation
High-resolution (HR) medical videos are vital for accurate diagnosis, yet are hard to acquire due to hardware limitations and physiological constraints. Clinically, the collected low-resolution (LR) medical videos present unique challenges for video super-resolution (VSR) models, including camera shake, noise, and abrupt frame transitions, which result in significant optical flow errors and alignment difficulties. Additionally, tissues and organs exhibit continuous and nuanced structures, but c…

Magnetic-field dependent vortex dynamics and critical currents in superconducting microwires with regular large-area perforation by pinholes
Dong Zhu, Ilya Charaev, Konstantin Ilin, Andreas Schilling
https://arxiv.org/abs/2509.23404

Magnetic-field dependent vortex dynamics and critical currents in superconducting microwires with regular large-area perforation by pinholes
We report on results of simulations and experiments of vortex states in superconducting micro-wires with periodic rectangular pinhole structures. The simulations have been performed by means of numerically solving the time-dependent Ginzburg-Landau (TDGL) equations. With increasing bias current and for different values of the external magnetic field applied normal to the structure plane, we observe at first a vortex free Meissner state, followed by a resistive vortex-flow mixed state and a stat…

🇺🇦 #NowPlaying on #BBC6Music's #6MusicsRaveForever
808 State:
🎵 Flow Coma ( Aphex Twin Remix)
#808State
https://royoftheravers.bandcamp.com/track/808-state-flow-coma-rotr-live-ekko-utrecht

A Bilevel Optimization Framework for Adversarial Control of Gas Pipeline Operations
Tejaswini Sanjay Katale, Lu Gao, Yunpeng Zhang, Alaa Senouci
https://arxiv.org/abs/2510.02503

A Bilevel Optimization Framework for Adversarial Control of Gas Pipeline Operations
Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber-physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bi-level formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed g…

ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing
Giacomo Rizzieri, Federico Lanteri, Liberato Ferrara, Massimiliano Cremonesi
https://arxiv.org/abs/2510.02009 …

ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing
This work introduces ShapeGen3DCP, a deep learning framework for fast and accurate prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP). The method is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes. To enhance generalization, some inputs are refo…

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilib...
Ken Ishihara, Hideaki Shimazaki

The influence of boundary conditions and interfacial slip on the time taken to achieve a nonequilibrium steady-state for highly confined flows
Carmelo Riccardo Civello, Luca Maffioli, Joseph Johnson, Edward R. Smith, James P. Ewen, Peter J. Daivis, Daniele Dini, B. D. Todd
https://arxiv.org/abs/2509.20944

The influence of boundary conditions and interfacial slip on the time taken to achieve a nonequilibrium steady-state for highly confined flows
We investigate the equilibration time to attain steady-state for a system of liquid molecules under boundary-driven planar Couette flow via nonequilibrium molecular dynamics (NEMD) simulation. In particular, we examine the equilibration time for the two common types of boundary driven flow: one in which both walls slide with equal and opposite velocity, and the other in which one wall is fixed and the other moves with twice the velocity. Both flows give identical steady-state strain rates, and …

Exploring parameter dependence of heavy-flavor dynamics in small collision systems
Grace Pang
https://arxiv.org/abs/2509.23081 https://arxiv.org/pdf/2509.2…

Exploring parameter dependence of heavy-flavor dynamics in small collision systems
Observations from high-multiplicity proton-lead ($p$-Pb) collisions indicate that small systems may exhibit collective behavior in both heavy and light hadrons. This work investigates the roles of initial- and final-state interactions in shaping the nuclear modification factor and elliptic flow of $D$ mesons measured in $p$-Pb collisions. Initial-state effects, including the Cronin and shadowing effects, are considered in the heavy-quark initial conditions, while final-state interactions are si…

Magneto-Tunable Thermal Diode Based on Bulk Superconductor
Poonam Rani, Masayuki Mashiko, Keisuke Hirata, Ken-ichi Uchida, Yoshikazu Mizuguchi
https://arxiv.org/abs/2510.00341 h…

Magneto-Tunable Thermal Diode Based on Bulk Superconductor
Thermal diode is a growing technology and important for active thermal flow control. Since the theoretical designing of thermal diode in 2004, various kinds of solid-state thermal diodes have been theoretically and experimentally investigated. Here, we report on the observation of thermal rectification in bulk-size superconductor-normal metal junctions. High-purity (5N) wires of Pb and Al are soldered, and thermal conductivity (\k{appa}) of the junctions is measured in two different directions …

A Target-Agnostic Protocol-Independent Interface for the Transport Layer
Pedro Mizuno, Kimiya Mohammadtaheri, Linfan Qian, Joshua Johnson, Danny Akbarzadeh, Chris Neely, Mario Baldi, Nacihket Kapre, Mina Tahmasbi Arashloo
https://arxiv.org/abs/2509.21550

A Target-Agnostic Protocol-Independent Interface for the Transport Layer
Transport protocols are fundamental to network communications, continuously evolving to meet the demands of new applications, workloads, and network architectures while running in a wide range of execution environments (a.k.a targets). We argue that this diversity across protocols and targets calls for a high-level, target-agnostic programming abstraction for the transport layer. Specifically, we propose to specify transport protocols as high-level programs that take an event and flow state as …

Untangling the Complex Nature of AGN Variability with Fairall 9
Scott Hagen (Durham-CEA), Chris Done (Durham-CEA), Edward M. Cackett (Wayne State), Ethan R. Partington (IA-FORTH), Rick Edelson (Eureka Scientific), Collin Lewin (MIT), Erin Kara (MIT), Jonathan Gelbord (Spectral Sciences Inc)
https://arxiv.org/abs/2509.25324

Untangling the Complex Nature of AGN Variability with Fairall 9
The accretion flow in AGN is not well understood, motivating intensive monitoring campaigns of multiwavelength variability to probe its structure. One of the best of these is the 3 year optical/UV/X-ray approximately daily monitoring campaign on Fairall\,9, a fairly typical moderate accretion rate AGN. The UV lightcurve shows a clear increase over $\sim 50$ days between years 1 and 2, strongly coherent with the X-ray lightcurve rise. This changes the average spectral energy distribution such th…

Projection-based model order reduction for residence time distribution analysis of an industrial-scale continuous casting tundish
Harshith Gowrachari, Mattia Giuseppe Barra, Moaad Khamlich, Giovanni Stabile, Gianluca Bazzaro, Gianluigi Rozza
https://arxiv.org/abs/2509.20366

Projection-based model order reduction for residence time distribution analysis of an industrial-scale continuous casting tundish
The flow behavior in the continuous casting tundish plays a critical role in steel quality and is typically characterized via residence time distribution (RTD) curves. This study investigates the fluid flow behaviour in a single-strand tundish using numerical and experimental approaches. Full-order model (FOM) steady-state simulations were conducted under both isothermal and non-isothermal conditions to assess the influence of thermal buoyancy on the flow characteristics. The results show that …

Effects of galactic environment on accretion dynamics onto a rotating centrally located black hole and on emergent analogue gravity
Ripon Sk, Sangita Chatterjee, Sankhasubhra Nag
https://arxiv.org/abs/2509.18833