Tootfinder

Cowboys land 'dynamic' RB in 2026 mock draft who could be missing piece https://www.si.com/nfl/cowboys/draft/dallas-cowboys-land-dynamic-running-back-2026-nfl-mock-draft-jeremiyah-love

Lions' Dan Campbell says 'sky's the limit' for Jameson Williams in 2025; how WR can make offense more dynamic

https://www.cbsspor…

DIGS: Dynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model
Yuliang Huang, Imraj Singh, Thomas Joyce, Kris Thielemans, Jamie R. McClelland
https://arxiv.org/abs/2506.22280

DIGS: Dynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model
3D Cone-Beam CT (CBCT) is widely used in radiotherapy but suffers from motion artifacts due to breathing. A common clinical approach mitigates this by sorting projections into respiratory phases and reconstructing images per phase, but this does not account for breathing variability. Dynamic CBCT instead reconstructs images at each projection, capturing continuous motion without phase sorting. Recent advancements in 4D Gaussian Splatting (4DGS) offer powerful tools for modeling dynamic scenes, …

You can play with (a supercharged server-driven version of it) today with Kitten:
https://kitten.small-web.org/tutorials/dynamic-pages/

LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals
Yanbin Wang, Xingyu Chen, Yumiao Wang, Xiang Wang, Chuanfei Zang, Guolong Cui, Jiahuan Liu
https://arxiv.org/abs/2505.23454

LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals
We propose the LCB-CV-UNet to tackle performance degradation caused by High Dynamic Range (HDR) radar signals. Initially, a hardware-efficient, plug-and-play module named Logarithmic Connect Block (LCB) is proposed as a phase coherence preserving solution to address the inherent challenges in handling HDR features. Then, we propose the Dual Hybrid Dataset Construction method to generate a semi-synthetic dataset, approximating typical HDR signal scenarios with adjustable target distributions. Si…

STDR: Spatio-Temporal Decoupling for Real-Time Dynamic Scene Rendering
Zehao Li, Hao Jiang, Yujun Cai, Jianing Chen, Baolong Bi, Shuqin Gao, Honglong Zhao, Yiwei Wang, Tianlu Mao, Zhaoqi Wang
https://arxiv.org/abs/2505.22400

STDR: Spatio-Temporal Decoupling for Real-Time Dynamic Scene Rendering
Although dynamic scene reconstruction has long been a fundamental challenge in 3D vision, the recent emergence of 3D Gaussian Splatting (3DGS) offers a promising direction by enabling high-quality, real-time rendering through explicit Gaussian primitives. However, existing 3DGS-based methods for dynamic reconstruction often suffer from \textit{spatio-temporal incoherence} during initialization, where canonical Gaussians are constructed by aggregating observations from multiple frames without te…

Change Point Localization and Inference in Dynamic Multilayer Networks
Fan Wang, Kyle Ritscher, Yik Lun Kei, Xin Ma, Oscar Hernan Madrid Padilla
https://arxiv.org/abs/2506.21878

Change Point Localization and Inference in Dynamic Multilayer Networks
We study offline change point localization and inference in dynamic multilayer random dot product graphs (D-MRDPGs), where at each time point, a multilayer network is observed with shared node latent positions and time-varying, layer-specific connectivity patterns. We propose a novel two-stage algorithm that combines seeded binary segmentation with low-rank tensor estimation, and establish its consistency in estimating both the number and locations of change points. Furthermore, we derive the l…

QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization
Danush Khanna, Aditya Kumar Guru, Srivarshinee Sridhar, Zidan Ahmed, Rubhav Bahirwani, Meetu Malhotra, Vinija Jain, Aman Chadha, Amitava Das, Kripabandhu Ghosh
https://arxiv.org/abs/2…

QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization
Inference accounts for the majority of latency and energy consumption in large language model (LLM) deployments, often exceeding 90% of total cost. While training-time efficiency has seen extensive progress, runtime optimization remains a key bottleneck, particularly under autoregressive decoding. Existing approaches -- such as pruning, quantization, early exits, and speculative decoding -- often require retraining, architectural changes, or disrupt decoding compatibility. We introduce QuickSil…

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Zhennan Lin, Kaixun Huang, Wei Ren, Linju Yang, Lei Xie
https://arxiv.org/abs/2505.23077

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Deep biasing improves automatic speech recognition (ASR) performance by incorporating contextual phrases. However, most existing methods enhance subwords in a contextual phrase as independent units, potentially compromising contextual phrase integrity, leading to accuracy reduction. In this paper, we propose an encoder-based phrase-level contextualized ASR method that leverages dynamic vocabulary prediction and activation. We introduce architectural optimizations and integrate a bias loss to ex…

‘They’ve Worked Together Very Well’—Raiders Owner Tom Brady On His Dynamic Duo https://raiderramble.com/2025/06/27/raiders-owner-tom-brady-on-dynamic-duo/

Coverage of the unit cube by dynamic Boolean models
Hanna D\"oring, Lianne de Jonge, Xiaochuan Yang
https://arxiv.org/abs/2506.22286 https://

Coverage of the unit cube by dynamic Boolean models
Motivated by peer-to-peer telecommunication, we study a dynamic Boolean model. We define a Poisson number of random lines through the $(d-1)$-dimensional base of a $d$-dimensional unit cube and dilate them to define cylinders. Letting $ρ$ be the expected number of cylinders, the random variable of interest is the coverage radius $R_ρ$, which is the cylinder radius required to cover the $d$-dimensional unit cube. We show that $R_ρ^{d-1}$ is of the order $\log ρ/ ρ$ with high probability as …

SealOS : A Sealos-based Approach for Adaptive Resource Optimization Under Dynamic Workloads for Securities Trading System
Haojie Jia, Zhenhao Li, Gen Li, Minxian Xu, Kejiang Ye
https://arxiv.org/abs/2505.23258

SealOS+: A Sealos-based Approach for Adaptive Resource Optimization Under Dynamic Workloads for Securities Trading System
As securities trading systems transition to a microservices architecture, optimizing system performance presents challenges such as inefficient resource scheduling and high service response delays. Existing container orchestration platforms lack tailored performance optimization mechanisms for trading scenarios, making it difficult to meet the stringent 50ms response time requirement imposed by exchanges. This paper introduces SealOS+, a Sealos-based performance optimization approach for securi…

A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems
Jonghan Lim, Ilya Kovalenko
https://arxiv.org/abs/2505.22814

A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems
Manufacturing environments are becoming more complex and unpredictable due to factors such as demand variations and shorter product lifespans. This complexity requires real-time decision-making and adaptation to disruptions. Traditional control approaches highlight the need for advanced control strategies capable of overcoming unforeseen challenges, as they demonstrate limitations in responsiveness within dynamic industrial settings. Multi-agent systems address these challenges through decentra…

Dyn-HTE: High-temperature expansion of the dynamic Matsubara spin correlator
Ruben Burkard, Benedikt Schneider, Bj\"orn Sbierski
https://arxiv.org/abs/2505.23699

Dyn-HTE: High-temperature expansion of the dynamic Matsubara spin correlator
The high-temperature series expansion for quantum spin models is a well-established tool to compute thermodynamic quantities and equal-time spin correlations, in particular for frustrated interactions. We extend the scope of this expansion to the dynamic Matsubara spin-spin correlator and develop a fully analytic algorithm to compute its expansion coefficients. We focus on Heisenberg models with a single coupling constant J and spin lengths S=1/2,1. The expansion coefficients up to 12th order i…

Two-gap superconductor ZrB12 with dynamic stripes and charge density waves: Crystal structure, physical properties and pairing mechanism
A. N. Azarevicha, N. B. Bolotinab, O. N. Khrykinab, A. V. Bogacha, K. M. Krasikova, A. Yu. Tsvetkovc, S. Yu. Gavrilkinc, V. V. Voronova, S. Gabanid, K. Flachbartd, A. N. Azarevich, N. B. Bolotina, O. N. Khrykina, A. V. Bogach, K. M. Krasikov, A. Yu. Tsvetkov, S. Yu. Gavrilkin, V. V. Voronov, S. Gabani, K. Flachbart, A. V. Kuznetsov, N. E. Sluchanko

Two-gap superconductor ZrB12 with dynamic stripes and charge density waves: Crystal structure, physical properties and pairing mechanism
A review of long-term studies of ZrB12 and LuB12 superconductors with very similar conduction bands and phonon spectra, but with radically different (by a factor of 15-20) critical temperatures and magnetic fields is presented. A detailed analysis of well-known studies in combination with new results of structural, thermodynamic and charge transport measurements obtained here for these metallic dodecaborides with Jahn-Teller instability of the rigid boron network and with dynamic charge stripes…

sp_hypertext: Hypertext 2009 dynamic contact network
The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.
This network has 113 nodes and 2196 edges.
Tags: Social, Offline, Unweighted, Temporal
https://networks.skewed.de/net/sp_hype…

Linear-Quadratic Discrete-Time Dynamic Games with Unknown Dynamics
Shengyuan Huang, Xiaoguang Yang, Zhigang Cao, Wenjun Mei
https://arxiv.org/abs/2506.22073

Linear-Quadratic Discrete-Time Dynamic Games with Unknown Dynamics
Considering linear-quadratic discrete-time games with unknown input/output/state (i/o/s) dynamics and state, we provide necessary and sufficient conditions for the existence and uniqueness of feedback Nash equilibria (FNE) in the finite-horizon game, based entirely on offline input/output data. We prove that the finite-horizon unknown-dynamics game and its corresponding known-dynamics game have the same FNEs, and provide detailed relationships between their respective FNE matrices. To simplify …

Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves
Amer Krivo\v{s}ija, Alexander Munteanu, Andr\'e Nusser, Chris Schwiegelshohn
https://arxiv.org/abs/2505.23431

Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves
This paper introduces $k$-Dynamic Time Warping ($k$-DTW), a novel dissimilarity measure for polygonal curves. $k$-DTW has stronger metric properties than Dynamic Time Warping (DTW) and is more robust to outliers than the Fréchet distance, which are the two gold standards of dissimilarity measures for polygonal curves. We show interesting properties of $k$-DTW and give an exact algorithm as well as a $(1+\varepsilon)$-approximation algorithm for $k$-DTW by a parametric search for the $k$-th lar…

#GrindayFriday for this week is TRADING HANDS from the UK and their new EP 'I'm Going Out of My Mind, Does Anyone Want Anything?'
Trading Hands play a wild mix of powerviolence, grind & fastcore. This stuff is FAST, heavy, raw, tons of breakdowns. You can listen to this in no time at all. Blink and you might miss it. Weird, dynamic, I can't get enough. Easily one of my favo…

And then there's just the random stuff, like Guitar Hero guitars and drums and keyboards (in a bundle with the associated vintage consoles), an old MakerBot 3D Printer, some telepresence robots, and a fair bit of A/V equipment. Hopefully some folks out there will enjoy these, too!
https://mastodon.omnigro…

Hybrid Learning for Cold-Start-Aware Microservice Scheduling in Dynamic Edge Environments
Jingxi Lu, Wenhao Li, Jianxiong Guo, Xingjian Ding, Zhiqing Tang, Tian Wang, Weijia Jia
https://arxiv.org/abs/2505.22424

Hybrid Learning for Cold-Start-Aware Microservice Scheduling in Dynamic Edge Environments
With the rapid growth of IoT devices and their diverse workloads, container-based microservices deployed at edge nodes have become a lightweight and scalable solution. However, existing microservice scheduling algorithms often assume static resource availability, which is unrealistic when multiple containers are assigned to an edge node. Besides, containers suffer from cold-start inefficiencies during early-stage training in currently popular reinforcement learning (RL) algorithms. In this pape…

Dynamic signature of the thermodynamic transition in a novel mean field system
Ehtesham Anwar, Ujjwal Kumar Nandi, Palak Patel, Sanket Kumawat, Sarika Maitra Bhattacharyya
https://arxiv.org/abs/2505.23371

Dynamic signature of the thermodynamic transition in a novel mean field system
Understanding the connection between thermodynamics and dynamics in glass-forming liquids remains a central challenge in condensed matter physics. In this study, we investigate a novel model system that enables a continuous crossover from a standard three dimensional liquid to a fully connected mean field like system by introducing pseudo neighbours. These pseudo neighbours enhance the effective connectivity of the system without altering its local structure. While their presence slows down the…

Single-shot HDR using conventional image sensor shutter functions and optical randomization
Xiang Dai, Kyrollos Yanny, Kristina Monakhova, Nicholas Antipa
https://arxiv.org/abs/2506.22426

Single-shot HDR using conventional image sensor shutter functions and optical randomization
High-dynamic-range (HDR) imaging is an essential technique for overcoming the dynamic range limits of image sensors. The classic method relies on multiple exposures, which slows capture time, resulting in motion artifacts when imaging dynamic scenes. Single-shot HDR imaging alleviates this issue by encoding HDR data into a single exposure, then computationally recovering it. Many established methods use strong image priors to recover improperly exposed image detail. These approaches struggle wi…

Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications
Xinye Tang, Haijun Zhai, Chaitanya Belwal, Vineeth Thayanithi, Philip Baumann, Yogesh K Roy
https://arxiv.org/abs/2506.20815

Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications
LLM-powered applications are highly susceptible to the quality of user prompts, and crafting high-quality prompts can often be challenging especially for domain-specific applications. This paper presents a novel dynamic context-aware prompt recommendation system for domain-specific AI applications. Our solution combines contextual query analysis, retrieval-augmented knowledge grounding, hierarchical skill organization, and adaptive skill ranking to generate relevant and actionable prompt sugges…

Optimal Auction Design for Dynamic Stochastic Environments: Myerson Meets Naor
Yeon-Koo Che, Andrew B. Choi
https://arxiv.org/abs/2505.22862 https://

Optimal Auction Design for Dynamic Stochastic Environments: Myerson Meets Naor
Allocation of goods and services often involves both stochastic supply and stochastic demand. Motivated by applications such as cloud computing, gig platforms, and blockchain auctions, we study the design of optimal selling mechanisms in an environment where buyers with private valuations arrive stochastically and are assigned goods that also arrive stochastically, and either buyers or goods can be held in a queue at costs until allocation. The optimal mechanism dynamically leverages competitiv…

This https://arxiv.org/abs/2502.11647 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with Model Editing
Large Language Models (LLMs) are widely applied in decision making, but their deployment is threatened by jailbreak attacks, where adversarial users manipulate model behavior to bypass safety measures. Existing defense mechanisms, such as safety fine-tuning and model editing, either require extensive parameter modifications or lack precision, leading to performance degradation on general tasks, which is unsuitable to post-deployment safety alignment. To address these challenges, we propose DELM…

Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
Elia Trevisan, Khaled A. Mustafa, Godert Notten, Xinwei Wang, Javier Alonso-Mora
https://arxiv.org/abs/2506.21205

Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
Deploying mobile robots safely among humans requires the motion planner to account for the uncertainty in the other agents' predicted trajectories. This remains challenging in traditional approaches, especially with arbitrarily shaped predictions and real-time constraints. To address these challenges, we propose a Dynamic Risk-Aware Model Predictive Path Integral control (DRA-MPPI), a motion planner that incorporates uncertain future motions modelled with potentially non-Gaussian stochastic pre…

This https://arxiv.org/abs/2407.09802 has been replaced.
link: https://scholar.google.com/scholar?q=a

Long-time signatures of chaos in large atom-light frequency ratios Rabi model
As one of the famous effects in quantum Rabi model (QRM), Rabi oscillation may lead to the occurrence of quantum dynamics behaviors without classical dynamic counterparts, such as quantum collapse and revival effects. In this paper, we focus on studying the long-time quantum signatures of chaos in the large atom-light frequency ratios Rabi model. It is shown that the saturated values of the entanglement entropy for initial states located in chaotic sea are higher than that in the regular region…

Effect of Haptic Feedback on Avoidance Behavior and Visual Exploration in Dynamic VR Pedestrian Environment
Kyosuke Ishibashi, Atsushi Saito, Zin Y. Tun, Lucas Ray, Megan C. Coram, Akihiro Sakurai, Allison M. Okamura, Ko Yamamoto
https://arxiv.org/abs/2506.20952

Effect of Haptic Feedback on Avoidance Behavior and Visual Exploration in Dynamic VR Pedestrian Environment
Human crowd simulation in virtual reality (VR) is a powerful tool with potential applications including emergency evacuation training and assessment of building layout. While haptic feedback in VR enhances immersive experience, its effect on walking behavior in dense and dynamic pedestrian flows is unknown. Through a user study, we investigated how haptic feedback changes user walking motion in crowded pedestrian flows in VR. The results indicate that haptic feedback changed users' collision av…

Belgian startup nox.energy, one 🚀 to watch in the smart energy space, has launched its new tool to optimize heat pumps: smart control based on weather forecast, to increase solar self-consumption, for (static/dynamic) tariff optimization and additional flexibility trading as independent aggregator
https://app.nox.energy

The CASE Framework -- A New Architecture for Participatory Research and Digital Health Surveillance
Marco Hirsch, Peter Hevesi, Paul Lukowicz
https://arxiv.org/abs/2505.23516

The CASE Framework -- A New Architecture for Participatory Research and Digital Health Surveillance
We present the CASE framework, an open-source platform for adaptive, context-aware participatory research, and pandemic preparedness. CASE implements an event-driven architecture that enables dynamic survey workflows, allowing real-time adaptation based on participant responses, external data, temporal conditions, and evolving user states. The framework supports a broad range of research needs, from simple one-time questionnaires to complex longitudinal studies with advanced conditional logic. …

This https://arxiv.org/abs/2308.15651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Ensuring User-side Fairness in Dynamic Recommender Systems
User-side group fairness is crucial for modern recommender systems, aiming to alleviate performance disparities among user groups defined by sensitive attributes like gender, race, or age. In the ever-evolving landscape of user-item interactions, continual adaptation to newly collected data is crucial for recommender systems to stay aligned with the latest user preferences. However, we observe that such continual adaptation often exacerbates performance disparities. This necessitates a thorough…

Dynamic Race Detection With O(1) Samples
Mosaad Al Thokair, Minjian Zhang, Umang Mathur, Mahesh Viswanathan
https://arxiv.org/abs/2506.20127 https://

Dynamic Race Detection With O(1) Samples
Happens before-based dynamic analysis is the go-to technique for detecting data races in large scale software projects due to the absence of false positive reports. However, such analyses are expensive since they employ expensive vector clock updates at each event, rendering them usable only for in-house testing. In this paper, we present a sampling-based, randomized race detector that processes only constantly many events of the input trace even in the worst case. This is the first sub-linear …

Robust and Efficient Autoregressive Speech Synthesis with Dynamic Chunk-wise Prediction Policy
Bohan Li, Zhihan Li, Haoran Wang, Hanglei Zhang, Yiwei Guo, Hankun Wang, Xie Chen, Kai Yu
https://arxiv.org/abs/2506.22023

Robust and Efficient Autoregressive Speech Synthesis with Dynamic Chunk-wise Prediction Policy
Recently, autoregressive (AR) language models have emerged as a dominant approach in speech synthesis, offering expressive generation and scalable training. However, conventional AR speech synthesis models relying on the next-token prediction paradigm often encounter significant challenges when handling long speech sequences. These models often struggle to construct stable frame-to-frame attention, leading to increased latency and degraded synthesis quality, thereby limiting their feasibility f…

Mitigating Hallucination of Large Vision-Language Models via Dynamic Logits Calibration
Jiahe Chen, Jiaying He, Qian Shao, Qiyuan Chen, Jiahe Ying, Hongxia Xu, Jintai Chen, Jianwei Zheng, Jian Wu
https://arxiv.org/abs/2506.21509

Mitigating Hallucination of Large Vision-Language Models via Dynamic Logits Calibration
Large Vision-Language Models (LVLMs) have demonstrated significant advancements in multimodal understanding, yet they are frequently hampered by hallucination-the generation of text that contradicts visual input. Existing training-free decoding strategies exhibit critical limitations, including the use of static constraints that do not adapt to semantic drift during generation, inefficiency stemming from the need for multiple forward passes, and degradation of detail due to overly rigid interve…

An Efficient Class of Bayesian Generalized Quadratic Nonlinear Dynamic Models with Application to Birth Rate Monitoring
Madelyn Clinch, Jonathan R. Bradley
https://arxiv.org/abs/2506.22188

An Efficient Class of Bayesian Generalized Quadratic Nonlinear Dynamic Models with Application to Birth Rate Monitoring
Many real-world spatio-temporal processes exhibit nonlinear dynamics that can often be described through stochastic partial differential equations. These models are flexible and scientifically motivated, however, implementing them in a fully Bayesian framework can be computationally challenging. We are motivated by birth rate data, which has important implications for public health and are known to follow nonlinear dynamics. We propose a covariance calibration strategy that specifies the covari…

Data-Driven Dynamic Factor Modeling via Manifold Learning
Graeme Baker, Agostino Capponi, J. Antonio Sidaoui
https://arxiv.org/abs/2506.19945 https://

Data-Driven Dynamic Factor Modeling via Manifold Learning
We propose a data-driven dynamic factor framework where a response variable depends on a high-dimensional set of covariates, without imposing any parametric model on the joint dynamics. Leveraging Anisotropic Diffusion Maps, a nonlinear manifold learning technique introduced by Singer and Coifman, our framework uncovers the joint dynamics of the covariates and responses in a purely data-driven way. We approximate the embedding dynamics using linear diffusions, and exploit Kalman filtering to pr…

This https://arxiv.org/abs/2410.20448 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

New results related to cutters and to an extrapolated block-iterative method for finding a common fixed point of a collection of them
Given a Hilbert space and a finite family of operators defined on the space, the common fixed point problem (CFPP) is to find a point in the intersection of the fixed point sets of these operators. Instances of the problem have numerous applications in science and engineering. We consider an extrapolated block-iterative method with dynamic weights for solving the CFPP assuming the operators belong to a wide class of operators called cutters. Global convergence is proved in two different scenari…

Network classification through random walks
Gonzalo Travieso, Joao Merenda, Odemir M. Bruno
https://arxiv.org/abs/2505.21706 https://…

Network classification through random walks
Network models have been widely used to study diverse systems and analyze their dynamic behaviors. Given the structural variability of networks, an intriguing question arises: Can we infer the type of system represented by a network based on its structure? This classification problem involves extracting relevant features from the network. Existing literature has proposed various methods that combine structural measurements and dynamical processes for feature extraction. In this study, we introd…

Jet precession in gamma-ray bursts: The roles of fallback accretion and disk dynamics
Yun-Peng Li, Da-Bin Lin, Guo-Yu Li, En-Wei Liang
https://arxiv.org/abs/2506.22043

Jet precession in gamma-ray bursts: The roles of fallback accretion and disk dynamics
The precession phenomenon of the jet in a gamma-ray burst (GRB) is a key probe of the physics of the central engine. Previous studies generally assumed a fixed precession period when analysing the temporal profiles in GRBs; however, the dynamic evolution of the fallback process and accretion disk can significantly affect the precession behaviour. In this work we present a jet precession model that incorporates the co-evolution of fallback accretion and the central black hole (BH). Our model dem…

A Matlab-based Toolbox for Automatic EMT Modeling and Small-Signal Stability Analysis of Modern Power Systems
Josep Arevalo-Soler, Dionysios Moutevelis, Elia Mateu-Barriendos, Onur Alican, Carlos Collados-Rodriguez, Marc Cheah-Ma\~ne, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt
https://arxiv.org/abs/2506.22201

A Matlab-based Toolbox for Automatic EMT Modeling and Small-Signal Stability Analysis of Modern Power Systems
The intensive integration of power converters is changing the way that power systems operate, leading to the emergence of new types of dynamic phenomena and instabilities. At the same time, converters act as an interface between traditional AC grids and their more recent DC counterparts, giving rise to hybrid AC/DC networks. These conditions increase the necessity for stability analysis tools that can simultaneously account for the newly-introduced dynamic phenomena and can also be applied for …

This https://arxiv.org/abs/2505.03458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal information on cortical, regional co-activity patterns. However, this approach has been largely unexplored within the Alzheimer's spectrum. We analysed age- and sex-matched static and dynamic fMRI brain…

Effects of thrust, tip-speed ratio, and time variations on wind-turbine wakes at high Reynolds numbers
Nathaniel J. Wei, Adina Y. Fleisher, John W. Kurelek, Marcus N. Hultmark
https://arxiv.org/abs/2505.22788

Effects of thrust, tip-speed ratio, and time variations on wind-turbine wakes at high Reynolds numbers
The evolution of rotor wakes is an important problem for a wide range of wind-energy and aerodynamic applications, and is of particular relevance to dynamic wake control strategies for wind farms. This study aims to clarify the influence of turbine thrust and tip-speed ratio on tip-vortex breakdown and the evolution of the near wake. Scaling arguments show that these parameters contribute to the wake dynamics in distinct ways, and that neither thrust nor tip-speed ratio are alone sufficient to …

This https://arxiv.org/abs/2305.13054 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Fluid limits for interacting queues in sparse dynamic graphs
Consider a network of $n$ single-server queues where tasks arrive independently at each server at rate $λ_n$. The servers are connected by a graph that is resampled at rate $μ_n$ in a way that is symmetric with respect to the servers, and each task is dispatched to the shortest queue in the graph neighborhood where it appears. We aim to gain insight in the impact of the dynamic network structure on the load balancing dynamics in terms of the occupancy process which describes the empirical dis…

Teaching Mathematics for Economists
Eric Hillebrand
https://arxiv.org/abs/2505.22671 https://arxiv.org/pdf/2505.22671

Teaching Mathematics for Economists
In this chapter, I discuss teaching mathematical tools specifically tailored for economics students. A typical one-semester course in this area seeks to blend a range of topics: from foundational elements of subjects such as linear algebra and multivariate calculus to intermediate areas like real and convex analysis and further into advanced topics such as dynamic optimization in both continuous and discrete time. This breadth of coverage corresponds to material usually spread across multiple y…

MHD simulation of tilt instability during the dynamic FRC magnetic compression process
Yiming Ma, Ping Zhu, Bo Rao, Haolong Li
https://arxiv.org/abs/2506.20443

MHD simulation of tilt instability during the dynamic FRC magnetic compression process
The nonlinear evolution of the tilt instability in a field reversed configuration (FRC) during the dynamic magnetic compression process has been investigated using magnetohydrodynamic (MHD) simulations with the NIMROD code [C. R. Sovinec \textit{et al.}, J. Comput. Phys. \textbf{195}, 355 (2004)]. The tilt mode induces significant deformations in the linear growth phase and results in complete confinement loss of the FRC in the nonlinear phase, with no evidence of dynamic nonlinear stabilizatio…

Intraday Functional PCA Forecasting of Cryptocurrency Returns
Joann Jasiak, Cheng Zhong
https://arxiv.org/abs/2505.20508 https://arxi…

Intraday Functional PCA Forecasting of Cryptocurrency Returns
We study the Functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account. The Karhunen-Loeve (KL) dynamic factor model is introduced to bridge the functional and discrete time dynamic models. It offers a convenient framework for functional time series analysis. For intraday forecasting, we intr…

Stochastic Coefficient of Variation: Assessing the Variability and Forecastability of Solar Irradiance
Cyril Voyant, Alan Julien, Milan Despotovic, Gilles Notton, Luis Antonio Garcia-Gutierrez, Claudio Francesco Nicolosi, Philippe Blanc, Jamie Bright
https://arxiv.org/abs/2506.21807

Stochastic Coefficient of Variation: Assessing the Variability and Forecastability of Solar Irradiance
This work presents a robust framework for quantifying solar irradiance variability and forecastability through the Stochastic Coefficient of Variation (sCV) and the Forecastability (F). Traditional metrics, such as the standard deviation, fail to isolate stochastic fluctuations from deterministic trends in solar irradiance. By considering clear-sky irradiance as a dynamic upper bound of measurement, sCV provides a normalized, dimensionless measure of variability that theoretically ranges from 0…

This https://arxiv.org/abs/2505.08249 has been replaced.
initial toot: https://mastoxiv.page/@a…

Modelling of time-dependent electrostatic effects and AFM-based surface conductivity characterization
Atomic Force Microscopy (AFM) combined with electrical modes provides a powerful contactless approach to characterize material electrical properties at the nanoscale. However, conventional electrostatic models often overlook dynamic charge effects, which are particularly relevant for 2D materials deposited on insulating substrates. In this work, we introduce a theoretical framework that extends traditional electrostatic models by incorporating charge dynamics, analyzing two key cases: quasi-ide…

Digital Twin-Empowered Cooperative Autonomous Car-sharing Services: Proof-of-Concept
Kazuma Nonomura, Kui Wang, Zongdian Li, Tao Yu, Kei Sakaguchi
https://arxiv.org/abs/2504.20542

This https://arxiv.org/abs/2501.05440 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Enabling GPU Portability into the Numba-JITed Monte Carlo Particle Transport Code MC/DC
The Center for Exascale Monte Carlo Neutron Transport is developing Monte Carlo / Dynamic Code (MC/DC) as a portable Monte Carlo neutron transport package for rapid numerical methods exploration on CPU- and GPU-based high-performance computers. In this paper, we describe MC/DC's current event-based GPU algorithm as well as the just-in-time (JIT) compilation scheme we use to enable GPU operability on Nvidia and AMD GPUs from MC/DC's Python source. To analyze performance, we conduct runtime tests…

Dual-Hierarchy Labelling: Scaling Up Distance Queries on Dynamic Road Networks
Muhammad Farhan, Henning Koehler, Qing Wang
https://arxiv.org/abs/2506.18013

Dual-Hierarchy Labelling: Scaling Up Distance Queries on Dynamic Road Networks
Computing the shortest-path distance between any two given vertices in road networks is an important problem. A tremendous amount of research has been conducted to address this problem, most of which are limited to static road networks. Since road networks undergo various real-time traffic conditions, there is a pressing need to address this problem for dynamic road networks. Existing state-of-the-art methods incrementally maintain an indexing structure to reflect dynamic changes on road networ…

MDC-R: The Minecraft Dialogue Corpus with Reference
Chris Madge, Maris Camilleri, Paloma Carretero Garcia, Mladen Karan, Juexi Shao, Prashant Jayannavar, Julian Hough, Benjamin Roth, Massimo Poesio
https://arxiv.org/abs/2506.22062

MDC-R: The Minecraft Dialogue Corpus with Reference
We introduce the Minecraft Dialogue Corpus with Reference (MDC-R). MDC-R is a new language resource that supplements the original Minecraft Dialogue Corpus (MDC) with expert annotations of anaphoric and deictic reference. MDC's task-orientated, multi-turn, situated dialogue in a dynamic environment has motivated multiple annotation efforts, owing to the interesting linguistic phenomena that this setting gives rise to. We believe it can serve as a valuable resource when annotated with reference,…

Dissecting Exciton-Polariton Transport in Organic Molecular Crystals: Emerging Conductivity Assisted by Intermolecular Vibrational Coupling
Guangming Liu, Hsing-Ta Chen
https://arxiv.org/abs/2505.21888

Dissecting Exciton-Polariton Transport in Organic Molecular Crystals: Emerging Conductivity Assisted by Intermolecular Vibrational Coupling
In this work, we systematically investigate the spectral and transport properties of exciton-polaritons under the explicit influence of intermolecular vibrational coupling, which introduces dynamic disorder. In the context of a one-dimensional molecular chain strongly interacting with a cavity photon, we demonstrate the polaritonic characteristics of the spectral function and its interactions with the electronic band broadened by the coupling disorder. We further dissect the current flux into i…

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Ganesh Parab, Zishan Ahmad, Dagnachew Birru
https://arxiv.org/abs/2506.18916 https:…

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Text-to-SQL generation bridges the gap between natural language and databases, enabling users to query data without requiring SQL expertise. While large language models (LLMs) have significantly advanced the field, challenges remain in handling complex queries that involve multi-table joins, nested conditions, and intricate operations. Existing methods often rely on multi-step pipelines that incur high computational costs, increase latency, and are prone to error propagation. To address these l…

Online Learning for Dynamic Vickrey-Clarke-Groves Mechanism in Sequential Auctions under Unknown Environments
Vincent Leon, S. Rasoul Etesami
https://arxiv.org/abs/2506.19038

Online Learning for Dynamic Vickrey-Clarke-Groves Mechanism in Sequential Auctions under Unknown Environments
We consider the problem of online dynamic mechanism design for sequential auctions in unknown environments, where the underlying market and, thus, the bidders' values vary over time as interactions between the seller and the bidders progress. We model the sequential auctions as an infinite-horizon average-reward Markov decision process (MDP), where the transition kernel and reward functions are unknown to the seller. In each round, the seller determines an allocation and a payment for each bidd…

Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation
Niklas S. Neher, Erik Faulhaber, Sven Berger, Christian Wei{\ss}enfels, Gregor J. Gassner, Michael Schlottke-Lakemper
https://arxiv.org/abs/2506.21206

Robust and efficient pre-processing techniques for particle-based methods including dynamic boundary generation
Obtaining high-quality particle distributions for stable and accurate particle-based simulations poses significant challenges, especially for complex geometries. We introduce a preprocessing technique for 2D and 3D geometries, optimized for smoothed particle hydrodynamics (SPH) and other particle-based methods. Our pipeline begins with the generation of a resolution-adaptive point cloud near the geometry's surface employing a face-based neighborhood search. This point cloud forms the basis for …

Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing
Qiong Li, Chao Fang, Longwei Huang, Jun Lin, Zhongfeng Wang
https://arxiv.org/abs/2505.19096

Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing
While posit format offers superior dynamic range and accuracy for transprecision computing, its adoption in RISC-V processors is hindered by the lack of a unified solution for lightweight, precision-scalable, and IEEE-754 arithmetic compatible hardware implementation. To address these challenges, we enhance RISC-V processors by 1) integrating dedicated posit codecs into the original FPU for lightweight implementation, 2) incorporating multi/mixed-precision support with dynamic exponent size for…

When you start playing an album that’s >20 years old and you have to turn up the master volume because no one compressed this to the max, and there’s more than 6dB of dynamic range. ❤️

This https://arxiv.org/abs/2505.19301 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control
Traditional Identity and Access Management (IAM) systems, primarily designed for human users or static machine identities via protocols such as OAuth, OpenID Connect (OIDC), and SAML, prove fundamentally inadequate for the dynamic, interdependent, and often ephemeral nature of AI agents operating at scale within Multi Agent Systems (MAS), a computational system composed of multiple interacting intelligent agents that work collectively. This paper posits the imperative for a novel Agentic AI I…

This https://arxiv.org/abs/2306.16904 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Endogenous Barriers to Learning
Building on the idea that lack of experience is a source of errors but that experience should reduce them, we model agents' behavior using a stochastic choice model (logit quantal response), leaving endogenous the accuracy of their choices. In some games, higher accuracy leads to unstable logit-response dynamics. Starting from the lowest possible accuracy, we define the barrier to learning as the maximum accuracy which keeps the logit-response dynamic stable (for all lower accuracies). This def…

This https://arxiv.org/abs/2502.10570 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World Mobile Interactions
Mobile gaze tracking involves inferring a user's gaze point or direction on a mobile device's screen from facial images captured by the device's front camera. While this technology inspires an increasing number of gaze-interaction applications, achieving consistent accuracy remains challenging due to dynamic user-device spatial relationships and varied motion conditions inherent in mobile contexts. This paper provides empirical evidence on how user mobility and behaviour affect mobile gaze trac…

Robust Dynamic Material Handling via Adaptive Constrained Evolutionary Reinforcement Learning
Chengpeng Hu, Ziming Wang, Bo Yuan, Jialin Liu, Chengqi Zhang, Xin Yao
https://arxiv.org/abs/2506.16795

Robust Dynamic Material Handling via Adaptive Constrained Evolutionary Reinforcement Learning
Dynamic material handling (DMH) involves the assignment of dynamically arriving material transporting tasks to suitable vehicles in real time for minimising makespan and tardiness. In real-world scenarios, historical task records are usually available, which enables the training of a decision policy on multiple instances consisting of historical records. Recently, reinforcement learning has been applied to solve DMH. Due to the occurrence of dynamic events such as new tasks, adaptability is hig…

Dallas Cowboys urged to trade for dynamic offensive weapon https://www.si.com/nfl/cowboys/news/dallas-cowboys-urged-to-trade-for-dynamic-offensive-weapon

sp_primary_school: Primary school dynamic contacts (2009)
Two temporal networks of contacts among students and teachers at a primary school in Lyon, France, on consecutive days of in October 2009. Each network accumulates all contacts over the course of a single day; contacts were sampled at 20-second intervals.
This network has 242 nodes and 125773 edges.
Tags: Social, Offline, Weighted, Temporal, Metadata

Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
Weilun Yu, Shixiang Tang, Yonggui Huang, Nanqing Dong, Li Fan, Honggang Qi, Wei Liu, Xiaoli Diao, Xi Chen, Wanli Ouyang
https://arxiv.org/abs/2506.18348

Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
Scientific progress increasingly relies on effective collaboration among researchers, a dynamic that large language models (LLMs) have only begun to emulate. While recent LLM-based scientist agents show promise in autonomous scientific discovery, they often lack the interactive reasoning and evaluation mechanisms essential to real-world research. We propose IDVSCI (Internal Discussion and Vote SCIentists), a multi-agent framework built on LLMs that incorporates two key innovations: a Dynamic Kn…

Topological Adaptive Least Mean Squares Algorithms over Simplicial Complexes
Lorenzo Marinucci, Claudio Battiloro, Paolo Di Lorenzo
https://arxiv.org/abs/2505.23160

Topological Adaptive Least Mean Squares Algorithms over Simplicial Complexes
This paper introduces a novel adaptive framework for processing dynamic flow signals over simplicial complexes, extending classical least-mean-squares (LMS) methods to high-order topological domains. Building on discrete Hodge theory, we present a topological LMS algorithm that efficiently processes streaming signals observed over time-varying edge subsets. We provide a detailed stochastic analysis of the algorithm, deriving its stability conditions, steady-state mean-square-error, and converge…

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
Ruijia Zhang, Zhengling Qi, Yue Wu, Xiangyu Zhang, Yanxun Xu
https://arxiv.org/abs/2506.20406

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
Dynamic treatment regimes (DTRs) provide a principled framework for optimizing sequential decision-making in domains where decisions must adapt over time in response to individual trajectories, such as healthcare, education, and digital interventions. However, existing statistical methods often rely on strong positivity assumptions and lack robustness under partial data coverage, while offline reinforcement learning approaches typically focus on average training performance, lack statistical gu…

This https://arxiv.org/abs/2410.20448 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

New results related to cutters and to an extrapolated block-iterative method for finding a common fixed point of a collection of them
Given a Hilbert space and a finite family of operators defined on the space, the common fixed point problem (CFPP) is to find a point in the intersection of the fixed point sets of these operators. Instances of the problem have numerous applications in science and engineering. We consider an extrapolated block-iterative method with dynamic weights for solving the CFPP assuming the operators belong to a wide class of operators called cutters. Global convergence is proved in two different scenari…

Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
Lingyun Chen, Xinrui Zhao, Marcos P. S. Campanha, Alexander Wegener, Abdeldjallil Naceri, Abdalla Swikir, Sami Haddadin
https://arxiv.org/abs/2506.20376

Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
This paper presents a novel approach for robot navigation in environments containing deformable obstacles. By integrating Learning from Demonstration (LfD) with Dynamical Systems (DS), we enable adaptive and efficient navigation in complex environments where obstacles consist of both soft and hard regions. We introduce a dynamic modulation matrix within the DS framework, allowing the system to distinguish between traversable soft regions and impassable hard areas in real-time, ensuring safe and…

From the dynamic to the static glass transition via hypersonic measurements using Brillouin spectroscopy
Jan-Kristian Kr\"uger, Rafael J. Jim\'enez Riob\'oo, Bernd Wetzel, Andreas Klingler
https://arxiv.org/abs/2506.20808

From the dynamic to the static glass transition via hypersonic measurements using Brillouin spectroscopy
For the fragile, low-molecular-weight liquid diglycidyl ether of bisphenol A (DGEBA), we report on the dynamic glass transition and a further acoustic anomaly in the vicinity of the thermal glass transition based on hypersonic investigations of the longitudinal elastic modulus using Brillouin spectroscopy. This additional acoustic anomaly of the longitudinally polarized phonon is confirmed by the occurrence of an anomaly of the shear phonon at the same thermal glass transition temperature. Anal…

Parallel batch queries on dynamic trees: algorithms and experiments
Humza Ikram, Andrew Brady, Daniel Anderson, Guy Blelloch
https://arxiv.org/abs/2506.16477

Parallel batch queries on dynamic trees: algorithms and experiments
Dynamic tree data structures maintain a forest while supporting insertion and deletion of edges and a broad set of queries in $O(\log n)$ time per operation. Such data structures are at the core of many modern algorithms. Recent work has extended dynamic trees so as to support batches of updates or queries so as to run in parallel, and these batch parallel dynamic trees are now used in several parallel algorithms. In this work we describe improvements to batch parallel dynamic trees, describe a…

Context-Aware Semantic Communication for the Wireless Networks
Guangyuan Liu, Yinqiu Liu, Jiacheng Wang, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Abbas Jamalipour
https://arxiv.org/abs/2505.23249

Context-Aware Semantic Communication for the Wireless Networks
In next-generation wireless networks, supporting real-time applications such as augmented reality, autonomous driving, and immersive Metaverse services demands stringent constraints on bandwidth, latency, and reliability. Existing semantic communication (SemCom) approaches typically rely on static models, overlooking dynamic conditions and contextual cues vital for efficient transmission. To address these challenges, we propose CaSemCom, a context-aware SemCom framework that leverages a Large L…

This https://arxiv.org/abs/2502.21026 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Artemis: Toward Accurate Detection of Server-Side Request Forgeries through LLM-Assisted Inter-Procedural Path-Sensitive Taint Analysis
Server-side request forgery (SSRF) vulnerabilities are inevitable in PHP web applications. Existing static tools in detecting vulnerabilities in PHP web applications neither contain SSRF-related features to enhance detection accuracy nor consider PHP's dynamic type features. In this paper, we present Artemis, a static taint analysis tool for detecting SSRF vulnerabilities in PHP web applications. First, Artemis extracts both PHP built-in and third-party functions as candidate source and sink fu…

Fluid Simulation on Vortex Particle Flow Maps
Sinan Wang, Junwei Zhou, Fan Feng, Zhiqi Li, Yuchen Sun, Duowen Chen, Greg Turk, Bo Zhu
https://arxiv.org/abs/2505.21946

Fluid Simulation on Vortex Particle Flow Maps
We propose the Vortex Particle Flow Map (VPFM) method to simulate incompressible flow with complex vortical evolution in the presence of dynamic solid boundaries. The core insight of our approach is that vorticity is an ideal quantity for evolution on particle flow maps, enabling significantly longer flow map distances compared to other fluid quantities like velocity or impulse. To achieve this goal, we developed a hybrid Eulerian-Lagrangian representation that evolves vorticity and flow map qu…

Feedforward equilibrium trajectory optimization with GSPulse
J. T. Wai, M. D. Boyer, D. J. Battaglia, A. Merle, F. Carpanese, F. Felici, M. Kochan, E. Kolemen
https://arxiv.org/abs/2506.21760

Feedforward equilibrium trajectory optimization with GSPulse
One of the common tasks required for designing new plasma scenarios or evaluating capabilities of a tokamak is to design the desired equilibria using a Grad-Shafranov (GS) equilibrium solver. However, most standard equilibrium solvers are time-independent and do not include dynamic effects such as plasma current flux consumption, induced vessel currents, or voltage constraints. Another class of tools, plasma equilibrium evolution simulators, do include time-dependent effects. These are generall…

This https://arxiv.org/abs/2408.09955 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs
LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on predefined Standard Operating Procedures (SOPs), which demand extensive human input. To address these limitations, we propose MegaAgent, a large-scale autonomous LLM-based multi-agent system. MegaAgent generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communic…

This https://arxiv.org/abs/2409.04668 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Performance Portable Monte Carlo Neutron Transport in MCDC via Numba
Finding a software engineering approach that allows for portability, rapid development, and open collaboration for high-performance computing on GPUs and CPUs is a challenge. We implement a portability scheme using the Numba compiler for Python in Monte Carlo / Dynamic Code (MC/DC), a new neutron transport application for rapidly developing Monte Carlo. Using this scheme, we have built MC/DC as an application that can run as a pure Python, compiled CPU, or compiled GPU solver. In GPU mode, we u…

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
Fangyuan Zhang, Zhengjun Huang, Yingli Zhou, Qintian Guo, Zhixun Li, Wensheng Luo, Di Jiang, Yixiang Fang, Xiaofang Zhou
https://arxiv.org/abs/2506.20963

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typically assume a static corpus, requiring expensive full-graph reconstruction whenever new documents arrive, limiting their scalability in dynamic, evolving environments. To address these limitations, we introduce EraRAG, a novel multi-layered Graph-RAG framework that supports efficient and scalable dynamic updates. Our met…

From Connectivity to Autonomy: The Dawn of Self-Evolving Communication Systems
Zeinab Nezami, Syed Danial Ali Shah, Maryam Hafeez, Karim Djemame, Syed Ali Raza Zaidi
https://arxiv.org/abs/2505.23710

From Connectivity to Autonomy: The Dawn of Self-Evolving Communication Systems
This paper envisions 6G as a self-evolving telecom ecosystem, where AI-driven intelligence enables dynamic adaptation beyond static connectivity. We explore the key enablers of autonomous communication systems, spanning reconfigurable infrastructure, adaptive middleware, and intelligent network functions, alongside multi-agent collaboration for distributed decision-making. We explore how these methodologies align with emerging industrial IoT frameworks, ensuring seamless integration within digi…

Maintaining a Bounded Degree Expander in Dynamic Peer-to-Peer Networks
Antonio Cruciani
https://arxiv.org/abs/2506.17757 https://arxi…

Maintaining a Bounded Degree Expander in Dynamic Peer-to-Peer Networks
We study the problem of maintaining robust and sparse overlay networks in fully distributed settings where nodes continuously join and leave the system. This scenario closely models real-world unstructured peer-to-peer networks, where maintaining a well-connected yet low-degree communication graph is crucial. We generalize a recent protocol by Becchetti et al. [SODA 2020] that relies on a simple randomized connection strategy to build an expander topology with high probability to a dynamic netw…

Securing AI Agents with Information-Flow Control
Manuel Costa, Boris K\"opf, Aashish Kolluri, Andrew Paverd, Mark Russinovich, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Santiago Zanella-B\'eguelin
https://arxiv.org/abs/2505.23643

Securing AI Agents with Information-Flow Control
As AI agents become increasingly autonomous and capable, ensuring their security against vulnerabilities such as prompt injection becomes critical. This paper explores the use of information-flow control (IFC) to provide security guarantees for AI agents. We present a formal model to reason about the security and expressiveness of agent planners. Using this model, we characterize the class of properties enforceable by dynamic taint-tracking and construct a taxonomy of tasks to evaluate security…

Rolling, sliding and trapping of driven particles in square obstacle lattices
Galor Geva, Arin Escobar, Paula Magrinya, Pablo Llombart, Alfredo Alexander-Katz, Laura R. Arriaga, Juan L. Aragones
https://arxiv.org/abs/2505.21702

Rolling, sliding and trapping of driven particles in square obstacle lattices
Transport phenomena in complex and dynamic microscopic environments are fundamentally shaped by hydrodynamic interactions. In particular, microparticle transport in porous media is governed by the delicate interplay between particle-substrate friction and pressure forces. Here, we systematically investigate the motion of externally driven rotating magnetic microparticles near a substrate patterned with a square lattice of cylindrical obstacles, a model porous medium. Remarkably, we observe a re…

ACTLLM: Action Consistency Tuned Large Language Model
Jing Bi, Lianggong Bruce Wen, Zhang Liu, Chenliang Xu
https://arxiv.org/abs/2506.21250 https://

ACTLLM: Action Consistency Tuned Large Language Model
This paper introduces ACTLLM (Action Consistency Tuned Large Language Model), a novel approach for robot manipulation in dynamic environments. Traditional vision-based systems often struggle to learn visual representations that excel in both task execution and spatial reasoning, thereby limiting their adaptability in dynamic environments. ACTLLM addresses these challenges by harnessing language to craft structured scene descriptors, providing a uniform interface for both spatial understanding a…

DCMF: A Dynamic Context Monitoring and Caching Framework for Context Management Platforms
Ashish Manchanda, Prem Prakash Jayaraman, Abhik Banerjee, Kaneez Fizza, Arkady Zaslavsky
https://arxiv.org/abs/2506.17226

DCMF: A Dynamic Context Monitoring and Caching Framework for Context Management Platforms
The rise of context-aware IoT applications has increased the demand for timely and accurate context information. Context is derived by aggregating and inferring from dynamic IoT data, making it highly volatile and posing challenges in maintaining freshness and real-time accessibility. Caching is a potential solution, but traditional policies struggle with the transient nature of context in IoT (e.g., ensuring real-time access for frequent queries or handling fast-changing data). To address this…

Exact Time-Varying Turnpikes for Dynamic Operation of District Heating Networks
Max Rose, Hannes Gernandt, Timm Faulwasser, Johannes Schiffer
https://arxiv.org/abs/2506.21239

Exact Time-Varying Turnpikes for Dynamic Operation of District Heating Networks
District heating networks (DHNs) are crucial for decarbonizing the heating sector. Yet, their efficient and reliable operation requires the coordination of multiple heat producers and the consideration of future demands. Predictive and optimization-based control is commonly used to address this task, but existing results for DHNs do not account for time-varying problem aspects. Since the turnpike phenomenon can serve as a basis for model predictive control design and analysis, this paper examin…

Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
Kai-Robin Lange, Tobias Schmidt, Matthias Reccius, Henrik M\"uller, Michael Roos, Carsten Jentsch
https://arxiv.org/abs/2506.20269

Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
With rapidly evolving media narratives, it has become increasingly critical to not just extract narratives from a given corpus but rather investigate, how they develop over time. While popular narrative extraction methods such as Large Language Models do well in capturing typical narrative elements or even the complex structure of a narrative, applying them to an entire corpus comes with obstacles, such as a high financial or computational cost. We propose a combination of the language understa…

Estimating causal distances with non-causal ones
Beatrice Acciaio, Songyan Hou, Gudmund Pammer
https://arxiv.org/abs/2506.22421 https://

Estimating causal distances with non-causal ones
The adapted Wasserstein ($AW$) distance refines the classical Wasserstein ($W$) distance by incorporating the temporal structure of stochastic processes. This makes the $AW$-distance well-suited as a robust distance for many dynamic stochastic optimization problems where the classical $W$-distance fails. However, estimating the $AW$-distance is a notably challenging task, compared to the classical $W$-distance. In the present work, we build a sharp estimate for the $AW$-distance in terms of the…

Adaptive Hybrid Sort: Dynamic Strategy Selection for Optimal Sorting Across Diverse Data Distributions
Shrinivass Arunachalam Balasubramanian
https://arxiv.org/abs/2506.20677

Adaptive Hybrid Sort: Dynamic Strategy Selection for Optimal Sorting Across Diverse Data Distributions
Sorting is an essential operation in computer science with direct consequences on the performance of large scale data systems, real-time systems, and embedded computation. However, no sorting algorithm is optimal under all distributions of data. The new adaptive hybrid sorting paradigm proposed in this paper is the paradigm that automatically selects the most effective sorting algorithm Counting Sort, Radix Sort, or QuickSort based on real-time monitoring of patterns in input data. The architec…

Voltage Control of the Boost Converter: PI vs. Nonlinear Passivity-based Control
Leyan Fang, Romeo Ortega, Robert Gri\~n\'o
https://arxiv.org/abs/2505.23112

Voltage Control of the Boost Converter: PI vs. Nonlinear Passivity-based Control
We carry-out a detailed analysis of direct voltage control of a Boost converter feeding a simple resistive load. First, we prove that using a classical PI control to stabilize a desired equilibrium leads to a very complicated dynamic behavior consisting of two equilibrium points, one of them always unstable for all PI gains and circuit parameter values. Interestingly, the second equilibrium point may be rendered stable -- but for all tuning gains leading to an extremely large value of the circu…

This https://arxiv.org/abs/2505.19934 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Disentangling hierarchical relaxations in glass formers via dynamic eigenmodes
Hierarchical dynamics in glass-forming systems span multiple timescales, from fast vibrations to slow structural rearrangements, appearing in both supercooled fluids and glassy states. Understanding how these diverse processes interact across timescales remains a central challenge. Here, by combining direct particle-level observations with a dynamic eigenmode approach that decomposes intermediate-timescale responses into distinct modes, we reveal the microscopic organisation of relaxation dynam…

Decoupling Periodic Systems: An Algebraic Approach
Vladim\'ir Ku\v{c}era
https://arxiv.org/abs/2505.23616 https://arxiv.org/pdf/2…

Decoupling Periodic Systems: An Algebraic Approach
This paper addresses the problem of row-by-row (or diagonal) decoupling of discrete-time linear multi-input multi-output systems with periodic time-varying coefficients using periodic state feedback. Previous solutions have tackled row-by-row decoupling using dynamic compensation for square systems and block-decoupling through regular state feedback for nonsquare systems with more outputs than inputs. While it appears likely that a row-by-row state feedback solution for square systems can be de…

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows
Jinjin Zhao, Sanjay Krishnan
https://arxiv.org/abs/2506.18257 https://

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows
Large Language Models (LLMs) have emerged as powerful tools for automating and executing complex data tasks. However, their integration into more complex data workflows introduces significant management challenges. In response, we present TableVault - a data management system designed to handle dynamic data collections in LLM-augmented environments. TableVault meets the demands of these workflows by supporting concurrent execution, ensuring reproducibility, maintaining robust data versioning, a…

SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging
Zijun Chen, Zhanpeng Zhou, Bo Zhang, Weinan Zhang, Xi Sun, Junchi Yan
https://arxiv.org/abs/2506.18135

SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging
Model merging has gained increasing attention due to its intriguing property: interpolating the parameters of different task-specific fine-tuned models leads to multi-task abilities. However, despite its empirical success, the underlying mechanisms of model merging remain poorly understood. In this work, we delve into the mechanism behind model merging from a representation perspective. Our analysis reveals that model merging achieves multi-task abilities through two key capabilities: i) distin…

Near-Optimal Dynamic Policies for Joint Replenishment in Continuous/Discrete Time
Danny Segev
https://arxiv.org/abs/2506.18491 https://

Near-Optimal Dynamic Policies for Joint Replenishment in Continuous/Discrete Time
While dynamic policies have historically formed the foundation of most influential papers dedicated to the joint replenishment problem, we are still facing profound gaps in our structural understanding of optimal such policies as well as in their surrounding computational questions. To date, the seminal work of Roundy (1985, 1986) and Jackson et al. (1985) remains unsurpassed in efficiently developing provably-good dynamic policies in this context. The principal contribution of this paper con…

This https://arxiv.org/abs/2503.05053 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Multi-phase-field Models of Biological Tissues
The development of complex multicellular organisms from a single parent cell is a highly orchestrated process that cells conduct collectively without central guidance, creating intricate dynamic patterns essential for development and regeneration. Despite significant advances in imaging spatiotemporal dynamics of cell collectives and mechanical characterization techniques, the role of physical forces in biological functions remains poorly understood. Physics-based models are crucial in compleme…

Online design of experiments by active learning for nonlinear system identification
Kui Xie, Alberto Bemporad
https://arxiv.org/abs/2506.21754 https://

Online design of experiments by active learning for nonlinear system identification
We investigate the use of active-learning (AL) strategies to generate the input excitation signal at runtime for system identification of linear and nonlinear autoregressive and state-space models. We adapt various existing AL approaches for static model regression to the dynamic context, coupling them with a Kalman filter to update the model parameters recursively, and also cope with the presence of input and output constraints. We show the increased sample efficiency of the proposed approache…

Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
Ioannis Panopoulos, Maria-Lamprini A. Bartsioka, Sokratis Nikolaidis, Stylianos I. Venieris, Dimitra I. Kaklamani, Iakovos S. Venieris
https://arxiv.org/abs/2506.18114

Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, necessitating efficient and adaptive Intrusion Detection Systems (IDS). Traditional IDS models often overlook the temporal characteristics of network traffic, limiting their effectiveness in early threat detection. We propose a Transformer-based Early Intrusion Detection System (EIDS) that incorporates dynamic temporal positional encodings to enhance detection accuracy while maintaining computati…

Softness and Hydrodynamic Interactions Regulate Lipoprotein Transport in Crowded Yolk Environments
Nimmi Das Anthuparambil, Michelle Dargasz, Sonja Timmermann, Anita Girelli, Sebastian Retzbach, Johannes M\"oller, Wonhyuk Jo, Agha Mohammad Raza, Aliaksandr Leonau, James Wrigley, Frederik Unger, Maddalena Bin, Prince Prabhu Rajaiah, Iason Andronis, William Ch\`evremont, J\"org Hallmann, Angel Rodriguez-Fernandez, Jan-Etienne Pudell, Felix Brausse, Ulrike Boesenberg, Mohamed Yo…

Softness and Hydrodynamic Interactions Regulate Lipoprotein Transport in Crowded Yolk Environments
Low-density lipoproteins (LDLs) serve as nutrient reservoirs in egg yolk for embryonic development and as promising drug carriers. Both roles critically depend on their mobility in densely crowded biological environments. Under these crowded conditions, diffusion is hindered by transient confinement within dynamic cages formed by neighboring particles, driven by solvent-mediated hydrodynamic interactions and memory effects -- phenomena that have remained challenging to characterize computationa…

Finite-Horizon Strategy in Infinite-Horizon Linear-Quadratic Discrete-Time Dynamic Games
Shengyuan Huang, Xiaoguang Yang, Yifen Mu, Wenjun Mei
https://arxiv.org/abs/2506.19565

Finite-Horizon Strategy in Infinite-Horizon Linear-Quadratic Discrete-Time Dynamic Games
This paper explores a finite-horizon strategy, ``watching $T$ steps into the future and moving one step now,'' in an $N$-person infinite-horizon discrete-time linear-quadratic dynamic game. The game involves linear input/output/state dynamics and quadratic cost functions with heterogeneous discount factors. For the finite-horizon version, which forms the basis of the infinite-horizon game, we analyze the structure of the coupled generalized discrete Riccati difference equations related to the f…