Tootfinder

Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation
Chenxu Wang, Yonggang Jin, Cheng Hu, Youpeng Zhao, Zipeng Dai, Jian Zhao, Shiyu Huang, Liuyu Xiang, Junge Zhang, Zhaofeng He
https://arxiv.org/abs/2506.16718

Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation
Adapting a single agent to a new multi-agent system brings challenges, necessitating adjustments across various tasks, environments, and interactions with unknown teammates and opponents. Addressing this challenge is highly complex, and researchers have proposed two simplified scenarios, Multi-agent reinforcement learning for zero-shot learning and Ad-Hoc Teamwork. Building on these foundations, we propose a more comprehensive setting, Agent Collaborative-Competitive Adaptation (ACCA), which ev…

"Pupils in England face lost learning from flooding and extreme heat, study finds"
#UK #UnitedKingdom #Climate #ClimateChange

#PleaseBoost #Poll
Do you feel informed about climate risks and adaptation to climate change?
#ClimateChangeAdaptation

Rayons is such a great musician. As I am reading a novel and listening to the soundtrack of the anime adaptation, I am getting murdered by feels. She nailed the soundtrack, she nailed the mood of the novels, everything is perfect. I know that wishing for more recognition for her is a bit off-tone given that she did the new era of Japan ceremony music too, but I feel like she deserves more public eye

We're having a hot June, here in the Netherlands. And it'll get hotter in the coming decades. We need more trees in our towns and cities. In the place in the picture, there once was a big tree. A civil servant explained that it had to be cut down, "because it took up two parking places" 🤦‍♂️🤦‍♀️🤦
#ClimateChange

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
Matteo Zecchin, Tomer Raviv, Dileep Kalathil, Krishna Narayanan, Nir Shlezinger, Osvaldo Simeone
https://arxiv.org/abs/2506.15176

In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. Leveraging programmable hardware architectures, deep learning-based receivers offer the potential to dynamically adapt to varying channel environments. However, current adaptation strategies, including joint training, hypernetwork-based methods, and meta-learning, either demonstrate limited flexibility or necessitate e…

Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation
Runhao Zeng, Qi Deng, Ronghao Zhang, Shuaicheng Niu, Jian Chen, Xiping Hu, Victor C. M. Leung
https://arxiv.org/abs/2506.12481

Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation
Test-time adaptation (TTA) aims to boost the generalization capability of a trained model by conducting self-/unsupervised learning during the testing phase. While most existing TTA methods for video primarily utilize visual supervisory signals, they often overlook the potential contribution of inherent audio data. To address this gap, we propose a novel approach that incorporates audio information into video TTA. Our method capitalizes on the rich semantic content of audio to generate audio-as…

Replaced article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
- A framework for the use of generative modelling in non-equilibrium statistical mechanics
Karl J Friston, Maxwell J D Ramstead, Dalton A R Sakthivadivel

Investitionen in #Klimaanpassung zahlen sich aus.
Laut einer Analyse des World Resources Institute bringen sie im Schnitt mehr als das Zehnfache des eingesetzten Kapitals zurück.
Besonders hohe Renditen wurden im #Gesundheitswesen und bei der

BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng
https://arxiv.org/abs/2506.13795

BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
Transferring extensive knowledge from relevant social networks has emerged as a promising solution to overcome label scarcity in detecting social bots and other anomalies with GNN-based models. However, effective transfer faces two critical challenges. Firstly, the network heterophily problem, which is caused by bots hiding malicious behaviors via indiscriminately interacting with human users, hinders the model's ability to learn sufficient and accurate bot-related knowledge from source domains…

Socially Aware Robot Crowd Navigation via Online Uncertainty-Driven Risk Adaptation
Zhirui Sun, Xingrong Diao, Yao Wang, Bi-Ke Zhu, Jiankun Wang
https://arxiv.org/abs/2506.14305

Socially Aware Robot Crowd Navigation via Online Uncertainty-Driven Risk Adaptation
Navigation in human-robot shared crowded environments remains challenging, as robots are expected to move efficiently while respecting human motion conventions. However, many existing approaches emphasize safety or efficiency while overlooking social awareness. This article proposes Learning-Risk Model Predictive Control (LR-MPC), a data-driven navigation algorithm that balances efficiency, safety, and social awareness. LR-MPC consists of two phases: an offline risk learning phase, where a Prob…

crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
Navodini Wijethilake, Reuben Dorent, Marina Ivory, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Mohamed Okasha, Anna Oviedova, Hexin Dong, Bogyeong Kang, Guillaume Sall\'e, Luyi Han, Ziyuan Zhao, Han Liu, Tao Yang, Shahad Hardan, Hussain Alasmawi, Santosh Sanjeev, Yuzhou Zhuang, Satoshi Kondo, Maria Baldeon Calisto, Shaikh Muh…

crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), focuses on unsupervised cross-modality segmentation, learning from contrast-enhanced T1 (ceT1) and transferring to T2 MRI. The task is an extreme example of domain shift chosen to serve as a meaningful and illustrative benchmark. From a clinical application perspective, it aims to automate Ve…

Oh hey, it's #ScreenshotSaturday , here's the latest mockup from the video game adaptation of Battle of Tarot I'm working on with some lovely people.
Everything is still a work-in-progress and not final at all, but trying to get the visual style nailed down.
#GameDev

Screen Reader Users in the Vibe Coding Era: Adaptation, Empowerment, and New Accessibility Landscape
Nan Chen, Luna K. Qiu, Arran Zeyu Wang, Zilong Wang, Yuqing Yang
https://arxiv.org/abs/2506.13270

Screen Reader Users in the Vibe Coding Era: Adaptation, Empowerment, and New Accessibility Landscape
The rise of generative AI agents has reshaped human-computer interaction and computer-supported cooperative work by shifting users' roles from direct task execution to supervising machine-driven actions, especially in programming (e.g., "vibe coding"). However, there is limited understanding of how screen reader users engage with these systems in practice. To address this gap, we conducted a longitudinal study with 16 screen reader users, exploring their experiences with AI code assistants in d…

Making deep neural networks work for medical audio: representation, compression and domain adaptation
Charles C Onu
https://arxiv.org/abs/2506.13970 https:…

Making deep neural networks work for medical audio: representation, compression and domain adaptation
This thesis addresses the technical challenges of applying machine learning to understand and interpret medical audio signals. The sounds of our lungs, heart, and voice convey vital information about our health. Yet, in contemporary medicine, these sounds are primarily analyzed through auditory interpretation by experts using devices like stethoscopes. Automated analysis offers the potential to standardize the processing of medical sounds, enable screening in low-resource settings where physici…

Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation
Nikhil Pawar, Guilherme Vieira Hollweg, Akhtar Hussain, Wencong Su, Van-Hai Bui
https://arxiv.org/abs/2506.14133

Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation
Accurate time series forecasting models are often compromised by data drift, where underlying data distributions change over time, leading to significant declines in prediction performance. To address this challenge, this study proposes an adaptive forecasting framework that integrates drift detection with targeted model retraining to compensate for drift effects. The framework utilizes the Pruned Exact Linear Time (PELT) algorithm to identify drift points within the feature space of time serie…

Rising sea levels: adaptation has never been more urgent https://www.theguardian.com/commentisfree/2025/may/25/the-guardian-view-on-rising-sea-levels-adaptation-has-never-been-m…

Facility Location Problem under Local Differential Privacy without Super-set Assumption
Kevin Pfisterer, Quentin Hillebrand, Vorapong Suppakitpaisarn
https://arxiv.org/abs/2506.15224

Facility Location Problem under Local Differential Privacy without Super-set Assumption
In this paper, we introduce an adaptation of the facility location problem and analyze it within the framework of local differential privacy (LDP). Under this model, we ensure the privacy of client presence at specific locations. When n is the number of points, Gupta et al. established a lower bound of $Ω(\sqrt{n})$ on the approximation ratio for any differentially private algorithm applied to the original facility location problem. As a result, subsequent works have adopted the super-set assu…

Robust Unsupervised Adaptation of a Speech Recogniser Using Entropy Minimisation and Speaker Codes
Rogier C. van Dalen, Shucong Zhang, Titouan Parcollet, Sourav Bhattacharya
https://arxiv.org/abs/2506.10653

Robust Unsupervised Adaptation of a Speech Recogniser Using Entropy Minimisation and Speaker Codes
Speech recognisers usually perform optimally only in a specific environment and need to be adapted to work well in another. For adaptation to a new speaker, there is often too little data for fine-tuning to be robust, and that data is usually unlabelled. This paper proposes a combination of approaches to make adaptation to a single minute of data robust. First, instead of estimating the adaptation parameters with cross-entropy on a single error-prone hypothesis or "pseudo-label", this paper pro…

A Variational Framework for Improving Naturalness in Generative Spoken Language Models
Li-Wei Chen, Takuya Higuchi, Zakaria Aldeneh, Ahmed Hussen Abdelaziz, Alexander Rudnicky
https://arxiv.org/abs/2506.14767

A Variational Framework for Improving Naturalness in Generative Spoken Language Models
The success of large language models in text processing has inspired their adaptation to speech modeling. However, since speech is continuous and complex, it is often discretized for autoregressive modeling. Speech tokens derived from self-supervised models (known as semantic tokens) typically focus on the linguistic aspects of speech but neglect prosodic information. As a result, models trained on these tokens can generate speech with reduced naturalness. Existing approaches try to fix this by…

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters
Anastasis Kratsios, Tin Sum Cheng, Aurelien Lucchi, Haitz S\'aez de Oc\'ariz Borde
https://arxiv.org/abs/2506.14530

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters
Low-Rank Adaptation (LoRA) has emerged as a widely adopted parameter-efficient fine-tuning (PEFT) technique for foundation models. Recent work has highlighted an inherent asymmetry in the initialization of LoRA's low-rank factors, which has been present since its inception and was presumably derived experimentally. This paper focuses on providing a comprehensive theoretical characterization of asymmetric LoRA with frozen random factors. First, while existing research provides upper-bound genera…

MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
Ken Yagel, Eyal German, Aviel Ben Siman Tov
https://arxiv.org/abs/2506.07563

MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
Personalized recommendation systems must adapt to user interactions across different domains. Traditional approaches like MLoRA apply a single adaptation per domain but lack flexibility in handling diverse user behaviors. To address this, we propose MoE-MLoRA, a mixture-of-experts framework where each expert is first trained independently to specialize in its domain before a gating network is trained to weight their contributions dynamically. We evaluate MoE-MLoRA across eight CTR models on Mov…

[2025-06-23 Mon (UTC), 2 new articles found for nlin.AO Adaptation and Self-Organizing Systems]
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This https://arxiv.org/abs/2505.00533 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Test-time Correlation Alignment
Deep neural networks often degrade under distribution shifts. Although domain adaptation offers a solution, privacy constraints often prevent access to source data, making Test-Time Adaptation (TTA, which adapts using only unlabeled test data) increasingly attractive. However, current TTA methods still face practical challenges: (1) a primary focus on instance-wise alignment, overlooking CORrelation ALignment (CORAL) due to missing source correlations; (2) complex backpropagation operations for…

Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments
Fatemeh Mohammadi Amin, Darwin G. Caldwell, Hans Wernher van de Venn
https://arxiv.org/abs/2506.09552

Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments
The robust interpretation of 3D environments is crucial for human-robot collaboration (HRC) applications, where safety and operational efficiency are paramount. Semantic segmentation plays a key role in this context by enabling a precise and detailed understanding of the environment. Considering the intense data hunger for real-world industrial annotated data essential for effective semantic segmentation, this paper introduces a pioneering approach in the Sim2Real domain adaptation for semantic…

[2025-05-23 Fri (UTC), no new articles found for nlin.AO Adaptation and Self-Organizing Systems]
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Degrowth: a dead end or the way out? Capital’s future scam
Aurora Despierta In the series Prospects for Degrowth This article by the Spanish writer Aurora Despierta is her adaptation (highly summarised for translation into English), for the series Prospects for Degrowth, of the original article: Decrecer ¿callejón o salida? La futura estafa del capital’ (2-5-2025) Translated by Mark Burton and Anna Gregoletto For a radically anti-capitalist degrowth that cannot be…

Degrowth: a dead end or the way out? Capital’s future scam
Aurora Despierta In the series Prospects for Degrowth This article by the Spanish writer Aurora Despierta is her adaptation (highly summarised for translation into English), for the series Prospect…

Bounded Memory in Distributed Networks
Ran Ben Basat, Keren Censor-Hillel, Yi-Jun Chang, Wenchen Han, Dean Leitersdorf, Gregory Schwartzman
https://arxiv.org/abs/2506.11644

Bounded Memory in Distributed Networks
The recent advent of programmable switches makes distributed algorithms readily deployable in real-world datacenter networks. However, there are still gaps between theory and practice that prevent the smooth adaptation of CONGEST algorithms to these environments. In this paper, we focus on the memory restrictions that arise in real-world deployments. We introduce the $μ$-CONGEST model where on top of the bandwidth restriction, the memory of nodes is also limited to $μ$ words, in line with rea…

GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors
Hengyuan Zhang, Xinrong Chen, Yingmin Qiu, Xiao Liang, Ziyue Li, Guanyu Wang, Weiping Li, Tong Mo, Wenyue Li, Hayden Kwok-Hay So, Ngai Wong
https://arxiv.org/abs/2506.14646

GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors
Parameter-efficient fine-tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), offer an efficient way to adapt large language models with reduced computational costs. However, their performance is limited by the small number of trainable parameters. Recent work combines LoRA with the Mixture-of-Experts (MoE), i.e., LoRA-MoE, to enhance capacity, but two limitations remain in hindering the full exploitation of its potential: 1) the influence of downstream tasks when assigning expert nu…

#TuneTuesday (June 10)
For #PrideMonth 🏳️‍🌈, I’m selecting #HeatherSmall’s song “Proud”, notably used in the American adaptation of the TV show

[2025-05-22 Thu (UTC), no new articles found for nlin.AO Adaptation and Self-Organizing Systems]
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Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning
Mahmoud Mbarak, Manmeet Singh, Naveen Sudharsan, Zong-Liang Yang
https://arxiv.org/abs/2506.12919

Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning
Accurate soil moisture prediction during extreme events remains a critical challenge for Earth system modeling, with profound implications for drought monitoring, flood forecasting, and climate adaptation strategies. While land surface models (LSMs) provide physically-based predictions, they exhibit systematic biases during extreme conditions when their parameterizations operate outside calibrated ranges. Here we present NoahMP-AI, a physics-guided deep learning framework that addresses this ch…

Non-exchangeable mean-field theory for adaptive weights: propagation of chaos and graphon sampling lemma
Datong Zhou
https://arxiv.org/abs/2506.13587 https…

Non-exchangeable mean-field theory for adaptive weights: propagation of chaos and graphon sampling lemma
We develop a mean-field theory for large, non-exchangeable particle (agent) systems where the states and interaction weights co-evolve in a coupled system of SDEs. A first main result is a generalization of the propagation of chaos. The weight adaptation in the SDEs makes the classical approach of using a static probability space as the continuum limit for the agent labels inadequate. We address this by introducing a label space endowed with a filtration that captures the stochasticity. While t…

This https://arxiv.org/abs/2505.21904 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Instance segmentation demands costly per-pixel annotations and large models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that compresses pretrained vision foundation models (VFM) into compact experts using limited labeled and abundant unlabeled data. CAST unfolds in three stages: (1) domain adaptation of the VFM teacher(s) via self-training with contrastive pixel calibration, (2) distillation into a compact student via a unified multi-objective loss that couples…

Modulating lipid membrane morphology by dynamic DNA origami networks
Juanjuan Yang, Kevin Jahnke, Ling Xin, Xinxin Jing, Pengfei Zhan, Andreas Peil, Alessandra Griffo, Marko \v{S}kugor, Donglei Yang, Sisi Fan, Kerstin G\"opfrich, Hao Yan, Pengfei Wang, Na Liu
https://arxiv.org/abs/2506.11522…

Modulating lipid membrane morphology by dynamic DNA origami networks
Membrane morphology and its dynamic adaptation regulate many cellular functions, which are often mediated by membrane proteins. Advances in DNA nanotechnology have enabled the realization of various protein-inspired structures and functions with precise control at the nanometer level, suggesting a viable tool to artificially engineer the membrane morphology. In this work, we demonstrate a DNA origami cross (DOC) structure that can be anchored onto giant unilamellar vesicles (GUVs) and subsequen…

The GAIN Model: A Nature-Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model
Gage K. R. Hooper
https://arxiv.org/abs/2506.04247

The GAIN Model: A Nature-Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model
While many neural networks focus on layers to process information, the GAIN model uses a grid-based structure to improve biological plausibility and the dynamics of the model. The grid structure helps neurons to interact with their closest neighbors and improve their connections with one another, which is seen in biological neurons. While also being implemented with the Izhikevich model this approach allows for a computationally efficient and biologically accurate simulation that can aid in the…

One and a bit episodes in, and I am completely here for that Netflix Asterix adaptation. So far, it's hitting all the right notes (and more than a few Romans. Paf! )

This https://arxiv.org/abs/2505.20089 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Homophily Enhanced Graph Domain Adaptation
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. In this paper, we highlight the significance of graph homophily, a pivotal factor for graph domain alignment, which, however, has long been overlooked in existing approaches. Specifically, our analysis first reveals that homophily discrepancies exist in benchmarks. Moreover, we also show that homophily discrepancies degrade GDA performance from bot…

Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
Xinran Li, Chenjia Bai, Zijian Li, Jiakun Zheng, Ting Xiao, Jun Zhang
https://arxiv.org/abs/2506.07232

Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environments. However, despite LLMs' advanced abilities and the sophisticated modular design of agentic methods, existing LLM-based planning algorithms remain limited by weak adaptation capabilities to multi-agent embodied scenarios. We address this limitation by introducing a framework that enables LLM agents to learn and ev…

Stereographic Projections on Some Quadric Surfaces
W. F. C. Barboza, T. F. Cruz, R. B. Leal
https://arxiv.org/abs/2506.08289 https://…

Stereographic Projections on Some Quadric Surfaces
In this work, we present an adaptation of the classical stereographic projection, originally formulated for the sphere, now considering the context of the ellipsoid and the elliptic paraboloid. We begin by constructing the stereographic projections for both quadric surfaces separately, analyzing the geometric particularities of each surface and the challenges arising from their variable curvatures and, in the case of the paraboloid, its non-compactness. In the final part of the work, we establi…

New paper alert:
Finkel & Lamm, Cultural evolution beyond the individual: what human collective knowledge adds to high fidelity copying.
This is the third in the Distributed Adaptation series.
Many thanks to the diligent reviewers! #philbio #cultevo
https://link.springer.com/article/10.1007/s10539-025-09982-4

MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
Wei Shen, Yaxiang Zhang, Minhui Huang, Mengfan Xu, Jiawei Zhang, Cong Shen
https://arxiv.org/abs/2506.01897

MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
With increasing size of large language models (LLMs), full-parameter fine-tuning imposes substantial memory demands. To alleviate this, we propose a novel memory-efficient training paradigm called Momentum Low-rank compression (MLorc). By directly compressing and reconstructing momentum rather than gradients, MLorc avoids imposing a fixed-rank constraint on weight update matrices and better preserves the training dynamics of full-parameter fine-tuning, in contrast to existing low-rank approache…

Containerized In-Storage Processing and Computing-Enabled SSD Disaggregation
Miryeong Kwon, Donghyun Gouk, Eunjee Na, Jiseon Kim, Junhee Kim, Hyein Woo, Eojin Ryu, Hyunkyu Choi, Jinwoo Baek, Hanyeoreum Bae, Mahmut Kandemir, Myoungsoo Jung
https://arxiv.org/abs/2506.06769

Containerized In-Storage Processing and Computing-Enabled SSD Disaggregation
ISP minimizes data transfer for analytics but faces challenges in adaptation and disaggregation. We propose DockerSSD, an ISP model leveraging OS-level virtualization and lightweight firmware to enable containerized data processing directly on SSDs. Key features include Ethernet over NVMe for network-based ISP management and Virtual Firmware for secure, efficient container execution. DockerSSD supports disaggregated storage pools, reducing host overhead and enhancing large-scale services like L…

Multi-Objective Covariance Matrix Adaptation MAP-Annealing
Shihan Zhao, Stefanos Nikolaidis
https://arxiv.org/abs/2505.20712 https://…

Multi-Objective Covariance Matrix Adaptation MAP-Annealing
Quality-Diversity (QD) optimization is an emerging field that focuses on finding a set of behaviorally diverse and high-quality solutions. While the quality is typically defined w.r.t. a single objective function, recent work on Multi-Objective Quality-Diversity (MOQD) extends QD optimization to simultaneously optimize multiple objective functions. This opens up multi-objective applications for QD, such as generating a diverse set of game maps that maximize difficulty, realism, or other propert…

Online-Optimized Gated Radial Basis Function Neural Network-Based Adaptive Control
Mingcong Li
https://arxiv.org/abs/2506.13168 https://

Online-Optimized Gated Radial Basis Function Neural Network-Based Adaptive Control
Real-time adaptive control of nonlinear systems with unknown dynamics and time-varying disturbances demands precise modeling and robust parameter adaptation. While existing neural network-based strategies struggle with computational inefficiency or inadequate temporal dependencies, this study proposes a hybrid control framework integrating a Temporal-Gated Radial Basis Function (TGRBF) network with a nonlinear robust controller. The TGRBF synergizes radial basis function neural networks (RBFNNs…

This https://arxiv.org/abs/2408.08968 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems
When a network slice spans multiple technology domains, it is crucial for each domain to uphold the End-to-End (E2E) Service Level Agreement (SLA) associated with the slice. Consequently, the E2E SLA must be properly decomposed into partial SLAs that are assigned to each domain involved. In a network slice management system with a two-level architecture, comprising an E2E service orchestrator and local domain controllers, we consider that the orchestrator has access only to historical data rega…

LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning
Haotian Zhang, Liu Liu, Baosheng Yu, Jiayan Qiu, Yanwei Ren, Xianglong Liu
https://arxiv.org/abs/2506.12394

LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning
The advent of parameter-efficient fine-tuning methods has significantly reduced the computational burden of adapting large-scale pretrained models to diverse downstream tasks. However, existing approaches often struggle to achieve robust performance under domain shifts while maintaining computational efficiency. To address this challenge, we propose Low-rAnk Regulated Gradient Projection (LARGO) algorithm that integrates dynamic constraints into low-rank adaptation methods. Specifically, LARGO …

Effortless adjustments with an adaptive paste #visualstudio :visualstudio:
https://devblogs.microsoft.com/visualstudio/effortless-adjust…

Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
Jarek Duda
https://arxiv.org/abs/2506.05354

Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving estimator, which estimates parameters separately for every time $t$: optimizing $F_t=\sum_{τ

Dimerization in $O(n)$-invariant quantum spin chains
J. E. Bj\"ornberg, K. Ryan
https://arxiv.org/abs/2506.06103 https://arxiv.o…

Dimerization in $O(n)$-invariant quantum spin chains
We establish dimerization in $O(n)$-invariant quantum spin chains with big enough $n$, in a large part of the phase diagram where this result is expected. This includes identifying two distinct ground states which are translations of one unit of eachother, and which both have exponentially decaying correlations. Our method relies on a probabilistic representation of the quantum system in terms of random loops, and an adaptation of a method developed for loop $O(n)$ models on the hexagonal latti…

Domain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification
Emiliano Acevedo, Mart\'in Rocamora, Magdalena Fuentes
https://arxiv.org/abs/2506.04376

Domain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification
Audio-text models are widely used in zero-shot environmental sound classification as they alleviate the need for annotated data. However, we show that their performance severely drops in the presence of background sound sources. Our analysis reveals that this degradation is primarily driven by SNR levels of background soundscapes, and independent of background type. To address this, we propose a novel method that quantifies and integrates the contribution of background sources into the classifi…

Gradient-Weighted, Data-Driven Normalization for Approximate Border Bases -- Concept and Computation
Hiroshi Kera, Achim Kehrein
https://arxiv.org/abs/2506.09529

Gradient-Weighted, Data-Driven Normalization for Approximate Border Bases -- Concept and Computation
This paper studies the concept and the computation of approximately vanishing ideals of a finite set of data points. By data points, we mean that the points contain some uncertainty, which is a key motivation for the approximate treatment. A careful review of the existing border basis concept for an exact treatment motivates a new adaptation of the border basis concept for an approximate treatment. In the study of approximately vanishing polynomials, the normalization of polynomials plays a vit…

Today is the release of #Netflix adaptation of El #Eternauta (The Eternaut). I haven't seen it yet, but I highly encourage you to do it. The Eternaut is the holy grail of Argentine comics and science fiction. Set on an alien invasion in Buenos Aires, Argentina, I can still remember the fascination the…

Warum ich bei der Klimakrise keine Hoffnung mehr habe? Weil das Wort Kapitalismus nicht mal mehr erwähnt wird.
Was soll das?
https://www.instagram.com/p/DKR8nWlRtZi/

Quarks on Instagram: "🌍🌱 Warum tun wir uns gerade mit dem Klimaschutz so schwer? Wird Klimaschutz immer mehr zum Luxus? . 2️⃣0️⃣1️⃣9️⃣ Erinnert ihr euch noch an 2019? Das Jahr war ein Wendepunkt der globalen Klimabewegung. Millionen Menschen gingen weltweit auf die Straße. Schüler:innen, Studierende, aber auch Wissenschaftler:innen (“Scientists for Future”) demonstrierten dabei für Klimaschutz, die Einhaltung des 1,5-Grad-Ziels des Pariser Abkommens und ein schnelles Ende der Nutzung fossiler Energien. Die Proteste rückten das Klimathema ins Zentrum der weltweiten Aufmerksamkeit. Doch das ist sechs Jahre her. . 🇩🇪🌱 Aktuell scheint den Menschen (in Deutschland) Klimaschutz und Nachhaltigkeit nicht mehr so wichtig, Konsument:innen haben immer seltener Schuldgefühle, wenn sie sich nicht umweltfreundlich verhalten. 2019 gaben das in einer Umfrage eines Marktforschungsunternehmens (NIQ) 30 Prozent an. Im Jahr 2024 waren es nur noch 22 Prozent. 2️⃣0️⃣2️⃣5️⃣ Immerhin: Der Nachhaltigkeitsindex zeigt seit April 2025 wieder einen leichten Aufwärtstrend, liegt aber weit unter den Werten von 2019. Die Menschen seien pragmatischer geworden, so die Umfrage: Klimaschutz dürfe nicht zu viel kosten und dürfe nicht allzu unbequem sein, finden die Konsument:innen. . Unsere Quellen, u.a.: NielsenIQ: „NIQ Consumer Life“-Langzeitstudie: So shoppen (und leben) wir anders als 2019 Osberghaus et al. (2025): The intention-behavior gap in climate change adaptation: Evidence from longitudinal survey data Ajzen I. (1985) From Intentions to Actions: A Theory of Planned Behavior"
2,385 likes, 168 comments - quarks.de on May 30, 2025: "🌍🌱 Warum tun wir uns gerade mit dem Klimaschutz so schwer? Wird Klimaschutz immer mehr zum Luxus? . 2️⃣0️⃣1️⃣9️⃣ Erinnert ihr euch noch an 2019? Das Jahr war ein Wendepunkt der globalen Klimabewegung. Millionen Menschen gingen weltweit auf die Straße. Schüler:innen, Studierende, aber auch Wissenschaftler:innen (“Scientists for Future”) demonstrierten dabei für Klimaschutz, die Einhaltung des 1,5-Grad-Ziels des …

This https://arxiv.org/abs/2410.00059 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method
Illegitimate reproduction, distribution and derivation of Deep Neural Network (DNN) models can inflict economic loss, reputation damage and even privacy infringement. Passive DNN intellectual property (IP) protection methods such as watermarking and fingerprinting attempt to prove the ownership upon IP violation, but they are often too late to stop catastrophic damage of IP abuse and too feeble against strong adversaries. In this paper, we propose IDEA, an Inverse Domain Expert Adaptation based…

[2025-05-21 Wed (UTC), no new articles found for nlin.AO Adaptation and Self-Organizing Systems]
#toXiv_bot_toot

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
Hariharan Ramesh, Jyotikrishna Dass
https://arxiv.org/abs/2506.09199

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
Integrating Low-Rank Adaptation (LoRA) into federated learning offers a promising solution for parameter-efficient fine-tuning of Large Language Models (LLMs) without sharing local data. However, several methods designed for federated LoRA present significant challenges in balancing communication efficiency, model accuracy, and computational cost, particularly among heterogeneous clients. These methods either rely on simplistic averaging of local adapters, which introduces aggregation noise, re…

Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
Yangui Fang, Jing Peng, Xu Li, Yu Xi, Chengwei Zhang, Guohui Zhong, Kai Yu
https://arxiv.org/abs/2506.05671

Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
Recent advances in automatic speech recognition (ASR) have combined speech encoders with large language models (LLMs) through projection, forming Speech LLMs with strong performance. However, adapting them to new domains remains challenging, especially in low-resource settings where paired speech-text data is scarce. We propose a text-only fine-tuning strategy for Speech LLMs using unpaired target-domain text without requiring additional audio. To preserve speech-text alignment, we introduce a …

"Degrowth: a dead end or the way out? Capital’s future scam" –
https://degrowthuk.org/2025/05/31/degrowth-a-dead-end-or-the-way-out-capitals-future-scam/
New piece in our

Degrowth: a dead end or the way out? Capital’s future scam
Aurora Despierta In the series Prospects for Degrowth This article by the Spanish writer Aurora Despierta is her adaptation (highly summarised for translation into English), for the series Prospect…

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…

This https://arxiv.org/abs/2505.17663 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States
As Large Language Models (LLMs) increasingly participate in human-AI interactions, evaluating their Theory of Mind (ToM) capabilities - particularly their ability to track dynamic mental states - becomes crucial. While existing benchmarks assess basic ToM abilities, they predominantly focus on static snapshots of mental states, overlooking the temporal evolution that characterizes real-world social interactions. We present \textsc{DynToM}, a novel benchmark specifically designed to evaluate LLM…

Multi-Objective Covariance Matrix Adaptation MAP-Annealing
Shihan Zhao, Stefanos Nikolaidis
https://arxiv.org/abs/2505.20712 https://…

Multi-Objective Covariance Matrix Adaptation MAP-Annealing
Quality-Diversity (QD) optimization is an emerging field that focuses on finding a set of behaviorally diverse and high-quality solutions. While the quality is typically defined w.r.t. a single objective function, recent work on Multi-Objective Quality-Diversity (MOQD) extends QD optimization to simultaneously optimize multiple objective functions. This opens up multi-objective applications for QD, such as generating a diverse set of game maps that maximize difficulty, realism, or other propert…

DS-TTS: Zero-Shot Speaker Style Adaptation from Voice Clips via Dynamic Dual-Style Feature Modulation
Ming Meng, Ziyi Yang, Jian Yang, Zhenjie Su, Yonggui Zhu, Zhaoxin Fan
https://arxiv.org/abs/2506.01020

DS-TTS: Zero-Shot Speaker Style Adaptation from Voice Clips via Dynamic Dual-Style Feature Modulation
Recent advancements in text-to-speech (TTS) technology have increased demand for personalized audio synthesis. Zero-shot voice cloning, a specialized TTS task, aims to synthesize a target speaker's voice using only a single audio sample and arbitrary text, without prior exposure to the speaker during training. This process employs pattern recognition techniques to analyze and replicate the speaker's unique vocal features. Despite progress, challenges remain in adapting to the vocal style of uns…

This https://arxiv.org/abs/2409.12949 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A Learning-based Quadcopter Controller with Extreme Adaptation
This paper introduces a learning-based low-level controller for quadcopters, which adaptively controls quadcopters with significant variations in mass, size, and actuator capabilities. Our approach leverages a combination of imitation learning and reinforcement learning, creating a fast-adapting and general control framework for quadcopters that eliminates the need for precise model estimation or manual tuning. The controller estimates a latent representation of the vehicle's system parameters …

This https://arxiv.org/abs/2506.00605 has been replaced.
link: https://scholar.google.com/scholar?q=a

ABCDEFGH: An Adaptation-Based Convolutional Neural Network-CycleGAN Disease-Courses Evolution Framework Using Generative Models in Health Education
With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy concerns and a shortage of educational resources (Balogh et al., 2015). In this context, image data generated by machine learning models, partic…

Fodor and Pylyshyn's Legacy - Still No Human-like Systematic Compositionality in Neural Networks
Tim Woydt, Moritz Willig, Antonia W\"ust, Lukas Helff, Wolfgang Stammer, Constantin A. Rothkopf, Kristian Kersting
https://arxiv.org/abs/2506.01820

Fodor and Pylyshyn's Legacy - Still No Human-like Systematic Compositionality in Neural Networks
Strong meta-learning capabilities for systematic compositionality are emerging as an important skill for navigating the complex and changing tasks of today's world. However, in presenting models for robust adaptation to novel environments, it is important to refrain from making unsupported claims about the performance of meta-learning systems that ultimately do not stand up to scrutiny. While Fodor and Pylyshyn famously posited that neural networks inherently lack this capacity as they are unab…

This https://arxiv.org/abs/2412.09840 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

LAVA: Lifetime-Aware VM Allocation with Learned Distributions and Adaptation to Mispredictions
Scheduling virtual machines (VMs) on hosts in cloud data centers dictates efficiency and is an NP-hard problem with incomplete information. Prior work improved VM scheduling with predicted VM lifetimes. Our work further improves lifetime-aware scheduling using repredictions with lifetime distributions versus one-shot prediction. Our approach repredicts and adjusts VM and host lifetimes when incorrect predictions emerge. We also present novel approaches for defragmentation and regular system mai…

AI Agent Behavioral Science
Lin Chen, Yunke Zhang, Jie Feng, Haoye Chai, Honglin Zhang, Bingbing Fan, Yibo Ma, Shiyuan Zhang, Nian Li, Tianhui Liu, Nicholas Sukiennik, Keyu Zhao, Yu Li, Ziyi Liu, Fengli Xu, Yong Li
https://arxiv.org/abs/2506.06366

AI Agent Behavioral Science
Recent advances in large language models (LLMs) have enabled AI systems to behave in increasingly human-like ways, exhibiting planning, adaptation, and social dynamics across increasingly diverse, interactive, and open-ended scenarios. These behaviors are not solely the product of the models' internal architecture, but emerge from their integration into agentic systems that operate within situated contexts, where goals, feedback, and interactions shape behavior over time. This shift calls for a…

Warum ich bei der Klimakrise keine Hoffnung mehr habe? Weil das Wort Kapitalismus nicht mal mehr erwähnt wird.
Was soll das?
https://www.instagram.com/p/DKR8nWlRtZi/

Quarks on Instagram: "🌍🌱 Warum tun wir uns gerade mit dem Klimaschutz so schwer? Wird Klimaschutz immer mehr zum Luxus? . 2️⃣0️⃣1️⃣9️⃣ Erinnert ihr euch noch an 2019? Das Jahr war ein Wendepunkt der globalen Klimabewegung. Millionen Menschen gingen weltweit auf die Straße. Schüler:innen, Studierende, aber auch Wissenschaftler:innen (“Scientists for Future”) demonstrierten dabei für Klimaschutz, die Einhaltung des 1,5-Grad-Ziels des Pariser Abkommens und ein schnelles Ende der Nutzung fossiler Energien. Die Proteste rückten das Klimathema ins Zentrum der weltweiten Aufmerksamkeit. Doch das ist sechs Jahre her. . 🇩🇪🌱 Aktuell scheint den Menschen (in Deutschland) Klimaschutz und Nachhaltigkeit nicht mehr so wichtig, Konsument:innen haben immer seltener Schuldgefühle, wenn sie sich nicht umweltfreundlich verhalten. 2019 gaben das in einer Umfrage eines Marktforschungsunternehmens (NIQ) 30 Prozent an. Im Jahr 2024 waren es nur noch 22 Prozent. 2️⃣0️⃣2️⃣5️⃣ Immerhin: Der Nachhaltigkeitsindex zeigt seit April 2025 wieder einen leichten Aufwärtstrend, liegt aber weit unter den Werten von 2019. Die Menschen seien pragmatischer geworden, so die Umfrage: Klimaschutz dürfe nicht zu viel kosten und dürfe nicht allzu unbequem sein, finden die Konsument:innen. . Unsere Quellen, u.a.: NielsenIQ: „NIQ Consumer Life“-Langzeitstudie: So shoppen (und leben) wir anders als 2019 Osberghaus et al. (2025): The intention-behavior gap in climate change adaptation: Evidence from longitudinal survey data Ajzen I. (1985) From Intentions to Actions: A Theory of Planned Behavior"
2,385 likes, 168 comments - quarks.de on May 30, 2025: "🌍🌱 Warum tun wir uns gerade mit dem Klimaschutz so schwer? Wird Klimaschutz immer mehr zum Luxus? . 2️⃣0️⃣1️⃣9️⃣ Erinnert ihr euch noch an 2019? Das Jahr war ein Wendepunkt der globalen Klimabewegung. Millionen Menschen gingen weltweit auf die Straße. Schüler:innen, Studierende, aber auch Wissenschaftler:innen (“Scientists for Future”) demonstrierten dabei für Klimaschutz, die Einhaltung des 1,5-Grad-Ziels des …

This https://arxiv.org/abs/2506.06105 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Text-to-LoRA: Instant Transformer Adaption
While Foundation Models provide a general tool for rapid content creation, they regularly require task-specific adaptation. Traditionally, this exercise involves careful curation of datasets and repeated fine-tuning of the underlying model. Fine-tuning techniques enable practitioners to adapt foundation models for many new applications but require expensive and lengthy training while being notably sensitive to hyper-parameter choices. To overcome these limitations, we introduce Text-to-LoRA (T2…

This https://arxiv.org/abs/2505.19912 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation
We present Adjacent Possible Exploration (APE), a selective fine-tuning method for adapting large language models that systematically explores parameter modifications while maintaining model stability. Inspired by evolutionary optimization principles, APE evaluates multiple candidate parameter updates through fine-tuning on small data subsets and accepts only those exceeding a performance threshold. Unlike standard fine-tuning that follows single gradient directions, APE implements a filtered s…

Replaced article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
- Inverted Inference and Recursive Bootstrapping: A Primal-Dual Theory of Structured Cognition
Xin Li

Beware diluted, fake, scam "degrowth". Insist on the real thing.
New piece in our #ProspectsForDegrowth series.
"Degrowth: a dead end or the way out? Capital’s future scam" – #degrowthUK

Degrowth: a dead end or the way out? Capital’s future scam
Aurora Despierta In the series Prospects for Degrowth This article by the Spanish writer Aurora Despierta is her adaptation (highly summarised for translation into English), for the series Prospect…

Enhanced V2X Communication Using Game-Theory Based Adaptive MAC Protocols
Dhrumil Bhatt, Nirbhay Singhal
https://arxiv.org/abs/2506.09817 https://

Enhanced V2X Communication Using Game-Theory Based Adaptive MAC Protocols
This paper presents an enhanced Vehicle-to-Everything (V2X) communication system featuring adaptive Medium Access Control (MAC) using game theory. Our approach integrates dynamic transmission power control, dynamic beacon rates, contention window adaptation, and implicit acknowledgment mechanisms within a Manhattan-like grid-based mobility scenario. Simulations are conducted in a circular coverage area, incorporating refined signal propagation models and probabilistic vehicle mobility with boun…

Hybrid Wave-wind System Power Optimisation Using Effective Ensemble Covariance Matrix Adaptation Evolutionary Algorithm
Mehdi Neshat, Nataliia Y. Sergiienko, Leandro S. P. da Silva, Seyedali Mirjalili, Amir H. Gandomi, Ossama Abdelkhalik, John Boland
https://arxiv.org/abs/2505.20720

Hybrid Wave-wind System Power Optimisation Using Effective Ensemble Covariance Matrix Adaptation Evolutionary Algorithm
Floating hybrid wind-wave systems combine offshore wind platforms with wave energy converters (WECs) to create cost-effective and reliable energy solutions. Adequately designed and tuned WECs are essential to avoid unwanted loads disrupting turbine motion while efficiently harvesting wave energy. These systems diversify energy sources, enhancing energy security and reducing supply risks while providing a more consistent power output by smoothing energy production variability. However, optimisin…

[2025-05-20 Tue (UTC), no new articles found for nlin.AO Adaptation and Self-Organizing Systems]
#toXiv_bot_toot

Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
Neta Glazer, David Chernin, Idan Achituve, Sharon Gannot, Ethan Fetaya
https://arxiv.org/abs/2505.23619

Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
Recent advancements in Text-to-Speech (TTS) models, particularly in voice cloning, have intensified the demand for adaptable and efficient deepfake detection methods. As TTS systems continue to evolve, detection models must be able to efficiently adapt to previously unseen generation models with minimal data. This paper introduces ADD-GP, a few-shot adaptive framework based on a Gaussian Process (GP) classifier for Audio Deepfake Detection (ADD). We show how the combination of a powerful deep e…

On-board Mission Replanning for Adaptive Cooperative Multi-Robot Systems
Elim Kwan, Rehman Qureshi, Liam Fletcher, Colin Laganier, Victoria Nockles, Richard Walters
https://arxiv.org/abs/2506.06094

On-board Mission Replanning for Adaptive Cooperative Multi-Robot Systems
Cooperative autonomous robotic systems have significant potential for executing complex multi-task missions across space, air, ground, and maritime domains. But they commonly operate in remote, dynamic and hazardous environments, requiring rapid in-mission adaptation without reliance on fragile or slow communication links to centralised compute. Fast, on-board replanning algorithms are therefore needed to enhance resilience. Reinforcement Learning shows strong promise for efficiently solving mi…

AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill Diversification
Geonwoo Cho, Jaemoon Lee, Jaegyun Im, Subi Lee, Jihwan Lee, Sundong Kim
https://arxiv.org/abs/2506.05980

AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill Diversification
Skill-based reinforcement learning (SBRL) enables rapid adaptation in environments with sparse rewards by pretraining a skill-conditioned policy. Effective skill learning requires jointly maximizing both exploration and skill diversity. However, existing methods often face challenges in simultaneously optimizing for these two conflicting objectives. In this work, we propose a new method, Adaptive Multi-objective Projection for balancing Exploration and skill Diversification (AMPED), which expli…

[2025-06-20 Fri (UTC), no new articles found for nlin.AO Adaptation and Self-Organizing Systems]
toXiv_bot_toot

Beware diluted, fake, scam "degrowth".  Insist on the real thing.
New piece in our #ProspectsForDegrowth series.
"Degrowth: a dead end or the way out? Capital’s future scam" – #degrowthUK

Degrowth: a dead end or the way out? Capital’s future scam
Aurora Despierta In the series Prospects for Degrowth This article by the Spanish writer Aurora Despierta is her adaptation (highly summarised for translation into English), for the series Prospect…

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
Moshi Wei, Sparks Li
https://arxiv.org/abs/2506.09335 https://…

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
The Intelligent System of Emergent Knowledge (ISEK) establishes a decentralized network where human and artificial intelligence agents collaborate as peers, forming a self-organizing cognitive ecosystem. Built on Web3 infrastructure, ISEK combines three fundamental principles: (1) a decentralized multi-agent architecture resistant to censorship, (2) symbiotic AI-human collaboration with equal participation rights, and (3) resilient self-adaptation through distributed consensus mechanisms. The…

This https://arxiv.org/abs/2505.00849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

TherMod Communication: Low Power or Hot Air?
The Kirchhoff-Law-Johnson-Noise (KLJN) secure key exchange scheme leverages statistical physics to enable secure communication with zero average power flow in a wired channel. While the original KLJN scheme requires significant power for operation, a recent wireless modification, TherMod, proposed by Basar claims a "low power" implementation. This paper critically examines this claim. We explain that the additional components inherent in Basar's wireless adaptation substantially increase power …

This https://arxiv.org/abs/2506.01921 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

MedEBench: Revisiting Text-instructed Image Editing
Text-guided image editing has seen rapid progress in natural image domains, but its adaptation to medical imaging remains limited and lacks standardized evaluation. Clinically, such editing holds promise for simulating surgical outcomes, creating personalized teaching materials, and enhancing patient communication. To bridge this gap, we introduce \textbf{MedEBench}, a comprehensive benchmark for evaluating text-guided medical image editing. It consists of 1,182 clinically sourced image-prompt …

Hybrid Wave-wind System Power Optimisation Using Effective Ensemble Covariance Matrix Adaptation Evolutionary Algorithm
Mehdi Neshat, Nataliia Y. Sergiienko, Leandro S. P. da Silva, Seyedali Mirjalili, Amir H. Gandomi, Ossama Abdelkhalik, John Boland
https://arxiv.org/abs/2505.20720

Hybrid Wave-wind System Power Optimisation Using Effective Ensemble Covariance Matrix Adaptation Evolutionary Algorithm
Floating hybrid wind-wave systems combine offshore wind platforms with wave energy converters (WECs) to create cost-effective and reliable energy solutions. Adequately designed and tuned WECs are essential to avoid unwanted loads disrupting turbine motion while efficiently harvesting wave energy. These systems diversify energy sources, enhancing energy security and reducing supply risks while providing a more consistent power output by smoothing energy production variability. However, optimisin…

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Axel TahmasebiMoradi, Vincent Ren, Benjamin Le-Creurer, Chetra Mang
https://arxiv.org/abs/2506.08033

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Aiming to reduce the computational cost of numerical simulations, a convolutional neural network (CNN) and a multi-layer perceptron (MLP) are introduced to build a surrogate model to approximate radiative heat transfer solutions in a 2-D walled domain with participative gases. The originality of this work lays in the adaptation of the inputs of the problem (gas and wall properties) in order to fit with the CNN architecture, more commonly used for image processing. Two precision datasets have be…

Text-to-LoRA: Instant Transformer Adaption
Rujikorn Charakorn, Edoardo Cetin, Yujin Tang, Robert Tjarko Lange
https://arxiv.org/abs/2506.06105 https://

Text-to-LoRA: Instant Transformer Adaption
While Foundation Models provide a general tool for rapid content creation, they regularly require task-specific adaptation. Traditionally, this exercise involves careful curation of datasets and repeated fine-tuning of the underlying model. Fine-tuning techniques enable practitioners to adapt foundation models for many new applications but require expensive and lengthy training while being notably sensitive to hyper-parameter choices. To overcome these limitations, we introduce Text-to-LoRA (T2…

This https://arxiv.org/abs/2411.08832 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Offline Adaptation of Quadruped Locomotion using Diffusion Models
We present a diffusion-based approach to quadrupedal locomotion that simultaneously addresses the limitations of learning and interpolating between multiple skills and of (modes) offline adapting to new locomotion behaviours after training. This is the first framework to apply classifier-free guided diffusion to quadruped locomotion and demonstrate its efficacy by extracting goal-conditioned behaviour from an originally unlabelled dataset. We show that these capabilities are compatible with a m…

This https://arxiv.org/abs/2505.23868 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Noise-Robustness Through Noise: Asymmetric LoRA Adaption with Poisoning Expert
Current parameter-efficient fine-tuning methods for adapting pre-trained language models to downstream tasks are susceptible to interference from noisy data. Conventional noise-handling approaches either rely on laborious data pre-processing or employ model architecture modifications prone to error accumulation. In contrast to existing noise-process paradigms, we propose a noise-robust adaptation method via asymmetric LoRA poisoning experts (LoPE), a novel framework that enhances model robustne…

[2025-06-19 Thu (UTC), no new articles found for nlin.AO Adaptation and Self-Organizing Systems]
toXiv_bot_toot

Adaptive Event-triggered Formation Control of Autonomous Vehicles
Ziming Wang, Yihuai Zhang, Chenguang Zhao, Huan Yu
https://arxiv.org/abs/2506.06746 https…

Adaptive Event-triggered Formation Control of Autonomous Vehicles
This paper presents adaptive event-triggered formation control strategies for autonomous vehicles (AVs) subject to longitudinal and lateral motion uncertainties. The proposed framework explores various vehicular formations to enable safe and efficient navigation in complex traffic scenarios, such as narrow passages, collaborative obstacle avoidance, and adaptation to cut-in maneuvers. In contrast to conventional platoon control strategies that rely on predefined communication topologies and con…

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

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

This https://arxiv.org/abs/2505.20455 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

HAND Me the Data: Fast Robot Adaptation via Hand Path Retrieval
We hand the community HAND, a simple and time-efficient method for teaching robots new manipulation tasks through human hand demonstrations. Instead of relying on task-specific robot demonstrations collected via teleoperation, HAND uses easy-to-provide hand demonstrations to retrieve relevant behaviors from task-agnostic robot play data. Using a visual tracking pipeline, HAND extracts the motion of the human hand from the hand demonstration and retrieves robot sub-trajectories in two stages: fi…

Replaced article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
Extremum Seeking Nonlinear Regulator with Concurrent Uncertainties in Exosystems and Control Dire...

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
Yujia Huo, Jianchun Liu, Hongli Xu, Zhenguo Ma, Shilong Wang, Liusheng Huang
https://arxiv.org/abs/2506.05977

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
Federated fine-tuning (FedFT) of large language models (LLMs) has emerged as a promising solution for adapting models to distributed data environments while ensuring data privacy. Existing FedFT methods predominantly utilize parameter-efficient fine-tuning (PEFT) techniques to reduce communication and computation overhead. However, they often fail to adequately address the catastrophic forgetting, a critical challenge arising from continual adaptation in distributed environments. The tradit…

[2025-06-18 Wed (UTC), no new articles found for nlin.AO Adaptation and Self-Organizing Systems]
toXiv_bot_toot

This https://arxiv.org/abs/2506.02965 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs
Mixture-of-Experts (MoE) has been gaining popularity due to its successful adaptation to large language models (LLMs). In this work, we introduce Privacy-preserving Collaborative Mixture-of-Experts (PC-MoE), which leverages the sparsity of the MoE architecture for memory-efficient decentralized collaborative LLM training, enabling multiple parties with limited GPU-memory and data resources to collectively train more capable LLMs than they could achieve individually. At the same time, this appro…

[2025-06-17 Tue (UTC), 1 new article found for nlin.AO Adaptation and Self-Organizing Systems]
toXiv_bot_toot

This https://arxiv.org/abs/2505.23378 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models
Speaker-dependent modelling can substantially improve performance in speech-based health monitoring applications. While mixed-effect models are commonly used for such speaker adaptation, they require computationally expensive retraining for each new observation, making them impractical in a production environment. We reformulate this task as a meta-learning problem and explore three approaches of increasing complexity: ensemble-based distance models, prototypical networks, and transformer-based…

[2025-06-16 Mon (UTC), 2 new articles found for nlin.AO Adaptation and Self-Organizing Systems]
toXiv_bot_toot