Tootfinder

Mitigating Cross-Image Information Leakage in LVLMs for Multi-Image Tasks
Yeji Park, Minyoung Lee, Sanghyuk Chun, Junsuk Choe
https://arxiv.org/abs/2508.13744 https://

Mitigating Cross-Image Information Leakage in LVLMs for Multi-Image Tasks
Large Vision-Language Models (LVLMs) demonstrate strong performance on single-image tasks. However, we observe that their performance degrades significantly when handling multi-image inputs. This occurs because visual cues from different images become entangled in the model's output. We refer to this phenomenon as cross-image information leakage. To address this issue, we propose FOCUS, a training-free and architecture-agnostic decoding strategy that mitigates cross-image information leakage du…

Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
Jie Shi, Arno P. J. M. Siebes, Siamak Mehrkanoon
https://arxiv.org/abs/2508.13715 https://

Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
This paper proposes a Trans-XFed architecture that combines federated learning with explainable AI techniques for supply chain credit assessment. The proposed model aims to address several key challenges, including privacy, information silos, class imbalance, non-identically and independently distributed (Non-IID) data, and model interpretability in supply chain credit assessment. We introduce a performance-based client selection strategy (PBCS) to tackle class imbalance and Non-IID problems. T…

The US is supplying weapons to Ukraine via Europe: a new strategy: https://benborges.xyz/2025/07/18/the-us-is-supplying-weapons.html

A Novel APVD Steganography Technique Incorporating Pseudorandom Pixel Selection for Robust Image Security
Mehrab Hosain, Rajiv Kapoor
https://arxiv.org/abs/2507.13367

A Novel APVD Steganography Technique Incorporating Pseudorandom Pixel Selection for Robust Image Security
Steganography is the process of embedding secret information discreetly within a carrier, ensuring secure exchange of confidential data. The Adaptive Pixel Value Differencing (APVD) steganography method, while effective, encounters certain challenges like the "unused blocks" issue. This problem can cause a decrease in security, compromise the embedding capacity, and lead to lower visual quality. This research presents a novel steganographic strategy that integrates APVD with pseudorandom pixel …

Towards Better Requirements from the Crowd: Developer Engagement with Feature Requests in Open Source Software
Pragyan K C, Rambod Ghandiparsi, Thomas Herron, John Heaps, Mitra Bokaei Hosseini
https://arxiv.org/abs/2507.13553

Towards Better Requirements from the Crowd: Developer Engagement with Feature Requests in Open Source Software
As user demands evolve, effectively incorporating feature requests is crucial for maintaining software relevance and user satisfaction. Feature requests, typically expressed in natural language, often suffer from ambiguity or incomplete information due to communication gaps or the requester's limited technical expertise. These issues can lead to misinterpretation, faulty implementation, and reduced software quality. While seeking clarification from requesters is a common strategy to mitigate th…

NASA is looking for a CISO.
https://www.usajobs.gov/job/839985100?mod=djemCybersecruityPro&tpl=cs

Chief Information Security Officer (CISO)/Senior Agency Information Security Officer (SAISO)
The incumbent serves as the Chief Information Security Officer (CISO) and Senior Agency Information Security Officer (SAISO) leading the cybersecurity and privacy strategy and program implementation to protect NASA's missions and enterprise. Responsibilities include establishing strategy, policies, programs, and frameworks for the security of classified and unclassified information and information systems at NASA. Also, manages the Cybersecurity and Privacy Division within the Office of the CIO.

Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
Amin Masoumi, Mert Korkali
https://arxiv.org/abs/2507.13265

Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
This paper introduces a framework to address the critical loss of transient stability caused by reduced inertia in grids with high inverter-based resource (IBR) penetration. The proposed method integrates a predictive deep learning (DL) model with information gap decision theory (IGDT) to create a risk-averse dispatch strategy. By reformulating the conventional virtual inertia scheduling (VIS) problem, the framework uses early predictions of post-fault dynamics to proactively redispatch resourc…

Unwavering EU stance – strategy to support and strengthen Ukraine | Wrap-Up Analytics: https://benborges.xyz/2025/07/13/unwavering-eu-stance-strategy-to.html

Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment
Adam Yang, Gustavo Penha, Enrico Palumbo, Hugues Bouchard
https://arxiv.org/abs/2507.11042

Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment
With the breakthroughs in large language models (LLMs), query generation techniques that expand documents and queries with related terms are becoming increasingly popular in the information retrieval field. Such techniques have been shown to improve the effectiveness of traditional lexical retrieval methods by dealing with the vocabulary mismatch problem. Recent work has found that generating queries with a greedy decoding strategy can produce sub-optimal queries, including hallucinations, and …

UCD: Unlearning in LLMs via Contrastive Decoding
Vinith M. Suriyakumar, Ayush Sekhari, Ashia Wilson
https://arxiv.org/abs/2506.12097 https://

UCD: Unlearning in LLMs via Contrastive Decoding
Machine unlearning aims to remove specific information, e.g. sensitive or undesirable content, from large language models (LLMs) while preserving overall performance. We propose an inference-time unlearning algorithm that uses contrastive decoding, leveraging two auxiliary smaller models, one trained without the forget set and one trained with it, to guide the outputs of the original model using their difference during inference. Our strategy substantially improves the tradeoff between unlearni…

The Russian Orthodox Church as part of Russia's war strategy – How does it work in Europe?: https://benborges.xyz/2025/08/17/the-russian-orthodox-church-as.html

High-fidelity entanglement and coherent multi-qubit mapping in an atom array
Aruku Senoo, Alexander Baumg\"artner, Joanna W. Lis, Gaurav M. Vaidya, Zhongda Zeng, Giuliano Giudici, Hannes Pichler, Adam M. Kaufman
https://arxiv.org/abs/2506.13632

High-fidelity entanglement and coherent multi-qubit mapping in an atom array
Neutral atoms in optical tweezer arrays possess broad applicability for quantum information science, in computing, simulation, and metrology. Among atomic species, Ytterbium-171 is unique as it hosts multiple qubits, each of which is impactful for these distinct applications. Consequently, this atom is an ideal candidate to bridge multiple disciplines, which, more broadly, has been an increasingly effective strategy within the field of quantum science. Realizing the full potential of this syner…

#GiffordFire
Firing operations began this afternoon.
This methodical strategy is aimed at removing dense vegetation ahead of the main fire to deprive it of fuel and help keep it contained within the Garcia Wilderness.
Ground crews initiated the operation along containment lines around the wilderness border.
Helicopters were used to ignite steep, difficult to reach terrain alon…

Watch Duty - Wildfire Maps & Alerts
Real-time information about wildfire and firefighting efforts nearby

YOLOv11-RGBT: Towards a Comprehensive Single-Stage Multispectral Object Detection Framework
Dahang Wan, Rongsheng Lu, Yang Fang, Xianli Lang, Shuangbao Shu, Jingjing Chen, Siyuan Shen, Ting Xu, Zecong Ye
https://arxiv.org/abs/2506.14696

YOLOv11-RGBT: Towards a Comprehensive Single-Stage Multispectral Object Detection Framework
Multispectral object detection, which integrates information from multiple bands, can enhance detection accuracy and environmental adaptability, holding great application potential across various fields. Although existing methods have made progress in cross-modal interaction, low-light conditions, and model lightweight, there are still challenges like the lack of a unified single-stage framework, difficulty in balancing performance and fusion strategy, and unreasonable modality weight allocatio…

Proxitaxis: an adaptive search strategy based on proximity and stochastic resetting
Giuseppe Del Vecchio Del Vecchio, Manas Kulkarni, Satya N. Majumdar, Sanjib Sabhapandit
https://arxiv.org/abs/2507.05800

Proxitaxis: an adaptive search strategy based on proximity and stochastic resetting
We introduce \emph{proxitaxis}, a simple search strategy where the searcher has only information about the distance from the target but not the direction. The strategy consists of three crucial components: (i) local adaptive moves with distance-dependent hopping rate, (ii) intermittent long range returns via stochastic resetting to a certain location $\vec{R}_0$, and (iii) an inspection move where the searcher dynamically updates the resetting position $\vec{R}_0$. We compute analytically the c…

This https://arxiv.org/abs/2409.06990 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SIS: Seam-Informed Strategy for T-shirt Unfolding
Seams are information-rich components of garments. The presence of different types of seams and their combinations helps to select grasping points for garment handling. In this paper, we propose a new Seam-Informed Strategy (SIS) for finding actions for handling a garment, such as grasping and unfolding a T-shirt. Candidates for a pair of grasping points for a dual-arm manipulator system are extracted using the proposed Seam Feature Extraction Method (SFEM). A pair of grasping points for the ro…

Surgical Knowledge Rewrite in Compact LLMs: An 'Unlearn-then-Learn' Strategy with ($IA^3$) for Localized Factual Modulation and Catastrophic Forgetting Mitigation
Stanley Ngugi
https://arxiv.org/abs/2508.07075

Surgical Knowledge Rewrite in Compact LLMs: An 'Unlearn-then-Learn' Strategy with ($IA^3$) for Localized Factual Modulation and Catastrophic Forgetting Mitigation
Large Language Models (LLMs) struggle with dynamic knowledge updates, especially when new information conflicts with deeply embedded facts. Such conflicting factual edits often lead to two critical issues: resistance to adopting the new fact and severe catastrophic forgetting of unrelated knowledge. This paper introduces and evaluates a novel "unlearn-then-learn" strategy for precise knowledge editing in LLMs, leveraging the parameter-efficient fine-tuning (PEFT) technique, Infused Adapter by I…

China’s Ukraine war strategy: Whose side is Beijing on?: https://benborges.xyz/2025/07/09/chinas-ukraine-war-strategy-whose.html

This https://arxiv.org/abs/2506.06197 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Metainformation in Quantum Guessing Games
Quantum guessing games offer a structured approach to analyzing quantum information processing, where information is encoded in quantum states and extracted through measurement. An additional aspect of this framework is the influence of partial knowledge about the input on the optimal measurement strategies. This kind of side information can significantly influence the guessing strategy and earlier work has shown that the timing of such side information, whether revealed before or after the mea…

Cognitive Load and Information Processing in Financial Markets: Theory and Evidence from Disclosure Complexity
Yimin Du, Guolin Tang
https://arxiv.org/abs/2507.07037 https://

Cognitive Load and Information Processing in Financial Markets: Theory and Evidence from Disclosure Complexity
We develop a theoretical framework for understanding how cognitive load affects information processing in financial markets and test it using exogenous variation in disclosure complexity. Our model distinguishes between attention allocation and cognitive processing capacity, showing that complex information creates differential effects across investor types. Using a comprehensive dataset of corporate disclosures and a novel identification strategy based on regulatory changes, we find that cogni…

Intuitive dissection of the Gaussian information bottleneck method with an application to optimal prediction
Vahe Galstyan, Age Tjalma, Pieter Rein ten Wolde
https://arxiv.org/abs/2507.05183

Intuitive dissection of the Gaussian information bottleneck method with an application to optimal prediction
Efficient signal representation is essential for the functioning of living and artificial systems operating under resource constraints. A widely recognized framework for deriving such representations is the information bottleneck method, which yields the optimal strategy for encoding a random variable, such as the signal, in a way that preserves maximal information about a functionally relevant variable, subject to an explicit constraint on the amount of information encoded. While in its genera…

An efficiency ordering of k-price auctions under complete information
Sumit Goel, Jeffrey Zeidel
https://arxiv.org/abs/2507.05738 https://

An efficiency ordering of k-price auctions under complete information
We study $k$-price auctions in a complete information environment and characterize all pure-strategy Nash equilibrium outcomes. In a setting with $n$ agents having ordered valuations, we show that any agent, except those with the lowest $k-2$ valuations, can win in equilibrium. As a consequence, worst-case welfare increases monotonically as we go from $k=2$ (second-price auction) to $k=n$ (lowest-price auction), with the first-price auction achieving the highest worst-case welfare.

Information Bulletin Strategy in Impatient Queuing
Anthony Kiggundu, Bin Han, Hans D. Schotten
https://arxiv.org/abs/2508.04241 https://arxiv.org/pdf/2508.…

Information Bulletin Strategy in Impatient Queuing
In Sixth Generation (6G) networks, decentralized control in multi-tenant systems is a suggested enabler for autonomous network operations. However, autonomy requires independent rationale decisions be taken by tenants. This rationality can only be underpinned by timely and continuous access to status information. Despite its importance, the questions of what information should be shared, how much should be communicated, and how frequently updates should be dispatched remain open research challe…

(PDF) Guiding sightless people one dimension at a time: how the strategy used in spontaneous voice instructions benefits sonification-based guidance https://hal.science/hal-05165934v1 On sensory substitution for the blind

A variable dimension sketching strategy for nonlinear least-squares
Stefania Bellavia, Greta Malaspina, Benedetta Morini
https://arxiv.org/abs/2506.03965 h…

A variable dimension sketching strategy for nonlinear least-squares
We present a stochastic inexact Gauss-Newton method for the solution of nonlinear least-squares. To reduce the computational cost with respect to the classical method, at each iteration the proposed algorithm approximately minimizes the local model on a random subspace. The dimension of the subspace varies along the iterations, and two strategies are considered for its update: the first is based solely on the Armijo condition, the latter is based on information from the true Gauss-Newton model.…

Last-Iterate Convergence in Adaptive Regret Minimization for Approximate Extensive-Form Perfect Equilibrium
Hang Ren, Xiaozhen Sun, Tianzi Ma, Jiajia Zhang, Xuan Wang
https://arxiv.org/abs/2508.07699

Last-Iterate Convergence in Adaptive Regret Minimization for Approximate Extensive-Form Perfect Equilibrium
The Nash Equilibrium (NE) assumes rational play in imperfect-information Extensive-Form Games (EFGs) but fails to ensure optimal strategies for off-equilibrium branches of the game tree, potentially leading to suboptimal outcomes in practical settings. To address this, the Extensive-Form Perfect Equilibrium (EFPE), a refinement of NE, introduces controlled perturbations to model potential player errors. However, existing EFPE-finding algorithms, which typically rely on average strategy converge…

A Kalman-smoother based data imputation strategy to data gaps in spaceborne gravitational wave detectors
Tingyang Shen, He Wang, Jibo He
https://arxiv.org/abs/2507.02458

A Kalman-smoother based data imputation strategy to data gaps in spaceborne gravitational wave detectors
Massive black hole binaries (MBHBs) and other sources within the frequency band of spaceborne gravitational wave observatories like the Laser Interferometer Space Antenna (LISA), Taiji and Tianqin pose unique challenges, as gaps and glitches during the years-long observation lead to both loss of information and spectral leakage. We propose a novel data imputation strategy based on Kalman filter and smoother to mitigate gap-induced biases in parameter estimation. Applied to a scenario where trad…

Conditional Residual Coding with Explicit-Implicit Temporal Buffering for Learned Video Compression
Yi-Hsin Chen, Kuan-Wei Ho, Martin Benjak, J\"orn Ostermann, Wen-Hsiao Peng
https://arxiv.org/abs/2508.01818

Conditional Residual Coding with Explicit-Implicit Temporal Buffering for Learned Video Compression
This work proposes a hybrid, explicit-implicit temporal buffering scheme for conditional residual video coding. Recent conditional coding methods propagate implicit temporal information for inter-frame coding, demonstrating superior coding performance to those relying exclusively on previously decoded frames (i.e. the explicit temporal information). However, these methods require substantial memory to store a large number of implicit features. This work presents a hybrid buffering strategy. For…

Optimizing information transmission in the canonical Wnt pathway
Olivier Witteveen, Samuel J. Rosen, Ryan S. Lach, Maxwell Z. Wilson, Marianne Bauer
https://arxiv.org/abs/2506.22633

Optimizing information transmission in the canonical Wnt pathway
Populations of cells regulate gene expression in response to external signals, but their ability to make reliable collective decisions is limited by both intrinsic noise in molecular signaling and variability between individual cells. In this work, we use optogenetic control of the canonical Wnt pathway as an example to study how reliably information about an external signal is transmitted to a population of cells, and determine an optimal encoding strategy to maximize information transmission …

Chapter 11 Students' interaction with and appreciation of automated informative tutoring feedback
Gerben van der Hoek, Bastiaan Heeren, Rogier Bos, Paul Drijvers, Johan Jeuring
https://arxiv.org/abs/2507.15650

Chapter 11 Students' interaction with and appreciation of automated informative tutoring feedback
Computer aided formative assessment can be used to enhance a learning process, for instance by providing feedback. There are many design choices for delivering feedback, that lead to a feedback strategy. In an informative feedback strategy, students do not immediately receive information about the correct response, but are offered the opportunity to retry a task to apply feedback information. In this small-scale qualitative study, we explore an informative feedback strategy designed to offer a …

Higher-Order Uncoupled Learning Dynamics and Nash Equilibrium
Sarah A. Toonsi, Jeff S. Shamma
https://arxiv.org/abs/2506.10874 https://

Higher-Order Uncoupled Learning Dynamics and Nash Equilibrium
We study learnability of mixed-strategy Nash Equilibrium (NE) in general finite games using higher-order replicator dynamics as well as classes of higher-order uncoupled heterogeneous dynamics. In higher-order uncoupled learning dynamics, players have no access to utilities of opponents (uncoupled) but are allowed to use auxiliary states to further process information (higher-order). We establish a link between uncoupled learning and feedback stabilization with decentralized control. Using this…

Metainformation in Quantum Guessing Games
Teiko Heinosaari, Hanwool Lee
https://arxiv.org/abs/2506.06197 https://arxiv.org/pdf/2506.0…

Metainformation in Quantum Guessing Games
Quantum guessing games offer a structured approach to analyzing quantum information processing, where information is encoded in quantum states and extracted through measurement. An additional aspect of this framework is the influence of partial knowledge about the input on the optimal measurement strategies. This kind of side information can significantly influence the guessing strategy and earlier work has shown that the timing of such side information, whether revealed before or after the mea…

Trump reportedly asked Zelensky if Ukraine could strike Moscow: https://benborges.xyz/2025/07/15/trump-reportedly-asked-zelensky-if.html

A Multimessenger Strategy for Downselecting the Orientations of Galactic Close White Dwarf Binaries
Naoki Seto
https://arxiv.org/abs/2507.01250 https://

A Multimessenger Strategy for Downselecting the Orientations of Galactic Close White Dwarf Binaries
The planned space-based gravitational wave detector, LISA, will provide a fundamentally new means of studying the orbital alignment of close white dwarf binaries. However, due to the inherent symmetry of their gravitational wave signals, a fourfold degeneracy arises in the transverse projections of their angular momentum vectors. In this paper, we demonstrate that by incorporating timing information from electromagnetic observations, such as radial velocity modulations and light curves, this de…

Learn Like Feynman: Developing and Testing an AI-Driven Feynman Bot
Akshaya Rajesh, Sumbul Khan
https://arxiv.org/abs/2506.09055 https://

Learn Like Feynman: Developing and Testing an AI-Driven Feynman Bot
The Feynman learning technique is an active learning strategy that helps learners simplify complex information through student-led teaching and discussion. In this paper, we present the development and usability testing of the Feynman Bot, which uses the Feynman technique to assist self-regulated learners who lack peer or instructor support. The Bot embodies the Feynman learning technique by encouraging learners to discuss their lecture material in a question-answer-driven discussion format. Th…

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models
Elena Sofia Ruzzetti, Giancarlo A. Xompero, Davide Venditti, Fabio Massimo Zanzotto
https://arxiv.org/abs/2506.10024

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models
Large Language Models (LLMs) memorize, and thus, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information (PII), which should not be stored and, consequently, not leaked. In this paper, we introduce Private Memorization Editing (PME), an approach for preventing private data leakage that turns an apparent limitation, that is, the LLMs' memorization ability, into a powerful privacy defense strategy. While attacks against LLMs have been performed exploiting previou…

HRIPBench: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs
Kaixuan Wang, Chenxin Diao, Jason T. Jacques, Zhongliang Guo, Shuai Zhao
https://arxiv.org/abs/2507.21815

HRIPBench: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs
Millions of individuals' well-being are challenged by the harms of substance use. Harm reduction as a public health strategy is designed to improve their health outcomes and reduce safety risks. Some large language models (LLMs) have demonstrated a decent level of medical knowledge, promising to address the information needs of people who use drugs (PWUD). However, their performance in relevant tasks remains largely unexplored. We introduce HRIPBench, a benchmark designed to evaluate LLM's accu…

CSI Obfuscation: Single-Antenna Transmitters Can Not Hide from Adversarial Multi-Antenna Radio Localization Systems
Phillip Stephan, Florian Euchner, Stephan ten Brink
https://arxiv.org/abs/2508.02553 …

CSI Obfuscation: Single-Antenna Transmitters Can Not Hide from Adversarial Multi-Antenna Radio Localization Systems
The ability of modern telecommunication systems to locate users and objects in the radio environment raises justified privacy concerns. To prevent unauthorized localization, single-antenna transmitters can obfuscate the signal by convolving it with a randomized sequence prior to transmission, which alters the channel state information (CSI) estimated at the receiver. However, this strategy is only effective against CSI-based localization systems deploying single-antenna receivers. Inspired by t…

Formal Bayesian Transfer Learning via the Total Risk Prior
Nathan Wycoff, Ali Arab, Lisa O. Singh
https://arxiv.org/abs/2507.23768 https://arxiv.org/pdf/25…

Formal Bayesian Transfer Learning via the Total Risk Prior
In analyses with severe data-limitations, augmenting the target dataset with information from ancillary datasets in the application domain, called source datasets, can lead to significantly improved statistical procedures. However, existing methods for this transfer learning struggle to deal with situations where the source datasets are also limited and not guaranteed to be well-aligned with the target dataset. A typical strategy is to use the empirical loss minimizer on the source data as a pr…

Quantum Advantage in Storage and Retrieval of Isometry Channels
Satoshi Yoshida, Jisho Miyazaki, Mio Murao
https://arxiv.org/abs/2507.10784 https://…

Quantum Advantage in Storage and Retrieval of Isometry Channels
Storage and retrieval refer to the task of encoding a quantum channel $Λ$ into a quantum state, known as the program state, such that the channel can later be retrieved. This task is closely related to quantum channel estimation, where multiple queries to $Λ$ are used to prepare a quantum state $ϕ_Λ$ that encodes information about the channel. The channel can then be retrieved by measuring $ϕ_Λ$, following a measure-and-prepare strategy. In this work, we analyze the asymptotic performance…

This https://arxiv.org/abs/2505.11292 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Status of the International Linear Collider
This paper is not a proposal for a CERN future project but provides information on the International Linear Collider (ILC) considered for Japan in order to facilitate the European Strategy discussion in a global context. It describes progress to date, ongoing engineering studies, updated cost estimate for the machine at $\sqrt{s}=250~\rm GeV$ and the situation in Japan. The physics of the ILC is not presented here, but jointly for all Linear Collider projects in a separate document ``A Linear C…

Reservoir Computing with Evolved Critical Neural Cellular Automata
Sidney Pontes-Filho, Stefano Nichele, Mikkel Lepper{\o}d
https://arxiv.org/abs/2508.02218 https://

Reservoir Computing with Evolved Critical Neural Cellular Automata
Criticality is a behavioral state in dynamical systems that is known to present the highest computation capabilities, i.e., information transmission, storage, and modification. Therefore, such systems are ideal candidates as a substrate for reservoir computing, a subfield in artificial intelligence. Our choice of a substrate is a cellular automaton (CA) governed by an artificial neural network, also known as neural cellular automaton (NCA). We apply evolution strategy to optimize the NCA to ach…

Information and fitness in two-state systems: self-replicating individuals in a fluctuating environment
Poulami Chatterjee, Cesar Nieto, Juan Manuel Pedraza, Abhyudai Singh
https://arxiv.org/abs/2508.00150

Information and fitness in two-state systems: self-replicating individuals in a fluctuating environment
A population of individuals with the same genes can present heterogeneous traits (phenotypes). The prevalence of this heterogeneity can be explained as a bet-hedging strategy that improves the population proliferation rate (fitness) in fluctuating environments. The phenotype distribution is influenced by factors such as competition between phenotypes, the duration of environmental states, and the rate of phenotype-switching. We illustrate these effects in a system where both the environment and…

Quantize-Sample-and-Verify: LLM Acceleration via Adaptive Edge-Cloud Speculative Decoding
Guangyi Zhang, Yunlong Cai, Guanding Yu, Petar Popovski, Osvaldo Simeone
https://arxiv.org/abs/2507.00605

Quantize-Sample-and-Verify: LLM Acceleration via Adaptive Edge-Cloud Speculative Decoding
In edge-cloud speculative decoding (SD), edge devices equipped with small language models (SLMs) generate draft tokens that are verified by large language models (LLMs) in the cloud. A key bottleneck in such systems is the limited communication bandwidth between edge and cloud, which necessitates quantization of the information transmitted about generated tokens. In this work, we introduce a novel quantize-sample (Q-S) strategy that provably preserves the output distribution of the cloud-based …

Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting
Nikita Sukhorukov, Danil Gusak, Evgeny Frolov
https://arxiv.org/abs/2508.06455

Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting
Cold-start challenges in recommender systems necessitate leveraging auxiliary features beyond user-item interactions. However, the presence of irrelevant or noisy features can degrade predictive performance, whereas an excessive number of features increases computational demands, leading to higher memory consumption and prolonged training times. To address this, we propose a feature selection strategy that prioritizes the user behavioral information. Our method enhances the feature representa…

Two-Stage Lithium Niobate Nonlinear Photonic Circuits for Low-Crosstalk and Broadband All Optical Wavelength Conversion
Xiaoting Li, Haochuan Li, Zhaoxi Chen, Fei Ma, Ke Zhang, Wenzhao Sun, Cheng Wang
https://arxiv.org/abs/2508.01777

Two-Stage Lithium Niobate Nonlinear Photonic Circuits for Low-Crosstalk and Broadband All Optical Wavelength Conversion
All optical wavelength converters (AOWCs) that can effectively and flexibly switch optical signals between different wavelength channels are essential elements in future optical fiber communications and quantum information systems. A promising strategy for achieving high-performance AOWCs is to leverage strong three-wave mixing processes in second-order nonlinear nanophotonic devices, specifically thin-film periodically poled lithium niobate (TF-PPLN) waveguides. By exploiting the advantages of…

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Mingkai Jia, Wei Yin, Xiaotao Hu, Jiaxin Guo, Xiaoyang Guo, Qian Zhang, Xiao-Xiao Long, Ping Tan
https://arxiv.org/abs/2507.07997

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Vector Quantized Variational Autoencoders (VQ-VAEs) are fundamental models that compress continuous visual data into discrete tokens. Existing methods have tried to improve the quantization strategy for better reconstruction quality, however, there still exists a large gap between VQ-VAEs and VAEs. To narrow this gap, we propose \NickName, a novel method to augment the representation capability of discrete codebooks, facilitating easier optimization for codebooks and minimizing information loss…

MeshLLM: Empowering Large Language Models to Progressively Understand and Generate 3D Mesh
Shuangkang Fang, I-Chao Shen, Yufeng Wang, Yi-Hsuan Tsai, Yi Yang, Shuchang Zhou, Wenrui Ding, Takeo Igarashi, Ming-Hsuan Yang
https://arxiv.org/abs/2508.01242

MeshLLM: Empowering Large Language Models to Progressively Understand and Generate 3D Mesh
We present MeshLLM, a novel framework that leverages large language models (LLMs) to understand and generate text-serialized 3D meshes. Our approach addresses key limitations in existing methods, including the limited dataset scale when catering to LLMs' token length and the loss of 3D structural information during mesh serialization. We introduce a Primitive-Mesh decomposition strategy, which divides 3D meshes into structurally meaningful subunits. This enables the creation of a large-scale da…

Strategy Evolution in the Adoption of Conservation Tillage Technology under Time Preference Heterogeneity and Lemon Market: Insights from Evolutionary Dynamics
Dingyi Wang, Ruqiang Guo, Qian Lu
https://arxiv.org/abs/2507.15497

Strategy Evolution in the Adoption of Conservation Tillage Technology under Time Preference Heterogeneity and Lemon Market: Insights from Evolutionary Dynamics
The promotion of Conservation Tillage Technology (CTT) is critical for mitigating global soil degradation, yet their actual adoption rates remain substantially lower than anticipated targets. Existing research predominantly focuses on static factor analyses, failing to adequately capture the dynamic evolutionary mechanisms of farmer strategic interactions and the impacts of information asymmetries in agricultural markets. This study constructs an evolutionary game model integrating heterogeneou…

REF-VC: Robust, Expressive and Fast Zero-Shot Voice Conversion with Diffusion Transformers
Yuepeng Jiang, Ziqian Ning, Shuai Wang, Chengjia Wang, Mengxiao Bi, Pengcheng Zhu, Lei Xie, Zhonghua Fu
https://arxiv.org/abs/2508.04996

REF-VC: Robust, Expressive and Fast Zero-Shot Voice Conversion with Diffusion Transformers
In real-world voice conversion applications, environmental noise in source speech and user demands for expressive output pose critical challenges. Traditional ASR-based methods ensure noise robustness but suppress prosody, while SSL-based models improve expressiveness but suffer from timbre leakage and noise sensitivity. This paper proposes REF-VC, a noise-robust expressive voice conversion system. Key innovations include: (1) A random erasing strategy to mitigate the information redundancy inh…

Optimal Messaging Strategy for Incentivizing Agents in Dynamic Systems
Renyan Sun, Ashutosh Nayyar
https://arxiv.org/abs/2508.00188 https://arxiv.org/pdf/2…

Optimal Messaging Strategy for Incentivizing Agents in Dynamic Systems
We consider a finite-horizon discrete-time dynamic system jointly controlled by a designer and one or more agents, where the designer can influence the agents' actions through selective information disclosure. At each time step, the designer sends a message to the agent(s) from a prespecified message space. The designer may also take an action that directly influences system dynamics and rewards. Each agent uses its received message (and its own information) to choose its action. We are interes…

Boosting Ensembles for Statistics of Tails at Conditionally Optimal Advance Split Times
Justin Finkel, Paul A. O'Gorman
https://arxiv.org/abs/2507.22310 https://

Boosting Ensembles for Statistics of Tails at Conditionally Optimal Advance Split Times
Climate science needs more efficient ways to study high-impact, low-probability extreme events, which are rare by definition and costly to simulate in large numbers. Rare event sampling (RES) and ensemble boosting offer a novel strategy to extract more information from those occasional simulated events: small perturbations in advance can turn a moderate event into a severe one, which otherwise might not come for many more simulation-years. But how to choose this ``advance split time'' (AST) rem…

PROTEAN: Federated Intrusion Detection in Non-IID Environments through Prototype-Based Knowledge Sharing
Sara Chennoufi, Yufei Han, Gregory Blanc, Emiliano De Cristofaro, Christophe Kiennert
https://arxiv.org/abs/2507.05524

PROTEAN: Federated Intrusion Detection in Non-IID Environments through Prototype-Based Knowledge Sharing
In distributed networks, participants often face diverse and fast-evolving cyberattacks. This makes techniques based on Federated Learning (FL) a promising mitigation strategy. By only exchanging model updates, FL participants can collaboratively build detection models without revealing sensitive information, e.g., network structures or security postures. However, the effectiveness of FL solutions is often hindered by significant data heterogeneity, as attack patterns often differ drastically a…

Mask6D: Masked Pose Priors For 6D Object Pose Estimation
Yuechen Xie, Haobo Jiang, Jin Xie
https://arxiv.org/abs/2507.06486 https://a…

Mask6D: Masked Pose Priors For 6D Object Pose Estimation
Robust 6D object pose estimation in cluttered or occluded conditions using monocular RGB images remains a challenging task. One reason is that current pose estimation networks struggle to extract discriminative, pose-aware features using 2D feature backbones, especially when the available RGB information is limited due to target occlusion in cluttered scenes. To mitigate this, we propose a novel pose estimation-specific pre-training strategy named Mask6D. Our approach incorporates pose-aware 2D…

Blind Passive Beamforming for MIMO System
Wenhai Lai, Jiawei Yao, Kaiming Shen
https://arxiv.org/abs/2506.00987 https://arxiv.org/pdf…

Blind Passive Beamforming for MIMO System
Passive beamforming for the intelligent surface (IS)-aided multiple-input multiple-output (MIMO) communication is a difficult nonconvex problem. It becomes even more challenging under the practical discrete constraints on phase shifts. Unlike most of the existing approaches that rely on the channel state information (CSI), this work advocates a blind beamforming strategy without any CSI. Simply put, we propose a statistical method that learns the main feature of the wireless environment from th…

Quantum MIMO Diversity over Discrete-Variable Crosstalk Channels
Shehbaz Tariq, Junaid ur Rehman, Symeon Chatzinotas
https://arxiv.org/abs/2508.07344 https://

Quantum MIMO Diversity over Discrete-Variable Crosstalk Channels
Quantum communication plays a pivotal role in enabling distributed quantum computing and sensing. Diversity strategies can be used to increase the communication reliability (in the sense of output fidelity with respect to the input quantum state) when multiple communication channels are available. The current paper proposes a diversity strategy for quantum discrete variable (DV) multiple-input-multiple-output (MIMO) channels, utilizing approximate cloning to distribute information across multip…

High-performance neuromorphic computing architecture of brain
Jinxuan Ma, Wanlin Guo
https://arxiv.org/abs/2508.03191 https://arxiv.org/pdf/2508.03191

High-performance neuromorphic computing architecture of brain
Artificial intelligence can outperform humans in specific tasks but consumes substantial energy. How the human brain can work at just 20 watts with complex cognitive intelligence? Here we decode the fundamental information strategy unit of brain, neural sphere, which agglomerates neurons into sphere to achieve energy-efficient and exhibits many ultra-long period or random electrophysiological activities. Chaos dynamics and fractal theory demonstrated the mathematical principle of neural spheres…

RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction
Huy-Son Nguyen, Quang-Huy Nguyen, Duc-Hoang Pham, Duc-Trong Le, Hoang-Quynh Le, Padipat Sitkrongwong, Atsuhiro Takasu, Masoud Mansoury
https://arxiv.org/abs/2507.14361

RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction
Existing studies on bundle construction have relied merely on user feedback via bipartite graphs or enhanced item representations using semantic information. These approaches fail to capture elaborate relations hidden in real-world bundle structures, resulting in suboptimal bundle representations. To overcome this limitation, we propose RaMen, a novel method that provides a holistic multi-strategy approach for bundle construction. RaMen utilizes both intrinsic (characteristics) and extrinsic (c…

This https://arxiv.org/abs/2505.16652 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often suffer from hallucinations. In this work, hallucinations are categorized into two main types: initial hallucinations and snowball hallucinations. We argue that adequate contextual information can be extracted directly from the token interaction process. Inspired by causal inference in the decoding strategy, we propose to leverage causal masks …

Dehumanization of opponents – the core strategy of Russian propaganda | Hate Speech: https://benborges.xyz/2025/07/23/dehumanization-of-opponents-the-core.html

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
Sinuo Cheng, Ruyi Zhou, Wenhao Feng, Huaiguang Yang, Haibo Gao, Zongquan Deng, Liang Ding
https://arxiv.org/abs/2506.16703

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
The increasingly complex and diverse planetary exploration environment requires more adaptable and flexible rover navigation strategy. In this study, we propose a VLM-empowered multi-mode system to achieve efficient while safe autonomous navigation for planetary rovers. Vision-Language Model (VLM) is used to parse scene information by image inputs to achieve a human-level understanding of terrain complexity. Based on the complexity classification, the system switches to the most suitable naviga…

Cooperative Target Capture in 3D Engagements over Switched Dynamic Graphs
Abhinav Sinha, Shashi Ranjan Kumar
https://arxiv.org/abs/2507.01350 https://

Cooperative Target Capture in 3D Engagements over Switched Dynamic Graphs
This paper presents a leaderless cooperative guidance strategy for simultaneous time-constrained interception of a stationary target when the interceptors exchange information over switched dynamic graphs. We specifically focus on scenarios when the interceptors lack radial acceleration capabilities, relying solely on their lateral acceleration components. This consideration aligns with their inherent kinematic turn constraints. The proposed strategy explicitly addresses the complexities of cou…

SSEmb: A Joint Structural and Semantic Embedding Framework for Mathematical Formula Retrieval
Ruyin Li, Xiaoyu Chen
https://arxiv.org/abs/2508.04162 https://

SSEmb: A Joint Structural and Semantic Embedding Framework for Mathematical Formula Retrieval
Formula retrieval is an important topic in Mathematical Information Retrieval. We propose SSEmb, a novel embedding framework capable of capturing both structural and semantic features of mathematical formulas. Structurally, we employ Graph Contrastive Learning to encode formulas represented as Operator Graphs. To enhance structural diversity while preserving mathematical validity of these formula graphs, we introduce a novel graph data augmentation approach through a substitution strategy. Sema…

ranDecepter: Real-time Identification and Deterrence of Ransomware Attacks
Md Sajidul Islam Sajid, Jinpeng Wei, Ehab Al-Shaer
https://arxiv.org/abs/2508.00293 https://

ranDecepter: Real-time Identification and Deterrence of Ransomware Attacks
Ransomware (RW) presents a significant and widespread threat in the digital landscape, necessitating effective countermeasures. Active cyber deception is a promising strategy to thwart RW and limiting its propagation by misleading it with false information and revealing its true behaviors. Furthermore, RW often acts as a communication conduit between attackers and defenders, allowing deception to return false data to attackers and deplete their resources. This paper introduces ranDecepter, a no…

Thresholds for sensitive optimality and Blackwell optimality in stochastic games
St\'ephane Gaubert, Julien Grand-Cl\'ement, Ricardo D. Katz
https://arxiv.org/abs/2506.18545

Thresholds for sensitive optimality and Blackwell optimality in stochastic games
We investigate refinements of the mean-payoff criterion in two-player zero-sum perfect-information stochastic games. A strategy is Blackwell optimal if it is optimal in the discounted game for all discount factors sufficiently close to $1$. The notion of $d$-sensitive optimality interpolates between mean-payoff optimality (corresponding to the case $d=-1$) and Blackwell optimality ($d=+\infty$). The Blackwell threshold $α_{\sf Bw} \in [0,1[$ is the discount factor above which all optimal strat…

Evaluation of PID Performance at CEPC and Optimization with Combined dN/dx and Time-of-Flight Data
Dian Yu (Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shanghai, China), Houqian Ding (Department of Physics, Nanjing University, Nanjing, China), Yunyun Fan (Institute of High Energy Physics, CAS, Beijing, China), Yongfeng Zhu (Institute of High Energy Physics, CAS, Beijing, China), Ming Qi (Department of Physics, Nanjing University, Nanjing, China)

Evaluation of PID Performance at CEPC and Optimization with Combined dN/dx and Time-of-Flight Data
This work presents a comprehensive study of charged-hadron particle identification (PID) at the Circular Electron-Positron Collider (CEPC), based on full simulation of hadronic $Z$-pole events. A unified PID strategy is developed by combining energy loss measurements (dN/dx) from the time projection chamber (TPC) and time-of-flight (ToF) information from both the inner (ITK) and outer (OTK) silicon trackers. The PID discriminant is constructed using residuals between measured and expected obser…

This https://arxiv.org/abs/2505.01875 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

MolQAE: Quantum Autoencoder for Molecular Representation Learning
We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to quantum states using parameterized rotation gates, preserving vital structural information. Its quantum encoder-decoder framework enables latent space compression and reconstruction. A dual-objective strategy optimizes fidelity and minimizes trash state deviation. Our evaluations demonstrate effective capture of molecul…

Probing graph topology from local quantum measurements
F. Romeo, J. Settino
https://arxiv.org/abs/2507.23689 https://arxiv.org/pdf/2507.23689

Probing graph topology from local quantum measurements
We show that global properties of an unknown quantum network, such as the average degree, hub density, and the number of closed paths of fixed length, can be inferred from strictly local quantum measurements. In particular, we demonstrate that a malicious agent with access to only a small subset of nodes can initialize quantum states locally and, through repeated short-time measurements, extract sensitive structural information about the entire network. The intrusion strategy is inspired by ext…

Magneto-Ionic Hardware Security Primitives: Embedding Data Protection at the Material Level
Irena Spasojevic, Federica Celegato, Alessandro Magni, Paola Tiberto, Jordi Sort
https://arxiv.org/abs/2507.14213

Magneto-Ionic Hardware Security Primitives: Embedding Data Protection at the Material Level
The Big Data revolution has heightened the demand for robust, energy-efficient security hardware capable of withstanding increasingly sophisticated cyber threats. Conventional encryption schemes, reliant on complex algorithms, are resource-intensive and remain vulnerable. To fortify sensitive information, society needs innovative anti-hacking and anti-counterfeiting technologies that exploit new materials and designs. Here, we present a magneto-ionic strategy for hardware-level security based o…

A Distributed Actor-Critic Algorithm for Fixed-Time Consensus in Nonlinear Multi-Agent Systems
Aria Delshad, Maryam Babazadeh
https://arxiv.org/abs/2507.16520

A Distributed Actor-Critic Algorithm for Fixed-Time Consensus in Nonlinear Multi-Agent Systems
This paper proposes a reinforcement learning (RL)-based backstepping control strategy to achieve fixed time consensus in nonlinear multi-agent systems with strict feedback dynamics. Agents exchange only output information with their neighbors over a directed communication graph, without requiring full state measurements or symmetric communication. Achieving fixed time consensus, where convergence occurs within a pre-specified time bound that is independent of initial conditions is faced with si…