Tootfinder

#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

Today, I'm at Bundesarchiv in Koblenz for the Strategy & Planning meeting of our project "Wiedergutmachung". Our task in this project is to develop efficient information extraction from historical case files of the German Postwar recompensation process of nationalsocialist injustice.
@… @…

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…

Leading the Follower: Learning Persuasive Agents in Social Deduction Games
Zhang Zheng, Deheng Ye, Peilin Zhao, Hao Wang
https://arxiv.org/abs/2510.09087 https://

Leading the Follower: Learning Persuasive Agents in Social Deduction Games
Large language model (LLM) agents have shown remarkable progress in social deduction games (SDGs). However, existing approaches primarily focus on information processing and strategy selection, overlooking the significance of persuasive communication in influencing other players' beliefs and responses. In SDGs, success depends not only on making correct deductions but on convincing others to response in alignment with one's intent. To address this limitation, we formalize turn-based dialogue in…

Scalable Multi-Agent Path Finding using Collision-Aware Dynamic Alert Mask and a Hybrid Execution Strategy
Bharath Muppasani, Ritirupa Dey, Biplav Srivastava, Vignesh Narayanan
https://arxiv.org/abs/2510.09469

Scalable Multi-Agent Path Finding using Collision-Aware Dynamic Alert Mask and a Hybrid Execution Strategy
Multi-agent pathfinding (MAPF) remains a critical problem in robotics and autonomous systems, where agents must navigate shared spaces efficiently while avoiding conflicts. Traditional centralized algorithms that have global information, such as Conflict-Based Search (CBS), provide high-quality solutions but become computationally expensive in large-scale scenarios due to the combinatorial explosion of conflicts that need resolution. Conversely, distributed approaches that have local informatio…

Soft Guessing Under Logarithmic Loss Allowing Errors and Variable-Length Source Coding
Shota Saito, Hamdi Joudeh
https://arxiv.org/abs/2510.09015 https://a…

Soft Guessing Under Logarithmic Loss Allowing Errors and Variable-Length Source Coding
This paper considers the problem of soft guessing under a logarithmic loss distortion measure while allowing errors. We find an optimal guessing strategy, and derive single-shot upper and lower bounds for the minimal guessing moments as well as an asymptotic expansion for i.i.d. sources. These results are extended to the case where side information is available to the guesser. Furthermore, a connection between soft guessing allowing errors and variable-length lossy source coding under logarithm…

Integrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation Systems
Shuangquan Lyu, Ming Wang, Huajun Zhang, Jiasen Zheng, Junjiang Lin, Xiaoxuan Sun
https://arxiv.org/abs/2510.10109

Integrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation Systems
This paper designs and implements an explainable recommendation model that integrates knowledge graphs with structure-aware attention mechanisms. The model is built on graph neural networks and incorporates a multi-hop neighbor aggregation strategy. By integrating the structural information of knowledge graphs and dynamically assigning importance to different neighbors through an attention mechanism, the model enhances its ability to capture implicit preference relationships. In the proposed me…

D\'er\'everb\'eration non-supervis\'ee de la parole par mod\`ele hybride
Louis Bahrman (IDS, S2A), Mathieu Fontaine (IDS, S2A), Ga\"el Richard (IDS, S2A)
https://arxiv.org/abs/2510.09025

Déréverbération non-supervisée de la parole par modèle hybride
This paper introduces a new training strategy to improve speech dereverberation systems in an unsupervised manner using only reverberant speech. Most existing algorithms rely on paired dry/reverberant data, which is difficult to obtain. Our approach uses limited acoustic information, like the reverberation time (RT60), to train a dereverberation system. Experimental results demonstrate that our method achieves more consistent performance across various objective metrics than the state-of-the-ar…

Russia's failed strategy: Drone perations against EU and failures on the front line: https://benborges.xyz/2025/10/06/russias-failed-strategy-drone-perations.html

Strategy Logic, Imperfect Information, and Hyperproperties
Raven Beutner, Bernd Finkbeiner
https://arxiv.org/abs/2510.03952 https://arxiv.org/pdf/2510.0395…

Strategy Logic, Imperfect Information, and Hyperproperties
Strategy logic (SL) is a powerful temporal logic that enables first-class reasoning over strategic behavior in multi-agent systems (MAS). In many MASs, the agents (and their strategies) cannot observe the global state of the system, leading to many extensions of SL centered around imperfect information, such as strategy logic with imperfect information (SL$_\mathit{ii}$). Along orthogonal lines, researchers have studied the combination of strategic behavior and hyperproperties. Hyperproperties …

A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning
Cheng Peng, Xinyu Dong, Mengxian Lyu, Daniel Paredes, Yaoyun Zhang, Yonghui Wu
https://arxiv.org/abs/2509.04753

A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning
Natural language processing (NLP) is a key technology to extract important patient information from clinical narratives to support healthcare applications. The rapid development of large language models (LLMs) has revolutionized many NLP tasks in the clinical domain, yet their optimal use in patient information extraction tasks requires further exploration. This study examines LLMs' effectiveness in patient information extraction, focusing on LLM architectures, fine-tuning strategies, and multi…

Constructing a Nanopipette-based DNA Electro-Mechanical Device
Cengiz J. Khan, Oliver J. Irving, Rand A. Al-Waqfi, Giorgio Ferrari, Tim Albrecht
https://arxiv.org/abs/2510.10515

Constructing a Nanopipette-based DNA Electro-Mechanical Device
Solid-state nanopore and nanopipette sensors are powerful devices for the detection, quantification and structural analysis of biopolymers such as DNA and proteins, especially in carrier-enhanced resistive-pulse sensing. However, hundreds of different molecules typically need to be sampled from solution and analysed to obtain statistically robust information. This limits the applicability of such sensors and complicates associated workflows. Here, we present a new strategy to trap DNA structure…

Tuning Layer Orbital Hall Effect via Spin Rotation in Ferromagnetic Transition Metal Dichalcogenides
Shilei Ji, Jianping Yang, Li Gao, Xing'ao Li
https://arxiv.org/abs/2510.11180

Tuning Layer Orbital Hall Effect via Spin Rotation in Ferromagnetic Transition Metal Dichalcogenides
Orbitronics, which leverages the angular momentum of atomic orbitals for information transmission, provides a novel strategy to overcome the limitations of electronic devices. Unlike electron spin, orbital angular momentum (OAM) is strongly influenced by crystal field effects and band topology, making its orientation difficult to manipulate with external fields. In this work, by using first principle calculations, we investigate quantum anomalous Hall insulators (QAHIs) as a model system to stu…

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…

A multi-strategy improved gazelle optimization algorithm for solving numerical optimization and engineering applications
Qi Diao, Chengyue Xie, Yuchen Yin, Hoileong Lee, Haolong Yang
https://arxiv.org/abs/2509.07211

A multi-strategy improved gazelle optimization algorithm for solving numerical optimization and engineering applications
Aiming at the shortcomings of the gazelle optimization algorithm, such as the imbalance between exploration and exploitation and the insufficient information exchange within the population, this paper proposes a multi-strategy improved gazelle optimization algorithm (MSIGOA). To address these issues, MSIGOA proposes an iteration-based updating framework that switches between exploitation and exploration according to the optimization process, which effectively enhances the balance between local …

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…

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…

Mixture of Semantics Transmission for Generative AI-Enabled Semantic Communication Systems
Junjie Ni, Tong Wu, Zhiyong Chen, Yin Xu, Meixia Tao, Wenjun Zhang
https://arxiv.org/abs/2509.09499

Mixture of Semantics Transmission for Generative AI-Enabled Semantic Communication Systems
In this paper, we propose a mixture of semantics (MoS) transmission strategy for wireless semantic communication systems based on generative artificial intelligence (AI). At the transmitter, we divide an image into regions of interest (ROI) and reigons of non-interest (RONI) to extract their semantic information respectively. Semantic information of ROI can be allocated more bandwidth, while RONI can be represented in a compact form for transmission. At the receiver, a diffusion model reconstru…

Inteligencia Artificial jur\'idica y el desaf\'io de la veracidad: an\'alisis de alucinaciones, optimizaci\'on de RAG y principios para una integraci\'on responsable
Alex Dantart
https://arxiv.org/abs/2509.09467

Inteligencia Artificial jurídica y el desafío de la veracidad: análisis de alucinaciones, optimización de RAG y principios para una integración responsable
This technical report analyzes the challenge of "hallucinations" (false information) in LLMs applied to law. It examines their causes, manifestations, and the effectiveness of the RAG mitigation strategy, highlighting its limitations and proposing holistic optimizations. The paper explores the ethical and regulatory implications, emphasizing human oversight as an irreplaceable role. It concludes that the solution lies not in incrementally improving generative models, but in adopting a "consulta…

from my link log —
Bluffing in Scrabble.
https://arxiv.org/abs/2509.10471
saved 2025-09-18 https://dotat.at/:/WNJZZ.html…

Bluffing in Scrabble
It is well known that in games with imperfect information, such as poker, bluffing with some probability can be a component of the optimal strategy. However, as far as we know, nobody has ever exhibited a Scrabble position in which the optimal strategy involves bluffing, or even a Scrabble position in which the optimal strategy is a mixed (i.e., randomized) strategy. We present a carefully constructed Scrabble position, that could actually arise in a tournament game with no invalid words played…

Femtosecond low-threshold all-optical switching enabled by giant broadband optical nonlinearity from heteroatom doping
Xuefeng Zhang, Zhongquan Nie, Jinhai Zou, Hanke Zhang, Xiaobin Wang, Jian Wu, Yuqiu Zhang, Jinyong Leng, Guohui Li, Yue Tian, Baohua Jia, Pu Zhou
https://arxiv.org/abs/2509.08663

Femtosecond low-threshold all-optical switching enabled by giant broadband optical nonlinearity from heteroatom doping
Ultrafast all-optical switching (AOS) is pivotal for advancing integrated photonic devices, from high-speed photonic information processing to next generation all-optical computing and communication networks. However, conventional nonlinear materials suffer from sluggish response time, high power threshold, weak and narrow-bandwidth optical nonlinearities, critically limiting their viability. Here, we report a heteroatom engineering strategy to overcome these limitations by designing zero-dimen…

Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution
Haosong Liu, Xiancheng Zhu, Huanqiang Zeng, Jianqing Zhu, Jiuwen Cao, Junhui Hou
https://arxiv.org/abs/2509.04824

Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution
Recently, Mamba-based methods, with its advantage in long-range information modeling and linear complexity, have shown great potential in optimizing both computational cost and performance of light field image super-resolution (LFSR). However, current multi-directional scanning strategies lead to inefficient and redundant feature extraction when applied to complex LF data. To overcome this challenge, we propose a Subspace Simple Scanning (Sub-SS) strategy, based on which we design the Subspace …

On Performance of IoT Networks with Coordinated NOMA Transmission: Covert Monitoring and Information Decoding
Thai-Hoc Vu, Anh-Tu Le, Ngo Hoang Tu, Tan N. Nguyen, Miroslav Voznak
https://arxiv.org/abs/2509.01935

On Performance of IoT Networks with Coordinated NOMA Transmission: Covert Monitoring and Information Decoding
This work investigates the covertness and security performance of Internet-of-Things (IoTs) networks under Rayleigh fading environments. Specifically, a cellular source transmits covert information to cell-edge users with the assistance of an IoT master node, employing a coordinated direct and relay transmission strategy combined with non-orthogonal multiple access (NOMA). This approach not only enhances spectrum utilization but also generates friendly interference to complicate a warden's surv…

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…

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…

Prospects for a Ukrainian breakthrough: strategy, weapons and actions of allies | Wrap-Up Analytics: https://benborges.xyz/2025/08/31/prospects-for-a-ukrainian-breakthrough.html

(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

Optimal Data Reduction under Information-Theoretic Criteria
Taotao He, Jun Luo, Junkai Zhao
https://arxiv.org/abs/2508.16123 https://arxiv.org/pdf/2508.161…

Optimal Data Reduction under Information-Theoretic Criteria
Selecting an optimal subset of features or instances under an information theoretic criterion has become an effective preprocessing strategy for reducing data complexity while preserving essential information. This study investigates two representative problems within this paradigm: feature selection based on the maximum relevance minimum redundancy criterion, and instance selection grounded in the Kullback Leibler divergence. To address the intrinsic nonconvexities of these problems, we develo…

Combined machine learning for stock selection strategy based on dynamic weighting methods
Lin Cai (Department of Statistics, Columbia University, New York, USA), Zhiyang He (Department of Engineering and Informatics, University of Sussex, Brighton, UK), Caiya Zhang (Department of Statistics and Data Science, Hangzhou City University, Hangzhou, China)
https://…

Combined machine learning for stock selection strategy based on dynamic weighting methods
This paper proposes a novel stock selection strategy framework based on combined machine learning algorithms. Two types of weighting methods for three representative machine learning algorithms are developed to predict the returns of the stock selection strategy. One is static weighting based on model evaluation metrics, the other is dynamic weighting based on Information Coefficients (IC). Using CSI 300 index data, we empirically evaluate the strategy' s backtested performance and model predic…

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…

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 …

A Distributed Gradient-Based Deployment Strategy for a Network of Sensors with a Probabilistic Sensing Model
Hesam Mosalli, Amir G. Aghdam
https://arxiv.org/abs/2509.02869 https…

A Distributed Gradient-Based Deployment Strategy for a Network of Sensors with a Probabilistic Sensing Model
This paper presents a distributed gradient-based deployment strategy to maximize coverage in hybrid wireless sensor networks (WSNs) with probabilistic sensing. Leveraging Voronoi partitioning, the overall coverage is reformulated as a sum of local contributions, enabling mobile sensors to optimize their positions using only local information. The strategy adopts the Elfes model to capture detection uncertainty and introduces a dynamic step size based on the gradient of the local coverage, ensur…

A State-Update Prompting Strategy for Efficient and Robust Multi-turn Dialogue
Ziyi Liu
https://arxiv.org/abs/2509.17766 https://arxiv.org/pdf/2509.17766…

A State-Update Prompting Strategy for Efficient and Robust Multi-turn Dialogue
Large Language Models (LLMs) struggle with information forgetting and inefficiency in long-horizon, multi-turn dialogues. To address this, we propose a training-free prompt engineering method, the State-Update Multi-turn Dialogue Strategy. It utilizes "State Reconstruction" and "History Remind" mechanisms to effectively manage dialogue history. Our strategy shows strong performance across multiple multi-hop QA datasets. For instance, on the HotpotQA dataset, it improves the core information fil…

Wagner’s failures in Mali undermine Kremlin’s Africa strategy, Ukrainian intelligence says: https://benborges.xyz/2025/08/31/wagners-failures-in-mali-undermine.html

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…

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…

CRouting: Reducing Expensive Distance Calls in Graph-Based Approximate Nearest Neighbor Search
Zhenxin Li, Shuibing He, Jiahao Guo, Xuechen Zhang, Xian-He Sun, Gang Chen
https://arxiv.org/abs/2509.00365

CRouting: Reducing Expensive Distance Calls in Graph-Based Approximate Nearest Neighbor Search
Approximate nearest neighbor search (ANNS) is a crucial problem in information retrieval and AI applications. Recently, there has been a surge of interest in graph-based ANNS algorithms due to their superior efficiency and accuracy. However, the repeated computation of distances in high-dimensional spaces constitutes the primary time cost of graph-based methods. To accelerate the search, we propose a novel routing strategy named CRouting, which bypasses unnecessary distance computations by expl…

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…

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…

The Kottke.org "rollodex" is a good idea that is well executed, imo.
https://kottke.org/25/07/the-kottkeorg-rolodex
"The Rolodex is part of this 'strategy' of relationship-building and strengthening of trusted sources of information. You readers are cur…

The Kottke.org Rolodex
Over the weekend, I added a new feature to the site that, for now, is only accessible from the front page of the site, right after

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…

Bayesimax Theory: Selecting Priors by Minimizing Total Information
Sitaram Vangala
https://arxiv.org/abs/2508.15976 https://arxiv.org/pdf/2508.15976…

Bayesimax Theory: Selecting Priors by Minimizing Total Information
We introduce Bayesimax theory, a paradigm for objective Bayesian analysis which selects priors by applying minimax theory to prior disclosure games. In these games, the uniquely optimal strategy for a Bayesian agent upon observing the data is to reveal their prior. As such, the prior chosen by minimax theory is, in effect, the implicit prior of minimax agents. Since minimax analysis disregards prior information, this prior is arguably noninformative. We refer to minimax solutions of certain pri…

Multicollinearity-Aware Parameter-Free Strategy for Hyperspectral Band Selection: A Dependence Measures-Based Approach
Dibyabha Deb, Ujjwal Verma
https://arxiv.org/abs/2509.21973

Multicollinearity-Aware Parameter-Free Strategy for Hyperspectral Band Selection: A Dependence Measures-Based Approach
Hyperspectral bands offer rich spectral and spatial information; however, their high dimensionality poses challenges for efficient processing. Band selection (BS) methods aim to extract a smaller subset of bands to reduce spectral redundancy. Existing approaches, such as ranking-based, clustering-based, and iterative methods, often suffer from issues like sensitivity to initialization, parameter tuning, and high computational cost. This work introduces a BS strategy integrating three dependence…

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…

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…

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…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Robust Set Partitioning Strategy for Malicious Information Detection in Large-Scale Internet of T...
Yuhan Suo, Runqi Chai, Kaiyuan Chen, Senchun Chai, Wannian Liang, Yuanqing Xia

Node Classification via Simplicial Interaction with Augmented Maximal Clique Selection
Eunho Koo, Tongseok Lim
https://arxiv.org/abs/2509.23568 https://arx…

Node Classification via Simplicial Interaction with Augmented Maximal Clique Selection
Considering higher-order interactions allows for a more comprehensive understanding of network structures beyond simple pairwise connections. While leveraging all cliques in a network to handle higher-order interactions is intuitive, it often leads to computational inefficiencies due to overlapping information between higher-order and lower-order cliques. To address this issue, we propose an augmented maximal clique strategy. Although using only maximal cliques can reduce unnecessary overlap an…

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…

Ukraine holds its ground: Russia's "1,000 cuts" strategy fails: https://benborges.xyz/2025/08/27/ukraine-holds-its-ground-russias.html

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…

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…

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…

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…

Near-Optimal Simultaneous Estimation of Quantum State Moments
Xiao Shi, Jiyu Jiang, Xian Wu, Jingu Xie, Hongshun Yao, Xin Wang
https://arxiv.org/abs/2509.24842 https://

Near-Optimal Simultaneous Estimation of Quantum State Moments
Estimating nonlinear properties such as Rényi entropies and observable-weighted moments serves as a central strategy for spectrum spectroscopy, which is fundamental to property prediction and analysis in quantum information science, statistical mechanics, and many-body physics. However, existing approaches are susceptible to noise and require significant resources, making them challenging for near-term quantum hardware. In this work, we introduce a framework for resource-efficient simultaneous…

Machine Learning-Enhanced Colorimetric Sensing: Achieving over 5700-fold Accuracy Improvement via Full-Spectrum Modeling
Majid Aalizadeh, Chinmay Raut, Ali Tabartehfarahani, Xudong Fan
https://arxiv.org/abs/2509.03398

Machine Learning-Enhanced Colorimetric Sensing: Achieving over 5700-fold Accuracy Improvement via Full-Spectrum Modeling
Conventional colorimetric sensing methods typically rely on signal intensity at a single wavelength, often selected heuristically based on peak visual modulation. This approach overlooks the structured information embedded in full-spectrum transmission profiles, particularly in intensity-based systems where linear models may be highly effective. In this study, we experimentally demonstrate that applying a forward feature selection strategy to normalized transmission spectra, combined with linea…

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…

HiPerformer: A High-Performance Global-Local Segmentation Model with Modular Hierarchical Fusion Strategy
Dayu Tan, Zhenpeng Xu, Yansen Su, Xin Peng, Chunhou Zheng, Weimin Zhong
https://arxiv.org/abs/2509.20280

HiPerformer: A High-Performance Global-Local Segmentation Model with Modular Hierarchical Fusion Strategy
Both local details and global context are crucial in medical image segmentation, and effectively integrating them is essential for achieving high accuracy. However, existing mainstream methods based on CNN-Transformer hybrid architectures typically employ simple feature fusion techniques such as serial stacking, endpoint concatenation, or pointwise addition, which struggle to address the inconsistencies between features and are prone to information conflict and loss. To address the aforemention…

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…

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

A Novel Framework for Honey-X Deception in Zero-Sum Games
Brendan Gould, Kyriakos Vamvoudakis
https://arxiv.org/abs/2509.20329 https://arxiv.org/pdf/2509.2…

A Novel Framework for Honey-X Deception in Zero-Sum Games
In this paper, we present a novel, game-theoretic model of deception in two-player, zero-sum games. Our framework leverages an information asymmetry: one player (the deceiver) has access to accurate payoff information, while the other (the victim) observes a modified version of these payoffs due to the deception strategy employed. The deceiver's objective is to choose a deception-action pair that optimally exploits the victim's best response to the altered payoffs, subject to a constraint on th…

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…

Embodying computation in nonlinear perturbative metamaterials
Sima Zahedi Fard, Paolo Tiso, Parisa Omidvar, Marc Serra-Garcia
https://arxiv.org/abs/2509.01625 https://

Embodying computation in nonlinear perturbative metamaterials
Designing metamaterials that carry out advanced computations poses a significant challenge. A powerful design strategy splits the problem into two steps: First, encoding the desired functionality in a discrete or tight-binding model, and second, identifying a metamaterial geometry that conforms to the model. Applying this approach to information-processing tasks requires accurately mapping nonlinearity -- an essential element for computation -- from discrete models to geometries. Here we formul…

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…

Design and Development of an Intelligent LLM-based LDAP Honeypot
Javier Jim\'enez-Rom\'an, Florina Almenares-Mendoza, Alfonso S\'anchez-Maci\'an
https://arxiv.org/abs/2509.16682

Design and Development of an Intelligent LLM-based LDAP Honeypot
Cybersecurity threats continue to increase, with a growing number of previously unknown attacks each year targeting both large corporations and smaller entities. This scenario demands the implementation of advanced security measures, not only to mitigate damage but also to anticipate emerging attack trends. In this context, deception tools have become a key strategy, enabling the detection, deterrence, and deception of potential attackers while facilitating the collection of information about t…

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…

A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment
Shangqing Yuan, Wenshuang Zhai, Shengwen Guo
https://arxiv.org/abs/2509.19334

A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment
To address the issue of limited channels and insufficient information collection in portable EEG devices, this study explores an EEG virtual channel signal generation network using a novel spatio-temporal feature fusion strategy. Based on the EEG signals from four frontal lobe channels, the network aims to generate virtual channel EEG signals for other 13 important brain regions. The architecture of the network is a two-dimensional convolutional neural network and it includes a parallel module …

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

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…

DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization
Feliks Ba\'nka (Warsaw University of Technology, Faculty of Electronics,Information Technology), Jaros{\l}aw A. Chudziak (Warsaw University of Technology)
https://arxiv.org/abs/2509.12753

DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization
In volatile financial markets, balancing risk and return remains a significant challenge. Traditional approaches often focus solely on equity allocation, overlooking the strategic advantages of options trading for dynamic risk hedging. This work presents DeltaHedge, a multi-agent framework that integrates options trading with AI-driven portfolio management. By combining advanced reinforcement learning techniques with an ensembled options-based hedging strategy, DeltaHedge enhances risk-adjusted…

On-device Internet of Sounds Sonification with Wavetable Synthesis Techniques for Soil Moisture Monitoring in Water Scarcity Contexts
Stephen Roddy
https://arxiv.org/abs/2509.19097

Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
Mayank Kumar Jain, Dinesh Gopalani, Yogesh Kumar Meena, Nishant Jain
https://arxiv.org/abs/2508.16223

Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
With the rapid evolution of technology and the Internet, the proliferation of fake news on social media has become a critical issue, leading to widespread misinformation that can cause societal harm. Traditional fact checking methods are often too slow to prevent the dissemination of false information. Therefore, the need for rapid, automated detection of fake news is paramount. We introduce DaCFake, a novel fake news detection model using a divide and conquer strategy that combines content and…

Beyond the Portal: Enhancing Recognition in Virtual Reality Through Multisensory Cues
Siyeon Bak, Dongyun Han, Inho Jo, Sun-Jeong Kim, Isaac Cho
https://arxiv.org/abs/2509.11347

Beyond the Portal: Enhancing Recognition in Virtual Reality Through Multisensory Cues
While Virtual Reality (VR) systems have become increasingly immersive, they still rely predominantly on visual input, which can constrain perceptual performance when visual information is limited. Incorporating additional sensory modalities, such as sound and scent, offers a promising strategy to enhance user experience and overcome these limitations. This paper investigates the contribution of auditory and olfactory cues in supporting perception within the portal metaphor, a VR technique that …

An early termination strategy for the distributed biased min-consensus protocol under disturbances
Zicheng Huang, Wangzhi Zhou, Yuanqiu Mo
https://arxiv.org/abs/2509.19832 https…

An early termination strategy for the distributed biased min-consensus protocol under disturbances
The distributed biased min-consensus (DBMC) protocol is an iterative scheme that solves the shortest path problem asymptotically, requiring only local information exchange between neighboring nodes. By appropriately designing the gain function, prior work [1] proposed a DBMC-based system that ensures convergence within a pre-specified time interval. However, this guarantee assumes the absence of disturbances. In this paper, we study the DBMC-based system under disturbances affecting the edge we…

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

Here is CUPW's fact sheet on Postal Banking “A Bank for Everyone”
Fun fact!
"our post office used to have a national savings bank – up until 1969 – and there is no reason we shouldn't have one today”
Some other reasons:
"Banks are failing to meet the needs of a growing number of Canadians. Thousands of towns and villages across our country do not have a bank. But many of them have a post office that could provide access to financial and banking services.”
"Postal banking is lucrative!
New Zealand: Kiwibank generated 81% of New Zealand Post's after tax profits.”
"France: Banque Postale has an obligation to provide products and services to as many people as possible. It provides a Livret A or passbook savings account, at no charge, to anyone who requests it. It also provides banking services to the financially vulnerable and financing for social housing, voluntary organizations and microentrepreneurs lacking bank credit.”
"Canada Post's secret postal banking study
Canada Post conducted a secret four-year study on postal banking that indicates that adding this service "would be a win-win strategy" for the corporation. This study was obtained though an Access to Information (ATI) request. Unfortunately, 701 of the study's 811 pages were redacted. CUPW has asked Canada Post's President to release the full report, but he has refused.”
#CanadaPost #UPW #Strike #Union #Solidarity
https://www.cupw.ca/en/campaign/resources/postal-banking-–-bank-everyone-fact-sheet

Semantic-Aware Ship Detection with Vision-Language Integration
Jiahao Li, Jiancheng Pan, Yuze Sun, Xiaomeng Huang
https://arxiv.org/abs/2508.15930 https://…

Semantic-Aware Ship Detection with Vision-Language Integration
Ship detection in remote sensing imagery is a critical task with wide-ranging applications, such as maritime activity monitoring, shipping logistics, and environmental studies. However, existing methods often struggle to capture fine-grained semantic information, limiting their effectiveness in complex scenarios. To address these challenges, we propose a novel detection framework that combines Vision-Language Models (VLMs) with a multi-scale adaptive sliding window strategy. To facilitate Seman…

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 …

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 …

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…

DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis
J\'er\'emie Stym-Popper, Nathan Painchaud, Cl\'ement Rambour, Pierre-Yves Courand, Nicolas Thome, Olivier Bernard
https://arxiv.org/abs/2509.15990

DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis
Multimodal data fusion is a key approach for enhancing diagnosis in medical applications. We propose an asymmetric fusion strategy starting from a primary modality and integrating secondary modalities by disentangling shared and modality-specific information. Validated on a dataset of 239 patients with echocardiographic time series and tabular records, our model outperforms existing methods, achieving an AUC over 90%. This improvement marks a crucial benchmark for clinical use.

The superconducting grid-states qubit
Long B. Nguyen, Hyunseong Kim, Dat T. Le, Thomas Ersevim, Sai P. Chitta, Trevor Chistolini, Christian J\"unger, W. Clarke Smith, T. M. Stace, Jens Koch, David I. Santiago, Irfan Siddiqi
https://arxiv.org/abs/2509.14656

The superconducting grid-states qubit
Decoherence errors arising from noisy environments remain a central obstacle to progress in quantum computation and information processing. Quantum error correction (QEC) based on the Gottesman-Kitaev-Preskill (GKP) protocol offers a powerful strategy to overcome this challenge, with successful demonstrations in trapped ions, superconducting circuits, and photonics. Beyond active QEC, a compelling alternative is to engineer Hamiltonians that intrinsically enforce stabilizers, offering passive p…

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…

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

Optimization of Latent-Space Compression using Game-Theoretic Techniques for Transformer-Based Vector Search
Kushagra Agrawal, Nisharg Nargund, Oishani Banerjee
https://arxiv.org/abs/2508.18877

Optimization of Latent-Space Compression using Game-Theoretic Techniques for Transformer-Based Vector Search
Vector similarity search plays a pivotal role in modern information retrieval systems, especially when powered by transformer-based embeddings. However, the scalability and efficiency of such systems are often hindered by the high dimensionality of latent representations. In this paper, we propose a novel game-theoretic framework for optimizing latent-space compression to enhance both the efficiency and semantic utility of vector search. By modeling the compression strategy as a zero-sum game b…

On the Monotonicity of relative entropy: A Comparative Study of Petz's and Uhlmann's Approaches
Santiago Matheus, Francesco Bottacin, Edoardo Provenzi
https://arxiv.org/abs/2509.11221

On the Monotonicity of relative entropy: A Comparative Study of Petz's and Uhlmann's Approaches
We revisit the monotonicity of relative entropy under the action of quantum channels, a foundational result in quantum information theory. Among the several available proofs, we focus on those by Petz and Uhlmann, which we reformulate within a unified, finite-dimensional operator-theoretic framework. In the first part, we examine Petz's strategy, identify a subtle flaw in his original use of Jensen's contractive operator inequality, and point out how it was corrected to restore the validity of …

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…

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…

Nonlinear Cooperative Salvo Guidance with Seeker-Limited Interceptors
Lohitvel Gopikannan, Shashi Ranjan Kumar, Abhinav Sinha
https://arxiv.org/abs/2509.15136 https://

Nonlinear Cooperative Salvo Guidance with Seeker-Limited Interceptors
This paper presents a cooperative guidance strategy for the simultaneous interception of a constant-velocity, non-maneuvering target, addressing the realistic scenario where only a subset of interceptors are equipped with onboard seekers. To overcome the resulting heterogeneity in target observability, a fixed-time distributed observer is employed, enabling seeker-less interceptors to estimate the target state using information from seeker-equipped agents and local neighbors over a directed com…

Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs
Briti Gangopadhyay, Zhao Wang, Shingo Takamatsu
https://arxiv.org/abs/2509.09683 https://

Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs
Forecasting click volume is a key task in digital advertising, influencing both revenue and campaign strategy. Traditional time series models rely solely on numerical data, often overlooking rich contextual information embedded in textual elements, such as keyword updates. We present a multimodal forecasting framework that combines click data with textual logs from real-world ad campaigns and generates human-interpretable explanations alongside numeric predictions. Reinforcement learning is use…