Tootfinder

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…

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy
Utkarsh Pratiush, Austin Houston, Kamyar Barakati, Aditya Raghavan, Dasol Yoon, Harikrishnan KP, Zhaslan Baraissov, Desheng Ma, Samuel S. Welborn, Mikolaj Jakowski, Shawn-Patrick Barhorst, Alexander J. Pattison, Panayotis Manganaris, Sita Sirisha Madugula, Sai Venkata Gayathri Ayyagari, Vishal Kennedy, Ralph Bulanadi, Michelle Wang, Kieran J. Pang, Ian Addison-Smith, Willy Menacho, Horacio V. G…

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy
Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. A…

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…

This https://arxiv.org/abs/2501.09023 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Cyber-Physical Security Vulnerabilities Identification and Classification in Smart Manufacturing -- A Defense-in-Depth Driven Framework and Taxonomy
The increasing cybersecurity threats to critical manufacturing infrastructure necessitate proactive strategies for vulnerability identification, classification, and assessment. Traditional approaches, which define vulnerabilities as weaknesses in computational logic or information systems, often overlook the physical and cyber-physical dimensions critical to manufacturing systems, comprising intertwined cyber, physical, and human elements. As a result, existing solutions fall short in addressin…

Controlling the Spread of Epidemics on Networks with Differential Privacy
Dung Nguyen, Aravind Srinivasan, Renata Valieva, Anil Vullikanti, Jiayi Wu
https://arxiv.org/abs/2506.00745

Controlling the Spread of Epidemics on Networks with Differential Privacy
Designing effective strategies for controlling epidemic spread by vaccination is an important question in epidemiology, especially in the early stages when vaccines are limited. This is a challenging question when the contact network is very heterogeneous, and strategies based on controlling network properties, such as the degree and spectral radius, have been shown to be effective. Implementation of such strategies requires detailed information on the contact structure, which might be sensitiv…

Optimizing entanglement distribution via noisy quantum channels
Piotr Masajada, Marco Fellous-Asiani, Alexander Streltsov
https://arxiv.org/abs/2506.06089 …

Optimizing entanglement distribution via noisy quantum channels
Entanglement distribution is a crucial problem in quantum information science, owing to the essential role that entanglement plays in enabling advanced quantum protocols, including quantum teleportation and quantum cryptography. We investigate strategies for distributing quantum entanglement between two distant parties through noisy quantum channels. Specifically, we compare two configurations: one where the entanglement source is placed at the midpoint of the communication line, and another wh…

Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion
Lingyuan Liu, Mengxiang Zhang
https://arxiv.org/abs/2506.04760

Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion
Large Language Models (LLMs) have shown potential in generating hypothetical documents for query expansion, thereby enhancing information retrieval performance. However, the efficacy of this method is highly dependent on the quality of the generated documents, which often requires complex prompt strategies and the integration of advanced dense retrieval techniques. This can be both costly and computationally intensive. To mitigate these limitations, we explore the use of zero-shot LLM-based que…

This https://arxiv.org/abs/2504.18253 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances and depth constraints. Traditional navigation strategies struggle with limited sensor information, making safe and efficient operation difficult. In this paper, we propose a reinforcement learning (RL) framework for ASV navigation under depth constraints, where the vehicle must reach a target while avoiding unsafe areas…

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.…

Distributionally Robust Wireless Semantic Communication with Large AI Models
Long Tan Le, Senura Hansaja Wanasekara, Zerun Niu, Yansong Shi, Nguyen H. Tran, Phuong Vo, Walid Saad, Dusit Niyato, Zhu Han, Choong Seon Hong, H. Vincent Poor
https://arxiv.org/abs/2506.03167

Distributionally Robust Wireless Semantic Communication with Large AI Models
6G wireless systems are expected to support massive volumes of data with ultra-low latency. However, conventional bit-level transmission strategies cannot support the efficiency and adaptability required by modern, data-intensive applications. The concept of semantic communication (SemCom) addresses this limitation by focusing on transmitting task-relevant semantic information instead of raw data. While recent efforts incorporating deep learning and large-scale AI models have improved SemCom's …

Learning Recommender Mechanisms for Bayesian Stochastic Games
Bengisu Guresti, Chongjie Zhang, Yevgeniy Vorobeychik
https://arxiv.org/abs/2505.22979 https:…

Learning Recommender Mechanisms for Bayesian Stochastic Games
An important challenge in non-cooperative game theory is coordinating on a single (approximate) equilibrium from many possibilities - a challenge that becomes even more complex when players hold private information. Recommender mechanisms tackle this problem by recommending strategies to players based on their reported type profiles. A key consideration in such mechanisms is to ensure that players are incentivized to participate, report their private information truthfully, and follow the recom…

Conceal Truth while Show Fake: T/F Frequency Multiplexing based Anti-Intercepting Transmission
Zhisheng Yin, Nan Cheng, Tom H. Luan, Mingjie Wang, Dongbo Li, Yiliang Liu, Changle Li
https://arxiv.org/abs/2506.00811

Conceal Truth while Show Fake: T/F Frequency Multiplexing based Anti-Intercepting Transmission
In wireless communication adversarial scenarios, signals are easily intercepted by non-cooperative parties, exposing the transmission of confidential information. This paper proposes a true-and-false (T/F) frequency multiplexing based anti-intercepting transmission scheme capable of concealing truth while showing fake (CTSF), integrating both offensive and defensive strategies. Specifically, through multi-source cooperation, true and false signals are transmitted over multiple frequency bands u…

Integrating Expert Knowledge and Recursive Bayesian Inference: A Framework for Spatial and Spatio-Temporal Data Challenges
Mario Figueira, David Conesa, Antonio L\'opez-Qu\'ilez, H{\aa}vard Rue
https://arxiv.org/abs/2506.00221

Integrating Expert Knowledge and Recursive Bayesian Inference: A Framework for Spatial and Spatio-Temporal Data Challenges
Expert elicitation is a critical approach for addressing data scarcity across various disciplines. But moreover, it can also complement big data analytics by mitigating the limitations of observational data, such as incompleteness and reliability issues, thereby enhancing model estimates through the integration of disparate or conflicting data sources. The paper also outlines various strategies for integrating prior information within the Integrated Nested Laplace Approximation method and propo…

Trade among moral agents with information asymmetries
Jos\'e Ignacio Rivero-Wildemauwe
https://arxiv.org/abs/2505.20551 https://a…

Trade among moral agents with information asymmetries
I study a sequential bilateral trade setting with asymmetric information, where one side has all the bargaining power and makes a take-it-or-leave-it price offer. Both agents hold a certain degree of Kantian morality and thus care about what would have happened had their actions been adopted by their counterpart. In order to capture this, I implement a Veil-of-Ignorance approach, whereby players are uncertain about their role and are thus forced to submit strategies for both the case where they…

Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems
Patrick Gerard, Hans W. A. Hanley, Luca Luceri, Emilio Ferrara
https://arxiv.org/abs/2505.21729

Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems
Political discourse has grown increasingly fragmented across different social platforms, making it challenging to trace how narratives spread and evolve within such a fragmented information ecosystem. Reconstructing social graphs and information diffusion networks is challenging, and available strategies typically depend on platform-specific features and behavioral signals which are often incompatible across systems and increasingly restricted. To address these challenges, we present a platform…

This https://arxiv.org/abs/2505.02093 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

A Deep Learning-Aided Approach for Estimating Field Permeability Map by Fusing Well Logs, Well Tests, and Seismic Data
Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and, therefore, designing effective recovery strategies. This problem, however, remains challenging, as it requires the integration of various data sources by experts from different disciplines. Moreover, there are no sources to provide direct information about the inter-well space. In this work, a new method based on the data-fusion approach is proposed for predicting…

Evaluation of clinical utility in emulated clinical trials
Johannes Hruza, Arvid Sj\"olander, Erin Gabriel, Samir Bhatt, Michael Sachs
https://arxiv.org/abs/2506.03991

Evaluation of clinical utility in emulated clinical trials
Dynamic treatment regimes have been proposed to personalize treatment decisions by utilizing historical patient data, but optimization can only be done over information available in the database. In contrast, the standard of care or physicians' decisions may be complex algorithms based on information that is not available to researchers. It is thus meaningful to integrate the standard of care into the evaluation of treatment strategies, and previous works have suggested doing so through the con…

Gated Multimodal Graph Learning for Personalized Recommendation
Sibei Liu, Yuanzhe Zhang, Xiang Li, Yunbo Liu, Chengwei Feng, Hao Yang
https://arxiv.org/abs/2506.00107

Gated Multimodal Graph Learning for Personalized Recommendation
Multimodal recommendation has emerged as a promising solution to alleviate the cold-start and sparsity problems in collaborative filtering by incorporating rich content information, such as product images and textual descriptions. However, effectively integrating heterogeneous modalities into a unified recommendation framework remains a challenge. Existing approaches often rely on fixed fusion strategies or complex architectures , which may fail to adapt to modality quality variance or introduc…

This https://arxiv.org/abs/2505.20551 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Trade among moral agents with information asymmetries
I study a sequential bilateral trade setting with asymmetric information, where one side has all the bargaining power and makes a take-it-or-leave-it price offer. Both agents hold a certain degree of Kantian morality and thus care about what would have happened had their actions been adopted by their counterpart. In order to capture this, I implement a Veil-of-Ignorance approach, whereby players are uncertain about their role and are thus forced to submit strategies for both the case where they…

This https://arxiv.org/abs/2411.01217 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Preference-CFR$\:$ Beyond Nash Equilibrium for Better Game Strategies
Artificial intelligence (AI) has surpassed top human players in a variety of games. In imperfect information games, these achievements have primarily been driven by Counterfactual Regret Minimization (CFR) and its variants for computing Nash equilibrium. However, most existing research has focused on maximizing payoff, while largely neglecting the importance of strategic diversity and the need for varied play styles, thereby limiting AI's adaptability to different user preferences. To address…

Bayesian and Markovian classical feedforward for discriminating amplitude damping channels
Milajiguli Rexiti, Stefano Mancini
https://arxiv.org/abs/2506.01851

Bayesian and Markovian classical feedforward for discriminating amplitude damping channels
We address the issue of multishot discrimination between two qubit-amplitude damping channels by invoking a simple adaptive protocol that employs Helstrom measurement at each step and classical information feedforward. We contrast the performance of Bayesian and Markovian strategies. We show that the former is only slightly advantageous for a limited parameters' region.