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EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Zhou Chen, Sanjoy Kundu, Harsimran S. Baweja, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2506.17516

EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Active event perception, the ability to dynamically detect, track, and summarize events in real time, is essential for embodied intelligence in tasks such as human-AI collaboration, assistive robotics, and autonomous navigation. However, existing approaches often depend on predefined action spaces, annotated datasets, and extrinsic rewards, limiting their adaptability and scalability in dynamic, real-world scenarios. Inspired by cognitive theories of event perception and predictive coding, we p…

Pricing under the Benchmark Approach
Eckhard Platen
https://arxiv.org/abs/2506.16264 https://arxiv.org/pdf/2506.16264

Pricing under the Benchmark Approach
The paper summarizes key results of the benchmark approach with a focus on the concept of benchmark-neutral pricing. It applies these results to the pricing of an extreme-maturity European put option on a well-diversified stock index. The growth optimal portfolio of the stocks is approximated by a well-diversified stock portfolio and modeled by a drifted time-transformed squared Bessel process of dimension four. It is shown that the benchmark-neutral price of a European put option is theoretica…

Sketched Sum-Product Networks for Joins
Brian Tsan, Abylay Amanbayev, Asoke Datta, Florin Rusu
https://arxiv.org/abs/2506.14034 https://

Sketched Sum-Product Networks for Joins
Sketches have shown high accuracy in multi-way join cardinality estimation, a critical problem in cost-based query optimization. Accurately estimating the cardinality of a join operation -- analogous to its computational cost -- allows the optimization of query execution costs in relational database systems. However, although sketches have shown high efficacy in query optimization, they are typically constructed specifically for predefined selections in queries that are assumed to be given a pr…

Introducing the PIT-plot -- a new tool in the portfolio manager's toolkit
Stig-Johan Wiklund, Magnus Ytterstad
https://arxiv.org/abs/2506.12068 https:/…

Introducing the PIT-plot -- a new tool in the portfolio manager's toolkit
Project portfolio management is an essential process for organizations aiming to optimize the value of their R&D investments. In this article, we introduce a new tool designed to support the prioritization of projects within project portfolio management. We label this tool the PIT-plot, an acronym for Project Impact Tornado plot, with reference to the similarity to the Tornado plot often used for sensitivity analyses. Many traditional practices in portfolio management focus on the properties of…

Secure Time-Modulated Intelligent Reflecting Surface via Generative Flow Networks
Zhihao Tao, Athina P. Petropulu
https://arxiv.org/abs/2506.14992 https://…

Secure Time-Modulated Intelligent Reflecting Surface via Generative Flow Networks
We propose a novel directional modulation (DM) design for OFDM transmitters aided by a time-modulated intelligent reflecting surface (TM-IRS). The TM-IRS is configured to preserve the integrity of transmitted signals toward multiple legitimate users while scrambling the signal in all other directions. Existing TM-IRS design methods typically target a single user direction and follow predefined rule-based procedures, making them unsuitable for multi-user scenarios. Here, we propose a generative …

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
Yu Tan, Yongxiang Li, Xiaowu Dai, Kwok-Leung Tsui
https://arxiv.org/abs/2506.12701

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
Functional ANOVA (FANOVA) is a widely used variance-based sensitivity analysis tool. However, studies on functional-output FANOVA remain relatively scarce, especially for black-box computer experiments, which often involve complex and nonlinear functional-output relationships with unknown data distribution. Conventional approaches often rely on predefined basis functions or parametric structures that lack the flexibility to capture complex nonlinear relationships. Additionally, strong assumptio…

Intrinsic and Extrinsic Organized Attention: Softmax Invariance and Network Sparsity
Oluwadamilola Fasina, Ruben V. C. Pohle, Pei-Chun Su, Ronald R. Coifman
https://arxiv.org/abs/2506.15541

Intrinsic and Extrinsic Organized Attention: Softmax Invariance and Network Sparsity
We examine the intrinsic (within the attention head) and extrinsic (amongst the attention heads) structure of the self-attention mechanism in transformers. Theoretical evidence for invariance of the self-attention mechanism to softmax activation is obtained by appealing to paradifferential calculus, (and is supported by computational examples), which relies on the intrinsic organization of the attention heads. Furthermore, we use an existing methodology for hierarchical organization of tensors …

Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
Vicente P. Soloviev, Antonio M\'arquez Romero, Josh Kirsopp, Michal Krompiec
https://arxiv.org/abs/2506.08947

Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
Quantum Approximate Optimization Algorithms (QAOA) have demonstrated a strong potential in addressing graph-based optimization problems. However, the execution of large-scale quantum circuits remains constrained by the limitations of current quantum hardware. In this work, we introduce QuantCut, an automatic framework for circuit cutting that enables efficient execution of large quantum circuits by decomposing entangling two-qubit gates into manageable sub-circuits. Specifically, we focus on ga…

This https://arxiv.org/abs/2410.10239 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Sample Average Approximation for Portfolio Optimization under CVaR constraint in an (re)insurance context
We consider optimal allocation problems with Conditional Value-At-Risk (CVaR) constraint. We prove, under very mild assumptions, the convergence of the Sample Average Approximation method (SAA) applied to this problem, and we also exhibit a convergence rate and discuss the uniqueness of the solution. These results give (re)insurers a practical solution to portfolio optimization under market regulatory constraints, i.e. a certain level of risk.

Numerical approximation of a PDE-constrained Optimization problem that appears in Data-Driven Computational Mechanics
Pedro B. Bazon, Cristian G. Gebhardt, Gustavo C. Buscaglia, Roberto F. Ausas
https://arxiv.org/abs/2506.10894

Numerical approximation of a PDE-constrained Optimization problem that appears in Data-Driven Computational Mechanics
We investigate an optimization problem that arises when working within the paradigm of Data-Driven Computational Mechanics. In the context of the diffusion-reaction problem, such an optimization problem seeks for the continuous primal fields (gradient and flux) that are closest to some predefined discrete fields taken from a material data set. The optimization is performed over primal fields that satisfy the physical conservation law and the geometrical compatibility. We consider a reaction ter…