Tootfinder

Enhanced Ideal Objective Vector Estimation for Evolutionary Multi-Objective Optimization
Ruihao Zheng, Zhenkun Wang, Yin Wu, Maoguo Gong
https://arxiv.org/abs/2505.21903

Enhanced Ideal Objective Vector Estimation for Evolutionary Multi-Objective Optimization
The ideal objective vector, which comprises the optimal values of the $m$ objective functions in an $m$-objective optimization problem, is an important concept in evolutionary multi-objective optimization. Accurate estimation of this vector has consistently been a crucial task, as it is frequently used to guide the search process and normalize the objective space. Prevailing estimation methods all involve utilizing the best value concerning each objective function achieved by the individuals in…

Enhanced Ideal Objective Vector Estimation for Evolutionary Multi-Objective Optimization
Ruihao Zheng, Zhenkun Wang, Yin Wu, Maoguo Gong
https://arxiv.org/abs/2505.21903

Enhanced Ideal Objective Vector Estimation for Evolutionary Multi-Objective Optimization
The ideal objective vector, which comprises the optimal values of the $m$ objective functions in an $m$-objective optimization problem, is an important concept in evolutionary multi-objective optimization. Accurate estimation of this vector has consistently been a crucial task, as it is frequently used to guide the search process and normalize the objective space. Prevailing estimation methods all involve utilizing the best value concerning each objective function achieved by the individuals in…

Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization
Stephanie Riedm\"uller (Zuse Institute Berlin), Janina Zittel (Zuse Institute Berlin), Thorsten Koch (Zuse Institute Berlin, Technische Universit\"at Berlin)
https://arxiv.org/abs/2507.21933

Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization
We explore how warm-starting strategies can be integrated into scalarization-based approaches for multi-objective optimization in (mixed) integer linear programming. Scalarization methods remain widely used classical techniques to compute Pareto-optimal solutions in applied settings. They are favored due to their algorithmic simplicity and broad applicability across continuous and integer programs with an arbitrary number of objectives. While warm-starting has been applied in this context befor…

Source: David Ellison plans to "bring back a performance-based culture" to CBS, "Not quotas. Not ideology. Just objective journalism" (Financial Times)
https://www.ft.com/content/b0771100-9180-4a73-adb4-38595aa8c15c

I started the morning with the objective of putting the plants we brought from our old house into our new yard. But I quickly realized all the tools I needed were sprinkled throughout my disorganized garage. So I spent 3 hours ripping out old cabinets left by the previous owners to make some space and then hunting through our piles of stuff to find the garden tools. Got most of it organized, so at least I can actually put my hands on stuff. A small step forward.
#Gardening #FengShui

Qatar, Saudi, Egypt join call for Hamas to disarm, give up Gaza rule - #France24
https://www.france24.com/en/live-news/20250729-qatar…

Minimum Attention Control (MAC) in a Receding Horizon Framework with Applications
T. Ganesh Teja, Santhosh Kumar Varanasi, Phanindra Jampana
https://arxiv.org/abs/2507.20835 htt…

Minimum Attention Control (MAC) in a Receding Horizon Framework with Applications
Minimum Attention Control (MAC) is a control technique that provides minimal input changes to meet the control objective. Mathematically, the zero norm of the input changes is used as a constraint for the given control objective and minimized with respect to the process dynamics. In this paper, along with the zero norm constraint, stage costs are also considered for reference tracking in a receding horizon framework. For this purpose, the optimal inputs of the previous horizons are also conside…

Quality assessment of 3D human animation: Subjective and objective evaluation
Rim Rekik, Stefanie Wuhrer, Ludovic Hoyet, Katja Zibrek, Anne-H\'el\`ene Olivier
https://arxiv.org/abs/2505.23301

Quality assessment of 3D human animation: Subjective and objective evaluation
Virtual human animations have a wide range of applications in virtual and augmented reality. While automatic generation methods of animated virtual humans have been developed, assessing their quality remains challenging. Recently, approaches introducing task-oriented evaluation metrics have been proposed, leveraging neural network training. However, quality assessment measures for animated virtual humans that are not generated with parametric body models have yet to be developed. In this contex…

Deep Learning for Skeleton Based Human Motion Rehabilitation Assessment: A Benchmark
Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
https://arxiv.org/abs/2507.21018

Deep Learning for Skeleton Based Human Motion Rehabilitation Assessment: A Benchmark
Automated assessment of human motion plays a vital role in rehabilitation, enabling objective evaluation of patient performance and progress. Unlike general human activity recognition, rehabilitation motion assessment focuses on analyzing the quality of movement within the same action class, requiring the detection of subtle deviations from ideal motion. Recent advances in deep learning and video-based skeleton extraction have opened new possibilities for accessible, scalable motion assessment …

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

A systematic literature review to unveil users objective reaction to virtual experiences: Complemented with a conceptual model (QoUX in VE)
Alireza Mortezapour, Andrea Antonio Cantone, Monica Maria Lucia Sebillo, Giuliana Vitiello
https://arxiv.org/abs/2507.19104

A systematic literature review to unveil users objective reaction to virtual experiences: Complemented with a conceptual model (QoUX in VE)
In pursuit of documenting users Neurophysiological responses during experiencing virtual environments (VE), this systematic review presents a novel conceptual model of UX in VE. Searching across seven databases yielded to 1743 articles. Rigorous screenings, included only 66 articles. Notably, UX in VE lacks a consensus definition. Obviously, this UX has many unique sub-dimensions that are not mentioned in other products. The presented conceptual model contains 26 subdimensions which mostly not …

Thompson Sampling in Function Spaces via Neural Operators
Rafael Oliveira, Xuesong Wang, Kian Ming A. Chai, Edwin V. Bonilla
https://arxiv.org/abs/2506.21894

Thompson Sampling in Function Spaces via Neural Operators
We propose an extension of Thompson sampling to optimization problems over function spaces where the objective is a known functional of an unknown operator's output. We assume that functional evaluations are inexpensive, while queries to the operator (such as running a high-fidelity simulator) are costly. Our algorithm employs a sample-then-optimize approach using neural operator surrogates. This strategy avoids explicit uncertainty quantification by treating trained neural operators as approxi…

How (Not) To Write a Software Engineering Abstract
Lutz Prechelt, Lloyd Montgomery, Julian Frattini, Franz Zieris
https://arxiv.org/abs/2506.21634 https://…

How (Not) To Write a Software Engineering Abstract
Background: Abstracts are a particularly valuable element in a software engineering research article. However, not all abstracts are as informative as they could be. Objective: Characterize the structure of abstracts in high-quality software engineering venues. Observe and quantify deficiencies. Suggest guidelines for writing informative abstracts. Methods: Use qualitative open coding to derive concepts that explain relevant properties of abstracts. Identify the archetypical structure of abstra…

Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics
Florian Krellner, Abhishek Awasthi, Nico Kraus, Sarah Braun, Michael Poppel, Daniel Porawski
https://arxiv.org/abs/2507.21701

Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics
This study evaluates the performance of a quantum-classical metaheuristic and a traditional classical mathematical programming solver, applied to two mathematical optimization models for an industry-relevant scheduling problem with autonomous guided vehicles (AGVs). The two models are: (1) a time-indexed mixed-integer linear program, and (2) a novel binary optimization problem with linear and quadratic constraints and a linear objective. Our experiments indicate that optimization methods are ve…

Exploring the change in scientific readability following the release of ChatGPT
Abdulkareem Alsudais
https://arxiv.org/abs/2506.21825 https://

Exploring the change in scientific readability following the release of ChatGPT
The rise and growing popularity of accessible large language models have raised questions about their impact on various aspects of life, including how scientists write and publish their research. The primary objective of this paper is to analyze a dataset consisting of all abstracts posted on arXiv.org between 2010 and June 7th, 2024, to assess the evolution of their readability and determine whether significant shifts occurred following the release of ChatGPT in November 2022. Four standard re…

This https://arxiv.org/abs/2502.02165 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Broadcast in Almost Mixing Time
We study the problem of broadcasting multiple messages in the CONGEST model. In this problem, a dedicated source node $s$ possesses a set $M$ of messages with every message of size $O(\log n)$ where $n$ is the total number of nodes. The objective is to ensure that every node in the network learns all messages in $M$. The execution of an algorithm progresses in rounds, and we focus on optimizing the round complexity of broadcasting multiple messages. Our primary contribution is a randomized al…

A Computationally Aware Multi Objective Framework for Camera LiDAR Calibration
Venkat Karramreddy, Rangarajan Ramanujam
https://arxiv.org/abs/2506.20636 ht…

A Computationally Aware Multi Objective Framework for Camera LiDAR Calibration
Accurate extrinsic calibration between LiDAR and camera sensors is important for reliable perception in autonomous systems. In this paper, we present a novel multi-objective optimization framework that jointly minimizes the geometric alignment error and computational cost associated with camera-LiDAR calibration. We optimize two objectives: (1) error between projected LiDAR points and ground-truth image edges, and (2) a composite metric for computational cost reflecting runtime and resource usa…

Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems
Hiba Bederina, Jill-J\^enn Vie
https://arxiv.org/abs/2506.21617 https://

Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems
The challenge of balancing user relevance and content diversity in recommender systems is increasingly critical amid growing concerns about content homogeneity and reduced user engagement. In this work, we propose a novel framework that leverages a multi-objective, contextual sequential sampling strategy. Item selection is guided by Bayesian updates that dynamically adjust scores to optimize diversity. The reward formulation integrates multiple diversity metrics-including the log-determinant vo…

A Bi-Objective Mathematical Model for the Multi-Skilled Resource-Constrained Project Scheduling Problem Considering Reliability: An AUGMECON2VIKOR Hybrid Method
Mohammad Ghasemi, Asef Nazari, Dhananjay Thiruvady, Reza Tavakkoli-Moghaddam, Reza Shahabi-Shahmiri, Seyed-Ali Mirnezami
https://arxiv.org/abs/2507.21436

A Bi-Objective Mathematical Model for the Multi-Skilled Resource-Constrained Project Scheduling Problem Considering Reliability: An AUGMECON2VIKOR Hybrid Method
In recent years, resources with multiple skills have received attention as an extension of the resource-constrained project scheduling problem known as MSRCPSP. Although the disruption rate is well-estimated in today's manufacturing projects, its impact on project makespan and cost need further investigation. Hence, this study presents a novel mathematical model for the MSRCPSP considering reliability, namely MSRCPSPR. The model proposes both objectives of minimizing project makespan and projec…

A Statistical Analysis of Magnetic Parameters in Solar Source Regions of Halo-CMEs with and without SEP events
Xuchun Duan, Ting Li, Yijun Hou, Yang Wang, Yue Li, Yining Zhang, Zheng Sun, Guiping Zhou
https://arxiv.org/abs/2506.21907

A Statistical Analysis of Magnetic Parameters in Solar Source Regions of Halo-CMEs with and without SEP events
Large SEPs can cause adverse space weather hazard to humans technology and such events are especially associated with halo coronal mass ejections (CMEs). But in turn, a significant portion of halo-CMEs are not associated with large SEPs. The objective of this study is to gain an understanding of the source region distinctions between halo-CMEs in SEP and No-SEP events. Among the 176 halo-CMEs observed from 2010-2024, we screen out 45 large SEP events and 131 No-SEP events from this dataset. It …

A Markov switching discrete-time Hawkes process: application to the monitoring of bats behavior
Anna Bonnet, St\'ephane Robin
https://arxiv.org/abs/2507.20153 https://

A Markov switching discrete-time Hawkes process: application to the monitoring of bats behavior
Over the past few decades, the Hawkes process has become a popular framework for modeling temporal events thanks to its flexibility to capture different dependency structures. The objective of this work is to model call sequences emitted by bats for echolocation, whose patterns are known to change depending on the animal's activity. The novelty of the model lies in the combination of a Hawkes-type dependency from past events, as well as a latent variable that encodes changes in bat behavior. Mo…

Pareto-Grid-Guided Large Language Models for Fast and High-Quality Heuristics Design in Multi-Objective Combinatorial Optimization
Minh Hieu Ha, Hung Phan, Tung Duy Doan, Tung Dao, Dao Tran, Huynh Thi Thanh Binh
https://arxiv.org/abs/2507.20923

Pareto-Grid-Guided Large Language Models for Fast and High-Quality Heuristics Design in Multi-Objective Combinatorial Optimization
Multi-objective combinatorial optimization problems (MOCOP) frequently arise in practical applications that require the simultaneous optimization of conflicting objectives. Although traditional evolutionary algorithms can be effective, they typically depend on domain knowledge and repeated parameter tuning, limiting flexibility when applied to unseen MOCOP instances. Recently, integration of Large Language Models (LLMs) into evolutionary computation has opened new avenues for automatic heuristi…

ChipletPart: Scalable Cost-Aware Partitioning for 2.5D Systems
Alexander Graening, Puneet Gupta, Andrew B. Kahng, Bodhisatta Pramanik, Zhiang Wang
https://arxiv.org/abs/2507.19819

ChipletPart: Scalable Cost-Aware Partitioning for 2.5D Systems
Industry adoption of chiplets has been increasing as a cost-effective option for making larger high-performance systems. Consequently, partitioning large systems into chiplets is increasingly important. In this work, we introduce ChipletPart - a cost-driven 2.5D system partitioner that addresses the unique constraints of chiplet systems, including complex objective functions, limited reach of inter-chiplet I/O transceivers, and the assignment of heterogeneous manufacturing technologies to diffe…

NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis
Chengkai Wang, Di Wu, Yunsheng Liao, Wenyao Zheng, Ziyi Zeng, Xurong Gao, Hemmings Wu, Zhoule Zhu, Jie Yang, Lihua Zhong, Weiwei Cheng, Yun-Hsuan Chen, Mohamad Sawan
https://arxiv.org/abs/2507.20189

NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis
Methamphetamine dependence poses a significant global health challenge, yet its assessment and the evaluation of treatments like repetitive transcranial magnetic stimulation (rTMS) frequently depend on subjective self-reports, which may introduce uncertainties. While objective neuroimaging modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer alternatives, their individual limitations and the reliance on conventional, often hand-crafted, feature…

Research on Sectionalizing Switches Placement Problem of Distribution System Automation Based on Multi-Objective Optimization Analysis
Selma Cheshmeh Khavar, Arya Abdollahi
https://arxiv.org/abs/2507.19029

Research on Sectionalizing Switches Placement Problem of Distribution System Automation Based on Multi-Objective Optimization Analysis
Achieving high distribution-reliability levels and concurrently minimizing operating costs can be considered as the main issues in distribution system optimization. Determination of the optimal number and location of automation devices in the distribution system network is an essential issue from the reliability and economical points of view. To address these issues, this paper develops a multi-objective model, wherein the primary objective, optimal automation devices placement is implemented a…

\textit{FedABC}: Attention-Based Client Selection for Federated Learning with Long-Term View
Wenxuan Ye, Xueli An, Junfan Wang, Xueqiang Yan, Georg Carle
https://arxiv.org/abs/2507.20871

\textit{FedABC}: Attention-Based Client Selection for Federated Learning with Long-Term View
Native AI support is a key objective in the evolution of 6G networks, with Federated Learning (FL) emerging as a promising paradigm. FL allows decentralized clients to collaboratively train an AI model without directly sharing their data, preserving privacy. Clients train local models on private data and share model updates, which a central server aggregates to refine the global model and redistribute it for the next iteration. However, client data heterogeneity slows convergence and reduces mo…

This https://arxiv.org/abs/2505.07501 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

The Complexity of Pure Strategy Relevant Equilibria in Concurrent Games
We study rational synthesis problems for concurrent games with $ω$-regular objectives. Our model of rationality considers only pure strategy Nash equilibria that satisfy either a social welfare or Pareto optimality condition with respect to an $ω$-regular objective for each agent. This extends earlier work on equilibria in concurrent games, without consideration about their quality. Our results show that the existence of Nash equilibria satisfying social welfare conditions can be computed as …

This https://arxiv.org/abs/2503.09663 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csOS_…

BYOS: Knowledge-driven Large Language Models Bring Your Own Operating System More Excellent
Operating System (OS) kernel tuning involves systematically adjusting kernel configurations to optimize system performance. Despite recent advancements in large language models (LLMs), kernel tuning remains a critical challenge due to: (1) the semantic gap between abstract tuning objective and concrete config options, (2) insufficient environmental interaction induces LLM hallucinations, and (3) the rapid evolution of kernel versions. To address these challenges, we propose BYOS, a LLM-powered …

Decoding the role of $\rho$ mesonic states for elucidating the $e^ e^-\to a_2(1320)\pi$ data and other reactions
Qin-Song Zhou, Zi-Yue Bai, Hao Xu, Jun-Zhang Wang, Xiang Liu
https://arxiv.org/abs/2507.21554

Decoding the role of $ρ$ mesonic states for elucidating the $e^+e^-\to a_2(1320)π$ data and other reactions
Recently, the BESIII Collaboration observed a $ρ$-like structure $Y(2044)$ in $e^+e^-\to a_2(1320)π$, suggesting that $Y(2044)$ may be a candidate of vector meson $ρ(2D)$ by comparing resonance parameters. However, the theoretical prediction for the combined branching ratio $Γ_{e^+e^-}\mathcal{B}_{a_2(1320)π}$ for the pure $ρ(2D)$ state is about two orders of magnitude smaller than the experimental value. Resolving this discrepancy and deciphering the nature of $Y(2044)$ is a primary obje…

A general framework for the OSRC-preconditioned EFIE in computational electromagnetics
Marion Darbas, Ignacia Fierro-Piccardo
https://arxiv.org/abs/2507.20707 https://

A general framework for the OSRC-preconditioned EFIE in computational electromagnetics
This work presents a comprehensive study of preconditioning strategies for the Electric Field Integral Equation (EFIE) using On-Surface Radiation Condition (OSRC) operators. We examine two distinct formulations -- the Magnetic-to-Electric (MtE) and Electric-to-Magnetic (EtM) maps -- used to precondition the EFIE, and we analyze their spectral properties, discretization behavior, and numerical performance. A central objective is to bridge the gap between theoretical development and practical imp…

In the hands of someone like Trump,
deals are ways to evade, postpone or subvert the efficient work of markets.
Trump does not like markets,
precisely because they are impersonal and objective.
Their results – for corporations, entrepreneurs, investors and shareholders – are subject to clear measures of success and failure.
Because deals are personal, adversarial and incomplete,
they are perfect grist for Trump’s relentless publicity machine,
and a…

Exploring the fusion power plant design space: comparative analysis of positive and negative triangularity tokamaks through optimization
T. Slendebroek, A. O. Nelson, O. M. Meneghini, G. Dose, A. G. Ghiozzi, J. Harvey, B. C. Lyons, J. McClenaghan, T. F. Neiser, D. B. Weisberg, M. G. Yoo, E. Bursch, C. Holland
https://arxiv.org/abs/2507.196…

Exploring the fusion power plant design space: comparative analysis of positive and negative triangularity tokamaks through optimization
The optimal configuration choice between positive triangularity (PT) and negative triangularity (NT) tokamaks for fusion power plants hinges on navigating different operational constraints rather than achieving specific plasma performance metrics. This study presents a systematic comparison using constrained multi-objective optimization with the integrated FUsion Synthesis Engine (FUSE) framework. Over 200,000 integrated design evaluations were performed exploring the trade-offs between capital…

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

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

Proper Orthogonal Decomposition-based Model-Order Reduction for Smoothed Particle Hydrodynamics Simulation
Lidong Fang, Zilong Song, Kirk Fraser, Faisal Habib, Christopher Drummond, Huaxiong Huang
https://arxiv.org/abs/2507.19825

Proper Orthogonal Decomposition-based Model-Order Reduction for Smoothed Particle Hydrodynamics Simulation
In this paper, we present a projection-based model-order reduction (MOR) technique for smoothed particle hydrodynamics (SPH) simulations, which is a mesh-free approach within the Lagrangian framework. Our approach utilizes the proper orthogonal decomposition (POD) technique to generate a subspace basis for the reduction process. The main objective of this study is to conduct an initial exploration of the feasibility of employing POD-based MOR (POD-MOR) in SPH simulations and to quantify the a…

Data Augmentation for Spoken Grammatical Error Correction
Penny Karanasou, Mengjie Qian, Stefano Bann\`o, Mark J. F. Gales, Kate M. Knill
https://arxiv.org/abs/2507.19374 https:…

Data Augmentation for Spoken Grammatical Error Correction
While there exist strong benchmark datasets for grammatical error correction (GEC), high-quality annotated spoken datasets for Spoken GEC (SGEC) are still under-resourced. In this paper, we propose a fully automated method to generate audio-text pairs with grammatical errors and disfluencies. Moreover, we propose a series of objective metrics that can be used to evaluate the generated data and choose the more suitable dataset for SGEC. The goal is to generate an augmented dataset that maintains…

This https://arxiv.org/abs/2505.20527 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Financial literacy, robo-advising, and the demand for human financial advice: Evidence from Italy
This paper investigates the impact of objective financial knowledge, confidence in one's financial skills, and digital financial literacy on individuals' decisions to seek financial advice from robo-advice platforms. Using microdata from the Bank of Italy's survey on adults' "Financial Literacy and Digital Financial Skills in Italy", we find that individuals with greater financial knowledge are less inclined to rely on online services for automated financial advice. On the contrary, confidence …

Evaluating Redundancy Mitigation in Vulnerable Road User Awareness Messages for Bicycles
Nico Ostendorf, Keno Garlichs, Lars Wolf
https://arxiv.org/abs/2506.22052

Evaluating Redundancy Mitigation in Vulnerable Road User Awareness Messages for Bicycles
V2X communication has become crucial for enhancing road safety, especially for Vulnerable Road Users (VRU) such as pedestrians and cyclists. However, the increasing number of devices communicating on the same channels will lead to significant channel load. To address this issue this study evaluates the effectiveness of Redundancy Mitigation (RM) for VRU Awareness Messages (VAM), focusing specifically on cyclists. The objective of RM is to minimize the transmission of redundant information. We c…

The Impact of Simple, Brief, and Adaptive Instructions within Virtual Reality Training: Components of Cognitive Load Theory in an Assembly Task
Rebecca L. Pharmer, Christopher D. Wickens, Lucas Plabst, Benjamin A. Clegg, Leanne M. Hirshfield, Joanna E. Lewis, Jalynn B. Nicoly, Cara A. Spencer, Francisco R. Ortega
https://arxiv.org/abs/2507…

The Impact of Simple, Brief, and Adaptive Instructions within Virtual Reality Training: Components of Cognitive Load Theory in an Assembly Task
Objective: The study examined the effects of varying all three core elements of cognitive load on learning efficiency during a shape assembly task in virtual reality (VR). Background: Adaptive training systems aim to improve learning efficiency and retention by dynamically adjusting difficulty. However, design choices can impact the cognitive workload imposed on the learner. The present experiments examined how aspects of cognitive load impact training outcomes. Method: Participants learned…

This https://arxiv.org/abs/2502.06044 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Scalable Differentially Private Bayesian Optimization
In recent years, there has been much work on scaling Bayesian Optimization to high-dimensional problems, for example hyperparameter tuning in large machine learning models. These scalable methods have been successful, finding high objective values much more quickly than traditional global Bayesian Optimization or random search-based methods. At the same time, these large models often use sensitive data, but preservation of Differential Privacy has not scaled alongside these modern Bayesian Opti…

Economic Model Predictive Control with a Non-Fixed Reference Trajectory for Optimal Microgrid Dispatch
Avik Ghosh, Adil Khurram, Jan Kleissl, Sonia Martinez
https://arxiv.org/abs/2506.22406

Economic Model Predictive Control with a Non-Fixed Reference Trajectory for Optimal Microgrid Dispatch
Economic Model Predictive Control (EMPC), instead of stabilizing a reference trajectory/state in the objective function like a Tracking MPC, optimizes the economic performance over the prediction horizon, making it attractive for economical microgrid (MG) dispatch. However, the demand charge component in the monthly electricity cost, make it difficult to be encapsulated in additive stage costs, and can make solutions violate the principle of optimality if naively introduced in the objective fun…

Generating Highly Structured Test Inputs Leveraging Constraint-Guided Graph Refinement
Zhaorui Yang, Yuxin Qiu, Haichao Zhu, Qian Zhang
https://arxiv.org/abs/2507.21271 https://…

Generating Highly Structured Test Inputs Leveraging Constraint-Guided Graph Refinement
[Context] Modern AI applications increasingly process highly structured data, such as 3D meshes and point clouds, where test input generation must preserve both structural and semantic validity. However, existing fuzzing tools and input generators are typically handcrafted for specific input types and often generate invalid inputs that are subsequently discarded, leading to inefficiency and poor generalizability. [Objective] This study investigates whether test inputs for structured domains can…

Differential contributions of low-frequency phase and power in crossmodal temporal prediction: A MEG study https://www.biorxiv.org/content/10.1101/2025.05.26.656128v1 on "tactile-to-visual influences on temporal prediction", "broad applicability of …

Differential contributions of low-frequency phase and power in crossmodal temporal prediction: A MEG study
Introduction Our representation of time is embedded within multisensory perception, based on sight, sound, or touch. However, despite being a crucial aspect of daily life, the neural dynamics of cross-modal temporal predictions remain elusive. The objective of this study was to investigate neural correlates of tactile-to-visual influences on temporal prediction using magnetoencephalography (MEG). We hypothesized to observe increased inter-trial phase consistency (ITPC) in the low-frequency delt…

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

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

"Objective-C is a West Coast language. Objective-C is a hippie. You throw stuff at it and it just trusts you. “Peace maaaaaannn… if you say this is an NSArray I believe you…” and it leans back in its armchair, smoking weed, listening to Marley, Floyd, Doors, or Janis, looking at you with deep eyes, telling you stories about Buddhism and how important is ecology, and that tonight they are going to a retreat in the beach to pray for Yemanjš and the salvation of dolphins."

The Developer Guide to Migrate Across Galaxies
This is the presentation I gave at the second App Builders Conference in Lausanne, Switzerland, April 25th, 2017.

John Oliver's Last Week Tonight looks at AI slop, how it corrodes the concept of objective reality, the slop economy, tech leaders' enthusiasm for gen AI, more (LastWeekTonight on YouTube)
https://www.youtube.com/watch?v=TWpg1RmzAbc

String Consensus Problems with Swaps and Substitutions
Est\'eban Gabory, Laurent Bulteau, Gabriele Fici, Hilde Verbeek
https://arxiv.org/abs/2507.19139 https://

String Consensus Problems with Swaps and Substitutions
String consensus problems aim at finding a string that minimizes some given distance with respect to an input set of strings. In particular, in the \textsc{Closest String} problem, we are given a set of strings of equal length and a radius $d$. The objective is to find a new string that differs from each input string by at most $d$ substitutions. We study a generalization of this problem where, in addition to substitutions, swaps of adjacent characters are also permitted, each operation incurri…

This https://arxiv.org/abs/2412.12485 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Rydberg Atomic Receiver: Next Frontier of Wireless Communications
Rydberg Atomic REceiver (RARE) is driving a paradigm shift in electromagnetic (EM) wave measurement by harnessing the electron transition phenomenon of Rydberg atoms. Operating at the quantum scale, such receivers have the potential to breakthrough the performance limit of classic receivers, sparking a revolution in physical-layer wireless communications. The objective of this paper is to offer insights into RARE-empowered communications. We first provide a comprehensive introduction to the fun…

Multi-objective Portfolio Optimization Via Gradient Descent
Christian Oliva, Pedro R. Ventura, Luis F. Lago-Fern\'andez
https://arxiv.org/abs/2507.16717 https://

Multi-objective Portfolio Optimization Via Gradient Descent
Traditional approaches to portfolio optimization, often rooted in Modern Portfolio Theory and solved via quadratic programming or evolutionary algorithms, struggle with scalability or flexibility, especially in scenarios involving complex constraints, large datasets and/or multiple conflicting objectives. To address these challenges, we introduce a benchmark framework for multi-objective portfolio optimization (MPO) using gradient descent with automatic differentiation. Our method supports any …

Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals
Junyan Liu, Arnab Maiti, Artin Tajdini, Kevin Jamieson, Lillian J. Ratliff
https://arxiv.org/abs/2505.23124

Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals
We initiate the study of a repeated principal-agent problem over a finite horizon $T$, where a principal sequentially interacts with $K\geq 2$ types of agents arriving in an adversarial order. At each round, the principal strategically chooses one of the $N$ arms to incentivize for an arriving agent of unknown type. The agent then chooses an arm based on its own utility and the provided incentive, and the principal receives a corresponding reward. The objective is to minimize regret against the…

NIRS: An Ontology for Non-Invasive Respiratory Support in Acute Care
Md Fantacher Islam, Jarrod Mosier, Vignesh Subbian
https://arxiv.org/abs/2507.19992 https://

NIRS: An Ontology for Non-Invasive Respiratory Support in Acute Care
Objective: Develop a Non Invasive Respiratory Support (NIRS) ontology to support knowledge representation in acute care settings. Materials and Methods: We developed the NIRS ontology using Web Ontology Language (OWL) semantics and Protege to organize clinical concepts and relationships. To enable rule-based clinical reasoning beyond hierarchical structures, we added Semantic Web Rule Language (SWRL) rules. We evaluated logical reasoning by adding 17 hypothetical patient clinical scenarios. W…

Evaluating Personalized Beneficial Interventions in the Daily Lives of Older Adults Using a Camera
Longfei Chen, Christopher Lochhead, Robert B. Fisher, Nusa Faric, Jacques Fleuriot, Subramanian Ramamoorthy
https://arxiv.org/abs/2507.19494

Evaluating Personalized Beneficial Interventions in the Daily Lives of Older Adults Using a Camera
Beneficial daily activity interventions have been shown to improve both the physical and mental health of older adults. However, there is a lack of robust objective metrics and personalized strategies to measure their impact. In this study, two older adults aged over 65, living in Edinburgh, UK, selected their preferred daily interventions (mindful meals and art crafts), which are then assessed for effectiveness. The total monitoring period across both participants was 8 weeks. Their physical b…

Toward Effective AI Governance: A Review of Principles
Danilo Ribeiro, Thayssa Rocha, Gustavo Pinto, Bruno Cartaxo, Marcelo Amaral, Nicole Davila, Ana Camargo
https://arxiv.org/abs/2505.23417

Toward Effective AI Governance: A Review of Principles
Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We…

A direct approach of the existence of the solution to a Riccati equation
Gabriela Marinoschi
https://arxiv.org/abs/2507.20171 https://arxiv.org/pdf/2507.20…

A direct approach of the existence of the solution to a Riccati equation
Finding the state feedback control in an $% H^{\infty }$-optimal control problem involves a challenging approach of the associated algebraic Riccati equation of the generic form $A^{\ast }P+PA+PΓP=F$. In view of this objective, we explore first in this paper the existence of the solution to this algebraic Riccati equation by a direct operatorial approach in the space of Hilbert-Schmidt operators. The proofs are provided, under certain assumptions on the operators $Γ$ and $F,$ for the cases wi…

This https://arxiv.org/abs/2503.17189 has been replaced.
initial toot: https://mastoxiv.page/@a…

Bayesian optimisation of poloidal field coil positions in tokamaks
The tokamak is a world-leading concept for producing sustainable energy via magnetically-confined nuclear fusion. Identifying where to position the magnets within a tokamak, specifically the poloidal field (PF) coils, is a design problem which requires balancing a number of competing economic, physical, and engineering objectives and constraints. In this paper, we show that multi-objective Bayesian optimisation (BO), an iterative optimisation technique utilising probabilistic machine learning m…

Disney names Jilly Pearce as head of unscripted programming for ABC and Hulu; Pearce was previously the president of Objective Media Group America (Rick Porter/The Hollywood Reporter)
https://www.hollywoodreporter.com/tv/tv-ne

Jilly Pearce to Head Unscripted Programming at ABC and Hulu
The executive was most recently president of Objective Media Group America.

Guarding Offices with Maximum Dispersion
S\'andor P. Fekete, Kai Kobbe, Dominik Krupke, Joseph S. B. Mitchell, Christian Rieck, Christian Scheffer
https://arxiv.org/abs/2506.21307

Guarding Offices with Maximum Dispersion
We investigate the Dispersive Art Gallery Problem with vertex guards and rectangular visibility ($r$-visibility) for a class of orthogonal polygons that reflect the properties of real-world floor plans: these office-like polygons consist of rectangular rooms and corridors. In the dispersive variant of the Art Gallery Problem, the objective is not to minimize the number of guards but to maximize the minimum geodesic $L_1$-distance between any two guards, called the dispersion distance. Our mai…

Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Yuanfang Ren, Esra Adiyeke, Ziyuan Guan, Zhenhong Hu, Mackenzie J Meni, Benjamin Shickel, Parisa Rashidi, Tezcan Ozrazgat-Baslanti, Azra Bihorac
https://arxiv.org/abs…

Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Despite advances in surgical techniques and care, postoperative complications are prevalent and effects up to 15% of the patients who underwent a major surgery. The objective of this study is to develop and validate models for predicting postoperative complications and death after major surgery on a large and multicenter dataset, following the previously validated MySurgeryRisk algorithm. This retrospective, longitudinal and multicenter cohort analysis included 508,097 encounters from 366,875 a…

Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs
Maria Camporese, Fabio Massacci
https://arxiv.org/abs/2507.20977 https://

Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs
Background: Automated Vulnerability Repair (AVR) is a fast-growing branch of program repair. Recent studies show that large language models (LLMs) outperform traditional techniques, extending their success beyond code generation and fault detection. Hypothesis: These gains may be driven by hidden factors -- "invisible hands" such as training-data leakage or perfect fault localization -- that let an LLM reproduce human-authored fixes for the same code. Objective: We replicate prior AVR studi…

"The Research Data Management Workbook: Building a Collection of Data Management Exercises to Bridge Data Information Literacy and Data Management Implementation" @ Journal of eScience Librarianship: https://doi.org/10.7191/jeslib.937

Martingale Consumption
Peter Holm Nielsen
https://arxiv.org/abs/2505.20504 https://arxiv.org/pdf/2505.20504

Martingale Consumption
We propose martingale consumption as a natural, desirable consumption pattern for any given (proportional) investment strategy. The idea is to always adjust current consumption so as to achieve level expected future consumption under the arbitrarily chosen investment strategy. This approach avoids the formulation of an optimization objective based on preferences towards risk, intertemporal consumption, habit formation etc. We identify general explicit solutions in deterministic-coefficient mode…

Bridging Classical Molecular Dynamics and Quantum Foundations for Comprehensive Protein Structural Analysis
Don Roosan, Rubayat Khan, Tiffany Khou, Saif Nirzhor, Fahmida Hai, Brian Provencher
https://arxiv.org/abs/2506.20830

Bridging Classical Molecular Dynamics and Quantum Foundations for Comprehensive Protein Structural Analysis
The objective of this paper is to investigate the structural stability, dynamic properties, and potential interactions among Amyloid Precursor Protein (APP), Tau, and Alpha-synuclein through a series of molecular dynamics simulations that integrate publicly available structural data, detailed force-field parameters, and comprehensive analytical protocols. By focusing on these three proteins, which are each implicated in various neurodegenerative disorders, the study aims to elucidate how their …

A Model-Based Derivative-Free Optimization Algorithm for Partially Separable Problems
Yichuan Liu, Yingzhou Li
https://arxiv.org/abs/2506.21948 https://

A Model-Based Derivative-Free Optimization Algorithm for Partially Separable Problems
We propose UPOQA, a derivative-free optimization algorithm for partially separable unconstrained problems, leveraging quadratic interpolation and a structured trust-region framework. By decomposing the objective into element functions, UPOQA constructs underdetermined element models and solves subproblems efficiently via a modified projected gradient method. Innovations include an approximate projection operator for structured trust regions, improved management of elemental radii and models, a …

MEDEA: A Design-Time Multi-Objective Manager for Energy-Efficient DNN Inference on Heterogeneous Ultra-Low Power Platforms
Hossein Taji, Jos\'e Miranda, Miguel Pe\'on-Quir\'os, David Atienza
https://arxiv.org/abs/2506.19067

MEDEA: A Design-Time Multi-Objective Manager for Energy-Efficient DNN Inference on Heterogeneous Ultra-Low Power Platforms
The growing demand for on-device AI necessitates energy-efficient execution of DNN based applications on resource-constrained ultra-low power (ULP) platforms. Heterogeneous architectures, combining specialized processing elements (PEs), have emerged as a key solution for achieving the required performance and energy efficiency. However, optimizing energy while executing applications on these platforms requires efficiently managing platform resources like PEs, power features, and memory footprin…

Towards Effective Human Performance in XR Space Framework based on Real-time Eye Tracking Biofeedback
Barbara Karpowicz, Tomasz Kowalewski, Pavlo Zinevych, Adam Kuzdrali\'nski, Grzegorz Marcin W\'ojcik, Wies{\l}aw Kope\'c
https://arxiv.org/abs/2507.21000

Towards Effective Human Performance in XR Space Framework based on Real-time Eye Tracking Biofeedback
This paper proposes an eye tracking module for the XR Space Framework aimed at enhancing human performance in XR-based applications, specifically in training, screening, and teleoperation. This framework provides a methodology and components that streamline the development of adaptive real-time virtual immersive systems. It contains multimodal measurements - declarative in the form of in-VR questionnaires and objective, including eye tracking, body movement, and psychophysiological data (e.g., …

Simultaneous improvement of control and estimation for battery management systems
Mohammad S. Ramadan, Marfred Barrera, Mihai Anitescu, Sylvia Herbert
https://arxiv.org/abs/2507.21300

Simultaneous improvement of control and estimation for battery management systems
The state of charge of battery systems is an important metric typically estimated by observation models, represented by open-circuit voltage graphs. These observation models are often nonlinear in the state of charge, resulting in varying observability from a state estimation perspective. In this paper, we employ a stochastic optimal control (also known as dual control) approach to simultaneously satisfy the control objective in the state of charge of battery systems and improve estimation accu…

Reinforcement Learning for Optimal Control of Spin Magnetometers
Logan W. Cooke, Stefanie Czischek
https://arxiv.org/abs/2506.21475 https://

Reinforcement Learning for Optimal Control of Spin Magnetometers
Quantum optimal control in the presence of decoherence is difficult, particularly when not all Hamiltonian parameters are known precisely, as in quantum sensing applications. In this context, maximizing the sensitivity of the system is the objective, for which the optimal target state or unitary transformations are unknown, especially in the case of multi-parameter estimation. Here, we investigate the use of reinforcement learning (RL), specifically the soft actor-critic (SAC) algorithm, for pr…

Stochastic gradient with least-squares control variates
Fabio Nobile, Matteo Raviola, Nathan Schaeffer
https://arxiv.org/abs/2507.20981 https://arxiv.org/p…

Stochastic gradient with least-squares control variates
The stochastic gradient descent (SGD) method is a widely used approach for solving stochastic optimization problems, but its convergence is typically slow. Existing variance reduction techniques, such as SAGA, improve convergence by leveraging stored gradient information; however, they are restricted to settings where the objective functional is a finite sum, and their performance degrades when the number of terms in the sum is large. In this work, we propose a novel approach which is well suit…

The Trump administration plans to vet AI models for "ideological bias" and limit government contracts to tech companies whose models offer "objective truth" (Financial Times)
https://www.ft.com/content/406bc127-e1c3-41d5-9e68-b8921856c3c7

This survey has a few hours of life left, but I've gone ahead and chosen "Innocuous In Innisfil." That feels right and suits my general objective to be harmless and not give offense or arouse strong feelings. Although I've failed that objective on occasion.
#NewBeginning

Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests
Masaki Uto, Yuma Ito
https://arxiv.org/abs/2506.20119

Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests
Evaluating the abilities of learners is a fundamental objective in the field of education. In particular, there is an increasing need to assess higher-order abilities such as expressive skills and logical thinking. Constructed-response tests such as short-answer and essay-based questions have become widely used as a method to meet this demand. Although these tests are effective, they require substantial manual grading, making them both labor-intensive and costly. Item response theory (IRT) prov…

Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects
Zihan Hong, Yushi Wu, Zhiting Zhao, Shanshan Feng, Jianghong Ma, Jiao Liu, Tianjun Wei
https://arxiv.org/abs/2506.16893

Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects
With the recent progress in generative artificial intelligence (Generative AI), particularly in the development of large language models, recommendation systems are evolving to become more versatile. Unlike traditional techniques, generative AI not only learns patterns and representations from complex data but also enables content generation, data synthesis, and personalized experiences. This generative capability plays a crucial role in the field of recommendation systems, helping to address t…

Fast convex optimization via inertial systems with asymptotic vanishing viscous and Hessian-driven damping
Zepeng Wang, Juan Peypouquet
https://arxiv.org/abs/2506.21730

Fast convex optimization via inertial systems with asymptotic vanishing viscous and Hessian-driven damping
We study the convergence rate of a family of inertial algorithms, which can be obtained by discretization of an inertial system combining asymptotic vanishing viscous and Hessian-driven damping. We establish a fast sublinear convergence rate in case the objective function is convex and satisfies Polyak-Łojasiewicz inequality. We also establish a linear convergence rate for smooth strongly convex functions. The results can provide more insights into the convergence property of Nesterov's accele…

Max-Min Beamforming for Large-Scale Cell-Free Massive MIMO: A Randomized ADMM Algorithm
Bin Wang, Jun Fang, Yue Xiao, Martin Haardt
https://arxiv.org/abs/2507.18980 https://

Max-Min Beamforming for Large-Scale Cell-Free Massive MIMO: A Randomized ADMM Algorithm
We consider the problem of max-min beamforming (MMB) for cell-free massive multi-input multi-output (MIMO) systems, where the objective is to maximize the minimum achievable rate among all users. Existing MMB methods are mainly based on deterministic optimization methods, which are computationally inefficient when the problem size grows large. To address this issue, we, in this paper, propose a randomized alternating direction method of multiplier (ADMM) algorithm for large-scale MMB problems. …

Financial literacy, robo-advising, and the demand for human financial advice: Evidence from Italy
David Aristei, Manuela Gallo
https://arxiv.org/abs/2505.20527

Financial literacy, robo-advising, and the demand for human financial advice: Evidence from Italy
This paper investigates the impact of objective financial knowledge, confidence in one's financial skills, and digital financial literacy on individuals' decisions to seek financial advice from robo-advice platforms. Using microdata from the Bank of Italy's survey on adults' "Financial Literacy and Digital Financial Skills in Italy", we find that individuals with greater financial knowledge are less inclined to rely on online services for automated financial advice. On the contrary, confidence …

Inverse initial data reconstruction for Maxwell's equations via time-dimensional reduction method
Thuy T. Le, Cong B. Van, Trong D. Dang, Loc H. Nguyen
https://arxiv.org/abs/2506.20777

Inverse initial data reconstruction for Maxwell's equations via time-dimensional reduction method
We study an inverse problem for the time-dependent Maxwell system in an inhomogeneous and anisotropic medium. The objective is to recover the initial electric field $\mathbf{E}_0$ in a bounded domain $Ω\subset \mathbb{R}^3$, using boundary measurements of the electric field and its normal derivative over a finite time interval. Informed by practical constraints, we adopt an under-determined formulation of Maxwell's equations that avoids the need for initial magnetic field data and charge densi…

This https://arxiv.org/abs/2503.11688 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Towards Resilient and Sustainable Global Industrial Systems: An Evolutionary-Based Approach
This paper presents a new complex optimization problem in the field of automatic design of advanced industrial systems and proposes a hybrid optimization approach to solve the problem. The problem is multi-objective as it aims at finding solutions that minimize CO2 emissions, transportation time, and costs. The optimization approach combines an evolutionary algorithm and classical mathematical programming to design resilient and sustainable global manufacturing networks. Further, it makes use o…

Route Optimization Over Scheduled Services For Large-Scale Package Delivery Networks
Mohammed Faisal Ahmed, Pascal Van Hentenryck, Ahmed El Nashar
https://arxiv.org/abs/2507.20844

Route Optimization Over Scheduled Services For Large-Scale Package Delivery Networks
This paper introduces the Trailer Path Optimization with Schedule Services Problem (TPOSSP) and proposes a column- generation heuristic (CG-heuristic) to find high-quality solutions to large-scale instances. The TPOSSP aims at determining trailer routes over a time-dependent network using existing scheduled services, while considering tractor capacity constraints and time windows for trailer pickups and deliveries. The objective is to minimize both the number of schedules used and the total mil…

This https://arxiv.org/abs/2503.11688 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Towards Resilient and Sustainable Global Industrial Systems: An Evolutionary-Based Approach
This paper presents a new complex optimization problem in the field of automatic design of advanced industrial systems and proposes a hybrid optimization approach to solve the problem. The problem is multi-objective as it aims at finding solutions that minimize CO2 emissions, transportation time, and costs. The optimization approach combines an evolutionary algorithm and classical mathematical programming to design resilient and sustainable global manufacturing networks. Further, it makes use o…

Post-estimation Adjustments in Data-driven Decision-making with Applications in Pricing
Michael Albert, Max Biggs, Ningyuan Chen, Guan Wang
https://arxiv.org/abs/2507.20501 http…

Post-estimation Adjustments in Data-driven Decision-making with Applications in Pricing
The predict-then-optimize (PTO) framework is a standard approach in data-driven decision-making, where a decision-maker first estimates an unknown parameter from historical data and then uses this estimate to solve an optimization problem. While widely used for its simplicity and modularity, PTO can lead to suboptimal decisions because the estimation step does not account for the structure of the downstream optimization problem. We study a class of problems where the objective function, evaluat…

Fair Allocation with Money: What is Your Objective?
Noga Klein Elmalem, Rica Gonen, Erel Segal-Halevi
https://arxiv.org/abs/2506.18794 https://

Fair Allocation with Money: What is Your Objective?
When allocating indivisible items, there are various ways to use monetary transfers for eliminating envy. Particularly, one can apply a balanced vector of transfer payments, or charge each agent a positive amount, or -- contrarily -- give each agent a positive amount as a ``subsidy''. In each model, one can aim to minimize the amount of payments used; this aim translates into different optimization objectives in each setting. This note compares the various models, and the relations between uppe…

Max-Min Rate Optimization for Multigroup Multicast MISO Systems Via Novel Transmissive RIS Transceiver
Yuan Guo, Wen Chen, Qingqing Wu, Yanze Zhu, Yang Liu, Zhendong Li, Ying Wang
https://arxiv.org/abs/2507.18733

Max-Min Rate Optimization for Multigroup Multicast MISO Systems Via Novel Transmissive RIS Transceiver
This paper investigates a novel transmissive reconfigurable intelligent surface (RIS) transceiver architectureenabled multigroup multicast downlink communication system. Under this setup, an optimization problem is formulated to maximize the minimum rate of users across all groups, subject to the maximum available power of each RIS unit. Due to the nondifferentiable nature of the objective function, the max-min rate problem is challenging to solve. To tackle this difficult problem, we develop a…

Second-order methods for provably escaping strict saddle points in composite nonconvex and nonsmooth optimization
Alexander Bodard, Masoud Ahookhosh, Panagiotis Patrinos
https://arxiv.org/abs/2506.22332

Second-order methods for provably escaping strict saddle points in composite nonconvex and nonsmooth optimization
This study introduces two second-order methods designed to provably avoid saddle points in composite nonconvex optimization problems: (i) a nonsmooth trust-region method and (ii) a curvilinear linesearch method. These developments are grounded in the forward-backward envelope (FBE), for which we analyze the local second-order differentiability around critical points and establish a novel equivalence between its second-order stationary points and those of the original objective. We show that the…

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
Jose Guadalupe Hernandez, Attri Ghosh, Philip J. Freda, Yufei Meng, Nicholas Matsumoto, Jason H. Moore
https://arxiv.org/abs/2505.22746

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
We present the Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. StarBASE-GP uses a genetic programming-based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximize explanatory power (r2) and minimize pipeline complexity. Biological domain knowledge is integrated …

A Complete-Electrode-Model-Based Forward Approach for Transcranial Temporal Interference Stimulation with Linearization: Numerical Simulation Study
Santtu S\"oderholm, Maryam Samavaki, Sampsa Pursiainen
https://arxiv.org/abs/2506.18436

A Complete-Electrode-Model-Based Forward Approach for Transcranial Temporal Interference Stimulation with Linearization: Numerical Simulation Study
Background and Objective: Transcranial temporal interference stimulation (tTIS) is a promising non-invasive brain stimulation technique in which interference between electrical current fields extends the possibilities of electrical brain stimulation. The objective of this study is to develop an efficient mathematical tTIS forward modelling scheme that allows for realistic and adaptable simulation and can be updated accurately when the stimulation frequency is modified in one or more electrodes.…

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
Jose Guadalupe Hernandez, Attri Ghosh, Philip J. Freda, Yufei Meng, Nicholas Matsumoto, Jason H. Moore
https://arxiv.org/abs/2505.22746

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
We present the Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. StarBASE-GP uses a genetic programming-based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximize explanatory power (r2) and minimize pipeline complexity. Biological domain knowledge is integrated …

Limits at a Distance: Design Directions to Address Psychological Distance in Policy Decisions Affecting Planetary Boundaries
Eshta Bhardwaj, Han Qiao, Christoph Becker
https://arxiv.org/abs/2507.18913 …

Limits at a Distance: Design Directions to Address Psychological Distance in Policy Decisions Affecting Planetary Boundaries
Policy decisions relevant to the environment rely on tools like dashboards, risk models, and prediction models to provide information and data visualizations that enable decision-makers to make trade-offs. The conventional paradigm of data visualization practices for policy and decision-making is to convey data in a supposedly neutral, objective manner for rational decision-makers. Feminist critique advocates for nuanced and reflexive approaches that take into account situated decision-makers a…

Automating Capacitor Part Selection with Dual-Objective Optimization
Luke Brantingham, Jason Grover
https://arxiv.org/abs/2507.16071 https://

Automating Capacitor Part Selection with Dual-Objective Optimization
This paper presents a novel framework for optimizing capacitor selection in electronic design using multi-objective linear and non-linear constrained optimization techniques. We demonstrate the effectiveness of this approach in minimizing cost and board area while meeting critical performance requirements.

Stable formulations for the Capacitated Facility Location Problem with Customer Preferences
Concepci\'on Dom\'inguez, Juan de Dios Jaime-Alc\'antara
https://arxiv.org/abs/2507.21944

Stable formulations for the Capacitated Facility Location Problem with Customer Preferences
In the Simple Plant Location Problem with Order (SPLPO), the aim is to open a subset of plants to assign every customer taking into account their preferences. Customers rank the plants in strict order and are assigned to their favorite open plant, and the objective is to minimize the location plus allocation costs. Here, we study a generalization of the SPLPO named the Capacitated Facility Location Problem with Customer Preferences (CFLCP) where a limited number of customers can be allocated to…

Smoothness Meets Autobidding: Tight Price of Anarchy Bounds for Simultaneous First-Price Auctions
Riccardo Colini-Baldeschi, Sophie Klumper, Twan Kroll, Stefano Leonardi, Guido Sch\"afer, Artem Tsikiridis
https://arxiv.org/abs/2506.20908

Smoothness Meets Autobidding: Tight Price of Anarchy Bounds for Simultaneous First-Price Auctions
Online advertising systems have recently transitioned to autobidding, enabling advertisers to delegate bidding decisions to automated agents. Each advertiser directs their agent to optimize a valuation-dependent objective subject to return-on-investment (ROI) or budget constraints. Given their relevance, there has been a surge in literature studying the liquid welfare price of anarchy (POA) of core auction formats in autobidding, among which simultaneous first-price auctions (FPA). These models…

An Explainable Equity-Aware P2P Energy Trading Framework for Socio-Economically Diverse Microgrid
Abhijan Theja, Mayukha Pal
https://arxiv.org/abs/2507.18738 https://

An Explainable Equity-Aware P2P Energy Trading Framework for Socio-Economically Diverse Microgrid
Fair and dynamic energy allocation in community microgrids remains a critical challenge, particularly when serving socio-economically diverse participants. Static optimization and cost-sharing methods often fail to adapt to evolving inequities, leading to participant dissatisfaction and unsustainable cooperation. This paper proposes a novel framework that integrates multi-objective mixed-integer linear programming (MILP), cooperative game theory, and a dynamic equity-adjustment mechanism driven…

Integrating Movable Antennas and Intelligent Reflecting Surfaces for Coverage Enhancement
Ying Gao, Qingqing Wu, Weidong Mei, Guangji Chen, Wen Chen, Ziyuan Zheng
https://arxiv.org/abs/2506.21375

Integrating Movable Antennas and Intelligent Reflecting Surfaces for Coverage Enhancement
This paper investigates an intelligent reflecting surface (IRS)-aided movable antenna (MA) system, where multiple IRSs cooperate with a multi-MA base station to extend wireless coverage to multiple designated target areas. The objective is to maximize the worst-case signal-to-noise ratio (SNR) across all locations within these areas through joint optimization of MA positions, IRS reflection coefficients, and transmit beamforming. To achieve this while balancing the performance-cost trade-off, w…

ROBBO: An Efficient Method for Pareto Front Estimation with Guaranteed Accuracy
Roberto Boffadossi, Marco Leonesio, Lorenzo Fagiano
https://arxiv.org/abs/2506.18004

ROBBO: An Efficient Method for Pareto Front Estimation with Guaranteed Accuracy
A new method to estimate the Pareto Front (PF) in bi-objective optimization problems is presented. Assuming a continuous PF, the approach, named ROBBO (RObust and Balanced Bi-objective Optimization), needs to sample at most a finite, pre-computed number of PF points. Upon termination, it guarantees that the worst-case approximation error lies within a desired tolerance range, predefined by the decision maker, for each of the two objective functions. Theoretical results are derived, about the wo…

Speed-Aware Network Design: A Parametric Optimization Approach
Ugo Rosolia, Marc Bataillou Almagro, George Iosifidis, Martin Gross, Georgios Paschos
https://arxiv.org/abs/2506.20216

Speed-Aware Network Design: A Parametric Optimization Approach
Network design problems have been studied from the 1950s, as they can be used in a wide range of real-world applications, e.g., design of communication and transportation networks. In classical network design problems, the objective is to minimize the cost of routing the demand flow through a graph. In this paper, we introduce a generalized version of such a problem, where the objective is to tradeoff routing costs and delivery speed; we introduce the concept of speed-coverage, which is defined…

A Multi-Objective Optimization framework for Decentralized Learning with coordination constraints
Roberto Morales, Umberto Biccari
https://arxiv.org/abs/2507.13983

A Multi-Objective Optimization framework for Decentralized Learning with coordination constraints
This article introduces a generalized framework for Decentralized Learning formulated as a Multi-Objective Optimization problem, in which both distributed agents and a central coordinator contribute independent, potentially conflicting objectives over a shared model parameter space. Unlike traditional approaches that merge local losses under a common goal, our formulation explicitly incorporates coordinator-side criteria, enabling more flexible and structured training dynamics. To navigate the …

Assessing an evolutionary search engine for small language models, prompts, and evaluation metrics
Cl\'audio L\'ucio do Val Lopes, Lucca Machado
https://arxiv.org/abs/2506.21512

Assessing an evolutionary search engine for small language models, prompts, and evaluation metrics
The concurrent optimization of language models and instructional prompts presents a significant challenge for deploying efficient and effective AI systems, particularly when balancing performance against computational costs like token usage. This paper introduces and assesses a bi-objective evolutionary search engine designed to navigate this complex space, focusing specifically on Small Language Models (SLMs). We employ the NSGA-II algorithm and prompt grammar to simultaneously optimize for ta…

Distributionally Robust Optimization is a Multi-Objective Problem
Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim
https://arxiv.org/abs/2507.11350 https:/…

Distributionally Robust Optimization is a Multi-Objective Problem
Distributionally Robust Optimization (DRO) is a worst-case approach to decision making when there is model uncertainty. Though formulated as a single-objective problem, we show that it is intrinsically multi-objective in that DRO solutions map out a near-Pareto-optimal frontier between expected cost and a measure of robustness called worst-case sensitivity (WCS). We take this as the starting point and explore robust decision making through a multi-objective lens. We show that WCS is a measure o…

An Optimization-Based Framework for Solving Forward-Backward Stochastic Differential Equations: Convergence Analysis and Error Bounds
Yutian Wang, Yuan-Hua Ni, Xun Li
https://arxiv.org/abs/2507.15234

An Optimization-Based Framework for Solving Forward-Backward Stochastic Differential Equations: Convergence Analysis and Error Bounds
In this paper, we develop an optimization-based framework for solving coupled forward-backward stochastic differential equations. We introduce an integral-form objective function and prove its equivalence to the error between consecutive Picard iterates. Our convergence analysis establishes that minimizing this objective generates sequences that converge to the true solution. We provide explicit upper and lower bounds that relate the objective value to the error between trial and exact solution…

An Adaptive Order Caputo Fractional Gradient Descent Method for Multi-objective Optimization Problems
Barsha Shaw, Md Abu Talhamainuddin Ansary
https://arxiv.org/abs/2507.07674

An Adaptive Order Caputo Fractional Gradient Descent Method for Multi-objective Optimization Problems
This article introduces the multi-objective adaptive order Caputo fractional gradient descent (MOAOCFGD) algorithm for solving unconstrained multi-objective problems. The proposed method performs equally well for both smooth and non-smooth multi-objective optimization problems. Moreover, the proposed method does not require any a priori chosen parameters or ordering information of the objective functions. At every iteration of the proposed method, a subproblem is solved to identify a suitable d…

MultiObjectiveAlgorithms.jl: a Julia package for solving multi-objective optimization problems
Oscar Dowson, Xavier Gandibleux, G\"okhan Kof
https://arxiv.org/abs/2507.05501 …

MultiObjectiveAlgorithms.jl: a Julia package for solving multi-objective optimization problems
We present MultiObjectiveAlgorithms.jl, an open-source Julia library for solving multi-objective optimization problems written in JuMP. MultiObjectiveAlgorithms.jl implements a number of different solution algorithms, which all rely on an iterative scalarization of the problem from a multi-objective optimization problem to a sequence of single-objective subproblems. As part of this work, we extended JuMP to support vector-valued objective functions. Because it is based on JuMP, MultiObjectiveAl…

Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
Hao Huang, Zelda B. Zabinsky
https://arxiv.org/abs/2506.04554 https://

Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
A multiple objective simulation optimization algorithm named Multiple Objective Probabilistic Branch and Bound with Single Observation (MOPBnB(so)) is presented for approximating the Pareto optimal set and the associated efficient frontier for stochastic multi-objective optimization problems. MOPBnB(so) evaluates a noisy function exactly once at any solution and uses neighboring solutions to estimate the objective functions, in contrast to a variant that uses multiple replications at a solution…