Tootfinder

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.

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…

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

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…

The second amendment is a cancer on the Constitution. Its primary objective is to preserve to the states the ability to raise militia to act as slave patrols.

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…

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.…

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 …

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.

Some days editing means researching the fine points of further vs farther or everyday vs every day. Some days you read up on why mental health professional recommend saying "death by suicide" instead of "committed suicide"
https://www.iasp.info/languageguidelines/

Language Guidelines - IASP
The Language of Suicide  An important objective of suicide prevention is to remove the stigma associated with suicide and mental health issues so that people will be more likely to seek the help they need. One of the ways we can do this is to be conscious of our use […]

"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.

A Unifying Scheme for Extractive Content Selection Tasks
Shmuel Amar, Ori Shapira, Aviv Slobodkin, Ido Dagan
https://arxiv.org/abs/2507.16922 https://

A Unifying Scheme for Extractive Content Selection Tasks
A broad range of NLP tasks involve selecting relevant text spans from given source texts. Despite this shared objective, such \textit{content selection} tasks have traditionally been studied in isolation, each with its own modeling approaches, datasets, and evaluation metrics. In this work, we propose \textit{instruction-guided content selection (IGCS)} as a beneficial unified framework for such settings, where the task definition and any instance-specific request are encapsulated as instructio…

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

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
Omid Orang, Patricia O. Lucas, Gabriel I. F. Paiva, Petronio C. L. Silva, Felipe Augusto Rocha da Silva, Adriano Alonso Veloso, Frederico Gadelha Guimaraes
https://arxiv.org/abs/2507.17016

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
In recent years, the application of Large Language Models (LLMs) to time series forecasting (TSF) has garnered significant attention among researchers. This study presents a new frame of LLMs named CGF-LLM using GPT-2 combined with fuzzy time series (FTS) and causal graph to predict multivariate time series, marking the first such architecture in the literature. The key objective is to convert numerical time series into interpretable forms through the parallel application of fuzzification and c…

Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos, Antonio A. Gentile, Osvaldo Simeone
https://arxiv.org/abs/2506.19714 …

Conservative quantum offline model-based optimization
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experimentation. Recent work has introduced quantum extremal learning (QEL), which leverages the expressive power of variational quantum circuits to learn accurate surrogate functions by training on a few data points. However, as widely studied in the classical machine learning literature, predictive models may incorrectly e…

XtalOpt Version 14: Variable-Composition Crystal Structure Search for Functional Materials Through Pareto Optimization
Samad Hajinazar, Eva Zurek
https://arxiv.org/abs/2506.17246 …

XtalOpt Version 14: Variable-Composition Crystal Structure Search for Functional Materials Through Pareto Optimization
Version 14 of XtalOpt, evolutionary multi-objective global optimization algorithm for crystal structure prediction, is now available for download from its official website https://xtalopt.github.io. The new version of the code is designed to perform ground state search for novel crystal structures with variable composition by integrating a suite of ab initio methods alongside classical and machine-learning potentials for structural relaxation. The multi-objective search framework has been furth…

Bayesian preference elicitation for decision support in multiobjective optimization
Felix Huber, Sebastian Rojas Gonzalez, Raul Astudillo
https://arxiv.org/abs/2507.16999

Bayesian preference elicitation for decision support in multiobjective optimization
We present a novel approach to help decision-makers efficiently identify preferred solutions from the Pareto set of a multi-objective optimization problem. Our method uses a Bayesian model to estimate the decision-maker's utility function based on pairwise comparisons. Aided by this model, a principled elicitation strategy selects queries interactively to balance exploration and exploitation, guiding the discovery of high-utility solutions. The approach is flexible: it can be used interactively…

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…

Integrating Pair Programming as a Work Practice
Nina Haugland Andersen, Anastasiia Tkalich, Nils Brede Moe, Darja Smite, Asgaut Mj{\o}lne S\"oderbom, Ola Hast, Viktoria Stray
https://arxiv.org/abs/2506.19511

Integrating Pair Programming as a Work Practice
Context: Pair programming (PP) is more relevant than ever. As modern systems grow in complexity, knowledge sharing and collaboration across teams have become essential. However, despite well-documented benefits of PP, its adoption remains inconsistent across software teams. Objective: This study aims to understand the factors that facilitate or hinder team members' adoption as well as lasting engagement in PP. Method: We have conducted an exploratory single-case study in a mature agile company …

MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems
Kushagra Agrawal, Polat Goktas, Anjan Bandopadhyay, Debolina Ghosh, Junali Jasmine Jena, Mahendra Kumar Gourisaria
https://arxiv.org/abs/2508.14830

MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems
The rapid growth of Internet of Things (IoT) ecosystems has intensified the challenge of efficiently allocating heterogeneous resources in highly dynamic, distributed environments. Conventional centralized mechanisms and single-objective auction models, focusing solely on metrics such as cost minimization or revenue maximization, struggle to deliver balanced system performance. This paper proposes the Multi-Objective Hierarchical Auction Framework (MOHAF), a distributed resource allocation mech…

TTSDS2: Resources and Benchmark for Evaluating Human-Quality Text to Speech Systems
Christoph Minixhofer, Ondrej Klejch, Peter Bell
https://arxiv.org/abs/2506.19441

TTSDS2: Resources and Benchmark for Evaluating Human-Quality Text to Speech Systems
Evaluation of Text to Speech (TTS) systems is challenging and resource-intensive. Subjective metrics such as Mean Opinion Score (MOS) are not easily comparable between works. Objective metrics are frequently used, but rarely validated against subjective ones. Both kinds of metrics are challenged by recent TTS systems capable of producing synthetic speech indistinguishable from real speech. In this work, we introduce Text to Speech Distribution Score 2 (TTSDS2), a more robust and improved versio…

Computational Intelligence based Land-use Allocation Approaches for Mixed Use Areas
Sabab Aosaf, Muhammad Ali Nayeem, Afsana Haque, M Sohel Rahmana
https://arxiv.org/abs/2508.15240

Computational Intelligence based Land-use Allocation Approaches for Mixed Use Areas
Urban land-use allocation represents a complex multi-objective optimization problem critical for sustainable urban development policy. This paper presents novel computational intelligence approaches for optimizing land-use allocation in mixed-use areas, addressing inherent trade-offs between land-use compatibility and economic objectives. We develop multiple optimization algorithms, including custom variants integrating differential evolution with multi-objective genetic algorithms. Key contrib…

How weak are weak factors? Uniform inference for signal strength in signal plus noise models
Anna Bykhovskaya, Vadim Gorin, Sasha Sodin
https://arxiv.org/abs/2507.18554 https://…

How weak are weak factors? Uniform inference for signal strength in signal plus noise models
The paper analyzes four classical signal-plus-noise models: the factor model, spiked sample covariance matrices, the sum of a Wigner matrix and a low-rank perturbation, and canonical correlation analysis with low-rank dependencies. The objective is to construct confidence intervals for the signal strength that are uniformly valid across all regimes - strong, weak, and critical signals. We demonstrate that traditional Gaussian approximations fail in the critical regime. Instead, we introduce a u…

Compatibility of Max and Sum Objectives for Committee Selection and $k$-Facility Location
Yue Han, Elliot Anshelevich
https://arxiv.org/abs/2507.17063 https://

Compatibility of Max and Sum Objectives for Committee Selection and $k$-Facility Location
We study a version of the metric facility location problem (or, equivalently, variants of the committee selection problem) in which we must choose $k$ facilities in an arbitrary metric space to serve some set of clients $C$. We consider four different objectives, where each client $i\in C$ attempts to minimize either the sum or the maximum of its distance to the chosen facilities, and where the overall objective either considers the sum or the maximum of the individual client costs. Rather than…

And yes, the US Congress, with many #Democrats voting in favor, continues to send weapons to Israel. Why?
“If they recycle the Israeli bombs that didn’t explode there is an endless supply of explosives, and the genocide provides an infinite recruiting pool for new members.”
"American intelligence estimates suggest Hamas has recruited 15,000 operatives during the war, about twice a…

And yes, the US Congress, with many #Democrats voting in favor, continues to send weapons to Israel. Why?
“If they recycle the Israeli bombs that didn’t explode there is an endless supply of explosives, and the genocide provides an infinite recruiting pool for new members.”
"American intelligence estimates suggest Hamas has recruited 15,000 operatives during the war, about twice a…

Replaced article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms
Simon Wietheger, Benjamin Doerr

Longitudinal analysis of heart rate variability as it pertains to anxiety and readiness
Tucker Paron
https://arxiv.org/abs/2506.19128 https://

Longitudinal analysis of heart rate variability as it pertains to anxiety and readiness
The aim of this study is to explore the relationship between lifestyle choices, subjective experiences and objective biometric data in a single individual. The participant, at the time a male in his twenties, used the EliteHRV app to perform Heart Rate Variability Readings across twenty-six months accompanied by logs about the previous days activity as well as current emotional and physical state. The study will use a mixed-methods approach to analyze the data, including quantitative analysis o…

HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search
Ruiyu Zhang, Lin Nie, Xin Zhao
https://arxiv.org/abs/2508.15555 ht…

HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search
Hierarchical Evolutionary Agent Simulation (HEAS) is a Python framework that unifies layered agent-based modeling with evolutionary optimization and tournament evaluation in a single, reproducible workflow. HEAS represents models as hierarchies of lightweight processes ("streams") scheduled in deterministic layers that read and write a shared context, making cross-scale couplings explicit and auditable. A compact API and CLI-simulate, optimize, evaluate-expose single- and multi-objective evolut…

Solution Methods and Perturbation Analysis for the Equality-Constrained Total Least Squares Problem over Reduced Biquaternions
Neha Bhadala
https://arxiv.org/abs/2506.18319

Solution Methods and Perturbation Analysis for the Equality-Constrained Total Least Squares Problem over Reduced Biquaternions
This paper proposes a theoretical framework to address the reduced biquaternion equality-constrained total least squares (RBTLSE) problem. The objective is to find an approximate solution to the system $AX \approx B$, subject to linear constraints $CX = D$, while explicitly accounting for errors in both the coefficient matrix $A$ and the observation matrix $B$. We establish conditions under which real and complex solutions exist and develop corresponding solution methods using real and complex …

"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

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

Frequentist Asymptotics of Variational Laplace
Janis Keck
https://arxiv.org/abs/2507.17697 https://arxiv.org/pdf/2507.17697

Frequentist Asymptotics of Variational Laplace
Variational inference is a general framework to obtain approximations to the posterior distribution in a Bayesian context. In essence, variational inference entails an optimization over a given family of probability distributions to choose the member of this family best approximating the posterior. Variational Laplace, an iterative update scheme motivated by this objective, is widely used in different contexts in the cognitive neuroscience community. However, until now, the theoretical properti…

Empirical Models of the Time Evolution of SPX Option Prices
Alessio Brini, David A. Hsieh, Patrick Kuiper, Sean Moushegian, David Ye
https://arxiv.org/abs/2506.17511

Empirical Models of the Time Evolution of SPX Option Prices
The key objective of this paper is to develop an empirical model for pricing SPX options that can be simulated over future paths of the SPX. To accomplish this, we formulate and rigorously evaluate several statistical models, including neural network, random forest, and linear regression. These models use the observed characteristics of the options as inputs -- their price, moneyness and time-to-maturity, as well as a small set of external inputs, such as the SPX and its past history, dividend …

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique
Mohammad Asifur Rahim, Muhammad Nazmul Arefin, Md. Mizanur Rahman, Md Ali Hossain, Ahmed Moustafa
https://arxiv.org/abs/2507.15915

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique
Context: Mpox is a zoonotic disease caused by the Mpox virus, which shares similarities with other skin conditions, making accurate early diagnosis challenging. Artificial intelligence (AI), especially Deep Learning (DL), has a strong tool for medical image analysis; however, pre-trained models like CNNs and XAI techniques for mpox detection is underexplored. Objective: This study aims to evaluate the effectiveness of pre-trained CNN models (VGG16, VGG19, InceptionV3, MobileNetV2) for the early…

See What I Mean? Expressiveness and Clarity in Robot Display Design
Matthew Ebisu, Hang Yu, Reuben Aronson, Elaine Short
https://arxiv.org/abs/2506.16643 h…

See What I Mean? Expressiveness and Clarity in Robot Display Design
Nonverbal visual symbols and displays play an important role in communication when humans and robots work collaboratively. However, few studies have investigated how different types of non-verbal cues affect objective task performance, especially in a dynamic environment that requires real time decision-making. In this work, we designed a collaborative navigation task where the user and the robot only had partial information about the map on each end and thus the users were forced to communicat…

Can Limited Liability Increase Stability for Banks: A Dynamic Portfolio Approach
Deb Narayan Barik, Siddhartha P. Chakrabarty
https://arxiv.org/abs/2507.16494 https://

Can Limited Liability Increase Stability for Banks: A Dynamic Portfolio Approach
We present a novel approach for the bank's decision problem, incorporating Limited Liability in the objective function. Accordingly, we consider continuous time models, with and without Limited Liability. We compare the solutions of these two models to demonstrate the effect of inclusion of Limited Liability. To solve the problem with the objective function incorporating Limited Liability, we approximate the payoff function to another set of functions for which we have closed-form solutions. Th…

Typicality with Feedback
Thomas Sturma, Mich\`ele Wigger
https://arxiv.org/abs/2507.16384 https://arxiv.org/pdf/2507.16384…

Typicality with Feedback
The main objective of this paper is to analyze a closed-loop feedback system where a transmitter probes a discrete memoryless channel (DMC) and can adapt its inputs based on the previous channel outputs. We prove that, regardless of the transmitter's strategy, the conditional type of the outputs given the inputs remains close to the DMC transition law $P_{Y|X}$. This general result enables the study of fundamental limits in certain adaptive systems. As an application, we establish a conv…

How Do Code Smells Affect Skill Growth in Scratch Novice Programmers?
Ricardo Hidalgo Arag\'on, Jes\'us M. Gonz\'alez-Barahona, Gregorio Robles
https://arxiv.org/abs/2507.17314

How Do Code Smells Affect Skill Growth in Scratch Novice Programmers?
Context. Code smells, which are recurring anomalies in design or style, have been extensively researched in professional code. However, their significance in block-based projects created by novices is still largely unknown. Block-based environments such as Scratch offer a unique, data-rich setting to examine how emergent design problems intersect with the cultivation of computational-thinking (CT) skills. Objective. This research explores the connection between CT proficiency and design-level c…

Experiential marketing strategy and tourism demand in the contribution of the positioning of the floating islands Los Uros, Puno
Guina Flores Montalico
https://arxiv.org/abs/2506.18937

Experiential marketing strategy and tourism demand in the contribution of the positioning of the floating islands Los Uros, Puno
Experiential focused on creating memorable and meaningful experiences for consumers, has emerged as a key strategy in promoting tourist destinations. particularly in destinations seeking to highlight their unique cultural characteristics. In this study, the influence of experiential marketing on the tourism demand of the floating islands Los Uros, Puno, an emblematic destination in Peru, is analyzed. The objective of the research was to evaluate how experiential marketing strategies impact tour…

Combining Cost-Constrained Runtime Monitors for AI Safety
Tim Tian Hua, James Baskerville, Henri Lemoine, Mia Hopman, Aryan Bhatt, Tyler Tracy
https://arxiv.org/abs/2507.15886 h…

Combining Cost-Constrained Runtime Monitors for AI Safety
Monitoring AIs at runtime can help us detect and stop harmful actions. In this paper, we study how to combine multiple runtime monitors into a single monitoring protocol. The protocol's objective is to maximize the probability of applying a safety intervention on misaligned outputs (i.e., maximize recall). Since running monitors and applying safety interventions are costly, the protocol also needs to adhere to an average-case budget constraint. Taking the monitors' performance and cost as given…

AWR6843 focus stacked dataset at 100x stitching now.
11663 tiles, downsampled from 1992 x 1992 to 498 x 498. This should give a final resolution of 188 nm/pix, so we're still very slightly oversampled from the ~300nm theoretical optical resolution of the objective.
Raw dataset is 46.27 Gpix, down to 2.892 Gpix after downsampling. Given the 30% tile overlap we're probably looking at about a 1.4 Gpix final stitch.

Regular Tree Search for Simulation Optimization
Du-Yi Wang, Guo Liang, Guangwu Liu, Kun Zhang
https://arxiv.org/abs/2506.17696 https://

Regular Tree Search for Simulation Optimization
Tackling simulation optimization problems with non-convex objective functions remains a fundamental challenge in operations research. In this paper, we propose a class of random search algorithms, called Regular Tree Search, which integrates adaptive sampling with recursive partitioning of the search space. The algorithm concentrates simulations on increasingly promising regions by iteratively refining a tree structure. A tree search strategy guides sampling decisions, while partitioning is tri…

A fun way to celebrate the destruction of our shared objective reality.
https://www.tumblr.com/teledyn/792427531321147392/ai-slop?source=share

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Sarah Pungitore, Shashank Yadav, Molly Douglas, Jarrod Mosier, Vignesh Subbian
https://arxiv.org/abs/2506.16359

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Objective: Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review, limited automation, and difficulties in adapting algorithms across sources. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To suppor…

Efficient and Scalable Self-Healing Databases Using Meta-Learning and Dependency-Driven Recovery
Joydeep Chandra, Prabal Manhas
https://arxiv.org/abs/2507.13757

Efficient and Scalable Self-Healing Databases Using Meta-Learning and Dependency-Driven Recovery
This study explored the development of a novel self-healing framework for databases using meta-learning and reinforcement learning techniques. The primary objective was to address the challenges of real-time adaptability and minimal retraining in dynamic workload environments. The proposed approach integrated Model-Agnostic Meta-Learning (MAML) with reinforcement learning to enable anomaly detection and corrective actions that adapted swiftly to evolving database conditions. Multi-objective opt…

Design and fabrication of ultrasound linear array transducer used in ultrasound endoscope
Yuan Zhang, Mingtong Chen, Zhengbao Yang
https://arxiv.org/abs/2507.18628 https://

Design and fabrication of ultrasound linear array transducer used in ultrasound endoscope
This report details the successful construction of an ultrasound imaging platform and the design and fabrication of a novel ultrasound endoscope probe. The projects primary objective was to establish a functional system for acquiring and processing ultrasound signals, specifically targeting minimally invasive endoscopic applications. The ultrasound imaging platform was primarily designed and developed based on Texas Instruments (TI) Evaluation Modules (EVMs). It enables the transmission of 32-c…

Improved Wake-Up Time For Euclidean Freeze-Tag Problem
Sharareh Alipour, Arash Ahadi, Kajal Baghestani
https://arxiv.org/abs/2507.16269 https://

Improved Wake-Up Time For Euclidean Freeze-Tag Problem
The Freeze-Tag Problem (FTP) involves activating a set of initially asleep robots as quickly as possible, starting from a single awake robot. Once activated, a robot can assist in waking up other robots. Each active robot moves at unit speed. The objective is to minimize the makespan, i.e., the time required to activate the last robot. A key performance measure is the wake-up ratio, defined as the maximum time needed to activate any number of robots in any primary positions. This work focuses o…

Model Reference Adaptive Control of Networked Systems with State and Input Delays
Moh Kamalul Wafi, Katherin Indriawati, Bambang L. Widjiantoro
https://arxiv.org/abs/2506.19138

Model Reference Adaptive Control of Networked Systems with State and Input Delays
Adaptive control strategies have progressively advanced to accommodate increasingly uncertain, delayed, and interconnected systems. This paper addresses the model reference adaptive control (MRAC) of networked, heterogeneous, and unknown dynamical agents subject to both state and input delays. The objective is to ensure that all follower agents asymptotically track the trajectory of a stable leader system, despite system uncertainties and communication constraints. Two communication topologies …

An unconditional lower bound for the active-set method in convex quadratic maximization
Eleon Bach, Yann Disser, Sophie Huiberts, Nils Mosis
https://arxiv.org/abs/2507.16648

An unconditional lower bound for the active-set method in convex quadratic maximization
We prove that the active-set method needs an exponential number of iterations in the worst-case to maximize a convex quadratic function subject to linear constraints, regardless of the pivot rule used. This substantially improves over the best previously known lower bound [IPCO 2025], which needs objective functions of polynomial degrees $ω(\log d)$ in dimension $d$, to a bound using a convex polynomial of degree 2. In particular, our result firmly resolves the open question [IPCO 2025] of whe…

EffortNet: A Deep Learning Framework for Objective Assessment of Speech Enhancement Technologies Using EEG-Based Alpha Oscillations
Ching-Chih Sung, Cheng-Hung Hsin, Yu-Anne Shiah, Bo-Jyun Lin, Yi-Xuan Lai, Chia-Ying Lee, Yu-Te Wang, Borchin Su, Yu Tsao
https://arxiv.org/abs/2508.15473

EffortNet: A Deep Learning Framework for Objective Assessment of Speech Enhancement Technologies Using EEG-Based Alpha Oscillations
This paper presents EffortNet, a novel deep learning framework for decoding individual listening effort from electroencephalography (EEG) during speech comprehension. Listening effort represents a significant challenge in speech-hearing research, particularly for aging populations and those with hearing impairment. We collected 64-channel EEG data from 122 participants during speech comprehension under four conditions: clean, noisy, MMSE-enhanced, and Transformer-enhanced speech. Statistical an…

Low complexity convergence rate bounds for push-sum algorithms with homogeneous correlation structure
Bal\'azs Gerencs\'er, Mikl\'os Kornyik
https://arxiv.org/abs/2507.16601

Low complexity convergence rate bounds for push-sum algorithms with homogeneous correlation structure
The objective of this work is to establish an upper bound for the almost sure convergence rate for a class of push-sum algorithms. The current work extends the methods and results of the authors on a similar low-complexity bound on push-sum algorithms with some particular synchronous message passing schemes and complements the general approach of Gerencsér and Gerencsér from 2022 providing an exact, but often less accessible description. Furthermore, a parametric analysis is presented on the …

Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation
Phurinut Srisawad, Juergen Branke, Long Tran-Thanh
https://arxiv.org/abs/2508.14705 https://

Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation
We study payoff manipulation in repeated multi-objective Stackelberg games, where a leader may strategically influence a follower's deterministic best response, e.g., by offering a share of their own payoff. We assume that the follower's utility function, representing preferences over multiple objectives, is unknown but linear, and its weight parameter must be inferred through interaction. This introduces a sequential decision-making challenge for the leader, who must balance preference elicita…

The Hyper-Kamiokande experiment: input to the update of the European Strategy for Particle Physics
Kamiokande Collaboration
https://arxiv.org/abs/2506.16641

The Hyper-Kamiokande experiment: input to the update of the European Strategy for Particle Physics
Hyper-Kamiokande is a large infrastructure for particle and astroparticle physics being built in Japan and aiming to start operations by the end of 2027 whose objective is to address the most important questions in science today, for instance how the universe began and evolved. It aims to measure with the highest precision the leptonic Charge-Parity violation parameter using both a Mega-Watt intense neutrino beam and high-statistics atmospheric neutrino samples. The combination of these sampl…

Efficient Visual Appearance Optimization by Learning from Prior Preferences
Zhipeng Li, Yi-Chi Liao, Christian Holz
https://arxiv.org/abs/2507.15355 https:…

Efficient Visual Appearance Optimization by Learning from Prior Preferences
Adjusting visual parameters such as brightness and contrast is common in our everyday experiences. Finding the optimal parameter setting is challenging due to the large search space and the lack of an explicit objective function, leaving users to rely solely on their implicit preferences. Prior work has explored Preferential Bayesian Optimization (PBO) to address this challenge, involving users to iteratively select preferred designs from candidate sets. However, PBO often requires many rounds …

LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Code
Shahbaz Siddeeq, Muhammad Waseem, Zeeshan Rasheed, Md Mahade Hasan, Jussi Rasku, Mika Saari, Henri Terho, Kalle Makela, Kai-Kristian Kemell, Pekka Abrahamsson
https://arxiv.org/abs/2506.19481

LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Code
Refactoring is a constant activity in software development and maintenance. Scale and maintain software systems are based on code refactoring. However, this process is still labor intensive, as it requires programmers to analyze the codebases in detail to avoid introducing new defects. In this research, we put forward a large language model (LLM)-based multi-agent system to automate the refactoring process on Haskell code. The objective of this research is to evaluate the effect of LLM-based ag…

Broadband Low-Resolution Spectrograph -- SpectrumMate LR
Nguyen Nguyen-Duc, Thuy Le-Quang, Tobias C. Hinse, Tue Nguyen-Van, Quang Nguyen-Luong
https://arxiv.org/abs/2506.16153

Broadband Low-Resolution Spectrograph -- SpectrumMate LR
This paper presents the development and application of SpectrumMate LR, a broadband, low-resolution spectrograph for small telescope use. SpectrumMate LR is designed to offer affordable, accessible spectroscopic capabilities for amateur astronomers and educators, inspired by the need for versatile instruments in amateur and educational settings. Utilising a 300 grooves/mm grating, 80 mm collimator and objective lenses, SpectrumMate LR is optimised to perform analyses across the visible spectrum…

Nonconvex Nonsmooth Multicomposite Optimization and Its Applications to Recurrent Neural Networks
Lingzi Jin, Xiao Wang, Xiaojun Chen
https://arxiv.org/abs/2506.17884

Nonconvex Nonsmooth Multicomposite Optimization and Its Applications to Recurrent Neural Networks
We consider a class of nonconvex nonsmooth multicomposite optimization problems where the objective function consists of a Tikhonov regularizer and a composition of multiple nonconvex nonsmooth component functions. Such optimization problems arise from tangible applications in machine learning and beyond. To define and compute its first-order and second-order d(irectional)-stationary points effectively, we first derive the closed-form expression of the tangent cone for the feasible region of it…

Bladder Cancer Diagnosis with Deep Learning: A Multi-Task Framework and Online Platform
Jinliang Yu, Mingduo Xie, Yue Wang, Tianfan Fu, Xianglai Xu, Jiajun Wang
https://arxiv.org/abs/2508.15379

Bladder Cancer Diagnosis with Deep Learning: A Multi-Task Framework and Online Platform
Clinical cystoscopy, the current standard for bladder cancer diagnosis, suffers from significant reliance on physician expertise, leading to variability and subjectivity in diagnostic outcomes. There is an urgent need for objective, accurate, and efficient computational approaches to improve bladder cancer diagnostics. Leveraging recent advancements in deep learning, this study proposes an integrated multi-task deep learning framework specifically designed for bladder cancer diagnosis from cy…

Spatial filtering of interlayer exciton ground state in WSe2/MoS2 heterobilayer
Disheng Chen, Kevin Dini, Abdullah Rasmita, Zumeng Huang, Qinghai Tan, Hongbing Cai, Ruihua He, Yansong Miao, Timothy C. H. Liew, Wei-bo Gao
https://arxiv.org/abs/2507.16180

Spatial filtering of interlayer exciton ground state in WSe2/MoS2 heterobilayer
Long-life interlayer excitons (IXs) in transition metal dichalcogenide (TMD) heterostructure are promising for realizing excitonic condensates at high temperatures. Critical to this objective is to separate the IX ground state (the lowest energy of IX state) emission from other states emissions. Filtering the IX ground state is also essential in uncovering the dynamics of correlated excitonic states, such as the excitonic Mott insulator. Here, we show that the IX ground state in WSe2/MoS2 heter…

Estimating quantile treatments without strict overlap
Marco Avella-Medina, Richard Davis, Gennady Samorodnitsky
https://arxiv.org/abs/2506.18215 https://…

Estimating quantile treatments without strict overlap
We consider the problem of estimating quantile treatment effects without assuming strict overlap , i.e., we do not assume that the propensity score is bounded away from zero. More specifically, we consider an inverse probability weighting (IPW) approach for estimating quantiles in the potential outcomes framework and pay special attention to scenarios where the propensity scores can tend to zero as a regularly varying function. Our approach effectively considers a heavy-tailed objective functio…

Revised identification of strain gradient elastic parameters
Luca Placidi, Anil Misra, Gabriele La Valle, Casey Rodriguez
https://arxiv.org/abs/2506.14932 …

Revised identification of strain gradient elastic parameters
The work reported in ``Granular micromechanics-based identification of isotropic strain gradient parameters for elastic geometrically nonlinear deformations" misidentified key terms in the grain-pair objective relative displacement when accounting for the second gradient of placement. In this paper, we correct that oversight by deriving a revised expression for the grain-pair objective relative displacement within the granular micromechanics framework. The amended terms, which resemble Christof…

Replaced article(s) found for econ.TH. https://arxiv.org/list/econ.TH/new
[1/1]:
- Shapley-Scarf Markets with Objective Indifferences
Will Sandholtz, Andrew Tai

A new 1D $V_p$ and $V_s$ velocity model of the western Rift of Corinth, Greece, using a fully non-linear tomography algorithm
Mark S. Noble (GEOSCIENCES), Alexandrine Gesret (GEOAZUR 7329), H\'el\`ene Lyon-Caen (GEOAZUR 7329), Anne Deschamps (GEOAZUR 7329)
https://arxiv.org/abs/2506.16222

A new 1D $V_p$ and $V_s$ velocity model of the western Rift of Corinth, Greece, using a fully non-linear tomography algorithm
The objective of this study is to propose a new updated accurate 1-D $V_p$ and $V_s$ velocity model of the western Rift of Corinth for precise absolute earthquake locations. The methodology used to obtain this new model associates two fully non-linear algorithms, a complete exploration of the $V_p$ and $V_s$ model space combined with a grid search method for earthquake locations to minimize the P and S arrival time residuals. We calculated the misfit function for approximately 2.10 6 velocity m…

Managing Technical Debt in a Multidisciplinary Data Intensive Software Team: an Observational Case Study
Ulrike M. Graetsch, Rashina Hoda, Hourieh Khalazjadeh, Mojtaba Shahin, John Grundy
https://arxiv.org/abs/2506.18219

Managing Technical Debt in a Multidisciplinary Data Intensive Software Team: an Observational Case Study
Context: There is an increase in the investment and development of data-intensive (DI) solutions, systems that manage large amounts of data. Without careful management, this growing investment will also grow associated technical debt (TD). Delivery of DI solutions requires a multidisciplinary skill set, but there is limited knowledge about how multidisciplinary teams develop DI systems and manage TD. Objective: This research contributes empirical, practice based insights about multidisciplina…

Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control
J\'an Boldock\'y, Shahriar Dadras Javan, Martin Gulan, Martin M\"onnigmann, J\'an Drgo\v{n}a
https://arxiv.org/abs/2506.19646

Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control
We propose a novel approach to solving input- and state-constrained parametric mixed-integer optimal control problems using Differentiable Predictive Control (DPC). Our approach follows the differentiable programming paradigm by learning an explicit neural policy that maps control parameters to integer- and continuous-valued decision variables. This policy is optimized via stochastic gradient descent by differentiating the quadratic model predictive control objective through the closed-loop fin…

The Generalized Matrix Separation Problem: Algorithms
Xuemei Chen, Owen Deen
https://arxiv.org/abs/2507.17069 https://arxiv.org/pdf/2507.17069

The Generalized Matrix Separation Problem: Algorithms
When given a generalized matrix separation problem, which aims to recover a low rank matrix $L_0$ and a sparse matrix $S_0$ from $M_0=L_0+HS_0$, the work \cite{CW25} proposes a novel convex optimization problem whose objective function is the sum of the $\ell_1$-norm and nuclear norm. In this paper we detail the iterative algorithms and its associated computations for solving this convex optimization problem. We present various efficient implementation strategies, with attention to practical ca…

Variance Reduced Policy Gradient Method for Multi-Objective Reinforcement Learning
Davide Guidobene, Lorenzo Benedetti, Diego Arapovic
https://arxiv.org/abs/2508.10608 https://

Variance Reduced Policy Gradient Method for Multi-Objective Reinforcement Learning
Multi-Objective Reinforcement Learning (MORL) is a generalization of traditional Reinforcement Learning (RL) that aims to optimize multiple, often conflicting objectives simultaneously rather than focusing on a single reward. This approach is crucial in complex decision-making scenarios where agents must balance trade-offs between various goals, such as maximizing performance while minimizing costs. We consider the problem of MORL where the objectives are combined using a non-linear scalarizati…

Revela: Dense Retriever Learning via Language Modeling
Fengyu Cai, Tong Chen, Xinran Zhao, Sihao Chen, Hongming Zhang, Sherry Tongshuang Wu, Iryna Gurevych, Heinz Koeppl
https://arxiv.org/abs/2506.16552

Revela: Dense Retriever Learning via Language Modeling
Dense retrievers play a vital role in accessing external and specialized knowledge to augment language models (LMs). Training dense retrievers typically requires annotated query-document pairs, which are costly and hard to obtain in specialized domains such as code-motivating growing interest in self-supervised retriever learning. Since LMs are trained to capture token-level dependencies through a self-supervised learning objective (i.e., next-token prediction), we can analogously cast retrieva…

Aethorix v1.0: AI-Driven Inverse Design of Inorganic Materials for Scalable Industrial Innovation
Yingjie Shi, Runtian Miao
https://arxiv.org/abs/2506.16609

Aethorix v1.0: AI-Driven Inverse Design of Inorganic Materials for Scalable Industrial Innovation
Artificial intelligence for Science (AI4S) is poised to transform industrial manufacturing by enabling the accelerated discovery and optimization of advanced (bio)materials, dramatically reducing development cycles, and unlocking novel high-performance solutions. We introduce Aethorix v1.0, a platform that integrates large language models for objective mining, diffusion-based generative models for zero-shot inorganic crystal design, and machine-learned interatomic potentials for rapid property …

Sub-sampled Trust-Region Methods with Deterministic Worst-Case Complexity Guarantees
Max L. N. Goncalves, Geovani N. Grapiglia
https://arxiv.org/abs/2507.17556 https://

Sub-sampled Trust-Region Methods with Deterministic Worst-Case Complexity Guarantees
In this paper, we develop and analyze sub-sampled trust-region methods for solving finite-sum optimization problems. These methods employ subsampling strategies to approximate the gradient and Hessian of the objective function, significantly reducing the overall computational cost. We propose a novel adaptive procedure for deterministically adjusting the sample size used for gradient (or gradient and Hessian) approximations. Furthermore, we establish worst-case iteration complexity bounds for o…

Learn to optimize for automatic proton PBS treatment planning for H&N cancers
Qingqing Wang, Liqiang Xiao, Chang Chang
https://arxiv.org/abs/2508.11085 https://

Learn to optimize for automatic proton PBS treatment planning for H&N cancers
Proton PBS treatment planning for H&N cancers involves numerous conflicting objectives, requiring significant effort from human planners to balance and satisfy multiple clinical goals during planning. To achieve this, experience-demanding objective parameter adjustment and computationally expensive inverse optimization are performed iteratively. Extensive efforts have been made to automatically adjust objective parameters, but the most time-consuming component, i.e., inverse optimization, still…

Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbent
Jingyi Wang, Haowei Wang, Szu Hui Ng, Cosmin G. Petra
https://arxiv.org/abs/2508.15674 htt…

Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbent
Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven empirical success in applications, the cumulative regret upper bound of EI remains an open question. In this paper, we analyze the classic noisy Gaussian process expected improvement (GP-EI) algorithm. We consider the Bayesian setting, where the objective is a sample from a GP. Three commonly used incumbents, namely the best posterior mean incumbent (BPMI), the best s…

Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track
Brian Ondov, William Xia, Kush Attal, Ishita Unde, Jerry He, Hoa Dang, Ian Soboroff, Dina Demner-Fushman
https://arxiv.org/abs/2507.14096

Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track
Objective: Recent advances in language models have shown potential to adapt professional-facing biomedical literature to plain language, making it accessible to patients and caregivers. However, their unpredictability, combined with the high potential for harm in this domain, means rigorous evaluation is necessary. Our goals with this track were to stimulate research and to provide high-quality evaluation of the most promising systems. Methods: We hosted the Plain Language Adaptation of Biome…

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…

Efficient Routing of Inference Requests across LLM Instances in Cloud-Edge Computing
Shibo Yu, Mohammad Goudarzi, Adel Nadjaran Toosi
https://arxiv.org/abs/2507.15553

Efficient Routing of Inference Requests across LLM Instances in Cloud-Edge Computing
The rising demand for Large Language Model (LLM) inference services has intensified pressure on computational resources, resulting in latency and cost challenges. This paper introduces a novel routing algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute inference requests across heterogeneous LLM instances in a cloud-edge computing environment. Formulated as a multi-objective optimization problem, the algorithm balances response quality, response time, and i…

An improved input-constrained funnel controller for nonlinear systems
Thomas Berger
https://arxiv.org/abs/2506.18417 https://arxiv.or…

An improved input-constrained funnel controller for nonlinear systems
We present an improvement of a recent funnel controller design for uncertain nonlinear multi-input, multi-output systems modeled by higher order functional differential equations in the presence of input constraints. The objective is to guarantee the evolution of the tracking error within a performance funnel with prescribed desired shape for the case of inactive saturation. Compared to its precursor, controller complexity is significantly reduced, much fewer design parameters are involved and …

Job Scheduling under Base and Additional Fees, with Applications to Mixed-Criticality Scheduling
Yi-Ting Hsieh, Mong-Jen Kao, Jhong-Yun Liu, Hung-Lung Wang
https://arxiv.org/abs/2507.15434

Job Scheduling under Base and Additional Fees, with Applications to Mixed-Criticality Scheduling
We are concerned with the problem of scheduling $n$ jobs onto $m$ identical machines. Each machine has to be in operation for a prescribed time, and the objective is to minimize the total machine working time. Precisely, let $c_i$ be the prescribed time for machine $i$, where $i\in[m]$, and $p_j$ be the processing time for job $j$, where $j\in[n]$. The problem asks for a schedule $σ\colon\, J\to M$ such that $\sum_{i=1}^m\max\{c_i, \sum_{j\inσ^{-1}(i)}p_j\}$ is minimized, where $J$ and $M$ de…

The Dispersion Leverage Coronagraph (DLC): A nulling coronagraph for use on primary objective grating telescopes
Leaf Swordy, Heidi Jo Newberg, Becket Hill, Richard K. Barry, Marina Cousins, Kerrigan Nish, Frank Ravizza, Sarah Rickborn
https://arxiv.org/abs/2507.11745

The Dispersion Leverage Coronagraph (DLC): A nulling coronagraph for use on primary objective grating telescopes
We present the Dispersion Leverage Coronagraph (DLC), a novel variation of the Achromatic Interfero Coronagraph (AIC) that is designed for optical systems featuring large, dispersive primary objective gratings. DLC was originally designed for the Diffractive Interfero Coronagraph Exoplanet Resolver (DICER), a notional 20m class infrared space telescope utilizing the enhanced one-dimensional angular resolution of large diffraction gratings in order to discover and characterize near-Earth exoplan…

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…

NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data
Dzung Dinh, Boqi Chen, Marc Niethammer, Junier Oliva
https://arxiv.org/abs/2507.12412

NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data
In many critical applications, resource constraints limit the amount of information that can be gathered to make predictions. For example, in healthcare, patient data often spans diverse features ranging from lab tests to imaging studies. Each feature may carry different information and must be acquired at a respective cost of time, money, or risk to the patient. Moreover, temporal prediction tasks, where both instance features and labels evolve over time, introduce additional complexity in dec…

Frank-Wolfe algorithm for star-convex functions
R. Diaz Millan, Orizon Pereira Ferreira, Julien Ugon
https://arxiv.org/abs/2507.17272 https://arxiv.org/pdf…

Frank-Wolfe algorithm for star-convex functions
We study the Frank-Wolfe algorithm for minimizing a differentiable function with Lipschitz continuous gradient over a compact convex set. To extend classical complexity bounds to certain non-convex functions, we focus on the class of \emph{star-convex functions}, which retain essential geometric properties despite the lack of convexity. We establish iteration-complexity bounds of $\mathcal{O}(1/k)$ for both the objective values and the duality gap under star-convexity, using diminishing, Armijo…

Integral action for bilinear systems with application to counter current heat exchanger
Francesco Ripa, Daniele Astolfi, Boussad Hamroun, Diego Regruto
https://arxiv.org/abs/2507.16553

Integral action for bilinear systems with application to counter current heat exchanger
In this study, we propose a robust control strategy for a counter-current heat exchanger. The primary objective is to regulate the outlet temperature of one fluid stream by manipulating the flow rate of the second counter-current fluid stream. By leveraging the energy balance equations, we develop a structured bilinear system model derived by using a uniform spatial discretization of each stream into a cascade of homogeneous volumes and by considering the heat transfer and convective phenomena …

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…

General Proximal Quasi-Newton Methods based on model functions for nonsmooth nonconvex problems
Xiaoxi Jia, Peter Ochs
https://arxiv.org/abs/2507.18363 https://

General Proximal Quasi-Newton Methods based on model functions for nonsmooth nonconvex problems
In this manuscript, we propose a general proximal quasi-Newton method tailored for nonconvex and nonsmooth optimization problems, where we do not require the sequence of the variable metric (or Hessian approximation) to be uniformly bounded as a prerequisite, instead, the variable metric is updated by a continuous matrix generator. From the respective of the algorithm, the objective function is approximated by the so-called local model function and subproblems aim to exploit the proximal point(…

KKT-based optimality conditions for neural network approximation
Vinesha Peiris, Nadezda Sukhorukova, Julien Ugon
https://arxiv.org/abs/2506.17305 https://…

KKT-based optimality conditions for neural network approximation
In this paper, we obtain necessary optimality conditions for neural network approximation. We consider neural networks in Manhattan ($l_1$ norm) and Chebyshev ($\max$ norm). The optimality conditions are based on neural networks with at most one hidden layer. We reformulate nonsmooth unconstrained optimisation problems as larger dimension constrained problems with smooth objective functions and constraints. Then we use KKT conditions to develop the necessary conditions and present the optimalit…

Investigating the Use of LLMs for Evidence Briefings Generation in Software Engineering
Mauro Marcelino, Marcos Alves, Bianca Trinkenreich, Bruno Cartaxo, S\'ergio Soares, Simone D. J. Barbosa, Marcos Kalinowski
https://arxiv.org/abs/2507.15828

Investigating the Use of LLMs for Evidence Briefings Generation in Software Engineering
[Context] An evidence briefing is a concise and objective transfer medium that can present the main findings of a study to software engineers in the industry. Although practitioners and researchers have deemed Evidence Briefings useful, their production requires manual labor, which may be a significant challenge to their broad adoption. [Goal] The goal of this registered report is to describe an experimental protocol for evaluating LLM-generated evidence briefings for secondary studies in terms…

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 …

A Deep Reinforcement Learning Method for Multi-objective Transmission Switching
Ding Lin, Jianhui Wang, Tianqiao Zhao, Meng Yue
https://arxiv.org/abs/2507.11726

A Deep Reinforcement Learning Method for Multi-objective Transmission Switching
Transmission switching is a well-established approach primarily applied to minimize operational costs through strategic network reconfiguration. However, exclusive focus on cost reduction can compromise system reliability. While multi-objective transmission switching can balance cost savings with reliability improvements, feasible solutions become exceedingly difficult to obtain as system scale grows, due to the inherent nonlinearity and high computational demands involved. This paper proposes …

Approximation of risk-averse optimal feedback control
Philipp A. Guth, Karl Kunisch
https://arxiv.org/abs/2508.15618 https://arxiv.org/pdf/2508.15618

Approximation of risk-averse optimal feedback control
The challenge of constructing feedback control laws for risk-averse optimal control of partial differential equations (PDEs) with random coefficients is addressed. The control objective composes a tracking-type cost with the nonlinear entropic risk measure. A sequential quadratic programming scheme is derived that iteratively solves linear quadratic subproblems obtained through second-order Taylor expansions of the objective functional, with each subproblem re-centered at the previous iterate. …

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…

The Intrinsic Riemannian Proximal Gradient Method for Convex Optimization
Ronny Bergmann, Hajg Jasa, Paula John, Max Pfeffer
https://arxiv.org/abs/2507.16055

The Intrinsic Riemannian Proximal Gradient Method for Convex Optimization
We consider a class of (possibly strongly) geodesically convex optimization problems on Hadamard manifolds, where the objective function splits into the sum of a smooth and a possibly nonsmooth function. We introduce an intrinsic convex Riemannian proximal gradient (CRPG) method that employs the manifold proximal map for the nonsmooth step, without operating in the embedding or tangent space. A sublinear convergence rate for convex problems and a linear convergence rate for strongly convex prob…

A Distributional View of High Dimensional Optimization
Felix Benning
https://arxiv.org/abs/2507.16315 https://arxiv.org/pdf/2507.1631…

A Distributional View of High Dimensional Optimization
This PhD thesis presents a distributional view of optimization in place of a worst-case perspective. We motivate this view with an investigation of the failure point of classical optimization. Subsequently we consider the optimization of a randomly drawn objective function. This is the setting of Bayesian Optimization. After a review of Bayesian optimization we outline how such a distributional view may explain predictable progress of optimization in high dimension. It further turns out that th…

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…

First Order Algorithm on an Optimization Problem with Improved Convergence when Problem is Convex
Chee-Khian Sim
https://arxiv.org/abs/2508.13302 https://a…

First Order Algorithm on an Optimization Problem with Improved Convergence when Problem is Convex
We propose a first order algorithm, a modified version of FISTA, to solve an optimization problem with an objective function that is a sum of a possibly nonconvex function, with Lipschitz continuous gradient, and a convex function which can be nonsmooth. The algorithm is shown to have an iteration complexity of $\mathcal{O}(ε^{-2})$ to find an $ε$-approximate solution to the problem, and this complexity improves to $\mathcal{O}(ε^{-2/3})$ when the objective function turns out to be convex. W…

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…

Adaptive Parameter Optimization in Gaussian Processes: A Comprehensive Study of Uncertainty Quantification and Dimensional Scaling
Nishant Gadde
https://arxiv.org/abs/2507.15138

Adaptive Parameter Optimization in Gaussian Processes: A Comprehensive Study of Uncertainty Quantification and Dimensional Scaling
Gaussian Process (GP) models have also become extremely useful for optimization under uncertainty algorithms, especially where the objective functions are costly to compute. Yet, the more classical methods usually adopt strategies that, in certain circumstances, might be effective but not flexible to be applied to a wide range of problem terrains. This study aims to adapt parameter optimization in GP models and especially how uncertainty quantification can assist in the learning process. We inv…

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…

Kurdyka-\L ojasiewicz exponent via sqaure parametrization
Wenqing Ouyang
https://arxiv.org/abs/2506.10110 https://arxiv.org/pdf/2506.…

Kurdyka-Łojasiewicz exponent via sqaure parametrization
We consider one of the most common reparameterization techniques, the square transformation. Assuming the original objective function is the sum of a smooth function and a polyhedral function, we study the variational properties of the objective function after reparameterization. In particular, we first study the minimal norm of the subdifferential of the reparameterized objective function. Second, we compute the second subderivative of the reparameterized objective function on a linear subspac…

MOSS: Multi-Objective Optimization for Stable Rule Sets
Brian Liu, Rahul Mazumder
https://arxiv.org/abs/2506.08030 https://arxiv.org/…

MOSS: Multi-Objective Optimization for Stable Rule Sets
We present MOSS, a multi-objective optimization framework for constructing stable sets of decision rules. MOSS incorporates three important criteria for interpretability: sparsity, accuracy, and stability, into a single multi-objective optimization framework. Importantly, MOSS allows a practitioner to rapidly evaluate the trade-off between accuracy and stability in sparse rule sets in order to select an appropriate model. We develop a specialized cutting plane algorithm in our framework to rapi…