Tootfinder

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…

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…

Clicks Versus Conversion: Choosing a Recommender's Training Objective in E-Commerce
Michael Weiss, Robert Rosenbach, Christian Eggenberger
https://arxiv.org/abs/2508.10377 h…

Clicks Versus Conversion: Choosing a Recommender's Training Objective in E-Commerce
Ranking product recommendations to optimize for a high click-through rate (CTR) or for high conversion, such as add-to-cart rate (ACR) and Order-Submit-Rate (OSR, view-to-purchase conversion) are standard practices in e-commerce. Optimizing for CTR appears like a straightforward choice: Training data (i.e., click data) are simple to collect and often available in large quantities. Additionally, CTR is used far beyond e-commerce, making it a generalist, easily implemented option. ACR and OSR, on…

My friend as moved his main domain from .nl to .eu , Interesting move.
#Apple #iPhone

THERMOS: Thermally-Aware Multi-Objective Scheduling of AI Workloads on Heterogeneous Multi-Chiplet PIM Architectures
Alish Kanani, Lukas Pfromm, Harsh Sharma, Janardhan Rao Doppa, Partha Pratim Pande, Umit Y. Ogras
https://arxiv.org/abs/2508.10691

THERMOS: Thermally-Aware Multi-Objective Scheduling of AI Workloads on Heterogeneous Multi-Chiplet PIM Architectures
Chiplet-based integration enables large-scale systems that combine diverse technologies, enabling higher yield, lower costs, and scalability, making them well-suited to AI workloads. Processing-in-Memory (PIM) has emerged as a promising solution for AI inference, leveraging technologies such as ReRAM, SRAM, and FeFET, each offering unique advantages and trade-offs. A heterogeneous chiplet-based PIM architecture can harness the complementary strengths of these technologies to enable higher perfo…

'They’ll chip away at it gradually with bills that disenfranchise the “wrong” sort of people and mechanisms that make voting more difficult. But we should not mistake their ultimate objective. [W]hen they hint that they are interested in getting rid of women’s suffrage, we should take them very seriously indeed.'
Women’s suffrage is apparently up for debate again in America | Arwa Mahdawi | The Guardian
https://www.theguardian.com/commentisfree/2025/sep/13/womens-suffrage-week-in-patriarchy

Amorphous silicon resistors enable smaller pixels in photovoltaic retinal prosthesis https://www.biorxiv.org/content/10.1101/2025.05.01.651774v1 retinal implant

Amorphous silicon resistors enable smaller pixels in photovoltaic retinal prosthesis
Objective Clinical trials of the photovoltaic subretinal prosthesis PRIMA demonstrated feasibility of prosthetic central vision with resolution matching its 100µm pixel size. To improve prosthetic acuity further, pixel size should be decreased. However, there are multiple challenges, one of which is related to accommodating a compact shunt resistor within each pixel that discharges the electrodes between stimulation pulses and helps increase the contrast of the electric field pattern. Unfortun…

Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation Models
Paul A. Bereuter, Benjamin Stahl, Mark D. Plumbley, Alois Sontacchi
https://arxiv.org/abs/2507.11427

Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation Models
Traditional Blind Source Separation Evaluation (BSS-Eval) metrics were originally designed to evaluate linear audio source separation models based on methods such as time-frequency masking. However, recent generative models may introduce nonlinear relationships between the separated and reference signals, limiting the reliability of these metrics for objective evaluation. To address this issue, we conduct a Degradation Category Rating listening test and analyze correlations between the obtained…

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
Hanxue Gu, Yaqian Chen, isoo Lee, Diego Schaps, Regina Woody, Roy Colglazier, Maciej A. Mazurowski, Christopher Mantyh
https://arxiv.org/abs/2506.11996

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after colectomy, either alone or combined with clinical variables or existing risk predictors. Main outcomes and measures: The primary outcome was the predictive performance for 1-year all-cause mortality following colectomy. A Cox proportional hazards model with 1-year follow-up was used, and performance was evaluated using the concordance index (C-index…

Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
Mani Hamidi, Terrence W. Deacon
https://arxiv.org/abs/2507.11482 https://

Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
Three core tenets of reinforcement learning (RL)--concerning the definition of agency, the objective of learning, and the scope of the reward hypothesis--have been highlighted as key targets for conceptual revision, with major implications for theory and application. We propose a framework, inspired by open-ended evolutionary theory, to reconsider these three "dogmas." We revisit each assumption and address related concerns raised alongside them. To make our arguments relevant to RL as a model …

Reporte de vulnerabilidades en IIoT. Proyecto DEFENDER
Pedro Almansa Jim\'enez, Lorenzo Fern\'andez Maim\'o, \'Angel Luis Per\'ales G\'omez
https://arxiv.org/abs/2507.10819

Reporte de vulnerabilidades en IIoT. Proyecto DEFENDER
The main objective of this technical report is to conduct a comprehensive study on devices operating within Industrial Internet of Things (IIoT) environments, describing the scenarios that define this category and analysing the vulnerabilities that compromise their security. To this end, the report seeks to identify and examine the main classes of IIoT devices, detailing their characteristics, functionalities, and roles within industrial systems. This analysis enables a better understanding of …

Multi-Objective Optimizations of High Gradient C-band Photoinjector for High Bunch Charge Applications
M. Kaemingk, P. M. Anisimov, J. M. Maxson, J. B. Rosenzweig, E. I. Simakov, H. Xu
https://arxiv.org/abs/2509.11014

Multi-Objective Optimizations of High Gradient C-band Photoinjector for High Bunch Charge Applications
The high gradients potentially achievable in distributed-coupling C-band photoinjectors make them attractive for many high brightness applications. Here we discuss optimization results for a 1.6 cell C-band photoinjector with a 240 MV/m peak field at the cathode that delivers a 250 pC electron bunch charge. We use a Multi-Objective Genetic Algorithm (MOGA), obtaining a Pareto front of emittance vs. bunch length. We also perform MOGA optimizations including an aperture to retain only a bright be…

An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
Johanna D\"ungler, Amartya Sanyal
https://arxiv.org/abs/2508.10879 https://

An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
Given $n$ i.i.d. random matrices $A_i \in \mathbb{R}^{d \times d}$ that share a common expectation $Σ$, the objective of Differentially Private Stochastic PCA is to identify a subspace of dimension $k$ that captures the largest variance directions of $Σ$, while preserving differential privacy (DP) of each individual $A_i$. Existing methods either (i) require the sample size $n$ to scale super-linearly with dimension $d$, even under Gaussian assumptions on the $A_i$, or (ii) introduce excessiv…

Fast Non-Episodic Adaptive Tuning of Robot Controllers with Online Policy Optimization
James A. Preiss, Fengze Xie, Yiheng Lin, Adam Wierman, Yisong Yue
https://arxiv.org/abs/2507.10914

Fast Non-Episodic Adaptive Tuning of Robot Controllers with Online Policy Optimization
We study online algorithms to tune the parameters of a robot controller in a setting where the dynamics, policy class, and optimality objective are all time-varying. The system follows a single trajectory without episodes or state resets, and the time-varying information is not known in advance. Focusing on nonlinear geometric quadrotor controllers as a test case, we propose a practical implementation of a single-trajectory model-based online policy optimization algorithm, M-GAPS,along with rep…

Simulating Mass-Dependent Decoherence in Quantum Computers: Baseline Signatures for Testing Gravity-Induced Collapse
Viswak R Balaji, Samuel Punch
https://arxiv.org/abs/2508.10590

Simulating Mass-Dependent Decoherence in Quantum Computers: Baseline Signatures for Testing Gravity-Induced Collapse
We present a quantum computing simulation study of mass-dependent decoherence models inspired by Penrose's gravity-induced collapse hypothesis. According to objective reduction (OR) theory, quantum superpositions become unstable when the gravitational self-energy difference between branches exceeds a certain threshold, leading to a collapse time $τ\approx \hbar / E_G$. In this work, we implement a mass-dependent dephasing noise channel, $p(m) = 1 - e^{-k m^α}$, within the Qiskit AerSimulator,…

Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling
Andrea Gargano, Jasin Machkour, Mimma Nardelli, Enzo Pasquale Scilingo, Michael Muma
https://arxiv.org/abs/2508.10561

Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling
In Affective Computing, a key challenge lies in reliably linking subjective emotional experiences with objective physiological markers. This preliminary study addresses the issue of reproducibility by identifying physiological features from cardiovascular and electrodermal signals that are associated with continuous self-reports of arousal levels. Using the Continuously Annotated Signal of Emotion dataset, we analyzed 164 features extracted from cardiac and electrodermal signals of 30 participa…

Rather than assuming the war can be ended through a comprehensive battlefield victory or a negotiated compromise,
Ukraine and its allies must plan to build a viable, sovereign, and secure state under constant military pressure.
This reality demands a redefinition of what a successful outcome looks like.
In this context, the objective should not be to defeat Russia outright or expect its regime to end the war because of economic or diplomatic pressure
-- but to syste…

Ukraine’s New Theory of Victory Should be Strategic Neutralization
By ensuring that Russia’s war is operationally pointless, Ukraine can survive, adapt, and achieve success, no matter how prolonged the war.

Reduction of motion artifacts from photoplethysmography signals using learned convolutional sparse coding
Giulio Basso, Xi Long, Reinder Haakma, Rik Vullings
https://arxiv.org/abs/2508.10805

Reduction of motion artifacts from photoplethysmography signals using learned convolutional sparse coding
Objective. Wearable devices with embedded photoplethysmography (PPG) enable continuous non-invasive monitoring of cardiac activity, offering a promising strategy to reduce the global burden of cardiovascular diseases. However, monitoring during daily life introduces motion artifacts that can compromise the signals. Traditional signal decomposition techniques often fail with severe artifacts. Deep learning denoisers are more effective but have poorer interpretability, which is critical for clini…

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Nan Mu, Ruiqi Song, Xiaoning Li, Zhihui Xu, Jingfeng Jiang, Chen Zhao
https://arxiv.org/abs/2506.11454

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of …

Superstructure Optimization with Embedded Neural Networks for Sustainable Aviation Fuel Production
Alexander Klimek, Christoph Plate, Sebastian Sager, Kai Sundmacher, Caroline Ganzer
https://arxiv.org/abs/2509.09796

Superstructure Optimization with Embedded Neural Networks for Sustainable Aviation Fuel Production
This study presents a multi-objective optimization framework for sustainable aviation fuel (SAF) production, integrating artificial neural networks (ANNs) within a mixed-integer quadratically constrained programming (MIQCP) formulation. By embedding data-driven surrogate models into the mathematical optimization structure, the proposed methodology addresses key limitations of conventional superstructure-based approaches, enabling simultaneous optimization of discrete process choices and continu…

Enhancing Decision Space Diversity in Multi-Objective Evolutionary Optimization for the Diet Problem
Gustavo V. Nascimento, Ivan R. Meneghini, Val\'eria Santos, Eduardo Luz, Gladston Moreira
https://arxiv.org/abs/2508.07077

Enhancing Decision Space Diversity in Multi-Objective Evolutionary Optimization for the Diet Problem
Multi-objective evolutionary algorithms (MOEAs) are essential for solving complex optimization problems, such as the diet problem, where balancing conflicting objectives, like cost and nutritional content, is crucial. However, most MOEAs focus on optimizing solutions in the objective space, often neglecting the diversity of solutions in the decision space, which is critical for providing decision-makers with a wide range of choices. This paper introduces an approach that directly integrates a H…

Bicriteria Polygon Aggregation with Arbitrary Shapes
Lotte Blank, David Eppstein, Jan-Henrik Haunert, Herman Haverkort, Benedikt Kolbe, Philip Mayer, Petra Mutzel, Alexander Naumann, Jonas Sauer
https://arxiv.org/abs/2507.11212

Bicriteria Polygon Aggregation with Arbitrary Shapes
We study the problem of aggregating polygons by covering them with disjoint representative regions, thereby inducing a clustering of the polygons. Our objective is to minimize a weighted sum of the total area and the total perimeter of the regions. This problem has applications in cartographic map generalization and urban analytics. Here, the polygons represent building footprints and the clusters may represent urban areas. Previous approaches forced the boundaries of the regions to come from a…

Facilitating Personalized TTS for Dysarthric Speakers Using Knowledge Anchoring and Curriculum Learning
Yejin Jeon, Solee Im, Youngjae Kim, Gary Geunbae Lee
https://arxiv.org/abs/2508.10412

Facilitating Personalized TTS for Dysarthric Speakers Using Knowledge Anchoring and Curriculum Learning
Dysarthric speakers experience substantial communication challenges due to impaired motor control of the speech apparatus, which leads to reduced speech intelligibility. This creates significant obstacles in dataset curation since actual recording of long, articulate sentences for the objective of training personalized TTS models becomes infeasible. Thus, the limited availability of audio data, in addition to the articulation errors that are present within the audio, complicates personalized sp…

Inverse Optimal Control with Constraint Relaxation
Rahel Rickenbach, Amon Lahr, Melanie N. Zeilinger
https://arxiv.org/abs/2507.11392 https://

Inverse Optimal Control with Constraint Relaxation
Inverse optimal control (IOC) is a promising paradigm for learning and mimicking optimal control strategies from capable demonstrators, or gaining a deeper understanding of their intentions, by estimating an unknown objective function from one or more corresponding optimal control sequences. When computing estimates from demonstrations in environments with safety-preserving inequality constraints, acknowledging their presence in the chosen IOC method is crucial given their strong influence on t…

Foreground and internal free-free absorption in particle-accelerating colliding-wind binaries : Insights from the radio emission of WR 147
M. Tasseroul, M. De Becker, A. B Blanco, P. Benaglia, S. del Palacio
https://arxiv.org/abs/2508.10506

Foreground and internal free-free absorption in particle-accelerating colliding-wind binaries : Insights from the radio emission of WR 147
Radio emission from massive binary systems is generally of composite nature, showing both a thermal emission component from the winds and a non-thermal component from relativistic electrons accelerated in the colliding-wind region. Understanding the processes ruling their radio spectrum is essential to investigate the role of these objects in the production of non-thermal particle populations in our galaxy. Our objective is to explore how the processes at work in particle-accelerating colliding…

Obesity & diet
I wouldn't normally share a positive story about the new diet drugs, because I've seen someone get obsessed with them who was at a perfectly acceptable weight *by majority standards* (surprise: every weight is in fact perfectly acceptable by *objective* standards, because every "weight-associated" health risk is its own danger that should be assessed *in individuals*). I think two almost-contradictory things:
1. In a society shuddering under the burden of metastasized fatmisia, there's a very real danger in promoting the new diet drugs because lots of people who really don't need them will be psychologically bullied into using them and suffer from the cost and/or side effects.
2. For many individuals under the assault of our society's fatmisia, "just ignore it" is not a sufficient response, and also for specific people for whom decreasing their weight can address *specific* health risks/conditions that they *want* to address that way, these drugs can be a useful tool.
I know @… to be a trustworthy & considerate person, so I think it's responsible to share this:
#Fat #Diet #Obesity

In response to Elon Musk's claims that the App Store favors the ChatGPT app, Apple says the App Store "is designed to be fair and free of bias" (Mark Gurman/@markgurman)
https://x.com/markgurman/status/1955383759853007198

Mark Gurman (@markgurman) on X
Apple on @elonmusk’s claims: "The App Store is designed to be fair and free of bias. We feature thousands of apps through charts, algorithmic recommendations, and curated lists selected by experts using objective criteria.” https://t.co/w4NFINOF9x

Nonlinear optical microscopy with an obscuration-free, freeform reflective objective
Yryx Y. Luna Palacios, Tuyet N. A. Hoang, Salile Khandani, Stephan Clark, Aaron Bauer, Jannick P. Rolland, Eric O. Potma, Adam M. Hanninen
https://arxiv.org/abs/2507.08185

Nonlinear optical microscopy with an obscuration-free, freeform reflective objective
Nonlinear optical (NLO) imaging platforms traditionally rely on refractive microscope objectives, which suffer from chromatic aberrations and temporal dispersion of pulsed excitation light. These issues degrade spatial imaging properties and signal brightness. Furthermore, the limited transmission range of refractive materials restricts NLO imaging, especially for applications requiring short- to mid-wave infrared excitation. While reflective microscope objectives offer an achromatic solution a…

Distinct neurodynamics of functional brain networks in Alzheimer's disease and frontotemporal dementia as revealed by EEG
Sungwoo Ahn, Evie A. Malaia, Leonid L Rubchinsky
https://arxiv.org/abs/2507.08728

Distinct neurodynamics of functional brain networks in Alzheimer's disease and frontotemporal dementia as revealed by EEG
Objective While Alzheimer's disease (AD) and frontotemporal dementia (FTD) show some common memory deficits, these two disorders show partially overlapping complex spatiotemporal patterns of neural dynamics. The objective of this study is to characterize these patterns to better understand the general principles of neurodynamics in these conditions. Methods A comprehensive array of methods to study brain rhythms and functional brain networks are used in the study, from spectral power measures…

A Review of the Design of Cone-Cylinder-Flare Geometries for Stability Analyses and Conventional/Quiet Wind Tunnel Tests
Sebastien Esquieu, Steven P. Schneider, Elizabeth K. Benitez, Jean-Philippe Brazier
https://arxiv.org/abs/2509.10411

A Review of the Design of Cone-Cylinder-Flare Geometries for Stability Analyses and Conventional/Quiet Wind Tunnel Tests
In order to study the effects of pressure gradients, flow expansion, and recompression on the stability of hypersonic boundary-layers, axisymmetric cone-cylinder-flare configurations have been specifically designed for wind tunnel experiments. The objective is to create well-suited geometries for hypersonic laminar-turbulent transition analyses, while also adding the capability to generate attached and separated flows. After a thorough review of the aerodynamic flows obtained at Mach 6 and zero…

Developing an Inhaled NEU1 Inhibitor for Cystic Fibrosis via Pharmacokinetic and Biophysical Modeling
Yousra Hassan Alsaad Almeshale, Abdulelah Hassan Almeshali, Omar Alsaddique, Noura Jandali, Nadeen Garaween, Bin Hu
https://arxiv.org/abs/2508.10082

Developing an Inhaled NEU1 Inhibitor for Cystic Fibrosis via Pharmacokinetic and Biophysical Modeling
Background: Cystic fibrosis (CF) airway mucus exhibits reduced mucin sialylation, increasing viscosity and impairing mucociliary clearance (MCC). NEU1 inhibition has been proposed to restore MCC, but its quantitative pharmacokinetic and rheological effects, particularly with inhaled delivery, remain uncharacterized. Objective: To develop an integrated pharmacokinetic/pharmacodynamic (PK/PD) and biophysical model to assess the efficacy of an inhaled NEU1 inhibitor. Methods: Empirical and pre…

Emergence of Hierarchies in Multi-Agent Self-Organizing Systems Pursuing a Joint Objective
Gang Chen, Guoxin Wang, Anton van Beek, Zhenjun Ming, Yan Yan
https://arxiv.org/abs/2508.09541

Emergence of Hierarchies in Multi-Agent Self-Organizing Systems Pursuing a Joint Objective
Multi-agent self-organizing systems (MASOS) exhibit key characteristics including scalability, adaptability, flexibility, and robustness, which have contributed to their extensive application across various fields. However, the self-organizing nature of MASOS also introduces elements of unpredictability in their emergent behaviors. This paper focuses on the emergence of dependency hierarchies during task execution, aiming to understand how such hierarchies arise from agents' collective pursuit …

Active Learning and Explainable AI for Multi-Objective Optimization of Spin Coated Polymers
Brendan Young, Brendan Alvey, Andreas Werbrouck, Will Murphy, James Keller, Mattias J. Young, Matthew Maschmann
https://arxiv.org/abs/2509.08988

Active Learning and Explainable AI for Multi-Objective Optimization of Spin Coated Polymers
Spin coating polymer thin films to achieve specific mechanical properties is inherently a multi-objective optimization problem. We present a framework that integrates an active Pareto front learning algorithm (PyePAL) with visualization and explainable AI techniques to optimize processing parameters. PyePAL uses Gaussian process models to predict objective values (hardness and elasticity) from the design variables (spin speed, dilution, and polymer mixture), guiding the adaptive selection of sa…

New Lower Bounds for the Minimum Singular Value in Matrix Selection
Zhiqiang Xu
https://arxiv.org/abs/2508.10452 https://arxiv.org/pdf/2508.10452

New Lower Bounds for the Minimum Singular Value in Matrix Selection
The objective of the matrix selection problem is to select a submatrix $A_{S}\in \mathbb{R}^{n\times k}$ from $A\in \mathbb{R}^{n\times m}$ such that its minimum singular value is maximized. In this paper, we employ the interlacing polynomial method to investigate this problem. This approach allows us to identify a submatrix $A_{S_0}\in \mathbb{R}^{n\times k}$ and establish a lower bound for its minimum singular value. Specifically, unlike common interlacing polynomial approaches that estimate …

Single and multi-objective optimal designs for group testing experiments
Chi-Kuang Yeh, Weng Kee Wong, Julie Zhou
https://arxiv.org/abs/2508.08445 https://…

Single and multi-objective optimal designs for group testing experiments
Group testing, or pooled sample testing, is an active research area with increasingly diverse applications across disciplines. This paper considers design issues for group testing problems when a statistical model, an optimality criterion and a cost function are given. We use the software CVX to find designs that best estimate all or some of the model parameters ($D$ -, $D_s$ -, $A$-optimality) when there is one or more objectives in the study. A novel feature is that we include maximin types o…

Human-AI Collaboration Increases Efficiency in Regulatory Writing
Umut Eser, Yael Gozin, L. Jay Stallons, Ari Caroline, Martin Preusse, Brandon Rice, Scott Wright, Andrew Robertson
https://arxiv.org/abs/2509.09738

Human-AI Collaboration Increases Efficiency in Regulatory Writing
Background: Investigational New Drug (IND) application preparation is time-intensive and expertise-dependent, slowing early clinical development. Objective: To evaluate whether a large language model (LLM) platform (AutoIND) can reduce first-draft composition time while maintaining document quality in regulatory submissions. Methods: Drafting times for IND nonclinical written summaries (eCTD modules 2.6.2, 2.6.4, 2.6.6) generated by AutoIND were directly recorded. For comparison, manual draftin…

Technical Aspects of Plasma Operational Simulation (POPSIM): A Framework for Data-Driven Simulation and Control
Allen M. Wang, Zander Keith, Mark Dan Boyer, Andrew Oakleigh Nelson, Alex Saperstein, Alessandro Pau, Cristina Rea
https://arxiv.org/abs/2509.10244

Technical Aspects of Plasma Operational Simulation (POPSIM): A Framework for Data-Driven Simulation and Control
This paper reports on technical aspects of Plasma Operational Simulation (POPSIM), a research framework for data-driven simulation and control built in the machine learning framework JAX. The objective of the project is to address the extremely challenging simulation and modeling requirements of tokamak operations and control by combining simple principles-based models with data-driven models, spanning everything from power laws to new neural network architectures. This paper reports on key sof…

Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction
Luyao Tang, Kunze Huang, Chaoqi Chen, Yuxuan Yuan, Chenxin Li, Xiaotong Tu, Xinghao Ding, Yue Huang
https://arxiv.org/abs/2508.10731

Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction
Human perceptual systems excel at inducing and recognizing objects across both known and novel categories, a capability far beyond current machine learning frameworks. While generalized category discovery (GCD) aims to bridge this gap, existing methods predominantly focus on optimizing objective functions. We present an orthogonal solution, inspired by the human cognitive process for novel object understanding: decomposing objects into visual primitives and establishing cross-knowledge comparis…

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…

Sensor Model Identification via Simultaneous Model Selection and State Variable Determination
Christian Brommer, Alessandro Fornasier, Jan Steinbrener, Stephan Weiss
https://arxiv.org/abs/2506.11263

Sensor Model Identification via Simultaneous Model Selection and State Variable Determination
We present a method for the unattended gray-box identification of sensor models commonly used by localization algorithms in the field of robotics. The objective is to determine the most likely sensor model for a time series of unknown measurement data, given an extendable catalog of predefined sensor models. Sensor model definitions may require states for rigid-body calibrations and dedicated reference frames to replicate a measurement based on the robot's localization state. A health metric is…

Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
Nico Meyer, Christopher Mutschler, Andreas Maier, Daniel D. Scherer
https://arxiv.org/abs/2506.11552

Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
Quantum error correction is crucial for protecting quantum information against decoherence. Traditional codes like the surface code require substantial overhead, making them impractical for near-term, early fault-tolerant devices. We propose a novel objective function for tailoring error correction codes to specific noise structures by maximizing the distinguishability between quantum states after a noise channel, ensuring efficient recovery operations. We formalize this concept with the distin…

Generative Representational Learning of Foundation Models for Recommendation
Zheli Zhou, Chenxu Zhu, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
https://arxiv.org/abs/2506.11999

Generative Representational Learning of Foundation Models for Recommendation
Developing a single foundation model with the capability to excel across diverse tasks has been a long-standing objective in the field of artificial intelligence. As the wave of general-purpose foundation models sweeps across various domains, their influence has significantly extended to the field of recommendation systems. While recent efforts have explored recommendation foundation models for various generative tasks, they often overlook crucial embedding tasks and struggle with the complexit…

Polars is a lightning fast DataFrame library/in-memory query engine with parallel execution and cache efficiency. And now you can use is with the tidyverse syntax: #rstats

Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep Reinforcement Learning
Mohammadamin Moradi, Runyu Jiang, Yingzi Liu, Malvern Madondo, Tianming Wu, James J. Sohn, Xiaofeng Yang, Yasmin Hasan, Zhen Tian
https://arxiv.org/abs/2506.11957…

Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep Reinforcement Learning
High-dose-rate (HDR) brachytherapy plays a critical role in the treatment of locally advanced cervical cancer but remains highly dependent on manual treatment planning expertise. The objective of this study is to develop a fully automated HDR brachytherapy planning framework that integrates reinforcement learning (RL) and dose-based optimization to generate clinically acceptable treatment plans with improved consistency and efficiency. We propose a hierarchical two-stage autoplanning framework.…

Projected Coupled Diffusion for Test-Time Constrained Joint Generation
Hao Luan, Yi Xian Goh, See-Kiong Ng, Chun Kai Ling
https://arxiv.org/abs/2508.10531 https://

Projected Coupled Diffusion for Test-Time Constrained Joint Generation
Modifications to test-time sampling have emerged as an important extension to diffusion algorithms, with the goal of biasing the generative process to achieve a given objective without having to retrain the entire diffusion model. However, generating jointly correlated samples from multiple pre-trained diffusion models while simultaneously enforcing task-specific constraints without costly retraining has remained challenging. To this end, we propose Projected Coupled Diffusion (PCD), a novel te…

Data-Driven Differential Evolution in Tire Industry Extrusion: Leveraging Surrogate Models
Eider Garate-Perez, Kerman L\'opez de Calle-Etxabe, Susana Ferreiro
https://arxiv.org/abs/2507.11191

Data-Driven Differential Evolution in Tire Industry Extrusion: Leveraging Surrogate Models
The optimization of industrial processes remains a critical challenge, particularly when no mathematical formulation of objective functions or constraints is available. This study addresses this issue by proposing a surrogate-based, data-driven methodology for optimizing complex real-world manufacturing systems using only historical process data. Machine learning models are employed to approximate system behavior and construct surrogate models, which are integrated into a tailored metaheuristic…

Uplink RSMA for Pinching-Antenna Systems
Apostolos A. Tegos, Yue Xiao, Sotiris A. Tegos, George K. Karagiannidis, Panagiotis D. Diamantoulakis
https://arxiv.org/abs/2509.10076 h…

Uplink RSMA for Pinching-Antenna Systems
One of the key goals of next-generation wireless networks is to adapt to changing conditions and meet the growing demand for reliable, high-capacity communications from emerging applications. Overcoming the limitations of conventional technologies, such as fixed antenna positions, is essential to achieving this objective because it mitigates the impact of path loss on the received signal and creates strong line-of-sight links, enhancing system performance. With this in mind, the newly proposed …

A thermonuclear supernova interacting with hydrogen- and helium-deficient circumstellar material. SN 2020aeuh as a SN Ia-CSM-C/O?
K. Tsalapatas, J. Sollerman, R. Chiba, E. Kool, J. Johansson, S. Rosswog, S. Schulze, T. J. Moriya, I. Andreoni, T. G. Brink, T. X. Chen, S. Covarrubias, K. De, G. Dimitriadis, A. V. Filippenko, C. Fremling, A. Gangopadhyay, K. Maguire, G. Mo, Y. Sharma, N. Sravan, J. H. Terwel, Y. Yang

A thermonuclear supernova interacting with hydrogen- and helium-deficient circumstellar material. SN 2020aeuh as a SN Ia-CSM-C/O?
Identifying the progenitors of thermonuclear supernovae (Type Ia supernovae; SNe Ia) remains a key objective in contemporary astronomy. The rare subclass of SNe Ia that interacts with circumstellar material (Type Ia-CSM) allows for studies of the progenitor's environment before explosion, and generally favours single-degenerate progenitor channels. The case of SN Ia-CSM PTF11kx clearly connected thermonuclear explosions with hydrogen-rich CSM-interacting events, and the more recent SN 2020eyj c…

Adaptique: Multi-objective and Context-aware Online Adaptation of Selection Techniques in Virtual Reality
Chao-Jung Lai, Mauricio Sousa, Tianyu Zhang, Ludwig Sidenmark, Tovi Grossman
https://arxiv.org/abs/2508.08505

Adaptique: Multi-objective and Context-aware Online Adaptation of Selection Techniques in Virtual Reality
Selection is a fundamental task that is challenging in virtual reality due to issues such as distant and small targets, occlusion, and target-dense environments. Previous research has tackled these challenges through various selection techniques, but complicates selection and can be seen as tedious outside of their designed use case. We present Adaptique, an adaptive model that infers and switches to the most optimal selection technique based on user and environmental information. Adaptique con…

Objective Soups: Multilingual Multi-Task Modeling for Speech Processing
A F M Saif, Lisha Chen, Xiaodong Cui, Songtao Lu, Brian Kingsbury, Tianyi Chen
https://arxiv.org/abs/2508.09228

Objective Soups: Multilingual Multi-Task Modeling for Speech Processing
Training a single model for multilingual, multi-task speech processing (MSP) is severely hampered by conflicting objectives between tasks like speech recognition and translation. While multi-objective optimization (MOO) aims to align gradient updates, its effectiveness diminishes as the number of tasks grows, making it difficult to find a common descent direction. This raises a fundamental question: should highly conflicting objectives be optimized jointly or separated into a hierarchical struc…

Whisper Smarter, not Harder: Adversarial Attack on Partial Suppression
Zheng Jie Wong, Bingquan Shen
https://arxiv.org/abs/2508.09994 https://arxiv.org/pdf…

Whisper Smarter, not Harder: Adversarial Attack on Partial Suppression
Currently, Automatic Speech Recognition (ASR) models are deployed in an extensive range of applications. However, recent studies have demonstrated the possibility of adversarial attack on these models which could potentially suppress or disrupt model output. We investigate and verify the robustness of these attacks and explore if it is possible to increase their imperceptibility. We additionally find that by relaxing the optimisation objective from complete suppression to partial suppression, w…

1935: birth
1955: fresh out of college, first job writing IBM 701 assembler
1965: independent COBOL contractor
1975: buys an Altair after attending a Homebrew meeting, gets into BASIC
1985: unboxes IBM PC, inserts Turbo Pascal diskette
1995: learns HTML and JavaScript, publishes first home page
2005: retired, first steps in Python and Ruby, starts blog
2015: publishes mobile apps written in Objective-C and Java, starts podcast
2025: at age 90, vibe cod…

Knowledge Graph-Based approach for Sustainable 6G End-to-End System Design
Akshay Jain, Sylvaine Kerboeuf, Sokratis Barmpounakis, Crist\'obal Vinagre Z., Stefan Wendt, Dinh Thai Bui, Pol Alemany, Riccardo Nicolicchia, Jos\'e Mar\'ia Jorquera Valero, Dani Korpi, Mohammad Hossein Moghaddam, Mikko A. Uusitalo, Patrik Rugeland, Abdelkader Outtagarts, Karthik Upadhya, Panagiotis Demestichas, Raul Mu\~noz, Manuel Gil P\'erez, Daniel Adanza, Ricard Vilalta

Knowledge Graph-Based approach for Sustainable 6G End-to-End System Design
Previous generations of cellular communication, such as 5G, have been designed with the objective of improving key performance indicators (KPIs) such as throughput, latency, etc. However, to meet the evolving KPI demands as well as the ambitious sustainability targets for the ICT industry, 6G will need to be designed differently. Concretely, 6G will need to consider both the performance and sustainability targets for the various use cases it will serve. Moreover, like previous generations, 6G w…

A Markovian Framing of WaveFunctionCollapse for Procedurally Generating Aesthetically Complex Environments
Franklin Yiu, Mohan Lu, Nina Li, Kevin Joseph, Tianxu Zhang, Julian Togelius, Timothy Merino, Sam Earle
https://arxiv.org/abs/2509.09919

A Markovian Framing of WaveFunctionCollapse for Procedurally Generating Aesthetically Complex Environments
Procedural content generation often requires satisfying both designer-specified objectives and adjacency constraints implicitly imposed by the underlying tile set. To address the challenges of jointly optimizing both constraints and objectives, we reformulate WaveFunctionCollapse (WFC) as a Markov Decision Process (MDP), enabling external optimization algorithms to focus exclusively on objective maximization while leveraging WFC's propagation mechanism to enforce constraint satisfaction. We emp…

Machine Learning-Assisted Surrogate Modeling with Multi-Objective Optimization and Decision-Making of a Steam Methane Reforming Reactor
Seyed Reza Nabavi, Zonglin Guo, Zhiyuan Wang
https://arxiv.org/abs/2507.07641

Machine Learning-Assisted Surrogate Modeling with Multi-Objective Optimization and Decision-Making of a Steam Methane Reforming Reactor
This study presents an integrated modeling and optimization framework for a steam methane reforming (SMR) reactor, combining a mathematical model, artificial neural network (ANN)-based hybrid modeling, advanced multi-objective optimization (MOO) and multi-criteria decision-making (MCDM) techniques. A one-dimensional fixed-bed reactor model accounting for internal mass transfer resistance was employed to simulate reactor performance. To reduce the high computational cost of the mathematical mode…

Solving Distance-Based Optimization Problems Using Optical Hardware
Guangyao Li, Richard Zhipeng Wang, Natalia G. Berloff
https://arxiv.org/abs/2507.11378 …

Solving Distance-Based Optimization Problems Using Optical Hardware
We present a practical approach to solving distance-based optimization problems using optical computing hardware. The objective is to minimize an energy function defined as the weighted sum of squared differences between measured distances and the squared Euclidean distances of point coordinates. By representing coordinates as complex numbers, we map the optimization problem onto optical fields, enabling its solution through either the canonical transformation (CT) method or the gain-based bifu…

The Space Between Us: A Methodological Framework for Researching Bonding and Proxemics in Situated Group-Agent Interactions
Ana M\"uller, Anja Richert
https://arxiv.org/abs/2506.11829

The Space Between Us: A Methodological Framework for Researching Bonding and Proxemics in Situated Group-Agent Interactions
This paper introduces a multimethod framework for studying spatial and social dynamics in real-world group-agent interactions with socially interactive agents. Drawing on proxemics and bonding theories, the method combines subjective self-reports and objective spatial tracking. Applied in two field studies in a museum (N = 187) with a robot and a virtual agent, the paper addresses the challenges in aligning human perception and behavior. We focus on presenting an open source, scalable, and fiel…

Provable avoidance of barren plateaus for GM-QAOA
Boris Tsvelikhovskiy, Matthew Nuyten, Bojko N. Bakalov
https://arxiv.org/abs/2509.10424 https://arxiv.org…

Provable avoidance of barren plateaus for GM-QAOA
We analyze the dynamical Lie algebras (DLAs) associated with the Grover-mixer variant of the Quantum Approximate Optimization Algorithm (GM-QAOA). When the initial state is the uniform superposition of computational basis states, we show that the corresponding DLA is isomorphic to either $\mathfrak{su}_{r} \oplus \mathfrak{u}_{1}^{\oplus 2}$ or $\mathfrak{su}_{r} \oplus \mathfrak{u}_{1}$, where \(r\) denotes the number of distinct values of the objective function. We also establish an analogous…

Accelerated Design of Mechanically Hard Magnetically Soft High-entropy Alloys via Multi-objective Bayesian Optimization
Mian Dai, Yixuan Zhang, Weijia He, Chen Shen, Xiaoqing Li, Stephan Sch\"onecker, Liuliu Han, Ruiwen Xie, Tianhang Zhou, Hongbin Zhang
https://arxiv.org/abs/2509.05702

Accelerated Design of Mechanically Hard Magnetically Soft High-entropy Alloys via Multi-objective Bayesian Optimization
Designing high-entropy alloys (HEAs) that are both mechanically hard and possess soft magnetic properties is inherently challenging, as a trade-off is needed for mechanical and magnetic properties. In this study, we optimize HEA compositions using a multi-objective Bayesian optimization (MOBO) framework to achieve simultaneous optimal mechanical and magnetic properties. An ensemble surrogate model is constructed to enhance the accuracy of machine learning surrogate models, while an efficient sa…

Recursive Bound-Constrained AdaGrad with Applications to Multilevel and Domain Decomposition Minimization
Serge Gratton, Alena Kopani\v{c}\'akov\'a, Philippe Toint
https://arxiv.org/abs/2507.11513

Recursive Bound-Constrained AdaGrad with Applications to Multilevel and Domain Decomposition Minimization
Two OFFO (Objective-Function Free Optimization) noise tolerant algorithms are presented that handle bound constraints, inexact gradients and use second-order information when available.The first is a multi-level method exploiting a hierarchical description of the problem and the second is a domain-decomposition method covering the standard addditive Schwarz decompositions. Both are generalizations of the first-order AdaGrad algorithm for unconstrained optimization. Because these algorithms shar…

Estimation for the logarithmic partial derivative and Wiman-Valiron theory in several complex variables
Junfeng Xu, Sujoy Majumder, Nabadwip Sarkar
https://arxiv.org/abs/2509.07113

Estimation for the logarithmic partial derivative and Wiman-Valiron theory in several complex variables
The first objective of the paper is to estimate logarithmic partial derivative for meromorphic functions in several complex variables. Our estimations for logarithmic partial derivatives extend the results of Gundersen \cite{GG2} to the higher dimensions. The second objective of the paper is to study Wiman-Valiron theory into higher dimensions. The third objective of this paper is to study the growth of solutions of a class of complex partial differential equations and our obtained result is th…

Objective Bayesian inference for the Dhillon distribution
Pedro Luiz Ramos, Enrique Achire Quispe, Ricardo Puziol de Oliveira, Jorge A. Achcar
https://arxiv.org/abs/2509.06344 h…

Objective Bayesian inference for the Dhillon distribution
In this work, we develop an objective Bayesian framework for the Dhillon probability distribution. We explicitly derive three objective priors: the Jeffreys prior, the overall reference prior, and the maximal data information prior. We show that both Jeffreys and reference priors yields a proper posterior distribution, whereas the maximal data information prior leads to an improper posterior. Moreover, we establish sufficient conditions for the existence of its respective posterior moments. Bay…

BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
Donipolo Ghimire, Aamodh Suresh, Carlos Nieto-Granda, Solmaz S. Kia
https://arxiv.org/abs/2508.10363

BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized signal strength as a surrogate probability of source location. Building on Behavioral Entropy(BE) with Prelec's probability weighting function, we define an objective function that adapts robot behavior …

A Universal Framework for Large-Scale Multi-Objective Optimization Based on Particle Drift and Diffusion
Jia-Cheng Li, Min-Rong Chen, Guo-Qiang Zeng, Jian Weng, Man Wang, Jia-Lin Mai
https://arxiv.org/abs/2507.05847

A Universal Framework for Large-Scale Multi-Objective Optimization Based on Particle Drift and Diffusion
Large-scale multi-objective optimization poses challenges to existing evolutionary algorithms in maintaining the performances of convergence and diversity because of high dimensional decision variables. Inspired by the motion of particles in physics, we propose a universal framework for large-scale multi-objective optimization based on particle drift and diffusion to solve these challenges in this paper. This framework innovatively divides the optimization process into three sub-stages: two coa…

Automatic Generation of Explicit Quadratic Programming Solvers
Maximilian Schaller, Daniel Arnstr\"om, Alberto Bemporad, Stephen Boyd
https://arxiv.org/abs/2506.11513

Automatic Generation of Explicit Quadratic Programming Solvers
We consider a family of convex quadratic programs in which the coefficients of the linear objective term and the righthand side of the constraints are affine functions of a parameter. It is well known that the solution of such a parametrized quadratic program is a piecewise affine function of the parameter. The number of (polyhedral) regions in the solution map can grow exponentially in problem size, but when the number of regions is moderate, a so-called explicit solver is practical. Such a so…

Adaptive Backtracking for Privacy Protection in Large Language Models
Zhihao Yao, Yuxuan Gu, Xiachong Feng, Weitao Ma, Bo Li, Xiaocheng Feng
https://arxiv.org/abs/2508.06087 htt…

Adaptive Backtracking for Privacy Protection in Large Language Models
The preservation of privacy has emerged as a critical topic in the era of artificial intelligence. However, current work focuses on user-oriented privacy, overlooking severe enterprise data leakage risks exacerbated by the Retrieval-Augmented Generation paradigm. To address this gap, our paper introduces a novel objective: enterprise-oriented privacy concerns. Achieving this objective requires overcoming two fundamental challenges: existing methods such as data sanitization severely degrade mod…

Using generative AI in most capacities is wrong for the exact same reason using steroids in sports or at work is wrong (also for additional bonus reasons, too, of course).
We may one day invent safer tools, but that's not meaningfully an objective of any of the biggest players right now.
#AI #GenAI #LLMs

Three-Dimensional Isotropic STED Nanoscopy using a Single Objective
Renlong Zhang, Xiaoyu Weng, Haoxian Zhou, Luwei Wang, Fangrui Lin, Wei Yan, Xiumin Gao, Bin Yu, Danying Lin, Liwei Liu, Chenshuang Zhang, Kayla K. Green, Ewoud R. E. Schmidt, Songlin Zhuang, Junle Qu
https://arxiv.org/abs/2507.06718

Three-Dimensional Isotropic STED Nanoscopy using a Single Objective
Accurate three-dimensional (3D) imaging requires an isotropic point spread function (PSF). However, the inherent missing aperture of a single objective lens results in an elongated, cigar-like PSF, which has rendered isotropic resolution in fluorescence microscopy seemingly insurmountable without a 4π configuration for decades. To address this long-standing challenge, we introduce ISO-STED (Isotropic Single-Objective STED) Nanoscopy, a novel approach that employs a single objective lens and a …

Intracortical bipolar stimulation allows selective activation of neuronal populations in the cortex #BCI

Intracortical bipolar stimulation allows selective activation of neuronal populations in the cortex
Background Intracortical electrical stimulation has emerged as a promising approach for sensory restoration, such as a cortical visual prosthesis, yet its effectiveness is limited by current spread and electrode density constraints. Objective To determine whether intracortical bipolar current steering—via modulation of the return electrode position—can enhance neural activation selectivity compared to traditional monopolar stimulation, with the aim of improving spatial precision in sensory…

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise
Yuchen Yang, Kaihong Lu, Long Wang
https://arxiv.org/abs/2506.11647

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise
In this paper, the problem of distributed optimization is studied via a network of agents. Each agent only has access to a noisy gradient of its own objective function, and can communicate with its neighbors via a network. To handle this problem, a distributed clipped stochastic gradient descent algorithm is proposed, and the high probability convergence of the algorithm is studied. Existing works on distributed algorithms involving stochastic gradients only consider the light-tailed noises. Di…

The Paradigm of Massive Wireless Human Sensing: Concept, Architecture and Challenges
Mauro De Sanctis
https://arxiv.org/abs/2508.09756 https://arxiv.org/pd…

The Paradigm of Massive Wireless Human Sensing: Concept, Architecture and Challenges
This article is a position paper which introduces the paradigm of ``Massive Wireless Human Sensing'', i.e. an infrastructure for wireless human sensing based on a plethora of heterogeneous wireless communication signals. More specifically, we aim to exploit signal diversity in the time, frequency, and space domains using opportunistically both device-free and device-based wireless sensing approaches, with the objective of enhancing human sensing capabilities in terms of accuracy and service ava…

Multi-Objective Nonlinear Power Split Control For BESS With Real-Time Simulation Feedback
Vivek Teja Tanjavooru, Prashant Pant, Thomas Hamacher, Holger Hesse
https://arxiv.org/abs/2507.04800

Multi-Objective Nonlinear Power Split Control For BESS With Real-Time Simulation Feedback
This paper presents a mixed-integer, nonlinear, multi-objective optimization strategy for optimal power allocation among parallel strings in Battery Energy Storage Systems (BESS). High-fidelity control is achieved by co-simulating the optimizer with a BESS electro-thermal simulation that models spatial thermal dynamics of the battery, providing real-time State of Charge (SOC) and temperature feedback. The optimizer prioritizes reliability by enforcing power availability as a hard constraint and…

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
Tiantian He, Minzhi Xie, Runtong Li, Xiaoxiao Xu, Jiaqi Yu, Zixiu Wang, Lantao Hu, Han Li, Kun Gai
https://arxiv.org/abs/2508.05093

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short video recommendation system. EMER enhances personalization by replacing manually-designed heuristic formulas with an end-to-end modeling paradigm. EMER introduces a meticulously designed loss function to address the fundamental challenge of defining effective supervision for ensemble ranking, where no single ground-tru…

A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages
Kayhan Latifzadeh, Jacek Gwizdka, Luis A. Leiva
https://arxiv.org/abs/2507.08003

A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages
We contribute a comprehensive dataset to study user attention and purchasing behavior on Search Engine Result Pages (SERPs). Previous work has relied on mouse movements as a low-cost large-scale behavioral proxy but also has relied on self-reported ground-truth labels, collected at post-task, which can be inaccurate and prone to biases. To address this limitation, we use an eye tracker to construct an objective ground-truth of continuous visual attention. Our dataset comprises 2,776 transaction…

P.808 Multilingual Speech Enhancement Testing: Approach and Results of URGENT 2025 Challenge
Marvin Sach, Yihui Fu, Kohei Saijo, Wangyou Zhang, Samuele Cornell, Robin Scheibler, Chenda Li, Anurag Kumar, Wei Wang, Yanmin Qian, Shinji Watanabe, Tim Fingscheidt
https://arxiv.org/abs/2507.11306

P.808 Multilingual Speech Enhancement Testing: Approach and Results of URGENT 2025 Challenge
In speech quality estimation for speech enhancement (SE) systems, subjective listening tests so far are considered as the gold standard. This should be even more true considering the large influx of new generative or hybrid methods into the field, revealing issues of some objective metrics. Efforts such as the Interspeech 2025 URGENT Speech Enhancement Challenge also involving non-English datasets add the aspect of multilinguality to the testing procedure. In this paper, we provide a brief reca…

A DC-Reformulation for Gradient-$L^0$-Constrained Problems in Function Spaces
Bastian Dittrich, Evelyn Herberg, Roland Herzog, Georg M\"uller
https://arxiv.org/abs/2506.11917

A DC-Reformulation for Gradient-$L^0$-Constrained Problems in Function Spaces
Cardinality constraints in optimization are commonly of $L^0$-type, and they lead to sparsely supported optimizers. An efficient way of dealing with these constraints algorithmically, when the objective functional is convex, is reformulating the constraint using the difference of suitable $L^1$- and largest-$K$-norms and subsequently solving a sequence of penalized subproblems in the difference-of-convex (DC) class. We extend this DC-reformulation approach to problems with $L^0$-type cardinalit…

Robust inference under Benford's law
Lucio Barabesi, Andrea Cerioli, Andrea Cerasa, Domenico Perrotta
https://arxiv.org/abs/2507.08650 https://

Robust inference under Benford's law
We address the task of identifying anomalous observations by analyzing digits under the lens of Benford's law. Motivated by the crucial objective of providing reliable statistical analysis of customs declarations, we answer one major and still open question: How can we detect the behavior of operators who are aware of the prevalence of the Benford's pattern in the digits of regular observations and try to manipulate their data in such a way that the same pattern also holds after data fabricatio…

Reshaping the Forward-Forward Algorithm with a Similarity-Based Objective
James Gong, Raymond Luo, Emma Wang, Leon Ge, Bruce Li, Felix Marattukalam, Waleed Abdulla
https://arxiv.org/abs/2509.08697

Reshaping the Forward-Forward Algorithm with a Similarity-Based Objective
Backpropagation is the pivotal algorithm underpinning the success of artificial neural networks, yet it has critical limitations such as biologically implausible backward locking and global error propagation. To circumvent these constraints, the Forward-Forward algorithm was proposed as a more biologically plausible method that replaces the backward pass with an additional forward pass. Despite this advantage, the Forward-Forward algorithm significantly trails backpropagation in accuracy, and i…

Convergence of Momentum-Based Optimization Algorithms with Time-Varying Parameters
Mathukumalli Vidyasagar
https://arxiv.org/abs/2506.11904 https://…

Convergence of Momentum-Based Optimization Algorithms with Time-Varying Parameters
In this paper, we present a unified algorithm for stochastic optimization that makes use of a "momentum" term; in other words, the stochastic gradient depends not only on the current true gradient of the objective function, but also on the true gradient at the previous iteration. Our formulation includes the Stochastic Heavy Ball (SHB) and the Stochastic Nesterov Accelerated Gradient (SNAG) algorithms as special cases. In addition, in our formulation, the momentum term is allowed to vary as a f…

AudioBERTScore: Objective Evaluation of Environmental Sound Synthesis Based on Similarity of Audio embedding Sequences
Minoru Kishi, Ryosuke Sakai, Shinnosuke Takamichi, Yusuke Kanamori, Yuki Okamoto
https://arxiv.org/abs/2507.00475

AudioBERTScore: Objective Evaluation of Environmental Sound Synthesis Based on Similarity of Audio embedding Sequences
We propose a novel objective evaluation metric for synthesized audio in text-to-audio (TTA), aiming to improve the performance of TTA models. In TTA, subjective evaluation of the synthesized sound is an important, but its implementation requires monetary costs. Therefore, objective evaluation such as mel-cepstral distortion are used, but the correlation between these objective metrics and subjective evaluation values is weak. Our proposed objective evaluation metric, AudioBERTScore, calculates …

Array-Aware Ambisonics and HRTF Encoding for Binaural Reproduction With Wearable Arrays
Yhonatan Gayer, Vladimir Tourbabin, Zamir Ben Hur, David Lou Alon, Boaz Rafaely
https://arxiv.org/abs/2507.11091

Array-Aware Ambisonics and HRTF Encoding for Binaural Reproduction With Wearable Arrays
This work introduces a novel method for binaural reproduction from arbitrary microphone arrays, based on array-aware optimization of Ambisonics encoding through Head-Related Transfer Function (HRTF) pre-processing. The proposed approach integrates array-specific information into the HRTF processing pipeline, leading to improved spatial accuracy in binaural rendering. Objective evaluations demonstrate superior performance under simulated wearable-array and head rotations compared to conventional…

Lyapunov analysis for FISTA under strong convexity
Luis M. Brice\~no-Arias
https://arxiv.org/abs/2506.11785 https://arxiv.org/pdf/250…

Lyapunov analysis for FISTA under strong convexity
In this paper, we conduct a theoretical and numerical study of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) under strong convexity assumptions. We propose an autonomous Lyapunov function that reflects the strong convexity of the objective function, whether it arises from the smooth or non-smooth component. This Lyapunov function decreases monotonically at a linear rate along the iterations of the algorithm for a fixed inertial parameter. Our analysis demonstrates that the best th…

Locality in Image Diffusion Models Emerges from Data Statistics
Artem Lukoianov, Chenyang Yuan, Justin Solomon, Vincent Sitzmann
https://arxiv.org/abs/2509.09672 https://…

Locality in Image Diffusion Models Emerges from Data Statistics
Among generative models, diffusion models are uniquely intriguing due to the existence of a closed-form optimal minimizer of their training objective, often referred to as the optimal denoiser. However, diffusion using this optimal denoiser merely reproduces images in the training set and hence fails to capture the behavior of deep diffusion models. Recent work has attempted to characterize this gap between the optimal denoiser and deep diffusion models, proposing analytical, training-free mode…

Multi-Objective Infeasibility Diagnosis for Routing Problems Using Large Language Models
Kai Li, Ruihao Zheng, Xinye Hao, Zhenkun Wang
https://arxiv.org/abs/2508.03406 https://

Multi-Objective Infeasibility Diagnosis for Routing Problems Using Large Language Models
In real-world routing problems, users often propose conflicting or unreasonable requirements, which result in infeasible optimization models due to overly restrictive or contradictory constraints, leading to an empty feasible solution set. Existing Large Language Model (LLM)-based methods attempt to diagnose infeasible models, but modifying such models often involves multiple potential adjustments that these methods do not consider. To fill this gap, we introduce Multi-Objective Infeasibility D…

Improving AEBS Validation Through Objective Intervention Classification Leveraging the Prediction Divergence Principle
Daniel Betschinske, Steven Peters
https://arxiv.org/abs/2507.07872

Improving AEBS Validation Through Objective Intervention Classification Leveraging the Prediction Divergence Principle
The safety validation of automatic emergency braking system (AEBS) requires accurately distinguishing between false positive (FP) and true positive (TP) system activations. While simulations allow straightforward differentiation by comparing scenarios with and without interventions, analyzing activations from open-loop resimulations - such as those from field operational testing (FOT) - is more complex. This complexity arises from scenario parameter uncertainty and the influence of driver inter…

A risk-sensitive ergodic singular stochastic control problem
Justin Gwee, Mihail Zervos
https://arxiv.org/abs/2509.09835 https://arxiv.org/pdf/2509.09835…

A risk-sensitive ergodic singular stochastic control problem
We consider a two-sided singular stochastic control problem with a risk-sensitive ergodic criterion. In particular, we consider a stochastic system whose uncontrolled dynamics are modelled by a linear diffusion. The control that can be applied to the system is modelled by an additive finite variation process. The objective of the control problem is to minimise a risk-sensitive long-term average criterion that penalises deviations of the controlled process from a given interval, as well as the e…

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization
Yiming Yao, Fei Liu, Liang Zhao, Xi Lin, Qingfu Zhang
https://arxiv.org/abs/2509.03244 https://

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization
Expensive multi-objective optimization is a prevalent and crucial concern in many real-world scenarios, where sample-efficiency is vital due to the limited evaluations to recover the true Pareto front for decision making. Existing works either involve rebuilding Gaussian process surrogates from scratch for each objective in each new problem encountered, or rely on extensive past domain experiments for pre-training deep learning models, making them hard to generalize and impractical to cope with…

Heavy-ball dynamics with Hessian-driven damping for non-convex optimization under the {\L}ojasiewicz condition
Vassilis Apidopoulos, Vasiliki Mavrogeorgou, Theodoros G. Tsironis
https://arxiv.org/abs/2506.11705

Heavy-ball dynamics with Hessian-driven damping for non-convex optimization under the Łojasiewicz condition
In this paper, we examine the convergence properties of heavy-ball dynamics with Hessian-driven damping in smooth non-convex optimization problems satisfying a Łojasiewicz condition. In this general setting, we provide a series of tight, worst-case optimal convergence rate guarantees as a function of the dynamics' friction coefficients and the Łojasiewicz exponent of the problem's objective function. Importantly, the linear rates that we obtain improve on previous available rates and they sug…

An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes
Rodrigo Lankaites Pinheiro, Dario Landa-Silva, Wasakorn Laesanklang, Ademir Aparecido Constantino
https://arxiv.org/abs/2508.08297

An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes
Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many-objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario present similarities in their fitness landscapes. That is, there are recurring features in the fitness landscapes when searching for solutions to differ…

Worst-Case Complexity of High-Order Algorithms for Pareto-Front Reconstruction
Andrea Cristofari, Marianna De Santis, Stefano Lucidi, Giampaolo Liuzzi
https://arxiv.org/abs/2506.11929

Worst-Case Complexity of High-Order Algorithms for Pareto-Front Reconstruction
In this paper, we are concerned with a worst-case complexity analysis of a-posteriori algorithms for unconstrained multiobjective optimization. Specifically, we propose an algorithmic framework that generates sets of points by means of $p$th-order models regularized with a power $p+1$ of the norm of the step. Through a tailored search procedure, several trial points are generated at each iteration and they can be added to the current set if a decrease is obtained for at least one objective func…

Policy Newton methods for Distortion Riskmetrics
Soumen Pachal, Mizhaan Prajit Maniyar, Prashanth L. A
https://arxiv.org/abs/2508.07249 https://arxiv.org/p…

Policy Newton methods for Distortion Riskmetrics
We consider the problem of risk-sensitive control in a reinforcement learning (RL) framework. In particular, we aim to find a risk-optimal policy by maximizing the distortion riskmetric (DRM) of the discounted reward in a finite horizon Markov decision process (MDP). DRMs are a rich class of risk measures that include several well-known risk measures as special cases. We derive a policy Hessian theorem for the DRM objective using the likelihood ratio method. Using this result, we propose a natu…

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…

Acoustic Non-Stationarity Objective Assessment with Hard Label Criteria for Supervised Learning Models
Guilherme Zucatelli, Ricardo Barioni, Gabriela Dantas
https://arxiv.org/abs/2508.06405

Acoustic Non-Stationarity Objective Assessment with Hard Label Criteria for Supervised Learning Models
Objective non-stationarity measures are resource intensive and impose critical limitations for real-time processing solutions. In this paper, a novel Hard Label Criteria (HLC) algorithm is proposed to generate a global non-stationarity label for acoustic signals, enabling supervised learning strategies to be trained as stationarity estimators. The HLC is first evaluated on state-of-the-art general-purpose acoustic models, demonstrating that these models encode stationarity information. Furtherm…

OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent
Bowen Chen, Zhao Wang, Shingo Takamatsu
https://arxiv.org/abs/2507.02353

OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent
Keyword decision in Sponsored Search Advertising is critical to the success of ad campaigns. While LLM-based methods offer automated keyword generation, they face three major limitations: reliance on large-scale query-keyword pair data, lack of online multi-objective performance monitoring and optimization, and weak quality control in keyword selection. These issues hinder the agentic use of LLMs in fully automating keyword decisions by monitoring and reasoning over key performance indicators s…

Cost-Effective Strategies for Infectious Diseases: A Multi-Objective Framework with an Interactive Dashboard
Jongmin Lee, Renier Mendoza, Victoria May P. Mendoza, Eunok Jung
https://arxiv.org/abs/2509.09087

Cost-Effective Strategies for Infectious Diseases: A Multi-Objective Framework with an Interactive Dashboard
During an infectious disease outbreak, policymakers must balance medical costs with social and economic burdens and seek interventions that minimize both. To support this decision-making process, we developed a framework that integrates multi-objective optimization, cost-benefit analysis, and an interactive dashboard. This platform enables users to input cost parameters and immediately obtain a cost-optimal intervention strategy. We applied this framework to the early outbreak of COVID-19 in So…

Tree-Structured Parzen Estimator Can Solve Black-Box Combinatorial Optimization More Efficiently
Kenshin Abe, Yunzhuo Wang, Shuhei Watanabe
https://arxiv.org/abs/2507.08053 https://arxiv.org/pdf/2507.08053 https://arxiv.org/html/2507.08053
arXiv:2507.08053v1 Announce Type: new
Abstract: Tree-structured Parzen estimator (TPE) is a versatile hyperparameter optimization (HPO) method supported by popular HPO tools. Since these HPO tools have been developed in line with the trend of deep learning (DL), the problem setups often used in the DL domain have been discussed for TPE such as multi-objective optimization and multi-fidelity optimization. However, the practical applications of HPO are not limited to DL, and black-box combinatorial optimization is actively utilized in some domains, e.g., chemistry and biology. As combinatorial optimization has been an untouched, yet very important, topic in TPE, we propose an efficient combinatorial optimization algorithm for TPE. In this paper, we first generalize the categorical kernel with the numerical kernel in TPE, enabling us to introduce a distance structure to the categorical kernel. Then we discuss modifications for the newly developed kernel to handle a large combinatorial search space. These modifications reduce the time complexity of the kernel calculation with respect to the size of a combinatorial search space. In the experiments using synthetic problems, we verified that our proposed method identifies better solutions with fewer evaluations than the original TPE. Our algorithm is available in Optuna, an open-source framework for HPO.
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Attention's forward pass and Frank-Wolfe
Albert Alcalde, Borjan Geshkovski, Dom\`enec Ruiz-Balet
https://arxiv.org/abs/2508.09628 https://arxiv.org/pdf…

Attention's forward pass and Frank-Wolfe
We study the hardmax limit of self-attention dynamics for token embeddings obtained in the zero-temperature ($β\to+\infty$) regime, and relate it to the finite-$β$ setting. In this limit, the update rule can be viewed as a Frank-Wolfe step for a quadratic objective over the convex hull of the current token embeddings. When the key-query matrix is negative semidefinite, the method linearly contracts all tokens to a single cluster at the origin. When it is positive semidefinite, extending the h…

Hierarchical exact controllability for a parabolic equation with Hardy potential
Haiyang Lin, Bo You
https://arxiv.org/abs/2509.08471 https://arxiv.org/pdf…

Hierarchical exact controllability for a parabolic equation with Hardy potential
The main objective of this paper is to study the hierarchical exact controllability for a parabolic equation with Hardy potential by Stackelberg-Nash strategy. In linear case, we employ Lax-Milgram theorem to prove the existence of an associated Nash equilibrium pair corresponding to a bi-objective optimal control problem for each leader, which is responsible for an exact controllability property. Then the observability inequality of a coupled parabolic system is established by using global Car…

Global Descent Method for Non-convex Multi-objective Optimization Problems
Bikram Adhikary, Md Abu Talhamainuddin Ansary, Savin Treanta
https://arxiv.org/abs/2507.22390 https://…

Global Descent Method for Non-convex Multi-objective Optimization Problems
In this paper, we develop a global descent method for non-convex multi-objective optimization problems. The proposed approach builds upon foundational concepts from single-objective global descent techniques while removing the need for predefined scalars or ordering information of objective functions. Initially, the proposed method identifies a local weak efficient solution using any suitable descent algorithm, then applies an auxiliary function termed the multi-objective global descent functio…

Optimization-Free Fast Optimal Control: Bang-Ride Property, Monotonicity, and Applications to Fast Battery Charging
Shengling Shi, Jacob Sass, Jiaen Wu, Minsu Kim, Yingjie Ma, Sungho Shin, Richard D. Braatz
https://arxiv.org/abs/2508.09010

Optimization-Free Fast Optimal Control: Bang-Ride Property, Monotonicity, and Applications to Fast Battery Charging
Single-input fast optimal control problems, which aim to achieve the optimal objective as fast as possible, occur in various real-world applications. In the case of fast battery charging, the associated optimal control problem becomes computationally challenging when detailed battery models are used. A recent heuristic optimization-free algorithm can significantly reduce the computational cost and provide an approximate solution, consistent with many heuristic input profiles in practice. These …