Tootfinder

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison
Parsa Hassani Shariat Panahi, Amir Hossein Jalilvand, M. Hasan Najafi
https://arxiv.org/abs/2506.00924

Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison
This paper introduces a dual-layer framework for network operator-side quality of experience (QoE) assessment that integrates both objective network modeling and subjective user perception extracted from live-streaming platforms. On the objective side, we develop a machine learning model trained on mean opinion scores (MOS) computed via the ITU-T P.1203 reference implementation, allowing accurate prediction of user-perceived video quality using only network parameters such as packet loss, delay…

Multi-Objective Neural Network Assisted Design Optimization of Soft Fin-Ray Grippers for Enhanced Grasping Performance
Ali Ghanizadeh, Ali Ahmadi, Arash Bahrami
https://arxiv.org/abs/2506.00494

Multi-Objective Neural Network Assisted Design Optimization of Soft Fin-Ray Grippers for Enhanced Grasping Performance
Soft Fin-Ray grippers can perform delicate and careful manipulation, which has caused notable attention in different fields. These grippers can handle objects of various forms and sizes safely. The internal structure of the Fin-Ray finger plays a significant role in its adaptability and grasping performance. However, modeling the non-linear grasp force and deformation behaviors for design purposes is challenging. Moreover, when the Fin-Ray finger becomes more rigid and capable of exerting highe…

Regionalized Metric Framework: A Novel Approach for Evaluating Multimodal Multi-Objective Optimization Algorithms
Jintai Chen, Fangqing Liu, Xueming Yan, Han Huang
https://arxiv.org/abs/2506.00468

Regionalized Metric Framework: A Novel Approach for Evaluating Multimodal Multi-Objective Optimization Algorithms
This study aims to optimize the evaluation metric of multimodal multi-objective optimization problems using a Regionalized Metric Framework, which provides a certain boost to research in this field. Existing evaluation metrics usually use the reference set as the evaluation basis, which inevitably leads to reference set dependence. To optimize this problem, this study proposes an evaluation metric based on a Regionalized Metric Framework. The algorithm divides the set of solutions to be evaluat…

This https://arxiv.org/abs/2305.12867 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

An Output-Polynomial Time Algorithm to Determine all Supported Efficient Solutions for Multi-Objective Integer Network Flow Problems
This paper addresses the problem of enumerating all supported efficient solutions for a linear multi-objective integer minimum cost flow problem (MOIMCF). It derives an output-polynomial time algorithm to determine all supported efficient solutions for MOIMCF problems. This is the first approach to solve this general problem in output-polynomial time. Moreover, we prove that the existence of an output-polynomial time algorithm to determine all weakly supported nondominated vectors (or all weakl…

This https://arxiv.org/abs/2312.01786 has been replaced.
link: https://scholar.google.com/scholar?q=a

Output-sensitive Complexity of Multi-Objective Integer Network Flow Problems
This paper addresses the output-sensitive complexity for linear multi-objective integer minimum cost flow (MOIMCF) problems and provides insights about the time complexity for enumerating all supported nondominated vectors. The paper shows that there can not exist an output-polynomial time algorithm for the enumeration of all supported nondominated vectors that determine the vectors in an ordered way in the outcome space unless NP = P. Moreover, novel methods for identifying supported nondomina…

I have just submitted a grant application to the Swedish Science Council with the title "Functional multi-objective optimization under uncertainty".
https://patrikja.owlstown.net/posts/3782
If granted it would pay for a PhD student supervision time.
Some prelimina…

Crowdsourcing MUSHRA Tests in the Age of Generative Speech Technologies: A Comparative Analysis of Subjective and Objective Testing Methods
Laura Lechler, Chamran Moradi, Ivana Balic
https://arxiv.org/abs/2506.00950

Crowdsourcing MUSHRA Tests in the Age of Generative Speech Technologies: A Comparative Analysis of Subjective and Objective Testing Methods
The MUSHRA framework is widely used for detecting subtle audio quality differences but traditionally relies on expert listeners in controlled environments, making it costly and impractical for model development. As a result, objective metrics are often used during development, with expert evaluations conducted later. While effective for traditional DSP codecs, these metrics often fail to reliably evaluate generative models. This paper proposes adaptations for conducting MUSHRA tests with non-ex…

A High-Performance Evolutionary Multiobjective Community Detection Algorithm
Guilherme O. Santos, Lucas S. Vieira, Giulio Rossetti, Carlos H. G. Ferreira, Gladston Moreira
https://arxiv.org/abs/2506.01752

A High-Performance Evolutionary Multiobjective Community Detection Algorithm
Community structure is a key feature of complex networks, underpinning a diverse range of phenomena across social, biological, and technological systems. While traditional methods, such as Louvain and Leiden, offer efficient solutions, they rely on single-objective optimization, often failing to capture the multifaceted nature of real-world networks. Multi-objective approaches address this limitation by considering multiple structural criteria simultaneously, but their high computational cost r…

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

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

Human-Centric Evaluation for Foundation Models
Yijin Guo, Kaiyuan Ji, Xiaorong Zhu, Junying Wang, Farong Wen, Chunyi Li, Zicheng Zhang, Guangtao Zhai
https://arxiv.org/abs/2506.01793

Human-Centric Evaluation for Foundation Models
Currently, nearly all evaluations of foundation models focus on objective metrics, emphasizing quiz performance to define model capabilities. While this model-centric approach enables rapid performance assessment, it fails to reflect authentic human experiences. To address this gap, we propose a Human-Centric subjective Evaluation (HCE) framework, focusing on three core dimensions: problem-solving ability, information quality, and interaction experience. Through experiments involving Deepseek R…

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Shuohao Ping, Naren Sathishkumar, Wan-Hsuan Lin, Hanyu Wang, Jason Cong
https://arxiv.org/abs/2505.24169

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Quantum Layout Synthesis (QLS) is a critical compilation stage that adapts quantum circuits to hardware constraints with an objective of minimizing the SWAP overhead. While heuristic tools demonstrate good efficiency, they often produce suboptimal solutions, and exact methods suffer from limited scalability. In this work, we propose ML-SABRE, a high-performance multilevel framework for QLS that improves both solution quality and compilation time through a hierarchical optimization approach. We …

Understanding Underrepresented Groups in Open Source Software
Reydne Santos, Rafa Prado, Ana Paula de Holanda Silva, Kiev Gama, Fernando Castor, Ronnie de Souza Santos
https://arxiv.org/abs/2506.00142

Understanding Underrepresented Groups in Open Source Software
Context: Diversity can impact team communication, productivity, cohesiveness, and creativity. Analyzing the existing knowledge about diversity in open source software (OSS) projects can provide directions for future research and raise awareness about barriers and biases against underrepresented groups in OSS. Objective: This study aims to analyze the knowledge about minority groups in OSS projects. We investigated which groups were studied in the OSS literature, the study methods used, their im…

This https://arxiv.org/abs/2505.21356 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Towards Robust Automated Perceptual Voice Quality Assessment with Deep Learning
Objective: Perceptual voice quality assessment plays a critical role in diagnosing and monitoring voice disorders by providing standardized evaluation of vocal function. Traditionally, this process relies on expert raters utilizing standard scales, such as the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) and Grade, Roughness, Breathiness, Asthenia, and Strain (GRBAS). However, these metrics are inherently subjective and susceptible to inter-rater variability, motivating the need f…

A Sequential Computation Algorithm for the Center of the Smallest Enclosing Ball
Kenji Nakagawa, Yoshinori Takei
https://arxiv.org/abs/2506.00734 https://

A Sequential Computation Algorithm for the Center of the Smallest Enclosing Ball
In this paper, we consider the problem of finding the center $Q^\ast$ of the SEB (smallest enclosing ball) for $n$ points in $d$-dimensional Euclidean space. One application of the SEB is SVDD (support vector data description) in support vector machines. Our objective is to develop a sequential computation algorithm for determining the barycentric coordinate of $Q^\ast$. To achieve it, we apply the concept of the Arimoto-Blahut algorithm, which is a sequential computation algorithm used to comp…

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…

Data-assimilated model-informed reinforcement learning
Defne E. Ozan, Andrea N\'ovoa, Georgios Rigas, Luca Magri
https://arxiv.org/abs/2506.01755 https…

Data-assimilated model-informed reinforcement learning
The control of spatio-temporally chaos is challenging because of high dimensionality and unpredictability. Model-free reinforcement learning (RL) discovers optimal control policies by interacting with the system, typically requiring observations of the full physical state.In practice, sensors often provide only partial and noisy measurements (observations) of the system. The objective of this paper is to develop a framework that enables the control of chaotic systems with partial and noisy obse…

A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors
Zheng Gong, Ziyi Jiang, Weihao Gao, Deng Zhuo, Lan Ma
https://arxiv.org/abs/2505.23862

A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors
The mRNA optimization is critical for therapeutic and biotechnological applications, since sequence features directly govern protein expression levels and efficacy. However, current methods face significant challenges in simultaneously achieving three key objectives: (1) fidelity (preventing unintended amino acid changes), (2) computational efficiency (speed and scalability), and (3) the scope of optimization variables considered (multi-objective capability). Furthermore, existing methods often…

A mean field game model with non-local spatial interactions and resources accumulation
Daria Ghilli, Fausto Gozzi, Cristiano Ricci, Giovanni Zanco
https://arxiv.org/abs/2506.01200

A mean field game model with non-local spatial interactions and resources accumulation
We study a family of Mean Field Games arising in modeling the behavior of strategic economic agents which move across space maximizing their utility from consumption and have the possibility to accumulate resources for production (such as human capital). The resulting mean field game PDE system is not covered in the actual literature on the topic as it displays weaker assumptions on the regularity of the data (in particular Lipschitz continuity and boundedness of the objective are lost), state …

Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning
Pengcheng Dai, Yuanqiu Mo, Wenwu Yu, Wei Ren
https://arxiv.org/abs/2505.24113

Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning
This paper studies the networked multi-agent reinforcement learning (NMARL) problem, where the objective of agents is to collaboratively maximize the discounted average cumulative rewards. Different from the existing methods that suffer from poor expression due to linear function approximation, we propose a distributed neural policy gradient algorithm that features two innovatively designed neural networks, specifically for the approximate Q-functions and policy functions of agents. This distri…

Discriminating Tail Behavior Using Halfspace Depths: Population and Empirical Perspectives
Sibsankar Singha, Marie Kratz, Sreekar Vadlamani
https://arxiv.org/abs/2506.01126

Discriminating Tail Behavior Using Halfspace Depths: Population and Empirical Perspectives
We study the empirical version of halfspace depths with the objective of establishing a connection between the rates of convergence and the tail behaviour of the corresponding underlying distributions. The intricate interplay between the sample size and the parameter driving the tail behaviour forms one of the main results of this analysis. The chosen approach is mainly based on weighted empirical processes indexed by sets by Alexander (1987), which leads to relatively direct and elegant proofs…

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…

This https://arxiv.org/abs/2505.01875 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

MolQAE: Quantum Autoencoder for Molecular Representation Learning
We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to quantum states using parameterized rotation gates, preserving vital structural information. Its quantum encoder-decoder framework enables latent space compression and reconstruction. A dual-objective strategy optimizes fidelity and minimizes trash state deviation. Our evaluations demonstrate effective capture of molecul…

Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications
Vahid Garousi, Zafar Jafarov, Aytan Movsumova, Atif Namazov, Huseyn Mirzayev
https://arxiv.org/abs/2506.00682

Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications
Context: As generative AI (GenAI) tools such as ChatGPT and GitHub Copilot become pervasive in education, concerns are rising about students using them to complete rather than learn from coursework-risking overreliance, reduced critical thinking, and long-term skill deficits. Objective: This paper proposes and empirically applies a causal model to help educators scaffold responsible GenAI use in Software Engineering (SE) education. The model identifies how professor actions, student factors, …

The Use of Alendronate to Enhance Transcranial Transmission of Focused Ultrasound for Successful Ablations in Brain
G. Sakharova, A. Krokhmal, R. Galimova, A. Khatmullina, D. Nabiullina, I. Buzaev, D. Avzaletdinova, D. Chupova, V. Khokhlova
https://arxiv.org/abs/2505.24349

The Use of Alendronate to Enhance Transcranial Transmission of Focused Ultrasound for Successful Ablations in Brain
Objective: The aim of this study was to evaluate the efficacy of alendronate therapy in improving bone density distribution in skull bones and corresponding ultrasound permeability in patients who had previously experienced unsuccessful transcranial MR-guided focused ultrasound (MRgFUS) ablation. The ability of alendronate treatment to modify skull bone characteristics and enhance the success rate of repeat MRgFUS procedures was assessed. Methods: Five patients with initially unsuccessful MRg…

This https://arxiv.org/abs/2503.14328 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Risk-Sensitive Model Predictive Control for Interaction-Aware Planning -- A Sequential Convexification Algorithm
This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot interaction scenarios. We derive computationally tractable convex upper bounds to both the objective function, and to frequently used penalty terms for collision avoidance, allowing us to efficiently solve the generally nonconvex optimal control problem as a sequence of convex problems. Simulations of a robot navigatin…

FUSE: Universal Speech Enhancement using Multi-Stage Fusion of Sparse Compression and Token Generation Models for the URGENT 2025 Challenge
Nabarun Goswami, Tatsuya Harada
https://arxiv.org/abs/2506.00809

FUSE: Universal Speech Enhancement using Multi-Stage Fusion of Sparse Compression and Token Generation Models for the URGENT 2025 Challenge
We propose a multi-stage framework for universal speech enhancement, designed for the Interspeech 2025 URGENT Challenge. Our system first employs a Sparse Compression Network to robustly separate sources and extract an initial clean speech estimate from noisy inputs. This is followed by an efficient generative model that refines speech quality by leveraging self-supervised features and optimizing a masked language modeling objective on acoustic tokens derived from a neural audio codec. In the f…

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

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

This https://arxiv.org/abs/2204.12015 has been replaced.
link: https://scholar.google.com/scholar?q=a

Reassessing the strength of a class of Wigner's friend no-go theorems
Two recent, prominent theorems--the "no-go theorem for observer-independent facts" and the "Local Friendliness no-go theorem"--employ so-called extended Wigner's friend scenarios to try to impose novel, non-trivial constraints on the possible nature of physical reality. While the former is argued to entail that there can be no theory in which the results of Wigner and his friend can both be considered objective, the latter is said to place on reality stronger constraints than the Bell and Koche…

This https://arxiv.org/abs/2501.16815 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination
This paper introduces two novel criteria: one for feature selection and another for feature elimination in the context of best subset selection, which is a benchmark problem in statistics and machine learning. From the perspective of optimization, we revisit the classical selection and elimination criteria in traditional best subset selection algorithms, revealing that these classical criteria capture only partial variations of the objective function after the entry or exit of features. By form…

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

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

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

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

What if there was a game like Deathloop and Hitman but with Final Destination vibes where the objective was to run around manipulating the environment so everyone lives? And there was an old Bioshock-esque Hifi system where one could save memories or artifacts or worldstate changes across loops? And it wasn't a game?
#Dream #JustThisOnce #EveryoneLives

This https://arxiv.org/abs/2404.17386 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Stochastic Bregman Subgradient Methods for Nonsmooth Nonconvex Optimization Problems
This paper focuses on the problem of minimizing a locally Lipschitz continuous function. Motivated by the effectiveness of Bregman gradient methods in training nonsmooth deep neural networks and the recent progress in stochastic subgradient methods for nonsmooth nonconvex optimization problems \cite{bolte2021conservative,bolte2022subgradient,xiao2023adam}, we investigate the long-term behavior of stochastic Bregman subgradient methods in such context, especially when the objective function lack…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Soledad O'Brien praised a company for firing a man for being creepy. OK.
So to be fair all people who seem creepy should be fired from all jobs, right?
What is the objective criteria for creepiness that shows the line between odd behavior and fire-able "creepiness"?
#StopTheHysteria

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

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

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

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

This https://arxiv.org/abs/2505.07501 has been replaced.
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The Complexity of Pure Strategy Relevant Equilibria in Concurrent Games
We study rational synthesis problems for concurrent games with $ω$-regular objectives. Our model of rationality considers only pure strategy Nash equilibria that satisfy either a social welfare or Pareto optimality condition with respect to an $ω$-regular objective for each agent. This extends earlier work on equilibria in concurrent games, without consideration about their quality. Our results show that the existence of Nash equilibria satisfying social welfare conditions can be computed as …

US political contradictions; knowledge systems
As Trump at least partially succeeds in constructing an alternate reality for his most ardent followers, it's tempting to think of his dogma as false, in contrast to some imagined "truth" which his non-followers are smart enough to believe in. But a more nuanced view of knowledge would admit that different groups of people have different shared truths, constituting different knowledge systems which each deviate from what's objectively measurable in different ways, and in fact they each accept different standards of what is objective, so there's not really a single "ground truth" we can even compare to to determine which of these knowledge systems is "more correct" (similar problems arise even if we only care about "more useful").
To make this more concrete, we can see that e.g., competing quantum physics theories, or likewise competing religious beliefs, have no reasonable basis on which to judge between them, either in terms of "truth" or "utility." So the Trump-dogma knowledge system, although bad, morally repugnant, etc., can't so easily be dismissed as "false" in my view. "Distorted" or "malignant" or "evil" or "contradictory" are better monikers, in my opinion.
But what I'm even more interested in thinking about is: in what ways does the current American liberal "common sense" knowledge system already bear the scars of past fascist lies & contradictions? I can think of a few:
"Columbus was an explorer."
This is "factually accurate" in the same way some of Trump's propaganda is, but it's also a cruel distortion of "Columbus was a child murderer," and it's a misrepresentation that serves an evil purpose, yet which is widely taught in elementary schools today.
Another: "dropping atomic bombs on civilians in Japan was necessary to end WWII."
Perhaps in the future we'll have "family separation & the 2025 ICE crackdowns were necessary to end the immigration crisis," although I dearly hope not.
"Reparations for slavery aren't reasonable," is yet another...
I'll close this rambling with a question: what other fascist lies have you noticed that are normalized in America right now from past Trump-like leaders (or even from less overtly fascist institutions)?

Material did\'actico de L\'ogica Proposicional para Estructuras Discretas
Margarita Carrera Fournier
https://arxiv.org/abs/2505.13459 https://

Material didáctico de Lógica Proposicional para Estructuras Discretas
One of the most difficult topics in the subject of Discrete Mathematics is the subject of Propositional Logic, therefore the present work had as objective to facilitate the learning of Propositional Logic through the implementation of didactic material with the use of educational technology that contributes to the achievement of the objectives established in the syllabus of the subject of Discrete Mathematics, because the knowledge that it provides to Computer Engineers is essential for their p…

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

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

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

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

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

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

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

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

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

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