Tootfinder

A fourth-order active flux method for parabolic problems with application to porous medium equation
Junming Duan
https://arxiv.org/abs/2510.11527 https://a…

A fourth-order active flux method for parabolic problems with application to porous medium equation
The active flux (AF) method is a compact high-order finite volume method originally proposed for solving hyperbolic conservation laws, in which cell averages and point values at cell interfaces are evolved simultaneously. This paper develops a fourth-order AF method for one- and two-dimensional parabolic problems, employing the explicit strong-stability-preserving Runge-Kutta (SSP-RK) method for time integration. The proposed method is built on a degenerate first-order system with auxiliary var…

A PCA-based Data Prediction Method
Peteris Daugulis, Vija Vagale, Emiliano Mancini, Filippo Castiglione
https://arxiv.org/abs/2510.09246 https://arxiv.org/…

A PCA-based Data Prediction Method
The problem of choosing appropriate values for missing data is often encountered in the data science. We describe a novel method containing both traditional mathematics and machine learning elements for prediction (imputation) of missing data. This method is based on the notion of distance between shifted linear subspaces representing the existing data and candidate sets. The existing data set is represented by the subspace spanned by its first principal components. Solutions for the case of th…

An inexact semismooth Newton-Krylov method for semilinear elliptic optimal control problem
Shiqi Chen, Xuesong Chen
https://arxiv.org/abs/2511.10058 https://arxiv.org/pdf/2511.10058 https://arxiv.org/html/2511.10058
arXiv:2511.10058v1 Announce Type: new
Abstract: An inexact semismooth Newton method has been proposed for solving semi-linear elliptic optimal control problems in this paper. This method incorporates the generalized minimal residual (GMRES) method, a type of Krylov subspace method, to solve the Newton equations and utilizes nonmonotonic line search to adjust the iteration step size. The original problem is reformulated into a nonlinear equation through variational inequality principles and discretized using a second-order finite difference scheme. By leveraging slanting differentiability, the algorithm constructs semismooth Newton directions and employs GMRES method to inexactly solve the Newton equations, significantly reducing computational overhead. A dynamic nonmonotonic line search strategy is introduced to adjust stepsizes adaptively, ensuring global convergence while overcoming local stagnation. Theoretical analysis demonstrates that the algorithm achieves superlinear convergence near optimal solutions when the residual control parameter $\eta_k$ approaches to 0. Numerical experiments validate the method's accuracy and efficiency in solving semilinear elliptic optimal control problems, corroborating theoretical insights.
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A novel spatial distribution method for wind farm parameterizations based on the Gaussian function
Bowen Du, Qi Li, Mingwei Ge, Xintao Li, Yongqian Liu
https://arxiv.org/abs/2510.11392

A novel spatial distribution method for wind farm parameterizations based on the Gaussian function
Wind farm parameterizations are crucial for quantifying the wind-farm atmosphere interaction, where wind turbines are typically modeled as elevated momentum sinks and sources of turbulence kinetic energy (TKE). These quantities must be properly distributed to the mesoscale grid. Existing parameterizations use the single-column method. However, this method can easily lead to the errors of the spatial distribution of the sink and source, thereby impacting the accuracy of mesoscale flow simulation…

Lattice Boltzmann method for electromagnetic wave scattering
Mohd. Meraj Khan, Sumesh P. Thampi, Anubhab Roy
https://arxiv.org/abs/2510.11042 https://arxiv…

Lattice Boltzmann method for electromagnetic wave scattering
In this paper, we propose the lattice Boltzmann method (LBM) as an alternative numerical approach for electromagnetic scattering. The method is systematically validated over a wide range of size parameters, thereby covering the Rayleigh, Mie, and geometric optics regimes, through comparison with established reference solutions. For circular cylinders, both perfect electrically conducting (PEC) and dielectric, LBM results are benchmarked against analytical Mie theory. For dielectric cylinders, c…

A Morphology-Adaptive Random Feature Method for Inverse Source Problem of the Helmholtz Equation
Xinwei Hu, Jingrun Chen, Haijun Yu
https://arxiv.org/abs/2510.09213 https://

A Morphology-Adaptive Random Feature Method for Inverse Source Problem of the Helmholtz Equation
The inverse source problem for the Helmholtz equation poses significant challenges, particularly when sources exhibit complex or discontinuous geometries. Traditional numerical methods suffer from prohibitive computational costs, while machine learning-based approaches such as Physics-Informed Neural Networks (PINNs) and the Random Feature Method (RFM) -- though computationally efficient for inverse problems -- lack the intrinsic machinery to handle the sharp morphological features in such sing…

Non-unitary Time Evolution via the Chebyshev Expansion Method
\'Aron Holl\'o, D\'aniel Varjas, Cosma Fulga, L\'aszl\'o Oroszl\'any, Viktor K\"onye
https://arxiv.org/abs/2510.10643

Non-unitary Time Evolution via the Chebyshev Expansion Method
The Chebyshev expansion method is a well-established technique for computing the time evolution of quantum states, particularly in Hermitian systems with a bounded spectrum. Here, we show that the applicability of the Chebyshev expansion method extends well beyond this constraint: It remains valid across the entire complex plane and is thus suitable for arbitrary non-Hermitian matrices. We identify that numerical rounding errors are the primary source of errors encountered when applying the met…

Scalable Quantum Monte Carlo Method for Polariton Chemistry via Mixed Block Sparsity and Tensor Hypercontraction Method
Yu Zhang
https://arxiv.org/abs/2510.11634 https://…

Scalable Quantum Monte Carlo Method for Polariton Chemistry via Mixed Block Sparsity and Tensor Hypercontraction Method
We present a reduced-scaling auxiliary-field quantum Monte Carlo (AFQMC) framework designed for large molecular systems and ensembles, with or without coupling to optical cavities. Our approach leverages the natural block sparsity of Cholesky decomposition (CD) of electron repulsion integrals in molecular ensembles and employs tensor hypercontraction (THC) to efficiently compress low-rank Cholesky blocks. By representing the Cholesky vectors in a mixed format, keeping high-rank blocks in block-…

Graph Signal Wiener Filtering in the Linear Canonical Domain: Theory and Method Design
Xiaopeng Cheng, Zhichao Zhang
https://arxiv.org/abs/2510.10512 https://

Graph Signal Wiener Filtering in the Linear Canonical Domain: Theory and Method Design
The graph linear canonical transform (GLCT)-based filtering methods often optimize transform parameters and filters separately, which results in high computational costs and limited stability. To address this issue, this paper proposes a trainable joint optimization framework that combines GLCT parameters and Wiener filtering into an end-to-end learning process, allowing for synergistic optimization between transform domain construction and filtering operations. The proposed method not only eli…

Anthropic open sources a method to score AI model political evenhandedness; Gemini 2.5 Pro got 97%, Grok 4 96%, Claude Opus 4.1 95%, GPT-5 89%, and Llama 4 66% (Ina Fried/Axios)
https://www.axios.com/2025/11/13/anthropic-bot-bias-data

Anthropic tries to measure bot bias
There's no consensus over what constitutes political bias in AI systems.

Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): II. A Physics-Informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Kai E. Yang, Xudong Sun, Lucas A. Tarr, Jiayi Liu, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
https://…

Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): II. A Physics-Informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Inferring the three-dimensional (3D) solar atmospheric structures from observations is a critical task for advancing our understanding of the magnetic fields and electric currents that drive solar activity. In this work, we introduce a novel, Physics-Informed Machine Learning method to reconstruct the 3D structure of the lower solar atmosphere based on the output of optical depth sampled spectropolarimetric inversions, wherein both the fully disambiguated vector magnetic fields and the geometri…

A Neural Surrogate-Enhanced Multi-Method Framework for Robust Wing Design Optimization
Arash Fath Lipaei, AmirHossein Ghaemi, Melika Sabzikari
https://arxiv.org/abs/2510.08582 h…

A Neural Surrogate-Enhanced Multi-Method Framework for Robust Wing Design Optimization
This paper introduces a modular and scalable design optimization framework for the wing design process that enables faster early-phase design while ensuring aerodynamic stability. The pipeline starts with the generation of initial wing geometries and then proceeds to optimize the wing using several algorithms. Aerodynamic performance is assessed using a Vortex Lattice Method (VLM) applied to a carefully selected dataset of wing configurations. These results are employed to develop surrogate neu…

Selecting Clusters and Protoclusters via Stellar Mass Density: I. Method and tests on Mock HSC-SSP catalogs
Marcelo C. Vicentin, Pablo Araya-Araya, Laerte Sodr\'e Jr., Michael A. Strauss
https://arxiv.org/abs/2510.10735

Selecting Clusters and Protoclusters via Stellar Mass Density: I. Method and tests on Mock HSC-SSP catalogs
We present an algorithm designed to identify galaxy (proto)clusters in wide-area photometric surveys by first selecting their dominant galaxy-i.e., the Brightest Cluster Galaxy (BCG) or protoBCG-through the local stellar mass density traced by massive galaxies. We focus on its application to the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Wide Survey to detect candidates up to $\rm z \sim 2$. In this work, we apply the method to mock galaxy catalogs that replicate the observational con…

Ab-initio calculation of magnetic exchange interactions using the spin-spiral method in VASP: Self-consistent versus magnetic force theorem approaches
Umit Dogan Daglum, Maria Stamenova, Ersoy Sasioglu, Stefano Sanvito
https://arxiv.org/abs/2510.11603

Ab-initio calculation of magnetic exchange interactions using the spin-spiral method in VASP: Self-consistent versus magnetic force theorem approaches
We present an ab initio investigation of magnetic exchange interactions using the spin-spiral method implemented in the VASP code, with a comparative analysis of the self-consistent (SC) and magnetic force theorem (MFT) approaches. Using representative 3d ferromagnets (Fe, Co, Ni) and Mn-based full Heusler compounds, we compute magnon dispersion relations directly from spin-spiral total energies and extract real-space Heisenberg exchange parameters via Fourier transformation. Curie temperatures…

EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
Tobias Preintner, Weixuan Yuan, Adrian K\"onig, Thomas B\"ack, Elena Raponi, Niki van Stein
https://arxiv.org/abs/2510.11631

EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
Combining large language models with evolutionary computation algorithms represents a promising research direction leveraging the remarkable generative and in-context learning capabilities of LLMs with the strengths of evolutionary algorithms. In this work, we present EvoCAD, a method for generating computer-aided design (CAD) objects through their symbolic representations using vision language models and evolutionary optimization. Our method samples multiple CAD objects, which are then optimiz…

"New Teflon Recycling Method Turns The “Forever Chemical” into Toothpaste "
#PFAS #ForeverChemicals
https://

New Teflon Recycling Method Turns The “Forever Chemical” into Toothpaste  | Happy Eco News
Scientists from Newcastle University and the University of Birmingham developed a room-temperature Teflon recycling method that turns Teflon waste into sodium fluoride using mechanical shaking with sodium metal. The process breaks strong carbon-fluorine bonds without heat or solvents, producing pure sodium fluoride for toothpaste, water treatment, and pharmaceutical applications.

Experimental investigations on Lehmer's conjecture for elliptic curves
Sven Cats, John Michael Clark, Charlotte Dombrowsky, Mar Curco Iranzo, Krystal Maughan, Eli Orvis
https://arxiv.org/abs/2510.08871

Experimental investigations on Lehmer's conjecture for elliptic curves
In this short note, we give a method for computing a non-torsion point of smallest canonical height on a given elliptic curve $E/\mathbb{Q}$ over all number fields of a fixed degree. We then describe data collected using this method, and investigate related conjectures of Lehmer and Lang using these data.

Parameterized crack modelling based on a localized non-intrusive reduced basis method
Margarita Chasapi
https://arxiv.org/abs/2510.10624 https://arxiv.org/…

Parameterized crack modelling based on a localized non-intrusive reduced basis method
This contribution presents a model order reduction strategy for fast parametric modelling of problems with cracks formulated on spline discretizations. In the context of damage detection, parametric reduced order models (ROMs) are well suited for fast computations by establishing an efficient offline/online split of the simulation process. The problems of interest focus on geometric parameters that describe the crack configuration and may pose challenges to constructing efficient ROMs. This wor…

Science ouverte et collaborative pour l'\'elaboration d'un banc automatis\'e de caract\'erisation de pertes en commutation par opposition
Nicolas Rouger, Luiz Villa, Matthieu Masson, Pauline Kergus, Joseph Kemdeg, Lorenzo Leijnen, Jean Alinei, Adrien Colomb, Ayoub Farah-Hassan, Arnauld Biganzoli
https://arxiv.org/abs/25…

Science ouverte et collaborative pour l'élaboration d'un banc automatisé de caractérisation de pertes en commutation par opposition
The switching losses of power transistors are generally measured using the so-called double pulse method. Measuring the opposition of two switching cells is a complementary method that is more accurate but indirect. However, implementing this method can be more complex and requires calibration steps and comprehensive control, with the added issue of thermal management. In this context, we proposed to address this topic through open and collaborative science, first in the form of a two-day hacka…

Dual Data Scaling for Robust Two-Stage User-Defined Keyword Spotting
Zhiqi Ai, Han Cheng, Yuxin Wang, Shiyi Mu, Shugong Xu, Yongjin Zhou
https://arxiv.org/abs/2510.10740 https:/…

Dual Data Scaling for Robust Two-Stage User-Defined Keyword Spotting
In this paper, we propose DS-KWS, a two-stage framework for robust user-defined keyword spotting. It combines a CTC-based method with a streaming phoneme search module to locate candidate segments, followed by a QbyT-based method with a phoneme matcher module for verification at both the phoneme and utterance levels. To further improve performance, we introduce a dual data scaling strategy: (1) expanding the ASR corpus from 460 to 1,460 hours to strengthen the acoustic model; and (2) leveraging…

A Localized Orthogonal Decomposition method for heterogeneous mixed-dimensional problems
Moritz Hauck, Axel M{\aa}lqvist, Malin Mosquera
https://arxiv.org/abs/2510.09442 https:/…

A Localized Orthogonal Decomposition method for heterogeneous mixed-dimensional problems
We propose a multiscale method for mixed-dimensional elliptic problems with highly heterogeneous coefficients arising, for example, in the modeling of fractured porous media. The method is based on the Localized Orthogonal Decomposition (LOD) framework and constructs locally supported, problem-adapted basis functions on a coarse mesh that does not need to resolve the coefficient oscillations. These basis functions are obtained in parallel by solving localized fine-scale problems. Our a priori e…

Our puritanical streak strikes again...
Many of us endure pain. Pain from many causes. Often intense, often of long duration.
In the race to prevent opioid adiction the US Federal government has denied many of us who are careful users, who have no history of abuse, access to an effective remedy.
Now the puritans want to deny another method of meeting pain. Why, because they want to control our lives and our bodies (sound familiar?)
The bill to fund the government w…

THC gummies and drinks face ban under provision in government spending bill
The bill prohibits products containing over 0.4 milligrams of THC per container.

Halpern Acceleration of the Inexact Proximal Point Method of Rockafellar
Liwei Zhang, Fanli Zhuang, Ning Zhang
https://arxiv.org/abs/2511.10372 https://arxiv.org/pdf/2511.10372 https://arxiv.org/html/2511.10372
arXiv:2511.10372v1 Announce Type: new
Abstract: This paper investigates a Halpern acceleration of the inexact proximal point method for solving maximal monotone inclusion problems in Hilbert spaces. The proposed Halpern inexact proximal point method (HiPPM) is shown to be globally convergent, and a unified framework is developed to analyze its worst-case convergence rate. Under mild summability conditions on the inexactness tolerances, HiPPM achieves an $\mathcal{O}(1/k^{2})$ rate in terms of the squared fixed-point residual. Furthermore, under additional mild condition, the method retains a fast linear convergence rate. Building upon this framework, we further extend the acceleration technique to constrained convex optimization through the augmented Lagrangian formulation. In analogy to Rockafellar's classical results, the resulting accelerated inexact augmented Lagrangian method inherits the convergence rate and complexity guarantees of HiPPM. The analysis thus provides a unified theoretical foundation for accelerated inexact proximal algorithms and their augmented Lagrangian extensions.
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A Faster Randomized Algorithm for Vertex Cover: An Automated Approach
Katie Clinch, Serge Gaspers, Tao Zixu He, Simon Mackenzie, Tiankuang Zhang
https://arxiv.org/abs/2510.09027

A Faster Randomized Algorithm for Vertex Cover: An Automated Approach
This work introduces two techniques for the design and analysis of branching algorithms, illustrated through the case study of the Vertex Cover problem. First, we present a method for automatically generating branching rules through a systematic case analysis of local structures. Second, we develop a new technique for analyzing randomized branching algorithms using the Measure & Conquer method, offering greater flexibility in formulating branching rules. By combining these innovations with addi…

I used to work at McDonald's in my youth. Can confirm
#ShamelesslyStolenFromSomewhereElseOnTheInternetHonestlyICantKeepTrackOfThisStuffAnymore #Shitpost

Advancing credibility and transparency in brain-to-image reconstruction research: Reanalysis of Koide-Majima, Nishimoto, and Majima https://arxiv.org/abs/2511.07960 by @… et al.;

Advancing credibility and transparency in brain-to-image reconstruction research: Reanalysis of Koide-Majima, Nishimoto, and Majima (Neural Networks, 2024)
A recent high-profile study by Koide-Majima et al. (2024) claimed a major advance in reconstructing visual imagery from brain activity using a novel variant of a generative AI-based method. However, our independent reanalysis reveals multiple methodological concerns that raise questions about the validity of their conclusions. Specifically, our evaluation demonstrates that: (1) the reconstruction results are biased by selective reporting of only the best-performing examples at multiple levels; …

A new puzzle: normally in #XOrg I can blank the screen with "xset dpms force off" and with KDE/Plasma I could bind this to a hot key, but in Gnome #Ubuntu 25.10 using #XFCE4 it will work from inside a terminal, but as a hotkey it blanks, pauses, then refreshes the screens. Why would this be? Or perhaps, is there a prefered method to instantly blank all screens in XFCE-4? Not to lock, just to turn them off.

I love how simple Kitten’s Streaming HTML workflow makes building features like this, especially when using class-based Kitten pages and components :)
#Kitten

Query-Specific GNN: A Comprehensive Graph Representation Learning Method for Retrieval Augmented Generation
Yuchen Yan, Zhihua Liu, Hao Wang, Weiming Li, Xiaoshuai Hao
https://arxiv.org/abs/2510.11541 …

Query-Specific GNN: A Comprehensive Graph Representation Learning Method for Retrieval Augmented Generation
Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multiple knowledge targets to form a synthesized answer, raise new challenges for RAG systems. Under the multi-hop settings, existing methods often struggle to fully understand the questions with complex semantic structures and are susceptible to irrelevant noise during the retrieval…

🔋 Feeding off spent battery waste, a novel bacterium signals a new method for self-sufficient battery recycling
https://phys.org/news/2025-10-spent-battery-bacterium-method-sufficient.html

Feeding off spent battery waste, a novel bacterium signals a new method for self-sufficient battery recycling
A unique bacterium that thrives in highly acidic environments feeds on spent battery "waste," making it a promising new method for self-sufficient battery recycling, according to new research from Boston College chemists.

🇺🇦 #NowPlaying on KEXP's #StreetSounds
Missy Elliot ft. Method Man:
🎵 Bring the Pain
#MissyElliotftMethodMan
https://skillzbeats-fr.bandcamp.com/track/16-method-man-feat-missy-elliot-bring-the-pain
https://open.spotify.com/track/6a2mBuXce3F5rmV2CNvDXx

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/7]:
- A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class...
Cheng Yin, Yi Chen

A new approach to inverse Sturm-Liouville problems based on point interaction II. The singular case
Min Zhao, Jiangang Qi, Xiao Chen
https://arxiv.org/abs/2510.08904 https://

A new approach to inverse Sturm-Liouville problems based on point interaction II. The singular case
In this paper, further to the point interaction method for inverse Sturm-Liouville problems on finite intervals firstly proposed in our previous work, we will continue to generalize this method to the inverse eigenvalue problems for singular Sturm-Liouville problems on the half real axis.

LLMAtKGE: Large Language Models as Explainable Attackers against Knowledge Graph Embeddings
Ting Li, Yang Yang, Yipeng Yu, Liang Yao, Guoqing Chao, Ruifeng Xu
https://arxiv.org/abs/2510.11584

LLMAtKGE: Large Language Models as Explainable Attackers against Knowledge Graph Embeddings
Adversarial attacks on knowledge graph embeddings (KGE) aim to disrupt the model's ability of link prediction by removing or inserting triples. A recent black-box method has attempted to incorporate textual and structural information to enhance attack performance. However, it is unable to generate human-readable explanations, and exhibits poor generalizability. In the past few years, large language models (LLMs) have demonstrated powerful capabilities in text comprehension, generation, and reas…

Interesting... Yamaha has a process called Acoustic Resonance Enhancement that does something similar, and they do it to guitars starting in the mid range price.
https://www.guitarworld.com/artists/guitarists/jason-isbells-method-to-age-acou…

“The longer it goes, the better the guitar is going to sound”: Jason Isbell has shared his bizarre method for breaking in new acoustics – and you’ll never guess what it is
There’s science behind the madness. We promise

Agentic Property-Based Testing: Finding Bugs Across the Python Ecosystem
Muhammad Maaz, Liam DeVoe, Zac Hatfield-Dodds, Nicholas Carlini
https://arxiv.org/abs/2510.09907 https:/…

Agentic Property-Based Testing: Finding Bugs Across the Python Ecosystem
Property-based testing (PBT) is a lightweight formal method, typically implemented as a randomized testing framework. Users specify the input domain for their test using combinators supplied by the PBT framework, and the expected properties or invariants as a unit-test function. The framework then searches for a counterexample, e.g. by generating inputs and calling the test function. In this work, we demonstrate an LLM-based agent which analyzes Python modules, infers function-specific and cros…

NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
Neil C. Janwani, Varun Madabushi, Maegan Tucker
https://arxiv.org/abs/2510.11542 https…

NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
Reinforcement learning (RL) has emerged as a powerful method to learn robust control policies for bipedal locomotion. Yet, it can be difficult to tune desired robot behaviors due to unintuitive and complex reward design. In comparison, offline trajectory optimization methods, like Hybrid Zero Dynamics, offer more tuneable, interpretable, and mathematically grounded motion plans for high-dimensional legged systems. However, these methods often remain brittle to real-world disturbances like exter…

Lecture Notes on Verifying Graph Neural Networks
Fran\c{c}ois Schwarzentruber
https://arxiv.org/abs/2510.11617 https://arxiv.org/pdf/2510.11617

Lecture Notes on Verifying Graph Neural Networks
In these lecture notes, we first recall the connection between graph neural networks, Weisfeiler-Lehman tests and logics such as first-order logic and graded modal logic. We then present a modal logic in which counting modalities appear in linear inequalities in order to solve verification tasks on graph neural networks. We describe an algorithm for the satisfiability problem of that logic. It is inspired from the tableau method of vanilla modal logic, extended with reasoning in quantifier-free…

Spin-Locking Spectroscopy of Harmonic Motion
Florian Kranzl, Adria Rospars, Johannes Franke, Manoj K. Joshi, Rainer Blatt, Christian F. Roos
https://arxiv.org/abs/2510.08732 htt…

Spin-Locking Spectroscopy of Harmonic Motion
Characterization of noise of a quantum harmonic oscillator is important for many experimental platforms. We experimentally demonstrate motional spin-locking spectroscopy, a method that allows us to directly measure the motional noise spectrum of a quantum harmonic oscillator. We measure motional noise of a single trapped ion in a frequency range from 200 Hz to 5 kHz with a power spectral density that resolves noise over two orders of magnitude. Coherent modulations in the oscillation frequency …

Conditional Flow Matching for Bayesian Posterior Inference
So Won Jeong, Percy S. Zhai, Veronika Ro\v{c}ov\'a
https://arxiv.org/abs/2510.09534 https://…

Conditional Flow Matching for Bayesian Posterior Inference
We propose a generative multivariate posterior sampler via flow matching. It offers a simple training objective, and does not require access to likelihood evaluation. The method learns a dynamic, block-triangular velocity field in the joint space of data and parameters, which results in a deterministic transport map from a source distribution to the desired posterior. The inverse map, named vector rank, is accessible by reversibly integrating the velocity over time. It is advantageous to levera…

N-output Mechanism: Estimating Statistical Information from Numerical Data under Local Differential Privacy
Incheol Baek, Yon Dohn Chung
https://arxiv.org/abs/2510.11116 https:/…

N-output Mechanism: Estimating Statistical Information from Numerical Data under Local Differential Privacy
Local Differential Privacy (LDP) addresses significant privacy concerns in sensitive data collection. In this work, we focus on numerical data collection under LDP, targeting a significant gap in the literature: existing LDP mechanisms are optimized for either a very small ($|Ω| \in \{2, 3\}$) or infinite output spaces. However, no generalized method for constructing an optimal mechanism for an arbitrary output size $N$ exists. To fill this gap, we propose the \textbf{N-output mechan…

Perceptual Compensation of Ambisonics Recordings for Reproduction in Room
Ali Fallah, Shun Nakamura, Steven van de Par
https://arxiv.org/abs/2510.10883 https://

Perceptual Compensation of Ambisonics Recordings for Reproduction in Room
Ambisonics is a method for capturing and rendering a sound field accurately, assuming that the acoustics of the playback room does not significantly influence the sound field. However, in practice, the acoustics of the playback room may lead to a noticeable degradation in sound quality. We propose a recording and rendering method based on Ambisonics that utilizes a perceptually-motivated approach to compensate for the reverberation of the playback room. The recorded direct and reverberant sound…

A generalized alternating NGMRES method for PDE-constrained optimization problems governed by transport equations
Yunhui He, Andreas Mang
https://arxiv.org/abs/2510.08782 https:…

A generalized alternating NGMRES method for PDE-constrained optimization problems governed by transport equations
In this work, we propose a generalized alternating nonlinear generalized minimal residual method (GA-NGMRES) to accelerate first-order optimization schemes for PDE-constrained optimization problems governed by transport equations. We apply GA-NGMRES to a preconditioned first-order optimization scheme by interpreting the update rule as a fixed-point (FP) iteration. Our approach introduces a novel periodic mixing strategy that integrates NGMRES updates with FP steps. This new scheme improves effi…

Superoscillations and the Klein-Gordon equation via the Fourier method
Kamal Diki, Simon Verbruggen
https://arxiv.org/abs/2510.10700 https://arxiv.org/pdf/…

Superoscillations and the Klein-Gordon equation via the Fourier method
We investigate the time-evolution problem associated with the Klein-Gordon equation, using superoscillations as initial data. Additionally, the Segal-Bargmann transform is used to derive integral representations of the resulting solutions.

Divergent Infinite Series -- Ramanujan's Initial Intuition
Mario M. Attard
https://arxiv.org/abs/2510.08652 https://arxiv.org/pdf/2510.08652

Divergent Infinite Series -- Ramanujan's Initial Intuition
This paper investigates Srinivasa Ramanujan's initial intuitive methodology for assigning the finite value -1/12 to the sum of the divergent infinite series of all positive integers. We systematically examine Ramanujan's initial method, originally sketched in his notebooks, and set the methodology into an algebraic framework. The methodology has limited applicability to other classes of divergent series. The methodology is extended to assign a Ramanujan smoothed sum to the infinite sequences of…

The border rank of the $4 \times 4$ determinant tensor is twelve
Jong In Han, Jeong-Hoon Ju, Yeongrak Kim
https://arxiv.org/abs/2510.11051 https://arxiv.or…

The border rank of the $4 \times 4$ determinant tensor is twelve
We show that the border rank of the $4 \times 4$ determinant tensor is at least $12$ over $\mathbb{C}$, using the fixed ideal theorem introduced by Buczyńska-Buczyński and the method by Conner-Harper-Landsberg. Together with the known upper bound, this implies that the border rank is exactly $12$.

Variation of the disk thickness across ice bands: A method to determine ice abundances in highly inclined protoplanetary disks
Laurine Martinien, Gaspard Duch\^ene, Fran\c{c}ois M\'enard, Karl R. Stapelfeldt, Ryo Tazaki, Jennifer B. Bergner, Emmanuel Dartois, Jennifer A. Noble, William Thompson
https://arxiv.org/abs/2510.11359

Variation of the disk thickness across ice bands: A method to determine ice abundances in highly inclined protoplanetary disks
The James Webb Space Telescope provides unprecedented information to study ices in protoplanetary disks. However, the saturation of ice bands in highly inclined disks hinders the measurement of ice abundances using classical spectroscopy. This is unfortunate as the presence and more importantly abundance of ices plays a key role in, e.g., the evolution of dust (because it modifies the sticking properties) and the composition of planetesimals and exoplanetary atmospheres. To overcome this issue …

An information theorist's tour of differential privacy
Anand D. Sarwate, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar
https://arxiv.org/abs/2510.10316 https://

An information theorist's tour of differential privacy
Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, differential privacy measures risk in terms of the differences between probability distributions, which is a central topic in information theory. A differentially private algorithm is a channel between the underlying data and the output of the analysis. Seen in this way, the guarantees made by differential privacy can be…

Synchrosqueezed windowed linear canonical transform: A method for mode retrieval from multicomponent signals with crossing instantaneous frequencies
Shuixin Li, Jiecheng Chen, Qingtang Jiang, Jian Lu
https://arxiv.org/abs/2510.10438

Synchrosqueezed windowed linear canonical transform: A method for mode retrieval from multicomponent signals with crossing instantaneous frequencies
In nature, signals often appear in the form of the superposition of multiple non-stationary signals. The overlap of signal components in the time-frequency domain poses a significant challenge for signal analysis. One approach to addressing this problem is to introduce an additional chirprate parameter and use the chirplet transform (CT) to elevate the two-dimensional time-frequency representation to a three-dimensional time-frequency-chirprate representation. From a certain point of view, the …

Chord Colourizer: A Near Real-Time System for Visualizing Musical Key
Paul Haimes
https://arxiv.org/abs/2510.10173 https://arxiv.org/pdf/2510.10173

Chord Colourizer: A Near Real-Time System for Visualizing Musical Key
This paper introduces Chord Colourizer, a near real-time system that detects the musical key of an audio signal and visually represents it through a novel graphical user interface (GUI). The system assigns colours to musical notes based on Isaac Newton's original colour wheel, preserving historical links between pitch and hue, and also integrates an Arduino-controlled LED display using 3D-printed star-shaped diffusers to offer a physical ambient media representation. The method employs Constant…

Fair Kernel-Lock-Free Claim/Release Protocol for Shared Object Access in Cooperatively Scheduled Runtimes
Kevin Chalmers, Jan B{\ae}kgaard Pedersen
https://arxiv.org/abs/2510.10818

Fair Kernel-Lock-Free Claim/Release Protocol for Shared Object Access in Cooperatively Scheduled Runtimes
We present the first spin-free, kernel-lock-free mutex that cooperates with user-mode schedulers and is formally proven FIFO-fair and linearizable using CSP/FDR. Our fairness oracle and stability-based proof method are reusable across coroutine runtime designs. We designed the claim/release protocol for a process-oriented language -- ProcessJ -- to manage the race for claiming shared inter-process communication channels. Internally, we use a lock-free queue to park waiting processes for gaining…

Indirect method for nuclear reactions and the role of the self energy
Gregory Potel
https://arxiv.org/abs/2510.09140 https://arxiv.org/pdf/2510.09140

Indirect method for nuclear reactions and the role of the self energy
When a nuclear species (e.g., a nucleon or a deuteron nucleus) propagating freely is made to collide with a target nucleus, its trajectory is modified by exchanging variable amounts of energy, mass, linear and angular momentum with the target, according to its interaction with the nuclear medium. By addressing this perturbation away from the free path, one hopes to learn something about the nature of the medium through which our probe propagates. This is the essence of the experimental use of n…

HERO: Hardware-Efficient RL-based Optimization Framework for NeRF Quantization
Yipu Zhang, Chaofang Ma, Jinming Ge, Lin Jiang, Jiang Xu, Wei Zhang
https://arxiv.org/abs/2510.09010

HERO: Hardware-Efficient RL-based Optimization Framework for NeRF Quantization
Neural Radiance Field (NeRF) has emerged as a promising 3D reconstruction method, delivering high-quality results for AR/VR applications. While quantization methods and hardware accelerators have been proposed to enhance NeRF's computational efficiency, existing approaches face crucial limitations. Current quantization methods operate without considering hardware architecture, resulting in sub-optimal solutions within the vast design space encompassing accuracy, latency, and model size. Additio…

Interlaced dynamic XCT reconstruction with spatio-temporal implicit neural representations
Mathias Boulanger, Ericmoore Jossou
https://arxiv.org/abs/2510.08641 https://

Interlaced dynamic XCT reconstruction with spatio-temporal implicit neural representations
In this work, we investigate the use of spatio-temporalImplicit Neural Representations (INRs) for dynamic X-ray computed tomography (XCT) reconstruction under interlaced acquisition schemes. The proposed approach combines ADMM-based optimization with INCODE, a conditioning framework incorporating prior knowledge, to enable efficient convergence. We evaluate our method under diverse acquisition scenarios, varying the severity of global undersampling, spatial complexity (quantified via spatial in…

Novel superconvergence and ultraconvergence structures for the finite volume element method
Xiang Wang, Yuqing Zhang, Zhimin Zhang
https://arxiv.org/abs/2510.10668 https://

Novel superconvergence and ultraconvergence structures for the finite volume element method
This paper develops novel natural superconvergence and ultraconvergence structures for the bi-$k$-order finite volume element (FVE) method on rectangular meshes. These structures furnish tunable and possibly asymmetric superconvergence and ultraconvergence points. We achieve one-order-higher superconvergence for both derivatives and function values, and two-orders-higher ultraconvergence for derivatives--a phenomenon that standard bi-$k$-order finite elements do not exhibit. Derivative ultracon…

An Efficient Solution Method for Solving Convex Separable Quadratic Optimization Problems
Shaoze Li, Junhao Wu, Cheng Lu, Zhibin Deng, Shu-Cherng Fang
https://arxiv.org/abs/2510.11554

An Efficient Solution Method for Solving Convex Separable Quadratic Optimization Problems
Convex separable quadratic optimization problems occur in many practical applications. In this paper, based on an iterative resolution scheme of the KKT system, we develop an efficient method for solving a quadratic programming problem with a convex separable objective function subject to multiple convex separable constraints. We show that the proposed approach leads to a dual coordinate ascent algorithm and provide a convergence proof. Numerical experiments support the superior performance of …

A Constrained Multi-Fidelity Bayesian Optimization Method
Jingyi Wang, Nai-Yuan Chiang, Tucker Hartland, J. Luc Peterson, Jerome Solberg, Cosmin G. Petra
https://arxiv.org/abs/2510.10984

A Constrained Multi-Fidelity Bayesian Optimization Method
Recently, multi-fidelity Bayesian optimization (MFBO) has been successfully applied to many engineering design optimization problems, where the cost of high-fidelity simulations and experiments can be prohibitive. However, challenges remain for constrained optimization problems using the MFBO framework, particularly in efficiently identifying the feasible region defined by the constraints. In this paper, we propose a constrained multi-fidelity Bayesian optimization (CMFBO) method with novel acq…

Utilizing dynamic sparsity on pretrained DETR
Reza Sedghi, Anand Subramoney, David Kappel
https://arxiv.org/abs/2510.09380 https://arxiv.org/pdf/2510.09380…

Utilizing dynamic sparsity on pretrained DETR
Efficient inference with transformer-based models remains a challenge, especially in vision tasks like object detection. We analyze the inherent sparsity in the MLP layers of DETR and introduce two methods to exploit it without retraining. First, we propose Static Indicator-Based Sparsification (SIBS), a heuristic method that predicts neuron inactivity based on fixed activation patterns. While simple, SIBS offers limited gains due to the input-dependent nature of sparsity. To address this, we i…

OSCAR: Orthogonal Stochastic Control for Alignment-Respecting Diversity in Flow Matching
Jingxuan Wu, Zhenglin Wan, Xingrui Yu, Yuzhe Yang, Bo An, Ivor Tsang
https://arxiv.org/abs/2510.09060

OSCAR: Orthogonal Stochastic Control for Alignment-Respecting Diversity in Flow Matching
Flow-based text-to-image models follow deterministic trajectories, forcing users to repeatedly sample to discover diverse modes, which is a costly and inefficient process. We present a training-free, inference-time control mechanism that makes the flow itself diversity-aware. Our method simultaneously encourages lateral spread among trajectories via a feature-space objective and reintroduces uncertainty through a time-scheduled stochastic perturbation. Crucially, this perturbation is projected …

Verifying Chain-of-Thought Reasoning via Its Computational Graph
Zheng Zhao, Yeskendir Koishekenov, Xianjun Yang, Naila Murray, Nicola Cancedda
https://arxiv.org/abs/2510.09312 …

Verifying Chain-of-Thought Reasoning via Its Computational Graph
Current Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into why a computation fails. We introduce a white-box method: Circuit-based Reasoning Verification (CRV). We hypothesize that attribution graphs of correct CoT steps, viewed as execution traces of the model's latent reasoning circuits, possess distinct structural fingerprints from those of incorrect steps. By training a classifier o…

Quantum Alternating Direction Method of Multipliers for Semidefinite Programming
Hantao Nie, Dong An, Zaiwen Wen
https://arxiv.org/abs/2510.10056 https://a…

Quantum Alternating Direction Method of Multipliers for Semidefinite Programming
Semidefinite programming (SDP) is a fundamental convex optimization problem with wide-ranging applications. However, solving large-scale instances remains computationally challenging due to the high cost of solving linear systems and performing eigenvalue decompositions. In this paper, we present a quantum alternating direction method of multipliers (QADMM) for SDPs, building on recent advances in quantum computing. An inexact ADMM framework is developed, which tolerates errors in the iterates …

Search-based Hyperparameter Tuning for Python Unit Test Generation
Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.08716 https://arxiv.org/pdf/…

Search-based Hyperparameter Tuning for Python Unit Test Generation
Search-based test-generation algorithms have countless configuration options. Users rarely adjust these options and usually stick to the default values, which may not lead to the best possible results. Tuning an algorithm's hyperparameters is a method to find better hyperparameter values, but it typically comes with a high demand of resources. Meta-heuristic search algorithms -- that effectively solve the test-generation problem -- have been proposed as a solution to also efficiently tune param…

A geometrical approach to solve the proximity of a point to an axisymmetric quadric in space
Bibekananda Patra, Aditya Mahesh Kolte, Sandipan Bandyopadhyay
https://arxiv.org/abs/2510.08973

A geometrical approach to solve the proximity of a point to an axisymmetric quadric in space
This paper presents the classification of a general quadric into an axisymmetric quadric (AQ) and the solution to the problem of the proximity of a given point to an AQ. The problem of proximity in $R^3$ is reduced to the same in $R^2$, which is not found in the literature. A new method to solve the problem in $R^2$ is used based on the geometrical properties of the conics, such as sub-normal, length of the semi-major axis, eccentricity, slope and radius. Furthermore, the problem in $R^2$ is ca…

A Davydov Ansatz approach to accurate system-bath dynamics in the presence of multiple baths with distinct temperatures
Chenlin Ma, Fulu Zheng, Kewei Sun, Lu Wang, Yang Zhao
https://arxiv.org/abs/2510.09029

A Davydov Ansatz approach to accurate system-bath dynamics in the presence of multiple baths with distinct temperatures
We perform benchmark simulations using the time-dependent variational approach with the multiple Davydov Ansatz (mDA) to study realtime nonequilibrium dynamics in a single qubit model coupled to two thermal baths with distinct temperatures. A broad region of the parameter space has been investigated, accompanied by a detailed analysis of the convergence behavior of the mDA method. In addition, we have compared our mD2 results to those from two widely adopted, numerically "exact" techniques: the…

Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
Meixia Lin, Meijiao Shi, Yunhai Xiao, Qian Zhang
https://arxiv.org/abs/2510.11546 http…

Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
High-dimensional regression often suffers from heavy-tailed noise and outliers, which can severely undermine the reliability of least-squares based methods. To improve robustness, we adopt a non-smooth Wilcoxon score based rank objective and incorporate structured group sparsity regularization, a natural generalization of the lasso, yielding a group lasso regularized rank regression method. By extending the tuning-free parameter selection scheme originally developed for the lasso, we introduce …

🥼 Room-temperature RNA detection method eliminates need for complex lab equipment
#rna …

Room-temperature RNA detection method eliminates need for complex lab equipment
A new method to detect ribonucleic acid (RNA) without needing any complex lab procedures or heating equipment has been developed by researchers at UConn Health.

The Second Moment of $\mathrm{GL}_4 \times \mathrm{GL}_2$ $L$-functions at Special Points
Zhi Qi, Ruihua Qiao
https://arxiv.org/abs/2510.11186 https://arxi…

The Second Moment of $\mathrm{GL}_4 \times \mathrm{GL}_2$ $L$-functions at Special Points
In this paper, we provide an alternative proof of Chandee and Li's result on the second moment of $\mathrm{GL}_4 \times \mathrm{GL}_2$ special $L$-values. Our method is conceptually more direct as it neither detects the `Eisenstein--Kloosterman' cancellation nor uses the Poisson summation.

SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use Agents
Longjie Guo, Chenjie Yuan, Mingyuan Zhong, Robert Wolfe, Ruican Zhong, Yue Xu, Bingbing Wen, Hua Shen, Lucy Lu Wang, Alexis Hiniker
https://arxiv.org/abs/2510.11035

SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use Agents
As LLM-based computer-use agents (CUAs) begin to autonomously interact with real-world interfaces, understanding their vulnerability to manipulative interface designs becomes increasingly critical. We introduce SusBench, an online benchmark for evaluating the susceptibility of CUAs to UI dark patterns, designs that aim to manipulate or deceive users into taking unintentional actions. Drawing nine common dark pattern types from existing taxonomies, we developed a method for constructing believab…

Special points on intersections of hypersurfaces
Claudio G\'omez-Gonz\'ales
https://arxiv.org/abs/2510.10272 https://arxiv.org/pdf/2510.10272

Special points on intersections of hypersurfaces
We establish lower bounds on the ambient dimension for an intersection of hypersurfaces to have a dense collection of ``level $\ell$" points, in the sense introduced by Arnold-Shimura, given as a polynomial in the numbers of hypersurfaces of each degree. Our method builds upon the framework for solvable points of Gómez-Gonzáles-Wolfson to include other classes of accessory irrationality, towards the problem of understanding the arithmetic of "special points." We deduce improved upper bounds o…

Linear Algebra Problems Solved by Using Damped Dynamical Systems on the Stiefel Manifold
M Gulliksson, A Oleynik, M Ogren, R Bakhshandeh-Chamazkoti
https://arxiv.org/abs/2510.10535

Linear Algebra Problems Solved by Using Damped Dynamical Systems on the Stiefel Manifold
We develop a new method for solving minimization problems on the Stiefel Manifold using damped dynamical systems. The constraints are satisfied in the limit by an additional damped dynamical system. The method is illustrated by numerical experiments and compared to a state-of-the-art conjugate gradient method.

A Fast-Converging Decentralized Approach to the Weighted Minimum Vertex Cover Problem
Matteo Mordacchini, Emanuele Carlini, Patrizio Dazzi
https://arxiv.org/abs/2510.11697 https…

A Fast-Converging Decentralized Approach to the Weighted Minimum Vertex Cover Problem
We address the problem of computing a Minimum Weighted Vertex Cover (MWVC) in a decentralized network. MWVC, a classical NP-hard problem, is foundational in applications such as network monitoring and resource placement. We propose a fully decentralized protocol where each node makes decisions using only local knowledge and communicates with its neighbors. The method is adaptive, communication-efficient, and avoids centralized coordination. We evaluate the protocol on real-world and synthetic g…

Site-Specific RIS Deployment in Cellular Networks via Calibrated Ray Tracing
Sina Beyraghi, Javad Shabanpour, Giovanni Geraci, Paul Almasan, Angel Lozano
https://arxiv.org/abs/2510.09478

Site-Specific RIS Deployment in Cellular Networks via Calibrated Ray Tracing
This work introduces a fully-automated RIS deployment strategy validated through a digital twin, powered by Sionna ray tracing, of a UK city. On a scene calibrated with measured data, the method jointly optimizes RIS placement, orientation, configuration, and BS beamforming across 4G, 5G, and hypothetical 6G frequencies. Candidate RIS sites are identified via scattering-based rays, while user clustering reduces deployment overhead. Results show that meaningful coverage enhancement requires dens…

An Eulerian Perspective on Straight-Line Sampling
Panos Tsimpos, Youssef Marzouk
https://arxiv.org/abs/2510.11657 https://arxiv.org/pdf/2510.11657

An Eulerian Perspective on Straight-Line Sampling
We study dynamic measure transport for generative modeling: specifically, flows induced by stochastic processes that bridge a specified source and target distribution. The conditional expectation of the process' velocity defines an ODE whose flow map achieves the desired transport. We ask \emph{which processes produce straight-line flows} -- i.e., flows whose pointwise acceleration vanishes and thus are exactly integrable with a first-order method? We provide a concise PDE characterization of s…

Mono4DEditor: Text-Driven 4D Scene Editing from Monocular Video via Point-Level Localization of Language-Embedded Gaussians
Jin-Chuan Shi, Chengye Su, Jiajun Wang, Ariel Shamir, Miao Wang
https://arxiv.org/abs/2510.09438

Mono4DEditor: Text-Driven 4D Scene Editing from Monocular Video via Point-Level Localization of Language-Embedded Gaussians
Editing 4D scenes reconstructed from monocular videos based on text prompts is a valuable yet challenging task with broad applications in content creation and virtual environments. The key difficulty lies in achieving semantically precise edits in localized regions of complex, dynamic scenes, while preserving the integrity of unedited content. To address this, we introduce Mono4DEditor, a novel framework for flexible and accurate text-driven 4D scene editing. Our method augments 3D Gaussians wi…

One Sentence, Two Embeddings: Contrastive Learning of Explicit and Implicit Semantic Representations
Kohei Oda, Po-Min Chuang, Kiyoaki Shirai, Natthawut Kertkeidkachorn
https://arxiv.org/abs/2510.09293

One Sentence, Two Embeddings: Contrastive Learning of Explicit and Implicit Semantic Representations
Sentence embedding methods have made remarkable progress, yet they still struggle to capture the implicit semantics within sentences. This can be attributed to the inherent limitations of conventional sentence embedding methods that assign only a single vector per sentence. To overcome this limitation, we propose DualCSE, a sentence embedding method that assigns two embeddings to each sentence: one representing the explicit semantics and the other representing the implicit semantics. These embe…

An efficient iteration method to reconstruct the drift term from the final measurement
Dakang Cen, Wenlong Zhang, Zhidong Zhang
https://arxiv.org/abs/2510.10940 https://

An efficient iteration method to reconstruct the drift term from the final measurement
This work investigates the inverse drift problem in the one-dimensional parabolic equation with the final time data. The authors construct an operator first, whose fixed points are the unknown drift, and then apply it to prove the uniqueness. The proof of uniqueness contains an iteration converging to the drift, which inspires the numerical algorithm. To handle the ill-posedness of the inverse problem, the authors add the mollification on the data first in the iterative algorithm, and then prov…

The Tournament Tree Method for preference elicitation in Multi-criteria decision-making
Diego Garc\'ia-Zamora, \'Alvaro Labella, Jos\'e Rui Figueira
https://arxiv.org/abs/2510.08197

The Tournament Tree Method for preference elicitation in Multi-criteria decision-making
Pairwise comparison methods, such as Fuzzy Preference Relations and Saaty's Multiplicative Preference Relations, are widely used to model expert judgments in multi-criteria decision-making. However, their application is limited by the high cognitive load required to complete $m(m-1)/2$ comparisons, the risk of inconsistency, and the computational complexity of deriving consistent value scales. This paper proposes the Tournament Tree Method (TTM), a novel elicitation and evaluation framework tha…

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
Vasudha Yanuganti, Ishaan Puri, Swapnil Chhatre, Mantinder Singh, Ashok Jallepalli, Hritvik Shrivastava, Pradeep Kumar Sharma
https://arxiv.org/abs/2510.08850

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
Modern codebases make it hard for developers and AI coding assistants to find the right source files when answering questions like "How does this feature work?" or "Where was the bug introduced?" Traditional code search (keyword or IR based) often misses semantic context and cross file links, while large language models (LLMs) understand natural language but lack repository specific detail. We present a method for file path retrieval that fine tunes a strong LLM (Qwen3-8B) with QLoRA and Unslot…

Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf, Paolo Tonella, Sebastiano Panichella
https://arxiv.org/abs/2510.09396

Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for industrial inspection. Our method uses a search-based algorithm to automatically generate challenging obstacle avoidance scenarios, uncovering fa…

On the Proof of the Gen\v{c}ev-Rucki Conjecture for Multiple Ap\'ery-Like Series
Ce Xu
https://arxiv.org/abs/2510.09052 https://arxiv.org/pdf/2510.0905…

On the Proof of the Genčev-Rucki Conjecture for Multiple Apéry-Like Series
In this paper, we employ the theories and techniques of hypergeometric functions to provide two distinct proofs of the conjectured identities involving multiple Apéry-like series with central binomial coefficients and multiple harmonic star sums, as recently proposed by Genčev and Rucki. Furthermore, we establish several more general identities for multiple Apéry-like series. Furthermore, by utilizing the method of iterated integrals, a class of multiple mixed values can be expressed as comb…

⌨️ The 8pen input method for phones [2010]
#phones #hci

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
Chuan He, Zhaosong Lu
https://arxiv.org/abs/2510.11676 https://a…

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
We study convex composite optimization problems, where the objective function is given by the sum of a prox-friendly function and a convex function whose subgradients are estimated under heavy-tailed noise. Existing work often employs gradient clipping or normalization techniques in stochastic first-order methods to address heavy-tailed noise. In this paper, we demonstrate that a vanilla stochastic algorithm -- without additional modifications such as clipping or normalization -- can achieve op…

Learning to Make MISTAKEs: Modeling Incorrect Student Thinking And Key Errors
Alexis Ross, Jacob Andreas
https://arxiv.org/abs/2510.11502 https://arxiv.org…

Learning to Make MISTAKEs: Modeling Incorrect Student Thinking And Key Errors
Research on reasoning in language models (LMs) predominantly focuses on improving the correctness of their outputs. But some important applications require modeling reasoning patterns that are incorrect. For example, automated systems that can reason about and simulate student errors are useful for providing real-time feedback in the classroom or offline practice for educators-in-training. This paper presents a new method, MISTAKE, that (1) constructs high-quality synthetic examples of reasonin…

Multilevel correction type of adaptive finite element method for Hartree-Fock equation
Fei Xu
https://arxiv.org/abs/2510.10879 https://arxiv.org/pdf/2510.1…

Multilevel correction type of adaptive finite element method for Hartree-Fock equation
This paper proposes an efficient algorithm for solving the Hartree--Fock equation combining a multilevel correction scheme with an adaptive refinement technique to improve computational efficiency. The algorithm integrates a multilevel correction framework with an optimized implementation strategy. Within this framework, a series of linearized boundary value problems are solved, and their approximate solutions are corrected by solving small-scale Hartree--Fock equations in low-dimensional corre…

StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models
Zehao Chen, Rong Pan, Haoran Li
https://arxiv.org/abs/2510.11618

StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models
Human writers often begin their stories with an overarching mental scene, where they envision the interactions between characters and their environment. Inspired by this creative process, we propose a novel approach to long-form story generation, termed hybrid bottom-up long-form story generation, using multi-agent simulations. In our method, agents interact within a dynamic sandbox environment, where their behaviors and interactions with one another and the environment generate emergent events…

Convergence analysis of inexact MBA method for constrained upper-$\mathcal{C}^2$ optimization problems
Ruyu Liu, Shaohua Pan
https://arxiv.org/abs/2511.09940 https://arxiv.org/pdf/2511.09940 https://arxiv.org/html/2511.09940
arXiv:2511.09940v1 Announce Type: new
Abstract: This paper concerns a class of constrained optimization problems in which, the objective and constraint functions are both upper-$\mathcal{C}^2$. For such nonconvex and nonsmooth optimization problems, we develop an inexact moving balls approximation (MBA) method by a workable inexactness criterion for the solving of subproblems. By leveraging a global error bound for the strongly convex program associated with parametric optimization problems, we establish the full convergence of the iterate sequence under the partial bounded multiplier property (BMP) and the Kurdyka-{\L}ojasiewicz (KL) property of the constructed potential function, and achieve the local convergence rate of the iterate and objective value sequences if the potential function satisfies the KL property of exponent $q\in[1/2,1)$. A verifiable condition is also provided to check whether the potential function satisfies the KL property of exponent $q\in[1/2,1)$ at the given critical point. To the best of our knowledge, this is the first implementable inexact MBA method with a full convergence certificate for the constrained nonconvex and nonsmooth optimization problem.
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Online IMU-odometer Calibration using GNSS Measurements for Autonomous Ground Vehicle Localization
Baoshan Song, Xiao Xia, Penggao Yan, Yihan Zhong, Weisong Wen, Li-Ta Hsu
https://arxiv.org/abs/2510.08880

Online IMU-odometer Calibration using GNSS Measurements for Autonomous Ground Vehicle Localization
Accurate calibration of intrinsic (odometer scaling factors) and extrinsic parameters (IMU-odometer translation and rotation) is essential for autonomous ground vehicle localization. Existing GNSS-aided approaches often rely on positioning results or raw measurements without ambiguity resolution, and their observability properties remain underexplored. This paper proposes a tightly coupled online calibration method that fuses IMU, odometer, and raw GNSS measurements (pseudo-range, carrier-phase…

Cost-Efficient Long Code Translation using LLMs while Leveraging Identifier Replacements
Manojit Chakraborty, Madhusudan Ghosh, Rishabh Gupta
https://arxiv.org/abs/2510.09045 ht…

Cost-Efficient Long Code Translation using LLMs while Leveraging Identifier Replacements
In the domain of software development, LLMs have been utilized to automate tasks such as code translation, where source code from one programming language is translated to another while preserving its functionality. However, LLMs often struggle with long source codes that don't fit into the context window, which produces inaccurate translations. To address this, we propose a novel zero-shot code translation method that incorporates identifier replacement. By substituting user-given long identif…

LR-WaveHoltz: A Low-Rank Helmholtz Solver
Andreas Granath, Daniel Appel\"o, Siyang Wang
https://arxiv.org/abs/2510.09352 https://arxiv.org/pdf/2510.09…

LR-WaveHoltz: A Low-Rank Helmholtz Solver
We propose a low-rank method for solving the Helmholtz equation. Our approach is based on the WaveHoltz method, which computes Helmholtz solutions by applying a time-domain filter to the solution of a related wave equation. The wave equation is discretized by high-order multiblock summation-by-parts finite differences. In two dimensions we use the singular value decomposition and in three dimensions we use tensor trains to compress the numerical solution. To control rank growth we use step-trun…

Re$^3$MCN: Cubic Newton Variance Reduction Momentum Quadratic Regularization for Finite-sum Non-convex Problems
Dmitry Pasechnyuk-Vilensky, Dmitry Kamzolov, Martin Tak\'a\v{c}
https://arxiv.org/abs/2510.08714

Re$^3$MCN: Cubic Newton + Variance Reduction + Momentum + Quadratic Regularization for Finite-sum Non-convex Problems
We analyze a stochastic cubic regularized Newton method for finite sum optimization $\textstyle\min_{x\in\mathbb{R}^d} F(x) \;=\; \frac{1}{n}\sum_{i=1}^n f_i(x)$, that uses SARAH-type recursive variance reduction with mini-batches of size $b\sim n^{1/2}$ and exponential moving averages (EMA) for gradient and Hessian estimators. We show that the method achieves a $(\varepsilon,\sqrt{L_2\varepsilon})$-second-order stationary point (SOSP) with total stochastic oracle calls $n + \widetilde{\mathcal…

Weighted implicit-explicit discontinuous Galerkin methods for two-dimensional Ginzburg-Landau equations on general meshes
Zhen Guan, Xianxian Cao
https://arxiv.org/abs/2510.10283

Weighted implicit-explicit discontinuous Galerkin methods for two-dimensional Ginzburg-Landau equations on general meshes
In this paper, a second-order linearized discontinuous Galerkin method on general meshes, which treats the backward differentiation formula of order two (BDF2) and Crank-Nicolson schemes as special cases, is proposed for solving the two-dimensional Ginzburg-Landau equations with cubic nonlinearity. By utilizing the discontinuous Galerkin inverse inequality and the mathematical induction method, the unconditionally optimal error estimate in $L^2$-norm is obtained. The core of the analysis in thi…

The Method of Infinite Descent
Reza T. Batley, Sourav Saha
https://arxiv.org/abs/2510.05489 https://arxiv.org/pdf/2510.05489…

The Method of Infinite Descent
Training - the optimisation of complex models - is traditionally performed through small, local, iterative updates [D. E. Rumelhart, G. E. Hinton, R. J. Williams, Nature 323, 533-536 (1986)]. Approximating solutions through truncated gradients is a paradigm dating back to Cauchy [A.-L. Cauchy, Comptes Rendus Mathématique 25, 536-538 (1847)] and Newton [I. Newton, The Method of Fluxions and Infinite Series (Henry Woodfall, London, 1736)]. This work introduces the Method of Infinite Descent, a s…

Exact deflation for accurate SVD computation of nonnegative bidiagonal products of arbitrary rank
Rong Huang, Jungong Xue
https://arxiv.org/abs/2510.10502 https://

Exact deflation for accurate SVD computation of nonnegative bidiagonal products of arbitrary rank
Dealing with zero singular values can be quite challenging, as they have the potential to cause numerous numerical difficulties. This paper presents a method for computing the singular value decomposition (SVD) of a nonnegative bidiagonal product of arbitrary rank, regardless of whether the factors are of full rank or rank-deficient, square or rectangular. A key feature of our method is its ability to exactly deflate all zero singular values with a favorable complexity, irrespective of rank def…

dHPR: A Distributed Halpern Peaceman--Rachford Method for Non-smooth Distributed Optimization Problems
Zhangcheng Feng, Defeng Sun, Yancheng Yuan, Guojun Zhang
https://arxiv.org/abs/2511.10069 https://arxiv.org/pdf/2511.10069 https://arxiv.org/html/2511.10069
arXiv:2511.10069v1 Announce Type: new
Abstract: This paper introduces the distributed Halpern Peaceman--Rachford (dHPR) method, an efficient algorithm for solving distributed convex composite optimization problems with non-smooth objectives, which achieves a non-ergodic $O(1/k)$ iteration complexity regarding Karush--Kuhn--Tucker residual. By leveraging the symmetric Gauss--Seidel decomposition, the dHPR effectively decouples the linear operators in the objective functions and consensus constraints while maintaining parallelizability and avoiding additional large proximal terms, leading to a decentralized implementation with provably fast convergence. The superior performance of dHPR is demonstrated through comprehensive numerical experiments on distributed LASSO, group LASSO, and $L_1$-regularized logistic regression problems.
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S-D-RSM: Stochastic Distributed Regularized Splitting Method for Large-Scale Convex Optimization Problems
Maoran Wang, Xingju Cai, Yongxin Chen
https://arxiv.org/abs/2511.10133 https://arxiv.org/pdf/2511.10133 https://arxiv.org/html/2511.10133
arXiv:2511.10133v1 Announce Type: new
Abstract: This paper investigates the problems large-scale distributed composite convex optimization, with motivations from a broad range of applications, including multi-agent systems, federated learning, smart grids, wireless sensor networks, compressed sensing, and so on. Stochastic gradient descent (SGD) and its variants are commonly employed to solve such problems. However, existing algorithms often rely on vanishing step sizes, strong convexity assumptions, or entail substantial computational overhead to ensure convergence or obtain favorable complexity. To bridge the gap between theory and practice, we integrate consensus optimization and operator splitting techniques (see Problem Reformulation) to develop a novel stochastic splitting algorithm, termed the \emph{stochastic distributed regularized splitting method} (S-D-RSM). In practice, S-D-RSM performs parallel updates of proximal mappings and gradient information for only a randomly selected subset of agents at each iteration. By introducing regularization terms, it effectively mitigates consensus discrepancies among distributed nodes. In contrast to conventional stochastic methods, our theoretical analysis establishes that S-D-RSM achieves global convergence without requiring diminishing step sizes or strong convexity assumptions. Furthermore, it achieves an iteration complexity of $\mathcal{O}(1/\epsilon)$ with respect to both the objective function value and the consensus error. Numerical experiments show that S-D-RSM achieves up to 2--3$\times$ speedup compared to state-of-the-art baselines, while maintaining comparable or better accuracy. These results not only validate the algorithm's theoretical guarantees but also demonstrate its effectiveness in practical tasks such as compressed sensing and empirical risk minimization.
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Low-Discrepancy Set Post-Processing via Gradient Descent
Fran\c{c}ois Cl\'ement, Linhang Huang, Woorim Lee, Cole Smidt, Braeden Sodt, Xuan Zhang
https://arxiv.org/abs/2511.10496 https://arxiv.org/pdf/2511.10496 https://arxiv.org/html/2511.10496
arXiv:2511.10496v1 Announce Type: new
Abstract: The construction of low-discrepancy sets, used for uniform sampling and numerical integration, has recently seen great improvements based on optimization and machine learning techniques. However, these methods are computationally expensive, often requiring days of computation or access to GPU clusters. We show that simple gradient descent-based techniques allow for comparable results when starting with a reasonably uniform point set. Not only is this method much more efficient and accessible, but it can be applied as post-processing to any low-discrepancy set generation method for a variety of standard discrepancy measures.
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Measuring dissimilarity between convex cones by means of max-min angles
Welington de Oliveira, Valentina Sessa, David Sossa
https://arxiv.org/abs/2511.10483 https://arxiv.org/pdf/2511.10483 https://arxiv.org/html/2511.10483
arXiv:2511.10483v1 Announce Type: new
Abstract: This work introduces a novel dissimilarity measure between two convex cones, based on the max-min angle between them. We demonstrate that this measure is closely related to the Pompeiu-Hausdorff distance, a well-established metric for comparing compact sets. Furthermore, we examine cone configurations where the measure admits simplified or analytic forms. For the specific case of polyhedral cones, a nonconvex cutting-plane method is deployed to compute, at least approximately, the measure between them. Our approach builds on a tailored version of Kelley's cutting-plane algorithm, which involves solving a challenging master program per iteration. When this master program is solved locally, our method yields an angle that satisfies certain necessary optimality conditions of the underlying nonconvex optimization problem yielding the dissimilarity measure between the cones. As an application of the proposed mathematical and algorithmic framework, we address the image-set classification task under limited data conditions, a task that falls within the scope of the \emph{Few-Shot Learning} paradigm. In this context, image sets belonging to the same class are modeled as polyhedral cones, and our dissimilarity measure proves useful for understanding whether two image sets belong to the same class.
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Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity
Ilyas Fatkhullin, Niao He, Guanghui Lan, Florian Wolf
https://arxiv.org/abs/2511.10626 https://arxiv.org/pdf/2511.10626 https://arxiv.org/html/2511.10626
arXiv:2511.10626v1 Announce Type: new
Abstract: Constrained non-convex optimization is fundamentally challenging, as global solutions are generally intractable and constraint qualifications may not hold. However, in many applications, including safe policy optimization in control and reinforcement learning, such problems possess hidden convexity, meaning they can be reformulated as convex programs via a nonlinear invertible transformation. Typically such transformations are implicit or unknown, making the direct link with the convex program impossible. On the other hand, (sub-)gradients with respect to the original variables are often accessible or can be easily estimated, which motivates algorithms that operate directly in the original (non-convex) problem space using standard (sub-)gradient oracles. In this work, we develop the first algorithms to provably solve such non-convex problems to global minima. First, using a modified inexact proximal point method, we establish global last-iterate convergence guarantees with $\widetilde{\mathcal{O}}(\varepsilon^{-3})$ oracle complexity in non-smooth setting. For smooth problems, we propose a new bundle-level type method based on linearly constrained quadratic subproblems, improving the oracle complexity to $\widetilde{\mathcal{O}}(\varepsilon^{-1})$. Surprisingly, despite non-convexity, our methodology does not require any constraint qualifications, can handle hidden convex equality constraints, and achieves complexities matching those for solving unconstrained hidden convex optimization.
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Characterizing nonconvex boundaries via scalarization
Jin Ma, Weixuan Xia, Jianfeng Zhang
https://arxiv.org/abs/2510.09918 https://arxiv.org/pdf/2510.09918…

Characterizing nonconvex boundaries via scalarization
We present a unified approach for characterizing the boundary of a possibly nonconvex domain. Motivated by the well-known Pascoletti--Serafini method of scalarization, we recast the boundary characterization as a multi-criteria optimization problem with respect to a local partial order induced by a spherical cone with varying orient. Such an approach enables us to trace the whole boundary and can be considered a general dual representation for arbitrary (nonconvex) sets satisfying an exterior c…

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