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Dunkl derivative from moment differentiation
Edmundo J. Huertas, Alberto Lastra, Judit Minguez Ceniceros
https://arxiv.org/abs/2510.11357 https://arxiv.org…

Dunkl derivative from moment differentiation
The work analyzes the theory of Dunkl operator as a moment differential operator. This last operator generalizes the first one whenever the sequence of moments satisfies appropriate classical properties, classically considered in the general theory of ultraholomorphic and ultradifferentiable classes of functions. In this sense, the theory of Dunkl operator is then generalized. On the other hand, some features developed in Dunkl theory, such as Dunkl translation, have not been considered in the …

Tensor Logic: The Language of AI
Pedro Domingos
https://arxiv.org/abs/2510.12269 https://arxiv.org/pdf/2510.12269…

Tensor Logic: The Language of AI
Progress in AI is hindered by the lack of a programming language with all the requisite features. Libraries like PyTorch and TensorFlow provide automatic differentiation and efficient GPU implementation, but are additions to Python, which was never intended for AI. Their lack of support for automated reasoning and knowledge acquisition has led to a long and costly series of hacky attempts to tack them on. On the other hand, AI languages like LISP an Prolog lack scalability and support for learn…

Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles
Rongchen Guo, Vincent Francoeur, Isar Nejadgholi, Sylvain Gagnon, Miodrag Bolic
https://arxiv.org/abs/2510.03060

Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles
Speech Emotion Recognition (SER) is essential for improving human-computer interaction, yet its accuracy remains constrained by the complexity of emotional nuances in speech. In this study, we distinguish between descriptive semantics, which represents the contextual content of speech, and expressive semantics, which reflects the speaker's emotional state. After watching emotionally charged movie segments, we recorded audio clips of participants describing their experiences, along with the inte…

An algebra modality admitting countably many deriving transformations
Jean-Baptiste Vienney
https://arxiv.org/abs/2510.03953 https://arxiv.org/pdf/2510.039…

An algebra modality admitting countably many deriving transformations
A differential category is an additive symmetric monoidal category, that is, a symmetric monoidal category enriched over commutative monoids, with an algebra modality, axiomatizing smooth functions, and a deriving transformation on this algebra modality, axiomatizing differentiation. Lemay proved that a comonoidal algebra modality has at most one deriving transformation, thus differentiation is unique in models of differential linear logic. It was then an open problem whether this result extend…

Reverse Fa\`a di Bruno's Formula for Cartesian Reverse Differential Categories
Aaron Biggin (Macquarie University), Jean-Simon Pacaud Lemay (Macquarie University)
https://arxiv.org/abs/2509.20931

Reverse Faà di Bruno's Formula for Cartesian Reverse Differential Categories
Reverse differentiation is an essential operation for automatic differentiation. Cartesian reverse differential categories axiomatize reverse differentiation in a categorical framework, where one of the primary axioms is the reverse chain rule, which is the formula that expresses the reverse derivative of a composition. Here, we present the reverse differential analogue of Faa di Bruno's Formula, which gives a higher-order reverse chain rule in a Cartesian reverse differential category. To prop…

SCANS: A Soft Gripper with Curvature and Spectroscopy Sensors for In-Hand Material Differentiation
Nathaniel Hanson, Austin Allison, Charles DiMarzio, Ta\c{s}k{\i}n Pad{\i}r, Kristen L. Dorsey
https://arxiv.org/abs/2510.02164

SCANS: A Soft Gripper with Curvature and Spectroscopy Sensors for In-Hand Material Differentiation
We introduce the soft curvature and spectroscopy (SCANS) system: a versatile, electronics-free, fluidically actuated soft manipulator capable of assessing the spectral properties of objects either in hand or through pre-touch caging. This platform offers a wider spectral sensing capability than previous soft robotic counterparts. We perform a material analysis to explore optimal soft substrates for spectral sensing, and evaluate both pre-touch and in-hand performance. Experiments demonstrate ex…

Intrinsic cause-effect power: the tradeoff between differentiation and specification
William G. P. Mayner, William Marshall, Giulio Tononi
https://arxiv.org/abs/2510.03881 https…

Intrinsic cause-effect power: the tradeoff between differentiation and specification
Integrated information theory (IIT) starts from the existence of consciousness and characterizes its essential properties: every experience is intrinsic, specific, unitary, definite, and structured. IIT then formulates existence and its essential properties operationally in terms of cause-effect power of a substrate of units. Here we address IIT's operational requirements for existence by considering that, to have cause-effect power, to have it intrinsically, and to have it specifically, substr…

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…

Argus: JAX state-space filtering for gravitational wave detection with a pulsar timing array
Tom Kimpson, Nicholas J. O'Neill, Patrick M. Meyers, Andrew Melatos
https://arxiv.org/abs/2510.11077

Argus: JAX state-space filtering for gravitational wave detection with a pulsar timing array
Argus is a high-performance Python package for detecting and characterising nanohertz gravitational waves in pulsar timing array data. The package provides a complete Bayesian inference framework based on state-space models, using Kalman filtering for efficient likelihood evaluation. Argus leverages JAX for just-in-time compilation, GPU acceleration, and automatic differentiation, facilitating rapid Bayesian inference with gradient-based samplers. The state-space approach provides a computation…

Series C, Episode 07 - Children of Auron
DERAL: Is that all it takes, then? A sample of blood?
FRANTON: It's the genetic print we need. All body cells with a nucleus carry that. The white cells are here. As long as they're capable of differentiation, they're capable of growth, into another you.
https://blake.torpid…

Price discrimination, algorithmic decision-making, and European non-discrimination law
Frederik Zuiderveen Borgesius
https://arxiv.org/abs/2509.23851 https://

Price discrimination, algorithmic decision-making, and European non-discrimination law
Our society can benefit immensely from algorithmic decision-making and similar types of artificial intelligence. But algorithmic decision-making can also have discriminatory effects. This paper examines that problem, using online price differentiation as an example of algorithmic decision-making. With online price differentiation, a company charges different people different prices for identical products, based on information the company has about those people. The main question in this paper i…

A review of cultural heritage inspection: Toward terahertz from mid-infrared region
Pengfei Zhu, Hai Zhang, Stefano Sfarra, Dazhi Yang, Xavier Maldague
https://arxiv.org/abs/2510.11521

A review of cultural heritage inspection: Toward terahertz from mid-infrared region
This review explores non-invasive imaging (NII) methods covering the mid- and far-infrared to the terahertz spectral regions (up to approximately 1000 um) for the detection and analysis of cultural heritage artifacts. In the thermal infrared domain, where radiation follows Planck's law, the self-emission of materials reveals intrinsic properties and internal degradation. By contrast, in the near-infrared range, external illumination enhances surface details and pigment differentiation. Far-infr…

Acoustic-based Gender Differentiation in Speech-aware Language Models
Junhyuk Choi, Jihwan Seol, Nayeon Kim, Chanhee Cho, EunBin Cho, Bugeun Kim
https://arxiv.org/abs/2509.21125

Acoustic-based Gender Differentiation in Speech-aware Language Models
Speech-aware Language Models (SpeechLMs) have fundamentally transformed human-AI interaction by enabling voice-based communication, yet they may exhibit acoustic-based gender differentiation where identical questions lead to different responses based on the speaker's gender. This paper propose a new dataset that enables systematic analysis of this phenomenon, containing 9,208 speech samples across three categories: Gender-Independent, Gender-Stereotypical, and Gender-Dependent. We further evalu…

Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors
Guangyao Zhai, Yue Zhou, Xinyan Deng, Lars Heckler, Nassir Navab, Benjamin Busam
https://arxiv.org/abs/2510.01934

Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors
Few-shot anomaly detection streamlines and simplifies industrial safety inspection. However, limited samples make accurate differentiation between normal and abnormal features challenging, and even more so under category-agnostic conditions. Large-scale pre-training of foundation visual encoders has advanced many fields, as the enormous quantity of data helps to learn the general distribution of normal images. We observe that the anomaly amount in an image directly correlates with the differenc…

Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
Junsei Ito, Yasuaki Wasa
https://arxiv.org/abs/2510.04591 https://arxiv.org/pdf/…

Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
This article proposes a data-driven PID controller design based on the principle of adaptive gain optimization, leveraging Physics-Informed Neural Networks (PINNs) generated for predictive modeling purposes. The proposed control design method utilizes gradients of the PID gain optimization, achieved through the automatic differentiation of PINNs, to apply model predictive control using a cost function based on tracking error and control inputs. By optimizing PINNs-based PID gains, the method ac…

Machine learning approaches to seismic event classification in the Ostrava region
Marek Pecha, Michael Skotnica, Jana Ru\v{s}ajov\'a, Bohdan Rieznikov, V\'it Wandrol, Mark\'eta R\"osnerov\'a, Jarom\'ir Knejzl\'ik
https://arxiv.org/abs/2509.22574

Machine learning approaches to seismic event classification in the Ostrava region
The northeastern region of the Czech Republic is among the most seismically active areas in the country. The most frequent seismic events are mining-induced since there used to be strong mining activity in the past. However, natural tectonic events may also occur. In addition, seismic stations often record explosions in quarries in the region. Despite the cessation of mining activities, mine-induced seismic events still occur. Therefore, a rapid differentiation between tectonic and anthropogeni…

microJAX: A Differentiable Framework for Microlensing Modeling with GPU-Accelerated Image-Centered Ray Shooting
Shota Miyazaki (Institute of Space,Astronautical Science, Japan Aerospace Exploration Agency, Japan), Hajime Kawahara (Institute of Space,Astronautical Science, Japan Aerospace Exploration Agency, Japan)
https://arxiv.org/abs/251…

microJAX: A Differentiable Framework for Microlensing Modeling with GPU-Accelerated Image-Centered Ray Shooting
We introduce microJAX, the first fully differentiable implementation of the image-centered ray-shooting (ICRS) algorithm for gravitational microlensing. Built on JAX and its XLA just-in-time compiler, microJAX exploits GPU parallelism while providing exact gradients through automatic differentiation. The current release supports binary- and triple-lens geometries, including limb-darkened extended-source effects, and delivers magnifications that remain differentiable for all model parameters. Be…

Variational optimization of projected entangled-pair states on the triangular lattice
Jan Naumann, Jens Eisert, Philipp Schmoll
https://arxiv.org/abs/2510.04907 https://

Variational optimization of projected entangled-pair states on the triangular lattice
We introduce a general corner transfer matrix renormalization group algorithm tailored to projected entangled-pair states on the triangular lattice. By integrating automatic differentiation, our approach enables direct variational energy minimization on this lattice geometry. In contrast to conventional approaches that map the triangular lattice onto a square lattice with diagonal next-nearest-neighbour interactions, our native formulation yields improved variational results at the same bond di…

The Trajectory Bundle Method: Unifying Sequential-Convex Programming and Sampling-Based Trajectory Optimization
Kevin Tracy, John Z. Zhang, Jon Arrizabalaga, Stefan Schaal, Yuval Tassa, Tom Erez, Zachary Manchester
https://arxiv.org/abs/2509.26575

The Trajectory Bundle Method: Unifying Sequential-Convex Programming and Sampling-Based Trajectory Optimization
We present a unified framework for solving trajectory optimization problems in a derivative-free manner through the use of sequential convex programming. Traditionally, nonconvex optimization problems are solved by forming and solving a sequence of convex optimization problems, where the cost and constraint functions are approximated locally through Taylor series expansions. This presents a challenge for functions where differentiation is expensive or unavailable. In this work, we present a der…

SymBoltz.jl: a symbolic-numeric, approximation-free and differentiable linear Einstein-Boltzmann solver
Herman Sletmoen
https://arxiv.org/abs/2509.24740 https://

SymBoltz.jl: a symbolic-numeric, approximation-free and differentiable linear Einstein-Boltzmann solver
SymBoltz is a new Julia package that solves the linear Einstein-Boltzmann equations. It features a symbolic-numeric interface for specifying equations, is free of approximation switching schemes and is compatible with automatic differentiation. Cosmological models are built from replaceable physical components in a way that scales well in model space. The modeler should simply write down their equations, and SymBoltz solves them and eliminates much of the friction in the process. SymBoltz enabl…

Non-locally averaged pruned reassigned spectrograms: a tool for glottal pulse visualization and analysis
Gabriel J. Griswold, Mark A. Griswold
https://arxiv.org/abs/2509.19686 h…

Non-locally averaged pruned reassigned spectrograms: a tool for glottal pulse visualization and analysis
Reassigned spectrograms have shown advantages in precise formant measuring and inter-speaker differentiation. However, reassigned spectrograms suffer from their inability to visualize larger amounts of data in an easily comprehensible and reproducible manner. Utilizing the techniques and tools developed by Fulop and Fitz, a variation of the reassigned spectrogram is proposed. Non-locally Averaged Pruned Reassigned Spectrograms (NAPReS) provide a simplified view into the characteristics of a spe…

Crosslisted article(s) found for cs.FL. https://arxiv.org/list/cs.FL/new
[1/1]:
- AD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods ...
Yunhao Yang, Junyuan Hong, Gabriel Jacob Perin, Zhiwen Fan, Li Yin, Zhangyang Wang, Ufuk Topcu

EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition
Pengcheng Li, Botao Zhao, Zuheng Kang, Junqing Peng, Xiaoyang Qu, Yayun He, Jianzong Wang
https://arxiv.org/abs/2509.15654

EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition
Although Large Audio-Language Models (LALMs) have exhibited outstanding performance in auditory understanding, their performance in affective computing scenarios, particularly in emotion recognition, reasoning, and subtle sentiment differentiation, remains suboptimal. Recent advances in Reinforcement Learning (RL) have shown promise in improving LALMs' reasoning abilities. However, two critical challenges hinder the direct application of RL techniques to Speech Emotion Recognition (SER) tasks: …

Design principles of the cytotoxic CD8 T-cell response
Obinna A. Ukogu, Zachary Montague, Gr\'egoire Altan-Bonnet, Armita Nourmohammad
https://arxiv.org/abs/2509.22997 http…

Design principles of the cytotoxic CD8+ T-cell response
Cytotoxic T lymphocytes eliminate infected or malignant cells, safeguarding surrounding tissues. Although experimental and systems-immunology studies have cataloged many molecular and cellular actors involved in an immune response, the design principles governing how the speed and magnitude of T-cell responses emerge from cellular decision-making remain elusive. Here, we recast a T-cell response as a feedback-controlled program, wherein the rates of activation, proliferation, differentiation an…

First-principles calculation of higher-order elastic constants from divided differences
Ruvini Attanayake, Umesh C. Roy, Abhiyan Pandit, Angelo Bongiorno
https://arxiv.org/abs/2509.15468

First-principles calculation of higher-order elastic constants from divided differences
A method is presented to calculate from first principles the higher-order elastic constants of a solid material. The method relies on finite strain deformations, a density functional theory approach to calculate the Cauchy stress tensor, and a recursive numerical differentiation technique homologous to the divided differences polynomial interpolation algorithm. The method is applicable as is to any material, regardless its symmetry, to calculate elastic constants of, in principle, any order. He…

Room Impulse Response Prediction with Neural Networks: From Energy Decay Curves to Perceptual Validation
Imran Muhammad, Gerald Schuller
https://arxiv.org/abs/2509.24834 https:/…

Room Impulse Response Prediction with Neural Networks: From Energy Decay Curves to Perceptual Validation
Prediction of room impulse responses (RIRs) is essential for room acoustics, spatial audio, and immersive applications, yet conventional simulations and measurements remain computationally expensive and time-consuming. This work proposes a neural network framework that predicts energy decay curves (EDCs) from room dimensions, material absorption coefficients, and source-receiver positions, and reconstructs corresponding RIRs via reverse-differentiation. A large training dataset was generated us…