Tootfinder

🌍 The climate crisis demands urgent action. But which actions are best?
Decision makers face tough trade-offs:
Policy A lowers emissions at home but increases reliance on imports.
Policy B cuts emissions long-term but raises unemployment short-term.
Policy C boosts jobs now but increases emissions in the near term.
None of these choices are simple. A policy that looks good locally may increase global emissions, or its effects may depend on what other countries d…

Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling
Seiji Maekawa, Jackson Hassell, Pouya Pezeshkpour, Tom Mitchell, Estevam Hruschka
https://arxiv.org/abs/2509.26553

Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling
As language models gain access to external tools via structured function calls, they become increasingly more capable of solving complex, multi-step tasks. However, existing benchmarks for tool-augmented language models (TaLMs) provide insufficient control over factors such as the number of functions accessible, task complexity, and input size, and remain vulnerable to data contamination. We present FuncBenchGen, a unified, contamination-free framework that evaluates TaLMs by generating synthet…

Model Reference Adaptive Control with Time-Varying State and Input Constraints
Poulomee Ghosh, Shubhendu Bhasin
https://arxiv.org/abs/2508.21586 https://ar…

Model Reference Adaptive Control with Time-Varying State and Input Constraints
This paper presents a model reference adaptive control (MRAC) framework for uncertain linear time-invariant (LTI) systems subject to user-defined, time-varying state and input constraints. The proposed design seamlessly integrates a time-varying barrier Lyapunov function (TVBLF) to enforce state constraints with a time-varying saturation function to handle input limits. These time-varying constraints can be designed as performance functions to shape transient and steady-state behaviors for both…

Dual-Function Beam Pattern Design for Multi-Target ISAC Systems: A Decoupled Approach
Wilson de Souza Junior, Taufik Abrao, Amine Mezghani, Ekram Hossain
https://arxiv.org/abs/2509.23302

Dual-Function Beam Pattern Design for Multi-Target ISAC Systems: A Decoupled Approach
We investigate the beampattern design problem for mono-static multi-user (MU) multi-point-target integrated sensing and communication (ISAC) systems, where a dual-function multiple-input multiple-output (DF-MIMO) base station (BS) performs downlink communication and radar sensing simultaneously. In ISAC systems, sensing and communication inherently compete for resources. As communication demand increases, the beam pattern is reshaped, which might degrade the direction of arrival (DoA) sensing a…

A brief introduction to dispersive methods
Gilberto Colangelo
https://arxiv.org/abs/2509.24548 https://arxiv.org/pdf/2509.24548

A brief introduction to dispersive methods
These lectures aim to provide a basic introduction to dispersive methods and their modern applications to the phenomenology of the Standard Model at low energy. This approach exploits analyticity properties of Green functions and scattering amplitude, often combined with unitarity constraints. To find a logically coherent set of topics in this vast subject, I start with the two-point Green's function, show that this needs the three-point function as input which in turn needs the four-point func…

Neural Optimal Transport Meets Multivariate Conformal Prediction
Vladimir Kondratyev, Alexander Fishkov, Nikita Kotelevskii, Mahmoud Hegazy, Remi Flamary, Maxim Panov, Eric Moulines
https://arxiv.org/abs/2509.25444

Neural Optimal Transport Meets Multivariate Conformal Prediction
We propose a framework for conditional vector quantile regression (CVQR) that combines neural optimal transport with amortized optimization, and apply it to multivariate conformal prediction. Classical quantile regression does not extend naturally to multivariate responses, while existing approaches often ignore the geometry of joint distributions. Our method parametrizes the conditional vector quantile function as the gradient of a convex potential implemented by an input-convex neural network…

State and Input Constrained Model Reference Adaptive Control with Robustness and Feasibility Analysis
Poulomee Ghosh, Shubhendu Bhasin
https://arxiv.org/abs/2508.21584 https://

State and Input Constrained Model Reference Adaptive Control with Robustness and Feasibility Analysis
We propose a model reference adaptive controller (MRAC) for uncertain linear time-invariant (LTI) plants with user-defined state and input constraints in the presence of unmatched bounded disturbances. Unlike popular optimization-based approaches for constrained control, such as model predictive control (MPC) and control barrier function (CBF) that solve a constrained optimization problem at each step using the system model, our approach is optimization-free and adaptive; it combines a saturate…

Catwalk: Unary Top-K for Efficient Ramp-No-Leak Neuron Design for Temporal Neural Networks
Devon Lister, Prabhu Vellaisamy, John Paul Shen, Di Wu
https://arxiv.org/abs/2508.21267

Catwalk: Unary Top-K for Efficient Ramp-No-Leak Neuron Design for Temporal Neural Networks
Temporal neural networks (TNNs) are neuromorphic neural networks that utilize bit-serial temporal coding. TNNs are composed of columns, which in turn employ neurons as their building blocks. Each neuron processes volleys of input spikes, modulated by associated synaptic weights, on its dendritic inputs. Recently proposed neuron implementation in CMOS employs a Spike Response Model (SRM) with a ramp-no-leak (RNL) response function and assumes all the inputs can carry spikes. However, in actual s…

Noise estimation of SDE from a single data trajectory
Munawar Ali, Purba Das, Qi Feng, Liyao Gao, Guang Lin
https://arxiv.org/abs/2509.25484 https://arxiv.…

Noise estimation of SDE from a single data trajectory
In this paper, we propose a data-driven framework for model discovery of stochastic differential equations (SDEs) from a single trajectory, without requiring the ergodicity or stationary assumption on the underlying continuous process. By combining (stochastic) Taylor expansions with Girsanov transformations, and using the drift function's initial value as input, we construct drift estimators while simultaneously recovering the model noise. This allows us to recover the underlying $\mathbb P$ B…

Staying on the Manifold: Geometry-Aware Noise Injection
Albert Kj{\o}ller Jacobsen, Johanna Marie Gegenfurtner, Georgios Arvanitidis
https://arxiv.org/abs/2509.20201 https://

Staying on the Manifold: Geometry-Aware Noise Injection
It has been shown that perturbing the input during training implicitly regularises the gradient of the learnt function, leading to smoother models and enhancing generalisation. However, previous research mostly considered the addition of ambient noise in the input space, without considering the underlying structure of the data. In this work, we propose several methods of adding geometry-aware input noise that accounts for the lower dimensional manifold the input space inhabits. We start by proj…

The properties of a Leontief production technology for Health System Modeling: the Thanzi la Onse model for Malawi
Martin Chalkley, Sakshi Mohan, Margherita Molaro, Bingling She, Wiktoria Tafesse
https://arxiv.org/abs/2508.21699

The properties of a Leontief production technology for Health System Modeling: the Thanzi la Onse model for Malawi
As health system modeling (HSM) advances to include more complete descriptions of the production of healthcare, it is important to establish a robust conceptual characterisation of the production process. For the Thanzi La Onse model in Malawi we have incorporated an approach to production that is based on a form of Leontief technology -- fixed input proportions. At first sight, this form of technology appears restrictive relative to the general conception of a production function employed in e…

A Graph-based Hybrid Beamforming Framework for MIMO Cell-Free ISAC Networks
Yanan Du, Sai Xu, Jagmohan Chauhan
https://arxiv.org/abs/2509.25385 https://arx…

A Graph-based Hybrid Beamforming Framework for MIMO Cell-Free ISAC Networks
This paper develops a graph-based hybrid beamforming framework for multiple-input multiple-output (MIMO) cell-free integrated sensing and communication (ISAC) networks. Specifically, we construct a novel MIMO cell-free ISAC network model. In this model, multiple dual-function base station (BS) transmitters employ distributed hybrid beamforming to enable simultaneous communication and sensing, while maintaining physical separation between the transmitters and the radar receiver. Building on this…

Sensitivity Analysis for Antenna Devices through Interval Arithmetic -- A Generalized Approach
Nicola Anselmi, Paolo Rocca, Andrea Massa
https://arxiv.org/abs/2509.23361 https:/…

Sensitivity Analysis for Antenna Devices through Interval Arithmetic -- A Generalized Approach
This paper presents a novel method for the sensitivity analysis of electromagnetic (EM) systems whose transfer function (TF), that is the input-output (I/O) relationship between the input parameters affected by tolerance and the system response (i.e., the arising EM performance of interest), is not available in closed (or explicit) form. The method is a generalized analytic technique based on the Interval Analysis (IA). First, an analytic surrogate model (SM) of the TF is defined by means of a …

TextOnly: A Unified Function Portal for Text-Related Functions on Smartphones
Minghao Tu, Chun Yu, Xiyuan Shen, Zhi Zheng, Li Chen, Yuanchun Shi
https://arxiv.org/abs/2508.16926

TextOnly: A Unified Function Portal for Text-Related Functions on Smartphones
Text boxes serve as portals to diverse functionalities in today's smartphone applications. However, when it comes to specific functionalities, users always need to navigate through multiple steps to access particular text boxes for input. We propose TextOnly, a unified function portal that enables users to access text-related functions from various applications by simply inputting text into a sole text box. For instance, entering a restaurant name could trigger a Google Maps search, while a gre…

Re-uploading quantum data: A universal function approximator for quantum inputs
Hyunho Cha, Daniel K. Park, Jungwoo Lee
https://arxiv.org/abs/2509.18530 https://

Re-uploading quantum data: A universal function approximator for quantum inputs
Quantum data re-uploading has proved powerful for classical inputs, where repeatedly encoding features into a small circuit yields universal function approximation. Extending this idea to quantum inputs remains underexplored, as the information contained in a quantum state is not directly accessible in classical form. We propose and analyze a quantum data re-uploading architecture in which a qubit interacts sequentially with fresh copies of an arbitrary input state. The circuit can approximate …

🔧 Strict-Mode Function Calling
Beta API now supports strict-mode for function calling, ensuring the output always complies with the defined JSON schema.
🔗 Anthropic API Compatibility
The API is now compatible with Anthropic API, allowing seamless integration with Claude Code.
💰 Pricing Adjustment (Effective Sept. 5, 2025, 16:00 UTC)
Input tokens: $ 0.07/million (cache hit) / $ 0.56/million (cache miss)
Output tokens: $ 1.68/million
The off-peak discount e…

PU-Gaussian: Point Cloud Upsampling using 3D Gaussian Representation
Mahmoud Khater, Mona Strauss, Philipp von Olshausen, Alexander Reiterer
https://arxiv.org/abs/2509.20207 htt…

PU-Gaussian: Point Cloud Upsampling using 3D Gaussian Representation
Point clouds produced by 3D sensors are often sparse and noisy, posing challenges for tasks requiring dense and high-fidelity 3D representations. Prior work has explored both implicit feature-based upsampling and distance-function learning to address this, but often at the expense of geometric interpretability or robustness to input sparsity. To overcome these limitations, we propose PU-Gaussian, a novel upsampling network that models the local neighborhood around each point using anisotropic 3…

Polynomial Property Testing
Lior Gishboliner, Asaf Shapira
https://arxiv.org/abs/2508.16878 https://arxiv.org/pdf/2508.16878…

Polynomial Property Testing
Property testers are fast, randomized "election polling"-type algorithms that determine if an input (e.g., graph or hypergraph) has a certain property or is $\varepsilon$-far from the property. In the dense graph model of property testing, it is known that many properties can be tested with query complexity that depends only on the error parameter $\varepsilon$ (and not on the size of the input), but the current bounds on the query complexity grow extremely quickly as a function of $1/\varepsil…

Spent half a day adding error checking for errors that can't really happen anyway on the advice of a robot.
Now, instead of crashing with an error message if the input data is impossibly broken, the function instead checks if the error exists, and if so quits with an error code and a slightly different error message.
This is better code apparently. I guess when it's longer there's more to read so it's more readable.

Replaced article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[1/1]:
- Exponent Function for Stationary Memoryless Channels with Input Cost at Rates above the Capacity
Yasutada Oohama

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Joshua R. Jandrell, Mitchell A. Cox
https://arxiv.org/abs/2508.19751 https://

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Modelling the effects of perturbations on optical fields often requires learning highly oscillatory complex-valued functions. Standard multi-layer perceptrons (MLPs) struggle with this task due to an inherent spectral bias, preventing them from fitting high-frequency sinusoids. To overcome this, we incorporate Fourier features - a set of predefined sinusoids dependent on the perturbation - as an additional network input. This reframes the learning problem from approximating a complex function t…

Efficient & Correct Predictive Equivalence for Decision Trees
Joao Marques-Silva, Alexey Ignatiev
https://arxiv.org/abs/2509.17774 https://arxiv.org/pd…

Efficient & Correct Predictive Equivalence for Decision Trees
The Rashomon set of decision trees (DTs) finds importance uses. Recent work showed that DTs computing the same classification function, i.e. predictive equivalent DTs, can represent a significant fraction of the Rashomon set. Such redundancy is undesirable. For example, feature importance based on the Rashomon set becomes inaccurate due the existence of predictive equivalent DTs, i.e. DTs with the same prediction for every possible input. In recent work, McTavish et al. proposed solutions for s…

Output-Sensitive Evaluation of Acyclic Conjunctive Regular Path Queries
Mahmoud Abo Khamis, Alexandru-Mihai Hurjui, Ahmet Kara, Dan Olteanu, Dan Suciu, Zilu Tian
https://arxiv.org/abs/2509.20204

Output-Sensitive Evaluation of Acyclic Conjunctive Regular Path Queries
Conjunctive Regular Path Queries, or CRPQs for short, are an essential construct in graph query languages. In this paper, we propose the first output-sensitive algorithm for evaluating acyclic CRPQs. It is output-sensitive in the sense that its complexity is a function of the sizes of the input graph and of the query output. In particular, it does not depend on the output sizes of the regular expressions that appear in the query, as these sizes can be much larger than the query output size. O…

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…

Getting a s***-ton of CSV files as input for spatial analysis can be a pain.
Yes, you can write a quick Python loop in the #QGIS console to load them but it's not a great workflow, imho.
The DuckDB read_csv function has been very convenient for this kind of ETL workflow:

Why Compressed Metal Hydrides are Near-room-temperature Superconductors
Warren E. Pickett
https://arxiv.org/abs/2508.18429 https://arxiv.org/pdf/2508.18429…

Why Compressed Metal Hydrides are Near-room-temperature Superconductors
This contribution provides a partial response to the titular statement since, it will be claimed,the ``why'' is not yet understood, but there is a pathway for achieving a more complete understanding. The sense of the community has been that, given a prospective metal hydride and pressure, the energy landscape can be surveyed computationally for thermodynamic and dynamic stability, the Eliashberg spectral function with its required input (energy bands, phonon modes, coupling matrix elements) can…

Efficient Input-Constrained Impulsive Optimal Control of Linear Systems with Application to Spacecraft Relative Motion
Ethan Foss, Simone D'Amico
https://arxiv.org/abs/2510.03423

Efficient Input-Constrained Impulsive Optimal Control of Linear Systems with Application to Spacecraft Relative Motion
This work presents a novel algorithm for impulsive optimal control of linear time-varying systems with the inclusion of input magnitude constraints. Impulsive optimal control problems, where the optimal input solution is a sum of delta functions, are typically formulated as an optimization over a normed function space subject to integral equality constraints and can be efficiently solved for linear time-varying systems in their dual formulation. In this dual setting, the problem takes the form …

Attention Mechanism in Randomized Time Warping
Yutaro Hiraoka, Kazuya Okamura, Kota Suto, Kazuhiro Fukui
https://arxiv.org/abs/2508.16366 https://arxiv.org…

Attention Mechanism in Randomized Time Warping
This paper reveals that we can interpret the fundamental function of Randomized Time Warping (RTW) as a type of self-attention mechanism, a core technology of Transformers in motion recognition. The self-attention is a mechanism that enables models to identify and weigh the importance of different parts of an input sequential pattern. On the other hand, RTW is a general extension of Dynamic Time Warping (DTW), a technique commonly used for matching and comparing sequential patterns. In essence,…

Decidability of polynomial equations over function fields in positive characteristic
Nicolas Daans
https://arxiv.org/abs/2509.02290 https://arxiv.org/pdf/2…

Decidability of polynomial equations over function fields in positive characteristic
Let $K$ be a field of positive characteristic with no algebraically closed subfield. Let $F$ be a function field over $K$ and $t \in F$ transcendental over $K$. Refining a result of Eisentr{ä}ger and Shlapentokh, we show that there is no algorithm which, on input a polynomial $f \in \mathbb{Z}[t][X_1, \ldots, X_n]$, determines whether $f$ has a zero in $F^n$. To this end, we revisit and partially extend several recent results from the literature on existential definability in function fields.

Phase Transitions of the Additive Uniform Noise Channel with Peak Amplitude and Cost Constraint
Jonas Stapmanns, Catarina Dias, Luke Eilers, Tobias K\"uhn, Jean-Pascal Pfister
https://arxiv.org/abs/2510.12427

Phase Transitions of the Additive Uniform Noise Channel with Peak Amplitude and Cost Constraint
Under which condition is quantization optimal? We address this question in the context of the additive uniform noise channel under peak amplitude and cost constraints. We compute analytically the capacity-achieving input distribution as a function of the noise level, the average cost constraint, and the curvature of the cost function. We find that when the cost function is concave, the capacity-achieving input distribution is discrete, whereas when the cost function is convex and the cost const…

Inequalities, identities, and bounds for divided differences of the exponential function
Qiulin Zeng, Nicholas Ezzell, Arman Babakhani, Itay Hen, Lev Barash
https://arxiv.org/abs/2510.10724

Inequalities, identities, and bounds for divided differences of the exponential function
Let $\exp[x_0,x_1,\dots,x_n]$ denote the divided difference of the exponential function. (i) We prove that exponential divided differences are log-submodular. (ii) We establish the four-point inequality $ \exp[a,a,b,c]\,\exp[d,d,b,c]+\exp[b,b,a,d]\,\exp[c,c,a,d]-\exp[a,b,c,d]^2 \ge 0 $ for all $ a,b,c,d \in \mathbb{R} $. (iii) We obtain sharp two-sided bounds for $\exp[x_0,\dots,x_n]$ at fixed mean and variance; as a consequence, we derive their large-input asymptotics. (iv) We present …

A model free approach for continuous-time optimal tracking control with unknown user-define cost and constrained control input via advantage function
Duc Cuong Nguyen, Quang Huy Dao, Phuong Nam Dao
https://arxiv.org/abs/2509.16821

A model free approach for continuous-time optimal tracking control with unknown user-define cost and constrained control input via advantage function
This paper presents a pioneering approach to solving the linear quadratic regulation (LQR) and linear quadratic tracking (LQT) problems with constrained inputs using a novel off-policy continuous-time Q-learning framework. The proposed methodology leverages a novel concept of the Advantage function for linear continuous systems, enabling solutions to be obtained without the need for prior knowledge of the reward matrix weights, state resetting, or assuming the existence of a predefined admissib…

Triplet Loss Based Quantum Encoding for Class Separability
Marco Mordacci, Mahul Pandey, Paolo Santini, Michele Amoretti
https://arxiv.org/abs/2509.15705 https://

Triplet Loss Based Quantum Encoding for Class Separability
An efficient and data-driven encoding scheme is proposed to enhance the performance of variational quantum classifiers. This encoding is specially designed for complex datasets like images and seeks to help the classification task by producing input states that form well-separated clusters in the Hilbert space according to their classification labels. The encoding circuit is trained using a triplet loss function inspired by classical facial recognition algorithms, and class separability is meas…

A New Algorithm for Computing Integer Hulls of 2D Polyhedral Sets
Chirantan Mukherjee
https://arxiv.org/abs/2509.09134 https://arxiv.org/pdf/2509.09134

A New Algorithm for Computing Integer Hulls of 2D Polyhedral Sets
The $\texttt{IntegerHull}$ function is part of Maple's $\texttt{PolyhedralSets}$ library, which calculates the integer hull of a given polyhedral set. This algorithm works by translating the supporting hyperplanes of the facets of the input polyhedral set inwards till each hyperplane encounters at least one integer point. The polyhedral set is then divided into smaller regions and a brute force method is applied to find the remaining vertices. There are certain edge case scenarios where the c…

Nessie: A Rust-Powered, Fast, Flexible, and Generalized Friends-of-Friends Galaxy-Group Finder in R and Python
Trystan S. Lambert, A. S. G. Robotham, M. Bravo, C. del P. Lagos, R. Tobar, S. Driver, A. Aufan Stoffels d'Hautefort
https://arxiv.org/abs/2509.13647

Nessie: A Rust-Powered, Fast, Flexible, and Generalized Friends-of-Friends Galaxy-Group Finder in R and Python
We introduce Nessie, a galaxy group finder implemented in Rust and distributed as both a Python and R package. Nessie employs the friends-of-friends (FoF) algorithm and requires only on-sky position and redshift as input, making it immediately applicable to surveys that lack a well-defined luminosity function. We implement several algorithmic optimizations including binary search and k-d tree pre-selection that significantly improve performance by reducing unnecessary galaxy pair checks. To val…

Replaced article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[1/1]:
- Exponent Function for Stationary Memoryless Channels with Input Cost at Rates above the Capacity
Yasutada Oohama

Cellular Learning: Scattered Data Regression in High Dimensions via Voronoi Cells
Shankar Prasad Sastry
https://arxiv.org/abs/2510.03810 https://arxiv.org/…

Cellular Learning: Scattered Data Regression in High Dimensions via Voronoi Cells
I present a regression algorithm that provides a continuous, piecewise-smooth function approximating scattered data. It is based on composing and blending linear functions over Voronoi cells, and it scales to high dimensions. The algorithm infers Voronoi cells from seed vertices and constructs a linear function for the input data in and around each cell. As the algorithm does not explicitly compute the Voronoi diagram, it avoids the curse of dimensionality. An accuracy of around 98.2% on the MN…

Neural network-based singularity detection and applications
Nadiia Derevianko, Ioannis G. Kevrekidis, Felix Dietrich
https://arxiv.org/abs/2509.10110 https://

Neural network-based singularity detection and applications
We present a method for constructing a special type of shallow neural network that learns univariate meromorphic functions with pole-type singularities. Our method is based on using a finite set of Laurent coefficients as input information, which we compute by FFT, employing values of the investigated function on some contour $Γ$ in the complex plane. The primary components of our methodology are the following: (1) the adaptive construction of rational polynomial activation functions, (2) a no…

Ergodic approach to the study of boundedness of solutions of one type of the first-order semilinear difference equations
Andrii Chaikovskyi, Oleksandr Liubimov
https://arxiv.org/abs/2509.11320

Ergodic approach to the study of boundedness of solutions of one type of the first-order semilinear difference equations
We investigate the sufficient conditions for boundedness of one type of difference equations of the form $x(n+1)=ax(n)+f(x(n)) + y(n), \ n\geq 1$ in critical case $|a|=1$. For this equation the following assumptions are introduced: 1) The function $f: \mathbb{C} \to \mathbb{C}$ and the input sequence $\{y(n)\}_{n\geq1}$ are assumed to be bounded. 2) $\text{Re}\left(\overline{f(ρ\, e^{2πi θ})} \; \cdot ae^{2πi θ}\right)$ converges uniformly on $[0,1) \ni θ$ to some real-val…

Improved Search-to-Decision Reduction for Random Local Functions
Kel Zin Tan, Prashant Nalini Vasudevan
https://arxiv.org/abs/2510.02944 https://arxiv.org/…

Improved Search-to-Decision Reduction for Random Local Functions
A random local function defined by a $d$-ary predicate $P$ is one where each output bit is computed by applying $P$ to $d$ randomly chosen bits of its input. These represent natural distributions of instances for constraint satisfaction problems. They were put forward by Goldreich as candidates for low-complexity one-way functions, and have subsequently been widely studied also as potential pseudo-random generators. We present a new search-to-decision reduction for random local functions defi…

Customizing the Inductive Biases of Softmax Attention using Structured Matrices
Yilun Kuang, Noah Amsel, Sanae Lotfi, Shikai Qiu, Andres Potapczynski, Andrew Gordon Wilson
https://arxiv.org/abs/2509.07963

Customizing the Inductive Biases of Softmax Attention using Structured Matrices
The core component of attention is the scoring function, which transforms the inputs into low-dimensional queries and keys and takes the dot product of each pair. While the low-dimensional projection improves efficiency, it causes information loss for certain tasks that have intrinsically high-dimensional inputs. Additionally, attention uses the same scoring function for all input pairs, without imposing a distance-dependent compute bias for neighboring tokens in the sequence. In this work, we …

On the Invariance of Cross-Correlation Peak Positions Under Monotonic Signal Transformations, with Application to Fast Time Difference Estimation
Natsuki Ueno, Ryotaro Sato, Nobutaka Ono
https://arxiv.org/abs/2509.19974

On the Invariance of Cross-Correlation Peak Positions Under Monotonic Signal Transformations, with Application to Fast Time Difference Estimation
We present a theorem concerning the invariance of cross-correlation peak positions, which provides a foundation for a new method for time difference estimation that is potentially faster than the conventional fast Fourier transform (FFT) approach for real/complex sequences. This theoretical result shows that the peak position of the cross-correlation function between two shifted discrete-time signals remains unchanged under arbitrary monotonic transformations of the input signals. By exploiting…

Edges In Coadded Images
Erin Sheldon
https://arxiv.org/abs/2509.06929 https://arxiv.org/pdf/2509.06929

Edges In Coadded Images
We investigate how discontinuities in the point spread function (PSF) and image noise affect weak gravitational lensing shear measurements. Our focus is on discontinuities that arise in coadded images, particularly when edges from input images cross the coadd region. Using Metacalibration and Metadetection for shear calibration and a simple weighted mean coaddition, we find no significant biases for typical galaxy populations, typical edge hit rates (a few percent), or coadds with tens to hundr…

On Random Fields Associated with Analytic Wavelet Transform
Gi-Ren Liu, Yuan-Chung Sheu, Hau-Tieng Wu
https://arxiv.org/abs/2508.10495 https://arxiv.org/pd…

On Random Fields Associated with Analytic Wavelet Transform
Despite the broad application of the analytic wavelet transform (AWT), a systematic statistical characterization of its magnitude and phase as inhomogeneous random fields on the time-frequency domain when the input is a random process remains underexplored. In this work, we study the magnitude and phase of the AWT as random fields on the time-frequency domain when the observed signal is a deterministic function plus additive stationary Gaussian noise. We derive their marginal and joint distribu…

Space Mapping Optimization using Neural Networks for Efficient Parameter Estimation
Dhruvil Kamleshkumar Kotecha
https://arxiv.org/abs/2509.06899 https://a…

Space Mapping Optimization using Neural Networks for Efficient Parameter Estimation
This project focuses on optimizing input parameters of a partial derivative function of a fine model using Neural network-based Space Mapping Optimization (SMO). The fine model is known for its high accuracy but is computationally expensive. On the other hand, the coarse model is represented by a neural network, which is much faster but less accurate. The SMO approach is applied to bridge the gap between these two models and estimate the optimal input parameters for the fine model. Additionally…

Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions
Ersin Das, Rahal Nanayakkara, Xiao Tan, Ryan M. Bena, Joel W. Burdick, Paulo Tabuada, Aaron D. Ames
https://arxiv.org/abs/2508.19159

Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions
Measurements and state estimates are often imperfect in control practice, posing challenges for safety-critical applications, where safety guarantees rely on accurate state information. In the presence of estimation errors, several prior robust control barrier function (R-CBF) formulations have imposed strict conditions on the input. These methods can be overly conservative and can introduce issues such as infeasibility, high control effort, etc. This work proposes a systematic method to improv…

Data-Free Knowledge Distillation for LiDAR-Aided Beam Tracking in MmWave Systems
Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb, Markku Juntti
https://arxiv.org/abs/2509.19092

Data-Free Knowledge Distillation for LiDAR-Aided Beam Tracking in MmWave Systems
Multimodal sensing reduces beam training overhead but is constrained by machine learning complexity and dataset demands. To address this, we propose a data-free (DF) knowledge distillation (KD) framework for efficient LiDAR-aided mmWave beam tracking, i.e., predicting the best current and future beams. Specifically, we propose a knowledge inversion framework, where a generator synthesizes LiDAR input data from random noise, guided by a loss function defined on the features and outputs of a pre-…

Binaural Sound Event Localization and Detection Neural Network based on HRTF Localization Cues for Humanoid Robots
Gyeong-Tae Lee
https://arxiv.org/abs/2508.04333 https://

Binaural Sound Event Localization and Detection Neural Network based on HRTF Localization Cues for Humanoid Robots
Humanoid robots require simultaneous sound event type and direction estimation for situational awareness, but conventional two-channel input struggles with elevation estimation and front-back confusion. This paper proposes a binaural sound event localization and detection (BiSELD) neural network to address these challenges. BiSELDnet learns time-frequency patterns and head-related transfer function (HRTF) localization cues from binaural input features. A novel eight-channel binaural time-freque…

Replacement dynamics of binary quadratic forms
Raghav Bhutani, Frederick Saia
https://arxiv.org/abs/2508.05816 https://arxiv.org/pdf/2508.05816

Replacement dynamics of binary quadratic forms
For an $S$-valued function $f$ of $m \geq 1$ variables we define a dynamical process in which the output $f(\overline{v})$ replaces exactly one entry of the input $\overline{v} \in S^m$ at each step in the iterative process. Our study focuses on periodic vectors with respect to this process. We define a stratification of periodic vectors according to their type, and characterize types for which the determination of periodic vectors comes down to dynamics of univariate polynomials. We then restr…

Asymptotic Scaling Law Analysis of Multicast Satellite Communications with Massive MIMO
Seyong Kim, Jeonghun Park
https://arxiv.org/abs/2509.16921 https://…

Asymptotic Scaling Law Analysis of Multicast Satellite Communications with Massive MIMO
In this paper, we consider a geostationary orbit (GEO) satellite communication system that employs massive multiple-input multiple-output (MIMO) for multicast transmission. By modeling the spatial distribution of ground users using a Poisson point process (PPP) and assuming a fixed-beam precoding is adopted, we find a closed-form expression for the asymptotical rate scaling law as a function of the number of antennas and the scaling factors of user density and multicast users. From the derived …

Analysis of (p,$\gamma$) capture cross-sections relevant to p-process using TALYS for A=75-110
Satabdi Mondal, Enakshi Senapati, Deepak Pandit, Balaram Dey, Pampa Das, Alokkumar De, Srijit Bhattacharya
https://arxiv.org/abs/2508.05054

Analysis of (p,$γ$) capture cross-sections relevant to p-process using TALYS for A=75-110
The proton capture (p, $γ$) cross-sections for eight different atomic nuclei in the mass region A=75-110 were calculated within the nuclear reaction model code TALYS. For all the reactions, we tested different combinations of inputs for level density (l.d) parameter and gamma strength function ($γ_{sf}$). Finally, it was observed that application of hybrid input in TALYS (macroscopic l.d and microscopic or semi-microscopic $γ_{sf}$ or in abbreviation mac-mic) resulted successful agreement of…

On the Gaussian Limit of the Output of IIR Filters
Yashaswini Murthy, Bassam Bamieh, R. Srikant
https://arxiv.org/abs/2508.12705 https://arxiv.org/pdf/2508…

On the Gaussian Limit of the Output of IIR Filters
We study the asymptotic distribution of the output of a stable Linear Time-Invariant (LTI) system driven by a non-Gaussian stochastic input. Motivated by longstanding heuristics in the stochastic describing function method, we rigorously characterize when the output process becomes approximately Gaussian, even when the input is not. Using the Wasserstein-1 distance as a quantitative measure of non-Gaussianity, we derive upper bounds on the distance between the appropriately scaled output and a …

Energy Efficiency Maximization for Movable Antenna-Enhanced MIMO Downlink System Based on S-CSI
Xintai Chen, Biqian Feng, Yongpeng Wu, Xiang-Gen Xia, Chengshan Xiao
https://arxiv.org/abs/2509.12036

Energy Efficiency Maximization for Movable Antenna-Enhanced MIMO Downlink System Based on S-CSI
This paper presents an innovative movable antenna (MA)-enhanced multi-user multiple-input multiple-output (MIMO) downlink system. We aim to maximize the energy efficiency (EE) under statistical channel state information (S-CSI) through a joint optimization of the precoding matrix and the antenna position vectors (APVs). To solve the resulting stochastic problem, we first resort to deterministic equivalent (DE) tecnology to formulate the deterministic minorizing function of the system EE and the…

Boundaried Kernelization via Representative Sets
Leonid Antipov, Stefan Kratsch
https://arxiv.org/abs/2510.00832 https://arxiv.org/pdf/2510.00832

Boundaried Kernelization via Representative Sets
A kernelization is an efficient algorithm that given an instance of a parameterized problem returns an equivalent instance of size bounded by some function of the input parameter value. It is quite well understood which problems do or (conditionally) do not admit a kernelization where this size bound is polynomial, a so-called polynomial kernelization. Unfortunately, such polynomial kernelizations are known only in fairly restrictive settings where a small parameter value corresponds to a stron…

Networked Control and Mean Field Problems Under Diagonal Dominance: Decentralized and Social Optimality
Vivek Khatana, Duo Wang, Petros Voulgaris, Nicola Elia, Naira Hovakimyan
https://arxiv.org/abs/2510.01067

Networked Control and Mean Field Problems Under Diagonal Dominance: Decentralized and Social Optimality
In this article, we employ an input-output approach to expand the study of cooperative multi-agent control and optimization problems characterized by mean-field interactions that admit decentralized and selfish solutions. The setting involves $n$ independent agents that interact solely through a shared cost function, which penalizes deviations of each agent from the group's average collective behavior. Building on our earlier results established for homogeneous agents, we extend the framework t…

Designing learning in high dimensional oscillator networks with low dimensional read-out
Thomas Geert de Jong
https://arxiv.org/abs/2509.00848 https://arxi…

Designing learning in high dimensional oscillator networks with low dimensional read-out
In this paper we investigate how to configure an oscillator network based reservoir computer with a high number of oscillators and a low dimensional read-out. The read-out is a function on the average phases with respect to each oscillator population. Hence, this read-out provides a robust measurement of the oscillator states. We consider a low number of populations which leads to a low-dimensional read-out. Here, the task is time-series prediction. The input time-series is introduced via a for…

Factored Output Feedback Controller Synthesis with Locality Constraints for Spatially-Invariant Systems
Walden Marshall
https://arxiv.org/abs/2509.14168 https://

Factored Output Feedback Controller Synthesis with Locality Constraints for Spatially-Invariant Systems
We consider H2 output feedback controller synthesis with pre-specified constraints on spatial communication distance (locality) for spatially-invariant systems using two factored controller frameworks: the system-level parameterization and the input-output parameterization. In our main result, we show that in both frameworks, output feedback controller synthesis with locality constraints can be formulated as a convex problem in finitely many transfer function variables, admitting the use of sta…

Joint Spatial and Spectral Hybrid Precoding for Multi-User MIMO-OFDM Systems
Navid Reyhanian, Reza Ghaderi Zefreh, Parisa Ramezani, Emil Bj\"ornson
https://arxiv.org/abs/2509.07229

Joint Spatial and Spectral Hybrid Precoding for Multi-User MIMO-OFDM Systems
The deployment of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems cannot rely solely on digital precoding due to hardware constraints. Instead, hybrid precoding, which combines digital and radio frequency (RF) techniques, has emerged as a potential alternative. This approach strikes a balance between performance and cost, addressing the limitations of signal mixers and analog-to-digital converters in mmWave systems. mmWave systems are designed to function in wideband chan…

Guaranteed Time Control using Linear Matrix Inequalities
V\'ictor Costa da Silva Campos, Mariella Maia Quadros, Luciano Frezzato, Leonardo Mozelli, Anh-Tu Nguyen
https://arxiv.org/abs/2510.02636

Guaranteed Time Control using Linear Matrix Inequalities
This paper presents a synthesis approach aiming to guarantee a minimum upper-bound for the time taken to reach a target set of non-zero measure that encompasses the origin, while taking into account uncertainties and input and state constraints. This approach is based on a harmonic transformation of the Lyapunov function and a novel piecewise quadratic representation of this transformed Lyapunov function over a simplicial partition of the state space. The problem is solved in a policy iteration…

Designing Control Barrier Functions Using a Dynamic Backup Policy
Victor Freire, Marco M. Nicotra
https://arxiv.org/abs/2510.09810 https://arxiv.org/pdf/25…

Designing Control Barrier Functions Using a Dynamic Backup Policy
This paper presents a systematic approach to construct control barrier functions for nonlinear control affine systems subject to arbitrary state and input constraints. Taking inspiration from the reference governor literature, the proposed method defines a family of backup policies, parametrized by the equilibrium manifold of the system. The control barrier function is defined on the augmented state-and-reference space: given a state-reference pair, the approach quantifies the distance to const…

Predictive Control Barrier Functions for Discrete-Time Linear Systems with Unmodeled Delays
Juan Augusto Paredes Salazar, James Usevitch, Ankit Goel
https://arxiv.org/abs/2510.01059

Predictive Control Barrier Functions for Discrete-Time Linear Systems with Unmodeled Delays
This paper introduces a predictive control barrier function (PCBF) framework for enforcing state constraints in discrete-time systems with unknown relative degree, which can be caused by input delays or unmodeled input dynamics. Existing discrete-time CBF formulations typically require the construction of auxiliary barrier functions when the relative degree is greater than one, which complicates implementation and may yield conservative safe sets. The proposed PCBF framework addresses this chal…