Tootfinder

Sharp bilinear eigenfunction estimate, $L^\infty_{x_2}L^p_{t,x_1}$-type Strichartz estimate, and energy-critical NLS
Yangkendi Deng, Yunfeng Zhang, Zehua Zhao
https://arxiv.org/abs/2509.09565

Sharp bilinear eigenfunction estimate, $L^\infty_{x_2}L^p_{t,x_1}$-type Strichartz estimate, and energy-critical NLS
We establish sharp bilinear and multilinear eigenfunction estimates for the Laplace-Beltrami operator on the standard three-sphere $\mathbb{S}^3$, eliminating the logarithmic loss that has persisted in the literature since the pioneering work of Burq, Gérard, and Tzvetkov over twenty years ago. This completes the theory of multilinear eigenfunction estimates on the standard spheres. Our approach relies on viewing $\mathbb{S}^3$ as the compact Lie group $\mathrm{SU}(2)$ and exploiting its repre…

Number of integers represented by families of binary forms III: fewnomials
Etienne Fouvry, Michel Waldschmidt
https://arxiv.org/abs/2509.08335 https://arxi…

Number of integers represented by families of binary forms III: fewnomials
In a series of papers we investigated the following question: given a family $\calF$ of binary forms having nonzero discriminant and integer coefficients, for each $d\geqslant 3$, we estimate the number of integers $m$ with $|m|\leqslant N$ which are represented by an element in $\calF$ of degree $\geqslant d$. Under suitable assumptions, asymptotically as $N\to\infty$, the main term in the estimate is given by the forms in $\calF$ having degree $d$ (if any), while the forms of degree…

Asymptotic behaviour of the weak inverse anisotropic mean curvature flow
Chaoqun Gao, Yong Wei, Rong Zhou
https://arxiv.org/abs/2510.08168 https://arxiv.or…

Asymptotic behaviour of the weak inverse anisotropic mean curvature flow
We first establish a local gradient estimate for anisotropic $p$-harmonic functions. A key feature of our estimate is that the constant remains bounded as $p\to 1$; consequently, in the limit $p\to 1$, this estimate yields the local gradient estimate for weak solutions of the inverse anisotropic mean curvature flow (IAMCF). As an application, we show that the weak IAMCF is asymptotic to the expanding Wulff shape solution at the infinity, thereby extending the result of Huisken and Ilmanen in [7…

Forecasting Generative Amplification
Henning Bahl, Sascha Diefenbacher, Nina Elmer, Tilman Plehn, Jonas Spinner
https://arxiv.org/abs/2509.08048 https://ar…

Forecasting Generative Amplification
Generative networks are perfect tools to enhance the speed and precision of LHC simulations. It is important to understand their statistical precision, especially when generating events beyond the size of the training dataset. We present two complementary methods to estimate the amplification factor without large holdout datasets. Averaging amplification uses Bayesian networks or ensembling to estimate amplification from the precision of integrals over given phase-space volumes. Differential am…

Interesting thread to get a first-hand account on the current state of LLMs for assisting US tax filings, even in complex situations. tl;dr: Very helpful for a sophisticated user that can check the output.
Another point towards my very narrow defense of LLMs, i.e., feel free to dislike LLMs for many good reasons, but please don't call them useless.

Patrick McKenzie
So October 15th, the extended US tax deadline, is just around the corner, and I have some observations which are more about LLM progress than taxes. Background: many people professionally involved with LLMs estimate 2026-2028 as the year where one can get an LLM to "do taxes."

DualTrack: Sensorless 3D Ultrasound needs Local and Global Context
Paul F. R. Wilson, Matteo Ronchetti, R\"udiger G\"obl, Viktoria Markova, Sebastian Rosenzweig, Raphael Prevost, Parvin Mousavi, Oliver Zettinig
https://arxiv.org/abs/2509.09530

DualTrack: Sensorless 3D Ultrasound needs Local and Global Context
Three-dimensional ultrasound (US) offers many clinical advantages over conventional 2D imaging, yet its widespread adoption is limited by the cost and complexity of traditional 3D systems. Sensorless 3D US, which uses deep learning to estimate a 3D probe trajectory from a sequence of 2D US images, is a promising alternative. Local features, such as speckle patterns, can help predict frame-to-frame motion, while global features, such as coarse shapes and anatomical structures, can situate the sc…

Robust Non-Linear Correlations via Polynomial Regression
Luca Giuliani, Michele Lombardi
https://arxiv.org/abs/2509.09380 https://arxiv.org/pdf/2509.09380

Robust Non-Linear Correlations via Polynomial Regression
The Hirschfeld-Gebelein-Rényi (HGR) correlation coefficient is an extension of Pearson's correlation that is not limited to linear correlations, with potential applications in algorithmic fairness, scientific analysis, and causal discovery. Recently, novel algorithms to estimate HGR in a differentiable manner have been proposed to facilitate its use as a loss regularizer in constrained machine learning applications. However, the inherent uncomputability of HGR requires a bias-variance trade-of…

Online Dynamic SLAM with Incremental Smoothing and Mapping
Jesse Morris, Yiduo Wang, Viorela Ila
https://arxiv.org/abs/2509.08197 https://arxiv.org/pdf/250…

Online Dynamic SLAM with Incremental Smoothing and Mapping
Dynamic SLAM methods jointly estimate for the static and dynamic scene components, however existing approaches, while accurate, are computationally expensive and unsuitable for online applications. In this work, we present the first application of incremental optimisation techniques to Dynamic SLAM. We introduce a novel factor-graph formulation and system architecture designed to take advantage of existing incremental optimisation methods and support online estimation. On multiple datasets, we …

Superresolution for two incoherent optical sources with arbitrary intensities in two dimensions
Junyan Li, Shengshi Pang
https://arxiv.org/abs/2508.08049 https://

Superresolution for two incoherent optical sources with arbitrary intensities in two dimensions
The Rayleigh criterion has long served as a fundamental limit for the resolution of classical optical imaging. However, recent advances in quantum metrology have led to the quantum superresolution technique that can break Rayleigh's curse and estimate the separation between a pair of incoherent point sources with nonvanishing precision. For two-dimensional optical systems, the precision limit of estimating the whole separation, i.e., the distance, between two point sources remains unknown so fa…

A Priest, a Rabbi, and an Atheist Walk Into an Error Bar: Religious Meditations on Uncertainty Visualization
Michael Correll, Lane Harrison
https://arxiv.org/abs/2509.08213 http…

A Priest, a Rabbi, and an Atheist Walk Into an Error Bar: Religious Meditations on Uncertainty Visualization
In this provocation, we suggest that much (although not all) current uncertainty visualization simplifies the myriad forms of uncertainty into error bars around an estimate. This apparent simplification into error bars comes only as a result of a vast metaphysics around uncertainty and probability underlying modern statistics. We use examples from religion to present alternative views of uncertainty (metaphysical or otherwise) with the goal of enriching our conception of what kind of uncertaint…

On the Optimality of Tracking Fisher Information in Adaptive Testing with Stochastic Binary Responses
Sanghwa Kim (KAIST), Dohyun Ahn (The Chinese University of Hong Kong), Seungki Min (Seoul National University)
https://arxiv.org/abs/2510.07862

On the Optimality of Tracking Fisher Information in Adaptive Testing with Stochastic Binary Responses
We study the problem of estimating a continuous ability parameter from sequential binary responses by actively asking questions with varying difficulties, a setting that arises naturally in adaptive testing and online preference learning. Our goal is to certify that the estimate lies within a desired margin of error, using as few queries as possible. We propose a simple algorithm that adaptively selects questions to maximize Fisher information and updates the estimate using a method-of-moments …

POW: Political Overton Windows of Large Language Models
Leif Azzopardi, Yashar Moshfeghi
https://arxiv.org/abs/2509.08853 https://arxiv.org/pdf/2509.08853

POW: Political Overton Windows of Large Language Models
Political bias in Large Language Models (LLMs) presents a growing concern for the responsible deployment of AI systems. Traditional audits often attempt to locate a model's political position as a point estimate, masking the broader set of ideological boundaries that shape what a model is willing or unwilling to say. In this paper, we draw upon the concept of the Overton Window as a framework for mapping these boundaries: the range of political views that a given LLM will espouse, remain neutra…

Improved Receiver Chain Performance via Error Location Inference
Michael Greenwood, Robert Hunter
https://arxiv.org/abs/2509.08869 https://arxiv.org/pdf/25…

Improved Receiver Chain Performance via Error Location Inference
Modern spacecraft communication systems rely on concatenated error correction schemes, typically combining convolutional and Reed-Solomon (RS) codes. This paper presents a decoder-side method that uses a machine learning model to estimate the likelihood of byte-level corruption in received data frames. These estimates are used to mark erasures prior to RS decoding, enhancing its correction capacity without requiring changes to spacecraft hardware or encoding standards. The approach enables impr…

Ergodicity and error estimate of laws for a random splitting Langevin Monte Carlo
Lei Li, Chen Wang, Mengchao Wang
https://arxiv.org/abs/2510.07676 https://

Ergodicity and error estimate of laws for a random splitting Langevin Monte Carlo
The random splitting Langevin Monte Carlo could mitigate the first order bias in Langevin Monte Carlo with little extra work compared other high order schemes. We develop in this work an analysis framework for the sampling error under Wasserstein distance regarding the random splitting Langevin Monte Carlo. First, the sharp local truncation error is obtained by the relative entropy approach together with the explicit formulas for the commutator of related semi-groups. The necessary pointwise es…

WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks
Felipe \'Alvarez Barrientos, Tom\'as Banduc, Isabeau Sirven, Francisco Sahli Costabal
https://arxiv.org/abs/2509.08872

WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks
The contractile motion of the heart is strongly determined by the distribution of the fibers that constitute cardiac tissue. Strain analysis informed with the orientation of fibers allows to describe several pathologies that are typically associated with impaired mechanics of the myocardium, such as cardiovascular disease. Several methods have been developed to estimate strain-derived metrics from traditional imaging techniques. However, the physical models underlying these methods do not inclu…

Sequential Spectral Clustering of Data Sequences
G Dhinesh Chandran, Kota Srinivas Reddy, Srikrishna Bhashyam
https://arxiv.org/abs/2509.09144 https://arxi…

Sequential Spectral Clustering of Data Sequences
We study the problem of nonparametric clustering of data sequences, where each data sequence comprises i.i.d. samples generated from an unknown distribution. The true clusters are the clusters obtained using the Spectral clustering algorithm (SPEC) on the pairwise distance between the true distributions corresponding to the data sequences. Since the true distributions are unknown, the objective is to estimate the clusters by observing the minimum number of samples from the data sequences for a …

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Joaqu\'in Mart\'inez-Minaya, Lore Zumeta-Olaskoaga, Dae-Jin Lee
https://arxiv.org/abs/2508.07708

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Compositional data (CoDa) plays an important role in many fields such as ecology, geology, or biology. The most widely used modeling approaches are based on the Dirichlet and the logistic-normal formulation under Aitchison geometry. Recent developments in the mathematical field on the simplex geometry allow to express the regression model in terms of coordinates and estimate its coefficients. Once the model is projected in the real space, we can employ a multivariate Gaussian regression to deal…

Ennen kielimalleja huolehttiin Bitcoinin tolkuttomasta energiankulutuksesta. Joku muistutti Mastodonissa, että sekin jatkaa kasvamistaan, tosin tekoäly saattaa piankin ajaa sen edelle - ellei ole jo.
https://digiconomist.net/bitcoin-energy-consumption
Nyt puuttuukin enää …

Bitcoin Energy Consumption Index - Digiconomist
The Bitcoin Energy Consumption Index provides the latest estimate of the total energy consumption of the Bitcoin network.

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design
Ruixuan Zhao, Guitao Yang, Peng Li, Boli Chen
https://arxiv.org/abs/2509.08914 https://

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design
State estimation for linear time-invariant systems with unknown inputs is a fundamental problem in various research domains. In this article, we establish conditions for the design of unknown input observers (UIOs) from a geometric approach perspective. Specifically, we derive a necessary and sufficient geometric condition for the existence of a centralized UIO. Compared to existing results, our condition offers a more general design framework, allowing designers the flexibility to estimate par…

Xia & Lindell have also published a response (https://doi.org/10.1073/pnas.2518837122) – unsurprisingly, we don’t agree with most of their arguments. What puzzles us most is their claim that the re-analysis (their Table 1) "strengthens [their] conclusions." On the contrary:
In the Polysynthesis analysis, Small_Family has a minuscule averaged effect estimate (0.02) with a standard error more than four times larger (0.085). Together with a posterior inclusion probability (PIP) of just 0.085, this means the variable is rarely included in the best-supported models and its estimated effect is highly uncertain – essentially indistinguishable from zero. We therefore still struggle to see how this could be taken as support for their earlier statement that "different measures of language isolation – social, physical and *phylogenetic* – are important predictors of polysynthesis."

Numerical analysis of the homogeneous Landau equation: approximation, error estimates and simulation
Francis Filbet (IMT), Yanzhi Gui (THU), Ling-Bing He (THU)
https://arxiv.org/abs/2509.09276

Numerical analysis of the homogeneous Landau equation: approximation, error estimates and simulation
We construct a numerical solution to the spatially homogeneous Landau equation with Coulomb potential on a domain $D_L$ with N retained Fourier modes. By deriving an explicit error estimate in terms of $L$ and $N$, we demonstrate that for any prescribed error tolerance and fixed time interval $[0, T ]$, there exist choices of $D_L$ and $N$ satisfying explicit conditions such that the error between the numerical and exact solutions is below the tolerance. Specifically, the estimate shows that su…

Hybrid estimation for a mixed fractional Black-Scholes model with random effects from discrete time observations
Nerine Chebli, Hamdi Fathallah, Yousri Slaoui
https://arxiv.org/abs/2508.07936

Hybrid estimation for a mixed fractional Black-Scholes model with random effects from discrete time observations
We propose a hybrid estimation procedure to estimate global fixed parameters and subject-specific random effects in a mixed fractional Black-Scholes model based on discrete time observations. Specifically, we consider $N$ independent stochastic processes, each driven by a linear combination of standard Brownian motion and an independent fractional Brownian motion, and governed by a drift term that depends on an unobserved random effect with unknown distribution. Based on discrete-time statistic…

JWST ALMA reveal the ISM kinematics and stellar structure of MAMBO-9, a merging pair of DSFGs in an overdense environment at $z=5.85$
Hollis B. Akins, Caitlin M. Casey, Jaclyn B. Champagne, Olivia Cooper, Maximilien Franco, Seiji Fujimoto, Kirsten K. Knudsen, Anton M. Koekemoer, Arianna S. Long, Allison Man, Sinclaire M. Manning, Jed McKinney, Jorge Zavala, Pablo Arrabal Haro, Mark Dickinson, Vasily Kokorev, Anthony J. Taylor

JWST+ALMA reveal the ISM kinematics and stellar structure of MAMBO-9, a merging pair of DSFGs in an overdense environment at $z=5.85$
We present high-resolution ALMA [CII] 158 micron observations and JWST/NIRCam+MIRI imaging of MAMBO-9, a pair of optically-dark, dusty star-forming galaxies at $z=5.85$. MAMBO-9 is among the most massive, gas-rich, and actively star-forming galaxies at this epoch, when the Universe was less than 1 Gyr old. The new, 400 pc-resolution [CII] observations reveal velocity gradients in both objects; we estimate dynamical masses and find a relative mass ratio of 1:5. The kinematics of both objects sug…

A transport approach to the cutoff phenomenon
Francesco Pedrotti, Justin Salez
https://arxiv.org/abs/2509.08560 https://arxiv.org/pdf/2509.08560

A transport approach to the cutoff phenomenon
Substantial progress has recently been made in the understanding of the cutoff phenomenon for Markov processes, using an information-theoretic statistics known as varentropy [Sal23; Sal24; Sal25a; PS25]. In the present paper, we propose an alternative approach which bypasses the use of varentropy and exploits instead a new W-TV transport inequality, combined with a classical parabolic regularization estimate [BGL01; OV01]. While currently restricted to non-negatively curved processes on smooth …

Whitehead doubling, rank estimate and nonembeddability of contractible open manifolds
Shijie Gu, Jian Wang, Yanqing Zou
https://arxiv.org/abs/2510.06598 https://

Whitehead doubling, rank estimate and nonembeddability of contractible open manifolds
Let $K$ be a nontrivial knot. For each $n\in \mathbb{N}$, we prove that the rank of its $n$th iterated Whitehead doubled knot group $π_1(S^3 \setminus \operatorname{WD}^n(K))$ is bounded below by $n+1$. As an application, we show that there exist infinitely many non-homeomorphic contractible open $n$-manifolds ($n\geq 3$) which cannot embed in a compact, locally connected and locally 1-connected $n$-dimensional metric space.

🐓 Online cockfighting thrives in Philippines despite ban and murders
https://www.japantimes.co.jp/news/2025/08/15/asia-pacific/crime-legal/philippines-online-cockfighting-gambling/
🆓

Online cockfighting thrives in Philippines despite ban and murders
Authorities estimate cockfight bettors are fueling an industry that generates millions of dollars in revenue each week.

What's Coming Next? Short-Term Simulation of Business Processes from Current State
Maksym Avramenko, David Chapela-Campa, Marlon Dumas, Fredrik Milani
https://arxiv.org/abs/2509.07747

What's Coming Next? Short-Term Simulation of Business Processes from Current State
Business process simulation is an approach to evaluate business process changes prior to implementation. Existing methods in this field primarily support tactical decision-making, where simulations start from an empty state and aim to estimate the long-term effects of process changes. A complementary use-case is operational decision-making, where the goal is to forecast short-term performance based on ongoing cases and to analyze the impact of temporary disruptions, such as demand spikes and sh…

Characterization of syndrome-dependent logical noise in detector regions
Matthew Girling, Ben Criger, Cristina Cirstoiu
https://arxiv.org/abs/2508.08188 https://

Characterization of syndrome-dependent logical noise in detector regions
Characterizing how quantum error correction circuits behave under realistic hardware noise is essential for testing the premises that enable scalable fault tolerance. Logical error rates conditioned on syndrome outcomes are needed to enable noise-aware decoding and validate threshold-relevant assumptions. We introduce a protocol to directly estimate the logical Pauli channels (and pure errors) associated with detector regions formed of two or more syndrome extraction gadgets, conditioned on obs…

Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation
Thorbj{\o}rn Mosekj{\ae}r Iversen, Lars Car{\o}e S{\o}rensen, Simon Faarvang Mathiesen, Henrik Gordon Petersen
https://arxiv.org/abs/2509.09238

Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation
Many optimization problems in robotics involve the optimization of time-expensive black-box functions, such as those involving complex simulations or evaluation of real-world experiments. Furthermore, these functions are often stochastic as repeated experiments are subject to unmeasurable disturbances. Bayesian optimization can be used to optimize such methods in an efficient manner by deploying a probabilistic function estimator to estimate with a given confidence so that regions of the search…

Duolingo stock falls 25% after saying it expects bookings to range between $329.5M and $335.5M this quarter, falling short of a $344.3M estimate (Samantha Subin/CNBC)
https://www.cnbc.com/2025/11/06/duolingo-duol-stock-q3-earnings-2025.html

Duolingo stock plunges 27% on light guidance as company prioritizes user growth
CEO and co-founder Luis von Ahn told CNBC that the company has shifted its investment strategy in recent months to focus on long-term initiatives.

American Option Pricing Under Time-Varying Rough Volatility: A Signature-Based Hybrid Framework
Roshan Shah
https://arxiv.org/abs/2508.07151 https://arxiv.…

American Option Pricing Under Time-Varying Rough Volatility: A Signature-Based Hybrid Framework
We introduce a modular framework that extends the signature method to handle American option pricing under evolving volatility roughness. Building on the signature-pricing framework of Bayer et al. (2025), we add three practical innovations. First, we train a gradient-boosted ensemble to estimate the time-varying Hurst parameter H(t) from rolling windows of recent volatility data. Second, we feed these forecasts into a regime switch that chooses either a rough Bergomi or a calibrated Heston sim…

Young's inequality for Banach function spaces and its application to the maximal regularity estimate
Toru Nogayama
https://arxiv.org/abs/2509.05252 https://

Young's inequality for Banach function spaces and its application to the maximal regularity estimate
The goal of this paper is to give the necessary and sufficient condition for Banach function spaces on which Young's inequality holds. As an application, we consider the maximal regularity estimate of heat equations for Besov spaces associated with Banach function spaces.

From the dept of of #GoodNews https://flipboard.com/@gizmodo/earther-ensdu4a6z/-/a-943k_LaXTiG-wkjauL-0Og:a:1876139665-/0

Scientists Turned Plastic Trash Into a Material That Eats Carbon
A new study presents an innovative approach to CO2 removal that addresses two environmental crises at once. Experts estimate that the global …

How long can you sleep? Idle Time System Inefficiencies and Opportunities
Georgia Antoniou (University of Cyprus), Haris Volos (University of Cyprus), Jawad Haj Yahya (Rivos Inc), Yiannakis Sazeides (University of Cyprus)
https://arxiv.org/abs/2510.07449

How long can you sleep? Idle Time System Inefficiencies and Opportunities
This work introduces a model-based framework that reveals the idle opportunity of modern servers running latency-critical applications. Specifically, three queuing models, M/M/1, cxM/M/1, and M/M/c, are used to estimate the theoretical idle time distribution at the CPU core and system (package) level. A comparison of the actual idleness of a real server and that from the theoretical models reveals significant missed opportunities to enter deep idle states. This inefficiency is attributed to the…

The anabelian restricted Burnside problem
Andreas Thom
https://arxiv.org/abs/2509.05242 https://arxiv.org/pdf/2509.05242

The anabelian restricted Burnside problem
Let $n,d \in \mathbb N$ and $w \in \mathbb F_n$ be non-trivial. We prove that the relatively free group of rank $d$ in the variety defined by the group law $w$ has a largest anabelian finite quotient and estimate its size. Here, a finite group is called anabelian if it has only non-abelian composition factors. The estimate is based on explicit bounds for the length of laws for finite simple groups obtained by Bradford and the author and on recent work by Fumagalli--Leinen--Puglisi.

A log-free estimate for the diagonal paraproduct high $\times$ high $\to$ low in the 3D Navier-Stokes equation
Pylyp Cherevan
https://arxiv.org/abs/2510.07848 https://

A log-free estimate for the diagonal paraproduct high x high -> low in the 3D Navier-Stokes equation
We consider the diagonal paraproduct arising in the nonlinearity (u * grad) u for the three-dimensional Navier-Stokes equations. On scale-critical windows and in the range 1/6 < delta <= 5/8 we obtain a log-free estimate at the level L^2_t H^-1_x for the projection P_{< N^(1-delta)} div(u_N otimes v_N), consistent with the critical energy scheme. The main tools are phase-geometric integration, anisotropic local estimates on cylinders, and bilinear ell^2 decoupling on a finite-rank surface; the …

Dynamical Dark Energy or Modified Gravity? Signatures in Gravitational Wave Propagation
Purnendu Karmakar, Sandeep Haridasu
https://arxiv.org/abs/2509.07976 https://

Dynamical Dark Energy or Modified Gravity? Signatures in Gravitational Wave Propagation
We discuss the implications of the most recent DESI Baryon Acoustic Oscillations (BAO) DR2 and DESyr5 compilation of supernovae (SNe) datasets for modified gravity focusing on non-metricity based $f(Q)$ theory, by employing a `model-independent' approach. We reconstruct of dark energy density which is then extended to estimate the perturbation-level quantities, sourced by the modified gravity, namely the effective gravitational coupling, $μ$, and the amplitude damping parameter of gravitationa…

you need to catch the 9:44, and if you hurry you can just make it, only, crap, forgot your keys, and to go back will take at least 5 minutes.
so you get the sched and historical records, and you find there is quite a bit of variation so you crunch the numbers and figure the odds are still reasonable it will be at least ten minutes late. You weigh your probabilities and decide to go.
Meanwhile I call a friend who is on that train and ask their estimate. I now have a better estimate? After my call, they ask a porter and get another even better estimate?
Meanwhile, 12km down the rails a protest has blocked a crossing, determined to hold that train at least until the police arrive.
Probability is a subjective measure of ignorance 😅

Object recognition from sparse simulated phosphenes and curved segments https://www.sciencedirect.com/science/article/pii/S0042698925001798 "lower bound estimate on the minimal number of phosphenes needed to recognize objects" in uncluttered views;

Reassessment of Kepler's habitable zone Earth-like exoplanets with data-driven null-signal templates
Jakob Robnik, Uro\v{s} Seljak
https://arxiv.org/abs/2509.07409 https://

Reassessment of Kepler's habitable zone Earth-like exoplanets with data-driven null-signal templates
One of the primary mission goals of the Kepler space telescope is to detect Earth-like terrestrial planets in the habitable zone around Sun-like stars. Unfortunately, such planets are at the detection limit. Estimating their statistical significance via false alarm probability (FAP) is crucial for their validation, and has a large impact on the estimate of their occurrence rate, which is of central importance for future spectroscopic missions searching for life signatures. Current methods estim…

How many unique domains do y’all estimate you’ve purchased (all time, for yourself)?

Calculating the Projective Norm of higher-order tensors using a gradient descent algorithm
Aaditya Rudra, Maria Anastasia Jivulescu
https://arxiv.org/abs/2508.07933 https://

Calculating the Projective Norm of higher-order tensors using a gradient descent algorithm
Projective Norms are a class of tensor norms that map on the input and output spaces. These norms are useful for providing a measure of entanglement. Calculating the projective norms is an NP-hard problem, which creates challenges in computing due to the complexity of the exponentially growing parameter space for higher-order tensors. We develop a novel gradient descent algorithm to estimate the projective norm of higher-order tensors. The algorithm guarantees convergence to a minimum nuclear r…

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

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

Sum of elements preceding records in set partitions
Walaa Asakly, Noor Kezil
https://arxiv.org/abs/2510.06521 https://arxiv.org/pdf/2510.06521

Sum of elements preceding records in set partitions
In this paper, we aim to derive an explicit formula for the total number of elements preceding records over all set partitions of $[n]$ with exactly $k$ blocks, as well as an asymptotic estimate for the total sum of elements preceding records in all set partitions of $[n]$, expressed in terms of Bell numbers. To achieve this, we analyze the generating function that enumerates set partitions of $[n]$ according to this statistic, which we denote by $\sumelements$.

COSMOS-Web galaxy groups: Evolution of red sequence and quiescent galaxy fraction
Greta Toni, Matteo Maturi, Gianluca Castignani, Lauro Moscardini, Ghassem Gozaliasl, Alexis Finoguenov, Sina Taamoli, B. Hollis Akins, C. Rafael Arango-Toro, M. Caitlin Casey, E. Nicole Drakos, L. Andreas Faisst, Carter Flayhart, Maximilien Franco, Fabrizio Gentile, Ali Hadi, Santosh Harish, Hossein Hatamnia, Olivier Ilbert, Shuowen Jin, S. Jeyhan Kartaltepe, Ali Ahmad Khostovan, M. Anton Koekemoer, Gavin…

COSMOS-Web galaxy groups: Evolution of red sequence and quiescent galaxy fraction
We investigate the redshift and group richness dependence of the quiescent fraction and red-sequence (RS) parameters in COSMOS galaxy groups from z=0 to z=3.7. We analyzed the deep and well-characterized sample of groups detected with AMICO in the COSMOS(-Web) field. Our study of the quiescent galaxy population is based on a machine-learning classification tool based on rest-frame magnitudes. The algorithm learns from several traditional methods to estimate the probability of a galaxy being qui…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
Jonathan Teagan
https://arxiv.org/abs/2509.07813 https://arxiv.org/pdf/2509…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
This study applies a range of forecasting techniques,including ARIMA, Prophet, Long Short Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and XGBoost, to model and predict Russian equipment losses during the ongoing war in Ukraine. Drawing on daily and monthly open-source intelligence (OSINT) data from WarSpotting, we aim to assess trends in attrition, evaluate model performance, and estimate future loss patterns through the end of 2025. Our findings show that deep learning …

A gradient estimate for the linearized translator equation
Kyeongsu Choi, Robert Haslhofer, Or Hershkovits
https://arxiv.org/abs/2509.07629 https://arxiv.o…

A gradient estimate for the linearized translator equation
In this paper, we develop some analytic foundations for the linearized translator equation in $\mathbb{R}^4$, i.e. in the first dimension where the Bernstein property fails. This equation governs how the (noncompact) singularity models of the mean curvature flow in $\mathbb{R}^4$ fit together in a common moduli space. Here, we prove a gradient estimate, which gives a sharp bound for $W_v$, namely for the derivative of the variation field $W$ in the tip region. This serves as a substitute for th…

Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization
Philippe J. Giabbanelli, Ameeta Agrawal
https://arxiv.org/abs/2509.04646

Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization
Modeling & Simulation (M&S) approaches such as agent-based models hold significant potential to support decision-making activities in health, with recent examples including the adoption of vaccines, and a vast literature on healthy eating behaviors and physical activity behaviors. These models are potentially usable by different stakeholder groups, as they support policy-makers to estimate the consequences of potential interventions and they can guide individuals in making healthy choices in co…

Replaced article(s) found for math.CA. https://arxiv.org/list/math.CA/new
[1/1]:
- A Kakeya maximal function estimate in four dimensions using planebrushes
Nets Hawk Katz, Joshua Zahl
…

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…

Asteroseismology of 35 Kepler and TESS $\delta$ Scuti stars near the red edge of the instability strip. The limitations of $\delta$ Scuti stars for dating open clusters
David Pamos Ortega
https://arxiv.org/abs/2509.05684

Asteroseismology of 35 Kepler and TESS $δ$ Scuti stars near the red edge of the instability strip. The limitations of $δ$ Scuti stars for dating open clusters
Aims. The aim of this work is to determine the maximum ages that can be unambiguously established for $δ$ Sct stars using seismic observables, and, by extension, the oldest open clusters that can be dated using this type of star. Methods. I estimate the large frequency separation using various techniques applied to two samples of $δ$ Sct located near the red edge of the instability strip. One sample consists of 18 targets observed by the Kepler mission, and the other comprises 17 targets obse…

Xi : Uncertainty Supervision for Robust Speaker Embedding
Junjie Li, Kong Aik Lee, Duc-Tuan Truong, Tianchi Liu, Man-Wai Mak
https://arxiv.org/abs/2509.05993 https://

Xi+: Uncertainty Supervision for Robust Speaker Embedding
There are various factors that can influence the performance of speaker recognition systems, such as emotion, language and other speaker-related or context-related variations. Since individual speech frames do not contribute equally to the utterance-level representation, it is essential to estimate the importance or reliability of each frame. The xi-vector model addresses this by assigning different weights to frames based on uncertainty estimation. However, its uncertainty estimation model is …

Time-Varying Volatility of Bank Betas
Matt Brigida
https://arxiv.org/abs/2510.07671 https://arxiv.org/pdf/2510.07671

Time-Varying Volatility of Bank Betas
Research has shown banks match interest income and expense betas, and thereby obtain net interest income margins which are insensitive to changes in short-term interest rates. The present analysis extends this research in a number of ways. First, we use state-space methods to estimate time-varying betas and test whether they are matched at each time interval. We find substantial variation in interest income and expense betas, which drives variation in net interest margin beta coefficients. Seco…

The linearized translator equation and applications
Kyeongsu Choi, Robert Haslhofer, Or Hershkovits
https://arxiv.org/abs/2509.06667 https://arxiv.org/pdf/…

The linearized translator equation and applications
In this paper, we consider the linearized translator equation $L_ϕu=f$, around entire convex translators $M=\textrm{graph}(ϕ)\subset\mathbb{R}^4$, i.e. in the first dimension where the Bernstein property fails. Here, $L_ϕu=\mathrm{div} (a_ϕD u)+ b_ϕ\cdot Du$ is a mean curvature type elliptic operator, whose coefficients degenerate as the slope tends to infinity. We derive two fundamental barrier estimates, specifically an upper-lower estimate and an inner-outer estimate, which allow to pro…

Gamma-ray lines, electron-positron annihilation, and possible radio emission in X-ray pulsars
Alexander A. Mushtukov, Emir Tataroglu, Alex J. Cooper, Sergey S. Tsygankov
https://arxiv.org/abs/2509.05427

Gamma-ray lines, electron-positron annihilation, and possible radio emission in X-ray pulsars
Accretion onto neutron stars (NSs) in X-ray pulsars (XRPs) results in intense X-ray emission, and under specific conditions, high-energy nuclear interactions that produce gamma-ray photons at discrete energies. These interactions are enabled by the high free-fall velocities of accreting nuclei near the NS surface and give rise to characteristic gamma-ray lines, notably at 2.2 MeV, 5.5 MeV, and 67.5 MeV. We investigate the production mechanisms of these lines and estimate the resulting gamma-ray…

On fixed points of pseudo-Anosov maps
Tarik Aougab, David Futer, Samuel J. Taylor
https://arxiv.org/abs/2509.07818 https://arxiv.org/pdf/2509.07818

On fixed points of pseudo-Anosov maps
We give a formula to estimate the number of fixed points of a pseudo-Anosov homeomorphism of a surface. When the homeomorphism satisfies a mild property called strong irreducibility, the log of the number of fixed points is coarsely equal to the Teichmuller translation length. We also discuss several applications, including an inequality relating the hyperbolic volume of a mapping torus to the rank of its Heegaard Floer homology.

Optimal Policy Learning for Multi-Action Treatment with Risk Preference using Stata
Giovanni Cerulli
https://arxiv.org/abs/2509.06851 https://arxiv.org/pdf…

Optimal Policy Learning for Multi-Action Treatment with Risk Preference using Stata
This paper presents the Stata community-distributed command "opl_ma_fb" (and the companion command "opl_ma_vf"), for implementing the first-best Optimal Policy Learning (OPL) algorithm to estimate the best treatment assignment given the observation of an outcome, a multi-action (or multi-arm) treatment, and a set of observed covariates (features). It allows for different risk preferences in decision-making (i.e., risk-neutral, linear risk-averse, and quadratic risk-averse), and provides a graph…

Triple convolution sums of the generalised divisor functions
Bikram Misra, Biswajyoti Saha
https://arxiv.org/abs/2509.04610 https://arxiv.org/pdf/2509.0461…

Triple convolution sums of the generalised divisor functions
We study the triple convolution sum of the generalised divisor functions given by $$\sum_{n\leq x} d_k(n+h)d_l(n)d_m(n-h),$$ where $h \le x^{1-ε}$ for any $ε>0$ and $d_k(n)$ denotes the generalised divisor function which counts the number of ways $n$ can be written as product of $k$ many positive integers. The purpose of this paper is two-fold. Firstly, we note a predicted asymptotic estimate for the above sum, where the constant appearing in the estimate can be obtained from the theory of Di…

Exponential Error Bounds for Information Bottleneck Source Coding Problems
Han Wu, Hamdi Joudeh
https://arxiv.org/abs/2510.08364 https://arxiv.org/pdf/2510…

Exponential Error Bounds for Information Bottleneck Source Coding Problems
We study the information bottleneck (IB) source coding problem, also known as remote lossy source coding under logarithmic loss. Based on a rate-limited description of noisy observations, the receiver produces a soft estimate for the remote source, i.e., a probability distribution, evaluated under the logarithmic loss. We focus on the excess distortion probability of IB source coding and investigate how fast it converges to 0 or 1, depending on whether the rate is above or below the rate-distor…

Multi-Level Multi-Fidelity Methods for Path Integral and Safe Control
Zhuoyuan Wang, Takashi Tanaka, Yongxin Chen, Yorie Nakahira
https://arxiv.org/abs/2510.07756 https://

Multi-Level Multi-Fidelity Methods for Path Integral and Safe Control
Sampling-based approaches are widely used in systems without analytic models to estimate risk or find optimal control. However, gathering sufficient data in such scenarios can be prohibitively costly. On the other hand, in many situations, low-fidelity models or simulators are available from which samples can be obtained at low cost. In this paper, we propose an efficient approach for risk quantification and path integral control that leverages such data from multiple models with heterogeneous …

Attitude and Heading Estimation in Symmetrical Inertial Arrays
Yaakov Libero, Itzik Klein
https://arxiv.org/abs/2510.08161 https://arxiv.org/pdf/2510.08161…

Attitude and Heading Estimation in Symmetrical Inertial Arrays
Attitude and heading reference systems (AHRS) play a central role in autonomous navigation systems on land, air and maritime platforms. AHRS utilize inertial sensor measurements to estimate platform orientation. In recent years, there has been increasing interest in multiple inertial measurement units (MIMU) arrays to improve navigation accuracy and robustness. A particularly challenging MIMU implementation is the gyro-free (GF) configuration, in which angular velocity is derived solely from ac…

A Linear Programming Approach to Estimate the Core in Cooperative Games
J Camacho, JC Gon\c{c}alves-Dosantos, J S\'anchez-Soriano
https://arxiv.org/abs/2510.01766 https://…

A Linear Programming Approach to Estimate the Core in Cooperative Games
This paper proposes a novel algorithm to approximate the core of transferable utility (TU) cooperative games via linear programming. Given the computational hardness of determining the full core, our approach provides a tractable approximation by sampling extreme points through randomized linear problems (LPs). We analyze its convergence and computational complexity, and validate its effectiveness through extensive simulations on various game models. Our results show that the method is scalable…

DETERring more than Deforestation: Environmental Enforcement Reduces Violence in the Amazon
Rafael Araujo, Vitor Possebom, Gabriela Setti
https://arxiv.org/abs/2509.06076 https:…

DETERring more than Deforestation: Environmental Enforcement Reduces Violence in the Amazon
We estimate the impact of environmental law enforcement on violence in the Brazilian Amazon. The introduction of the Real-Time Deforestation Detection System (DETER), which enabled the government to monitor deforestation in real time and issue fines for illegal clearing, significantly reduced homicides in the region. To identify causal effects, we exploit exogenous variation in satellite monitoring generated by cloud cover as an instrument for enforcement intensity. Our estimates imply that the…

The Space Coronagraph Optical Bench (SCoOB): 8. end-to-end numerical modeling of the testbed to estimate the contrast limits
Ramya M Anche, Kyle Van Gorkom, Kian Milani, Kevin Derby, Emory Jenkins, Jaren Ashcraft, Saraswathi Kalyani Subramanian, Patrick Ingraham, Daewook Kim, Heejoo Choi, Olivier Durney, Ewan Douglas
https://arxiv.org/abs/…

The Space Coronagraph Optical Bench (SCoOB): 8. end-to-end numerical modeling of the testbed to estimate the contrast limits
The space coronagraph optical bench (SCoOB) at the University of Arizona is a high-contrast imaging testbed designed to operate in a vacuum to obtain a contrast better than 1e-8 in optical wavelengths using vector vortex coronagraph (VVC) masks. The testbed performance in a half-sided D-shaped dark hole is 2.2e-9 in a << 1% BW, 4e-9 in a 2% BW, and 2.5e-8 in a 15% BW. While the testbed has met the design specification contrast requirements in monochromatic wavelengths, comprehensive end-to-end …

Zero-Inflated Bayesian Multi-Study Infinite Non-Negative Matrix Factorization
Blake Hansen, Dafne Zorzetto, Valeria Edefonti, Roberta De Vito
https://arxiv.org/abs/2510.07518 ht…

Zero-Inflated Bayesian Multi-Study Infinite Non-Negative Matrix Factorization
Understanding the association between dietary patterns and health outcomes, such as the cancer risk, is crucial to inform public health guidelines and shaping future dietary interventions. However, dietary intake data present several statistical challenges: they are high-dimensional, often sparse with excess zeros, and exhibit heterogeneity driven by individual-level covariates. Non-Negative Matrix Factorization (NMF), commonly used to estimate patterns in high-dimensional count data, typically…

A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling
Meher V. R. Malladi, Tiziano Guadagnino, Luca Lobefaro, Cyrill Stachniss
https://arxiv.org/abs/2509.06593

A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling
Accurate odometry is a critical component in a robotic navigation stack, and subsequent modules such as planning and control often rely on an estimate of the robot's motion. Sensor-based odometry approaches should be robust across sensor types and deployable in different target domains, from solid-state LiDARs mounted on cars in urban-driving scenarios to spinning LiDARs on handheld packages used in unstructured natural environments. In this paper, we propose a robust LiDAR-inertial odometry sy…

A Honest Cross-Validation Estimator for Prediction Performance
Tianyu Pan, Vincent Z. Yu, Viswanath Devanarayan, Lu Tian
https://arxiv.org/abs/2510.07649 https://

A Honest Cross-Validation Estimator for Prediction Performance
Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the model performance across different data splits. A well-known criticism is that such cross-validation procedure does not directly estimate the performance of the particular model recommended for future use. In this paper, w…

Log-free estimate for the resonant paraproduct in the 3D Navier-Stokes equations
Pylyp Cherevan
https://arxiv.org/abs/2510.06246 https://arxiv.org/pdf/2510…

Log-free estimate for the resonant paraproduct in the 3D Navier-Stokes equations
We consider the resonant paraproduct (high-high -> low regime) in the nonlinearity (u\cdot\nabla)u for the three-dimensional Navier-Stokes equations. For sufficiently smooth, divergence-free u, we establish the a priori estimate without logarithmic loss \|R_N(u)\|{\dot H^{-1}} \lesssim N^{-1}\,\|u\|{\dot H^{1/2}}\,\|u\|_{\dot H^{1}}, with a constant independent of the dyadic frequency N. The proof combines phase-geometric integration by parts along an adapted frame…

DeFi protocol Balancer says its V2 Composable Stable Pools suffered an exploit, which security experts estimate resulted in total losses worth about $128M (Ryan S. Gladwin/Decrypt)
https://decrypt.co/347173/balancer-exploited-128-million-across…

Balancer Exploited for $128 Million Across Ethereum Chains as Berachain Halts Network
Some $128 million in crypto was stolen from Balancer liquidity pools on Ethereum and beyond, with Berachain halting its chain as a result.

Latent Uncertainty Representations for Video-based Driver Action and Intention Recognition
Koen Vellenga, H. Joe Steinhauer, Jonas Andersson, Anders Sj\"ogren
https://arxiv.org/abs/2510.05006

Latent Uncertainty Representations for Video-based Driver Action and Intention Recognition
Deep neural networks (DNNs) are increasingly applied to safety-critical tasks in resource-constrained environments, such as video-based driver action and intention recognition. While last layer probabilistic deep learning (LL-PDL) methods can detect out-of-distribution (OOD) instances, their performance varies. As an alternative to last layer approaches, we propose extending pre-trained DNNs with transformation layers to produce multiple latent representations to estimate the uncertainty. We ev…

Sparse domination via the Calder\'on-Zygmund decomposition: the example of Dini-smooth kernels
Fernando Ballesta-Yag\"ue, Jos\'e M. Conde-Alonso
https://arxiv.org/abs/2509.07659

Sparse domination via the Calderón-Zygmund decomposition: the example of Dini-smooth kernels
In this expository article, we briefly survey the main known schemes of proof of sparse domination principles within harmonic analysis. We then use the one based on the Calderón-Zygmund decomposition to prove a dual sparse domination estimate for Calderón-Zymgund operators with Dini-smooth kernels, with an eye on the difficulties that arise when trying to transfer the argument to spaces with nondoubling measures.

Autoencoding Coordinate Sequences from Psychophysiologic Signals
Timothy L. Hutcheson, Anil K. Raj
https://arxiv.org/abs/2510.07415 https://arxiv.org/pdf/2…

Autoencoding Coordinate Sequences from Psychophysiologic Signals
We present a method for converting 24 channels of psychophysiologic time series data collected from individual participants via electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), respiration rate (RR) into trackable three dimensional (3D) coordinates sufficient to estimate participation in specific task and cognitive states.

On Pauling's residual entropy estimate for regular graphs with growing degree
M. Hasheminezhad, M. Isaev, B. D. McKay, R-R. Zhang
https://arxiv.org/abs/2509.20671 https://…

On Pauling's residual entropy estimate for regular graphs with growing degree
In 1935, Pauling proposed an estimate for the number of Eulerian orientations of a graph in the context of the theoretical behaviour of water ice. The logarithm of the number of Eulerian orientations, normalised by the number of vertices, is called the residual entropy. In an earlier paper, we conjectured that the residual entropy of a sequence of regular graphs of increasing degree was asymptotically equal to Pauling's estimate. Here we prove the conjecture under constraints on the number of s…

Design of Input-Output Observers for a Population of Systems with Bounded Frequency-Domain Variation using $DK$-iteration
Timothy Everett Adams, James Richard Forbes
https://arxiv.org/abs/2509.07201

Design of Input-Output Observers for a Population of Systems with Bounded Frequency-Domain Variation using $DK$-iteration
This paper proposes a linear input-output observer design methodology for a population of systems in which each observer uses knowledge of the linear time-invariant dynamics of the particular device. Observers are typically composed of a known model of the system and a correction mechanism to produce an estimate of the state. The proposed design procedure characterizes the variation within the population in the frequency domain and synthesizes a single robust correction filter. The correction f…

Estimate of the exit time for the Long Range Ising model on random regular graphs
Vanessa Jacquier
https://arxiv.org/abs/2509.02188 https://arxiv.org/pdf/2…

Estimate of the exit time for the Long Range Ising model on random regular graphs
We investigate the metastable behavior of the long-range Ising model on random regular graphs under Glauber dynamics at low-temperature. We estimate the energy barrier and exit time from the metastable state using a nontrivial path-wise approach that explicitly accounts for the spatial decay of the interactions and the structural properties of the graph, such as the Cheeger constant and known estimates of the diameter. Our results generalize those of Dommers \cite{dommers2017metastability} for …

On divisibility of Hecke eigenvalues of Ikeda lifts
Sanoli Gun, Sunil Naik
https://arxiv.org/abs/2510.07056 https://arxiv.org/pdf/2510.07056

On divisibility of Hecke eigenvalues of Ikeda lifts
In this article, we estimate the density of the set of primes $p$ such that the $p$-th Hecke eigenvalue of an Ikeda lift is divisible by a fixed positive integer. One of the main ingredients involves the study of abelian subfields of fixed fields of the kernel of Galois representations attached to elliptic Hecke eigenforms. Further, we study the distribution of Fourier coefficients of elliptic Hecke eigenforms in arithmetic progressions.

Boundary estimates for singular elliptic problems involving a gradient term
Phuong Le
https://arxiv.org/abs/2508.07360 https://arxiv.org/pdf/2508.07360

Boundary estimates for singular elliptic problems involving a gradient term
We study the behavior of weak solutions to the singular quasilinear elliptic problem $-Δ_p u + \vartheta |\nabla u|^q = \frac{1}{u^γ} + f(u)$, in a bounded domain with the Dirichlet boundary condition, where $p>1$, $γ>0$, $0

Bayesian Portfolio Optimization by Predictive Synthesis
Masahiro Kato, Kentaro Baba, Hibiki Kaibuchi, Ryo Inokuchi
https://arxiv.org/abs/2510.07180 https://

Bayesian Portfolio Optimization by Predictive Synthesis
Portfolio optimization is a critical task in investment. Most existing portfolio optimization methods require information on the distribution of returns of the assets that make up the portfolio. However, such distribution information is usually unknown to investors. Various methods have been proposed to estimate distribution information, but their accuracy greatly depends on the uncertainty of the financial markets. Due to this uncertainty, a model that could well predict the distribution infor…

Toward the fabric of the Milky Way I. The density of disk streams from a local $250^3$ pc$^3$ volume
Sebastian Ratzenb\"ock, Jo\~ao Alves, Emily L. Hunt, N \'uria Miret-Roig, Stefan Meingast, Torsten M\"oller
https://arxiv.org/abs/2509.07075

Toward the fabric of the Milky Way I. The density of disk streams from a local $250^3$ pc$^3$ volume
We studied 12 disk streams found in a 250$^3$ pc$^3$ volume in the solar neighborhood, which we define as coeval and comoving stellar structures with aspect ratios greater than 3:1. Using \emph{Gaia} Data Release 3 data and the advanced clustering algorithms \texttt{SigMA} and \texttt{Uncover}, we identified and characterized these streams beyond the search volume, doubling, on average, their known populations. We estimate the number density of disk streams to be $\approx 820$ objects\,/\,kpc$^…

Just watched a video about project plowshare, which also contained an estimate of the price of a small nuclear weapon, with the point of reference „For the price of a small house in a city, you can wipe that city off the map“.
I feel like this is a promising extension to the „rent vs. buy“ binary.
https://www.youtube.com/watch…

Tangential Velocity Estimation Using Near-Field Automotive Radar Model
Michael Shifrin, Joseph Tabrikian, Igal Bilik
https://arxiv.org/abs/2509.04692 https://

Tangential Velocity Estimation Using Near-Field Automotive Radar Model
This work investigates the problem of tangential velocity estimation in automotive radar systems, addressing the limitations of conventionally considered models. Conventional automotive radars are usually based on far-field models and estimate the target's range, radial velocity, and direction-of-arrival (DOA) but are not able to estimate the tangential component of the target 2-D velocity, which is a critical parameter for reliable perception of dynamic environments. To address this challenge,…

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives
Matthieu Bou, Nyal Patel, Arjun Jagota, Satyapriya Krishna, Sonali Parbhoo
https://arxiv.org/abs/2510.06096

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives
The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a grand challenge. While Inverse Reinforcement Learning (IRL) can infer reward functions from behaviour, existing approaches either produce a single, overconfident reward estimate or fail to address the fundamental ambiguity of the task (non-identifiability). This paper introduces a principled auditing framework that re-frames reward inference from a simple e…

The solar sulphur abundance in view of large-scale atomic structure calculations and 3D non-LTE models
A. M. Amarsi, W. Li, N. Grevesse, A. J. G. Jurewicz
https://arxiv.org/abs/2509.05225

The solar sulphur abundance in view of large-scale atomic structure calculations and 3D non-LTE models
The solar chemical composition is a fundamental yardstick in astrophysics and the topic of heated debate in recent literature. We re-evaluate the abundance of sulphur in the photosphere by studying seven S I lines in the solar disc-centre intensity spectrum. Our analysis considers independent sets of experimental and theoretical oscillator strengths together with, for the first time, three-dimensional non-local thermodynamic equilibrium (3D non-LTE) S I spectrum synthesis. Our best estimate is …

Precise and Efficient Collision Prediction under Uncertainty in Autonomous Driving
Marc Kaufeld, Johannes Betz
https://arxiv.org/abs/2510.05729 https://arx…

Precise and Efficient Collision Prediction under Uncertainty in Autonomous Driving
This research introduces two efficient methods to estimate the collision risk of planned trajectories in autonomous driving under uncertain driving conditions. Deterministic collision checks of planned trajectories are often inaccurate or overly conservative, as noisy perception, localization errors, and uncertain predictions of other traffic participants introduce significant uncertainty into the planning process. This paper presents two semi-analytic methods to compute the collision probabili…

A generalized Cheeger inequality and the Steklov Problem on finite graphs
Bobo Hua, Supanat Kamtue, Shiping Liu, Florentin M\"unch, Norbert Peyerimhoff
https://arxiv.org/abs/2509.05667

A generalized Cheeger inequality and the Steklov Problem on finite graphs
We prove generalized Cheeger inequalities for eigenvalues of Laplacians for reversible Markov chains. Then we apply Hassannezhad and Miclo's convergence result to obtain Jammes Cheeger inequalities for Steklov eigenvalues. In particular, we get a sharp estimate for the first non-trivial Steklov eigenvalue via Escobar Cheeger constant. At the end, we extend Hassannezhad and Miclo's convergence result to non-reversible Markov chains via a different method based on resolvent convergence, answering…

Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
Yuxuan Bai, Gauri Pradhan, Marlon Tobaben, Antti Honkela
https://arxiv.org/abs/2510.05753 https://…

Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
With the emergence of powerful large-scale foundation models, the training paradigm is increasingly shifting from from-scratch training to transfer learning. This enables high utility training with small, domain-specific datasets typical in sensitive applications.Membership inference attacks (MIAs) provide an empirical estimate of the privacy leakage by machine learning models. Yet, prior assessments of MIAs against models fine-tuned with transfer learning rely on a small subset of possible att…

Amazon raises its forecast for capital expenditures to $125B in 2025, up from its earlier estimate of $118B, and says that number will likely increase in 2026 (Annie Palmer/CNBC)
https://www.cnbc.com/2025/10/30/amazon-amzn-q3-earnings-report-2025.html

Amazon stock soars 10% as earnings beat estimates, company posts strong cloud growth
AWS revenue accelerated 20.2% to $33 billion, which CEO Andy Jassy said was a pace it hadn't seen since 2022.

Coordinate-Consistent Localization via Continuous-Time Calibration and Fusion of UWB and SLAM Observations
Tien-Dat Nguyen, Thien-Minh Nguyen, Vinh-Hao Nguyen
https://arxiv.org/abs/2510.05992

Coordinate-Consistent Localization via Continuous-Time Calibration and Fusion of UWB and SLAM Observations
Onboard simultaneous localization and mapping (SLAM) methods are commonly used to provide accurate localization information for autonomous robots. However, the coordinate origin of SLAM estimate often resets for each run. On the other hand, UWB-based localization with fixed anchors can ensure a consistent coordinate reference across sessions; however, it requires an accurate assignment of the anchor nodes' coordinates. To this end, we propose a two-stage approach that calibrates and fuses UWB d…

A Simplification of the Aubin-Yau Proof and a New $C^{0}$ Estimate for the Monge-Amp\`ere Equation on Calabi-Yau Manifolds
Junyu Pan
https://arxiv.org/abs/2510.00609 https://

A Simplification of the Aubin-Yau Proof and a New $C^{0}$ Estimate for the Monge-Ampère Equation on Calabi-Yau Manifolds
In this paper, a simplified exposition of the celebrated Aubin-Yau proof for the existence of Kähler-Einstein metrics is provided. For the case of a compact Kähler manifold with vanishing first Chern class, we improve Yau's celebrated $C^0$ a priori estimate. Instead of relying on the $L^{\infty}$ norm of the Kähler potential $F$ as in the original proof, we establish a new uniform bound for the solution to the Monge-Ampère equation that depends only on the $L^{p}$ norm of $e^{F}$. This new…

Learning Survival Models with Right-Censored Reporting Delays
Yuta Shikuri, Hironori Fujisawa
https://arxiv.org/abs/2510.04421 https://arxiv.org/pdf/2510.0…

Learning Survival Models with Right-Censored Reporting Delays
Survival analysis is a statistical technique used to estimate the time until an event occurs. Although it is applied across a wide range of fields, adjusting for reporting delays under practical constraints remains a significant challenge in the insurance industry. Such delays render event occurrences unobservable when their reports are subject to right censoring. This issue becomes particularly critical when estimating hazard rates for newly enrolled cohorts with limited follow-up due to admin…

Formation of A Nuclear Star Cluster Through A Merger Event In The Low Surface Brightness Galaxy AGC 223218
Tian-Wen Cao, Zi-Qi Chen, Zi-Jian Li, Cheng Cheng, Gaspar Galaz, Venu M. Kalari, Jun-feng Wang, Chun-Yi Zhang, Pei-Bin Chen, Meng-Ting Shen, Hong Wu
https://arxiv.org/abs/2510.06668

Formation of A Nuclear Star Cluster Through A Merger Event In The Low Surface Brightness Galaxy AGC 223218
We present the properties of the nuclear star cluster (NSC) in the low surface brightness galaxy AGC 223218. The disk of the galaxy can be modeled using two S$\acute{\rm e}$rsic components with distinct central positions: one representing the inner bright disk and the other corresponding to the extended outer disk. We estimate the stellar masses of the NSC and the host galaxy using two methods: spectral energy distribution (SED) fitting and mass-to-light versus color relations (MLCRs). The stel…

Correlation-Aware Dual-View Pose and Velocity Estimation for Dynamic Robotic Manipulation
Mahboubeh Zarei, Robin Chhabra, Farrokh Janabi-Sharifi
https://arxiv.org/abs/2510.05536

Correlation-Aware Dual-View Pose and Velocity Estimation for Dynamic Robotic Manipulation
Accurate pose and velocity estimation is essential for effective spatial task planning in robotic manipulators. While centralized sensor fusion has traditionally been used to improve pose estimation accuracy, this paper presents a novel decentralized fusion approach to estimate both pose and velocity. We use dual-view measurements from an eye-in-hand and an eye-to-hand vision sensor configuration mounted on a manipulator to track a target object whose motion is modeled as random walk (stochasti…

Duality estimates for subdiffusion problems including time-fractional porous medium type equations
Arl\'ucio Viana, Patryk Wolejko, Rico Zacher
https://arxiv.org/abs/2509.07862

Duality estimates for subdiffusion problems including time-fractional porous medium type equations
We prove duality estimates for time-fractional and more general subdiffusion problems. An important example is given by subdiffusive porous medium type equations. Our estimates can be used to prove uniqueness of weak solutions to such problems, and they allow to extend a key estimate from classical reaction-diffusion systems to the subdiffusive case. Besides concrete equations involving a Laplacian, we also consider abstract problems in a Hilbert space setting.

Distributional Inverse Reinforcement Learning
Feiyang Wu, Ye Zhao, Anqi Wu
https://arxiv.org/abs/2510.03013 https://arxiv.org/pdf/2510.03013

Distributional Inverse Reinforcement Learning
We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and full distributions of returns. Unlike conventional IRL approaches that recover a deterministic reward estimate or match only expected returns, our method captures richer structure in expert behavior, particularly in learning the reward distribution, by minimizing first-order stochastic dominance (FSD) violations and thus integrating distortion risk mea…

Estimation of Resistance Training RPE using Inertial Sensors and Electromyography
James Thomas, Johan Walhstr\"om
https://arxiv.org/abs/2510.03197 https://

Estimation of Resistance Training RPE using Inertial Sensors and Electromyography
Accurate estimation of rating of perceived exertion (RPE) can enhance resistance training through personalized feedback and injury prevention. This study investigates the application of machine learning models to estimate RPE during single-arm dumbbell bicep curls, using data from wearable inertial and electromyography (EMG) sensors. A custom dataset of 69 sets and over 1000 repetitions was collected, with statistical features extracted for model training. Among the models evaluated, a random f…

On sharp Strichartz estimate for hyperbolic Schr\"{o}dinger equation on $\mathbb{T}^3$
Baoping Liu, Xu Zheng
https://arxiv.org/abs/2510.01886 https://…

On sharp Strichartz estimate for hyperbolic Schrödinger equation on $\mathbb{T}^3$
We prove the sharp Strichartz estimate for hyperbolic Schrödinger equation on $\mathbb{T}^3 $ via an incidence geometry approach. As application, we obtain optimal local well-posedness of nonlinear hyperbolic Schrödinger equations.

On some divergence-form singular elliptic equations with codimension-two boundary: $L^p$-estimates
Jie Ji, Jingang Xiong
https://arxiv.org/abs/2510.06716 https://

On some divergence-form singular elliptic equations with codimension-two boundary: $L^p$-estimates
We establish a global weighted $L^p$ estimate for the gradient of the solution to a divergence-form elliptic equations, where the coefficients are in a weighted VMO space and the equations have singularities on a co-dimension two boundary.

Integrated Local Energy Decay for Waves with Time-Dependent Damping
Perry Kleinhenz, Michael McNulty
https://arxiv.org/abs/2510.00179 https://arxiv.org/pdf…

Integrated Local Energy Decay for Waves with Time-Dependent Damping
We prove integrated local energy decay for solutions of the damped wave equation with time-dependent damping satisfying an appropriate generalization of the geometric control condition on asymptotically flat, stationary space-times. We first obtain a high frequency estimate, which we prove via a positive commutator estimate using an escape function explicitly constructed in terms of the damping around individual space-time trajectories. We combine the high frequency estimate with low and medium…