May 2024 arXiv papers — page 37

Matan Goren, Eran Treister

Full Waveform Inversion (FWI) is an inverse problem for estimating the wave velocity distribution in a given domain, based on observed data on the boundaries. The inversion is computationally demanding because we are required to solve multiple forward problems, either in time or frequency domains, to simulate data that are then iteratively fitted to the obse

Bozorgmehr Vaziri, Farhad Rahmati

The present paper investigates the limit $G$-space $\mathcal{J}_{G}$ generated by the self-similar action of automatic groups on a regular rooted tree. The limit space $\mathcal{J}_{G}$ is the Gromov-Hausdorff limit of the family of Schreier graphs $\Gamma_{n}$; therefore, $\mathcal{J}_{G}$ can be approximated by Schreier graphs on level $n$-th when $n$ tend

Yiling Chen, Tao Lin, Ariel D. Procaccia, Aaditya Ramdas

We introduce and study the problem of detecting whether an agent is updating their prior beliefs given new evidence in an optimal way that is Bayesian, or whether they are biased towards their own prior. In our model, biased agents form posterior beliefs that are a convex combination of their prior and the Bayesian posterior, where the more biased an agent i

Hari Srikanth

Structure fires are responsible for the majority of fire-related deaths nationwide. In order to assist with the rapid evacuation of trapped people, this paper proposes the use of a system that determines optimal search paths for firefighters and exit paths for civilians in real time based on environmental measurements. Through the use of a LiDAR mapping syst

Iosif Lytras, Panayotis Mertikopoulos

Motivated by applications to deep learning which often fail standard Lipschitz smoothness requirements, we examine the problem of sampling from distributions that are not log-concave and are only weakly dissipative, with log-gradients allowed to grow superlinearly at infinity. In terms of structure, we only assume that the target distribution satisfies eithe

Marius Costandin

In this paper we study the Product Partition Problem (PPP), i.e. we are given a set of $n$ natural numbers represented on $m$ bits each and we are asked if a subset exists such that the product of the numbers in the subset equals the product of the numbers not in the subset. Our approach is to obtain the integer factorization of each number. This is the sube

Saeedeh Ghanadbashi, Fatemeh Golpayegani

Agents, whether software or hardware, perceive their environment through sensors and act using actuators, often operating in dynamic, partially observable settings. They face challenges like incomplete and noisy data, unforeseen situations, and the need to adapt goals in real-time. Traditional reasoning and ML methods, including Reinforcement Learning (RL),

Hari Srikanth

Neural Network based approximations of the Value function make up the core of leading Policy Based methods such as Trust Regional Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). While this adds significant value when dealing with very complex environments, we note that in sufficiently low State and action space environments, a computationa

Jinjin Zhao, Avidgor Gal, Sanjay Krishnan

With the goal of identifying common practices in data science projects, this paper proposes a framework for logging and understanding incremental code executions in Jupyter notebooks. This framework aims to allow reasoning about how insights are generated in data science and extract key observations into best data science practices in the wild. In this paper

Brian Nugent

Let $X$ be a projective variety over a field. In this paper, we will construct a moduli space of very ample line bundles on $X$. In doing so, we develop a generalization of Fitting ideals to complexes of sheaves on $X$. We give other applications of these Fitting ideals such as constructing Brill-Noether spaces for higher dimensional varieties and giving a s

Allyson Silva, Xiangyi Zhang, Zak Webb, Mia Kramer

Fault-tolerant quantum computation using two-dimensional topological quantum error correcting codes can benefit from multi-qubit long-range operations. By using simple commutation rules, a quantum circuit can be transpiled into a sequence of solely non-Clifford multi-qubit gates. Prior work on fault-tolerant compilation avoids optimal scheduling of such gate

Mathew D. Penrose, Frankie Higgs

Given a compact planar region $A$, let $\tau_A$ be the (random) time it takes for the Johnson-Mehl tessellation of $A$ to be complete, i.e. the time it takes for $A$ to be fully covered by a spatial birth-growth process in $A$ with seeds arriving as a unit-intensity Poisson point process in $A \times [0,\infty)$, where upon arrival each seed grows at unit ra

Jinjin Zhao, Ted Shaowang, Stavos Sintos, Sanjay Krishnan

Video analytics systems based on deep learning models are often opaque and brittle and require explanation systems to help users debug. Current model explanation system are very good at giving literal explanations of behavior in terms of pixel contributions but cannot integrate information about the physical or systems processes that might influence a predic

Gerd Kiene, Sadik Ilik, Luigi Mastrodomenico, Masoud Babaie

This paper presents an extensive characterization of the low-frequency noise (LFN) at room temperature (RT) and cryogenic temperature (4.2 K) of 40-nm bulk-CMOS transistors. The noise is measured over a wide range of bias conditions and geometries to generate a comprehensive overview of LFN in this technology. While the RT results are in-line with the litera

Tianying Wang, Wenfei Zhang, Ying Wei

Utilizing natural history data as external control plays an important role in the clinical development of rare diseases, since placebo groups in double-blind randomization trials may not be available due to ethical reasons and low disease prevalence. This article proposed an innovative approach for utilizing natural history data to support rare disease clini

Andres Guevara, Juan Achury, Juan Sierra, Sebastian Benavides

From the implementation of the Grafcet programming language we obtain in this document the different ways of applying this method, the advantages it obtains with respect to efficiency, cost, quality and operational and time optimization. The behavior of a pump alternation process is analyzed using GRAFCET, which is easier to design, verify and implement sequ

Eduardo C. Marino

The detailed structure of the $T \times doping$ phase diagram of hole doped High-Tc superconducting cuprates is investigated from the perspective of a recently proposed comprehensive theory for these materials. Our theory is compared to Anderson's RVB theory for High-$T_c$ cuprates and it is demonstrated that the ground-eigenstate of our theory's Hamiltonian

Victor Cardenas

The document discusses the financial climate risk in the context of the banking industry, emphasizing the need for a comprehensive understanding of climate change across different spatial and temporal scales. It highlights the challenges in estimating physical and transition risks, specifically extreme events and limitations of current climate models. The do

Jack Stanek, Daniel Killough

JSON (JavaScript Object Notation) is a data encoding that allows structured data to be used in a standardized and straightforward manner across systems. Schemas for JSON-formatted data can be constructed using the JSON Schema standard, which describes the data types, structure, and meaning of JSON-formatted data. JSON is commonly used for storing and transmi

Yi Xu, Yun Fu

Understanding multi-agent movement is critical across various fields. The conventional approaches typically focus on separate tasks such as trajectory prediction, imputation, or spatial-temporal recovery. Considering the unique formulation and constraint of each task, most existing methods are tailored for only one, limiting the ability to handle multiple ta

Dylan Heberle, Noah Flemens, Connor Davis, Philippe Lassonde

Dispersion management of few-cycle pulses is crucial for ultrafast optics and photonics. Often, nontrivial dispersion is compensated using complex optical systems or minimized through careful design of waveguides. Here, we present dispersion-managed adiabatic frequency conversion enabling efficient downconversion of an 11.1-fs near-IR pulse to an 11.6-fs mid

Fengji Ma, Hei Victor Cheng, Chenxing Li, Li Liu

Achieving zero-shot adversarial robustness without sacrificing generalization remains challenging for foundation models such as CLIP, especially under large adversarial perturbations. Through empirical analyses, we identify three critical yet overlooked issues: (1) Logit margins exhibit a stable offset between small and large adversarial perturbations, sugge

Yoel Zimmermann, Joonas Keski-Rahkonen, Anton M. Graf, Eric J. Heller

The intricate relationship between electrons and the crystal lattice is a linchpin in condensed matter, traditionally described by the Fr\"ohlich model encompassing the lowest-order lattice-electron coupling. Recently developed quantum acoustics, emphasizing the wave nature of lattice vibrations, has enabled the exploration of previously uncharted territorie

Yanqi Xu, Yiqiu Shen, Carlos Fernandez-Granda, Laura Heacock

Transformer-based detectors have shown success in computer vision tasks with natural images. These models, exemplified by the Deformable DETR, are optimized through complex engineering strategies tailored to the typical characteristics of natural scenes. However, medical imaging data presents unique challenges such as extremely large image sizes, fewer and s

Mayowa Ayodele

The intersection between quantum computing and optimisation has been an area of interest in recent years. There have been numerous studies exploring the application of quantum and quantum-hybrid solvers to various optimisation problems. This work explores scalarisation methods within the context of solving the bi-objective quadratic assignment problem using

Lin Tang, Yi-Xuan Yang, Zhu-Fang Cui, Craig D. Roberts

A reaction model for $\gamma + p \to J/\psi + p$ photoproduction, which exposes the $c \bar c$ content of the photon in making the transition $\gamma\to c\bar c + \mathbb P \to J/\psi$ and couples the intermediate $c \bar c$ system to the proton's valence quarks via Pomeron ($\mathbb P $) exchange, is used to deliver a description of available data, viz. bot

Paul Hagelstein, Blanca Radillo-Murguia, Alexander Stokolos

We provide a condition on a set of directions $\Omega \subset \mathbb{S}^1$ ensuring that the associated directional maximal operator $M_\Omega$ is unbounded on $L^p(\mathbb{R}^2)$ for every $1 \leq p < \infty$. The techniques of proof extend ideas of Bateman and Katz involving probabilistic construction of Kakeya-type sets involving sticky maps and Bernoull

Kushagra Pandey, Ruihan Yang, Stephan Mandt

Constructing fast samplers for unconditional diffusion and flow-matching models has received much attention recently; however, existing methods for solving inverse problems, such as super-resolution, inpainting, or deblurring, still require hundreds to thousands of iterative steps to obtain high-quality results. We propose a plug-and-play framework for const

Lukasz Sztukiewicz, Jack Henry Good, Artur Dubrawski

In the real world, data is often noisy, affecting not only the quality of features but also the accuracy of labels. Current research on mitigating label errors stems primarily from advances in deep learning, and a gap exists in exploring interpretable models, particularly those rooted in decision trees. In this study, we investigate whether ideas from deep l

Jeremy Redding, Emma Cavanaugh, Luis Bravo, Muthuvel Murugan

This work investigates the unsteady behavior of unstart phenomena within a scramjet inlet using advanced computational techniques. Scramjets and ramjets, with their reliance on inlet compression, offer promising airbreathing propulsion for hypersonic regimes. This research focuses on understanding and modeling the onset of unstart phenomena in supersonic inl

Pascal Sikorski, Leendert Schrader, Kaleb Yu, Lucy Billadeau

This paper investigates the possibility of intuitive human-robot interaction through the application of Natural Language Processing (NLP) and Large Language Models (LLMs) in mobile robotics. This work aims to explore the feasibility of using these technologies for edge-based deployment, where traditional cloud dependencies are eliminated. The study specifica

Dafne Zorzetto, Antonio Canale, Fabrizia Mealli, Francesca Dominici

Principal stratification provides a causal inference framework for investigating treatment effects in the presence of a post-treatment variable. Principal strata play a key role in characterizing the treatment effect by identifying groups of units with the same or similar values for the potential post-treatment variable at all treatment levels. The literatur

Agata Małgorzata Wilk, Andrzej Swierniak, Andrea d'Amico, Rafał Suwiński

Background. Radiomic features, derived from a region of interest (ROI) in medical images, are valuable as prognostic factors. Selecting an appropriate ROI is critical, and many recent studies have focused on leveraging multiple ROIs by segmenting analogous regions across patients - such as the primary tumour and peritumoral area or subregions of the tumour.

Malia L. Kao, Keith Hawkins, Laura K. Rogers, Amy Bonsor

White dwarfs (WDs) polluted by exoplanetary material provide the unprecedented opportunity to directly observe the interiors of exoplanets. However, spectroscopic surveys are often limited by brightness constraints, and WDs tend to be very faint, making detections of large populations of polluted WDs difficult. In this paper, we aim to increase considerably

Tommy Rochussen

Bayesian neural network posterior distributions have a great number of modes that correspond to the same network function. The abundance of such modes can make it difficult for approximate inference methods to do their job. Recent work has demonstrated the benefits of partial stochasticity for approximate inference in Bayesian neural networks; inference can

Pascal Sikorski, Kaleb Yu, Lucy Billadeau, Flavio Esposito

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting a

José E. Gallardo, Carlos Cotta

The FAR FROM MOST STRING PROBLEM (FFMSP) is a string selection problem. The objective is to find a string whose distance to other strings in a certain input set is above a given threshold for as many of those strings as possible. This problem has links with some tasks in computational biology and its resolution has been shown to be very hard. We propose a me

Shisheng Hu, Mushu Li, Jie Gao, Conghao Zhou

Device-edge collaboration on deep neural network (DNN) inference is a promising approach to efficiently utilizing network resources for supporting artificial intelligence of things (AIoT) applications. In this paper, we propose a novel digital twin (DT)-assisted approach to device-edge collaboration on DNN inference that determines whether and when to stop l

Cory Efird, Alex Murphy, Joel Zylberberg, Alona Fyshe

Prior work has offered evidence for functional localization in the brain; different anatomical regions preferentially activate for certain types of visual input. For example, the fusiform face area preferentially activates for visual stimuli that include a face. However, the spectrum of visual semantics is extensive, and only a few semantically-tuned patches

Harini Desiraju, Alexander R. Its, Andrei Prokhorov

We obtain rigorous large time asymptotics for the Landau-Lifshitz equation in the soliton free case by extending the nonlinear steepest descent method to genus 1 surfaces. The methods presented in this paper pave the way to a rigorous analysis of other integrable equations on the torus and enable asymptotic analysis on different regimes of the Landau-Lifshit

Jiaojiao Fan, Haotian Xue, Qinsheng Zhang, Yongxin Chen

There is a rapidly growing interest in controlling consistency across multiple generated images using diffusion models. Among various methods, recent works have found that simply manipulating attention modules by concatenating features from multiple reference images provides an efficient approach to enhancing consistency without fine-tuning. Despite its popu

Shaohua Dong, Yunhe Feng, Qing Yang, Yuewei Lin

High-performance Transformer trackers have shown excellent results, yet they often bear a heavy computational load. Observing that a smaller input can immediately and conveniently reduce computations without changing the model, an easy solution is to adopt the low-resolution input for efficient Transformer tracking. Albeit faster, this hurts tracking accurac

Jiahao Huang, Liutao Yang, Fanwen Wang, Yang Nan

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with globa

Kaustubh D. Dhole, Ramraj Chandradevan, Eugene Agichtein

Query Reformulation (QR) is a set of techniques used to transform a user's original search query to a text that better aligns with the user's intent and improves their search experience. Recently, zero-shot QR has been a promising approach due to its ability to exploit knowledge inherent in large language models. Inspired by the success of ensemble prompting

Mingi Jeong

Research on coastal regions traditionally involves methods like manual sampling, monitoring buoys, and remote sensing, but these methods face challenges in spatially and temporally diverse regions of interest. Autonomous surface vehicles (ASVs) with artificial intelligence (AI) are being explored, and recognized by the International Maritime Organization (IM

Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin

Graph diffusion models, dominant in graph generative modeling, remain underexplored for graph-to-graph translation tasks like chemical reaction prediction. We demonstrate that standard permutation equivariant denoisers face fundamental limitations in these tasks due to their inability to break symmetries in noisy inputs. To address this, we propose aligning

P. Suárez, F. Álcantara-Ávila, A. Miró, J. Rabault

This study presents novel drag reduction active-flow-control (AFC) strategies} for a three-dimensional cylinder immersed in a flow at a Reynolds number based on freestream velocity and cylinder diameter of $Re_D=3900$. The cylinder in this subcritical flow regime has been extensively studied in the literature and is considered a classic case of turbulent flo

Mehmet Fatih Ozkan, James Farrell, Marcello Telloni, Luis Mendez

In urban traffic environments, driver behaviors exhibit considerable diversity in vehicle operation, encompassing a range of acceleration and braking maneuvers as well as adherence to traffic regulations, such as speed limits. It is well-established that these intrinsic driving behaviors significantly influence vehicle energy consumption. Therefore, establis

Xinting Huang, Madhur Panwar, Navin Goyal, Michael Hahn

The inner workings of neural networks can be better understood if we can fully decipher the information encoded in neural activations. In this paper, we argue that this information is embodied by the subset of inputs that give rise to similar activations. We propose InversionView, which allows us to practically inspect this subset by sampling from a trained

Juan Pablo Borthagaray, Ricardo H. Nochetto, Abner J. Salgado, Céline Torres

We develop a monotone, two-scale discretization for a class of integrodifferential operators of order $2s$, $s \in (0,1)$. We apply it to develop numerical schemes, and derive pointwise convergence rates, for linear and obstacle problems governed by such operators. As applications of the monotonicity, we provide error estimates for free boundaries and a conv

Nikita Blinov, Stefania Gori, Nick Hamer

We analyze the unique capability of the DarkQuest proton beam-dump experiment at Fermilab to discover new light resonances decaying into photons. As an example model, we focus on muon-philic scalar particles that decay to photons. This is one of the few minimal models that can address the $(g-2)_\mu$ anomaly at low mass. These scalars can be copiously produc

Mathieu Dugré, Yohan Chatelain, Tristan Glatard

Magnetic Resonance Image (MRI) pre-processing is a critical step for neuroimaging analysis. However, the computational cost of MRI pre-processing pipelines is a major bottleneck for large cohort studies and some clinical applications. While High-Performance Computing (HPC) and, more recently, Deep Learning have been adopted to accelerate the computations, th

Esther Banaian, Kyle Celano, Megan Chang-Lee, Laura Colmenarejo

The operation of twinning a graph at a vertex was introduced by Foley, Ho\`ang, and Merkel (2019), who conjectured that twinning preserves $e$-positivity of the chromatic symmetric function. A counterexample to this conjecture was given by Li, Li, Wang, and Yang (2021). In this paper, we prove that $e$-positivity is preserved by the twinning operation on cyc

Finn Larsen, Siyul Lee

Supersymmetric AdS black hole solutions exist only when their angular momenta and charges satisfy a certain constraint that depends on the dimension. We show that these nonlinear relations on the conserved charges agree with a computation in the dual supersymmetric CFT in its free limit, with interactions entering only through a uniform rescaling of all char

O. Pavón-Torres, M. A. Agüero-Granados, M. E. Maguiña-Palma

Unlike expected from the Hodgkin-Huxley model predictions, in which there is annihilation once orthodromic and antidromic impulses collide, the Heimburg-Jackson model demonstrates that both impulses penetrate each other as it has been shown experimentally. These impulses can be depicted as low amplitude nonlinear excitations in a weakly dissipative soliton m

Nugzar Shavlakadze, Nana Odishelidze, Francisco Criado-Aldeanueva

The exact and approximate solutions of singular integro-differential equations relating to the problems of interaction of an elastic thin finite or infinite non-homogeneous patch with a plate are considered, provided that the materials of plate and patch possess the creep property. Using the method of orthogonal polynomials the problem is reduced to the infi

Binaya Bhandari, Debra Cunningham, Grace Morrell, SuHo Oh

We study the difference between the number of facets of the order polytope and the chain polytope of a poset. Hibi and Li classified posets where the gap is exactly zero. We describe the bounds on this gap using the new notion of crossing numbers, and then use this result to classify the posets where the gap is exactly one.

Oliver Pechenik, Matthew St. Denis

We prove a formula for the degrees of Ikeda and Naruse's $P$-Grothendieck polynomials using combinatorics of shifted tableaux. We show this formula can be used in conjunction with results of Hamaker, Marberg, and Pawlowski to obtain an upper bound on the Castelnuovo-Mumford regularity of certain Pfaffian varieties known as vexillary skew-symmetric matrix Sch

Tom Goertzen

The study of interlocking assemblies is an emerging field with applications in various disciplines. However, to this day, the mathematical treatment of these assemblies has been sparse. In this work, we develop a comprehensive mathematical theory for interlocking assemblies, providing a precise definition and a method for proving the interlocking property ba

Arianna Favale, Adrià Gómez-Valent, Marina Migliaccio

Looking at the well-known Hubble tension as a tension in the calibrators of the cosmic distance ladder, i.e. the absolute magnitude $M$ of standard candles such as supernovae of Type Ia (SNIa) and the standard ruler represented by the comoving sound horizon at the baryon-drag epoch, $r_d$, we propose a model-independent method to measure these distance calib

Oleksii Furman, Patryk Wielopolski, Łukasz Lenkiewicz, Jerzy Stefanowski

The growing complexity of AI systems has intensified the need for transparency through Explainable AI (XAI). Counterfactual explanations (CFs) offer actionable "what-if" scenarios on three levels: Local CFs providing instance-specific insights, Global CFs addressing broader trends, and Group-wise CFs (GWCFs) striking a balance and revealing patterns within c

Christian Janos Lebeda, Matthew Regehr, Gautam Kamath, Thomas Steinke

We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the privacy parameters to arbitrary precision but must be carefully applied. Our main contribution is to address two common points of confusion. First, some privacy accountants assume tha

Trishna Chakraborty, Erfan Shayegani, Zikui Cai, Nael Abu-Ghazaleh

Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing safety training techniques like Supervised Fine-tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF). While further SFT and RLHF-based safety training can be conducte

Kaif Hilman, Dominik Kirstein, Christian Kremer

In this article, we introduce and develop the notion of parametrised Poincar\'{e} duality in the formalism of parametrised higher category theory by Martini-Wolf, in part generalising Cnossen's theory of twisted ambidexterity to the nonpresentable setting. We prove several basechange results, allowing us to move between different coefficient categories and a

Patryk Wielopolski, Oleksii Furman, Jerzy Stefanowski, Maciej Zięba

We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization of plausibility within a unified framework. Compared to reference approaches, our method enforces plausibility

Erfei Yue

A family of disjoint pairs of finite sets $\mathcal{P}=\{(A_i,B_i)\mid i\in[m]\}$ is called a Bollob\'as system if $A_i\cap B_j\neq\emptyset$ for every $i\neq j$, and a skew Bollob\'as system if $A_i\cap B_j\neq\emptyset$ for every $i<j$. Bollob\'as proved that for a Bollob\'as system, the inequality \begin{equation*} \sum_{i=1}^m\binom{|A_i|+|B_i|}{|A_i|}^{

Xiaoou Cheng, Jonathan Weare

We quantify the efficiency of temporal difference (TD) learning over the direct, or Monte Carlo (MC), estimator for policy evaluation in reinforcement learning, with an emphasis on estimation of quantities related to rare events. Policy evaluation is complicated in the rare event setting by the long timescale of the event and by the need for \emph{relative a

Geraldo Xexéo, Filipe Braida, Marcus Parreiras, Paulo Xavier

Selecting language models in business contexts requires a careful analysis of the final financial benefits of the investment. However, the emphasis of academia and industry analysis of LLM is solely on performance. This work introduces a framework to evaluate LLMs, focusing on the earnings and return on investment aspects that should be taken into account in

Mobina Tavangarifard, Wendy Rodriguez Ovalle, Farshid Alambeigi

To address the challenges associated with shape sensing of continuum manipulators (CMs) using Fiber Bragg Grating (FBG) optical fibers, we feature a unique shape sensing assembly utilizing solely a single Optical Frequency Domain Reflectometry (OFDR) fiber attached to a flat nitinol wire (NiTi). Integrating this easy-to-manufacture unique sensor with a long

Bilal Karaman, Ilhan Basturk, Sezai Taskin, Ferdi Kara

Natural disasters can have catastrophic consequences, a poignant example is the series of $7.7$ and $7.6$ magnitude earthquakes that devastated T\"urkiye on February 6, 2023. To limit the damage, it is essential to maintain the communications infrastructure to ensure individuals impacted by the disaster can receive critical information. The disastrous earthq

Lisa Hartung, Oren Louidor, Tianqi Wu

We study the limiting extremal and cluster point processes of branching Brownian motion. The former records the heights of all extreme values of the process, while the latter records the relative heights of extreme values in a genealogical neighborhood of order unity around a local maximum thereof. For the extremal point process, we show that the mass of upp

Jocelyn Shen, Joel Mire, Hae Won Park, Cynthia Breazeal

Empathy serves as a cornerstone in enabling prosocial behaviors, and can be evoked through sharing of personal experiences in stories. While empathy is influenced by narrative content, intuitively, people respond to the way a story is told as well, through narrative style. Yet the relationship between empathy and narrative style is not fully understood. In t

Reid Buchanan

We study the Macaulay coefficients induced by the ideal and quotient segments of a degree-$\delta$ monomial in $n$ variables. We give explicit formulas for these coefficients and establish a duality between the two theories. Our main result is that the ideal and quotient coefficients form a set partition of $\{0,1,\ldots,n+\delta-2\}$.

Yusuf Roohani, Andrew Lee, Qian Huang, Jian Vora

Agents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities. In this paper, we introduce BioDiscoveryAgent, an agent that designs new experiments, reasons about their outcomes, and efficiently navigates the hypothesis space to reach desired solu

G. Ghirlanda, L. Nava, O. Salafia, F. Fiore

Gamma Ray Bursts (GRBs) bridge relativistic astrophysics and multi-messenger astronomy. Space-based gamma/X-ray wide field detectors have proven essential to detect and localize the highly variable GRB prompt emission, which is also a counterpart of gravitational wave events. We study the capabilities to detect long and short GRBs by the High Energy Rapid Mo

Teodor Knapik

Combinatorial generation of expander families and Lindenmayer-style development models are both parallel in nature. Both can be handled within proposed parallel graph grammar formalism. Their first-order properties can then be checked by encompassing the generated graph language into an appropriate automatic structure.

Sergio Rozada, Antonio G. Marques

The goal of reinforcement learning is estimating a policy that maps states to actions and maximizes the cumulative reward of a Markov Decision Process (MDP). This is oftentimes achieved by estimating first the optimal (reward) value function (VF) associated with each state-action pair. When the MDP has an infinite horizon, the optimal VFs and policies are st

Wenxiao Xiao, Hongfu Liu

Active learning strategically selects informative unlabeled data points and queries their ground truth labels for model training. The prevailing assumption underlying this machine learning paradigm is that acquiring these ground truth labels will optimally enhance model performance. However, this assumption may not always hold true or maximize learning capac

Sergio Rozada, Antonio G. Marques

Estimating a policy that maps states to actions is a central problem in reinforcement learning. Traditionally, policies are inferred from the so called value functions (VFs), but exact VF computation suffers from the curse of dimensionality. Policy gradient (PG) methods bypass this by learning directly a parametric stochastic policy. Typically, the parameter

Sergio Rozada, Antonio G. Marques

Most methods in reinforcement learning use a Policy Gradient (PG) approach to learn a parametric stochastic policy that maps states to actions. The standard approach is to implement such a mapping via a neural network (NN) whose parameters are optimized using stochastic gradient descent. However, PG methods are prone to large policy updates that can render l

Sergio Rozada, Antonio G. Marques

The (efficient and parsimonious) decomposition of higher-order tensors is a fundamental problem with numerous applications in a variety of fields. Several methods have been proposed in the literature to that end, with the Tucker and PARAFAC decompositions being the most prominent ones. Inspired by the latter, in this work we propose a multi-resolution low-ra

Alexander Wessel, Emin Semih Perdahcıoğlu, Ton van den Boogaard, Alexander Butz

Crystal plasticity finite element simulations are frequently employed to predict the plastic anisotropy of polycrystalline metals based on their crystallographic texture. In age-hardenable aluminium alloys, however, the texture-induced plastic anisotropy is known to affect by precipitation. This paper presents a new modelling approach to incorporate this eff

C. A. Argüelles, F. Halzen, N. Kurahashi

Over the past decade, neutrino astronomy has emerged as a new window into the extreme and hidden universe. Current-generation experiments have detected high-energy neutrinos of astrophysical origin and identified the first sources, opening the field to discovery. Looking ahead, the authors of this Perspective identify seven major open questions in neutrino a

Kevin Ludwick

An electron-multiplying charge-coupled device (EMCCD) is often used for taking images with space telescopes and other devices. Photons hit the pixels and photo-electrons are created, and these are multiplied via impact ionization as they travel through the gain register from one gain stage to the next. A high gain means a high multiplication factor, and this

Manuel D. Contreras, Francisco J. Cruz-Zamorano, Maria Kourou, Luis Rodríguez-Piazza

Let $\Omega$ be a regular Koenigs domain in the complex plane $\mathbb{C}$. We prove that the Hardy number of $\Omega$ is greater or equal to $1/2$. That is, every holomorphic function in the unit disc $f \colon \mathbb{D} \to \Omega$ belongs to the Hardy space $H^{p}(\mathbb{D})$ for all $p<1/2$.

Alexander M. Berezhkovskii, Dmitrii E. Makarov

A recent experimental study reports on measuring the temporal duration and the spatial extent of failed attempts to cross an activation barrier (i.e., "loops") for a folding transition in a single molecule and for a Brownian particle trapped within a bistable potential. Within the model of diffusive dynamics, however, both of these quantities are, on the ave

Rajneesh Kumar, Ramanujam Srinivasan, Debasish Chaudhuri

The tubulin-like protein FtsZ is crucial for cytokinesis in bacteria and many archaea, forming a ring-shaped structure called the Z-ring at the site of cell division. Despite extensive research, the self-assembly of Z-rings is not entirely understood. We propose a theoretical model based on FtsZ's known filament structures, treating them as semiflexible poly

Ju-Seung Byun, Andrew Perrault

Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional difficulty. Differing preferences can complicate the alignment process, and prediction errors in a trained

Shuyu Xiao

We investigate the abelianization of a Lie algebroid and provide a necessary and sufficient condition for its existence. We also study the abelianization of groupoids and provide sufficient conditions for its existence in the smooth category and a necessary and sufficient condition for its existence in the diffeological category.

Muhammad Asfar Saeed, Augustine O. Nwajana

This paper presents the design and characterization of a rectangular microstrip patch antenna array optimized for operation within the Ku-band frequency range. The antenna array is impedance-matched to 50 Ohms and utilizes a microstrip line feeding mechanism for excitation. The design maintains compact dimensions, with the overall antenna occupying an area o

Francesco Paissan, Luca Della Libera, Mirco Ravanelli, Cem Subakan

Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthiness of this technology. In this paper, we introduce LMAC-ZS (Listenable Maps for Audio Classifiers in the Zero-Shot context), which, to the best of our knowledge, is the first decoder-based post-hoc interpretation method f

Mikaël Pichot, Erik Séguin

We establish new metric characterizations for the norm (respectively, ultraweak) closure of the convex hull of a bounded set in an arbitrary $C^*$-algebra (respectively, von Neumann algebra), and provide applications of these results to the majorization theory.

Divyam Madaan, Taro Makino, Sumit Chopra, Kyunghyun Cho

Supervised multi-modal learning involves mapping multiple modalities to a target label. Previous studies in this field have concentrated on capturing in isolation either the inter-modality dependencies (the relationships between different modalities and the label) or the intra-modality dependencies (the relationships within a single modality and the label).

Dibyajyoti Chakraborty, Shivam Barwey, Hong Zhang, Romit Maulik

In this work, we provide a mathematical formulation for error propagation in flow trajectory prediction using data-driven turbulence closure modeling. Under the assumption that the predicted state of a large eddy simulation prediction must be close to that of a subsampled direct numerical simulation, we retrieve an upper bound for the prediction error when u

Enrico Bertuzzo, Yuber F. Perez-Gonzalez, Gabriel M. Salla, Renata Zukanovich Funchal

We examine how the existence of a population of primordial black holes (PBHs) influences cosmological gravitational particle production (CGPP) for spin-0 and spin-1 particles. In addition to the known effects of particle production and entropy dilution resulting from PBH evaporation, we find that the generation of dark matter (DM) through CGPP is profoundly

Francisco de Arriba-Pérez, Silvia García-Méndez, Francisco J. González-Castaño, Jaime González-González

Artificial Intelligence techniques such as Machine Learning (ML) have not been exploited to their maximum potential in the legal domain. This has been partially due to the insufficient explanations they provided about their decisions. Automatic expert systems with explanatory capabilities can be specially useful when legal practitioners search jurisprudence

Maria Korosteleva, Timur Levent Kesdogan, Fabian Kemper, Stephan Wenninger

Recent research interest in the learning-based processing of garments, from virtual fitting to generation and reconstruction, stumbles on a scarcity of high-quality public data in the domain. We contribute to resolving this need by presenting the first large-scale synthetic dataset of 3D made-to-measure garments with sewing patterns, as well as its generatio

D. J. Lennon, P. L. Dufton, J. I. Villaseñor, N. Langer

The spin evolution of stars in close binary systems can be strongly affected by tides. We investigate the rotational synchronisation of the stellar components for 69 SB1 systems and 14 SB2 B-type systems in the 30 Doradus region of the Large Magellanic Cloud using observations from the VFTS and BBC surveys. Their orbital periods range from a few to a few hun

Armin Moradi, Nicola Neophytou, Golnoosh Farnadi

Popularity bias in music recommendation systems -- where artists and tracks with the highest listen counts are recommended more often -- can also propagate biases along demographic and cultural axes. In this work, we identify these biases in recommendations for artists from underrepresented cultural groups in prototype-based matrix factorization methods. Unl

Susheela Sharma, Sarah Go, Zeynep Yakay, Yash Kulkarni

In this paper, with the goal of enhancing the minimally invasive spinal fixation procedure in osteoporotic patients, we propose a first-of-its-kind image-guided robotic framework for performing an autonomous and patient-specific procedure using a unique concentric tube steerable drilling robot (CT-SDR). Particularly, leveraging a CT-SDR, we introduce the con