April 2024 arXiv papers — page 84

Ahmed Maged, Salah Haridy, Herman Shen

As the manufacturing industry advances with sensor integration and automation, the opaque nature of deep learning models in machine learning poses a significant challenge for fault detection and diagnosis. And despite the related predictive insights Artificial Intelligence (AI) can deliver, advanced machine learning engines often remain a black box. This pap

Luca Maria Aiello, Anastassia Vybornova, Sándor Juhász, Michael Szell

Urban highways are common, especially in the US, making cities more car-centric. They promise the annihilation of distance but obstruct pedestrian mobility, thus playing a key role in limiting social interactions locally. Although this limiting role is widely acknowledged in urban studies, the quantitative relationship between urban highways and social ties

Soneya Binta Hossain, Nan Jiang, Qiang Zhou, Xiaopeng Li

Large language models (LLMs) have shown impressive effectiveness in various software engineering tasks, including automated program repair (APR). In this study, we take a deep dive into automated bug fixing utilizing LLMs. In contrast to many deep learning-based APR methods that assume known bug locations, rely on line-level localization tools, or address bu

Konstantin Batygin, Alessandro Morbidelli, Michael E. Brown, David Nesvorny

The solar system's distant reaches exhibit a wealth of anomalous dynamical structure, hinting at the presence of a yet-undetected, massive trans-Neptunian body - Planet 9. Previous analyses have shown how orbital evolution induced by this object can explain the origins of a broad assortment of exotic orbits, ranging from those characterized by high perihelia

Xi Chen, Sida Peng, Dongchen Yang, Yuan Liu

This paper aims to recover object materials from posed images captured under an unknown static lighting condition. Recent methods solve this task by optimizing material parameters through differentiable physically based rendering. However, due to the coupling between object geometry, materials, and environment lighting, there is inherent ambiguity during the

Juan Lanchares, Oscar Garnica, José L. Risco-Martín, J. Ignacio Hidalgo

The design of a cusp-like digital pulse shaper for particle energy measurements requires the definition of four parameters whose values are defined based on the nature of the shaper input signal (timing, noise, ...) provided by a sensor. However, after high doses of radiation, sensors degenerate and their output signals do not meet the original characteristi

Feng Yu, Teng Zhang, Gilad Lerman

We present the subspace-constrained Tyler's estimator (STE) designed for recovering a low-dimensional subspace within a dataset that may be highly corrupted with outliers. STE is a fusion of the Tyler's M-estimator (TME) and a variant of the fast median subspace. Our theoretical analysis suggests that, under a common inlier-outlier model, STE can effectively

Zezhong Fan, Xiaohan Li, Chenhao Fang, Topojoy Biswas

The rapid evolution of text-to-image diffusion models has opened the door of generative AI, enabling the translation of textual descriptions into visually compelling images with remarkable quality. However, a persistent challenge within this domain is the optimization of prompts to effectively convey abstract concepts into concrete objects. For example, text

Mary Aiyetigbo, Alexander Korte, Ethan Anderson, Reda Chalhoub

In this paper, we introduce a novel unsupervised network to denoise microscopy videos featured by image sequences captured by a fixed location microscopy camera. Specifically, we propose a DeepTemporal Interpolation method, leveraging a temporal signal filter integrated into the bottom CNN layers, to restore microscopy videos corrupted by unknown noise types

Xiangci Li, Jessica Ouyang

To convince readers of the novelty of their research paper, authors must perform a literature review and compose a coherent story that connects and relates prior works to the current work. This challenging nature of literature review writing makes automatic related work generation (RWG) academically and computationally interesting, and also makes it an excel

Shinnosuke Hattori, Qiang Zhu

In this study, we present a systematic computational investigation to analyze the long debated crystal stability of two well known aspirin polymorphs, labeled as Form I and Form II. Specifically, we developed a strategy to collect training configurations covering diverse interatomic interactions between representative functional groups in the aspirin crystal

Pablo. J. Bilbao, Robert J. Ewart, Francisco Assunçao, Thales Silva

We study how radiation reaction leads plasmas initially in kinetic equilibrium to develop features in momentum space, such as anisotropies and population inversion, resulting in a ring-shaped momentum distribution that can drive kinetic instabilities. We employ the Landau-Lifshiftz radiation reaction model for a plasma in a strong magnetic field, and we obta

Carlos Penarrubia, Carlos Garrido-Munoz, Jose J. Valero-Mas, Jorge Calvo-Zaragoza

Handwritten Text Recognition (HTR) is a relevant problem in computer vision, and implies unique challenges owing to its inherent variability and the rich contextualization required for its interpretation. Despite the success of Self-Supervised Learning (SSL) in computer vision, its application to HTR has been rather scattered, leaving key SSL methodologies u

Tula Masterman, Sandi Besen, Mason Sawtell, Alex Chao

This survey paper examines the recent advancements in AI agent implementations, with a focus on their ability to achieve complex goals that require enhanced reasoning, planning, and tool execution capabilities. The primary objectives of this work are to a) communicate the current capabilities and limitations of existing AI agent implementations, b) share ins

Raimon Luna, Daniela D. Doneva, José A. Font, Jr-Hua Lien

In this paper, we apply a novel approach based on physics-informed neural networks to the computation of quasinormal modes of black hole solutions in modified gravity. In particular, we focus on the case of Einstein-scalar-Gauss-Bonnet theory, with several choices of the coupling function between the scalar field and the Gauss-Bonnet invariant. This type of

Halvard Hummel

We consider the problem of fairly allocating a set of indivisible items under the criteria of the maximin share guarantee. Specifically, we study approximation of maximin share allocations under hereditary set system valuations, in which each valuation function is based on the independent sets of an underlying hereditary set systems. Using a lone divider app

Xinmei Huang, Haoyang Li, Jing Zhang, Xinxin Zhao

Database knob tuning is a significant challenge for database administrators, as it involves tuning a large number of configuration knobs with continuous or discrete values to achieve optimal database performance. Traditional methods, such as manual tuning or learning-based approaches, typically require numerous workload replays and are both time-consuming an

Mike Verostek, Casey W. Miller, Benjamin M. Zwickl

Studying the factors that influence the quality of physics PhD students' doctoral experiences, especially those that motivate them to stay or leave their programs, is critical for providing them with more holistic and equitable support. Prior literature on doctoral attrition has found that students with clear research interests who establish an advisor-advis

Xin Zhang, Shan Yu, Zhengyuan Zhu, Xin Wang

Identifying spatial heterogeneous patterns has attracted a surge of research interest in recent years, due to its important applications in various scientific and engineering fields. In practice the spatially heterogeneous components are often mixed with components which are spatially smooth, making the task of identifying the heterogeneous regions more chal

Ameesh Shah, Cameron Voloshin, Chenxi Yang, Abhinav Verma

Linear Temporal Logic (LTL) offers a precise means for constraining the behavior of reinforcement learning agents. However, in many settings where both satisfaction and optimality conditions are present, LTL is insufficient to capture both. Instead, LTL-constrained policy optimization, where the goal is to optimize a scalar reward under LTL constraints, is n

Amirhossein Nazerian, Joseph D Hart, Matteo Lodi, Francesco Sorrentino

Synchronization of coupled oscillators is a fundamental process in both natural and artificial networks. While much work has investigated the asymptotic stability of the synchronous solution, the fundamental question of the transient behavior toward synchronization has received far less attention. In this work, we present the transverse reactivity as a metri

Yiwen Tu, Pingbang Hu, Jiaqi Ma

Machine unlearning updates machine learning models to remove information from specific training samples, complying with data protection regulations that allow individuals to request the removal of their personal data. Despite the recent development of numerous unlearning algorithms, reliable evaluation of these algorithms remains an open research question. I

Fei Cui, Jiaojiao Fang, Xiaojiang Wu, Zelong Lai

Stochastic video prediction enables the consideration of uncertainty in future motion, thereby providing a better reflection of the dynamic nature of the environment. Stochastic video prediction methods based on image auto-regressive recurrent models need to feed their predictions back into the latent space. Conversely, the state-space models, which decouple

Hamidreza Golmohammadi, Saeid Alikhani, Nima Ghanbari, I. I. Takhonov

For a graph $G=(V,E)$, a set $D\subset V(G)$ is a strong dominating set of $G$, if for every vertex $x\in V (G)\setminus D$ there is a vertex $y\in D$ with $xy \in E(G)$ and $deg(x)\leq deg(y)$. A strong coalition consists of two disjoint sets of vertices $V_{1}$ and $V_{2}$, neither of which is a strong dominating set but whose union $V_{1}\cup V_{2}$, is a

Ting Mao, Xufeng Xu, Pamina M. Winkler, Cécilia Siri

Despite being used for decades as stabilizers, amino acids (AAs) remain mysterious components of many medical and biological formulations. Hypotheses on their role vary ranging from hydrotropic to protein-specific effects (stabilization against misfolding). Here, we deduce that AAs possess a new and broad colloidal property by finding that stabilizing effect

Tina M. Hayward, Robert Stewart, Rajesh Menon, Apratim Majumder

We demonstrate a high-NA (0.88), ultra-low-f-number (f/0.2714), multi-wavelength (480nm, 550nm and 650nm) multilevel diffractive MicroLens Array (MLA) using inverse design. Each microlens in the array is close-packed with diameter of 70 {\mu}m and focal length of only 19 {\mu}m in air. The MLA was patterned on one surface of a polymer film via UV casting, su

Carrie Filion, Rosemary F. G. Wyse, Hannah Richstein, Nitya Kallivayalil

The stellar initial mass function (IMF) describes the distribution of stellar masses that form in a given star formation event. The long main-sequence lifetimes of low-mass stars mean that the IMF in this regime (below $\sim 1 \rm{M}_\odot$) can be investigated through star counts. Ultrafaint dwarf galaxies are low-luminosity systems with ancient, metal-poor

Abdul Rahaman Shaikh, Rathin Adhikari

For understanding the hierarchies of fermion masses and mixing, we extend the Standard Model gauge group with \( U(1)_X \) and \( Z_2 \) symmetry. The field content of the Standard Model is augmented by three heavy right-handed neutrinos, two new scalar singlets, and a scalar doublet. \( U(1)_X \) charges of different fields are determined after satisfying a

Omar Elezabi, Marcos V. Conde, Radu Timofte

In modern smartphone cameras, the Image Signal Processor (ISP) is the core element that converts the RAW readings from the sensor into perceptually pleasant RGB images for the end users. The ISP is typically proprietary and handcrafted and consists of several blocks such as white balance, color correction, and tone mapping. Deep learning-based ISPs aim to tr

Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei, Nia Dickson

Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong relationship between model size, dataset size, and performance. However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower effi

Cameron Heather, Teeraparb Chantavat, Siri Chongchitnan, Joseph Silk

Data from the James Webb Space Telescope have revealed an intriguing population of bright galaxies at high redshifts. In this work, we use extreme-value statistics to calculate the distribution (in UV magnitude) of the brightest galaxies in the redshift range $9 \lesssim z \lesssim 16$. We combine the Generalised Extreme Value (GEV) approach with modelling o

Rahul Somasundaram, Cassandra L. Armstrong, Pablo Giuliani, Kyle Godbey

The validation, verification, and uncertainty quantification of computationally expensive theoretical models of quantum many-body systems require the construction of fast and accurate emulators. In this work, we develop emulators for auxiliary field diffusion Monte Carlo (AFDMC), a powerful many-body method for nuclear systems. We introduce a reduced-basis m

Kuan-Chieh Wang, Daniil Ostashev, Yuwei Fang, Sergey Tulyakov

We introduce a new architecture for personalization of text-to-image diffusion models, coined Mixture-of-Attention (MoA). Inspired by the Mixture-of-Experts mechanism utilized in large language models (LLMs), MoA distributes the generation workload between two attention pathways: a personalized branch and a non-personalized prior branch. MoA is designed to r

Irene Ayuso Ventura, Quentin Berger

In this article, we study concave recursions on trees, which appear widely in information theory through algorithms such as belief propagation, and in statistical mechanics through models on tree-like graphs, including the Ising model, percolation, and more generally, the random cluster model. These tree recursions can, in fact, be compared with non-linear c

Kemal Tezgin, Brean Maynard, Peter Schweitzer

A study of chiral-odd generalized parton distributions (GPDs) of the nucleon is presented in the bag model demonstrating that in this model all four chiral-odd GPDs are non-zero contrary to other claims in literature. The bag model results for the GPDs $H_T^q(x,\xi,t)$, $E_T^q(x,\xi,t)$, $\tilde{H}_T^q(x,\xi,t)$ agree with other models within a typical quark

Edmund Heng

We give a brief introduction to the relationship between Bridgeland stability conditions and the $K(\pi,1)$ conjecture for Artin groups. These notes have been written as pre-reading for the MFO mini-workshop 2405a: Artin groups meet triangulated categories.

Allen Herman

The Terwilliger algebras of asymmetric association schemes of rank $3$, whose nonidentity relations correspond to doubly regular tournaments, are shown to have thin irreducible modules, and to always be of dimension $4k+9$ for some positive integer $k$. It is determined that asymmetric rank $3$ association schemes of order up to $23$ are determined up to com

Anup B. Dixit, Sushant Kala

In this paper, we introduce the notion of asymptotically positive infinite extensions of $\mathbb{Q}$, in the spirit of the Tsfasman-Vl\u{a}du\c{t} theory of asymptotically exact families of number fields. For asymptotically positive extensions, we obtain lower bounds on the logarithmic Weil height, establishing the Bogomolov property for a wide range of inf

S. S. Baturin, A. V. Volotka

We develop a simple model and propose a scheme that allows the production of twisted atoms in free space using the absorption of twisted photons by a bound electron. We show that in the inelastic collision of a photon and an atom, the twisted state of the photon is transferred to the center-of-mass state, so that the projection of the orbital momentum of the

Taerim Yoon, Dongho Kang, Seungmin Kim, Jin Cheng

This work presents a motion retargeting approach for legged robots, aimed at transferring the dynamic and agile movements to robots from source motions. In particular, we guide the imitation learning procedures by transferring motions from source to target, effectively bridging the morphological disparities while ensuring the physical feasibility of the targ

Valérie Hayot-Sasson, Tristan Glatard

Neuroimaging open-data initiatives have led to increased availability of large scientific datasets. While these datasets are shifting the processing bottleneck from compute-intensive to data-intensive, current standardized analysis tools have yet to adopt strategies that mitigate the costs associated with large data transfers. A major challenge in adapting n

Bhavin A. Joshi, Louis-Gregory Strolger, Yossef Zenati

We present a numerical analysis investigating the reliability of type Ia supernova (SN~Ia) delay-time distributions recovered from individual host galaxy star-formation histories. We utilize star-formation histories of mock samples of galaxies generated from the IllustrisTNG simulation at two redshifts to recover delay-time distributions. The delay-time dist

Tarasha Khurana, Deva Ramanan

Our work explores the task of generating future sensor observations conditioned on the past. We are motivated by `predictive coding' concepts from neuroscience as well as robotic applications such as self-driving vehicles. Predictive video modeling is challenging because the future may be multi-modal and learning at scale remains computationally expensive fo

Zihao Li, Yucheng Shi, Zirui Liu, Fan Yang

The development of Large Language Models (LLMs) relies on extensive text corpora, which are often unevenly distributed across languages. This imbalance results in LLMs performing significantly better on high-resource languages like English, German, and French, while their capabilities in low-resource languages remain inadequate. Currently, there is a lack of

Anuj Abhishek, Thilo Strauss, Taufiquar Khan

In this article, we propose a non-parametric Bayesian level-set method for simultaneous reconstruction of two different piecewise constant coefficients in an elliptic partial differential equation. We show that the Bayesian formulation of the corresponding inverse problem is well-posed and that the posterior measure as a solution to the inverse problem satis

Ziyu Shu, Zhixin Pan

Deep image prior (DIP) proposed in recent research has revealed the inherent trait of convolutional neural networks (CNN) for capturing substantial low-level image statistics priors. This framework efficiently addresses the inverse problems in image processing and has induced extensive applications in various domains. However, as the whole algorithm is initi

Dipanjan Mitra, Mattia Negrello, Gianfranco De Zotti, Zhen-Yi Cai

We investigate the constraints provided by the Euclid space observatory on the physical properties of dusty star forming galaxies (DSFGs) at z>~1.5 detected in wide area sub millimetre surveys with Herschel. We adopt a physical model for the high z progenitors of spheroidal galaxies, which form the bulk of the DSFGs at z>~1.5. We improve the model by combini

Daniela E. Olave-Rojas, José A. Hernandez-Jimenez, Sergio Torres-Flores, Marcelo D. Mora

We study the properties of one of the most luminous hinge clumps, located on the compact group of galaxies NGC6845. Using IFS from GMOS/Gemini, complemented with archival MUSE data, we obtain oxygen abundances, ages, star formation rates, velocity fields and we also performed a single stellar populations modeling to understand the SFH of the hinge clump loca

Konstantin Isaev, Rinad Yulmukhametov

We introduce a continuous scale of Hilbert spaces of entire functions $P_\beta (D)$ for a bounded convex domain $D$ on the complex plane. For the parameters $\beta \in (\frac 12;\frac 32)$ a complete description of the spaces of Borel transforms of functions from $P_\beta (D)$ is given.

Chen-Kai Ren, Zhi-Wei Sun

Let $p>3$ be a prime, and let $d\in\mathbb Z$ with $p\nmid d$. For the determinants $$S_m(d,p)=\det\left[(i^2+dj^2)^{m}\right]_{1\leqslant i,j \leqslant (p-1)/2}\ \ \left(\frac{p-1}2\leqslant m\leqslant p-1\right),$$ Sun recently determined $S_m(d,p)$ modulo $p$ when $m\in\{p-2,p-3\}$ and $(\frac {-d}p)=-1$. In this paper, we obtain $S_{p-2}(d,p)$ modulo $p$

Danila Cherkashin, Emanuele Paolini, Yana Teplitskaya

The Euclidean Steiner problem is the problem of finding a set $St$, with the shortest length, such that $St \cup A$ is connected, where $A$ is a given set in a Euclidean space. The solutions $St$ to the Steiner problem will be called Steiner sets while the set $A$ will be called input. Since every Steiner set is acyclic we call it Steiner tree in the case wh

Bastián Bahamondes, Mathieu Dahan

We consider a zero-sum inspection game, in which a defender positions detectors across a critical system to detect multiple attacks caused by an attacker. We assume that detection is imperfect, and each detector location is associated with a probability of detecting attacks within its set of monitored system components. The objective of the defender (resp. a

Fabiola Antonietta Gerosa, Jérémie Bec, Héloïse Méheut, Anand Utsav Kapoor

Dust particles in protoplanetary disks, lacking support from pressure, rotate at velocities exceeding those of the surrounding gas. Consequently, they experience a head-wind from the gas that drives them toward the central star. Radial drift occurs on timescales much shorter than those inferred from disk observations or those required for dust to aggregate a

Pasin Manurangsi, Warut Suksompong

We investigate fairness in the allocation of indivisible items among groups of agents using the notion of maximin share (MMS). While previous work has shown that no nontrivial multiplicative MMS approximation can be guaranteed in this setting for general group sizes, we demonstrate that ordinal relaxations are much more useful. For example, we show that if $

Jia Li, Behrad Moeini, Shiva Nejati, Mehrdad Sabetzadeh

The Internet of Things connects a plethora of smart devices globally across various applications like smart cities, autonomous vehicles and health monitoring. Simulation plays a key role in the testing of IoT systems, noting that field testing of a complete IoT product may be infeasible or prohibitively expensive. This paper addresses a specific yet importan

Lewis Hill, Claudia Maraston, Daniel Thomas, Renbin Yan

Carbon- and Oxygen-rich stars populating the Thermally-Pulsing Asymptotic Giant Branch (TP-AGB) phase of stellar evolution are relevant contributors to the spectra of ~1 Gyr old populations. Atmosphere models for these types are uncertain, due to complex molecules and mass-loss effects. Empirical spectra are then crucial, but samples are small due to the sho

Huasheng Xie

In fusion plasmas, where electron temperatures $T_e$ range from keV to hundreds of keV, Bremsstrahlung radiation constitutes a significant energy loss mechanism. While various thermal average fitting formulas exist in the literature, their accuracy is limited, particularly for $T_e \leq 20$ keV with error $>10\%$. Additionally, non-relativistic fitting formu

Soyoung Yang, Won Ik Cho

In the era of rapid evolution of generative language models within the realm of natural language processing, there is an imperative call to revisit and reformulate evaluation methodologies, especially in the domain of aspect-based sentiment analysis (ABSA). This paper addresses the emerging challenges introduced by the generative paradigm, which has moderate

Md Athikul Islam, Edoardo Serra, Sushil Jajodia

Adversarial attacks pose significant challenges to deep neural networks (DNNs) such as Transformer models in natural language processing (NLP). This paper introduces a novel defense strategy, called GenFighter, which enhances adversarial robustness by learning and reasoning on the training classification distribution. GenFighter identifies potentially malici

Yu Zhong, Xiao Wu, Liang-Jian Deng, Zihan Cao

Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images. Recently, denoising diffusion probabilistic models have been gradually applied to visual tasks, enhancing controllable image generation through low-rank adaptation (LoRA)

Bela Bajnok

We provide the problems and their solutions to the 2020 USA Mathematical Olympiad.

Zhaopeng Peng, Xiaoliang Fan, Yufan Chen, Zheng Wang

Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs. Existing methods fine-tune FM by allocating sub-FM to clients in FL, however, leading to suboptimal performance due to insufficient tuning and inevitable error accumulations of gradients. In this pap

Jay Jorgenson, Anders Karlsson, Lejla Smajlović

Let $G$ be an infinite, edge- and vertex-weighted graph with certain reasonable restrictions. We construct the heat kernel of the associated Laplacian using an adaptation of the parametrix approach due to Minakshisundaram-Pleijel in the setting of Riemannian geometry. This is partly motivated by the wish to relate the heat kernels of a graph and a subgraph,

Harshay Shah, Andrew Ilyas, Aleksander Madry

How does the internal computation of a machine learning model transform inputs into predictions? In this paper, we introduce a task called component modeling that aims to address this question. The goal of component modeling is to decompose an ML model's prediction in terms of its components -- simple functions (e.g., convolution filters, attention heads) th

Pablo Soberón, Shira Zerbib

A theorem of Gr\"unbaum, which states that every $m$-polytope is a refinement of an $m$-simplex, implies the following generalization of Tverberg's theorem: if $f$ is a linear function from an $m$-dimensional polytope $P$ to $\mathbb{R}^d$ and $m \ge (d + 1)(r - 1)$, then there are $r$ pairwise disjoint faces of $P$ whose images intersect. Moreover, the topo

Harry Walsh, Ben Saunders, Richard Bowden

Sign languages, often categorised as low-resource languages, face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets. This paper introduces Select and Reorder (S&R), a novel approach that addresses data scarcity by breaking down the translation process into two distinct steps: Gloss Selection (GS) and

Costas Mavromatis, Petros Karypis, George Karypis

Fusing knowledge from multiple Large Language Models (LLMs) can combine their diverse strengths to achieve improved performance on a given task. However, current fusion approaches either rely on learning-based fusers that do not generalize to new LLMs, or do not take into account how well each LLM understands the input. In this work, we study LLM fusion at t

Z. Ye, S. M. Barnett, S. Franke-Arnold, J. B. Götte

Vector light beams, characterised by a spatially varying polarisation, can exhibit localised structures reminiscent of the Skyrmions familiar from the study of magnetic media. We present a theory of such Skyrmions within paraxial optics, exploiting mathematical analogies with the study of superfluids, especially the A phase of superfluid $\textrm{He}^3$. The

Ross G. Pinsky

A parking function on $[n]$ creates a permutation in $S_n$ via the order in which the $n$ cars appear in the $n$ parking spaces. Placing the uniform probability measure on the set of parking functions on $[n]$ induces a probability measure on $S_n$. We initiate a study of some properties of this distribution. Let $P_n^{\text{park}}$ denote this distribution

Hitoshi Mori, Takuya Nomoto, Ryotaro Arita, Elena R. Margine

In this study, we combine the ab initio Migdal-Eliashberg approach with the intermediate representation for the Green's function, enabling accurate and efficient calculations of the momentum-dependent superconducting gap function while fully considering the effect of the Coulomb retardation. Unlike the conventional scheme that relies on a uniform sampling ac

Guillermo L. Incatasciato, Pedro Sánchez Terraf

We present an exposition of the *Chain Bounding Lemma*, which is a common generalization of both Zorn's Lemma and the Bourbaki-Witt fixed point theorem. The proofs of these results through the use of Chain Bounding are amongst the simplest ones that we are aware of. As a by-product, we show that for every poset $P$ and function $f$ from the powerset of $P$ i

Reiner Czerwinski

The arithmetical hierarchy (AH) is similar to the polynomial hierarchy (PH). Unlike the PH, the AH does not collapse relative to any oracle. A language in the (k + 1)-st level of the AH is computable enumerable (c.e.) relative to the kth level. So, given an oracle in the kth level of the AH, we could use a black-box search to decide whether the input word is

Mengyang Li, Daniele Paolo Anderle, Hongxi Xing, Yuxiang Zhao

In this research, we conduct a global QCD analysis of fragmentation functions (FFs) for neutral pions ($\pi^0$), neutral kaons ($K_S^0$), and eta mesons ($\eta$), utilizing world data of single inclusive hadron production in $e^+e^-$ annihilation involving the most recent BESIII data with low collision energy, to test the operational region of QCD collinear

Jacob Fein-Ashley

We consider the Ornstein-Uhlenbeck (OU) process, a stochastic process widely used in finance, physics, and biology. Parameter estimation of the OU process is a challenging problem. Thus, we review traditional tracking methods and compare them with novel applications of deep learning to estimate the parameters of the OU process. We use a multi-layer perceptro

Zeyu Zhang, Xuyin Qi, Mingxi Chen, Guangxi Li

The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders. However, continuous monitoring of SaO2 is time-consuming and highly variable depending on patients' conditions. Recently, optical coherence tomography angiography (OCTA) has shown promising development in rapidly and effective

I Scott

Existentially closed groups are, informally, groups that contain solutions to every consistent finite system of equations and inequations. They were introduced in 1951 in an algebraic context and subsequent research elucidated deep connections with group theory and computability theory. We continue this investigation, with particular emphasis on illuminating

J. C. Costes, J. W. Xuan, A. Vigan, J. Wang

Context. High-resolution spectroscopy has the potential to drive a better understanding of the atmospheric composition, physics, and dynamics of young exoplanets and brown dwarfs, bringing clear insights into the formation channel of individual objects. Aims. Using the Keck Planet Imager and Characterizer (KPIC; R = 35,000), we aim to characterize a young br

Bijaya Acharya

Chiral effective field theory ($\chi$EFT) is a powerful tool for studying electroweak processes in nuclei. I discuss $\chi$EFT calculations of three key nuclear electroweak processes: primordial deuterium production, proton-proton fusion, and magnetic dipole excitations of $^{48}\mathrm{Ca}$. This article showcases $\chi$EFT's ability to quantify theory unce

Enzo Orsingher, Manfred Marvin Marchione

We study a planar random motion $\big(X(t),Y(t)\big)$ with orthogonal directions which can turn clockwise, counterclockwise and reverse its direction each with a different probability. The support of the process is given by a time-varying square and the singular distributions on the boundary and the diagonals of the square are obtained. In the interior of th

Madeleine Pollack, Ryan Piansky, Swati Gupta, Daniel Molzahn

Social vulnerability indices have increased traction for guiding infrastructure investment decisions to prioritize communities that need these investments most. One such plan is the Biden-Harris Justice40 initiative, which aims to guide equitable infrastructure investments by ensuring that disadvantaged communities defined by the Climate & Economic Justice S

Zhiyong Cheng, Jianhua Dong, Fan Liu, Lei Zhu

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various u

Marco Robbio, Michael G. Jabbour, Leonardo Novo, Nicolas J. Cerf

The quantum central limit theorem derived by Cushen and Hudson provides the foundations for understanding how subsystems of large bosonic systems evolving unitarily do reach equilibrium. It finds important applications in the context of quantum interferometry, for example, with photons. A practical feature of current photonic experiments, however, is that ph

Akash Unnikrishnan, Vinod Narayanan, Surya Pratap Vanka

Recently, meshless methods have become popular in numerically solving partial differential equations and have been employed to solve equations governing fluid flows, heat transfer, and species transport. In the present study, a numerical solver is developed employing the meshless framework to efficiently compute the hydrodynamic stability of fluid flows in c

Florian Johst, Jannik Rebmann, Hans Werners, Lars Klemeyer

Exciton-phonon coupling limits the homogeneous emission linewidth of nanocrystals. Hence, a full understanding of it is crucial. In this work, we statistically investigate exciton-phonon coupling by performing single-particle spectroscopy on Zn$_{1-x}$Cd$_{x}$Se/CdS dot-in-rod nanocrystals at cryogenic temperatures ($T\approx 10~\rm{K}$). In situ cation exch

Keenan Jones, Fatima Zahrah, Jason R. C. Nurse

Privacy is a human right. It ensures that individuals are free to engage in discussions, participate in groups, and form relationships online or offline without fear of their data being inappropriately harvested, analyzed, or otherwise used to harm them. Preserving privacy has emerged as a critical factor in research, particularly in the computational social

Ana O Henriques, Hugo Nicolau, Kyle Montague

This paper introduces a relational perspective on ethics within the context of Feminist Digital Civics and community-led design. Ethics work in HCI has primarily focused on prescriptive machine ethics and bioethics principles rather than people. In response, we advocate for a community-led, processual approach to ethics, acknowledging power dynamics and loca

Gianfranco Bertone

The formation and growth of black holes can strongly influence the distribution of dark matter around them. I discuss here the different types of dark matter overdensities around black holes, including dark matter cusps, spikes, mounds, crests, and gravitational atoms. I then review recent results on the evolution of a black holes binary in presence of dark

Stephen Cantrell, Mark Pollicott

Suppose we have two finitely supported, admissible, probability measures on a hyperbolic group $\Gamma$. In this article we prove that the corresponding two Green metrics satisfy a counting central limit theorem when we order the elements of $\Gamma$ according to one of the metrics. Our results also apply to various other metrics including length functions a

Manasi Muglikar, Siddharth Somasundaram, Akshat Dave, Edoardo Charbon

Traditional cameras face a trade-off between low-light performance and high-speed imaging: longer exposure times to capture sufficient light results in motion blur, whereas shorter exposures result in Poisson-corrupted noisy images. While burst photography techniques help mitigate this tradeoff, conventional cameras are fundamentally limited in their sensor

Julien D. Laurendeau, Aaron L. Sarvet, Mats J. Stensrud

Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, informative bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cover null effects, they can guide practical decisions based on formal decision theoretic criteria. Here we deriv

Yaqi Xie, Will Ma, Linwei Xin

There has been growing interest in applying reinforcement learning (RL) to inventory management, either by optimizing over temporal transitions or by learning directly from full historical demand trajectories. This contrasts sharply with classical data-driven approaches, which first estimate demand distributions from past data and then compute well-structure

Maria Tsedrik, Benjamin Bose, Pedro Carrilho, Alkistis Pourtsidou

We forecast constraints on minimal model-independent parametrisations of several Modified Gravity theories using mock Stage-IV cosmic shear data. We include nonlinear effects and screening, which ensures recovery of General Relativity on small scales. We introduce a power spectrum emulator to accelerate our analysis and evaluate the robustness of the growth

Sovanlal Mondal, Joe Rosenblatt, Máté Wierdl

For an ergodic map $T$ and a non-constant, real-valued $f \in L^1$, the ergodic averages $\mathbb{A}_N f(x) = \frac{1} {N} \sum_{n=1}^N f(T^n x)$ converge a.e., but the convergence is never monotone. Depending on particular properties of the function $f$, the averages $\mathbb{A}_N f(x)$ may or may not actually fluctuate around the mean value infinitely ofte

Eli Ben-Michael, Mitchell L. Doucette, Avi Feller, Alexander D. McCourt

Gun violence is a critical public health and safety concern in the United States. There is considerable variability in policy proposals meant to curb gun violence, ranging from increasing gun availability to deter potential assailants (e.g., concealed carry laws or arming school teachers) to restricting access to firearms (e.g., universal background checks o

Pan Guo, Hong-Bo Jin, Cong-Feng Qiao, Yue-Liang Wu

For space-based gravitational wave (GW) detection, the main noise source for massive black hole binaries (MBHBs) is attributed to approximately $10^7$ double white dwarf binaries in the foreground. For a GW source, the amplitude of the detector response, recorded by a space-based gravitational wave detector, exhibits a modulation effect with a year period wh

Ishay Haviv, Michal Parnas

A set family ${\cal F}$ is called intersecting if every two members of ${\cal F}$ intersect, and it is called uniform if all members of ${\cal F}$ share a common size. A uniform family ${\cal F} \subseteq \binom{[n]}{k}$ of $k$-subsets of $[n]$ is $\varepsilon$-far from intersecting if one has to remove more than $\varepsilon \cdot \binom{n}{k}$ of the sets

Di Fang, Jianfeng Lu, Yu Tong

Understanding the mixing of open quantum systems is a fundamental problem in physics and quantum information science. Existing approaches for estimating the mixing time often rely on the spectral gap estimation of the Lindbladian generator, which can be challenging to obtain in practice. We propose a novel theoretical framework to estimate the mixing time of

Yushuo Chen, Tianyi Tang, Erge Xiang, Linjiang Li

In real world, large language models (LLMs) can serve as the assistant to help users accomplish their jobs, and also support the development of advanced applications. For the wide application of LLMs, the inference efficiency is an essential concern, which has been widely studied in existing work, and numerous optimization algorithms and code libraries have

Pramod Padmanabhan, Vladimir Korepin

Bethe Ansatz was discoverd in 1932. Half a century later its algebraic structure was unearthed: Yang-Baxter equation was discovered, as well as its multidimensional generalizations [tetrahedron equation and $d$-simplex equations]. Here we describe a universal method to solve these equations using Clifford algebras. The Yang-Baxter equation ($d=2$), Zamalodch

Yue Zhou, Yada Zhu, Diego Antognini, Yoon Kim

This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models. We find that subtle alterations in the surface form can significantly impact the answer distribution and the solve rate, exposing the language model's lack of robustness and sensitivity to the surface form in reasoning through