February 2024 arXiv papers — page 108

Libang Mao, Ivan Toftul, Sivacarendran Balendhran, Mohammad Taha

Optical tweezers revolutionized the manipulation of nanoscale objects. Typically, tunable manipulations of optical tweezers rely on adjusting either the trapping laser beams or the optical environment surrounding the nanoparticles. We present a novel approach to achieve tunable and switchable trapping using nanoparticles made of a phase-change material (vana

Jack Miller, Patrick Gleeson, Charles O'Neill, Thang Bui

Neural networks sometimes exhibit grokking, a phenomenon where perfect or near-perfect performance is achieved on a validation set well after the same performance has been obtained on the corresponding training set. In this workshop paper, we introduce a robust technique for measuring grokking, based on fitting an appropriate functional form. We then use thi

Saeed Rasouli, Seyed Naser Hosseini, Amin Dehghani

This paper explores the concept of \'{e}tal\'{e} spaces associated with residuated lattices. Notions of bundles and \'{e}tal\'{e}s of residuated lattices over a given topological space are introduced and investigated. For a topological space $\mathscr{B}$, we establish that the category of \'{e}tal\'{e}s of residuated lattices over $\mathscr{B}$ with morphis

Sarah Post, Sébastien Bertrand

The goals of this paper are threefold. First, we provide a new ''universal'' definition for the Racah algebra of rank 2 as an extension of the rank-1 Racah algebra where the generators are indexed by subsets and any three disjoint indexing sets define a subalgebra isomorphic to the rank-1 case. With this definition, we explore some of the properties of the a

Kishansingh Rajput, Duong Binh Nguyen, Guoning Chen

Time-series data originate from various applications that describe specific observations or quantities of interest over time. Their analysis often involves the comparison across different time-series data sequences, which in turn requires the alignment of these sequences. Dynamic Time Warping (DTW) is the standard approach to achieve an optimal alignment bet

Berta Hudak

Let $R_n$ denote the KLR algebra of type $A^{(1)}_{e-1}$. Using the presentation of Specht modules given by Kleschev-Mathas-Ram, Loubert completely determined $\hom_{R_n}(S^\mu,S^\lambda)$ where $\mu$ is an arbitrary partition, $\lambda$ is a hook and $e\neq2$. In this paper, we investigate the same problem when $e=2$. First we give a complete description of

Masayuki Sawada, Takuya Ishihara, Daisuke Kurisu, Yasumasa Matsuda

We study a multivariate regression discontinuity design in which treatment is assigned by crossing a boundary in the space of multiple running variables. We document that the existing bandwidth selector is suboptimal for a multivariate regression discontinuity design when the distance to a boundary point is used for its running variable, and introduce a mult

Kaitong Sun, Yinghao Zhu, Shinichiro Yano, Qian Zhao

We have successfully grown large and good-quality single crystals of the La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-\delta}$ compound using the floating-zone method with laser diodes. We investigated the crystal quality, crystallography, chemical composition, magnetic properties and the oxidation state of Cr in the grown single crystals by employing a combinatio

Xinyun Chen, Ryan A. Chi, Xuezhi Wang, Denny Zhou

Large language models (LLMs) have accomplished remarkable reasoning performance in various domains. However, in the domain of reasoning tasks, we discover a frailty: LLMs are surprisingly brittle to the ordering of the premises, despite the fact that such ordering does not alter the underlying task. In particular, we observe that LLMs achieve the best perfor

Dae Hyun Kim, Hyungyu Shin, Shakhnozakhon Yadgarova, Jinho Son

Clients often partner with AI experts to develop AI applications tailored to their needs. In these partnerships, careful planning and clear communication are critical, as inaccurate or incomplete specifications can result in misaligned model characteristics, expensive reworks, and potential friction between collaborators. Unfortunately, given the complexity

Sebastian Bahamonde, Jorge Gigante Valcarcel

We analyse the stability of the vector and axial sectors of Poincar\'e gauge theory around general backgrounds in the presence of cubic order invariants defined from the curvature and torsion tensors, showing how the latter can in fact cancel out well-known instabilities arising from the quadratic curvature invariants of the theory and accordingly help in th

Yiming Bu, Jiayang Liu, Qinru Qiu

The Dynamic Vision Sensor (DVS) is an innovative technology that efficiently captures and encodes visual information in an event-driven manner. By combining it with event-driven neuromorphic processing, the sparsity in DVS camera output can result in high energy efficiency. However, similar to many embedded systems, the off-chip communication between the cam

Ling Sun, Bram J. J. Slagmolen, Jiayi Qin

Ultralight bosons with masses in the range from $\sim 10^{-22}$ eV/$c^2$ to $\sim 1$ eV/$c^2$, are well-motivated, wave-like dark matter candidates. Particles on the lower-mass end are less explored in experiments due to their vanishingly small mass and weak coupling to the Standard Model. We propose a sensor with dual torsion pendulums for the direct detect

Bohan Li, Yiming Liu, Xueyan Niu, Bo Bai

Diffusion models have achieved remarkable success in generating high quality image and video data. More recently, they have also been used for image compression with high perceptual quality. In this paper, we present a novel approach to extreme video compression leveraging the predictive power of diffusion-based generative models at the decoder. The conditio

Manju S Nair, Aparna Lakshmanan S, S Arumugam

Let $G = (V,E)$ be a graph of order $n$ with chromatic number $\chi(G) = k$, let $S \subset V$ and let $C_0$ be a $k$-coloring of the induced subgraph $G[S]$. The coloring $C_0$ is called an extendable coloring, if $C_0$ can be extended to a $k$-coloring of $G$ and it is a Sudoku coloring of $G$ if the extension is unique. The smallest order of such an induc

Desh Raj

Since the first speech recognition systems were built more than 30 years ago, improvement in voice technology has enabled applications such as smart assistants and automated customer support. However, conversation intelligence of the future requires recognizing free-flowing multi-party conversations, which is a crucial and challenging component that still re

Tong Zhao, Mingyu Ding, Wei Zhan, Masayoshi Tomizuka

Stereo matching plays a crucial role in 3D perception and scenario understanding. Despite the proliferation of promising methods, addressing texture-less and texture-repetitive conditions remains challenging due to the insufficient availability of rich geometric and semantic information. In this paper, we propose a lightweight volume refinement scheme to tac

Jiankun Hou, Jiefu Zhu, Ruixin Ma, Boyi Xue

Subwavelength gratings play a fundamental and pivotal role in numerous science and applications for wave manipulation, exhibiting distinctive features such as filtering, phase manipulation, and anti-reflection. However, conventional fabrication methods for ultrasmall periodic structures are constrained by the fundamental optical diffraction limit, making it

Arun Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan

Bandit convex optimization (BCO) is a general framework for online decision making under uncertainty. While tight regret bounds for general convex losses have been established, existing algorithms achieving these bounds have prohibitive computational costs for high dimensional data. In this paper, we propose a simple and practical BCO algorithm inspired by t

Ananda G. Maity, Joshua C. A. Casapao, Naphan Benchasattabuse, Michal Hajdušek

Estimating noise processes is an essential step for practical quantum information processing. Standard estimation tools require consuming valuable quantum resources. Here we ask the question of whether the noise affecting entangled states can be learned solely from the measurement statistics obtained during a distillation protocol. As a first step, we consid

Eren Metin Elci, Timothy M. Garoni

We study the autocorrelation time of the size of the cluster at the origin in discrete-time dynamical percolation. We focus on binary trees and high-dimensional tori, and show in both cases that this autocorrelation time is linear in the volume in the subcritical regime, but strictly sublinear in the volume at criticality. This establishes rigorously that th

Ankur, Ram Jiwari, Akil Narayan

We present a novel and comparative analysis of finite element discretizations for a nonlinear Rosenau-Burgers model including a biharmonic term. We analyze both continuous and mixed finite element approaches, providing stability, existence, and uniqueness statements of the corresponding variational methods. We also obtain optimal error estimates of the semid

Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel

Reinforcement Learning from Human Feedback (RLHF) aligns language models to human preferences by employing a singular reward model derived from preference data. However, such an approach overlooks the rich diversity of human preferences inherent in data collected from multiple users. In this work, we first derive an impossibility result of alignment with sin

Kohei Fukai

In this thesis, I study the local charges $\{Q_k\}$ in the one-dimensional Hubbard model, which is an integrable system used for theoretical studies of non-perturbative effects in strongly correlated electron systems. I obtained their explicit expressions in a closed form. An expression of the $k$-local charge $Q_k$ for $k > 5$, where $k$ denotes the support

Varun Ramani, Hossein Khayami, Yang Bai, Nakul Garg

This paper presents a novel approach for predicting human poses using IMU data, diverging from previous studies such as DIP-IMU, IMUPoser, and TransPose, which use up to 6 IMUs in conjunction with bidirectional RNNs. We introduce two main innovations: a data-driven strategy for optimal IMU placement and a transformer-based model architecture for time series

Juanhui Li, Haoyu Han, Zhikai Chen, Harry Shomer

Session-based recommendation has gained increasing attention in recent years, with its aim to offer tailored suggestions based on users' historical behaviors within sessions. To advance this field, a variety of methods have been developed, with ID-based approaches typically demonstrating promising performance. However, these methods often face challenges wit

Tao Xiao, Zhili Zeng, Dong Wang, Hideaki Hata

Self-Admitted Technical Debt (SATD) annotates development decisions that intentionally exchange long-term software artifact quality for short-term goals. Recent work explores the existence of SATD clones (duplicate or near duplicate SATD comments) in source code. Cloning of SATD in build systems (e.g., CMake and Maven) may propagate suboptimal design choices

Alessandro Achille, Greg Ver Steeg, Tian Yu Liu, Matthew Trager

Quantifying the degree of similarity between images is a key copyright issue for image-based machine learning. In legal doctrine however, determining the degree of similarity between works requires subjective analysis, and fact-finders (judges and juries) can demonstrate considerable variability in these subjective judgement calls. Images that are structural

Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye

Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive applications, such as real-time financial fraud detection. To address this limitation, recent studies have proposed distilling knowledge from t

Xiang Li, Linfeng Wen, Minxian Xu, Kejiang Ye

Container orchestration technologies are widely employed in cloud computing, facilitating the co-location of online and offline services on the same infrastructure. Online services demand rapid responsiveness and high availability, whereas offline services require extensive computational resources. However, this mixed deployment can lead to resource contenti

Shen Gao, Peihao Dong, Zhiwen Pan, Xiaohu You

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) systems introduce the much higher channel dimensionality and incur the additional near-field propagation effect, aggravating the computation load and the difficulty to acquire the prior knowledge for channel estimation. In this article, an XL-MIMO channel network (XLCNet) is developed to

Norman H. Christ, Xu Feng, Luchang Jin, Christopher T. Sachrajda

We describe a first-principles method to apply lattice QCD to compute the order $\alpha_{\mathrm{EM}}$ corrections to $K\to\pi\ell\nu_\ell$ decay. This method formulates the calculation in infinite volume with the conventional infinite-volume, continuum treatment of QED. Infinite volume reconstruction is used to replace the QCD components of the calculation

L. Xayavong, N. A. Smirnova, Y. Lim

The long-standing discrepancy between shell-model and experimental values of the mirror asymmetry in Gamow-Teller transition rates for $^{28}$Al($\beta^-$)$^{28m}$Si and $^{28}$P($\beta^+$)$^{28m}$Si is partially resolved by integrating the $p$-orbital contribution into the radial mismatch correction term. This approach offers a potential estimate of the rad

Yaowei Xie, Quansen Jiu, Jitao Liu

Whether the global existence and uniqueness of strong solutions of $n$-dimensional incompressible magnetohydrodynamic (MHD for short) equations with only kinematic viscosity or magnetic diffusion holds true or not remains an outstanding open problem. In recent years, more attention has been paid to the case when the magnetic field close to an equilibrium sta

Xiaoqi Ma, Jin Zhang, Xinyi Feng, Chunxiao Zhang

This paper investigates the supercloseness of a singularly perturbed convection diffusion problem using the direct discontinuous Galerkin (DDG) method on a Shishkin mesh. The main technical difficulties lie in controlling the diffusion term inside the layer, the convection term outside the layer, and the inter element jump term caused by the discontinuity of

Yurina Nakazato, Daniel Ceverino, Naoki Yoshida

Recent JWST observations with superb angular resolution have revealed the existence of clumpy galaxies at high redshift through the detection of rest-frame optical emission lines. We use the FirstLight simulation to study the properties of (sub-)galactic clumps that are bright in [OIII] 5007$\mathrm{\mathring{A}}$ line with flux greater than $\sim 10^{-18} \

Shiqi Peng, Bolin Lai, Guangyu Yao, Xiaoyun Zhang

Spinal metastasis is the most common disease in bone metastasis and may cause pain, instability and neurological injuries. Early detection of spinal metastasis is critical for accurate staging and optimal treatment. The diagnosis is usually facilitated with Computed Tomography (CT) scans, which requires considerable efforts from well-trained radiologists. In

Huangxin Chen, Piaopiao Dong, Shuyu Sun, Zixuan Wang

In this work we present an enriched Petrov-Galerkin (EPG) method for the simulation of the Darcy flow in porous media. The new method enriches the approximation trial space of the conforming continuous Galerkin (CG) method with bubble functions and enriches the approximation test space of the CG method with piecewise constant functions, and it does not requi

Xiaolin Zhang, Kailun Qin, Shipei Qu, Tengfei Wang

Remote Attestation (RA) enables the integrity and authenticity of applications in Trusted Execution Environment (TEE) to be verified. Existing TEE RA designs employ a centralized trust model where they rely on a single provisioned secret key and a centralized verifier to establish trust for remote parties. This model is however brittle and can be untrusted u

Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye

Transfer learning aims to enhance performance on a target task by using knowledge from related tasks. However, when the source and target tasks are not closely aligned, it can lead to reduced performance, known as negative transfer. Unlike in image or text data, we find that negative transfer could commonly occur in graph-structured data, even when source an

Aashish Sah, Suman Kundu, Heikki Suominen, Qiming Chen

Achieving fast gates and long coherence times for superconducting qubits presents challenges, typically requiring either a stronger coupling of the drive line or an excessively strong microwave signal to the qubit. To address this, we introduce on-chip filters of the qubit drive exhibiting a stopband at the qubit frequency, thus enabling long coherence times

Takeshi Kato

Based on interactions between individuals and others and references to social norms, this study reveals the impact of heterogeneity in time preference on wealth distribution and inequality. We present a novel approach that connects the interactions between microeconomic agents that generate heterogeneity to the dynamic equations for capital and consumption i

Vasilii Chsherbakov, Ilia Karpov

Inflation is one of the most important macroeconomic indicators that have a great impact on the population of any country and region. Inflation is influenced by range of factors, one of which is inflation expectations. Many central banks take this factor into consideration while implementing monetary policy within the inflation targeting regime. Nowadays, a

Fei Ma, Sipei Zhao, Ian S. Burnett

Sound field reconstruction (SFR) augments the information of a sound field captured by a microphone array. Conventional SFR methods using basis function decomposition are straightforward and computationally efficient, but may require more microphones than needed to measure the sound field. Recent studies show that pure data-driven and learning-based methods

Benjamin Metha, Michele Trenti, Andrew Battisti, Tingjin Chu

Geostatistical methods are powerful tools for understanding the spatial structure of the metallicity distribution of galaxies, and enable construction of accurate predictive models of the 2D metallicity distribution. However, so far these methods have only been applied to very high spatial resolution metallicity maps, leaving it uncertain if they will work o

Xinjie Liu, Lasse Peters, Javier Alonso-Mora, Ufuk Topcu

When multiple agents interact in a common environment, each agent's actions impact others' future decisions, and noncooperative dynamic games naturally capture this coupling. In interactive motion planning, however, agents typically do not have access to a complete model of the game, e.g., due to unknown objectives of other players. Therefore, we consider th

D. Matakias, G. Carini, H. Chen, M. Dabrowski

ALFE2 is an ATLAS Liquid Argon Calorimeter (LAr) Front-End ASIC designed for the HL-LHC upgrade. ALFE2 comprises four channels of pre-amplifiers and CR-(RC)2 shapers with adjustable input impedance. ALFE2 features two separate gain outputs to provide 16-bit dynamic-range coverage and an optimum resolution. ALFE2 is characterized using a Front-End Test Board

Jiawei Peng, Hong Liu, Zhenggang Lan

The comprehension of nonadiabatic dynamics in polyatomic systems relies heavily on the simultaneous advancements in theoretical and experimental domains. The gas-phase electron diffraction (GUED) technique has attracted widespread attention as a promising tool for observing the photochemical and photophysical features at all-atomic level with high temporal a

Shawn M. P. McSorley, Benjamin P. Dix-Matthews, Alex M. Frost, Ayden S. McCann

The comparison of optical reference frequency signals over free-space optical links is limited by the relative motion between local and remote sites. For ground to low earth orbit comparison, the expected Doppler shift and Doppler rate typically reach 4 GHz at 100 MHz/s, which prevents the narrow-band detection required to compare optical frequencies at the

Ruchao Fan, Natarajan Balaji Shanka, Abeer Alwan

Non-autoregressive automatic speech recognition (NASR) models have gained attention due to their parallelism and fast inference. The encoder-based NASR, e.g. connectionist temporal classification (CTC), can be initialized from the speech foundation models (SFM) but does not account for any dependencies among intermediate tokens. The encoder-decoder-based NAS

Adam Seewald, Marvin Chancán, Connor M. McCann, Seonghoon Noh

This systems paper presents the implementation and design of RB5, a wheeled robot for autonomous long-term exploration with fewer and cheaper sensors. Requiring just an RGB-D camera and low-power computing hardware, the system consists of an experimental platform with rocker-bogie suspension. It operates in unknown and GPS-denied environments and on indoor a

Lucas Johns

Collective neutrino flavor instabilities are believed to be prevalent in core-collapse supernovae and neutron star mergers. This work establishes two points related to instability, both in the spirit of developing a more fundamental understanding of collective flavor dynamics. First, a conservation law related to lepton number implies that spectral crossings

Lucas Schneider, Christian von Bredow, Howon Kim, Khai That Ton

Adding superconducting (SC) electron pairing via the proximity effect to pristinely non-superconducting materials can lead to a variety of interesting physical phenomena. Particular interest has recently focused on inducing SC into two-dimensional surface states (SSs), potentially also combined with non-trivial topology. We study the mechanism of proximity-i

H. Okabe, M. Hiraishi, A. Koda, Y. Matsushita

Hydrogen dynamics in the nanoscale region of VO$_{2}$ was investigated by muon spin rotation/relaxation ($\mu $SR) technique. Positively charged muon acts as a light radioisotope of proton and can be used as a probe to explore the inside of materials from an atomic perspective. By analyzing the muon hopping rate and the spatial distribution of the $^{51}$V n

Yilin Bi, Xinshan Jiao, Yan-Li Lee, Tao Zhou

Link prediction is a paradigmatic and challenging problem in network science, which aims to predict missing links, future links and temporal links based on known topology. Along with the increasing number of link prediction algorithms, a critical yet previously ignored risk is that the evaluation metrics for algorithm performance are usually chosen at will.

Shiqi Peng, Bolin Lai, Guangyu Yao, Xiaoyun Zhang

Vertebral body (VB) segmentation is an important preliminary step towards medical visual diagnosis for spinal diseases. However, most previous works require pixel/voxel-wise strong supervisions, which is expensive, tedious and time-consuming for experts to annotate. In this paper, we propose a Weakly supervised Iterative Spinal Segmentation (WISS) method lev

Daniel T. Yimam, Minpeng Liang, Jianting Ye, Bart J. Kooi

The switching and optical properties of phase-change thin films are actively investigated for future smart optical devices. The possibility of having more than one stable state, the large optical contrast between phases, and the fast and reversible switching are some attractive properties driving the research interest. Optical devices based on phase change a

William Graham Hoover, Carol Griswold Hoover

"Pedagogical derivations for Nos\'e's dynamics can be developed in two different ways, (i) by starting with a temperature-dependent Hamiltonian in which the variable $s$ scales the time or the mass, or (ii) by requiring that the equations of motion generate the canonical distribution including a Gaussian distribution in the friction coefficient $\zeta$. Nos\

Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan

To create early warning capabilities for upcoming Space Weather disturbances, we have selected a dataset of 61 emerging active regions, which allows us to identify characteristic features in the evolution of acoustic power density to predict continuum intensity emergence. For our study, we have utilized Doppler shift and continuum intensity observations from

T. Ghosh, Sangeeta, B. Maheshwari, G. Saxena

Spin and parity dependent nuclear level densities (NLDs) are obtained for configuration interaction shell model using a numerically efficient spectral distribution method. The calculations are performed for $^{24}$Na, $^{25,26,27}$Mg nuclei using full $sd$-$pf$ model space that incorporates the cross-shell excitations from $sd$ to $pf$-shell. The NLDs so obt

Hong Zeng, Zhao-Qin He, Yun-Ru Fan, Yue Luo

Integrated quantum light source is increasingly desirable in large-scale quantum information processing.~Despite recent remarkable advances, new material platform is constantly being explored for the fully on-chip integration of quantum light generation, active and passive manipulation, and detection. Here, for the first time, we demonstrate a gallium nitrid

Yu-Shan Wang, Zhen-Ming Xu, Bin Wu

The phase transition has always been a major focus in the study of black hole thermodynamics. This study employs the Kramer escape rate from stochastic processes to investigate the first-order phase transition strength between the large and small black hole states. The results indicate that the phase transition of the third-order Lovelock black holes exhibit

Zhanqiang Bai, Markus Hunziker, Xun Xie, Roger Zierau

We prove a simple formula that calculates the associated variety of a highest weight Harish-Chandra module directly from its highest weight. We also give a formula for the Gelfand--Kirillov dimension of highest weight Harish-Chandra module which is uniform across Cartan types and is valid for arbitrary infinitesimal character.

Shun-Cai Zhao, Qi-Xuan Wu, Ai-Ling Gong

This paper presents a algebraic analysis of electromagnetic chirality-induced negative refractive index in a four-level atomic medium. According to analyze mathematically its argument of the complex refractive index for one circular polarization, it found that the negative refractive index without simultaneously negative permittivity and permeability can be

Sindhu Jammi, Andrew R. Ferdinand, Zheng Luo, Zachary L. Newman

We demonstrate the formation of a complex, multi-wavelength, three-dimensional laser beam configuration with integrated metasurface optics. Our experiments support the development of a compact Sr optical-lattice clock, which leverages magneto-optical trapping on atomic transitions at 461 nm and 689 nm without bulk free-space optics. We integrate six, mm-scal

Vaidish Sumaria, Takat B. Rawal, Young Feng Li, David Sommer

The photoconversion of CO$_2$ to hydrocarbons is a sustainable route to its transformation into value-added compounds and, thereby, crucial to mitigating the energy and climate crises. CuPt nanoparticles on TiO$_2$ surfaces have been reported to show promising photoconversion efficiency. For further progress, a mechanistic understanding of the catalytic prop

Nicholas Loutrel, Sajal Mukherjee, Andrea Maselli, Paolo Pani

We develop a fully analytical waveform model for precessing binaries with arbitrary spin vectors using post-Newtonian~(PN) theory in the extreme mass-ratio limit and a hierarchical multi-scale analysis. The analytical model incorporates leading PN order spin precession dynamics from spin-orbit, spin-spin, and quadrupole-monopole couplings, and 2PN order diss

Ge Shi, Zhili Yang

Dynamic scene understanding is one of the most conspicuous field of interest among computer vision community. In order to enhance dynamic scene understanding, pixel-wise segmentation with neural networks is widely accepted. The latest researches on pixel-wise segmentation combined semantic and motion information and produced good performance. In this work, w

Carlos Kenig, Zihui Zhao

Let $u$ be a harmonic function in a $C^1$ domain $D\subset \mathbb{R}^d$, which vanishes on an open subset of the boundary. In this note we study its critical set $\{x \in \overline{D}: \nabla u(x) = 0 \}$. When $D$ is a $C^{1,\alpha}$ domain for some $\alpha \in (0,1]$, we give an upper bound on the $(d-2)$-dimensional Hausdorff measure of the critical set

Fan Zhang, Lee Lindblom

The effectiveness of the hyperbolic relaxation method for solving the Einstein constraint equations numerically is studied here on a variety of compact orientable three-manifolds. Convergent numerical solutions are found using this method on manifolds admitting negative Ricci scalar curvature metrics, i.e. those from the $H^3$ and the $H^2\times S^1$ geometr

Yiqi Liu, Francesca Molinari

Algorithms are increasingly used to aid with high-stakes decision making. Yet, their predictive ability frequently exhibits systematic variation across population subgroups. To assess the trade-off between fairness and accuracy using finite data, we propose a debiased machine learning estimator for the fairness-accuracy frontier introduced by Liang, Lu, Mu,

Shoma Matsui, Kai Cai, Karen Rudie

In this paper, we study a security problem of protecting secrets in distributed systems. Specifically, we employ discrete-event systems to describe the structure and behaviour of distributed systems, in which global secret information is separated into pieces and stored in local component agents. The goal is to prevent such secrets from being exposed to intr

Renaud Alie, David A. Stephens, Alexandra M. Schmidt

In the last two decades, the linear model of coregionalization (LMC) has been widely used to model multivariate spatial processes. However, it can be a challenging task to conduct likelihood-based inference for such models because of the cubic cost associated with Gaussian likelihood evaluations. Starting from an analogy with matrix normal models, we propose

Miguel Fainstein, Viviana Siless, Emmanuel Iarussi

In recent years, there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. However, UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients, generally resulting in fragmented and discont

Yang Qian, Yinan Sun, Ali Kargarandehkordi, Parnian Azizian

The increasing variety and quantity of tagged multimedia content on a variety of online platforms offer a unique opportunity to advance the field of human action recognition. In this study, we utilize 283,582 unique, unlabeled TikTok video clips, categorized into 386 hashtags, to train a domain-specific foundation model for action recognition. We employ Vide

Chenxi Lin, Jiayu Ren, Guoxiu He, Zhuoren Jiang

While large language models (LLMs) excel at understanding and generating plain text, they are not tailored to handle hierarchical text structures or directly predict task-specific properties such as text rating. In fact, selectively and repeatedly grasping the hierarchical structure of large-scale text is pivotal for deciphering its essence. To this end, we

Jianing Dong, Raymond K. W. Wong, Kwun Chuen Gary Chan

Balancing weights have been widely applied to single or monotone missingness due to empirical advantages over likelihood-based methods and inverse probability weighting approaches. This paper considers non-monotone missing data under the complete-case missing variable condition (CCMV), a case of missing not at random (MNAR). Using relationships between each

Jing Xu, Changchun Zhong, Shihao Zhuang, Chen Qian

Cavity magnonics is an emerging research area focusing on the coupling between magnons and photons. Despite its great potential for coherent information processing, it has been long restricted by the narrow interaction bandwidth. In this work, we theoretically propose and experimentally demonstrate a novel approach to achieve broadband photon-magnon coupling

Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural cho

Ignacio Huerta, Pablo Monzón, Gonzalo Robledo

We propose a new controllability property for linear time varying control systems in finite dimension: the nonuniform complete controllability, which is halfway between the classical Kalman's properties of complete controllability and uniform complete controllability. This new concept is described in terms of two gramian inequalities, which have a strong rel

Stefan Erben, Andreas Waldis

The long-standing problem of spam and fraudulent messages in the comment sections of Instagram pages in the financial sector claims new victims every day. Instagram's current spam filter proves inadequate, and existing research approaches are primarily confined to theoretical concepts. Practical implementations with evaluated results are missing. To solve th

Antonio De Felice, Shinji Tsujikawa

The axion-photon coupling allows the existence of a magnetically and electrically charged black hole (BH) solution endowed with a pseudo-scalar hair. For the Reissner-Nordstrom BH with a given total charge and mass, it is known that the quasinormal modes (QNMs) are independent of the mixture between the magnetic and electric charges due to the presence of el

Arash Asgharivaskasi, Nikolay Atanasov

This work develops a distributed optimization algorithm for multi-robot 3-D semantic mapping using streaming range and visual observations and single-hop communication. Our approach relies on gradient-based optimization of the observation log-likelihood of each robot subject to a map consensus constraint to build a common multi-class map of the environment.

Ben Cameron, Jeannette Janssen, Rogers Matthew, Zhiyuan Zhang

We give an algorithm that finds a zero forcing set which approximates the optimal size by a factor of $\text{pw}(G)+1$, where $\text{pw}(G)$ is the pathwidth of $G$. Starting from a path decomposition, the algorithm runs in $O(nm)$ time, where $n$ and $m$ are the order and size of the graph, respectively. As a corollary, we obtain a new upper bound on the ze

J. Aalbers, D. S. Akerib, A. K. Al Musalhi, C. S. Amarasinghe

Searches for dark matter with liquid xenon time projection chamber experiments have traditionally focused on the region of the parameter space that is characteristic of weakly interacting massive particles, ranging from a few GeV/$c^2$ to a few TeV/$c^2$. Models of dark matter with a mass much heavier than this are well motivated by early production mechanis

S Ashwin Hebbar, Sravan Kumar Ankireddy, Hyeji Kim, Sewoong Oh

Progress in designing channel codes has been driven by human ingenuity and, fittingly, has been sporadic. Polar codes, developed on the foundation of Arikan's polarization kernel, represent the latest breakthrough in coding theory and have emerged as the state-of-the-art error-correction code for short-to-medium block length regimes. In an effort to automate

Roberto Perera, Vinamra Agrawal

Mesh-based Graph Neural Networks (GNNs) have recently shown capabilities to simulate complex multiphysics problems with accelerated performance times. However, mesh-based GNNs require a large number of message-passing (MP) steps and suffer from over-smoothing for problems involving very fine mesh. In this work, we develop a multiscale mesh-based GNN framewor

Oguzhan Gungordu, A. Murat Tekalp

Effective compression of 360$^\circ$ images, also referred to as omnidirectional images (ODIs), is of high interest for various virtual reality (VR) and related applications. 2D image compression methods ignore the equator-biased nature of ODIs and fail to address oversampling near the poles, leading to inefficient compression when applied to ODI. We present

Younghan Bae, Davesh Maulik, Junliang Shen, Qizheng Yin

Motivated by the Beauville decomposition of an abelian scheme and the "Perverse = Chern" phenomenon for a compactified Jacobian fibration, we study in this paper splittings of the perverse filtration for compactified Jacobian fibrations. On the one hand, we prove for the Beauville-Mukai system associated with an irreducible curve class on a $K3$ surface the

Saak Gabriyelyan

For $1\leq p\leq q\leq\infty$ and a locally convex space $E$, we introduce and study the $(V^\ast)$ subsets of order $(p,q)$ of $E$ and the $(V)$ subsets of order $(p,q)$ of the topological dual $E'$ of $E$. Using these sets we define and study the (sequential) Pe{\l}czy\'{n}ski's property $V^\ast$ of order $(p,q)$, the (sequential) Pe{\l}czy\'{n}ski's prope

Yingpeng Du, Ziyan Wang, Zhu Sun, Haoyan Chua

In recent years, efforts have been made to use text information for better user profiling and item characterization in recommendations. However, text information can sometimes be of low quality, hindering its effectiveness for real-world applications. With knowledge and reasoning capabilities capsuled in Large Language Models (LLMs), utilizing LLMs emerges a

Gautier Hamon, Mayalen Etcheverry, Bert Wang-Chak Chan, Clément Moulin-Frier

The research field of Artificial Life studies how life-like phenomena such as autopoiesis, agency, or self-regulation can self-organize in computer simulations. In cellular automata (CA), a key open-question has been whether it it is possible to find environment rules that self-organize robust "individuals" from an initial state with no prior existen

Tommaso Puccetti, Andrea Ceccarelli

The ever-evolving landscape of attacks, coupled with the growing complexity of ICT systems, makes crafting anomaly-based intrusion detectors (ID) and error detectors (ED) a difficult task: they must accurately detect attacks, and they should promptly perform detections. Although improving and comparing the detection capability is the focus of most research w

Xinqiang Ding

The multistate Bennett acceptance ratio (MBAR) method is a prevalent approach for computing free energies of thermodynamic states. In this work, we introduce BayesMBAR, a Bayesian generalization of the MBAR method. By integrating configurations sampled from thermodynamic states with a prior distribution, BayesMBAR computes a posterior distribution of free en

Daniel Marti-Dafcik, Nicholas Lee, Hugh G. A. Burton, David P. Tew

Molecular orbital theory is powerful both as a conceptual tool for understanding chemical bonding, and as a theoretical framework for ab initio quantum chemistry. Despite its undoubted success, MO theory has well documented shortcomings, most notably that it fails to correctly describe diradical states and homolytic bond fission. In this contribution, we int

Jonathan Michaux, Adam Li, Qingyi Chen, Che Chen

Generating safe motion plans in real-time is necessary for the wide-scale deployment of robots in unstructured and human-centric environments. These motion plans must be safe to ensure humans are not harmed and nearby objects are not damaged. However, they must also be generated in real-time to ensure the robot can quickly adapt to changes in the environment

Awni Altabaa, John Lafferty

Inner products of neural network feature maps arise in a wide variety of machine learning frameworks as a method of modeling relations between inputs. This work studies the approximation properties of inner products of neural networks. It is shown that the inner product of a multi-layer perceptron with itself is a universal approximator for symmetric positiv

Catherine Yeh, Gonzalo Ramos, Rachel Ng, Andy Huntington

Large language models (LLMs) have become ubiquitous in providing different forms of writing assistance to different writers. However, LLM-powered writing systems often fall short in capturing the nuanced personalization and control needed to effectively support users -- particularly for those who lack experience with prompt engineering. To address these chal

Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie

Subsurface seismic velocity structure is essential for earthquake source studies, including hypocenter determination. Conventional hypocenter determination methods ignore the inherent uncertainty in seismic velocity structure models, and the impact of this oversight has not been thoroughly investigated. Here, we address this issue by employing a physics-info

Jeremy Booher, Everett W. Howe, Andrew V. Sutherland, José Felipe Voloch

Let $C$ and $C'$ be curves over a finite field $K$, provided with embeddings $\iota$ and $\iota'$ into their Jacobian varieties. Let $D\to C$ and $D'\to C'$ be the pullbacks (via these embeddings) of the multiplication-by-$2$ maps on the Jacobians. We say that $(C,\iota)$ and $(C',\iota')$ are \emph{doubly isogenous} if $\mathrm{Jac}(C)$ and $\mathrm{Jac}(C'