October 2024 arXiv papers — page 13

Timo Keller, Mulun Yin

Let $E/\mathbf{Q}$ be an elliptic curve and $p\geq 3$ be a prime. We prove the $p$-converse theorems for elliptic curves of potentially good ordinary reduction at Eisenstein primes (i.e., such that the residual representation $E[p]$ is reducible) when the $p$-Selmer rank is $0$ or $1$. The key step is to obtain the anticyclotomic Iwasawa Main Conjectures for

Levi Harris

We present a reliable temporal grounding pipeline for video-to-analytic alignment of basketball broadcast footage. Given a series of frames as input, our method quickly and accurately extracts time-remaining and quarter values from basketball broadcast scenes. Our work intends to expedite the development of large, multi-modal video datasets to train data-hun

Jason Hao, Jeffrey Owrutsky, Daniel Ratchford, Blake Simpkins

In this paper, we have developed a theory describing surface exciton polariton (SEPs) that accounts for the spatial dispersion of the dielectric constant connected with exciton momentum. Due to strong coupling between light and bulk excitons in the frequency separation, $\hbar\omega_{LT}$, between the longitudinal and transverse exciton, the SEP is formed an

Yuh Kobayashi, Shin-ichiro Seki

The $(k,l)$-G\"{o}bel sequences defined by Ibstedt remain integers for the first (in some cases, many) terms, but for selected values of $(k,l)$, computations show that the terms eventually stop being integers. It is still unresolved whether the integrality of these sequences breaks down for all $k, l\geq 2$. In this article, we prove the non-integrality for

Yiran Zhao, Maria Alinea-Bravo, Niti Parikh

CRAFT@Large (C@L) is an initiative launched by the MakerLAB at Cornell Tech to create an inclusive environment for the intercultural and intergenerational exchange of ideas through making. With our approach, we challenge the traditional definition of community outreach performed by academic makerspaces. Existing academic makerspaces often perform community e

A. A. Saoulis, D. Piras, A. Spurio Mancini, B. Joachimi

This paper presents a novel framework for full-waveform seismic source inversion using simulation-based inference (SBI). Traditional probabilistic approaches often rely on simplifying assumptions about data errors, which we show can lead to inaccurate uncertainty quantification. SBI addresses this limitation by building an empirical probabilistic model of th

Ali Fasihi, Grzegorz Kudra, Maryam Ghandchi Tehrani, Jan Awrejcewicz

Nonlinear dynamics of fluid conveying pipe, rotating with constant velocity about its longitudinal axis is analyzed. Considering boundary conditions and internal damping, the nonlinear equation of motion is derived, and it is discretized via the Galerkin method. Afterward, the stability of the system is investigated by characterizing the eigenvalues under th

Nilotpal Kakati, Etienne Dreyer, Anna Ivina, Francesco Armando Di Bello

In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been explored in the context of single jets. In this paper, we expand the scope to full proton-proton and electron-positron collis

Shiqiao Zhang

We examine a non-axisymmetric perturbation of a family of axisymmetric toric Einstein manifolds and Ricci solitons studied in Firester-Tsiamis (2024). We establish a rigidity result stating that these axisymmetric Ricci solitons do not admit constant-angle non-axisymmetric perturbations except for conformally flat cases. For these new cases, our result leads

Merel L. R. van 't Hoff, Jennifer B. Bergner

Knowledge of the composition of material that will form planets is crucial to understand planetary diversity and the occurrence of potentially habitable planets. Ultimately, it is the chemistry in circumstellar disks that determines the global make up of planetary systems, as the dust in these disks grows into giant planet cores and rocky planets, the gas be

Peide Huang, Yuhan Hu, Nataliya Nechyporenko, Daehwa Kim

This paper introduces a framework, called EMOTION, for generating expressive motion sequences in humanoid robots, enhancing their ability to engage in humanlike non-verbal communication. Non-verbal cues such as facial expressions, gestures, and body movements play a crucial role in effective interpersonal interactions. Despite the advancements in robotic beh

Benjamin Church, Francisco García-Cortés

Let $\ell$ be a prime number, $k$ a positive integer and consider the group $\Gamma_{\ell^k} :=\langle a,b\ \vert\ a^{\ell^k(\ell^k-1)}ba^{-\ell^k}b^{-2}\rangle$. We prove that $\Gamma_{\ell^k}$ is not $\mathrm{SL}_2$-weakly integral with obstruction at exactly the prime $\ell$. We also give a general description of the character varieties of $2$-generated g

Kristian Georgiev, Roy Rinberg, Sung Min Park, Shivam Garg

Machine unlearning -- efficiently removing the effect of a small "forget set" of training data on a pre-trained machine learning model -- has recently attracted significant research interest. Despite this interest, however, recent work shows that existing machine unlearning techniques do not hold up to thorough evaluation in non-convex settings. In this work

Yucheng Huang, Luping Ji, Hudong Liu, Mao Ye

Deep visual Simultaneous Localization and Mapping (SLAM) techniques, e.g., DROID, have made significant advancements by leveraging deep visual odometry on dense flow fields. In general, they heavily rely on global visual similarity matching. However, the ambiguous similarity interference in uncertain regions could often lead to excessive noise in corresponde

Shentong Mo, Yibing Song

Visual content and accompanied audio signals naturally formulate a joint representation to improve audio-visual (AV) related applications. While studies develop various AV representation learning frameworks, the importance of AV data alignment is usually undermined for achieving high-quality representation. We observe that an audio signal may contain backgro

Patrick Aschenbrenner, Norbert Przybilla

The majority of massive stars are located in binary or multiple star systems. Compared to single stars, these objects pose additional challenges to quantitative analyses based on model atmospheres. In particular, little information is currently available on the chemical composition of such systems. The members of the quadruple star system HD 37061, which exc

Giuseppe Bruno, Federico Pasqualotto, Andrea Agazzi

We model the evolution of tokens within a deep stack of Transformer layers as a continuous-time flow on the unit sphere, governed by a mean-field interacting particle system, building on the framework introduced in (Geshkovski et al., 2023). Studying the corresponding mean-field Partial Differential Equation (PDE), which can be interpreted as a Wasserstein g

Seungjoo Lee, Thanh-Long V. Le, Jaemin Shin, Sung-Ju Lee

Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. However, most FL approaches assume that clients possess labeled data, which is often not the case in practice. Federated Semi-Supervised Learning (FSSL) addresses this label deficiency problem, targeting sit

Nolan R. Wallach

This paper, in particular, gives a complete proof of the direct integral version of the Whittaker Plancherel Theorem. The main emphasis is on certain Hilbert and Fr\'echet vector bundles over a space that has a submersion onto the tempered dual. This allows for an approach to the Plancherel Theorems (both for L^2 and the Whittaker case) that is representatio

Xiaoyu Chen, Weiming Feng, Heng Guo, Xinyuan Zhang

We show that spin systems with bounded degrees and coupling independence admit fully polynomial time approximation schemes (FPTAS). We design a new recursive deterministic counting algorithm to achieve this. As applications, we give the first FPTASes for $q$-colourings on graphs of bounded maximum degree $\Delta\ge 3$, when $q\ge (11/6-\varepsilon_0)\Delta$

Damien Gaboriau, François Le Maître, Yves Stalder

We continue our study of the perfect kernel of the space of transitive actions of Baumslag-Solitar groups by investigating high transitivity. We show that actions of finite phenotype are never highly transitive, except when the phenotype is 1, in which case high transitivity is actually generic. In infinite phenotype, high transitivity is generic, except whe

Yixin Liu, Argyris Oikonomou, Weiqiang Zheng, Yang Cai

Many alignment methods, including reinforcement learning from human feedback (RLHF), rely on the Bradley-Terry reward assumption, which is not always sufficient to capture the full range and complexity of general human preferences. We explore RLHF under a general preference framework by modeling the alignment problem as a two-player zero-sum game in a game-t

Gaurav Kumar Gupta, D. N. Sheng, C. S. Ting

Recent experiment on MoTe$_2$-WTe$_2$ twisted bi-layer demonstrated physics of Kane-Mele (KM) as well as Haldane models. Although topological properties of KM model has been studied extensively, effects of interaction are still less explored beyond half filling. In this work we study the effect of Hubbard interaction in KM model at small hole doping around h

Anthonette Adanyin

The rapid spread of digital technologies has produced data-driven feedback loops, wearable devices, social media networks, and mobile applications that shape user behavior, motivation, and mental well-being. While these systems encourage self-improvement and the development of healthier habits through real-time feedback, they also create psychological risks

Phillip Lo, Yuehaw Khoo

In this paper, we study the problem of recovering a ground truth high dimensional piecewise linear curve $C^*(t):[0, 1]\to\mathbb{R}^d$ from a high noise Gaussian point cloud with covariance $\sigma^2I$ centered around the curve. We establish that the sample complexity of recovering $C^*$ from data scales with order at least $\sigma^6$. We then show that rec

Yitong Li, Morteza Ghahremani, Youssef Wally, Christian Wachinger

Diagnosing dementia, particularly for Alzheimer's Disease (AD) and frontotemporal dementia (FTD), is complex due to overlapping symptoms. While magnetic resonance imaging (MRI) and positron emission tomography (PET) data are critical for the diagnosis, integrating these modalities in deep learning faces challenges, often resulting in suboptimal performance c

Zhiyong Wu, Zhenyu Wu, Fangzhi Xu, Yian Wang

Existing efforts in building GUI agents heavily rely on the availability of robust commercial Vision-Language Models (VLMs) such as GPT-4o and GeminiProVision. Practitioners are often reluctant to use open-source VLMs due to their significant performance lag compared to their closed-source counterparts, particularly in GUI grounding and Out-Of-Distribution (

Bhaskar Gaur, Himanshu Thapliyal

As quantum computers scale, the rise of multi-user and cloud-based quantum platforms can lead to new security challenges. Attacks within shared execution environments become increasingly feasible due to the crosstalk noise that, in combination with quantum computer's hardware specifications, can be exploited in form of crosstalk attack. Our work pursues cros

Raúl M. Falcón, Lorenzo Mella

A Heffter array over an additive group $G$ is any partially filled array $A$ satisfying that: (1) each one of its rows and columns sum to zero in $G$, and (2) if $i\in G\setminus\{0\}$, then either $i$ or $-i$ appears exactly once in $A$. In this paper, this notion is naturally generalized to that of $\mathcal{B}$-Heffter array over a partial loop, where $\m

Shikhar Kumar, Omer Keidar, Yael Edan

In this work, we focused on constructing and evaluating levels of explanation(LOE) that address two basic aspect of HRI: 1. What information should be communicated to the user by the robot? 2. When should the robot communicate this information? For constructing the LOE, we defined two terms, verbosity and explanation patterns, each with two levels (verbosity

Sheryl Hsu, Omar Khattab, Chelsea Finn, Archit Sharma

The hallucinations of large language models (LLMs) are increasingly mitigated by allowing LLMs to search for information and to ground their answers in real sources. Unfortunately, LLMs often struggle with posing the right search queries, especially when dealing with complex or otherwise indirect topics. Observing that LLMs can learn to search for relevant f

Muhammad Salman Ali, Sung-Ho Bae, Enzo Tartaglione

3D models have recently been popularized by the potentiality of end-to-end training offered first by Neural Radiance Fields and most recently by 3D Gaussian Splatting models. The latter has the big advantage of naturally providing fast training convergence and high editability. However, as the research around these is still in its infancy, there is still a g

Momonari Kudo, Kazuhiro Yokoyama

Nowadays, the notion of semi-regular sequences, originally proposed by Fr\"oberg, becomes very important not only in Mathematics, but also in Information Science, in particular Cryptology. For example, it is highly expected that randomly generated polynomials form a semi-regular sequence, and based on this observation, secure cryptosystems based on polynomia

Eliel Camargo-Molina, Rikard Enberg, Johan Löfgren

We use modern dimensionally-reduced effective field theory methods, with careful attention to scale hierarchies, to analyze and catalog the types of first-order electroweak phase transitions that are possible in the Standard Model Effective Field Theory (SMEFT). Our calculations lay the necessary groundwork to perform gauge invariant, properly resummed pertu

Maciej Łebek, Miłosz Panfil

Integrable systems feature an infinite number of conserved charges and on hydrodynamic scales are described by generalised hydrodynamics (GHD). This description breaks down when the integrability is weakly broken and sufficiently large space-time-scales are probed. The emergent hydrodynamics depends then on the charges conserved by the perturbation. We focus

Michael Matthews, Michael Beukman, Chris Lu, Jakob Foerster

While large models trained with self-supervised learning on offline datasets have shown remarkable capabilities in text and image domains, achieving the same generalisation for agents that act in sequential decision problems remains an open challenge. In this work, we take a step towards this goal by procedurally generating tens of millions of 2D physics-bas

Alireza Abbaspour, Aliasghar Arab, Yashar Mousavi

The advent of autonomous driving technology has accentuated the need for comprehensive hazard analysis and risk assessment (HARA) to ensure the safety and reliability of vehicular systems. Traditional HARA processes, while meticulous, are inherently time-consuming and subject to human error, necessitating a transformative approach to fortify safety engineeri

Hiranya Kishore Dey, Umesh Shankar, Sivaramakrishnan Sivasubramanian

It is well known that descents and excedances are equidistributed in the symmetric group. We show that the descent and excedance enumerators, summed over permutations with a fixed first letter are identical when we perform a simple change of the first letter. We generalize this to type B and other colored permutation groups. We are led to defining descents a

Finja Tietjen, R. Matthias Geilhufe

We present an ultrafast thermodynamics framework to model heat generation and entropy production in laser-driven ferromagnetic systems. By establishing a connection between the magnetic field strength of the laser pulse and magnetization dynamics we model time-dependent entropy production rates and deduce the associated heat dissipation in epitaxial and poly

Jing Song, Wei-Hong Liang, Chu-Wen Xiao, Jorgivan Morais Dias

We report on the reactions $\psi(3770)\to \bar{\Omega}^+ \bar{K} \Xi $ and $\psi(3770)\to \bar{\Omega}^+ \bar{K}\Xi^*(1530)~(\Xi^*(1530)\to \pi\Xi $), and calculate the mass distributions $\frac{{\rm d}\Gamma}{{\rm d}M_\text{inv}(\bar{K}\Xi)}$ and $\frac{{\rm d}\Gamma}{{\rm d}M_\text{inv}(\bar{K}\Xi^*)}$, respectively. We obtain clear peaks corresponding to

Nurul Huda Mahmood, Sumudu Samarakoon, Pawani Porambage, Mehdi Bennis

Our future society will be increasingly digitalized, hyper-connected and globally data driven. The sixth generation (6G) and beyond 6G wireless networks are expected to bridge the digital and physical worlds by providing wireless connectivity as a service to different vertical sectors, making the society increasingly dependent on wireless networks. Thus, any

Jiaying Yang, Maryam Khanahmadi, Ingrid Strandberg, Akshay Gaikwad

A distributed quantum computing network requires a quantum communication channel between spatially separated processing units. In superconducting circuits, such a channel can be implemented based on propagating microwave photons to encode and transfer quantum information between an emitter and a receiver. However, traveling microwave photons can be lost duri

Alessandro Monteverdi, Elizabeth Winstanley

We present some addition theorems for spin-weighted spherical harmonics, generalizing previous results for scalar (spin-zero) spherical harmonics. These addition theorems involve sums over the azimuthal quantum number of products of two spin-weighted spherical harmonics at different points on the two-sphere, either (or both) of which are differentiated with

Kiran Kokilepersaud, Seulgi Kim, Mohit Prabhushankar, Ghassan AlRegib

In this paper, we propose an algorithm that can be used on top of a wide variety of self-supervised (SSL) approaches to take advantage of hierarchical structures that emerge during training. SSL approaches typically work through some invariance term to ensure consistency between similar samples and a regularization term to prevent global dimensional collapse

M. Victoria Ale Crivillero, Priscila F. S. Rosa, Z. Fisk, J. Müller

While SmB$_6$ attracts attention as a possible topological Kondo insulator, EuB$_6$ is known to host magnetic polarons that give rise to large magnetoresistive effects above its ferromagnetic order transition. Here we investigate single crystals of Sm$_{1-x}$Eu$_x$B$_6$ by magnetic and magnetotransport measurements to explore a possible interplay of these tw

R. G. Bullis, U. D. Jentschura, D. C. Yost

We propose a measurement of laser-induced vacuum birefringence through the use of pulsed lasers coupled to femtosecond optical enhancement cavities. This measurement technique features cavity-enhanced pump and probe pulses, as well as an independent control pulse. The control pulse allows for a differential measurement where the final signal is obtained usin

Momchil Molnar, Roberto Casini

The magnetic field vector of the solar corona is not regularly and comprehensively being measured, because of the complexity and degeneracy inherently present in the types of observations currently available. To address some of the current limitations of coronal polarimetry, we present computations that demonstrate the possibility of magnetometry using the u

Fabio Deelan Cunden, Jakub Czartowski, Giovanni Gramegna, A. de Oliveira Junior

Any semigroup $\mathcal{S}$ of stochastic matrices induces a semigroup majorization relation $\prec^{\mathcal{S}}$ on the set $\Delta_{n-1}$ of probability $n$-vectors. Pick $X,Y$ at random in $\Delta_{n-1}$: what is the probability that $X$ and $Y$ are comparable under $\prec^{\mathcal{S}}$? We review recent asymptotic ($n\to\infty$) results and conjectures

Bruno Staffa

We prove the Parametric Coarea Inequality for $1$-cycles conjectured by Guth and Liokumovich.

Sakshi Maurya, Joysankar Majumdar, Varun, Neetu Sahu

In this work, we studied the broadband temporal and spectral properties of the flat-spectrum radio quasar (FSRQ) Ton 599. We collected the long-term data from Jan 2019 to August 2024 when the source was in a long flaring episode. We used the Bayesian block methodology to identify the various flux states, including three flares. The broadband fractional varia

Yudai Tanaka, Neil Weiss, Robert Cole Bolger-Cruz, Jess Hartcher-O'Brien

We present a technique for providing remote tactile feedback to the thumb and index finger via a wristband device. This enables haptics for touch and pinch interactions in mixed reality (MR) while keeping the hand entirely free. We achieve this through a novel cross-modal stimulation, which we term visually augmented electro-tactile feedback. This consists o

Bruno Staffa

We prove the Weyl law for the volume spectrum for $1$-cycles in $n$-dimensional manifolds which was conjectured by Gromov. We follow the strategy of Guth and Liokumovich of obtaining the Weyl law from parametric versions of the coarea inequality and the isoperimetric inequality. A version of the later for families of $0$-cycles is shown in this article. We a

Meng Ye, Bingyu Xin, Leon Axel, Dimitris Metaxas

Current cardiac cine magnetic resonance image (cMR) studies focus on the end diastole (ED) and end systole (ES) phases, while ignoring the abundant temporal information in the whole image sequence. This is because whole sequence segmentation is currently a tedious process and inaccurate. Conventional whole sequence segmentation approaches first estimate the

P. Cristofari, V. Tatischeff, M. Chabot

Diffusive shock acceleration (DSA) is a prominent mechanism for energizing charged particles up to very large rigidities at astrophysical collisionless shocks. In addition to ions and electrons, it has been proposed that interstellar dust grains could also be accelerated through diffusive shock acceleration, for instance, at supernova remnants (SNRs). Consid

Andrés Cremades, Sergio Hoyas, Ricardo Vinuesa

For the last 140 years, the mechanisms of transport and dissipation of energy in a turbulent flow have not been completely understood. Previous research has focused on analyzing the so-called coherent structures, organized flow patterns characterized by their spatial coherence, lifespan and significant contribution to momentum and energy transfer. However, t

Manon Thbaut, Basile Audoly, Claire Lestringant

Energy functionals produced by second-order homogenization of periodic elastic structures commonly feature negative gradient moduli. We show that this undesirable property is caused by the truncation of the energy expansion in powers of the small scale separation parameter. By revisiting Cholesky's LDLT decomposition, we propose an alternative truncation met

Kayla Schroeder, Zach Wood-Doughty

Topic models allow researchers to extract latent factors from text data and use those variables in downstream statistical analyses. However, these methodologies can vary significantly due to initialization differences, randomness in sampling procedures, or noisy data. Reliability of these methods is of particular concern as many researchers treat learned top

Di Zhang, Rohan Dhall, Matthew M. Schneider, Chengyu Song

Interface-type resistive switching (RS) devices with lower operation current and more reliable switching repeatability exhibits great potential in the applications for data storage devices and ultra-low-energy computing. However, the working mechanism of such interface-type RS devices are much less studied compared to that of the filament-type devices, which

Raúl M. Falcón, L. Mella, P. Vojtěchovský

The Hadamard quasigroup product has recently been introduced as a natural generalization of the classical Hadamard product of matrices. It is defined as the superposition operator of three binary operations, one of them being a quasigroup operation. This paper delves into the fundamentals of this superposition operator by considering its more general version

Zhichao Hou, Weizhi Gao, Yuchen Shen, Feiyi Wang

Transformer-based architectures have dominated various areas of machine learning in recent years. In this paper, we introduce a novel robust attention mechanism designed to enhance the resilience of transformer-based architectures. Crucially, this technique can be integrated into existing transformers as a plug-and-play layer, improving their robustness with

Siddhesh Pawar, Junyeong Park, Jiho Jin, Arnav Arora

Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure inclusivity. Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive in LLMs that

Sikandar A. Dar, Zahir Shah, Ranjeev Misra, Naseer Iqbal

We conducted a comprehensive temporal and spectral study of the FSRQ PKS 0805-07 by using the broadband observations from the Fermi-LAT and Swift-XRT/UVOT instruments over the period MJD 54684-60264. The 3-day binned $\gamma$-ray light curve during the active state, revealed eleven distinct peak structures with the maximum integral flux (E $>$ 100 MeV) reach

Millennium Bismay, Xiangjue Dong, James Caverlee

This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpretable explanations. In contrast to conventional recommendation systems that rely on implicit user-item interactions, ReasoningRec employs LLMs to model users and items, focusing o

Johann Brehmer, Sönke Behrends, Pim de Haan, Taco Cohen

Given large datasets and sufficient compute, is it beneficial to design neural architectures for the structure and symmetries of each problem? Or is it more efficient to learn them from data? We study empirically how equivariant and non-equivariant networks scale with compute and training samples. Focusing on a benchmark problem of rigid-body interactions an

Mostafa Cherif, Tobías I. Liaudat, Jonathan Kern, Christophe Kervazo

The advent of next-generation radio interferometers like the Square Kilometer Array promises to revolutionise our radio astronomy observational capabilities. The unprecedented volume of data these devices generate requires fast and accurate image reconstruction algorithms to solve the ill-posed radio interferometric imaging problem. Most state-of-the-art rec

Eva Rifà, Joan Massachs, Emanuele Cozzo, Julian Vicens

Political polarization has attracted increasing attention in recent years, driven by the rise of social media and the global emergence of far-right populist movements. This study investigates the dynamics of structural polarization during electoral campaigns in multi-party systems, with a particular focus on the presence of far-right actors and their influen

Robert Hunt, Eli Silver, Daniel M. Harris

Fluid velocimetry is fundamental to a breadth of applications spanning academia and industry, however velocimetry at high temporal resolution is often prohibitively costly. Here, we introduce a Fast and InExpensive Velocimeter (FIEVel) based on an optical mouse sensor. At its core, the optical mouse sensor consists of a small pixel array that acquires image

Luis M. Báez, Andrés Santos

The Laplace transform is a valuable tool in physics, particularly in solving differential equations with initial or boundary conditions. A 2014 study by Tsaur and Wang (2014 \emph{Eur.~J.~Phys.} \textbf{35} 015006) introduced a Laplace-transform-based method to solve the stationary Schr\"odinger equation for various potentials. However, their approach contai

Fei Song, Hong-Yi Wang, Zhong Wang

In non-Hermitian systems, it is a counterintuitive feature of the non-Hermitian skin effect (NHSE) that the energy spectrum and eigenstates can be totally different under open or periodic boundary conditions, suggesting that non-Hermitian spectra can be extremely sensitive to non-local perturbations. Here, we show that a wide range of non-Hermitian models wi

Francesco Pozza, Giacomo Zanella

We study multiproposal Markov chain Monte Carlo algorithms, such as Multiple-try or generalised Metropolis-Hastings schemes, which have recently received renewed attention due to their amenability to parallel computing. First, we prove that no multiproposal scheme can speed-up convergence relative to the corresponding single proposal scheme by more than a fa

Alberto Perro, Mitja Vodnik, Paolo Durante

High Energy Physics (HEP) data acquisition systems are often built from high-end FPGAs. As such systems scale in the HL-LHC era, severe under-utilization of FPGA transceivers can occur because front-end links prioritize radiation hardness and power consumption over raw data bandwidth. This work evaluates recently introduced low-power, low-cost FPGA devices a

Zhijin Chen, Branko Ristic, Du Yong Kim

Statistical dependencies between information sources are rarely known, yet in practical distributed tracking schemes, they must be taken into account in order to prevent track divergences. Chernoff fusion is well-known and universally accepted method that can address the problem of track fusion when the statistical dependence between the fusing sources is un

Xiangyu Zhang, Brandon S. Hensley, Gregory M. Green

The first all-sky, high-resolution, 3D map of the optical extinction curve of the Milky Way (Zhang & Green 2024) revealed an unexpected steepening of the extinction curve in the moderate-density, "translucent" interstellar medium (ISM). We argue that this trend is driven by growth of polycyclic aromatic hydrocarbons (PAHs) through gas-phase accretion. We fin

Shiyue Zhang, Longlin Yu, Ziheng Cheng, Cheng Zhang

Recently, through a unified gradient flow perspective of Markov chain Monte Carlo (MCMC) and variational inference (VI), particle-based variational inference methods (ParVIs) have been proposed that tend to combine the best of both worlds. While typical ParVIs such as Stein Variational Gradient Descent (SVGD) approximate the gradient flow within a reproducin

Connall Garrod, Jonathan P. Keating

Neural collapse (NC) and its multi-layer variant, deep neural collapse (DNC), describe a structured geometry that occurs in the features and weights of trained deep networks. Recent theoretical work by Sukenik et al. using a deep unconstrained feature model (UFM) suggests that DNC is suboptimal under mean squared error (MSE) loss. They heuristically argue th

Azadeh Sharafi, Nikolai J. Mickevicius, Mehran Baboli, Andrew S. Nencka

Purpose: This study presents a variable resolution (VR) sampling and deep learning reconstruction approach for multi-spectral MRI near metal implants, aiming to reduce scan times while maintaining image quality. Background: The rising use of metal implants has increased MRI scans affected by metal artifacts. Multi-spectral imaging (MSI) reduces these artifac

Haiyang Wang, Yue Fan, Muhammad Ferjad Naeem, Yongqin Xian

Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on a fixed number of parameters within linear projections. When architectural modificatio

Conor M Byrne, Jan J Eldridge, Elizabeth R Stanway

Stellar evolution modelling is fundamental to many areas of astrophysics including stellar populations in both nearby and distant galaxies. It is heavily influenced by chemical composition. Observations of distant galaxies and nucleosynthesis calculations show that $\alpha$-process elements are enriched faster than iron group elements. We present a dense gri

Wenxiao Wang, Lihui Gu, Liye Zhang, Yunxiang Luo

The rapid advancement of large language models (LLMs) has opened new possibilities for automating the proposal of innovative scientific ideas. This process involves two key phases: literature retrieval and idea generation. However, existing approaches often fall short due to their reliance on keyword-based search tools during the retrieval phase, which negle

A. A. Araújo Filho

We explore the gravitational properties of a nonlinear electromagnetic extension of an AdS Reissner-Nordstr\"om black hole. Our study begins with an analysis of the metric function and horizon structure, followed by calculations of the Ricci and Kretschmann scalars and an evaluation of the non-vanishing Christoffel symbols. These calculations allow us to exa

Ezequiel Maderna, Andrea Venturelli

For the N-body problem we prove that any two hyperbolic rays having the same limit shape define the same Busemann function. We localize a region of differentiability for these functions, of which we know that they are viscosity solutions of the stationary Hamilton-Jacobi equation. As a first corollary, we deduce that every hyperbolic motion of the $N$-body p

Filippo Giovagnini, Dan Crisan

We study a model of interacting particles represented by a system of N stochastic differential equations. We establish that the mollified empirical distribution of the system converges uniformly with respect to both time and spatial variables to the solution of the two dimensional Navier Stokes equation with transport noise. The proofs are based on a semigro

Roland Jaha, Connor A. Graham-Scott, Adrian S. Abazi, Wolfram Pernice

The ability to resolve photon numbers is crucial in quantum information science and technology, driving the development of detectors with intrinsic photon-number resolving (PNR) capabilities. Although transition edge sensors represent the state-of-the-art in PNR performance, superconducting nanowire single-photon detectors (SNSPDs) offer superior efficiency,

Navideh Ghafouri, John S. Vardakas, Kostas Ramantas, Christos Verikoukis

Since the 6th Generation (6G) of wireless networks is expected to provide a new level of network services and meet the emerging expectations of the future, it will be a complex and intricate networking system. 6Gs sophistication and robustness will be accompanied by complexities, which will require novel strategies to tackle them. This research work focuses

Jingge Xiao, Yile Chen, Gao Cong, Wolfgang Nejdl

Forecasting time series with irregular temporal structures remains challenging for universal pre-trained models. Existing approaches often assume regular sampling or depend heavily on imputation, limiting their applicability in real-world scenarios where irregularities are prevalent due to diverse sensing devices and recording practices. We introduce FlexTSF

Chiu-Wai Yan, Shi Quan Foo, Van Hoan Trinh, Dit-Yan Yeung

Deep learning approaches have been widely adopted for precipitation nowcasting in recent years. Previous studies mainly focus on proposing new model architectures to improve pixel-wise metrics. However, they frequently result in blurry predictions which provide limited utility to forecasting operations. In this work, we propose a new Fourier Amplitude and Co

Oliver Urs Lenz, Matthijs van Leeuwen

Semi-supervised anomaly detection is based on the principle that potential anomalies are those records that look different from normal training data. However, in some cases we are specifically interested in anomalies that correspond to high attribute values (or low, but not both). We present two asymmetrical distance measures that take this monotonicity into

Chin-Chun Wu, Kan Liou, Brian E. Wood, Y. M. Wang

Global magnetohydrodynamic (MHD) models play an important role in the infrastructure of space weather forecasting. Validating such models commonly utilizes in situ solar wind measurements made near the orbit of the Earth. The purpose of this study is to test the performance of G3DMHD (a data driven, time-dependent, 3-D MHD model of the solar wind) with Parke

Yichao Liang, Nishanth Kumar, Hao Tang, Adrian Weller

Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space. In this work, we present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations. We

Mohammad Shahverdikondori, Ehsan Mokhtarian, Negar Kiyavash

Causal discovery is essential for understanding relationships among variables of interest in many scientific domains. In this paper, we focus on permutation-based methods for learning causal graphs in Linear Gaussian Acyclic Models (LiGAMs), where the permutation encodes a causal ordering of the variables. Existing methods in this setting are not scalable du

Songyu Xu, Yicheng Hu, Jionglong Su, Daniel Elson

Purpose: Drop-in gamma probes are widely used in robotic-assisted minimally invasive surgery (RAMIS) for lymph node detection. However, these devices only provide audio feedback on signal intensity, lacking the visual feedback necessary for precise localisation. Previous work attempted to predict the sensing area location using laparoscopic images, but the p

Edwin Kitaeff

Gilmer and Masbaum use Witten-Reshetikhin-Turaev (WRT) invariants to define a map from the Kauffman bracket skein module to a set of complex-valued functions defined on roots of unity in order to provide a lower bound for its dimension. We compute the image of the evaluation map for a family of mapping tori of the 2-torus and find that the restriction of the

Juan Marcelo Parra-Ullauri, Oscar Dilley, Hari Madhukumar, Dimitra Simeonidou

The rapid growth of end-user AI applications, such as computer vision and generative AI, has led to immense data and processing demands often exceeding user devices' capabilities. Edge AI addresses this by offloading computation to the network edge, crucial for future services in 6G networks. However, it faces challenges such as limited resources during simu

Vahideh Hayyolalam, Öznur Özkasap

Diabetes is a chronic disorder identified by the high sugar level in the blood that can cause various different disorders such as kidney failure, heart attack, sightlessness, and stroke. Developments in the healthcare domain by facilitating the early detection of diabetes risk can help not only caregivers but also patients. AIoMT is a recent technology that

Tai-Hsuan Yang, Mehdi Soleimanifar, Thiago Bergamaschi, John Preskill

A naive classical representation of an n-qubit state requires specifying exponentially many amplitudes in the computational basis. Past works have demonstrated that classical neural networks can succinctly express these amplitudes for many physically relevant states, leading to computationally powerful representations known as neural quantum states. What und

S. B. White, P. B. Rimmer, Z. Liu

One way in which we can attempt to relate chemical pathways to geochemical environments is by studying the kinetics of a given sequence of reactions and identifying the conditions under which this chemistry is the most productive. Many prebiotic reactions rely on a source of fixed carbon, therefore chemical pathways that suggest prebiotically plausible ways

Lukas Junge, François Louis Antoine Visconti

We study the ground state energy of trapped two-dimensional Bose gases with mean-field type interactions that can be attractive. We prove the stability of second kind of the many-body system and the convergence of the ground state energy per particle to that of a non-linear Schr\"odinger (NLS) energy functional. Notably, we can take any polynomial scaling of

Yan Yang, Tao Qian

Denote by ${\mathcal D}$ the open unit disc in the complex plane and $\partial {\mathcal D}$ its boundary. Douglas showed through an identical quantity represented by the Fourier coefficients of the concerned function $u$ that \begin{eqnarray}\label{abs} A(u)=\int_{\mathcal D}|\bigtriangledown U|^2dxdy&=&\frac{1}{2\pi}\int\int_{\partial {\mathcal D}\times \p

Adolfo Guarino, Colin Sterckx, Mario Trigiante

Fetching techniques from Generalised Geometry and Exceptional Field Theory, we develop a new method to identify consistent subsectors of four-dimensional gauged maximal supergravities that possess a (locally) geometric embedding in type IIB or 11D supergravity. We show that a subsector that is invariant under a structure group $\textrm{G}_\textrm{S} \subset

Wenxuan Li, Taiyi Wang, Eiko Yoneki

Optimizing black-box functions in high-dimensional search spaces has been known to be challenging for traditional Bayesian Optimization (BO). In this paper, we introduce HiBO, a novel hierarchical algorithm integrating global-level search space partitioning information into the acquisition strategy of a local BO-based optimizer. HiBO employs a search-tree-ba