March 2024 arXiv papers — page 72

Ravi Raghunathan

If $L(s,\pi)$ and $L(s,\rho)$ are the Dirichlet series attached to cuspidal automorphic representations $\pi$ and $\rho$ of ${\rm GL}_n({\mathbb A}_{\mathbb Q})$ and ${\rm GL}_{n-2}({\mathbb A}_{\mathbb Q})$ respectively, we show that $F_2(s)=L(s,\pi)/L(s,\rho)$ has infinitely many poles. We also establish analogous results for Artin $L$-functions and other

Sergio Barbosa, Sylvain Fichet

Light particles quadratically coupled to nucleons induce macroscopic forces in matter. While a quantum effect always exists, an additional force occurs in the presence of a finite density of the light particles. We compute and classify such background-induced forces for particles of spin $0,\frac{1}{2},1$ in the framework of effective field theory. We show t

Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma

The acquisition of training data is crucial for machine learning applications. Data markets can increase the supply of data, particularly in data-scarce domains such as healthcare, by incentivizing potential data providers to join the market. A major challenge for a data buyer in such a market is choosing the most valuable data points from a data seller. Unl

C. S. Kochanek, J. C. Raymond, N. Caldwell

In the absence of a parallax distance to a pulsar or a surviving binary in a supernova remnant (SNR), distances to Galactic SNRs are generally very uncertain. However, by combining Gaia data with wide field, multi-fiber echelle spectroscopy, it is now possible to obtain accurate distances to many SNRs with limited extinction by searching for the appearance o

Wen-Jie Tseng

Virtual Reality (VR) moves away from well-controlled laboratory environments into public and personal spaces. As users are visually disconnected from the physical environment, interacting in an uncontrolled space frequently leads to collisions and raises safety concerns. In my thesis, I investigate this phenomenon which I define as the physical breakdown in

Istvan Kadar

We study the energy critical wave equation in 3 dimensions around a single soliton. We obtain energy boundedness (modulo unstable modes) for the linearised problem. We use this to construct scattering solutions in a neighbourhood of timelike infinity ($i_+$), provided the data on null infinity ($\scri$) decay polynomially. Moreover, the solutions we construc

Richard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi

Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent administration is not only associated with adverse health risks, but also restricted for patients during pregnanc

Pablo Antonio Moreno Casares, Modjtaba Shokrian Zini, Juan Miguel Arrazola

Hamiltonian simulation is arguably the most fundamental application of quantum computers. The Magnus operator is a popular method for time-dependent Hamiltonian simulation in computational mathematics, yet its usage requires the implementation of exponentials of commutators, which has previously made it unappealing for quantum computing. The development of c

Alexis E. Block

Creating a standardized introduction course becomes more critical as the field of human-robot interaction (HRI) becomes more established. This paper outlines the key components necessary to provide an undergraduate with a sufficient foundational understanding of the interdisciplinary nature of this field and provides proposed course content. It emphasizes th

Drisya Karinkuzhi, Banibrata Mukhopadhyay, Dayal Wickramasinghe, Christopher A. Tout

We present the observational mass-radius (M-R) relation for a sample of 47 magnetized white dwarfs (WDs) with the magnetic field strength (B) ranging from 1 to 773 MG, identified from the SDSS data release 7 (DR7). We derive their effective temperature, surface gravity (log g), luminosity, radius, and mass. While atmospheric parameters are derived using a Vi

Patrick D. Bolton, Svjetlana Fajfer, Jernej F. Kamenik, Martín Novoa-Brunet

The recent Belle II observation of $B \to K E_{\rm miss}$ challenges theoretical interpretations in terms of Standard Model neutrino final states. Instead, we consider new physics scenarios where up to two new light-invisible particles of spin 0 up to 3/2 are present in the final state. We identify viable scenarios by reconstructing the (binned) likelihoods

Sulagna Bhattacharya, Andrew L. Miller, Anupam Ray

Sun-like stars can transmute into comparable mass black holes by steadily accumulating heavy non-annihilating dark matter particles over the course of their lives. If such stars form in binary systems, they could give rise to quasi-monochromatic, persistent gravitational waves, commonly known as continuous gravitational waves, as they inspiral toward one ano

Erik R. Peterson, Daniel Scolnic, David O. Jones, Aaron Do

Measurements of Type Ia Supernovae (SNe Ia) in the near-infrared (NIR) have been used both as an alternate path to cosmology compared to optical measurements and as a method of constraining key systematics for the larger optical studies. With the DEHVILS sample, the largest published NIR sample with consistent NIR coverage of maximum light across three NIR b

Patrick Orman, Hrant Gharibyan, John Preskill

The Sachdev-Ye-Kitaev (SYK) model is a system of $N$ Majorana fermions with random interactions and strongly chaotic dynamics, which at low energy admits a holographically dual description as two-dimensional Jackiw-Teitelboim gravity. Hence the SYK model provides a toy model of quantum gravity that might be feasible to simulate with near-term quantum hardwar

Fabio Magistrelli, Sebastiano Bernuzzi, Albino Perego, David Radice

Understanding the details of $r$-process nucleosynthesis in binary neutron star mergers (BNSM) ejecta is key to interpreting kilonova observations and identifying the role of BNSMs in the origin of heavy elements. We present a self-consistent two-dimensional, ray-by-ray radiation-hydrodynamic evolution of BNSM ejecta with an online nuclear network (NN) up to

Weiyu Hu, Kazunori Nakayama, Volodymyr Takhistov, Yong Tang

Particle decays are always accompanied by the emission of graviton quanta of gravity through bremsstrahlung processes. However, the corresponding branching ratio is suppressed by the square of the ratio of particle's mass to the Planck scale. The resulting present abundance of gravitational waves (GWs), composed of gravitons, is analogously suppressed. We sh

Joshua Fagin, Georgios Vernardos, Grigorios Tsagkatakis, Yannis Pantazis

Strong gravitational lensing can be used as a tool for constraining the substructure in the mass distribution of galaxies. In this study we investigate the power spectrum of dark matter perturbations in a population of 23 Hubble Space Telescope images of strong galaxy-galaxy lenses selected from The Sloan Lens ACS (SLACS) survey. We model the dark matter sub

Ho Tat Lam, Jung Hoon Han, Yizhi You

We expand the concept of two-dimensional topological insulators to encompass a novel category known as topological dipole insulators (TDIs), characterized by conserved dipole moments along the $x$-direction in addition to charge conservation. By generalizing Laughlin's flux insertion argument, we prove a no-go theorem and predict possible edge patterns and a

Ramanjit Sohal, Abhinav Prem

We propose a general framework for studying two-dimensional (2D) topologically ordered states subject to local correlated errors and show that the resulting mixed-state can display intrinsically mixed-state topological order (imTO) -- topological order which is not expected to occur in the ground state of 2D local gapped Hamiltonians. Specifically, we show t

Adam Ehrenberg, Joseph T. Iosue, Abhinav Deshpande, Dominik Hangleiter

Gaussian Boson Sampling is a popular method for experimental demonstrations of quantum advantage, but many subtleties remain in fully understanding its theoretical underpinnings. An important component in the theoretical arguments for approximate average-case hardness of sampling is anticoncentration, which is a second-moment property of the output probabili

Luis F. Alday, Shai M. Chester, Tobias Hansen, De-liang Zhong

We compute the AdS Veneziano amplitude for type IIB gluon scattering in $AdS_5 \times S^3$ to all orders in $\alpha'$ in a small curvature expansion. This is achieved by combining a dispersion relation in the dual $4d$ $\mathcal{N}=2$ SCFT with an ansatz for the amplitude as a worldsheet integral in terms of multiple polylogarithms. The first curvature corre

Mark Ho-Yeuk Cheung, Ken K. Y. Ng, Miguel Zumalacárregui, Emanuele Berti

When gravitational waves pass near a gravitating object, they are deflected, or lensed. If the object is massive, such that the wavelength of the waves is small compared to its gravitational size, lensed gravitational wave events can be identified when multiple signals are detected at different times. However, when the wavelength is long, wave-optics diffrac

Hasan Abed Al Kader Hammoud, Tuhin Das, Fabio Pizzati, Philip Torr

We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed computational budget. Our findings consistently demonstrate that increasing pretraining data diversity enhances SSL performance, albeit only when the distribution distance to the downs

Tianwei Xiong, Yue Wu, Enze Xie, Yue Wu

Text-to-image diffusion models suffer from the risk of generating outdated, copyrighted, incorrect, and biased content. While previous methods have mitigated the issues on a small scale, it is essential to handle them simultaneously in larger-scale real-world scenarios. We propose a two-stage method, Editing Massive Concepts In Diffusion Models (EMCID). The

Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona, Michael Oechsle

Recent advances in view synthesis and real-time rendering have achieved photorealistic quality at impressive rendering speeds. While Radiance Field-based methods achieve state-of-the-art quality in challenging scenarios such as in-the-wild captures and large-scale scenes, they often suffer from excessively high compute requirements linked to volumetric rende

Ruozhen He, Ziyan Yang, Paola Cascante-Bonilla, Alexander C. Berg

This paper extensively investigates the effectiveness of synthetic training data to improve the capabilities of vision-and-language models for grounding textual descriptions to image regions. We explore various strategies to best generate image-text pairs and image-text-box triplets using a series of pretrained models under different settings and varying deg

Yang Yang, Wenhai Wang, Zhe Chen, Jifeng Dai

Bounding boxes uniquely characterize object detection, where a good detector gives accurate bounding boxes of categories of interest. However, in the real-world where test ground truths are not provided, it is non-trivial to find out whether bounding boxes are accurate, thus preventing us from assessing the detector generalization ability. In this work, we f

Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova

The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. Firstly, we identify a critical oversight in most current M

Yusuke Mikami, Andrew Melnik, Jun Miura, Ville Hautamäki

We demonstrate experimental results with LLMs that address robotics task planning problems. Recently, LLMs have been applied in robotics task planning, particularly using a code generation approach that converts complex high-level instructions into mid-level policy codes. In contrast, our approach acquires text descriptions of the task and scene objects, the

Jingxi Chen, Brandon Y. Feng, Haoming Cai, Mingyang Xie

This paper addresses the novel challenge of ``rewinding'' time from a single captured image to recover the fleeting moments missed just before the shutter button is pressed. This problem poses a significant challenge in computer vision and computational photography, as it requires predicting plausible pre-capture motion from a single static frame, an inheren

Murat O. Mamchuev, Felix N. Chukhovskii

The fractional diffraction optics theory has been elaborated using the Green function technique. The optics-fractional equation describing the diffraction X-ray scattering by imperfect crystals has been derived as the fractional matrix integral Fredholm--Volterra equation of the second kind. In the paper, to solve the Cauchy problems, the Liouville--Neumann-

Olga Golovneva, Zeyuan Allen-Zhu, Jason Weston, Sainbayar Sukhbaatar

Large language models (LLMs) have a surprising failure: when trained on "A has a feature B", they do not generalize to "B is a feature of A", which is termed the Reversal Curse. Even when training with trillions of tokens this issue still appears due to Zipf's law - hence even if we train on the entire internet. This work proposes an alternative training sch

Lauren Okamoto, Paritosh Parmar

Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic para

Chao Yi, Yu-Hang He, De-Chuan Zhan, Han-Jia Ye

Vision Language Models (VLMs) excel in zero-shot image classification by pairing images with textual category names. The expanding variety of Pre-Trained VLMs enhances the likelihood of identifying a suitable VLM for specific tasks. To better reuse the VLM resource and fully leverage its potential on different zero-shot image classification tasks, a promisin

H. Netzel, R. I. Anderson, G. Viviani

The photometric observations from the recent decade revolutionized our view on classical pulsators. Low-amplitude signals have been detected photometrically in addition to the dominant high-amplitude radial mode pulsations in many RR Lyrae stars and classical Cepheids. First overtone (1O) pulsators with an additional low-amplitude signal at a period ratio of

Carlos García-García, Matteo Zennaro, Giovanni Aricò, David Alonso

We present a cosmological analysis of the combination of the DES-Y3, KiDS-1000 and HSC-DR1 weak lensing samples under a joint harmonic-space pipeline making use of angular scales down to $\ell_{\rm max}=4500$, corresponding to significantly smaller scales ($\delta\theta\sim2.4'$) than those commonly used in cosmological weak lensing studies. We are able to d

Mary Phuong, Matthew Aitchison, Elliot Catt, Sarah Cogan

To understand the risks posed by a new AI system, we must understand what it can and cannot do. Building on prior work, we introduce a programme of new "dangerous capability" evaluations and pilot them on Gemini 1.0 models. Our evaluations cover four areas: (1) persuasion and deception; (2) cyber-security; (3) self-proliferation; and (4) self-reasoning. We d

José Alvarado, Gabriel Dias, Simon Griffiths

Let $N_{\triangle}(G)$ be the number of triangles in a graph $G$. In [14] and [25] (respectively) the following bounds were proved on the lower tail behaviour of triangle counts in the dense Erd\H{o}s-R\'enyi random graphs $G_m\sim G(n,m)$: \[ \mathbb{P}\big(N_{\triangle}(G_m) \, < \, (1-\delta)\mathbb{E}[N_{\triangle}(G_m)]\big) \,=\, \exp\left(-\Theta\left

Tahir Choulli, Martin Schweizer

The classic stochastic Fubini theorem says that if one stochastically integrates with respect to a semimartingale $S$ an $\eta(dz)$-mixture of $z$-parametrized integrands $\psi^z$, the result is just the $\eta(dz)$-mixture of the individual $z$-parametrized stochastic integrals $\int\psi^z{d}S.$ But if one wants to use such a result for the study of Volterra

Fan Yang, Hadi Yarloo, Hua-Chen Zhang, Klaus Mølmer

Hilbert space fragmentation provides a mechanism to break ergodicity in closed many-body systems. Here, we propose a feasible scheme to explore this exotic paradigm on a Rydberg quantum simulator. We show that the Rydberg Ising model in the large detuning regime can be mapped to a generalized folded XXZ model featuring a strongly fragmented Hilbert space. Th

S. Aune, B. Azmoun, A. Bonenfant, S. Boose

The sPHENIX Time Projection Chamber Outer Tracker (TPOT) is a Micromegas based detector. It is a part of the sPHENIX experiment that aims to facilitate the calibration of the Time Projection Chamber, in particular the correction of the time-averaged and beam-induced distortions of the electron drift. This paper describes the detector mission, setup, construc

Ming Gui, Johannes Schusterbauer, Ulrich Prestel, Pingchuan Ma

Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transport. Our method addresses these challenges by framing depth estimation as a direct transport between image and depth distributions. We are the first to explore flow matching in thi

Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda

Reward models (RMs) are at the crux of successfully using RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those models. Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models and which values are embedded in the

Guangzeng Han, Weisi Liu, Xiaolei Huang, Brian Borsari

Automatic coding patient behaviors is essential to support decision making for psychotherapists during the motivational interviewing (MI), a collaborative communication intervention approach to address psychiatric issues, such as alcohol and drug addiction. While the behavior coding task has rapidly adapted machine learning to predict patient states during t

Roberta De Fazio, Stefano Marrone, Laura Verde, Vincenzo Reccia

One of the most appreciated features of Fault Trees (FTs) is their simplicity, making them fit into industrial processes. As such processes evolve in time, considering new aspects of large modern systems, modelling techniques based on FTs have adapted to these needs. This paper proposes an extension of FTs to take into account the problem of Predictive Maint

Matt White, Ibrahim Haddad, Cailean Osborne, Xiao-Yang Yanglet Liu

Generative artificial intelligence (AI) offers numerous opportunities for research and innovation, but its commercialization has raised concerns about the transparency and safety of frontier AI models. Most models lack the necessary components for full understanding, auditing, and reproducibility, and some model producers use restrictive licenses whilst clai

Zeshun Zong, Chenfanfu Jiang, Xuchen Han

In this paper, we introduce a novel convex formulation that seamlessly integrates the Material Point Method (MPM) with articulated rigid body dynamics in frictional contact scenarios. We extend the linear corotational hyperelastic model into the realm of elastoplasticity and include an efficient return mapping algorithm. This approach is particularly effecti

Mohan Das, KangJin Lee, Christopher L. Wirth

Slurry electrodes are an effective means to improve flow battery performance by reducing electrode fouling and increasing the active surface area necessary for electrochemical reactions. It is critical to understand how the slurry formulation impacts its rheological profile and ultimately battery performance. We study the linear and nonlinear rheology of act

Francesco Della Santa

Graph Neural Networks (GNNs) have emerged as effective tools for learning tasks on graph-structured data. Recently, Graph-Informed (GI) layers were introduced to address regression tasks on graph nodes, extending their applicability beyond classic GNNs. However, existing implementations of GI layers lack efficiency due to dense memory allocation. This paper

Jaehun Jung, Ximing Lu, Liwei Jiang, Faeze Brahman

The current winning recipe for automatic summarization is using proprietary large-scale language models (LLMs) such as ChatGPT as is, or imitation learning from them as teacher models. While increasingly ubiquitous dependence on such large-scale language models is convenient, there remains an important question of whether small-scale models could have achiev

Zhaohui Yang, Kshitij Jerath

Traffic flow modeling is typically performed at one of three different scales (microscopic, mesoscopic, or macroscopic), each with distinct modeling approaches. Recent works that attempt to merge models at different scales have yielded some success, but there still exists a need for a single modeling framework that can seamlessly model traffic flow across se

Mohammadhossein Bahari, Saeed Saadatnejad, Amirhossein Askari Farsangi, Seyed-Mohsen Moosavi-Dezfooli

Predicting human trajectories is essential for the safe operation of autonomous vehicles, yet current data-driven models often lack robustness in case of noisy inputs such as adversarial examples or imperfect observations. Although some trajectory prediction methods have been developed to provide empirical robustness, these methods are heuristic and do not o

Hugues Thomas, Mouli Sivapurapu, Jian Zhang

This paper presents the Embedding Pose Graph (EPG), an innovative method that combines the strengths of foundation models with a simple 3D representation suitable for robotics applications. Addressing the need for efficient spatial understanding in robotics, EPG provides a compact yet powerful approach by attaching foundation model features to the nodes of a

Jonas Glatthard, Guillem Aznar-Menargues, José P. Palao, Daniel Alonso

The accurate characterisation of energy exchanges between nanoscale quantum systems and their environments is of paramount importance for quantum technologies, and central to quantum thermodynamics. Here, we show that, in order to accurately approximate steady-state heat currents via perturbative master equations, the coupling-induced reorganisation correcti

Alberto Daza-Garcia

On this work we study associative triple systems of the second kind. We show that for simple triple systems the automorphism group scheme is isomorphic to the automorphism group scheme of the $3$-graded associative algebra with involution constructed by Loos. This result will allow us to prove our main result which is a complete classification up to isomorph

L. L. Lage, N. C. Rappe, A. Latgé

Fractal lattices, with their self-similar and intricate structures, offer potential platforms for engineering physical properties on the nanoscale and also for realizing and manipulating high order topological insulator states in novel ways. Here we present a theoretical study on localized corner and edge states, emerging from topological phases in Sierpinsk

Christopher P. Chambers, Christopher Turansick

This paper uncovers tight bounds on the number of preferences permissible in identified random utility models. We show that as the number of alternatives in a discrete choice model becomes large, the fraction of preferences admissible in an identified model rapidly tends to zero. We propose a novel sufficient condition ensuring identification, which is stric

Anna S. Bodrova, Alexander I. Osinsky

We investigate both ensemble and time-averaged mean-squared displacements of particles in a polydisperse granular system in a homogeneous cooling state. The system contains an arbitrary number of species of different sizes and masses. The collisions between granular particles are described in terms of the models both of constant and time-dependent restitutio

Nicholas Bai, Rahul A. Iyer, Tuomas Oikarinen, Akshay Kulkarni

In this paper, we propose Describe-and-Dissect (DnD), a novel method to describe the roles of hidden neurons in vision networks. DnD utilizes recent advancements in multimodal deep learning to produce complex natural language descriptions, without the need for labeled training data or a predefined set of concepts to choose from. Additionally, DnD is training

Markus Bachmayr, Martin Eigel, Henrik Eisenmann, Igor Voulis

The subject of this work is an adaptive stochastic Galerkin finite element method for parametric or random elliptic partial differential equations, which generates sparse product polynomial expansions with respect to the parametric variables of solutions. For the corresponding spatial approximations, an independently refined finite element mesh is used for e

Michael J. Wilensky, Jacob Burba, Philip Bull, Hugh Garsden

Accurate modelling of the primary beam is an important but difficult task in radio astronomy. For high dynamic range problems such as 21cm intensity mapping, small modelling errors in the sidelobes and spectral structure of the beams can translate into significant systematic errors. Realistic beams exhibit complex spatial and spectral structure, presenting a

Zheng Zhang, Philip Bull, Katrine A. Glasscock

The existence of a radio synchrotron background (RSB) excess is implied by a number of measurements, including excess emission seen by the ARCADE~2 and LWA experiments. Highly sensitive wideband radio arrays, of the kind used to measure the cosmic 21cm signal, provide a promising way to further constrain the RSB excess through its anisotropy, providing addit

Jacob Burba, Philip Bull, Michael J. Wilensky, Fraser Kennedy

Power spectrum estimators are an important tool in efforts to detect the 21 cm brightness temperature fluctuations from neutral hydrogen at early times. An initial detection will likely be statistical in nature, meaning that it will not be possible to make a coherent map of the brightness temperature fluctuations; instead, only their variance will be measure

Katrine A. Glasscock, Philip Bull, Jacob Burba, Hugh Garsden

An important application of next-generation wide-field radio interferometers is making high dynamic range maps of radio emission. Traditional deconvolution methods like CLEAN can give poor recovery of diffuse structure, prompting the development of wide-field alternatives like Direct Optimal Mapping and $m$-mode analysis. In this paper, we propose an alterna

R. Kenny Jones, Siddhartha Chaudhuri, Daniel Ritchie

People grasp flexible visual concepts from a few examples. We explore a neurosymbolic system that learns how to infer programs that capture visual concepts in a domain-general fashion. We introduce Template Programs: programmatic expressions from a domain-specific language that specify structural and parametric patterns common to an input concept. Our framew

Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb

We study pre-training representations for decision-making using video data, which is abundantly available for tasks such as game agents and software testing. Even though significant empirical advances have been made on this problem, a theoretical understanding remains absent. We initiate the theoretical investigation into principled approaches for representa

Pál Burai, Paweł Pasteczka

The main goal of this paper to introduce a new model of evolvement of narratives (common opinions, information bubble) on networks. Our main tools come from invariant mean theory and graph theory. The case, when the root set of the network (influencers, news agencies, etc.) is ergodic is fully discussed. The other possibility, when the root contains more tha

David González-Álvaro, Masoumeh Zarei

We prove that there exist $\mathsf{SU}_{3}$-invariant metrics on Aloff-Wallach spaces $W^7_{k_1, k_2}$, as well as $\mathsf{SU}_{5}$-invariant metrics on the Berger space $B^{13}$, which have positive sectional curvature and evolve under the Ricci flow to metrics with non-positively curved planes.

Li Tuobang

In descriptive statistics, $U$-statistics arise naturally in producing minimum-variance unbiased estimators. In 1984, Serfling considered the distribution formed by evaluating the kernel of the $U$-statistics and proposed generalized $L$-statistics which includes Hodges-Lehamnn estimator and Bickel-Lehmann spread as special cases. However, the structures of

Jiří Mayer, Milan Straka, Jan Hajič, Pavel Pecina

The majority of recent progress in Optical Music Recognition (OMR) has been achieved with Deep Learning methods, especially models following the end-to-end paradigm, reading input images and producing a linear sequence of tokens. Unfortunately, many music scores, especially piano music, cannot be easily converted to a linear sequence. This has led OMR resear

Giulia Rizzoli, Matteo Caligiuri, Donald Shenaj, Francesco Barbato

In Federated Learning (FL), multiple clients collaboratively train a global model without sharing private data. In semantic segmentation, the Federated source Free Domain Adaptation (FFreeDA) setting is of particular interest, where clients undergo unsupervised training after supervised pretraining at the server side. While few recent works address FL for au

Yuyi Zhang, Yuanzhi Zhu, Dezhi Peng, Peirong Zhang

Text recognition, especially for complex scripts like Chinese, faces unique challenges due to its intricate character structures and vast vocabulary. Traditional one-hot encoding methods struggle with the representation of hierarchical radicals, recognition of Out-Of-Vocabulary (OOV) characters, and on-device deployment due to their computational intensity.

Djavan De Clercq, Elias Nehring, Harry Mayne, Adam Mahdi

Coverage of ChatGPT-style large language models (LLMs) in the media has focused on their eye-catching achievements, including solving advanced mathematical problems and reaching expert proficiency in medical examinations. But the gradual adoption of LLMs in agriculture, an industry which touches every human life, has received much less public scrutiny. In th

Chih-Chun Hsu, Adam J. Burgasser, Christopher A. Theissen, Jessica L. Birky

We present precise measurements of radial (RV) and projected rotational ($v\sin{i}$) velocities, effective temperatures, and surface gravities of a sample of 258 M6 to L2 dwarfs with multi-epoch, high-resolution ($\lambda/\Delta\lambda$ = 22500), near-infrared (1.514-1.696 $\mu$m) spectroscopic observations reported in the Apache Point Observatory Galactic E

Adrian Yao, Sally M. Benson, William C. Chueh

Sodium-ion batteries have garnered significant attention as a potentially low-cost alternative to lithium-ion batteries, which have experienced supply shortages and pricing volatility of key minerals. Here we assess their techno-economic competitiveness against incumbent lithium-ion batteries using a modeling framework incorporating componential learning cur

Gordon W. Semenoff

Some aspects of the theory of fermions living on three dimensional spacetime with a flat co-dimension one boundary are discussed, particularly a case where the boundary condition preserves scale and translation invariance but violates the residual Lorentz and conformal symmetries. This case is interesting because the Dirac equation has normalizable stationar

David Grabovsky

We discuss correlators of light fields in heavy states in 3d gravity and holographic 2d CFTs. In the bulk, the propagator of free fields in AdS backgrounds containing a conical defect or a BTZ black hole can be obtained by solving the wave equation, as well as by the method of images. On the boundary, these geometries are sourced by heavy operator insertions

Jeremy R. Harper

In the evolving landscape of clinical informatics, the integration and utilization of software tools developed through governmental funding represent a pivotal advancement in research and application. However, the dispersion of these tools across various repositories, with no centralized knowledge base, poses significant challenges to leveraging their full p

Diwei Wang, Kun Yuan, Candice Muller, Frédéric Blanc

We present a knowledge augmentation strategy for assessing the diagnostic groups and gait impairment from monocular gait videos. Based on a large-scale pre-trained Vision Language Model (VLM), our model learns and improves visual, textual, and numerical representations of patient gait videos, through a collective learning across three distinct modalities: ga

Abdallah Daddi-Moussa-Ider, Elsen Tjhung, Thomas Richter, Andreas M. Menzel

To make progress towards the development of a theory on the motion of inclusions in thin structured films and membranes, we here consider as an initial step a circular disk in a two-dimensional, uniaxially anisotropic fluid layer. We assume overdamped dynamics, incompressibility of the fluid, and global alignment of the axis of anisotropy. Motion within this

Catherine Arnett, Pamela D. Rivière, Tyler A. Chang, Sean Trott

The relationship between language model tokenization and performance is an open area of research. Here, we investigate how different tokenization schemes impact number agreement in Spanish plurals. We find that morphologically-aligned tokenization performs similarly to other tokenization schemes, even when induced artificially for words that would not be tok

Ashu Kushwaha

In this thesis, we aim to investigate the mechanisms underlying the genesis of magnetic fields in the Universe and explore their potential in addressing the matter-antimatter asymmetry. We construct consistent models that generate the helical magnetic field consistent with observations. We also develop an effective field theory approach to magnetogenesis, wh

Junyan Li, Shengshi Pang

Abstract Superresolution has been demonstrated to overcome the limitation of the Rayleigh's criterion and achieve significant improvement of the precision in resolving the separation of two incoherent optical point sources. However, in recent years, it was found that if the photon numbers of the two incoherent optical sources are unknown, the precision of su

Ivana Beslic, Ashley T. Barnes, Frank Bigiel, Maria Jesus Jimenez-Donaire

Studying molecular gas in nearby galaxies using hydrogen cyanide (HCN) as a tracer for higher densities than CO emission still poses a significant challenge. Even though several galaxies have HCN maps on a few kpc scales, higher-resolution maps are still required. Our goal is to examine the contrast in intensity between two tracers that probe different densi

Raffaele Paolino, Sohir Maskey, Pascal Welke, Gitta Kutyniok

We introduce $r$-loopy Weisfeiler-Leman ($r$-$\ell{}$WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, $r$-$\ell{}$MPNN, that can count cycles up to length $r + 2$. Most notably, we show that $r$-$\ell{}$WL can count homomorphisms of cactus graphs. This strictly extends classical 1-WL, which can only count homomorphisms of

Rinaldo B. Schinazi

Some species of salmon and sea turtle are famously good at finding their birth place to reproduce after having travelled vast expanses of ocean. In contrast, imagine now a species (maybe ancestral to the salmon or turtle) which has to find its birth place to reproduce but has no navigation skills and relies on chance alone. Would such an imaginary species su

Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul

Given an intractable distribution $p$, the problem of variational inference (VI) is to find the best approximation from some more tractable family $Q$. Commonly, one chooses $Q$ to be a family of factorized distributions (i.e., the mean-field assumption), even though $p$ itself does not factorize. We show that this mismatch can lead to an impossibility theor

Aymene Berriche, Mehdi Adjal Zakaria, Riyadh Baghdadi

Deep hashing techniques have emerged as the predominant approach for efficient image retrieval. Traditionally, these methods utilize pre-trained convolutional neural networks (CNNs) such as AlexNet and VGG-16 as feature extractors. However, the increasing complexity of datasets poses challenges for these backbone architectures in capturing meaningful feature

Daobo Zhang, Josef Kiendl

We propose a novel variationally consistent membrane wrinkling model for analyzing the mechanical responses of wrinkled thin membranes. The elastic strain energy density is split into tensile and compressive terms via a spectral decomposition of the strain tensor. Tensile and compressive parts of the stress and constitutive tensors are then obtained via cons

Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi

Video outpainting is a challenging task, aiming at generating video content outside the viewport of the input video while maintaining inter-frame and intra-frame consistency. Existing methods fall short in either generation quality or flexibility. We introduce MOTIA Mastering Video Outpainting Through Input-Specific Adaptation, a diffusion-based pipeline tha

Dimitrios Charamaras

In this paper we are concerned with the study of additive ergodic averages in multiplicative systems and the investigation of the "pretentious" dynamical behaviour of these systems. We prove a mean ergodic theorem (Theorem A) that generalises Hal\'asz's mean value theorem for finitely generated multiplicative functions taking values in the unit circle. In ad

Girraj Kumar Verma, Nahida Majeed Wani, Prosanta Gope

Vehicular Ad-hoc Networks (VANETs) marked a pronounced change in the Intelligent Transport System and Smart Cities through seamless vehicle communication to intensify safety and efficacy. However, a few authentication schemes have been devised in the literature to ensure the authenticity of the source and information in the post-quantum era. The most popular

Lucas Aragão, João Pedro Marciano, Walner Mendonça

A classical result of Chv\'atal implies that if $n \geq (r-1)(t-1) +1$, then any colouring of the edges of $K_n$ in red and blue contains either a monochromatic red $K_r$ or a monochromatic blue $P_t$. We study a natural generalization of his result, determining the exact minimum degree condition for a graph $G$ on $n = (r - 1)(t - 1) + 1$ vertices which gua

Raven Beutner, Bernd Finkbeiner

Strategy logic (SL) is a powerful temporal logic that enables strategic reasoning in multi-agent systems. SL supports explicit (first-order) quantification over strategies and provides a logical framework to express many important properties such as Nash equilibria, dominant strategies, etc. While in SL the same strategy can be used in multiple strategy prof

Jon Vadillo, Roberto Santana, Jose A. Lozano, Marta Kwiatkowska

The lack of transparency of Deep Neural Networks continues to be a limitation that severely undermines their reliability and usage in high-stakes applications. Promising approaches to overcome such limitations are Prototype-Based Self-Explainable Neural Networks (PSENNs), whose predictions rely on the similarity between the input at hand and a set of prototy

Maxime Ingremeau, Martin Vogel

It has been known since the work of Avakumov\'ic, H\"ormander and Levitan that, on any compact smooth Riemannian manifold, if $-\Delta_g \psi_\lambda = \lambda \psi_\lambda$, then $\|\psi_\lambda\|_{L^\infty} \leq C \lambda^{\frac{d-1}{4}} \|\psi_\lambda\|_{L^2}$. It is believed that, on manifolds of negative curvature, such a bound can be largely improved;

Takuya Ura, Lina Zhang

This paper provides a unified framework for bounding policy relevant treatment effects using instrumental variables. In this framework, the treatment selection may depend on multidimensional unobserved heterogeneity. We derive bilinear constraints on the target parameter by extracting information from identifiable estimands. We apply a convex relaxation meth

Atnafu Lambebo Tonja, Israel Abebe Azime, Tadesse Destaw Belay, Mesay Gemeda Yigezu

Large language models (LLMs) have gained popularity recently due to their outstanding performance in various downstream Natural Language Processing (NLP) tasks. However, low-resource languages are still lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. Ethiopian languages exhibit rema

Mary Milad, Christina Ovezik, Dimitris Karakostas, Daniel W. Woods

The security of blockchain systems depends on the distribution of mining power across participants. If sufficient mining power is controlled by one entity, they can force their own version of events. This may allow them to double spend coins, for example. For Proof of Work (PoW) blockchains, however, the distribution of mining power cannot be read directly f

Bram Mesland, Adam Rennie

We combine Hilbert module and algebraic techniques to give necessary and sufficient conditions for the existence of an Hermitian torsion-free connection on the bimodule of differential one-forms of a first order differential calculus. In the presence of the extra structure of a bimodule connection, we give sufficient conditions for uniqueness. We prove that