October 2024 arXiv papers — page 55

Alberto Rolandi

The emergence of irreversibility in physical processes, despite the fundamentally reversible nature of quantum mechanics, remains an open question in physics. This thesis explores the intricate relationship between quantum mechanics and thermodynamics, with a particular focus on minimizing entropy production in finite-time processes. By employing tools from

Taido Purason, Hele-Andra Kuulmets, Mark Fishel

The advancement of large language models (LLMs) has predominantly focused on high-resource languages, leaving low-resource languages, such as those in the Finno-Ugric family, significantly underrepresented. This paper addresses this gap by focusing on V\~oro, Livonian, and Komi. We cover almost the entire cycle of LLM creation, from data collection to instru

Nick S. Blunt, Laura Caune, Javiera Quiroz-Fernandez

Quantum Monte Carlo (QMC) methods are powerful approaches for solving electronic structure problems. Although they often provide high-accuracy solutions, the precision of most QMC methods is ultimately limited by a trial wave function that must be used. Recently, an approach has been demonstrated to allow the use of trial wave functions prepared on a quantum

Ksenia Pereverdieva, André Deutz, Tessa Ezendam, Thomas Bäck

Indicator-based (multiobjective) diversity optimization aims at finding a set of near (Pareto-)optimal solutions that maximizes a diversity indicator, where diversity is typically interpreted as the number of essentially different solutions. Whereas, in the first diversity-oriented evolutionary multiobjective optimization algorithm, the NOAH algorithm by Ulr

Ayodele Ore, Caroline Heneka, Tilman Plehn

The Square Kilometer Array will initiate a new era of radio astronomy by allowing 3D imaging of the Universe during Cosmic Dawn and Reionization. Modern machine learning is crucial to analyse the highly structured and complex signal. However, accurate training data is expensive to simulate, and supervised learning may not generalize. We introduce a self-supe

David Abella

In this doctoral thesis, we investigate the complex interplay between temporal dynamics associated with aging and memory and their effects on social and economic systems. To do so, we combine theoretical modeling, to explore the aging implications in threshold (peer pressure) models, and empirical analysis, to address the impact of temporal and spatial patte

Tomonori Takahashi, Takayuki Mizuno

Despite its practical significance, generating realistic synthetic financial time series is challenging due to statistical properties known as stylized facts, such as fat tails, volatility clustering, and seasonality patterns. Various generative models, including generative adversarial networks (GANs) and variational autoencoders (VAEs), have been employed t

Junheng Peng, Yong Li, Yingtian LIu, Mingwei Wang

Seismic exploration is currently the most mature approach for studying subsurface structures, yet the presence of noise greatly restricts its imaging accuracy. Previous methods still face significant challenges: traditional computational methods are often computationally complex and their effectiveness is hard to guarantee; deep learning methods rely heavily

Sicong Huang, Roozbeh Jafari, Bobak J. Mortazavi

Goal: Continuous arterial blood pressure (ABP) waveform is invasive but essential for hemodynamic monitoring. Current non-invasive techniques reconstruct ABP waveforms with pulsatile signals but derived inaccurate systolic and diastolic blood pressure (SBP/DBP) and were sensitive to individual variability. Methods: ArterialNet integrates generalized pulsatil

Zhaofeng Si, Shu Hu, Kaiyi Ji, Siwei Lyu

Meta-learning is a general approach to equip machine learning models with the ability to handle few-shot scenarios when dealing with many tasks. Most existing meta-learning methods work based on the assumption that all tasks are of equal importance. However, real-world applications often present heterogeneous tasks characterized by varying difficulty levels,

Claire Schlesinger, Arjun Guha, Joydeep Biswas

Using Large Language Models (LLMs) to produce robot programs from natural language has allowed for robot systems that can complete a higher diversity of tasks. However, LLM-generated programs may be faulty, either due to ambiguity in instructions, misinterpretation of the desired task, or missing information about the world state. As these programs run, the

W. Liu, S. Li, N. -J. Guo, X. -D. Zeng

Spin defects in atomically thin two-dimensional (2D) materials such as hexagonal boron nitride (hBN) attract significant attention for their potential quantum applications. The layered host materials not only facilitate seamless integration with optoelectronic devices but also enable the formation of heterostructures with on-demand functionality. Furthermore

Kristen Dawson, Serkan Hoşten, Kaie Kubjas, Lilja Metsälampi

We characterize when a size-2 positive semidefinite (psd) factorization of a positive matrix of rank 3 and psd rank 2 is unique. The characterization is obtained using tools from rigidity theory. In the first step, we define s-infinitesimally rigid psd factorizations and characterize 1- and 2-infinitesimally rigid size-2 psd factorizations. In the second ste

Graziano A. Manduzio, Federico A. Galatolo, Mario G. C. A. Cimino, Enzo Pasquale Scilingo

Recent advancements in Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language understanding and generation. While these models excel in general complex reasoning tasks, they still face challenges in mathematical problem-solving and logical reasoning. To address these limitations, researchers have explored function calling

Omer Nahum, Nitay Calderon, Orgad Keller, Idan Szpektor

NLP benchmarks rely on standardized datasets for training and evaluating models and are crucial for advancing the field. Traditionally, expert annotations ensure high-quality labels; however, the cost of expert annotation does not scale well with the growing demand for larger datasets required by modern models. While crowd-sourcing provides a more scalable s

Willem Esterhuizen, Bernhard Maschke, Till Preuster, Manuel Schaller

We investigate the existence of solutions of reversible and irreversible port-Hamiltonian systems. To this end, we utilize the associated exergy, a function that is composed of the system's Hamiltonian and entropy, to prove global existence in time for bounded control functions. The results are then leveraged to prove existence of solutions of energy- and en

Nelson Hernández Rodríguez, Maria J. Rodriguez

We uncover the thermodynamical properties of a class of non-asymptotically flat geometries, referred here as the Kerr effective geometries, that realize the hidden symmetries of Kerr black hole spacetimes via Teukolsky's equation in the theory of linear gravitational perturbations. While the thermodynamic properties, such as entropy, remain the same as those

Taushif Ahmed, Syed Mehedi Hasan, Andreas Rapakoulias

This letter introduces a novel analytical approach to calculating phase-space integrals, crucial for precision in particle physics. We develop a method to compute angular components using multifold Mellin-Barnes integrals, yielding results in terms of Goncharov polylogarithms for integrals involving three denominators. Our results include expressions for mas

Yaowei Long, Seth Pettie, Thatchaphol Saranurak

We consider the problem of assigning short labels to the vertices and edges of a graph $G$ so that given any query $\langle s,t,F\rangle$ with $|F|\leq f$, we can determine whether $s$ and $t$ are still connected in $G-F$, given only the labels of $F\cup\{s,t\}$. This problem has been considered when $F\subset E$ (edge faults), where correctness is guarantee

Richard Stiskalek, Harry Desmond

We perform a Bayesian analysis of anisotropy in binary galaxy spin directions in the Hyper-Suprime Cam Data Release 3 catalogue, in response to a recent claim that it exhibits a dipole (Shamir 2024). We find no significant evidence for anisotropy, or for a direction-independent spin probability that differs from 0.5. These results are unchanged allowing for

Luca Capogna, Ryan Gibara, Riikka Korte, Nageswari Shanmugalingam

We prove well-posedness, Harnack inequality and sharp regularity of solutions to a fractional $p$-Laplace non-homogeneous equation $(-\Delta_p)^su =f$, with $0<s<1$, $1<p<\infty$, for data $f$ satisfying a weighted $L^{p'}$ condition in a doubling metric measure space $(Z,d_Z,\nu)$ that is possibly unbounded. Our approach is inspired by the work of Caffarell

Michael Kodochian

In 2023, Lubineau et al. published an article [1] detailing several experiments carried out with dyslexic readers. These authors attempted to measure the change in reading performance under different reading conditions using flickering devices. Beyond the low-frequency systems which have nevertheless shown their interest for some cases, we restrict here our

Shafkat Farabi, Tharindu Ranasinghe, Diptesh Kanojia, Yu Kong

Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text

Weijian Luo

One-step text-to-image generator models offer advantages such as swift inference efficiency, flexible architectures, and state-of-the-art generation performance. In this paper, we study the problem of aligning one-step generator models with human preferences for the first time. Inspired by the success of reinforcement learning using human feedback (RLHF), we

Shahar Mendelson, Grigoris Paouris, Roman Vershynin

The problem of detecting fake data inspires the following seemingly simple mathematical question. Sample a data point $X$ from the standard normal distribution in $\mathbb{R}^n$. An adversary observes $X$ and corrupts it by adding a vector $rt$, where they can choose any vector $t$ from a fixed set $T$ of the adversary's ``tricks'', and where $r>0$ is a fixe

Arnav Samal, Ranya Batsyas

We present CapsoNet, a deep learning framework developed for the Capsule Vision 2024 Challenge, designed to perform multi-class abnormality classification in video capsule endoscopy (VCE) frames. CapsoNet leverages an ensemble of convolutional neural networks (CNNs) and transformer-based architectures to capture both local and global visual features. The mod

Matthias Bentert, Fedor V. Fomin, Petr A. Golovach, Tuukka Korhonen

Cycle packing is a fundamental problem in optimization, graph theory, and algorithms. Motivated by recent advancements in finding vertex-disjoint paths between a specified set of vertices that either minimize the total length of the paths [Bj\"orklund, Husfeldt, ICALP 2014; Mari, Mukherjee, Pilipczuk, and Sankowski, SODA 2024] or request the paths to be shor

Hannah Beaux, Pegah Karimi, Otilia Pop, Rob Clark

In this innovative practice full paper, we address the equity gap for neurodivergent and situationally limited learners by identifying the spectrum of dynamic factors that impact learning and function. Educators have shown a growing interest in identifying learners' cognitive abilities and learning preferences to measure their impact on academic achievement.

Alina A. Volnova, Patrick D. Aleo, Anastasia Lavrukhina, Etienne Russeil

SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. The work carried out by SNAD not only contributes to the discovery and classification of various astronomical phenomena but also enhances our understanding and implementation of mac

Rubén Gómez González, Vicente Garzó, Ricardo Brito, Rodrigo Soto

Mass transport of impurities immersed in a confined quasi-two-dimensional moderately dense granular gas of inelastic hard spheres is studied. The effect of the confinement on granular particles is modeled through a collisional model (the so-called $\Delta$-model) that includes an effective mechanism to transfer the kinetic energy injected by vibration in the

Mohammed J. Karaki, Ahmed E. Fahmy, Archibald J. Williams, Sara Haravifard

Topological magnons give rise to possibilities for engineering novel spintronics devices with critical applications in quantum information and computation, due to its symmetry-protected robustness and low dissipation. However, to make reliable and systematic predictions about material realization of topological magnons has been a major challenge, due to the

Junming Lao, Tao Zhou

In the realm of condensed matter physics, higher-order topological insulators and superconductors have become a focal point of research due to their unique gapless boundary states at lower-dimensional boundaries such as corners and edges. This paper delves into the effects of a $d$-density wave (DDW) on first-order topological insulators and the competitive

Paul Friedrich, Barna Pásztor, Giorgia Ramponi

Pricing algorithms have demonstrated the capability to learn tacit collusion that is largely unaddressed by current regulations. Their increasing use in markets, including oligopolistic industries with a history of collusion, calls for closer examination by competition authorities. In this paper, we extend the study of tacit collusion in learning algorithms

Zhaolin Gao, Joyce Zhou, Yijia Dai, Thorsten Joachims

There is a growing interest in natural language-based user profiles for recommender systems, which aims to enhance transparency and scrutability compared with embedding-based methods. Existing studies primarily generate these profiles using zero-shot inference from large language models (LLMs), but their quality remains insufficient, leading to suboptimal re

Erhan Bayraktar, Nikolaos Kolliopoulos

We consider an $N$-player game where the states of the players evolve with time as Stochastic Differential Equations (SDEs) with interaction only in the drift terms. Each player controls the drift of the SDE satisfied by her state process, aiming to minimize the expected value of a cost that depends on the paths of the player's state and the empirical measur

Katharina Friedl, Noémie Jaquier, Jens Lundell, Tamim Asfour

By incorporating physical consistency as inductive bias, deep neural networks display increased generalization capabilities and data efficiency in learning nonlinear dynamic models. However, the complexity of these models generally increases with the system dimensionality, requiring larger datasets, more complex deep networks, and significant computational e

Carlos Hermoso, Juan Gerardo Alcázar

We characterize the polynomials $p_1(t), ... , p_n(t)$ whose Wronskian $W(p_1, ... , p_n)$ is a nonzero constant. Then, we generalize our results to characterize the Laurent polynomials with the same property. Finally, for rational functions we prove an impossibility result for $n=2$, and pose the case $n \geq 3$ as an open question, although we suggest an i

Linda Laurier, Ave Giulietta, Arlo Octavia, Meade Cleti

The emergence of diffusion models has transformed synthetic media generation, offering unmatched realism and control over content creation. These advancements have driven innovation across fields such as art, design, and scientific visualization. However, they also introduce significant ethical and societal challenges, particularly through the creation of hy

Sian Nie, Panjun Tan, Qingchao Yu

In this paper, we study convex elements in a (twisted) Weyl group introduced by Ivanov and the first named author. We show that each conjugacy class of the twisted Weyl group contains a convex element, and moreover, the Steinberg cross-sections exist for all convex elements. This result strictly enlarges the cases of Steinberg cross-sections from a new persp

Sebastián Espinel-Ríos, José Montaño López, José L. Avalos

This work presents an omics-driven modeling pipeline that integrates machine-learning tools to facilitate the dynamic modeling of multiscale biological systems. Random forests and permutation feature importance are proposed to mine omics datasets, guiding feature selection and dimensionality reduction for dynamic modeling. Continuous and differentiable machi

Mehmet Celik, Mathis Duguin, Jia Guo, Dianlun Luo

In 2021, Dan Reznik made a YouTube video demonstrating that power circles of Poncelet triangles have an invariant total area. He made a simulation based on this observation and put forward a few conjectures. One of these conjectures suggests that the sum of the areas of three circles, each centered at the midpoint of a side of the Poncelet triangle and passi

I-Cheng Lin, Osman Yagan, Carlee Joe-Wong

Federated learning has recently gained popularity as a framework for distributed clients to collaboratively train a machine learning model using local data. While traditional federated learning relies on a central server for model aggregation, recent advancements adopt a decentralized framework, enabling direct model exchange between clients and eliminating

Miranda Christ, Sam Gunn, Tal Malkin, Mariana Raykova

The recent explosion of high-quality language models has necessitated new methods for identifying AI-generated text. Watermarking is a leading solution and could prove to be an essential tool in the age of generative AI. Existing approaches embed watermarks at inference and crucially rely on the large language model (LLM) specification and parameters being s

Aryo Pradipta Gema, Chen Jin, Ahmed Abdulaal, Tom Diethe

Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context or incorrectly recalling internal knowledge. Recent studies have identified specific attention heads within the Transformer architecture, known as retrieval heads, responsible for extracting relevant contextual informati

Somdeb Lahiri

We present a theory of expected utility with state-dependent linear utility functions for monetary returns, that incorporates the possibility of loss-aversion. Our results relate to first order stochastic dominance, mean-preserving spread, increasing-concave linear utility profiles and risk aversion. As an application of the expected utility theory developed

Philipp Reiser, Francesca Tripaldi

We consider the problem of preserving weighted Riemannian metrics of positive Bakry-\'Emery Ricci curvature along surgery. We establish two theorems of this type: One for connected sums, and one for surgeries along higher-dimensional spheres. In contrast to known surgery results for positive Ricci curvature, these results are local, i.e. we only impose assum

Vittorio Erba, Emanuele Troiani, Luca Biggio, Antoine Maillard

Current progress in artificial intelligence is centered around so-called large language models that consist of neural networks processing long sequences of high-dimensional vectors called tokens. Statistical physics provides powerful tools to study the functioning of learning with neural networks and has played a recognized role in the development of modern

Sanghyuk Chun, Wonjae Kim, Song Park, Sangdoo Yun

Vision-language models (VLMs) embed aligned image-text pairs into a joint space but often rely on deterministic embeddings, assuming a one-to-one correspondence between images and texts. This oversimplifies real-world relationships, which are inherently many-to-many, with multiple captions describing a single image and vice versa. We introduce Probabilistic

F. Robicheaux

We theoretically and computationally investigate the role that the spatial spread of atoms plays in the transmission and reflection of weak light from atom arrays. In particular, we investigate whether coherent wave functions for the atoms' positions leads to different results from a thermal distribution with the same spatial spread. We find that the coheren

Yin-Kai Yu, Zhi-Xuan Li, Shuai Yin, Zi-Xiang Li

The notorious fermion sign problem, arising from fermion statistics, presents a fundamental obstacle to the numerical simulation of quantum many-body systems. Here, we introduce a framework that circumvents the sign problem in the studies of quantum criticality and its associated phases by leveraging imaginary-time nonequilibrium critical dynamics. We demons

Vladimir Juričić, Olivera Miskovic, Francisca Ramírez Carrasco

We study torsional topological defects in Einstein-Gauss-Bonnet gravity in ($4+1$)-dimensional anti-de Sitter spacetime. In the holographic interpretation, these correspond to crystalline dislocation defects associated with the discrete lattice translational symmetry. The Gauss-Bonnet coupling is fixed at the Chern-Simons point. By solving the equations of m

Yuxuan Yu, Yuzhuo Fang, Hua Tong, Yongjie Jessica Zhang

In this paper, we present a novel algorithm that integrates deep learning with the polycube method (DL-Polycube) to generate high-quality hexahedral (hex) meshes, which are then used to construct volumetric splines for isogeometric analysis. Our DL-Polycube algorithm begins by establishing a connection between surface triangular meshes and polycube structure

Advait Sarkar

Among the many narratives of the transformative power of Generative AI is one that sees in the world a latent nation of programmers who need to wield nothing but intentions and natural language to render their ideas in software. In this paper, this outlook is problematised in two ways. First, it is observed that generative AI is not a neutral vehicle of inte

Maya K. Nachesa, Vlad Niculae

Speech recognition performance varies by language, domain, and speaker characteristics such as accent, but fine-tuning a model on any of these categories may lead to catastrophic forgetting. Token-level $k$ nearest neighbor search ($k$NN), first proposed for neural sequence decoders for natural language generation (NLG) and machine translation (MT), is a non

Justin D. Yeakel, Matthew C. Hutchinson, Christopher P. Kempes, Paul L. Koch

Body size drives the energetic demands of organisms, constraining trophic interactions between species and playing a significant role in shaping the feasibility of species' populations in a community. On macroevolutionary timescales, these demands feed back to shape the selective landscape driving the evolution of body size and diet. We develop a theoretical

Rohit Roy, Koushik Ballav Goswami, Debadri Bhattacharjee, Pradip Kumar Chattopadhyay

In this work, a detailed investigation of compact stars composed of deconfined quark matter with finite strange quark mass ($m_s \neq 0$) admixed with dark energy is presented. The quark sector is modeled using the MIT bag model equation of state, while the dark energy component obeys a linear equation of state, $p^{de} = \omega \rho^{de}$ with $\omega$ in t

Mohamed-Amine Lahmeri, Víctor Mustieles-Pérez, Martin Vossiek, Gerhard Krieger

In this paper, we study the optimization of the sensing accuracy of unmanned aerial vehicle (UAV)-based dual-baseline interferometric synthetic aperture radar (InSAR) systems. A swarm of three UAV-synthetic aperture radar (SAR) systems is deployed to image an area of interest from different angles, enabling the creation of two independent digital elevation m

Ji-Chong Yang, Shuai Zhang, Chong-Xing Yue

Due to the success of the Standard Model~(SM), it is reasonable to anticipate that the signal of new physics~(NP) beyond the SM is small. Consequently, future searches for NP and precision tests of the SM will require high luminosity collider experiments. Moreover, as precision tests advance, rare processes with many final-state particles require considerati

Jason DeVito, Miguel Domínguez-Vázquez, David González-Álvaro, Alberto Rodríguez-Vázquez

We construct metrics of positive $2^{\rm nd}$ intermediate Ricci curvature, $\mathrm{Ric}_2>0$, on closed manifolds of dimensions 10, 11, 12, 13 and 14, including $\mathbb{S}^6\times\mathbb{S}^7$, $\mathbb{S}^7\times\mathbb{S}^7$ and all their simply connected isometric quotients. In particular, we obtain infinitely many examples in dimension 13. We also pro

Ashish Hingle, Aditya Johri

As the application of AI continues to expand, students in technology programs are poised to be both producers and users of the technologies. They are also positioned to engage with AI applications within and outside the classroom. While focusing on the curriculum when examining students' AI knowledge is common, extending this connection to students' everyday

Udvas Das, Debabrota Basu

Pure exploration in bandits formalises multiple real-world problems, such as tuning hyper-parameters or conducting user studies to test a set of items, where different safety, resource, and fairness constraints on the decision space naturally appear. We study these problems as pure exploration in multi-armed bandits with unknown linear constraints, where the

Alexandru Macridin, Andrew Cameron, Cristian Pena, Si Xie

We present a hybrid continuous variable-discrete variable entanglement generation protocol using linear optics and homodyne measurements, capable of producing multiple high-fidelity Bell pairs per protocol iteration, with an approximate $0.5$ success probability. The effectiveness of the protocol is determined by the squeezing strength. To increase the numbe

Erick J Canales-Rodríguez, Chantal M. W. Tax, Elda Fischi-Gomez, Derek K. Jones

Quantifying the myelin sheath radius of myelinated axons in vivo is important for understanding, diagnosing, and monitoring various neurological disorders. Despite advancements in diffusion MRI (dMRI) microstructure techniques, there are currently no models specifically designed to estimate myelin sheath radii. This proof-of-concept theoretical study present

Shreeyash Gowaikar, Hugo Berard, Rashid Mushkani, Shin Koseki

As AI systems, particularly generative models, increasingly influence decision-making, ensuring that they are able to fairly represent diverse human preferences becomes crucial. This paper introduces a novel framework for evaluating epistemic fairness in preference learning models inspired by economic theories of inequality and Rawlsian justice. We propose m

Mingyu Xu, Greeshma C. Jose, Aya Rutherford, Haozhe Wang

La$_3$Ni$_2$O$_7$ exists in two polymorphs: an unconventional structure with alternating layers of single- and triple-layered nickel-oxygen octahedra, and a classical double-layered Ruddlesden-Popper phase. In this study, we report the growth of single crystals of classical double-layered La$_3$Ni$_2$O$_7$ using the floating zone method. Structural character

Matthew L. Evans, Gian-Marco Rignanese, David Elbert, Peter Kraus

Two key issues hindering the transition towards FAIR data science are the poor discoverability and inconsistent instructions for the use of data extractor tools, i.e., how we go from raw data files created by instruments, to accessible metadata and scientific insight. If the existing format conversion tools are hard to find, install, and use, their reimpleme

Lukas Weber, Leonardo dos Anjos Cunha, Miguel A. Morales, Angel Rubio

We present a generalization of the phaseless auxiliary-field quantum Monte Carlo (AFQMC) method to cavity quantum-electrodynamical (QED) matter systems. The method can be formulated in both the Coulomb and the dipole gauge. We verify its accuracy by benchmarking calculations on a set of small molecules against full configuration interaction and state-of-the-

M. Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga, Marco Mondelli

A growing number of machine learning scenarios rely on knowledge distillation where one uses the output of a surrogate model as labels to supervise the training of a target model. In this work, we provide a sharp characterization of this process for ridgeless, high-dimensional regression, under two settings: (i) model shift, where the surrogate model is arbi

Artur Kiulian, Anton Polishko, Mykola Khandoga, Yevhen Kostiuk

In this paper, we propose a model-agnostic cost-effective approach to developing bilingual base large language models (LLMs) to support English and any target language. The method includes vocabulary expansion, initialization of new embeddings, model training and evaluation. We performed our experiments with three languages, each using a non-Latin script - U

Roman Freiberg, Alexander Qualmann, Ngo Anh Vien, Gerhard Neumann

Grasping is a fundamental skill in robotics with diverse applications across medical, industrial, and domestic domains. However, current approaches for predicting valid grasps are often tailored to specific grippers, limiting their applicability when gripper designs change. To address this limitation, we explore the transfer of grasping strategies between va

Aya Ghoul, Kerstin Hammernik, Andreas Lingg, Patrick Krumm

In Magnetic Resonance Imaging (MRI), high temporal-resolved motion can be useful for image acquisition and reconstruction, MR-guided radiotherapy, dynamic contrast-enhancement, flow and perfusion imaging, and functional assessment of motion patterns in cardiovascular, abdominal, peristaltic, fetal, or musculoskeletal imaging. Conventionally, these motion est

Frederic Cerou, Patrick Heas, Mathias Rousset

This work proposes an adaptive sequential Monte Carlo sampling algorithm to solve Bayesian inverse problems in scenarios where likelihood evaluations are costly but can be approximated using a surrogate model built from previous evaluations of the true likelihood. A rough estimate of the surrogate error is required. The method relies on an adaptive SMC frame

Gabriel Díaz Ramos, Toros Arikan, Richard G. Baraniuk

The Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem, which seeks the shortest interconnection of a given number of terminals in a rectilinear plane while avoiding obstacles, is a critical task in integrated circuit design, network optimization, and robot path planning. Since OARSMT is NP-hard, exact algorithms scale poorly with the number

Pedro. C. Rijo, Francisco J. Galindo-Rosales

The electrorheological (ER) characterization of low-viscosity fluids is paramount for producing micro- and nanoscale products through electrohydrodynamic (EHD) techniques, such as EHD-jet printing, electrospray, and electrospinning. Key properties such as viscosity, surface tension, dielectric properties, electrical conductivity, and relaxation time signific

Xiaoyu Zhang, Teng Zhou, Xinlong Zhang, Jia Wei

Diffusion models have recently gained recognition for generating diverse and high-quality content, especially in image synthesis. These models excel not only in creating fixed-size images but also in producing panoramic images. However, existing methods often struggle with spatial layout consistency when producing high-resolution panoramas due to the lack of

Marina Maneyro, David D'Enterria

The production of six energetic jets in proton-proton (pp) collisions at the LHC is studied as a means to directly observe for the first time the simultaneous scattering of three partons. The single-parton-scattering (SPS) cross sections for the production 2-, 3-, 4-, and 6-jets in pp collisions at center-of-mass energies of $\sqrt{s} = 14$ TeV, are calculat

Igor Haladjian

The family of $J$-reflection groups can be seen as a combinatorial generalisation of irreducible rank two complex reflection groups and was introduced by the author in a previous article. In this article, we define the braid groups associated to $J$-reflection groups, which coincide with the complex braid group when the $J$-reflection group is finite. We sho

Bharti Arora, Michaël De Becker

The study of the X-ray emission from massive binaries constitutes a relevant approach to investigate shock physics. The case of short period binaries may turn out to be quite challenging, especially in very asymmetric systems where the primary wind may overwhelm that of the secondary in the wind interaction. Our objective consists in providing an observation

Mingyu Xu, Haozhe Wang, Krishna Prasad Koirala, Corey Melnick

The interplay between crystal electric field splitting of d states and Hund's rule exchange energy in cobalt-based perovskites offers a promising avenue for inducing spin-state transitions. This study reports a new body-centered tetragonal (BCT) phase of BaCoO$_3$ (BCT-BaCoO$_3$), synthesized under high pressure (15 GPa) and high temperature (1200 {\deg}C) c

Florian Mirus, Frederik Pasch, Nikhil Singhal, Kay-Ulrich Scholl

Offloading computationally expensive algorithms to the edge or even cloud offers an attractive option to tackle limitations regarding on-board computational and energy resources of robotic systems. In cloud-native applications deployed with the container management system Kubernetes (K8s), one key problem is ensuring resilience against various types of failu

Yulian Sun, Li Duan, Yong Li

Privacy vulnerabilities in LLMs, such as leakage from memorization, have been constantly identified, and various mitigation proposals have been proposed. LoRA is usually used in fine-tuning LLMs and a good entry point to insert privacy-enhancing modules. In this ongoing research, we introduce PSY, a Posterior Sampling based PrivacY enhancer that can be used

Yejin Choi, Jiwan Chung, Sumin Shim, Giyeong Oh

Visual text design plays a critical role in conveying themes, emotions, and atmospheres in multimodal formats such as film posters and album covers. Translating these visual and textual elements across languages extends the concept of translation beyond mere text, requiring the adaptation of aesthetic and stylistic features. To address this, we introduce a n

Liang Han, Junsheng Zhou, Yu-Shen Liu, Zhizhong Han

Novel view synthesis from sparse inputs is a vital yet challenging task in 3D computer vision. Previous methods explore 3D Gaussian Splatting with neural priors (e.g. depth priors) as an additional supervision, demonstrating promising quality and efficiency compared to the NeRF based methods. However, the neural priors from 2D pretrained models are often noi

Corentin Le Bars

We consider a non-elementary group action $G \curvearrowright X$ of a locally compact second countable group $G$ on a possibly exotic non-discrete affine building $X$ of type $\tilde{A}_2$. We prove that if $\mu$ is an admissible symmetric probability measure on $G$, there is a unique $\mu$-stationary measure supported on the chambers of the spherical buildi

Tatsuya Terao

In the matroid intersection problem, we are given two matroids $\mathcal{M}_1 = (V, \mathcal{I}_1)$ and $\mathcal{M}_2 = (V, \mathcal{I}_2)$ defined on the same ground set $V$ of $n$ elements, and the objective is to find a common independent set $S \in \mathcal{I}_1 \cap \mathcal{I}_2$ of largest possible cardinality, denoted by $r$. In this paper, we consi

Sirui Chen, Shu Yu, Shengjie Zhao, Chaochao Lu

Self-consciousness, the introspection of one's existence and thoughts, represents a high-level cognitive process. As language models advance at an unprecedented pace, a critical question arises: Are these models becoming self-conscious? Drawing upon insights from psychological and neural science, this work presents a practical definition of self-consciousnes

Grant Hutchings, Kellin Rumsey, Derek Bingham, Gabriel Huerta

In modular Bayesian analyses, complex models are composed of distinct modules, each representing different aspects of the data or prior information. In this context, fully Bayesian approaches can sometimes lead to undesirable feedback between modules, compromising the integrity of the inference. This paper focuses on the "cut-distribution" which prevents unw

Tom Claeys, Julian Mauersberger

We conjecture an explicit expression for the lower tail large deviation rate function of the partition function of the log-Gamma polymer. We rigorously prove our result, except for one step for which we only provide heuristic evidence. We show that the large deviation rate function matches with that of last passage percolation with exponential weights in the

Ai-Yu Bai, Hanjie Cai, Chang-Lin Chen, Siyuan Chen

The spontaneous conversion of muonium to antimuonium is one of the interesting charged lepton flavor violation phenomena offering a sensitive probe of potential new physics and serving as a tool to constrain the parameter space beyond the Standard Model. The Muonium-to-Antimuonium Conversion Experiment (MACE) is designed to utilize a high-intensity muon beam

Drew Behrendt, Atanu Samanta, Andrew M. Rappe

The advent of wurtzite ferroelectrics is enabling a new generation of ferroelectric devices for computer memory that has the potential to bypass the von Neumann bottleneck, due to their robust polarization and silicon compatibility. However, the microscopic switching mechanism of wurtzites is still undetermined due to the limitations of density functional th

Vera Delfavero, K. E. Saavik Ford, Barry McKernan, Harrison E. Cook

The Active Galactic Nuclei (AGN) channel for the formation of binary black hole (BBH) mergers has been previously studied as a potential formation channel for the merging compact binaries observed by the LIGO/Virgo/KAGRA (LVK) scientific collaboration. The first two papers in this series explored the McFACTS code for the evolution of black hole orbits in AGN

Donovan Slabbert, Francesco Petruccione

In order to leverage quantum computers for machine learning tasks such as image classification, careful consideration is required: NISQ-era quantum computers have limitations, which include noise, scalability, read-in and read-out times, and gate operation times. Therefore, strategies should be devised to mitigate the impact that complex datasets can have on

Aldana Grichener

The study of massive binary systems has steadily progressed over the past decades, with increasing focus on their evolution, interactions and mergers, driven by improvements in computational modelling and observational techniques. In particular, when a binary system involves a massive giant and a neutron star (NS) or a black hole (BH) that go through common

Grzegorz Czelusta, Jakub Mielczarek

Tensor networks provide a powerful tool for studying many-body quantum systems, particularly making quantum simulations more efficient. In this article, we construct a tensor network representation of the spin network states, which correspond to $SU(2)$ gauge-invariant discrete field theories. Importantly, the spin network states play a central role in the L

Chandan Watts, Sudhanshu Barway, Omkar Bait, Yogesh Wadadekar

We present the discovery of an extended and faint tail observed in the isolated environment associated with galaxy NGC 3785. This study additionally provides observational evidence supporting the formation of ultra-diffuse galaxies at the end of the tail. We utilized the Gnuastro software to detect and analyse the low surface brightness structures in the opt

Siqi Xie, Yu Liu, Lingyun Yu

In this paper, we provide an overview of our attempts to harness data physicalizations as pedagogical tools for enhancing the understanding of visual channels. We first elaborate the research goals that we have crafted for the physicalization prototype, shedding light on the key principles that guided our design choices. Then we detail the materials and data

Tonglin Chen, Yinxuan Huang, Zhimeng Shen, Jinghao Huang

Humans can discern scene-independent features of objects across various environments, allowing them to swiftly identify objects amidst changing factors such as lighting, perspective, size, and position and imagine the complete images of the same object in diverse settings. Existing object-centric learning methods only extract scene-dependent object-centric r

Zhengkai Lin, Zhihang Fu, Kai Liu, Liang Xie

While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A". In this paper,

Martin Bojowald, Erick I. Duque, Aiden Shah

Deformations of spacelike hypersurfaces in space-time play an important role in discussions of general covariance and slicing independence in gravitational theories. In a canonical formulation, they provide the geometrical meaning of gauge transformations generated by the diffeomorphism and Hamiltonian constraints. However, it has been known for some time th

Zheyuan Zhang

Substantial research on deep learning-based emergent communication uses the referential game framework, specifically the Lewis signaling game, however we argue that successful communication in this game typically only need one or two symbols for target image classification because of a sampling pitfall in the training data. To address this issue, we provide