December 2024 arXiv papers — page 174

Eliseo Fuentes-Martínez, Antonio Ríos-Vila, Juan C. Martinez-Sevilla, David Rizo

The digitization of vocal music scores presents unique challenges that go beyond traditional Optical Music Recognition (OMR) and Optical Character Recognition (OCR), as it necessitates preserving the critical alignment between music notation and lyrics. This alignment is essential for proper interpretation and processing in practical applications. This paper

Carolin Wille, Maksimilian Usoltcev, Jens Eisert, Alexander Altland

This work proposes a minimal model extending the duality between classical statistical spin systems and fermionic systems beyond the case of free fermions. A Jordan-Wigner transformation applied to a two-dimensional tensor network maps the partition sum of a classical statistical mechanics model to a Grassmann variable integral, structurally similar to the p

A. Evans, D. P. K. Banerjee, T. R. Geballe, A. Polin

We have obtained near-infrared ($0.80-2.45\mu$m) spectra of the recurrent nova LMCN 1968-12a on two occasions during its 2024 August eruption. This is the first near-infrared spectroscopy of an extragalactic nova. The initial spectrum, on day 8.48, caught the nova in the coronal phase, with the [SiX] $1.43\mu$m line being extremely strong. This line had a lu

Jörn Krenzer, Felix Reichenbach, Jochen Schein

Non-destructive X-ray imaging of thruster parts and assemblies down to the scale of several micrometers is a key technology for electric propulsion research and engineering. It allows for thorough product assurance, rapid state acquisition and implementation of more detailed simulation models to understand the physics of device wear and erosion. Being able t

Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di

Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have

Emily Ewers, Tatyana Turova

A random planar quadrangulation process is introduced as an approximation for certain cellular automata in terms of random growth of rays from a given set of points. This model turns out to be a particular (rectangular) case of the well-known Gilbert tessellation, which originally models the growth of needle-shaped crystals from the initial random points wit

Takahiro Morishita, Charlotte A. Mason, Kimi C. Kreilgaard, Michele Trenti

We introduce the Bias-free Extragalactic Analysis for Cosmic Origins with NIRCam (BEACON) survey, a JWST Cycle2 program allocated up to 600 pure-parallel hours of observations. BEACON explores high-latitude areas of the sky with JWST/NIRCam over $\sim100$ independent sightlines, totaling $\sim0.3$deg$^2$, reaching a median F444W depth of $\approx28.2$AB mag

Yingbin Lin, Feng Wang, Xiao Zhang, Guojun Han

This paper considers a hybrid reconfigurable intelligent surface (RIS) assisted integrated sensing and communication (ISAC) system, where each RIS element can flexibly switch between the active and passive modes. Subject to the signal-to-interference-plus-noise ratio (SINR) constraint for each communication user (CU) and the transmit power constraints for bo

Ruoyu Yao, Yubin Wang, Haichao Liu, Rui Yang

Decision-making and motion planning constitute critical components for ensuring the safety and efficiency of autonomous vehicles (AVs). Existing methodologies typically adopt two paradigms: decision then planning or generation then scoring. However, the former architecture often suffers from decision-planning misalignment that incurs risky situations. Meanwh

Bengt E. W. Nilsson

The entire eigenvalue spectrum of the operators on the squashed $S^7$ that appear in the Freund-Rubin compactification of eleven-dimensional supergravity was recently derived in [1 - 4]. Here we give a brief account of this work which started with [1] where the complete spectrum of irreducible isometry representations of the fields in $AdS_4$ was derived for

Shivesh Yadav, Shikhar Kumar Gupta, Mohit Verma, Debjoty Paul

Antiferromagnets are emerging as promising alternatives to ferromagnets in spintronics applications. A key feature of antiferromagnets is their anisotropic magnetoresistance (AMR), which has the potential to serve as a sensitive marker for the antiferromagnetic order parameter. However, the underlying origins of this behavior remains poorly understood, parti

Peter Werner, Alexander K. Hartmann

For statistics of rare events in systems obeying a large-deviation principle, the rate function is a key quantity. When numerically estimating the rate function one is always restricted to finite system sizes. Thus, if the interest is in the limiting rate function for infinite system sizes, first, several system sizes have to be studied numerically. Here, ra

José Pombal, Sweta Agrawal, Patrick Fernandes, Emmanouil Zaranis

Automatic translation systems offer a powerful solution to bridge language barriers in scenarios where participants do not share a common language. However, these systems can introduce errors leading to misunderstandings and conversation breakdown. A key issue is that current systems fail to incorporate the rich contextual information necessary to resolve am

Valerio Marsocci, Yuru Jia, Georges Le Bellier, David Kerekes

Geospatial Foundation Models (GFMs) have emerged as powerful tools for extracting representations from Earth observation data, but their evaluation remains inconsistent and narrow. Existing works often evaluate on suboptimal downstream datasets and tasks, that are often too easy or too narrow, limiting the usefulness of the evaluations to assess the real-wor

Gidon Ernst, Jiří Fejlek

In this paper, we consider an analysis of temporal properties of hybrid systems based on simulations, so-called falsification of requirements. We present a novel exploration-based algorithm for falsification of black-box models of hybrid systems based on the Voronoi bias in the output space. This approach is inspired by techniques used originally in motion p

Callie C. Liao, Duoduo Liao, Ellie L. Zhang

Artificial Intelligence (AI) song generation has emerged as a popular topic, yet the focus on exploring the latent correlations between specific lyrical and rhythmic features remains limited. In contrast, this pilot study particularly investigates the relationships between keywords and rhythmically stressed features such as strong beats in songs. It focuses

Shuang Xu, Zixiang Zhao, Haowen Bai, Chang Yu

Hyperspectral images (HSIs) are frequently noisy and of low resolution due to the constraints of imaging devices. Recently launched satellites can concurrently acquire HSIs and panchromatic (PAN) images, enabling the restoration of HSIs to generate clean and high-resolution imagery through fusing PAN images for denoising and super-resolution. However, previo

Igor Grzelec, Alfréd Onderko, Mariusz Woźniak

A multigraph in which adjacent vertices have different degrees is called locally irregular. The locally irregular edge coloring is an edge coloring of a multigraph $G$ in which every color induces a locally irregular submultigraph of $G$. We denote by $\operatorname{lir}(G)$ the locally irregular chromatic index of a multigraph $G$, which is the smallest num

Koushik Goswami, K. L. Sebastian

The diffusion of colloids inside an active system-e.g. within a living cell or the dynamics of active particles itself (e.g. self-propelled particles) can be modeled through overdamped Langevin equation which contains an additional noise term apart from the usual white Gaussian noise, originating from the thermal environment. The second noise is referred to

Ivan A. Pshenichnyuk, Muneeb Farooq, Daniil S. Zemtsov, Denis M. Zhigunov

We investigate theoretically photonic chips with monolithically integrated blue light sources. According to our evaluations, a group-III nitride light emitting heterostructure can be efficiently combined with silicon nitride waveguiding layers. Low losses, high level of miniaturization and built-in light injection mechanism potentially make the selected plat

Alexander Marx, Sascha W. Epp

The analysis of ultrafast electron diffraction (UED) data from low-symmetry single crystals of small molecules is often challenged by the difficulty of assigning unique Laue indices to the observed Bragg reflections. For a variety of technical and physical reasons, UED diffraction images are typically of lower quality when viewed from the perspective of stru

Daniel Loughran, Tim Santens

We put forward a conjecture for the leading constant in Malle's conjecture on number fields of bounded discriminant, guided by stacky versions of conjectures of Batyrev-Manin, Batyrev-Tschinkel, and Peyre on rational points of bounded height on Fano varieties. A new framework for Brauer groups of stacks plays a key role in our conjecture, and we define a new

A. V. Jayanthan, Aniketh Sivakumar, Adam Van Tuyl

We introduce the notion of a partial Betti splitting of a homogeneous ideal, generalizing the notion of a Betti splitting first given by Francisco, H\`a, and Van Tuyl. Given a homogeneous ideal $I$ and two ideals $J$ and $K$ such that $I = J+K$, a partial Betti splitting of $I$ relates some of the graded Betti of $I$ with those of $J, K$, and $J\cap K$. As a

Mira Varma

Quantum entanglement, a fundamental feature of quantum mechanics, has become a powerful tool impacting various areas of physics. In this proceeding, we investigate the presence of quantum entanglement within the Higgs to ZZ* (HZZ*) interaction at the ATLAS experiment. Utilizing quantum tomography techniques, we extract the complete spin density matrix charac

Nathaniel R. Robinson, Shahd Abdelmoneim, Kelly Marchisio, Sebastian Ruder

Dialectal Arabic (DA) varieties are under-served by language technologies, particularly large language models (LLMs). This trend threatens to exacerbate existing social inequalities and limits LLM applications, yet the research community lacks operationalized performance measurements in DA. We present a framework that comprehensively assesses LLMs' DA modeli

Marta Russo, Antonella Maselli, Dagmar Sternad, Giovanni Pezzulo

Humans perform exquisite sensorimotor skills, both individually and in teams, from athletes performing rhythmic gymnastics to everyday tasks like carrying a cup of coffee. The "predictive brain" framework suggests that mastering these skills relies on predictive mechanisms, raising the question of how we deploy predictions for real-time control and coordinat

Zeki Doruk Erden, Boi Faltings

Adaptive networks today rely on overparameterized fixed topologies that cannot break through the statistical conflicts they encounter in the data they are exposed to, and are prone to "catastrophic forgetting" as the network attempts to reuse the existing structures to learn new task. We propose a structural adaptation method, DIRAD, that can complexify as n

Yayuan Li, Zhi Cao, Jason J. Corso

Despite the recent strides in video generation, state-of-the-art methods still struggle with elements of visual detail. One particularly challenging case is the class of videos in which the intricate motion of the hand coupled with a mostly stable and otherwise distracting environment is necessary to convey the execution of some complex action and its effect

Tamme Emunds, Nils Nießen

To ensure the effective and objective development of transportation networks, it is crucial to identify performance limitations across various subsystems. A timetable-independent assessment of infrastructure capacity at railway junctions is a fundamental aspect of long-term rail network planning. While recent research introduced queueing-based methods to qua

Konstantinos Chalas, Pasquale Calabrese, Colin Rylands

The linear growth of entanglement after a quench from a state with short-range correlations is a universal feature of many body dynamics. It has been shown to occur in integrable and chaotic systems undergoing either Hamiltonian, Floquet or circuit dynamics and has also been observed in experiments. The entanglement dynamics emerging from long-range correlat

Y. Hino, K. Abe, R. Asaka, S. Han

Neutron tagging is a fundamental technique for electron anti-neutrino detection via the inverse beta decay channel. A reported discrepancy in neutron detection efficiency between observational data and simulation predictions prompted an investigation into neutron capture modeling in Geant4. The study revealed that an overestimation of the thermal motion of h

Dominic Lohr, Marc Berges, Abhishek Chugh, Michael Kohlhase

Background: Over the past few decades, the process and methodology of automated question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models (LLMs) for gene

Shailendra Bhandari, Pedro Lencastre, Rujeena Mathema, Alexander Szorkovszky

Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a GAN framework em

Md Shihab Reza, Monirul Islam Mahmud, Ifti Azad Abeer, Nova Ahmed

The development of computing has made credit scoring approaches possible, with various machine learning (ML) and deep learning (DL) techniques becoming more and more valuable. While complex models yield more accurate predictions, their interpretability is often weakened, which is a concern for credit scoring that places importance on decision fairness. As fe

Rodrigo P. Câmara, Tatiana G. Rappoport, Mário G. Silveirinha

In this work, we develop a symmetry-based classification of Chern phases in honeycomb photonic crystals, considering arbitrary nonreciprocal couplings compatible with energy conservation. Our analysis focuses on crystals formed through nonreciprocal perturbations of photonic graphene. These perturbations, which can have arbitrary spatial variations, are gene

Jens Niklas Eberhardt, Francisco Revson F. Pereira, Vincent Steffan

Quantum low-density parity-check codes are promising candidates for quantum error correcting codes as they might offer more resource-efficient alternatives to surface code architectures. In particular, bivariate bicycle codes have recently gained attention due to their 2D-local structure, high encoding rate, and promising performance under simulation. In thi

Runsheng Bai, Bo Liu, Qiang Liu

Large Language Models (LLMs) exhibit impressive performance across various tasks, but deploying them for inference poses challenges. Their high resource demands often necessitate complex, costly multi-GPU pipelines, or the use of smaller, less capable models. While quantization offers a promising solution utilizing lower precision for model storage, existing

Kai Bergermann, Francesco Tudisco

Multilayer networks provide a powerful framework for modeling complex systems that capture different types of interactions between the same set of entities across multiple layers. Core-periphery detection involves partitioning the nodes of a network into core nodes, which are highly connected across the network, and peripheral nodes, which are densely connec

Florens Rohde, Victor Christen, Martin Franke, Erhard Rahm

Privacy-Preserving Record linkage (PPRL) is an essential component in data integration tasks of sensitive information. The linkage quality determines the usability of combined datasets and (machine learning) applications based on them. We present a novel privacy-preserving protocol that integrates clerical review in PPRL using a multi-layer active learning p

Deepak Kumar, Dinesh Tripathi, Sunil Hans

The main purpose of this paper is to present the generalization of the inequalities between the modulus of the polar derivative and the polynomial itself, depending on consideration of the zeros inside and outside of a closed disk and the extremal coefficient position of the underlying polynomial.

Koushik Goswami, Norman Hsia, Cheng-Hung Chang, Hong-Yan Shih

We investigate the conformational and dynamical properties of a partially active Rouse chain, where activity is localized within a specific segment, positioned at various locations along the chain and spanning any given length. Through analytical methods and simulations, we reveal how the location and size of the active segment influence polymer swelling pat

Joan Badia Torres, Eric Carmona, Abhijit Makhal, Omid Heidari

One of the strategies to detect the pose and shape of unknown objects is their geometric modeling, consisting on fitting known geometric entities. Classical geometric modeling fits simple shapes such as spheres or cylinders, but often those don't cover the variety of shapes that can be encountered. For those situations, one solution is the use of superquadri

Luca Francone

We construct a graded cluster algebra structure on the Cox ring of a smooth complex variety $Z$, depending on a base cluster structure on the ring of regular functions of an open subset $Y$ of $Z$. After considering some elementary examples of our construction, including toric varieties, we discuss the two main applications. First: if $Z$ is a flag variety a

Rong Chen, Zijian Deng

Hadwiger's conjecture for the immersion relation posits that every graph $G$ contains an immersion of the complete graph $K_{\chi(G)}$. Vergara showed that this is equivalent to saying that every $n$-vertex graph $G$ with $\alpha(G)=2$ contains an immersion of the complete graph on $\lceil\frac{n}{2}\rceil$ vertices. Recently, Botler et al. showed that every

Arthur Fyon, Guillaume Drion

Neurons rely on two interdependent mechanisms, homeostasis and neuromodulation, to maintain robust and adaptable functionality. Calcium homeostasis stabilizes neuronal activity by adjusting ionic conductances, whereas neuromodulation dynamically modifies ionic properties in response to external signals carried by neuromodulators. Combining these mechanisms i

D. Mesa, R. Gratton, V. D'Orazi, E. Carolo

The combination of detection techniques enhances our ability to identify companions orbiting nearby stars. We employed high-contrast imaging to constrain mass and separation of possible companions responsible for the significant proper motion anomalies of the nearby stars HIP 11696, HIP 47110 and HIP 36277. These targets were observed using the LBT's high-co

Lawrence Hollom, Lyuben Lichev, Adva Mond, Julien Portier

For a tree $T$ and a function $f \colon E(T)\to \mathbb{S}^d$, the imbalance of a subtree $T'\subseteq T$ is given by $|\sum_{e \in E(T')} f(e)|$. The $d$-dimensional discrepancy of the tree $T$ is the minimum, over all functions $f$ as above, of the maximum imbalance of a subtree of $T$. We prove tight asymptotic bounds for the discrepancy of a tree $T$, co

Nuno Hultberg

This article introduces the study of toric bundles and the morphisms between them from the perspective of adelic fibre bundles, as introduced by Chambert-Loir and Tschinkel. We study the Okounkov bodies and Boucksom-Chen transforms of suitable adelic line bundles on toric bundles. Finally, we prove an arithmetic analogue of a formula for intersection numbers

Samuel Morales, Silvia Pappalardi, Reinhold Egger

We introduce and analyze an active steering protocol designed to target multipartite entangled states. The protocol involves multiple qubits subjected to weak Bell pair measurements with active feedback, where the feedback operations are optimized to maximize the Quantum Fisher Information. Our scheme efficiently reaches a genuinely entangled one-parameter s

Yuanshuai Wang, Xingjian Zhang, Jinkun Zhao, Siwei Wen

Large Language Models (LLMs) are key technologies driving intelligent systems to handle multiple tasks. To meet the demands of various tasks, an increasing number of LLMs-driven experts with diverse capabilities have been developed, accompanied by corresponding benchmarks to evaluate their performance. This paper proposes the Bench-CoE framework, which enabl

Disha Ghandwani, Neeraj Sarna, Yuanyuan Li, Yang Lin

Advanced classification algorithms are being increasingly used in safety-critical applications like health-care, engineering, etc. In such applications, miss-classifications made by ML algorithms can result in substantial financial or health-related losses. To better anticipate and prepare for such losses, the algorithm user seeks an estimate for the probabi

Shobhna Misra, Reshma Peremadathil Pradeep, Yaoxuan Feng, Urs Grob

The separation of physical forces acting on the tip of a magnetic force microscope (MFM) is essential for correct magnetic imaging. Electrostatic forces can be modulated by varying the tip-sample potential and minimized to map the local Kelvin potential. However, distinguishing magnetic forces from van der Waals forces typically requires two measurements wit

Lu Wang, Hong Liang, Jiangrong Xu

The discrete unified gas kinetic scheme (DUGKS) has emerged as a promising Boltzmann solver capable of effectively capturing flow physics across all Knudsen numbers. However, simulating rarefied flows at high Knudsen numbers remains computationally demanding. This paper introduces a parametric Gaussian quadrature (PGQ) rule designed to improve the computatio

Gianluca Capozzi, Tong Tang, Jie Wan, Ziqi Yang

Binary function similarity, which often relies on learning-based algorithms to identify what functions in a pool are most similar to a given query function, is a sought-after topic in different communities, including machine learning, software engineering, and security. Its importance stems from the impact it has in facilitating several crucial tasks, from r

Ethan André, Jingyi Li, David Loiseaux, Steve Oudot

Given an unknown $\mathbb{R}^n$-valued function $f$ on a metric space $X$, can we approximate the persistent homology of $f$ from a finite sampling of $X$ with known pairwise distances and function values? This question has been answered in the case $n=1$, assuming $f$ is Lipschitz continuous and $X$ is a sufficiently regular geodesic metric space, and using

Riccardo Cristoferi, Gabriele Fissore, Marco Morandotti

We study geometrically constrained magnetic walls in a three dimensional geometry where two bulks are connected by a thin neck. Without imposing any symmetry assumption on the domain, we investigate the scaling of the energy as the size of the neck vanishes. We identify five significant scaling regimes, for all of which we characterise the energy scaling and

Manjith Bose, David L. Cortie, Sergey Rubanov, Anton P. Le Brun

Vanadium silicide (V$_3$Si) is a promising superconductor for integration with silicon-based electronics, however the interfacial growth kinetics have a strong influence on the resulting superconducting properties and are not yet fully understood. In this study, we have used neutron reflectometry to reveal the phase transformation during thin film growth dri

Tim Wibiral, Mohamed Karim Belaid, Maximilian Rabus, Ansgar Scherp

Assessing the importance of individual training samples is a key challenge in machine learning. Traditional approaches retrain models with and without specific samples, which is computationally expensive and ignores dependencies between data points. We introduce LossVal, an efficient data valuation method that computes importance scores during neural network

Seth Siriya, Jingge Zhu, Dragan Nešić, Ye Pu

We investigate the problem of least squares parameter estimation from single-trajectory data for discrete-time, unstable, closed-loop nonlinear stochastic systems. Specifically, we consider nonlinear systems with linearly parametrised uncertainty and additive i.i.d. process noise, in feedback with a control policy that is intentionally perturbed by an explor

Petra Berenbrink, Amin Coja-Oghlan, Colin Cooper, Thorsten Götte

In an influential article Papadimitriou [FOCS 1991] proved that a local search algorithm called WalkSAT finds a satisfying assignment of a satisfiable 2-CNF with $n$ variables in $O(n^2)$ expected time. Variants of the WalkSAT algorithm have become a mainstay of practical SAT solving (e.g., [Hoos and St\"utzle 2000]). In the present article we analyse the ex

Amos Uderzo

In this paper, stability and sensitivity properties of a class of parametric constrained optimization problem, whose feasible region is defined by a set-valued inclusion, are investigated through the associated optimal value function. Set-valued inclusions are a kind of constraint system, which naturally emerges in contexts requiring the robust fulfilment of

M. Farahani, T. Kozák, A. D. Pajdarová, T. Tölg

High-power impulse magnetron sputtering (HiPIMS) delivers a high target power in short pulses, enhancing the ionization and energy of sputtered atoms and providing thus more possibilities to control the film properties. This study explores the effect of various pulse configurations (unipolar HiPIMS, bipolar HiPIMS, chopped unipolar, and chopped bipolar HiPIM

Murad Dawood, Ahmed Shokry, Maren Bennewitz

Reinforcement learning (RL) has been successfully applied to a variety of robotics applications, where it outperforms classical methods. However, the safety aspect of RL and the transfer to the real world remain an open challenge. A prominent field for tackling this challenge and ensuring the safety of the agents during training and execution is safe reinfor

Yi Peng, Chao Yang, Haiping Hu, Yucheng Wang

Robust states emerging at the boundaries of a system are an important hallmark of topological matter. Here, using the Su-Schrieffer-Heeger model and the Kitaev chain as examples, we study the impact of a type of experimentally realizable bond dissipation on topological systems by calculating the steady-state density matrix, and demonstrate that such dissipat

Boris Kruglikov

We relate rational integrals of the geodesic flow of a (pseudo-)Riemannian metric to relative Killig tensors, describe the spaces they span and discuss upper bounds on their dimensions.

Alvaro Suarez, Marcelo Vachetta

A classroom activity for high school physics students is presented to explore the distinction between correlation and causation. Using data linking ice cream sales to drowning deaths, presented within a fictional news article, students analyze the relationship, create plots, and identify temperature as the hidden variable explaining the correlation. The acti

Haitian Zhang, Xiangyuan Wang, Chang Xu, Xinya Wang

Fusing Events and RGB images for object detection leverages the robustness of Event cameras in adverse environments and the rich semantic information provided by RGB cameras. However, two critical mismatches: low-latency Events \textit{vs.}~high-latency RGB frames; temporally sparse labels in training \textit{vs.}~continuous flow in inference, significantly

Tomoya Hirobe, Kenta Kasai

We study recursively extended permutation (REP) codes under the Chebyshev distance. An REP code is built by repeatedly inserting an allowed symbol in the first coordinate and relabeling the remaining symbols. The central question is how large such a code can be for a prescribed length and minimum distance. A condition imposed separately at every extension st

Sokratis Nikolaidis, Stylianos I. Venieris, Iakovos S. Venieris

Cascade systems, consisting of a lightweight model processing all samples and a heavier, high-accuracy model refining challenging samples, have become a widely-adopted distributed inference approach to achieving high accuracy and maintaining a low computational burden for mobile and IoT devices. As intelligent indoor environments, like smart homes, continue

Xinghui Li, Qichao Sun, Pengze Zhang, Fulong Ye

Recent advances in garment-centric image generation from text and image prompts based on diffusion models are impressive. However, existing methods lack support for various combinations of attire, and struggle to preserve the garment details while maintaining faithfulness to the text prompts, limiting their performance across diverse scenarios. In this paper

Sayan Bandyapadhyay, William Lochet, Daniel Lokshtanov, Dániel Marx

We prove a robust contraction decomposition theorem for $H$-minor-free graphs, which states that given an $H$-minor-free graph $G$ and an integer $p$, one can partition in polynomial time the vertices of $G$ into $p$ sets $Z_1,\dots,Z_p$ such that $\operatorname{tw}(G/(Z_i \setminus Z')) = O(p + |Z'|)$ for all $i \in [p]$ and $Z' \subseteq Z_i$. Here, $\oper

Muhammad Khalifa, Yi-Chern Tan, Arash Ahmadian, Tom Hosking

Model merging has shown great promise at combining expert models, but the benefit of merging is unclear when merging "generalist" models trained on many tasks. We explore merging in the context of large (~100B) models, by recycling checkpoints that exhibit tradeoffs among different tasks. Such checkpoints are often created in the process of developing a fron

Ben Jarvis

Pin sequences play an important role in the structural study of permutation classes. In this paper, we study the permutation classes that comprise all the finite subpermutations contained in an infinite pin sequence. We prove that these permutation classes have proper growth rates and establish a procedure for calculating these growth rates.

Juan Sandubete-López, José L. Risco-Martín, Alexander H. McMillan, Eva Besada-Portas

Microfluidic devices are increasingly used in biological and chemical experiments due to their cost-effectiveness for rheological estimation in fluids. However, these devices often face challenges in terms of accuracy, size, and cost. This study presents a methodology, integrating deep learning, modeling and simulation to enhance the design of microfluidic s

Hongshen Xu, Zichen Zhu, Lei Pan, Zihan Wang

Large Language Models (LLMs) have expanded their capabilities beyond language generation to interact with external tools, enabling automation and real-world applications. However, tool hallucinations, where models either select inappropriate tools or misuse them, pose significant challenges, leading to erroneous task execution, increased computational costs,

Dongjae Jeon, Dueun Kim, Albert No

In this paper, we introduce a geometric framework to analyze memorization in diffusion models through the sharpness of the log probability density. We mathematically justify a previously proposed score-difference-based memorization metric by demonstrating its effectiveness in quantifying sharpness. Additionally, we propose a novel memorization metric that ca

Jungwoo Park, Young Jin Ahn, Kee-Eung Kim, Jaewoo Kang

Understanding the internal computations of large language models (LLMs) is crucial for aligning them with human values and preventing undesirable behaviors like toxic content generation. However, mechanistic interpretability is hindered by polysemanticity -- where individual neurons respond to multiple, unrelated concepts. While Sparse Autoencoders (SAEs) ha

Davide Perego, Matteo Tarocchi

Generalizing work by Belk and Forrest, we develop almost expanding hyperedge replacement systems that build fractal topological spaces as quotients of edge shifts under certain ``gluing'' equivalent relations. We define ESS groups, which are groups of homeomorphisms of these spaces that act as a finitary asynchronous transformations followed by self-similar

Doyoung Park, Naresh Reddy Yarram, Sunjin Kim, Minkyu Kim

Document comparison typically relies on optical character recognition (OCR) as its core technology. However, OCR requires the selection of appropriate language models for each document and the performance of multilingual or hybrid models remains limited. To overcome these challenges, we propose text change detection (TCD) using an image comparison model tail

Zhaobing Fan, Haitao Ma, Zhicheng Zhang

We establish a duality between a pair of mirabolic quantum groups, i.e., the mirabolic counterpart of quantum Howe duality.

Jibril Ben Achour

This manuscript reviews the construction of exact solutions describing both (rotating) black holes and non-linear radiative spacetimes in the context of degenerate higher order scalar-tensor (DHOST) theories. We start be reviewing the structure of the DHOST theory space, the notion of degeneracy conditions and the stability of these degeneracy classes under

Tao Zhang, Zhenhai Liu, Feipeng Qi, Yongjun Jiao

Multiphysics simulation, which models the interactions between multiple physical processes, and multi-component simulation of complex structures are critical in fields like nuclear and aerospace engineering. Previous studies use numerical solvers or ML-based surrogate models for these simulations. However, multiphysics simulations typically require integrati

Enrico M. Brehm, Yibin Guo, Karl Jansen, Enrico Rinaldi

Matrix models, as quantum mechanical systems without explicit spatial dependence, provide valuable insights into higher-dimensional gauge and gravitational theories, especially within the framework of string theory, where they can describe quantum black holes via the holographic principle. Simulating these models allows for exploration of their kinematic and

Guillaume Nguyen, Manon Knockaert, Michael Lognoul, Xavier Devroey

While procedures prevail on the European market for the greater good of its citizens, it might be daunting when trying to introduce a product, whether innovative or not. In the current world, Cyber-Physical Systems (CPSs) are ubiquitous in our daily lives. Cars can provide intrusive assistance as they can brake or turn wheels on their own, buildings are gett

Libei Sun, Yongduan Song, Maolong Lv

This study addresses the intricate challenge of decentralized output-feedback control for stochastic non-triangular nonlinear interconnected systems with unknown time-varying sensor sensitivity in a dynamic event-triggered context. The presence of stochastic disturbances, non-triangular structural uncertainties, and evolving sensor sensitivity distinguishes

Biquard Maud, Marie Chabert, Florence Genin, Christophe Latry

Satellite optical images, upon their on-ground receipt, offer a distorted view of the observed scene. Their restoration, including denoising, deblurring, and sometimes super-resolution, is required before their exploitation. Moreover, quantifying the uncertainties related to this restoration helps to reduce the risks of misinterpreting the image content. Dee

Seth Siriya, Mo Chen, Ye Pu

When deploying autonomous systems in unknown and changing environments, it is critical that their motion planning and control algorithms are computationally efficient and can be reapplied online in real time, whilst providing theoretical safety guarantees in the presence of disturbances. The satisfaction of these objectives becomes more challenging when cons

Hun Jang, Minkyoo Kim, Hocheol Lee, Jeong-Hyuck Park

We propose a Lorentzian wormhole geometry characterized by a closed string massless sector with nontrivial $H$-flux and a scalar dilaton. In the string frame, the dilaton exhibits a negative kinetic term, enabling the existence of the wormhole. The geometry consists of three distinct regions. The middle region contains the throat, and its boundaries with the

Jiun-Shiuan Shiu, Chang-Wei Lin, Yu-Chiao Huang, Meng-Jung Lin

We present experimental results on tuning biphoton frequency by introducing a detuned coupling field in spontaneous four-wave mixing (SFWM), and examine its impact on the pairing ratio. This tunability is achieved by manipulating the inherent electromagnetically induced transparency (EIT) effect in the double-$\Lambda$ scheme. Introducing a detuned coupling

Horst Foidl, Tanja Rindler-Daller

The measurements of the CMB have determined the cosmological parameters with high accuracy, and the observation of the flatness of space have contributed to the status of the concordance $\Lambda$CDM model. However, the cosmological constant $\Lambda$, necessary to close the model to critical density, remains an open conundrum. We explore the observed late-t

Gabriel Torrens, Abdel Alheyasat, Bartomeu Alorda, Sebastia A. Bota

This work proposes a methodology to estimate the statistical distribution of the probability that a 6T bit-cell starts up to a given logic value in SRAM memories for PUF applications. First, the distribution is obtained experimentally in a 65-nm CMOS device. As this distribution cannot be reproduced by electrical simulation, we explore the use of an alternat

Zixing Zhang, Q. P. Liu

A two-component generalization of the Camassa-Holm equation and its reduction proposed recently by Xue, Du and Geng [Appl. Math. Lett. {\bf 146} (2023) 108795] are studied. For this two-component equation, its missing bi-Hamiltonian structure is constructed and a Miura transformation is introduced so that it may be regarded as a modification of the very firs

Oriel A. Humes, Josef Hanuš

The Main Belt asteroid (203) Pompeja shows evidence of extreme variability in visible and near-infrared spectral slope with time. The observed spectral variability has been hypothesized to be attributed to spatial variations across Pompeja's surface. In this scenario, the observed spectrum of Pompeja is dependent on the geometry of the Sun and the observer r

Rong-Xin Miao

Inspired by the Kovtun-Son-Starinet bound, we propose that holography imposes a lower bound on the Casimir effect. For simplicity, we focus on the Casimir effect between parallel planes for three-dimensional conformal field theories and briefly comment on the generalizations to other boundary shapes and higher dimensions. Remarkably, the ghost-free holograph

Georgios Triantafyllou, Panagiotis G. Kalozoumis, George Dimas, Dimitris K. Iakovidis

Finite Element Analysis (FEA) is a powerful but computationally intensive method for simulating physical phenomena. Recent advancements in machine learning have led to surrogate models capable of accelerating FEA. Yet there are still limitations in developing surrogates of transient FEA models that can simultaneously predict the solutions for both nodes and

Thomas Walker, Salvatore Esposito, Daniel Rebain, Amir Vaxman

Reconstructing complex structures from planar cross-sections is a challenging problem, with wide-reaching applications in medical imaging, manufacturing, and topography. Out-of-the-box point cloud reconstruction methods can often fail due to the data sparsity between slicing planes, while current bespoke methods struggle to reconstruct thin geometric structu

Cristian-George Crăciun, Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel, Mihaela-Claudia Cercel

Pre-trained Language Models (PLMs) have shown remarkable performances in recent years, setting a new paradigm for NLP research and industry. The legal domain has received some attention from the NLP community partly due to its textual nature. Some tasks from this domain are represented by question-answering (QA) tasks. This work explores the legal domain Mul

Francois Brucker, Pascal Préa, Christopher Thraves Caro

In this work, we extend the concept of Robinson spaces to asymmetric dissimilarities, enhancing their applicability in representing and analyzing complex data. Within this generalized framework, we introduce two different problems that extend the classical seriation problem: an optimization problem and a decision problem. We establish that these problems are

Erik Brorsson, Lennart Svensson, Kristofer Bengtsson, Knut Åkesson

We address multi-view pedestrian detection in a setting where labeled data is collected using a multi-camera setup different from the one used for testing. While recent multi-view pedestrian detectors perform well on the camera rig used for training, their performance declines when applied to a different setup. To facilitate seamless deployment across varied

Lewis Stanton, Stephen Theriault

We study the homotopy theory of polyhedral products associated to a combinatorial generalisation of manifolds known as pseudomanifolds. As special cases, we show that loop spaces of moment-angle manifolds associated to triangulations of $S^2$ and $S^3$ decompose as a product of spheres and loops on spheres.