December 2024 arXiv papers — page 97

Felix Brandt, Patrick Lederer

An important -- but very demanding -- property in collective decision-making is strategyproofness, which requires that voters cannot benefit from submitting insincere preferences. Gibbard (1977) has shown that only rather unattractive rules are strategyproof, even when allowing for randomization. However, Gibbard's theorem is based on a rather strong interpr

Qi Sun, Pengfei Hong, Tej Deep Pala, Vernon Toh

Traditional reinforcement learning-based robotic control methods are often task-specific and fail to generalize across diverse environments or unseen objects and instructions. Visual Language Models (VLMs) demonstrate strong scene understanding and planning capabilities but lack the ability to generate actionable policies tailored to specific robotic embodim

Janni Yuval, Ian Langmore, Dmitrii Kochkov, Stephan Hoyer

Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. Here, we present a hybrid model that is trained directly on satellite-based precipitation observations. Our model runs at 2.8$^\circ$ resolution and is built on the differentiable NeuralGCM framework. The model demonstrates significant improvements over

Onur Tasar, Clément Chadebec, Benjamin Aubin

Realistic shadow generation is a critical component for high-quality image compositing and visual effects, yet existing methods suffer from certain limitations: Physics-based approaches require a 3D scene geometry, which is often unavailable, while learning-based techniques struggle with control and visual artifacts. We introduce a novel method for fast, con

Iuliia Promskaia, Adrian O'Hagan, Michael Fop

Network data often represent multiple types of relations, which can also denote exchanged quantities, and are typically encompassed in a weighted multiplex. Such data frequently exhibit clustering structures, however, traditional clustering methods are not well-suited for multiplex networks. Additionally, standard methods treat edge weights in their raw form

Tong Xie, Yuwei Wan, Yixuan Liu, Yuchen Zeng

Materials discovery and design aim to find compositions and structures with desirable properties over highly complex and diverse physical spaces. Traditional solutions, such as high-throughput simulations or machine learning, often rely on complex descriptors, which hinder generalizability and transferability across different material systems. Moreover, Thes

T. Bloom, D. Dauvergne, N. Levenberg

We consider random polynomials of the form $G_n(z):= \sum_{|\alpha|\leq n} \xi^{(n)}_{\alpha}p_{n,\alpha}(z)$ where $\{\xi^{(n)}_{\alpha}\}_{|\alpha|\leq n}$ are i.i.d. (complex) random variables and $\{p_{n,\alpha}\}_{|\alpha|\leq n}$ form a basis for $\mathcal P_n$, the holomorphic polynomials of degree at most $n$ in ${\mathbb C}^d$. In particular, this i

Jiacheng Tang

We define what it means for a condensed group action to be open (following Scholze) and show that for open subgroups, many elementary results about abstract modules hold for condensed modules, such as the existence of Mackey's Formula for condensed groups. We also indicate how these results can be "solidified" to obtain their solid versions.

Kamaljyoti Nath, Varun Kumar, Daniel J. Smith, George Em Karniadakis

Improving diesel engine efficiency, reducing emissions, and enabling robust health monitoring have been critical research topics in engine modelling. While recent advancements in the use of neural networks for system monitoring have shown promising results, such methods often focus on component-level analysis, lack generalizability, and physical interpretabi

Michele Caselle, Nicodemo Magnoli, Alessandro Nada, Marco Panero

We study the effective bosonic string that describes confining flux tubes in three-dimensional SU(N) Yang--Mills theories. Although the low-energy properties are universal and well described by the Nambu--Got\=o action, the subtle dependence on the gauge group is embedded in a series of corrections, which remain undetermined, appearing in the expansion aroun

Hao-Ning He, Eiji Kido, Kai-Kai Duan, Yang Yang

Ultrahigh-energy cosmic rays (UHECRs) are the highest energy messenger from space, with energies exceeding 1 EeV. Although UHECRs were discovered over 60 years ago, their origin still remains a mystery. Pinpointing sources of UHECRs is crucial for understanding the extreme astrophysical processes that accelerate particles to such extraordinary energies. We s

Amit Elhelo, Mor Geva

Attention heads are one of the building blocks of large language models (LLMs). Prior work on investigating their operation mostly focused on analyzing their behavior during inference for specific circuits or tasks. In this work, we seek a comprehensive mapping of the operations they implement in a model. We propose MAPS (Mapping Attention head ParameterS),

Whitney Sloneker, Shalin Patel, Michael Wang, Lorin Crawford

Graph neural networks (GNNs) are powerful tools for conducting inference on graph data but are often seen as "black boxes" due to difficulty in extracting meaningful subnetworks driving predictive performance. Many interpretable GNN methods exist, but they cannot quantify uncertainty in edge weights and suffer in predictive accuracy when applied to challengi

Praneeth Kacham, David P. Woodruff

When rows of an $n \times d$ matrix $A$ are given in a stream, we study algorithms for approximating the top eigenvector of the matrix ${A}^TA$ (equivalently, the top right singular vector of $A$). We consider worst case inputs $A$ but assume that the rows are presented to the streaming algorithm in a uniformly random order. We show that when the gap paramet

Ludmila Yu. Tsiovkina

The paper is devoted to the study of abelian (in the sense defined by Godsil and Hensel) distance-regular $r$-covers of the complete graphs $K_n$. According to the construction by Coutinho, Godsil, Shirazi, and Zhan (2016), each such cover yields an equiangular set of lines of size $n$ that attains the relative bound. Moreover, there are four families of abe

Laura Di Marino, Luigi Di Palma, Michele Riccio, Francesco Fienga

Quantum computation requires high-fidelity qubit readout, preserving the quantum state. In the case of superconducting (SC) qubits, readout is typically performed using a complex analog experimental setup operated at room temperature, which poses significant technological and economic barriers to large system scalability. An alternative approach is to perfor

Giordano Cicchetti, Eleonora Grassucci, Luigi Sigillo, Danilo Comminiello

Human perception integrates multiple modalities, such as vision, hearing, and language, into a unified understanding of the surrounding reality. While recent multimodal models have achieved significant progress by aligning pairs of modalities via contrastive learning, their solutions are unsuitable when scaling to multiple modalities. These models typically

Yang-Yang Li, Zheyan Wan, Juven Wang, Shing-Tung Yau

Recent research has revealed that the CRT symmetry for fermions exhibits a fractionalization distinct from the $\mathbb{Z}_2^{\mathcal{C}}\times\mathbb{Z}_2^{\mathcal{R}}\times\mathbb{Z}_2^{\mathcal{T}}$ for scalar bosons. In fact, the CRT symmetry for fermions can be extended by internal symmetries such as fermion parity, thereby forming a group extension o

Arun G. Chandrasekhar, Vasu Chaudhary, Benjamin Golub, Matthew O. Jackson

Social and economic networks are often multiplexed, meaning that people are connected by different types of relationships -- such as borrowing goods and giving advice. We make two contributions to the study of multiplexing and the understanding of simple versus complex contagion. On the theoretical side, we introduce a model and theoretical results about dif

Zhiqing Yin

We study the deacy and Strichartz estimates for the massive Dirac Hamiltonian in a constant magnetic fields in $\mathbb{R}_t\times\mathbb{R}^2_x$: \begin{equation*} \begin{cases} i\partial_tu(t,x)-\mathcal{D}_Au(t,x)=0, u(0,x)=f, \end{cases} \end{equation*} where $\mathcal{D}_A=-i{\bf \sigma}\cdot (\nabla-i{\bf A}(x))+\sigma_3m$ with $m\geq0$ being the mass

Dario Cilluffo

The decomposition of multiport interferometers is a fundamental tool in quantum optics and computing. This note aims to serve as a concise reference for performing the decomposition according to the most common design approaches, offering a self-contained treatment with essential mathematical details and practical working examples. Specifically, we provide a

Luca Pascal Staus, Christian Komusiewicz, Frank Sommer, Manuel Sorge

Decision trees are a classic model for summarizing and classifying data. To enhance interpretability and generalization properties, it has been proposed to favor small decision trees. Accordingly, in the minimum-size decision tree training problem (MSDT), the input is a set of training examples in $\mathbb{R}^d$ with class labels and we aim to find a decisio

Julio Alberto Silva Dias

Hydroelectric power generation is a critical component of the global energy matrix, particularly in countries like Brazil, where it represents the majority of the energy supply. However, its strong dependence on river discharges, which are inherently uncertain due to climate variability, poses significant challenges. River discharges are linked to precipitat

Mohaddeseh Chegini, Ali Mahloojifar

Breast cancer is a heterogeneous disease with different molecular subtypes, clinical behavior, treatment responses as well as survival outcomes. The development of a reliable, accurate, available and inexpensive method to predict the molecular subtypes using medical images plays an important role in the diagnosis and prognosis of breast cancer. Recently, dee

Yuti Liu, Shice Liu, Junyuan Gao, Pengtao Jiang

Image Aesthetic Assessment (IAA) is a vital and intricate task that entails analyzing and assessing an image's aesthetic values, and identifying its highlights and areas for improvement. Traditional methods of IAA often concentrate on a single aesthetic task and suffer from inadequate labeled datasets, thus impairing in-depth aesthetic comprehension. Despite

Ahmad Hassanpour, Amir Zarei, Khawla Mallat, Anderson Santana de Oliveira

This study investigates the trade-offs between fairness, privacy, and utility in image classification using machine learning (ML). Recent research suggests that generalization techniques can improve the balance between privacy and utility. One focus of this work is sharpness-aware training (SAT) and its integration with differential privacy (DP-SAT) to furth

Zewen Yang, Xiaobing Dai, Sandra Hirche

This is a complementary document for the paper titled "Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems".

Tobias Rohe, Barbara Böhm, Michael Kölle, Jonas Stein

Drones have revolutionized various domains, including agriculture. Recent advances in deep learning have propelled among other things object detection in computer vision. This study utilized YOLO, a real-time object detector, to identify and count coconut palm trees in Ghanaian farm drone footage. The farm presented has lost track of its trees due to differe

Maximilian Idahl, Zahra Ahmadi

We present OpenReviewer, an open-source system for generating high-quality peer reviews of machine learning and AI conference papers. At its core is Llama-OpenReviewer-8B, an 8B parameter language model specifically fine-tuned on 79,000 expert reviews from top conferences. Given a PDF paper submission and review template as input, OpenReviewer extracts the f

Juliano C. S. Neves

In a brane-world context in which our universe would be a four-dimensional brane embedded into a five-dimensional spacetime or bulk, wormhole geometries are induced on branes. In this article, the Morris-Thorne wormhole and the Molina-Neves wormhole are obtained on the brane using the Nakas-Kanti approach, which starts from a regular five-dimensional spaceti

A. Andrews, J. Jasche, G. Lavaux, F. Leclercq

A primary target of the \Euclid space mission is to constrain early-universe physics by searching for deviations from a primordial Gaussian random field. A significant detection of primordial non-Gaussianity would rule out the simplest models of cosmic inflation and transform our understanding of the origin of the Universe. This paper forecasts how well fiel

Madjid Sadallah

Delivering high-quality content is crucial for effective reading comprehension and successful learning. Ensuring educational materials are interpreted as intended by their authors is a persistent challenge, especially with the added complexity of multimedia and interactivity in the digital age. Authors must continuously revise their materials to meet learner

Simon Rampp, Andreas Triantafyllopoulos, Manuel Milling, Björn W. Schuller

This work introduces the key operating principles for autrainer, our new deep learning training framework for computer audition tasks. autrainer is a PyTorch-based toolkit that allows for rapid, reproducible, and easily extensible training on a variety of different computer audition tasks. Concretely, autrainer offers low-code training and supports a wide ra

Zhigang Ou, Congyi Nai, Baoxiang Pan, Yi Zheng

Extreme floods pose escalating risks in a changing climate, yet forecasting remains challenging due to peak flow underestimation and high uncertainty. We introduce DRUM, a diffusion-based probabilistic deep learning approach that advances extreme flood forecasting across representative basins in the contiguous United States. DRUM outperforms state-of-the-art

Sina Moradi

Efficient scheduling of periodic meetings is a critical challenge in various service-oriented domains, including academic settings, healthcare, and legal consultancy. This study presents a robust Integer Linear Programming (ILP) model to optimize the scheduling of faculty-student meetings. The proposed model incorporates practical constraints such as minimum

Byung-Doh Oh, William Schuler

Word-by-word language model surprisal is often used to model the incremental processing of human readers, which raises questions about how various choices in language modeling influence its predictive power. One factor that has been overlooked in cognitive modeling is the granularity of subword tokens, which explicitly encodes information about word length a

Jianxiang Yu, Jiaqi Tan, Zichen Ding, Jiapeng Zhu

Peer review, as a cornerstone of scientific research, ensures the integrity and quality of scholarly work by providing authors with objective feedback for refinement. However, in the traditional peer review process, authors often receive vague or insufficiently detailed feedback, which provides limited assistance and leads to a more time-consuming review cyc

Matouš Elphick, Samra Turajlic, Guang Yang

Self-supervised foundation models for digital pathology encode small patches from H\&E whole slide images into latent representations used for downstream tasks. However, the invariance of these representations to patch rotation remains unexplored. This study investigates the rotational invariance of latent representations across twelve foundation models by q

Bradley Butcher, Michael O'Keefe, James Titchener

Large Language Models (LLMs) are increasingly used in production systems, powering applications such as chatbots, summarization, and question answering. Despite their success, controlling the length of their response remains a significant challenge, particularly for tasks requiring structured outputs or specific levels of detail. In this work, we propose a m

Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu

Mathematical reasoning, a core aspect of human cognition, is vital across many domains, from educational problem-solving to scientific advancements. As artificial general intelligence (AGI) progresses, integrating large language models (LLMs) with mathematical reasoning tasks is becoming increasingly significant. This survey provides the first comprehensive

Shah Jahan, P. Sam Johnson

We start by introducing and studying the definition of a Riesz basis in a Krein space $(\mathcal{K},[.,.])$, along with a condition under which a Riesz basis becomes a Bessel sequence. The concept of biorthogonal sequence in Krein spaces is also introduced, providing an equivalent characterization of a Riesz basis. Additionally, we explore the concept of the

Jingyu Peng, Maolin Wang, Xiangyu Zhao, Kai Zhang

Large language models (LLMs) have made remarkable strides in complex reasoning tasks, but their safety and robustness in reasoning processes remain underexplored. Existing attacks on LLM reasoning are constrained by specific settings or lack of imperceptibility, limiting their feasibility and generalizability. To address these challenges, we propose the Step

Ashleigh Ratcliffe, Bogdan Grechuk

Generalised Fermat equation (GFE) is the equation of the form $ax^p+by^q=cz^r$, where $a,b,c,p,q,r$ are positive integers. If $1/p+1/q+1/r<1$, GFE is known to have at most finitely many primitive integer solutions $(x,y,z)$. A large body of the literature is devoted to finding such solutions explicitly for various six-tuples $(a,b,c,p,q,r)$, as well as for i

Subhajyoti Bid, Henning Schomerus

Non-Hermitian systems display remarkable response effects that reflect a variety of distinct spectral scenarios, such as exceptional points where the eigensystem becomes defective. However, present frameworks treat the different scenarios as separate cases, following the singular mathematical change between the spectral decompositions from one scenario to an

Benjamin Doerr, Tudor Ivan, Martin S. Krejca

The non-dominated sorting genetic algorithm~II (NSGA-II) is the most popular multi-objective optimization heuristic. Recent mathematical runtime analyses have detected two shortcomings in discrete search spaces, namely, that the NSGA-II has difficulties with more than two objectives and that it is very sensitive to the choice of the population size. To overc

Minjae Cho, Chuangchuang Sun

Meta-Reinforcement Learning (Meta-RL) enables fast adaptation to new testing tasks. Despite recent advancements, it is still challenging to learn performant policies across multiple complex and high-dimensional tasks. To address this, we propose a novel architecture with three hierarchical levels for 1) learning task representations, 2) discovering task-agno

Rudolf Haindl, Valerio Di Giulio, Armin Feist, Claus Ropers

Temporal correlations in pulsed electron beams reflect the microscopic dynamics of emission and interparticle interaction. In femtosecond electron emission from nanoscale field emitters, Coulomb interactions result in structured few-electron states with strong correlations in energy, time, and transverse momentum. Interactions with external fields may be use

Éric Vacelet

We study propagation in a system consisting of two topological insulators without a magnetic field, whose interface is a non-compact, smooth, and connected curve without boundary. The dynamics are governed by an adiabatic modulation of a Dirac operator with a smooth, effective variable mass. We determine the evolution of the semiclassical measure of the solu

Shane Storks, Itamar Bar-Yossef, Yayuan Li, Zheyuan Zhang

Procedural mistake detection (PMD) is a challenging problem of classifying whether a human user (observed through egocentric video) has successfully executed a task (specified by a procedural text). Despite significant recent efforts, machine performance in the wild remains nonviable, and the reasoning processes underlying this performance are opaque. As suc

Jian Wang, Mark Williams

In a recent paper [WW23] we studied the transport of oscillations in solutions to linear and some semilinear second-order hyperbolic boundary problems along rays that graze a convex obstacle to any order. We showed that high frequency exact solutions are well approximated in $H^1$ by much simpler approximate solutions constructed from explicit solutions to p

Martina Flammer, Knut Hüper

A method to apply and visualize persistent homology of time series is proposed. The method captures persistent features in space and time, in contrast to the existing procedures, where one usually chooses one while keeping the other fixed. An extended zigzag module that is built from a time series is defined. This module combines ideas from zigzag persistent

Dongxin Gao, Daojin Fan, Chen Zha, Jiahao Bei

In the relentless pursuit of quantum computational advantage, we present a significant advancement with the development of Zuchongzhi 3.0. This superconducting quantum computer prototype, comprising 105 qubits, achieves high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62% and 99.18%, respectively. Our

Sepideh Mamooler, Syrielle Montariol, Alexander Mathis, Antoine Bosselut

In-context learning (ICL) enables Large Language Models (LLMs) to perform tasks using few demonstrations, facilitating task adaptation when labeled examples are hard to obtain. However, ICL is sensitive to the choice of demonstrations, and it remains unclear which demonstration attributes enable in-context generalization. In this work, we conduct a perturbat

Ying Li, Valentin Leeb, Krzysztof Wohlfeld, Roser Valentí

The antiferromagnetic parent phase of high-T$_c$ cuprates has been established as a N\'eel state of copper moments, but early work pointed out the important role of ligand oxygen orbitals. Using the three-orbital Emery model, we explore how, and under which conditions, doping-induced antiferromagnetic ordering of weak magnetic moments on the oxygen sites can

Pratibha Jangra, Daniele Gaggero, Bradley J. Kavanagh, J. M. Diego

Primordial Black Holes (PBHs) have not been experimentally detected so far, but their existence would provide important insights about the early Universe and serve as one of the possible candidates of dark matter (DM). In this work, we explore the accretion of radiation and matter by PBHs, with relevance for the growth of PBH seeds to form early Supermassive

Thi Tam Dang, Trung Hau Hoang

This paper investigates the performance of a subclass of exponential integrators, specifically explicit exponential Runge--Kutta methods. It is well known that third-order methods can suffer from order reduction when applied to linearized problems involving unbounded and non-commuting operators. In this work, we consider a fourth-stage third-order Runge--Kut

Xiaoxi Li, Jiajie Jin, Yujia Zhou, Yongkang Wu

Large language models (LLMs) exhibit remarkable generative capabilities but often suffer from hallucinations. Retrieval-augmented generation (RAG) offers an effective solution by incorporating external knowledge, but existing methods still face several limitations: additional deployment costs of separate retrievers, redundant input tokens from retrieved text

S. Cepollaro, S. Cusumano, A. Hamma, G. Lo Giudice

The harvesting of quantum resources from the vacuum state of a quantum field is a central topic in relativistic quantum information. While several proposals for the harvesting of entanglement from the quantum vacuum exist, less attention has been paid to other quantum resources, such as non-stabilizerness, commonly dubbed {\em magic} and quantified by the St

Pingchuan Ma, Lennart Rietdorf, Dmytro Kotovenko, Vincent Tao Hu

Accurately describing images with text is a foundation of explainable AI. Vision-Language Models (VLMs) like CLIP have recently addressed this by aligning images and texts in a shared embedding space, expressing semantic similarities between vision and language embeddings. VLM classification can be improved with descriptions generated by Large Language Model

Hemjyoti Nath, Manjil P. Saikia, Abhishek Sarma

In this paper, we study arithmetic properties satisfied by the $k$-tuple $\ell$-regular partitions. A $k$-tuple of partitions $(\xi_1, \xi_2, \ldots, \xi_k)$ is said to be $\ell$-regular if all the $\xi_i$'s are $\ell$-regular. We study the cases $(\ell, k)=(2,3), (4,3), (\ell, p)$, where $p$ is a prime, and even the general case when both $\ell$ and $k$ are

James C. Ward, Ryan McConville, Edmund R. Hunt

The problem of decentralized multi-robot patrol has previously been approached primarily with hand-designed strategies for minimization of 'idlenes' over the vertices of a graph-structured environment. Here we present two lightweight neural network-based strategies to tackle this problem, and show that they significantly outperform existing strategies in bot

Simon Ekhammar, Nikolay Gromov, Bogdan Stefański

We show that in the asymptotic large-volume limit, the original proposal for Quantum Spectral Curve for AdS3 x S3 x T4 with R-R flux has a wider class of solutions, than studied previously. We argue that in this limit the QSC reduces to a finite set of Bethe equations for both massive and massless particle types. We also find that the QSC imposes more constr

Boris Alexeev, Dustin G. Mixon, Hans Parshall

We improve the best known upper bound on the number of edges in a unit-distance graph on $n$ vertices for each $n\in\{16,\ldots,30\}$. When $n\leq 21$, our bounds match the best known lower bounds, and we fully enumerate the densest unit-distance graphs in these cases. On the combinatorial side, our principle technique is to more efficiently generate $\mathc

Jason Qin, Shikun Ban, Wentao Zhu, Yizhou Wang

Developing robots that can assist humans efficiently, safely, and adaptively is crucial for real-world applications such as healthcare. While previous work often assumes a centralized system for co-optimizing human-robot interactions, we argue that real-world scenarios are much more complicated, as humans have individual preferences regarding how tasks are p

Jinfeng Zhou, Yongkang Huang, Bosi Wen, Guanqun Bi

Character-based dialogue (aka role-playing) enables users to freely customize characters for interaction, which often relies on LLMs, raising the need to evaluate LLMs' character customization capability. However, existing benchmarks fail to ensure a robust evaluation as they often only involve a single character category or evaluate limited dimensions. More

Luis Morales-Navarro, Yasmin B. Kafai, Eric Yang, Asep Suryana

The prominence of artificial intelligence and machine learning in everyday life has led to efforts to foster AI literacy for all K-12 students. In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in particular with machine learning and societal impact. We found that a large majority of activities focus on perception and

Hongkai Liu, Daiki Ueda

Multi-TeV muon colliders offer a powerful means of accessing new physics coupled to muons while generating clean and intense high-energy neutrino beams via muon decays. We study a fixed-target experiment leveraging the neutrino beams and a forward detector pointing at the interaction point of the muon collider. The sensitivity to neutrino self-interactions i

Tomas Ondro, Bhaskar Arya, Rudolf Galis

This study aims to test a potential application of lognormal seminumerical simulations to recover the thermal parameters and Jeans length. This could be suitable for generating large number of synthetic spectra with various input data and parameters, and thus ideal for interpreting the high-quality data obtained from QSO absorption spectra surveys. We use a

Andrii Nikolaiev, Yiannos Stathopoulos, Simone Teufel

In this paper we look at the ability of recent large language models (LLMs) at solving mathematical problems in combinatorics. We compare models LLaMA-2, LLaMA-3.1, GPT-4, and Mixtral against each other and against human pupils and undergraduates with prior experience in mathematical olympiads. To facilitate these comparisons we introduce the Combi-Puzzles d

Qisheng Xu, Yulin Sun, Yi Su, Qian Zhu

Deep learning, with its robust aotomatic feature extraction capabilities, has demonstrated significant success in audio signal processing. Typically, these methods rely on static, pre-collected large-scale datasets for training, performing well on a fixed number of classes. However, the real world is characterized by constant change, with new audio classes e

Kun Ouyang, Yuanxin Liu, Shicheng Li, Yi Liu

Multimodal punchlines, which involve humor or sarcasm conveyed in image-caption pairs, are a popular way of communication on online multimedia platforms. With the rapid development of multimodal large language models (MLLMs), it is essential to assess their ability to effectively comprehend these punchlines. However, existing benchmarks on punchline comprehe

Huishi Luo, Yiwen Chen, Yiqing Wu, Fuzhen Zhuang

Multi-domain recommendation (MDR) aims to enhance recommendation performance across various domains. However, real-world recommender systems in online platforms often need to handle dozens or even hundreds of domains, far exceeding the capabilities of traditional MDR algorithms, which typically focus on fewer than five domains. Key challenges include a subst

Jens Keim, Hasel-Cicek Konan, Christian Rohde

We consider the two-phase dynamics of two incompressible and immiscible fluids. As a mathematical model we rely on the Navier-Stokes-Cahn-Hilliard system that belongs to the class of diffuse-interface models. Solutions of the Navier-Stokes-Cahn-Hilliard system exhibit strong non-local effects due to the velocity divergence constraint and the fourth-order Cah

Valentin Vankov Iliev

Here we establish conditions for some pairs of quantum logic gates which operate on one qubit to be protected against crosstalk.

Alberto Enciso, Pablo Hidalgo-Palencia, Xavier Ros-Oton

We establish the existence of positive solutions to a general class of overdetermined semilinear elliptic boundary problems on suitable bounded open sets $\Omega\subset\mathbb{R}^n$. Specifically, for $n\leq 4$ and under mild technical hypotheses on the coefficients and the nonlinearity, we show that there exist open sets $\Omega\subset\mathbb{R}^n$ with smo

Séverin Philip

The finite monodromy groups of abelian varieties over number fields have been introduced by Grothendieck. They represent the local obstruction to semi-stable reduction. In this paper we prove a criteria for finite groups to be realized as finite monodromy groups in given dimension. An application to the degree of semi-stability gives an effective version of

Anna Büttner, Frank Hellmann

This study applies the Probabilistic Behavioral Tuning (ProBeTune) framework to transient power grid simulations to address challenges posed by increasing grid complexity. ProBeTune offers a probabilistic approach to model aggregation, using a behavioral distance measure to quantify and minimize discrepancies between a full-scale system and a simplified mode

Ane Sanz, David Franco, Asier Atutxa, Jasone Astorga

This paper presents the SareQuant project, which aims to evolve the Basque NREN (National Research and Education Networks) into a quantum-based communication infrastructure. SareQuant focuses on the network design and on the integration of quantum technologies into real-world scenarios and applications. Therefore, this paper provides insights into the opport

Shahar Elisha, Andrew McDowell, Mariano Beguerisse-Díaz, Emmanouil Benetos

Distinguishing scripted from spontaneous speech is an essential tool for better understanding how speech styles influence speech processing research. It can also improve recommendation systems and discovery experiences for media users through better segmentation of large recorded speech catalogues. This paper addresses the challenge of building a classifier

Yurii A. Aleshchenko, Andrey V. Muratov, Elena S. Zhukova, Lenar S. Kadyrov

The broad-band optical spectroscopy was used to study the optical and the hidden transport properties of the Ba(Fe$_{1-x}$Ni$_x$)$_2$As$_2$ superconducting films with different Ni contents. The normal state data were analyzed using a Drude-Lorentz model with two Drude components: narrow and broad ones. In the superconducting state, two gaps with $2\Delta _{0

Serena Valtolina, Rutger van Haasteren

The recent announcement of evidence for a stochastic background of gravitational waves (GWB) in pulsar timing array (PTA) data has piqued interest across the scientific community. A combined analysis of all currently available data holds the promise of confirming the announced evidence as a solid detection of a GWB. However, the complexity of individual puls

Guanglong Yu

The extremal eigenvalues including maximum eigenvalues and the minimum eigenvalues about outerplanar graphs are investigated in this paper. Some structural characterizations about the (edge) maximal bipartite outerplanar graphs are represented. With these characterizations, among all bipartite outerplanar graphs of order $n\geq 55$, the maximum spectral radi

Xilin Wang, Jia Zheng, Yuanchao Hu, Hao Zhu

In this paper, we present CAD2Program, a new method for reconstructing 3D parametric models from 2D CAD drawings. Our proposed method is inspired by recent successes in vision-language models (VLMs), and departs from traditional methods which rely on task-specific data representations and/or algorithms. Specifically, on the input side, we simply treat the 2D

Dongxiao Zhao, Hussein Aluie

We expand on the method of sequential filtering for calculating spectra of inhomogeneous fields. Sadek & Aluie [Phys. Rev. Fluids, 3, 124610 (2018)] showed that the kernel has to have at least $p$ vanishing moments to extract a power-law spectrum $k^{-\alpha}$ with $\alpha<p+2$ by low-pass filtering. Here, we show that sequential high-pass filtering allows f

Yunxiang Fu, Meng Lou, Yizhou Yu

High-quality semantic segmentation relies on three key capabilities: global context modeling, local detail encoding, and multi-scale feature extraction. However, recent methods struggle to possess all these capabilities simultaneously. Hence, we aim to empower segmentation networks to simultaneously carry out efficient global context modeling, high-quality l

Zachary P. Bradshaw, Ethan N. Evans, Matthew Cook, Margarite L. LaBorde

Geometric quantum machine learning uses the symmetries inherent in data to design tailored machine learning tasks with reduced search space dimension. The field has been well-studied recently in an effort to avoid barren plateau issues while improving the accuracy of quantum machine learning models. This work explores the related problem of learning an equiv

Evgeny Zamyatin

A major problem of making friend suggestions in social networks is the large size of social graphs, which can have hundreds of millions of people and tens of billions of connections. Classic methods based on heuristics or factorizations are often used to address the difficulties of scaling more complex models. However, the unsupervised nature of these method

Jules Duraz, Hassen Souissi, Maksym Gromovyi, David Troadec

The Ni-Au-Ga interdiffusion mechanisms taking place during rapid thermal annealing (RTA) under oxygen atmosphere of a Ni-Au/p-GaN contact are investigated by high-resolution transmission electron microscopy (HR-TEM) coupled to energy dispersive X-ray spectroscopy (EDX). It is shown that oxygen-assisted, Ni diffusion to the top surface of the metallic contact

A. V. Belitsky

We study the collinear factorization of off-shell scattering amplitudes in maximally supersymmetric Yang-Mills (sYM) theory. These are constructed starting from six-dimensional N = (1,1) sYM, taking advantage of an available unconstrained spinor-helicity formalism combined with a unitarity-cut sewing procedure. After generalized dimensional reduction, their

Nicolas Torres-Ulloa, Erick Kracht, Urban Fasel, Benjamin Herrmann

Modal analysis has long been consolidated as a basic tool to interpret dynamics and build low-order models of mechanical, thermal, and fluid systems. Eigenmodes arising from the spectral decomposition of the underlying linearized dynamics represent spatial patterns in vibration, temperature, or velocity fields associated with simple time dynamics. However, f

Fabian Paschke, Ricardo Ortiz, Shantanu Mishra, Manuel Vilas-Varela

Engineering sublattice imbalance is an intuitive way to induce high-spin ground states in bipartite polycyclic conjugated hydrocarbons (PCHs). Such high-spin molecules can be employed as building blocks of quantum spin chains, which are outstanding platforms to study many-body physics and fundamental models in quantum magnetism. Recent reports on the bottom-

Ian J. Maquignaz

Accurate environment maps are a key component in rendering photorealistic outdoor scenes with coherent illumination. They enable captivating visual arts, immersive virtual reality and a wide range of engineering and scientific applications. Recent works have extended sky-models to be more comprehensive and inclusive of cloud formations but existing approache

Songnan Yang, Shiliang Zhang, Qianyun Zhang, Xiaohui Zhang

Geomagnetic navigation leverages the ubiquitous Earth's magnetic signals to navigate missions, without dependence on GPS services or pre-stored geographic maps. It has drawn increasing attention and is promising particularly for long-range navigation into unexplored areas. Current geomagnetic navigation studies are still in the early stages with simulations

Ryo Fujita

We introduce a collection of injective homomorphisms among the quantum Grothendieck rings of finite-dimensional modules over the quantum loop algebras of type $\mathrm{A}$. In the classical limit, it specializes to the inflation among the usual Grothendieck rings studied by Brito-Chari [J. Reine Angew. Math. 804, 2023]. We show that our homomorphisms respect

Andrew Gelman, Jonas M. Mikhaeil

It has been proposed in medical decision analysis to express the ``first do no harm'' principle as an asymmetric utility function in which the loss from killing a patient would count more than the gain from saving a life. Such a utility depends on unrealized potential outcomes, and we show how this yields a paradoxical decision recommendation in a simple hyp

Heinz H. Bauschke, Walaa M. Moursi, Shambhavi Singh

Finding zeros of the sum of two maximally monotone operators involving a continuous linear operator is a central problem in optimization and monotone operator theory. We revisit the duality framework proposed by Eckstein, Ferris, Pennanen, and Robinson from a quarter of a century ago. Paramonotonicity is identified as a broad condition ensuring that saddle p

Sam Relins, Daniel Birks, Charlie Lloyd

Objectives: Compare qualitative coding of instruction tuned large language models (IT-LLMs) against human coders in classifying the presence or absence of vulnerability in routinely collected unstructured text that describes police-public interactions. Evaluate potential bias in IT-LLM codings. Methods: Analyzing publicly available text narratives of police-

Mahesha Kodithuwakku Arachchige, Zakaria Siddiquee, Hend Baza, Robert Twieg

The dynamics of swimming bacteria depend on the properties of their habitat media. Recently it was shown that the motion of swimming bacteria dispersed directly in a non-toxic water-based lyotropic chromonic liquid crystal can be controlled by the director field of the liquid crystal. Here we investigate whether the macroscopic polar order of a ferroelectric

Anna Lentz

Capacitary measures form a class of measures that vanish on sets of capacity zero. These measures are compact with respect to so-called $\gamma$-convergence, which relates a sequence of measures to the sequence of solutions of relaxed Dirichlet problems. This compactness result is already known for the classical $H^1(\Omega)$-capacity. This paper extends it

Philipp Reiser, Paul-Christian Bürkner, Anneli Guthke

Surrogate models are often used as computationally efficient approximations to complex simulation models, enabling tasks such as solving inverse problems, sensitivity analysis, and probabilistic forward predictions, which would otherwise be computationally infeasible. During training, surrogate parameters are fitted such that the surrogate reproduces the sim