November 2024 arXiv papers — page 145

Arthur Braida, Shantanav Chakraborty, Alapan Chaudhuri, Joseph Cunningham

In the circuit model of quantum computing, amplitude amplification techniques can be used to find solutions to NP-hard problems defined on $n$-bits in time $\text{poly}(n) 2^{n/2}$. In this work, we investigate whether such general statements can be made for adiabatic quantum optimization, as provable results regarding its performance are mostly unknown. Alt

Mayee F. Chen, Michael Y. Hu, Nicholas Lourie, Kyunghyun Cho

Language model performance depends on identifying the optimal mixture of data groups to train on (e.g., law, code, math). Prior work has proposed a diverse set of methods to efficiently learn mixture proportions, ranging from fitting regression models over training runs to dynamically updating proportions throughout training. Surprisingly, we find that no ex

Agamdeep Singh, Sujit PB, Mayank Vatsa

Access to expert coaching is essential for developing technique in sports, yet economic barriers often place it out of reach for many enthusiasts. To bridge this gap, we introduce Poze, an innovative video processing framework that provides feedback on human motion, emulating the insights of a professional coach. Poze combines pose estimation with sequence c

Lucas Rosenblatt, Yuliia Lut, Eitan Turok, Marco Avella-Medina

Imbalanced learning occurs in classification settings where the distribution of class-labels is highly skewed in the training data, such as when predicting rare diseases or in fraud detection. This class imbalance presents a significant algorithmic challenge, which can be further exacerbated when privacy-preserving techniques such as differential privacy are

Yandika Sirgabsou, Benjamin Hardin, François Leblanc, Efi Raili

The rapid advancement of artificial intelligence and autonomous driving technologies has significantly propelled the development of autonomous vehicles (AVs). However, psychological barriers continue to impede widespread AV adoption, despite technological progress. This paper addresses the critical yet often overlooked aspect of psychological safety in AV de

Junxi Jin, Xiulai Li, Haiping Huang, Lianjun Liu

Recently, 3D Gaussian Splatting (3D-GS) has achieved significant success in real-time, high-quality 3D scene rendering. However, it faces several challenges, including Gaussian redundancy, limited ability to capture view-dependent effects, and difficulties in handling complex lighting and specular reflections. Additionally, methods that use spherical harmoni

G. J. E. van Otterdijk, S. Moradi, S. Weiland, R. Tóth

Port-Hamiltonian neural networks (pHNNs) are emerging as a powerful modeling tool that integrates physical laws with deep learning techniques. While most research has focused on modeling the entire dynamics of interconnected systems, the potential for identifying and modeling individual subsystems while operating as part of a larger system has been overlooke

Marcello Calvanese Strinati, Claudio Conti

We report on the emergence of a highly non-classical collective behavior in quantum parametric oscillators, which we name quantum hyperspin, induced by a tailored nonlinear interaction. This is the second quantized version of classical multidimensional spherical spins, as XY spins in two dimensions, and Heisenberg spins in three dimensions. In the phase spac

Kamal Kumar Paul, Ashok Mondal, Jae Woo Kim, Ji-Hee Kim

Extraction of hot carriers (HCs) over the band-edge is a key to harvest solar energy beyond Shockley-Queisser limit1. Graphene is known as a HC-layered material due to phonon bottleneck effect near Dirac point, but limited by low photocarrier density2. Graphene/transition metal dichalcogenide (TMD) heterostructures circumvent this issue by ultrafast carrier

Benjamin Testart

This work concerns a construction of pattern-avoiding inversion sequences from right to left we call the generating tree growing on the left. We first apply this construction to inversion sequences avoiding 201 and 210, resulting in a new way of computing their generating function. We then use a slightly modified construction to compute the generating functi

Wen Wan, Maria N. Gastiasoro, Daniel Muñoz-Segovia, Paul Dreher

When the period of an incommensurate charge density wave (ICDW) approaches an integer multiple of a lattice vector, the energy gain obtained from locking the period to the lattice can lead to a fascinating transition into a commensurate state. This transition actually occurs through an intermediate near-commensurate (NC) phase, with locally commensurate regi

Vardan Oganesyan

We construct a monotone spin Lagrangian cobordism from L to (L_1, L_2) such that there is no monotone spin Lagrangian cobordism from L to (L_2, L_1), where L, L_1, L_2 are Lagrangians of CP^7.

Sanaz Zarei

Photonic neural networks capable of rapid programming are indispensable to realize many functionalities. Phase change technology can provide nonvolatile programmability in photonic neural networks. Integrating direct laser writing technique with phase change material (PCM) can potentially enable programming and in-memory computing for on-chip photonic neural

Hunter Ng

Wash trading, the practice of simultaneously placing buy and sell orders for the same asset to inflate trading volume, has been prevalent in cryptocurrency markets. This paper investigates whether wash traders in Bitcoin act deliberately to exploit market conditions and identifies the characteristics of such manipulative behavior. Using a unique dataset of 1

Elaine Li, Felix Stutz, Thomas Wies, Damien Zufferey

We study the implementability problem for an expressive class of symbolic communication protocols involving multiple participants. Our symbolic protocols describe infinite states and data values using dependent refinement predicates. Implementability asks whether a global protocol specification admits a distributed, asynchronous implementation, namely one fo

Tomasz Mańdziuk, Emanuele Ventura

One of the fundamental open problems in the field of tensors is the border Comon's conjecture: given a symmetric tensor $F\in(\mathbb{C}^n)^{\otimes d}$ for $d\geq 3$, its border and symmetric border ranks are equal. In this paper, we prove the conjecture for large classes of concise tensors in $(\mathbb{C}^n)^{\otimes d}$ of border rank $n$, i.e., tenso

Sijin Li, Yifan Li, Xujun Liu

For a sequence $S = (s_1, s_2, \ldots, s_k)$ of non-decreasing positive integers, an $S$-packing edge-coloring (S-coloring) of a graph $G$ is a partition of $E(G)$ into $E_1, E_2, \ldots, E_k$ such that the distance between each pair of distinct edges $e_1,e_2 \in E_i$, $1 \le i \le k$, is at least $s_i + 1$. In particular, a $(1^{\ell},2^k)$-coloring is a p

Allan McRobie

We extend graphic statics to describe the forces and moments in 3D rigid-jointed frame structures. Graphic statics relates the form diagram (the geometrical layout of structural bars) to a reciprocal force diagram representing the forces in those bars. For 3D structures, Rankine reciprocals represent bar forces by areas of polygons perpendicular to bars. Unf

Puze Liu, Jonas Günster, Niklas Funk, Simon Gröger

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots. When deployi

Vaibhav Raj Singh Parmar, Sayantan Chanda, Sri Vishnu Bharat Sivasubramaniam, Ranjini Bandyopadhyay

The adsorption of charged clay nanoplatelets plays an important role in stabilizing emulsions by forming a barrier around the emulsion droplets and preventing coalescence. In this work, the adsorption of charged clay nanoplatelets on a preformed Latex microsphere in an aqueous medium is investigated at high temporal resolution using optical tweezer-based sin

Basdouri Imed, Bouzid Mosbahi

We introduce and study a generalized form of derivations for dendriform algebras, specifying all admissible parameter values that define these derivations. Additionally, we present a complete classification of generalized derivations for two-dimensional left-symmetric dialgebras over the field $\mathbb{K}$.

Lukas Grasse, Matthew S. Tata

Humans are able to fuse information from both auditory and visual modalities to help with understanding speech. This is demonstrated through a phenomenon known as the McGurk Effect, during which a listener is presented with incongruent auditory and visual speech that fuse together into the percept of illusory intermediate phonemes. Building on a recent frame

Ethan King, Jaime Rodriguez, Diego Llanes, Timothy Doster

We present a sensor-agnostic spectral transformer as the basis for spectral foundation models. To that end, we introduce a Universal Spectral Representation (USR) that leverages sensor meta-data, such as sensing kernel specifications and sensing wavelengths, to encode spectra obtained from any spectral instrument into a common representation, such that a sin

Martin Bullinger, Matan Gilboa

We study coalition formation in the framework of hedonic games. There, a set of agents needs to be partitioned into disjoint coalitions, where agents have a preference order over coalitions. A partition is called popular if it does not lose a majority vote among the agents against any other partition. Unfortunately, hedonic games need not admit popular parti

Ben Koska, Mojmír Horváth

We present a novel 4.5B parameter small language model that can handle multiple input and output modalities, including text, images, videos, and audio. Despite its small size, the model achieves near state-of-the-art performance on a variety of tasks, demonstrating the potential of multi-modal models to tackle complex real-world problems. Our approach levera

Jing Xiong, Gongye Liu, Lun Huang, Chengyue Wu

Autoregressive modeling has been a huge success in the field of natural language processing (NLP). Recently, autoregressive models have emerged as a significant area of focus in computer vision, where they excel in producing high-quality visual content. Autoregressive models in NLP typically operate on subword tokens. However, the representation strategy in

Abdulkadir Gokce, Martin Schrimpf

When trained on large-scale object classification datasets, certain artificial neural network models begin to approximate core object recognition behaviors and neural response patterns in the primate brain. While recent machine learning advances suggest that scaling compute, model size, and dataset size improves task performance, the impact of scaling on bra

Yaiza Aragonés-Soria, Julia Kotovich, Chitsutha Soomlek, Manuel Oriol

Upon release, ChatGPT3.5 shocked the software engineering community by its ability to generate answers to specialized questions about coding. Immediately, many educators wondered if it was possible to use the chatbot as a support tool that helps students answer their programming questions. This article evaluates this possibility at three levels: fundamental

Ya. A. Nikiforov, V. A. Danilovsky, N. I. Baklanova

This work investigates the solid-state reaction between iridium and zirconium carbide, resulting in the formation of carbon and $\mathrm{ZrIr}_{3}$ -- an intermetallic compound of great interest for modern high-temperature materials science. We have found a transition of kinetic regimes in this reaction: from linear kinetics (when the chemical reaction is a

Sukŗti Bansal, Silvia Nagy, Antonio Padilla, Ivonne Zavala

We advance the study of pure de Sitter supergravity by introducing a finite formulation of unimodular supergravity via the super-St\"uckelberg mechanism. Building on previous works, we construct a complete four-dimensional action of spontaneously broken ${\cal N}\!\!=\!\!1$ supergravity to all orders, which allows for de Sitter solutions. The introduction of

L. L. Bonilla, R. González-Albaladejo

The discrete time Vicsek model confined by a harmonic potential explains many aspects of swarm formation in insects. We have found exact solutions of this model without alignment noise in two or three dimensions. They are periodic or quasiperiodic (invariant circle) solutions with positions on a circular orbit or on several concentric orbits and exist for qu

Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas

A single-index model (SIM) is a function of the form $\sigma(\mathbf{w}^{\ast} \cdot \mathbf{x})$, where $\sigma: \mathbb{R} \to \mathbb{R}$ is a known link function and $\mathbf{w}^{\ast}$ is a hidden unit vector. We study the task of learning SIMs in the agnostic (a.k.a. adversarial label noise) model with respect to the $L^2_2$-loss under the Gaussian dis

Gino Biondini, Alexander Bivolcic, Mark A. Hoefer

The oblique collisions and dynamical interference patterns of two-dimensional dispersive shock waves are studied numerically and analytically via the temporal dynamics induced by wedge-shaped initial conditions for the Kadomtsev-Petviashvili II equation. Various asymptotic wave patterns are identified, classified and characterized in terms of the incidence a

Jia-Hong Huang, Hongyi Zhu, Yixian Shen, Stevan Rudinac

Evaluating the quality of automatically generated image descriptions is a complex task that requires metrics capturing various dimensions, such as grammaticality, coverage, accuracy, and truthfulness. Although human evaluation provides valuable insights, its cost and time-consuming nature pose limitations. Existing automated metrics like BLEU, ROUGE, METEOR,

Kourosh Kiani, Razieh Rastgoo, Alireza Chaji, Sergio Escalera

Image inpainting, the process of restoring missing or corrupted regions of an image by reconstructing pixel information, has recently seen considerable advancements through deep learning-based approaches. In this paper, we introduce a novel deep learning-based pre-processing methodology for image inpainting utilizing the Vision Transformer (ViT). Our approac

Emily Lichko, Damiano Caprioli, Benedikt Schroer, Siddhartha Gupta

A critical component of particle acceleration in astrophysical shocks is the non-resonant (Bell) instability, where the streaming of cosmic rays (CRs) leads to the amplification of magnetic fields necessary to scatter particles. In this work we use kinetic particle-in-cells simulations to investigate the high-CR current regime, where the typical assumptions

Mithilesh Kumar

Schmidt decomposition is a powerful tool in quantum information. While Schmidt decomposition is universal for bipartite states, its not for multipartite states. In this article, we review properties of bipartite Schmidt decompositions and study which of them extend to multipartite states. In particular, Schmidt number (the number of non-zero terms in Schmidt

Pierre Bieliavsky, Maxime Willaert

Answering a conjecture by S. Kobayashi, in 1986, K. Sekigawa and L. Vanhecke proved that an almost hermitian manifold whose local geodesic symmetries preserve the K\"ahler 2-form is a locally symmetric hermitian space. In the present paper, we relax the hermitean hypothesis by only requiring the manifold to be symplectic. In other words, we study the symplec

Ignacio Breva Ribes, R. Giménez Conejero

We prove several results regarding the homology and homotopy type of images of real maps and their complexification. In particular, we study the local behavior of singular points after deformations. In this context, we prove a restrictive necessary condition for a real perturbation to have the same homology than its complexification, which is known as good r

Serge Bouc, Deniz Yılmaz

Let $p$ be a prime number. We consider diagonal $p$-permutation functors over a (commutative, unital) ring $\mathsf{R}$ in which all prime numbers different from $p$ are invertible. We first determine the finite groups $G$ for which the associated essential algebra $\mathcal{E}_\mathsf{R}(G)$ is non zero: These are groups of the form $G=L\rtimes \langle u\ra

Anselme Ndikumana, Kim Khoa Nguyen, Mohamed Cheriet

Due to the high costs of optical fiber deployment in Low-Density and Rural Areas (LDRAs), 5G Fixed Wireless Access (5G FWA) recently emerged as an affordable solution. A widely adopted deployment scenario of 5G FWA includes edge cloud that supports computing services and Radio Access Network (RAN) functions. Such edge cloud requires network and energy resour

Hongyi Pan, Ziliang Hong, Gorkem Durak, Elif Keles

Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 652 T1-weighted and 655 T2-weighted MRI im

Massimiliano Marcellino, Andrea Renzetti, Tommaso Tornese

We develop a Functional Augmented Vector Autoregression (FunVAR) model to explicitly incorporate firm-level heterogeneity observed in more than one dimension and study its interaction with aggregate macroeconomic fluctuations. Our methodology employs dimensionality reduction techniques for tensor data objects to approximate the joint distribution of firm-lev

Tabea Bühler, Timo Zwettler, Gaia Bolognini, Aurélien Fabre

We present the production of quantum degenerate, superfluid gases of $^6$Li through direct evaporative cooling in a cavity-enhanced optical dipole trap. The entire evaporative cooling process is performed in a trap created by the TEM$_{00}$ mode of a Fabry-P\'erot cavity, simultaneously driven on several successive longitudinal modes. This leads to near-comp

Yi Li, Zhichun Guo, Guanpeng Li, Bingzhe Li

Graph neural networks (GNNs) have become essential tools for analyzing non-Euclidean data across various domains. During training stage, sampling plays an important role in reducing latency by limiting the number of nodes processed, particularly in large-scale applications. However, as the demand for better prediction performance grows, existing sampling alg

Abhisek Ray, Ayush Raj, Maheshkumar H. Kolekar

Extracting multiscale contextual information and higher-order correlations among skeleton sequences using Graph Convolutional Networks (GCNs) alone is inadequate for effective action classification. Hypergraph convolution addresses the above issues but cannot harness the long-range dependencies. The transformer proves to be effective in capturing these depen

Andrew Semenov

This paper describes Asterisk, a compact GPT-based model for generating text embeddings. The model uses a minimalist architecture with two layers, two attention heads, and 256 embedding dimensions. By applying knowledge distillation from larger pretrained models, we explore the trade-offs between model size and performance while minimizing computational and

Jun Wei Zhang, Zhen Jie Qi, Li Jie Wu, Wan Wan Cao

With the versatile manipulation capability, programmable metasurfaces are rapidly advancing in their intelligence, integration, and commercialization levels. However, as the programmable metasurfaces scale up, their control configuration becomes increasingly complicated, posing significant challenges and limitations. Here, we propose a multiple-partition cro

Mazen Alamir

In this paper, a new python package (optipoly) is described that solves box-constrained optimization problem over multivariate polynomial cost functions. The principle of the algorithm is described before its performance is compared to three general purpose NLP solvers implemented in the state-of-the-art Gekko and scipy packages. The comparison show statisti

Yifei Yang, Sreejith Nair, Yihong Fan, Yu-Chia Chen

Crystal symmetry plays an important role in the Hall effects. Unconventional spin Hall effect (USHE), characterized by Dresselhaus and out-of-plane spins, has been observed in materials with low crystal symmetry. Recently, antisymmetric planar Hall effect (APHE) was discovered in rutile RuO2 and IrO2 (101) thin films, which also exhibit low crystal symmetry.

Jan Gilles, Stephan Fritzsche, Lukas J. Spieß, Piet O. Schmidt

Recent advances in high-precision spectroscopy of highly charged ions necessitate an understanding of energy shifts of ionic levels caused by external electric and magnetic fields. Beyond the well-known Stark and linear Zeeman shifts, trapped ions may also exhibit quadratic Zeeman and electric quadrupole shifts. In this contribution, we present a systematic

Keli Zhang, Shizhi Lin, Peiqing Liu, Shihao Liu

This study presents wall-resolved large-eddy simulations (WRLES) of a high-lift airfoil, based on high-order flux reconstruction (FR) commercial software Dimaxer, which runs on consumer level GPUs. A series of independence tests are conducted, including various Ffowcs Williams-Hawkings sampling surfaces, different mesh densities, simulations at 4th and 5th o

Al Amin, Kamrul Hasan, Sharif Ullah, M. Shamim Hossain

Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is not always easy and often compromises performance and security. To address these limitations, this research introduces

Junhap Bian, Tao Zhou, Yilin Bi

Complex systems, such as economic, social, biological, and ecological systems, usually feature interactions not only between pairwise entities but also among three or more entities. These multi-entity interactions are known as higher-order interactions. Hypergraph, as a mathematical tool, can effectively characterize higher-order interactions, where nodes de

Leonardo Chaves-Velasquez, Gilberto C. Gómez, Ángeles Pérez-Villegas

The innermost region of the Milky Way harbors the central molecular zone (CMZ). This region contains a large amount of molecular gas but a poor star formation rate considering the densities achieved by the gas in this region. We used the arepo code to perform a hydrodynamic and star formation simulation of the Galaxy, where a Ferrers bar was adiabatically in

Francesco Girlanda, Olga Demler, Bjoern Menze, Neda Davoudi

Accurate prediction of cardiovascular diseases remains imperative for early diagnosis and intervention, necessitating robust and precise predictive models. Recently, there has been a growing interest in multi-modal learning for uncovering novel insights not available through uni-modal datasets alone. By combining cardiac magnetic resonance images, electrocar

Indranil Sur, Aswin Raghavan, Abrar Rahman, James Z Hare

The integration of unmanned platforms equipped with advanced sensors promises to enhance situational awareness and mitigate the "fog of war" in military operations. However, managing the vast influx of data from these platforms poses a significant challenge for Command and Control (C2) systems. This study presents a novel multi-agent learning framework to ad

Yifei Yang, Seungjun Lee, Yu-Chia Chen, Qi Jia

Spin-orbit torque (SOT) can be used to efficiently manipulate the magnetic state of magnetic materials, which is an essential element for memory and logic applications. Due to symmetry constraints, only in-plane spins can be injected into the ferromagnet from the underlying SOT layer for conventional SOT materials such as heavy metals and topological materia

Kai-Fung Chu, Haiyue Yuan, Jinsheng Yuan, Weisi Guo

Mobility-as-a-Service (MaaS) integrates different transport modalities and can support more personalisation of travellers' journey planning based on their individual preferences, behaviours and wishes. To fully achieve the potential of MaaS, a range of AI (including machine learning and data mining) algorithms are needed to learn personal requirements and ne

F. Wragg, S. Kamann, S. Saracino, M. Latour

NGC 5139 ($\omega$ Cen), is the closest candidate of a Nuclear Star Cluster that has been stripped of its host galaxy in the Milky Way. Despite extensive studies through the last decades, many open questions about the cluster remain, including the properties of the binary population. In this study we use MUSE multi-epoch spectroscopy to identify binary syste

Xiulong Liu, Kun Su, Eli Shlizerman

The content of visual and audio scenes is multi-faceted such that a video can be paired with various audio and vice-versa. Thereby, in video-to-audio generation task, it is imperative to introduce steering approaches for controlling the generated audio. While Video-to-Audio generation is a well-established generative task, existing methods lack such controll

Peter Bubenik, Alex Elchesen

We develop a theory of optimal transport relative to a distinguished subset, which acts as a reservoir of mass, allowing us to compare measures of different total variation. This relative transportation problem has an optimal solution and we obtain relative versions of the Kantorovich-Rubinstein norm, Wasserstein distance, Kantorovich-Rubinstein duality and

Luke C. Rhodes, Dylan C. Houston, Olivia R. Armitage, Peter Wahl

Moir\'e lattices are a general feature of bilayer structures, where an additional periodic superstructure is generated by either lattice mismatch or from a twist angle. They have been shown to stabilise new ground states, including unconventional superconductivity and Mott insulating phases, attributed to strong electron correlations. However, controlling th

Xiaoyang Hou, Tian Zhu, Milong Ren, Dongbo Bu

Generating molecular graphs is crucial in drug design and discovery but remains challenging due to the complex interdependencies between nodes and edges. While diffusion models have demonstrated their potentiality in molecular graph design, they often suffer from unstable training and inefficient sampling. To enhance generation performance and training stabi

Mateo Anarella, Michaël Liefsoens

Through the means of an alternative and less algebraic method, an explicit expression for the isometry groups of the six-dimensional homogeneous nearly K\"ahler manifolds is provided.

Ludia T. Khusainova, Konstantin S. Kolegov

The work is devoted to the development of an information system ISANM that will be useful for teaching students and applicable in the work of engineers and researchers for automating the analysis of colloidal structure morphology. The ISANM system integrates various analysis methods and is designed prioritize usability, catering primarily to users rather tha

Tony Lindeberg

When observing the surface patterns of objects delimited by smooth surfaces, the projections of the surface patterns to the image domain will be subject to substantial variabilities, as induced by variabilities in the geometric viewing conditions, and as generated by either monocular or binocular imaging conditions, or by relative motions between the object

Yueke Hu, Ian Petrow, Matthew P. Young

We develop generalized Petersson/Bruggeman-Kuznetsov (PBK) formulas for specified local components at non-archimedean places. In fact, we introduce two hypotheses on non-archimedean test function pairs $f \leftrightarrow \pi(f)$, called geometric and spectral hypotheses, under which one obtains `nice' PBK formulas by the adelic relative trace function approa

Sebastian C. Carrasco, Sean Lourette, Ignacio Sola, Vladimir S. Malinovsky

A novel quantum control protocol utilizing two-photon processes with trigonometric pulse modulation is developed, enabling the intermediate state population's dynamic elimination (DE). The proposed DE technique excels at single-photon resonance in contrast to the well-known adiabatic elimination (AE) regime, which requires large single-photon detuning and st

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The dissociation of quarkonium states with different binding energies produced in heavy-ion collisions is a powerful probe for investigating the formation and properties of the quark-gluon plasma. The ratio of production cross-sections of $\psi(2S)$ and $J/\psi$ mesons times the ratio of their branching fractions into the dimuon final state is measured as a

Ulderico Filippozzi, Graham Kimbell, Davide Pizzirani, Siobhan McKeown Walker

We study the superconducting critical fields of two-dimensional electron systems at (111)$\mathrm{LaAlO_3/KTaO_3}$ interfaces as a function of electrostatic back-gating. Our work reveals inplane critical fields of unprecedented magnitudes at oxide interfaces. By comparing the critical fields in-plane and out-of-plane we discover an extremely anisotropic supe

Domenik Ehlert, Foteini Oikonomou, Enrico Peretti

We investigate ultra-fast outflows (UFOs) in active galactic nuclei (AGN) as potential sources of ultra-high-energy cosmic rays (UHECRs). We focus on cosmic-ray nuclei, an aspect not explored previously. These large-scale, mildly-relativistic outflows, characterised by velocities up to half the speed of light, are a common feature of AGN. We study the cosmic

John Fearnley, Rahul Savani

We define the Super-Unique-Tarski problem, which is a Tarski instance in which all slices are required to have a unique fixed point. We show that Super-Unique-Tarski lies in UEOPL under promise-preserving reductions.

Fuka Matsuzaki, Haru-Tada Sato

This paper sheds light on the limitations of Large Language Models (LLMs) by rigorously evaluating their ability to process masked text. We introduce two novel tasks: MskQA, measuring reasoning on masked question-answering datasets like RealtimeQA, and MskCal, assessing numerical reasoning on masked arithmetic problems.Testing GPT-4o and 4o-mini reveals that

Anselme Ndikumana, Kim Khoa Nguyen, Mohamed Cheriet

Internet access in rural areas should be improved to support digital inclusion and 5G services. Due to the high deployment costs of fiber optics in these areas, Fixed Wireless Access (FWA) has become a preferable alternative. Additionally, the Open Radio Access Network (O-RAN) can facilitate the interoperability of FWA elements, allowing some FWA functions t

Xiwen Wei, Guihong Li, Radu Marculescu

Catastrophic forgetting is a significant challenge in online continual learning (OCL), especially for non-stationary data streams that do not have well-defined task boundaries. This challenge is exacerbated by the memory constraints and privacy concerns inherent in rehearsal buffers. To tackle catastrophic forgetting, in this paper, we introduce Online-LoRA,

Alejo Costa Duran, Mauricio Sturla, Ludovic D. C. Jaubert, Han Yan

In addition to describing our universe, gravitational theories profoundly inspire the study of emergent properties of exotic phases of matter. While the Anti-de Sitter/conformal field theory (AdS/CFT) correspondence is one of the most celebrated examples, the field of fractonic matter -- driven in part by gapless phases resembling linearized gravity -- has a

Lennard Miller, Bruno Deremble, Antoine Venaille

This study examines the role of stratification in the formation and persistence of eastward jets (like the Gulf Stream and Kuroshio currents). Using a wind-driven, two-layer quasi-geostrophic model in a double-gyre configuration, we construct a phase diagram to classify flow regimes. The parameter space is defined by a criticality parameter \( \xi \), which

Askin Altinoklu, Leila Musavian

This work focuses on designing a power-efficient network for Dynamic Metasurface Antennas (DMA)-aided multi-user multiple-input single-output (MISO) antenna systems. Power efficiency is achieved through holographic beamforming in a DMA-aided network, minimizing total transmission power while ensuring a guaranteed signal-to-noise-and-interference ratio (SINR)

Mohamed Suliman, Anisa Halimi, Swanand Kadhe, Nathalie Baracaldo

Data forging attacks provide counterfactual proof that a model was trained on a given dataset, when in fact, it was trained on another. These attacks work by forging (replacing) mini-batches with ones containing distinct training examples that produce nearly identical gradients. Data forging appears to break any potential avenues for data governance, as adve

Iridanos Loulas, Evangelos Almpanis, Minas Kouroublakis, Kosmas L. Tsakmakidis

We develop a full-wave electromagnetic (EM) theory for calculating the multipole decomposition in two-dimensional (2-D) structures consisting of isolated, arbitrarily shaped, inhomogeneous, anisotropic cylinders or a collection of such. To derive the multipole decomposition, we first solve the scattering problem by expanding the scattered electric field in d

Yusuke Oumi, Yuto Shibata, Go Irie, Akisato Kimura

This paper explores the problem of 3D human pose estimation from only low-level acoustic signals. The existing active acoustic sensing-based approach for 3D human pose estimation implicitly assumes that the target user is positioned along a line between loudspeakers and a microphone. Because reflection and diffraction of sound by the human body cause subtle

A. Kataria, R. Kumar, D. T. Adroja, C. Ritter

We present the results of microscopic investigations of antiferromagnetic CeNiGe$_3$, using neutron powder diffraction (NPD), inelastic neutron scattering (INS), and muon spin relaxation ($\mu$SR) measurements. CeNiGe$_3$ crystallizes in a centrosymmetric orthorhombic crystal structure (space group: $Cmmm$) and undergoes antiferromagnetic (AFM) ordering. The

Tiago da Silva, Daniel Augusto de Souza, Diego Mesquita

Bayes' rule naturally allows for inference refinement in a streaming fashion, without the need to recompute posteriors from scratch whenever new data arrives. In principle, Bayesian streaming is straightforward: we update our prior with the available data and use the resulting posterior as a prior when processing the next data chunk. In practice, however, th

Afang Yuan, Zhouyong Hu, Zhili Sun, Qinyu Zhang

With the ever-increasing lunar missions, a growing interest develops in designing data relay satellite constellations for cislunar communications, which is challenged by the constrained visibility and huge distance between the earth and moon in pursuit of establishing real-time communication links. In this work, therefore, we propose an age and coverage opti

Hiraku Kawanoue

We give an explicit formula for the generators of the logarithmic vector field of the coning of the extended Catalan arrangement of type $B_\ell$.

Linfeng He, Yiming Sun, Sihao Wu, Jiaxu Liu

In this paper, we propose a novel framework for enhancing visual comprehension in autonomous driving systems by integrating visual language models (VLMs) with additional visual perception module specialised in object detection. We extend the Llama-Adapter architecture by incorporating a YOLOS-based detection network alongside the CLIP perception network, add

Nicholas Wan, Qiao Jin, Joey Chan, Guangzhi Xiong

Although large language models (LLMs) have been assessed for general medical knowledge using licensing exams, their ability to support clinical decision-making, such as selecting medical calculators, remains uncertain. We assessed nine LLMs, including open-source, proprietary, and domain-specific models, with 1,009 multiple-choice question-answer pairs acros

Leon O. H. Kroczek, Alexander May, Selina Hettenkofer, Andreas Ruider

Large Language Models (LLMs) have demonstrated remarkable capabilities in conversational tasks. Embodying an LLM as a virtual human allows users to engage in face-to-face social interactions in Virtual Reality. However, the influence of person- and task-related factors in social interactions with LLM-controlled agents remains unclear. In this study, forty-si

Gernot Eichmann, Christian S. Fischer, Tim Haeuser, Oliver Regenfelder

We present results for single axial-vector and scalar meson pole contributions to the hadronic light-by-light scattering (HLbL) part of the muon's anomalous magnetic moment. In the dispersive approach to these quantities (in narrow width approximation) the central inputs are the corresponding space-like electromagnetic transition form factors. We determine t

Yuheng Zhao, Junjie Wang, Linbin Xiang, Xiaowen Zhang

Visual analytics (VA) requires analysts to iteratively propose analysis tasks based on observations and execute tasks by creating visualizations and interactive exploration to gain insights. This process demands skills in programming, data processing, and visualization tools, highlighting the need for a more intelligent, streamlined VA approach. Large langua

Charles M. Elliott, Thomas Sales

In this paper we study semi-discrete and fully discrete evolving surface finite element schemes for the Cahn-Hilliard equation with a logarithmic potential. Specifically we consider linear finite elements discretising space and backward Euler time discretisation. Our analysis relies on a specific geometric assumption on the evolution of the surface. Our main

Elena V. Epure, Gabriel Meseguer-Brocal, Darius Afchar, Romain Hennequin

Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categories or consumption contexts, from training data or user preferences. This has been proven effective in domains like movies or products. However, in the music domain, understandin

Samira Maghool, Paolo Ceravolo

In this paper, we propose an innovative approach to thoroughly explore dataset features that introduce bias in downstream machine-learning tasks. Depending on the data format, we use different techniques to map instances into a similarity feature space. Our method's ability to adjust the resolution of pairwise similarity provides clear insights into the rela

Ulrich Pfister, Daniel Wendland, Florian Hornung, Lena Engel

Photonic integrated circuits find ubiquitous use in various technologies, from communication, to computing and sensing, and therefore play a crucial role in the quantum technology counterparts. Several systems are currently under investigation, each showing distinct advantages and drawbacks. For this reason, efforts are made to effectively combine different

Jia-Zheng Ma

In this thesis, we give three examples of applying high energy physics method or idea in strongly correlated electron system. The first one is considering a curved space-time hydrodynamics theory for twisted bilayer graphene (TBG) and rotating bilayer graphene (RBG).We have predicted the possible quantized Thouless pumping in RBG for low driven frequency as

Hongbo Fang, Patrick Park, James Evans, James Herbsleb

In a real-world social network, weak ties (reflecting low-intensity, infrequent interactions) act as bridges and connect people to different social circles, giving them access to diverse information and opportunities that are not available within one's immediate, close-knit vicinity. Weak ties can be crucial for creativity and innovation, as they introduce i

Romy Müller, Judith Schmidt

When trains collide with obstacles, the consequences are often severe. To assess how artificial intelligence might contribute to avoiding collisions, we need to understand how train drivers do it. What aspects of a situation do they consider when evaluating the risk of collision? In the present study, we assumed that train drivers do not only identify potent

Nils Deppe, Lavinia Heisenberg, Henri Inchauspé, Lawrence E. Kidder

We assess the prospects for detecting gravitational wave echoes arising due to the quantum nature of black hole horizons with LISA. In a recent proposal, Bekenstein's black hole area quantization is connected to a discrete absorption spectrum for black holes in the context of gravitational radiation. Consequently, for incoming radiation at the black hole hor

Xingye Yang, Alexander Antonov, Andreas Aigner, Thomas Weber

Metasurfaces offer a powerful platform for effective light manipulation, which is crucial for advanced optical technologies. While designs of polarization-independent structures have reduced the need for polarized illumination, they are often limited by either low Q factors or low resonance modulation. Here, we design and experimentally demonstrate a metasur