April 2024 arXiv papers — page 3

Arturo Winters, Hans Christian Öttinger, Jan Vermant

Traditionally, stress fluctuations in flowing and deformed materials are overlooked, with an obvious focus on average stresses in a continuum mechanical approximation. However, these fluctuations, often dismissed as noise, hold the potential to provide direct insights into the material structure and its structure-stress coupling, uncovering detailed aspects

Nicholas Faucher, Michael R. Blanton

We use simulated galaxy observations from the NIHAO-SKIRT-Catalog to test the accuracy of Spectral Energy Distribution (SED) modeling techniques. SED modeling is an essential tool for inferring star-formation histories from nearby galaxy observations, but is fraught with difficulty due to our incomplete understanding of stellar populations, chemical enrichme

Neda Moradi, Gholam Hossein Fath-Tabara, Alain Bretto

In this paper the structure of the Cayley graphs and G-graphs of some gyro-groups are studied and some properties of them will be proved. Moreover we review some special gyro-groups including: gyro-commutative gyrogroups, dihedral gyro-groups and dihedralized gyro-groups. Then we try to establish some important properties of their associated G-graphs. Finall

Hadi Hosseini, Aghaheybat Mammadov, Tomasz Wąs

In fair division of indivisible items, domain restriction has played a key role in escaping from negative results and providing structural insights into the computational and axiomatic boundaries of fairness. One notable subdomain of additive preferences, the lexicographic domain, has yielded several positive results in dealing with goods, chores, and mixtur

Ian Dunn, David Ryan Koes

Deep generative models that produce novel molecular structures have the potential to facilitate chemical discovery. Diffusion models currently achieve state of the art performance for 3D molecule generation. In this work, we explore the use of flow matching, a recently proposed generative modeling framework that generalizes diffusion models, for the task of

Junze Li, Changyang He, Jiaxiong Hu, Boyang Jia

Elicitation diary studies, a type of qualitative, longitudinal research method, involve participants to self-report aspects of events of interest at their occurrences as memory cues for providing details and insights during post-study interviews. However, due to time constraints and lack of motivation, participants' diary entries may be vague or incomplete,

Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz

Large language models such as GPT and Llama are trained with a next-token prediction loss. In this work, we suggest that training language models to predict multiple future tokens at once results in higher sample efficiency. More specifically, at each position in the training corpus, we ask the model to predict the following n tokens using n independent outp

Xinlong Dong, Dragomir Šarić, Zhe Wang

The Liouville map, introduced by Bonahon, assigns to each point in the Teichm\"uller space a natural Radon measure on the space of geodesics of the base surface. The Liouville map is real analytic and it even extends to a holomorphic map of a neighborhood of the Teichm\"uller space in the Quasi-Fuchsian space of an arbitrary conformally hyperbolic Riemann su

Mohsen Koohi Esfahani, Marco D'Antonio, Syed Ibtisam Tauhidi, Thai Son Mai

Comprehensive evaluation is one of the basis of experimental science. In High-Performance Graph Processing, a thorough evaluation of contributions becomes more achievable by supporting common input formats over different frameworks. However, each framework creates its specific format, which may not support reading large-scale real-world graph datasets. This

Tengfei Wu, YoonSeok Baek, Fei Xia, Sylvain Gigan

Far-field super-resolution fluorescence microscopy has been rapidly developed for applications ranging from cell biology to nanomaterials. However, it remains a significant challenge to achieve super-resolution imaging at depth in opaque materials. In this study, we present a super-resolution microscopy technique for imaging hidden fluorescent objects throug

Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He

Iterative preference optimization methods have recently been shown to perform well for general instruction tuning tasks, but typically make little improvement on reasoning tasks (Yuan et al., 2024, Chen et al., 2024). In this work we develop an iterative approach that optimizes the preference between competing generated Chain-of-Thought (CoT) candidates by o

K. Océlotl. C. López, Alan M. Watson, William H. Lee, Rosa L. Becerra

The Fermi/GBM instrument is a vital source of detections of gamma-ray bursts and has an increasingly important role to play in understanding gravitational-wave transients. In both cases, its impact is increased by accurate positions with reliable uncertainties. We evaluate the RoboBA and BALROG algorithms for determining the position of gamma-ray bursts dete

Pranjal Talukdar

An integer partition of a positive integer $n$ is called to be $t$-core if none of its hook lengths are divisible by $t$. Recently, Gireesh, Ray and Shivashankar [`A new analogue of $t$-core partitions', \textit{Acta Arith.} \textbf{199} (2021), 33-53] introduced an analogue $\overline{a}_t(n)$ of the $t$-core partition function $c_t(n)$. They obtained certa

Ming-Xuan Lu, Yun-Feng Liang, Xuerui Ouyang, Rong-Lan Li

Given that gamma rays with energies larger than TeV are severely absorbed by background radiation fields, for many extragalactic sources, the GeV-TeV gamma-ray observations are the messengers that are closest in energy to the TeV-PeV neutrinos observed by IceCube. Investigating whether there is a correlation between the gamma-ray and neutrino observations ca

Steph Buongiorno, Corey Clark

External knowledge graphs (KGs) can be used to augment large language models (LLMs), while simultaneously providing an explainable knowledge base of facts that can be inspected by a human. This approach may be particularly valuable in domains where explainability is critical, like human trafficking data analysis. However, creating KGs can pose challenges. KG

Thuy Huong Pham, Gerhard Pfister, Gert-Martin Greuel

The aim of this article is the classification of simple 0-dimensional isolated complete intersection singularities in positive characteristic. As usual, a singularity is called simple or 0-modal if there are only finitely many isomorphism classes of singularities into which the given singularity can deform. The notion of simpleness was introduced by V. I. Ar

Jorge M. Ramirez, Elaine Wong, Caio Alves, Sarah Chehade

This study investigates the frame potential and expressiveness of commutative quantum circuits. Based on the Fourier series representation of these circuits, we express quantum expectation and pairwise fidelity as characteristic functions of random variables, and expressiveness as the recurrence probability of a random walk on a lattice. A central outcome of

Kohei Kamada, Jun'ya Kume

Gravitational chiral anomaly connects the topological charge of spacetime and the chirality of fermions. It has been known that the chirality is carried by the particles (or the excited states) and also by vacuum. While the gravitational anomaly equation has been applied to cosmology, distinction between these two contributions has been rarely discussed. In

Harshit Sharma, Shaily Roy, Asif Salekin

Modern human sensing applications often rely on data distributed across users and devices, where privacy concerns prevent centralized training. Federated Learning (FL) addresses this challenge by enabling collaborative model training without exposing raw data or attributes. However, achieving fairness in such settings remains difficult, as most human sensing

Rupak Majumdar, V. R. Sathiyanarayana

Deciding termination is a fundamental problem in the analysis of probabilistic imperative programs. We consider the qualitative and quantitative probabilistic termination problems for an imperative programming model with discrete probabilistic choice and demonic bounded nondeterminism. The qualitative question asks if the program terminates almost-surely, no

Hankun Wang, Chenpeng Du, Yiwei Guo, Shuai Wang

Recent popular decoder-only text-to-speech models are known for their ability of generating natural-sounding speech. However, such models sometimes suffer from word skipping and repeating due to the lack of explicit monotonic alignment constraints. In this paper, we notice from the attention maps that some particular attention heads of the decoder-only model

Jingbo Wang, Zhengyi Luo, Ye Yuan, Yixuan Li

We address the challenge of content diversity and controllability in pedestrian simulation for driving scenarios. Recent pedestrian animation frameworks have a significant limitation wherein they primarily focus on either following trajectory [46] or the content of the reference video [57], consequently overlooking the potential diversity of human motion wit

Yuanhang Qi, M. Shamim Hossain

The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL),

Steph Buongiorno, Lawrence Jake Klinkert, Tanishq Chawla, Zixin Zhuang

This research introduces Procedural Artificial Narrative using Generative AI (PANGeA), a structured approach for leveraging large language models (LLMs), guided by a game designer's high-level criteria, to generate narrative content for turn-based role-playing video games (RPGs). Distinct from prior applications of LLMs used for video game design, PANGeA inn

Samuel Oslovich, Bing Wang, Walter Krawec, Kenneth Goodenough

Multiparty quantum key distribution (QKD) is useful for many applications that involve secure communication or collaboration among multiple parties. While it can be achieved using pairwise QKD, a more efficient approach is to achieve it using multipartite entanglement distributed over quantum networks that connect the multiple parties. Existing studies on mu

Trinath Sai Subhash Reddy Pittala, Uma Maheswara R Meleti, Hemanth Vasireddy

In the burgeoning market of short-term rentals, understanding pricing dynamics is crucial for a range of stake-holders. This study delves into the factors influencing Airbnb pricing in major European cities, employing a comprehensive dataset sourced from Kaggle. We utilize advanced regression techniques, including linear, polynomial, and random forest models

Dhruva Karkada

A central theme of the modern machine learning paradigm is that larger neural networks achieve better performance on a variety of metrics. Theoretical analyses of these overparameterized models have recently centered around studying very wide neural networks. In this tutorial, we provide a nonrigorous but illustrative derivation of the following fact: in ord

Adrian Rettich

We study a variant of domination, called Roman domination, where we must assign to each vertex one of the labels 0, 1, or 2 and require that every vertex with label 0 has a neighbour with label 2. We study the problem of finding a low-cost Roman dominating function on Lebesgue-measurable graphings, that is, on infinite graphs whose vertices are the points of

Lukasz Lacinski, Lee Liming, Steven Turoscy, Cameron Harr

We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) climate simulation data from Lawrence Livermore National Laboratory (LLNL) in California to Argonne National Laboratory (ANL) in Illinois and Oak Ridge National Laboratory (ORNL) in Tennessee. This movement of some 29 million files, twice, undertaken in order t

Michele Caprio, Mengqi Chen

We introduce the concept of an imprecise Markov semigroup \(\mathbf Q\). It is a tool that allows us to represent ambiguity around both the transition probabilities and the invariant measure of a continuous-time Markov process via a collection of Markov semigroups, each associated with a (possibly different) Markov process. We use techniques from topology, g

Connor P Horn, Christina Wicker, Antoni Wellisz, Cyrus Zeledon

We detail several scientific and engineering innovations which enable the controlled spalling of 10 - 50 micron thick films of single crystal 4H silicon carbide (4H-SiC) from bulk substrates. 4H-SiC's properties, including high thermal conductivity and a wide bandgap, make it an ideal candidate for high-temperature, high-voltage power electronic devices. Mor

Constantinos Patsakis, Fran Casino, Nikolaos Lykousas

The integration of large language models (LLMs) into various pipelines is increasingly widespread, effectively automating many manual tasks and often surpassing human capabilities. Cybersecurity researchers and practitioners have recognised this potential. Thus, they are actively exploring its applications, given the vast volume of heterogeneous data that re

Hoang-Thang Ta, Abu Bakar Siddiqur Rahman, Lotfollah Najjar, Alexander Gelbukh

This paper describes our participation in Task 3 and Task 5 of the #SMM4H (Social Media Mining for Health) 2024 Workshop, explicitly targeting the classification challenges within tweet data. Task 3 is a multi-class classification task centered on tweets discussing the impact of outdoor environments on symptoms of social anxiety. Task 5 involves a binary cla

Scott Sumpter

This study introduces a transformative framework for medical education by integrating semi-structured data with Large Language Models (LLMs), primarily OpenAIs ChatGPT3.5, to automate the creation of medical simulation scenarios. Traditionally, developing these scenarios was a time-intensive process with limited flexibility to meet diverse educational needs.

Peiyi Li, Ji Liu, Alvin Gonzales, Zain Hamid Saleem

Quantum error mitigation plays a crucial role in the current noisy-intermediate-scale-quantum (NISQ) era. As we advance towards achieving a practical quantum advantage in the near term, error mitigation emerges as an indispensable component. One notable prior work, Jigsaw, demonstrates that measurement crosstalk errors can be effectively mitigated by measuri

Hossein Saberi, Guoxing Xia, Linbo Liang, John Patrick Farmer

The long proton beams present at CERN have the potential to evolve into a train of microbunches through the self-modulation instability process. The resonant wakefield generated by a periodic train of proton microbunches can establish a high acceleration field within the plasma, facilitating electron acceleration. This paper investigates the impact of plasma

Kosio Beshkov, Gaute T. Einevoll

Neural networks can be thought of as applying a transformation to an input dataset. The way in which they change the topology of such a dataset often holds practical significance for many tasks, particularly those demanding non-homeomorphic mappings for optimal solutions, such as classification problems. In this work, we leverage the fact that neural network

S. V. Molkov, A. A. Lutovinov, S. S. Tsygankov, V. F. Suleimanov

We report on the discovery of the new accreting millisecond X-ray pulsar SRGAJ144459.2-604207 using the SRG/ART-XC data. The source was observed twice in February 2024 during the declining phase of the outburst. Timing analysis revealed a coherent signal near 447.8~Hz modulated by the Doppler effect due to the orbital motion. The derived parameters for the b

Nicholas S. Kersting, Mohammad Rahman, Suchismitha Vedala, Yang Wang

We introduce an intuitive method to test the robustness (stability and explainability) of any black-box LLM in real-time via its local deviation from harmoniticity, denoted as $\gamma$. To the best of our knowledge this is the first completely model-agnostic and unsupervised method of measuring the robustness of any given response from an LLM, based upon the

Zhexi Peng, Tianjia Shao, Yong Liu, Jingke Zhou

We present Real-time Gaussian SLAM (RTG-SLAM), a real-time 3D reconstruction system with an RGBD camera for large-scale environments using Gaussian splatting. The system features a compact Gaussian representation and a highly efficient on-the-fly Gaussian optimization scheme. We force each Gaussian to be either opaque or nearly transparent, with the opaque o

Tiziano Labruna, Jon Ander Campos, Gorka Azkune

In this paper, we demonstrate how Large Language Models (LLMs) can effectively learn to use an off-the-shelf information retrieval (IR) system specifically when additional context is required to answer a given question. Given the performance of IR systems, the optimal strategy for question answering does not always entail external information retrieval; rath

Amichai Lampert

In this note, we present an elementary proof of the fact that the slice rank of a trilinear form over a finite field is bounded above by a linear expression in the analytic rank. The existing proofs by Adiprasito-Kazhdan-Ziegler and Cohen-Moshkovitz both rely on results of Derksen via geometric invariant theory. A novel feature of our proof is that the linea

Alan Dow, Klaas Pieter Hart, Jan van Mill, Hans Vermeer

We show that in the class of Lindel\"of \v{C}ech-complete spaces the property of being $C$-embedded is quite well-behaved. It admits a useful characterization that can be used to show that products and perfect preimages of $C$-embedded spaces are again $C$-embedded. We also show that both properties, Lindel\"of and \v{C}ech-complete, are needed in the produc

Kai Zhang, Sai Bi, Hao Tan, Yuanbo Xiangli

We propose GS-LRM, a scalable large reconstruction model that can predict high-quality 3D Gaussian primitives from 2-4 posed sparse images in 0.23 seconds on single A100 GPU. Our model features a very simple transformer-based architecture; we patchify input posed images, pass the concatenated multi-view image tokens through a sequence of transformer blocks,

J. Zhang, D. N. Sibley, D. Tseluiko, A. J. Archer

We consider the dynamic wetting and dewetting processes of films and droplets of complex liquids on planar surfaces, focusing on the case of colloidal suspensions, where the particle interactions can be sufficiently attractive to cause agglomeration of the colloids within the film. This leads to an interesting array of dynamic behaviours within the liquid an

Sibsankar Singha, Marie Kratz, Sreekar Vadlamani

Quantile-Quantile (Q-Q) plots are widely used for assessing the distributional similarity between two datasets. Traditionally, Q-Q plots are constructed for univariate distributions, making them less effective in capturing complex dependencies present in multivariate data. In this paper, we propose a novel approach for constructing multivariate Q-Q plots, wh

Janik Ole Wecks, Johannes Voshaar, Benedikt Jost Plate, Jochen Zimmermann

This study evaluates the impact of students' usage of generative artificial intelligence (GenAI) tools such as ChatGPT on their exam performance. We analyse student essays using GenAI detection systems to identify GenAI users among the cohort. Employing multivariate regression analysis, we find that students using GenAI tools score on average 6.71 (out of 10

Jerry Chun-Wei Lin, Pi-Wei Chen, Chao-Chun Chen

Reconstruction networks are prevalently used in unsupervised anomaly and Out-of-Distribution (OOD) detection due to their independence from labeled anomaly data. However, in multi-class datasets, the effectiveness of anomaly detection is often compromised by the models' generalized reconstruction capabilities, which allow anomalies to blend within the expand

Noè Angelo Caruso

This paper presents a study of the inherent structural properties of Krylov subspaces, in particular for the self-adjoint class of operators, and how they relate with the important phenomenon of `Krylov solvability' of linear inverse problems. Owing to the complexity of the problem in the unbounded setting, recently developed perturbative techniques are used

Thomas Baier, Ana Cristina Ferreira, Joachim Hilgert, José M. Mourão

In this paper, we describe holomorphic quantizations of the cotangent bundle of a symmetric space of compact type $T^*(U/K)\cong U_\mathbb{C}/K_\mathbb{C}$, along Mabuchi rays of $U$-invariant K\"ahler structures. At infinite geodesic time, the K\"ahler polarizations converge to a mixed polarization $\mathcal{P}_\infty$. We show how a generalized coherent st

Chun Feng, Joy Hsu, Weiyu Liu, Jiajun Wu

3D visual grounding is a challenging task that often requires direct and dense supervision, notably the semantic label for each object in the scene. In this paper, we instead study the naturally supervised setting that learns from only 3D scene and QA pairs, where prior works underperform. We propose the Language-Regularized Concept Learner (LARC), which use

H. Mohseni Sadjadi

We present a model in which the Gauss-Bonnet invariant holds the quintessence at a fixed point, respecting an initial $Z_2$ symmetry in the radiation-dominated era. This results in an early dark energy that becomes significant around the matter-radiation equality era. However, due to $Z_2$ symmetry breaking, scalarization occurs, leading to a rapid reduction

R. Durrer, R. von Eckardstein, Deepen Garg, K. Schmitz

We study how Abelian-gauge-field production during inflation affects scalar perturbations in the case when the gauge field interacts with the inflaton directly (by means of generic kinetic and axial couplings) and via gravity. The homogeneous background solution is defined by self-consistently taking into account the backreaction of the gauge field on the ev

Yuto Nakashima, Mingzhe Yang, Yukino Baba

Generating preferred images using generative adversarial networks (GANs) is challenging owing to the high-dimensional nature of latent space. In this study, we propose a novel approach that uses simple user-swipe interactions to generate preferred images for users. To effectively explore the latent space with only swipe interactions, we apply principal compo

M. W. C. Dharma-wardana

Theoretical prediction of the thermal conductivity $\kappa$ of metal-like electron-ion systems would be greatly simplified if a convenient generalization of the Lorentz number $L_N$ for arbitrary temperatures ($T$) and densities were available. Such calculations are needed in astrophysics, high-energy-density physics, semiconductor physics as well as in mate

Pavamana K J, Chandramani Kishore Singh

We study content caching with recommendations in a wireless network where the users are connected through a base station equipped with a finite-capacity cache. We assume a fixed set of contents with unknown user preferences and content popularities. The base station can cache a subset of the contents and can also recommend subsets of the contents to differen

Marcelo Tala Pinto, Adrian Kaminski, Andreas Quirrenbach, Mathias Zechmeister

We present $\texttt{moes}$, a ray tracing software package that computes the path of rays through echelle spectrographs. Our algorithm is based on sequential direct tracing with Seidel aberration corrections applied at the detector plane. As a test case, we model the CARMENES VIS spectrograph. After subtracting the best model from the data, the residuals yie

M. De Simone, L. Podio, L. Chahine, C. Codella

Context. Understanding the connection between outflows, winds, accretion and disks in the inner protostellar regions is crucial for comprehending star and planet formation process. Aims. We aim to we explore the inner 300 au of the protostar IRAS 4A2 as part of the ALMA FAUST Large Program. Methods. We analysed the kinematical structures of SiO and CH$_3$OH

Kehan Shi, Martin Burger

This paper studies the $p$-biharmonic equation on graphs, which arises in point cloud processing and can be interpreted as a natural extension of the graph $p$-Laplacian from the perspective of hypergraph. The asymptotic behavior of the solution is investigated when the random geometric graph is considered and the number of data points goes to infinity. We s

Emily King, Rocio Nores, Victoria Paternostro

We investigate the reproducing properties of Gabor systems within the context of expansible groups. These properties are established in terms of density conditions. The concept of density that we employ mirrors the well-known Beurling density defined in Euclidean space, which is made possible due to the expansive structure. Along the way, for groups with a c

Jules Pitcho

We construct a set of bounded vector fields dense in $L^p((0,2);W^{s,p}_{loc}(\mathbb{R}^2;\mathbb{R}^2))$ for $1\leq p <+\infty$ and $0\leq s<1$ with $p<1/s$ for which smooth regularisation of the vector field does not give a selection criterion for the continuity equation, thereby showing that the two examples constructed in [Calc. Var. Partial Differ. Equ

Gabriele Gemmi, Pedram Johari, Paolo Casari, Michele Polese

The quest for safer and more efficient transportation through cooperative, connected and automated mobility (CCAM) calls for realistic performance analysis tools, especially with respect to wireless communications. While the simulation of existing and emerging communication technologies is an option, the most realistic results can be obtained by employing re

Manuel Odelli, Andreas Ruschhaupt, Vladimir M. Stojanovic

The twist-and-turn dynamics of spin squeezing results from the interplay of the one-axis-twisting (nonlinear in the collective-spin operators) and the transverse-field turning (linear) term in the underlying Lipkin-Meshkov-Glick-type Hamiltonian, both with constant (time-independent) prefactors. Using shortcuts to adiabaticity (STA) and the recently develope

Yuri A. Kordyukov

We study asymptotic spectral properties of the Bochner-Schr\"odinger operator $H_{p}=\frac 1p\Delta^{L^p\otimes E}+V$ on high tensor powers of a Hermitian line bundle $L$ twisted by a Hermitian vector bundle $E$ on a Riemannian manifold $X$ of bounded geometry under assumption that the curvature form of $L$ is non-degenerate. At an arbitrary point $x_0$ of $

Hao Qin, Weishi Zhang

To address the low efficiency in priority signal control within intelligent transportation systems, this study introduces a novel eight-phase priority signal control method, CBQL-TSP, leveraging a hybrid decision-making framework that integrates cooperative game theory and reinforcement learning. This approach conceptualizes the allocation of bus signal prio

Pablo Banon Perez, Maarten DeKieviet

In this paper, we present a series of techniques to describe General Relativity using Geometric Algebra (GA). We emphasize the physical interpretation of quantities and provide a step-by-step guide for performing calculations. In doing so, we show how GA offers insightful information on the physical meaning of the connection coefficients, the Riemann tensor,

Ryoichiro Noda

We present a new general framework for metrization of Gromov-Hausdorff-type topologies on non-compact metric spaces. We also give easy-to-check conditions for separability and completeness and hence the measure theoretic requirements are provided to study convergence of random spaces with additional random objects. In particular, our framework enables us to

Pavel P. Popov, Valentin Kasper, Maciej Lewenstein, Erez Zohar

Gauge-field configurations with nontrivial topology have profound consequences for the physics of Abelian and non-Abelian gauge theories. Over time, arguments have been gathering for the existence of gauge-field configurations with fractional topological charge, called fractons. Ground-state properties of gauge theories can drastically change in presence of

Martin Hayhurst Appel, Alexander Ghorbal, Noah Shofer, Leon Zaporski

Quantum networks require quantum nodes with coherent optical interfaces and multiple stationary qubits. In terms of optical properties, semiconductor quantum dots are highly compelling, but their adoption as quantum nodes has been impaired by the lack of auxiliary qubits. Here, we demonstrate a functional quantum register in a semiconductor quantum dot lever

Noah Shofer, Leon Zaporski, Martin Hayhurst Appel, Santanu Manna

A central spin qubit interacting coherently with an ensemble of proximal spins can be used to engineer entangled collective states or a multi-qubit register. Making full use of this many-body platform requires tuning the interaction between the central spin and its spin register. GaAs quantum dots offer a model realization of the central spin system where an

Yanghao Meng, Yi Yang, Hualei Sun, Sasa Zhang

Density wave (DW) order is believed to be correlated with superconductivity in the recently discovered high-temperature superconductor La$_3$Ni$_2$O$_7$. However, experimental investigations of its evolution under high pressure are still lacking. Here, we explore the quasiparticle dynamics in bilayer nickelate La$_3$Ni$_2$O$_7$ single crystals using ultrafas

Karwan Kareem

This paper comprehensively analyzes the Pegasus spyware and its implications for digital privacy and security. The Israeli cyber intelligence company NSO Group's Pegasus has gained recognition as a potent surveillance tool capable of hacking into smartphones and extracting data without the user's knowledge [49], [50]. The research emphasizes the technical as

Norbert S. Schulz, David P. Huenemoerder, David A. Principe, Marc Gagne

The Orion Nebula Cluster (ONC) is the closest site of very young ($\sim$ 1 Myrs) massive star formation. The ONC hosts more than 1600 young and X-ray bright stars with masses ranging from $\sim$ 0.1 to 35 $M_\odot$. The Chandra HETGS Orion Legacy Project observed the ONC with the Chandra high energy transmission grating spectrometer (HETGS) for $2.1\,$Ms. We

Bernhard Schmiedmayer, Georg Kresse

We develop a strategy that integrates machine learning and first-principles calculations to achieve technical accurate predictions of infrared spectra. Specifically, the methodology allows to predict infrared spectra for complex systems at finite temperatures. The method's effectiveness is demonstrated in challenging scenarios, such as the analysis of water

Lingyi Yang, Zhen Shao

We introduce a class of neural controlled differential equation inspired by quantum mechanics. Neural quantum controlled differential equations (NQDEs) model the dynamics by analogue of the Schr\"{o}dinger equation. Specifically, the hidden state represents the wave function, and its collapse leads to an interpretation of the classification probability. We i

Julio Backhoff-Veraguas, Xin Zhang

Defining a divergence between the laws of continuous martingales is a delicate task, owing to the fact that these laws tend to be singular to each other. An important idea, put forward by N. Gantert, is to instead consider a scaling limit of the relative entropy between such continuous martingales sampled over a finite time grid. This gives rise to the conce

Mateusz Bocheński, Jakub Dobosz, Mariusz Semczuk

We present a dual isotope magneto-optical trap (MOT), simultaneous sub-Doppler laser cooling, and magnetic trapping of a spin-polarized $^{39}$K-$^{40}$K Bose-Fermi mixture realized in a single-chamber setup with an unenriched potassium dispenser as the source of atoms. We are able to magnetically confine more than $2.2\times10^5$ fermions ($F=9/2\,m_F=9/2$)

Patrick Kinnear

We construct a relative version of the Crane-Yetter topological quantum field theory in four dimensions, from non-semisimple data. Our theory is defined relative to the classical $G$-gauge theory in five dimensions -- this latter theory assigns to each manifold $M$ the appropriate linearization of the moduli stack of $G$-local systems, called the character s

Lei Wang, Desen Yuan

Image quality assessment often relies on raw opinion scores provided by subjects in subjective experiments, which can be noisy and unreliable. To address this issue, postprocessing procedures such as ITU-R BT.500, ITU-T P.910, and ITU-T P.913 have been standardized to clean up the original opinion scores. These methods use annotator-based statistical priors,

Dimitrios Karkalousos, Ivana Išgum, Henk A. Marquering, Matthan W. A. Caan

AI is revolutionizing MRI along the acquisition and processing chain. Advanced AI frameworks have been developed to apply AI in various successive tasks, such as image reconstruction, quantitative parameter map estimation, and image segmentation. Existing frameworks are often designed to perform tasks independently or are focused on specific models or datase

Robert McCarthy, Daniel C. H. Tan, Dominik Schmidt, Fernando Acero

Scaling deep learning to massive and diverse internet data has driven remarkable breakthroughs in domains such as video generation and natural language processing. Robot learning, however, has thus far failed to replicate this success and remains constrained by a scarcity of available data. Learning from videos (LfV) methods aim to address this data bottlene

Harri Hakula, Mohamed M. S. Nasser, Matti Vuorinen

We consider constellations of disks which are unions of disjoint hyperbolic disks in the unit disk with fixed radii and unfixed centers. We study the problem of maximizing the conformal capacity of a constellation with a fixed number of disks under constraints on the centers in two cases. In the first case the constraint is that the centers are at most at di

Franck Picard, Vincent Rivoirard, Angelina Roche, Victor Panaretos

We introduce a novel statistical framework for the analysis of replicated point processes that allows for the study of point pattern variability at a population level. By treating point process realizations as random measures, we adopt a functional analysis perspective and propose a form of functional Principal Component Analysis (fPCA) for point processes.

Felix Soest, Konstantin Beyer, Walter T. Strunz

Gradient descent algorithms on Riemannian manifolds have been used recently for the optimization of quantum channels. In this contribution, we investigate the influence of various regularization terms added to the cost function of these gradient descent approaches. Motivated by Lasso regularization, we apply penalties for large ranks of the quantum channel,

Zonghai Li

The Gibbons-Werner method for calculating deflection angles using the Gauss-Bonnet theorem and optical/Jacobi metric has become widely popular in recent years. Werner extended this method to stationary spacetimes, where the optical/Jacobi metric takes the form of a Finsler metric of Randers type, by adopting an osculating Riemannian metric. Werner's method i

J. R. Espinosa

The Tunneling Potential Formalism was introduced to calculate the tunneling actions that control vacuum decay as an alternative to the standard Euclidean Formalism. The new approach sets the problem as a simple variational problem in field space with decay described by a tunneling potential function $V_t$ that extremizes a simple action functional $S[V_t]$ a

Serge A. Krasnokutski, Cornelia Jager, Thomas Henning, Claude Geffroy

The formation of protein precursors, due to the condensation of atomic carbon under the low-temperature conditions of the molecular phases of the interstellar medium, opens alternative pathways for the origin of life. We perform peptide synthesis under conditions prevailing in space and provide a comprehensive analytic characterization of its products. The a

Felix Grün, Marcus Nolte, Markus Maurer

The foundational role of datasets in defining the capabilities of deep learning models has led to their rapid proliferation. At the same time, published research focusing on the process of dataset development for environment perception in automated driving has been scarce, thereby reducing the applicability of openly available datasets and impeding the devel

Hai Zhu, Rui Guo, Juntai Shen, Jianglai Liu

Numerical simulations indicate that correlations exist between the velocity distributions of stars and dark matter (DM). We study the local DM velocity distribution based on these correlations. We select K giants from LAMOST DR8 cross-matched with {\gaia} DR3, which have robust measurements of velocity and metallicity, and separate them into the disk, halo s

Rishav Pramanik, José-Fabian Villa-Vásquez, Marco Pedersoli

Unsupervised object discovery is becoming an essential line of research for tackling recognition problems that require decomposing an image into entities, such as semantic segmentation and object detection. Recently, object-centric methods that leverage self-supervision have gained popularity, due to their simplicity and adaptability to different settings an

John Strahan, Chatipat Lorpaiboon, Jonathan Weare, Aaron R. Dinner

An issue for molecular dynamics simulations is that events of interest often involve timescales that are much longer than the simulation time step, which is set by the fastest timescales of the model. Because of this timescale separation, direct simulation of many events is prohibitively computationally costly. This issue can be overcome by aggregating infor

Yuliang Liu, Mingxin Huang, Hao Yan, Linger Deng

Text spotting, a task involving the extraction of textual information from image or video sequences, faces challenges in cross-domain adaption, such as image-to-image and image-to-video generalization. In this paper, we introduce a new method, termed VimTS, which enhances the generalization ability of the model by achieving better synergy among different tas

Ge Yan, Yaniv Romano, Tsui-Wei Weng

Conformal prediction is a powerful tool to generate uncertainty sets with guaranteed coverage using any predictive model, under the assumption that the training and test data are i.i.d.. Recently, it has been shown that adversarial examples are able to manipulate conformal methods to construct prediction sets with invalid coverage rates, as the i.i.d. assump

T. Y. Tao, Neil N. Y. Yang

Hindman conjectured that any finite partition of $\mathbb{N}$ has a monochromatic $\{x,y,x+y,xy\}$. Recently, Bowen proved the result for all 2-partition. In this paper, we extend Bowen's result to any semiring $(S,+,\cdot)$ such that $Ss$ is piecewise syndetic for all $s\in S$. As a method, we gave a combinatorial proof for a piecewise syndetic version of B

Elhabib Jaloum, Mohamed Amazioug

In this article, we investigate the hierarchy of quantum correlations between two gravitational cats states (modeled by two qubits). We use concurrence to quantify the entanglement between the two gravitational cat states. Quantum steering is employed to measure the steerabilities. We consider geometric quantum discord to quantify quantum correlations beyond

Brian Conrey

We prove that an innocent looking inequality implies the Riemann Hypothesis and show a way to approach this inequality through sums of Legendre symbols.

Yiming Yang, Mohammad Vaseem, Ruiqi Wang, Behrooz Makki

Reconfigurable Intelligent Surface (RIS) is attracting more and more research interest because of its ability to reprogram the radio environment. Designing and implementing the RIS, however, is challenging because of limitations of printed circuit board (PCB) technology related to manufacturing of large sizes as well as the cost of switches. Thus, a low-cost

Stefano Pozza

We present the first formulation of the optimal polynomial approximation of the solution of linear non-autonomous systems of ODEs in the framework of the so-called $\star$-product. This product is the basis of new approaches for the solution of such ODEs, both in the analytical and the numerical sense. The paper shows how to formally state the problem and de

Min Zhang, Haoxuan Li, Fei Wu, Kun Kuang

Out-of-distribution (OOD) problems in few-shot classification (FSC) occur when novel classes sampled from testing distributions differ from base classes drawn from training distributions, which considerably degrades the performance of deep learning models deployed in real-world applications. Recent studies suggest that the OOD problems in FSC mainly includin

Mazen Mohamad, Ramana Reddy Avula, Peter Folkesson, Pierre Kleberger

The increased importance of cybersecurity in autonomous machinery is becoming evident in the forestry domain. Forestry worksites are becoming more complex with the involvement of multiple systems and system of systems. Hence, there is a need to investigate how to address cybersecurity challenges for autonomous systems of systems in the forestry domain. Using