December 2023 arXiv papers — page 84

Nan Huang, Ting Zhang, Yuhui Yuan, Dong Chen

In this paper, we address the critical bottleneck in robotics caused by the scarcity of diverse 3D data by presenting a novel two-stage approach for generating high-quality 3D models from a single image. This method is motivated by the need to efficiently expand 3D asset creation, particularly for robotics datasets, where the variety of object types is curre

Djohan Bonnet, Tifenn Hirtzlin, Tarcisius Januel, Thomas Dalgaty

Catastrophic forgetting remains a challenge for neural networks, especially in lifelong learning scenarios. In this study, we introduce MEtaplasticity from Synaptic Uncertainty (MESU), inspired by metaplasticity and Bayesian inference principles. MESU harnesses synaptic uncertainty to retain information over time, with its update rule closely approximating t

Justin W. Cole, Casey Papovich, Steven L. Finkelstein, Micaela B. Bagley

We present the star-formation-rate -- stellar-mass (SFR-M$_\ast$) relation for galaxies in the CEERS survey at $4.5\leq z\leq 12$. We model the \jwst\ and \hst\ rest-UV and rest-optical photometry of galaxies with flexible star-formation histories (SFHs) using \bagpipes. We consider SFRs averaged from the SFHs over 10~Myr (\sfrten) and 100~Myr (\sfrcen), whe

Purnendu Das, Valentin Leeb, Johannes Knolle, Michael Knap

Altermagnetism represents a type of collinear magnetism, that is in some aspects distinct from ferromagnetism and from conventional antiferromagnetism. In contrast to the latter, sublattices of opposite spin are related by spatial rotations and not only by translations and inversions. As a result, altermagnets have spin-split bands leading to unique experime

T. O'Shea, M. Giannotti, I. G. Irastorza, L. M. Plasencia

Dark (hidden) photons are widely recognised as well motivated candidates for physics beyond the standard model, and have been invoked for the solution of several outstanding problems, including to account for the dark matter in the universe. In this paper, we consider a simple model for dark photons, which is coupled to ordinary matter only through kinetic m

David Osten

A novel classically integrable model is proposed. It is a deformation of the two-dimensional principal chiral model, embedded into a heterotic $\sigma$-model, by a particular heterotic gauge field. This is inspired by the bosonic part of the heterotic $\sigma$-model and its recent Hamiltonian formulation in terms of O$(d,d+n)$-generalised geometry by Hatsuda

Jan-Erik Christian, Jürgen Schaffner-Bielich, Stephan Rosswog

We examine which first order phase transitions are consistent with today's astrophysical constraints. In particular, we explore how a well-constrained mass-radius data point would restrict the admissible parameter space and to this end, we employ the most likely candidates of the recent NICER limits of PSR J0030+0451. To systematically vary the stiffness of

Guilherme Zambon, Diogo O. Soares-Pinto

In the dynamics of open quantum systems, information may propagate in time through either the system or the environment, giving rise to Markovian and non-Markovian temporal correlations, respectively. However, despite their notable coexistence in most physical situations, it is not yet clear how these two quantities may limit the existence of one another. He

Massimiliano Parente, Cinthia Ragone-Figueroa, Pablo López, Héctor J. Martínez

The large-scale environment of the cosmic web is believed to impact galaxy evolution, but there is still no consensus regarding the mechanisms. We use a semi-analytic model (SAM) galaxy catalog to study the star formation and dust content of local galaxies in different cosmic environments of the cosmic web, namely voids, filaments, walls, and nodes. We find

Teodor Strömberg, Peter Schiansky, Philip Walther

Single-mode optical fibres exhibit a small but non-negligible birefringence that induces random polarisation rotations during light propagation. In classical interferometry these rotations give rise to polarisation-induced fading of the interferometric visibility, and in fibre-based polarimetric sensors as well as quantum optics experiments they scramble the

Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze

The goal of multimodal alignment is to learn a single latent space that is shared between multimodal inputs. The most powerful models in this space have been trained using massive datasets of paired inputs and large-scale computational resources, making them prohibitively expensive to train in many practical scenarios. We surmise that existing unimodal encod

Alexey Uvarov, Dmitry Vinichenko

Gaussian Boson Samplers aim to demonstrate quantum advantage by performing a sampling task believed to be classically hard. The probabilities of individual outcomes in the sampling experiment are determined by the Hafnian of an appropriately constructed symmetric matrix. For nonnegative matrices, there is a family of randomized estimators of the Hafnian base

Artur Czerwinski, Xiangji Cai, Saeed Haddadi

In this study, we investigate the effects of chromatic dispersion on single-photon temporal wave functions (TWFs) in the context of quantum communications. Departing from classical beam analysis, we focus on the temporal shape of single photons, specifically exploring generalized Gaussian modes. From this foundation, we introduce chirped and unchirped Gaussi

Beatriz Pérez-González, Gloria Platero, Álvaro Gómez-León

Anomalous Floquet topological phases are a hallmark, without a static analog, of periodically driven systems. Recently, Quantum Floquet Engineering has emerged as an interesting approach to cavity-QED materials, which recovers the physics of Floquet engineering in its semi-classical limit. However, the mapping between these two widely different scenarios rem

Margarita Gavrilova, Yuval Grossman, Stefan Schacht

We study the penguin over tree ratio in $D\rightarrow \pi\pi$ decays. This ratio can serve as a probe for rescattering effects. Assuming the Standard Model and in the isospin limit, we derive expressions that relate both the magnitude and the phase of this ratio to direct CP asymmetries and branching fractions. We find that the current data suggest that resc

Weiguang Cao, Xiaochuan Lu, Tom Melia

We identify constraints in the energy spectra of quantum theories that have a global $O(N)$ symmetry, where $N$ is treated as a continuous parameter. We point out that a class of evanescent states fall out of the spectrum at integer values of $N$ in pairs, via an annihilation mechanism. This forces the energies of the states in such a pair to approach equali

Lionel Mason, Romain Ruzziconi, Akshay Yelleshpur Srikant

Carrollian holography aims to express gravity in four-dimensional asymptotically flat spacetime in terms of a dual three-dimensional Carrollian CFT living at null infinity. Carrollian amplitudes are massless scattering amplitudes written in terms of asymptotic or null data at $\mathscr I$. These position space amplitudes at $\mathscr I$ are to be re-interpre

Temple He, Sergio Hernández-Cuenca, Cynthia Keeler

Motivated by bit threads, we introduce a new prescription for computing entropy vectors outside the holographic entropy cone. By utilizing cycle flows on directed graphs, we show that the maximum cycle flow associated to any subset of vertices, which corresponds to a subsystem, manifestly obeys purification symmetry. Furthermore, by restricting ourselves to

Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu

This paper is not motivated to seek innovation within the attention mechanism. Instead, it focuses on overcoming the existing trade-offs between accuracy and efficiency within the context of point cloud processing, leveraging the power of scale. Drawing inspiration from recent advances in 3D large-scale representation learning, we recognize that model perfor

Shiran Yuan, Hao Zhao

Neural Radiance Field (NeRF) and its variants have recently emerged as successful methods for novel view synthesis and 3D scene reconstruction. However, most current NeRF models either achieve high accuracy using large model sizes, or achieve high memory-efficiency by trading off accuracy. This limits the applicable scope of any single model, since high-accu

Brant Robertson, Benjamin D. Johnson, Sandro Tacchella, Daniel J. Eisenstein

We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters spanning $0.4-0.9\mu\mathrm{m}$) and novel JWST images with 14 filters spanning $0.8-5\mu\mathrm{m}$, including 7 medium-band filters, and reaching total exposure times of

Zhi Zhang, Qizhe Zhang, Zijun Gao, Renrui Zhang

With the growing size of pre-trained models, full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible. In this paper, we propose a new parameter-efficient fine-tuning method, Gradient-based Parameter Selection (GPS), demonstrating that only tuning a few selected parameters from the pre-trained model while keeping

Yuqian Yuan, Wentong Li, Jian Liu, Dongqi Tang

Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However, current MLLMs primarily focus on image-level or box-level understanding, falling short in achieving fine-grained vision-language alignment at pixel level. Besides, the lack of mask-based instructi

Qing Yang, Daniel Tamayo

The secular Laplace-Lagrange orbital solution, decomposing eccentricities into a set of uniformly precessing eigenmodes is a classical result that is typically solved numerically. However, in the limit where orbits are closely spaced, several simplifications make it possible to make analytical progress. We derive simple expressions for the eccentricity eigen

Alexander Drewitz, Alexis Prévost, Pierre-François Rodriguez

We investigate the bond percolation model on transient weighted graphs ${G}$ induced by the excursion sets of the Gaussian free field on the corresponding metric graph. We assume that balls in ${G}$ have polynomial volume growth with growth exponent $\alpha $ and that the Green's function for the random on ${G}$ exhibits a power law decay with exponent $\nu

Sebastian Farquhar, Vikrant Varma, Zachary Kenton, Johannes Gasteiger

We show that existing unsupervised methods on large language model (LLM) activations do not discover knowledge -- instead they seem to discover whatever feature of the activations is most prominent. The idea behind unsupervised knowledge elicitation is that knowledge satisfies a consistency structure, which can be used to discover knowledge. We first prove t

Vanja Marić, Saverio Bocini, Maurizio Fagotti

We take the paradigm of interacting spin chains, the Heisenberg spin-$\frac{1}{2}$ XXZ model, as a reference system and consider interacting models that are related to it by Jordan-Wigner transformations and restrictions to sub-chains. An example is the fermionic analogue of the gapless XXZ Hamiltonian, which, in a continuum scaling limit, is described by th

Maryam Salamatmoghadasi, Amir Mehrabian, Halim Yanikomeroglu

The growing demand for radio access networks (RANs) driven by advanced wireless technology and the everincreasing mobile traffic, faces significant energy consumption challenges that threaten sustainability. To address this, an architecture referring to the vertical heterogeneous network (vHetNet) has recently been proposed. Our study seeks to enhance networ

Marcelo Campos, Matthew Jenssen, Marcus Michelen, Julian Sahasrabudhe

We show there exists a packing of identical spheres in $\mathbb{R}^d$ with density at least \[ (1-o(1))\frac{d \log d}{2^{d+1}}\, , \] as $d\to\infty$. This improves upon previous bounds for general $d$ by a factor of order $\log d$ and is the first asymptotically growing improvement to Rogers' bound from 1947.

Jonathan E. Thompson, Eleanor Hamilton, Lionel London, Shrobana Ghosh

In this work we introduce PhenomXO4a, the first phenomenological, frequency-domain gravitational waveform model to incorporate multipole asymmetries and precession angles tuned to numerical relativity. We build upon the modeling work that produced the PhenomPNR model and incorporate our additions into the IMRPhenomX framework, retuning the coprecessing frame

Sahil Nokhwal, Priyanka Chilakalapudi, Preeti Donekal, Suman Nokhwal

The process of training a deep neural network is characterized by significant time requirements and associated costs. Although researchers have made considerable progress in this area, further work is still required due to resource constraints. This study examines innovative approaches to expedite the training process of deep neural networks (DNN), with spec

Kwanghee Choi, Jee-weon Jung, Shinji Watanabe

With the success of self-supervised representations, researchers seek a better understanding of the information encapsulated within a representation. Among various interpretability methods, we focus on classification-based linear probing. We aim to foster a solid understanding and provide guidelines for linear probing by constructing a novel mathematical fra

Xia Zhu, Ke Wu, Stephan W. Anderson, Xin Zhang

Recent advancements in metamaterials have yielded the possibility of a wireless solution to improve signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI). Unlike traditional closely packed local coil arrays with rigid designs and numerous components, these lightweight, cost-effective metamaterials eliminate the need for radio frequency (RF) cabling

Alessandro Conigli, Julien Frison, Patrick Fritzsch, Antoine Gérardin

We present preliminary results for B-physics from a combination of non-perturbative results in the static limit with relativistic computations satisfying $am_{\mathrm{heavy}}\ll 1$. Relativistic measurements are carried out at the physical b-quark mass using the Schr\"{o}dinger Functional in a $0.5 \ \mathrm{fm}$ box. They are connected to large volume obser

Steffen Gielen, Lisa Mickel

We study a group field theory (GFT) for quantum gravity coupled to four massless scalar fields, using these matter fields to define a (relational) coordinate system. We exploit symmetries of the GFT action, in particular under shifts in the values of the scalar fields, to derive a set of classically conserved currents, and show that the same conservation law

Federico Buccioni, Philipp Alexander Kreer, Xiao Liu, Lorenzo Tancredi

We compute the one-loop corrections to \tth up to order $\mathcal{O}(\epsilon^2)$ in the dimensional regularization parameter. We apply the projector method to compute polarized amplitudes, which generalize massless helicity amplitudes to the massive case. We employ a semi-numerical strategy to evaluate the scattering amplitudes. We express the form factors

R. Unnikrishnan, E. De Beck, L. A. Nyman, H. Olofsson

AGB stars are major contributors to the chemical enrichment of the ISM through nucleosynthesis and extensive mass loss. Most of our current knowledge of AGB atmospheric and circumstellar chemistry, in particular in a C-rich environment, is based on observations of the carbon star IRC+10216. We aim to obtain a more generalised understanding of the chemistry i

Cesar Abascal Machado, Victor O. Costa, Cesar Augusto Prior, Cesar Ramos Rodrigues

Purpose. Photoplethysmography (PPG) is a non-invasive technique that measures changes in blood flow volume through optical means. Previous research has established the feasibility of PPG peak detection based on the crossover of moving averages. This paper proposes the Smoothed Recuarsive Moving Average Crossover, which eliminates the need for post-processing

Ivan I. Kyrchei, Eran Treister, Volodymyr O. Pelykh

A quaternion unit gain graph is a graph where each orientation of an edge is given a quaternion unit, and the opposite orientation is assigned the inverse of this quaternion unit. In this paper, we provide a combinatorial description of the determinant of the Laplacian matrix of a quaternion unit gain graph by using row-column noncommutative determinants rec

Dhrubajyoti Mandal

An important field of research in defense-related technology is the design of guidance laws. A guided missile is generally challenging to intercept if its trajectory becomes unpredictable. In this short communication, we have discussed a possible application of the chaos theory in developing an advanced guided missile, where the guidance law is based upon a

Oliver Clarke, Serkan Hoşten, Nataliia Kushnerchuk, Janike Oldekop

We study the maximum likelihood (ML) degree of discrete exponential independence models and models defined by the second hypersimplex. For models with two independent variables, we show that the ML degree is an invariant of a matroid associated to the model. We use this description to explore ML degrees via hyperplane arrangements. For independence models wi

Noah Zipper

We present the deployment and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS Level-1 Global Trigger (GT) test crate during LHC Run 3. The GT test crate is a copy of the main GT system, receiving the same input data, but whose output is not used to trigger the readout of CMS, providing a platform for thorough tes

Paul Maria Scheikl, Nicolas Schreiber, Christoph Haas, Niklas Freymuth

Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions. To this end, we introduce Movement Primitive Diffusion (MPD), a novel method for imitation learning (IL) in RAS that focuses on gentle manipulation of deformable objects. The approach combines

Pegah Jandaghi, XiangHai Sheng, Xinyi Bai, Jay Pujara

High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a

Vera Fischer, Lukas Schembecker

We improve the state-of-the-art proof techniques for realizing various spectra of $\mathfrak{a}_{\text{T}}$ in order to realize arbitrarily large spectra. Thus, we make significant progress in addressing a question posed by Brian in his recent work. As a by-product, we obtain many complete subforcings and an algebraic analysis of the automorphisms of the for

Steffen Bisswanger, Henning Bonart, Pyi Thein Khaing, Steffen Hardt

The influence of chaotic capillary waves on the time-averaged shape of a liquid volume is studied experimentally and theoretically. In that context, a liquid film containing a stable hole is subjected to Faraday waves. The waves induce a shrinkage of the hole compared to the static film, which can be described using the Young-Laplace equation by incorporatin

Benedikt Brantner, Michael Kraus

Many applications, such as optimization, uncertainty quantification and inverse problems, require repeatedly performing simulations of large-dimensional physical systems for different choices of parameters. This can be prohibitively expensive. In order to save computational cost, one can construct surrogate models by expressing the system in a low-dimensiona

Renat Aksitov, Sobhan Miryoosefi, Zonglin Li, Daliang Li

Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval with a large language model (LLM) to answer such questions. These systems, however, suffer from various failure cases, and we cannot directly train them end-to-end to fix such failures, a

Mattie Ji

The Euler characteristic transform (ECT) is an integral transform used widely in topological data analysis. Previous efforts by Curry et al. and Ghrist et al. have independently shown that the ECT is injective on all compact definable sets. In this work, we first study the injectivity of the ECT on definable sets that are not necessarily compact and prove a

Zhongshu Xu, Yuan Chen, Qifan Chen, Dongbin Xiu

We present a numerical method to learn an accurate predictive model for an unknown stochastic dynamical system from its trajectory data. The method seeks to approximate the unknown flow map of the underlying system. It employs the idea of autoencoder to identify the unobserved latent random variables. In our approach, we design an encoding function to discov

Vera Fischer, Lukas Schembecker

We introduce the notion of an arithmetical type of combinatorial family of reals, which serves to generalize different types of families such as mad families, maximal cofinitary groups, ultrafilter bases, splitting families and other similar types of families commonly studied in combinatorial set theory. We then prove that every combinatorial family of reals

Ervin Győri, Binlong Li, Nika Salia, Casey Tompkins

Bollob\'as proved that for every $k$ and $\ell$ such that $k\mathbb{Z}+\ell$ contains an even number, an $n$-vertex graph containing no cycle of length $\ell \bmod k$ can contain at most a linear number of edges. The precise (or asymptotic) value of the maximum number of edges in such a graph is known for very few pairs $\ell$ and $k$. In this work we precis

M. Avendaño-Camacho, J. C. Ruíz-Pantaleón, Yu. Vorobiev

In the context of averaging method, we describe a reconstruction of invariant connection-dependent Poisson structures from canonical actions of compact Lie groups on fibered phase spaces. Some symmetry properties of Wong's type equations are derived from the main results.

Mikołaj Małkiński, Jacek Mańdziuk

Abstract Visual Reasoning (AVR) comprises a wide selection of various problems similar to those used in human IQ tests. Recent years have brought dynamic progress in solving particular AVR tasks, however, in the contemporary literature AVR problems are largely dealt with in isolation, leading to highly specialized task-specific methods. With the aim of devel

Jiangcheng Chen, Markus Berg, Kimmo Rasilainen, Zeeshan Siddiqui

This communication proposes a dual-wideband differentially fed dual-polarized magnetoelectric (ME) dipole with second-order bandstop filtering for millimeter-wave (mm-Wave) applications at 24.25-29.5 GHz and 37-43.5 GHz. Without disturbing the complementary antenna operation, two resonator types (hairpin and coupled {\lambda}/4 open-/short-circuited stub res

Miguel Terra-Neves, José Amaral, Alexandre Lemos, Rui Quintino

Graph matching is a fundamental problem in pattern recognition, with many applications such as software analysis and computational biology. One well-known type of graph matching problem is graph isomorphism, which consists of deciding if two graphs are identical. Despite its usefulness, the properties that one may check using graph isomorphism are rather lim

Vera Fischer, Lukas Schembecker

Under $\text{CH}$ we construct a partition of Baire space into compact sets, which is indestructible by countably supported iteration and product of Sacks forcing of any length, answering a question of Newelski. Further, we present an in-depth isomorphism-of-names argument for $\text{spec}(\mathfrak{a}_\text{T}) = \{\aleph_1, \mathfrak{c}\}$ in the product-S

Pierpaolo Basile, Elio Musacchio, Marco Polignano, Lucia Siciliani

Large Language Models represent state-of-the-art linguistic models designed to equip computers with the ability to comprehend natural language. With its exceptional capacity to capture complex contextual relationships, the LLaMA (Large Language Model Meta AI) family represents a novel advancement in the field of natural language processing by releasing found

A. M. Escobar-Ruiz, L. Jiménez-Lara, P. M. Juárez-Florez, F. Montoya-Molina

In this study, the classical two-dimensional potential $V_N=\frac{1}{2}\,m\,\omega^2\,r^2 + \frac{1}{N}\,r^N\,\sin(N\,\theta)$, $N \in {\mathbb Z}^+$, is considered. At $N=1,2$, the system is superintegrable and integrable, respectively, whereas for $N>2$ it exhibits a richer chaotic dynamics. For instance, at $N=3$ it coincides with the H\'enon-Heiles syste

Carlos Navarrete-Leon, P. Stephen Patrick, Adam Doherty, Harry Allan

Two-directional beam-tracking (2DBT) is a method for phase-contrast imaging and tomography that uses an intensity modulator to structure the X-ray beam into an array of independent circular beamlets that are resolved by a high-resolution detector. It features isotropic spatial resolution, provides two-dimensional phase sensitivity, and enables the three-dime

Pranav Kairon, John Sous, Mona Berciu, Roman V. Krems

Feasibility of accurate quantum calculations is often restricted by the dimensionality of the truncated Hilbert space required for the numerical computations. The present work demonstrates Bayesian machine learning (ML) models that use quantum properties in an effectively lower-dimensional Hilbert space to make predictions for the Hamiltonian parameters that

Ed Younis, Noah Goss

The synthesis of single-qudit unitaries has mainly been understudied, resulting in inflexible and non-optimal analytical solutions, as well as inefficient and impractical numerical solutions. To address this challenge, we introduce QSweep, a guided numerical synthesizer that produces pulse-optimal single-qudit decompositions for any subspace gateset, outperf

M. Billi, R. B. Barreiro, E. Martínez-González

The lack of power anomaly is an unexpected feature observed at large angular scales in the CMB maps produced by the COBE, WMAP and Planck satellites. This signature, which consists in a missing of power with respect to that predicted by the LCDM model, might hint at a new cosmological phase before the standard inflationary era. The main point of this paper i

Yilin Liu, Yunkui Pang, Jiang Li, Yong Chen

Untrained networks inspired by deep image priors have shown promising capabilities in recovering high-quality images from noisy or partial measurements without requiring training sets. Their success is widely attributed to implicit regularization due to the spectral bias of suitable network architectures. However, the application of such network-based priors

Cristian Conde, Kais Feki

This paper explores the concept of approximate Birkhoff-James orthogonality in the context of operators on semi-Hilbert spaces. These spaces are generated by positive semi-definite sesquilinear forms. We delve into the fundamental properties of this concept and provide several characterizations of it. Using innovative arguments, we extend a widely known resu

Lolita I. Knyazeva, Vladimir I. Yudson

We consider a pair of identical fermions with a short-range attractive interaction on a finite lattice cluster in the presence of strong site disorder. This toy model imitates a low density regime of the strongly disordered Hubbard model. In contrast to spinful fermions, which can simultaneously occupy a site with a minimal energy and thus always form a boun

Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta

We study the task of $(\epsilon, \delta)$-differentially private online convex optimization (OCO). In the online setting, the release of each distinct decision or iterate carries with it the potential for privacy loss. This problem has a long history of research starting with Jain et al. [2012] and the best known results for the regime of {\epsilon} not bein

Kimberly J. Harry

Using Kostant's weight multiplicity formula, we describe and enumerate the terms contributing a nonzero value to the multiplicity of a positive root $\mu$ in the adjoint representation of $\mathfrak{sl}_{r+1}(\mathbb{C})$, which we denote $L(\tilde{\alpha})$, where $\tilde{\alpha}$ is the highest root of $\mathfrak{sl}_{r+1}(\mathbb{C})$. We prove that the n

Pedro-José Cazorla García

Given a prime number $q$ and a squarefree integer $C_1$, we develop a method to explicitly determine the tuples $(y, n, \alpha)$ for which the difference $y^n-q^\alpha$ has squarefree part equal to $C_1$. Our techniques include the combination of the local information provided by Galois representations of Frey-Hellegouarch curves with the effective resolutio

John Femi-Oyetoro, Sasha Sypkens, Henry LeDuc, Matthew Dickie

This study presents a comprehensive investigation into the exceptional superconducting attributes of titanium nitride (TiN) achieved through plasma-enhanced atomic layer deposition (PEALD) on both planar and intricate three-dimensional (3D) structures. We introduced an additional substrate biasing cycle to densify the film and remove ligand residues, augment

Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw

Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems. This work motivates the use of potential-based reward shaping to reduce the computational burden of each RL sub-problem. This work serves as a proof-of-concept and we hope will inspire future

Amir H. Ashouri, Muhammad Asif Manzoor, Duc Minh Vu, Raymond Zhang

The key to performance optimization of a program is to decide correctly when a certain transformation should be applied by a compiler. This is an ideal opportunity to apply machine-learning models to speed up the tuning process; while this realization has been around since the late 90s, only recent advancements in ML enabled a practical application of ML to

D. J. Gauthier, N. Biederman, B. Gyovai, J. P. Wilhelm

A digital twin of a direct current brushless (BLDC) electric motor and propeller is developed for predicting the generated thrust when there is no motion of the system (static conditions). The model accounts for the back electromotive force, the propeller drag force, and the finite response time arising from the electromagnet winding inductance and DC resist

J. M. Khalil, J. van Roestel, E. C. Bellm, J. S. Bloom

Context. Accreting ultracompact binaries contain a white dwarf that is accreting from a degenerate object and have orbital periods shorter than 65 minutes. Aims. The aims of this letter are to report the discovery and the orbital period of four new eclipsing accreting ultracompact binaries found using the Zwicky Transient Facility, and to discuss their photo

Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao

Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks. Increasing instruction data substantially is a direct solution to align the model with a broader range of downstream tasks or notably improve its performance on a specific task. Ho

Márcio Sampaio Gomes-Filho, Luciano Calheiros Lapas, Ewa Gudowska-Nowak, Fernando Albuquerque Oliveira

In this review, we scrutinize historical and modern results on the linear response of dynamical systems to external perturbations with a particular emphasis on the celebrated relationship between fluctuations and dissipation expressed by the fluctuation-dissipation theorem (FDT). The conceptual foundation of FDT originates from the definition of the equilibr

C. J. Wright, N. Biederman, B. Gyovai, D. J. Gauthier

Machine learning was applied to create a digital twin of a numerical simulation of a single-scroll jet engine. A similar model based on the insights gained from this numerical study was used to create a digital twin of a JetCat P100-RX jet engine using only experimental data. Engine data was collected from a custom sensor system measuring parameters such as

Ulf Gran, Eric Nilsson, Johannes Hofmann

In interaction-dominated two-dimensional electron gases at intermediate temperatures, electron transport is not diffusive as in the conventional Drude picture but instead hydrodynamic. The relevant transport coefficient in this regime is the shear viscosity. Here, we develop a numerically exact basis expansion to solve the Fermi liquid equation, and apply it

Lior Gishboliner, Stefan Glock, Amedeo Sgueglia

In this paper, we study discrepancy questions for spanning subgraphs of $k$-uniform hypergraphs. Our main result is that, for any integers $k \ge 3$ and $r \ge 2$, any $r$-colouring of the edges of a $k$-uniform $n$-vertex hypergraph $G$ with minimum $(k-1)$-degree $\delta(G) \ge (1/2+o(1))n$ contains a tight Hamilton cycle with high discrepancy, that is, wi

Kean Fallon, Joseph W. Iverson

We show the optimal coherence of $2d$ lines in $\mathbb{C}^{d}$ is given by the Welch bound whenever a skew Hadamard of order $d+1$ exists. Our proof uses a variant of Hadamard doubling that converts any equiangular tight frame of size $\tfrac{d-1}{2} \times d$ into another one of size $d \times 2d$. Among $d < 150$, this produces equiangular tight frames of

Vahagn Aslanyan, Sebastian Eterović, Vincenzo Mantova

We show that for any polynomial $F(X,Y_0,Y_1,Y_2) \in \mathbb{C}[X, Y_0, Y_1, Y_2]$, the equation $F(z,j(z),j'(z),j''(z))=0$ has a Zariski dense set of solutions in the hypersurface $F(X,Y_0,Y_1,Y_2)=0$, unless $F$ is in $\mathbb{C}[X]$ or it is divisible by $Y_0$, $Y_0-1728$, or $Y_1$. Our methods establish criteria for finding solutions to more general equ

Michael J. Schlosser, Nicolas Allen Smoot

We introduce a finite-bound extension of a partition equinumerosity result which was orignally proposed as a problem by Andrews and Deutsch in 2016, and given a generalized form in 2018 by Smoot and Yang. We also give a simple bijective proof, itself an extension of the proof given of the 2018 generalization.

Nor Pirzkal, Barry Rothberg, Casey Papovich, Lu Shen

The Next Generation Deep Extragalactic Exploratory Public (NGDEEP) survey program was designed specifically to include Near Infrared Slitless Spectroscopic observations (NGDEEP-NISS) to detect multiple emission lines in as many galaxies as possible and across a wide redshift range using the Near Infrared Imager and Slitless Spectrograph (NIRISS). We present

Hui-Yuan Chen, Rolf B. Versteeg, Michele Puppin, Ludmila Leroy

We present a new set up for resonant inelastic hard X-ray scattering at the Bernina beamline of SwissFEL with energy, momentum, and temporal resolution. The compact R=0.5 m Johann-type spectrometer can be equipped with up to 3 crystal analysers and allows efficient collection of RIXS spectra. Optical pumping for time-resolved studies can be realized with a b

Jose Duato, Jose I. Mestre, Manuel F. Dolz, Enrique S. Quintana-Ortí

The number and complexity of artificial intelligence (AI) applications is growing relentlessly. As a result, even with the many algorithmic and mathematical advances experienced over past decades as well as the impressive energy efficiency and computational capacity of current hardware accelerators, training the most powerful and popular deep neural networks

Ian Cordeiro, Enrico Speranza, Kevin Ingles, Fábio S. Bemfica

We derive necessary and sufficient conditions under which a large class of relativistic generalizations of Braginskii's magnetohydrodynamics with shear, bulk, and heat diffusion effects is causal and strongly hyperbolic in the fully nonlinear regime in curved spacetime. We find that causality severely constrains the size of nonideal effects and the onset of

François Portier, Lionel Truquet, Ikko Yamane

Many existing covariate shift adaptation methods estimate sample weights given to loss values to mitigate the gap between the source and the target distribution. However, estimating the optimal weights typically involves computationally expensive matrix inversion and hyper-parameter tuning. In this paper, we propose a new covariate shift adaptation method wh

Doruk Aksoy, Huolin L. Xin, Timothy J. Rupert, William J. Bowman

Automated detection of grain boundaries (GBs) in electron microscope images of polycrystalline materials could help accelerate the nanoscale characterization of myriad engineering materials and novel materials under scientific research. Accurate segmentation of interconnected line networks, such as GBs in polycrystalline material microstructures, poses a sig

Tyler Gorda

Does deconfined cold quark matter occur in nature? This is currently one of the fundamental open questions in nuclear astrophysics. In these proceedings, I review the current state-of-the-art techniques to address this question in a model-agnostic manner, by synthesizing inputs from astrophysical observations of neutron stars and their binary mergers, and fi

Thorben Schomacker, Michael Gille, Jörg von der Hülls, Marina Tropmann-Frick

This paper examines the current state-of-the-art of German text simplification, focusing on parallel and monolingual German corpora. It reviews neural language models for simplifying German texts and assesses their suitability for legal texts and accessibility requirements. Our findings highlight the need for additional training data and more appropriate app

Mostafa Bendahmane, Youssef Ouakrim, Yassine Ouzrour, Mohamed Zagour

This paper deals with the mathematical analysis and numerical simulation of a new nonlinear ablation system modeling radiofrequency ablation phenomena in cardiac tissue, {which incorporates the effects of blood flow on the heat generated when ablation by radiofrequency. The model also considers the effects of viscous energy dissipation. It consists of a coup

Mikel Sanchez-Garitaonandia, Jorinde van de Vis

The bubble expansion velocity is an important parameter in the prediction of gravitational waves from first order phase transitions. This parameter is difficult to compute, especially in phase transitions in strongly coupled theories. In this work, we present a method to estimate the wall velocity for phase transitions with a large enthalpy jump, valid for w

Andrea Beraudo, Arturo De Pace, Daniel Pablos, Francesco Prino

We study heavy-flavor hadron production in high-energy pp collisions, assuming the formation of a small, deconfined and expanding fireball where charm quarks can undergo rescattering and hadronization. We adopt the same in-medium hadronization mechanism developed for heavy-ion collisions, which involves Local Color-Neutralization (LCN) through recombination

Matteo Cardellini, Enrico Giunchiglia, Marco Maratea

In this paper, we propose a novel approach for solving linear numeric planning problems, called Symbolic Pattern Planning. Given a planning problem $\Pi$, a bound $n$ and a pattern -- defined as an arbitrary sequence of actions -- we encode the problem of finding a plan for $\Pi$ with bound $n$ as a formula with fewer variables and/or clauses than the state-

Domenico Marinucci, Maurizia Rossi, Anna Paola Todino

We investigate the nodal volume of random hyperspherical harmonics $\lbrace T_{\ell;d}\rbrace_{\ell\in \mathbb N}$ on the $d$-dimensional unit sphere ($d\ge 2$). We exploit an orthogonal expansion in terms of Laguerre polynomials; this representation entails a drastic reduction in the computational complexity and allows to prove \emph{isotropy} for chaotic c

Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez

Recent advances in learning techniques have garnered attention for their applicability to a diverse range of real-world sequential decision-making problems. Yet, many practical applications have critical constraints for operation in real environments. Most learning solutions often neglect the risk of failing to meet these constraints, hindering their impleme

Andres Luna, Nathan Moynihan, Donal O'Connell, Alasdair Ross

We study the classical dynamics of spinning particles using scattering amplitudes and eikonal exponentiation. We show that observables are determined by a simple algorithm. A wealth of complexity arises in perturbation theory as positions, momenta and spins must be iteratively corrected at each order. Even though we restrict ourselves to one-loop computation

Ramesh Mondal, Aditi Sengupta

Bounded variation estimates of Galerkin approximations are established in order to extract an almost everywhere convergent subsequence of Galerkin approximations. As a result we prove existence of weak solutions of initial boundary value problems for quasilinear parabolic equations. Uniqueness of weak solutions is derieved applying a standard argument.

Mauro Nievas, Aditya Basu, Yanshan Wang, Hrituraj Singh

The recent success of large language models (LLMs) has paved the way for their adoption in the high-stakes domain of healthcare. Specifically, the application of LLMs in patient-trial matching, which involves assessing patient eligibility against clinical trial's nuanced inclusion and exclusion criteria, has shown promise. Recent research has shown that GPT-

Fátima Castro-Jul, Rebeca Díaz Redondo, Ana Fernández-Vilas

Ad hoc architectures have emerged as a valuable alternative to centralized participatory sensing systems due to their infrastructureless nature, which ensures good availability, easy maintenance and direct user communication. As a result, they need to incorporate content-aware assessment mechanisms to deal with a common problem in participatory sensing: info