October 2023 arXiv papers — page 49

Sandra May, Fabian Laakmann

The solution of time-dependent hyperbolic conservation laws on cut cell meshes causes the small cell problem: standard schemes are not stable on the arbitrarily small cut cells if an explicit time stepping scheme is used and the time step size is chosen based on the size of the background cells. In [J. Sci. Comput. 71, 919-943 (2017)], the mixed explicit imp

Youshan Zhang, Brian D. Davison

While unsupervised domain adaptation has been explored to leverage the knowledge from a labeled source domain to an unlabeled target domain, existing methods focus on the distribution alignment between two domains. However, how to better align source and target features is not well addressed. In this paper, we propose a deep feature registration (DFR) model

Sukesh Adiga, Jose Dolz, Herve Lombaert

Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to regularize model predictions. Since the predictions of unlabeled data can be unreliable, uncertainty-aware schemes are typically employed to gradually learn from meaningful and rel

David W. Cohen, Bruce M. Boghosian

We present analytical results for the Gini coefficient of economic inequality under the dynamics of a modified Yard-Sale Model of kinetic asset exchange. A variant of the Yard-Sale Model is introduced by modifying the underlying binary transaction of the classical system. It is shown that the Gini coefficient is monotone under the resulting dynamics but the

P. Haskell, S. Das, D. J. B. Smith, R. K. Cochrane

We investigate how the recovery of galaxy star formation rates (SFRs) using energy-balance spectral energy distribution (SED) fitting codes depends on their recent star formation histories (SFHs). We use the Magphys and Prospector codes to fit 6,706 synthetic spectral energy distributions of simulated massive galaxies at $1 < z < 8$ from the Feedback in Real

Dean Foster, Randy Jia, Dhruv Madeka

Solutions to address the periodic review inventory control problem with nonstationary random demand, lost sales, and stochastic vendor lead times typically involve making strong assumptions on the dynamics for either approximation or simulation, and applying methods such as optimization, dynamic programming, or reinforcement learning. Therefore, it is import

Ye Eun Chun, Sunjae Kwon, Kyunghwan Sohn, Nakwon Sung

In this paper, we introduce CR-COPEC called Causal Rationale of Corporate Performance Changes from financial reports. This is a comprehensive large-scale domain-adaptation causal sentence dataset to detect financial performance changes of corporate. CR-COPEC contributes to two major achievements. First, it detects causal rationale from 10-K annual reports of

Ziwei Wang, Yves H. Kwan, Glenn Wagner, Nick Bultinck

We study the phase diagram of magic-angle twisted symmetric trilayer graphene in the presence of uniaxial heterostrain and interlayer displacement field. For experimentally reasonable strain, our mean-field analysis finds robust Kekul\'e spiral order whose doping-dependent ordering vector is incommensurate with the moir\'e superlattice, consistent with recen

Haonan Zheng, Sownak Bose, Carlos S. Frenk, Liang Gao

We use the Voids-within-Voids-within-Voids (VVV) simulations, a suite of successive nested N-body simulations with extremely high resolution (denoted, from low to high resolution, by L0 to L7), to test the Press-Schechter (PS), Sheth-Tormen (ST), and extended Press-Schechter (EPS) formulae for the halo abundance over the entire mass range, from mini-haloes o

Jennifer E. Andrews, Jeniveve Pearson, Griffin Hosseinzadeh, K. Azalee Bostroem

We present high cadence optical and ultraviolet observations of the Type II supernova (SN), SN 2022jox which exhibits early spectroscopic high ionization flash features of \ion{H}{1}, \ion{He}{2}, \ion{C}{4}, and \ion{N}{4} that disappear within the first few days after explosion. SN 2022jox was discovered by the Distance Less than 40 Mpc (DLT40) survey $\si

Gianpaolo Torre, Jovan Odavić, Pierre Fromholz, Salvatore Marco Giampaolo

Topological order comes in different forms, and its classification and detection is an important field of modern research. In this work, we show that the Disconnected Entanglement Entropy, a measure originally introduced to identify topological phases, is also able to unveil the long-range entanglement (LRE) carried by a single, fractionalized excitation. We

Ben Gripaios, Oscar Randal-Williams, Joseph Tooby-Smith

Dynamical quantum field theories (QFTs), such as those in which spacetimes are equipped with a metric and/or a field in the form of a smooth map to a target manifold, can be formulated axiomatically using the language of $\infty$-categories. According to a geometric version of the cobordism hypothesis, such QFTs collectively assemble themselves into objects

Alba V. Alonso-Tetilla, Francesco Shankar, Fabio Fontanot, Nicola Menci

The origin of obscuration in Active Galactic Nuclei (AGN) is still an open debate. In particular, it is unclear what drives the relative contributions to the line-of-sight column densities from galaxy-scale and torus-linked obscuration. The latter source is expected to play a significant role in Unification Models, while the former is thought to be relevant

Rhea-Silvia Remus, Lucas C. Kimmig

A puzzling population of extremely massive quiescent galaxies at redshifts beyond z=3 has recently been revealed by JWST and ALMA, some of them with stellar ages that show their quenching times to be as high as z=6, while their stellar masses are already above 5e10Msun. These extremely massive yet quenched galaxies challenge our understanding of galaxy forma

Trevor Z. Dorn-Wallenstein, Kathryn F. Neugent, Emily M. Levesque

Characterizing the physical properties of cool supergiants allows us to probe the final stages of a massive star's evolution before it undergoes core collapse. Despite their importance, the fundamental properties for these stars -- $T_{\rm eff}$ and $\log L/L_\odot$ -- are only known for a limited number of objects. The third data release of the Gaia mission

Andrew Cheek, Jacek K. Osiński, Leszek Roszkowski

We examine the cosmological consequences of the heavy quarks in KSVZ-type axion models. We find that their presence often causes an early matter domination phase, altering the evolution of the Universe. This extends the axion mass into the region where standard cosmology leads to overproduction, and allows for a greater number of axion models with non-renorm

Lindsey Byrne, Claude-André Faucher-Giguère, Sarah Wellons, Philip F. Hopkins

Feedback from supermassive black holes is believed to be a critical driver of the observed color bimodality of galaxies above the Milky Way mass scale. AGN feedback has been modeled in many galaxy formation simulations, but most implementations have involved simplified prescriptions or a coarse-grained interstellar medium (ISM). We present the first set of F

Lucas C. Kimmig, Rhea-Silvia Remus, Benjamin Seidel, Lucas M. Valenzuela

Recent observations with JWST and ALMA have revealed extremely massive quiescent galaxies at redshifts of z=3 and higher, indicating both rapid onset and quenching of star formation. Using the cosmological simulation suite Magneticum Pathfinder we reproduce the observed number densities and stellar masses, with 36 quenched galaxies of stellar mass larger tha

Philip Zechmann, Julian Boesl, Johannes Feldmeier, Michael Knap

Quantum many-body systems with fractonic excitations can realize fascinating phases of matter. Here, we study the low-energy excitations of a constrained Bose-Hubbard model in one dimension, which conserves the center of mass or, equivalently, the dipole moment in addition to the particle number. This model is known to realize fractonic phases, including a d

Bruno de S. L. Torres

Using the Schwinger-Keldysh path integral, we draw a connection between localized quantum field theories and more commonly used models of local probes in Relativistic Quantum Information (RQI). By integrating over and then tracing out the inaccessible modes of the localized field being used as a probe, we show that, at leading order in perturbation theory, t

Gilad Kishony, Mark S. Rudner, Achim Rosch, Erez Berg

Simulated cooling is a robust method for preparing low-energy states of many-body Hamiltonians on near-term quantum simulators. In such schemes, a subset of the simulator's spins (or qubits) are treated as a ``bath,'' which extracts energy and entropy from the system of interest. However, such protocols are inefficient when applied to systems whose excitatio

Guanda Lin, Mykhaylo Usatyuk

We revisit the gravity path integral formalism of JT gravity. We explain how to gauge fix the path integral in the presence of asymptotic boundaries and conical defects, and resolve an ambiguity regarding the dilaton gravity operator that creates a conical defect. Along the way we study JT gravity coupled to matter on surfaces with defects of special opening

Pedro M. Cônsoli, Matthias Vojta

Spiral spin liquids are correlated states of matter in which a frustrated magnetic system evades order by fluctuating between a set of (nearly) degenerate spin spirals. Here, we investigate the response of spiral spin liquids to quenched disorder in a $J_1$-$J_2$ honeycomb-lattice Heisenberg model. At the single-impurity level, we identify different order-by

Falk Hassler, Sylvain Lacroix, Benoit Vicedo

We study the renormalisation of a large class of integrable $\sigma$-models obtained in the framework of affine Gaudin models. They are characterised by a simple Lie algebra $\mathfrak{g}$ and a rational twist function $\varphi(z)$ with simple zeros, a double pole at infinity but otherwise no further restrictions on the pole structure. The crucial tool used

Khaled Said

The observed radial velocity of a galaxy consists of two main components: the recession velocity caused by the smooth Hubble expansion and the peculiar velocity resulting from the gravitational attraction of growing structures due to matter density fluctuations. To isolate the recession velocity component and calculate the Hubble constant, accurate measureme

Qixin Hu, Alan Yuille, Zongwei Zhou

This study leverages synthetic data as a validation set to reduce overfitting and ease the selection of the best model in AI development. While synthetic data have been used for augmenting the training set, we find that synthetic data can also significantly diversify the validation set, offering marked advantages in domains like healthcare, where data are ty

Muqing Yu, Nicholas Hougland, Qianheng Du, Junyi Yang

The discovery of two-dimensional superconductivity in LaAlO3/KTaO3 (111) and (110) interfaces has raised significant interest in this system. In this manuscript we report the first successful fabrication of a superconducting quantum interference device (DC-SQUID) in the KTO system. The key device elements, superconducting weak links, are created by conductiv

Jingyun Yang, Congyue Deng, Jimmy Wu, Rika Antonova

If a robot masters folding a kitchen towel, we would expect it to master folding a large beach towel. However, existing policy learning methods that rely on data augmentation still don't guarantee such generalization. Our insight is to add equivariance to both the visual object representation and policy architecture. We propose EquivAct which utilizes SIM(3)

Veysi Adin, Chunwoo Kim

As the more sensors and actuators are used in the robotic systems to provide more features, complexity of the system is increasing. When it comes to medical robotics, it becomes harder to ensure safety and determinism in the system. To deal with increasing complexity and ensure precise periodicity and execution timing for a medical robot, in this paper we re

Zayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri

While large language models (LLMs) equipped with techniques like chain-of-thought prompting have demonstrated impressive capabilities, they still fall short in their ability to reason robustly in complex settings. However, evaluating LLM reasoning is challenging because system capabilities continue to grow while benchmark datasets for tasks like logical dedu

Abhilash Mishra

Aligning AI agents to human intentions and values is a key bottleneck in building safe and deployable AI applications. But whose values should AI agents be aligned with? Reinforcement learning with human feedback (RLHF) has emerged as the key framework for AI alignment. RLHF uses feedback from human reinforcers to fine-tune outputs; all widely deployed large

Noa Cohen, Hila Manor, Yuval Bahat, Tomer Michaeli

Image restoration problems are typically ill-posed in the sense that each degraded image can be restored in infinitely many valid ways. To accommodate this, many works generate a diverse set of outputs by attempting to randomly sample from the posterior distribution of natural images given the degraded input. Here we argue that this strategy is commonly of l

Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we i

Shukang Yin, Chaoyou Fu, Sirui Zhao, Tong Xu

Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content. In order to mitigate hallucinations, existing studies mainly resort to an instruction-tuning manner that requires retraining the models with specific data. In thi

Zhengfei Kuang, Yunzhi Zhang, Hong-Xing Yu, Samir Agarwala

We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark. Recent advances in inverse rendering have enabled a wide range of real-world applications in 3D content generation, moving rapidly from research and commercial use cases to consumer devices. While the results continue to improve, there is no real-world benchmark that can quant

Jerome Lloyd, Tibor Rakovszky, Frank Pollmann, Curt von Keyserlingk

Charge and energy are expected to diffuse in interacting systems of fermions at finite temperatures, even in the absence of disorder, with the interactions inducing a crossover from the coherent and ballistic streaming of quasi-particles at early times, to incoherent diffusive behavior at late times. The relevant crossover timescales and the transport coeffi

Heyi Tao, Sethuraman T, Michal Shlapentokh-Rothman, Derek Hoiem

The paper investigates using a Large Language Model (LLM) to automatically perform web software tasks using click, scroll, and text input operations. Previous approaches, such as reinforcement learning (RL) or imitation learning, are inefficient to train and task-specific. Our method uses filtered Document Object Model (DOM) elements as observations and perf

Yizhu Jiao, Ming Zhong, Sha Li, Ruining Zhao

Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users. To address this, we propose a novel paradigm, te

Johannes Popp, Johannes Stowasser, Michael A. Schreiber, Lukas Seitner

We present a full-wave Maxwell-density matrix simulation tool including c-number stochastic noise terms for the modeling of the spatiotemporal dynamics in active photonic devices, such as quantum cascade lasers (QCLs) and quantum dot (QD) structures. The coherent light-matter interaction in such devices plays an important role in the generation of frequency

Ildar Khabibullin, Massimiliano Galeazzi, Akos Bogdan, Jenna M. Cann

The Line Emission Mapper (LEM) is an X-ray Probe with with spectral resolution ~2 eV FWHM from 0.2 to 2.5 keV and effective area >2,500 cm$^2$ at 1 keV, covering a 33 arcmin diameter Field of View with 15 arcsec angular resolution, capable of performing efficient scanning observations of very large sky areas and enabling the first high spectral resolution su

Yu-Hsuan Teng, I-Da Chiang, Karin M. Sandstrom, Jiayi Sun

Determining how galactic environment, especially the high gas densities and complex dynamics in bar-fed galaxy centers, alters the star formation efficiency (SFE) of molecular gas is critical to understanding galaxy evolution. However, these same physical or dynamical effects also alter the emissivity properties of CO, leading to variations in the CO-to-H$_2

Michelle Kovarik, Tyler Allcroft, Per Sebastian Skardal

Detecting communities in large complex networks has found a wide range of applications in physical, biological, and social sciences by identifying mesoscopic groups based on the links between individual units. Moreover, community detection approaches have been generalized to various data analysis tasks by constructing networks whose links depend on individua

Victor V. Braguta, Maxim N. Chernodub, Ilya E. Kudrov, Artem A. Roenko

We discuss the negativity of the moment of inertia of (quark-)gluon plasma in a window of "supervortical" range of temperatures above the deconfining phase transition, $T \simeq (1\dots 1.5) T_c $ found recently in numerical Monte Carlo simulations by two independent methods. In our work, we confirm numerically that the origin of this effect is rooted in the

Joy Hsu, Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu

Recent works such as VisProg and ViperGPT have smartly composed foundation models for visual reasoning-using large language models (LLMs) to produce programs that can be executed by pre-trained vision-language models. However, they operate in limited domains, such as 2D images, not fully exploiting the generalization of language: abstract concepts like "left

Marcos Riojas, Hao-Yu Sun

In this letter, we show the Unruh temperature of the photon sphere for an AdS$_4$-Schwarzschild black hole can be determined holographically from the retarded Green's function and is proportional to its circumference according to a boundary observer. We then sharpen the conjecture that the photon sphere, as seen by a boundary observer, is the spatial Fourier

Jiarui Zhang, Mahyar Khayatkhoei, Prateek Chhikara, Filip Ilievski

Multimodal Large Language Models (MLLMs) have recently achieved promising zero-shot accuracy on visual question answering (VQA) -- a fundamental task affecting various downstream applications and domains. Given the great potential for the broad use of these models, it is important to investigate their limitations in dealing with different image and question

Tibor Rakovszky, Vedika Khemani

Low-depth parity check (LDPC) codes are a paradigm of error correction that allow for spatially non-local interactions between (qu)bits, while still enforcing that each (qu)bit interacts only with finitely many others. On expander graphs, they can give rise to ``good codes'' that combine a finite encoding rate with an optimal scaling of the code distance, wh

Adrià Delhom, Killian Guerrero, Paula Calizaya, Kévin Falque

The amplification of radiation by superradiance is a universal phenomenon observed in numerous physical systems. We demonstrate that superradiant scattering generates entanglement for different input states, including coherent states, thereby establishing the inherently quantum nature of this phenomenon. To put these concepts to the test, we propose a novel

Yushi Hamaguchi

We establish weak existence and uniqueness in law for stochastic Volterra equations (SVEs for short) with completely monotone kernels and non-degenerate noise under mild regularity assumptions. In particular, our results reveal the regularization-by-noise effect for SVEs with singular kernels, allowing for multiplicative noise with H\"{o}lder diffusion coeff

Yunhai Feng, Nicklas Hansen, Ziyan Xiong, Chandramouli Rajagopalan

Reinforcement Learning (RL) is notoriously data-inefficient, which makes training on a real robot difficult. While model-based RL algorithms (world models) improve data-efficiency to some extent, they still require hours or days of interaction to learn skills. Recently, offline RL has been proposed as a framework for training RL policies on pre-existing data

Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin

Large language models exhibit surprising emergent generalization properties, yet also struggle on many simple reasoning tasks such as arithmetic and parity. This raises the question of if and when Transformer models can learn the true algorithm for solving a task. We study the scope of Transformers' abilities in the specific setting of length generalization

Travers Rhodes, Daniel D. Lee

Human demonstrations of trajectories are an important source of training data for many machine learning problems. However, the difficulty of collecting human demonstration data for complex tasks makes learning efficient representations of those trajectories challenging. For many problems, such as for dexterous manipulation, the exact timings of the trajector

Young Min Seo, Karen Willacy, Geoffrey Bryden, Dariusz C. Lis

We discuss statistical relationships between the mass of protoplanetary disks and the hydrogen deuteride (HD) line emission and the dust spectral energy distribution (SED) determined using 3000 ProDiMo disk models. The models have 15 free parameters describing disk physical properties, the central star, and the local radiation field. The sampling of physical

Sophia Morton, Stefano Rinaldi, Alejandro Torres-Orjuela, Andrea Derdzinski

GW190521, the most massive binary black hole merger confidently detected by the LIGO-Virgo-KAGRA collaboration, is the first gravitational-wave observation of an intermediate-mass black hole. The signal was followed approximately 34 days later by flare ZTF19abanrhr, detected in AGN J124942.3+344929 by the Zwicky Transient Facility at the 78% spatial contour

Alexander Hedrick, Heiko Kabutz, Lawrence Smith, Robert MacCurdy

Nitinol is a smart material that can be used as an actuator, a sensor, or a structural element, and has the potential to significantly enhance the capabilities of microrobots. Femtosecond laser technology can be used to process nitinol while avoiding heat-affected zones (HAZ), thus retaining superelastic properties. In this work, we manufacture living hinges

Jorge Cervantes-Villanueva, Fulvio Paleari, Alberto García-Cristóbal, Davide Sangalli

We carry out a detailed theoretical study of the electronic and optical properties of bulk and monolayer bismuth triiodide (BiI$_{3}$), a layered metal halide, using the ab initio GW+BSE scheme with a full spinorial formulation. We discuss in detail the effects due to the change of dimensionality and the role of spin-orbit coupling. Moreover, we compute the

Siddhartha Solanki, Manohar Lal, Rishabh Sharma, Vineet Kumar Agotiya

In this article, we employed the Quasi-particle debye mass at finite baryonic chemical potential which can be used in the medium modified heavy quark potential to solve the N-dimensional Schroedinger equation. The bound state solution of the Schroedinger equation using Cornell potential is obtained by Super-Symmetry Quantum Mechanics (SUSYQM) method. The the

Dana Fisman, Emmanuel Goldberg, Oded Zimerman

Families of DFAs (FDFAs) are a computational model recognizing $\omega$-regular languages. They were introduced in the quest of finding a Myhill-Nerode theorem for $\omega$-regular languages, and obtaining learning algorithms. FDFAs have been shown to have good qualities in terms of the resources required for computing Boolean operations on them (complementa

Vitus Benson, Jonathan F. Donges, Niklas Boers, Marina Hirota

The Amazon rainforest is considered one of the Earth's tipping elements and may lose stability under ongoing climate change. Recently a decrease in tropical rainforest resilience has been identified globally from remotely sensed vegetation data. However, the underlying theory assumes a Gaussian distribution of forest disturbances, which is different from mos

Joey Wilson, Yuewei Fu, Joshua Friesen, Parker Ewen

In this paper, we develop a modular neural network for real-time {\color{black}(> 10 Hz)} semantic mapping in uncertain environments, which explicitly updates per-voxel probabilistic distributions within a neural network layer. Our approach combines the reliability of classical probabilistic algorithms with the performance and efficiency of modern neural net

Alexander Zapryagaev

B\"uchi arithmetics $\mathop{\mathbf{BA}}\nolimits_n$, $n\ge 2$, are extensions of Presburger arithmetic with an unary functional symbol $V_n(x)$ denoting the largest power of $n$ that divides $x$. A rank of a linear order is the minimal number of condensations required to reach a finite order. We show that linear orders of arbitrarily large finite rank can

E. Donoway, T. V. Trevisan, A. Liebman - Peláez, R. P. Day

One of the prime material candidates to host the axion insulator state is EuIn$_{2}$As$_{2}$. First-principles calculations predicted the emergence of this exotic topological phase based on the assumption of a collinear antiferromagnetic structure. However, neutron scattering measurements revealed a more intricate magnetic ground state, characterized by two

Pedro Neto Mendes, Gonçalo Lobato Teixeira, David Pinho, Rui Rocha

Quantum key distribution is costly and, at the moment, offers low performance in space applications. Other more recent protocols could offer a potential practical solution to this problem. In this work, a preliminary optical payload design using commercial off-the-shelf elements for a quantum communication downlink in a 3U CubeSat is proposed. It is shown th

T. M. Dunster

Reformulated uniform asymptotic expansions are derived for ordinary differential equations having a large parameter and a simple turning point. These involve Airy functions, but not their derivatives, unlike traditional asymptotic expansions. From these, asymptotic expansions are derived for the zeros of Bessel functions that are valid for large positive val

Da Fan, Steven J. Greybush, David John Gagne, Eugene E. Clothiaux

Convection initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, object-based probabilistic deep learning models are developed to predict CI based on multichannel infrared GOES-R satellite observations. The data come from patches surrounding potential CI events

Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber

Recent studies of the computational power of recurrent neural networks (RNNs) reveal a hierarchy of RNN architectures, given real-time and finite-precision assumptions. Here we study auto-regressive Transformers with linearised attention, a.k.a. linear Transformers (LTs) or Fast Weight Programmers (FWPs). LTs are special in the sense that they are equivalent

Jiayu Qian, Yuanyuan Liu, Jingya Yang, Qingping Zhou

Bayesian inference with deep generative prior has received considerable interest for solving imaging inverse problems in many scientific and engineering fields. The selection of the prior distribution is learned from, and therefore an important representation learning of, available prior measurements. The SA-Roundtrip, a novel deep generative prior, is intro

Ajay Mandlekar, Caelan Garrett, Danfei Xu, Dieter Fox

Imitation learning from human demonstrations can teach robots complex manipulation skills, but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) systems are automated and excel at solving long-horizon tasks, but they are difficult to apply to contact-rich tasks. In this paper, we present Human-in-the-Loop Task and Motion Pla

Juan F. Hernández-Cabrera, Javier De Miguel, E. Hernández-Suárez, Enrique Joven-Álvarez

The axion is a hypothetical boson arising from the most natural solution to the problem of charge and parity symmetry in the strong nuclear force. Moreover, this pseudoscalar emerges as a dark matter candidate in a parameter space extending several decades in mass. The Dark-photons \& Axion-Like particles Interferometer (DALI) is a proposal to search for axi

Rene Cabrera, Maria Gualdani, Nestor Guillen

The Landau-Coulomb equation is an important model in plasma physics featuring both nonlinear diffusion and reaction terms. In this manuscript we focus on the diffusion operator within the equation by dropping the potentially nefarious reaction term altogether. We show that the diffusion operator in the Landau-Coulomb equation provides a much stronger L^1 to

Jonathan Mauro, Gwenhaël de Wasseige

Solar flares are highly energetic events that happen in the solar atmosphere. They are mostly observed as X-ray or gamma-ray bursts located on the Sun's surface. While they are known to be sites of particle acceleration, the acceleration process(es) responsible for the observed fluxes remain unsure. The diversity in shape and duration of the gamma-ray fluxes

Raymond Cheng, Christopher Felder

This work studies optimal polynomial approximants (OPAs) in the classical Hardy spaces on the unit disk, $H^p$ ($1 < p < \infty$). For fixed $f\in H^p$ and $n\in\mathbb{N}$, the OPA of degree $n$ associated to $f$ is the polynomial which minimizes the quantity $\|qf-1\|_p$ over all complex polynomials $q$ of degree less than or equal to $n$. We begin with so

Maximilian Dreisbach, Jochen Kriegseis, Alexander Stroh

The present work introduces a deep learning approach for the three-dimensional reconstruction of the spatio-temporal dynamics of the gas-liquid interface in two-phase flows on the basis of monocular images obtained via optical measurement techniques. The dynamics of liquid droplets impacting onto structured solid substrates are captured through high-speed im

Luis Álvarez-Gaumé, Moshe M. Chaichian, Markku A. Oksanen, Anca Tureanu

The CPT theorem originally proven by L\"uders and Pauli ensures the equality of masses, lifetimes, magnetic moments and cross sections of any particle and its antiparticle. We show that in a Lorentz invariant quantum field theory described by its Lagrangian, CPT-violating interaction alone does not split the masses of an elementary particle and its antiparti

Katsuki Aoki, Sebastian Bahamonde, Jorge Gigante Valcarcel, Mohammad Ali Gorji

We formulate cosmological perturbation theory around the spatially curved FLRW background in the context of metric-affine gauge theory of gravity which includes torsion and nonmetricity. Performing scalar-vector-tensor decomposition of the spatial perturbations, we find that the theory displays a rich perturbation spectrum with helicities 0, 1, 2 and 3, on t

M. Link, K. Gao, A. Kell, M. Breyer

We determine the phase diagram of strongly correlated fermions in the crossover from Bose-Einstein condensates of molecules (BEC) to Cooper pairs of fermions (BCS) utilizing an artificial neural network. By applying advanced image recognition techniques to the momentum distribution of the fermions, a quantity which has been widely considered as featureless f

Andrej Bauer, Matej Petković, Ljupčo Todorovski

We introduce MLFMF, a collection of data sets for benchmarking recommendation systems used to support formalization of mathematics with proof assistants. These systems help humans identify which previous entries (theorems, constructions, datatypes, and postulates) are relevant in proving a new theorem or carrying out a new construction. Each data set is deri

Frederik Van Daele, Olivier Honnay, Hanne De Kort

Understanding the combined effects of climate change and habitat fragmentation on the adaptive potential of plant populations is essential for devising effective conservation strategies. This is particularly important where mating system variation impacts the evolutionary consequences of habitat fragmentation. Here we aimed to reveal how habitat fragmentatio

Jay Zhangjie Wu, Xiuyu Li, Difei Gao, Zhen Dong

Humans watch more than a billion hours of video per day. Most of this video was edited manually, which is a tedious process. However, AI-enabled video-generation and video-editing is on the rise. Building on text-to-image models like Stable Diffusion and Imagen, generative AI has improved dramatically on video tasks. But it's hard to evaluate progress in the

Sarbajaya Kundu, David Sénéchal

We examine the phase diagram of the extended Hubbard model on a square lattice, for both attractive and repulsive nearest-neighbor interactions, using CDMFT+HFD, a combination of Cluster Dynamical Mean Field theory (CDMFT) and a Hartree-Fock mean-field decoupling of the inter-cluster extended interaction. For attractive non-local interactions, this model exh

Jinbin Bai, Zhen Dong, Aosong Feng, Xiao Zhang

In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge. This paper introduces a pioneering framework that integrates viewpoint information to enhance the control of image editing tasks, especially for interior design scenes. By surveying existing object editing methodologies, we distil

Zulaihat Hassan, Wenxian Shen, Yuming Paul Zhang

While much literature on chemotaxis systems focuses on bounded domains, this paper emphasizes the global existence of classical solutions for three primary chemotaxis systems with a logistic source on $\mathbb{R}^n$. We present a unified proof demonstrating global existence of solutions can be deduced from their locally uniform boundedness in $L^p(\mathbb{R}

Abel J. Abraham, Stepan Malkov, Frane A. Ljubetic, Matthew Durey

Understanding the ability of particles to maneuver through disordered environments is a central problem in innumerable settings, from active matter and biology to electronics. Macroscopic particles ultimately exhibit diffusive motion when their energy exceeds the characteristic potential barrier of the random landscape. In stark contrast, wave-particle duali

Abhra Chaudhuri, Massimiliano Mancini, Zeynep Akata, Anjan Dutta

Recent advances in fine-grained representation learning leverage local-to-global (emergent) relationships for achieving state-of-the-art results. The relational representations relied upon by such methods, however, are abstract. We aim to deconstruct this abstraction by expressing them as interpretable graphs over image views. We begin by theoretically showi

H. K. Li, H. L. Ding, Y. Li, J. F. Zhu

In many experiments, the simultaneous emission of multiple wavelengths of FEL (Free-Electron Laser) is significant. For the pulsed-mode FEL facility, we must accelerate multiple electron beams in one microwave pulse, and they may be in different amplitudes and phases in the acceleration field. Therefore, we implement a microwave excitation, whose amplitude a

Ciro Pinto, James F. Steiner, Arash Bodaghee, Priyanka Chakraborty

We investigate outflows and the physics of super-Eddington versus sub-Eddington regimes in black hole systems. Our focus is on prospective science using next-generation high-resolution soft X-ray instruments. We highlight the properties of black hole ultraluminous X-ray source (ULX) systems in particular. Owing to scale invariance in accreting black holes, U

Adrián Esparza-Amador, Irene Inoquio-Renteria

We provide a version of a thermodynamic formalism of entire transcendental maps that exhibit Baker domains, denoted as $f_{\ell, c}: \mathbb C\to \mathbb C$ and defined by $f_{\ell, c}(z)= c-(\ell-1)\log c+ \ell z- e^z$, where $\ell \in \mathbb N$, with $\ell \geq 2 $ and $c$ belongs to the disk $ D(\ell, 1)$ in the complex plane. We show in particular the e

L. Y. Cheung, R. Haller, A. Kononov, C. Ciaccia

In a superconducting weak link, the supercurrent is carried by Andreev bound states (ABSs) formed by the phase-coherent reflection of electrons and their time-reversed partners. A single, highly transmissive ABS can serve as an ideal, compact two-level system, due to a potentially large energy difference to the next ABS. While the coherent manipulation of su

David M. Ceperley, Scott Jensen, Yubo Yang, Hongwei Niu

Quantum Monte Carlo (QMC) can play a very important role in generating accurate data needed for constructing potential energy surfaces. We argue that QMC has advantages in terms of a smaller systematic bias and an ability to cover phase space more completely. The stochastic noise can ease the training of the machine learning model. We discuss how stochastic

Tomoki Moriya, Momonari Kudo

In the past several years, Howe curves have been studied actively in the field of algebraic curves over fields of positive characteristic. Here, a Howe curve is defined as the desingularization of the fiber product over a projective line of two hyperelliptic curves. In this paper, we construct an explicit plane sextic model for non-hyperelliptic Howe curves

Ramses M. Ramirez

Energy balance models (EBMs), alongside radiative-convective climate models (RCMs) and global climate models (GCMs), are useful tools for simulating planetary climates. Historically, planetary and exoplanetary EBMs have solely been 1D latitudinally-dependent models with no longitudinal dependence, until the study of Okuya et al., which focused on simulating

Chenyuan Yang, Yinlin Deng, Runyu Lu, Jiayi Yao

Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences. Fuzzing has been studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates tests without sufficient understanding of internal compiler behaviors. Meanw

Shicheng Li, Bharat Singhal, Jr-Shin Li

In complex networks, interactions between multiple agents give rise to an array of intricate global dynamics, ranging from synchronization to cluster formations. Decoding the connectivity structure as well as the types of interactions from measurement data is the first step toward understanding these intriguing behaviors. In this paper, we present a bilinear

D. Eberz, M. Link, A. Kell, M. Breyer

We study the critical temperature of the superfluid phase transition of strongly-interacting fermions in the crossover regime between a Bardeen-Cooper-Schrieffer (BCS) superconductor and a Bose-Einstein condensate (BEC) of dimers. To this end, we employ the technique of unsupervised machine learning using an autoencoder neural network which we directly apply

John E. Hurtado

The relationship between the Hamiltonian and Lagrangean functions in analytical mechanics is a type of duality. The two functions, while distinct, are both descriptive functions encoding the behavior of the same dynamical system. One difference is that the Lagrangean naturally appears as one investigates the fundamental equation of classical dynamics. It is

Pezhman Nasirifard, Ruben Mayer, Hans-Arno Jacobsen

With the increased adaption of blockchain technologies, permissioned blockchains such as Hyperledger Fabric provide a robust ecosystem for developing production-grade decentralized applications. However, the additional latency between executing and committing transactions, due to Fabric's three-phase transaction lifecycle of Execute-Order-Validate (EOV), is

Vikas Raunak, Hany Hassan Awadalla, Arul Menezes

Most of the recent work in leveraging Large Language Models (LLMs) such as GPT-3 for Machine Translation (MT) has focused on selecting the few-shot samples for prompting. In this work, we try to better understand the role of demonstration attributes for the in-context learning of translations through perturbations of high-quality, in-domain demonstrations. W

Krister DG. Barclay, Sophie Rosu, Noel D. Richardson, André-Nicolas Chené

The bright WN4 star EZ CMa exhibits a 3.77 day periodicity in photometry, spectroscopy, and polarimetry but the variations in the measurements are not strictly phase-locked, exhibiting changes in reference times, amplitudes, and the shape of the variability happening over times as short as a few weeks. Recently, 137 days of contiguous, variable photometry fr

Xin Xing, Zhexiao Xiong, Abby Stylianou, Srikumar Sastry

This paper presents a novel approach to Single-Positive Multi-label Learning. In general multi-label learning, a model learns to predict multiple labels or categories for a single input image. This is in contrast with standard multi-class image classification, where the task is predicting a single label from many possible labels for an image. Single-Positive

Zicheng Zhang, Yingjie Zhou, Wei Sun, Xiongkuo Min

Dynamic Digital Humans (DDHs) are 3D digital models that are animated using predefined motions and are inevitably bothered by noise/shift during the generation process and compression distortion during the transmission process, which needs to be perceptually evaluated. Usually, DDHs are displayed as 2D rendered animation videos and it is natural to adapt vid