October 2023 arXiv papers — page 79

Danyang Wang, Dan Chavas

Tropical cyclones are known to expand to an equilibrium size on the $f$-plane, but the expansion process is not understood. In this study, an analytical model for tropical cyclone size expansion on the $f$-plane is proposed. Conceptually, the storm expands because the imbalance between latent heating and radiative cooling drives a lateral inflow that imports

A. P. M. Towner, A. Ginsburg, P. Dell'Ova, A. Gusdorf

We present a catalog of 315 protostellar outflow candidates detected in SiO J=5-4 in the ALMA-IMF Large Program, observed with ~2000 au spatial resolution, 0.339 km/s velocity resolution, and 2-12 mJy/beam (0.18-0.8 K) sensitivity. We find median outflow masses, momenta, and kinetic energies of ~0.3 M$_{\odot}$, 4 M$_{\odot}$ km/s, and 10$^{45}$ erg, respect

Xinmiao Luan, Qing Zou, Jian Li, Andi Wang

The development of the manufacturing systems has made it increasingly necessary to monitor the data generated by multiple interconnected subsystems with rapid incoming of samples. Based on incremental Singular Value Decomposition (ISVD), we develop a general online monitoring approach for the relationship of data generated from two interconnected subsystems,

Pedram Agand, Allison Kennedy, Trevor Harris, Chanwoo Bae

As the importance of eco-friendly transportation increases, providing an efficient approach for marine vessel operation is essential. Methods for status monitoring with consideration to the weather condition and forecasting with the use of in-service data from ships requires accurate and complete models for predicting the energy efficiency of a ship. The mod

Alexander Austregesilo

The GlueX experiment at Jefferson Lab was specifically designed for precision studies of the light-meson spectrum. For this purpose, a photon beam with energies up to 12 GeV is directed onto a liquid hydrogen target contained within a hermetic detector with near-complete neutral and charged particle coverage. Linear polarization of the photon beam with a max

Philip Quirke, Fazl Barez

Understanding the inner workings of machine learning models like Transformers is vital for their safe and ethical use. This paper provides a comprehensive analysis of a one-layer Transformer model trained to perform n-digit integer addition. Our findings suggest that the model dissects the task into parallel streams dedicated to individual digits, employing

Yuduo Wang, Pedram Ghamisi

In recent years, with the rapid advancement of transformer models, transformer-based multimodal architectures have found wide application in various downstream tasks, including but not limited to Image Captioning, Visual Question Answering (VQA), and Image-Text Generation. However, contemporary approaches to Remote Sensing (RS) VQA often involve resource-int

Bangbang Yang, Wenqi Dong, Lin Ma, Wenbo Hu

Diffusion-based methods have achieved prominent success in generating 2D media. However, accomplishing similar proficiencies for scene-level mesh texturing in 3D spatial applications, e.g., XR/VR, remains constrained, primarily due to the intricate nature of 3D geometry and the necessity for immersive free-viewpoint rendering. In this paper, we propose a nov

Lorenzo Iorio, Bahram Mashhoon

The general relativistic gravitomagnetic clock effect, in its simplest form, consists of the non-vanishing difference in the orbital periods of two counter-orbiting objects moving in opposite directions along circular orbits lying in the equatorial plane of a central rotating source. We briefly review both the theoretical and observational aspects of such an

Erwan Hardy, Jonathan Poree, Hatim Belgharbi, Chloe Bourquin

Ultrasound Localization Microscopy (ULM) has recently enabled the mapping of the cerebral vasculature in vivo with a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20.000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer ra

Zhihao Wang

When the quantum parameter $q^{\frac{1}{2}}$ is a root of unity of odd order and the punctured bordered surface has nonempty boundary, we prove the fraction ring of the stated skein algebra (that is the localization over all nonzero elements) is a symmetric Frobenius algebra over both the field of fractions of the image of the Frobenius map and the field of

Kevin O'Neill

The purpose of this paper is to address a manifold-based version of Whitney's extension problem: Given a compact set $E\subset\mathbb{R}^n$, how can we tell if there exists a $d$-dimensional, $C^m$-smooth manifold $\mathcal{M}\supset E$? We provide an answer for compact manifolds with boundary in terms of a Glaeser refinement much like that used in the solut

Shohini Bhattacharya, Krzysztof Cichy, Martha Constantinou, Jack Dodson

Recently, we made significant advancements in improving the computational efficiency of lattice QCD calculations for Generalized Parton Distributions (GPDs). This progress was achieved by adopting calculations of matrix elements in asymmetric frames, deviating from the computationally-expensive symmetric frame typically used, and allowing freedom in the choi

Farzan Sepahi, Roberto Verzicco, Detlef Lohse, Dominik Krug

Direct numerical simulations are utilised to investigate mass transfer processes at gas-evolving electrodes that experience successive formation and detachment of bubbles. The gas-liquid interface is modeled employing an Immersed Boundary Method. We simulate the growth phase of the bubbles followed by their departure from the electrode surface in order to st

Seraphim Jarov, Mark Van Raamsdonk

For a quantum system with Hilbert space ${\cal H}$ of dimension $N$ and a set $S$ of $n$ Hermitian operators ${\cal O}_i$, a basic question is to understand the set $E_S \subset \mathbb{R}^n$ of points $\vec{e}$ where $e_i = {\rm tr}(\rho {\cal O}_i)$ for an allowed state $\rho$. A related question is to determine whether a given set of expectation values $\

Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana, Jan Drgona

Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Recently, data-driven approaches such as Neural Ordinary Differential Equations (NODE) have shown promising results by leveraging the expressive power of neural networks to model unkn

Ilya Shirokov, Valentina Shirokova

In ${\cal N}=1$ SQED with $N_f$ flavors regularized by higher derivatives we obtain the four-loop beta function using a method based on calculating vacuum supergraphs. For this purpose we use a special C++ program which obtain contributions to the $\beta$-function from supergraphs without external legs in the form of integrals of double total derivatives. Af

Javed Hossain, Md. Touhidul Islam, Md. Taufiqul Haque Khan Tusar

Brain tumors are increasingly prevalent, characterized by the uncontrolled spread of aberrant tissues in the brain, with almost 700,000 new cases diagnosed globally each year. Magnetic Resonance Imaging (MRI) is commonly used for the diagnosis of brain tumors and accurate classification is a critical clinical procedure. In this study, we propose an efficient

Saeed Khan, Bryce A. Primavera, Richard P. Mirin, Sae Woo Nam

We demonstrate superconducting single-photon detectors that integrate signals locally at each pixel. This capability is realized by the monolithic integration of superconducting-nanowire single-photon detectors with Josephson electronics. The motivation is to realize superconducting sensor elements with integrating capabilities similar to their CMOS-sensor c

Zhuoer Wang, Yicheng Wang, Ziwei Zhu, James Caverlee

Question generation is a widely used data augmentation approach with extensive applications, and extracting qualified candidate answers from context passages is a critical step for most question generation systems. However, existing methods for candidate answer extraction are reliant on linguistic rules or annotated data that face the partial annotation issu

Simon Blais, Jonathan Poree, Gerardo Ramos-Palacios, Samuel Desmarais

The development of neurologically active drugs faces a challenge in treating brain diseases due to the blood-brain barrier's impermeability. High-intensity focused ultrasound can open the barrier in a targeted manner, expanding treatment options. However, this procedure risks brain damage and off-target effects without proper monitoring. Current monitoring m

Zhiru Zhu, Raul Castro Fernandez

Differential privacy (DP) enables private data analysis. In a typical DP deployment, controllers manage individuals' sensitive data and are responsible for answering analysts' queries while protecting individuals' privacy. They do so by choosing the privacy parameter $\epsilon$, which controls the degree of privacy for all individuals in all possible dataset

Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been compreh

Gabriele Corso, Yilun Xu, Valentin de Bortoli, Regina Barzilay

In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time. However, generative models are often sampled multiple times to obtain a diverse set incurring a cost that is orthogonal to sampling time. We tackle the question of how to improve diversity and sample efficiency by moving be

Amy M. Schertz

Excited vector mesons, some of which are predicted to include gluonic excitation in their wavefunctions, and certain classes of axial vector mesons, some of which are implicated in the decay of spin-exotic mesons, decay dominantly to vector-pseudoscalar final states. In particular, the axial vector $b_1(1235)$ decays dominantly to $\omega\pi$, and is produce

Adrien Green, Jeremy Lawrence, George Siopsis, Nicholas Peters

Hydropower facilities are often remotely monitored or controlled from a centralized remote-control room. Additionally, major component manufacturers monitor the performance of installed components. While these communications enable efficiencies and increased reliability, they also expand the cyber-attack surface. Communications may use the internet to remote

Sergey Berezin, Reza Farahbakhsh, Noel Crespi

We introduce a simple yet efficient sentence-level attack on black-box toxicity detector models. By adding several positive words or sentences to the end of a hateful message, we are able to change the prediction of a neural network and pass the toxicity detection system check. This approach is shown to be working on seven languages from three different lang

Piotr Gramacki, Kacper Leśniara, Kamil Raczycki, Szymon Woźniak

Spatial Representations for Artificial Intelligence (srai) is a Python library for working with geospatial data. The library can download geospatial data, split a given area into micro-regions using multiple algorithms and train an embedding model using various architectures. It includes baseline models as well as more complex methods from published works. T

Meng Shang, Camilla De Bleecker, Jos Vanrenterghem, Roel De Ridder

Monitoring the number of jumps for volleyball players during training or a match can be crucial to prevent injuries, yet the measurement requires considerable workload and cost using traditional methods such as video analysis. Also, existing methods do not provide accurate differentiation between different types of jumps. In this study, an unobtrusive system

STAR Collaboration, M. I. Abdulhamid, B. E. Aboona, J. Adam

For the search of the chiral magnetic effect (CME), STAR previously presented the results from isobar collisions (${^{96}_{44}\text{Ru}}+{^{96}_{44}\text{Ru}}$, ${^{96}_{40}\text{Zr}}+{^{96}_{40}\text{Zr}}$) obtained through a blind analysis. The ratio of results in Ru+Ru to Zr+Zr collisions for the CME-sensitive charge-dependent azimuthal correlator ($\Delt

Brice Le Grignou, Victor Roca i Lucio

Algebraic operads provide a powerful tool to understand the homotopy theory of the types of (co)algebras they encode. So far, the principal results and methods that this theory provides were only available in characteristic zero. The reason is that operads carry an action of all the symmetric groups, whose representation theory becomes much more involved in

Toshikazu Natsume, Ryszard Nest

We study an analogue of chirality operators associated with quantum walks on the binary tree. For those operators we introduce a K-theoretic invariant, an analogue of the index of Fredholm operators, and compute its values in the ring of di-adic integers

Thomas Amestoy, Naty Sidaty, Wassim Hamidouche, Pierrick Philippe

In recent years, the proliferation of multimedia applications and formats, such as IPTV, Virtual Reality (VR, 360-degree), and point cloud videos, has presented new challenges to the video compression research community. Simultaneously, there has been a growing demand from users for higher resolutions and improved visual quality. To further enhance coding ef

Claire Barale, Michael Rovatsos, Nehal Bhuta

Language Models (LMs) have proven their ability to acquire diverse linguistic knowledge during the pretraining phase, potentially serving as a valuable source of incidental supervision for downstream tasks. However, there has been limited research conducted on the retrieval of domain-specific knowledge, and specifically legal knowledge. We propose to explore

Jeffrey Mao, Jennifer Yeom, Suraj Nair, Giuseppe Loianno

Aerial robots are required to remain operational even in the event of system disturbances, damages, or failures to ensure resilient and robust task completion and safety. One common failure case is propeller damage, which presents a significant challenge in both quantification and compensation. We propose a novel adaptive control scheme capable of detecting

Tianqi Zheng, John W. Simpson-Porco, Enrique Mallada

Motion planning and control are two core components of the robotic systems autonomy stack. The standard approach to combine these methodologies comprises an offline/open-loop stage, planning, that designs a feasible and safe trajectory to follow, and an online/closed-loop stage, tracking, that corrects for unmodeled dynamics and disturbances. Such an approac

Richard Lechner, Pavlos Motakis, Paul F. X. Müller, Thomas Schlumprecht

Let $(h_I)$ denote the standard Haar system on $[0,1]$, indexed by $I\in \mathcal D$, the set of dyadic intervals and $h_I\otimes h_J$ denote the tensor product $(s,t)\mapsto h_I(s) h_J(t)$, $I,J\in \mathcal D$. We consider a class of two-parameter function spaces which are completions of the linear span $\mathcal{V}(\delta^2)$ of $h_I\otimes h_J$, $I,J\in \

Jacob Trauger, Ambuj Tewari

This paper provides norm-based generalization bounds for the Transformer architecture that do not depend on the input sequence length. We employ a covering number based approach to prove our bounds. We use three novel covering number bounds for the function class of bounded linear transformations to upper bound the Rademacher complexity of the Transformer. F

Matthew Macauley

In this paper, we take the classic dihedral and quaternion groups and explore questions like "what if we replace $i=e^{2\pi i/4}$ in $Q_8$ with a larger root of unity?" and "what if we add a reflection to $Q_8$?" The delightful answers reveal lesser-known families like the dicyclic, diquaternion, semidihedral, and semiabelian groups, which come to life with

Stefanos Theodorakopoulos

In this paper we present new, short and elementary proofs of the famous projection and section theorems that are used in Stochastic Calculus.

Atik Faysal, Mohammad Rostami, Huaxia Wang, Avimanyu Sahoo

Few-shot learning or meta-learning leverages the data scarcity problem in machine learning. Traditionally, training data requires a multitude of samples and labeling for supervised learning. To address this issue, we propose a one-shot unsupervised meta-learning to learn the latent representation of the training samples. We use augmented samples as the query

Zhi-Hao Cui, Arkajit Mandal, David R. Reichman

We apply the Lang-Firsov (LF) transformation to electron-boson coupled Hamiltonians and variationally optimize the transformation parameters and molecular orbital coefficients to determine the ground state. M\o{}ller-Plesset (MP-$n$, with $n = 2$ and $4$) perturbation theory is then performed on top of the optimized LF mean-field state to improve the descrip

Maram Sakr, Zhikai Zhang, Benjamin Li, Haomiao Zhang

Learning from Demonstration (LfD) is a framework that allows lay users to easily program robots. However, the efficiency of robot learning and the robot's ability to generalize to task variations hinges upon the quality and quantity of the provided demonstrations. Our objective is to guide human teachers to furnish more effective demonstrations, thus facilit

Nemanja Draganić, Rajko Nenadov

We consider the problem of finding edge-disjoint paths between given pairs of vertices in a sufficiently strong $d$-regular expander graph $G$ with $n$ vertices. In particular, we describe a deterministic, polynomial time algorithm which maintains an initially empty collection of edge-disjoint paths $\mathcal P$ in $G$ and fulfills any series of two types of

Diego Marcondes, Adilson Simonis

Metastability is a phenomenon observed in stochastic systems which stay in a false-equilibrium within a region of its state space until the occurrence of a sequence of rare events that leads to an abrupt transition to a different region. This paper presents financial markets as metastable systems and shows that, under this assumption, financial time series e

Shivani Kumar, Ramaneswaran S, Md Shad Akhtar, Tanmoy Chakraborty

Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable research has focused on discerning emotions of individual speakers in monolingual dialogues, understanding the emotional dynamics in code-mixed conversations has received relatively

Sònia Leal Díaz, Sergio Pastrana, Azqa Nadeem

Although intrusion alerts can provide threat intelligence regarding attacker strategies, extracting such intelligence via existing tools is expensive and time-consuming. Earlier work has proposed SAGE, which generates attack graphs from intrusion alerts using unsupervised sequential machine learning. This paper proposes a querying and prioritization-enabled

Justin C. Goodrich, Ryan Mahon, Joseph Hanrahan, Monika Dziubelski

Spontaneous parametric down-conversion is a vital method for generating correlated photon pairs in the visible and near-infrared spectral regions; however, its extension to X-ray frequencies has faced substantial barriers. Here, we present an advancement in correlated X-ray pair generation and detection by employing a two-dimensional pixelated detector to ob

Kaustab Pal, Aditya Sharma, Mohd Omama, Parth N. Shah

In this paper we show an effective means of integrating data driven frameworks to sampling based optimal control to vastly reduce the compute time for easy adoption and adaptation to real time applications such as on-road autonomous driving in the presence of dynamic actors. Presented with training examples, a spatio-temporal CNN learns to predict the optima

Chong Xiang, Tong Wu, Sihui Dai, Jonathan Petit

State-of-the-art defenses against adversarial patch attacks can now achieve strong certifiable robustness with a marginal drop in model utility. However, this impressive performance typically comes at the cost of 10-100x more inference-time computation compared to undefended models -- the research community has witnessed an intense three-way trade-off betwee

Kayol Soares Mayer, Jonathan Aguiar Soares, Ariadne Arrais Cruz, Dalton Soares Arantes

Complex-valued neural networks (CVNNs) are nonlinear filters used in the digital signal processing of complex-domain data. Compared with real-valued neural networks~(RVNNs), CVNNs can directly handle complex-valued input and output signals due to their complex domain parameters and activation functions. With the trend toward low-power systems, computational

Amama Mahmood, Chien-Ming Huang

Commercial voice assistants are largely feminized and associated with stereotypically feminine traits such as warmth and submissiveness. As these assistants continue to be adopted for everyday uses, it is imperative to understand how the portrayed gender shapes the voice assistant's ability to mitigate errors, which are still common in voice interactions. We

Parth Padalkar, Gopal Gupta

Within the realm of deep learning, the interpretability of Convolutional Neural Networks (CNNs), particularly in the context of image classification tasks, remains a formidable challenge. To this end we present a neurosymbolic framework, NeSyFOLD-G that generates a symbolic rule-set using the last layer kernels of the CNN to make its underlying knowledge int

Kala Agbo Bidi, Jean-Michel Coron, Amaury Hayat, Nathan Lichtlé

One of the central questions in control theory is achieving stability through feedback control. This paper introduces a novel approach that combines Reinforcement Learning (RL) with mathematical analysis to address this challenge, with a specific focus on the Sterile Insect Technique (SIT) system. The objective is to find a feedback control that stabilizes t

Saumya Ghosh, Arnab Acharya, Sunandan Gangopadhyay, Prasanta K. Panigrahi

Motivated by the recent development in quantum cosmology, we revisit the anisotropic Kantowski-Sachs model in the light of a Lorentzian path integral formalism. Studies so far have considered the Euclidean method where the choice of the lapse integration contour is constrained by certain physical considerations rather than mathematical justification. In this

Alexander Baur, Marcos A. G. Garcia, Raul Henriquez-Ortiz, Mauricio Hernandez-Neri

Forthcoming missions probing the absolute intensity of the CMB are expected to be able to measure spectral distortions, which are deviations from its blackbody distribution. As cosmic inflation can induce spectral distortions, these experiments offer a possibility to further test the various promising inflationary proposals, whose predictions need to be care

Chao Zhang, Xin Qian, Muriel Fallot

Reactor antineutrinos have played a significant role in establishing the standard model of particle physics and the theory of neutrino oscillations. In this article, we review the reactor antineutrino flux and in particular the reactor antineutrino anomaly (RAA) coined over a decade ago. RAA refers to a deficit of the measured antineutrino inverse beta decay

Hou-Zun Chen, Xi Kang, Andrea V. Macciò, Tobias Buck

We utilize the public GIZMO code to simulate twelve disc galaxies from the NIHAO suite simulated with the GASOLINE code, then compare the corresponding galaxies in the two simulations. We find that while both codes with the same initial conditions and large-scale environments can successfully produce similar disc galaxies, significant differences are still s

Muhammad Asif Ali, Maha Alshmrani, Jianbin Qin, Yan Hu

Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on the relative isomorphism of individual embedding spaces. Existing attempts aimed at controlling the relative isomorphism of different embedding spaces fail to incorporate the impact of semantically related words in the model training objective. To address this, we propose GARI that co

Dylan Fillmore, Bennet Goeckner, Rachel Kirsch, Kirin Martin

A universal partial cycle (or upcycle) for $\mathcal{A}^n$ is a cyclic sequence that covers each word of length $n$ over the alphabet $\mathcal{A}$ exactly once -- like a De Bruijn cycle, except that we also allow a wildcard symbol $\mathord{\diamond}$ that can represent any letter of $\mathcal{A}$. Chen et al. in 2017 and Goeckner et al. in 2018 showed that

Rintaro Kirikawa, Takahiro Sumi, David P. Bennett, Daisuke Suzuki

We present the analysis of microlensing event OGLE-2014-BLG-0221, a planetary candidate event discovered in 2014. The photometric light curve is best described by a binary-lens single-source model. Our light curve modeling finds two degenerate models, with event timescales of $t_\mathrm{E}\sim70$ days and $\sim110$ days. These timescales are relatively long,

Mengdi Xu, Peide Huang, Wenhao Yu, Shiqi Liu

Tool use is a hallmark of advanced intelligence, exemplified in both animal behavior and robotic capabilities. This paper investigates the feasibility of imbuing robots with the ability to creatively use tools in tasks that involve implicit physical constraints and long-term planning. Leveraging Large Language Models (LLMs), we develop RoboTool, a system tha

Taylor Brysiewicz, Aida Maraj

We express the maximum likelihood (ML) degrees of a family toric varieties in terms of Mobius invariants of matroids. The family of interest are those parametrized by monomial maps given by Lawrence lifts of totally unimodular matrices with even circuits. Specifying these matrices to be vertex-edge incidence matrices of bipartite graphs gives the ML degrees

E. O. Ofek, Y. Shvartzvald, A. Sharon, C. Tishler

The Large Array Survey Telescope (LAST) is a wide-field telescope designed to explore the variable and transient sky with a high cadence and to be a test-bed for cost-effective telescope design. A LAST node is composed of 48 (32 already deployed), 28-cm f/2.2 telescopes. A single telescope has a 7.4 deg^2 field of view and reaches a 5-sigma limiting magnitud

Xuan Chen, Petr Jakubčík, Matteo Marcoli, Giovanni Stagnitto

We compute the quantum chromodynamics (QCD) corrections to the decay of a neutralino to gluinos and partons at next-to-next-to-next-to-leading order (N$^3$LO) in the strong coupling constant $\alpha_s$, integrated separately over the phase-space of two, three, four or five particles in the final state. The resulting matrix elements are related to the quark-g

Darshil Doshi, Aritra Das, Tianyu He, Andrey Gromov

Robust generalization is a major challenge in deep learning, particularly when the number of trainable parameters is very large. In general, it is very difficult to know if the network has memorized a particular set of examples or understood the underlying rule (or both). Motivated by this challenge, we study an interpretable model where generalizing represe

Yanou Cui, Pankaj Saha, Evangelos I. Sfakianakis

We investigate a generic source of stochastic gravitational wave background due to the parametric resonance of oscillating scalar fields in the early Universe. By systematically analyzing benchmark models through lattice simulations and considering a wide range of parameters, we demonstrate that such a scenario can lead to detectable signals in gravitational

Felix Janda, Tony Yue Yu

We introduce Gromov-Witten invariants with naive tangency conditions at the marked points of the source curve. We then establish an explicit formula which expresses Gromov-Witten invariants with naive tangency conditions in terms of descendent Gromov-Witten invariants. Several examples of genus zero Gromov-Witten invariants with naive tangencies are computed

Jorge G. Russo, Miguel Tierz

New exact formulas are derived for systems involving Landau-Zener transition rates and for absorption spectra in quantum dots. These rectify previous inaccurate approximations utilized in experimental studies. The exact formulas give an explicit expression for the maxima and minima of the transition rate at any oscillating period and reveal a number of strik

Marat Freytsis, Maxim Perelstein, Yik Chuen San

Experiments at particle colliders are the primary source of insight into physics at microscopic scales. Searches at these facilities often rely on optimization of analyses targeting specific models of new physics. Increasingly, however, data-driven model-agnostic approaches based on machine learning are also being explored. A major challenge is that such met

Mario Cadelano, Cristina Pallanca, Emanuele Dalessandro, Maurizio Salaris

We used ultra-deep observations obtained with the NIRCam aboard the James Webb Space Telescope to explore the stellar population of NGC 6440: a typical massive, obscured and contaminated globular cluster formed and orbiting within the Galactic bulge. Leveraging the exceptional capabilities of this camera, we sampled the cluster down to ~5 magnitudes below th

R. A. Jackson, S. Kaviraj, S. K. Yi, S. Peirani

The `core-cusp' problem is considered a key challenge to the LCDM paradigm. Halos in dark matter only simulations exhibit `cuspy' profiles, where density continuously increases towards the centre. However, the dark matter profiles of many observed galaxies (particularly in the dwarf regime) deviate strongly from this prediction, with much flatter central reg

Danilo González-Díaz, José G. Fernández-Trincado, Sandro Villanova, Doug Geisler

This study presents the results concerning six red giant stars members of the globular cluster NGC 6558. Our analysis utilized high-resolution near-infrared spectra obtained through the CAPOS initiative (the APOgee Survey of Clusters in the Galactic Bulge), which focuses on surveying clusters within the Galactic Bulge, as a component of the Apache Point Obse

A. Jiménez-Rosales, A. I. Yfantis, M. A. Mościbrodzka, J. Dexter

We apply image moment invariant analysis to total intensity and polarimetric images calculated from general relativistic magnetohydrodynamic simulations of accreting black holes. We characterize different properties of the models in our library by their invariant distributions and their evolution in time. We show that they are highly sensitive to different p

Federico Biassoni, Andrea Caldiroli, Elena Gallo, Francesco Haardt

Absorption of stellar X-ray and Extreme Ultraviolet radiation in the upper atmosphere of close-in exoplanets can give rise to hydrodynamic outflows, which may lead to the gradual shedding of their primordial, light element envelopes. Excess absorption by neutral helium atoms in the metastable state has recently emerged as a viable diagnostic of atmospheric e

Luca Naterop, Peter Stoffer

The low-energy effective field theory below the electroweak scale (LEFT) describes the effects at low energies of both the weak interaction and physics beyond the Standard Model. We study the one-loop renormalization of the LEFT in the 't Hooft-Veltman scheme, which offers an algebraically consistent definition of the Levi-Civita symbol and $\gamma_5$ in dim

Vedant Chandra, Vadim A. Semenov, Hans-Walter Rix, Charlie Conroy

We illustrate the formation and evolution of the Milky Way over cosmic time, utilizing a sample of 10 million red giant stars with full chemodynamical information, including metallicities and $\alpha$-abundances from low-resolution Gaia XP spectra. The evolution of angular momentum as a function of metallicity - a rough proxy for stellar age, particularly fo

Arthur J. Parzygnat, James Fullwood, Francesco Buscemi, Giulio Chiribella

The quantum no-broadcasting theorem states that it is impossible to produce perfect copies of an arbitrary quantum state, even if the copies are allowed to be correlated. Here we show that, although quantum broadcasting cannot be achieved by any physical process, it can be achieved by a virtual process, described by a Hermitian-preserving trace-preserving ma

Charlie Conroy, Benjamin D. Johnson, Pieter van Dokkum, Alis Deason

We report the serendipitous discovery of an extended stellar halo surrounding the low-mass galaxy Ark 227 ($M_\ast=5\times10^9 M_\odot$; d=35 Mpc) in deep JWST NIRCam imaging from the Blue Jay Survey. The F200W-F444W color provides robust star-galaxy separation, enabling the identification of stars at very low density. By combining resolved stars at large ga

Pietro Pelliconi, Julian Sonner

Open quantum systems are defined as ordinary unitary quantum theories coupled to a set of external degrees of freedom, which are introduced to take on the r\^ole of an unobserved environment. Here we study examples of open quantum field theories, with the aid of the so-called Feynman- Vernon Influence Functional (IF), including field theories that arise in h

Tzu-Chi Hsieh, Leo Radzihovsky

A unidirectional "density" wave order in an otherwise isotropic environment is guaranteed to display a smecticlike Goldstone mode. Examples of such "soft" states include conventional smectic liquid crystals, putative Fulde-Ferrell-Larkin-Ovchinnikov superfluids, and helical states of frustrated bosons and spins. Here we develop generalized spin-smectic $\sig

Michał Piotrak, Marek Kopciuch, Arash Dezhang Fard, Magdalena Smolis

In this paper we introduce and investigate the concept of a perfect quantum protractor, a pure quantum state $|\psi\rangle\in\mathcal{H}$ that generates three different orthogonal bases of $\mathcal{H}$ under rotations around each of the three perpendicular axes. Such states can be understood as pure states of maximal uncertainty with regards to the three co

Ahmed Barbar, Anatoly Dymarsky, Alfred D. Shapere

We consider Abelian topological quantum field theories (TQFTs) in 3d and show that gaugings of invertible global symmetries naturally give rise to additive codes. These codes emerge as nonanomalous subgroups of the 1-form symmetry group, parameterizing the fusion rules of condensable TQFT anyons. The boundary theories dual to TQFTs with the anyons condensed,

Gonzalo Alonso-Álvarez, Miguel Escudero

Motivated by the recent evidence for $B^+\to K^+\bar{\nu} \nu$ decays at Belle II, we point out that fully invisible $B_d$ and $B_s$ meson decays are strongly constrained by LEP. A reinterpretation of an old inclusive ALEPH search for $b$-hadron decays with large missing energy allows us to place the limits $\mathrm{Br}(B_d \rightarrow \mathrm{invisible}) <

Christian Herwig, Joshua Isaacson, Bo Jayatilaka, Pedro A. N. Machado

The origin of the excess of low-energy events observed by the MiniBooNE experiment remains a mystery, despite exhaustive investigations of backgrounds and a series of null measurements from complementary experiments. One intriguing explanation is the production of beyond-the-Standard-Model particles that could mimic the experimental signature of additional $

Quentin Bonnefoy, Gauthier Durieux, Jasper Roosmale Nepveu

We extend the covariant color-kinematics duality introduced by Cheung and Mangan to effective field theories. We focus in particular on relations between the effective field theories of gluons only and of gluons coupled to bi-adjoint scalars. Maps are established between their respective equations of motion and between their tree-level scattering amplitudes.

Ho Kei Cheng, Seoung Wug Oh, Brian Price, Joon-Young Lee

We present Cutie, a video object segmentation (VOS) network with object-level memory reading, which puts the object representation from memory back into the video object segmentation result. Recent works on VOS employ bottom-up pixel-level memory reading which struggles due to matching noise, especially in the presence of distractors, resulting in lower perf

Linnea Grans-Samuelsson, Ryan V. Mishmash, David Aasen, Christina Knapp

We devise a new realization of the surface code on a rectangular lattice of qubits utilizing single-qubit and nearest-neighbor two-qubit Pauli measurements and three auxiliary qubits per plaquette. This realization gains substantial advantages over prior pairwise measurement-based realizations of the surface code. It has a short operation period of 4 steps a

Florent Baume, Jonathan J. Heckman, Max Hübner, Ethan Torres

The global symmetries of a $D$-dimensional QFT can, in many cases, be captured in terms of a $(D+1)$-dimensional symmetry topological field theory (SymTFT). In this work we construct a $(D+1)$-dimensional theory which governs the symmetries of QFTs with multiple sectors which have connected correlators that admit a decoupling limit. The associated symmetry f

Jeffrey Ouyang-Zhang, Daniel J. Diaz, Adam R. Klivans, Philipp Krähenbühl

Stabilizing proteins is a foundational step in protein engineering. However, the evolutionary pressure of all extant proteins makes identifying the scarce number of mutations that will improve thermodynamic stability challenging. Deep learning has recently emerged as a powerful tool for identifying promising mutations. Existing approaches, however, are compu

Shunlin Lu, Ling-Hao Chen, Ailing Zeng, Jing Lin

This work targets a novel text-driven whole-body motion generation task, which takes a given textual description as input and aims at generating high-quality, diverse, and coherent facial expressions, hand gestures, and body motions simultaneously. Previous works on text-driven motion generation tasks mainly have two limitations: they ignore the key role of

Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

The study of Deep Network (DN) training dynamics has largely focused on the evolution of the loss function, evaluated on or around train and test set data points. In fact, many DN phenomenon were first introduced in literature with that respect, e.g., double descent, grokking. In this study, we look at the training dynamics of the input space partition or li

Yinpeng Chen, Dongdong Chen, Xiyang Dai, Mengchen Liu

In this paper, we empirically reveal an invariance over images-images share a set of one-way wave equations with latent speeds. Each image is uniquely associated with a solution to these wave equations, allowing for its reconstruction with high fidelity from an initial condition. We demonstrate it using an intuitive encoder-decoder framework where each image

Rembert Daems, Manfred Opper, Guillaume Crevecoeur, Tolga Birdal

We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM). SDEs offer a versatile tool for modeling real-world continuous-time dynamic systems with inherent noise and randomness. Combining SDEs with the powerful inference capabilities of

Jonathan Crabbé, Pau Rodríguez, Vaishaal Shankar, Luca Zappella

What distinguishes robust models from non-robust ones? While for ImageNet distribution shifts it has been shown that such differences in robustness can be traced back predominantly to differences in training data, so far it is not known what that translates to in terms of what the model has learned. In this work, we bridge this gap by probing the representat

Mayank Lunayach, Sergey Zakharov, Dian Chen, Rares Ambrus

In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data. Our goal is to predict the 3D shape, size, and 6D pose of objects within a single RGB-D image, operating at the category level and eliminating the need for CAD models during inference. While existing self-supervised methods have made str

Ziqi Pang, Ziyang Xie, Yunze Man, Yu-Xiong Wang

This paper reveals that large language models (LLMs), despite being trained solely on textual data, are surprisingly strong encoders for purely visual tasks in the absence of language. Even more intriguingly, this can be achieved by a simple yet previously overlooked strategy -- employing a frozen transformer block from pre-trained LLMs as a constituent enco

Lijuan Zhou, Xiang Meng, Zhihuan Liu, Mengqi Wu

Human pose analysis has garnered significant attention within both the research community and practical applications, owing to its expanding array of uses, including gaming, video surveillance, sports performance analysis, and human-computer interactions, among others. The advent of deep learning has significantly improved the accuracy of pose capture, makin

Liyiming Ke, Yunchu Zhang, Abhay Deshpande, Siddhartha Srinivasa

We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding errors and disturbances. While existing methods rely on interactive expert labeling, additional offline datasets, or domain-specific invariances, our approach requires minimal additional assumptions beyond access to expe

David Chan, Suzanne Petryk, Joseph E. Gonzalez, Trevor Darrell

The evaluation of machine-generated image captions poses an interesting yet persistent challenge. Effective evaluation measures must consider numerous dimensions of similarity, including semantic relevance, visual structure, object interactions, caption diversity, and specificity. Existing highly-engineered measures attempt to capture specific aspects, but f