March 2024 arXiv papers — page 176

Raven Beutner

Hyperproperties relate multiple executions of a program and are commonly used to specify security and information-flow policies. Most existing work has focused on the verification of $k$-safety properties, i.e., properties that state that all $k$-tuples of execution traces satisfy a given property. In this paper, we study the automated verification of richer

Jingzhi Gong, Tao Chen

Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern software, modeling and predicting how various configurations can impact performance becomes one of the major challenges in software maintenance. As such, performance is often modeled with

Kensuke Arakawa

We develop Weiss's manifold calculus in the setting of $\infty$-categories, where we allow the target $\infty$-category to be any $\infty$-category with small limits. We will establish the connection between polynomial functors, Kan extensions, and Weiss sheaves, and will classify homogeneous functors. We will also generalize Weiss and Boavida de Brito's the

Antti Autio, Henrik Garde, Markus Hirvensalo, Nuutti Hyvönen

This work implements and numerically tests the direct reconstruction algorithm introduced in [Garde & Hyv\"onen, SIAM J. Math. Anal., 2024] for two-dimensional linearized electrical impedance tomography. Although the algorithm was originally designed for a linearized setting, we numerically demonstrate its functionality when the input data is the correspondi

Francesco Amoroso, Lea Terracini

In 2013 P. Habegger proved the Bogomolov property for the field generated over Q by the torsion points of a rational elliptic curve. We explore the possibility of applying the same strategy of proof to the case of field extensions fixed by the kernel of some modular Galois representations.

Zahir Alsulaimawi

This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a simplified consensus-based verification process integrated with an adaptive thresholding mechanism. This dynamic thresholding is designed to adjust based on the evolving landscape of model updates, offering a refined layer of

Diek W. Wheeler, Giorgio A. Ascoli

Many fields, such as neuroscience, are experiencing the vast proliferation of cellular data, underscoring the need for organizing and interpreting large datasets. A popular approach partitions data into manageable subsets via hierarchical clustering, but objective methods to determine the appropriate classification granularity are missing. We recently introd

Seungjin Han, Andrew Leal

This paper proposes Competing Mechanism Games Played Through Agent (CMGPTA), an extension of the GPTA (Prat and Rustichini (2003)), where a Principal can offer any arbitrary mechanism that specifies a transfer schedule for each agent conditional on all Agents' messages. We identify the set of equilibrium allocations using deviator-reporting mechanisms (DRMs)

Kye E. Hunter, Yuezhi Mao, Alex W. Chin, Tim J. Zuehlsdorff

Nonadiabatic couplings between several electronic excited states are ubiquitous in many organic chromophores and can significantly influence optical properties. A recent experimental study demonstrated that the proflavine molecule exhibits surprising dual fluorescence in the gas phase, that is suppressed in polar solvent environments. Here, we uncover the or

Jonas Richter

Strong disorder inhibits thermalization in isolated quantum systems and may lead to many-body localization (MBL). In realistic situations, however, the observation of MBL is hindered by residual couplings of the system to an environment, which acts as a bath and pushes the system to thermal equilibrium. This paper is concerned with the transient dynamics pri

Abyay Ghosh, Piotr Chudzinski, Myrta Grüning

We calculated the structural, electronic and magnetic properties of FeSe within density-functional theory at the generalized gradient approximation level. First, we studied how the bandwidth of the d-bands at the Fermi energy are renormalized by adding simple corrections: Hubbard U, Hunds J and by introducing long-range magnetic orders. We found that introdu

Zihao Dong, Shayegan Omidshafiei, Michael Everett

For many multiagent control problems, neural networks (NNs) have enabled promising new capabilities. However, many of these systems lack formal guarantees (e.g., collision avoidance, robustness), which prevents leveraging these advances in safety-critical settings. While there is recent work on formal verification of NN-controlled systems, most existing tech

Aleksander Ćwiszewski, Sławomir Plaskacz

Single species fisheries and prey-predator models with marine protected areas (MPA) are studied. The single species case is considered when the fishing effort is around the species extinction threshold and the influence of implementing MPA on catch quantity are studied. In the prey-predator fishery model, the situation with the fishing effort close to the pr

Aamir Hasan, D. Livingston McPherson, Melissa Miles, Katherine Driggs-Campbell

Distracted driving is a major cause of road fatalities. With improvements in driver (in)attention detection, these distracted situations can be caught early to alert drivers and improve road safety and comfort. However, drivers may have differing preferences for the modes of such communication based on the driving scenario and their current distraction state

Gabriela Wojtkowska, Radosław Poleski

We verify candidate hypervelocity red clump stars located in the Galactic bulge that were selected based on the VVV and the Gaia DR2 data by Luna et al. (2019). To do so, we analyze data from the OGLE-IV survey: difference images and astrometric time series. We have data for 30 stars out of 34 hypervelocity candidates. We confirmed high proper motion of only

Zhiding Liang, Gang Liu, Zheyuan Liu, Jinglei Cheng

In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods. Among these, the Quantum Approximate Optimization Algorithm (QAOA) stands out for its potential to efficiently solve the Max-Cut

Sagi Eppel, Jolina Li, Manuel Drehwald, Alan Aspuru-Guzik

Visual recognition of materials and their states is essential for understanding the physical world, from identifying wet regions on surfaces or stains on fabrics to detecting infected areas on plants or minerals in rocks. Collecting data that captures this vast variability is complex due to the scattered and gradual nature of material states. Manually annota

Valerie S. Lang, Naman Kumar Bhalla, Simran Sunil Gurdasani, Pardis Niknejadi

As the climate crisis intensifies, understanding the environmental impact of professional activities is paramount, especially in sectors with historically significant resource utilisation. This includes High Energy Physics (HEP) and related fields, which investigate the fundamental laws of our universe. As members of the young High Energy Physicists (yHEP) a

Junling Wang, Jakub Macina, Nico Daheim, Sankalan Pal Chowdhury

Educational chatbots are a promising tool for assisting student learning. However, the development of effective chatbots in education has been challenging, as high-quality data is seldom available in this domain. In this paper, we propose a framework for generating synthetic teacher-student interactions grounded in a set of textbooks. Our approaches capture

Lanjing Liu, Chao Zhang, Zhicong Lu

Adolescent peer relationships, essential for their development, are increasingly mediated by digital technologies. As this trend continues, wearable devices, especially smartwatches tailored for adolescents, are reshaping their socialization. In China, smartwatches like XTC have gained wide popularity, introducing unique features such as "Bump-to-Connect" an

Robert Vacareanu, Fahmida Alam, Md Asiful Islam, Haris Riaz

This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. This approach capitalizes on the strengths of both paradigms: the adaptability of rule-based systems and the generalization power of neural networks. Our architecture consists of two component

Joseph Gatto, Parker Seegmiller, Omar Sharif, Sarah M. Preum

Event Argument Extraction (EAE) is an extremely difficult information extraction problem -- with significant limitations in few-shot cross-domain (FSCD) settings. A common solution to FSCD modeling is data augmentation. Unfortunately, existing augmentation methods are not well-suited to a variety of real-world EAE contexts including (i) The need to model lon

Manuel Rocamora, Yago Ascasibar, Miguel A. Sánchez-Conde, Maneenate Wechakama

We study the origin of the positron excess observed in the local cosmic-ray spectrum at high energies, and relate it to the cosmic rays and gamma-ray emission across the entire Galaxy. In particular, we explore the hypothesis of a single, dominant source accountable for primary electron-positron pairs. Since we are agnostic about the physical nature of the u

Roman Pico, Paula Abufager, Ignacio Hamad, Roberto Robles

The properties of 2D materials are strongly influenced by their substrate, leading to a variety of "proximity effects" like screening, charge transfer, and hybridization. Surprisingly, there is a dearth of theoretical studies on these effects. Particularly, previous theoretical research on the Star of David (SOD) structure in 1T-NbSe$_2$ has focused on singl

Frits Beukers

Many generating series of combinatorially interesting numbers have the property that the sum of the terms of order $<p$ at some suitable point is congruent to a zero of a zeta-function modulo infinitely many primes $p$. Surprisingly, very often these congruences turn out to hold modulo $p^2$ or even $p^3$. We call such congruences supercongruences and in the

Siddharth Savyasachi Malu

We discuss new techniques and ideas in mm-wave instrumentation that can be used in CMB (Cosmic Microwave background) polarization experiments. Novel techniques in antenna receiver, beam combining and detector systems have resulted in greatly improved sensitivities. We present a few promising approaches and discuss briefly plans for feasibility studies for de

Tanvi Shinkre, Chad Hazlett

Researchers frequently estimate treatment effects by regressing outcomes (Y) on treatment (D) and covariates (X). Even without unobserved confounding, the coefficient on D yields a conditional-variance-weighted average of strata-wise effects, not the average treatment effect. Scholars have proposed characterizing the severity of these weights, evaluating res

Soobin Cho, Renming Song

In this paper, we study Feynman-Kac semigroups of symmetric $\alpha$-stable processes with supercritical killing potentials belonging to a large class of functions containing functions of the form $b|x|^{-\beta}$, where $b>0$ and $\beta>\alpha$. We obtain two-sided estimates on the densities $p(t, x, y)$ of these semigroups for all $t>0$, along with estimate

JiWoong Jang, Sanika Moharana, Patrick Carrington, Andrew Begel

Autistic adults often experience stigma and discrimination at work, leading them to seek social communication support from coworkers, friends, and family despite emotional risks. Large language models (LLMs) are increasingly considered an alternative. In this work, we investigate the phenomenon of LLM use by autistic adults at work and explore opportunities

Katia Jodogne-Del Litto, Guillaume-Alexandre Bilodeau

Increasing the accuracy of instance segmentation methods is often done at the expense of speed. Using coarser representations, we can reduce the number of parameters and thus obtain real-time masks. In this paper, we take inspiration from the set cover problem to predict mask approximations. Given ground-truth binary masks of objects of interest as training

Ashwin Nayak, Pulkit Sinha

A basic question in the PAC model of learning is whether proper learning is harder than improper learning. In the classical case, there are examples of concept classes with VC dimension $d$ that have sample complexity $\Omega\left(\frac d\epsilon\log\frac1\epsilon\right)$ for proper learning with error $\epsilon$, while the complexity for improper learning i

Meghna Kalra, Maxime Ferreira Da Costa, Kiryung Lee

A fundamental small-noise sensitivity analysis of spike localization in the presence of adversarial perturbations and an arbitrary point spread function (PSF) is presented. The analysis leverages the local Lipschitz property of the inverse map from measurement noise to parameter estimate. In the small noise regime, the local Lipschitz constant converges to t

Anjalee De Silva, Janaka L. Wijekoon, Rashini Liyanarachchi, Rrubaa Panchendrarajan

This paper discusses the effectiveness of leveraging Chatbot: Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research papers for effective writing of scientific literature surveys. The study selected the \textit{Application of Artificial Intelligence in Breast Cancer Treatment} as the research topic. Research papers related to

Sai Aparna Aketi, Sakshi Choudhary, Kaushik Roy

State-of-the-art decentralized learning algorithms typically require the data distribution to be Independent and Identically Distributed (IID). However, in practical scenarios, the data distribution across the agents can have significant heterogeneity. In this work, we propose averaging rate scheduling as a simple yet effective way to reduce the impact of he

M. Sohaib Alam, Eleanor Rieffel

The Bacon-Shor code is a quantum error correcting subsystem code composed of weight 2 check operators that admits a single logical qubit, and has distance $d$ on a $d \times d$ square lattice. We show that when viewed as a Floquet code, by choosing an appropriate measurement schedule of the check operators, it can additionally host several dynamical logical

Hadi Khodabandeh, David Eppstein

We study the problem of maintaining a lightweight bounded-degree $(1+\varepsilon)$-spanner of a dynamic point set in a $d$-dimensional Euclidean space, where $\varepsilon>0$ and $d$ are arbitrary constants. In our fully-dynamic setting, points are allowed to be inserted as well as deleted, and our objective is to maintain a $(1+\varepsilon)$-spanner that has

Babak Tarighi, Reyhaneh Khasseh, M. A. Rajabpour

This paper introduces an innovative approach for representing Gaussian fermionic states, pivotal in quantum spin systems and fermionic models, within a range of alternative quantum bases. We focus on transitioning these states from the conventional computational (\sigma^z) basis to more complex bases, such as ((\phi, \frac{\pi}{2}, \alpha)), which are essent

Jianqiiu Zhang

In the rapidly evolving field of Large Language Models (LLMs), there is a critical need to thoroughly analyze their capabilities and risks. Central to our investigation are two novel elements. Firstly, it is the innovative parallels between the statistical patterns of word relationships within LLMs and Martin Heidegger's concepts of "ready-to-hand" and "pres

Keiichi Ohnaka, Gerd Weigelt, Karl-Heinz Hofmann, Dieter Schertl

Mass loss at the asymptotic giant branch (AGB) plays an important role not only in the final fates of stars, but also in the chemical evolution of galaxies. Nevertheless, the metallicity effects on AGB mass loss are not yet fully understood. We present spatially resolved observations of an AGB star, V3, in the metal-poor globular cluster 47 Tuc (NGC 104). Th

An-Jun Liu, Bryan K. Clark

The ground state of second-quantized quantum chemistry Hamiltonians provides access to an important set of chemical properties. Wavefunctions based on ML architectures have shown promise in approximating these ground states in a variety of physical systems. In this work, we show how to achieve state-of-the-art energies for molecular Hamiltonians using the th

Konstantinos C. Rigatos, Shaodong Zhou

We bootstrap the $4$-point amplitude of $\mathcal{N}=2$ hypermultiplets in $\text{AdS}_2 \times \text{S}^2$ at tree-level and for arbitrary external weights. We hereby explicitly demonstrate the existence of a hidden four-dimensional conformal symmetry that was used as an assumption in previous studies to derive this result.

Philipp Sohr, Philipp Koller, Sebastian Ecker, Matthias Fink

Quantum communication promises unprecedented capabilities enabled by the transmission of quantum states of light. However, current implementations face severe distance limitations due to photon loss. Silicon carbide (SiC) defects have emerged as a promising quantum device platform, offering strong optical transitions, long spin coherence lifetimes and the op

Aly M. Kassem, Omar Mahmoud, Niloofar Mireshghallah, Hyunwoo Kim

In this paper, we introduce a black-box prompt optimization method that uses an attacker LLM agent to uncover higher levels of memorization in a victim agent, compared to what is revealed by prompting the target model with the training data directly, which is the dominant approach of quantifying memorization in LLMs. We use an iterative rejection-sampling op

David Raveh, Rafael I. Nepomechie

We present an explicit quantum circuit that prepares an arbitrary $U(1)$-eigenstate on a quantum computer, including the exact eigenstates of the spin-1/2 XXZ quantum spin chain with either open or closed boundary conditions. The algorithm is deterministic, does not require ancillary qubits, and does not require QR decompositions. The circuit prepares such a

Adam Fisher, Julie B. Staunton, Huan Wu, Peter Brommer

Precipitates in Nickel-based superalloys form during heat treatment on a time scale inaccessible to direct molecular dynamics simulation, but could be studied using kinetic Monte Carlo (KMC). This requires reliable values for the barrier energies separating distinct configurations over the trajectory of the system. In this study, we validate vacancy migratio

Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch

We consider the problem of late multi-modal fusion for discriminative learning. Motivated by noisy, multi-source domains that require understanding the reliability of each data source, we explore the notion of credibility in the context of multi-modal fusion. We propose a combination function that uses probabilistic circuits (PCs) to combine predictive distr

Laura Colmenarejo, Aleyah Dawkins, Jennifer Elder, Pamela E. Harris

Stirling permutations are parking functions, and we investigate two parking function statistics in the context of these objects: lucky cars and displacement. Among our results, we consider two extreme cases: extremely lucky Stirling permutations (those with maximally many lucky cars) and extremely unlucky Stirling permutations (those with exactly one lucky c

Ksenija Rozman, Primož Šparl

A graph of order $n$ is distance magic if it admits a bijective labeling of its vertices with integers from $1$ to $n$ such that each vertex has the same sum of the labels of its neighbors. In this paper we classify all distance magic Cartesian products of two cycles, thereby correcting an error in a widely cited paper from 2004. Additionally, we show that e

Netanel Raviv

Hyperdimensional Computing (HDC) is an emerging computational paradigm for representing compositional information as high-dimensional vectors, and has a promising potential in applications ranging from machine learning to neuromorphic computing. One of the long-standing challenges in HDC is factoring a compositional representation to its constituent factors,

Marcel Lamač, Petr Valenta, Jaroslav Nejdl, Uddhab Chaulagain

Bright ultrashort X-ray pulses allow scientists to observe ultrafast motion of atoms and molecules. Coherent light sources, such as the X-ray free electron laser (XFEL), enable remarkable discoveries in cell biology, protein crystallography, chemistry or materials science. However, in contrast to optical lasers, lack of X-ray mirrors demands XFELs to amplify

Salim Rukhsar, Anil Kumar Tiwari

Electroencephalography (EEG) is a widely used tool for diagnosing brain disorders due to its high temporal resolution, non-invasive nature, and affordability. Manual analysis of EEG is labor-intensive and requires expertise, making automatic EEG interpretation crucial for reducing workload and accurately assessing seizures. In epilepsy diagnosis, prolonged E

Wlodek Bryc, Pavel Zatitskii

We show that the stationary measure for the totally asymmetric simple exclusion process on a segment with open boundaries is given by a marginal of a two-line measure with a simple and explicit description. We use this representation to analyze asymptotic fluctuations of the height function near the triple point for a larger set of parameters than was previo

Samira Pakravan, Nikolaos Evangelou, Maxime Usdin, Logan Brooks

Digital health technologies (DHT), such as wearable devices, provide personalized, continuous, and real-time monitoring of patient. These technologies are contributing to the development of novel therapies and personalized medicine. Gaining insight from these technologies requires appropriate modeling techniques to capture clinically-relevant changes in dise

Lev Ayzenberg, Raja Giryes, Hayit Greenspan

Deep learning models have emerged as the cornerstone of medical image segmentation, but their efficacy hinges on the availability of extensive manually labeled datasets and their adaptability to unforeseen categories remains a challenge. Few-shot segmentation (FSS) offers a promising solution by endowing models with the capacity to learn novel classes from l

Madelyn Cain, Chen Zhao, Hengyun Zhou, Nadine Meister

Quantum error correction is believed to be essential for scalable quantum computation, but its implementation is challenging due to its considerable space-time overhead. Motivated by recent experiments demonstrating efficient manipulation of logical qubits using transversal gates (Bluvstein et al., Nature 626, 58-65 (2024)), we show that the performance of l

S. Sowmya, Gokularam Muthukrishnan, K. Giridhar

Massive MIMO (mMIMO) enables users with different requirements to get connected to the same base station (BS) on the same set of resources. In the uplink of Multiuser massive MIMO (MU-mMIMO), while such heterogeneous users are served, decoupling facilitates the use of user-specific detection schemes. In this paper, we propose a low-complexity linear decoupli

Jianfeng Gao, Xiaoshu Jin, Franziska Krebs, Noémie Jaquier

Visual imitation learning has achieved impressive progress in learning unimanual manipulation tasks from a small set of visual observations, thanks to the latest advances in computer vision. However, learning bimanual coordination strategies and complex object relations from bimanual visual demonstrations, as well as generalizing them to categorical objects

Raihan Islam Arnob, Gregory J. Stein

We address the task of long-horizon navigation in partially mapped environments for which active gathering of information about faraway unseen space is essential for good behavior. We present a novel planning strategy that, at training time, affords tractable computation of the value of information associated with revealing potentially informative regions of

Neelakantan Padmanabhan

An equation based reduced order model applicable to generalized heat equation and thermal simulations of power electronics systems developed in commercial CFD tools, is presented in this work. The model considers the physics of heat transfer between multiple objects in different mediums and presents a set of equations that can be applied to a wide range of h

Nanda Rani, Bikash Saha, Vikas Maurya, Sandeep Kumar Shukla

Understanding the modus operandi of adversaries aids organizations in employing efficient defensive strategies and sharing intelligence in the community. This knowledge is often present in unstructured natural language text within threat analysis reports. A translation tool is needed to interpret the modus operandi explained in the sentences of the threat re

Miguel Llamas Lanza, Olivier Godet, Benjamin Arcier, Manal Yassine

Context. Gamma-ray bursts (GRBs) can be detected at cosmological distances and therefore can be used to study the contents and phases of the early Universe. The 4-150 keV wide-field trigger camera ECLAIRs to fly on board the Space-based multi-band Variable Object Monitor (SVOM) mission, dedicated to studying the high-energy transient sky in synergy with mult

Lampis Alevizos, Martijn Dekker

Cyber threats continue to evolve in complexity, thereby traditional Cyber Threat Intelligence (CTI) methods struggle to keep pace. AI offers a potential solution, automating and enhancing various tasks, from data ingestion to resilience verification. This paper explores the potential of integrating Artificial Intelligence (AI) into CTI. We provide a blueprin

Luke Weisenbach, Thomas Collett, Ana Sainz de Murieta, Coleman Krawczyk

The standardizable nature of gravitationally lensed Type Ia supernovae (glSNe Ia) makes them an attractive target for time delay cosmography, since a source with known luminosity breaks the mass sheet degeneracy. It is known that microlensing by stars in the lensing galaxy can add significant stochastic uncertainty to the unlensed luminosity which is often m

S Pumpluen

We look at classes of semiassociative algebras, with an emphasis on those that canonically generalize associative (generalized) cyclic algebras, and at their behaviour in the semiassociative Brauer monoid defined by Blachar, Haile, Matzri, Rein, and Vishne. A possible way to generalize this monoid in characteristic $p$ that includes nonassociative differenti

M. Scalco, R. Gerasimov, L. R. Bedin, E. Vesperini

We exploit the astro-photometric dataset of the multi-epoch infrared parallel field of a $\textit{Hubble Space Telescope}$ Large Programme aimed at studying the faintest stars of the globular cluster NGC$\,$6752 to determine the luminosity and mass functions of the multiple stellar populations of this cluster. Thanks to the measurement of proper motions and

Sydney Vach, George Zhou, Chelsea X. Huang, James G Rogers

Within the first few hundreds of millions of years, many physical processes sculpt the eventual properties of young planets. NASA's TESS mission has surveyed young stellar associations across the entire sky for transiting planets providing glimpses into the various stages of planetary evolution. Using our own detection pipeline, we search a magnitude-limited

Zehao Zhong, Gang Zhao

The stellar kinematics of low-mass galaxies are usually observed to be very unsmooth with significant kinematical fluctuations in small scales, which cannot be consistent with the projected centrosymmetric stellar kinematics obtained from commonly used dynamical models. In this work, we aim to test whether the high degree of kinematical fluctuations affects

Wen-Tan Xue, Ching Hua Lee

The entanglement entropy encodes fundamental characteristics of quantum many-body systems, and is particularly subtle in non-Hermitian settings where eigenstates generically become non-orthogonal. In this work, we find that negative biorthogonal entanglement generically arises from topologically protected non-orthogonal edge states in free fermion systems, e

Hongyu Lu, Han-Qing Wu, Bin-Bin Chen, Kai Sun

The interplay of topological order and charge order exhibits rich physics. Recent experiments that succesfully realized the frational quantum anomalous Hall (FQAH) effect in twisted MoTe$_2$ bilayers and rhombohedral multilayer graphene without external magnetic field further call for deeper understanding of the relation between topological order and charge

Ze-Hao Zhong, Gang Zhao, Hans-Walter Rix, Luis C. Ho

The Galaxy inner parts are generally considered to be optically symmetric, as well as kinematically symmetric for most massive early-type galaxies. At the lower-mass end, many galaxies contain lots of small patches in their velocity maps, causing their kinematics to be nonsmooth in small scales and far from symmetry. These small patches can easily be mistake

Kurt Hinterbichler, Austin Joyce, Grégoire Mathys

We explore the physics of relativistic gapless phases defined by a mixed anomaly between two generalized conserved currents. The gapless modes can be understood as Goldstone modes arising from the nonlinear realization of (generically higher-form) symmetries arising from these currents. In some cases, the anomaly cannot be reproduced by any local and unitary

Mikhail Stephanov

These lecture notes consist of two major connected parts. The first part (Sections 1, 2), after a brief historical introduction, deals with the physics of critical points in thermodynamic equilibrium. The features of the fluctuations relevant for the QCD critical point search are highlighted. The second part (Sections 3, 4) focuses on the recent developments

Abhishek Hegade K. R., Justin L. Ripley, Nicolás Yunes

Accurately modeling the tidal response of neutron stars is crucial to connecting gravitational wave observations of binaries to ultra-dense nuclear physics. Most current models of the tidal response of relativistic stars either assume a static response model, or use phenomenological models inspired by Newtonian gravity. In this work, we present a general for

Daniel Gilman, Simon Birrer, Anna Nierenberg, Maverick S. H. Oh

Strong gravitational lensing provides a purely gravitational means to infer properties of dark matter halos and thereby constrain the particle nature of dark matter. Strong lenses sometimes appear as four lensed images of a background quasar accompanied by spatially-resolved emission from the quasar host galaxy encircling the main deflector (lensed arcs). We

Gian F. Giudice, Hyun Min Lee, Alex Pomarol, Bibhushan Shakya

We study nonthermal production of heavy dark matter from the dynamics of the background scalar field during a first-order phase transition, predominantly from bubble collisions. In scenarios where bubble walls achieve runaway behavior and get boosted to very high energies, we find that it is possible to produce dark matter with mass several orders of magnitu

L. Barchiesi, C. Vignali, F. Pozzi, R. Gilli

We investigated the properties of 94 [NeV]3426AA-selected type 2 AGN in COSMOS at z=0.6-1.2, performing optical-to-far-infrared spectral energy distribution fitting. In addition, we analyze the X-ray spectra of the X-ray-detected sources to obtain reliable values of the AGN obscuration and intrinsic luminosity. We found that more than two-thirds of our sampl

Bin Liu, Dong Lai

We study the dynamics of a star orbiting a merging black-hole binary (BHB) in a coplanar triple configuration. During the BHB's orbital decay, the system can be driven across the apsidal precession resonance, where the apsidal precession rate of the stellar orbit matches that of the inner BHB. As a result, the system gets captured into a state of resonance a

John D. Roberts, Marc H. Pinsonneault, Jennifer A. Johnson, Joel C. Zinn

The surface [C/N] ratios of evolved giants are strongly affected by the first dredge-up (FDU) of nuclear-processed material from stellar cores. C and N also have distinct nucleosynthetic origins and serve as diagnostics of mixing and mass loss. We use subgiants to find strong trends in the birth [C/N] with [Fe/H], which differ between the low-$\alpha$ and hi

E. O. Ofek, D. Polishook, D. Kushnir, G. Nir

Asteroid collisions are one of the main processes responsible for the evolution of bodies in the main belt. Using observations of the Dimorphos impact by the DART spacecraft, we estimate how asteroid collisions in the main belt may look in the first hours after the impact. If the DART event is representative of asteroid collisions with a ~1m size impactor, t

Alessandro Alberto Trani, Nathan W. C. Leigh, Tjarda C. N. Boekholt, Simon Portegies Zwart

The three-body problem (3BP) poses a longstanding challenge in physics and celestial mechanics. Despite the impossibility of obtaining general analytical solutions, statistical theories have been developed based on the ergodic principle. This assumption is justified by chaos, which is expected to fully mix the accessible phase space of the 3BP. This study pr

Christian Ecker, Tyler Gorda, Aleksi Kurkela, Luciano Rezzolla

Multimessenger signals from binary neutron star (BNS) mergers are promising tools to infer the largely unknown properties of nuclear matter at densities that are presently inaccessible to laboratory experiments. The gravitational waves (GWs) emitted by BNS merger remnants, in particular, have the potential of setting tight constraints on the neutron-star equ

Stefano Lanza

The landscape of low-energy effective field theories stemming from string theory is too vast for a systematic exploration. However, the meadows of the string landscape may be fertile ground for the application of machine learning techniques. Employing neural network learning may allow for inferring novel, undiscovered properties that consistent theories in t

Måns Holmberg, Nikku Madhusudhan

The JWST has ushered in a new era in atmospheric characterisations of temperate low-mass exoplanets with recent detections of carbon-bearing molecules in the candidate Hycean world K2-18 b. We investigated JWST observations of the TOI-270 system, with two sub-Neptunes simultaneously transiting the nearby M dwarf during the visit. We report our atmospheric ch

William McClymont, Sandro Tacchella, Aaron Smith, Rahul Kannan

We present an analysis of the diffuse ionised gas (DIG) in a high-resolution simulation of an isolated Milky Way-like galaxy, incorporating on-the-fly radiative transfer and non-equilibrium thermochemistry. We utilise the Monte-Carlo radiative transfer code COLT to self-consistently obtain ionisation states and line emission in post-processing. We find a cle

R. Timmerman, R. J. van Weeren, A. Botteon, H. J. A. Röttgering

Radio-mode feedback associated with the active galactic nuclei (AGN) at the cores of galaxy clusters injects large amount of energy into the intracluster medium (ICM), offsetting radiative losses through X-ray emission. This mechanism prevents the ICM from rapidly cooling down and fueling extreme starburst activity as it accretes onto the central galaxies, a

Chris Rockwell, Nilesh Kulkarni, Linyi Jin, Jeong Joon Park

Estimating relative camera poses between images has been a central problem in computer vision. Methods that find correspondences and solve for the fundamental matrix offer high precision in most cases. Conversely, methods predicting pose directly using neural networks are more robust to limited overlap and can infer absolute translation scale, but at the exp

Nhat-Minh Nguyen, Fabian Schmidt, Beatriz Tucci, Martin Reinecke

We present optimal Bayesian field-level cosmological constraints from nonlinear tracers of the large-scale structure, specifically the amplitude $\sigma_8$ of linear matter fluctuations inferred from rest-frame simulated dark matter halos in a comoving volume of $8\,(h^{-1}\mathrm{Gpc})^3$. Our constraint on $\sigma_8$ is entirely due to nonlinear informatio

Masahiro Kato, Shinji Ito

We investigate the \emph{linear contextual bandit problem} with independent and identically distributed (i.i.d.) contexts. In this problem, we aim to develop a \emph{Best-of-Both-Worlds} (BoBW) algorithm with regret upper bounds in both stochastic and adversarial regimes. We develop an algorithm based on \emph{Follow-The-Regularized-Leader} (FTRL) with Tsall

Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current ev

Meng Zheng, Benjamin Planche, Xuan Gong, Fan Yang

3D patient body modeling is critical to the success of automated patient positioning for smart medical scanning and operating rooms. Existing CNN-based end-to-end patient modeling solutions typically require a) customized network designs demanding large amount of relevant training data, covering extensive realistic clinical scenarios (e.g., patient covered b

Jose Beltrán Jiménez, David Figueruelo, Florencia Anabella Teppa Pannia

Cosmological models featuring an elastic interaction in the dark sector at linear order has been shown to provide a promising scenario for alleviating the $\sigma_8$ tension. A natural question for these scenarios is whether there could be a degeneracy between the interaction and massive neutrinos that also contribute to erasing structures at late times. In

Skylar X. Wei, Lu Gan, Joel W. Burdick

This work presents a novel data-driven multi-layered planning and control framework for the safe navigation of a class of unmanned ground vehicles (UGVs) in the presence of unknown stationary obstacles and additive modeling uncertainties. The foundation of this framework is a novel robust model predictive planner, designed to generate optimal collision-free

Chakradhar Rangi, Juana Moreno, Ka-Ming Tam

The out of time order correlator (OTOC) serves as a powerful tool for investigating quantum information spreading and chaos in complex systems. We present a method employing non-equilibrium dynamical mean-field theory (DMFT) and coherent potential approximation (CPA) combined with diagrammatic perturbation on the Schwinger-Keldysh contour to calculate the OT

Andreas A. Buchheit, Torsten Keßler, Kirill Serkh

This paper introduces a new method for the efficient computation of oscillatory multidimensional lattice sums in geometries with boundaries. Such sums are ubiquitous in both pure and applied mathematics, and have immediate applications in condensed matter physics and topological quantum physics. The challenge in their evaluation results from the combination

DUNE Collaboration, A. Abed Abud, B. Abi, R. Acciarri

The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day

Haofan Lu, Christopher Vattheuer, Baharan Mirzasoleiman, Omid Abari

We present NeWRF, a deep learning framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by a site survey which is a cumbersome process requiring an experienced eng

Eric Easthope

I show that a one-dimensional (1D) conditional generative adversarial network (cGAN) with an adversarial training architecture is capable of unpaired signal-to-signal ("sig2sig") translation. Using a simplified CycleGAN model with 1D layers and wider convolutional kernels, mirroring WaveGAN to reframe two-dimensional (2D) image generation as 1D audio generat

Thibault Didier Décoppet

We study group graded extensions of fusion 2-categories. As an application, we obtain a homotopy theoretic classification of fermionic strongly fusion 2-categories. We examine various examples in detail.

Kate Davis, Laszlo B. Kish, Chanan Singh

Unconditional security for smart grids is defined. Cryptanalyses of the watermarked security of smart grids indicate that watermarking cannot guarantee unconditional security unless the communication within the grid system is unconditionally secure. The successful attack against the dynamically watermarked smart grid remains valid even with the presence of i

Pyeongjae Park, E. A. Ghioldi, Andrew F. May, James A. Kolopus

The S = 1/2 triangular lattice antiferromagnet (TLAF) is a paradigmatic example of frustrated quantum magnetism. An ongoing challenge involves understanding the influence of exchange anisotropy on the collective behavior within such systems. Using inelastic neutron scattering (INS) and advanced calculation techniques, we have studied the low and high-tempera