October 2023 arXiv papers — page 2

Alberto Del Pia

In pure integer linear programming it is often desirable to work with polyhedra that are full-dimensional, and it is well known that it is possible to reduce any polyhedron to a full-dimensional one in polynomial time. More precisely, using the Hermite normal form, it is possible to map a non full-dimensional polyhedron to a full-dimensional isomorphic one i

Amirreza Sadeghpour, Daryoush Abdollahpour

We numerically study the filamentation of femtosecond vector beams with spatially varying polarization profiles in air. The vector beams include azimuthal, radial, and spiral cylindrical vector beams (CVBs), as well as low-order Full Poincar\'e beams (FPBs) with star and lemon polarization topologies. Comparing the filamentation of CVBs to that of a circular

Ada Lamba, Max Taylor, Vincent Beardsley, Jacob Bambeck

Information flow control (IFC) provides confidentiality by enforcing noninterference, which ensures that high-secrecy values cannot affect low-secrecy values. Prior work introduces fine-grained IFC approaches that modify the programming language and use nonstandard compilation tools, impose run-time overhead, or report false secrecy leaks -- all of which hin

Michal Lisicki, Mihai Nica, Graham W. Taylor

We introduce a novel approach for batch selection in Stochastic Gradient Descent (SGD) training, leveraging combinatorial bandit algorithms. Our methodology focuses on optimizing the learning process in the presence of label noise, a prevalent issue in real-world datasets. Experimental evaluations on the CIFAR-10 dataset reveal that our approach consistently

Frank Alvarez Borges, Kleber Carrapatoso, Stéphane Mischler

We consider the parabolic-parabolic Keller-Segel equation in the plane and prove the nonlinear exponential stability of the self-similar profile in a quasi parabolic-elliptic regime. We first perform a perturbation argument in order to obtain exponential stability for the semigroup associated to part of the first component of the linearized operator, by expl

Wolfdieter Lang

It is shown that each sequence giving the number of times a given day of the month falls on a certain day of the week for $400$ successive years of the Gregorian cycle can be composed of various pieces of various length of one of 4 sequences of length 28, used periodically.

Xuejie Liu, Anji Liu, Guy Van den Broeck, Yitao Liang

A popular paradigm for offline Reinforcement Learning (RL) tasks is to first fit the offline trajectories to a sequence model, and then prompt the model for actions that lead to high expected return. In addition to obtaining accurate sequence models, this paper highlights that tractability, the ability to exactly and efficiently answer various probabilistic

T. Daniel Brennan, Aiden Sheckler

Anomalous symmetries are known to strongly constrain the possible IR behavior along any renormalization group (RG) flow. Recently, the extension of the notion of symmetry in QFT has provided new types of anomalies with a corresponding new class of constraints on RG flows. In this paper, we derive the constraints imposed on RG flows from anomalies that can on

Denis-Charles Cisinski, Bruno Kahn

We prove that, for any smooth and projective scheme $X$ over a field $k$ of char. $0$, the set of maps from Spec $k$ to $X$ in the $\mathbf{A}^1$-homotopy category of schemes $\mathcal{H}_{\mathbf{A}^1}(k)$ is in bijection with the quotient of $X(k)$ by $R$-equivalence, and is a birational invariant of $X$. This is achieved by establishing a precise relation

Andronick Arutyunov, Alexey Naianzin

The derivation problem is a familiar one concerning group algebras, particularly $L_1(G)$ and von Neumann algebras. In this paper, we study the Banach bimodule $\ell_p(G)$, which is generated by the $\ell_p$ norm over a specific class of groups with well-organized conjugacy classes. For this case, we will demonstrate that all $\ell_p(G)$ derivations are inne

Edward N. Ward, Rebecca M. McClelland, Jacob R. Lamb, Roger Rubio-Sánchez

Structured illumination can reject out-of-focus signal from a sample, enabling high-speed and high-contrast imaging over large areas with widefield detection optics. Currently, this optical-sectioning technique is limited by image reconstruction artefacts and the need for sequential imaging of multiple colour channels. We combine multicolour interferometric

Zhiyan Ding, Taehee Ko, Jiahao Yao, Lin Lin

Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learning is not directly applicable in this setting due to the collapse of quantum states after measurements. Thus, gradient estimations constitute a significant overhead in a gradient-

Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner

The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' criteria like beauty, naturalness, etc. We design loss functions for performing both of those tasks with machine learning techniques. We use the Yukawa quark sector as a toy example to

Anders Dyhr Sandgaard, Noam Shemesh, Leif Østergaard, Valerij G. Kiselev

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to macroscopic scale. The MRI Larmor frequency shift measured in white m

Wei Wang, Brian A. Camley

Cells that collide with each other repolarize away from contact, in a process called contact inhibition of locomotion (CIL), which is necessary for correct development of the embryo. CIL can occur even when cells make a micron-scale contact with a neighbor - much smaller than their size. How precisely can a cell sense cell-cell contact and repolarize in the

Dylan Albrecht, N. Tobias Jacobson

NoMoPy is a code for fitting, analyzing, and generating noise modeled as a hidden Markov model (HMM) or, more generally, factorial hidden Markov model (FHMM). This code, written in Python, implements approximate and exact expectation maximization (EM) algorithms for performing the parameter estimation process, model selection procedures via cross-validation,

Moniruzzaman Shaikh, Amit D Lad, Devshree Mandal, Kamalesh Jana

Plasmas are beset with instabilities of all types, hydrodynamic, magneto-hydrodynamic, and electromagnetic. These instabilities are complex, occur over a large range of temporal and spatial scales, are most often unmanageable, and have seriously challenged our efforts at applications, even as they have shed light on the understanding of the physics of plasma

Mohit K. Sharma, Chen-Feng Liu, Ibrahim Farhat, Nassim Sehad

Over the past decade, the utilization of UAVs has witnessed significant growth, owing to their agility, rapid deployment, and maneuverability. In particular, the use of UAV-mounted 360-degree cameras to capture omnidirectional videos has enabled truly immersive viewing experiences with up to 6DoF. However, achieving this immersive experience necessitates enc

Maria López-Fernández, Łukasz Płociniczak

We construct a Convolution Quadrature (CQ) scheme for the quasilinear subdiffusion equation of order $\alpha$ and supply it with the fast and oblivious implementation. In particular, we find a condition for the CQ to be admissible and discretize the spatial part of the equation with the Finite Element Method. We prove the unconditional stability and converge

Maxim Ivanov

For a group $ G $ we consider its tensor square $G \otimes G$ and exterior square $G \wedge G$. We prove that for a circularly orderable group $G$, under some assumptions on $H_1(G)$ and $H_2(G)$, its exterior square and tensor square are left-orderable. This yields an obstruction for a circularly orderable group $G$ to have torsion. We apply these results t

Mohammad Azizmalayeri, Reza Abbasi, Amir Hosein Haji Mohammad rezaie, Reihaneh Zohrabi

Deep neural networks have exhibited remarkable performance in various domains. However, the reliance of these models on spurious features has raised concerns about their reliability. A promising solution to this problem is last-layer retraining, which involves retraining the linear classifier head on a small subset of data without spurious cues. Nevertheless

Colin L. Sarkis, John W. Villanova, Casey Eichstaedt, Adolfo G. Eguiluz

The Mott insulator $\alpha$-RuCl$_3$ has generated great interest in the community due to its possible field-induced Kitaev quantum spin liquid state. Despite enormous effort spent trying to obtain the form of the low energy Hamiltonian, there is currently no agreed upon set of parameters which is able to explain all of the data. A key piece of missing infor

David Casas, Mikhail V. Volkov

A deterministic finite automaton in which every non-empty set of states occurs as the image of the whole state set under the action of a suitable input word is called completely reachable. It was conjectured that in each completely reachable automaton with $n$ states, every set of $k>0$ states is the image of a word of length at most $n(n-k)$. We confirm the

O. Cavichia, M. Mollá, J. J. Bazán, A. Castrillo

The role of type Ia supernovae (SN Ia), mainly the Delay Time Distributions (DTDs) determined by the binary systems, and the yields of elements created by different explosion mechanisms, are studied by using the MulChem chemical evolution model applied to our Galaxy. We explored 15 DTDs and 12 tables of elemental yields produced by different SN Ia explosion

Stijn Cambie, Jan Goedgebeur, Jorik Jooken

We improve the best known lower bounds on the exponential behavior of the maximum of the number of connected sets, $N(G)$, and dominating connected sets, $N_{dom}(G)$, for regular graphs. These lower bounds are improved by constructing a family of graphs defined in terms of a small base graph (a Moore graph), using a combinatorial reduction of these graphs t

Sahrima Jannat Oishwee, Natalia Stakhanova, Zadia Codabux

The Android permission system is a set of controls to regulate access to sensitive data and platform resources (e.g., camera). The fast evolving nature of Android permissions, coupled with inadequate documentation, results in numerous challenges for third-party developers. This study investigates the permission-related challenges developers face and the solu

Om M. Khare, Shubham Gandhi, Aditya M. Rahalkar, Sunil Mane

Effective detection of road hazards plays a pivotal role in road infrastructure maintenance and ensuring road safety. This research paper provides a comprehensive evaluation of YOLOv8, an object detection model, in the context of detecting road hazards such as potholes, Sewer Covers, and Man Holes. A comparative analysis with previous iterations, YOLOv5 and

Mateusz Rałowski, Krzysztof Hryniewicz, Agnieszka Pollo, Łukasz Stawarz

In every proposed unification scheme for Active Galactic Nuclei (AGN), an integral element is the presence of circumnuclear dust arranged in torus-like structures. A crucial model parameter in this context is the covering factor (CF), defined as the ratio between the infrared luminosity of the dusty torus $L_{\rm IR}$, and the accretion disk bolometric lumin

Shixiong Wang, Wei Dai, Haowei Wang, Geoffrey Ye Li

Integrated sensing and communication (ISAC), which enables hardware, resources (e.g., spectra), and waveforms sharing, is becoming a key feature in future-generation communication systems. This paper investigates performance characterization and waveform design for ISAC systems when the underlying true communication channels are not accurately known. With un

Marco Benini, Alastair Grant-Stuart, Giorgio Musante, Alexander Schenkel

M{\o}ller maps are identifications between the observables of a perturbatively interacting physical system and the observables of its underlying free (i.e. non-interacting) system. This work studies and characterizes obstructions to the existence of such identifications. The main results are existence and importantly also non-existence theorems, which in par

Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu

In the domain of autonomous driving, the offline Reinforcement Learning~(RL) approaches exhibit notable efficacy in addressing sequential decision-making problems from offline datasets. However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets. In

Shyam Sundar, M. Yakovlev, N. Azari, M. Abedi

We report muon spin rotation (muSR) measurements of the temperature dependence of the absolute value of the magnetic penetration depth and the magnetic field dependence of the vortex core size in the mixed state of the non-symmorphic superconductor LaNiGa2. The temperature dependence of the normalized superfluid density is shown to be well described by a two

Tomo Lazovich

Echoing the history of search engines and social media content rankings, the advent of large language models (LLMs) has led to a push for increased personalization of model outputs to individual users. In the past, personalized recommendations and ranking systems have been linked to the development of filter bubbles (serving content that may confirm a user's

Parthapratim Saha Bipan Hazarika, Hemanta Kalita

Kuelbs-Steadman spaces are introduced in this article on a separable metric space with finite diameter and finite positive Borel measure. Kuelbs-Steadman spaces of the Lipschitz type are also discussed. Various inclusion properties are also discussed. In the sequel, we introduce HK-Sobolev spaces on metric mesure space which coincides with HK-Sobolev space i

Derya Gol Gungor, Bimba Rao, Cynthia Wolverton, Ismayil Guracar

Echocardiography provides an important tool for clinicians to observe the function of the heart in real time, at low cost, and without harmful radiation. Automated localization and classification of heart valves enables automatic extraction of quantities associated with heart mechanical function and related blood flow measurements. We propose a machine learn

Viswa Narayanan Sankaranarayanan, Sumeet Gajanan Satpute, Spandan Roy, George Nikolakopoulos

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the

Joao Marono, Catarina Silva, Joao P. Barraca, Vitor Cunha

The proliferation of mobile applications and the subsequent sharing of personal data with service and application providers have given rise to substantial privacy concerns. Application marketplaces have introduced mechanisms to conform to regulations and provide individuals with control over their data. However, a notable absence persists regarding clear ind

Alex McSweeney-Davis, R. S. MacKay, Shibabrat Naik

Escape from a potential well occurs in a wide variety of physical systems from chemical reactions to ship capsize. In these situations escape often occurs by passage over a normally hyperbolic submanifold (NHS). This paper describes the computational implementation of an algorithm to identify the NHS of a two degree of freedom model for ship motion under the

Matteo Magoni, Chris Nill, Igor Lesanovsky

We investigate the dynamics of a spin system with facilitation constraint that can be studied using Rydberg atoms in arrays of optical tweezer traps. The elementary degrees of freedom of the system are domains of Rydberg excitations that expand ballistically through the lattice. Due to mechanical forces, Rydberg excited atoms are coupled to vibrations within

Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty, Rien Quirynen

We consider the problem of safe multi-agent motion planning for drones in uncertain, cluttered workspaces. For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning. First, we use single-agent reinforcement learning to learn motion plans from data that re

Daniel J. Slonim

We study random walks in i.i.d. random environments on $\mathbb{Z}^d$ when there are two basic types of vertices, which we call "blue" and "red". Each color represents a different probability distribution on transition probability vectors. We introduce a method of studying these walks that compares the expected amount of time spent at a specific site on the

Erik Bollt, Jeremie Fish, Anil Kumar, Edmilson Roque dos Santos

An interesting feature of the brain is its ability to respond to disparate sensory signals from the environment in unique ways depending on the environmental context or current brain state. In dynamical systems, this is an example of multi-stability, the ability to switch between multiple stable states corresponding to specific patterns of brain activity/con

Victor J. Leon, Noah Ford, Honest Mrema, Jeffrey Gilbert

High-fidelity computational simulations and physical experiments of hypersonic flows are resource intensive. Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situations that have not been seen before. However, high-fidelity data is itself in limited quantity to validate all

Peter West, Ximing Lu, Nouha Dziri, Faeze Brahman

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models now take only seconds to produce outputs that would challenge or exceed the capabilities even of expert humans. At the same time, models still show basic errors in understanding th

Utkarsh Agrawal, Javier Lopez-Piqueres, Romain Vasseur, Sarang Gopalakrishnan

The mechanism by which an effective macroscopic description of quantum measurement in terms of discrete, probabilistic collapse events emerges from the reversible microscopic dynamics remains an enduring open question. Emerging quantum computers offer a promising platform to explore how measurement processes evolve across a range of system sizes while retain

David Berenstein, P. N. Thomas Lloyd

We study systems of staggered boson Hamiltonians in a one dimensional lattice and in particular how the translation symmetry by one unit in these systems is in reality a non-invertible symmetry closely related to T-duality. We also study the simplest systems of clock models derived from these staggered boson Hamiltonians. We show that the non-invertible symm

David Marwood, Shumeet Baluja, Yair Alon

Recent progress in text-to-image (TTI) systems, such as StableDiffusion, Imagen, and DALL-E 2, have made it possible to create realistic images with simple text prompts. It is tempting to use these systems to eliminate the manual task of obtaining natural images for training a new machine learning classifier. However, in all of the experiments performed to d

Han-Jia Ye, Qi-Le Zhou, Huai-Hong Yin, De-Chuan Zhan

Pre-training is prevalent in deep learning for vision and text data, leveraging knowledge from other datasets to enhance downstream tasks. However, for tabular data, the inherent heterogeneity in attribute and label spaces across datasets complicates the learning of shareable knowledge. We propose Tabular data Pre-Training via Meta-representation (TabPTM), a

S. Gokmen, Z. Eker, T. Yontan, S. Bilir

A detailed analysis of ground-based CCD UBV photometry and space-based Gaia Data Release 3 (DR3) data for the open clusters King 6 and NGC 1605 was performed. Using the pyUPMASK algorithm on Gaia astrometric data to estimate cluster membership probabilities, we have identified 112 stars in King 6 and 160 stars in NGC 1605 as the statistically most likely mem

Tamar Bar-On, Shira Gilat, Eli Matzri, Uzi Vishne

The algebra of supernatural matrices is a key example in the theory of locally finite central simple algebras, which studied in a previous paper of the authors (\cite{Local}). It is also a stand-alone object admits a rich study and various connections to other fields. The goal of this paper is to expose some new information about supernatural matrices, mainl

Brenden Martin, Md Zobaer Islam, Carly Gotcher, Tyler Martinez

An experimental testbed has been constructed to assess the capabilities of Light-Wave Sensing, a promising new vitals monitoring approach. A Light-Wave Sensing apparatus utilizes infrared radiation to contactlessly monitor the subtle respiratory motions of a subject from meters away. A respiration-simulating robot was programmed to produce controllable, huma

Marcel R. R. Hughes, Samir D. Mathur, Madhur Mehta

We consider D1-D5-P states in the untwisted sector of the D1-D5 orbifold CFT where we excite one copy of the seed CFT with a left-moving superconformal descendant. When the theory is deformed away from this region of moduli space these states can `lift', despite being BPS at the orbifold point. For descendants formed from the supersymmetry $G^{\alpha}_{\!\do

Elisa Todarello, Francesca Calore, Marco Regis

If the dark matter in the Universe is made of $\mu$eV axion-like particles (ALPs), then a rich phenomenology can emerge in connection to their stimulated decay into two photons. We discuss the ALP stimulated decay induced by electromagnetic radiation from Galactic radio sources. Three signatures, made by two echoes and one collinear emission, are associated

Rui Wen, Andrew C. Potter

Symmetry topological field theory (SymTFT) gives a holographic correspondence between systems with a global symmetry and a higher-dimensional topological field theory. In this framework, classification of gapped phases of matter in spacetime dimension 1+1D correspond to classifications of mechanisms to confine the SymTFT by condensing anyons. In this work, w

Aysu Ismayilova, Vugar Ismailov

In this paper, we show that the Kolmogorov two hidden layer neural network model with a continuous, discontinuous bounded or unbounded activation function in the second hidden layer can precisely represent continuous, discontinuous bounded and all unbounded multivariate functions, respectively.

Peijie Qiu, Pan Xiao, Wenhui Zhu, Yalin Wang

Multiple Instance Learning (MIL) has been widely used in weakly supervised whole slide image (WSI) classification. Typical MIL methods include a feature embedding part, which embeds the instances into features via a pre-trained feature extractor, and an MIL aggregator that combines instance embeddings into predictions. Most efforts have typically focused on

Yichi Zhang, Jiayi Pan, Yuchen Zhou, Rui Pan

Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world. However, known as visual illusions, human's perception of reality isn't always faithful to the physical world. This raises a key question: do VLMs have the similar kind of illusions as humans do, or do they faithfully learn to repres

Yongchan Yoo, Brian Swingle

We employ matrix product state simulations to study energy transport within the non-integrable regime of the one-dimensional $\mathbb{Z}_3$ chiral clock model. To induce a non-equilibrium steady state throughout the system, we consider open system dynamics with boundary driving featuring jump operators with adjustable temperature and footprint in the system.

Marina Berbel, Miquel Miravet-Tenés, Sushant Sharma Chaudhary, Simone Albanesi

Because of the electromagnetic radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave signal with electromagnetic telescopes, it is critical to promptly identify the properties of these sources. This identification must rely on the proper

Mikhail Gorchtein, Chien Yeah Seng

For many decades, the main source of information on the top-left corner element of the Cabibbo-Kobayashi-Maskawa quark mixing matrix $V_{ud}$ were superallowed nuclear beta decays with an impressive 0.01\% precision. This precision, apart from experimental data, relies on theoretical calculations in which nuclear structure-dependent effects and uncertainties

Safa Ben Othman, Armita Jalooli, Sean Tulin

New GeV-scale dark forces coupling predominantly to quarks offer novel signatures that can be produced directly and searched for at high-luminosity colliders. We compute the photon-proton and electron-proton cross sections for producing a GeV-scale gauge boson arising from a $U(1)_B$ gauge symmetry. Our calculation relies on vector meson dominance and a phen

Tuna Demircik, Domingo Gallegos, Umut Gürsoy, Matti Järvinen

We introduce a novel and effective method to compute transport coefficients in strongly interacting plasma states in holographic QFTs. Our method is based on relating the IR limit of fluctuations on a gravitational background to its variations providing a previously overlooked connection between boundary and near horizon data. We use this method to derive an

Andrew C. Mason, Robert A. Crain, Ricardo P. Schiavon, David H. Weinberg

We examine the stellar [$\alpha$/Fe]-[Fe/H] distribution of $\simeq1000$ present-day galaxies in a high-resolution EAGLE simulation. A slight majority of galaxies exhibit the canonical distribution, characterised by a sequence of low-metallicity stars with high [$\alpha$/Fe] that transitions at a "knee" to a sequence of declining [$\alpha$/Fe] with increasin

Sandeep Kumar Kataria, M. Vivek

We conduct a statistical study of black hole masses of barred and unbarred galaxies in the IllustrisTNG100 cosmological magneto-hydrodynamical simulations. This work aims to understand the role of the bars in the growth of central supermassive black hole mass and its implications on AGN fueling. Our sample consists of 1191 barred galaxies and 2738 unbarred g

Oguzhan Kasikci, Mehmet Ozkan, Yi Pang, Utku Zorba

This work challenges the conventional notion that in spacetime dimension higher than one, a supersymmetric Lagrangian invariably consists of purely bosonic terms, purely fermionic terms, as well as boson-fermion mixing terms. By recasting a relativistic Lagrangian in terms of its non-relativistic and ultra-relativistic sectors, we reveal that an ultra-relati

Earl Patrick Bellinger, Selma E. de Mink, Walter E. van Rossem, Stephen Justham

Despite major progress in our understanding of massive stars, concerning discrepancies still remain between observations and theory. Most notable are the numerous stars observed beyond the theoretical main sequence, an evolutionary phase expected to be short-lived and hence sparsely populated. This is the "Blue Supergiant Problem." Stellar models with abnorm

P. Peter Stavropoulos, Yang Yang, Ioannis Rousochatzakis, Natalia B. Perkins

It is well-recognized that the low-energy physics of many Kitaev materials is governed by two dominant energy scales, the Ising-like Kitaev coupling $K$ and the symmetric off-diagonal $\Gamma$ coupling. An understanding of the interplay between these two scales is therefore the natural starting point toward a quantitative description that includes sub-domina

Ian Hothi, Erwan Allys, Benoit Semelin, Francois Boulanger

We propose a new approach to improve the precision of astrophysical parameter constraints for the 21cm signal from the epoch of reionisation (EoR). Our method introduces new sets of summary statistics, hereafter `evolution compressed' statistics, which quantify the spectral evolution of the 2D spatial statistics computed at fixed redshift. We defined such co

Jason L. Sanders, Daisuke Kawata, Noriyuki Matsunaga, Mattia C. Sormani

A key event in the history of the Milky Way is the formation of the bar. This event affects the subsequent structural and dynamical evolution of the entire Galaxy. When the bar formed, gas was likely rapidly funnelled to the centre of the Galaxy settling in a star-forming nuclear disc. The Milky Way bar formation can then be dated by considering the oldest s

Jonatan Jacquemin-Ide, François Rincon, Alexander Tchekhovskoy, Matthew Liska

Jetted astrophysical phenomena with black hole (BH) engines, including binary mergers, jetted tidal disruption events, and X-ray binaries, require a large-scale vertical magnetic field for efficient jet formation. However, a dynamo mechanism that could generate these crucial large-scale magnetic fields has not been identified and characterized. We have emplo

Pierluca Carenza, Giuseppe Lucente, Leonardo Mastrototaro, Alessandro Mirizzi

Sterile neutrinos with masses up to $\mathcal{O} (100)$ MeV can be copiously produced in a supernova (SN) core, through the mixing with active neutrinos. In this regard the SN 1987A detection of neutrino events has been used to put constraints on active-sterile neutrino mixing, exploiting the well-known SN cooling argument. We refine the calculation of this

Asher Berlin, Gordan Krnjaic, Elena Pinetti

Thermal relic dark matter below $\sim 10 \ \text{GeV}$ is excluded by cosmic microwave background data if its annihilation to visible particles is unsuppressed near the epoch of recombination. Usual model-building measures to avoid this bound involve kinematically suppressing the annihilation rate in the low-velocity limit, thereby yielding dim prospects for

Pedro Luis Espino, Peter Hammond, David Radice, Sebastiano Bernuzzi

We study out-of-thermodynamic equilibrium effects in neutron star mergers with 3D general-relativistic neutrino-radiation large-eddy simulations. During merger, the cores of the neutron stars remain cold ($T \sim$ a few MeV) and out of thermodynamic equilibrium with trapped neutrinos originating from the hot collisional interface between the stars. However,

Mikhail Denissenya, Eric V. Linder, Sangwoo Park, Arman Shafieloo

We develop a model independent, robust method for determining galaxy rotation velocities across a 2D array of spaxels from an integral field spectrograph. Simulations demonstrate the method is accurate down to lower spectral signal-to-noise than standard methods: 99\% accurate when median $S/N=4$. We apply it to MaNGA data to construct the galaxy velocity ma

Yann Gouttenoire, Eric Kuflik, Di Liu

Confinement in $SU(N_{\rm DC})$ Yang-Mills theories is known to proceed through first-order phase transition. The wall velocity is bounded by $v_w \lesssim 10^{-6}$ due to the needed time for the substantial latent heat released during the phase transition to dissipate through Hubble expansion. Quarks much heavier than the confinement scale can be introduced

P. Chris Fragile, Peter Anninos, Nathaniel Roth, Bhupendra Mishra

We present a set of six general relativistic, multi-frequency, radiation magnetohydrodynamic simulations of thin accretion disks with different target mass accretion rates around black holes with spins ranging from non-rotating to rapidly spinning. The simulations use the $\mathbf{M}_1$ closure scheme with twelve, independent frequency (or energy) bins rangi

Stefano Baiguera

Non-relativistic quantum field theory is a framework that describes systems where the velocities are much smaller than the speed of light. A large class of those obey Schr\"{o}dinger invariance, which is the equivalent of the conformal symmetry in the relativistic world. In this review, we pedagogically introduce the main theoretical tools used to study non-

Teerthal Patel, Tanmay Vachaspati

We study the annihilation of electroweak dumbbells and the dependence of their dynamics on initial dumbbell length and twist. Untwisted dumbbells decay rapidly while maximally twisted dumbbells collapse to form a compact sphaleron-like object, before decaying into radiation. The decay products of a dumbbell include electromagnetic magnetic fields with energy

Aditya Chowdhury, Nissim Kanekar, Jayaram N. Chengalur

We present here estimates of the average rates of accretion of neutral gas onto main-sequence galaxies and the conversion of atomic gas to molecular gas in these galaxies at two key epochs in galaxy evolution: (i) $z\approx1.3-1.0$, towards the end of the epoch of peak star-formation activity in the Universe, and (ii) $z\approx1-0$, when the star-formation a

Carlos Nunez, Leonardo Santilli, Konstantin Zarembo

Quiver theories constitute an important class of supersymmetric gauge theories with well-defined holographic duals. Motivated by holographic duality, we use localisation on $S^d$ to study long linear quivers at large-N. The large-N solution shows a remarkable degree of universality across dimensions, including $d = 4$ where quivers are genuinely superconform

Minju M. Lee, Charles C. Steidel, Gabriel Brammer, Natascha Förster-Schreiber

We report the detection of cold dust in an apparently quiescent massive galaxy ($\log({M_{\star}/M_{\odot}})\approx11$) at $z\sim2$ (G4). The source is identified as a serendipitous 2 mm continuum source in a deep ALMA observation within the field of Q2343-BX610, a $z=2.21$ massive star-forming disk galaxy. Available multi-band photometry of G4 suggests reds

Suman Kundu, Eran Palti, Joan Quirant

We consider an effective theory with a single massive spin-2 particle and a gap to the cutoff. We couple the spin-2 particle to gravity, and to other lower-spin fields, and study the growth of scattering amplitudes of the particle in the Regge regime: where $s$ is much larger than $t$ and also any mass scales in the effective theory, but still much lower tha

Vincenzo Cirigliano, Wouter Dekens, Jordy de Vries, Emanuele Mereghetti

Recent developments in the Standard Model analysis of semileptonic charged-current processes involving light quarks have revealed $\sim 3\sigma$ tensions in Cabibbo universality tests involving meson, neutron, and nuclear beta decays. In this paper, we explore beyond the Standard Model explanations of this so-called Cabibbo Angle Anomaly in the framework of

Lukas Allwicher, Claudia Cornella, Gino Isidori, Ben A. Stefanek

We present a comprehensive analysis of electroweak, flavor, and collider bounds on the complete set of dimension-six SMEFT operators in the $U(2)^5$-symmetric limit. This operator basis provides a consistent framework to describe a wide class of new physics models and, in particular, the motivated class of models where the new degrees of freedom couple mostl

Giuseppe De Tomasi, Ivan M. Khaymovich

In this work, we investigate the localization properties of a paradigmatic model, coupled to a monitoring environment and possessing a many-body localized (MBL) phase. We focus on the post-selected no-click limit with quench random rates, i.e., random gains and losses. In this limit, the system is modeled by adding an imaginary random potential, rendering no

Valeriia Rohoza, Aretaios Lalakos, Max Paik, Koushik Chatterjee

Accreting supermassive black holes (SMBHs) produce highly magnetized relativistic jets that tend to collimate gradually as they propagate outward. However, recent radio interferometric observations of the 3C 84 galaxy reveal a stunning, cylindrical jet already at several hundred SMBH gravitational radii, $r\gtrsim350r_{\rm g}$. We explore how such extreme co

Saskia Rabich, Patrick Stotko, Reinhard Klein

Fourier PlenOctrees have shown to be an efficient representation for real-time rendering of dynamic Neural Radiance Fields (NeRF). Despite its many advantages, this method suffers from artifacts introduced by the involved compression when combining it with recent state-of-the-art techniques for training the static per-frame NeRF models. In this paper, we per

Suiqi Lu, Yu Qiu, Dongjian Wu

We prove that the principal component of the exchange graph of hearts of a graded skew-gentle algebra can be identified with the corresponding exchange graph of S-graphs, using the geometric models and the intersection formula in \cite{QZZ}. Using the similar argument in \cite{BS, BMQS, CHQ}, we extend this identification to an isomorphism between the spaces

Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat

Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often exceeded by that of more recent methods. Notably, EI and its variants, including for the parallel and multi-objective settings, are challenging to optimize because their acquisition

Yanai Elazar, Akshita Bhagia, Ian Magnusson, Abhilasha Ravichander

Large text corpora are the backbone of language models. However, we have a limited understanding of the content of these corpora, including general statistics, quality, social factors, and inclusion of evaluation data (contamination). In this work, we propose What's In My Big Data? (WIMBD), a platform and a set of sixteen analyses that allow us to reveal and

Mohammed Khaleed Almansoori, Mustansar Fiaz, Hisham Cholakkal

Person search (PS) is a challenging computer vision problem where the objective is to achieve joint optimization for pedestrian detection and re-identification (ReID). Although previous advancements have shown promising performance in the field under fully and weakly supervised learning fashion, there exists a major gap in investigating the domain adaptation

Yufan Cao, Tunhou Zhang, Wei Wen, Feng Yan

Weight-sharing Neural Architecture Search (WS-NAS) provides an efficient mechanism for developing end-to-end deep recommender models. However, in complex search spaces, distinguishing between superior and inferior architectures (or paths) is challenging. This challenge is compounded by the limited coverage of the supernet and the co-adaptation of subnet weig

Srijan Das, Tanmay Jain, Dominick Reilly, Pranav Balaji

Vision Transformers (ViTs) have become ubiquitous in computer vision. Despite their success, ViTs lack inductive biases, which can make it difficult to train them with limited data. To address this challenge, prior studies suggest training ViTs with self-supervised learning (SSL) and fine-tuning sequentially. However, we observe that jointly optimizing ViTs

Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak

Pretrained language models are commonly aligned with human preferences and downstream tasks via reinforcement finetuning (RFT), which refers to maximizing a (possibly learned) reward function using policy gradient algorithms. This work identifies a fundamental optimization obstacle in RFT: we prove that the expected gradient for an input vanishes when its re

Konstantin Grishunin, Axel Weiss, Dario Colombo, Mélanie Chevance

Stellar feedback plays a crucial role in star formation and the life cycle of molecular clouds. The intense star formation region 30 Doradus, which is located in the Large Magellanic Cloud (LMC), is a unique target for detailed investigation of stellar feedback owing to the proximity of the hosting galaxy and modern observational capabilities that together a

Xinyuan Chen, Yaohui Wang, Lingjun Zhang, Shaobin Zhuang

Recently video generation has achieved substantial progress with realistic results. Nevertheless, existing AI-generated videos are usually very short clips ("shot-level") depicting a single scene. To deliver a coherent long video ("story-level"), it is desirable to have creative transition and prediction effects across different clips. This paper presents a

Adwait Gaikwad, Victor Gorbenko, Andrea L. Guerrieri

The worldsheet axion plays a crucial role in the dynamics of the Yang-Mills confining flux tubes. According to the lattice measurements, its mass is of order the string tension and its coupling is close to a certain critical value. Using the S-matrix Bootstrap, we construct non-perturbative $2 \to 2$ branon scattering amplitudes which also feature a weakly c

Florian Honz, Nemanja Vokić, Philip Walther, Hannes Hübel

We investigate a polarization-encoded BB84-QKD transmitter that is simplified from an architectural and technological point-of-view, demonstrating a silicon emitter sourcing a low-complexity polarization modulator for secure-key generation at a raw-key rate of 2.8kb/s and QBER of 10.47%, underpinning the feasibility of an all-silicon QKD transmitter.

Victoria Lin, Louis-Philippe Morency, Eli Ben-Michael

As language technologies gain prominence in real-world settings, it is important to understand how changes to language affect reader perceptions. This can be formalized as the causal effect of varying a linguistic attribute (e.g., sentiment) on a reader's response to the text. In this paper, we introduce Text-Transport, a method for estimation of causal effe

Adrian B. Culver, Pratik Sathe, Albert Brown, Fenner Harper

Periodically driven systems often exhibit behavior distinct from static systems. In single-particle, static systems, any amount of disorder generically localizes all eigenstates in one dimension. In contrast, we show that in topologically nontrivial, single-particle Floquet loop drives with chiral symmetry in one dimension, a localization-delocalization tran