July 2023 arXiv papers — page 68

Hugo Maruri-Aguilar

We propose a new method for computing the lasso path, using the fact that the Manhattan norm of the coefficient vector is linear over every orthant of the parameter space. We use simple calculus and present an algorithm in which the lasso path is series of orthant moves. Our proposal gives the same results as standard literature, with the advantage of neat i

Michael L. Brodie

Data science is not a science. It is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery that is not otherwise possible and can be beyond human reasoning. It is changing our world practically and profoundly already widely deployed in tens of thousands of applications in every discipline in an AI Arms Race that,

Andrei V. Konstantinov, Lev V. Utkin

A new computationally simple method of imposing hard convex constraints on the neural network output values is proposed. The key idea behind the method is to map a vector of hidden parameters of the network to a point that is guaranteed to be inside the feasible set defined by a set of constraints. The mapping is implemented by the additional neural network

Jacintha Walters, Diptish Dey, Debarati Bhaumik, Sophie Horsman

The EU AI Act is the proposed EU legislation concerning AI systems. This paper identifies several categories of the AI Act. Based on this categorization, a questionnaire is developed that serves as a tool to offer insights by creating quantitative data. Analysis of the data shows various challenges for organizations in different compliance categories. The in

Somayeh Ghanbarzadeh, Hamid Palangi, Yan Huang, Radames Cruz Moreno

The reusability of state-of-the-art Pre-trained Language Models (PLMs) is often limited by their generalization problem, where their performance drastically decreases when evaluated on examples that differ from the training dataset, known as Out-of-Distribution (OOD)/unseen examples. This limitation arises from PLMs' reliance on spurious correlations, which

M. Ali Saif, Bassam M. Mughalles

Recently, machine learning algorithms have been used remarkably to study the equilibrium phase transitions, however there are only a few works have been done using this technique in the nonequilibrium phase transitions. In this work, we use the supervised learning with the convolutional neural network (CNN) algorithm and unsupervised learning with the densit

Zahra Gharaee, ZeMing Gong, Nicholas Pellegrino, Iuliia Zarubiieva

In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is taxonomically classified by an expert, and also has associated genetic information including raw nucleotide barcode sequences and assigned barcode index numbers, which are genetically-based proxies for species

Austin Davis, Samuel Jones, John J. Charonko, Chris M. Malone

Comparisons studies between simulated variable density turbulent flows often consist of direct graphical representations where the level of agreement is determined by eye. This work demonstrates a formal validation methodology using an existing validation framework to examine the agreement between a simulated variable density jet flow and corresponding exper

Younghoon Kim, Marie-Christine Düker, Zachary F. Fisher, Vladas Pipiras

This work considers estimation and forecasting in a multivariate, possibly high-dimensional count time series model constructed from a transformation of a latent Gaussian dynamic factor series. The estimation of the latent model parameters is based on second-order properties of the count and underlying Gaussian time series, yielding estimators of the underly

B. Manuel Hegelich, Calin I. Hojbota, Lance A. Labun, Ou Z. Labun

High intensity laser facilities are expanding their scope from laser and particle-acceleration test beds to user facilities and nuclear physics experiments. A basic goal is to confirm long-standing predictions of strong-field quantum electrodynamics, but with the advent of high-repetition rate laser experiments producing GeV-scale electrons and photons, nove

Lynn D. Matthews

Intense mass loss through cool, low-velocity winds is a defining characteristic of low-to-intermediate mass stars during the asymptotic giant branch (AGB) evolutionary stage. Such winds return up ~80% of the initial stellar mass to the interstellar medium and play a major role in enriching it with dust and heavy elements. A challenge to understanding the phy

Irea Mosquera-Lois, Seán R. Kavanagh, Johan Klarbring, Kasper Tolborg

Defects determine many important properties and applications of materials, ranging from doping in semiconductors, to conductivity in mixed ionic-electronic conductors used in batteries, to active sites in catalysts. The theoretical description of defect formation in crystals has evolved substantially over the past century. Advances in supercomputing hardware

S. A. K. Wijethunga, N. Eddy, J. Eldred, C. Y. Tan

Fermilab Booster synchrotron requires an intensity upgrade from 4.5x1012 to 6.5x1012 protons per pulse as a part of Fermilab's Proton Improvement Plan-II (PIP-II). One of the factors which may limit the high-intensity performance is the fast transverse instabilities caused by electron cloud effects. According to the experience in the Recycler, the electron c

Shiping Cao, Zhen-Qing Chen, Takashi Kumagai

Let $(K,d)$ be a connected compact metric space and $p\in (1, \infty)$. Under the assumption of \cite[Assumption 2.15]{Ki2} and the conductive $p$-homogeneity, we show that $\mathcal{W}^p(K)\subset C(K)$ holds if and only if $p>\operatorname{dim}_{AR}(K,d)$, where $\mathcal{W}^p(K)$ is Kigami's $(1,p)$-Sobolev space and $\operatorname{dim}_{AR}(K,d)$ is the

Bosu Choi, Jihun Han, Yoonsang Lee

Regularization is a critical technique for ensuring well-posedness in solving inverse problems with incomplete measurement data. Traditionally, the regularization term is designed based on prior knowledge of the unknown signal's characteristics, such as sparsity or smoothness. Inhomogeneous regularization, which incorporates a spatially varying exponent $p$

Daniel Alpay, Liora Mayats-Alpay

Similarity metric which is not positive definite, and present a general theorem which provides a large family of similarity metrics which are positive definite.

Paul F. Goldsmith, William D. Langer, Youngmin Seo, Jorge Pineda

We present analysis of OI 63 micron and CO $J$ = 5-4 and 8-7 multi-position data in the W3A region and use it to develop a model for the extended low-density foreground gas that produces absorption features in the OI and $J$ = 5-4 CO lines. We employ the extinction to the exciting stars of the background HII region to constrain the total column density of th

Satoru Hayami

We investigate multiple-$Q$ instability on a square lattice at particular ordering wave vectors. We find that a superposition of quadruple-$Q$ spin density waves, which are connected by fourfold rotational and mirror symmetries, gives rise to a checkerboard bubble lattice with a collinear spin texture as a result of the geometry among the constituent orderin

Ziteng Sun, Ananda Theertha Suresh, Aditya Krishna Menon

Training machine learning models with differential privacy (DP) has received increasing interest in recent years. One of the most popular algorithms for training differentially private models is differentially private stochastic gradient descent (DPSGD) and its variants, where at each step gradients are clipped and combined with some noise. Given the increas

Shima Foolad, Kourosh Kiani

Despite the significant progress made by transformer models in machine reading comprehension tasks, they still fall short in handling complex reasoning tasks due to the absence of explicit knowledge in the input sequence. To address this limitation, many recent works have proposed injecting external knowledge into the model. However, selecting relevant exter

Xinran Zhao, Hongming Zhang, Xiaoman Pan, Wenlin Yao

Although large-scale pre-trained language models (PTLMs) are shown to encode rich knowledge in their model parameters, the inherent knowledge in PTLMs can be opaque or static, making external knowledge necessary. However, the existing information retrieval techniques could be costly and may even introduce noisy and sometimes misleading knowledge. To address

Lukas Mauth

We are going to show an exact formula for lower $1$-run overpartitions. The generating function is of mixed mock-modular type with an overall weight $0.$ We will apply an extended version of the classical Circle Method. The approach requires bounding modified Kloosterman sums and Mordell integrals.

Chen Li, Xiaoling Hu, Chao Chen

Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised setting, when most training labels are u

Ivan V. Bajić, Teo Saeedi-Bajić, Kai Saeedi-Bajić

When developing technologies for the Metaverse, it is important to understand the needs and requirements of end users. Relatively little is known about the specific perspectives on the use of the Metaverse by the youngest audience: children ten and under. This paper explores the Metaverse from the perspective of a young gamer. It examines their understanding

Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, Prasanna Balaprakash

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we intr

Franklin Abodo

Traffic simulation software is used by transportation researchers and engineers to design and evaluate changes to roadways. These simulators are driven by models of microscopic driver behavior from which macroscopic measures like flow and congestion can be derived. Many models are designed for a subset of possible traffic scenarios and roadway configurations

Ved Piyush, Yuchen Yan, Yuzhen Zhou, Yanbin Yin

Deep Learners (DLs) are the state-of-art predictive mechanism with applications in many fields requiring complex high dimensional data processing. Although conventional DLs get trained via gradient descent with back-propagation, Kalman Filter (KF)-based techniques that do not need gradient computation have been developed to approximate DLs. We propose a mult

V. M. Sampaio, A. Aragón-Salamanca, M. R. Merrifield, R. R. de Carvalho

We exploit a sample of 80,000 SDSS central galaxies to investigate the effect of AGN feedback on their evolution. We trace the demographics of optically-selected AGN (Seyferts) as a function of their internal properties and environment. We find that the preeminence of AGN as the dominant ionising mechanism increases with stellar mass, overtaking star-formati

Ameesh Shah, Marcell Vazquez-Chanlatte, Sebastian Junges, Sanjit A. Seshia

Active learning is a well-studied approach to learning formal specifications, such as automata. In this work, we extend active specification learning by proposing a novel framework that strategically requests a combination of membership labels and pair-wise preferences, a popular alternative to membership labels. The combination of pair-wise preferences and

C. Contreras-Martinez, B. Giaccone, O. Melnychuk, A. Netepenko

Two 2.6 GHz SRF cavities are being used for a dark photon search at the vertical test stand (VTS) in FNAL, for the second phase of the Dark SRF experiment. During testing at 2 K the cavities experience frequency detuning caused by microphonics and slow frequency drifts. The experiment requires that the two cavities have the same frequency within the cavity's

Zhengliang Liu, Zihao Wu, Mengxuan Hu, Bokai Zhao

In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists. Our methodology encompasses the utilization of LLMs to generate comprehensible patient clusters, formulate medication plans, and forecast patient outcomes. We conduct ou

Clemens Arndt, Sören Dittmer, Nick Heilenkötter, Meira Iske

Learning-based methods for inverse problems, adapting to the data's inherent structure, have become ubiquitous in the last decade. Besides empirical investigations of their often remarkable performance, an increasing number of works addresses the issue of theoretical guarantees. Recently, [3] exploited invertible residual networks (iResNets) to learn provabl

Rodrigo Castellon, Achintya Gopal, Brian Bloniarz, David Rosenberg

The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance. While traditional marginal-based methods have achieved impressive results, recent work has shown that deep learning-based approaches tend to lag behind. In this work, we present Differentially-Private TaBular AutoRegressive Transformer (DP-TBART),

Xingri Geng

We consider the Fisher-KPP equation on a three-dimensional domain surrounded by a thin layer whose diffusion rates are drastically different from that in the bulk. The bulk is isotropic, while the layer is considered to be anisotropic and ``optimally aligned", where the normal direction is always an eigenvector of the diffusion tensor. To see the effect of t

David M. Schmidtt

A generalization of the 4d Chern-Simons theory action introduced by Costello and Yamazaki is presented. We apply general arguments from symplectic geometry concerning the Hamiltonian action of a symmetry group on the space of gauge connections defined on a 4d manifold and construct an action functional that is quadratic in the moment map associated to the gr

E. Conti

The aim of this paper is to present an original approach that takes advantage from the geometric features of strictly convex functions to tackle the problem of finding the minimum from another perspective. The general idea is that near the point of minimum, the function is 'v-shaped' and so we can reduce the interval where the minimum lies by finding the int

M. J. Yantovski-Barth, Jeffrey A. Newman, Biprateep Dey, Brett H. Andrews

Galaxy clusters enable unique opportunities to study cosmology, dark matter, galaxy evolution, and strongly-lensed transients. We here present a new cluster-finding algorithm, CluMPR (Clusters from Masses and Photometric Redshifts), that exploits photometric redshifts (photo-z's) as well as photometric stellar mass measurements. CluMPR uses a 2-dimensional b

Ruben Ascoli, Livia Betti, Justin Cheigh, Alex Iosevich

Let $\mathbb{F}_q^d$ be the $d$-dimensional vector space over the finite field with $q$ elements. For a subset $E\subseteq \mathbb{F}_q^d$ and a fixed nonzero $t\in \mathbb{F}_q$, let $\mathcal{H}_t(E)=\{h_y: y\in E\}$, where $h_y$ is the indicator function of the set $\{x\in E: x\cdot y=t\}$. Two of the authors, with Maxwell Sun, showed in the case $d=3$ th

C. Contreras-Martinez, B. Giaccone, I. Gonin, T. Khabiboulline

At FNAL two single cell 2.6 GHz SRF cavities are being used to search for dark photons, the experiment can be conducted at 2 K or in a dilution refrigerator. Precise frequency tuning is required for these two cavities so they can be matched in frequency. A cooling capacity constraint on the dilution refrigerator only allows piezo actuators to be part of the

Gustavo J. Turiaci

We describe two puzzles that arise from a semiclassical treatment of near-extremal black hole thermodynamics. Both puzzles are resolved by realizing that quantum corrections become arbitrarily large at low temperatures, and we explain how the spectrum and dynamics of near-extremal black holes are modified. This analysis also implies that without low energy s

Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang

Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enabled data-driven forecasting models using deep learning techniques. These models have shown promise for diverse

Ish Gupta, Chaitanya Afle, K. G. Arun, Ananya Bandopadhyay

Gravitational-wave observations by the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo have provided us a new tool to explore the Universe on all scales from nuclear physics to the cosmos and have the massive potential to further impact fundamental physics, astrophysics, and cosmology for decades to come. In this paper we have studied th

Rebwar Khalid Hamad, Tarik A. Rashid

This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative st

Jacek Gosciniak, Jacob B. Khurgin

Photonics integrated circuits have a huge potential to serve as a framework for a new class of information processing machines and can enable ultrafast artificial neural networks. They can overcome the existing speed and power limits of the electronic processing elements and provide additional benefits of photonics such as high-bandwidth, sub-nanosecond late

I. K. Kozlov

We study commutative subalgebras in the symmetric algebra $S(\mathfrak{g})$ of a finite-dimensional Lie algebra $\mathfrak{g}$. A. M. Izosimov introduced extended Mischenko-Fomenko subalgebras $\tilde{\mathcal{F}}_a$ and gave a completeness criterion for them. We generalize his construction and extend Mischenko-Fomenko subalgebras with the shifts of all semi

Cong Hoang, Kabe Moen, Carlos Pérez

We investigate Sobolev inequalities for several rough operators. We prove that several operators satisfy a pointwise bound by the Riesz potential applied to the gradient. From this inequality, we derive several new Sobolev-type inequalities with an operator on the left-hand side.

Kamalesh Saha

Herzog, Hibi, and Zheng classified the Cohen-Macaulay edge ideals of chordal graphs. In this paper, we classify Cohen-Macaulay edge ideals of (vertex) weighted oriented chordal and simplicial graphs, a more general class of monomial ideals. In particular, we show that the Cohen-Macaulay property of these ideals is equivalent to the unmixed one and hence, ind

Javier Sendra-Arranz

Let $X$ be a hypersurface, of degree $d$, in an $n$--dimensional projective space. The Hessian map is a rational map from $X$ to the projective space of symmetric matrices that sends a point $p\in X$ to the Hessian matrix of the defining polynomial of $X$ evaluated at $p$. The Hessian correspondence is the map that sends a hypersurface to its Hessian variety

V. Chouhan, D. Bice, D. Burk, M. K. Ng

Surface preparation of niobium superconducting RF cavities is a critical step for achieving good RF performance under the superconducting state. Surface defect, roughness, and contamination affect the accelerating gradient and quality factor of the cavities. We report surface inspection methods used to control the surface processing of 650 MHz cavities desig

John Bulava, Bárbara Cid-Mora, Andrew D. Hanlon, Ben Hörz

This letter presents the first lattice QCD computation of the coupled channel $\pi\Sigma-\bar{K}N$ scattering amplitudes at energies near $1405\,{\rm MeV}$. These amplitudes contain the resonance $\Lambda(1405)$ with strangeness $S=-1$ and isospin, spin, and parity quantum numbers $I(J^P)=0(1/2^-)$. However, whether there is a single resonance or two nearby

Jack E. Birkin, Taylor A. Hutchison, Brian Welch, Justin S. Spilker

We present the first spatially resolved maps of gas-phase metallicity for two dust-obscured star-forming galaxies (DSFGs) at $z\sim$ 4, from the JWST TEMPLATES Early Release Science program, derived from NIRSpec integral field unit spectroscopy of the H$\alpha$ and [NII] emission lines. Empirical optical line calibrations are used to determine that the sourc

Ulrik Brandes, Gordana Marmulla, Ivana Smokovic

In the run-up to any major sports tournament, winning probabilities of participants are publicized for engagement and betting purposes. These are generally based on simulating the tournament tens of thousands of times by sampling from single-match outcome models. We show that, by virtue of the tournament schedule, exact computation of winning probabilties ca

Ali Chaouche, Menouar Boulif

The graph partitioning problem (GPP) is among the most challenging models in optimization. Because of its NP-hardness, the researchers directed their interest towards approximate methods such as the genetic algorithms (GA). The edge-based GA has shown promising results when solving GPP. However, for big dense instances, the size of the encoding representatio

Kavya. R. Nair, M. S. Sunitha

Topological indices play a vital role in the area of graph theory and fuzzy graph (FG) theory. It has wide applications in the areas such as chemical graph theory, mathematical chemistry, etc. Topological indices produce a numerical parameter associated with a graph. Numerous topological indices are studied due to its applications in various fields. In this

Shahin Atakishiyev, Mohammad Salameh, Housam Babiker, Randy Goebel

The end-to-end learning ability of self-driving vehicles has achieved significant milestones over the last decade owing to rapid advances in deep learning and computer vision algorithms. However, as autonomous driving technology is a safety-critical application of artificial intelligence (AI), road accidents and established regulatory principles necessitate

Kavya. R. Nair, M. S. Sunitha

The concepts of domination and topological index hold great significance within the realm of graph theory. Therefore, it is pertinent to merge these concepts to derive the domination index of a graph. A novel concept of the domination index is introduced, which utilizes the domination degree of a vertex. The domination degree of a vertex a is defined as the

Dinushi Munasinghe, Ben Webster

We study the representation theory of the type B Schur algebra $\mathcal{L}^n(m)$ with unequal parameters introduced in work of Lai, Nakano and Xiang. For generic values of $q,Q$, this algebra is semi-simple and Morita equivalent to the Hecke algebra, but for special values, its category of modules is more complicated. We study this representation theory by

Jonas Gillberg, Joakim Bergdahl, Alessandro Sestini, Andrew Eakins

Going from research to production, especially for large and complex software systems, is fundamentally a hard problem. In large-scale game production, one of the main reasons is that the development environment can be very different from the final product. In this technical paper we describe an effort to add an experimental reinforcement learning system to a

Meike Nauta, Johannes H. Hegeman, Jeroen Geerdink, Jörg Schlötterer

Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis support on real-world medical imaging data. PIP-Net learns human-understandable prototypical image parts and we ev

Thomas Takacs

In this study we consider domains that are composed of an infinite sequence of self-similar rings and corresponding finite element spaces over those domains. The rings are parameterized using piecewise polynomial or tensor-product B-spline mappings of degree $q$ over quadrilateral meshes. We then consider finite element discretizations which, over each ring,

Song Li, Anton Pershin, Pei Li, Adam Gali

Van der Waals structures present a unique opportunity for tailoring material interfaces and integrating photonic functionalities. By precisely manipulating the twist angle and stacking sequences, it is possible to elegantly tune and functionalize the electronic and optical properties of layered van der Waals structures. Among these materials, two-dimensional

Kritika Sharu, Shashwata Chattopadhyay, K. N. Prajapati, J. Mitra

Modeling light-matter interaction in hybrid plasmonic materials is vital to their widening relevance from optoelectronics to photocatalysis. Here, we explore photoluminescence from ZnO nanorods (ZNR) embedded with gold nanoparticles (Au NPs). A progressive increase in Au NP concentration introduces significant structural disorder and defects in the ZNRs, whi

K. Howard, Y. -K. Kim, D. Bafia, A. Grassellino

The onset of high field Q-slope (HFQS) around 25 MV/m prevents cavities in electropolished (EP) condition from reaching high quality factors at high gradients due to the precipitation of niobium hydrides during cooldown. These hydrides are non-superconducting at 2 K, and contribute to losses such as Q disease and HFQS. We are interested in exploring the para

Maria Korolik, Rachael M. Roettenbacher, Debra A. Fischer, Stephen R. Kane

To accurately characterize the planets a star may be hosting, stellar parameters must first be well-determined. $\tau$ Ceti is a nearby solar analog and often a target for exoplanet searches. Uncertainties in the observed rotational velocities have made constraining $\tau$ Ceti's inclination difficult. For planet candidates from radial velocity (RV) observat

Anand Manaparambil, Ireneusz Weymann

The spin-resolved thermoelectric transport properties of correlated nanoscale junctions, consisting of a quantum dot/molecule asymmetrically coupled to external ferromagnetic contacts, are studied theoretically in the far-from-equilibrium regime. One of the leads is assumed to be strongly coupled to the quantum dot resulting in the development of the Kondo e

Dominik Neuenfeld, Manu Srivastava

Holography on cutoff surfaces can appear to be in tension with causality. For example, as argued by Omiya and Wei [arxiv:2107.01219], double holography seemingly allows for superluminal signalling. In this paper we argue that the brane description of double holography should be treated as an effective theory and demonstrate that causality violations due to f

M. V. S. Saketh, Zihan Zhou, Mikhail M. Ivanov

We match scattering amplitudes in point particle effective field theory (EFT) and general relativity to extract low frequency dynamical tidal responses of rotating (Kerr) black holes to all orders in spin. In the conservative sector, we study local worldline couplings that correspond to the time-derivative expansion of the black hole tidal response function.

Xin Nie, Jun Li, Trinanjan Datta, Dao-Xin Yao

Spin-rotation coupling (SRC) is a fundamental phenomenon that connects electronic spins with the rotational motion of a medium. We elucidate the Einstein-de Haas (EdH) effect and its inverse with SRC as the microscopic mechanism using the dynamic spin-lattice equations derived by elasticity theory and Lagrangian formalism. By applying the coupling equations

Chunxiao Liu, Shubhayu Chatterjee, Thomas Scaffidi, Erez Berg

A recent experiment on the bulk compound 4Hb-TaS$_2$ reveals an unusual time-reversal symmetry-breaking superconducting state that possesses a magnetic memory not manifest in the normal state. Here we provide a mechanism for this observation by studying the magnetic and electronic properties of 4Hb-TaS$_2$. We discuss the criterion for a small magnetization

Costante Bellettini, Kobe Marshall-Stevens

In compact Riemannian manifolds of dimension 3 or higher with positive Ricci curvature, we prove that every constant mean curvature hypersurface produced by the Allen-Cahn min-max procedure of Bellettini-Wickramasekera (with constant prescribing function) is a local minimiser of the natural area-type functional around each isolated singularity. In particular

Rui Wang, Sophokles Ktistakis, Siwei Zhang, Mirko Meboldt

The surgical usage of Mixed Reality (MR) has received growing attention in areas such as surgical navigation systems, skill assessment, and robot-assisted surgeries. For such applications, pose estimation for hand and surgical instruments from an egocentric perspective is a fundamental task and has been studied extensively in the computer vision field in rec

Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo

Observational data from astronomical imaging surveys contain information about a variety of source populations and environments, and its complexity will increase substantially as telescopes become more sensitive. Even for existing observations, measuring the correlations between point-like and diffuse emission can be crucial to correctly inferring the proper

Avik Banerjee, Toshali Mitra, Ayan Mukhopadhyay, Alexander Soloviev

Gubser flow is an axis-symmetric and boost-invariant evolution in a relativistic quantum field theory which is best studied by mapping $\mathbf{R}^{3,1}$ to $dS_{3}\times \mathbf{R}$ when the field theory has conformal symmetry. We show that at late de-Sitter time, which corresponds to large proper time and central region of the future wedge within $\mathbf{

Leron Borsten, Branislav Jurco, Hyungrok Kim, Tommaso Macrelli

We construct a simple Lorentz-invariant action for maximally supersymmetric self-dual Yang-Mills theory that manifests colour-kinematics duality. We also show that this action double copies to a known action for maximally supersymmetric self-dual gravity. Both actions live on twistor space and illustrate nicely the homotopy algebraic perspective on the doubl

Nicolás Bernal, Yasaman Farzan, Alexei Yu. Smirnov

We propose a novel explanation for the 18 TeV gamma ray from GRB 221009A observed by LHAASO. High-energy neutrinos are converted into axion-like particles (ALPs) via their interaction with the cosmic neutrino background. Subsequently, ALPs are converted into high-energy photons in the magnetic field of our galaxy. We compute the fluxes of neutrinos, ALPs, an

Matthew R. Gomer, Sebastian Ertl, Luca Biggio, Han Wang

Strong gravitational lensing is a powerful tool to provide constraints on galaxy mass distributions and cosmological parameters, such as the Hubble constant, $H_0$. Nevertheless, inference of such parameters from images of lensing systems is not trivial as parameter degeneracies can limit the precision in the measured lens mass and cosmological results. Exte

Xu-Xiang Li, Zhe Ren, Jiang-Hao Yu

Finding all possible UV resonances of effective operators is an important task in the bottom-up approach of effective field theory. We present all the tree-level UV resonances for the dimension-5, -6 and -7 operators in the Standard Model effective field theory (SMEFT), and then obtain the correspondence between the UV resonances and the effective operators

Edoardo Alessandroni, Sergi Ramos-Calderer, Ingo Roth, Emiliano Traversi

A major obstacle for quantum optimizers is the reformulation of constraints as a quadratic unconstrained binary optimization (QUBO). Current QUBO translators exaggerate the weight $M$ of the penalty terms. Classically known as the "Big-$M$" problem, the issue becomes even more daunting for quantum solvers, since it affects the physical energy scale. We take

Kiril Hristov, Minwoo Suh

We construct new classes of supersymmetric AdS$_2 \times {\Sigma}$ solutions of 4d gauged supergravity in presence of charged hypermultiplet scalars, with ${\Sigma}$ the complex weighted projective space known as a spindle. These solutions can be viewed as near-horizon geometries of asymptotically Anti de-Sitter (AdS$_4$) black holes with magnetic fluxes tha

Connor A. Occhialini, Joshua J. Sanchez, Qian Song, Gilberto Fabbris

The origin of nematicity in FeSe remains a critical outstanding question towards understanding unconventional superconductivity in proximity to nematic order. To understand what drives the nematicity, it is essential to determine which electronic degree of freedom admits a spontaneous order parameter independent from the structural distortion. Here, we use X

Wen-Han Kao, Natalia B. Perkins, Gábor B. Halász

Spin vacancies in the non-Abelian Kitaev spin liquid are known to harbor Majorana zero modes, potentially enabling topological quantum computing at elevated temperatures. Here, we study the spectroscopic signatures of such Majorana zero modes in a scanning tunneling setup where a non-Abelian Kitaev spin liquid with a finite density of spin vacancies forms a

José María Arroyo-Polonio, Giuseppina Battaglia, Guillaume F. Thomas, Michael J. Irwin

Aims: We aim to compute the binary fraction of "classical" dwarf spheroidal galaxies (dSphs) that are satellites of the Milky Way (MW). This value can offer insights into the binary fraction in environments that are less dense and more metal-poor than our own galaxy. Additionally, knowledge of the binary fraction in dwarf galaxies is important with respect t

András L. Szabó, Mark H. Fischer, Manfred Sigrist

Recent experiments observed a phase transition within the superconducting regime of the heavy-fermion system CeRh$_2$As$_2$ when subjected to a $c$-axis magnetic field. This phase transition has been interpreted as a parity switching from even to odd parity as the field is increased, and is believed to be of first order. If correct, this scenario provides a

Michal Geyer, Omer Bar-Tal, Shai Bagon, Tali Dekel

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editi

Anke Biekötter, Javier Fuentes-Martín, Anne Mareike Galda, Matthias Neubert

In the presence of an axion or axion-like particle (ALP) that couples to the Standard Model via dimension-five interactions, dimension-six SMEFT interactions are generated via renormalization-group evolution. As many of these SMEFT contributions are experimentally tightly constrained, this "ALP-SMEFT interference" can be used to derive indirect bounds on the

Yi Jiang, Ziyin Song, Tiannian Zhu, Zhong Fang

Symmetries of three-dimensional periodic scalar fields are described by 230 space groups (SGs). Symmetries of three-dimensional periodic (pseudo-) vector fields, however, are described by the spin-space groups (SSGs), which were initially used to describe the symmetries of magnetic orders. In SSGs, the real-space and spin degrees of freedom are unlocked in t

Elie Hammou, Zahari Kassabov, Maeve Madigan, Michelangelo L. Mangano

The interpretation of LHC data, and the assessment of possible hints of new physics, require the precise knowledge of the proton structure in terms of parton distribution functions (PDFs). We present a systematic methodology designed to determine whether and how global PDF fits might inadvertently 'fit away' signs of new physics in the high-energy tails of t

Xiaobing Chen, Jun Ren, Yanzhou Zhu, Yutong Yu

Those fundamental physical properties, such as phase transitions, Weyl fermions, and spin excitation, in all magnetic ordered materials, were ultimately believed to rely on the symmetry theory of magnetic space groups. Recently, it has come to light that a more comprehensive group, known as the spin space group (SSG), which combines separate spin and spatial

Lorenzo Annulli, Carlos A. R. Herdeiro

In linear perturbation theory, a static perturber in the vicinity of a Schwarzschild black hole (BH) enhances [suppresses] the Gauss-Bonnet (GB) curvature invariant, $\mathcal{R}_{\rm GB}$, in the high [low] tide regions. By analysing exact solutions of the vacuum Einstein field equations describing one or two BHs immersed in a multipolar gravitational field

Chiara Arina, Benjamin Fuks, Jan Heisig, Michael Krämer

We analyse six classes of t-channel dark matter simplified models in which the Standard Model field content is extended by a coloured mediator and a dark matter state. The two new states are enforced to be odd under a new parity, while all Standard Model fields are taken even so that dark matter stability is guaranteed. We study several possibilities for the

Shashank Shalgar, Irene Tamborra

In core-collapse supernovae, the neutrino density is so large that neutrino flavor instabilities, leading to flavor conversion, can be triggered by the forward scattering of neutrinos among each other, if a crossing between the angular distributions of electron neutrinos and antineutrinos exists (fast instability in the limit of vanishing vacuum frequency) o

Nemanja Kaloper

Linear axion monodromy models modulated with higher powers of fields naturally realize the quantum-mechanical flux discharge mechanism for relaxing the cosmological constant toward zero. Working with multiple copies of superposed linear and quadratic flux monodromies, each copy spanned by a pair of fluxes, we show that when the axion is very massive and so e

Zhenyu Xiao, Jianzhou Zhao, Yanqi Li, Ryuichi Shindou

In this work, we exhaust all the spin-space symmetries, which fully characterize collinear, non-collinear, commensurate, and incommensurate spiral magnetism, and investigate enriched features of electronic bands that respect these symmetries. We achieve this by systematically classifying the so-called spin space groups (SSGs) - joint symmetry groups of spati

J. M. Diego, Bangzheng Sun, Haojing Yan, Lukas J. Furtak

We report the discovery of Mothra, an extremely magnified monster star, likely a binary system of two supergiant stars, in one of the strongly lensed galaxies behind the galaxy cluster MACS0416. The star is in a galaxy with spectroscopic redshift $z=2.091$ in a portion of the galaxy that is parsecs away from the cluster caustic. The binary star is observed o

Carl Beadle, Sebastian A. R. Ellis, Jérémie Quevillon, Pham Ngoc Hoa Vuong

We show that the QCD axion couples to the electromagnetic kinetic term at one loop. The result is that if axions make up dark matter, they induce temporal variation of the fine structure constant $\alpha$, which is severely constrained. We recast these constraints on the QCD axion parameter space. We also discuss how to generalise our finding to axion-like p

Thomas Steingasser, David I. Kaiser

Both parameters in the Higgs field's potential, its mass and quartic coupling, appear fine-tuned to near-critical values, which gives rise to the hierarchy problem and the metastability of the electroweak vacuum. Whereas such behavior appears puzzling in the context of particle physics, it is a common feature of dynamical systems, which has led to the sugges

Yanou Cui, Soubhik Kumar, Raman Sundrum, Yuhsin Tsai

Identifying the anisotropies in a cosmologically sourced stochastic gravitational wave background (SGWB) would be of significance in shedding light on the nature of primordial inhomogeneities. For example, if SGWB carries isocurvature fluctuations, it would provide evidence for a multi-field inflationary origin of these inhomogeneities. However, this is chal

Tobias Müller, Dominik Kiese, Nils Niggemann, Björn Sbierski

For decades, frustrated quantum magnets have been a seed for scientific progress and innovation in condensed matter. As much as the numerical tools for low-dimensional quantum magnetism have thrived and improved in recent years due to breakthroughs inspired by quantum information and quantum computation, higher-dimensional quantum magnetism can be considered

Benjamin F. Schiffer, Dyon van Vreumingen, Jordi Tura, Stefano Polla

Transitions out of the ground space limit the performance of quantum adiabatic algorithms, while hardware imperfections impose stringent limitations on the circuit depth. We propose an adiabatic echo verification protocol which mitigates both coherent and incoherent errors, arising from non-adiabatic transitions and hardware noise, respectively. Quasi-adiaba

Hannah Schäfer, Marvin Zanke, Yannis Korte, Bastian Kubis

We perform a theoretical analysis of the semileptonic decays $\eta^{(\prime)} \to \pi^0 \ell^+ \ell^-$ and $\eta' \to \eta \ell^+ \ell^-$, where $\ell = e, \mu$, via a charge-conjugation-conserving two-photon mechanism. The underlying form factors are modeled using vector-meson dominance, phenomenological input, and $\mathrm{U}(3)$ flavor symmetry. We consid