May 2023 arXiv papers — page 115

Satoshi Kikuta, Masami Ouchi, Takatoshi Shibuya, Yongming Liang

We present 20,567 Ly$\alpha$ emitters (LAEs) at $z=2.2-7.3$ that are photometrically identified by the SILVERRUSH program in a large survey area up to 25 deg$^2$ with deep images of five broadband filters (grizy) and seven narrowband filters targeting Ly$\alpha$ lines at $z=2.2$, $3.3$, $4.9$, $5.7$, $6.6$, $7.0$, and $7.3$ taken by the Hyper Suprime-Cam Sub

Giacomo Fragione, Abraham Loeb

Neutron stars are born out of core-collapse supernovae, and they are imparted natal kicks at birth as a consequence of asymmetric ejection of matter and possibly neutrinos. Unless the force resulting from the kicks is exerted exactly at their center, it will also cause the neutron star to rotate. In this paper, we discuss the possibility that neutron stars m

Lisa V. Drummond, Alexandra G. Hanselman, Devin R. Becker, Scott A. Hughes

The study of spinning bodies moving in curved spacetime has relevance to binary black hole systems with large mass ratios, as well as being of formal interest. At zeroth order in a binary's mass ratio, the smaller body moves on a geodesic of the larger body's spacetime. Post-geodesic corrections describing forces driving the small body's worldline away from

Shahar Hod

In this essay it is proved that there are black holes that are dangerously cold. In particular, by analyzing the emission spectra of highly charged black holes we reveal the fact that near-extremal black holes whose Bekenstein-Hawking temperatures lie in the regime $T_{\text{BH}}\lesssim m^6_e/e^3$ may turn into horizonless naked singularities, thus violatin

Emilie R. Simpson, Tara Fetherolf, Stephen R. Kane, Joshua Pepper

Both direct and indirect methods of exoplanet detection rely upon detailed knowledge of the potential host stars. Such stellar characterization allows for accurate extraction of planetary properties, as well as contributing to our overall understanding of exoplanetary system architecture. In this analysis, we examine the photometry of 264 known exoplanet hos

Debasish Banerjee, Emilie Huffman

Ab-initio Monte Carlo simulations of strongly-interacting fermionic systems are plagued by the fermion sign problem, making the non-perturbative study of many interesting regimes of dense quantum matter, or of theories of odd numbers of fermion flavors, challenging. Moreover, typical fermion algorithms require the computation (or sampling) of the fermion det

Miha Papič, Adrian Auer, Inés de Vega

Large-scale quantum computation requires a reliable assessment of the main sources of error in the implemented quantum gates. To this aim, we provide a learning-based framework that extracts the contribution of each physical error source to the infidelity of a series of gates, together with an uncertainty estimate for every contribution. Crucially, the uncer

Diptajyoti Mukherjee, Amir Siraj, Hy Trac, Abraham Loeb

Recent observations and detections of interstellar objects (ISOs) passing through the solar system have sparked a wave of interest into these objects. Although rare, these ISOs can be captured into bound orbits around the Sun. In this study, we investigate the novel idea of capture of ISOs into near-Earth orbits and find that a steady population of ISOs exis

David Poland, Valentina Prilepina, Petar Tadić

We study five-point correlation functions of scalar operators in d-dimensional conformal field theories. We develop a new approach to computing the five-point conformal blocks for exchanged primary operators of arbitrary spin by introducing a generalization of radial coordinates, using an appropriate ansatz, and perturbatively solving two quadratic Casimir d

Adrien Fiorucci, Daniel Grumiller, Romain Ruzziconi

We argue that the celestial conformal field theory exhibits patterns of a logarithmic conformal field theory. We uncover a Jordan block structure involving the celestial stress tensor and its logarithmic partner, a composite operator built from the stress tensor and the Liouville field. Using a limiting process whose parameter corresponds to the infrared cut

Benjamin Michen, Jan Carl Budich

We report on a non-linear scattering effect that challenges the notion of topological protection for wave packets propagating in chiral edge modes. Specifically, in a Floquet topological system close to resonant driving and with a non-linear potential, we demonstrate how a wave packet propagating in a chiral edge mode may be irreversibly deflected by scatter

Andrea Antinucci, Giovanni Galati, Giovanni Rizi, Marco Serone

We study Ward identities and selection rules for local correlators in disordered theories where a 0-form global symmetry of a QFT is explicitly broken by a random coupling $h$ but it re-emerges after quenched average. We consider $h$ space-dependent or constant. In both cases we construct the symmetry operator implementing the group action, topological after

Joshua Baines, Matt Visser

Using minimalist assumptions we develop a natural functional decomposition for the spacetime metric, and explicit tractable formulae for the surface gravities, in arbitrary stationary circular (PT symmetric) axisymmetric spacetimes. We relate rigidity results, (the existence of a Killing horizon), and the zeroth law to the absence of curvature singularities

Alexandre Toubiana, Michael L. Katz, Jonathan R. Gair

Some analyses of the third gravitational wave catalogue released by the LIGO-Virgo-KAGRA collaboration (LVK) suggest an excess of black holes around $15-20 M_{\odot}$. In order to investigate this feature, we introduce two flexible population models, a semi-parametric one and a non-parametric one. Both make use of reversible jump Markov chain Monte-Carlo to

Ferruccio Feruglio, Alessandro Strumia, Arsenii Titov

String compactifications on an orbi-folded torus with complex structure give rise to chiral fermions, spontaneously broken CP, modular invariance. We show that this allows simple effective theories of flavour and CP where: i) the QCD angle vanishes; ii) the CKM phase is large; iii) quark and lepton masses and mixings can be reproduced up to order one coeffic

Upamanyu Moitra

We study (near-)circular orbits of charged particles in the background of charged black holes in asymptotically Anti-de Sitter (AdS) spacetimes of arbitrary dimensionality. We calculate the energy and angular momentum of such particles in a large-radius limit. This allows us to compute the anomalous dimension of the dual charged double-twist operators in a l

Ida E. Nielsen, Jens Schulenborg, Reinhold Egger, Michele Burrello

Advances in hybrid fractional quantum Hall (FQH)-superconductor platforms pave the way for realisation of parafermionic modes. We analyse signatures of these non-abelian anyons in transport measurements across devices with $\mathbb{Z}_6$ parafermions (PFs) coupled to an external electrode. Simulating the dynamics of these open systems by a stochastic quantum

Kyle Kremer, Brenna Mockler, Anthony L. Piro, James C. Lombardi

Tidal disruptions of stars by stellar-mass black holes are expected to occur frequently in dense star clusters. Building upon previous studies that performed hydrodynamic simulations of these encounters, we explore the formation and long-term evolution of the thick, super-Eddington accretion disks formed. We build a disk model that includes fallback of mater

Michael P. Zaletel, Mikhail Lukin, Christopher Monroe, Chetan Nayak

The spontaneous breaking of time translation symmetry has led to the discovery of a new phase of matter - the discrete time crystal. Discrete time crystals exhibit rigid subharmonic oscillations, which result from a combination of many-body interactions, collective synchronization, and ergodicity breaking. This Colloquium reviews recent theoretical and exper

Yurii Kvasiuk, Moritz Münchmeyer

We develop an optimization-based maximum likelihood approach to analyze the cross-correlation of the Cosmic Microwave Background (CMB) and large-scale structure induced by the kinetic Sunyaev-Zeldovich (kSZ) effect. Our main goal is to reconstruct the radial velocity field of the universe. While the existing quadratic estimator (QE) is statistically optimal

Callum R. T. Jones

We introduce a novel point-particle effective description of ANO vortex solitons in the critical Abelian Higgs Model (AHM) in $d=2+1$ based on the small winding expansion. Identifying the effective vortices with the elementary quanta of a complex scalar field, relativistic vortex-vortex scattering amplitudes are calculated as a diagrammatic, perturbative exp

Anthony Ashmore, Yang-Hui He, Elli Heyes, Burt A. Ovrut

We give the first numerical calculation of the spectrum of the Laplacian acting on bundle-valued forms on a Calabi-Yau three-fold. Specifically, we show how to compute the approximate eigenvalues and eigenmodes of the Dolbeault Laplacian acting on bundle-valued $(p,q)$-forms on K\"ahler manifolds. We restrict our attention to line bundles over complex projec

David Riegler, Jannis Seufert, Eduardo H. da Silva Neto, Peter Wölfle

We study magnetic and charge order in the electron-doped high-$T_c$ cuprates based on the one-band Hubbard model with onsite ($U$) and nearest-neighbor $(V)$ interactions. To investigate the interplay between the orders, we employ the Kotliar-Ruckenstein slave-boson method and analyze fluctuations descending from an antiferromagnetic parent state. Our analys

Boryana Hadzhiyska, Andreu Font-Ribera, Andrei Cuceu, Solène Chabanier

The full-shape correlations of the Lyman alpha (Ly$\alpha$) forest contain a wealth of cosmological information through the Alcock-Paczy\'{n}ski effect. However, these measurements are challenging to model without robustly testing and verifying the theoretical framework used for analyzing them. Here, we leverage the accuracy and volume of the $N$-body simula

Admir Greljo, Ajdin Palavrić

Short-distance new physics at (or slightly) above the TeV scale should not excessively violate the approximate flavor symmetries of the SM in order to comply with stringent constraints from flavor-changing neutral currents. In this respect, flavor symmetries provide an effective organizing principle for the vast parameter space of the SMEFT. In this work, we

Matthew M. Roberts, Toby Wiseman

We consider the tight-binding model of graphene with slowly spatially varying hopping functions. We develop a low energy approximation as a derivative expansion in a Dirac spinor that is perturbative in the hopping function deformation. The leading description is the Dirac equation in flat 2+1-d spacetime with (strain-)gauge field. Prior work considered subl

Alberto Nicolis, Alessandro Podo, Luca Santoni

We consider ${\rm U}(1)$-symmetric scalar quantum field theories at zero temperature. At nonzero charge densities, the ground state of these systems is usually assumed to be a superfluid phase, in which the global symmetry is spontaneously broken along with Lorentz boosts and time translations. We show that, in $d>2$ spacetime dimensions, this expectation is

Juan S. Cruz, Florian Niedermann, Martin S. Sloth

In this work, we show that the Cold New Early Dark Energy (Cold NEDE) model in its original form can solve both the Hubble tension and the $S_8$ tension without adding any new ingredients at the fundamental level. So far, it was assumed that the trigger field in the Cold NEDE model is completely subdominant. However, relaxing this assumption and letting the

Alexander Gagliano, Gabriella Contardo, Daniel Foreman-Mackey, Alex I. Malz

Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase-coverage of an event, limiting their use for triggering rapid follow-up of ongoing phenomena. In this work, we introduce a neural network with a single recurrent layer des

Antoni Bigata Casademunt, Rodrigo Mira, Nikita Drobyshev, Konstantinos Vougioukas

Speech-driven animation has gained significant traction in recent years, with current methods achieving near-photorealistic results. However, the field remains underexplored regarding non-verbal communication despite evidence demonstrating its importance in human interaction. In particular, generating laughter sequences presents a unique challenge due to the

Satya Almasian, Vivian Kazakova, Philip Göldner, Michael Gertz

Quantities are essential in documents to describe factual information. They are ubiquitous in application domains such as finance, business, medicine, and science in general. Compared to other information extraction approaches, interestingly only a few works exist that describe methods for a proper extraction and representation of quantities in text. In this

Shuhei Watanabe

Hyperparameter optimization is crucial to achieving high performance in deep learning. On top of the performance, other criteria such as inference time or memory requirement often need to be optimized due to some practical reasons. This motivates research on multi-objective optimization (MOO). However, Pareto fronts of MOO methods are often shown without con

Ziyang Xie, Ziqi Pang, Yu-Xiong Wang

While bird's-eye-view (BEV) perception models can be useful for building high-definition maps (HD-Maps) with less human labor, their results are often unreliable and demonstrate noticeable inconsistencies in the predicted HD-Maps from different viewpoints. This is because BEV perception is typically set up in an 'onboard' manner, which restricts the computat

Yuyang Zhao, Enze Xie, Lanqing Hong, Zhenguo Li

The text-driven image and video diffusion models have achieved unprecedented success in generating realistic and diverse content. Recently, the editing and variation of existing images and videos in diffusion-based generative models have garnered significant attention. However, previous works are limited to editing content with text or providing coarse perso

Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti

In numerous robotics and mechanical engineering applications, among others, data is often constrained on smooth manifolds due to the presence of rotational degrees of freedom. Common datadriven and learning-based methods such as neural ordinary differential equations (ODEs), however, typically fail to satisfy these manifold constraints and perform poorly for

Canwen Xu, Yichong Xu, Shuohang Wang, Yang Liu

Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned with common hardware. As a result, effectively tuning these models with large-scale supervised data can be challenging. As an alternative, In-Context Learning (ICL) can only use a sma

Guochao Sun, Adam Lidz, Andreas L. Faisst, Claude-André Faucher-Giguère

Understanding the star formation rate (SFR) variability and how it depends on physical properties of galaxies is important for developing and testing the theory of galaxy formation. We investigate how statistical measurements of the extragalactic background light (EBL) can shed light on this topic and complement traditional methods based on observations of i

Thomas Steinke, Milad Nasr, Matthew Jagielski

We propose a scheme for auditing differentially private machine learning systems with a single training run. This exploits the parallelism of being able to add or remove multiple training examples independently. We analyze this using the connection between differential privacy and statistical generalization, which avoids the cost of group privacy. Our auditi

Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu

Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks. Along this line of research, this work aims to investigate the capacity of LLMs that act as the ranking model for recommender systems. We first formalize the recommendation problem

Afra Feyza Akyürek, Ekin Akyürek, Aman Madaan, Ashwin Kalyan

Despite their unprecedented success, even the largest language models make mistakes. Similar to how humans learn and improve using feedback, previous work proposed providing language models with natural language feedback to guide them in repairing their outputs. Because human-generated critiques are expensive to obtain, researchers have devised learned criti

P. P. Avelino

We investigate the properties of dark energy halos in models with a nonminimal coupling in the dark sector. We show, using a quasistatic approximation, that a coupling of the mass of dark matter particles to a standard quintessence scalar field $\phi$ generally leads to the formation of dark energy concentrations in and around compact dark matter objects. Th

Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola

This work examines the challenges of training neural networks using vector quantization using straight-through estimation. We find that a primary cause of training instability is the discrepancy between the model embedding and the code-vector distribution. We identify the factors that contribute to this issue, including the codebook gradient sparsity and the

Han Zhong, Tong Zhang

The proximal policy optimization (PPO) algorithm stands as one of the most prosperous methods in the field of reinforcement learning (RL). Despite its success, the theoretical understanding of PPO remains deficient. Specifically, it is unclear whether PPO or its optimistic variants can effectively solve linear Markov decision processes (MDPs), which are argu

Abhijay Ghildyal, Feng Liu

Perceptual similarity metrics have progressively become more correlated with human judgments on perceptual similarity; however, despite recent advances, the addition of an imperceptible distortion can still compromise these metrics. In our study, we systematically examine the robustness of these metrics to imperceptible adversarial perturbations. Following t

Juan C. Verduzco, Ernesto E. Marinero, Alejandro Strachan

Doped lithium lanthanum zirconium oxide (LLZO) is a promising class of solid electrolytes for lithium-ion batteries due to their good electrochemical stability and compatibility with Li metal anodes. Ionic diffusivity in these ceramics is known to occur via correlated, vacancy mediated, jumps of Li+ between alternating tetrahedral and octahedral sites. Aliov

Jose A. Vélez-Marulanda, Pedro Rizzo

Let $\mathbb{k}$ be a field, and let $\Lambda$ be a (not necessarily finite dimensional) $\mathbb{k}$-algebra. Let $V$ be a left $\Lambda$-module such that is finite dimensional over $\mathbb{k}$. Assume further that $V$ has a weak universal deformation ring $R^w(\Lambda,V)$, which is a complete Noetherian commutative local $\mathbb{k}$-algebra with residue

James Brown

We show that chemically-accurate potential energy surfaces (PESs) can be generated from quantum computers by measuring only the density along an adiabatic transition between different molecular geometries. In lieu of using phase estimation, the energy is evaluated by performing line-integration using the inverted real-space Time-Dependant Density Functional

Emma Page, Joshua Pepper, Duncan Wright, Joseph E. Rodriguez

We present the discovery of TOI-1994b, a low-mass brown dwarf transiting a hot subgiant star on a moderately eccentric orbit. TOI-1994 has an effective temperature of $7700^{+720}_{-410}$ K, V magnitude of 10.51 mag and log(g) of $3.982^{+0.067}_{-0.065}$. The brown dwarf has a mass of $22.1^{+2.6}_{-2.5}$ $M_J$, a period of 4.034 days, an eccentricity of $0

Johan E. Runeson, David E. Manolopoulos

We describe a multiple electronic state adaptation of the mapping approach to surface hopping introduced recently by Mannouch and Richardson (J. Chem. Phys. 158, 104111 (2023)). This adaptation treats populations and coherences on an equal footing and is guaranteed to give populations in any electronic basis that tend to the correct quantum-classical equilib

Devin Francom, J. Derek Tucker, Gabriel Huerta, Kurtis Shuler

Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used. However, many of these tool

Jinwoo Sung, Yilin Wang

We derive the variational formula of the Loewner driving function of a simple chord under infinitesimal quasiconformal deformations with Beltrami coefficients supported away from the chord. As an application, we obtain the first variation of the Loewner energy of a Jordan curve, defined as the Dirichlet energy of the driving function of the curve. This resul

Paula Díaz, Adrián Corrochano, Manuel López-Martín, Soledad Le Clainche

Fluid Dynamics problems are characterized by being multidimensional and nonlinear. Therefore, experiments and numerical simulations are complex and time-consuming. Motivated by this, the need arises to find new techniques to obtain data in a simpler way and in less time. In this article, we present a novel methodology based on physical principles to reconstr

Jane Kim, Gabriel Pescia, Bryce Fore, Jannes Nys

Ultra-cold Fermi gases display diverse quantum mechanical properties, including the transition from a fermionic superfluid BCS state to a bosonic superfluid BEC state, which can be probed experimentally with high precision. However, the theoretical description of these properties is challenging due to the onset of strong pairing correlations and the non-pert

J. Menezes, M. Tenorio

Climate changes may affect ecosystems destabilising relationships among species. We investigate the spatial rock-paper-scissors models with a regional unevenness that reduces the selection capacity of organisms of one species. Our results show that the regionally weak species predominates in the local ecosystem, while spiral patterns appear far from the regi

Lilia S. Xie, Oscar Gonzalez, Kejun Li, Matteo Michiardi

Magnetic materials with noncollinear spin textures are promising for spintronic applications. To realize practical devices, control over the length and energy scales of such spin textures is imperative. The chiral helimagnets Cr1/3NbS2 and Cr1/3TaS2 exhibit analogous magnetic phase diagrams with different real-space periodicities and field dependence, positi

Ashok Urlana, Pinzhen Chen, Zheng Zhao, Shay B. Cohen

This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India. Our corpus provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs. We detail our construction workflow including data acquisition, processing, and quality assuran

Carlos Cifuentes

M dwarfs are the most abundant stars in the Universe and are hosts of a rich diversity of planetary companions. In many cases, planets orbiting M dwarfs can be described in remarkable detail. What makes the difference is how deeply we can characterise the host star. This includes to properly model their atmospheres, their abundance of metals, and their activ

Fabrizio Zanello

In this paper we show that the higher currents of the sine-Gordon model are super-renormalizable by power counting in the framework of pAQFT. First we obtain closed recursive formulas for the higher currents in the classical theory and introduce a suitable notion of degree for their components. We then move to the pAQFT setting and, by means of some technica

Sheng Wang, Zixu Zhuang, Xi Ouyang, Lichi Zhang

Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims to minimizing distances between positive pairs. These methods usually apply random data augmentation to input images, expecting the augmented views of the same images to be similar and positively paired. However, random augmentation may overlook image sem

Aaron Fish, Dylan Helliwell

Taxicab space is a modification of Euclidean space that uses an alternative notion of distance. Similarly, the Poincar\'{e} ball is a model of hyperbolic geometry that consists of a subset of Euclidean space with an alternative notion of distance. In this paper, we merge these two variations to create a taxicab version of the Poincar\'{e} ball. We determine

Jingxia Jiang, Tian Ye, Jinbin Bai, Sixiang Chen

A lightweight underwater image enhancement network is of great significance for resource-constrained platforms, but balancing model size, computational efficiency, and enhancement performance has proven difficult for previous approaches. In this work, we propose the Five A$^{+}$ Network (FA$^{+}$Net), a highly efficient and lightweight real-time underwater i

Nicholas A. Featherstone, Kyle C. Augustson, Jonathan M. Aurnou, Catherine Blume

The operation of the solar dynamo, with all of its remarkable spatio-temporal ordering, remains an outstanding problem of modern solar physics. A number of mechanisms that might plausibly contribute to its operation have been proposed, but the relative role played by each remains unclear. This uncertainty stems from continuing questions concerning the speed

A. Ritchie-Yates, A. Deisting, G. Barker, S. Boyd

New neutrino-nucleus interaction cross-section measurements are required to improve nuclear models sufficiently for future long-baseline neutrino experiments to meet their sensitivity goals. A time projection chamber (TPC) filled with a high-pressure gas is a promising detector to characterise the neutrino sources planned for such experiments. A gas-filled T

Juan Carlos Riano-Rojas

In this paper, using an algebraic approach, it is intended to show that the Goldbach's and Twin primes conjectures are true, building, for each $m>2$, an isomorphism between posets. One of the posets is the set of coprimes less than $m$, while the other is endowed with an operation that grants it an abelian group structure. Special features of this operation

Weijun Gao, Chong Han, Zhi Chen

Terahertz (THz) wireless communications have the potential to realize ultra-high-speed and secure data transfer with miniaturized devices for unmanned aerial vehicle (UAV) communications. Existing THz channel models for aerial scenarios assume a homogeneous medium along the line-of-sight propagation path. However, the atmospheric turbulence due to random air

Zhiyi Zhang, Pengfei Zhang, Qi Wang

Java is very powerful, but in Deep Learning field, its capabilities probably has not been sufficiently exploited. Compared to the Java-based deep-learning-frameworks, the Python-based (PyTorch, TensorFlow, etc) are undoubtedly the mainstream, due to their easy-to-use, flexibility and better ecosystem. Dragon-Alpha is a Java-based Tensor Computing Framework,

Sean Paulsen

Designing machine intelligence to converse with a human user necessarily requires an understanding of how humans participate in conversation, and thus conversation modeling is an important task in natural language processing. New breakthroughs in architecture and data gathering continue to push the performance of such conversational AI models. However, desig

R. Ogul, A. S. Botvina, M. Bleicher, N. Buyukcizmeci

Isotopic yield distributions of nuclei produced in peripheral collisions of $^{80}$Kr+$^{40,48}$Ca at 35 MeV/nucleon are studied. Experimental results obtained by the FAZIA Collaboration at the LNS facility in Catania are compared with calculations performed with the statistical multifragmentation model (SMM). The fragments with atomic number $Z=19-24$ obser

M. Farasat Shamir, Aisha Rashid

The main emphasis of this paper is to find the viable solutions of Einstein Maxwell fields equations of compact star in context of modified $f(R)$ theory of gravity. Two different models of modified $f(R)$ gravity are considered. In particular, we choose isotropic matter distribution and Bardeen's model for compact star to find the boundary conditions as an

P. V. Ratnikov

The possibility of the appearance of a dielectric electron-hole liquid (EHL) in monolayers of transition metal dichalcogenides and heterostructures based on them is considered. It is shown that the coherent pairing of electrons and holes in them leads to the formation of a dielectric EHL when the degree of circular polarization of the exciting light exceeds

Volodymyr Biloshytskyi, Iulian Ciobotaru-Hriscu, Franziska Hagelstein, Vadim Lensky

We attempt to rehabilitate a sum rule (proposed long ago by Bernab\'eu and Tarrach) which relates the electric polarizability of a particle to the total photoabsorption of quasi-real longitudinally polarized photons by that particle. We discuss its perturbative verification in QED, which is largely responsible for the scepticism about its validity. The failu

Chaoyue Liu, Han Bi, Like Hui, Xiao Liu

Nonlinear activation functions are widely recognized for enhancing the expressivity of neural networks, which is the primary reason for their widespread implementation. In this work, we focus on ReLU activation and reveal a novel and intriguing property of nonlinear activations. By comparing enabling and disabling the nonlinear activations in the neural netw

Megan Strauss, Stefan Mitsch

Technology advances give us the hope of driving without human error, reducing vehicle emissions and simplifying an everyday task with the future of self-driving cars. Making sure these vehicles are safe is very important to the continuation of this field. In this paper, we formalize the Responsibility-Sensitive Safety model (RSS) for self-driving cars and pr

Xujia Chen, Aleksey Zinger

We describe a sequence of smooth quotients of the Deligne-Mumford moduli space ${\mathbb R}\overline{\mathcal M}_{0,\ell+1}$ of real rational curves with $\ell\!+\!1$ conjugate pairs of marked points that terminates at ${\mathbb R}\overline{\mathcal M}_{0,\ell}\!\times\!{\mathbb C}{\mathbb P}^1$. This produces an analogue of Keel's blowup construction of the

Yuang Wang, Xingyi He, Sida Peng, Haotong Lin

A fully automated object reconstruction pipeline is crucial for digital content creation. While the area of 3D reconstruction has witnessed profound developments, the removal of background to obtain a clean object model still relies on different forms of manual labor, such as bounding box labeling, mask annotations, and mesh manipulations. In this paper, we

Zhengxuan Wu, Atticus Geiger, Thomas Icard, Christopher Potts

Obtaining human-interpretable explanations of large, general-purpose language models is an urgent goal for AI safety. However, it is just as important that our interpretability methods are faithful to the causal dynamics underlying model behavior and able to robustly generalize to unseen inputs. Distributed Alignment Search (DAS) is a powerful gradient desce

Xiaoyu Tian, Haoxi Ran, Yue Wang, Hang Zhao

This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without annotations? To answer that, we introduce a point cloud representation learning framework, based on geometric feature reconstruction. In contrast to recent papers that directly adopt

Ronald Richman, Mario Wüthrich

Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the Generalized Linear Models (GLMs) currently used in industry. Whereas constraining GLM parameters relating to insurance

Tribhuban Parida, Sandeep Chatterjee

Electromagnetic field in heavy ion collisions are expected to cause charge dependent directed flow splitting ($\Delta v_1$). Such charge dependent $\Delta v_1$ has been observed by the STAR collaboration. We demonstrate that relativistic dissipative fluid dynamic simulations with baryon diffusion that include realistic model of baryon stopping in the initial

Jaemin Kim, Ida Ang, Francesco Ballarin, Chung-Yuen Hui

We present a theoretical and computational model for the behavior of a porous solid undergoing two interdependent processes, the finite deformation of a solid and species migration through the solid, which are distinct in bulk and on surface. Nonlinear theories allow us to systematically study porous solids in a wide range of applications, such as drug deliv

Hanieh Khorashadizadeh, Nandana Mihindukulasooriya, Sanju Tiwari, Jinghua Groppe

Knowledge graphs can represent information about the real-world using entities and their relations in a structured and semantically rich manner and they enable a variety of downstream applications such as question-answering, recommendation systems, semantic search, and advanced analytics. However, at the moment, building a knowledge graph involves a lot of m

Gerhard Kirsten, Luca Saluzzi

Optimal control problems driven by evolutionary partial differential equations arise in many industrial applications and their numerical solution is known to be a challenging problem. One approach to obtain an optimal feedback control is via the Dynamic Programming principle. Nevertheless, despite many theoretical results, this method has been applied only t

Yue Chen, Peng Yi

We propose a type of non-cooperative game, termed multi-cluster aggregative game, which is composed of clusters as players, where each cluster consists of collaborative agents with cost functions depending on their own decisions and the aggregate quantity of each participant cluster to modeling large-scale and hierarchical multi-agent systems. This novel gam

Robert Underwood, Julie Bessac, David Krasowska, Jon C. Calhoun

Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement. In contrast with the notion of entropy in lossless compression, no theoretical or data-based quantification of lossy compressibility exists for scientific data. Users rely o

Zewen Chi, Heyan Huang, Xian-Ling Mao

Recent studies have exhibited remarkable capabilities of pre-trained multilingual Transformers, especially cross-lingual transferability. However, current methods do not measure cross-lingual transferability well, hindering the understanding of multilingual Transformers. In this paper, we propose IGap, a cross-lingual transferability metric for multilingual

Saul Schleimer, Henry Segerman

From a transverse veering triangulation (not necessarily finite) we produce a canonically associated dynamic pair of branched surfaces. As a key idea in the proof, we introduce the shearing decomposition of a veering triangulation.

Xujia Chen, Penka Georgieva, Aleksey Zinger

It is a long-established and heavily-used fact that the integral cohomology ring of the Deligne-Mumford moduli space of (complex) rational curves is the polynomial ring on the boundary divisors modulo the ideal generated by the obvious geometric relations between them. We show that the rational cohomology ring of the Deligne-Mumford moduli space of real rati

Igor Ciganović

In this paper, we determine the composition series of the induced representation $\delta([\nu^{-a}\rho,\nu^{c}\rho]) \times \delta([\nu^{\frac{1}{2}}\rho, \nu^{b}\rho]) \rtimes \sigma $ where $a, b, c \in \mathbb{Z}+ \frac{1}{2} $ such that $\frac{1}{2} \leq a < b < c $, $ \rho$ is an irreducible cuspidal unitary representation of a general linear group and

Daniel Hothem, Jordan Hines, Karthik Nataraj, Robin Blume-Kohout

Holistic benchmarks for quantum computers are essential for testing and summarizing the performance of quantum hardware. However, holistic benchmarks -- such as algorithmic or randomized benchmarks -- typically do not predict a processor's performance on circuits outside the benchmark's necessarily very limited set of test circuits. In this paper, we introdu

Peter Schneider, Claus Sorensen

We continue our study of the monoidal category $D(G)$. At the level of cohomology we transfer the duality functor to the derived category of Hecke dg-modules. In the process we develop a more general and streamlined approach to the anti-involution first defined by Ollivier and Schneider. We also verify that the tensor product on $D(G)$ corresponds to an oper

Sérgio R. A. Gomes, Alexandre C. M. Correia

The orbits of the main satellites of Uranus are expected to slowly drift away owing to tides raised in the planet. As a result, the 5/3 mean motion resonance between Ariel and Umbriel was likely encountered in the past. Previous studies have shown that, in order to prevent entrapment in this resonance, the eccentricities of the satellites must be larger than

Zouhour Rezig

The linear Boltzmann equation governs the absorption and scattering of a population of particles in a medium with an ambient field, represented by a Riemannian metric, where particles follow geodesics. In this paper, we study the possible issues of uniqueness and stability in recovering the absorption and scattering coefficients from the boundary knowledge o

Zdenek Sekanina

In the context of a recently proposed contact-binary model of the Kreutz system, all its members are products of the process of cascading fragmentation of the two lobes of the parent, Aristotle's comet of 372 BC. This process presumably began with the lobes' separation from each other near aphelion. However, not every object in a Kreutz-like orbit is a Kreut

Braedy Kuzma, Ivan Korostelev, João P. L. de Carvalho, José E. Moreira

The resurgence of machine learning has increased the demand for high-performance basic linear algebra subroutines (BLAS), which have long depended on libraries to achieve peak performance on commodity hardware. High-performance BLAS implementations rely on a layered approach that consists of tiling and packing layers, for data (re)organization, and micro ker

Octavio Mesner, Elizaveta Levina, Ji Zhu

Information spread through social networks is ubiquitous. Influence maximiza- tion (IM) algorithms aim to identify individuals who will generate the greatest spread through the social network if provided with information, and have been largely devel- oped with marketing in mind. In social networks with community structure, which are very common, IM algorithm

Guillermo Granados-Garcia, Raquel Prado, Hernando Ombao

One of the goals of neuroscience is to study interactions between different brain regions during rest and while performing specific cognitive tasks. The Multivariate Bayesian Autoregressive Decomposition (MBMARD) is proposed as an intuitive and novel Bayesian non-parametric model to represent high-dimensional signals as a low-dimensional mixture of univariat

Yuichiro Nakano, Hideaki Hakoshima, Kosuke Mitarai, Keisuke Fujii

Quantum computation is expected to accelerate certain computational task over classical counterpart. Its most primitive advantage is its ability to sample from classically intractable probability distributions. A promising approach to make use of this fact is the so-called quantum-enhanced Markov chain Monte Carlo (MCMC) [D. Layden, et al., arXiv:2203.12497

Douglas Brambila, Paulo A. A. Lopes, André L. B. Ribeiro, Arianna Cortesi

In this work, we consider four different galaxy populations and two distinct global environments in the local Universe (z $\leq 0.11$) to investigate the evolution of transitional galaxies (such as star-forming spheroids and passive discs) across different environments. Our sample is composed of 3,899 galaxies within the R$_{200}$ radius of 231 clusters and

Christopher Meaney, Therese A Stukel, Peter C Austin, Michael Escobar

Background & Objective: Biomedical text data are increasingly available for research. Tokenization is an initial step in many biomedical text mining pipelines. Tokenization is the process of parsing an input biomedical sentence (represented as a digital character sequence) into a discrete set of word/token symbols, which convey focused semantic/syntactic mea

Rahul Shah, Ayan Mitra, Purba Mukherjee, Barun Pal

We study how future Type-Ia supernovae (SNIa) standard candles detected by the Vera C. Rubin Observatory (LSST) can constrain some cosmological models. We use a realistic three-year SNIa simulated dataset generated by the LSST Dark Energy Science Collaboration (DESC) Time Domain pipeline, which includes a mix of spectroscopic and photometrically identified c