October 2023 arXiv papers — page 11

Andrea Clini, Benjamin Fehrman

We prove a central limit theorem characterizing the small noise fluctuations of stochastic PDEs of fluctuating hydrodynamics type. The results apply to the case of nonlinear and potentially degenerate diffusions and irregular noise coefficients including the square root. In several cases, the fluctuations of the solutions agree to first order with the fluctu

Michael Günther, Jackmin Ong, Isabelle Mohr, Alaeddine Abdessalem

Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information. While these models are essential for tasks like information retrieval, semantic clustering, and text re-ranking, most existing open-source models, especially those built on architectures like BERT, struggle t

Rodolfo Gambini, Javier Olmedo, Jorge Pullin

Vacuum spherically symmetric loop quantum gravity in the midi-superspace approximation using inhomogeneous horizon-penetrating slices has been studied for a decade, and it has been noted that the singularity is eliminated. It is replaced by a region of high curvature and potentially large quantum fluctuations. It was recently pointed out that the effective s

Meagan Sundstrom, N. G. Holmes

Gaining recognition as a physics person by peers is an important contributor to undergraduate students' physics identity and their success in physics courses. Previous research has separately demonstrated that women perceive less recognition from peers than men in their physics courses (perceived recognition) and that women receive fewer nominations from the

Atefeh Rajabalizadeh, Danial Davarnia

Cut-generating linear programs (CGLPs) play a key role as a separation oracle to produce valid inequalities for the feasible region of mixed-integer programs. When incorporated inside branch-and-bound, the cutting planes obtained from CGLPs help to tighten relaxations and improve dual bounds. However, running the CGLPs at the nodes of the branch-and-bound tr

Rodrigo Carrasco-Davis, Javier Masís, Andrew M. Saxe

Biological and artificial learning agents face numerous choices about how to learn, ranging from hyperparameter selection to aspects of task distributions like curricula. Understanding how to make these meta-learning choices could offer normative accounts of cognitive control functions in biological learners and improve engineered systems. Yet optimal strate

Josep Fontana-McNally, Eva Miranda, Cédric Oms, Daniel Peralta-Salas

In this article, we study the dynamical properties of Reeb vector fields on b-contact manifolds. We show that in dimension 3, the number of so-called singular periodic orbits can be prescribed. These constructions illuminate some key properties of escape orbits and singular periodic orbits, which play a central role in formulating singular counterparts to th

Feng Chen, Liqin Wang, Julie Hong, Jiaqi Jiang

Objectives: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. Yet, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to detect and mitigate diverse forms of bias in AI models developed using EHR

Alessandro Bacchetta, Francesco Giovanni Celiberto, Marco Radici

We present novel analyses on accessing the 3D gluon content of the proton via spin-dependent TMD gluon densities, calculated through the spectator-model approach. Our formalism embodies a fit-based spectator-mass modulation function, suited to catch longitudinal-momentum effects in a wide kinematic range. Particular attention is paid to the time-reversal eve

Seongwon Kim, Parisa Mollaei, Akshay Antony, Rishikesh Magar

With the rise of Transformers and Large Language Models (LLMs) in Chemistry and Biology, new avenues for the design and understanding of therapeutics have opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence

André Roque, Diogo Cruz, Francisco A. Monteiro, Bruno C. Coutinho

In this paper, we propose a novel bipartite entanglement purification protocol built upon hashing and upon the guessing random additive noise decoding (GRAND) approach recently devised for classical error correction codes. Our protocol offers substantial advantages over existing hashing protocols, requiring fewer qubits for purification, achieving higher fid

Gregorio García-Valladares, Deepak Gupta, Antonio Prados, Carlos A. Plata

We look into the problem of stochastic resetting with refractory periods. The model dynamics comprises diffusive and motionless phases. The diffusive phase ends at random time instants, at which the system is reset to a given position -- where the system remains at rest for a random time interval, termed the refractory period. A pathway formulation is introd

Sanaea C. Rose, Morgan MacLeod

A supermassive black hole (SMBH) surrounded by a dense, nuclear star cluster resides at the center of many galaxies. In this dense environment, high-velocity collisions frequently occur between stars. About $10 \%$ of the stars within the Milky Way's nuclear star cluster collide with other stars before evolving off the main-sequence. Collisions preferentiall

Perry Kleinhenz, Ruoyu P. T. Wang

For general second order evolution equations, we prove an optimal condition on the degree of unboundedness of the damping, that rules out finite-time extinction. We show that control estimates give energy decay rates that explicitly depend on the degree of unboundedness, and establish a dilation method to turn existing control estimates for one propagator in

Florian List, Noemi Anau Montel, Christoph Weniger

Reconstructing astrophysical and cosmological fields from observations is challenging. It requires accounting for non-linear transformations, mixing of spatial structure, and noise. In contrast, forward simulators that map fields to observations are readily available for many applications. We present a versatile Bayesian field reconstruction algorithm rooted

Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu

Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an ImageNet-trained convolutional neural network. However, the recent past has seen the emergence of countless backbones pretrained using various algorithms and datasets. While this

M. Peterek, M. Paur, M. Vitek, D. Koutny

Enhancing the ability to resolve axial details is crucial in three-dimensional optical imaging. We provide experimental evidence showcasing the ultimate precision achievable in axial localization using vortex beams. For Laguerre-Gauss (LG) beams, this remarkable limit can be attained with just a single intensity scan. This proof-of-principle demonstrates tha

Jun-Sik Lee, Steven A. Kivelson, Heemin Lee, Tong Wang

Pair-density-wave (PDW) correlations have been proposed as an important ingredient in the complex phase diagram of high-$T_{\rm c}$ cuprates, yet bulk-sensitive experimental signatures remain scarce. Here we report resonant x-ray scattering measurements revealing a subharmonic charge-density-wave (CDW) scattering response in Sr-doped $1/8$-LBCO. The subharmo

Sangwoo Seo, Sungwon Kim, Chanyoung Park

The success of Graph Neural Networks (GNNs) has led to a need for understanding their decision-making process and providing explanations for their predictions, which has given rise to explainable AI (XAI) that offers transparent explanations for black-box models. Recently, the use of prototypes has successfully improved the explainability of models by learni

D. Dahlbom, D. Brooks, M. S. Wilson, S. Chi

Simulating quantum spin systems at finite temperatures is an open challenge in many-body physics. This work studies the temperature-dependent spin dynamics of a pivotal compound, FeI$_2$, to determine if universal quantum effects can be accounted for by a phenomenological renormalization of the dynamical spin structure factor $S(\mathbf{q}, \omega)$ measured

Lukas Eisert, Connor Bottrell, Annalisa Pillepich, Rhythm Shimakawa

Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to the real one is difficult. In this paper of the ERGO-ML series (Extracting Reality from Galaxy Observables with Machine Le

Surya Narayanan Hari, Rex Liu, Matt Thomson

Currently, over a thousand LLMs exist that are multi-purpose and are capable of performing real world tasks, including Q&A, text summarization, content generation, etc. However, accessibility, scale and reliability of free models prevents them from being widely deployed in everyday use cases. To address the first two issues of access and scale, organisations

F. Yusef-Zadeh, M. Wardle, R. Arendt, J. W. Hewitt

We present highly sensitive measurements taken with MeerKAT at 1280 MHz as well as archival GBT, MWA and VLA images at 333, 88 and 74 MHz. We report the detection of synchrotron radio emission from the infrared dark cloud (IRDC) associated with the halo of the Sgr B complex on a scale of ~60 pc. A strong spatial correlation between low-frequency radio contin

Jarosław Kopiński, Alberto Soria, Juan A. Valiente Kroon

We prove a new geometric inequality that relates the Arnowitt-Deser-Misner mass of initial data to a quasilocal angular momentum of a marginally future trapped surface inner boundary. The inequality is expressed in terms of a 1-spinor, which satisfies an intrinsic first-order Dirac-type equation. Furthermore, we show that if the initial data is axisymmetric,

Evan McDonough, J. Colin Hill, Mikhail M. Ivanov, Adrien La Posta

We review and update constraints on the Early Dark Energy (EDE) model from cosmological data sets, in particular Planck PR3 and PR4 cosmic microwave background (CMB) data and large-scale structure (LSS) data sets including galaxy clustering and weak lensing data from the Dark Energy Survey, Subaru Hyper Suprime-Cam, and KiDS+VIKING-450, as well as BOSS/eBOSS

Md Motiur Rahman, Shiva Shokouhmand, Smriti Bhatt, Miad Faezipour

One of the common and promising deep learning approaches used for medical image segmentation is transformers, as they can capture long-range dependencies among the pixels by utilizing self-attention. Despite being successful in medical image segmentation, transformers face limitations in capturing local contexts of pixels in multimodal dimensions. We propose

Paul Fendley, Balazs Pozsgay

The Jordan-Wigner transformation is frequently utilised to rewrite quantum spin chains in terms of fermionic operators. When the resulting Hamiltonian is bilinear in these fermions, i.e. the fermions are free, the exact spectrum follows from the eigenvalues of a matrix whose size grows only linearly with the volume of the system. However, several Hamiltonian

Siddhartha Solanki, Manohar Lal, Vineet Kumar Agotiya

The analytical exact iteration method (AEIM) have been used widely to calculate N-dimensional radial Schrodinger equation with medium modified form of Cornell potential and is generalized to the finite value of magnetic field (eB) with quasi-particle approach in hot quantum chromodynamics (QCD) medium. In N-dimensional space the energy eigen values have been

Jawaher Almutlaq, Jiangtao Wang, Linsen Li, Chao Li

Electrical control of individual spins and photons in solids is key for quantum technologies, but scaling down to small, static systems remains challenging. Here, we demonstrate nanoscale electrical tuning of neutral and charged excitons in monolayer WSe2 using 1-nm carbon nanotube gates. Electrostatic simulations reveal a confinement radius below 15 nm, rea

Martin Bojowald, Erick I. Duque

A new class of modified gravity theories, made possible by subtle features of the canonical formulation of general covariance, naturally allows MOND-like behavior (MOdified Newtonian Dynamics) in effective space-time solutions without introducing new fields. A detailed analysis reveals a relationship with various quantum-gravity features, in particular in ca

CMS Collaboration

A search for W' bosons decaying to a top and a bottom quark in final states including an electron or a muon is performed with the CMS detector at the LHC. The analyzed data correspond to an integrated luminosity of 138 fb$^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 Tev. Good agreement with the standard model expectation is observed an

Valentin D. Kunz, Raphael C. Assier

We provide a description of the far-field encountered in the diffraction problem resulting from the interaction of a monochromatic plane-wave and a right-angled no-contrast penetrable wedge. To achieve this, we employ a two-complex-variable framework and use the analytical continuation formulae derived in (Kunz $\&$ Assier, QJMAM, 76(2), 2023) to recover the

Leo Versteegen

A linear graph code is a family $\mathcal{C}$ of graphs on $n$ vertices with the property that the symmetric difference of the edge sets of any two graphs in $\mathcal{C}$ is also the edge set of a graph in $\mathcal{C}$. In this article, we investigate the maximal size of a linear graph code that does not contain a copy of a fixed graph $H$. In particular,

Michael Borinsky, Henrik J. Munch, Felix Tellander

The software feyntrop for direct numerical evaluation of Feynman integrals is presented. We focus on the underlying combinatorics and polytopal geometries facilitating these methods. Especially matroids, generalized permutohedra and normality are discussed in detail.

Sriram Balasubramanian, Gaurang Sriramanan, Vinu Sankar Sadasivan, Soheil Feizi

Despite the remarkable success of deep neural networks in a myriad of settings, several works have demonstrated their overwhelming sensitivity to near-imperceptible perturbations, known as adversarial attacks. On the other hand, prior works have also observed that deep networks can be under-sensitive, wherein large-magnitude perturbations in input space do n

Nikita S. Khatiya, Margot Boughelilba, Christopher M. Karwin, Alex McDaniel

FR0 galaxies constitute the most abundant jet population in the local Universe. With their compact jet structure, they are broadband photon emitters and have been proposed as multi-messenger sources. Recently, these sources have been detected for the first time in $\gamma$ rays. Using a revised FR0 catalog, we confirm that the FR0 population as a whole are $

Georgios Itsios

We construct the embedding of the $\lambda$-model on $SL(2, \mathbb{R}) \times SU(2) \times SU(2)$ in type-II supergravity. In the absence of deformation, the ten-dimensional background corresponds to the near-horizon limit of the NS1-NS5-NS5 brane intersection. We show that when the deformation is turned on, supersymmetry breaks by half, and the solution pr

Ngai Lam Ho, Roy Ka-Wei Lee, Kwan Hui Lim

An essential task for tourists having a pleasant holiday is to have a well-planned itinerary with relevant recommendations, especially when visiting unfamiliar cities. Many tour recommendation tools only take into account a limited number of factors, such as popular Points of Interest (POIs) and routing constraints. Consequently, the solutions they provide m

Lucas Daguerre

The effective low temperature dynamics of near-extremal black holes is governed by the quantum fluctuations of the Schwarzian mode of JT gravity. Utilizing as a proxy a planar charged black hole in asymptotically Anti-de-Sitter spacetime, we investigate the effects of these fluctuations on a probe scalar field. The corresponding holographic real-time boundar

Derek Perera, Liliya L. R. Williams, Jori Liesenborgs, Agniva Ghosh

SDSS J1004+4112 is a well studied gravitational lens with a recently measured time delay between its first and fourth arriving quasar images. Using this new constraint, we present updated free-form lens reconstructions using the lens inversion method {\tt GRALE}, which only uses multiple image and time delay data as inputs. In addition, we obtain hybrid lens

Elizabeth E. Jenkins, Aneesh V. Manohar, Luca Naterop, Julie Pagès

We derive a general formula for two-loop counterterms in Effective Field Theories (EFTs) using a geometric approach. This formula allows the two-loop results of our previous paper to be applied to a wide range of theories. The two-loop results hold for loop graphs in EFTs where the interaction vertices contain operators of arbitrarily high dimension, but at

Haimeng Zhao, Laura Lewis, Ishaan Kannan, Yihui Quek

While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers. Given that states and unitaries appearing in Nature are of bounded gate complexity, it is natural to ask if efficient learning becomes possible. In this work, we prove that to learn a state generated by a quantum circuit with $G$ two-qubit

Christian W. Bauer, So Chigusa, Masahito Yamazaki

Parton showers which can efficiently incorporate quantum interference effects have been shown to be run efficiently on quantum computers. However, so far these quantum parton showers did not include the full kinematical information required to reconstruct an event, which in classical parton showers requires the use of a veto algorithm. In this work, we show

Johannes S. Hofmann, Florian Goth, Wei Zhu, Yin-Chen He

We present a numerical quantum Monte Carlo (QMC) method for simulating the 3D phase transition on the recently proposed fuzzy sphere [Phys. Rev. X 13, 021009 (2023)]. By introducing an additional $SU(2)$ layer degree of freedom, we reformulate the model into a form suitable for sign-problem-free QMC simulation. From the finite-size-scaling, we show that this

Tyler Holland-Ashford, Patrick Slane, Xi Long

In this paper, we present updated estimates of the velocity of the neutron star (NS) in the supernova remnant (SNR) Cassiopeia A using over two decades of Chandra observations. We use two methods: 1.) recording NS positions from dozens of Chandra observations, including the astrometric uncertainty estimates on the data points but not correcting the astrometr

Hans K. C. Beukers, Matteo Pasini, Hyeongrak Choi, Dirk Englund

Generating entanglement between distant quantum systems is at the core of quantum networking. In recent years, numerous theoretical protocols for remote entanglement generation have been proposed, of which many have been experimentally realized. Here, we provide a modular theoretical framework to elucidate the general mechanisms of photon-mediated entangleme

Gautham Varma K, Azaz Ahmad, Sumanta Tewari, G. Sharma

Recently, chiral anomaly (CA) has been proposed to occur in spin-orbit coupled noncentrosymmetric metals (SOC-NCMs), motivating CA to be a Fermi surface property rather than a Weyl node property. Although the nature of the anomaly is similar in both SOC-NCMs and Weyl systems, here we point out significant fundamental differences between the two. We show that

J. H. Pixley, David A. Huse, Justin H. Wilson

We theoretically study the interplay of short-ranged random and quasiperiodic static potentials on the low-energy properties of three-dimensional Weyl semimetals. This setting allows us to investigate the connection between the semimetal to diffusive metal "magic-angle" phase transition due to quasiperiodicity and the rare-region induced crossover at an avoi

Anatoly Kuklov, Lode Pollet, Nikolay Prokof'ev, Leo Radzihovsky

We study universal off-diagonal correlations in transverse quantum fluids (TQF) -- a new class of quasi-one-dimensional superfluids featuring long-range-ordered ground states. These exhibit unique self-similar space-time relations scaling with $x^2/D\tau$ that serve as fingerprints of the specific states. The results obtained with the effective field theory

Cynthia Keeler, William Munizzi, Jason Pollack

Following on our previous work arXiv:2204.07593 and arXiv:2306.01043 studying the orbits of quantum states under Clifford circuits via `reachability graphs', we introduce `contracted graphs' whose vertices represent classes of quantum states with the same entropy vector. These contracted graphs represent the double cosets of the Clifford group, where the lef

Nicholas Z. Rui, J. M. Joel Ong, Stéphane Mathis

Strong magnetic fields are expected to significantly modify the pulsation frequencies of waves propagating in the cores of red giants or in the radiative envelopes of intermediate- and high-mass main-sequence stars. We calculate the g-mode frequencies of stars with magnetic dipole fields which are aligned with their rotational axes, treating both the Lorentz

Erica Bufanda, Jamie Tayar, Daniel Huber, Sten Hasselquist

The success of galactic archaeology and the reconstruction of the formation history of our galaxy critically relies on precise ages for large populations of stars. For evolved stars in the red clump and red giant branch, the carbon to nitrogen ratio ([C/N]) has recently been identified as a powerful diagnostic of mass and age that can be applied to stellar s

Carola M. A. Zanoletti, Brayden R. Hull, C. Danielle Leonard, Robert B. Mann

4-Dimensional Einstein-Gauss-Bonnet (4DEGB) gravity has garnered significant attention in the last few years as a phenomenological competitor to general relativity. We consider the theoretical and observational implications of this theory in both the early and late universe, (re-)deriving background and perturbation equations and constraining its characteris

James Halverson, Fabian Ruehle

We develop a general theory of flows in the space of Riemannian metrics induced by neural network gradient descent. This is motivated in part by recent advances in approximating Calabi-Yau metrics with neural networks and is enabled by recent advances in understanding flows in the space of neural networks. We derive the corresponding metric flow equations, w

Alexander Schuckert, Or Katz, Lei Feng, Eleanor Crane

One of the most striking many-body phenomena in nature is the sudden change of macroscopic properties as the temperature or energy reaches a critical value. Such equilibrium transitions have been predicted and observed in two and three spatial dimensions, but have long been thought not to exist in one-dimensional (1D) systems. Fifty years ago, Dyson and Thou

Ivan Esteban, John F. Beacom, Joachim Kopp

Common-envelope evolution - where a star is engulfed by a companion - is a critical but poorly understood step in, e.g., the formation pathways for gravitational-wave sources. However, it has been extremely challenging to identify observable signatures of such systems. We show that for systems involving a neutron star, the hypothesized super-Eddington accret

Yichul Choi, Da-Chuan Lu, Zhengdi Sun

We discuss two-dimensional conformal field theories (CFTs) which are invariant under gauging a non-invertible global symmetry. At every point on the orbifold branch of $c=1$ CFTs, it is known that the theory is self-dual under gauging a $\mathbb{Z}_2\times \mathbb{Z}_2$ symmetry, and has $\mathsf{Rep}(H_8)$ and $\mathsf{Rep}(D_8)$ fusion category symmetries

Stefan Arseneau, Vedant Chandra, Hsiang-Chih Hwang, Nadia L. Zakamska

Measuring the mass-radius relation of individual white dwarfs is an empirically challenging task that has been performed for only a few dozen stars. We measure the white dwarf mass-radius relation using gravitational redshifts and radii of 137 white dwarfs in wide binaries with main sequence companions. We obtain the space velocities to these systems using t

Thomas Steingasser, Morgane König, David I. Kaiser

We derive the finite-temperature quantum-tunneling rate from first principles. The rate depends on both real- and imaginary-time; we demonstrate that the relevant instantons should therefore be defined on a Schwinger-Keldysh contour, and how the familiar Euclidean-time result arises from it in the limit of large physical times. We generalize previous results

Chadwick A. Trujillo, Cesar Fuentes, David W. Gerdes, Larissa Markwardt

We present the DECam Ecliptic Exploration Project (DEEP) survey strategy including observing cadence for orbit determination, exposure times, field pointings and filter choices. The overall goal of the survey is to discover and characterize the orbits of a few thousand Trans-Neptunian Objects (TNOs) using the Dark Energy Camera (DECam) on the Cerro Tololo In

Jonathan Mercedes-Feliz, Daniel Anglés-Alcázar, Boon Kiat Oh, Christopher C. Hayward

We investigate the formation of dense stellar clumps in a suite of high-resolution cosmological zoom-in simulations of a massive, star forming galaxy at $z \sim 2$ under the presence of strong quasar winds. Our simulations include multi-phase ISM physics from the Feedback In Realistic Environments (FIRE) project and a novel implementation of hyper-refined ac

Yale Yauk, Rohit Patil, Yicheng Zhang, Marcos Rigol

We calculate the typical bipartite entanglement entropy $\langle S_A\rangle_N$ in systems containing indistinguishable particles of any kind as a function of the total particle number $N$, the volume $V$, and the subsystem fraction $f=V_A/V$, where $V_A$ is the volume of the subsystem. We expand our result as a power series $\langle S_A\rangle_N=a f V+b\sqrt

Xingyuan Sun, Chenyue Cai, Ryan P. Adams, Szymon Rusinkiewicz

It is common to manufacture an object by decomposing it into parts that can be assembled. This decomposition is often required by size limits of the machine, the complex structure of the shape, etc. To make it possible to easily assemble the final object, it is often desirable to design geometry that enables robust connections between the subcomponents. In t

Aditya Kannan, Kenneth Shaw, Shikhar Bahl, Pragna Mannam

Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to perform a host of skills with their hands, from making food to operating tools. In this paper, we investigate these challenges, especially in the case of soft, deformable objects as well as complex, relatively long-horizon tasks. However, learning such behaviors from scratch

Shuang Qiu, Ziyu Dai, Han Zhong, Zhaoran Wang

This paper investigates posterior sampling algorithms for competitive reinforcement learning (RL) in the context of general function approximations. Focusing on zero-sum Markov games (MGs) under two critical settings, namely self-play and adversarial learning, we first propose the self-play and adversarial generalized eluder coefficient (GEC) as complexity m

Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley

Automated Synthesis Planning has recently re-emerged as a research area at the intersection of chemistry and machine learning. Despite the appearance of steady progress, we argue that imperfect benchmarks and inconsistent comparisons mask systematic shortcomings of existing techniques, and unnecessarily hamper progress. To remedy this, we present a synthesis

Zhenhua Liu

In 1974, Federer proved that all area-minimizing hypersurfaces on orientable manifolds were calibrated by weakly closed differential forms. However, in this manuscript, we prove the contrary in higher codimensions: calibrated area-minimizers are non-generic. This is surprising given that almost all known examples of area-minimizing surfaces are confirmed to

Zeyinzi Jiang, Chaojie Mao, Ziyuan Huang, Ao Ma

Parameter-efficient tuning has become a trend in transferring large-scale foundation models to downstream applications. Existing methods typically embed some light-weight tuners into the backbone, where both the design and the learning of the tuners are highly dependent on the base model. This work offers a new tuning paradigm, dubbed Res-Tuning, which inten

Hao Dong, Ismail Nejjar, Han Sun, Eleni Chatzi

In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to unseen multi-modal distributions poses even greater difficulties due to the distinct properties exhibited by different modalities. To overcome the challenges of achieving domain ge

Marc Santolini, Leo Blondel, Megan J. Palmer, Robert N. Ward

Teams are a primary source of innovation in science and technology. Rather than examining the lone genius, scholarly and policy attention has shifted to understanding how team interactions produce new and useful ideas. Yet the organizational roots of innovation remain unclear, in part because of the limitations of current data. This paper introduces the inte

Zirui Yan, Arpan Mukherjee, Burak Varıcı, Ali Tajer

Sequential design of experiments for optimizing a reward function in causal systems can be effectively modeled by the sequential design of interventions in causal bandits (CBs). In the existing literature on CBs, a critical assumption is that the causal models remain constant over time. However, this assumption does not necessarily hold in complex systems, w

Alberto Bietti, Joan Bruna, Loucas Pillaud-Vivien

We study gradient flow on the multi-index regression problem for high-dimensional Gaussian data. Multi-index functions consist of a composition of an unknown low-rank linear projection and an arbitrary unknown, low-dimensional link function. As such, they constitute a natural template for feature learning in neural networks. We consider a two-timescale algor

Christoph Leiter, Juri Opitz, Daniel Deutsch, Yang Gao

With an increasing number of parameters and pre-training data, generative large language models (LLMs) have shown remarkable capabilities to solve tasks with minimal or no task-related examples. Notably, LLMs have been successfully employed as evaluation metrics in text generation tasks. Within this context, we introduce the Eval4NLP 2023 shared task that as

Gabriel Grand, Lionel Wong, Maddy Bowers, Theo X. Olausson

While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and readable programs. In this paper, we introduce LILO, a neurosymbolic framework that iteratively synthesizes, compresses, and documents code to build libraries tailored to particular

Mariona Badenas-Agusti, Andrew Vanderburg, Simon Blouin, Patrick Dufour

We present a catalogue of 62 polluted white dwarfs observed by the 9th Low-Resolution Data Release of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST LRS DR9v1; R$\approx$1,800) and the Early Data Release 3 (EDR3) of the Gaia Mission. Among these stellar remnants, 30 are new discoveries with previously unknown traces of calcium pollutio

Eleni Bagui, Sebastien Clesse, Valerio De Luca, Jose María Ezquiaga

In the recent years, primordial black holes (PBHs) have emerged as one of the most interesting and hotly debated topics in cosmology. Among other possibilities, PBHs could explain both some of the signals from binary black hole mergers observed in gravitational wave detectors and an important component of the dark matter in the Universe. Significant progress

Beatrix M. G. Nielsen, Anders Christensen, Andrea Dittadi, Ole Winther

Diffusion models may be viewed as hierarchical variational autoencoders (VAEs) with two improvements: parameter sharing for the conditional distributions in the generative process and efficient computation of the loss as independent terms over the hierarchy. We consider two changes to the diffusion model that retain these advantages while adding flexibility

Masahiro Kato

This study investigates the experimental design problem for identifying the arm with the highest expected outcome, referred to as best arm identification (BAI). In our experiments, the number of treatment-allocation rounds is fixed. During each round, a decision-maker allocates an arm and observes a corresponding outcome, which follows a Gaussian distributio

Xiaojun Zheng, Simon Mak, Liyan Xie, Yao Xie

Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low-rank background is critical for process

Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich

We provide a novel reduction from swap-regret minimization to external-regret minimization, which improves upon the classical reductions of Blum-Mansour [BM07] and Stolz-Lugosi [SL05] in that it does not require finiteness of the space of actions. We show that, whenever there exists a no-external-regret algorithm for some hypothesis class, there must also ex

Amita Kamath, Jack Hessel, Kai-Wei Chang

Recent vision-language (VL) models are powerful, but can they reliably distinguish "right" from "left"? We curate three new corpora to quantify model comprehension of such basic spatial relations. These tests isolate spatial reasoning more precisely than existing datasets like VQAv2, e.g., our What'sUp benchmark contains sets of photographs varying only the

Ziyang Yuan, Mingdeng Cao, Xintao Wang, Zhongang Qi

Incorporating a customized object into image generation presents an attractive feature in text-to-image generation. However, existing optimization-based and encoder-based methods are hindered by drawbacks such as time-consuming optimization, insufficient identity preservation, and a prevalent copy-pasting effect. To overcome these limitations, we introduce C

Daniel Belkin, James Allen, Soumik Ghosh, Christopher Kang

Unitary t-designs are distributions on the unitary group whose first t moments appear maximally random. Previous work has established several upper bounds on the depths at which certain specific random quantum circuit ensembles approximate t-designs. Here we show that these bounds can be extended to any fixed architecture of Haar-random two-site gates. This

Jordan Pack, Yinjie Guo, Ziyu Liu, Bjarke S. Jessen

Two-dimensional (2D) semiconductors, such as the transition metal dichalcogenides, have demonstrated tremendous promise for the development of highly tunable quantum devices. Realizing this potential requires low-resistance electrical contacts that perform well at low temperatures and low densities where quantum properties are relevant. Here we present a new

Tarek M. Elgindi, Federico Pasqualotto

We develop a computer-assisted symbolic method to show that a linearized Boussinesq flow in self-similar coordinates gives rise to an invertible operator.

Tarek M. Elgindi, Federico Pasqualotto

We establish finite-time singularity formation for $C^{1,\alpha}$ solutions to the Boussinesq system that are compactly supported on $\mathbb{R}^2$ and infinitely smooth except in the radial direction at the origin. The solutions are smooth in the angular variable at the blow-up point, which was a fundamental obstruction in previous works. This is done by ex

Richard Montgomery

A Latin square of order $n$ is an $n$ by $n$ grid filled using $n$ symbols so that each symbol appears exactly once in each row and column. A transversal in a Latin square is a collection of cells which share no symbol, row or column. The Ryser-Brualdi-Stein conjecture, with origins from 1967, states that every Latin square of order $n$ contains a transversa

Catalina Gomez, Sue Min Cho, Shichang Ke, Chien-Ming Huang

Leveraging Artificial Intelligence (AI) in decision support systems has disproportionately focused on technological advancements, often overlooking the alignment between algorithmic outputs and human expectations. A human-centered perspective attempts to alleviate this concern by designing AI solutions for seamless integration with existing processes. Determ

Giacomo Cristinelli, José A. Iglesias, Daniel Walter

Total variation regularization has proven to be a valuable tool in the context of optimal control of differential equations. This is particularly attributed to the observation that TV-penalties often favor piecewise constant minimizers with well-behaved jumpsets. On the downside, their intricate properties significantly complicate every aspect of their analy

Sarah Rastegar, Hazel Doughty, Cees G. M. Snoek

In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined category set. While strides have been made in the realms of self-supervised and open-world learning towards test-time category discovery, a crucial yet often overlooked question persis

Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and

Tushita Prasad, Markus Grassl

Entanglement-assisted classical communication (EACC) aims to enhance communication systems using entanglement as an additional resource. However, there is a scarcity of explicit protocols designed for finite transmission scenarios, which presents a challenge for real-world implementation. In response we introduce a new EACC scheme capable of correcting a fix

Kevin Lin, Faisal Ahmed, Linjie Li, Chung-Ching Lin

We present MM-VID, an integrated system that harnesses the capabilities of GPT-4V, combined with specialized tools in vision, audio, and speech, to facilitate advanced video understanding. MM-VID is designed to address the challenges posed by long-form videos and intricate tasks such as reasoning within hour-long content and grasping storylines spanning mult

Michał Papaj, Guangxin Ni, Cyprian Lewandowski

The variety of correlated phenomena in moir\'e systems is incredibly rich, spanning effects such as superconductivity, a generalized form of ferromagnetism, or even charge fractionalization. This wide range of quantum phenomena is partly enabled by the large number of internal degrees of freedom in these systems, such as the valley and spin degrees of freedo

Michael Finkelberg, Victor Ginzburg, Roman Travkin

Given a hyperspherical $G$-variety $\mathscr X$ we consider the zero moment level $\Lambda_{\mathscr X}\subset{\mathscr X}$ of the action of a Borel subgroup $B\subset G$. We conjecture that $\Lambda_{\mathscr X}$ is Lagrangian. For the dual $G^\vee$-variety ${\mathscr X}^\vee$, we conjecture that that there is a bijection between the sets of irreducible com

Soumik Banerjee, Aleksey S. Polunchenko

We consider the classical Shiryaev--Roberts martingale diffusion, $(R_t)_{t\ge0}$, restricted to the interval $[0,A]$, where $A>0$ is a preset absorbing boundary. We take yet another look at the well-known phenomenon of quasi-stationarity (time-invariant probabilistic behavior, conditional on no absorbtion hitherto) exhibited by the diffusion in the temporal

Pierre-François Loos, Filippo Lipparini, Denis Jacquemin

Molecules that violate Hund's rule and exhibit an inverted gap between the lowest singlet $S_1$ and triplet $T_1$ excited states have attracted considerable attention due to their potential applications in optoelectronics. Amongst these molecules, the triangular-shaped heptazine, and its derivatives, have been in the limelight. However, conflicting reports h

Kyriakos Stylianopoulos, Murat Bayraktar, Nuria González Prelcic, George C. Alexandropoulos

User localization and tracking in the upcoming generation of wireless networks have the potential to be revolutionized by technologies such as the Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches rely on assumptions about relatively dominant Line-of-Sight (LoS) paths, or require pilot transmission sequences whose length is compar

Luke Major Krauth, Jordy Davelaar, Zoltán Haiman, John Ryan Westernacher-Schneider

Self-lensing flares (SLFs) are expected to be produced once or twice per orbit by an accreting massive black hole binary (MBHB), if the eclipsing MBHBs are observed close to edge-on. SLFs can provide valuable electromagnetic (EM) signatures to accompany the gravitational waves (GWs) detectable by the upcoming Laser Interferometer Space Antenna (LISA). EM fol

Gerard J. Machado, Lluc Sendra, Adam Vallés, Juan P. Torres

We consider an interferometer based on the concept of induced coherence, where two photons that originate in different second-order nonlinear crystals can interfere. We derive a complementarity relationship that links the first-order coherence between the two interfering photons with a parameter that quantifies the distinguishing information regarding the no