October 2023 arXiv papers — page 102

Raluca M. Balan, Panqiu Xia, Guangqu Zheng

In this paper, we present an almost sure central limit theorem (ASCLT) for the hyperbolic Anderson model (HAM) with a L\'evy white noise in a finite-variance setting, complementing a recent work by Balan and Zheng (\emph{Trans.~Amer.~Math.~Soc.}, 2024) on the (quantitative) central limit theorems for the solution to the HAM. We provide two different proofs:

Arved Bartuska, Luis Espath, Raúl Tempone

Finding the optimal design of experiments in the Bayesian setting typically requires estimation and optimization of the expected information gain functional. This functional consists of one outer and one inner integral, separated by the logarithm function applied to the inner integral. When the mathematical model of the experiment contains uncertainty about

Giovanni Colombo, Boris S. Mordukhovich, Dao Nguyen, Trang Nguyen

The paper is devoted to the study of a new class of optimal control problems governed by discontinuous constrained differential inclusions of the sweeping type with involving the duration of the dynamic process into optimization. We develop a novel version of the method of discrete approximations of its own qualitative and numerical values with establishing

Saumajit Saha, Albert Nanda

This paper presents the system that we have developed while solving this shared task on violence inciting text detection in Bangla. We explain both the traditional and the recent approaches that we have used to make our models learn. Our proposed system helps to classify if the given text contains any threat. We studied the impact of data augmentation when t

Ganghua Wang, Xun Xian, Jayanth Srinivasa, Ashish Kundu

The growing dependence on machine learning in real-world applications emphasizes the importance of understanding and ensuring its safety. Backdoor attacks pose a significant security risk due to their stealthy nature and potentially serious consequences. Such attacks involve embedding triggers within a learning model with the intention of causing malicious b

Gabriel Malavé, Jorge Schifferli, Rodrigo Soto-Garrido, Pedro A. Orellana

We here introduce the concept of fragile topological dislocation modes, which are localized only in a fraction of a topological phase, while otherwise leak into the bulk continuum. We show that such dislocation modes are hosted in an obstructed atomic topological phase in the two-dimensional Su-Schrieffer-Heeger model, but only in a finite region with an ind

Ahmed Temtam, Dimitrie Popescu

In order to enable spectrum sharing, spectrum sensing plays a crucial role in wireless communication. The challenges in wireless spectrum require collaboration among stakeholders to devise innovative solutions. This research explores the use of a Cognitive Radio (CR) system that employs a Time-Domain Symbol Cross-correlation (TDSC) based spectrum sensing alg

Zongren Zou, Xuhui Meng, George Em Karniadakis

Data-driven discovery of governing equations in computational science has emerged as a new paradigm for obtaining accurate physical models and as a possible alternative to theoretical derivations. The recently developed physics-informed neural networks (PINNs) have also been employed to learn governing equations given data across diverse scientific disciplin

Andrés A. Plazas Malagón, Federica Bianco, Ranpal Gill, Robert D. Blum

Systemic racism is a ubiquitous theme in societies worldwide and plays a central role in shaping our economic, social, and academic institutions. The Vera C. Rubin Observatory is a major US ground-based facility based in Chile with international participation. The Observatory is an example of excellence and will deliver the largest survey of the sky ever att

José Nicolás Orce, Cebo Ngwetsheni

Novel equations for the electric dipole polarizability $\alpha_{_{E1}}$ of low-lying excited states in atomic nuclei -- and the related $(-2)$ moment of the total photo-absorption cross section, $\sigma_{_{-2}}$ -- are inferred in terms of electric dipole and quadrupole matrix elements. These equations are valid for arbitrary angular momenta of the initial/g

Alun Thomas

We describe the implementation of the Giudici-Green Metropolis sampling method for decomposable graphs using a variety of structures to represent the graph. These comprise the graph itself, the Junction tree, the Almond tree and the Ibarra clique-separator graph. For each structure, we describe the process for ascertaining whether adding or deleting a specif

Emmanuel Noutahi, Cristian Gabellini, Michael Craig, Jonathan S. C Lim

Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures. To address this issue, we introduce Sequential Attachment-based Fragment Embedding (SAFE), a novel line notation for chemical structures. SAFE reimagines SMILES strings as an unor

Zachary Novack, Nikita Srivatsan, Taylor Berg-Kirkpatrick, Julian McAuley

Lead sheets have become commonplace in generative music research, being used as an initial compressed representation for downstream tasks like multitrack music generation and automatic arrangement. Despite this, researchers have often fallen back on deterministic reduction methods (such as the skyline algorithm) to generate lead sheets when seeking paired le

Jeffrey McKaig, Claudio Ricci, Stéphane Paltani, K. K. Gupta

Most Active Galactic Nuclei (AGN) in the local Universe are obscured. In these obscured AGN an excess is usually observed in the soft X-rays below ~2 keV above the absorbed X-ray continuum. This spectral component is associated with the scattering of X-ray photons off free electrons in the Narrow Line Region (NLR), and/or to photoionised lines. Recent studie

Nicola Pranzini, Paola Verrucchi

We propose a classification of measurement apparatuses based on their reliability and accessibility. Our notion of reliability parameterises the possibility of getting unexpected wrong results when using the apparatus in a given time window, and the one of accessibility describes the energy cost required to make the apparatus interact with a measured system.

Ruiqi Wu, Liangyu Chen, Tong Yang, Chunle Guo

With the impressive progress in diffusion-based text-to-image generation, extending such powerful generative ability to text-to-video raises enormous attention. Existing methods either require large-scale text-video pairs and a large number of training resources or learn motions that are precisely aligned with template videos. It is non-trivial to balance a

Chelsea Medina, Lily Shaw, Dissy Vargas, Sundar Krishnan

This paper discusses the mechanisms of cryptocurrency, the idea of using security in the system, and the popularity of it. To begin, the authors provide a background on cryptocurrency and how it works. The authors understand that while most people may be familiar with the concept, they may not know how it works. Next, the authors discuss the security of cryp

Tianxiang Gao, Xiaokai Huo, Hailiang Liu, Hongyang Gao

Neural networks with wide layers have attracted significant attention due to their equivalence to Gaussian processes, enabling perfect fitting of training data while maintaining generalization performance, known as benign overfitting. However, existing results mainly focus on shallow or finite-depth networks, necessitating a comprehensive analysis of wide ne

Yahong Yang, Yue Wu, Haizhao Yang, Yang Xiang

This paper introduces deep super ReLU networks (DSRNs) as a method for approximating functions in Sobolev spaces measured by Sobolev norms $W^{m,p}$ for $m\in\mathbb{N}$ with $m\ge 2$ and $1\le p\le +\infty$. Standard ReLU deep neural networks (ReLU DNNs) cannot achieve this goal. DSRNs consist primarily of ReLU DNNs, and several layers of the square of ReLU

Yu Gu, Jianwei Yang, Naoto Usuyama, Chunyuan Li

Rapid progress has been made in instruction-learning for image editing with natural-language instruction, as exemplified by InstructPix2Pix. In biomedicine, such methods can be applied to counterfactual image generation, which helps differentiate causal structure from spurious correlation and facilitate robust image interpretation for disease progression mod

Jose M. Betancourt

The dynamics of network formation are generally very complex, making the study of distributions over the space of networks often intractable. Under a condition called conservativeness, I show that the stationary distribution of a network formation process can be found in closed form, and is given by a Gibbs measure. For conservative processes, the stationary

Shreecheta Chowdhury, Amit Chakraborty, Saunak Dutta

This paper describes a method for detecting a rare top quark decay into a charm quark and a Higgs boson (H), which decays further into b quarks, at the Large Hadron Collider (LHC), and introduces a tagging algorithm to identify boosted tops using large-R jets containing b- and c-tagged elements. We consider the associated production of the top quark with a W

Jixin Hou, Nicholas Filla, Xianyan Chen, Mir Jalil Razavi

Traditional computational methods, such as the finite element analysis, have provided valuable insights into uncovering the underlying mechanisms of brain physical behaviors. However, precise predictions of brain physics require effective constitutive models to represent the intricate mechanical properties of brain tissue. In this study, we aimed to identify

Lorenzo Rimella, Chris Jewell, Paul Fearnhead

Inference for high-dimensional hidden Markov models is challenging due to the exponential-in-dimension computational cost of calculating the likelihood. To address this issue, we introduce an innovative composite likelihood approach called "Simulation Based Composite Likelihood" (SimBa-CL). With SimBa-CL, we approximate the likelihood by the product of its m

Bhaskarjit Sarmah, Tianjie Zhu, Dhagash Mehta, Stefano Pasquali

For a financial analyst, the question and answer (Q\&A) segment of the company financial report is a crucial piece of information for various analysis and investment decisions. However, extracting valuable insights from the Q\&A section has posed considerable challenges as the conventional methods such as detailed reading and note-taking lack scalability and

Mainak Das, Chunli Huang

Recent experiments have uncovered a distinctive magnetic metal in lightly-doped multilayer graphene, coined the \textit{quarter metal}. This quarter metal consolidates all the doped carriers, originally distributed evenly across the four (or twelve) Fermi surfaces of the paramagnetic state, into one expansive Fermi surface by breaking time-reversal and/or in

Zihao Chen, Yeshwanth Cherapanamjeri

We investigate the quantitative performance of affine-equivariant estimators for robust mean estimation. As a natural stability requirement, the construction of such affine-equivariant estimators has been extensively studied in the statistics literature. We quantitatively evaluate these estimators under two outlier models which have been the subject of much

Flavio Riche, Miguel Gonçalves, Bruno Amorim, Eduardo V. Castro

We unveil a novel mechanism for quasicondensation of hard-core bosons in the presence of quasiperiodicity-induced multifractal single-particle states. The new critical state, here dubbed fractal quasicondensate, is characterized by natural orbitals with multifractal properties and by an occupancy of the lowest natural orbital, {\lambda}0 ~ L{\gamma}, which g

Athira J Jacob, Puneet Sharma, Daniel Ruckert

Delineation of anatomical structures is often the first step of many medical image analysis workflows. While convolutional neural networks achieve high performance, these do not incorporate anatomical shape information. We introduce a novel segmentation algorithm that uses Deep Conditional Shape models (DCSMs) as a core component. Using deep implicit shape r

Zhenchao Jin, Xiaowei Hu, Lingting Zhu, Luchuan Song

Co-occurrent visual patterns suggest that pixel relation modeling facilitates dense prediction tasks, which inspires the development of numerous context modeling paradigms, \emph{e.g.}, multi-scale-driven and similarity-driven context schemes. Despite the impressive results, these existing paradigms often suffer from inadequate or ineffective contextual info

Thomas Ransford

We show that, given a closed subset $E$ of the unit circle of Lebesgue measure zero, there exists a positive sequence $u_n\to\infty$ with the following property: if $T$ is a Hilbert-space contraction such that $\sigma(T)\subset E$ and $\|T^{-n}\|=O(u_n)$ and rank$(I-T^*T)<\infty$, then $T$ is a unitary operator. We further show that the condition of measure

Ayan Barui, Mayukha Pal, Prasanta K. Panigrahi

We propose a hybrid quantum approach to threshold and binarize a grayscale image through unsharp measurements (UM) relying on image histogram. Generally, the histograms are characterized by multiple overlapping normal distributions corresponding to objects, or image features with small but significant overlaps, making it challenging to establish suitable thr

Konrad Aguilar, Zoë X. Batterman

We introduce a new metric on the ideal space of an AF algebra that metrizes the Fell topology. The novelty of this metric lies in the use of a Hamming distance type metric in its construction. Furthermore, this metric captures more of the ideal structure of AF algebras in comparison to known metrics on the Fell topology of an AF algebra. We explicitly test t

Eugene Magadeev, Robert Vakhitov, Raushan Kanbekov

The work examines ferromagnetic films with strong uniaxial anisotropy of the "easy plane" type and substantiates that paired nano-scaled perforations in such films can be used as memory cells for recording and storing data in the ternary number system. The problem of reading the state of cells of this type has been studied, and an approach to solving it has

Parisa Khodabakhshi, Olena Burkovska, Karen Willcox, Max Gunzburger

This study is devoted to the construction of a multifidelity Monte Carlo (MFMC) method for the uncertainty quantification of a nonlocal, non-mass-conserving Cahn-Hilliard model for phase transitions with an obstacle potential. We are interested in the estimation of the expected value of an output of interest (OoI) that depends on the solution of the nonlocal

ESSnuSB, :, J. Aguilar, M. Anastasopoulos

In this paper we study non-standard interactions mediated by a scalar field (SNSI) in the context of ESSnuSB experiment. In particular we study the capability of ESSnuSB to put bounds on the SNSI parameters and also study the impact of SNSI in the measurement of the leptonic CP phase $\delta_{\rm CP}$. Existence of SNSI modifies the neutrino mass matrix and

V. N. Obridko, D. D. Sokoloff, M. K. Katsova

We investigate sharp structures visible in solar magnetic field tracers. It is shown that the sunspot magnetic boundaries do not coincide with the photometric ones. Moreover, there is no clear boundary of the magnetic field in the vicinity of sunspots. Thus, the widely accepted concept of magnetic tubes with sharp edges is not always correct and should be us

Venugopal Raskatla, Tongjun Liu, Jinxiang Li, Kevin F. MacDonald

Continuous time crystals (CTCs) - media with broken continuous time translation symmetry - are an eagerly sought state of matter that spontaneously transition from a time-independent state to one of periodic motion in response to a small perturbation. The state has been realized recently in an array of nanowires decorated with plasmonic metamolecules illumin

A. Eugene DePrince

The two-electron reduced density matrix (2RDM) carries enough information to evaluate the electronic energy of a many-electron system. The variational 2RDM (v2RDM) approach seeks to determine the 2RDM directly, without knowledge of the wave function, by minimizing this energy with respect to variations in the elements of the 2RDM, while also enforcing known

Priyam Gupta, Peter J. Schmid, Denis Sipp, Taraneh Sayadi

The Koopman operator presents an attractive approach to achieve global linearization of nonlinear systems, making it a valuable method for simplifying the understanding of complex dynamics. While data-driven methodologies have exhibited promise in approximating finite Koopman operators, they grapple with various challenges, such as the judicious selection of

Xinglong Chang, Gillian Dobbie, Jörg Wicker

Machine learning models are increasingly used in fields that require high reliability such as cybersecurity. However, these models remain vulnerable to various attacks, among which the adversarial label-flipping attack poses significant threats. In label-flipping attacks, the adversary maliciously flips a portion of training labels to compromise the machine

Yue Zhang

Neutrinos -- amongst the lightest known particles -- can mediate a force driving dark matter self-interaction and the small scale structure of the universe. We explore such a possibility in the simplest neutrino portal dark sector model where neutrino has a Yukawa coupling with a scalar $\phi$ and fermion $\chi$ that are degenerate in mass and together compr

Gaoyue Guo, Maxime Latypov, Milica Tomasevic

In this work, we consider one-dimensional particles interacting in mean-field type through a bounded kernel. In addition, when particles hit some barrier (say zero), they are removed from the system. This absorption of particles is instantaneously felt by the others, as, contrary to the usual mean-field setting, particles interact only with other non-absorbe

Kevin Fry, Jonathan E. Taylor

Many modern datasets, such as those in ecology and geology, are composed of samples with spatial structure and dependence. With such data violating the usual independent and identically distributed (IID) assumption in machine learning and classical statistics, it is unclear a priori how one should measure the performance and generalization of models. Several

Brandt A. L. Gaches, Tommaso Grassi, Stefan Vogt-Geisse, Giulia M. Bovolenta

(Abridged) Electron-molecule interaction is a fundamental process in radiation-driven chemistry in space, from the interstellar medium to comets. Therefore, knowledge of interaction cross-sections is key. While there has been a plethora of studies of total ionization cross-sections, data is often spread over many sources, or not public or readily available.

Tolga Altinoluk, Guillaume Beuf, Michael Lublinsky, Vladimir V. Skokov

Various conventional running coupling prescriptions reproducing $\beta_ 0$-dependent terms of NLO JIMWLK are reviewed and found to be theoretically inconsistent: the JIMWLK evolution Hamiltonian with running coupling violates the requirement of positive semidefiniteness. This requirement appears to be tightly related to the possibility of having a Langevin f

Peihong Yuan, Muriel Medard, Kevin Galligan, Ken R. Duffy

We establish that a large, flexible class of long, high redundancy error correcting codes can be efficiently and accurately decoded with guessing random additive noise decoding (GRAND). Performance evaluation demonstrates that it is possible to construct simple product codes with lengths of approximately 200 to 4000 bits and rates between 0.2 and 0.8 that ou

Ryan Raikman, Simone Bavera, Tassos Fragos

In the flourishing field of gravitational-wave astronomy, accurately inferring binary black hole merger formation channels is paramount. The Bayesian hierarchical model selection analysis offers a promising methodology (see, e.g., One Channel to Rule Them All, Zevin et al. 2021). However, recently, Cheng et al. (2023) highlighted a critical caveat: observed

Ryan Shea, Zhou Yu

Maintaining a consistent persona is a key quality for any open domain dialogue system. Current state-of-the-art systems do this by training agents with supervised learning or online reinforcement learning (RL). However, systems trained with supervised learning often lack consistency as they are never punished for uttering contradictions. Additional training

Daniel Lautzenheiser

We view space-filling circle packings as subsets of the boundary of hyperbolic space subject to symmetry conditions based on a discrete group of isometries. This allows for the application of counting methods which admit rigorous upper and lower bounds on the Hausdorff dimension of the residual set of a generalized Apollonian circle packing. This dimension (

Cheqiu Lyu, Yingjie Peng, Yipeng Jing, Xiaohu Yang

The assembly of galaxies over cosmic time is tightly connected to the assembly of their host dark matter halos. We investigate the stellar mass growth history and the chemical enrichment history of central galaxies in SDSS-MaNGA. We find that the derived stellar metallicity of passive central galaxies is always higher than that of the star-forming ones. This

Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi Jaakkola

Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry. Their modular nature has enabled the use of template-based methods to generate hypothetical MOFs by combining molecular building blocks in accordance with known network topologies. However, th

Agnès Ferté, Kevin Hong

We highlight the role of weak lensing measurements from current and upcoming stage-IV imaging surveys in the search for cosmic inflation, specifically in measuring the scalar spectral index $n_s$. To do so, we combine the Dark Energy Survey 3 years of observation weak lensing and clustering data with BICEP/Keck, Planck and Sloan Digital Sky Survey data in $r

Lewis R. Prole, Paul C. Clark, Felix D. Priestley, Simon C. O. Glover

Advanced primordial chemistry networks have been developed to model the collapse of metal-free baryonic gas within the gravitational well of dark matter (DM) halos and its subsequent collapse into Population III stars. At the low densities of 10^-26-10^-21 g cm-3 (10-3-10^2 cm-3) the collapse is dependent on H2 production, which is a function of the compress

Justin Berman, Henriette Elvang, Aidan Herderschee

We examine universal positivity constraints on $2 \to 2$ scattering in 4d planar $N=4$ supersymmetric Yang-Mills theory with higher-derivative corrections. We present numerical evidence that the convex region of allowed Wilson coefficients (the ``EFT-hedron'') flattens completely along about one-third of its dimensions when an increasing number of constraint

Konrad Topolski, Samuel Tootle, Luciano Rezzolla

The spectral properties of the post-merger gravitational-wave signal from a binary of neutron stars encode a variety of information about the features of the system and of the equation of state describing matter around and above nuclear saturation density. Characterising the properties of such a signal is an ``old'' problem, which first emerged when a number

E. A. Zimmerman, I. Irani, P. Chen, A. Gal-Yam

Observing a supernova explosion shortly after it occurs can reveal important information about the physics of stellar explosions and the nature of the progenitor stars of supernovae (SNe). When a star with a well-defined edge explodes in vacuum, the first photons to escape from its surface appear as a brief shock-breakout flare. The duration of this flare ca

Jonas von Milczewski, Xin Chen, Atac Imamoglu, Richard Schmidt

We study a mechanism to induce superconductivity in atomically thin semiconductors where excitons mediate an effective attraction between electrons. Our model includes interaction effects beyond the paradigm of phonon-mediated superconductivity and connects to the well-established limits of Bose and Fermi polarons. By accounting for the strong-coupling physi

Jorick S. Vink

Recent studies of high-redshift galaxies with James Webb Space Telescope (JWST), such as GN-z11 at $z=10.6,$ show unexpectedly significant amounts of nitrogen (N) in their spectra. As this phenomenology appears to extend to gravitionally lensed galaxies at Cosmic noon such as the Sunburst Arc at $z=2.37$, as well as globular clusters overall, we suggest that

Sebastian Franco, Diego Rodriguez-Gomez

A new type of quiver theories, denoted twin quivers, was recently introduced for studying $5d$ SCFTs engineered by webs of 5-branes ending on 7-branes. Twin quivers provide an alternative perspective on various aspects of such webs, including Hanany-Witten moves and the $s$-rule. More ambitiously, they can be regarded as a first step towards the construction

Sen Wang, Dandan Xu, Shengdong Lu, Cheng Li

The effect of angular momentum on galaxy formation and evolution has been studied for several decades. Our recent two papers using IllustrisTNG-100 simulation have revealed the acquisition path of the angular momentum from large-scale environment (satellites within hundreds of kpc) through the circum-galactic medium (CGM) to the stellar discs, putting forwar

Christophe Piveteau, Christopher T. Chubb, Joseph M. Renes

Decoding algorithms based on approximate tensor network contraction have proven tremendously successful in decoding 2D local quantum codes such as surface/toric codes and color codes, effectively achieving optimal decoding accuracy. In this work, we introduce several techniques to generalize tensor network decoding to higher dimensions so that it can be appl

Sylvia Ploeckinger, Folkert S. J. Nobels, Matthieu Schaller, Joop Schaye

Large-scale cosmological galaxy formation simulations typically prevent gas in the interstellar medium (ISM) from cooling below $\approx 10^4$ K. This has been motivated by the inability to resolve the Jeans mass in molecular gas (>>$10^5\,\mathrm{M}_{\odot}$) which would result in undesired artificial clumping. We show that the classical Jeans criteria deri

Caterina Zerba, Clemens Kuhlenkamp, Ataç Imamoğlu, Michael Knap

Heterostructures of two-dimensional transition metal dichalcogenides (TMDs) are emerging as a promising platform for investigating exotic correlated states of matter. Here, we propose to engineer Bose-Fermi mixtures in these systems by coupling inter-layer excitons to doped charges in a trilayer structure. Their interactions are determined by the inter-layer

Christopher J. Agostino, Samir Salim, Médéric Boquien, Steven Janowiecki

Despite powerful X-ray emission, some AGNs are known to either lack optical emission lines (so-called 'optically dull' AGNs) or have lines that fall on the star-forming branch of the BPT diagram ('misclassified' AGNs). Aperture effects have been proposed to explain such atypical spectra, especially when based on SDSS (3'') fibers. We use observations from VL

Boris Goncharov, Laura Donnay, Jan Harms

We revisit gravitational wave (GW) memory as the key to measuring spacetime symmetries, extending beyond its traditional role in GW searches. In particular, we show how these symmetries may be probed via displacement and spin memory observations, respectively. We further find that the Einstein Telescope's (ET) sensitivity enables constraining the strain ampl

Davide Piras, Lucas Lombriser

We present DE-VAE, a variational autoencoder (VAE) architecture to search for a compressed representation of dynamical dark energy (DE) models in observational studies of the cosmic large-scale structure. DE-VAE is trained on matter power spectra boosts generated at wavenumbers $k\in(0.01-2.5) \ h/\rm{Mpc}$ and at four redshift values $z\in(0.1,0.48,0.78,1.5

Lei Gioia, Ryan Thorngren

The characterization of ground states among all quantum states is an important problem in quantum many-body physics. For example, the celebrated entanglement area law for gapped Hamiltonians has allowed for efficient simulation of 1d and some 2d quantum systems using matrix product states. Among ground states, some types, such as cat states (like the GHZ sta

N. Locatelli, G. Ponti, X. Zheng, A. Merloni

The first all-sky maps of the diffuse emission of high ionization lines observed in X-rays by SRG/eROSITA, provide an excellent probe for the study of the warm-hot phase (T~10^6 K) of the circumgalactic medium (CGM) of the Milky Way (MW). In this work we analyse the O VIII line detected in the first eROSITA All-Sky Survey data (eRASS1). We fit a sky map made

Conor M. O'Riordan, Simona Vegetti

Strong gravitational lensing can be used to find otherwise invisible dark matter subhaloes. In such an analysis, the lens galaxy mass model is a significant source of systematic uncertainty. In this paper we analyse the effect of angular complexity in the lens model. We use multipole perturbations which introduce low-order deviations from pure ellipticity in

Daniel A. Paradiso, Eric R. Coughlin, Jonathan Zrake, Dheeraj R. Pasham

Observations and theory suggest that core-collapse supernovae can span a range of explosion energies, and when sub-energetic, the shockwave initiating the explosion can decelerate to speeds comparable to the escape speed of the progenitor. In these cases, gravity will complicate the explosion hydrodynamics and conceivably cause the shock to stall at large ra

Ava Polzin, Andrey V. Kravtsov, Vadim A. Semenov, Nickolay Y. Gnedin

We use a suite of hydrodynamics simulations of the interstellar medium (ISM) within a galactic disk, which include radiative transfer, a non-equilibrium model of molecular hydrogen, and a realistic model for star formation and feedback, to study the structure of the ISM and H$_2$ abundance as a function of local ISM properties. We show that the star formatio

E. M. May, Ryan J. MacDonald, Katherine A. Bennett, Sarah E. Moran

The search for rocky planet atmospheres with JWST has focused on planets transiting M dwarfs. Such planets have favorable planet-to-star size ratios, enhancing the amplitude of atmospheric features. Since the expected signal strength of atmospheric features is similar to the single-transit performance of JWST, multiple observations are required to confirm an

Li-Yuan Chiang, Yu-tin Huang, He-Chen Weng

We study the space of open string effective field theories by combining the constraint of unitarity and monodromy relations for the four-point amplitude. The latter is a reflection of an underlying disk correlator with singularities at the boundary. By assuming maximal susy the resulting bootstrap isolates Wilson coefficients to at least $10^{-4}$ of the Typ

Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao

Hair editing has made tremendous progress in recent years. Early hair editing methods use well-drawn sketches or masks to specify the editing conditions. Even though they can enable very fine-grained local control, such interaction modes are inefficient for the editing conditions that can be easily specified by language descriptions or reference images. Than

Leif Van Holland, Ruben Bliersbach, Jan U. Müller, Patrick Stotko

Implicit representations like Neural Radiance Fields (NeRF) showed impressive results for photorealistic rendering of complex scenes with fine details. However, ideal or near-perfectly specular reflecting objects such as mirrors, which are often encountered in various indoor scenes, impose ambiguities and inconsistencies in the representation of the reconstr

Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic

The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry (kinetic energy), and the regularization of density paths (potential energy). These combinations yield different variational problems (Lagrangians), encompassing many variations of the optimal transport problem such as the Schr\"odinger bridg

Rose E. Wang, Qingyang Zhang, Carly Robinson, Susanna Loeb

Scaling high-quality tutoring remains a major challenge in education. Due to growing demand, many platforms employ novice tutors who, unlike experienced educators, struggle to address student mistakes and thus fail to seize prime learning opportunities. Our work explores the potential of large language models (LLMs) to close the novice-expert knowledge gap i

Zhen Xing, Qijun Feng, Haoran Chen, Qi Dai

The recent wave of AI-generated content (AIGC) has witnessed substantial success in computer vision, with the diffusion model playing a crucial role in this achievement. Due to their impressive generative capabilities, diffusion models are gradually superseding methods based on GANs and auto-regressive Transformers, demonstrating exceptional performance not

Yusuke Manita, Hiroki Takeda, Katsuki Aoki, Tomohiro Fujita

We propose a novel method for distinguishing the spin of ultralight dark matter (ULDM) using interferometric gravitational wave detectors. ULDM can be a bosonic field of spin-0, 1, or 2, and each induces distinctive signatures in signals. We find that the finite-time traveling effect causes a dominant signal for spin-0 and spin-1 ULDM, while not for spin-2.

Boyi Li, Philipp Wu, Pieter Abbeel, Jitendra Malik

An interactive robot framework accomplishes long-horizon task planning and can easily generalize to new goals and distinct tasks, even during execution. However, most traditional methods require predefined module design, making it hard to generalize to different goals. Recent large language model based approaches can allow for more open-ended planning but of

Yukai Shi, Jianan Wang, He Cao, Boshi Tang

In this paper, we present TOSS, which introduces text to the task of novel view synthesis (NVS) from just a single RGB image. While Zero-1-to-3 has demonstrated impressive zero-shot open-set NVS capability, it treats NVS as a pure image-to-image translation problem. This approach suffers from the challengingly under-constrained nature of single-view NVS: the

Zeyu Yang, Hongye Yang, Zijie Pan, Li Zhang

Reconstructing dynamic 3D scenes from 2D images and generating diverse views over time is challenging due to scene complexity and temporal dynamics. Despite advancements in neural implicit models, limitations persist: (i) Inadequate Scene Structure: Existing methods struggle to reveal the spatial and temporal structure of dynamic scenes from directly learnin

Anish Agashe, Mustapha Ishak

The theory of macroscopic gravity provides a formalism to average the Einstein field equations from small scales to largest scales in space-time. It is well known that averaging is an operation that does not commute with calculating the Einstein tensor and this leads to a correction term in the field equations known as back-reaction. In this work, we derive

Hanan Gani, Shariq Farooq Bhat, Muzammal Naseer, Salman Khan

Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts describing complex scenes with multiple objects. While excelling in generating images from short, single-object descriptions, these models often struggle to faithfully capture all the nuanced detai

Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Walke

If generalist robots are to operate in truly unstructured environments, they need to be able to recognize and reason about novel objects and scenarios. Such objects and scenarios might not be present in the robot's own training data. We propose SuSIE, a method that leverages an image-editing diffusion model to act as a high-level planner by proposing interme

Weijia Shi, Sewon Min, Maria Lomeli, Chunting Zhou

Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which can be reduced to document completion. Existing pretraining pipelines train LMs by concatenating random sets of short documents to create input contexts but the prior documents pro

Kunal Handa, Margaret Clapper, Jessica Boyle, Rose E Wang

Teachers' growth mindset supportive language (GMSL)--rhetoric emphasizing that one's skills can be improved over time--has been shown to significantly reduce disparities in academic achievement and enhance students' learning outcomes. Although teachers espouse growth mindset principles, most find it difficult to adopt GMSL in their practice due the lack of e

Nilesh Gupta, Devvrit Khatri, Ankit S Rawat, Srinadh Bhojanapalli

Dual-encoder (DE) models are widely used in retrieval tasks, most commonly studied on open QA benchmarks that are often characterized by multi-class and limited training data. In contrast, their performance in multi-label and data-rich retrieval settings like extreme multi-label classification (XMC), remains under-explored. Current empirical evidence indicat

Thomas Decker, Ananta R. Bhattarai, Michael Lebacher

Modern AI techniques open up ever-increasing possibilities for autonomous vehicles, but how to appropriately verify the reliability of such systems remains unclear. A common approach is to conduct safety validation based on a predefined Operational Design Domain (ODD) describing specific conditions under which a system under test is required to operate prope

Tianbao Xie, Fan Zhou, Zhoujun Cheng, Peng Shi

Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs). Current language agent frameworks aim to facilitate the construction of proof-of-concept language agents while neglecting the non-expert user access to agents and payi

Hyunsoo Choi, Seungman Choi, Peter Menart, Angshuman Deka

The Rayleigh limit and low Signal-to-Noise Ratio (SNR) scenarios pose significant limitations to optical imaging systems used in remote sensing, infrared thermal imaging, and space domain awareness. In this study, we introduce a Stochastic Sub-Rayleigh Imaging (SSRI) algorithm to localize point objects and estimate their positions, brightnesses, and number i

Odhran O'Donoghue, Aleksandar Shtedritski, John Ginger, Ralph Abboud

The ability to automatically generate accurate protocols for scientific experiments would represent a major step towards the automation of science. Large Language Models (LLMs) have impressive capabilities on a wide range of tasks, such as question answering and the generation of coherent text and code. However, LLMs can struggle with multi-step problems and

Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos

We present Llemma, a large language model for mathematics. We continue pretraining Code Llama on the Proof-Pile-2, a mixture of scientific papers, web data containing mathematics, and mathematical code, yielding Llemma. On the MATH benchmark Llemma outperforms all known open base models, as well as the unreleased Minerva model suite on an equi-parameter basi

Nimish P. Nazirkar, Sowmya Srinivasan, Ross Harder, Edwin Fohtung

The topological properties of quasiparticles, such as skyrmions and vortices, have the potential to offer extraordinary metastability through topological protection, and drive motion with minimal electrical current excitation. This has promising implications for future applications in spintronics. Skyrmions frequently appear either in lattice form or as sepa

Hannah Helgesen, Michael Felsberg, Jan-Åke Larsson

The field of Quantum Machine Learning (QML) has emerged recently in the hopes of finding new machine learning protocols or exponential speedups for classical ones. Apart from problems with vanishing gradients and efficient encoding methods, these speedups are hard to find because the sampling nature of quantum computers promotes either simulating computation

Manley Roberts, Himanshu Thakur, Christine Herlihy, Colin White

Recent claims about the impressive abilities of large language models (LLMs) are often supported by evaluating publicly available benchmarks. Since LLMs train on wide swaths of the internet, this practice raises concerns of data contamination, i.e., evaluating on examples that are explicitly or implicitly included in the training data. Data contamination rem

Charlie George, Andreas Stuhlmüller

Hallucination plagues even frontier LLMs--but how bad is it really for summarizing academic papers? We evaluate Factored Verification, a simple automated method for detecting hallucinations in abstractive summaries. This method sets a new SotA on hallucination detection in the summarization task of the HaluEval benchmark, achieving 76.2% accuracy. We then us

C. J. Lang

We provide a framework to classify hyperbolic monopoles with continuous symmetries and find a Structure Theorem, greatly simplifying the construction of all those with spherically symmetry. In doing so, we reduce the problem of finding spherically symmetric hyperbolic monopoles to a problem in representation theory. Additionally, we determine constraints on

Yilun Du, Mengjiao Yang, Pete Florence, Fei Xia

We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pretrained on Internet-scale data. To this end, we present video language planning (VLP), an algorithm that consists of a tree search procedure, where we train (i) vision-language mo