May 2023 arXiv papers — page 32

M. O. D. Alotaibi, L. Al Sakkaf, U. Al Khawaja

We numerically demonstrate the unidirectional flow of flat-top solitons when interacting with two reflectionless potential wells with slightly different depths. The system is described by a nonlinear Schr\"{o}dinger equation with dual nonlinearity. The results show that for shallow potential wells, the velocity window for unidirectional flow is larger than f

Milko Estrada, Rodrigo Aros

We provide a new regular black hole solution (RBH) in Einstein Gauss-Bonnet (EGB) gravity with localized sources of matter in the energy--momentum tensor. We determine the necessary constraints in order for the solution to be regular. Although we use a specific form for the energy density as a test of proof, these constraints could serve as a recipe for cons

Klaus Ziegler

We consider the quantum evolution of a fermion-hole pair in a d-dimensional gas of non-interacting fermions in the presence of random phase scattering. This system is mapped onto an effective Ising model, which enables us to show rigorously that the probability of recombining the fermion and the hole decays exponentially with the distance of their initial sp

Jérôme Houdayer, Haggai Landa, Grégoire Misguich

We present an exactly solvable toy model for the continuous dissipative dynamics of permutation-invariant graph states of $N$ qubits. Such states are locally equivalent to an $N$-qubit Greenberger-Horne-Zeilinger (GHZ) state, a fundamental resource in many quantum information processing setups. We focus on the time evolution of the state governed by a Lindbl

Mohammad Noaman, Donald W. Booth, James P. Shaffer

Rydberg atom-based sensors are a new type of radio frequency sensor that is inherently quantum mechanical. Several configurations of the sensor use a local oscillator to determine the properties of the target radio frequency field. We explain how the physics of Rydberg atom-based sensors in two or more radio frequency fields can be precisely described by a m

Suman Jyoti De, Udit Khanna, Sumathi Rao, Sourin Das

We investigate the effects of introducing a boost (a Zeeman field parallel to the spin quantization axis) at the proximitized helical edge of a two-dimensional (2D) quantum spin Hall insulator. Our self-consistent analysis finds that a Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) superconducting phase may emerge at the edge when the boost is larger than a critica

Siddharth Gandhi, Aurora Kesseli, Yapeng Zhang, Amy Louca

Ground-based high-resolution spectroscopy (HRS) has detected numerous chemical species and atmospheric dynamics in exoplanets, most notably ultra-hot Jupiters (UHJs). However, quantitative estimates on abundances have been challenging but are essential for accurate comparative characterisation and to determine formation scenarios. In this work we retrieve th

Peter Blümler, Helmut Soltner

This review is a compilation of relevant concepts in designing Halbach multipoles for magnetic resonance applications. The main focus is on providing practical guidelines to plan, design and build such magnets. Therefore, analytical equations are presented for estimating the magnetic field from ideal to realistic systems. Various strategies of homogenizing m

Aymen Laadhari, Ahmad Deeb

We present a numerical method to model the dynamics of inextensible biomembranes in a quasi-Newtonian incompressible flow, which better describes hemorheology in the small vasculature. We consider a level set model for the fluid-membrane coupling, while the local inextensibility condition is relaxed by introducing a penalty term. The penalty method is straig

Liang Wendong, Armin Kekić, Julius von Kügelgen, Simon Buchholz

Independent Component Analysis (ICA) aims to recover independent latent variables from observed mixtures thereof. Causal Representation Learning (CRL) aims instead to infer causally related (thus often statistically dependent) latent variables, together with the unknown graph encoding their causal relationships. We introduce an intermediate problem termed Ca

Gavin Zhang, Hong-Ming Chiu, Richard Y. Zhang

Non-convex gradient descent is a common approach for estimating a low-rank $n\times n$ ground truth matrix from noisy measurements, because it has per-iteration costs as low as $O(n)$ time, and is in theory capable of converging to a minimax optimal estimate. However, the practitioner is often constrained to just tens to hundreds of iterations, and the slow

Youngeun Kim, Yuhang Li, Abhishek Moitra, Ruokai Yin

Due to increasing interest in adapting models on resource-constrained edges, parameter-efficient transfer learning has been widely explored. Among various methods, Visual Prompt Tuning (VPT), prepending learnable prompts to input space, shows competitive fine-tuning performance compared to training of full network parameters. However, VPT increases the numbe

Midhul Vuppalapati, Giannis Fikioris, Rachit Agarwal, Asaf Cidon

We consider the problem of fair resource allocation in a system where user demands are dynamic, that is, where user demands vary over time. Our key observation is that the classical max-min fairness algorithm for resource allocation provides many desirable properties (e.g., Pareto efficiency, strategy-proofness, and fairness), but only under the strong assum

Tianshu Zhang, Changchang Liu, Wei-Han Lee, Yu Su

This paper studies a new task of federated learning (FL) for semantic parsing, where multiple clients collaboratively train one global model without sharing their semantic parsing data. By leveraging data from multiple clients, the FL paradigm can be especially beneficial for clients that have little training data to develop a data-hungry neural semantic par

Bowen Li, Jiashun Wang, Yaoyu Hu, Chen Wang

Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully uti

Fnu Mohbat, Mohammed J. Zaki, Catherine Finegan-Dollak, Ashish Verma

The robustness of a model for real-world deployment is decided by how well it performs on unseen data and distinguishes between in-domain and out-of-domain samples. Visual document classifiers have shown impressive performance on in-distribution test sets. However, they tend to have a hard time correctly classifying and differentiating out-of-distribution ex

Lucius Gregory Meredith, Ben Goertzel, Jonathan Warrell, Adam Vandervorst

We present an operational semantics for the language MeTTa.

Karishma Patnaik, Aravind Adhith Pandian Saravanakumaran, Wenlong Zhang

This article introduces XPLORER, a passive deformable UAV with a spring-augmented chassis and proprioceptive state awareness, designed to endure collisions and maintain smooth contact. We develop a fast-converging external force estimation algorithm for XPLORER that leverages onboard sensors and proprioceptive data for contact and collision detection. Using

Jing Yu Koh, Daniel Fried, Ruslan Salakhutdinov

We propose a method to fuse frozen text-only large language models (LLMs) with pre-trained image encoder and decoder models, by mapping between their embedding spaces. Our model demonstrates a wide suite of multimodal capabilities: image retrieval, novel image generation, and multimodal dialogue. Ours is the first approach capable of conditioning on arbitrar

Swagata Acharya, Dimitar Pashov, Mikhail I Katsnelson, Mark van Schilfgaarde

Cubic BAs has received recent attention for its large electron and hole mobilities and large thermal conductivity. This is a rare and much desired combination in semiconductor industry: commercial semiconductors typically have high electron mobilities, or hole mobilities, or large thermal conductivities, but not all of them together. Here we report predictio

Jingyuan Sun, Mingxiao Li, Zijiao Chen, Yunhao Zhang

Decoding visual stimuli from neural responses recorded by functional Magnetic Resonance Imaging (fMRI) presents an intriguing intersection between cognitive neuroscience and machine learning, promising advancements in understanding human visual perception and building non-invasive brain-machine interfaces. However, the task is challenging due to the noisy na

Alexander Kuznetsov, Evgeny Shinder

Using the technique of categorical absorption of singularities we prove that the nontrivial components of the derived categories of del Pezzo threefolds of degree $d \in \{2,3,4,5\}$ and crepant categorical resolutions of the nontrivial components of the derived categories of nodal del Pezzo threefolds of degree $d = 1$ can be smoothly deformed to the nontri

Atli Kosson, Bettina Messmer, Martin Jaggi

This study investigates how weight decay affects the update behavior of individual neurons in deep neural networks through a combination of applied analysis and experimentation. Weight decay can cause the expected magnitude and angular updates of a neuron's weight vector to converge to a steady state we call rotational equilibrium. These states can be highly

Zalán Gyenis, Alexa Gopaulsingh, Övge Öztürk

Subobject independence as morphism co-possibility has recently been defined in [2] and studied in the context of algebraic quantum field theory. This notion of independence is handy when it comes to systems coming from physics, but when directly applied to classical algebras, subobject independence is not entirely satisfactory. The sole purpose of this note

Congcong Wang

During crisis situations, social media allows people to quickly share information, including messages requesting help. This can be valuable to emergency responders, who need to categorise and prioritise these messages based on the type of assistance being requested. However, the high volume of messages makes it difficult to filter and prioritise them without

Antoine Chambert-Loir, Camille Noûs

Nous étendons aux courbes de genre arbitraire le théorème de rationalité de Cantor, lui-même une extension de théorèmes de Borel, Pólya, Dwork, Bertrandias et Robinson. La démonstration s'effectue en deux étapes. La première est un critère d'algébricité, démontré par une méthode d'approximation diophantienne. La seconde repose sur le théorème de

Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth

We propose Functional Flow Matching (FFM), a function-space generative model that generalizes the recently-introduced Flow Matching model to operate in infinite-dimensional spaces. Our approach works by first defining a path of probability measures that interpolates between a fixed Gaussian measure and the data distribution, followed by learning a vector fie

Angeline Aguinaldo, Evan Patterson, James Fairbanks, William Regli

Classical planning representation languages based on first-order logic have preliminarily been used to model and solve robotic task planning problems. Wider adoption of these representation languages, however, is hindered by the limitations present when managing implicit world changes with concise action models. To address this problem, we propose an alterna

Yunhao Ge, Jie Ren, Jiaping Zhao, Kaifeng Chen

We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs. We propose a novel one-class open-set OOD detector t

Charles F. Manski

In medical treatment and elsewhere, it has become standard to base treatment intensity (dosage) on evidence in randomized trials. Yet it has been rare to study how outcomes vary with dosage. In trials to obtain drug approval, the norm has been to specify some dose of a new drug and compare it with an established therapy or placebo. Design-based trial analysi

Atli Kosson, Dongyang Fan, Martin Jaggi

Batch Normalization (BN) is widely used to stabilize the optimization process and improve the test performance of deep neural networks. The regularization effect of BN depends on the batch size and explicitly using smaller batch sizes with Batch Normalization, a method known as Ghost Batch Normalization (GBN), has been found to improve generalization in many

Alexander R. Klotz, Caleb J. Anderson

The ropelength of a knot is the minimum length required to tie it. Computational upper bounds have previously been computed for every prime knot with up to 11 crossings. Here, we present ropelength measurements for the 2176 knots with 12 crossings, of which 1288 are alternating and 888 are non-alternating. We report on the distribution of ropelengths within

Alberto Bressan, Camillo De Lellis

Given a strictly hyperbolic $n\times n$ system of conservation laws, it is well known that there exists a unique Lipschitz semigroup of weak solutions, defined on a domain of functions with small total variation, which are limits of vanishing viscosity approximations. Aim of this note is to prove that every weak solution taking values in the domain of the se

Claytone Sikasote, Eunice Mukonde, Md Mahfuz Ibn Alam, Antonios Anastasopoulos

We present BIG-C (Bemba Image Grounded Conversations), a large multimodal dataset for Bemba. While Bemba is the most populous language of Zambia, it exhibits a dearth of resources which render the development of language technologies or language processing research almost impossible. The dataset is comprised of multi-turn dialogues between Bemba speakers bas

Tong Zhou

Retail sales forecasting presents a significant challenge for large retailers such as Walmart and Amazon, due to the vast assortment of products, geographical location heterogeneity, seasonality, and external factors including weather, local economic conditions, and geopolitical events. Various methods have been employed to tackle this challenge, including t

Taras Banakh, Tetiana Martyniuk, Magdalena Nowak, Filip Strobin

For a metric Peano continuum $X$, let $S_X$ be a Sierpi\'nski function assigning to each $\varepsilon>0$ the smallest cardinality of a cover of $X$ by connected subsets of diameter $\le \varepsilon$. We prove that for any increasing function $\Omega:\mathbb R_+\to\mathbb R_+$ with $(0,1]\subseteq\Omega[\mathbb R_+]$ and $s:=\sum_{n=1}^\infty S_X(2^{-n})\sum_

Jorge Jaber-Urquiza, Angel Sanchez

In this work we study the interaction strength among a neutral scalar boson and two massless vector bosons in presence of an external magnetic field. Based on global symmetries, we build the general tensor structure amplitude $\mathcal{M}^{\mu\nu}$, for the process $V^\mu+V^\nu\longrightarrow\phi$, in terms of the vector bosons polarization states. Then, we

Paul Barde, Jakob Foerster, Derek Nowrouzezahrai, Amy Zhang

Training multiple agents to coordinate is an essential problem with applications in robotics, game theory, economics, and social sciences. However, most existing Multi-Agent Reinforcement Learning (MARL) methods are online and thus impractical for real-world applications in which collecting new interactions is costly or dangerous. While these algorithms shou

Yingjie Feng, Jun Wang, Xianfeng Gu, Xiaoyin Xu

In diagnosing challenging conditions such as Alzheimer's disease (AD), imaging is an important reference. Non-imaging patient data such as patient information, genetic data, medication information, cognitive and memory tests also play a very important role in diagnosis. Effect. However, limited by the ability of artificial intelligence models to mine such in

Jiaxin Ge, Hongyin Luo, Yoon Kim, James Glass

Entailment has been recognized as an important metric for evaluating natural language understanding (NLU) models, and recent studies have found that entailment pretraining benefits weakly supervised fine-tuning. In this work, we design a prompting strategy that formulates a number of different NLU tasks as contextual entailment. This approach improves the ze

Agnieszka Ławrynowicz

The aim of this primer is to introduce the subject of knowledge engineering in a concise but synthetic way to develop the reader's intuition about the area.

Kartik Chandra, Tony Chen, Tzu-Mao Li, Jonathan Ragan-Kelley

A single panel of a comic book can say a lot: it can depict not only where the characters currently are, but also their motions, their motivations, their emotions, and what they might do next. More generally, humans routinely infer complex sequences of past and future events from a *static snapshot* of a *dynamic scene*, even in situations they have never se

Tucker J. Ervin, Blake Jackson

In a post on the Open Problems in Algebraic Combinatorics (OPAC) blog, E. Bucher and J. Machacek posed three open problems: OPAC-033, OPAC-034, and OPAC-035. These three problems deal with the relationships between three infinite classes of quivers: the Banff, Louise, and $\mathcal{P}$ quivers. OPAC-034 asks whether or not every Banff quiver can be verified

Ivan R. Nabi, Ben Cardoen, Ismail M. Khater, Guang Gao

Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell, bridging the mesoscale gap to classical structural biology methodologies. Analysis of super-resolution data by artificial intelligence (AI), such as machine learning, offers treme

Kyle Boone, Ben Wurster, Seth Thao, Yu Hen Hu

This project is centered around building a neural network that is able to recognize ASL letters in images, particularly within the scope of a live video feed. Initial testing results came up short of expectations when both the convolutional network and VGG16 transfer learning approaches failed to generalize in settings of different backgrounds. The use of a

Atli Kosson, Martin Jaggi

Multiplications are responsible for most of the computational cost involved in neural network training and inference. Recent research has thus looked for ways to reduce the cost associated with them. Inspired by Mogami (2020), we replace multiplication with a cheap piecewise affine approximation that is achieved by adding the bit representation of the floati

Hongyu Lu, Kai Sun, Zi Yang Meng, Bin-Bin Chen

Quadratic band touching (QBT) points are widely observed in 2D and 3D materials, including bilayer graphene and Luttinger semimetals, and attract significant attention from theory to experiment. However, even in its simplest form, the 2D checkerboard lattice QBT model, the phase diagram characterized by temperature and interaction strength still remains unkn

Bidya Binay Karak

One obvious feature of the solar cycle is its variation from one cycle to another. In this article, we review the dynamo models for the long-term variations of the solar cycle. By long-term variations, we mean the cycle modulations beyond the 11-year periodicity and these include, the Gnevyshev-Ohl/Even-Odd rule, grand minima, grand maxima, Gleissberg cycle,

Paulo Roberto Bueno

Quantum rate theory is based on a first-principle quantum mechanical rate concept that comprises with the Planck-Einstein relationship $E = h\nu$, where $\nu = e^2/hC_q$ is a frequency associated with the quantum capacitance $C_q$ and $E = e^2/C_q$ is the energy associated with $\nu$. For a single state mode of transmittance, $e^2/C_q$ corresponds to the che

Jessica Dai, Paula Gradu, Christopher Harshaw

From clinical development of cancer therapies to investigations into partisan bias, adaptive sequential designs have become increasingly popular method for causal inference, as they offer the possibility of improved precision over their non-adaptive counterparts. However, even in simple settings (e.g. two treatments) the extent to which adaptive designs can

Kourosh Nozari, Sara Saghafi, Fateme Aliyan

In astrophysics, the process of a massive body acquiring matter is referred to as accretion. The extraction of gravitational energy occurs as a result of the infall. Since it converts gravitational energy into radiation, accretion onto dark compact objects, e.g. black holes, neutron stars, and white dwarfs is an extremely significant process in the astrophys

Lizhou Fan, Sara Lafia, Lingyao Li, Fangyuan Yang

Data users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter-university Consortium for Political and Social Research (ICPSR), offer standardized metadata and search tools to support data search. Metadata standards emphasize the machine-readability of data and its d

Xinge Yang, Qiang Fu, Yunfeng Nie, Wolfgang Heidrich

Classical lens design minimizes optical aberrations to produce sharp images, but is typically decoupled from downstream computer vision tasks. Existing end-to-end optical design learns optical encoding through joint optimization, but often suffers from an unstable training process. We propose task-driven lens design, a new optimization philosophy for joint o

Gururaj A. Wagle, Emmanouil Chatzopoulos, Ryan Wollaeger, Christopher J. Fontes

In this paper, we introduce \texttt{SuperLite}, an open-source Monte Carlo radiation transport code designed to produce synthetic spectra for astrophysical transient phenomena affected by circumstellar interaction. \texttt{SuperLite} utilizes Monte Carlo methods for semi-implicit, semi-relativistic radiation transport in high-velocity shocked outflows, emplo

Kai San Chan, Huimiao Chen, Chenyu Jin, Yuxuan Tian

Accurate tracking of cellular and subcellular structures, along with their dynamics, plays a pivotal role in understanding the underlying mechanisms of biological systems. This paper presents a novel approach, ProGroTrack, that combines the You Only Look Once (YOLO) and ByteTrack algorithms within the detection-based tracking (DBT) framework to track intrace

Yihong Liu, Alexandra Chronopoulou, Hinrich Schütze, Alexander Fraser

Although unsupervised neural machine translation (UNMT) has achieved success in many language pairs, the copying problem, i.e., directly copying some parts of the input sentence as the translation, is common among distant language pairs, especially when low-resource languages are involved. We find this issue is closely related to an unexpected copying behavi

Hsu-kuang Chiu, Stephen F. Smith

The reliability of current autonomous driving systems is often jeopardized in situations when the vehicle's field-of-view is limited by nearby occluding objects. To mitigate this problem, vehicle-to-vehicle communication to share sensor information among multiple autonomous driving vehicles has been proposed. However, to enable timely processing and use of s

Kyle T. Yoshida, Joel X. Kiernan, Allison M. Okamura, Cara M. Nunez

Auditory, haptic, and visual stimuli provide alerts, notifications, and information for a wide variety of applications ranging from virtual reality to wearable and hand-held devices. Response times to these stimuli have been used to assess motor control and design human-computer interaction systems. In this study, we investigate human response times to 26 co

Tomasz Limisiewicz, Jiří Balhar, David Mareček

Multilingual language models have recently gained attention as a promising solution for representing multiple languages in a single model. In this paper, we propose new criteria to evaluate the quality of lexical representation and vocabulary overlap observed in sub-word tokenizers. Our findings show that the overlap of vocabulary across languages can be act

Yali Bian, Chris North

In this paper, we design novel interactive deep learning methods to improve semantic interactions in visual analytics applications. The ability of semantic interaction to infer analysts' precise intents during sensemaking is dependent on the quality of the underlying data representation. We propose the $\text{DeepSI}_{\text{finetune}}$ framework that integra

Sibo Zhang, Bruno Clerckx, David Vargas, Oliver Haffenden

Rate-Splitting Multiple Access (RSMA) has emerged as a novel multiple access technique that enlarges the achievable rate region of Multiple-Input Multiple-Output (MIMO) broadcast channels with linear precoding. In this work, we jointly address three practical but fundamental questions: (1) How to exploit the benefit of RSMA under finite constellations? (2) W

John C. Stevenson

Redistribution of resources within a group as a method to reduce wealth inequality is a current area of debate. The evolutionary path to or away from wealth sharing is also a subject of active research. In order to investigate effects and evolution of wealth sharing, societies are simulated using a minimal model of a complex adapting system. These simulation

Anthony Noll, Sébastien Deheuvels

Convective cores are the hydrogen reservoirs of main sequence stars that are more massive than around 1.2 solar masses. The characteristics of the cores have a strong impact on the evolution and structure of the star. However, such results rely on stellar evolution codes, in which simplistic assumptions are often made on the physics in the core. Indeed, mixi

Hanwen Ren, Ahmed H. Qureshi

Non-monotone object rearrangement planning in confined spaces such as cabinets and shelves is a widely occurring but challenging problem in robotics. Both the robot motion and the available regions for object relocation are highly constrained because of the limited space. This work proposes a Multi-Stage Monte Carlo Tree Search (MS-MCTS) method to solve non-

Julia Mendelsohn, Ronan Le Bras, Yejin Choi, Maarten Sap

Dogwhistles are coded expressions that simultaneously convey one meaning to a broad audience and a second one, often hateful or provocative, to a narrow in-group; they are deployed to evade both political repercussions and algorithmic content moderation. For example, in the sentence 'we need to end the cosmopolitan experiment,' the word 'cosmopolitan' likely

Dark Energy Survey, Kilo-Degree Survey Collaboration, :, T. M. C. Abbott

We present a joint cosmic shear analysis of the Dark Energy Survey (DES Y3) and the Kilo-Degree Survey (KiDS-1000) in a collaborative effort between the two survey teams. We find consistent cosmological parameter constraints between DES Y3 and KiDS-1000 which, when combined in a joint-survey analysis, constrain the parameter $S_8 = \sigma_8 \sqrt{\Omega_{\rm

S. K. Stuber, E. Schinnerer, T. G. Williams, M. Querejeta

The morphology of a galaxy stems from secular and environmental processes during its evolutionary history. Thus galaxy morphologies have been a long used tool to gain insights on galaxy evolution. We visually classify morphologies on cloud-scales based on the molecular gas distribution of a large sample of 79 nearby main-sequence galaxies, using 1'' resoluti

Thomas W. Baumgarte, Bernd Brügmann, Daniela Cors, Carsten Gundlach

Fine-tuning generic but smooth spherically-symmetric initial data for general relativity to the threshold of dynamical black hole formation creates arbitrarily large curvatures, mediated by a universal self-similar solution that acts as an intermediate attractor. For vacuum gravitational waves, however, these critical phenomena have been elusive. We present,

Samuel Lanthaler, Nicholas H. Nelsen

This paper provides a comprehensive error analysis of learning with vector-valued random features (RF). The theory is developed for RF ridge regression in a fully general infinite-dimensional input-output setting, but nonetheless applies to and improves existing finite-dimensional analyses. In contrast to comparable work in the literature, the approach propo

Jay Chan, Xiangyang Ju, Adam Kania, Benjamin Nachman

Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. Deep generative models are a natural replacement for classical techniques, since they are mo

Laura Andrianopoli, Bianca Letizia Cerchiai, Ruggero Noris, Lucrezia Ravera

We consider general torsion components in three-dimensional Einstein-Cartan gravity, providing a geometrical interpretation for matter, and find new solutions of the corresponding equations for the Riemann curvature and torsion. These geometries involve a peculiar interplay between the vector $(\beta_i)$ and the singlet $(\tau)$ irreducible components of the

L. L. Cowie, A. J. Barger, F. E. Bauer

Finding high-redshift (z>>4) dusty star-forming galaxies is extremely challenging. It has recently been suggested that millimeter selections may be the best approach, since the negative K-correction makes galaxies at a given far-infrared (FIR) luminosity brighter at z>4 than those at z=2-3. Here we analyze this issue using a deep ALMA 2mm sample obtained by

Riccardo Gonzo, Anton Ilderton

We study the space and properties of global and local observables for radiation emitted in the scattering of a massive scalar field in gauge and gravitational plane-wave backgrounds, in both the quantum and classical theory. We first compute the radiated momentum and angular momentum flow, demonstrating that they are good local observables determined by the

Thomas Bartsch, Mathew Bullimore, Andrea Grigoletto

This paper addresses the question of how categorical symmetries act on extended operators in quantum field theory. Building on recent results in two dimensions, we introduce higher tube categories and algebras associated to higher fusion category symmetries. We show that twisted sector extended operators transform in higher representations of higher tube alg

Patrice Kolb, Kiryl Pakrouski

We study the stability of the many-body scars in spin-1/2 fermionic systems under the most typical perturbations in relevant materials. We find that some families of scars are completely insensitive to certain perturbations. In some other cases they are stable to the first order in perturbation theory. Our analytical results apply to a large class of Hamilto

Fereshte Shahbeigi, Christopher T. Chubb, Ryszard Kukulski, Łukasz Pawela

The classical embeddability problem asks whether a given stochastic matrix $T$, describing transition probabilities of a $d$-level system, can arise from the underlying homogeneous continuous-time Markov process. Here, we investigate the quantum version of this problem, asking of the existence of a Markovian quantum channel generating state transitions descr

Josephine F. W. Baggen, Pieter van Dokkum, Ivo Labbe, Gabriel Brammer

The first JWST data revealed an unexpected population of red galaxies that appear to have redshifts of $z\sim 7-9$ and high masses of $M_*$ $\sim$ 10$^{10}$ M$_{\odot}$ (Labb\'e et al. 2023). Here we fit S\'ersic profiles to the F200W NIRCam images of the 13 massive galaxy candidates of Labb\'e et al., to determine their structural parameters. Satisfactory f

Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green

Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging. Building on recent advances in generative modeling, we here present flow matching posterior estimation (FMPE), a technique for SBI using continuous normal

Christopher Dessert, Orion Ning, Nicholas L. Rodd, Benjamin R. Safdi

The Hitomi X-ray satellite mission carried unique high-resolution spectrometers that were set to revolutionize the search for sterile neutrino dark matter (DM) by looking for narrow X-ray lines arising from DM decays. Unfortunately, the satellite was lost shortly after launch, and to-date the only analysis using Hitomi for DM decay used data taken towards th

Lakshya Bhardwaj, Sakura Schafer-Nameki

Consider a d-dimensional quantum field theory (QFT) $\mathfrak{T}$, with a generalized symmetry $\mathcal{S}$, which may or may not be invertible. We study the action of $\mathcal{S}$ on generalized or $q$-charges, i.e. $q$-dimensional operators. The main result of this paper is that $q$-charges are characterized in terms of the topological defects of the Sy

Lillian Santos-Olmsted, Kirk Barrow, Tilman Hartwig

We present the Galaxy Assembly and Interaction Neural Networks (GAINN), a series of artificial neural networks for predicting the redshift, stellar mass, halo mass, and mass-weighted age of simulated galaxies based on JWST photometry. Our goal is to determine the best neural network for predicting these variables at $11.5 < z < 15$. The parameters of the opt

Sheila Sagear, Sarah Ballard

We investigate the underlying distribution of orbital eccentricities for planets around early-to-mid M dwarf host stars. We employ a sample of 163 planets around early- to mid-M dwarfs across 101 systems detected by NASA's Kepler Mission. We constrain the orbital eccentricity for each planet by leveraging the Kepler lightcurve together with a stellar density

Adam Smercina, Eric F. Bell, Paul A. Price, Jeremy Bailin

M64, often called the "Evil Eye" galaxy, is unique among local galaxies. Beyond its dramatic, dusty nucleus, it also hosts an outer gas disk that counter-rotates relative to its stars. The mass of this outer disk is comparable to the gas content of the Small Magellanic Cloud (SMC), prompting the idea that it was likely accreted in a recent minor merger. Yet,

Xinyue Wei, Fanbo Xiang, Sai Bi, Anpei Chen

We present a method for generating high-quality watertight manifold meshes from multi-view input images. Existing volumetric rendering methods are robust in optimization but tend to generate noisy meshes with poor topology. Differentiable rasterization-based methods can generate high-quality meshes but are sensitive to initialization. Our method combines the

David Beltran, Jennifer Duncan, Jonathan Hickman

The optimal $L^p \to L^q$ mapping properties for the (local) helical maximal function are obtained, except for endpoints. The proof relies on tools from multilinear harmonic analysis and, in particular, a localised version of the Bennett--Carbery--Tao restriction theorem.

Anik Halder, Zhengyangguang Gong, Alexandre Barreira, Oliver Friedrich

We present the integrated 3-point correlation functions (3PCF) involving both the cosmic shear and the galaxy density fields. These are a set of higher-order statistics that describe the modulation of local 2-point correlation functions (2PCF) by large-scale features in the fields, and which are easy to measure from galaxy imaging surveys. Based on previous

Gabriele Sarti, Phu Mon Htut, Xing Niu, Benjamin Hsu

Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation outputs. While ACT has garnered attention in recent years due to its usefulness in real-world applications, progress in the task is currently limited by dataset availability, since

Antonio Rossi, Riccardo Dettori, Cameron Johnson, Jesse Balgley

We investigate the electronic properties of a graphene and $\alpha$-ruthenium trichloride (hereafter RuCl$_3$) heterostructure, using a combination of experimental and theoretical techniques. RuCl$_3$ is a Mott insulator and a Kitaev material, and its combination with graphene has gained increasing attention due to its potential applicability in novel electr

Eugeny Babichev, Christos Charmousis, Nicolas Lecoeur

We present solutions of DHOST theories describing a rotating black hole embedded in an expanding universe. The solution is constructed by conformal transformation of a stealth Kerr(-de Sitter) black hole. The conformal factor depends explicitly on the scalar field -- but not on its derivative -- and defines the new theory. The scalar field of the stealth Ker

Sergey Frolov, Anton Pribytok, Alessandro Sfondrini

We use the lightcone $AdS_{3}\times S^{3}\times T^{4}$ superstring sigma model with fermions and bosons subject to twisted boundary conditions to find the ground state energy in the semi-classical approximation where effective string tension $h$ and the light-cone momentum $L$ are sent to infinity in such a way that ${\cal J}\equiv L/h$ is kept fixed. We the

Tyler A. Chang, Kishaloy Halder, Neha Anna John, Yogarshi Vyas

NLP models often degrade in performance when real world data distributions differ markedly from training data. However, existing dataset drift metrics in NLP have generally not considered specific dimensions of linguistic drift that affect model performance, and they have not been validated in their ability to predict model performance at the individual exam

Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen

Recent research has highlighted the potential of large language models (LLMs) to improve their problem-solving capabilities with the aid of suitable external tools. In our work, we further advance this concept by introducing a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving. Ou

Sebastián Bordakevich, Dudbil Pabón, Lorena Rebón, Silvia Ledesma

Spatial light modulators (SLMs) are widely used to coherently control quantum states of light. When carrying out these experiments, some assumptions are made. For instance, it is supposed that the position-momentum correlations between twin photon pairs are not affected by the use of a liquid crystal display (LCD) as a SLM. Furthermore, it is assumed that th

Andreas Krug

We prove formulas for the cohomology and the extension groups of tautological bundles on punctual Quot schemes over complex smooth projective curves. As a corollary, we show that the tautological bundle determines the isomorphism class of the original vector bundle on the curve. We also give a vanishing result for the push-forward along the Quot--Chow morphi

Jason Bernstein, Alec M. Dunton, Benjamin W. Priest

Probabilistic proofs of the Johnson-Lindenstrauss lemma imply that random projection can reduce the dimension of a data set and approximately preserve pairwise distances. If a distance being approximately preserved is called a success, and the complement of this event is called a failure, then such a random projection likely results in no failures. Assuming

Alessandro Goffi

In this note, we prove interior a priori first- and second-order estimates for solutions of fully nonlinear degenerate elliptic inequalities structured over the vector fields of Carnot groups, under the main assumption that $u$ is semiconvex along the fields. These estimates for supersolutions are new even for linear subelliptic inequalities in nondivergence

Alessandro Goffi

This paper studies a priori and regularity estimates of Evans-Krylov type in H\"older spaces for fully nonlinear uniformly elliptic and parabolic equations of second order when the operator fails to be concave or convex in the space of symmetric matrices. In particular, it is assumed that either the level sets are convex or the operator is concave, convex or

Claudio Bonanno, Francesco D'Angelo, Massimo D'Elia, Lorenzo Maio

We compute the sphaleron rate in quenched QCD for a temperature $T \simeq 1.24~T_c$ from the inversion of the Euclidean lattice time correlator of the topological charge density. We explore and compare two different strategies: one follows a new approach proposed in this study and consists in extracting the rate from finite lattice spacing correlators, and t

Vani Nagarajan, Durga Mandarapu, Milind Kulkarni

The problem of identifying the k-Nearest Neighbors (kNNS) of a point has proven to be very useful both as a standalone application and as a subroutine in larger applications. Given its far-reaching applicability in areas such as machine learning and point clouds, extensive research has gone into leveraging GPU acceleration to solve this problem. Recent work

Shadi Sartipi, Edgar A. Bernal

One of the prevailing trends in the machine- and deep-learning community is to gravitate towards the use of increasingly larger models in order to keep pushing the state-of-the-art performance envelope. This tendency makes access to the associated technologies more difficult for the average practitioner and runs contrary to the desire to democratize knowledg